air pollution research topics

Air Pollution Research Paper Topics

This comprehensive guide to air pollution research paper topics is designed to assist students studying environmental science in selecting a suitable topic for their research paper. The guide provides a broad range of topics divided into ten categories, each containing ten unique research topics. Additionally, the guide offers expert advice on how to choose a topic from the multitude of air pollution research paper topics and how to write a compelling research paper on air pollution. The guide also introduces iResearchNet’s writing services, which offer students the opportunity to order a custom air pollution research paper on any topic. The services include a range of features designed to ensure the delivery of high-quality, custom-written papers.

100 Air Pollution Research Paper Topics

Air pollution is a critical environmental issue that affects every living being on the planet. It is a topic that requires in-depth understanding and research. To aid students in their quest for knowledge and to help them in their academic pursuits, we have compiled a comprehensive list of air pollution research paper topics. These topics are categorized into ten different sections, each containing ten unique topics.

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Causes of Air Pollution

• The role of industrialization in air pollution.
• The impact of transportation on air pollution.
• The effect of agriculture on air pollution.
• The influence of waste disposal on air pollution.
• The role of deforestation in air pollution.
• The impact of urbanization on air pollution.
• The effect of household activities on air pollution.
• The influence of natural disasters on air pollution.
• The role of power generation in air pollution.
• The impact of mining activities on air pollution.

Effects of Air Pollution

• The impact of air pollution on human health.
• The effect of air pollution on the environment.
• The influence of air pollution on climate change.
• The role of air pollution in biodiversity loss.
• The impact of air pollution on agriculture.
• The effect of air pollution on water bodies.
• The influence of air pollution on the ozone layer.
• The role of air pollution in acid rain.
• The impact of air pollution on urban heat islands.
• The effect of air pollution on mental health.

Air Pollution and Climate Change

• The role of air pollution in global warming.
• The impact of air pollution on weather patterns.
• The influence of air pollution on greenhouse gas emissions.
• The role of air pollution in climate change mitigation.
• The impact of air pollution on climate change adaptation.
• The effect of air pollution on carbon sequestration.
• The influence of air pollution on climate change policies.
• The role of air pollution in climate change communication.
• The impact of air pollution on climate change denial.
• The effect of air pollution on climate change education.

Air Pollution Policies

• The effectiveness of the Clean Air Act in addressing air pollution.
• The impact of the Paris Agreement on air pollution.
• The role of national policies in mitigating air pollution.
• The influence of international cooperation on air pollution.
• The effectiveness of emission standards in addressing air pollution.
• The role of renewable energy policies in mitigating air pollution.
• The impact of transportation policies on air pollution.
• The influence of waste management policies on air pollution.
• The effectiveness of urban planning policies in addressing air pollution.
• The role of education policies in mitigating air pollution.

Air Pollution Solutions

• The role of renewable energy in mitigating air pollution.
• The impact of energy efficiency on air pollution.
• The influence of green building on air pollution.
• The effectiveness of public transportation in addressing air pollution.
• The role of waste management in mitigating air pollution.
• The impact of urban green spaces on air pollution.
• The influence of sustainable agriculture on air pollution.
• The effectiveness of carbon capture and storage in addressing air pollution.
• The role of education in mitigating air pollution.
• The impact of individual actions on air pollution.

Air Pollution and Society

• The social impacts of air pollution.
• The role of media in shaping perceptions of air pollution.
• The influence of air pollution on social inequality.
• The impact of air pollution on social movements.
• The role of community engagement in addressing air pollution.
• The influence of air pollution on public health policies.
• The impact of air pollution on economic development.
• The role of air pollution in urban planning.
• The influence of air pollution on migration patterns.
• The impact of air pollution on cultural practices.

Air Pollution and Health

• The impact of air pollution on respiratory diseases.
• The role of air pollution in cardiovascular diseases.
• The influence of air pollution on allergies.
• The impact of air pollution on mental health.
• The role of air pollution in premature deaths.
• The influence of air pollution on children’s health.
• The impact of air pollution on elderly health.
• The role of air pollution in health inequalities.
• The influence of air pollution on public health interventions.
• The impact of air pollution on health care costs.

Air Pollution and Technology

• The role of technology in monitoring air pollution.
• The impact of technology on reducing air pollution.
• The influence of technology on air pollution modeling.
• The role of technology in air pollution forecasting.
• The impact of technology on air pollution communication.
• The influence of technology on air pollution policies.
• The role of technology in air pollution education.
• The impact of technology on air pollution mitigation.
• The influence of technology on air pollution adaptation.
• The role of technology in air pollution research.

Air Pollution and Economy

• The economic impacts of air pollution.
• The role of air pollution in economic inequality.
• The influence of air pollution on economic development.
• The impact of air pollution on economic policies.
• The role of air pollution in economic planning.
• The influence of air pollution on economic growth.
• The impact of air pollution on economic sustainability.
• The role of air pollution in economic transitions.
• The influence of air pollution on economic resilience.
• The impact of air pollution on economic sectors.

Air Pollution and Ethics

• The ethical implications of air pollution.
• The role of ethics in air pollution policies.
• The influence of ethics on air pollution communication.
• The impact of ethics on air pollution mitigation.
• The role of ethics in air pollution adaptation.
• The influence of ethics on air pollution research.
• The impact of ethics on air pollution education.
• The role of ethics in air pollution decision-making.
• The influence of ethics on air pollution justice.
• The impact of ethics on air pollution futures.

This comprehensive list of topics is designed to inspire and guide students in their quest for knowledge about air pollution. Each topic is a doorway to a vast field of research and understanding. As you embark on your academic journey, remember that the goal is not just to write a research paper but to contribute to the global understanding of air pollution and its impacts. Your research could be the key to solving one of the most pressing environmental issues of our time.

Air Pollution Research Guide

Air pollution is a pressing global issue that poses significant threats to human health and the environment. As students studying environmental science, it is essential to delve into the complexities of air pollution and understand its causes, impacts, and potential solutions. Writing research papers on air pollution topics not only enhances our knowledge but also contributes to the collective effort in combating this environmental challenge. In this comprehensive guide, we will explore a wide range of air pollution research paper topics to inspire and assist you in your academic journey.

The field of environmental science has increasingly focused on air pollution due to its detrimental effects on air quality, climate change, and public health. As the world grapples with the consequences of human activities and industrialization, it becomes crucial to investigate the different dimensions of air pollution and develop innovative approaches to mitigate its impact. Research papers serve as a valuable tool for investigating and disseminating knowledge about air pollution, making them an integral part of environmental science education.

The primary aim of this page is to provide students like you with an extensive array of air pollution research paper topics. By exploring diverse and engaging topics, you can gain a deeper understanding of the various aspects related to air pollution, ranging from its sources and consequences to policy interventions and sustainable solutions. Whether you are just starting your research journey or seeking inspiration for a specific area of interest, this comprehensive list will serve as a valuable resource to guide your exploration and empower you to contribute meaningfully to the field.

Moreover, this page offers expert advice on how to choose the most suitable air pollution research paper topics. With the abundance of available topics, it is important to select a research question that aligns with your interests, academic goals, and the current needs of the field. Our expert tips will help you navigate through the vast landscape of air pollution research and enable you to select a topic that is both relevant and impactful.

In addition to topic selection, we will also provide guidance on how to write an effective air pollution research paper. Writing a research paper requires a systematic approach, from conducting a literature review and collecting data to analyzing findings and presenting a coherent argument. By following our step-by-step instructions and incorporating our writing tips, you can enhance the quality and rigor of your research paper, ensuring that your work makes a valuable contribution to the field of environmental science.

Furthermore, to support your academic journey, we introduce our writing services, offering you the opportunity to order a custom air pollution research paper tailored to your specific requirements. Our team of expert degree-holding writers specializes in environmental science and is equipped with the knowledge and skills to deliver top-quality research papers. With a commitment to in-depth research, customized solutions, and timely delivery, our writing services provide a convenient and reliable option for students seeking assistance in their academic endeavors.

Choosing an Air Topic for Research

Choosing the right air pollution research paper topic is a crucial step in the research process. It sets the foundation for your study and determines the direction of your research. With the vast scope of air pollution issues, it can be challenging to narrow down your focus and select a topic that is both relevant and compelling. In this section, we provide expert advice and 10 valuable tips to help you navigate the process of choosing air pollution research paper topics effectively.

• Identify your research interests : Start by reflecting on your personal interests within the field of air pollution. What aspects of air pollution intrigue you the most? Are you interested in studying the health effects, the impact on ecosystems, policy interventions, or technological solutions? Identifying your research interests will guide you towards topics that resonate with your passion and motivation.
• Consider current issues and debates : Stay informed about the latest developments and ongoing debates in the field of air pollution. Read scientific journals, news articles, and policy reports to understand the pressing issues and emerging trends. By choosing a topic that addresses current concerns, you contribute to the existing knowledge and engage in the relevant conversations.
• Conduct preliminary research : Before finalizing a topic, conduct preliminary research to familiarize yourself with the existing literature and identify research gaps. This will help you refine your research question and ensure that your topic contributes to the existing knowledge base. Look for recent studies, key theories, and seminal works that can provide a solid foundation for your research.
• Define the scope of your study : Determine the scope and boundaries of your research. Are you focusing on a specific geographic region, a particular pollutant, or a certain population group? Clarifying the scope of your study will help you narrow down your topic and ensure that it is manageable within the given time and resources.
• Consider interdisciplinary approaches : Air pollution is a complex issue that requires interdisciplinary perspectives. Consider integrating concepts and methods from various fields such as environmental science, public health, engineering, sociology, and policy studies. This interdisciplinary approach can lead to innovative research and contribute to a holistic understanding of air pollution.
• Engage with stakeholders : Air pollution affects various stakeholders, including communities, policymakers, industry professionals, and advocacy groups. Engaging with these stakeholders can provide valuable insights and enhance the relevance of your research. Consider topics that address the concerns and needs of different stakeholders, ensuring that your research has practical implications and can make a meaningful impact.
• Seek guidance from your professors and mentors : Consult with your professors and mentors who have expertise in the field of air pollution. They can provide valuable guidance, suggest potential research topics, and help you refine your research question. Utilize their knowledge and experience to ensure that your topic aligns with current research trends and academic standards.
• Consider the availability of data : Before finalizing your research topic, consider the availability of data and resources. Ensure that you have access to reliable and relevant data sources that will support your research objectives. Assess the feasibility of data collection and analysis, considering factors such as time constraints, cost, and ethical considerations.
• Aim for a balance between novelty and significance : While it is important to choose a topic that is unique and novel, also consider its significance within the broader field of air pollution research. Balance your desire to explore new avenues with the need for topics that contribute to the existing body of knowledge and have real-world implications.
• Think critically and creatively : Finally, approach the topic selection process with a critical and creative mindset. Think beyond the conventional boundaries and explore unconventional ideas. Consider innovative research methodologies, alternative perspectives, and emerging trends in air pollution research. By thinking critically and creatively, you can identify research topics that are both intellectually stimulating and have the potential for significant contributions.

By following these expert tips, you can navigate the process of choosing air pollution research paper topics with confidence and clarity. Remember that the topic you choose will shape the entire research process, so take the time to select a topic that aligns with your interests, expertise, and aspirations. Now, let’s move on to the next section, where we will provide you with valuable insights on how to write an impactful air pollution research paper.

How to Write an Air Pollution Research Paper

Writing an air pollution research paper requires careful planning, systematic research, and effective organization. In this section, we will guide you through the essential steps and provide you with 10 tips to help you write a well-structured and compelling research paper on air pollution.

• Understand the research question : Start by clearly understanding the research question or objective of your study. Define the specific aspect of air pollution that you intend to investigate and the key research aims. This will provide you with a focused direction and ensure that your paper addresses the core issues related to air pollution.
• Conduct a comprehensive literature review : Before diving into your research, conduct a thorough literature review to familiarize yourself with the existing body of knowledge on air pollution. Identify key theories, concepts, and empirical studies relevant to your topic. The literature review will help you identify research gaps and build a strong theoretical foundation for your study.
• Develop a clear research methodology : Determine the research methodology and data collection techniques that align with your research objectives. Will you conduct experiments, surveys, interviews, or analyze existing datasets? Clearly define your research design, sampling strategy, and data analysis methods to ensure the rigor and validity of your findings.
• Collect and analyze data : If your research involves primary data collection, carefully collect and organize your data using appropriate methods. If you are analyzing secondary data, ensure that the datasets are reliable and relevant to your research objectives. Apply appropriate statistical or qualitative analysis techniques to derive meaningful insights from your data.
• Structure your paper effectively : Organize your research paper using a clear and logical structure. Typically, a research paper includes an introduction, literature review, methodology, results, discussion, and conclusion. Ensure smooth transitions between sections and maintain a coherent flow of ideas throughout your paper.
• Write a compelling introduction : Begin your paper with an engaging introduction that provides context and background information on air pollution. Clearly state the research question, explain the significance of your study, and highlight the objectives and expected outcomes. Grab the reader’s attention and set the tone for the rest of the paper.
• Present your findings accurately : In the results section, present your findings in a clear and concise manner. Use appropriate tables, graphs, and figures to present data effectively. Provide relevant statistical measures and interpret the results objectively. Ensure that your findings directly address the research question and support your hypotheses or research objectives.
• Analyze and discuss your results : In the discussion section, analyze and interpret your findings in light of the existing literature. Compare your results with previous studies, identify similarities and differences, and explain any discrepancies. Discuss the implications of your findings and their significance for understanding air pollution and its effects.
• Address limitations and future research : Acknowledge the limitations of your study, such as sample size constraints, data limitations, or potential biases. Suggest avenues for future research to address these limitations and further advance knowledge in the field of air pollution. This demonstrates your critical thinking and opens up opportunities for future research contributions.
• Craft a strong conclusion : Conclude your research paper by summarizing the key findings, emphasizing their significance, and restating the research question and objectives. Discuss the implications of your study for theory, practice, and policy-making in the context of air pollution. Avoid introducing new information in the conclusion and leave the reader with a lasting impression of your research.

By following these tips, you can effectively structure and write an air pollution research paper that contributes to the existing knowledge, addresses key research questions, and provides valuable insights into this critical environmental issue. In the next section, we will introduce you to the writing services offered by iResearchNet, where you can order a custom research paper on any air pollution topic.

Custom Research Paper Writing Services

At iResearchNet, we understand the challenges faced by students in conducting research and writing a high-quality research paper on air pollution. That’s why we offer custom writing services tailored to meet your specific needs. Our team of expert writers, who hold advanced degrees in environmental science, are dedicated to delivering top-notch research papers that showcase your knowledge and understanding of air pollution. When you choose our writing services, you can expect the following:

• Expert degree-holding writers : Our team consists of skilled writers with expertise in environmental science and air pollution research. They have the knowledge and experience to tackle complex topics and deliver well-researched and insightful papers.
• Custom written works : We understand the importance of originality and uniqueness in academic writing. Our writers craft each research paper from scratch, ensuring that it is tailored to your specific requirements and adheres to the highest standards of quality.
• In-depth research : Our writers conduct thorough research using credible sources to gather the most relevant and up-to-date information on air pollution. They critically analyze the literature and integrate it seamlessly into your research paper to support your arguments and strengthen your findings.
• Custom formatting : We are well-versed in various formatting styles, including APA, MLA, Chicago/Turabian, and Harvard. Our writers will format your research paper according to the specified guidelines, ensuring consistency and professionalism throughout.
• Top quality : Quality is our utmost priority. We strive to deliver research papers that meet the highest academic standards. Our writers pay attention to detail, ensure accurate referencing, and use clear and concise language to convey your ideas effectively.
• Customized solutions : We understand that every research paper is unique. Our writers take a personalized approach, tailoring their writing to your specific research objectives, methodology, and findings. They adapt their writing style and tone to match your requirements and ensure a seamless integration of your ideas.
• Flexible pricing : We offer competitive and flexible pricing options to accommodate your budget. Our pricing is transparent, and there are no hidden fees or additional charges. You can select the pricing plan that suits your needs, whether it’s for a comprehensive research paper or a specific section.
• Short deadlines : We understand that time is of the essence when it comes to academic assignments. Our writers are capable of working under tight deadlines and can deliver your custom research paper within short timeframes, even as little as 3 hours.
• Timely delivery : We are committed to delivering your research paper on time. We understand the importance of meeting deadlines, and our writers work diligently to ensure that your paper is delivered within the agreed-upon timeframe.
• 24/7 support : Our dedicated support team is available 24/7 to assist you with any questions or concerns you may have. Whether you need updates on your order or have inquiries about our services, our friendly support staff is here to provide prompt and helpful assistance.
• Absolute Privacy : We prioritize the privacy and confidentiality of our clients. Your personal information and order details are kept secure and protected. We adhere to strict privacy policies to ensure that your information remains confidential.
• Easy order tracking : Our user-friendly platform allows you to easily track the progress of your order. You can stay updated on the status of your research paper, communicate with your writer, and receive notifications throughout the writing process.
• Money back guarantee : We are confident in the quality of our services and the expertise of our writers. If, for any reason, you are not satisfied with the final product, we offer a money-back guarantee. Your satisfaction is our priority, and we strive to ensure that you are fully content with the research paper you receive.

When you choose iResearchNet, you can be confident in receiving a well-written and thoroughly researched custom air pollution research paper that meets your academic requirements. We value your privacy and guarantee absolute confidentiality throughout the entire process. Our easy order tracking system allows you to stay updated on the progress of your paper, ensuring a seamless experience from start to finish. If, for any reason, you are not satisfied with the final product, we offer a money-back guarantee.

Order Your Custom Air Pollution Research Paper Today!

Are you ready to take the next step in your academic journey and submit a stellar research paper on air pollution? Look no further than iResearchNet. Our team of expert writers and comprehensive writing services are here to support you in your pursuit of academic excellence. With our custom air pollution research paper writing service, you can be confident in receiving a well-researched, high-quality paper tailored to your specific requirements. Don’t miss out on the opportunity to submit a top-notch air pollution research paper that impresses your professors and demonstrates your expertise in the field. Place your order with iResearchNet today and experience the benefits of our custom writing services.

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Air pollution

Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere.

Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide. Outdoor and indoor air pollution cause respiratory and other diseases and are important sources of morbidity and mortality. 

WHO data show that almost all of the global population (99%) breathe air that exceeds  WHO guideline limits  and contains high levels of  pollutants , with low- and middle-income countries suffering from the highest exposures.

Air quality is closely linked to the earth’s climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of greenhouse gas emissions. Policies to reduce air pollution, therefore, offer a win-win strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.

From smog hanging over cities to smoke inside the home, air pollution poses a major  threat to health  and climate.

Ambient (outdoor) air pollution in both cities and rural areas is causing fine particulate matter which result in strokes, heart diseases, lung cancer, acute and chronic respiratory diseases.  

Additionally, around 2.4 billion people are exposed to dangerous levels of household air pollution, while using polluting open fires or simple stoves for cooking fuelled by kerosene, biomass (wood, animal dung and crop waste) and coal.

The combined effects of ambient air pollution and household air pollution is associated with 7 million premature deaths annually.

Sources of air pollution are multiple and context specific. The major outdoor pollution sources include residential energy for cooking and heating, vehicles, power generation, agriculture/waste incineration, and industry. Policies and investments that support sustainable land use, cleaner household energy and transport, energy-efficient housing, power generation, industry, and better municipal waste management can effectively reduce key sources of ambient air pollution.

WHO promotes interventions and initiatives for healthy sectoral policies (including energy, transport, housing, urban development and electrification of health-care facilities), addressing key risks to health from air pollution indoors and outdoors, and contributing to achieving health co-benefits from climate change mitigation policies. 

WHO provides technical support to WHO’s Member States in the development of normative guidance, tools and provision of authoritative advice on health issues related to air pollution and its sources.

WHO monitors and reports on global trends and changes in health outcomes associated with actions taken to address air pollution at the national, regional and global levels.

WHO has also developed and implemented a strategy for raising awareness on the risk of air pollution, as well as available solutions that can be implemented to mitigate the risks of exposure to air pollution. Through digital outreach and partnerships, WHO has helped enrich the value proposition of addressing air pollution for health and environment ministries, city governments and other stakeholders from sectors with significant emissions. 

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NOAA, NASA spearheading massive air quality research campaign this summer

A hazy smog settles over the Hudson River in New York City. Undated photo. (Image credit: iStock)

Scientists from NOAA, NASA and 21 universities from three countries are deploying state-of-the-art instruments in multiple, coordinated research campaigns this month to investigate how air pollution sources have shifted over recent decades.  

Since the 1970s, U.S. scientists and environmental regulators made significant strides in reducing air pollution by cleaning up tailpipe and smokestack emissions. Yet levels of two of the most harmful types of pollution, ground-level ozone and fine particulates, have decreased only modestly in recent years. Both still contribute to the premature deaths of more than 100,000 Americans every year. 

“This is an unprecedented scientific investigation — in scope, scale and sophistication — of an ongoing public health threat that kills people every year,” said NOAA Administrator Rick Spinrad, PhD. “No one agency or university could do anything like this alone.”

Using multiple satellites, seven research aircraft, vehicles, dozens of stationary installations — even instrumented backpacks — scientists will measure air pollution from sources that include transportation, industrial facilities, agriculture, wildfires and consumer products such as paint, pesticides and perfumes. The data will be scrutinized, analyzed and run through sophisticated chemical and weather models by scientists and the U.S. Environmental Protection Agency (EPA) in an effort to improve air pollution forecasts. Findings will be shared with state and local environmental officials to inform decisions about the most effective ways to reduce air pollution.

The data will also be used to evaluate the first observations made by NASA’s groundbreaking TEMPO offsite link instrument — the first geostationary space-borne sensor to continuously measure air pollution across North America. Lessons learned will aid the development of the new GeoXO satellites being jointly developed by NOAA and NASA.

Probing the causes of persistent pollution  

EPA, which sets national air quality regulations, currently lists about 200 U.S. counties as failing to meet the 8-hour ozone standard established in 2015. Sixty-nine counties are failing to meet the standard for fine particulates, or PM2.5, set in 2006. After decades of decline in ground-level ozone and fine particulate matter in the U.S., downward trends have slowed in recent years.  

Scientists from four NOAA research labs, led by the Chemical Sciences Laboratory (CSL), along with NOAA satellite scientists and research pilots, are leading three of the research projects. The largest, AEROMMA, has NOAA scientists and collaborators operating 30 specialized instruments aboard NASA’s DC-8 flying laboratory, collecting a myriad of chemical measurements over highly populated cities, including New York City, Chicago, Toronto and Los Angeles. 

“In order to make progress on reducing air pollution that negatively affects millions of Americans, we need to have a better understanding of the current sources of pollutants and what happens to these pollutants once they are in the atmosphere,” said CSL scientist Carsten Warneke, one of the AEROMMA project’s mission scientists.  

For decades, fossil fuel emissions were the primary source of urban volatile organic compounds or VOCs, which along with nitrogen oxides, or NOx, act as precursors to both ground-level ozone and particulate pollution. As VOCs from the transportation sector have declined, recent NOAA research shows that consumer products derived from fossil fuels (so-called “volatile chemical products”) may now contribute as much as 50% of total petrochemical VOC emissions in densely populated urban cities. These may not be properly accounted for in emission inventories or considered in air quality management strategies.

The campaigns may also have an opportunity to investigate another emerging air pollution source: wildfire smoke that has blanketed the Midwest and East Coast states this summer. 

Collecting data from the sidewalks to satellites

NASA researchers are also deploying two of their Gulfstream research aircraft with the DC-8,  mapping air quality and methane from high altitudes over the five cities while the DC-8 collects measurements at lower altitudes. Similar to the other projects, data collected by NASA’s  STAQS mission will be compared to TEMPO’s high-resolution estimates of trace gas and aerosols, as well as with emission inventories and atmospheric processes.

“NASA is excited to partner with NOAA and EPA during these field campaigns to learn how best to use the TEMPO satellite to observe hourly changes in air quality at the neighborhood scale over North America," said Barry Lefer, NASA's program scientist for tropospheric composition. 

A concurrent NOAA research mission, CUPiDS , will use NOAA’s Twin Otter research plane to zero in on the meteorology and dynamics of the atmosphere that creates and transports pollutants from the New York metro area downwind over Southern New England. Another element pairs a University of Maryland instrumented Cessna offsite link aircraft and a NOAA instrumented SUV making simultaneous measurements in the air and at the surface to better understand the vertical distribution of air pollution and greenhouse gas emissions in the Northeast corridor from DC-Baltimore up to New York City and Long Island Sound.   

On the ground , researchers from Yale University, Aerodyne Research Inc. and other NOAA-funded collaborators will be taking measurements from a rooftop site at the The City College of New York campus, downwind in Guilford, Connecticut, from a 62-meter research tower on Long Island, in coordination with the DC-8 and Twin Otter flights. NOAA’s Climate Program Office is providing major funding for these and other affiliated studies. 

“This regional network of ground sites has enormous potential to help us understand urban and downwind air pollution — not just today but under a continually changing climate,” said Yale Professor Drew Gentner, who is coordinating ground sites in New York and Connecticut.

In Manhattan, scientists will be carrying air pollution sensors in backpacks in a NOAA pilot project to investigate surface ozone and PM2.5 in underserved neighborhoods in New York City, where pollution directly impacts human health, especially during heat wave events.

Tying it all together 

“The large number of participants, measurements, the variety of platforms involved, and the way they are working together in a highly choreographed and coordinated way is unique,” said CSL Director David Fahey. “Our goal is a comprehensive view of air pollution spanning the U.S. to improve forecasts of urban and regional air quality and advance the health of our nation.” 

Media contact

Theo Stein,  theo.stein@noaa.gov , (303) 819-7409

Related Features //

Article  

• Volume 22, issue 7
• ACP, 22, 4615–4703, 2022
• Peer review
• Related articles

Advances in air quality research – current and emerging challenges

Ranjeet s. sokhi, nicolas moussiopoulos, alexander baklanov, john bartzis, isabelle coll, sandro finardi, rainer friedrich, camilla geels, tiia grönholm, tomas halenka, matthias ketzel, androniki maragkidou, volker matthias, jana moldanova, leonidas ntziachristos, klaus schäfer, peter suppan, george tsegas, greg carmichael, vicente franco, steve hanna, jukka-pekka jalkanen, guus j. m. velders, jaakko kukkonen.

This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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Sokhi, R. S., Moussiopoulos, N., Baklanov, A., Bartzis, J., Coll, I., Finardi, S., Friedrich, R., Geels, C., Grönholm, T., Halenka, T., Ketzel, M., Maragkidou, A., Matthias, V., Moldanova, J., Ntziachristos, L., Schäfer, K., Suppan, P., Tsegas, G., Carmichael, G., Franco, V., Hanna, S., Jalkanen, J.-P., Velders, G. J. M., and Kukkonen, J.: Advances in air quality research – current and emerging challenges, Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, 2022.

We wish to dedicate this article to the following eminent scientists who made immense contributions to the science of air quality and its impacts: Paul J. Crutzen (1933–2021), atmospheric chemist, awarded the Nobel Prize in Chemistry 1995; Mario Molina (1943–2020), atmospheric chemist, awarded the Nobel Prize in Chemistry 1995; Samohineeveesu Trivikrama Rao (1944–2021), air pollution meteorology and atmospheric modelling; Kirk Smith (1947–2020), global environmental health; Martin Williams (1947–2020), air quality science and policy; Sergej Zilitinkevich (1936–2021), atmospheric turbulence, awarded the IMO Prize 2019.

Air pollution remains one of the greatest environmental risks facing humanity. WHO (2016) estimated that over 90 % of the global population is exposed to air quality that does not meet WHO guidelines, and Shaddick et al. (2020) report that 55 % of the world's population were exposed to PM 2.5 concentrations that were increasing between 2010 and 2016. Shaddick et al. (2020) also highlighted marked inequalities between global regions, with decreasing trends in annual average population-weighted concentrations in North America and Europe but increasing trends in central and southern Asia. WHO (2016) has evaluated that approximately 7 million people died prematurely in 2012 throughout the world as a result of air pollution exposure originating from emissions from outdoor and indoor anthropogenic sources. The recent update from the World Health Organization (WHO) of air quality guidelines (WHO, 2021) has emphasized the need to further curtail air pollution emissions and improve air quality globally.

Over the past decade there have been significant developments in the field of air quality research spanning improvements in characterizing sources and emissions of air pollution, new measurement technologies offering the possibility of low-cost sensors, advances in air quality prediction and forecasting, understanding interactions with meteorology and climate, and exposure assessment and management. However, there has not been a broader and comprehensive review of recent developments that push the boundaries of air quality research forward. This was recognized as a major gap in the literature at the last International Conference on Air Quality – Science and Application held online due to the COVID 19 restrictions during 18–26 May 2020. While the concept of this review originated at the International Conference on Air Quality and was stimulated by the presentations and discussions at the conference, this article has been extended to incorporate a wider landscape of research literature in the field of air quality, spanning in particular the developments occurring over the last decade. It is hoped that such a review will help to pave the path for further research in key areas where significant gaps of knowledge still exist and also to make recommendations to guide the direction for future research within the wider community. Although this paper has been written to be accessible to readers from a wide scientific and policy background, it does not seek to provide an introduction to the topic of air quality science. For readers less familiar with the research area, an introductory lecture with a focus on air quality in megacities has been published by Molina (2021). There are also other recent specific reviews, e.g. Manisalidis et al. (2020) on health impacts and Fowler et al. (2020) on air quality developments. This section begins with a short historical perspective on air quality research, before providing the underlying rationale for the key areas considered in this paper.

1.1  A brief historical perspective

In order to provide context to the topics considered in this review, this section briefly touches upon developments of air quality research since the last century. For a more thorough historical survey of air quality issues, the reader is referred to Fowler et al. (2020). Over the previous century there have been a number of landmark events of elevated air pollution that have brought air quality increasingly to prominence, especially in relation to the adverse health impacts. It has been well-known since the early 1900s that cold weather in winter can lead to increased mortality (e.g. Russell, 1926).

The perception that air pollution can have severe health impacts significantly changed when a high-air-pollution episode occurred from 1–5 December 1930 over an industrial town in the Meuse Valley in Belgium (Firket, 1936). The atmospheric conditions were foggy and stagnant. A large proportion of the population experienced acute respiratory symptoms; in addition, health conditions of people with pre-existing cardiorespiratory problems worsened (e.g. Nemery et al., 2001; Anderson, 2009). A similar event was recorded in Donora, Pennsylvania, USA, during October 1948, reported by Schrenk (1949). Although air pollution was generally treated as a nuisance, this “unusual episode” along with that over the Meuse Valley raised awareness and acceptance of the seriousness of air pollution for human health. Both air pollution events, Meuse Valley and Donora, were associated with air pollution from industrial emissions, which accumulated during cold winter periods exhibiting atmospheric stagnation caused by thermal inversions.

The so-called “Great London Smog” occurred from 5–9 December 1952, when similar stagnant atmospheric conditions were prevalent. However, in this case the cause of the severe air pollution was mainly the burning of low-grade, sulfur-rich coal for home heating (e.g. Anderson, 2009). Estimates of deaths resulting from this smog episode range from 4000 to 12 000 (e.g. Stone, 2002).

Since these historical events, the prominence of air pollution sources has changed from industrial and heating to road traffic and become a global threat to health. Trends of air pollution emissions over the past decades have been markedly different for different regions of the world, which has led to similar disparities in air quality concentrations (e.g. Sokhi, 2012). These disparities still exist, as shown in Fig. 1. Spatial distributions in this figure are based on recent analysis showing the large variations in population-weighted annual mean PM 2.5 concentrations across the globe. Commonly, now some of the highest concentrations occur in parts of Asia, Africa, and Latin America as reported by Health Effects Institute (HEI, 2020).

Figure 1 Global distribution of population-weighted annual PM 2.5 concentrations for 2019 (HEI, 2020). Figure produced from https://www.stateofglobalair.org/data/#/air/map (last access: 10 December 2021).

As the recognition of poor air quality has increased, so has the need for the capability to assess levels of key air pollutants not only through monitoring but also through modelling. Historically, although air pollution was obviously poor prior to the first World War (WWI), the primary impetus for development of transport and dispersion (T&D) models during and after WWI was the widespread use of chemical weapons. Fundamental theoretical advances were made by Lewis Fry Richardson, George Keith Batchelor, and many other famous fluid dynamicists. The earliest models were analytical (e.g. Gaussian and K-theory) models used for surface boundary layer releases. With the advent of nuclear weapons in WWII, new emphasis was placed on plume rise and dispersion of large thermal radiological explosions. Thus, the full troposphere and stratosphere had to be modelled.

Later in the 1980s the first investigations came up about the atmospheric consequences of a hypothetical nuclear war initiated by Paul Crutzen (Crutzen and Birks, 1982) and others (Aleksandrov and Stenchikov, 1983; Turco et al., 1983). The concept of a nuclear winter was created. It is one of the first examples that enormous emissions of dust into the atmosphere cause global effects and catastrophic long-term climate change. Also, the nuclear winter scenario was examined in recent years with current model tools for certain nuclear war scenarios (Robock et al., 2007; Toon et al., 2019).

Deposition (wet and dry) was a main concern for many radiological substances, especially for accidental plume dispersion monitoring and modelling of nuclear power plants. In the US, a major change was the introduction of the Clean Air Act in the 1970s. A similar legislation was also issued in other countries. This effort initially focused on T&D models for industrial sources, such as the stacks of fossil power plants. The first applied models were analytical plume rise and Gaussian T&D models. Soon computer codes were written to solve these equations and produce outputs at many spatial locations and for every hour of the year.

1.2  Sources and emissions of air pollutants

From a human health perspective, the key emission sources are those affecting concentration of particulate matter and its size fractions (PM 2.5 and PM 10 ), but also sources affecting other air pollutants, such as ozone and nitrogen dioxide (NO 2 ), especially in highly populated urban areas. Sources in the direct vicinity of urban areas could also be considered especially important, including vehicular traffic and shipping, local industrial sources, various abrasive processes, and residential and commercial heating.

An important component of PM is secondary; regional sources of the precursors of secondary PM are therefore of major importance. These include volatile organic compounds (VOCs), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ammonia (NH 3 ), the first two also being precursors of ozone (O 3 ). Important regional precursor sources are biogenic and industrial emissions of VOCs, agriculture (NH 3 ), road traffic (nitrogen oxides, NO x = NO + NO 2 ), shipping ( NO x and SO 2 ) , and industrial and power generation sources, along with biomass burning and forest fires (VOC, NO x , also primary PM). An important source of PM is the resuspension of dust, especially in arid regions and seasonally also in areas with intensive agriculture.

While Europe and many other parts of the world have experienced decreasing anthropogenic emissions since 1990, climate change and its associated impacts can lead to an increase in dust and wildfire emissions, as a result of increased drought and desertification. Climate change is also expected to lead to significantly higher biogenic VOC emissions in different regions, e.g. Arctic and China (Kramshøj et al., 2016; Liu et al., 2019), also from urban vegetation (Churkina et al., 2017).

The emission inventory work in Europe is harmonized through the official reporting of EU member states of their emissions to the European Commission through an e-reporting scheme (Implementing Provisions for Reporting, IPR of EU Air Quality Directive, 2008/50/EC). The methodologies applied by the individual member states can, however, differ, which can sometimes bring inconsistencies into the reported national emissions. Within the last decade the EU-funded MACC project and the on-going Copernicus service have been developing consistent European-wide and global gridded emission inventories, which are suitable for air quality modelling. The access to the different inventories and analysis of differences have been facilitated by centralized databases like Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD, https://eccad.aeris-data.fr/ , last access: 7 July 2021).

Developing innovative methods to refine the emission inventories feeding the models and conducting studies to discriminate the role of different sources in local air quality have become essential to reduce uncertainties in predictions of urban air quality and help target effective abatement measures (Borge et al., 2014). The emission compilation that needs to be carried out also requires (i) the involvement of all stakeholders (e.g. citizens, decision-makers, service providers, and industrialists) and (ii) the implementation of dedicated and specific tools for assessing quality of the urban environment. This type of research can be used for quantifying the impacts of different emission control scenarios and supporting incentive policies (Fulton et al., 2015).

One area that has been receiving increased attention recently is ship emissions, which are an important source of air pollution, especially in coastal areas and harbour cities. Detailed bottom-up emission inventories based on ship position data have been established for SO 2 , NO x , PM, carbon monoxide (CO), and VOCs for various marine regions and also globally (Jalkanen et al., 2009, 2012, 2016; Aulinger et al., 2016; Johansson et al., 2017). Despite these advances, the evaluation of the shipping emissions for products of incomplete combustion, such as black carbons (BC), CO, and VOCs, is uncertain, as these may depend on characteristics which are not known accurately, such as the service history of ships. Regional model applications have quantified the contribution of shipping to air pollution to be of the order of up to 30 %, depending on pollutant and region (e.g. Matthias et al., 2010; Jonson et al., 2015; Aulinger et al., 2016; Karl et al., 2019a; Kukkonen et al., 2018, 2020a). More recent studies focus on the harbour and city scale, where relative contributions from ships to NO 2 concentrations may be even higher (Ramacher et al., 2019, 2020). Effects of in-plume chemistry, e.g. regarding the NO x removal and secondary aerosol formation, are not sufficiently well considered in larger-scale dispersion models (e.g. Prank et al., 2016).

1.3  Air quality in cities

Extensive and growing urban sprawl in different cities of the world is leading to environmental degradation and the depletion of natural resources, including the availability of arable land, thereby resulting in per capita increases of resource use and greenhouse gas emissions as well as air pollution, with significant impacts on health (WHO, 2016). Urban features have a profound influence on air quality in cities due to diurnal changes in urban air temperature; the urban heat island, which develops in particular during heat waves (Halenka et al., 2019); stable stratification and air stagnations; and wind flow and turbulence near and around streets and buildings affecting air pollution hotspots. Climate change will modify urban meteorology patterns which will affect air quality in cities and may even affect atmospheric chemistry reaction rates. The relative role of urban meteorology and climate compared to local emissions and chemistry is complex, non-linear, and subject to continued research, especially with boundary layer feedback (Baklanov et al., 2016).

With air quality standards being regularly exceeded in many urban areas across the globe, air quality issues are today strongly centred on the phenomena of proximity to emitters such as traffic – or certain industrial activities present in urban areas – but they also call for better understanding of contributions from long-range regional, diffuse, or specific local sources (e.g. residential wood combustion and maritime traffic) to the daily exposure of city dwellers (e.g. EEA, 2020b). In particular, the prevalent issue of individual exposure calls for a better understanding of the variability of concentrations at street level and the dispersion of emissions in the built environment. However, the approach implemented should not only be local, since urban air quality management involves a set of scales going beyond the city limits, in terms of the economic, societal, or logistical levers involved, but also include the interplay of pollutant sources and transport extending to regional and even global scales.

Beyond the scales of governance and urban functioning, it becomes essential to take into account the fact that scale interactions also exist in a geophysical context. The urban dweller has become especially exposed and vulnerable to the impacts of natural disasters, weather, and climate extreme events and their environmental consequences. These events often result in domino effects in the densely populated, complex urban environment in which system and services have become interdependent. There has never been a bigger need for user-focused urban weather, climate, water, and related environmental services in support of safe, healthy, and resilient cities (Baklanov et al., 2018b; Grimmond et al., 2020). The 18th World Meteorological Congress (2019) noted the current rapid urbanization and recognized the need for an integrated approach providing weather, climate, water, and related environmental services tailored to the urban needs (WMO, 2019).

1.4  Measuring air pollution

Measurements in the atmosphere are necessary not only for air quality monitoring but also for different purposes in weather forecast and climate change study, energy production, agriculture, traffic, industry, health protection, or tourism (e.g. Foken, 2021). Additional areas of application include the detection of emissions into the atmosphere, disaster monitoring, and the initialization and evaluation of modelling. Depending on the different objectives, in situ measuring, and ground-based, aircraft-based, and space-based remote sensing techniques and integrated measuring techniques are available. Satellite observations are a growing field of development due to increasingly small and thus cost-effective platforms (down to nanosatellites). Another area of growth is the use of unmanned aerial vehicles (UAVs) for air pollution measurements (Gu et al., 2018).

Networks of ground-level measurements with continuous monitoring stations remain a major effort, but the coverage is starkly regionally dependent and with scarce measurements in the continent of Africa (Rees et al., 2019; Bauer et al., 2019).

Over the past decade, there has been increasing recognition that measuring air pollution at outdoor locations may not necessarily reflect the health impact on individuals or populations. The research should therefore be directed to the evaluation of both personal exposure and dynamic population exposure (Kousa et al., 2002; Soares et al., 2014). Temporal concentration and location information is needed on air pollution concentrations at all the relevant outdoor and indoor microenvironments. The actual exposure of individuals and populations cannot realistically be represented by selected concentrations at fixed outdoor locations, due to the fine-resolution spatial variability of concentrations in urban areas and the mobility of people (Kukkonen et al., 2016b; Singh et al., 2020b).

Further development of the installation of a larger number of cheap measurement devices, especially for PM 2.5 , that are operated by people interested in air quality in so-called citizen science projects is ongoing ( https://www.eea.europa.eu/publications/assessing-air-quality-through-citizen-science , last access: 21 February 2022). Examples of such projects are the Open Knowledge Foundation Germany; OK Labs ( https://luftdaten.info/ , last access: 21 February 2022), Opensense (open air quality, meteorological, and noise data platform), connected with OK Labs ( https://opensensemap.org/ , last access: 21 February 2022); or AirSensEUR, an open framework for air quality monitoring ( https://airsenseur.org/website/airsenseur-air-quality-monitoring-open-framework/ , last access: 21 February 2022). However, the accuracy of these measurements is still debated (Duvall et al., 2021; Concas et al., 2021), although the development of more accurate but still low-cost devices is ongoing for denser measurement networks, 3D measurements, and new modelling. Measurements are not only required for compliance and for monitoring long-term trends. Observations are used more and more for evaluating models and where measurements might also be used to nudge the model results, for example through data assimilation (see for example Campbell et al., 2015; K. Wang et al., 2015).

1.5  Air quality modelling from local to regional scales

Air pollution models have played and continue to play a pivotal role in furthering scientific understanding and supporting policy. Additionally, for air quality assessments by regulatory methods, it is also important to predict or even forecast peak pollutant concentrations to prevent or reduce health impacts from acute episodes. Both complex and simple models have also been developed for dispersion on urban and local scales. A review has been provided by Thunis et al. (2016) that examines local- and regional-scale models, especially from an air quality policy perspective. Briefly, the spectrum of finer- and urban-scale air quality models applied for urban areas is very broad and includes urbanized chemistry–transport models (CTMs) coupled with high-resolution meso-scale numerical weather prediction (NWP) models, computational fluid dynamics (CFD) obstacle-resolved models in Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) formulations (the latest mostly only for research studies), and statistical and land use regression (LUR) models. Developments in local-scale air quality models continue. For example, the dispersion on local or urban scales that also considers obstacle effects has recently been investigated using wind tunnels and CFD models (e.g. Badeke et al., 2021).

During the last decades many countries have established real-time air quality forecasting (AQF) programmes to forecast concentrations of pollutants of special health concerns. The history of AQF can be traced back to the 1960s, when the US Weather Bureau provided the first forecasts of air stagnation or pollution potential using numerical weather prediction (NWP) models to forecast conditions conducive to poor air quality (e.g. Niemeyer, 1960). Accurate AQF can offer tremendous societal and economic benefits by enabling advanced planning for individuals, organizations, and communities in order to avoid exposure and reduce adverse health impacts resulting from air pollution. Forecasts can also assist urban authorities, for example, in changing and managing traffic and hence reduce road emissions in a particular area. Air quality modelling, however, can provide a more holistic assessment of air pollution for policy makers and decision makers to develop strategies that do not compromise benefits in one area while worsening air pollution in another.

Two main approaches can be generally distinguished in AQF: empirical/statistical methods and chemical transport modelling. Until the mid-1990s, AQF was mainly performed using empirical approaches and statistical models trained with or fitted to historical air quality and meteorological data (e.g. Aron, 1980). The empirical/statistical approaches have several common drawbacks for AQF which are reviewed and discussed by Zhang et al. (2012a) and Baklanov and Zhang (2020).

The chemical transport models (CTMs) are more commonly used today for air quality assessment and forecasting. Over the last decade AQF systems based on CTMs have been developed rapidly and are currently in operation in many countries. Progress in CTM development and computing technologies has allowed daily AQFs using simplified or more comprehensive 3D CTMs, such as offline-coupled and online-coupled meteorology–chemistry models. There are several comprehensive review papers, e.g. Kukkonen et al. (2012), Zhang et al. (2012a, b), Baklanov et al. (2014), Bai et al. (2018), and Baklanov and Zhang (2020), which have more thoroughly examined the development and principles of 3D global and regional AQF models and identified areas of improvement in meteorological forecasts, chemical inputs, and model treatments of atmospheric physical, dynamic, and chemical processes.

Interest in regional pollution arose in the 1980s, initially spurred by the acid rain problem (Sokhi, 2012; Fowler et al., 2020). In the past few years, these regional air pollution models have become routinely linked with outputs of NWP models such as WRF and ECMWF. Models such as WRF coupled with CTMs are often run in a nested mode down to an inner domain with a grid size of 1 km. As computer speed and storage continually improve with developments in parameterization, in the future, these nested models may potentially take over most applied T&D analyses on local scales. Another development over the last decade is the increasing use of ensemble techniques which have also progressed and make it possible to cover an increasing range of pollutants and physical parameters, using a multiplicity of observations (e.g. ground, airborne, satellite) that enable the different dimensions of models to be investigated. At the same time that the use of regional Eulerian models has grown (e.g. Rao et al., 2020), the puff, particle, and plume T&D models for small scales and mesoscales have been improved. Several agencies and countries now have Lagrangian particle or puff models that are linked with an NWP model and are applied at all scales (Ngan et al., 2019).

1.6  Interactions of air quality, meteorology, and climate

Meteorological processes are the main driver for atmospheric pollutant dispersion, transformation, and removal. However, as studies have shown (e.g. Baklanov et al., 2016; Pfister et al., 2020), the chemistry dynamics feedbacks exist among the Earth system components, including the atmosphere. Potential impacts of aerosol feedbacks can be broadly explained in terms of four types of effects: direct, semidirect, first indirect, and second indirect (e.g. Kong et al., 2015; Fan et al., 2016). Such feedbacks, forcing mechanisms, and two-way interactions of atmospheric composition and meteorology can be important not only for air pollution modelling but also for NWP and climate change prediction (WMO, 2016).

There is a strong scientific need to increase interfacing or even coupling of prediction capabilities for weather, air quality, and climate. The first driver for improvement is the fact that information from predictions is needed at higher spatial resolutions (and longer lead times) to address societal needs. Secondly, there is the need to estimate the changes in air quality in the future driven by climate change. Thirdly, continued improvements in prediction skill require advances in observing systems, models, and assimilation systems. In addition, there is also growing awareness of the benefits of more closely integrating atmospheric composition, weather, and climate predictions, because of the important feedbacks resulting from the role that aerosols (and atmospheric composition in general) play in these systems. Recently, this trend for further integration has led to greater coupling of atmospheric dynamics and composition models to deliver seamless Earth system modelling (ESM) systems.

1.7  Air quality and health perspectives

Air pollution has serious impacts on our health by reducing our life span and exacerbating numerous illnesses. The Global Burden of Disease Study 2019 (GBDS, 2020) summarizes a comprehensive assessment of the impact of a large number of stressors including air pollution. One of the most hazardous air pollutants is particulate matter. Primary particles are directly released into the atmosphere and originate from natural and anthropogenic sources. Secondary particles are formed in the atmosphere by chemical reactions involving, in particular, gas-to-particle conversion. Primary particles tend to be larger than secondary particles. Ultra-fine and fine particles, on the other hand, deposit into the respiratory system; these may reach human lungs and blood circulation and may therefore cause severe adverse health effects (e.g. Maragkidou, 2018; Stone et al., 2017).

When considering numbers of particles, most of these in the atmosphere are smaller than 0.1  µm in diameter (e.g. Jesus et al., 2019). On the other hand, the majority of the particle volume and mass is found in particles larger than 0.1  µm (e.g. Filella, 2012). The particle number concentrations are therefore in most cases dominated by the ultra-fine aerosols, whereas the mass or volume concentrations are dominated by the coarse and accumulation mode aerosols (e.g. Seinfeld and Pandis, 2016). Other characteristics of PM have also been shown to be important in relation to health impact. The characteristics of atmospheric particles in addition to the size include mass, surface area, chemical composition, and shape and morphology (Gwaze, 2007).

It has been convincingly shown in previous literature that the exposure to particulate matter (PM) in ambient air can be associated with negative health impacts (e.g. Hime et al., 2018; Thurston et al., 2017). It is also known that PM can cause health effects combined with other environmental stressors, such as heat waves and cold spells, allergenic pollen, or airborne microorganisms. For understanding such associations, reliable methods are needed to evaluate the exposure of human populations to air pollution.

The strong association between the exposure to mass-based concentrations of ambient PM air pollution and severe health effects has been found by numerous epidemiological studies (e.g. Pope et al., 2020). In particular, there is extensive scientific evidence to suggest that exposure to PM air pollution can have acute effects on human health, resulting in respiratory, cardiovascular and lung problems, chronic obstructive pulmonary diseases (COPDs), asthma, oxidative stress, immune response, and even lung cancer (e.g. Chen et al., 2017; Hime et al., 2018; Falcon-Rodriguez et al., 2016; Thurston et al., 2017). For instance, a cohort study conducted across Montreal and Toronto (Canada) on 1.9 million adults during four cycles (1991, 1996, 2001, and 2006) resulted in a possible connection between ambient ultra-fine particles and incident brain tumours in adults (Weichenthal et al., 2020). Recent work has also investigated assessment of the health impacts of particulate matter in terms of its oxidative potential (e.g. Gao et al., 2020; He et al., 2021).

1.8  Air quality management and legislative and policy responses

Air quality management and policy is an important but also complex task for political decision makers. It started in the middle of the last century when concerns about smoke and London smog arose. The national authorities at that time reacted by stipulating efficient dust filters and high stacks for large firings. In the 1980s, forest dieback led to a shift in focus to other important air pollutants, especially SO 2 , NO x , and later ozone, and so also on the ozone precursors including VOCs. In the 1990s studies showed a relation between PM 10 and “chronic” mortality, thus drawing particular attention to the health effects of fine particles (WHO, 2013b). Also, in the 1990s, the European Commission (EC) increasingly took over the responsibility for air pollution control from the authorities of the member states, on the basis that there is free trade of goods in the European Union and also transboundary air pollutants.

The EC launched the first Air Quality Framework Directive 96/62/EC and its daughter directives, which regulated the concentrations for a range of pollutants including ozone, PM 10 , NO 2 , and SO 2 . The first standard for vehicles (Euro 1) was established in 1991. The sulfur content in many oil products was reduced starting in the late 1990s. Some of the problems with air pollution in the EU, e.g. the acidification of lakes, were caused by the transport of air pollutants from eastern Europe to the EU. This problem was discussed in the United Nations Economic Commission for Europe (UNECE), as all countries involved were members of this commission. The Convention on Long-range Transboundary Air Pollution within the UNECE agreed on eight protocols, which set aims for reducing emissions, starting in 1985 with reducing national SO 2 emissions, with the latest protocol being the revised Protocol to Abate Acidification, Eutrophication and Ground-level Ozone (Gothenburg Protocol), which limits national SO 2 , NO x , VOC, NH 3 , and PM 2.5 emissions.

Over time, regulation of air pollution has become more stringent and thus more complex and more costly. To achieve acceptance, it had to be demonstrated that the measures would achieve the environmental and climate protection goals safely and efficiently, i.e. with the lowest possible costs and other disadvantages, and that the advantages of environmental protection outweigh the disadvantages (Friedrich, 2016). It is a scientific task to support this demonstration, mainly by developing and applying integrated assessments of air pollution control strategies, e.g. by carrying out cost–effectiveness and cost–benefit analyses. With a cost–effectiveness analysis (CEA) the net costs (costs minus monetizable benefits) for improving an indicator used in an environmental aim with a certain measure are calculated, e.g. the costs of reducing the emission of 1 t of CO 2,eq . The lower the unit costs, the higher the effectiveness of a policy or measure. The CEA is mostly used for assessing the effects associated with climatically active species, as the effects are global. The situation is different for air pollution, where the avoided damage of emitting 1 t of a pollutant varies widely depending on time and place of the emission.

The more general methodology is cost–benefit analysis (CBA). In a CBA, the benefits, i.e. the avoided damage and risks due to an air pollution control measure or bundle of measures, are quantified and monetized. Then, costs including the monetized negative impacts of the measures are estimated. If the net present value of benefits minus costs is positive, benefits outweigh the costs. Thus the measure is beneficial for society; i.e. it increases welfare. Dividing the benefits minus the nonmonetary costs by the monetary costs will result in the net benefit per euro spent, which can be used for ranking policies and measures.

Of course, for performing mathematical operations like summing or dividing costs and benefits, they have first to be quantified and then converted into a common unit, for which a monetary unit, i.e. euros, is usually chosen.

The term “integrated” in the context of integrated assessment means that – as far as possible – all relevant aspects (disadvantages, benefits) should be considered, i.e. all aspects that might have a non-negligible influence on the result of the assessment. Given the high complexity of answering questions related to managing the impacts of air quality, a scientific approach is required to conduct an integrated assessment, which is defined here as “a multidisciplinary process of synthesizing knowledge across scientific disciplines with the purpose of providing all relevant information to decision makers to help to make decisions” (Friedrich, 2016).

The focus of this review is on research developments that have emerged over approximately the past decade. Where needed, older references are given, but these either provide a historical perspective or support emerging work or where no recent references were available. The following areas of air quality research have been examined in this review:

air pollution sources and emissions;

air quality observations and instrumentation;

air quality modelling from local to regional scales;

interactions between air quality, meteorology, and climate;

air quality exposure and health;

air quality management and policy development.

Each section begins with a brief overview and then examines the current status and challenges before proceeding to highlight emerging challenges and priorities in air quality research. In terms of climate research, the focus is more on the interactions between air quality and meteorology with climate and not on climate change per se.

The section on air quality observations focuses on new technological developments that have led to remote sensing, low-cost sensors, crowdsourcing, and modern methods of data mining rather than attempting to cover the more traditional instrumentations and measurements which are dealt with, e.g. in Foken (2021). After considering these themes of research, the Discussion section pulls together common strands on science and implications for policy makers.

3.1  Brief overview

A fundamental prerequisite of successful abatement strategies for reduction of air pollution is understanding the role of emission sources in ambient concentration levels of different air pollutants. This requires a good knowledge of air pollution sources regarding their strength, chemical characterization, spatial distribution, and temporal variation along with knowledge on their atmospheric transport and processing. In observations of ambient air pollution, typically a complex mixture of contributions from different pollution sources is observed. These source contributions have to be disentangled before efficient reduction strategies targeting specific sources can be set up. Consequently, our discussion below is divided into two main topics: (i) emission inventories and emission pre-processing for model applications and (ii) source apportionment methods and studies.

This paper cannot give a full overview of the status of and the emerging challenges in all emissions sectors. For example, we do not deal with aviation as the impact on air quality in cities is generally rather small or concentrated around the major airports, or with construction machinery or industrial sources which make significant contributions to air pollution in some areas. Instead, we put emphasis on two emission sectors that have experienced important methodology developments in recent years in terms of emission inventories and that are of major concern for health effects: exhaust emissions from road traffic and shipping. We also touch other anthropogenic emissions, e.g. from agriculture and wood burning, As later in this paper we will explain, since individual exposure including the exposure to indoor pollution should gain importance in assessing air pollution, emissions from indoor sources will be addressed in a subchapter. Natural and biogenic emissions encompass VOC emissions from vegetation, NO emissions from soil, primary biological aerosol particles, windblown dust, methane from wetlands and geological seepages, and various pollutants from forest fires and volcanoes; these are described in a series of papers edited by Friedrich (2009). As natural and biogenic emissions depend on meteorological data, which are input data for the atmospheric model, they are usually estimated in a submodule of the atmospheric model. They are not further discussed here.

3.2  Current status and challenges

3.2.1  emissions inventories.

In the European Union, emissions of the most important gaseous air pollutants have decreased during the last 30 years (see Fig. 2). SO 2 and CO show reductions of at least 60 % (CO) or almost 90 % (SO 2 ). Also, NO x and non-methane volatile organic compound (NMVOC) emissions decreased by approx. 50 % while NH 3 shows much lower reductions of 20 % only. Similar to NH 3 , PM emissions also stay at similar levels compared to 2000 (Fig. 2b). Only black carbon shows considerably larger reductions, because of larger efforts to reduce BC, in particular from traffic. While traffic is the most important sector for NO x emissions and an important source for BC, PM emissions stem mainly from numerous small emission units like households and commercial applications (Fig. 2c).

Figure 2 EU-28 emission trends in absolute and relative numbers for (a)  the main gaseous air pollutants and (b)  particulate matter. Panel  (c) shows the share of EU emissions of the main pollutants by sector in 2018 (EEA, 2020b).

In parallel, research came on the path of accompanying and evaluating local emission control measures in a more comprehensive and systemic approach to urban space. The main technical advances of this research field have consisted in producing a more reliable assessment of the predominant emissions on the scale of an agglomeration/region. This has been done in order to feed the models with activity-based emission data such as population energy-consuming practices or local characteristics of road traffic, with the concern to better include their temporal variability or weather condition dependency. The originality of these approaches has been to develop the emissions inventories and modelling efforts in collaboration with stakeholders, for better data reliability and greater realism in policy support.

Improved and innovative representation of emissions, such as real configuration of residential combustion emission sources (location of domestic households using biomass combustion and surveys regarding the characteristics and use of wood stoves, boilers, and other relevant appliances) allows more realistic diagnoses (e.g. Ots et al., 2018; Grythe et al., 2019; Savolahti et al., 2019; Plejdrup et al., 2016; Kukkonen et al., 2020b). Also, increased use of traffic flow models for the representation of mobile emissions have provided refined traffic and emission estimates in cities and on national levels, as a path for improved scenarios (e.g. Matthias et al., 2020a). Kukkonen et al. (2016a) presented an emission inventory for particulate matter numbers (PNs) in the whole of Europe, and in more detail in five target cities. The accuracy of the modelled PN concentrations (PNCs) was evaluated against experimental data on regional and urban scales. They concluded that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remained in the evaluation of both the emissions and atmospheric transformation of PNCs.

For shipping, and in most recent development also aviation, inventories based on position data from transponders on individual vessels are becoming more widely used and provide refined emission inventories with high spatial resolution for use in harbour-city and airport studies (e.g. Johansson et al., 2017; Ramacher et al., 2019, 2020). Refined emission inventory and emission modelling are in many cases integrated into a complete regional-to-local modelling chain, which allows these refined data to be taken into account and ensures the consistency of the final results. This links to the subsequent chapters on air quality and exposure modelling.

3.2.2  Preprocessing emission data for use in atmospheric models

Emission inventories usually contain annual data for administrative units apart from data for large point sources and line sources. Atmospheric models, however, need hourly emission data for the grid cells of the model domain. Furthermore the height of the emissions (above ground), and for NMVOC, PM, and NO x a breakdown into species or classes of species according to the chemical scheme of the atmospheric model, is necessary. For PM, information is also required on the size distribution. Thus, a transformation of the available data into structure and resolution as needed by the models has to be made (Matthias et al., 2018).

For the spatial resolution, standard procedures for several emission sectors are described in Chap. 7 of the EMEP/EEA air pollutant emission inventory guidebook 2019 (EMEP/EEA, 2019). In principle, proxy data that are available in high spatial resolution and that are correlated to the activity data of the emission sources are used. For point sources (larger sources like power plants) these are coordinates of the stack. For road transport, shape files with coordinates at least for the main road network are used together with traffic counts (for past times) or traffic flow modelling for scenarios for future years. Figure 3 shows as an example the result of a distribution of road transport emissions to grid elements for the EU countries Norway and Switzerland. The major roads as well as the urban areas can be identified as sites for the NO x emitters. For households, land use data (e.g. residential area with a certain density) combined with statistical data (number of inhabitants, use of heating technologies) are used. Especially for heating with wood-specific algorithms using data on forest density and specific residential wood combustion, emission inventories and models have been developed (Aulinger et al., 2011; Bieser et al., 2011a; Mues et al., 2014; Paunu et al., 2020; Kukkonen et al., 2020b). Thiruchittampalam (2014) contains a comprehensive description of the methodology for the spatial resolution of emissions for Europe for all emission source categories.

The algorithms for disaggregating annual emission data into hourly data follow a similar scheme. All kinds of available data containing information about the temporal course of activities leading to emissions are used for temporal disaggregation. For road transport, data from continuously monitoring the traffic volume are available, and statistical data provide the electricity production from power plants. The activity of firings for heating depends on the outside temperature or more precisely on the degree days, an indicator for the daily heating demand, together with an empirical daily course of the use of the heating (Aulinger et al., 2011; Bieser et al., 2011a; Mues et al., 2014).

Figure 3 Spatial distribution of national PM 10 emissions from road transport in the EU28 on a 5 km×5 km grid (Schmid, 2018).

A detailed description of the methodology for the temporal resolution of emission data for all source sectors in Europe is contained in Thiruchittampalam (2014). A compilation of temporal profiles for disaggregating annual into hourly data is published by Denier van der Gon (2011) and in Matthias et al. (2018). New sets of global time profiles for numerous emission sectors have recently been provided by Crippa et al. (2020) and Guevara et al. (2021). Crippa et al. (2020) provide high-resolution temporal profiles for all parts of the world including Europe. Guevara et al. (2021) developed temporal profiles as part of the Copernicus Atmosphere Monitoring Service and also include higher-resolution European profiles designed for regional air pollution forecasting. The temporal profiles include time-dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, country-specific weekly and daily profiles, and a flexible system to compute hourly emissions. Thus, a harmonized temporal distribution of emissions is given, which can be applied to any emission database as input for atmospheric models up to the global scale.

For the temporal and spatial distribution of agricultural emissions a number of approaches have been established; these are based on information on farmer practice, available proxy data, and meteorological data, e.g. farmland and animal densities and the consideration of temperature and wind speed for agricultural emissions (e.g. Skjøth et al., 2011; Backes et al., 2016; Hendriks et al., 2016; see Fig. 4).

Figure 4 Break-down of agricultural emissions into sub-sectors in order to improve the spatial and temporal distribution (from Backes et al., 2016).

Comprehensive VOC split vectors are provided by Theloke and Friedrich (2007) and more recently by Huang et al. (2017). Region- and source-specific speciation profiles of NMVOC species or species groups are compiled and provided, with corresponding quality codes specifying the quality of the mapping. They can then be allocated to the reduced number of VOC species used in the chemical reaction schemes implemented in atmospheric chemistry–transport models. Typical heights for the release of emissions, e.g. typical stack heights, are given by Pregger and Friedrich (2009) and Bieser et al. (2011b).

Model systems have been developed that perform the entire temporal and spatial emission distribution and the NMVOC and PM speciation in order to provide hourly gridded emission data for use in different chemistry–transport models. Recent examples are the HERMES model (Baldasano et al., 2008; Guevara et al., 2013, 2019, 2020), FUME (Benešová et al., 2018), and the Community Emissions Data System (CEDS) model system (Hoesly et al., 2018). Because natural emissions, e.g. biogenic emissions, sea spray, and dust, depend strongly on the meteorological conditions, these emissions are frequently calculated within the chemistry–transport models (CTMs). Other established CTMs like the EMEP model (Simpson et al., 2012) or LOTOS-EUROS (Manders et al., 2017) do not use emissions preprocessors but distribute gridded emissions in time based on standard temporal and speciation profiles alongside the chemistry–transport calculations in order to avoid storing and reading large emission data sets.

3.2.3  Road transport emissions

Exhaust emissions from road transport have been a significant source of primarily NO x and ultra-fine particles (UFPs) in urban areas around the world. In the EU, road transport is the single most important source of NO x , producing 28.1 % of total NO x emissions (EEA, 2019b). In terms of PM 10 , its contribution is 7.7 % when both exhaust and non-exhaust sources are counted and 2.9 % when only exhaust emissions are considered (EEA, 2019b). Road transport contributes 32 %–97 % of total UFP in urban areas (Kumar et al., 2014). The difference between PM 10 and UFP contributions from road transport is a direct outcome of the small size of exhaust particles that mostly reside in the UFP range (Vouitsis et al., 2017).

The proximity of people to the emission source (vehicles) significantly increases exposure to traffic-induced pollution (Żak et al., 2017). Consequently, traffic exhaust emissions have been extensively studied, and comprehensive sets of emission factors have been available for a long time. The two most widespread methods to estimate emissions in Europe include COPERT ( https://www.emisia.com/utilities/copert/ , last access: 22 February 2022) and HBEFA ( https://www.hbefa.net , last access: 22 February 2022). These methods share the same experimental database of vehicular emissions – the so-called ERMES database ( https://www.ermes-group.eu/ , last access: 22 February 2022) – but express emission factors in different modelling terms. COPERT is also a part of the EMEP/CORINAIR Emission Inventory Guidebook (EMEP/EEA, 2019).

These models define the emissions for several pollutant species, for a wide range of vehicles and operating conditions. Emission factors are regularly being updated in an effort to reflect the best knowledge of on-road vehicle emission levels. Despite this, there are still some uncertainties in estimating emissions from road transport, in particular when these are to be used as input to air quality models. More attention is therefore needed in the following directions.

Emission factors for the latest vehicle technologies always come with some delay. This is the result of the time lag between placement of a new vehicle technology on the road and the organization of measurement campaigns to collect the experimental information required to develop the emission factors. The latest regulation (Reg. (EU) 2018/858) – mandating a minimum number of market surveillance tests in the different member states – may help to reduce this lag and to extend the availability of vehicle tests on which to base emission factors.

The availability of measurements of pollutants which are currently not included in emissions regulations (NH 3 , N 2 O, CH 4 , PAHs, etc.) is limited compared to regulated pollutants. Moreover, any available measurements have been mostly collected in the laboratory, due to instrumentation limitations for on-road measurements. Therefore, emission models may miss on-road operation conditions that potentially lead to high emissions rates of non-regulated pollutants.

The increase in emissions with vehicle age is still subject to high uncertainty. Emission increases with age may be due to normal system degradation, the presence of high emitters on the road (Murena and Prati, 2020) or vehicle tampering to improve performance or decrease operational costs. Current models use degradation functions based on remote sensing data (e.g. Borken-Kleefeld and Chen, 2015). This is a useful source of information, but remote sensing data need to be collected in additional locations in the EU, covering a range of climatic and operation conditions.

Emission models may be conservative in their approach of estimating emissions in extreme conditions of temperature (Lozhkina et al., 2020), altitude, road gradient, or creeping speeds. Although such conditions may not be substantial for estimating the total emissions of most countries, they can potentially lead to a significant underestimation of emissions that have to be locally calculated for high-resolution air quality modelling.

Despite uncertainties in modelling emissions, there is a high level of confidence that exhaust gas emissions of mobile sources will continue to decrease in the years to come. For example, Matthias et al. (2020b) projected that the contribution of road traffic to ambient NO 2 concentrations will decrease from 40 %–60 % in 2010 to 10 %–30 % in 2040. This is the result of relevant technological development driven by demanding CO 2 reduction targets and air pollutant emission standards applicable to new vehicles. An example of such technological development is the increase in the availability of plug-in hybrid vehicles, which have exhibited great potential in reducing both pollutant emissions and CO 2 emissions from traffic (Doulgeris et al., 2020).

Technological improvement in decreasing emissions from internal combustion engines will be accelerated in the EU market due to the current Euro 6d emission standard and the upcoming Euro 7 regulation but also the proliferation of electric power trains to meet CO 2 targets. The only road transport pollutant not significantly affected by the introduction of electric vehicles is non-exhaust PM coming from tyre, brake, and road wear, with estimates suggesting both increases due to heavier vehicles and reductions due to wider exploitation of regenerating braking systems (Beddows and Harrison, 2021).

New techniques are also being developed with the capacity to monitor emissions of vehicles in operation. This can verify that emissions remain below limits in actual use and not just in type approval testing conditions. A current example of such on-board monitoring systems is the on-board fuel consumption measurement (OBFCM) device which is already mandatory for new light-duty vehicles and is being extended for heavy-duty vehicles (Zacharof et al., 2020). Information from such systems, together with new computation methods (big data), can provide very useful information for improving the reliability and temporal and spatial resolution of current emissions inventories.

3.2.4  Shipping emissions

Ships consume high amounts of fossil fuels. On the global scale they emit amounts of CO 2 comparable to big industrialized countries like Germany and Japan. Because ships use high-sulfur fuels, regardless of the global introduction of the 0.5 % sulfur cap in 2020, and typically are not equipped with advanced exhaust gas cleaning systems, their share from global CO 2 is 2.9 %, but corresponding shares of NO x and SO x are considerably higher, 13 % and 12 %, respectively (IPCC, 2014; Smith et al., 2015; Faber et al., 2020). Ship routes are frequently located in the vicinity of the coast, which may go along with significant contributions to air pollution in coastal areas. Effects on ozone formation and secondary aerosol formation also need to be considered.

The environmental regulation concerning the sulfur emissions from ships has been in place in the Baltic Sea since 2006, with the North Sea following in 2007. Currently, also North America and some Chinese coastal areas have stringent sulfur limits for ship fuels. Everywhere else the use of high-sulfur fuel in ships was allowed until the start of 2020, when sulfur reductions of a maximum of 0.5 % S were extended to all ships (IMO, 2019). This has been estimated to reduce the premature deaths by 137 000 each year (Sofiev et al., 2018). Nitrogen oxide emissions from ships are regulated by NO x Emission Control Areas (ECAs), which currently exist only in the coastlines of Canada and the US. The Baltic Sea and the North Sea areas will quickly follow, because in 2021 all new ships sailing these areas must comply with 80 % NO x reduction.

The introduction of the automatic identification system (AIS), long-range identification and tracking (LRIT), and vessel monitoring systems (VMSs) have enabled tracking of individual ships in unprecedented detail. These navigational aids offer an excellent description of vessel activities on both local and global scales.

Currently, ship emission models using AIS data as an activity source are most popular. They can have accurate information about quantity, location, and time of the emissions. Most of the model systems applied today use a bottom-up approach to calculate shipping emissions (e.g. Jalkanen et al., 2009, 2012, 2016; Johansson et al., 2017; Aulinger et al., 2016). The combination of vessel activity, technical description, and an emission model allows for prediction of emissions for individual ships. This also facilitates comparisons to fuel reports, like those of the EU Monitoring, Reporting, and Verification (MRV) scheme or IMO Data Collection System (DCS). Emission models may also include external contributions, like wind, waves, ice, or sea currents in vessel performance prediction, which brings them closer to realistic conditions experienced by ships than the assumptions applied for ideal conditions (Jalkanen et al., 2009; Yang et al., 2020). A vessel-level modelling approach allows for very high spatio-temporal resolution and flexible 4D grids (lat, long, height, time) on which the data can be given. New information about modified or new emission factors for certain chemical species can easily be adopted in the models. Ship emission data are available on a global grid at 0.1 ∘ × 0.1 ∘ and in higher resolution for regional domains in Europe (see Fig. 5), North America, and East Asia (e.g. Johansson et al., 2017). The emission model systems also allow for the construction of future scenarios; see e.g. Matthias et al. (2016) for the North Sea, Karl et al. (2019c) for the Baltic Sea or Geels et al. (2021) for a possible opening of new routes in the Arctic.

Figure 5 The predicted SO x emissions from ships in Europe in 2018, computed using the STEAM model (e.g. Johansson et al., 2017). Use of low-sulfur fuels and SO x scrubbers is concentrated to the North Sea and Baltic Sea ECAs. Background map © US Geological Survey, Landsat8 imagery.

Emissions from ships in ports can be quantified for arrival and departure following the same AIS-based approach as for regional and global shipping emissions. Emissions for ships at berth are estimated based on ship type and size, but with large uncertainties.

Introduction of emission limits gives shipowners a choice to comply with at least three options. The first of these is the use of low-sulfur fuels, and the second option involves the use of aftertreatment devices ( SO x scrubbers), which remove air pollutants by spraying the exhaust with seawater. The third option probably applies only to new ships, because it involves the use of liquid natural gas (LNG) as a marine fuel.

Exhaust aftertreatment systems, which are commonly used to remove NO x , SO x , or PM often involve chemical additives (urea, caustic soda) or large amounts of seawater. Use of so-called open-loop SO x scrubbers, which use seawater spray to wash the ship exhaust, releases the effluent back to the sea. This may lead to a creation of a new water quality problem, especially in areas where water volumes are small (estuaries, ports) or water exchange is slow (e.g. the Baltic Sea) (Teuchies et al., 2020).

The use of low-sulfur or LNG fuels is a fossil-based solution, unless the fuel was made using renewable or fully synthetic sources. However, emissions of NO x , SO x , and PM from LNG engines can be very low, but this depends very much on the engine type selected.

Methane, methanol, and ammonia are three fuels which can be produced by fossil, bio, and synthetic pathways. These three fuels are also suitable for use in internal combustion engines as well as fuel cells. All three are hydrogen carriers and processes, which lead to synthesis of these three fuels and have hydrogen production as an intermediary step. This could offer a viable pathway towards hydrogen-based shipping but also allows the use of current engine setups and existing fuel infrastructure (DNV-GL, 2019).

3.2.5  Emissions of indoor sources

The shift in focus from regulating the outdoor concentration of pollutants to putting more emphasis on reducing the individual exposure to pollutants, which is described later in Sects. 7.4 and 8 of this paper, makes it necessary to analyse not only possibilities for reducing emissions from outdoor sources, but also those from indoor sources. Thus, detailed knowledge about emission factors from indoor sources is needed.

Smoking, combustion appliances, and cooking are important sources of PM 2.5 , NO, NO 2 , and PAHs (Hu, 2012; Li, 2020; Weschler and Carslaw, 2018). Particularly important indoor sources of NO and NO 2 are gas appliances such as stoves and boilers (Farmer et al., 2019). For PM 2.5 , apart from diffuse abrasion processes, passive smoking is still the most important source, although the awareness that passive smoking is unhealthy has been increasing with the EU ban of smoking in public buildings. Schripp et al. (2013) report that not only smoking but also consuming e-cigarettes leads to a high emission of VOCs and fine and ultra-fine particles. Frying and baking lead to the evaporation and later condensation of fat and are a large source for PM 2.5 , especially if no kitchen hood is used; a larger number of studies on frying are available and listed in Li (2020) and Hu et al. (2012). Hu et al. (2012) reviewed emissions of PM 2.5 from the use of candles and incense sticks and found that incense sticks have much higher emission rates than candles. Zhao et al. (2020a) simultaneously measured indoor and outdoor concentrations of PM in homes in Germany and report abrasion and resuspension processes as major contributors of coarser particles (PM 2.5−10 ) and toasting, frying, baking, and burning of candles and incense sticks as important sources for ultra-fine particles. Also, the use of open chimneys and older wood stoves in the living area is an important source. For wood stoves, mostly measured indoor concentrations of PM 2.5 are used to characterize the pressure coming from indoor emissions, or emissions are estimated as a fraction of the overall emissions of a stove. As only a few studies measuring emissions from wood stoves into the interior exist (Li et al., 2019b; Salthammer et al., 2014), more measurements are necessary. Schripp et al. (2014) report very high emission factors of ethanol-burning fireplaces, as these have no chimney.

Laser printers emit ultra-fine particles, especially longer-chained alkanes (C21–C45) and siloxanes (Morawska et al., 2009). Also the new 3D printers are a source of nanoparticles, as Gu et al. (2019) found out. Schripp et al. (2011) analysed the emissions from electric household appliances and reported high emission rates in particular from toasters, raclette grills, flat irons, and hair blowers.

New furniture is often a source of formaldehyde. The use of chemicals such as cleaning agents and personal care products leads to VOC and semi-volatile organic compounds (SVOC) emissions, which are partly oxidized and condensate and thus transform into fine particles. McDonald et al. (2018) point out that with rapidly decreasing emissions of VOC from transportation, emissions from the use of volatile chemical products indoors are becoming the dominant sources in the urban VOC emission inventory, so that VOC concentrations often are higher indoors than outdoors (Kristensen et al., 2019).

Excreta of house dust mites use of fan heaters; vacuum cleaning; especially without HEPA filters; and pets are further indoor emission sources. Furthermore, all kinds of human activities produce abrasion. As there are numerous different processes causing these emissions, instead of estimating emissions, measured concentrations, which typically stem from abrasion processes, are used.

Apart from reducing emissions, the concentration of pollutants indoors can also be reduced by ventilation, i.e. by opening windows or using mechanical ventilation, or by filtering the air, e.g. with HEPA filters for the removal of fine particles.

3.2.6  Source apportionment methods and studies

The question of how much the different sources are contributing to the ambient levels of different air pollutants is critical for the design of effective strategies for urban air quality planning. Different methods are used for source apportionment of ambient concentrations, each including certain limitations given by the intrinsic assumptions underpinning the individual methods and by availability and robustness of data underpinning the source apportionment. In many cases these methods are complementary to each other, and implementation of a combination of different methods decreases the uncertainties (Thunis et al., 2019). There are two principally different source apportionment models: the receptor models apportioning the measured mass of an atmospheric pollutant at a given site to its emission sources and the source-oriented models based on sensitivity analyses performed with different types of air quality models (Gaussian, Lagrangian, or Eulerian chemistry–transport models) (Viana et al., 2008; Hopke, 2016; Mircea et al., 2020). Another method addressing the source–receptor relation of air pollution is inverse modelling used for improvement of emission inventories from global scale to individual industrial sources (e.g. Stohl et al., 2010; Henne et al., 2016; Bergamaschi et al., 2018).

The main receptor models are the incremental (Lenschow) method, the chemical mass balance (CMB) method, and the positive matrix factorization (PMF) (Mircea et al., 2020). The Lenschow method is based on the assumption that source contributions can be derived from the differences in measured concentrations at specific locations not affected and affected by the emission sources. This approach is based on the assumptions that the regional contribution is constant at both locations and that the sources do not contribute to the regional background. The CMB is based on known source composition profiles and measured receptor species concentrations. The result depends strongly on the availability of source profiles, which ideally are from the region where the receptor is located and that should be contemporary with the underpinning ambient air measurements. PMF is the most commonly used analytical technique operating linear transformation of the original variables to create a new set of variables, which better explain cause–effect patterns. Hopke (2016) provides a complete review of receptor models.

The source-oriented apportionment methods utilizing source-specific gridded emission inventories and air pollution models include two in principal different methods, the widely used sensitivity analysis, also called brute-force method, or emission reduction potential (Mircea et al., 2020) or emission reduction impact (ERI) method (Thunis et al., 2019), and the tagged species methodology which involves computational algorithms solving reactive tracer concentrations within the chemistry–transport models. ERI and tagged species methods are conceptually different and address different questions. Generally, the ERI method analyses how the concentrations predicted by an air quality model respond to variations in input emissions and their uncertainties. An important aspect to consider when using this method is that the relationship between precursor emissions and concentrations of secondary air pollutants may include non-linear effects. In non-linear situations, the sum of the concentrations of each source is different from the total concentration obtained in the base case. The magnitude of the emission variations considered in ERI may vary from small perturbations, studying the model response in the same chemical and physical regime as the base case, to removing 100 % of the studied emissions (the zero-out method), which may include non-linear effects present in the model response (Mircea et al., 2020). The tagged species method is based on CTM simulations with the tagging/labelling technique, which keeps track of the origin of air pollutants through the model simulation. This accountability makes it possible to quantify the mass contributed by every source or area to the pollutant concentration (Thunis et al., 2019; Im et al., 2019).

The principal differences between the different source-apportionment methods and implications of these differences on apportionment of sources to the observed or modelled ambient concentration levels are in detail explained and discussed in Clappier et al. (2017) and Thunis et al. (2019). Belis et al. (2020) evaluated 49 independent source apportionment results produced by 40 different research groups deploying both receptor and source-oriented models in the framework of the FAIRMODE intercomparison study of PM 10 source apportionment. The results have shown good performance and intercomparability of the receptor models for the overall data set while results for the time series were more diverse. The source contributions of the source-oriented models to PM 10 were less than the measured concentrations.

In this section we further focus on new developments in source characterization with the help of receptor-oriented models and in construction of emission inventories while the air quality models and emission sensitivity studies are the subject of Sect. 5 of this paper. Several new studies reported on characterization of local composition of particulate matter as well as of NMVOCs and PAHs, tracking the contribution of main emission sources (Christodoulou et al., 2020; Diémoz et al., 2020; Saraga et al., 2021; Liakakou et al., 2020; Kermenidou et al., 2020). The particulate matter has been characterized in terms of carbonaceous matter – elemental or black and organic carbon, organic matter, metals, ionic species, and elemental composition. An Aethalometer model to identify BC related to fossil fuel combustion and biomass burning has been applied in several studies (Grange et al., 2020; Christodoulou et al., 2020; Diémoz et al., 2020). Combination of the different analytical methods and analysis of temporal and spatial variation in the data allowed for identification of chemical fingerprints of different emission sources. Belis et al. (2019) present a multistep PMF approach where a high-time-resolution data set from Italy of aerosol organic and inorganic species measured with several online and offline techniques gave internally consistent results and could identify additional emission sources compared to earlier studies.

The local studies characterizing the local composition of PM, as well as NMVOCs and PAHs, revealed the important roles of road traffic and residential combustion for concentration levels of air pollutants in both urban and rural areas. Wood burning has an important share in many residential areas, especially those outside the city centres and in the countryside (Saraga et al., 2021; Fameli et al., 2020). Fuel oil is another important fuel in residential combustion; in some cities such as Athens it is the dominating one (Fameli et al., 2020). The studies show important differences in the diurnal and seasonal patterns of these two emission sources. While road traffic emissions have maxima in the morning and afternoon hours, contributions from residential combustion dominate at night-time and in the cold season. Important contributions of traffic are found in all studies. Saraga et al. (2021) show, as results from the ICARUS study performed in six European cities, that the main contribution to road-traffic-related PM 2.5 is the tyre and brake wear and resuspension of the particles. The fuel oil combustion source is, apart from residential heating, also associated with industrial emissions and shipping emissions. Contributions from these sources become important at specific locations, like in cities with certain industrial plants or in harbour cities.

Analyses of data from longer time series show a decreasing trend for exhaust gas emissions in road traffic. Its contributions to BC in the last decade decreased while the residential combustion, especially the wood burning contribution, does not show any clear trend (Grange et al., 2020). Efficient abatement measures for improvement of the local air quality need to address the important sources. In most cases these are the local traffic and residential combustion, but in many cases these also include industrial sources and in some cases shipping. Targeting these different sources requires a different approach for each.

Inverse modelling is mainly used for improvement of emission inventories with the help of measurements. Different inversion methods applied in Lagrangian dispersion models (e.g. Stohl et al., 2010; Manning et al., 2011; Henne et al., 2016) and global and regional Eulerian models have been widely used for improvements of emission inventories of greenhouse gases on a wide range of geographical scales from global to national, urban, and local. An overview of different inverse modelling approaches applied to a European CH 4 emission inventory is presented by Bergamaschi et al. (2018). Inverse modelling has the potential to reduce uncertainties of emission inventories comparable to other approaches, e.g. an incremental method combining aircraft measurements and a high-resolution emission inventory (Gurney et al., 2017).

3.3  Emerging challenges

3.3.1  emission inventories and preprocessors.

Emission inventories still have large uncertainties. In particular, PM emissions stemming from all kinds of diffuse processes, especially from abrasion processes in industry, households, agriculture, and traffic, show a large variability and uncertainty. For example, abrasion processes of trains may cause very large PM concentrations in underground train stations, but emission factors and total emissions are not well-known. With the ongoing reduction of exhaust gas emissions and the continuing introduction of electric vehicles, abrasion will become the most important process for traffic emissions.

For residential wood combustion many uncertainties relate to the quality and refinement of information about the use of wood and the heating device technologies, tree species, wood storage conditions, or combustion procedures implemented. Their impact on emission inventories is not well evaluated, but new research underlines how national characteristics need to be taken into account and also shows what type of data can be used in order to improve the spatial representation of these emissions.

Despite the activities to improve temporal profiles of agricultural emissions, more detailed information about the amount of NH 3 and PM emissions is still needed for many regions of the world. Also, natural emissions like dust, marine VOCs, and marine organic aerosols remain a challenge, in particular when climate change might lead to the formation of new source regions in high latitudes.

Chemical composition of NMVOC emissions from combustion processes remains highly uncertain, especially when new fuels enter the market like low-sulfur residual fuels in shipping or when new exhaust gas cleaning technologies are introduced that modify the chemical composition of the exhaust gas. Advanced instrumentation for the characterization of new emission profiles are needed here. Measurement techniques employed in the characterization of emissions impact the results; for example, the dilution methods used have a large impact on the measured gas-to-particle partitioning. Better understanding of these impacts and a robust assessment of the uncertainties and variabilities remain a challenge. Emission inventories should include air pollutants and greenhouse gases at the same time. Integrated assessments analyse measures and policies targeting air pollution control as well as climate protection at the same time and potential, and their co-benefits need to be investigated.

Emissions preprocessors aim at increasing the level of detail they take into account for calculating the spatial and temporal resolution of emissions. However, the availability of input data sets (e.g. traffic data from mobile phone positions, AIS ship position data), the huge size of these data sets, and also data protection rules currently hinder their use. Still, there is big potential in extending the data sources used for emissions preprocessing towards big data, e.g. from mobile phone positions, traffic counts, or online emission reporting, in order to reach real-time emission data and improved dynamic emission inventories to be used in air quality forecast systems. Monitoring data from numerous air quality sensors at multiple locations might help in advancing these inventories.

3.3.2  Road emissions

The accuracy and relevance of our current emission estimation and modelling approaches may in the future be challenged by relevant developments, the most important ones being the following.

The exhaust emissions from road transport are continuously decreasing, as exhaust filters become increasingly efficient and are used in a wider range of vehicle technologies, including gasoline vehicles, while the market share of electric cars is also increasing. However, PM 2.5 , PM 10 , and heavy metal emissions from wear and abrasion processes increase with increasing traffic volume as they are not regulated, and electric cars also produce emissions from tyre wear and road abrasion. For instance, the emissions of PM 2.5 reported by Germany to the EEA for 2018 show 9.9 kt a −1 for exhaust gases of cars, trucks, and motorcycles; 7.6 kt for tyre and brake wear; and 4.3 kt from road abrasion. A scenario reported by Germany for 2030 shows only 2.0 kt PM 2.5 for exhaust emissions, but 7.9 kt from tyre and brake wear and 4.4 kt from road abrasion (EIONET, 2019). Emissions from wear of tyres and brakes and abrasion of road surfaces are less studied than exhaust emissions. Wear emissions depend on a range of parameters including driving behaviour (acceleration and braking pattern), vehicle weight and loading, structure and material of brakes and tyres, road surface material, and weather conditions (e.g. road water coverage) (e.g. Denby et al., 2013; Stojiljkovic et al., 2019; Beddows and Harrison, 2021). Capturing the effect of technological developments in this area would be therefore important for relevant air quality estimates.

The profile of non-methane organic gases (NMOGs) is important to estimate the contribution of exhaust to secondary organic aerosol formation. NMOGs depend on fuel and lube oil use, combustion, aftertreatment, and operation conditions. The profile of emission species may be differentiated as new fuels, including renewable, oxygenated, and other organic components are being increasingly used to decarbonize fuels. Hence, although total hydrocarbon emissions are still controlled by emission standards, the speciation of these emissions may vary in the future. Monitoring those changes is cumbersome as the study of the chemistry and/or volatility of organic species is a tedious and expensive procedure. Hence any changes may escape relevant experimental campaigns.

Questions remain about the suitability of widespread emission factors and models to capture the effects of lane layouts, vehicle interactions, and driving behaviour, while lane-wide average traffic parameters are a structural limitation to emission modelling. As urban policies are advancing in an effort to decrease the usage of private vehicles in cities, the impact of traffic calming and banning measures may not be satisfactorily captured by today's available emission models. In order to take driving behaviours into account, it is necessary to improve so-called microscopic models such as the “Passenger car and Heavy duty Emission Model“ (PHEM) (Hausberger et al., 2003) that calculate emissions from high-temporal-frequency information on network configuration as well as traffic and driving conditions (see review by Franco et al., 2013). Their use calls for the development of new methodologies to provide the simulation with individual speed profiles, taking into account the actual road usage and the specificities of the emissions of the most recent vehicles.

3.3.3  Shipping emissions

The efforts of decarbonizing shipping have thus far concentrated on minimizing the energy need of ships, but a shift to carbon-neutral or non-carbon fuels is necessary. Methane, methanol, and ammonia are three fuels that could offer a viable pathway towards hydrogen-based shipping but also allow for the use of current engine setups and existing fuel infrastructure (DNV-GL, 2019). Regardless of the fuel or aftertreatment technique used, detailed emission factor measurements for various combinations of fuels and engines are needed (Anderson et al., 2015; Winnes et al., 2020) to reliably model the emissions.

Little is known about emissions of VOCs from ships and how much they contribute to particle formation and ozone formation. VOC emissions from ships are not included in most ship emission models, because emission factors are not available or stem from comparably old observations. In addition, VOC emissions are expected to vary considerably with the type of fuel burned and the lubricants used on board, both of which have changed considerably with the introduction of low-sulfur fuels in 2015 (in ECAs) and in 2020 (on a global level). The most recent greenhouse gas emission report from IMO (2021) states that evaporation might be the most important source for VOCs from shipping, which is not considered in any emission inventory, yet.

Current exhaust gas cleaning technologies, in particular scrubbers applied for removing SO 2 from the ship exhaust, dump large parts of the scrubbed pollutants into the sea. More comprehensive research is therefore urgently needed on the combined effects of shipping, which will treat both the impacts via the atmosphere and those on the marine environments. The impacts via the atmosphere include the health effects on humans, the deposition of pollutants to the sea, and climatic forcing. The impacts on the marine environment include acidification, eutrophication, accumulation of pollution in the seas, and marine biota. Recently, there have been attempts to combine the expertise of oceanic and atmospheric researchers for resolving these issues (Kukkonen et al., 2020a).

Ships have high emissions when they arrive in ports and also when they depart a short time later. In addition, they need electricity and heat when they stay at berth, leading to additional emissions in ports stemming from their auxiliary engines and boilers. The impact of these emissions on urban air quality in port areas is of high interest because of their large impact on human exposure.

3.3.4  Indoor sources

Even though people in industrialized countries spend more than 80 % of their time indoors, systematic knowledge on indoor air quality, source strength of the indoor air pollution sources, and physico-chemical transformation of indoor air pollutants is still limited. Therefore, systematic quantification of different indoor air pollution sources, such as building material, consumer products, and human activities, is needed, including exploitation of the already existing test chamber, and other relevant laboratory data are needed. Special attention is also needed to the outdoor source component. Besides obtaining new data on indoor-to-outdoor (I  /  O) ratios, the existing data need to be systematically analysed. One of the key challenges here is how to translate such data from the outdoor contribution into a real indoor environment with considerable heterogeneity in terms of ventilation, volume, microclimatic characteristics, and multiple indoor sources (Bartzis et al., 2015).

Development of indoor air quality models with accurate description of the key chemical and physical processes involved in outdoor–indoor air interaction as well as processing and transport of indoor air pollution inside the buildings is needed to properly address connection between the outdoor air quality and indoor air pollution sources. Additional advanced modelling is needed for air–surface interactions targeting emissions and sinks on different surfaces including those in the ventilation set-up (Liu et al., 2013) along with verification of the indoor air models with measurements in a variety of indoor air environments.

3.3.5  Source apportionment

Continuous improvement of emission inventories with help of verification with source- and receptor-oriented source apportionment methods is needed, especially as large changes in emissions, in terms of both the emission totals and profiles of emission species from individual sources, are expected as a result of upcoming new technologies, fuels, and changes in lifestyle emerging mainly from the Paris Agreement climate change targets.

Currently, apportionments of the overall measurement data sets usually give consistent results while source apportionment of data with high temporal resolution still remains challenging. With rapid development of both advanced online measurement instruments and low-cost measurement sensors, development of source apportionment methods towards high-temporal-resolution data and increasing number of parameters is necessary. This also requires improvements in characterization of sources in terms of both speciation and temporal profiles. This in particular concerns emission profiles for NMVOCs, PAHs, and particulate organic matter (e.g. most existing profiles for PAH emission from vehicles are quite old and do not follow vehicle technology evolution; Cecinato et al., 2014; Finardi et al., 2017). Inverse modelling methods are very powerful and promising tools for source estimation and improvement of emission inventories, but the current models provide large spread in results and need to be further improved and intercompared.

Here we concentrate on another growing field of development: low-cost sensor (LCS) networks, crowdsourcing, and citizen science together with small-scale air quality model simulations to provide personal air pollution exposure. Modern satellite and remote sensing techniques are not in focus here.

4.1  Brief overview

Europe's air quality has been improved over the past decade. This has led to a significant reduction in premature deaths over the same period in Europe, but all Europeans still suffer from air pollution (EEA, 2020a). The most serious air pollutants, in terms of harm to human health, are particulate matter (PM), NO 2 , and ground-level ozone (O 3 ). The analysis of concentrations in relation to the defined EU and World Health Organization (WHO) standards is based on measurements at fixed monitoring points, officially reported by the member states. Supplementary assessment by modelling is also considered, particularly when it results in exceeding the legislated EU standards. But in parallel new monitoring techniques and strategies for observation of ambient air quality are available and applied, which are discussed below.

The motivations for new developments in observation and instrumentation are, on the one hand, obtaining necessary information about air pollutant concentrations and exposure as a basis for compliance and health protection measures and on the other hand supporting improvements in weather, climate, and air quality forecasts. Remote sensing techniques are developed further to get 3D coverage of observations globally by establishment of networks with mini-lidar for example (so-called ceilometers), for evaluation of satellite measurements, to contribute to atmospheric super sites (extension of in situ measurements), or for chemistry–transport model (CTM) evaluations. These techniques can provide nearly continuous monitoring data, only interrupted by certain weather conditions. Satellite measurements are becoming more important for air quality management because their spatial resolution can reach down to 1 km, while their information content is suitable for the assessment of modelling results and combination with modelling tasks (Hirtl et al., 2020). All these techniques enable unattended detection at different altitudes and thus of the composition, clouds, structure, and radiation fluxes of the atmosphere as well as Earth surface characteristics, relevant for atmosphere–surface feedback processes.

Some examples of modern remote sensing techniques as described in Foken (2021) are the sun photometer networks (determination of aerosol optical depth), MAX-DOAS (e.g. NO 2 and HCHO column densities), lidar (e.g. water vapour, temperature, wind, and air pollutants), and more recently ceilometers (e.g. cloud altitude and mixing layer height). Machine learning algorithms, such as neural networks, are now deployed for remote sensing applications (Feng et al., 2020). Satellite observations have become available for column densities of aerosol, NO 2 , CO, HCHO, O 3 , PM 10 , CH 4 , and CO 2 as well as aerosol optical depth and various image analyses (Foken, 2021). Together with improved spatial coverage and high resolution, these data become increasingly important for assessment in urban areas (Letheren, 2016).

The distribution of ambient air composition exhibits large spatial variations; therefore high-resolution measurement networks are required. This has become possible with LCS networks, which are used in both research and operational applications of air pollution measurement and in global networks of observations such as the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) programme (Lewis et al., 2017). WMO/GAW (Global Atmosphere Watch Programme of the World Meteorological Organization; https://public.wmo.int/en , last access: 21 February 2022) addresses atmospheric composition on all scales from global and regional to local and urban (see GAW Station Information System, https://gawsis.meteoswiss.ch/GAWSIS/#/ , last access: 21 February 2022) and thus provides information and services on atmospheric composition to the public and to decision-makers, which requires quality assurance elements and procedures as described by the WMO/GAW Implementation Plan: 2016–2023 (WMO, 2017). This topic is further discussed with respect to the related sensor, network, and data analysis requirements.

4.2  Current status and challenges

To describe the current trends of air quality monitoring, certain lines of research and technical development are formulated in the following section. This section concentrates on high-resolution measurement networks by the installation of a larger number of small and low-cost measurement sensors. The measurements by traditional in situ measuring as well as ground-based, aircraft-based, and space-based remote sensing techniques or integrated measuring techniques are no longer considered. Also, satellite observations, which are a growing field of development towards even smaller and thus cost-effective platforms, are not the focus here.

The configurations of ambient air measurements can be described as a space, time, and precision-dimensional feature space shown as large arrows in Fig. 6 where crowds with LCS (green) are distributed irregularly in space and time at low precision and high number. Stationary measurements (yellow) are performed at high precision and thus of the highest quality as well as continuously over time but only at a few points in space requiring high effort and cost. Between the two layers, mobile measurements are available on a medium level of precision: in one case regularly on certain routes (red) and in another case with high spatial density at a few points during intensive measurement campaigns (blue). The crowd measurements by LCS can be geo-statistically projected onto a higher quality level together with the high-precision measurements (thin black arrows). Following this, an overall higher information density at an elevated quality level than the sum of the individual measurements alone is possible, so that continuous data by LCS can be applied (Budde et al., 2017).

There is an increasing interest in air quality forecast and assessment systems by decision makers to improve air quality and public health; mitigate the occurrence of acute air pollution episodes, particularly in urban areas; and reduce the associated impacts on agriculture, ecosystems, and climate. Current trends in the development of modern atmospheric composition modelling and air quality forecast systems are described in review by Baklanov and Zhang (2020), which includes for instance the multi-scale prediction approach, multi-platform observations, and data assimilation as well as data fusion, machine learning methods, and bias correction techniques. This shows the general development towards spatial and temporal high resolution as well as better knowledge of personal air pollution exposure.

Figure 6 Configuration of ambient air measurements modelled as a space, time, and precision-dimensional feature space (large arrows): crowds with low-cost sensors (green) scatter irregularly in space and time at low precision but high number (source: Budde et al., 2017).

4.2.1  Low-cost sensors and citizen science for atmospheric research

Many manufacturers (more than 50 worldwide, with their numbers growing fast) are working in the market for air quality monitoring with different business models (Alfano et al., 2020). There are companies which produce and/or sell medium-cost sensors (MCSs) with a cost per compound on the order of EUR 100 and EUR 1000 and LCS on the order of EUR 10 and EUR 100 for all key air pollutants (Concas et al., 2021). Furthermore, manufacturers and integrators often provide installation of LCS and MCS for networks and on mobile monitoring platforms. The operation of such networked and mobile platform measurements is also often supported by the companies which install the sensors. However, the monitoring of air pollutant limit value exceedances is still a task of governmental agencies which are responsible for air quality.

These developments point to a new era in detecting the quality of air which we breathe (Munir et al., 2019; Schade et al., 2019; Schäfer et al., 2021) where virtually everybody can measure air pollutants. Following this potential high number of sensors, fine-granular assessment of air quality in urban areas is possible at lower costs. The data platforms of these LCS and MCS networks collect enormous amounts of data, and new data products like personal exposure of air pollutants, spatial distribution of air pollutants down to 1 m resolution, information about least polluted areas, and forecast of air quality are supplied for users. Figure 7 shows these possibilities on the Internet of Everything with things, sensor data, open data platforms, and citizen actions.

Figure 7 Exploitation of Internet of Everything technology with things, sensor data, open data platforms, and actions of people.

Algorithms from machine learning and big data, together with data from reference instruments as well as monitoring data owned by governmental agencies, are often working on a central data server. Thus, an overall higher information density at an elevated quality level than the sum of the individual measurement components is possible. Also, a dynamic evaluation technique can be applied, which is built upon mobile sensors on board vehicles, for example, trams, buses, and taxis combined with the existing monitoring infrastructure by intercomparison between any two devices which requires a corresponding high dynamic of their sensitivity. Pre-/post-calibrations are possible by using high-end instruments or adjustment in a reference atmosphere under prescribed laboratory and/or field conditions. Based on these achievements in the monitoring networks it is possible to identify emission hot spots and thus to assess spatially resolved, high-resolution emission inventories. Such emission inventories are a prerequisite for supporting high-resolution numerical simulations of air pollutant concentrations and eventually the forecast of air quality.

Furthermore, because of the small size and low weight, sensors can be installed on board unmanned aerial vehicles (UAVs) so that these platforms become complex air quality (Burgués and Marco, 2020) and meteorological instruments. This means vertical profiling is possible with aerial atmospheric monitoring to understand the influence of air pollutant emissions upon air quality.

4.2.2  Quality of sensor-measured and numerical simulation data

An increasing number of evaluations of MCS and LCS as well as of networks based on such sensors are being performed, and conclusions are available from these studies such as Thompson (2016), Morawska et al. (2018), and Karagulian et al. (2019). It is well-known that these sensors suffer from drift and ageing (Brattich et al., 2020). The drift can vary even among the same model sensors that come from the same factory. Furthermore, sensor data evaluation is necessary due to cross-sensitivities of sensors with other air pollutants in ambient air and the influences of different temperatures and humidity in ambient air upon the sensor response.

Activities for the standardization of a protocol for evaluation of MCS and LCS at an international level and for inter-comparison exercises are ongoing, where MCS and LCS are tested at the same sites and at the same time (e.g. Williams et al., 2019). The European Committee for Standardization/Technical Committee (CEN/TC) 264/Working Group (WG) 42 “Ambient air – Air quality sensors” works for a Technical Specification of LCS (CEN/TS 17660-1; https://standards.cencenelec.eu/dyn/www/f?p=CEN:105::RESET:::: last access: 21 February 2022). Such guidelines and sensor certifications are required for data products such as personal air pollution exposure, emission source identification, and nowcasting of air quality as well as for applications as traffic management (Lewis et al., 2018; Morawska et al., 2018).

In the area of high-resolution modelling, the creation of a model data standard for obstacle-resolving models ( https://www.atmodat.de/ , last access: 21 February 2022) has started (Voss et al., 2020) as already done for coupled models (CESM – CMIP6 (ucar.edu); https://www.cesm.ucar.edu/projects/CMIP6/ , last access: 21 February 2022).

4.2.3  Importance of crowdsourcing, big data analysis, and data assimilation

Data from high-resolution measurement networks can provide the base for application of small-scale 3D process-based CTMs by means of assessment of emission inventory and model results. Additionally, it can support the operation of statistical, artificial intelligence, neural network, machine learning, and hybrid modelling methods (Bai et al., 2018; WMO, 2020; Baklanov and Zhang, 2020). Statistical methods are simple but require a large amount of historical data and are extremely sensitive to them. Artificial intelligence, neural network, and machine learning methods can have better performance but can be unstable and depend on data quality. Hybrid or combined methods often provide better performance. Such methods can also improve the CTM forecast by utilizing added observation data. For example, Mallet et al. (2009) have applied machine learning methods for the ozone ensemble forecast, performing sequential aggregation based on ensemble simulations and past observations. The latest results of the integration of air quality sensor network data with numerical simulation and neural network modelling results by data assimilation methods are for the Balkan region (Barmpas et al., 2020); Grenoble, France (Zanini et al., 2020); Leipzig, Germany (Heinold et al., 2020); and the inner city of Paris, France (Otalora et al., 2020), and they show how modelling can be used to support and consolidate information from observation data products.

The trend to improve air quality forecasting systems leads to the development of new methods of utilizing modern observational data in models, including data assimilation and data fusion algorithms, machine learning methods, and bias correction techniques (Baklanov and Zhang, 2020). Typically, as a first step data verification and validation of different data sources are performed, including data from LCS and MCS networks, permanent monitoring networks, and UAV-based, aircraft-based, and satellite-based measurements (in situ and remote sensing). Subsequently, emission information data assimilation methods are applied for integration with urban-scale CTM or neural network modelling or fluid dynamics modelling or combining these to provide a flexible framework for air quality modelling (Barmpas et al., 2020). Such approaches that combine the use of observations with models can lead to improved new tools to deliver high-quality information about air quality, spatial high-resolution forecasts of air quality for hours up to days, and health protection to the public.

Further, literature already provides QA–QC methods for MCS and LCS based on big data analyses and machine learning as well as data analyses in the cloud (Foken, 2021). Evaluation methods for measurement and modelling results are selected and combined to show the application potential of data sets of the new sensors, networks, and air quality model simulations. The further development and application of assimilation and quality evaluation methods is ongoing with the aim that distributed data sources will form the basis for new data products, making possible new applications for citizens, local authorities, and stakeholders.

4.2.4  Applicability of sensor observations

Crowdsourcing of sensor observations is applied to get information for personal air pollution exposure and for supporting decisions on personal health protection measures such as information about the least polluted areas for outdoor activities. Using this data-based information, citizens can recognize heavily polluted areas, which could be especially important for sensitive groups.

The platforms for the combination of ground-based stationary and mobile sensors, the complementation with 3D measurement data by in situ and remote sensing observations, and model evaluation and assessment can support such applications. This trend of cost-effective air quality monitoring includes user-oriented data services and education about air pollution and climate change to best exploit the knowledge and information content of measured data. Local authorities already use such data (e.g. English et al., 2020) for identifying emission hot spots, management of city infrastructure, and road traffic management towards improving air quality.

MCS and LCS and their advantages in operation and data availability via citizen sciences can also support the understanding of indoor air quality. The investigations of indoor air pollution in conjunction with outdoor air pollution monitoring provide more realistic data of personal air pollution exposure and for assessing measures of health protection.

4.2.5  Modelling for urban air quality to support observation data products

Numerical modelling results are traditionally evaluated against data from air quality monitoring networks (see also Sect. 5). At high resolution, this process requires the use of a sensor network specifically configured to meet the needs of the exercise. Conversely, modelling can also be used to support air quality mapping based on observational data. Indeed, while the use of LCS for high-density observations can provide information on the variability of pollutant concentration on a fine spatial scale, the spatial (and temporal) global coverage of the areas being monitored nevertheless can prove to be irregular and incomplete.

Data-driven modelling over combined stationary- and mobile-generated pollution data requires the deployment of dedicated statistical methodologies. Although little research effort has been devoted to such developments so far, recent advances in machine learning and artificial intelligence have highlighted the exciting potential of several statistical analysis tools (data envelopment analysis, unsupervised neural learning algorithms, decision trees, etc.) to predict air quality at the city scale from data generated by mobile sensors, which are supported by citizen involvement (Mihăiţă et al., 2019).

Another approach that appears very promising to meet the operational challenges associated with fine spatial mapping is to combine sensor data with mapped data from models. The technique used is geostatistical data fusion, an approach similar to data assimilation and based on kriging interpolation. It produces a new map whose added value lies in obtaining the most probable field of concentration, at the time when the sensor observations were made but also the combination of information provided by the two data sources (Ahangar et al., 2019; Schneider et al., 2017). A study carried out on a medium-density urban area in France showed that the bias found between the outputs of an urban model and the data from the local air quality network was reduced from 8 % to 2.5 % following fusion with the sensor data. However, the results of the fusion technique are characterized by a lower dispersion than the input data sets, which leads to a smoothing of the peaks and thus an underestimation of the maximum values. Finally, the performance of fusion is logically degraded by the uncertainty in the sensor measurements and the low correlation between the two data sources due to biases in the LCS measurements (Gressent et al., 2020). This underlines the importance of accurate calibration of portable devices to achieve reliable air quality mapping on a fine scale.

4.3  Emerging challenges

4.3.1  use of low-cost sensors.

Providing citizens and stakeholders with innovative information from large networks of sensors can yield added value and is fast becoming one of the main emerging challenges in air quality management. Nevertheless, with the greater range of observational techniques available now, there is a need for the application of instrumentation consistency, involving operation of mobile sensors by citizen for routine inter-calibrations and approaches for sensor intercomparison in networks, using correction algorithms for sensors which should be described in a common way. When sensors are installed on board vehicles or UAV, detailed information about the sensor response time should be provided taking account of the compatibility with its movement speed and data gathering frequency.

There is also the need to strengthen the linkages between existing measurement data sets. For example, air pollution monitoring networks of governmental agencies operating at local and national levels incorporating reference data with certified QA–QC methods need to be explored to exploit numerical algorithms, especially from artificial intelligence or dynamic data assimilation, for example as part of sensor and network certifications and standardization, so that these measurement methodologies and the available enormous amount of data can be useful for air quality research and assessment, including legislative reporting.

In the case of low-cost sensors, guidelines and sensor certifications for LCS and MCS are prerequisites for their application. Because such documentation has not been consistently available up to now, LCS and MCS data cannot be used for official assessment of WHO or EU limit value exceedances. Furthermore, the level of acceptable data quality of LCS and MCS is difficult to ascertain, and presently the LCS and MCS networks are difficult to integrate into or extend the air pollution monitoring networks of responsible authorities.

4.3.2  Multi-pollutant instruments

Depending on the monitoring task of air quality or personal exposure, sensors for detection of all air pollutants including ultra-fine particles (UFPs) and particle size distribution (PSD) but also greenhouse gases (GHGs) are necessary. In the application case of sensors embedded at the surface of clothes or carried by individuals, extended miniaturization of LCS and MCS must measure the personal air pollution exposure. Relevant developments could also include personal measurements of bioaerosols (e.g. pollen and fungi). Such data are required to study the combined health effects of air pollutants, bioaerosols, and meteorological parameters. In this sense the speciation or chemical composition and physical characteristics of particles of all sizes are needed too.

4.3.3  Modelling for urban air quality to support observation data products

The small-scale forecast of air quality for different applicants and personal health protection must be improved by adaptation of corresponding numerical simulations of air pollution, based on online input data, which requires readily accessible sources like traffic counting and household heating activities. Alternatively, inverse modelling approaches can help quantify the strengths of diffusive emission sources and identify hot spots. Running spatial and temporal highly resolved numerical simulations requires online evaluation data from the combination of different platforms and the application of data algorithms from the area of machine learning or artificial intelligence.

The assimilation of small-scale data from measurements and numerical simulation of air pollution should be used for reduction of the space-time gaps of measurement networks. This is needed because measurement networks cannot be as dense as the spatial grids of numerical simulations. This implies further development of integration of observations by different platforms and methods as well as the assessment of numerical simulation results together with the application of crowdsourcing. Big data analyses and data assimilation methods can provide new areas of modelling applications in the field of improvement of air quality, determination of air pollution emissions and emission inventories, and development of personal health protection measures. Finally, it is necessary that these data eventually become suitable for monitoring and assessment of air quality in agreement with national and international guidelines.

Measurements and numerical simulation of coupled outdoor and indoor air quality must be supported for obtaining more realistic personal air pollution exposure information, given that most people are mainly exposed to indoor air, which, in turn, is strongly influenced by the quality of the outdoor air.

5.1  Brief overview

Over the last years, it became obvious that our understanding of pollution and exposure processes at the urban scale could be improved by combining multi-scale models and creating new dedicated numerical approaches and that the representation of scale interactions for dynamic phenomena, pollutant emission sources, and pollutant ageing would be a critical element in the realism of the simulation outputs. New developments have therefore aimed at restoring the spatial variability and heterogeneity of air pollution due to the turbulent transport of pollutants, whether in urbanized valleys, city centres, or confined urban spaces such as canyon streets.

The motivation of these works is to address societal issues with a focus on street-level representations of pollutant concentration fields to support the assessment of individual exposure to pollution. In this context, it is now acknowledged that statistical and other data analysis techniques such as machine learning have an important role to play in identifying underlying patterns and trends as well as relationships between different parameters. At the same time, air quality monitoring has been progressing by improving ensemble techniques that allow for more in-depth model evaluation and provide a solid basis for consistent operational work on air quality. The following section reviews current challenges and highlights emerging areas of research covering the development, application, and evaluation of air quality models.

5.2  Current status and challenges

5.2.1  innovative combinations of models.

To meet the need to represent concentration gradients of primary pollutants in large agglomerations, the use of urban-scale dispersion models has increased since the 2010s (Singh et al., 2014; Soulhac et al., 2012). These models indeed allowed the resolution of dispersion effects in a complex emitting and built environment, whereas chemistry–transport models (CTMs) cannot provide an explicit representation of near-source characteristics and meet computational time issues as the resolution increases. However, both the lack of connection between local emission effects and the regional transport of pollutants and the absence of a relevant representation of atmospheric reactivity limit the scope of this type of model. Therefore, interest is progressively turned to the nesting of CTMs and urban models, which allows the exploitation of the advantages of both approaches. Over the last decade, approaches either coupling or nesting Eulerian models with Gaussian source dispersion models (Hood et al., 2018; Hamer et al., 2020), microscale CFD models (Tsegas et al., 2015), obstacle-resolving Lagrangian particle models (Veratti et al., 2020), and/or street models (Jensen et al., 2017; Kim et al., 2018; Khan et al., 2021) have thus been developed with the aim of producing comprehensive cross-scale simulations of air quality in the city. An organization chart for such combined models is illustrated in Fig. 8.

Figure 8 Schematic diagram of the EPISODE model with the CityChem extension (EPISODE–CityChem model), from Karl et al. (2019b).

The interest of the “CTM-Urban dispersion model” approaches called plume-in-grid or street-in-grid lies in the fact that they allow in a single time step the simulation of urban background and to solve at low cost the dispersion of near-field emissions, for more resolved and realistic pollutant concentration fields. Compared to an urban model alone, those systems improve NO 2 scores in areas upwind of urban sources, as well as the average concentration levels of compounds that have a strong long-range transport component such as PM 2.5 , PM 10 , and ozone (Hood et al., 2018). Implemented at the scale of an agglomeration or a region, this approach demonstrated its ability to represent the diversity of urban microenvironments (e.g. proximity to road traffic versus urban background, effect of building density, and street configuration) that were until now poorly considered by the Eulerian approach alone. The representation of road traffic and its influence on urban air quality have been the main focus of these studies. Reaching a resolution from a few metres to a few tens of metres, the simulation outputs indeed accurately reproduce the gradients observed along road axes (see Fig. 9) and show greater comparability with urban-scale measurement data than CTMs alone (especially for NO 2 ). Particularly improved performances have been observed under stable winter conditions, and for some studies, the deviation from measurements is within the 15 % maximum uncertainty allowed by the EU directive for continuous measurements (Hamer et al., 2020). Mostly, the results show a better representation of the amplitude of the local signal than an improvement of the correlation with the observed concentrations, and it is concluded that these multi-scale approaches are a significant advance to predict local peaks and episodes. These skills set them apart as essential tools for providing high-resolution air quality data for street-level exposure purposes (Singh et al., 2020b). Statistical evaluations of the model outputs based on the EU DELTA Tool have been carried out as part of several studies: they show that the models comply well with the quality objectives of the FAIRMODE approach ( https://fairmode.jrc.ec.europa.eu/document/fairmode/WG1/MQO_GuidanceV3.2_online.pdf , last access: 23 Febraury 2022). In the end, although the performances of the models remain dependent on the relative importance of local emissions, as well as transport and chemical processes at each computation grid point, most of the residual biases could be attributed to a lack of realism in the emissions. This includes the presence of poorly characterized local sources (works on the street, road particulate resuspension processes) but also insufficient temporal refinement of road traffic profiles. In this respect, it should be emphasized that the improvement of particulate representation in the model and the restitution of near-field chemical equilibria are also expected as major evolution pathways for the models.

Figure 9 NO 2 annual average concentrations from the coupled ADMS-Urban–EMEP4UK model for (a)  the whole of Greater London and (b)  an area of central London. Monitoring data are overlaid as coloured symbols (Hood et al., 2018).

The study of the impact of shipping activities on urban air quality has also benefited from these multi-scale modelling approaches. Indeed, while conventional CTM approaches simulating the effect of shipping emissions in coastal areas of the North and Baltic seas agreed on the average contribution of shipping to air pollution (around 15 %–30 % of elevated concentrations of SO 2 , NO 2 , ozone, and PM 2.5 ; see Aulinger et al., 2016; Jonson et al., 2015; Karl et al., 2019a; Geels et al., 2021; Moussiopoulos et al., 2019, 2020), the use of urban and plume dispersion models made it possible to refine this diagnosis and assess near-field effects. As for road traffic, the influence of ship emissions on air quality induces pollution gradients in the city. Karl et al. (2020) thus found out that, in residential areas up to 3600 m from a major harbour, the ultra-fine particle concentrations were increased by a factor of 2 or more compared with the urban background.

5.2.2  Improved turbulence and dynamics for higher-resolution assessment of urban air quality

In parallel, the need for higher-resolution assessment of urban air quality poses new demands on flow and dispersion modelling. As an additional difficulty besides complex-geometry-induced phenomena, we are reaching a spatial resolution of metres and a temporal resolution of seconds, thus entering the space scales and timescales of atmospheric turbulence. Therefore, the exposure-related parameters cannot be described only deterministically without considering their stochastic component. A recent step forward in this direction is the increased use of large-eddy simulation (LES) methodology dealing directly with the stochastic behaviour of flow and concentration parameters (Wolf et al., 2020).

Advanced computational fluid dynamics (CFD), including Reynolds-averaged Navier–Stokes (RANS) equations models that provide concentration standard deviation, have also appeared in literature for some time (Andronopoulos et al., 2019). More precisely, the implementation of LES class models solving the most energetic part of turbulence explicitly as well as 3D primitive hydro-thermodynamical equations and the structural details of the complex urban surface has been carried out at the scale of agglomerations, in meteorological conditions corresponding to typical stratified winter pollution situations, and fed with emission data from the city authorities (residential combustion as well as maritime and road traffic in particular). More specifically, advanced CFD models such as LESs, have shown to better characterize the very fine-scale variability of primary urban pollution, for example regarding the irregular spatial distribution of concentrations in proximity to road traffic at complex built-up intersections, which makes it possible to open a reflection on the representativeness of the levels measured and their regulatory use and to define criteria for the optimization of measurement networks. LES local-scale modelling has been used to refine urban air quality predictions either alone (Esau et al., 2020) or embedded in an urban-scale model (San José et al., 2020). Also, wider use of CFD has taken place to improve understanding of pollution distribution inside a built environment, especially for critical infrastructure protection (Karakitsios et al., 2020).

Microscale models are particularly powerful to resolve the turbulent flow and pollutant dispersion around urban obstacles to reconstruct pollutant concentration variability within the urban canopy. Recent microscale model simulations also showed the importance of barrier effects for emissions from large ships. It was thus shown that turbulence at the stern of the ship may cause a significant decrease in exhaust pollutants, leading to higher concentrations near the ground and, most likely, higher exposure of the nearby urban population (Badeke et al., 2021). The application of LES (Esau et al., 2020; Wolf-Grosse et al., 2017; Resler et al., 2020; Werhahn et al., 2020; Hellsten et al., 2020; Khan et al., 2021) and CFD (San José et al., 2020; Gao et al., 2018; Flageul et al., 2020; Koutsourakis et al., 2020; Nuterman et al., 2011; Buccolieri et al., 2021; Kurppa et al., 2018, 2019; Karttunen et al., 2020; Kurppa et al., 2020) models for air quality assessment in urban environments is becoming a frequent approach. Many papers implementing the PALM LES model (Maronga et al., 2015) have been presented at the 12th International Conference on Air Quality – Science and Application. Yet, their application is still limited by difficulties dealing with urban-scale atmospheric chemistry and by the relevant computational resources required – as the use of advanced models such as LESs requires increased computational capabilities. On the other hand, the heavy computational burden of urban LES computations can be reduced by approximately 80 % or even more by employing the two-way coupled LES–LES nesting technique, recently developed within the LES model PALM (Hellsten et al., 2021). Precomputation of LES in operational modelling can be an acceptable solution, especially combined with big data compression methodologies (Sakai et al., 2013). Another possibility is to focus on limited urban areas with special interest (e.g. street canyons and “hot spots”); however, one should in this case take into account the effect on turbulent transport from the surrounding larger-scale turbulent phenomena. In the problem of urban air quality, an assisted approach in the selection/classification process is the use of clustering (Chatzimichailidis et al., 2020) and artificial intelligence/machine learning technologies (Gariazzo et al., 2020).

5.2.3  Use of advanced numerical approaches and statistical models

At the same time, the complementary role of prognostic and diagnostic approaches has been explored. New methodologies based on artificial neural network models, machine learning, or autoregressive models have been developed in order to achieve a more realistic representation of air quality in inhabited areas than achieved by CTMs (Kukkonen et al., 2003; Niska et al., 2005; Carbajal-Hernández et al., 2012; P. Wang et al., 2015; Zhan et al., 2017; Just et al., 2020; Alimissis et al., 2018). Likewise, Pelliccioni and Tirabassi (2006) employed neural networks to improve the outputs of Gaussian and puff atmospheric dispersion models. Also, Mallet et al. (2009) applied machine learning methods for ozone ensemble forecast and performed sequential aggregation based on ensemble simulations and past observations.

Kukkonen et al. (2003), through an extensive evaluation of the predictions of various types of neural network and other statistical models, concluded that such approaches can be accurate and easily usable tools of air quality assessment but that they have inherent limitations related to the need to train the model using appropriate site- and time-specific data. This dependence has prevented their use in the evaluation of air pollution abatement scenarios or for the evaluation of multidecadal time series of pollutant concentrations. The works of X. Li et al. (2017) confirmed that methods based on machine learning, and more specifically neural networks, can accurately predict the temporal variability of PM 2.5 concentrations in urban areas but that the model performance may be improved using explanatory training variables. Prospective neural network modelling works were also conducted in a canyon street by Goulier et al. (2020). They proposed a comparison of model outputs with measurements (based mainly on Pearson correlation, rank correlation by Spearman, modelling quality indicator's index from FAIRMODE), for a set of gaseous and particulate pollutants. They confirmed that the modelled data were able to reproduce with a very good accuracy the variability of the concentrations of some gaseous pollutants (O 3 , NO 2 ) but that there was still a significant margin for improvement of the models, notably for particles. Again, an important part of the expected progress lies in the choice of model predictors.

As for multi-scale modelling, the main research efforts associated with these numerical approaches are directed towards the downscaling of simulated pollutant concentration fields in urban areas, the improvement of CTM forecast using additional observation data, and a refined representation of individual exposure at the street scale (Berrocal et al., 2020; Elessa Etuman et al., 2020). Gariazzo et al. (2020) used a random forest model to enhance CTM results and produce improved population exposure estimates at 200 m resolution, in a multi-pollutant, multi-city, and multi-year study conducted over Italy. In addition to reduced bias, the outputs presented much greater physical consistency in their temporal evolution, when compared to measurements.

Other applications, such as advancing knowledge about exposure in urban microenvironments, have also been made possible by these approaches Thus, the use of Bayesian statistics has shown an ability to predict the concentration gradients of primary pollutants in the immediate vicinity of an air quality monitoring station, by iterating between observations and the outputs of a microscale simulation approach – including both a CFD and a Lagrangian dispersion model (Rodriguez et al., 2019).

5.2.4  Implementation of activity-based data

To take full advantage of the high-resolution simulation capability of these new modelling tools, and to achieve a more comprehensive approach to the determinants of air quality in urban areas, modellers have relied on a new generation of activity-based emissions data.

As for traffic, new methodologies relying on individual data collected through surveys, geocoded activities, improved emission factors, and measured traffic flows (Gioli et al., 2015; Sun et al., 2017) or involving traffic models simulating origin–destination matrices for city dwellers on the road network (Fallah-Shorshani et al., 2017) have been developed to serve as input to the urban dispersion models. Their implementation in a case study in Italy, with a horizontal resolution of 4 m, showed that detailed traffic emission estimates were very effective in reproducing observed NO x variability and trends (Veratti et al., 2020).

Residential wood combustion has also proven to act as a major source of harmful air pollutants in many cities in Europe, and especially in northern-central and northern European countries which have a strong tradition of wood combustion. Yet, until the early 2010s, residential wood combustion (RWC) inventories were still heavily burdened with uncertainties related to actual wood consumption, the location of emitters, emission factors depending on heating equipment, and practices driving the temporality of emissions. To represent RWC emissions more accurately in urban air quality models, new emission estimation methods based on environmental and activity variables that drive pollutant emissions have been developed. They include for example outdoor temperature, housing characteristics and equipment, available heating technologies and associated emission factors, or temporal activity profiles from official wood consumption statistics (Grythe et al., 2019; Kukkonen et al., 2020b). Kukkonen et al. (2020b) notably showed with this approach that the annual average contribution of RWC to PM 2.5 levels could be as high as 15 % to 22 % in Helsinki, Copenhagen, and Umeå and up to 60 % in Oslo. Overall, although the results show a better horizontal and vertical spatial distribution of emissions compared to non-specific inventories, improvements are expected, especially on the use of meteorological parameters and regarding emission factors for specific devices.

Finally, for emissions associated with maritime activity in port areas, the inventories developed specifically for high-resolution modelling approaches include information on the fleet, the ship rotations in the harbour, and the emission heights. The implementation of the EPISODE-CityChem model within a CTM showed that in Baltic Sea harbour cities such as Rostock (Germany), Riga (Latvia), and Gdańsk–Gdynia (Poland), shipping activity could have contributed to 50 % to 80 % of NO 2 concentrations within the port area (Ramacher et al., 2019). As for the other sources, improvements are expected. They concern for instance the energy consumption of the different ships and the propulsion power of the auxiliary systems of the ships during their stay in port.

Because they allow detailed mapping of air quality in urban areas, and realistically represent emitting activities, those approaches allow tackling issues such as chronic exposure and source–concentration relationships, but they also provide elements for increased policy and technical measures, as discussed below: regulation, information campaigns, and economic steering.

5.2.5  Contribution of modelling to policy making and urban management strategies

Applying air quality and emission models allows for projections of future developments in air quality that can shed light on the different effects of alternative policy options, e.g. new regulations or effects of changes in the emissions from certain emission sectors. As an example (Fig. 10), the OSCAR model was run over London to quantify the contribution of sources – such as traffic – to the urban PM 2.5 concentration gradients.

Figure 10 (a)  Predicted spatial distributions of the annual mean PM 2.5 concentrations in µg m −3 , and (b)  urban traffic contributions to the total PM 2.5 concentrations, in %, for London for the year 2008 (Singh et al., 2014).

Air quality modelling is expected to gain relevance following the review of air quality legislation announced as part of the European Green Deal (EC, 2019), whereby the European Commission will also propose strengthening provisions on monitoring, modelling, and air quality plans to help local authorities achieve cleaner air. The construction of these future air quality modelling scenarios can be demanding, in particular when the goal is to be realistic and consistent with technological potentials as well as economic and societal developments (in particular reductions in the use of fossil fuels driven by climate policies).

Another field of action recently explored is that of technology-based and management-based traffic control strategies, and in particular the implementation of low-emission zones (LEZs) in urban areas (e.g. in Portugal, Dias et al., 2016; France, Host et al., 2020; and India, Sonawane et al., 2012). The quantification of the expected gains in terms of pollutant concentrations in ambient air, but also of economic benefits and reduction in the occurrence of chronic respiratory diseases or vascular accidents, provides concrete and robust elements for political and citizen debate and helps to move towards greater acceptability of the measures. In this framework, the degree of realism of the simulated scenarios, the spatial refinement of the approaches used, and also the capacity to evaluate them at the sub-urban scale (street, individual) can become determining elements of their scientific relevance and their legitimacy in the policy debate. Therefore, an increasing number of studies favour the use of multi-scale models with the introduction of puff or Gaussian dispersion models, as well as canyon-street models, with CTMs. When modelled scenarios serve as a basis for political decisions, it is highly valuable to include relevant authorities and decision makers from the beginning in the scenario design. This can be done in common workshops with relevant stakeholders where questions about technological trends and possibilities for emission reduction are discussed.

The analysis of simulation data for the estimation of health impacts can be ensured by integrated approaches – such as the EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) – or more simply by algorithms derived from epidemiology such as population-attributable fractions, which are standard methodology used to assess the contribution of a risk factor to disease. In terms of emissions, depending on the focus of the study, survey data on residential practices or activity-based road traffic models (as well as marine traffic models where appropriate) are increasingly used. Supplementary traffic algorithms can sometimes more accurately represent the effects of congestion on roadway emissions. Finally, for more realism, the scenarios considered can be derived from either the relevant air quality plans implemented at the scale of agglomerations or projections on vehicle fleet evolution (Andre et al., 2020). Some of the models also include the feedback effects of changes in practice, such as the estimate of emission increase due to the energy demand for electric vehicle charging (Soret et al., 2014).

Very small-scale modelling has also been used in other fields such as support in evaluating the effect of roadside structures on near-road air quality. Several studies, mainly based on CFD models, including LES approaches have thus focused on the performance of air pollution dispersion by green infrastructures in open areas and street canyons, even characterizing the capacity of parked vehicles to reduce pedestrian exposure to pollutants (see review article in Abhijith et al., 2017). Also, the link between the morphology of urban buildings, the dispersion of emissions, and air quality is often apprehended through CFD models (Hassan et al., 2020). At an even more operational level, LUR models (based on the spatial analysis of air quality data) have been coupled to high-resolution CTM runs to allow a precise identification of land use classes more exposed to PM 10 , SO 2 , and NO 2 . The results provided a methodological framework that could be used by authorities to assess the impact of specific plans on the exposed population and to include air quality in urban development policies (Ajtai et al., 2020).

Examples also exist in the area of shipping emissions, where several EU-funded projects either involved stakeholders such as IMO and HELCOM from the beginning (e.g. Clean North Sea Shipping, ENVISUM, CSHIPP, EMERGE) or made use of their knowledge in dedicated expert elicitation workshops (e.g. SHEBA). Future scenarios for shipping, some of them developed in these projects, were presented for the North and Baltic seas (Johansson et al., 2013; Matthias et al., 2016; Karl et al., 2019a; Jonson et al., 2015), for Chinese waters (Zhao et al., 2020b), and globally (Sofiev et al., 2018; Geels et al., 2020). However, the process of scenario generation in cooperation with authorities and other stakeholders is rarely described in scientific literature or fully detailed in publications that address various policy options.

5.2.6  Ensemble modelling for air quality research applications

In parallel, statistical developments also serve the evolution of ensemble models. During the last decade, ensemble-building methodologies have been questioned and improved in several international collaborations, and the inclusion of new observational data has allowed a better assessment of the relevance of these approaches. Ensemble forecasting can be implemented using multiple models or one model but with different inputs (e.g. varying meteorological input forcings, emission scenarios, chemical initial conditions), different process parameters (e.g. varying chemical reaction rates), different model configurations (e.g. varying grid spacings), or different models (Hu et al., 2017; Galmarini et al., 2012). A comprehensive study on ensemble modelling of surface O 3 was done as part of the Air Quality Model Evaluation International Initiative (AQMEII), including 11 CTMs operated by European and North American modelling groups (Solazzo et al., 2012). One of the main conclusions was that even if the multi-model ensemble based on all models performed better than the individual models, a selection of both top- and low-ranking models can lead to an even better ensemble (Kioutsioukis et al., 2016). It was also shown that outliers are needed in order to enhance the performance of the ensemble.

Within the CAMS regional forecasting system for Europe, multi-model ensemble modelling is a part of daily operational production ( https://www.regional.atmosphere.copernicus.eu/ , last access: 28 February 2022) for several air quality components. Statistical analyses have shown that an ensemble based on the median of the individual model gives a robust and efficient setup, also in the case of outliers and missing data (Marécal et al., 2015). By combining global- and regional-scale models, Galmarini et al. (2018) have taken this kind of ensemble modelling a step further, by setting up a hybrid ensemble to explore the full potential benefit of the diversity between models covering different scales. The analysis indeed showed that the multi-scale ensemble leads to a higher performance than the single-scale (e.g. regional-scale) ensemble, highlighting the complementary contribution of the two types of models.

5.3  Emerging challenges

5.3.1  on multiscale interaction and subgrid modelling.

The advances in computational capacity, the progress on big data management, and the recent developments on low-cost sensor technology, together with the significant developments in closing the gaps of knowledge when dealing with finer spatial and temporal scales (up to the order of metres and seconds, respectively) give the opportunity for further achievements in terms of innovation and outcome reliability in urban- to local-scale flow and air quality assessment. In such applications, very high spatial resolution modelling outputs are required together with dynamic and geocoded demographic data to conduct health monitoring on the impacts of air pollutants. However, new sub-grid/local approaches such as LESs, advanced CFD-RANS, machine learning statistical tools, and interfaces among different modelling scales (regional, urban, local/sub-grid) require further R&D work, especially when interfacing models using different parameterizations or computational approaches.

Of specific interest here is the case of model nesting in regimes where it has not been extensively applied in the past, as is the case of implementation and validation of multiply nested LESs (see e.g. Hellsten et al., 2021), as well as coupling of urban-scale deterministic models with local probabilistic models. In both areas, complications arise due to the nature of different parameterizations and the way boundary conditions are traditionally treated in LES models, highlighting the need for further validation and tools for the numerical evaluation of coupling implementations. Further areas of development include the better articulation between CTMs and subgrid models, towards solving overlay problems like emission double counting and mass conservation across interpolated interfaces, both critical points for their successful application as assessment tools.

5.3.2  On chemistry and aerosol modelling

One important aspect is the fact that local-scale models often include simple approaches to tropospheric chemistry. Although such an approach can be justified from the fact that computation domain timescales are usually well below lifetime scales of priority pollutants, it also poses limitations that need to be addressed. For example, the lack of full representation of NO x –VOC chemistry, or not considering a delay in establishing the photostationary NO–NO 2 –O 3 equilibrium, can introduce a significant bias in the restitution of concentration gradients at very fine scales. Particle-size-resolved schemes, including for example the discrimination of particle removal phenomena, are also expected to be important developments for these local models. How do simplified chemistry and physics impact on treating traffic emissions in cities? What is their role in the restitution of particle growth, secondary organic aerosol (SOA) formation, and ozone chemistry? These issues require special attention. They are also relevant to the treatment of other urban sources generating strong concentration gradients, such as shipping. Thus, the impact of the representation of VOC behaviour on particle formation and ageing, or the effect of NO 2 removal, both in the early phases of ship plume dispersion, should also be investigated.

More globally, there remain issues in the representation of reactivity in multi-scale modelling approaches and air quality forecasting. On the one hand, although some studies have shown that high-resolution models are good at predicting the occurrence (or non-occurrence) of local pollution events, it has been observed that they do not always capture the full range of pollutant concentrations and, especially, the amplitude of the strongest concentration peaks. On the other hand, there remains a very strong interaction between locally emitted pollutants and those resulting from long-range transport (LRT) to the city. This may be determinant for the operational forecasting of air quality at the urban scale. Thus, the representation, on a fine scale, of the fundamental processes of reactivity is one next challenging issue of multi-scale modelling. For local-scale modelling it is indeed important to make sure that at least we include chemical transformation with timescales significantly smaller than the time ranges imposed by the considered computational domain.

5.3.3  On fine-scale model input and emission data

As we move to finer scales and more advanced modelling, the input data – whether meteorological, descriptive of the urban environment, or related to the sources of pollutant – also require additional knowledge of their time and space variation, even including sufficiently detailed statistical behaviour. The refinement of meteorological and chemical input fields for statistical approaches is an important challenge. Indeed, the application of LES or statistical models in a fine domain embedded into a larger domain where ensemble-average modelling data are available and needed raises the question of how to generate fine-scale or statistical input data that are both mathematically consistent and physically correct. It was highlighted that the role of statistical models based on machine learning is increasing, especially for urban AQ applications. This is due to growing computer and IT networking possibilities, but also to new types of numerous observations, e.g. crowdsourcing, low-cost sensors, or citizen science approaches. The ability of machine learning to capture these new data sources and identify new applications in fine-scale air quality and personal exposure is therefore a great challenge for the coming years.

As far as emissions are concerned, the gain in realism has become a prerequisite to produce decision-support scenarios and requires a strong grounding in reality – i.e. emissions must be based on a census of the activities and on the specificities of the emitters (e.g. car engines, heating equipment, and rotation of boats in the port), which requires increasingly complex phases of model implementation over a territory and the intervention of a multiplicity of actors for data supply. In this context, tabulated emission inventories – even those based on actual activity data – have limited scope for use in future air quality and exposure scenarios. To be realistic, the scenarios must be able to reproduce the variation in emitting activity in relation to changes in transport supply, urban planning, energy costs, and individual or collective energy consumption practices. Therefore, a significant part of the work is now focused on developing air quality modelling platforms integrating emission models centred on the individual (see Fig. 11 in this paper; Elessa Etuman and Coll, 2018).

Figure 11 Schematic representation of OLYMPUS emission operating system (Elessa Etuman and Coll, 2018).

There, the main challenges are related to the representation of individual mobility for both commuting and private activities as well as domestic heating and more broadly energy consumption practices on one side and the consideration of traffic parameters such as urban freight, the distribution of traffic and its speciation, driving patterns, or the effects of road congestion on the other side (Lejri et al., 2018; Coulombel et al., 2019).

Another emerging issue is also how to cope with short-time hazardous emissions in urban areas. Such emissions can be related to accidents or deliberate releases that are of increased concern today. An important characteristic of associated exposures is their inherent stochastic behaviour (Bartzis et al., 2020). Novel modelling approaches are needed to properly assess the impact and support relevant mitigation measures.

5.3.4  On model evaluation

To act on these numerous and expected developments, and use their results for operational decision support, multi-scale models need validation. An often-overseen basic prerequisite here is the availability and representativeness of validation data, particularly at smaller scales. The model's performance indeed needs to be explored in more spatial detail and in all covered spatial scales, preferably as part of multi-scale urban-to-rural intercomparison projects, in order to be able to provide finer assessment on air quality and exposure. Such efforts can be supported by networks of inexpensive sensors as well as smart tags (Sevilla et al., 2018) and other sources of distributed information acting complementary to traditional local monitoring and flow-profiling technologies. To obtain methodology and data refinement as well as outcome reliability, more experience through additional case studies is also needed. Finally, consideration should be given to specific model performance evaluation criteria for various regulatory purposes, including prospective mode operation, i.e. the ability of a model to accurately predict the air quality response to changes in emissions. To this end, evaluations can draw on the very large methodological work that has been carried out since 2007 by the Forum for AIR quality MODelling in Europe (FAIRMODE) for the assessment of CTMs (Monteiro et al., 2018). The objective was to develop and support the harmonized use of models for regulatory applications, based on PM 10 , NO 2 , and O 3 assessments. The main strength of this approach was to produce an in-depth analysis of the performance of different model applications, combining innovative and traditional indicators (Modelling Quality Index and Modelling Quality Objectives) and considering measurement uncertainty. Although FAIRMODE was successful in promoting a harmonized reporting process, there remain major ways of improvement that can be critical for its regulatory acknowledgement – in particular regarding inconsistencies between indicators of different time horizons – and a methodology dedicated to data assimilation assessments.

6.1  Brief overview

There is a need to increase prediction capabilities for weather, air quality, and climate. The new trend in developing integrated atmospheric dynamics and composition models is based on the seamless Earth system modelling (ESM) approach (WWRP, 2015) to evolve from separate model components to seamless meteorology–composition–environment modelling systems, where the different components of the Earth system are taken into account in a coupled way (WMO, 2016). The Coupled Model Intercomparison Project (CMIP) is the main reference for the development ESM models that serve as input to the IPCC assessment reports (Eyring et al., 2016; IPCC, 2022). One driver for improvement is the fact that information from predictions is needed at higher spatial resolutions and longer lead times. In addition, we have to consider two-way feedbacks between meteorological and chemical processes on the one hand and aerosol–meteorology feedback on the other hand, where both are needed to meet societal needs. Continued improvements in prediction will require advances in observing systems, models, and assimilation systems. There is also growing awareness of the benefits of closely integrating atmospheric composition, weather, and climate predictions, because of the important role that aerosols (and atmospheric composition in general) play in these systems. Because the proposed review is focused on air quality and its atmospheric forcings, the present section discusses the atmospheric component of ESMs focusing on coupled chemistry–meteorology models.

While this section also considers challenges related to air quality modelling, it differs in emphasis to Sect. 5, by examining interactions that operate on multiple scales and including multiple processes that affect air quality, especially for cities.

6.2  Current status and challenges

6.2.1  interactions and coupled chemistry–meteorology modelling (ccmm).

Meteorology is one of the main uncertainties of air quality modelling and prediction. Many studies have investigated the role of meteorology in air quality in the past (e.g. Fisher et al., 2001, 2005, 2006; Kukkonen et al., 2005a, b) and even more recently (e.g. McNider and Pour-Biazar, 2020; Rao et al., 2020; Gilliam et al., 2015; Parra, 2020). The relationship between meteorology and air pollution cannot be interpreted as a one-way input process due to the complex two-way interaction between the atmospheric circulation and physical and chemical processes involving trace substances in both gas and aerosol form. The improvement of atmospheric phenomena prediction capability is, therefore, tied to progress in both fields and to their coupling.

The advances made by mesoscale planetary boundary layer meteorology during the last decades have been recently reviewed by Kristovich et al. (2019). During the last decade significant advances have been made even in the capabilities to predict air quality and to model the many feedbacks between air quality, meteorology, and climate, including radiative and microphysical responses (WMO, 2016, 2020; Pfister et al., 2020). Due to advances in air quality models themselves and the availability of more computing resources, air quality models can be run at high spatial resolution and can be tightly (online) or weakly linked to meteorological models (through couplers). This is a pre-requisite to improve prediction skills further, while air quality models themselves will be improved as our knowledge of key processes continues to advance.

Online-coupled meteorology and atmospheric chemistry models have greatly evolved during the last decade (Flemming et al., 2009; Zhang et al., 2012a, b; Pleim et al., 2014; WWRP, 2015; Baklanov et al., 2014; Mathur et al., 2017; Bai et al., 2018; Im et al., 2015a, b), a comprehensive evaluation of coupled model results has been provided by the outcome of AQMEII project (Galmarini and Hogrefe, 2015). Although mainly developed by the air quality modelling community, these integrated models are also of interest for numerical weather prediction and climate modelling as they can consider both the effects of meteorology on air quality and the potentially important effects of atmospheric composition on weather (WMO, 2016). Migration from offline to online integrated modelling and seamless environmental prediction systems are recommended for consistent treatment of processes and allowance of two-way interactions of physical and chemical components, particularly for AQ and numerical weather prediction (NWP) communities (WWRP, 2015; Baklanov et al., 2018a).

It has been demonstrated that prediction skills can be improved through running an ensemble of models. Intercomparison studies such as MICS and AQMEII (Tan et al., 2020; Galmarini et al., 2017; Zhang et al., 2016) serve as important functions of demonstrating the effectiveness of ensemble predictions and helping to improve the individual models. Predictions can also be improved through the assimilation of atmospheric composition data. Weather prediction has relied on data assimilation for many decades. In comparison, assimilation in air quality prediction is much more recent, but important advances have been made in data assimilation methods for atmospheric composition (Carmichael et al., 2008; Bocquet et al., 2015; Benedetti et al., 2018). Community available assimilation systems for ensemble and variational methods make it easier to utilize assimilation (Delle Monache et al., 2008; Mallet, 2010). Furthermore, the amount of atmospheric composition data available for assimilation is increasing, with expanding monitoring networks and the growing capabilities to observe aerosol and atmospheric composition from geostationary satellites (e.g. Kim et al., 2020). Operational systems such as CAMS (Copernicus Atmospheric Monitoring Service) have advanced current capabilities for air quality prediction (Marécal et al., 2015; Barré et al., 2021).

Currently, NWP centres around the world are moving towards explicitly incorporating aerosols into their operational forecast models. Demonstration projects are also showing a positive impact on seasonal to sub-seasonal forecast by including aerosols in their models (Benedetti and Vitart, 2018). Even the usual subdivision between global-scale NWP models and limited-area models employed to resolve regional to local scales is going to be revised. Many groups are building new Earth system models and taking advantage of global refined grid capabilities that facilitate multiscale simulations in a single model run, as in the case of the Model for Prediction Across Scales (MPAS) (Skamarock et al., 2018; Michaelis et al., 2019) and MUSICA (Pfister et al., 2020) approaches.

6.2.2  Aerosol–meteorology feedbacks for predicting and forecasting air quality for city scales

Multiscale CTMs are increasingly used for research and air quality assessment but less for urban air quality. Recently, there have been examples of coupled urban and regional models which allow the prediction and assessment of local, urban, and regional air quality affecting cities (Baklanov et al., 2009; Kukkonen et al., 2012; Sokhi et al., 2018; Kukkonen et al., 2018; Khan et al., 2019b). In particular, a downscaling modelling chain for prediction of weather and atmospheric composition on the regional, urban, and street scales is described and evaluated against observations by Nuterman et al. (2021). Kukkonen et al. (2018) described a modelling chain from global to regional (European and northern European domains) and urban scales and a multidecadal hindcast application of this modelling chain.

There are still uncertainties in prediction of PM components such as secondary organic aerosols (SOAs), especially during stable atmospheric conditions in urban areas which can cause severe air pollution conditions (Beekmann et al., 2015). Moreover, aerosol feedback and interaction with urban heat island (UHI) circulation is a source of uncertainty in CTM predictions. Several studies (Folberth et al., 2015; Baklanov et al., 2016; Huszar et al., 2016) demonstrated that urban emissions of pollutants, especially aerosols, are leading to climate forcing, mostly at local and regional scales through complex interactions with air quality (Fig. 12). These, in addition to almost 70 % of global CO 2 emissions, arise from urban areas, and hence urban areas pose a considerable source of climate forcing species.

Figure 12 The main linkages between urban emissions, air quality, and climate. (Baklanov et al., 2010).

It is necessary to highlight that the effects of aerosols and other chemical species on meteorological parameters have many different pathways (e.g. direct, indirect, semidirect effects) and must be prioritized in integrated modelling systems. Chemical species influencing weather and atmospheric processes over urban areas include greenhouse gases (GHGs), which warm near-surface air, and aerosols, such as sea salt, dust, and primary and secondary particles of anthropogenic and natural origin. Some aerosol particle components (black carbon, iron, aluminium, polycyclic and nitrated aromatic compounds) warm the air by absorbing solar and thermal-infrared radiation, while others (water, sulfate, nitrate, and most organic compounds) cool the air by backscattering incident short-wave radiation to space. It has been demonstrated (Sokhi et al., 2018; Baklanov et al., 2011; Huszar et al., 2016) that the indirect effects of urban aerosols modulate dispersion by affecting atmospheric stability (the difference in deposition fields is up to 7 %). In addition its effects on the urban boundary layer (UBL) thickness could be of the same order of magnitude as the effects of the UHI (a few hundred metres for the nocturnal boundary layer).

6.2.3  Urban-scale interactions

Meteorology is one of the main uncertainties in air quality assessment and forecast in urban areas where meteorological characteristics are very inhomogeneous (Hidalgo et al., 2008; Ching, 2013; Huszar et al., 2018, 2020). For these reasons, models used at the urban level must achieve greater accuracy in the meteorological fields (wind speed, temperature, turbulence, humidity, cloud water, precipitation).

Due to different characteristics of the surface properties (e.g. heat storage, reflection properties), a heat island effect occurs in cities. Urban areas can therefore be up to several degrees Celsius warmer than the surrounding rural areas and experience lighter winds due to the increased drag of urban canopy. This heating impacts the local environment directly, as well as affecting the regional air circulation with complex interactions that can induce pollutant recirculation, worsen stagnation episodes, and influence ozone and secondary aerosol formation and transport.

Studies over the past decade (e.g. McCarthy et al., 2010; Cui and Shi, 2012; González-Aparicio et al., 2014; Fallmann et al., 2016; Molina, 2021) have shown that the effects of the built environment, such as the change in roughness and albedo, the anthropogenic heat flux, and the feedbacks between urban pollutants and radiation, can have significant impacts on the urban air quality levels. A reliable urban-scale forecast of air flows and meteorological fields is of primary importance for urban air quality and emergency management systems in the case of accidental toxic releases, fires, or even chemical, radioactive, or biological substance releases by terrorists.

Improvements (so-called “urbanization”) are required for meteorological and NWP models that are used as drivers for urban air quality (UAQ) models. The requirements for the urbanization of UAQ models must include a better resolution in the vertical structure of the urban boundary layer and specific urban feature description. One of the key important characteristics for UAQ modelling is the mixing height, which has a strong specificity and inhomogeneity over urban areas because of the internal boundary layers and blending heights from different urban roughness neighbourhoods (Sokhi et al., 2018; Scherer et al., 2019).

Modern urban meteorology and UAQ models (e.g. WRF, COSMO, ENVIRO-HIRLAM) successfully implemented (a hierarchy of) urban parameterizations with different complexities and reached suitable spatial resolutions (Baklanov et al., 2008; Salamanca et al., 2011, 2018; Sharma et al., 2017; Huang et al., 2019; Mussetti et al., 2020; Trusilova et al., 2016; Wouters et al., 2016; Schubert and Grossman-Clarke, 2014) for an effective description of atmospheric flow in urban areas. The application of urban parameterizations implemented inside limited-area meteorological models is becoming a common approach to drive urban air quality analysis, allowing the improved urban meteorology description in different climatic and environmental conditions (Ribeiro et al., 2021; Salamanca et al., 2018; Gariazzo et al., 2020; Pavlovic et al., 2020; Badia et al., 2020). However, activities to improve the parameterizations (Gohil and Jin, 2019) and provide reliable estimation of the input urban features (Brousse et al., 2016) are continuing.

6.2.4  Integrated weather, air quality, and climate modelling

Since cities are still growing, intensification of urban effects is expected, contributing to regional or global climate changes, including intensification of floods, heat waves, and other extreme weather events; air quality issues caused by pollutant production; and transport. This requires a more integrated assessment of environmental hazards affecting towns and cities.

The numerical models most suitable to address the description of mentioned phenomena within integrated operational urban weather, air quality, and climate forecasting systems are the new-generation limited-area models with coupled dynamic and chemistry modules (so-called coupled chemistry–meteorology models, CCMMs). These models have benefited from rapid advances in computing resources, along with extensive basic science research (Martilli et al., 2015; WMO, 2016; Baklanov et al., 2011, 2018a). Current state-of-the-art CCMMs encompass interactive chemical and physical processes, such as aerosols–clouds–radiation, coupled to a non-hydrostatic and fully compressible dynamic core that includes monotonic transport for scalars, allowing feedbacks between the chemical composition and physical properties of the atmosphere. These models incorporate the physical characteristics of the urban built environment. However, simulations using fine resolutions, large domains, and detailed chemistry over long time durations for the aerosol and gas/aqueous phase are computationally demanding given the models' high degree of complexity. Therefore, CCMM weather and climate applications still make compromises between the spatial resolution, domain size, simulation length, and degree of complexity for the chemical and aerosol mechanisms.

Over the past decade integrated approaches have benefited from coupled modelling of air quality and weather, enabling a range of hazards to be assessed. Research applications have demonstrated the advantages of such integration and the capability to assimilate aerosol information in forecast cycles to improve emission estimates (e.g. for biomass burning) impacting both weather and air quality predictions (Grell and Baklanov, 2011; Kukkonen et al., 2012; Klein et al., 2012; Benedetti et al., 2018).

6.3  Emerging challenges

6.3.1  earth systems modelling for air quality research.

Full integration of aerosols across the various applications requires advances in Earth system modelling, with explicit coupling between the biosphere, oceans, and atmosphere, taking advantage of global refined grid capabilities that facilitate multiscale simulations in a single ESM run. The Earth system models offer many advantages but also create new challenges. Data assimilation in these tightly coupled systems is a future research area, and we can anticipate advances in assimilation of soil moisture and surface fluxes of pollutants and greenhouse gases.

The expected advance of the Earth system approach requires an increased research effort for the different communities to work more closely together to expand and to evolve the Earth observing system capacity. For what concerns the atmospheric models, the improvement of aerosol–cloud interaction description, related sulfate production, and oxidation processes in the aqueous phase are important to provide a better estimate of aerosol and cloud condensation nuclei (CCN) production impacting weather and climate. Their impact on surface PM concentrations, especially in areas with very low SO x emissions like Europe, still needs to be investigated (Schrödner et al., 2020; Genz et al., 2020; Suter and Brunner, 2020).

6.3.2  Constraining models with observations

The use of coupled regional-scale meteorology–chemistry models for AQF represents a desirable advancement in routine operations that would greatly improve the understanding of the underlying complex interplay of meteorology, emission, and chemistry. Chemical species data assimilation along with increased capabilities to measure plume heights will help to better constrain emissions in forecast applications.

While important advances have been made, present challenges require advances in observing systems and assimilation systems to support and improve air quality models. From the perspective of air quality modelling, there are still uncertainties in the emission estimates (especially those driven by meteorology and other conditions such as biomass burning and dust storms).

The impacts of data assimilation of atmospheric composition are limited by the remaining major gaps in spatial coverage in our observing systems. Major parts of the world have limited or no observations (Africa is an obvious case). This is changing thanks to the forthcoming new constellation of geostationary satellites (Sentinel-4, TEMPO, and GEMS; Kim et al., 2020) measuring atmospheric composition and with the advances in low-cost sensor technologies. Machine learning applications will play important roles in improving predictions through better parameterizations, better ways to deal with bias, and new approaches to utilize heterogeneous observations, for example new models for relating aerosol optical depth (AOD) to surface PM 2.5 mass and composition.

Reanalysis products of aerosols and other atmospheric constituents are now being produced (Inness et al., 2019). These can support many applications, and continued development is strongly encouraged and will benefit from the observations and data assimilation advances discussed above.

6.3.3  Multiscale interactions affecting urban areas

For urban applications the main science challenges related to multiscale interactions involved the non-linear interactions of urban heat island circulation and aerosol forcing and urban aerosol interactions with clouds and radiation. In order to improve air quality modelling for cities, advances are needed in data assimilation of urban observations (including meteorological, chemical, and aerosol species), development of model dynamic cores with efficient multi-tracer transport capability, and the general effects of aerosols on the evolution of weather and climate on different scales. All these research areas are concerned with optimized use of models on massively parallel computer systems, as well as modern techniques for assimilation or fusion of meteorological and chemical observation data (Nguyen and Soulhac, 2021).

In terms of atmospheric chemistry, the formation of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols) in urban environments is still an active research area, and there is an important need to improve the understanding and treatment within two-way coupled chemistry–meteorology models.

Urban areas interact at many scales with the atmosphere through their physical form, geographical distribution, and metabolism from human activities and functions. Urban areas are the drivers with the greatest impact on climate change. The exchange processes between the urban surface and the free troposphere need to be more precisely determined in order to define and implement improved climate adaptation strategies for cities and urban conglomerations. The knowledge of the 3D structure of the urban airshed is an important feature to define temperature, humidity, wind flow, and pollutant concentrations inside urban areas. Although computational resources had great improvement, time and spatial resolution are still imposing some limitations to the correct representation of urban features, especially for the street scale. Urban areas are responsible for the urban heat island circulation, which interacts with other mesoscale circulations, such as the sea breeze and mountain valley circulations, determining the pathways of primary pollutants emitted in the atmosphere but even the production and transport of ozone (see e.g. Finardi et al., 2018) and secondary aerosols (Fig. 13).

Figure 13 Near-surface ozone concentrations ( µg m −3 ) predicted for 15 July 2015 at (a)  08:00, (b)  12:00, and (c)  17:00 LST over Naples. Wind field at 10 m height is represented by grey arrows. (Finardi et al., 2018; © American Meteorological Society. Used with permission.)

Challenges remain on how to include scale-dependent processes and interactions for urban- and sub-urban-scale modelling. These include spatial and temporal distribution of heat, chemical, and aerosol emission source activities down to building-size resolution, flow modification at the micro-scale level by the urban canopy structure and by the urban surface heat balance, enhancement/damping of turbulent fluxes in the urban boundary layer due to surface and emission heterogeneity, and chemical transformation of pollutants during their lifetime within the urban canopy sublayer. Obviously, the scale interaction issues facing air quality–meteorology–climate models are quite in line with those described in Sect. 5 for multi-scale air quality modelling. Thus, on coupling regional to urban and building scales, CTMs coupled with urbanized meteorological models are needed to describe the city-scale atmospheric circulation and chemistry in the urban airshed and the building and evolution of the urban heat island, especially strong during heat waves (Halenka et al., 2019), including the combined effects of urban, sub-urban, and rural pollutant emissions. High spatial resolution is also needed to capture pollutant concentration spatial variability at the pedestrian level in an urban environment, answering epidemiological research questions or emergency preparedness issues. In the near future, microscale CFD, including LES modelling, will probably become an appropriate tool for urban air quality assessment and forecasting purposes due to the expected continuous increase in computational resources enabling the inclusion of chemical reactions (Fig. 14). Nevertheless, today computational resources still limit their application to short-term episodes and often to stationary conditions, while climatological studies require for instance a multi-year approach. Parameterized street-scale models (Singh et al., 2020a; Hamer et al., 2020; Kim et al., 2018) or a database created with CFD simulations of several scenarios (Hellsten et al., 2020) can be alternative ways for the downscaling from the mesoscale to the city and street scale, together with obstacle-resolving Lagrangian particle models driven by Rokle-type diagnostic flow models (Veratti et al., 2020; Tinarelli and Trini Castelli et al., 2019) that can be coupled with CTMs for long-term air quality assessment (Barbero et al., 2021).

Figure 14 Modelled distribution of ground-level nitrogen dioxide  (a) and ozone  (b) at 20:00 CEST for a 6.7 km×6.7 km subarea of Berlin around Ernst-Reuter-Platz. The simulation was performed with the chemistry mechanism CBM4 and a horizontal grid size of 10 m (Khan et al., 2021).

6.3.4  Nature-based solutions for improving air quality

The growing interest for nature-based solutions requires the improvement of models' capability to describe biogenic emissions (Cremona et al., 2020) and deposition processes (Petroff et al., 2008; Petroff and Zhang, 2010), resolving the different species leaf features, biomass density, and physiology. The balance between vegetation drag, pollutant absorption, and biogenic volatile organic compound (BVOC) emissions determines the net positive or negative air quality impact at local and city scales (Karttunen et al., 2020; San José et al., 2020; Santiago et al., 2017; Jeanjean et al., 2017; Jones et al., 2019; Anderson and Gough, 2020). In most cases this feature cannot be explicitly considered, with some parameterized approach, such as the canyon one being necessary, to deal with it. Nevertheless, the present capabilities of UAQ models to describe biogenic emissions together with gas and particle deposition over vegetation covered surfaces (including green roofs and vertical green surfaces) need to be improved to include nature-based solutions' impact in air quality plan evaluation.

7.1  Brief overview

A substantial amount of research has been conducted regarding the health effects of air pollution, especially those attributed to particulate matter (PM). Nevertheless, it is not conclusively known which properties of PM are the most important ones in terms of the health impacts (e.g. Brook et al., 2010; Beelen et al., 2014; Pope et al., 2019; Schraufnagel et al., 2019). For example, a review article by Hoek et al. (2013) addressed cohort studies and reported an excess risk for all-cause and cardiovascular mortality due to long-term exposure to PM 2.5 .

In this section, we have therefore addressed three topical research areas, associated with air quality and health: (i) the health impacts of particulate matter in ambient air; (ii) the combined effects on human health of various air pollutants, heat waves, and pandemics; and (iii) the assessment of the exposure of populations to air pollution. Research that has been reviewed is based on selected international research projects and publications, but generally these are expected to reflect the general consensus, as both the projects and resulting publications involved a significant section of the air quality and health research community. Regarding pandemics, we will focus on the most recent one that has been caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The research and interdependencies of these topics have been illustrated in Fig. 15.

Figure 15 A schematic diagram that illustrates some of the main factors in the evaluation of the exposure and health impacts of particulate matter.

As illustrated in the figure, particulate matter pollution originates from a wide range of anthropogenic and natural sources, and its characteristics can vary in terms of size distributions, chemical composition, and other properties. The resulting health outcomes also vary substantially, depending on the target physiological system or organ of an individual. In addition, the assessments of the interrelations of PM pollution and health outcomes are challenged by various combined and in some cases synergetic effects caused by, for example heat waves and cold spells, allergenic pollen, and airborne microorganisms.

7.2  Current status and challenges

7.2.1  health impacts of particulate matter, (i) overview of the health impacts of particulate matter pollution.

In addition to cardiovascular and respiratory diseases, exposure to ambient air PM may result in acute and severe health problems, such as cardiovascular mortality, cardiac arrhythmia, myocardial infarction (MI), myocardial ischemia, and heart failure (Dockery et al., 1993; Schwartz et al., 1996; Peters et al., 2001; Pope et al., 2002). The Organization for Economic Co-operation and Development (OECD) concluded in its outlook (OECD, 2012) that PM pollution will be the primary cause of deaths of the African population by 2050, in comparison to hazardous water and poor hygiene. Pražnikar and Pražnikar (2012) comprehensively addressed in their review several epidemiological studies throughout the world; they reported a strong association between the PM concentrations and respiratory morbidity, cardiovascular morbidity, and total mortality.

Global assessments of air quality and health require comprehensive estimates of the exposure to air pollution. However, in many developing countries (e.g. Africa; see Rees at al., 2019; Bauer et al., 2019) ground-based monitoring is sparse or non-existent; quality control and the evaluation of the representativeness of stations may also be insufficient. An inter-disciplinary approach to exposure assessment for burden of disease analyses on a global scale has been recently suggested jointly by WHO, WMO, and CAMS (Shaddick et al., 2021). Such an approach would combine information from available ground measurements with atmospheric chemical transport modelling and estimates from remote sensing satellites. The aim is to produce information that is required for health burden assessment and the calculation of air-pollution-related Sustainable Development Goal (SDG) indicators.

(ii) Health effects associated with the long-term exposure to particulate matter

Long-term exposure may potentially affect every organ in the body and hence worsen existing health conditions, and it may even result in premature mortality (see for example a recent review by Schraufnagel et al., 2019; Brook et al., 2010; Brunekreef and Holgate, 2002; Beelen et al., 2015; Im et al., 2018; Liang et al., 2018; Vodonos et al., 2018; Pope et al., 2019). For example, a review article by Hoek et al. (2013) addressed cohort studies and reported an excess risk for all-cause and cardiovascular mortality due to long-term exposure to PM 2.5 . Beelen et al. (2015) analysed an extensive set of data from 19 European cohort studies; they found that long-term exposure to PM 2.5 sulfur was associated with natural-case mortality. Similar results regarding long-term exposure to PM 2.5 and mortality were also presented in other recent studies conducted by Vodonos et al. (2018) and Pope et al. (2019).

Studies conducted in the framework of the European Study of Cohorts for Air Pollution Effects (ESCAPE) project showed that long-term exposure to PM air pollution was linked to incidences of acute coronary (Cesaroni et al., 2014), cerebrovascular events (Stafoggia et al., 2014), and lung cancer in adults (Adam et al., 2015). Moreover, findings from the same project revealed that other health effects related to PM air pollution were reduced lung function in children (Gehring et al., 2013), pneumonia in early childhood and possibly otitis media (MacIntyre et al., 2014), low birthweight (Pedersen et al., 2013), and the incidence of lung cancer (Raaschou-Nielsen et al., 2013). In addition, another finding of the ESCAPE project was the connection between traffic-related PM 2.5 absorbance and malignant brain tumours (Andersen et al., 2018).

The Biobank Standardisation and Harmonisation for Research Excellence in the European Union (BioSHaRE-EU) project, which included three European cohort studies, presented the association between long-term exposure to ambient PM 10 and asthma prevalence (Cai et al., 2017). In the framework of three major cohorts (HUNT, EPIC-Oxford, and UK Biobank) it was shown that, after adjustments for road traffic noise, incidences of cardiovascular disease (CVD) diseases were attributed to long-term PM exposure (Cai et al., 2018). Hoffmann et al. (2015) suggested that long-term exposure to both PM 10 and PM 2.5 is linked to an increased risk for stroke, and it might be responsible for incidences of coronary events.

(iii) Health effects associated with the short-term exposure to particulate matter

Collaborative studies such as the APHENA (Air Pollution and Health: A European and North American Approach) and the MED-PARTICLES project in Mediterranean Europe have evidenced that short-term exposure to PM has been associated with all-cause cardiovascular and respiratory mortality (Katsouyanni et al., 2009; Zanobetti and Schwartz, 2009; Samoli et al., 2013; Dai et al., 2014), hospital admissions (Stafoggia et al., 2013), and occurrence of asthma symptom episodes in children (Weinmayr et al., 2010).

(iv) Health effects associated with the chemical constituents of PM

The chemical composition of PM is associated with the health effects related to PM concentrations, in addition to the mass concentrations of particulate matter (e.g. Maricq, 2007). Chemical composition of particles is complex; generally, it depends on the source origin of particles and their chemical and physical transformations in the atmosphere (e.g. Prank et al., 2016). Some prominent examples of the components of PM are sulfate (SO 4 ), nitrate (NO 3 ), metals, elemental and organic carbon (Yang et al., 2018), ammonium (NH 3 ) (Pražnikar and Pražnikar, 2012), sea salt, and dust (Prank et al., 2016).

The PM components also include biological organisms (e.g. bacteria, fungi, and viruses) and organic compounds (e.g. polycyclic aromatic hydrocarbons, PAHs, and their nitro-derivatives, NPAHs) (Morakinyo et al., 2016; Kalisa et al., 2019). Their content can vary significantly with regard to time and for various climatic regions (Maki et al., 2015; Gou et al., 2016).

Hime et al. (2018) have reviewed studies which investigated which PM components could be mostly responsible for severe health effects. Such studies included the National Particle Component Toxicity (NPACT) initiative, which combined epidemiologic and toxicologic studies. That study concluded that the concentrations of SO 4 , EC, OC, and PM mainly originated from traffic and combustion and had a significant impact on human health (Adams et al., 2015). The European Study of Cohorts for Air Pollution Effects (ESCAPE) project aimed at examining the association of elemental components of PM (copper, Cu; iron, Fe; potassium, K; nickel, Ni; sulfur, S; silicon, Si; vanadium, V; and zinc, Zn) with inflammatory blood markers in European cohorts (Hampel et al., 2015). They focused, together with the TRANSPHORM project (Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter), on investigating the relationship of these components with cardiovascular (CVD) mortality (Wang et al., 2014).

Moreover, other studies conducted within the framework of ESCAPE and TRANSPHORM projects provided evidence that mortality was linked to long-term exposure to PM 2.5 sulfur (Beelen et al., 2015), as well as to the particle mass and nitrogen oxides (NO 2 and NO x ) (Beelen et al., 2014). As part of the NordicWelfAir project, Hvidtfeldt et al. (2019b) connected the risks of being exposed long-term to PM 2.5 , PM 10 , BC, and NO 2 with all-cause and CVD mortality. In another paper, Hvidtfeldt et al. (2019a) demonstrated the association between long-term exposure to PM 2.5 , elemental and primary organic carbonaceous particles ( BC / OC ), secondary organic aerosols (SOA), and all-cause mortality. They also demonstrated the connection between PM 2.5 , BC / OC , and secondary inorganic aerosols (SIAs) and CVD mortality. Recently, a continuation of this study included all Danes born between 1921 and 1985, showing higher mortality related to exposure to NO 2 , O 3 , PM 2.5 , and BC (Raaschou-Nielsen et al., 2020).

In the framework of the Particle Component Toxicity (NPACT) project, Lippmann et al. (2013) showed that PM 2.5 mass and EC were linked to all-cause mortality; EC was also connected with ischemic heart disease mortality. The latter result was quite similar to the findings of Ostro et al. (2010, 2011, 2015), including OC, SO 4 , NO 3 , and SO in addition to EC. Concerning cardiopulmonary disease mortality, a strong association was observed for the exposure to NO 3 and SO 4 (Ostro et al., 2010, 2011). Luben et al. (2017) and Hoek et al. (2013) in their reviews observed the association of BC with cardiovascular disease hospital admissions and mortality.

In a meta-analysis work conducted by Achilleos et al. (2017), elemental carbon (EC), black carbon (BC), black smoke (BS), organic carbon (OC), sodium (Na), silicon (Si), and sulfate (SO 4 ) were associated with all-cause mortality, and BS, EC, nitrate (NO 3 ), ammonium (NH 4 ), chlorine (Cl), and calcium (Ca) were linked to CVD mortality. In addition, some American cohort studies pointed out that long-term exposure to SO 4 was positively connected with all-cause, cardiopulmonary disease, and lung cancer mortality (Dockery et al., 1993; HEI, 2000; Pope et al., 2002; Ostro et al., 2010).

In addition, other kinds of severe health effects related to PM components have been reported. For example, Wolf et al. (2015) showed that long-term exposure to PM constituents, especially of K, Si, and Fe, which are indicators of road dust, provoked coronary events. The findings of a systematic review, where 59 studies were included, indicated that chronic obstructive pulmonary disease (COPD) emergency risk was attributed to short-term exposure to O 3 and NO 2 , whereas short-term exposure to SO 2 and NO 2 was responsible for acute COPD risk in developing countries (Li et al., 2016). The review of Li et al. (2016) also reported that short-term exposure to O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5 was linked to respiratory risks.

Poulsen et al. (2020), using detailed modelling and Danish registers from 1989–2014, showed stronger relationships between primarily emitted black carbon (BC), organic carbon (OC), and combined carbon ( OC / BC ) and malignant brain tumours. Furthermore, the risk for lung cancer was linked to several different compounds and sources of aerosol particles; they found that particles containing S and Ni might be two of the most important components associated with lung cancer (Raaschou-Nielsen, 2016). Park et al. (2018) found that PM 2.5 particles emitted from diesel and gasoline engines were more toxic for humans than, for example, particles from biomass burning or coal combustion. In a recent study, it was concluded that traffic-specific PM components, and in particular NH 4 and SO 4 , lead to higher risks of stroke than PM components linked to industrial sources (Rodins et al., 2020).

(vi) The uncertainties associated with concentration–response functions

Based on previous research, WHO and Europe recommended in 2015 a set of linear concentration–response functions for the main air pollutants and related health outcomes (Héroux et al., 2015). These functions are currently widely used for health assessments, e.g. on a European scale by EEA. EEA (2019) estimated that more than 340 000 premature deaths per year in Europe could be related to the exposure to PM 2.5 . However, it is currently widely debated what the optimal shape of the concentration–response functions is and whether there should be a threshold or lower limit.

A prominent example is the highly cited study by Burnett et al. (2018) on the developments of the Global Exposure Mortality Model (GEMM). By combining data from 41 cohorts from 16 different countries, Burnett et al. (2018) have constructed new hazard ratio functions that to a wider degree than previous studies include the full range of the global exposure to outdoor PM 2.5 . The GEMM functions for PM 2.5 and nonaccidental mortality generally follow a supralinear association at lower concentrations and near-linear association at higher concentrations (Burnett et al., 2018).

The GEMM functions would indicate that health impacts related to PM 2.5 exposure have been underestimated, at both the global and regional scales. In a recent European study on cardiovascular mortality, the GEMM functions were combined with concentration fields from a global atmospheric chemistry–climate model. The results pointed towards a total of 790 000 premature deaths attributed to air pollution in Europe per year, which is significantly higher than the value previously estimated by EEA for example (Lelieveld et al., 2019). Several reviews or meta-analyses have focused on low exposure levels; the conclusion has been that significant associations can be found between PM 2.5 and health effects also at levels below the concentrations of 10–12  µg m −3 . These values are equal to or below the WHO guidelines (10  µg m −3 ) and the US EPA standards (12  µg m −3 ) (Vodonos et al., 2018; Papadogeorgou et al., 2019).

(vii) The use of high-resolution multi-decadal data sets for extensive regions

Developments of air pollution modelling and more efficient computing resources have made it possible to compute high-resolution air pollution data sets that cover larger regions, as well as longer, even multi-decadal, time periods (Fig. 16). The combination of such data with national or international health registers, or cohorts from several countries, improves the representativeness of statistical analyses. The use of more extensive data sets will also reduce the selection biases related to the sizes of the cohorts.

This has resulted in, for example, a better detection of the links between air pollution exposure and new health endpoints, such as psychiatric disorders (e.g. Khan et al., 2019a; Antonsen et al., 2020) and cognitive abilities (e.g. Zhang et al., 2018). Based on high-resolution ( 1 km×1 km ) air pollution data covering the period 1979–2015 and population-based data from the Danish national registers, Thygesen et al. (2020) found that exposure to air pollution (specifically NO 2 ) during early childhood was associated with the development of attention-deficit/hyperactivity disorder (ADHD).

Figure 16 An illustration of how concentration predictions at a high spatial and temporal resolution (panels on the left-hand side) could be used for high-resolution health impact assessments (panel on the right-hand side). The concentration distributions were predicted with the chemical transport model SILAM. The health impact assessment was made with the EVA model in a high-resolution setup for the Nordic region, giving an estimate of the number of premature deaths due to exposure to air pollution (Lehtomäki et al., 2020). The concentrations used in EVA were from the chemical transport system DEHH-UBM, providing 1 km×1 km concentration across the Nordic region.

Kukkonen et al. (2018) presented a multi-decadal global- and European-scale modelling of a wide range of pollutants and the finer-resolution urban-scale modelling of PM 2.5 in the Helsinki metropolitan area. All of these computations were conducted for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the chemical transport model SILAM. These results have been used for health impact assessments by Siddika et al. (2019, 2020). The predicted air quality and meteorological data are also available to be used in any other region globally in health impact assessments.

7.2.2  Combined effects of air pollution, heat waves, and pandemics on human health

It is widely known that poor air quality has severe impacts on the human immune system (Genc et al., 2012). In particular, some of the acute health effects include chronic respiratory and cardiovascular diseases (Ghorani-Azam et al., 2016), respiratory infection (e.g. Conticini et al., 2020), and even cancer and death (IOM, 2011; Villeneuve et al., 2013). Polluted air can cause, for example, damage in epithelial cilia (Cao et al., 2020), which leads to a chronic inflammatory stimulus (Conticini et al., 2020). It has also been shown that the SARS-CoV-2 can stay viable and infectious on aerosol particles that are smaller than 5  µm in diameter for more than 3 h (van Doremalen et al., 2020). Therefore, atmospheric pollutants might play an important role in spreading the virus.

(i) The role of air pollution in pandemics

Previously, Cui et al. (2003) found that the long-term exposure to moderate or high air pollution levels was positively correlated with mortality caused by SARS-CoV-1 in the Chinese population. Therefore, it is possible that poor air quality would enhance the risk of mortality during epidemics or pandemics, such as the COVID-19 disease, caused by SARS-CoV-2. Moreover, poor air quality can enhance the human health effects of heat waves, cold spells, and allergenic pollen. This is because exposure to ambient air pollutants together with microorganisms tend to make the health impacts of pathogens more severe; at the same time, they weaken human immunity, resulting in an increased risk of respiratory infection (e.g. Xu et al., 2016; Horne et al., 2018; Xie et al., 2019; Phosri et al., 2019).

Conticini et al. (2020) concluded that weakened lung defence mechanisms due to continuous exposure to air pollution could partly explain the higher morbidity and mortality caused by SARS-CoV-2 in areas of poor air quality in Italy. Zhu et al. (2020) used the data of daily confirmed COVID-19 cases, air pollution, and meteorology from 120 cities in China to study the association between the concentrations of ambient air pollutants (PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 ), and COVID-19 cases. By applying a generalized additive model, they found a significant correlation between PM 2.5 , PM 10 , CO, NO 2 , and O 3 and daily counts of confirmed COVID-19 patients, while SO 2 was negatively associated with the daily number of new COVID-19 cases.

Ogen (2020) studied 66 regions in Italy, Spain, France, and Germany; he also found a spatial correlation between high NO 2 concentration and fatality from COVID-19. According to this study, 83 % of all fatalities occurred in the regions having a maximum NO 2 concentration above 100  µ molec . m - 2 , and only 1.5 % of all fatalities took place in areas in which the maximum NO 2 concentration was below 50  µ molec . m - 2 . However, Pisoni and Van Dingenen (2020) did not find a similar phenomenon in the UK, where the number of deaths was higher than in Italy, despite a significantly lower NO 2 concentration.

Xie and Zhu (2020) used temperature data from 122 cities mainly in the eastern part of China and observed a linear relationship between ambient temperature and daily number of confirmed COVID-19 counts in cases when the temperature was below 3  ∘ C. At higher temperatures, no correlation was found. This indicates that daily counts of COVID-19 did not decline at warmer atmospheric conditions, although such a dependency was expected based on the previous studies related to coronaviruses SARS-CoV and MERS-CoV (e.g. van Doremalen et al., 2013; Bi et al., 2007; Tan et al., 2005). However, the study of Xie and Zhu (2020) was conducted in winter; the highest temperatures were around 27  ∘ C.

Chen et al. (2017) statistically investigated the correlation between influenza incidences and the concentrations of PM 2.5 in 47 Chinese cities for 14 months during 2013–2014. Based on the results, they concluded that about 10 % of the influenza cases were induced by the exposure to ambient PM 2.5 . They also classified the days as cold, moderately cold, moderately hot, and hot separately for each city and found that the risk for influenza transmission associated with ambient air PM 2.5 was enhanced during cold days.

(ii) Combined effects of air pollutants and heat waves

Siddika et al. (2019) found that prenatal exposure to both PM 2.5 and O 3 increased the risk of preterm birth in Finland in the 1980s. The risk was more pronounced if the mother was exposed to both higher PM 2.5 and higher O 3 concentrations. They explained that O 3 might deplete antioxidants in the lung, and therefore the defence mechanism needed against reactive oxygen species formation was reduced due to the exposure to PM 2.5 . Also, the O 3 concentrations can cause changes in lung epithelium so that it is more permeable for particles to absorb into the circulatory system. The population selected for the study were living in southern Finland in the 1980s, in relatively good air quality. However, the concentrations of many pollutants, e.g. those of PM 2.5 , have been shown to have been twice as high in the 1980s, compared with the corresponding pollutant levels in the same region during the second decade of the 21st century (Kukkonen et al., 2018).

Wang et al. (2020) presented that PM 2.5 exposure strengthened the effect of moderate heat waves (short or only moderate temperature rise) associated with preterm births during January 2015–July 2017 in Guangdong Province, China. However, during the intensive heat waves, the effects were not additive.

Analitis et al. (2018) studied synergetic effects of temperature, PM 10 , O 3 , and NO 2 on cardiovascular and respiratory deaths. They found some correlation between the effects of high ambient temperatures and those caused by O 3 and PM 10 concentrations. However, during the heat waves, no clear synergetic effect was found. In a review article, Son et al. (2019) concluded that there is some evidence between the mortality related to high temperatures and air pollution.

J. Li et al. (2017) wrote a comprehensive literature review about the role of temperature and air pollution in mortality. They determined individual spatial temperature ranges and grouped them in “low”, “medium”, and “high” based on the information given in each study about typical local weather conditions. After a careful selection based on the quality of the data sets, they performed a meta-analysis by using data of 21 studies; they found that high temperature significantly increased the risk of non-accidental and cardiovascular mortality, caused by the exposure to PM 10 or O 3 . The risk of cardiovascular mortality due to PM 10 decreased during low-temperature days in the prevailing climate. However, the exposure to both low temperature and the concentrations of O 3 increased the risk of non-accidental mortality. Similar effects were not found for the concentrations of SO 2 or NO 2 and temperature. Lepeule et al. (2018) found that short-term rise in outdoor air temperature and relative humidity was linked to deteriorated lung function of elderly people. A simultaneous exposure to black carbon amplified the health effects.

7.2.3  Estimation of exposures

(i) modelling of individual exposure.

The currently available epidemiological studies use measured or modelled outdoor concentrations in residential areas or at home addresses, to correlate the concentrations with health effects. However, several studies have pointed out that it is critical to use the exposure of people as indicators for the health effects (Kousa et al., 2002; Soares et al., 2014; Kukkonen et al., 2016b; Smith et al., 2016; Singh et al., 2020a; Li and Friedrich, 2019; Li, 2020). It is obvious that the effects of air pollutants on human health are caused by the inhaled pollutants, instead of the pollutants at a certain point or area outdoors. Thus, exposure is a much better indicator for estimating health risks than outdoor concentrations. The individual exposure of a person to air pollutants is defined here as the concentration of pollutants at the sites where the person is staying weighted by the length of stay at each of the places of stay and averaged over a certain time span, e.g. a year. The places of stay are in this context called microenvironments. Exposure of a group of people with certain features (e.g. sex, age, place of living) is the average exposure of the individuals in the subgroup. In general, the exposure of a person is calculated by first estimating the concentration of air pollutants in the microenvironments where the person or population subgroup is staying and then by weighting this concentration with the length of time the person has been at the respective microenvironment (Li and Friedrich, 2019; Li, 2020). The result of modelling exposure can be verified by measuring the exposure with personal sensors (e.g. Dessimond et al., 2021).

Exposures to ambient concentrations of PM 2.5 can be substantially different in different microenvironments. The concentrations in microenvironments can be either measured or modelled. Computational results of activity-based dynamic exposures by Singh et al. (2020a) demonstrate that the total population exposure was over one-quarter ( −28  %) lower on a city-wide average level, compared with simply using outdoor concentrations at residential locations, in the case of London in the 2010s. Smith et al. (2016) have shown by modelling that exposure estimates based on space-time activity were 37 % lower than the outdoor exposure evaluated at residential addresses in London. However, this proportion will be different for other urban regions and time periods, or when addressing specific population sub-groups.

The exposure to particulate matter is substantially influenced by indoor environments, as people spend 80 %–95 % of their time indoors (e.g. Hänninen et al., 2005). Indoor air quality mainly depends on the penetration of pollutants in outdoor air, on ventilation, and on indoor pollution sources. For estimating the indoor concentration, commonly a mass-balance model is applied (Hänninen et al., 2004; Li, 2020). With a mass-balance model, the indoor concentration is calculated based on the outdoor concentration, a penetration factor, the air exchange rate, the decay rate, the emission rates of the indoor sources and the room volume, and, if available, by parameters of the mechanical ventilation system.

A complex stochastic model has been developed for estimating the annual individual exposure of people or groups of people in the European Union to PM 2.5 and NO 2 , using characteristics of the analysed subgroup, such as age, gender, place of residence, and socioeconomic status (Li et al., 2019a, c; Li and Friedrich, 2019; Li, 2020). The probabilistic model incorporates an atmospheric model for estimating the ambient pollutant concentrations in outdoor microenvironments and a mass-balance model for estimating indoor concentrations stemming from outdoor concentrations and from emissions from indoor sources. Time-activity patterns (which specify how long a person stays in each microenvironment) were derived from an advancement of the Multinational Time Use Study (MTUS) (Fisher and Gershuny, 2016). The exposures can also be estimated for past years. It is therefore possible to analyse the exposure for the whole lifetime of a person, by using a lifetime trajectory model that retrospectively predicts the possible transitions in the past life of a person.

An exemplary result from Li and Friedrich (2019) is shown in Fig. 17. It displays that the PM 2.5 annual average exposure averaged over all adult persons living in the EU increased since the 1950s from 19.0 (95 % confidence interval, CI: 3.3–55.7)  µg m −3 to a maximum of 37.2 (95 % CI: 9.2–113.8)  µg m −3 in the 1980s. The exposure then declined gradually afterwards until 2015 to 20.1 (95 % CI: 5.8–51.2)  µg m −3 . Indoor air pollution contributes considerably to exposure. In recent years more than 45 % of the PM 2.5 exposure of an average EU citizen has been caused by indoor sources.

Figure 17 Temporal evolution of the annual average exposure of EU adult persons to PM 2.5 from 1950 to 2015 (Li and Friedrich, 2019). All sources, except “outdoor”, refer to indoor sources. ETS denotes environmental tobacco smoke (passive smoking).

The most important indoor sources are environmental tobacco smoke (passive smoking), frying, wood burning in open fireplaces and stoves in the living area, and the use of incense sticks and candles. In addition, nearly all indoor activities include abrasion processes that produce fine dust. For NO 2 , indoor sources cause around 24 % of the exposure, with the main contributions from cooking with gas and from biomass burning in stoves and open fireplaces (Li and Friedrich, 2019). The solid black line in Fig. 7.3 shows the background outdoor concentration at the places where EU citizens spend their lives on average. Urban background concentrations refer to urban concentrations that are not in the immediate vicinity of the emission sources, especially of streets.

The average exposure is higher than the average outdoor background concentration. Epidemiological studies correlate outdoor concentrations with health risks and thus neglect the exposure caused by indoor sources. Such studies therefore implicitly assume that the contribution of indoor sources is the same at all places and for all people. Thus, calculating the burden of disease using exposures to PM 2.5 will yield years of lives lost and other chronic diseases that are about 40 % higher than those calculated with outdoor background concentrations (Li, 2020). Using exposure data, a 70-year-old male EU citizen will have experienced a reduction of life expectancy of about 13 (CI 2–43) d yr −1 of exposure to PM 2.5 , since the age of 30 (Li, 2020). For a person who is now 40 years old or younger, the life expectancy loss per year will be less than half as much as that of a 70-year-old person.

A similar approach for estimating the “integrated population-weighted exposure” of the Chinese population to PM 2.5 has been used by Aunan et al. (2018) and Zhao et al. (2018). Aunan et al. (2018) estimated a mean annual averaged PM 2.5 exposure of 103 [86–120]  µg m −3 in urban areas and 200 [161–238]  µg m −3 in rural areas, with 50 % in urban areas and 78 % in rural areas originating from domestic biomass and coal burning.

(ii) Measurements of indoor concentrations and individual exposure

Zhao et al. (2020a) took measurements of PM concentrations of different size classes in 40 homes in the German cities of Leipzig and Berlin. Measurements were taken in different seasons simultaneously inside and directly outside the homes. Only homes without smokers were analysed. Mean annual indoor PM 10 concentrations were 30 % larger than the outdoor concentrations near the houses. However, the mean indoor concentration of PM 2.5 was 6 % smaller than the outdoor concentration. The infiltration factor was evaluated to be 0.5. They therefore concluded that the indoor concentration of PM 2.5 was considerably influenced by both indoor and outdoor sources; the former included cooking and burning of candles.

Vardoulakis et al. (2020) made a comprehensive literature review on indoor concentration of selected air pollutants associated with negative health effects and listed the main results (concentrations) and other features (e.g. main sources) for the analysed studies. They express the need for “standardized IAQ (indoor air quality) measurement and analytical methods and longer monitoring periods over multiple sites”.

Some studies have focused on the measurements of personal exposure to ambient air concentrations using portable instruments in different microenvironments. For instance, Dessimond et al. (2021) describe the development and use of a personal sensor for measuring PM 1 , PM 2.5 PM 10 , BC, NO 2 , and VOC together with climate parameters, location, and sleep quality. Clearly, such measurements can provide valuable and accurate information on the spatial and temporal variations in exposure, and they can be used to validate exposure models.

7.3  Emerging challenges

7.3.1  emerging challenges for health impacts of particulate matter, (i) classification of particulate matter measures and characteristics and potential health outcomes.

Various studies have described PM in terms of the overall aerosol properties, such as the mass fractions (most commonly PM 2.5 and PM 10 ), the size distributions (mass, area, volume), the chemical composition, primary versus secondary PM, the morphology of particles, and source-attributed PM. Some studies have adopted more specific properties of PM derived based on the above-mentioned overall properties. Such properties include, to mention a few of the most common ones, particle number concentrations (PNCs), PNCs evaluated separately for each particulate mode, ultra-fine PM, nanoparticles, secondary organic PM, primary PM, other combinations of chemical composition, suspended dust (specific class of source-attributed PM), the content of metals, and toxic or hazardous pollutants.

An important emerging area is therefore to understand better which PM properties or measures would optimally describe the resulting health impacts. As mentioned above, one potentially crucial candidate for such a property is particulate number concentration (PNC). Kukkonen et al. (2016a) presented the modelling of the emissions and concentrations of particle numbers on a European scale and in five European cities. Frohn et al. (2021) and Ketzel et al. (2021) performed modelling of particle number concentrations for all Danish residential addresses for a 40-year time period. For all studies, the comparison of the predicted PNCs to measurements on regional and urban scales showed a reasonable agreement. However, there are still substantial uncertainties, especially in the modelling of the emissions of particulate numbers.

Health outcomes can also be classified as overall outcomes and physiologically more specific outcomes. Prominent examples of overall outcomes are mortality and morbidity. Relatively more specific impacts include respiratory and cardiovascular impacts, bronchitis, asthma, neurological impacts, and impacts on specific population groups (such as infants, children, the elderly, prenatal impacts, and persons suffering various diseases).

(ii) Uncertainties and challenges on evaluating the health impacts of particulate matter

Additional uncertainty is included in the concentration versus health response functions, which may be linear or logarithmic or a combination of both of these, and including or excluding a threshold value. When applied in health assessments, the shape of the response functions translates into large differences in the estimated number of premature deaths (Lehtomäki et al., 2020). EEA has made a sensitivity analysis showing that the application of a 2.5  µg m −3 threshold (equivalent to a natural background) will reduce the estimated number of premature deaths related to PM 2.5 by about 20 % in Europe (EEA, 2019a). Clearly, in the evaluation of the health impacts of PM, there are also numerous confounding factors. For population-based studies, these include active and passive smoking, sources and sinks that influence indoor pollution, gaseous pollutants, allergenic pollen, socio-economic effects, age, health status, and gender.

In addition, the health impacts of PM are related to the impacts of other environmental stressors, such as heat waves and cold spells, allergenic pollen, and airborne microorganisms. Commonly, it is challenging to decipher such effects in terms of each other. The factors may also have either synergistic or antagonistic effects. For instance, the health impacts of PM may be enhanced in the presence of a heat wave. The impacts of various PM properties are also known to be physiologically specific; i.e. such a property may contribute to a certain health outcome but not to some other outcomes.

In summary, there are many associations of various PM properties and measures to various health outcomes. Some of these inter-dependencies are known relatively better, either qualitatively or quantitatively, while there are also numerous associations, which are currently known poorly.

(iii) Research recommendations for deciphering the impacts of various particulate matter properties

For evaluating the relative significance of various PM properties and measures on human health, a denser measurement network on advanced PM properties would be needed, on both regional and urban scales. The required PM properties would include, in particular, size distributions and chemical composition. Clearly, such a network would be especially valuable in cities and regions which include high-quality population cohorts. The most important requirement in terms of PM modelling would be improved emission inventories, which would also include sufficiently accurate information on particle size distributions and chemical composition.

Pražnikar and Pražnikar (2012) and Rodins et al. (2020) stressed the importance of the identification of the specific sources and the evaluation of the chemical composition of PM responsible for acute health effects. For instance, Hime et al. (2018) reported that there is a severe lack of epidemiological studies investigating the health impacts originating from exposure to ambient diesel exhaust PM. In addition, they pointed out that there is no clear distinction between PM originating from diesel emissions and from other sources; thus, there is a limited number of studies assessing the respective health impacts.

Despite the substantial amount of research on the impacts of various PM properties and measures, the results on the importance of the more advanced measures (in addition to PM mass fractions) are to some extent inconclusive. One reason for this uncertainty is that there are so many associations of various PM properties and measures to various health outcomes. An emerging area related to assessing the health impact of PM is the associated oxidative stress when the particles are inhaled (e.g. see Gao et al., 2020; He et al., 2021). A possible explanation for the health effects from PM is based on PM-bound reactive oxygen species (ROS) being introduced to the surface of the lung, which leads to the depletion of the lung-lining fluid antioxidants as well as other damage (Gao et al., 2020).

One prominent emerging area is the evaluation of long-term, multi-decadal concentrations and meteorology on a sufficient spatial resolution. Long-term and lifetime exposures are known to be more important in terms of human health, compared with short-term exposures. Comprehensive data sets are therefore needed, which will include multi-decadal evaluation of air quality, meteorology, exposure, and a range of health impacts. Some first examples of such data sets have already been reported (Kukkonen et al., 2018; Siddika et al., 2019; Raaschou-Nielsen et al., 2020; Thygesen et al., 2020; Siddika et al., 2020). Although it is clear that chronic diseases and chronic mortality are caused by exposure to fine PM over many years, information is scarce regarding the critical length of the exposure period in terms of premature death for example.

Elderly people are generally regarded as more sensitive to air pollution. It is well-known that the overall trend towards an ageing population can counteract improvements in air pollution levels in the future (e.g. Geels et al., 2015). However, detailed knowledge is scarce regarding whether exposure during specific periods in life can increase the risk of chronic morbidity or mortality. Inequalities in both the exposure to PM and the related risks across different population groups (like gender, ethnicity, socioeconomic position, etc) due to underlying differences in health status will also need further investigations, to ascertain that future mitigation strategies will benefit all population groups (Fairburn et at., 2019; Raaschou-Nielsen et al., 2020). With regard to chronic diseases caused by NO 2 , it is still uncertain whether NO 2 is the cause of the diseases or whether other pressures or a combination of pressures that are correlated with the NO 2 concentration are responsible.

The introduction of green spaces in urban areas can contribute either negatively or positively to air quality. Green spaces can also potentially act as sources of allergenic pollen. The health impacts of introducing green spaces would therefore need to be clarified (Hvidtfeldt et al., 2019b; Engemann et al., 2020).

7.3.2  Emerging challenges for the combined effects of air pollution and viruses

Studying the combined effects of air pollution, heat waves or cold spells, and viruses is challenging, due to numerous confounding factors and incidental correlations. For instance, air pollution is commonly a serious problem in areas where the population density is also high. The high population density tends to allow viruses to spread more easily, compared with the situation in more sparsely populated areas.

Morbidity or mortality due to pandemics is also dependent on the age distribution of the population, cultural and social differences, the level of health care, living conditions, common hygiene, and other factors. Clearly, such demographic differences should be taken into account, when comparing the frequencies of virus infections in different areas.

Due to limited data and the still evolving COVID-19 pandemic, it is difficult to draw definite conclusions related to the role of air pollution or meteorological drivers (like temperature or relative humidity) in transmission rates or in the severity of the disease. Global interdisciplinary studies, open data sharing, and scientific collaboration are the key words towards better understanding of the interaction of COVID-19 and meteorological and environmental variables. Moreover, it is important to know what the role of, for example, PM is in spreading SARS-CoV-2. Indoor or laboratory dispersion experiments are needed to find out if the virus is spreading not only in droplets but also in smaller aerosol particles. Together with a fully validated computational fluid dynamics model, it is possible to get facts about dispersion distances in different conditions and to study for example the effect of ventilation systems, furniture placements, and air cleaners to give information-based recommendations to make the environment as safe as possible without complete lockdowns.

Allergenic pollen can periodically cause substantial health impacts for numerous people. As PM is transported in the atmosphere, microbial pathogens such as bacteria, fungi, and viruses can be attached on the surfaces of particles (Morakinyo et al., 2016); clearly, these may provide an additional risk (Kalisa et al., 2019). The combination of both biological and chemical components of PM can further enhance some health effects, such as asthma and COPD (Kalisa et al., 2019).

Adverse health impacts can also be associated with short-term exposure to atmospheric particles. The short-term impacts may be important, especially during air pollution episodes. Such episodes may be caused, for example, by the emissions originating from wildfires, dust storms, or severe accidents. Episodes can also be caused by extreme meteorological conditions; two prominent examples are heat waves and extremely stable atmospheric conditions.

7.3.3  Other emerging challenges

First attempts have been made to quantify exposures by estimating concentrations in microenvironments, combined with space-time activity data. However, improvements will be necessary for virtually all the components of exposure modelling. Regarding the emissions used for concentration modelling, in particular the evaluation of the emission rates from indoor sources should be improved on a broader empirical basis.

Emission rates depend on human behaviour, for which more detailed information is needed. For example, how many people smoke indoors, and how many family members are exposed to passive smoking? Are kitchen hoods used when cooking and frying? How often are chimneys open, and how often are wood stoves used? For estimating indoor concentrations, one would need further information of ventilation habits in different seasons. Better information would be needed regarding the use of mechanical ventilation with heat recovery in new homes and office buildings.

To validate the results of the indoor air pollution models, one would need more measurements of indoor air concentrations in rooms with different emission sources and ventilation systems. Furthermore, measurements of concentrations are needed in various microenvironments, such as in cars, buses, and the underground.

With growing knowledge of the relation between exposure and health impacts, more detailed exposure indicators might be necessary. For instance, a further differentiation according to size and species of PM 2.5 and PM 10 might be needed, as well as the specification of the temperature and of the breathing rate, in other words the intake of pollutants with respiration. The use of more dynamic exposure data in epidemiological studies in the future could substantially improve the accuracy of health impact assessments.

8.1  Brief overview

While decisions about air quality management and policy development are based on political considerations, it is a scientific task to provide evidence and decision support for designing efficient air pollution control strategies that lead to an optimization of welfare of the population. To do this, integrated assessments of the available policies and measures to reduce air pollution and their impacts are made. In such assessments, two questions are addressed.

Is a policy or measure or a bundle of policies or measures beneficial for society? Does it increase the welfare of society; i.e. do the benefits outweigh the costs (including disadvantages, risks, utility losses)?

If several alternative policy measures or bundles of policy measures are proposed, how can we prioritize them according to their efficiency; i.e. which should be used first to fulfil the environmental aims?

To analyse these questions, two methodologies have been developed: cost–effectiveness analyses and cost–benefit analyses. The concept of “costs” is used here in a broad sense, referring to all negative impacts including – in addition to financial costs – also non-monetary risks and disadvantages, such as time losses, increased health risks, risks caused by climate change, biodiversity losses, comfort losses, and so on, which are monetized to be able to add them to the monetary costs. Benefits encompass all positive impacts including avoided monetary costs, avoided health risks, avoided biodiversity losses, avoided material damage, reduced risks caused by climate change, and time and comfort gains.

With a cost–effectiveness analysis (CEA) the net costs (costs plus monetized disadvantages minus monetized benefits) for improving a non-monetary indicator used in an environmental aim with a certain measure are calculated, e.g. the costs of reducing the emission of 1 t of CO 2,eq . The lower the unit costs, the higher the cost–effectiveness or efficiency of a policy or measure. The CEA is mostly used for assessing the costs associated with climatically active species, as the effects are global. The situation is different for air pollution, where the damage caused by emitting 1 t of a pollutant varies widely depending on time and place of the emission.

Cost–benefit analysis (CBA) is a more general methodology. In a CBA, the benefits, i.e. the avoided damage and risks due to an air pollution control measure or bundle of measures, are quantified and monetized. Then, costs including the monetized negative impacts of the measures are estimated. If the net present value of benefits minus costs is positive, benefits outweigh the costs. Thus the measure is beneficial for society; i.e. it increases welfare. Dividing the benefits minus the nonmonetary costs by the monetary costs of a specific measure will result in the net benefit per euro spent, which can be used for ranking policies and measures. For performing mathematical operations like summing or dividing costs and benefits, they have first to be quantified and then converted into a common unit, for which a monetary unit, e.g. euros, is usually chosen. Integrated assessment means that – as far as possible – all relevant aspects (disadvantages, benefits) should be considered, i.e. all aspects that might have a non-negligible influence.

When setting up air pollution control plans, it is essential to also consider the effect of these plans on greenhouse gas emissions. Air pollution control measures usually lead to a decrease but sometimes also to an increase in GHG emissions. And vice versa, most measures for GHG reduction influence in fact reduce the emissions of air pollutants in most cases. Thus, an optimized combined air pollution control and climate protection plan is necessary to avoid contradictions and inconsistencies.

Looking at the current praxis in the EU countries, still separate plans are made for air pollution control and climate protection. Air pollution control plans currently estimate the reduction (or sometimes increase) of GHG emissions more and more, but they do not assess these reductions by monetizing them, and thus they cannot be accounted for in a cost–benefit analysis. In the assessment of the National Energy and Climate Plans the EC states:

Despite some efforts made, there continues to be insufficient reporting of the projected impacts of the planned policies and measures on the emissions of air pollutants by Member States in their final plans. Only 13 Member States provided a sufficient level of detail and/or improved analysis of the air impacts compared to the draft plans. The final plans provide insufficient analysis of potential trade-offs between air and climate/energy objectives (mostly related to increasing amounts of bioenergy). (EC, 2020)

So, in an integrated assessment, the assessment of air pollution control measures should always take into account the impact of changes in greenhouse gas emissions. Correspondingly, climate protection plans should take changes in air pollution into account (Friedrich, 2016). In the following, advancements in the quantification and monetizing of avoided impacts from reducing emissions from air pollutants and greenhouse gases are described.

8.2  Current status and challenges

8.2.1  estimation and monetization of impacts from air pollution.

Integrated assessments, which include as a relevant element the assessment of air pollution, encompass many areas, especially the assessment of energy and transport technologies and of policies for air pollution control and climate protection. The development of such integrated assessments started in the early 1990s with a series of EU research projects, which have been called “ExternE-external costs of energy”. A summarized description of the developed methodology can be found in Bickel and Friedrich (2005); further descriptions and project results are addressed in ExternE (2012), Friedrich and Kuhn (2011), Friedrich (2016) and Roos (2017). The framework for integrated environmental assessments has been further consolidated and developed within the EU research projects INTARESE and HEIMTSA. The advanced methodology and its application are described in Friedrich and Kuhn (2011). The processes of an integrated assessment are shown in Fig. 18 (Briggs, 2008; IEHIAS, 2014), where important elements like issue framing, scenario construction, provision of data and models, uncertainty estimation, and stakeholder consultation are addressed. In the beginning of an assessment, the relevant air pollutants have to be identified, which are those that cause substantial damage. In many cases, primary and secondary particulate matter of different size classes and NO 2 will cause the worst damage, followed by O 3 .

The element in the framework that is representing the assessment of air pollution, i.e. the “impact pathway approach”, is shown in detail in Fig. 19. This figure already includes one of the emerging developments described in Sect. 8.3, namely the estimation of individual exposure instead of outdoor concentration. First, scenarios of activities are collected, for instance the distance driven with a Euro 5 diesel car or the amount of wood used in wood stoves. Multiplying the activity data with the appropriate emission factors will result in emissions. The emission data are input for chemical–transport models that are used to calculate concentrations on regional, continental, or global scales; for Europe the EMEP model (Simpson et al., 2012) and worldwide the TM5-FASST model (van Dingenen et al., 2018) are often used – see Sect. 5 of this paper.

In the next phase, concentration–response functions derived from epidemiological studies are used to estimate health impacts. For the most relevant pollutants PM 10 , PM 2.5 , NO 2 , and O 3 , the WHO (2013a) made a meta-analysis of the epidemiological studies available until 2012 and recommended exposure–response relationships for use in integrated assessments, which are still widely used. Newer epidemiological studies in particular investigating the relation between fine particulate air pollution and human mortality have been analysed by Pope et al. (2020), who present a nonlinear exposure–response function with a decreasing slope for cardiopulmonary disease mortality caused by PM 2.5 . The most important concentration–response functions for impacts of air pollution on human health are described in Sarigiannis and Karakitsios (2018) and Friedrich and Kuhn (2011).

Beneath health damage, which is the most important damage category, impacts on ecosystems, especially biodiversity losses, and on materials and crop losses should also be considered. Impacts on ecosystems are usually quantified as pdf, “potentially disappeared fraction of species” per square metre land (Dorber et al., 2020) and thus as biodiversity losses. A first methodology was developed by Ott et al. (2006), which is still used in some studies. Further approaches, partly adopted from methods developed for LCIA (life cycle impact assessment), were developed later (e.g. Souza et al., 2015; Förster et al., 2019), but because of the simplifications and uncertain assumptions made, none of these approaches reached the same full acceptance as the approaches for the other damage categories. For material damage and crop loss, deposition–response relationships have been developed in the ExternE – External Costs of Energy (ExternE, 2012) project series and are described in Bickel and Friedrich (2005); they are still used.

Finally, the health effects and the other impacts are monetized, which means that they are converted into financial costs; for the non-monetary part of the impacts results of contingent valuation (willingness to pay) studies are used (as described in OECD, 2018). As numerous contingent valuation studies have been made in the past, it is not necessary to carry out a further willingness-to-pay study; instead results of existing studies which found monetary values for the damage endpoints to be analysed can be used. Of course, as the contingent valuation studies are usually made at another time, in another area, and with other cultural situations than the planned assessment, the monetary values must be transformed with a methodology called “benefit transfer” from the original time, place, and cultural features to the ones of the assessment (see Navrud and Ready, 2007). The most important monetary value in the context of air pollution is the value for a statistical life year lost (VLYL) caused by a premature death at the end of life after lifelong exposure to air pollutants. It is often based on a study of Desaigues et al. (2011). The result for average EU citizens – transformed to 2020 – is EUR 2020 75 200 (47 000–269 450) per VLYL. A list of monetary values for health endpoints, which are used in most studies, can be found in Friedrich and Kuhn (2011).

Figure 18 Integrated assessment process involving air pollution (Briggs, 2008).

Figure 19 Schematic presentation of the use of models and the flow of data in the enhanced impact pathway approach (Friedrich, 2016).

Based on this principal approach, a growing number of tools have been developed and applied for supporting air quality control for urban, national, and regional to global scales. The tool used for the assessments for DG Environment and for the Convention on Long-range Transboundary Air Pollution of the UN ECE is GAINS (Greenhouse gas – Air pollution Interactions and Synergies) developed by IIASA (Amann et al., 2017; Klimont, 2021).

A specific development in GAINS is the use of source–receptor matrices as a proxy for using an atmospheric model. A limitation of chemical transport models has been the substantial computational requirements for running the models for estimating hourly concentration values caused by an emission scenario for an entire year. To be able to simulate many scenarios within a short time, results of certain runs with the complex atmospheric model EMEP (Simpson et al., 2012) were transformed into source–receptor matrices, which provided information of the relationship between a change of emissions in a country and the change of the concentration in grid cells of a European grid. However, because of the relatively large size of the grid cells for European-wide models, concentrations in cities were underestimated; thus an “urban increment” was introduced for cities (Vautard et al., 2007; Torras and Friedrich, 2013; Torras, 2012). Thunis (2018), however, points out that this approach has certain weaknesses. Thus, newer approaches use nested modelling with regional atmospheric models using varying grid sizes (e.g. Brandt, 2012) or modelling of typical days instead of a whole year with a finer grid (Bartzis et al., 2020; Sakellaris et al., 2022). The ECOSENSE model uses a similar method as GAINS, however, distinguishing between parts of larger countries and emission heights. Furthermore a monetary assessment of greenhouse gas emissions is made (ExternE, 2012; Friedrich, 2016; Roos, 2017).

As a major application of the GAINS tool, the European Commission, DG Environment regularly assesses its directives for air pollution control. A well-known example is the impact assessment carried out for assessing the Thematic Strategy on Air Pollution and the Directive on “Ambient Air Quality and Cleaner Air for Europe” (EC, 2005). It was shown that the monetized benefits of implementing the thematic strategy for air pollution control are much higher than the costs. In the most recent assessment, DG Environment assessed the costs and benefits of the so-called NAPCPs, the national air pollution control programmes, which the member states had to provide by 2019 to show how they plan to comply with the emission reduction commitments of the National Emission Reduction Commitments Directive (NEC Directive). The benefits considered were the monetized reduced health and environmental impacts caused by the requested air pollution control measures. The results show that the health benefits alone with EUR 8 billion per year to EUR 42 billion per year are much larger than the costs of the considered measures with EUR 1.4 billion per year (EC, 2021) and that further emission reductions might also be efficient. The UN ECE (UN Economic Commission for Europe) has launched eight so-called protocols guided by the Convention on Long-range Transboundary Air Pollution, which require the member states to provide information on air pollution in their countries and to take actions to improve it (UNECE, 2020). The latest protocol entering into force was the revised Protocol to Abate Acidification, Eutrophication and Ground-level Ozone, as amended on 4 May 2012. To prepare for these protocols, the effects of air pollution on ecosystems, health, crops, and materials have been assessed with the same methods as used by the EC, i.e. using the GAINS model.

The OECD recommends carrying out cost–benefit analyses with the impact pathway methodology (OECD, 2018). Similarly, national authorities, e.g. the German Federal Environmental Agency, have proposed using the methodology for the assessment of environmental policies and infrastructure projects (Matthey and Bünger, 2019). In Denmark, the method has been used in the EVA system (Economic Valuation of Air pollution, Brandt et al., 2013) to estimate the external costs related to air pollution, as part of the national air quality monitoring programme (Ellermann et al., 2018). The same system has been used to assess the impact from different emission sectors and countries within the Nordic area, by using a CTM model with a tagging method (Im et al., 2019). Kukkonen et al. (2020c) developed an integrated assessment tool based on the impact pathway principle that can be used for evaluating the public health costs. The model was applied for evaluating the concentrations of fine particulate matter (PM 2.5 ) in ambient air and the associated public health costs of domestic PM 2.5 emissions in Finland. Several further integrated assessment models have been described in Thunis et al. (2016). Not only in Europe but also in the USA, integrated assessment of air pollution is an issue. Keiser and Muller (2017) provide an overview of integrated assessment models for air and water in the US and hint at the intersections between air and water pollution.

Several studies are using the impact pathway approach from Fig. 8.2 for estimating health impacts and aggregate them to DALYS (disability adjusted life years), but without monetizing the impacts; i.e. they calculate the burden of disease or the overall health impacts stemming from air pollution. The WHO has estimated the burden of disease from different causes, including air pollution, in the Global Burden of Disease Study (GBDS, 2020). The European Environmental Agency regularly estimates the health impacts from air pollution in Europe and found 4 381 000 life years lost attributable to the emissions of PM 2.5 in 2018 in the EU28 (EEA, 2020a). Hänninen et al. (2014) analysed the burden of disease of nine environmental stressors, including particulate matter, for Europe. Lehtomäki et al. (2020) quantified the health impacts of particles, ozone, and nitrogen dioxide in Finland and found a burden of 34 800 DALYs per year, with fine particles being the main contributor (74 %). Recent studies also include future projections of emissions and climate. Huang (2018) assessed and monetized the health impacts of air pollution in China for 2010 and for several scenarios until 2030. Likewise, Tarín-Carrasco et al. (2021) projected the number of premature deaths in Europe towards 2050 and found that a shift to renewable energy sources (to a share of 80 %) is effective in reducing negative health impacts.

In the following, we address recent improvements in the methodology of integrated assessment with a focus on air pollution control. A milestone was the publication of concentration–response functions for NO 2 by the WHO (2013a). Following this, more and more studies calculated health impacts from exposure not only to PM 10 , PM 2.5 , and ozone but also to NO 2 (e.g. Balogun et al., 2020; Siddika et al., 2019, 2020),

Ideally, human health risks should be evaluated based on exposures instead of ambient concentrations (see Sect. 7). Until now, measured or modelled ambient (outdoor) air concentrations are input to the concentration–response functions used to estimate health risks. However, it is obvious that people are affected by the pollutants that they inhale, and that is decisive for the health impact. Therefore, a better indicator for estimating health impacts than the outside background concentration is exposure, which is the concentration of pollutants in the inhaled air averaged over a certain time interval. Only recently, in the EU projects HEALS and ICARUS, have methodologies been developed to estimate personal exposure, i.e. the concentration in the inhaled air averaged over a year or a number of years as the basis for estimating health impacts from air pollutants. Furthermore, the time span used in the exposure–response relationships commonly ranges from hourly to annual mean concentration values. By far the most important health effects are chronic effects. Although the indicator used to estimate chronic impacts is the annual mean concentrations, chronic diseases develop over several years, or even during the whole lifetime. This is the reason why the EC regulates a 3-year “average exposure index” of PM 2.5 in the air quality directive. But the relevant time period for the exposure might be larger than 3 years. Thus, exposure over a lifetime is important for estimating risks to develop chronic diseases and premature deaths, which are the most important health impacts. The methods for evaluating lifetime exposure have been addressed in Sect. 7.2.3 (see Li and Friedrich, 2019; Li et al., 2019a, c).

Thus, as a major improvement of the impact pathway approach, the exposure to pollutants should be used as an indicator for health impacts, instead of the exposure estimated from outdoor air concentrations at permanent locations. However, epidemiological studies that directly relate health impacts to exposures to air pollutants are not yet available. Instead, the existing concentration–response functions are transformed into exposure–response functions by calculating the increase in the exposure (e.g. x   µg m −3 ) caused by the increase of 1  µg m −3 in the outdoor concentration. Dividing the concentration–response relationship by x will then convert it into an exposure–response relationship (Li, 2020). Of course, it would be better to use results of epidemiological studies that directly relate exposure data with health effects. Thus, such studies should be urgently conducted.

Clearly, indoor pollution sources also influence exposure. It is therefore important to assess possibilities to reduce the contributions of indoor sources to exposure. These might include raising awareness of the dangers of smoking at home indoors, the development of more effective kitchen hoods and promoting their use, ban of incense sticks, and mandatory use of inserts in open fireplaces.

Secondly, a reduction of exposure is also possible by increasing the air exchange rate with ventilation or by filtering the indoor air. For example, if old windows are replaced by new ones, the use of mechanical ventilation with heat recovery might be recommended or even made mandatory. Also, the enhancement of HEPA filters and their use in vacuum cleaners will help as well as using air purifiers/filters. These systems will also be helpful to reduce the indoor transmission of SARS-CoV-2.

Furthermore, there is growing evidence that PM 10 and PM 2.5 concentrations in underground trains and in metro stations can be much higher than the concentration in street canyons with dense traffic (e.g. Nieuwenhuijsen et al., 2007; Loxham and Nieuwenhuijsen, 2019; Mao et al., 2019; Smith et al., 2020). Using ventilation systems with filters might improve this situation.

8.2.2  Monetization of impacts of greenhouse gas emissions

As explained above, air pollution control strategies usually influence and, in most cases, reduce emissions of greenhouse gases (GHGs). Thus, in an integrated assessment, both the reduction of air pollution and of greenhouse gas emissions should be quantitatively assessed. In practice, however, many national air pollution control strategies do not take changes in GHGs into account in the assessment; instead the national authorities develop separate climate protection plans. Similarly, although DG Environment estimates the changes in GHG emissions in their assessment of air pollution control strategies, the changes are not assessed or monetized.

An exception is the UK, where estimations of the “social costs of carbon” are used in assessments (Watkiss and Downing, 2008; DBEIS, 2019). The UK government currently recommends using a carbon price of GBP 69 per tonne of CO 2,eq in 2020 rising to GBP 355 per tonne of CO 2,eq in 2075–2078 at 2018 prices.

How can the benefits of a reduction of greenhouse gas emissions be monetized? A possibility is to use the same approach as with air pollution; i.e. estimate the marginal damage costs (i.e. the monetized damages and disadvantages) of emitting 1 additional tonne of CO 2 . These marginal costs would then be internalized for example as a tax per tonne of CO 2 emitted to allow the market to create optimal solutions (Baumol, 1972). Thus, first scenarios of greenhouse gas (GHG) emissions would be set up, then concentrations of GHGs in the atmosphere would be calculated, and finally changes of the climate followed by the estimation of changes of risks and damages would be calculated. However, this does not lead to useful results. Uncertainties are too high and assumptions of economic parameters like the discount rate or the use of equity weighting influence the result considerably, so that the range of results encompass several orders of magnitude. Furthermore, the precautionary principle tells us that we should avoid possible impacts, even if they cannot (yet) be quantified and thus not be included in the quantitative estimation of impacts.

An alternative approach to estimating marginal damage costs is to use marginal abatement costs. A basic law of environmental economics is that for pollution control a pareto-optimal state should be achieved, where marginal damage costs (MDCs) are equal to marginal abatement costs (MACs). Thus, if MAC at the pareto-optimal state are known, they could be used instead of the MDCs. However, the pareto-optimal state is not known if MDCs are not known. But one could use an environmental aim that is universally agreed upon by society and assume that they represent the optimal solution in the view of society and then estimate the MACs to reach this aim, which is then used for the assessment. This approach was first proposed by Baumol and Oates (1971).

For assessing GHG emissions, especially the aim of the so-called Paris Agreement, which was agreed on at the 2015 United Nations Climate Change Conference, COP 21 in Paris by a large number of countries, the most important aim was to keep a global temperature rise this century well below 2  ∘ C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5  ∘ C. This objective could be used as the basis for generating MACs.

Bachmann (2020) has carried out a literature research of MDCs and MACs for GHG emissions. Based on this review, MACs calculated by a meta-analysis of Kuik et al. (2009) are used here as the basis for the calculation of marginal abatement costs for reaching the above aim, resulting in EUR 2019  286 (162–503) per tonne of CO 2,eq in 2050. However, we propose starting with the most efficient measures now and gradually increasing the specific costs, until they reach the costs mentioned above in 2050. If future innovations lead to a reduction of the avoidance costs, the costs of carbon can be adjusted accordingly. With a real discount rate of 3 % a −1 , social costs of CO 2,eq to be used in 2020 would be EUR 2020  118 (67–207) per tonne of CO 2,eq .

8.2.3  Effect of integrating air pollution control and climate protection

In most cases, especially if a substitution of fossil fuels with carbon-free energy carriers or a reduction of energy demand is foreseen, a reduction of emissions of greenhouse gases and air pollutants is foreseen; thus, taking both air pollution control and climate protection into account will considerably improve the efficiency of such measures.

An example, showing the choice and ranking of measures for combined air pollution control and climate protection are different from the ranking in separate plans is shown in Fig. 20. In the frame of the EU TRANSPHORM project, 24 measures to reduce air pollution and climate change caused by transport in the EU have been assessed with an integrated assessment. Figure 20 shows the 8 most effective measures for both avoided health impacts and reduced climate change, where both benefits are converted into monetary units and combined (Friedrich, 2016). As can be seen, measures with benefits in both air pollution control and climate protection improve their rank compared to the separate rankings for these damage categories. The most effective measure is travel with trains instead of aeroplanes for routes of less than 500 km.

Figure 20 Ranking of measures in transport according to their effectiveness in mitigating damage from air pollutant and greenhouse gas emissions in 2019. Recalculation of results of Friedrich (2016) with abatement costs of EUR 2020  118 per avoided tonne of CO 2,eq as recommended in Sect. 8.2.2.

With another example, Markandya et al. (2018) demonstrate that especially for developing and emerging countries the costs for meeting the aims of the Paris Agreement will be outweighed by the benefits that are achieved by avoiding health impacts from air pollution, so that the climate protection comes without net costs. This is due to the fact that in developing countries the use of fossil fuels is less accompanied by the use of emission reduction technologies (filters), so replacing fossil fuels by electricity from wind or solar energy or saving energy will result in a much higher reduction in air pollution than doing the same in OECD countries. For Europe, the effects of integrating the damage costs of air pollution into the optimization of energy scenarios have been analysed by Korkmaz et al. (2020) and Schmid et al. (2019). Two effects are important: firstly, biomass burning in particular in smaller boilers is significantly reduced, as firing biomass is climate friendly but leads to air pollution. Secondly, the marginal avoidance costs per tonne of avoided carbon are reduced, especially for the period 2020–2035. The reason is that in this period more efficient measures like the replacement of oil and coal with electricity from carbon-free energy carriers (except biomass) and measures for energy savings will also reduce emissions of air pollution significantly, while later more expensive measures like producing and using fuels that are produced from renewable electricity (power to X) will have a lower effect on air pollution reduction.

In most cases, integrated assessment improves the efficiency of measures for environmental and climate protection. In the following an example is shown where an efficient climate protection measure gets inefficient if air pollution is included in the assessment. This example is the use of small wood firings in cities. Wood firings are climate friendly but emit lots of fine particles and NO x . Huang et al. (2016) show that for wood firings that are operated in cities, the damage of more health impacts outweighs the benefit of less greenhouse gas emissions.

Figure 21 shows the social costs per year; this is the annuity of the monetary costs, the monetized impacts of climate change, and the monetized health impacts caused by air pollution for different heating techniques that are used in an older single-family house in the centre of the city of Stuttgart. The social costs are calculated for newly built state-of-the-art technologies fulfilling the currently valid strict regulations for small firings in Germany (BImSchV, 2021). Older stoves have emissions and thus impacts that are much larger than those shown. The social costs are highest for wood and pellet combustion caused by their high air pollution costs, although the climate change costs of wood combustion are very low. This means that the benefit of less greenhouse gas emissions of wood firings is much smaller than the additional burden caused by air pollution. Furthermore, even if we further enhance the emission reduction by equipping the wood and pellet combustion with an efficient particulate filter – these are represented by the columns marked with “ + part. filter” – the ranking is not changed. The reason is the high NO x emissions of wood combustion. The results suggest that wood combustion in rural areas should be equipped with a particulate filter, while in cities a ban on small wood combustion might be considered, unless wood and pellet firings are equipped not only with particulate filters but also with selective catalytic reduction (SCR) filters.

Figure 21 Social costs per year (annuity) of different heating boilers for an older single-family house in Stuttgart. Boilers are state-of-the-art technologies, and + part. filter means that wood or pellet heating is additionally equipped with efficient particulate filters (Huang et al., 2016).

8.3  Emerging challenges

8.3.1  challenges in improving the methodology for integrated assessments.

Estimations of damage costs caused by air pollution and climate change still show large uncertainties. Li (2020) reports a 95 % confidence interval of EUR  3.5×10 11 to EUR  2.4×10 12 for the damage costs caused by the exposure to PM 2.5 and NO 2 for 1 year (2015) for the adult EU28 + 2 population. Kuik et al. (2009) report an uncertainty range for the marginal avoidance costs to reach the “2 ∘ aim” of EUR 2020  162 to EUR 2020  503 per tonne of CO 2,eq in 2050. In addition, systematic errors might occur, for instance still unknown exposure–response relationships. Thus, methodological improvements are necessary.

In principle, all model steps and related input data shown in Fig. 19 would need improvement. Most of the improvements necessary for models and the data shown in Fig. 19 have already been addressed in the previous sections. Challenges for improving the estimation of emissions of indoor and outdoor sources are described in Sect. 3.3. Improvements in atmospheric modelling are addressed in Sect. 5.3. Exposure modelling is a relatively new field, so a lot of gaps have to be filled (see Sect. 7.3.3). Further epidemiological studies, especially for analysing the health impacts of specific PM species and PM size classes, are urgently needed, and contingent valuation studies are needed to improve the methodology. The challenges for these topics are addressed in the relevant sections above and will thus not be repeated here. However, two further methodological improvements have not been mentioned and are thus described in the following.

When assessing a policy measure for the reduction of air pollution, the first step is to estimate the reduction of emissions caused by the policy measure. Measures can be roughly classified in technical measures that improve emission factors (e.g. by demanding filters) and non-technical measures that change the behaviour or choices of emission source operators (e.g. by increasing prices of polluting goods). Especially if non-technical measures are chosen, e.g. the increase in the price for a good that is less environmentally friendly, the identification of the reaction of the operators of the emission sources is not straightforward. Do they keep using the good although it is more expensive? Do they substitute the good or do they renounce the utility of the good by using neither the good nor substitutes anymore? For energy-saving measures, it is well-known that after implementing such a measure, the users do not save the full expected energy amount but instead increase their comfort, for instance by increasing the room temperature. This is known as the rebound effect. The traditional way to deal with behavioural changes is using empirically found elasticity factors. For the transport sector, where most of the applications are made, Schieberle (2019) compiled a literature search for elasticities in the transport sector and demonstrated their use in integrated assessments. However, as a further development, recently first attempts to use agent-based modelling have been made to estimate the behavioural changes of people confronted with policy measures (Chapizanis et al., 2021).

With regard to the marginal costs of CO 2 reduction used in the assessments, further investigations taking into account emerging innovations are necessary. Furthermore, the stated estimates are quite high, so that the question arises of whether the values and thus the Paris aim gain worldwide social acceptance. More emphasis might be laid on research to develop measures for more efficient climate protection as well as measures to remove GHGs from the atmosphere and also to develop adaptation measures.

8.3.2  Challenges for the reduction of ambient air pollution

In recent years, regulations have been implemented that will decrease emissions in two important sectors considerably.

For ships, the IMO (International Maritime Organization) adopted a revised annex VI to the international Convention for the Prevention of Pollution from Ships, now known universally as MARPOL, which reduces the global sulfur limit from 3.50 % to 0.50 %, effective from 1 January 2020 (IMO, 2019). This will drastically reduce the SO 2 emissions around Europe outside of the sulfur emission control areas of the Baltic Sea, North Sea, and English Channel. Furthermore, the IMO has adopted a strategy to reduce greenhouse gas emissions by at least 40 % by 2030, pursuing efforts towards 70 % by 2050, compared to 2008. A revised, more ambitious plan is currently being discussed. Geels et al. (2021) assess the effects of these new regulations by generating several future emission scenarios and assessing their impact on air pollution and health in northern Europe.

Diesel cars now must comply with the Euro 6d norm, which will drastically reduce the real driving emissions of NO x on streets and roads. In addition, electric vehicles are now promoted and subsidized in many EU countries.

In the context of a revision of the EU rules on air quality announced in the European Green Deal, the European Commission is expected to strengthen provisions on monitoring, modelling, and air quality plans to help local authorities achieve cleaner air, notably proposing to revise air quality standards to align them more closely with the World Health Organization recommendations (which was updated in 2021).

The European Commission is also expected to introduce a new Euro 7 norm for passenger cars in 2025. The Industrial Emissions Directive demands permanent reviews of the EU Best Available Techniques reference documents (BREFs), resulting in decreasing emissions from large industrial emitters. The EU has decided to reduce greenhouse gas emissions by at least 55 % compared to 1990. Furthermore, national reduction plans for GHG lead to a further reduction of the combustion of fossil fuels. Thus, emissions of air pollutants from combustion processes will significantly decrease with one exception: wood and pellet firings <500  kW th . Hence, regarding combustion, the main challenge is the development of further PM 2.5 and NO x reduction measures for small wood firings. Similar trends will be observed in a number of other countries. For example the USA wants to reduce their greenhouse gas emissions from 2005 to 2030 by 50 % and China wants to reach carbon neutrality by 2060.

As emissions of particulates from combustion decrease, diffuse emissions, e.g. from abrasion processes, bulk handling, or demolition of buildings, and those from evaporation of volatile organic compounds get more and more dominant. So more emphasis should be put on the determination and reduction of these emissions. In particular, the processes leading to diffuse emissions are not well-known. In transport, emissions from tyre and brake wear and road abrasion heavily depend on driving habits, speed, weather conditions, and especially the traffic situation and layout of the road network. However, emission factors for diffuse emissions are still largely expressed in grammes per vehicle kilometre, not taking situations where braking is necessary, e.g. because of traffic jams or crossroads, into account. Furthermore, reduction measures like the development of tyres and brakes with longer durability should be considered and assessed.

A key challenge for reducing secondary particulates, especially ammonium nitrates, is a further reduction of NH 3 emissions from agriculture. Certain national reduction commitments for EU countries from 2005 until 2030 are regulated by the National Emission Reduction Commitments Directive (NEC Directive) of the EU, but further reductions might be necessary.

8.3.3  Challenges for the reduction of indoor air pollution

A more precise understanding of personal exposure to air pollution and the use of exposure–response relationships (instead of relationships linking outdoor concentration with responses) will potentially change the focus of air pollution control. As people are indoors most of the time, now the reduction of indoor pollution is becoming important. Of course, reducing ambient concentration will also reduce indoor pollution, as pollutants penetrate from outside into the houses. However, around 46 % of the total exposure with PM 2.5 for an average EU citizen stems from indoor sources; for NO 2 about 25 % is caused by indoor sources (Li and Friedrich, 2019). Thus, indoor sources cannot be neglected. The reduction of exposure to emissions from passive smoking, frying, and baking in the kitchen; using open fireplaces and older wood stoves; and incense sticks and candles is especially important. Indoor concentrations can be reduced by reducing the emission factor of the source, changing behaviour when using the source; by banning the use of a source; by increasing the air exchange rate with ventilation; and by using air filters.

This review has covered a larger number of research areas and identified not only the current status but also the emerging research needs. There are of course cross-cutting needs that are a prerequisite to further air quality research and develop more robust strategies for reducing the impact of air pollution on health. The following section discusses some of the key areas and synthesizes these in the form of recommendations for further research.

9.1  Connecting emissions and exposure to air pollution

There is a progressively important need to move from static annual inventories to those that are dynamic in terms of activity patterns and of higher temporal resolution. This is driven partly by the need for activity-dependent exposure modelling and because there is an increasing availability of online observations from sensors to arrive at a better spatial and temporal resolution of emission rates and factors. Clearly community efforts are necessary for identifying and reducing uncertainties in emissions that have a large impact on the resulting air quality and exposure predictions including benefiting from source apportionment methods.

One gap is the evaluation of agricultural emissions, which are still poorly understood, and improvements will support both air quality and climate change assessment, leading to co-benefits. While considerable effort has been devoted to estimating NO x emissions, there are still uncertainties in the estimation of VOC emissions. These uncertainties have direct implications when quantifying changes in ozone levels and contributions from secondary organic aerosols to regional and global scales. One prominent example of such uncertainties is the estimation of VOC profiles in terms of the chemical species and their evaporation rates, including in particular those from shipping activities in the vicinity of ports, as a shift has occurred to both low-sulfur and carbon-neutral or non-carbon fuels.

Similarly, as exhaust emissions decrease with the increase in electric vehicles, the assessment of the consequences of airborne non-exhaust emissions is becoming more and more important. However, this needs to be examined in the context of tighter policy-driven controls on petrol and diesel vehicles. Emission factors for ultra-fine particles are also uncertain; these are also spatially and temporally highly variable, which reduces the reliability of particle number predictions necessary for estimating exposure of people (Kukkonen et al., 2016a).

Exposure connects emissions to concentrations and their impact on health. As exposure to a particular air pollutant is determined by all sources of that air pollutant, both indoor and outdoor sources are important. Indoor sources are considerable in number and variety from tobacco smoking to cooking and heating fuels, indoor furniture, body care and cleaning products, and perfumes. Not only are emission factor data for these sources needed, stricter regulations are necessary for indoor sources (e.g. indoor cleaning products and wood burning for residential heating).

9.2  Extending observations for air quality research

Our review has highlighted the urgent need to strengthen the integration of observations from different platforms, including from reference instruments, mobile and networked low-cost sensors, and other data sources, such as satellite instruments and other forms of remote sensing. In addition to providing greater spatial extent and fine-scale resolution of observations in urban and other areas, these integrated data sets can form the basis of inputs for dynamic data assimilation. Data assimilation can also be performed using machine learning and/or artificial intelligence approaches. These developments can improve the accuracy of chemistry–transport models, including air quality forecasts.

Additional requirements for low-cost sensors are (i) improving their reliability for both the gaseous and particulate matter measurements (including in particular VOCs), (ii) extending the measured size range of particulate matter up to ultra-fine particles, and (iii) including their scope to also measure bioaerosols, such as allergenic pollen species and fungi. Integrating these sensors into existing infrastructures, such as permanent air quality measurement networks, traffic counting sites, and indoor monitoring, would provide a richer data set for air quality and exposure research. Further effort is required to determine the health-relevant PM information, including in particular the chemical composition of PM.

As citizen science and crowdsourcing increase, their use in air quality research needs to be more clearly defined. It could potentially provide near-real-time air pollution information as well as information to be used for personal health protection and lifestyle decisions. Most challenging for this objective is data quality characterization and acceptance of these new data provisioning tools, which do not easily allow an analytical quality assurance and control.

9.3  Bridging scales and processes with integrated air pollution modelling

Continuing developments in fine, urban, and regional modelling have elevated scale interactions as a key area of interest. As highlighted above, research is needed to develop new approaches to connect processes operating on different scales. Baklanov et al. (2014) reviewed online approaches that include coupling of meteorology and chemistry within an Eulerian nested framework, but on the whole air pollution applications are limited to different modelling systems including Eulerian for regional, Gaussian for urban and street, and LES and advanced CFD-RANS for even finer scales. Challenges remain on how to best integrate the fast-emerging machine learning statistical tools and how parameterizations and computational approaches have to be adapted. These scale interactions are of critical importance when examining the impact of air pollution in cities which are subject to heterogeneous distribution of emissions and rapidly changing dispersion gradients of concentrations. New modelling approaches will enable multiple air quality hazards that affect cities to be examined within a consistent multi-scale framework for air quality prediction and forecasting and local air quality management, quantifying the impact of episodic high-air-pollution events involving LRT and even meteorological and climate hazards as cities prepare for the future.

One major development in this vain is that of Earth system model (ESM) approaches, which in the past have been focussed on global scales but have the potential of higher-resolution applications (e.g. WWRP, 2015). Within Earth system models, there is potential for integration of observations (e.g. through data assimilation of soil moisture and surface fluxes of short-lived pollutants and greenhouse gases). These developments are to some degree being aided by the rapidly evolving area of parallel computer systems. While the representation of urban features and processes within ESMs still require further effort, these models have the potential to include dynamical and chemical interactions on a much wider scale than is possible with traditional approaches (e.g. mesoscale circulations, urban heat island circulation, sea-breeze and mountain-valley circulations, floods, heat waves, wildfires, air quality issues, and other extreme weather events).

As primary air pollution emissions are decreasing, the role of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols) in urban environments requires more research. Here coupled systems and potential ESMs in the future will have a key role based on two-way interaction chemistry–meteorology models combining the effects of urban, sub-urban, and rural pollutant emissions with dynamics. This is especially true in a changing climate scenario.

Cities are routinely facing multiple hazards in addition to high levels of air pollution including storm surges, flooding, heat waves, and a changing climate. Moving towards integrated urban systems and services poses research challenges but is viewed as essential to meet sustainable and environmentally smart city development goals, e.g. SDG11: Sustainable Cities and Communities (Baklanov et al., 2018b; Grimmond et al., 2020). More integrated assessment of risk to urban areas necessitates observation and modelling that brings together data from hydrometeorological, soil, hydrology, vegetation, and air quality communities including sophisticated and responsive early warning and forecast capabilities for city and regional administrations.

9.4  Improving air quality for better health

As air quality science continues to develop, the need to improve our understanding of PM properties and resulting health impacts remains a priority. In particular the areas that stand out are the need to better quantify particle number concentrations (PNCs), particle size distributions (PSDs), and the chemical composition of PM, especially in urban areas where population density is higher. An ongoing challenge for the science community is to investigate which of the PM properties or measures optimally describe the resulting health impacts. To aid research, a denser measurement network on advanced PM properties is needed for quantifying chemical and physical characteristics of PM in cities and regionally. Another important requirement is the availability of improved higher-resolution emission inventories of PM components and for different sizes (see Sect. 9.1). To support epidemiological studies, comprehensive long-term data sets are needed including both (i) multi-decadal evaluations of air quality, meteorology, and exposure and (ii) information on a range of health impacts.

9.5  Challenges of global pandemics

In addition to the multiple hazards facing cities mentioned in Sect. 9.3, the COVID-19 pandemic has starkly demonstrated how society can be dramatically affected across the world. Studies are indicating a dramatic impact on air quality due to the lockdown as well as possible connections between air pollutants such as aerosols in spreading the SARS-CoV-2 virus (e.g. Baldasano, 2020; Gkatzelis et al., 2021; Sokhi et al., 2021). To fully assess the interactions of viruses and air pollutants, studies need to consider both indoor and outdoor transmission as well as meteorological and climatological influences. A recent preliminary review (WMO, 2021) has concluded that there are mixed indications of links between meteorology and air quality with COVID-19, and more thorough studies are needed to ascertain the direct and indirect effects. Given the complexity of the topic, cross- and interdisciplinary studies would be needed, including a collaboration of microbiologists, epidemiologists, health professionals, and atmospheric and indoor pollution air scientists.

9.6  Integrating policy responses for air quality, climate, and health

Most control policies and measures targeted at air pollution will also change GHG emissions, which implies that taking them both into account in integrated assessments will in most cases provide considerable co-benefits. There are cases, for example in the case of biomass burning, which will increase air pollution emissions, and hence additional abatement measures (e.g. cleaning systems) will be required. On the whole, however, integrating climate change and air pollution policies where possible has the potential of making the integrated policy more efficient than separate policies for improving air quality and limiting the impact of climate change. Thus, integrated environmental policies based on assessing reductions of impacts on health, the environment, and materials caused by air pollution control and reductions of impacts of climate change caused by measures for climate protection simultaneously should be implemented. The assessment should be made following the impact pathway approach described in Sect. 8.2. The impact pathway approach uses the methods and data from all the sections of this paper, i.e. emission modelling, atmospheric modelling, exposure modelling, and health impact modelling. Thus, addressing the challenges described in this paper would help to reduce uncertainties and improve efficiency in the scientific recommendations for setting up integrated environmental policy plans. Within an integrated air pollution control and climate protection assessment, a particularly important new development would be to use the individual exposure (the concentration of a pollutant where it is inhaled by an individual averaged over a year) instead of some outdoor concentration as an indicator for health impacts, i.e. as input for the exposure–response relationship. In this case, the indoor concentration of air pollutants and thus indoor source emission rates and ventilation air exchange rates would be important elements in the assessment along with contributions from outdoor sources when planning air pollution control strategies.

9.7  Key recommendations

Below in Table 1 we present a synthesis of key recommendations for scientific research and the importance for air quality policy that have emerged from this review. The table also provides an indication of the confidence in the scientific knowledge in each of the areas, the urgency to complete the science gaps in our knowledge, and the importance of each of the listed areas for supporting policy. It should be noted that our approach provides more of an overview and does not consider the needs of specific areas or of national needs which may differ from the regional status of knowledge. For example, in the case of emission inventories for Europe and North America, there is generally high confidence but that may not be the case in other regions of the world or for specific countries or sub-regions.

Table 1 A synthesis of key recommendations for scientific research and the importance for air quality policy. A three-level scale is used to indicate the current confidence in the scientific knowledge and understanding and a measure of the urgency to fill the science gaps where they exist. Similarly, a three-level scale is used to indicate the importance of the specific issues for policy support. Scientific confidence – h: high (progress is useful but may not require significant specific research effort); m: medium (some further research is required); l: low (concerted research effort is required). Scientific urgency to meet gaps in knowledge – v: very urgent need to fill science gap; u: urgent need to fill scientific gap; w: widely accepted with less urgency to fill the science gap. Importance for policy support – H: high (is highly important for developments of new policies); M: medium (can lead to refinements of current policies); L: low (progress is useful but may not require significant effort in the short or medium term).

1  PM properties refer here to particulate matter size distributions, particle number, and chemical composition for example. 2  Dynamic exposure assessment refers to exposure studies, which treat the pollutant concentrations in different microenvironments as well as the infiltration of outdoor air to indoors. In dynamic exposure assessment, one can also treat pollution sources and sinks in indoor air.

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This review has mainly examined research developments that have emerged over the last decade. As part of the review, we have provided a short historical survey, before assessing the current status of the research field and then highlighting emerging challenges. We have had to be selective in the key areas of air quality research that have been examined. While the concept of this review emerged from the 12th International Conference on Air Quality (held virtually during 18–26 May 2020), each of the sections not only provided an air quality research community perspective but also included a wider literature examination of the areas.

10.1  Emissions of air pollution

The emphasis has been on air pollution emissions of major concern for health effects, namely exhaust and non-exhaust emissions from road traffic and shipping, and other anthropogenic emissions, e.g. those from agriculture and wood burning. Developments are continuing to improve global and regional emission inventories and integrating local emissions data into the larger-scale inventories. With increasing demand for cleaner vehicles, there is still the need to assess whether electric and hybrid vehicles actually reduce total PM 2.5 and PM 10 emissions, as emissions from non-exhaust PM from tyre, brake, and road wear are still present. Developments in on board monitoring to help improve estimation of real-world emission estimation is another growing area. Understanding the effects of non-exhaust emission will be important to design robust air quality management strategies in the context of other emissions, including windblown dust.

Uncertainties still exist in estimating emissions from diffuse processes, such as abrasion processes in industry, households, agriculture, and traffic, where large variabilities are still present. Other sources, which are not well characterized, include residential wood combustion as well as the spatial representation of these emissions across regions. While progress in source apportionment models has continued, inverse modelling used for improvement of emission inventories has the potential to reduce their uncertainties.

In terms of chemical speciation, while some improvements have taken place in estimating temporal profiles of agricultural emissions, the amount of NH 3 and PM emissions originating from agriculture are still uncertain for many regions. The impact of new fuels on the chemical composition of NMVOC emissions from combustion processes remains highly uncertain (e.g. low-sulfur residual fuels in shipping and new exhaust gas cleaning technologies).

Bringing together air pollution emission inventories with those of greenhouse gases will facilitate integrated assessment measures and policies benefitting from co-benefits. On the urban and street scales, emission models need to be able to simulate the spatial and temporal variations in emissions at a higher resolution from road traffic, taking account of traffic and driving conditions.

The importance of shipping emissions is growing, as there is a shift to carbon-neutral or zero-carbon fuels. Emission factors for VOC from shipping are generally less certain, and hence little is known about their contribution to particle and ozone formation. To estimate the total environmental impact of shipping, integrated approaches are needed that bring together (i) impacts from atmospheric emissions on air quality and health, deposition of pollutants to the sea; (ii) impacts of discharges to the sea on the marine environments and biota; and (iii) climatic forcing.

The greater emphasis on reducing exposure to air pollution requires consideration of both emissions from outdoor and indoor sources, as well as their exchange between indoor and outdoor environments. Emissions of VOC for example, from transportation and the use of volatile chemical products, such as pesticides, coatings, inks, personal care products, and cleaning agents, are becoming more important, as are combustion gas appliances such as stoves and boilers, smoking, heating, and cooking, which are important sources of PM 2.5 , NO, NO 2 , and PAHs. The complexity of integrated exposure models is expected to increase, as they have to include both indoor and outdoor emissions of air pollution, accurate description of the key chemical and physical processes, and treatment of dispersion of air pollution inside and outside and exchange between buildings and the ambient environment (Liu et al., 2013; Bartzis et al., 2015).

10.2  Observations to support air quality research

Regarding observation of air quality, this review has focused on low-cost sensor (LCS) networks, crowdsourcing, and citizen science and on the development of modern satellite and remote sensing technics. Connecting observational data with small-scale air quality model simulations to provide personal air pollution exposure has also been discussed.

Remote sensing measurements including satellite observations have a significant role in air quality management because of their spatial coverage, improving spatial resolution and their use in combination with modelling tasks (Hirtl et al., 2020), even for urban areas (Letheren, 2016). Machine learning algorithms are increasingly being used with remote sensing applications (e.g. Foken, 2021), and recent advances have highlighted the potential of statistical analysis tools (e.g. neural learning algorithms) for predicting air quality at the city scale based on data generated by stationary and mobile sensors (Mihăiţă et al., 2019). Geostatistical data fusion is allowing fine spatial mapping by combining sensor data with modelled spatial distribution of air pollutant concentrations (Johansson et al., 2015; Ahangar et al., 2019; Schneider et al., 2017).

Applications of LCS as well as networks based on such sensors have increased over the past decade (e.g. Thompson, 2016; Karagulian et al., 2019; Barmpas et al., 2020; Schäfer et al., 2021). These applications have also highlighted the need for proper evaluation, quality control, and calibration of these sensors. The analysis of LCS data should take account of cross-sensitivities with other air pollutants, effects of ageing, and the dependence of the sensor responses on temperatures and humidity in ambient air (e.g. Brattich et al., 2020).

10.3  Air quality modelling

Air quality research, including approaches to manage air pollution, has relied heavily on the continuing developments, applications, and evaluation of air quality models. Air quality models span a wide range of modelling approaches including CFD and RANS models used for very high resolution dispersion applications (e.g. Nuterman et al., 2011; Andronopoulos et al., 2019), and Lagrangian plume models to Eulerian grid CTMs used for urban to regional scales. An interesting development is that of the implementation of multiply nested LESs and coupling of urban-scale deterministic models with local probabilistic models (e.g. Hellsten et al., 2021), although complexities arise because of the different parameterizations and the treatment of boundary conditions. A limitation that needs addressing with CFD, including LES models, is that they are currently suited mainly for dispersion of tracer contaminants or where only simple tropospheric chemistry is relevant. Lack of more sophisticated or realistic description of NO x –VOC chemistry can cause significant bias in the concentration gradients at very fine scales.

Over the last decade new developments have focused on improving scale interactions and model resolution to resolve the spatial variability and heterogeneity of air pollution (e.g. Jensen et al., 2017; Singh et al., 2014, 2020a) at street scales in a city area. New approaches of artificial neural network models and machine learning have shown a more detailed representation of air quality in complex built-up areas (e.g. P. Wang et al., 2015; Zhan et al., 2017; Just et al., 2020; Alimissis et al., 2018). CTMs have also been developed to improve spatial resolution, for example, through downscaling approaches for predicting air quality in urban areas, forecasting air quality, and simulation of exposure at the street scale (Berrocal et al., 2020; Elessa Etuman et al., 2020; Jensen et al., 2017). Ensemble simulations have proven to be successful to provide more reliable air quality prediction and forecasting (e.g. Galmarini et al., 2012; Hu et al., 2017), and complementary hybrid approaches have been explored for multi-scale applications (Galmarini et al., 2018).

The strong interaction between local and regional contributions, especially to secondary air pollutants (PM 2.5 and O 3 ), has motivated the coupling of urban- and regional-scale models (e.g. Singh et al., 2014; Kukkonen et al., 2018). With the importance of exposure assessment increasing, the incorporation of finer spatial scales within a larger spatial domain is required, which introduces the challenging issue of representing multiscale dynamical and chemical processes, while maintaining realistic computational constraints (e.g. Tsegas et al., 2015). Similarly, machine learning approaches offer possibilities to use observational data to improve fine-scale air quality and personal exposure predictions (Shaddick et al., 2021).

10.4  Interactions between air quality, meteorology, and climate

Our review has highlighted the need to integrate predictions of weather, air quality, and climate where Earth system modelling (ESM) approaches play an increasing role (WWRP, 2015; WMO, 2016). There are also continued improvements from higher-spatial-resolution modelling and interconnected multiscale processes, while maintaining realistic computational times. Many advances have taken place in the development and use of coupled regional-scale meteorology–chemistry models for air quality prediction and forecasting applications (e.g. Kong et al., 2015; Baklanov et al., 2014, 2018a). These advances contribute to assess complex interactions between meteorology, emission, and chemistry, for example, relating to dust intrusion and wildfires (e.g. Kong et al., 2015). Data assimilation of chemical species data into CTM systems is still an evolving field of research; it has the potential to better constrain emissions in forecast applications. An example would be data assimilation of urban observations (including meteorological, chemical, and aerosol species) to investigate multiscale effects of the impacts of aerosols on weather and climate (Nguyen and Soulhac, 2021).

Urban- and finer-scale (e.g. built environment) studies are showing that improvements in the treatment of albedo, the anthropogenic heat flux, and the feedbacks between urban pollutants and radiation can influence urban air quality significantly (e.g. González-Aparicio et al., 2014; Fallmann et al., 2016; Molina, 2021). These considerations can be very important for urban air quality forecasting, as temporal variations in air pollutant concentrations in the short term are largely due to variabilities in meteorology. Understanding and parameterizing multiscale and non-linear interactions, for example evolution and dynamics of urban heat island circulation and aerosol forcing and urban aerosol interactions with clouds and radiation, remains an ongoing atmospheric science challenge. Another remaining research challenge that involves multiscale interactions includes the formation of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols), especially to describe air quality over urban, sub-urban, and rural environments.

Development and evaluation of nature-based solutions to improve air quality demand an improved understanding of the role of biogenic emissions (Cremona et al., 2020) as a function of vegetation species and characteristics. Interactions are influenced by several factors, such as vegetation drag, pollutant absorption, and biogenic emissions. These factors will determine the impact on air quality, be it positive or negative (Karttunen et al., 2020; San José et al., 2020; Santiago et al., 2017). Advanced approaches are needed to describe biogenic emissions together with gas and particle deposition over vegetation surfaces to further assess the effectiveness of nature-based solutions to improve air quality in cities.

10.5  Air quality exposure and health

Air-quality-related observations to support air quality health impact studies are heterogeneous; for many developing regions, such as Africa, ground-based monitoring is sparse or non-existent (Rees at al., 2019). The motivation is growing for an inter-disciplinary approach to assess exposure and the burden of disease from air pollution (Shaddick et al., 2021); this could benefit from the combined use of ground and remote sensing measurements, including satellite data, with atmospheric chemical transport and urban-scale dispersion modelling.

Air quality impact on health can occur on short and long timescales. PM, which is one of the most health-relevant air pollutants, is associated with many health effects, such as all-cause, cardiovascular, and respiratory mortality and childhood asthma (e.g. Dai et al., 2014; Samoli et al., 2013; Stafoggia et al., 2013; Weinmayr et al., 2010). There have been significant advances that reveal new evidence of the health impact of PM components, such as SO 4 , EC, OC, and metals (Wang et al., 2014; Adams et al., 2015; Hampel et al., 2015; Hime et al., 2018). Challenges remain to elucidate the relative role of PM components and measures in determining the total health impact. These include particle number concentrations (PNCs), secondary organic PM, primary PM, various chemical components, suspended dust, the content of metals, and toxic or hazardous pollutants.

Improved knowledge on the health impacts of PM components has also stimulated further debate on the optimal concentration–response functions and on the necessity of threshold or lower limit values, below which health impacts might not manifest (Burnett et al., 2018). These challenges will feed into health impact studies, such as EEA (2020a), which estimated that more than 3 848 000 years of life lost (about 374 000 premature deaths) were linked to exposure to PM 2.5 in 2018 in the EU-28. However, another study by Lelieveld et al. (2019) indicated that health impacts from PM 2.5 exposure may have been considerably underestimated.

The worldwide impact from the COVID-19 pandemic caused by the SARS-CoV-2 virus has raised global interest in the links between air quality and the spread of viruses (van Doremalen et al., 2020). However, the exact role and mechanisms are not yet clear and require concerted effort (e.g. Pisoni and Van Dingenen, 2020). There is also evidence that poor air quality can exacerbate health effects from other environmental stressors, including heat waves, cold spells, and allergenic pollen (e.g. Klein et al., 2012; Horne et al., 2018; Xie et al., 2019; Phosri et al., 2019).

The link between population activity and actual exposure is also becoming clearer, where dynamic diurnal activity patterns provide more accurate representation of exposures to air pollution (Soares et al., 2014; Kukkonen et al., 2016b; Smith et al., 2016; Singh et al., 2020a). Recent work by Ramacher et al. (2019), for example, has also demonstrated the importance of the movements of people to assess exposure.

For evaluating the relative significance of various PM properties and measures on human health, a denser measurement network on advanced PM properties would be needed, on both regional and urban scales. The required PM properties would include, in particular, size distributions and chemical composition. Clearly, such a network would be especially valuable in cities and regions that include high-quality population cohorts. The most important requirement in terms of PM modelling would be improved emission inventories, which would also include sufficiently accurate information on particle size distributions and chemical composition.

10.6  Air quality management and policy

Integrated assessment of air pollution control policies has progressively developed over the last 2 decades and has been widely used as a tool for air quality management (e.g. EC, 2021). Relatively recently, integrated assessment for air pollution control in research projects has started to take account of climate change. Correspondingly, integrated assessment activities for climate protection have started to include impacts of air pollution in the assessment (Friedrich, 2016). Some national authorities, such as the German Federal Environmental Agency or the UK Department for Business, Energy and Industrial Strategy, have also recommended an integrated assessment, combining the assessment of climate and air pollution impacts (Matthey and Bünger, 2019; DBEIS, 2019). Impact pathway approaches are also currently increasingly incorporating exposure to air pollutants as an indicator of health impacts, instead of the previously applied concentration of air pollutants at fixed outdoor locations (Li and Friedrich, 2019). This has an implication for epidemiological studies, which usually are based on correlation between modelled or measured concentrations at outdoor locations and health risks (e.g. Singh et al., 2020b).

Interdependence of air pollution and climatically active species allows co-benefits to be optimized. This approach also shows that costs of meeting policy obligations for climate protection (e.g. for the Paris Agreement) can be reduced or offset by the benefits of reduced health impacts from improved air quality (Markandya et al., 2018). On the other hand, for some climate protection measures, the benefits of reduced climate change are much smaller than the impacts caused by increased air pollution. This has been demonstrated for wood combustion, which while being more climate friendly than fossil fuels, will give rise to PM 2.5 and NO x emissions (Huang et al., 2016; Kukkonen et al., 2020b). Some recent studies (e.g. Schmid et al., 2019) have provided evidence on the advantages of using costs and benefits for both climate and air pollution abatement measures in integrated assessments.

Air quality management must adapt to the tightening of policy-driven regulations. Recently, the sulfur content of the fuel for ships has been reduced to 0.5 % worldwide (IMO, 2019). The EURO 6d norm has led to a significant reduction of NO x in the exhaust gas of diesel cars, whereas the EURO 7 norm planned to be implemented in 2025 will further reduce PM and NO x emissions from vehicle engines. The European Council has recently (in September 2020) agreed to reduce the EU's greenhouse gas emissions in 2030 by at least 55 % compared to the corresponding emissions in 1990. Together with national reduction plans for GHG, this will significantly reduce emissions of air pollutants from the combustion of fossil fuels. However, there is one exception: small wood and pellet firings ( <500  kW), where still further measures should be developed for reducing the PM and NO x emissions (e.g. Kukkonen et al., 2020b).

While direct combustion emissions are expected to decrease, a particular challenge will be to control diffuse emissions, e.g. from abrasion processes, bulk handling, demolition of buildings, and use of paints and cleaning agents. Despite cleaner vehicles, emissions from tyre and brake wear and road abrasion remain an important challenge. Other areas that pose challenges for air quality management are the need to reduce agricultural emissions, especially of ammonia, which can lead to the production of secondary aerosols (especially ammonium nitrates).

Using personal exposure instead of outdoor concentration as an indicator in health impact assessments offers the opportunity to assess the impacts of indoor air pollution control. Possibilities to reduce emissions from indoor sources, such as smoking, frying and cooking, candles and incense sticks, open chimneys and wood stoves, and cleaning agents, should be assessed. Furthermore, using HEPA filters in vacuum cleaners, air filters, and cooker bonnets and using mechanical ventilation with heat recovery should be analysed. In addition, possibilities for reducing PM concentrations in underground rail stations should be explored.

Finally, we consider cross-cutting needs as a synthesis of our findings and suggest recommendations for further research.

Specifically, we indicate the confidence in the scientific knowledge, the urgency to complete the science gaps and the importance of each area for supporting policy.

No data sets were used in this article.

All co-authors contributed to conceptualization and design of study, coordination, methodology development of the review, validation and checking, formal analysis, investigation and examination of the literature, writing of original draft, and review and editing of paper.

The contact author has declared that neither they nor their co-authors have any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The support of the following institutions and enterprises is gratefully acknowledged: University of Hertfordshire; Aristotle University Thessaloniki; TITAN Cement S.A., TSI GmbH; APHH UK-India Programme on Air Pollution and Human Health (funded by NERC, MOES, DBT, MRC, Newton Fund); and American Meteorological Society (AMS) Air & Waste Management Association (A&WMA).

We especially acknowledge the tireless effort of Ioannis Pipilis, Afedo Koukounaris, and Eva Angelidou.

World Meteorological Organization (WMO) GAW Urban Research Meteorology and Environment (GURME) programme for supporting and contributing to this review.

Klaus Schäfer is grateful for funding within the frame of the project Smart Air Quality Network by the German Federal Ministry of Transport and Digital Infrastructure – Bundesministerium für Verkehr und digitale Infrastruktur (BMVI).

Tomas Halenka is grateful for funding within the activity PROGRES Q16 by the Charles University, Prague.

Vikas Singh is thanked for providing Fig. 10.

This work reflects only the authors' view, and the Innovation and Networks Executive Agency is not responsible for any use that may be made of the information it contains.

We are also thankful for the funding of NordForsk.

We wish to thank Antti Hellsten (FMI) for his useful comments on CFD modelling.

This research has been supported by the European Union's Horizon 2020 Research and Innovation programme (HEALS (grant agreement no. 603946), ICARUS (grant agreement no. 690105), SCIPPER (grant agreement no. 814893), EXHAUSTION (grant agreement no. 820655), and EMERGE (grant agreement no. 874990)), the EU LIFE financial programme through the project VEG-GAP “Vegetation for Urban Green Air Quality Plans” (LIFE18 PRE IT003), German Federal Ministry of Transport and Digital Infrastructure – Bundesministerium für Verkehr und digitale Infrastruktur (BMVI; grant no. 19F2003A-F), and the funding of NordForsk under the Nordic Programme on Health and Welfare (project no. 75,007: NordicWelfAir – Understanding the link between Air pollution and Distribution of related Health Impacts and Welfare in the Nordic countries).

This paper was edited by Pedro Jimenez-Guerrero and reviewed by two anonymous referees.

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• Introduction
• Scope and structure of the review
• Air pollution sources and emissions
• Air quality observations and instrumentation
• Air quality modelling from local to regional scales
• Interactions between air quality, meteorology, and climate
• Air quality exposure and health
• Air quality management and policy development
• Discussion, synthesis, and recommendations
• Conclusions and future direction
• Data availability
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement

Atmospheric Air Pollution and Its Environmental and Health Effects

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Study finds natural sources of air pollution exceed air quality guidelines in many regions

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Alongside climate change, air pollution is one of the biggest environmental threats to human health. Tiny particles known as particulate matter or PM2.5 (named for their diameter of just 2.5 micrometers or less) are a particularly hazardous type of pollutant. These particles are produced from a variety of sources, including wildfires and the burning of fossil fuels, and can enter our bloodstream, travel deep into our lungs, and cause respiratory and cardiovascular damage. Exposure to particulate matter is responsible for millions of premature deaths globally every year.

In response to the increasing body of evidence on the detrimental effects of PM2.5, the World Health Organization (WHO) recently updated its air quality guidelines , lowering its recommended annual PM2.5 exposure guideline by 50 percent, from 10 micrograms per meter cubed (μm 3 ) to 5 μm 3 . These updated guidelines signify an aggressive attempt to promote the regulation and reduction of anthropogenic emissions in order to improve global air quality.

A new study by researchers in the MIT Department of Civil and Environmental Engineering explores if the updated air quality guideline of 5 μm 3 is realistically attainable across different regions of the world, particularly if anthropogenic emissions are aggressively reduced. 

The first question the researchers wanted to investigate was to what degree moving to a no-fossil-fuel future would help different regions meet this new air quality guideline.

“The answer we found is that eliminating fossil-fuel emissions would improve air quality around the world, but while this would help some regions come into compliance with the WHO guidelines, for many other regions high contributions from natural sources would impede their ability to meet that target,” says senior author Colette Heald, the Germeshausen Professor in the MIT departments of Civil and Environmental Engineering, and Earth, Atmospheric and Planetary Sciences. 

The study by Heald, Professor Jesse Kroll, and graduate students Sidhant Pai and Therese Carter, published June 6 in the journal Environmental Science and Technology Letters , finds that over 90 percent of the global population is currently exposed to average annual concentrations that are higher than the recommended guideline. The authors go on to demonstrate that over 50 percent of the world’s population would still be exposed to PM2.5 concentrations that exceed the new air quality guidelines, even in the absence of all anthropogenic emissions.

This is due to the large natural sources of particulate matter — dust, sea salt, and organics from vegetation — that still exist in the atmosphere when anthropogenic emissions are removed from the air. 

“If you live in parts of India or northern Africa that are exposed to large amounts of fine dust, it can be challenging to reduce PM2.5 exposures below the new guideline,” says Sidhant Pai, co-lead author and graduate student. “This study challenges us to rethink the value of different emissions abatement controls across different regions and suggests the need for a new generation of air quality metrics that can enable targeted decision-making.”

The researchers conducted a series of model simulations to explore the viability of achieving the updated PM2.5 guidelines worldwide under different emissions reduction scenarios, using 2019 as a representative baseline year. 

Their model simulations used a suite of different anthropogenic sources that could be turned on and off to study the contribution of a particular source. For instance, the researchers conducted a simulation that turned off all human-based emissions in order to determine the amount of PM2.5 pollution that could be attributed to natural and fire sources. By analyzing the chemical composition of the PM2.5 aerosol in the atmosphere (e.g., dust, sulfate, and black carbon), the researchers were also able to get a more accurate understanding of the most important PM2.5 sources in a particular region. For example, elevated PM2.5 concentrations in the Amazon were shown to predominantly consist of carbon-containing aerosols from sources like deforestation fires. Conversely, nitrogen-containing aerosols were prominent in Northern Europe, with large contributions from vehicles and fertilizer usage. The two regions would thus require very different policies and methods to improve their air quality. 

“Analyzing particulate pollution across individual chemical species allows for mitigation and adaptation decisions that are specific to the region, as opposed to a one-size-fits-all approach, which can be challenging to execute without an understanding of the underlying importance of different sources,” says Pai. 

When the WHO air quality guidelines were last updated in 2005, they had a significant impact on environmental policies. Scientists could look at an area that was not in compliance and suggest high-level solutions to improve the region’s air quality. But as the guidelines have tightened, globally-applicable solutions to manage and improve air quality are no longer as evident. 

“Another benefit of speciating is that some of the particles have different toxicity properties that are correlated to health outcomes,” says Therese Carter, co-lead author and graduate student. “It’s an important area of research that this work can help motivate. Being able to separate out that piece of the puzzle can provide epidemiologists with more insights on the different toxicity levels and the impact of specific particles on human health.”

The authors view these new findings as an opportunity to expand and iterate on the current guidelines.  

“Routine and global measurements of the chemical composition of PM2.5 would give policymakers information on what interventions would most effectively improve air quality in any given location,” says Jesse Kroll, a professor in the MIT departments of Civil and Environmental Engineering and Chemical Engineering. “But it would also provide us with new insights into how different chemical species in PM2.5 affect human health."

“I hope that as we learn more about the health impacts of these different particles, our work and that of the broader atmospheric chemistry community can help inform strategies to reduce the pollutants that are most harmful to human health,” adds Heald.

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• Published: 02 September 2024

Transforming air pollution management in India with AI and machine learning technologies

• Kuldeep Singh Rautela 1 &
• Manish Kumar Goyal 1  

Scientific Reports volume  14 , Article number:  20412 ( 2024 ) Cite this article

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A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM 2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM 2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM 2.5 concentrations across India. The results reveal its exceptional precision in PM 2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m 3 . However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM 2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

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Introduction.

Air pollution has emerged as a critical global environmental health issue, with 92% of the world's population exposed to pollutant levels exceeding air quality guidelines 1 , 2 . This widespread exposure poses significant health risks, including increased incidence of respiratory diseases, cardiovascular problems, and premature mortality 3 , 4 . In India specifically, ambient particulate matter (PM) exposure has been linked to an estimated 1.1 million premature deaths annually, with air pollution becoming the fourth leading cause of mortality nationwide 5 , 6 . The economic impact is also substantial, with the World Bank estimating that air pollution costs India 3–8% of its GDP due to healthcare expenses, reduced productivity, and premature deaths 7 .

Atmospheric aerosols, particularly black carbon, organic carbon, dust, sea salt, and sulfates, have been extensively researched in South and Southeast Asia over the past two decades 8 . However, the magnitude of these impacts is largely influenced by spatio-temporal variability and the composition of these aerosols 9 . Aerosols, including, are significant constituents of atmospheric PM and account for approximately 30–70% of the fine aerosol mass over urban areas in India 5 , 10 . However, in recent decades, this concern has increased notably, primarily attributed to the rapid surge in population, unplanned urban development, and the expansion of industries 11 , 12 . India, home to the world's largest population share at 17.76%, faces a significant environmental challenge, with many of its cities (eg; Delhi, Mumbai, Kolkata) ranking among the most polluted on the global scale 13 , 14 . An investigation based on World Health Organization (WHO) data from 2008–2013 brought attention to India's status among the most polluted nations 15 . India has faced alarming and extensive air pollution incidents in the last twenty years, prompting substantial concern among regulatory authorities. The Indo-Gangetic Plain (IGP) is highly susceptible to severe pollution incidents, notably prevalent in the post-monsoon and winter period 16 . Similarly, in many metropolitan cities across India, such as Delhi, air quality has deteriorated to hazardous levels. Concentrations of particulate matter (PM 2.5 and PM 10 ) have surged beyond 500 µg/m 3 , while nitrogen oxides (NO 2 ) have exceeded 10 µg/m 3 . Additionally, ozone (O 3 ) and sulfur dioxide (SO 2 ) levels have surpassed 5 µg/m 3 , alongside other pollutants 17 . The concentration of these pollutants, often surpassing 500 µg/m 3 , far exceeds WHO's safe annual limit of 10 µg/m 3 and India’s national ambient air quality standards (NAAQS) of 40 µg/m 3 during winters 18 . According to the Economic Times, 12.25 million vehicles are registered in Delhi, growing at a rate of 7% per annum, and they account for 67% of the total pollution 19 , 20 . Additionally, Coal-based thermal power plants and small-scale industries each contribute 12% to the pollution, including emissions from various industrial units followed by the agricultural and biomass burning in Delhi and surrounding areas 20 . This increased pollution level has raised considerable concern among authorities and stakeholders, prompting focused efforts towards addressing this critical issue 9 . The urgency of addressing air pollution in India is evident through compelling data illustrating its significant impact across various sectors.

AI & ML have become pivotal in addressing air pollution by harnessing big data analytics, utilising advanced computing systems, scalable storage, and parallel processing technologies 21 , 22 , 23 . These innovations enable comprehensive management and mitigation strategies for various air pollutants, bridging the gap between atmospheric and climate sciences through sophisticated data-driven approaches. Previous studies have proposed various AI&ML-based models as pivotal components for air pollution and aerosol transport 5 , 8 , 24 , 25 , 26 . Initially, researchers have introduced succinct and efficient statistical models for practical applications. These statistical models primarily encompass multiple linear regression (MLR) 27 and autoregression moving average (ARMA) 28 methods. The predominant use of linear hypotheses in developing statistical models contrasts with the inherent nonlinear properties exhibited by pollutant concentrations. Consequently, researchers have advocated for integrating data mining methods 29 and machine learning models 30 , 31 , 32 designed to accommodate nonlinear predictions in studying air pollutants. However, the notably nonlinear and non-stationary nature of pollutants poses challenges for achieving high prediction accuracy with these models. As a result, several studies have turned to various deep learning techniques 8 , 33 , 34 , 35 , 36 to enhance the prediction of air pollutant levels.

Despite numerous efforts to forecast concentration of major pollutants, comprehending the complex relationship among diverse influencing factors remains a persistently challenging task. Studies exploring the relevance of these factors in predicting pollutants have been scarce and constrained in scope 37 , 38 . Typically, researchers tend to utilize all accessible features and input them into prediction models. While it holds true that AI&ML models exhibit superior performance in scenarios with abundant data availability, the effectiveness of these models in pollutant prediction hinges on understanding and incorporating the most influential factors. Figure  1 illustrates the comprehensive AI/ML model development workflow for environmental or traffic-related predictions. The process includes data collection across various domains, preprocessing, algorithm selection, model development, training, testing, and validation. The process completes with prediction, incorporating a feedback loop for model refinement if needed, ensuring adaptability and continuous improvement in predictive accuracy.

Charting the sequential steps of AI and ML involvement in predicting air pollution concentrations.

Previous studies have conducted comparative analyses between AI&ML-based methodologies for forecasting concentrations of various pollutants. Initially, Mc Kendry 39 evaluated Artificial Neural Networks (ANN) with MLR for simulating the concentrations of PM 2.5 and PM 10 . Similarly, Dutta and Jinsart 40 compared the performances of decision tree and ANN algorithms in estimating PM 10 concentrations. Other comparisons include Turias et al. 41 pitting back-propagation based ANN against ARIMA for predicting the Sulfur Dioxide (SO 2 ), concentrations of Carbon Monoxide (CO) and Suspended Particulate Matter (SPM), over an industrialized region. Shang and He 42 formulated an innovative prediction method by coupling of ANN and Random forest (RF) to forecast hourly PM 2.5 concentrations. Bozdağ et al. 43 presented a comprehensive analysis for the simulation of PM 10 concentrations by comparing various modelling approaches—ANN, KNN (K-Nearest Neighbour Algorithm), SVM (Support Vector Machine) , LASSO (Least Absolute Shrinkage and Selection Operator), RF, and xGBoost.

This study systematically explores the consequences of severe air pollution in India, focusing on contributors like PM, Organic Aerosols (OAs), BC, Water-Soluble Brown Carbon (WS-BrC), and Volatile Organic Compounds (VOCs). Remediation techniques, including legislation, NAAQS, and an Air Quality Index (AQI), are inspected alongside the evolution of emission load studies and management strategies. Additionally, the study investigates the integration of AI&ML in mitigating and predicting air pollution. It details the application of AI&ML models and underscores the potential of deep learning algorithms, exemplified through a case study predicting PM 2.5 concentrations over India. Identifying challenges like technological barriers, regulatory hurdles, public awareness gaps, agricultural practices, urbanization impacts, cross-border pollution, climate change interlinkages, and socio-economic disparities, the study emphasizes the urgency of comprehensive solutions. Looking forward, the study discusses prospects involving emerging technologies and global collaborations. The study emphasizes the imperative to address air pollution in India holistically, leveraging AI&ML advancements, global cooperation, and technological innovations to formulate effective strategies for combatting the multifaceted challenges posed by air pollution in the region.

Results and discussion

Consequences of air pollution in india.

Air pollution in India specially in metropolitan cities has dire consequences for public health, stemming from increased levels of particulate matter, nitrogen oxides, and various pollutants. This increase pollution level is consistently linked to increased respiratory diseases, particularly asthma, chronic obstructive pulmonary disease (COPD), and bronchitis 7 , 44 . Children, with developing respiratory systems, are particularly vulnerable to irreversible health issues upon prolonged exposure, while the elderly, with compromised immune systems, face pre-eminent risks, including deep lung penetration, inflammation, and enduring damage caused by PM 2.5 . Beyond respiratory implications, air pollution has severe cardiovascular consequences, with nitrogen oxides significantly contributing to an increased risk of heart attacks and strokes, leading to heightened cardiovascular mortality with prolonged exposur 7 . The significant study conducted by the CPCB in Delhi highlighted robust correlations between air quality levels and negative health effects. Comparative analysis against a rural control population in West Bengal indicated a 1.7-fold higher occurrence of respiratory symptoms in Delhi, emphasizing the direct impact of air quality on public health 20 , 45 , 46 , 47 . Odds ratios for upper and lower respiratory symptoms were 1.59 and 1.67, respectively, emphasizing the profound impact of air pollution. The study also highlighted a significantly higher prevalence of current and physician-diagnosed asthma in Delhi, with lung function notably reduced in 40.3% of Delhi's participants compared to 20.1% in the control group 20 .

In addition to respiratory effects, non-respiratory impacts were observed in the cities as compared to rural controls. The prevalence of hypertension was notably higher in cities (36% vs. 9.5% in controls), correlating positively with respirable suspended particulate matter (PM 10 ) levels in ambient air 48 . Chronic headaches, eye irritation, and skin irritation were significantly more pronounced in most of the cities. Community-based studies consistently affirm the association between air pollution and respiratory morbidity. Studies focusing on indoor air pollution reveal similar correlations with respiratory morbidity, extending to conditions such as attention-deficit hyperactivity disorder in children, increased blood levels of lead, and decreased serum concentration of vitamin D metabolites 49 . Beyond health impacts, the environmental consequences of air pollution are profound. Pollutants harm plants and animals, disrupt ecosystems, and lead to biodiversity loss 50 . The issue extends beyond health and the environment, impacting economics and society, straining healthcare, productivity, and social equity, demanding holistic strategies spanning economic, social, and environmental facets making it imperative, in this crisis, to understand the existing and potential remediation techniques 51 .

The economic and social ramifications are substantial, with healthcare costs soaring as the incidence of pollution-related illnesses rises 7 . Treating respiratory and cardiovascular diseases places a significant burden on the healthcare system, affecting both public and private healthcare expenditures 44 . Air pollution in India incurred an estimated economic toll of $95 billion in 2019, amounting to 3% of the country's GDP, attributable to decreased productivity, increased work absences, and premature fatalities 52 . The economic implications of air pollution extend beyond direct healthcare costs, affecting labor markets and overall productivity 53 . Social disparities are accentuated by air pollution, with vulnerable communities facing disproportionate exposure to pollutants. Factors such as socio-economic status, access to healthcare, and geographic location contribute to disparities in exposure and health outcomes 54 . Addressing these social dimensions is crucial for devising equitable solutions that prioritize environmental justice. As India grapples with the immediate consequences of air pollution, emerging challenges require attention. Also, climate change exacerbates existing issues, influencing weather patterns and contributing to the persistence of stagnant air masses that trap pollutants and their transportation mechanism 8 . The increasing frequency of extreme weather events further complicates pollution dynamics 55 . Moreover, the complex interplay of indoor and outdoor air pollution adds another layer of complexity, with indoor air pollution often stemming from household activities such as cooking with solid fuels, compounding the overall burden on public health 49 . However, government policies and initiatives take center stage in this exploration, with regulatory measures, such as emission standards and vehicle restrictions, scrutinized for their effectiveness and implementation challenges 12 . Sustainable urban planning, including the creation of green spaces and transportation planning for pollution reduction, is examined as a proactive approach to mitigate pollution at its source 56 . Technological solutions, ranging from air purifiers to pollution monitoring devices, are also evaluated 57 . The challenges of scalability, accessibility, and integration into existing infrastructure are dissected to discern the practicality and potential impact of these technologies. Emerging technologies and global collaborations are explored as potential catalysts for change 57 , 58 .

Contributors to air pollution in India

Air pollution in India is a complex issue with multiple sources and contributors, as highlighted by various studies conducted by Lalchandani et al. 59 , Tobler et al. 60 , Rai et al. 61 , Talukdar et al. 62 and Wang et al. 63 . The sources and contributors to air pollution can be broadly categorized into particulate matter (PM 2.5 and PM 10 ), organic aerosols (OAs) including black carbon (BC), water-soluble brown carbon (WS-BrC), and volatile organic compounds (VOCs). Each of these components plays a signifsicant role in the overall air quality of the region.

Particulate matter (PM)

Particulate matter is a key component of air pollution, and Lalchandani et al. 59 conducted studies using the Positive Matrix Factorization (PMF) model to identify and apportion different sources of PM. The sources identified included traffic-related emissions, dust transportation, solid-fuel burning emissions, and secondary factors 62 , 64 . Traffic-related emissions in metropolitan cities were found to be the significant contributor to the total concentration of PM, for example, at the IIT Delhi site, emphasizing the impact of vehicular activities on air quality. Additionally, solid fuel burning emissions, often associated with residential cooking and heating, were identified as a major contributor to PM, particularly at night 62 . Rai et al. 61 conducted source apportionment of elements in PM 10 and PM 2.5 , identifying nine source profiles/factors, including dust, non-exhaust sources, solid fuel combustion, and industrial/combustion aerosol plume events. The contribution of anthropogenic sources to elements associated with health risks, such as carcinogenic elements. The geographical origins of these sources were also determined, emphasizing the regional and local influences on element concentrations in atmosphere 65 .

Organic aerosols (OAs)

Organic aerosols are another crucial component of air pollution, and studies by Tobler et al. 60 and Lalchandani et al. 62 revealed three main components of OAs: solid fuel combustion OAs (SFC OAs), hydrocarbon-like OA (HOAs) from vehicular emissions, and oxygenated OAs (OOAs). Lalchandani et al. 65 further categorized these components into sub-factors, providing a detailed understanding of the OA composition. Emissions stemming from traffic emerged as the primary contributor to the overall OA mass, underscoring the profound influence of vehicular pollution 59 .

Black carbon (BC)

BC, a product of incomplete combustion, was studied by Using the Absorption Ångström Exponent (AAE) method, contributions from biomass burning and vehicular emissions were apportioned 66 . Vehicular emissions were found to be a dominant source of BC, contributing around 67.5% 62 , 67 . The distinction between BC and brown carbon (BrC), which absorbs light in the near-UV to visible region, was also discussed, highlighting the need to consider multiple light-absorbing aerosols in air quality assessments.

Water-soluble brown carbon (WS-BrC)

Rastogi et al. 68 performed a PMF analysis of WS-BrC spectra, identifying six factors representing specific sources of BrC. The study revealed diurnal variability in BrC absorption, with factors associated with different emission sources. The presence of secondary BrC was indicated, suggesting the importance of atmospheric processes in the formation of brown carbon. This finding adds another layer of complexity to the sources of light-absorbing aerosols in the atmosphere 69 .

Volatile organic compounds (VOCs)

Wang et al. 63 investigated the characteristics and sources of VOCs, identifying six factors related to traffic, solid fuel combustion, and secondary sources. Traffic-related emissions were found to be the dominant source of VOCs at the urban site, while at the suburban site (MRIIRS), contributions from secondary formation and solid fuel combustion were more significant. The study highlighted the major role of anthropogenic sources in VOC pollution 70 .

Current remediation techniques

India has faced escalating challenges in managing air pollution over the years, necessitating the implementation of diverse remediation techniques. Figure  2 illustrates the legislative evolution of air quality management in India across three eras: Pre-Internet (1905–89), Transition (1990–99), and Internet Era (2000 onwards). This timeline showcases key acts and regulations implemented over time to address air pollution. The bottom timeline highlights the progression of NAAQS in India, from monitoring just 3 pollutants in 1982 to 7 in 1994, and 12 in 2009. The latest phase (2019–24) involves a comprehensive review of air quality standards under the National Clean Air Programme (NCAP) in 2019, demonstrating India's ongoing commitment to improving air quality management.

Legalisation and Evaluation of NAAQS in India 12 .

Legislation and regulatory measures

India's legislative landscape has evolved significantly to address air pollution. The introduction of key acts such as the Air (Prevention and Control of Air Pollution) Act in 1981 and subsequent amendments empowered central and state pollution control boards to handle severe air pollution emergencies 71 . The Environment (Protection) Act of 1986 served as an umbrella act for environmental protection, while the Motor Vehicles Act has been periodically amended to regulate vehicular pollution 72 . Recent developments include the Motor Vehicles (Amendment) Bill of 2019, allowing the government to recall vehicles causing environmental harm 73 . The establishment of institutions like the National Green Tribunal (NGT) and the National Environment Tribunal reflects a commitment to environmental accountability 74 .

National ambient air quality standards (NAAQS) and air quality index (AQI)

The formulation and periodic revision of National Ambient Air Quality Standards (NAAQS) have been pivotal in regulating air quality 18 . Beginning in 1982, the Central Pollution Control Board (CPCB) introduced NAAQS, initially covering SO 2 , NO 2 , and SPM 47 . Subsequent amendments expanded the list to include RSPM, Pb, NH 3 , and CO 75 . The National Air Quality Index (NAQI) was introduced to enhance public awareness, categorizing air quality into six levels from 'Good' to 'Severe' 76 . This index, based on the concentration of eight pollutants, guides interventions for improved air quality.

Air pollution monitoring network

India's air quality monitoring network has witnessed substantial growth. The initiation of the National Ambient Air Quality Monitoring (NAAQM) Network in 1984, expanded to the National Air Quality Monitoring Programme (NAMP), marked a critical step 77 . The network, comprising both manual and Continuous Ambient Air Quality Monitoring System (CAAQMS) stations, now stands at 1082 locations 78 , 79 . Real-time monitoring, as exemplified by CAAQMS, provides valuable data for prompt decision-making. The introduction of the System of Air Quality and Weather Forecasting and Research (SAFAR) further enhances forecasting capabilities 80 .

Evolution of studies on emission load

Emission inventories, critical for formulating air pollution control policies, have evolved over time. Initiatives by CSIR-NEERI and CPCB in the late twentieth century laid the foundation 12 . Emission inventory data, collected through GIS, has become integral in mapping pollution sources and understanding spatial distribution 81 . The Air Pollution Knowledge Assessments (APnA) city program and organizations like TERI contribute to city-specific inventories 82 . The emphasis on utilizing secondary data streamlines the process, enabling the creation of comprehensive databases for national and urban pollution inventories. The secondary data refers to datasets that include emission loads from various sources such as vehicular emissions, industrial outputs, construction activities, residential heating, and biomass burning 83 .

Management strategies and control policies

India's air pollution management strategies encompass a multifaceted approach, with a blend of judicial interventions and executive actions.

Judicial interventions

The judiciary, particularly through petitions filed by M.C. Mehta, has been instrumental in setting guidelines and policies 84 . For instance, interventions in the Taj Trapezium Zone and the oversight of air quality management plans for non-attainment cities by the National Green Tribunal (NGT) are notable 74 . The judiciary has played a significant role in shaping policies for better governance and legislation.

Executive actions

Several executive measures contribute to air pollution control. The Auto Fuel Policy, initiated in 2003 and updated in 2014, addresses vehicular emissions 85 . Emphasis on alternative fuels, as seen in the National Auto Fuel Policy and the Pradhan Mantri Ujwala Yojana (PMUY) for subsidized LPG connections, aligns with cleaner fuel initiatives 86 . Stricter emission standards for thermal power plants and the push for Hybrid and Electric Vehicles (EVs) under schemes like Faster Adoption and Manufacturing of Hybrid & Electric Vehicles (FAMHE) contribute to pollution reductions 87 .

AI&ML Techniques for addressing and forecasting air pollution

Overview of ai&ml models.

Various AI&ML techniques, such as ANN, Fuzzy logic (FL), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrence Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Autoencoder (CA) etc., are commonly used in previous studies to predict and forecast earth and atmospheric variables 8 , 25 , 88 , 89 , 90 , 91 (Table 1 ). AI&ML models have become pivotal in processing and simulating non-linear information, with a notable focus on ANNs 92 . ANNs emulate the human nervous system, comprising interconnected neurons that collectively address a spectrum of challenges, from function approximation to clustering and optimization 93 . The three-stage process involved in ANN modelling, encompassing design, training, and validation, underscores its versatility 92 . During the design phase, crucial parameters such as architecture, layers, neurons, and learning algorithms are thoroughly chosen 94 . Training involves iterative adjustments of synaptic weights to minimize errors, while validation gauges the network's generalization performance for unknown data.

Multilayer Perceptron (MLPs), a prominent type of ANN, have proven effective in predicting atmospheric pollution events. Typically featuring input, hidden, and output layers, MLPs can adapt to complex patterns by incorporating multiple hidden layers 92 . Configuring neurons in the hidden layers is of utmost importance, as an incorrect count can lead to over-fitting or under-fitting. Techniques like thumb rule and trial and error, network reduction offer solutions to optimize neuron numbers. FL, another AI technique, operates on a different paradigm by assigning truth values in a range. Developed from fuzzy set theory, it accommodates linguistic variables, making it adept at handling uncertainty in natural language statements. Fuzzy logic's three main phases—fuzzification, inference, and defuzzification—form a robust modelling system capable of addressing nuanced problems. SVM are popular for supervised learning, excelling in classification, prediction, density estimation, and pattern recognition. SVM seeks an optimal hyperplane to segregate data into predefined classes, with kernel functions playing a pivotal role in introducing non-linearity.

Deep Neural Networks (DNNs) represent an advanced version of ANNs, characterized by structural depth and scalability 8 . DNNs, with more than three layers, can automatically extract features from raw inputs, known as feature learning. Notable architectures within DNNs, such as CA, LSTM, CNNs and RNNs have demonstrated superior performance, especially in air pollution forecasting. The training of DNNs demands significant computational power, leading to advancements in processing capabilities and the development of sophisticated algorithms. Overcoming challenges like vanishing gradient and overfitting has prompted the application of advanced algorithms like SVM, RF, Greedy layer-wise, and Dropout. The application of these models extends across various domains due to their versatility and robust performance. The modelling of complex atmospheric variables such as air pollution forecasting, LSTM, CA, and CNNs emerge as particularly effective and popular architectures.

Application of AI&ML in addressing and forecasting air pollution

The application of AI&ML models, particularly ANNs, FL, SVM and DL models, have emerged as a crucial tool in addressing and forecasting air pollution. ANNs have helped in a transformative era in air pollution forecasting, with a diverse range of applications capturing the attention of researchers. Numerous studies attest to the success of ANNs in predicting both particulate and gaseous pollutants with desired accuracy over various spatio-temporal resolution. The early forays into air pollution forecasting by Mlakar et al. 95 marked a significant milestone, employing a trained nonlinear three-layered back propagation feed forward network. This model successfully predicted the concentration of SO 2 over a thermal power plant, showcasing the potential of ANNs. Subsequent research expanded the scope and sophistication of ANN applications. Similarly, Arena et al. 96 demonstrated the efficacy of multi-layer perceptron in predicting concentration of SO 2 over an industrial area, emphasizing the model's accuracy across diverse weather conditions. Sohn et al. 97 extended the ANN approach to model multiple pollutants, including NO, SO 2 , NO 2 , CO, O 3 , CH 4 and total hydrocarbons. The results indicated reasonable accuracy within a limited prediction range, highlighting the need for further optimization by incorporating additional weather-related input parameters. The application of ANNs in gaseous pollutants forecasting continued with studies by Slini et al. 98 and Kandya 99 both emphasizing the importance of optimizing input parameters for improved accuracy. Comparative assessments with other forecasting techniques consistently positioned ANNs as superior for gaseous pollutants. Chaloulakou et al. 100 found that ANN outperformed Multiple Linear Regression (MLR) in predicting ozone concentrations, showcasing the model's superior accuracy. Similar findings were reported by Mishra and Goyal 101 , compared Principal Component Analysis (PCA)-based ANN model with MLR for estimating the concentrations of NO 2 . In the realm of particulate matter forecasting, ANNs have proven equally effective. Fernando et al. 102 successfully used multi-layered MLP to predict PM 10 concentrations, considering parameters such as hourly meteorological data, particulate, matter with statistical indicators. Grivas and Chaloulakou 103 employed an ANN model for hourly PM 10 predictions, showcasing consistent accuracy even in the presence of noisy datasets. The versatility of ANNs extends to predicting roadside contributions to PM 10 concentrations, as demonstrated by Suleiman et al. 104 . Comparative studies with other models have affirmed the efficacy of ANNs in particulate matter forecasting. Zhang et al. 105 utilized BPANN to forecast the concentrations of PM 10 and found BPANN outperforming other models in predictive accuracy. Paschalidou et al. 106 evaluated the multi-layer perceptron-based ANN those models provided superior results compared to Radial Basis Function models, establishing the former's dominance in terms of forecasting capability. Contrasting trends were observed in certain studies, such as those by Mishra et al. 107 and Moisan et al. 108 , where alternative models outperformed ANN during extreme events. This highlights the nuanced nature of model performance, with specific conditions favouring different approaches. However, recent progress has witnessed researchers utilizing ensemble methods to improve both the stability and accuracy of ANN models. Liu et al. 109 combined Wavelet Packet Decomposition (WPD), Particle Swarm Optimization (PSO), and BPNN to create an ensemble model for PM 2.5 forecasting, demonstrating superior precision compared to individual models.

FL, renowned for its capacity to manage uncertainty, enhanced fault tolerance, and adeptness in handling highly complex nonlinear functions, has garnered extensive adoption in the realm of air pollution prediction. The advantages of FL are exemplified in various studies. For example, Chen et al. 110 innovatively introduced a novel fuzzy time series model specifically for O 3 prediction, showcasing its superior performance when compared to traditional fuzzy time series models. Jain and Khare 111 applied a neuro-fuzzy model to predicts the concentration of CO in Delhi, achieving accurate estimates at complex urban levels. Carbajal-Hernández et al. 112 predicts air quality in Mexico City by utilising FL model alongside autoregression model and signal processing. The introduction of a novel algorithm, the "Sigma operator," allowed for precise evaluation of air quality variables, showcasing the effectiveness of fuzzy-based models. Moreover, Al-Shammari et al. 113 , evaluates stochastic and FL-driven models to estimate the daily maximum concentrations of O 3 . The findings indicated that the FL-based model exhibited a marginal superiority over the statistical model particularly in instances of severe pollution events. Innovative approaches like the Fuzzy Inference Ensemble (FIE), as proposed by Bougoudis et al. 114 , demonstrated high accuracy in air pollution forecasting for Athens. Another significant application was presented by Song et al. 115 , where different probability density functions were employed to enhance particulate matter (PM) forecasting. They developed an adaptive neuro-fuzzy model, emphasizing the importance of density functions in addressing uncertainty associated with future PM trends. Furthermore, Wang et al. 116 presented a hybrid model for forecasting air pollution. This model merges uncertainty analysis with fuzzy time series, demonstrating precision in predicting PM and NO 2 concentrations. Behal and Singh 117 leveraged FL within an intelligent IoT sensor framework to monitor and simulate benzene, demonstrating satisfactory statistical efficacy in recent advancements. The versatility of fuzzy logic extends to unconventional pollutants as demonstrated by Arbabsiar et al. 118 , who modelled the leakage of CH 4 and H 2 S using a fuzzy inference technique. The suggested model demonstrated satisfactory performance when evaluating these contaminants.

Support Vector Machines (SVM), when combined with other machine learning algorithms, have been helpful in forecasting diverse types of pollutants. Feng et al. 119 compared SVM with other models for forecasting daily maximum concentrations of O 3 in Beijing, highlighting its stable and accurate performance. Yeganeh et al. 120 assessed the efficacy of a forecasting model utilizing SVM integrated with Partial Least Squares (PLS) for the prediction of CO concentrations, demonstrating positive outcomes. García Nieto et al. 121 conducted a comparative analysis of various prediction models for PM 10 concentrations, determining that the SVM method exhibited superior accuracy and robustness. Luna et al. 122 utilized Principal PCA in combination with SVM and ANN for the prediction of O 3 levels in Rio de Janeiro. Their study specifically investigated the influence of meteorological parameters on the concentrations of O 3 . Wang et al. 123 proposed hybrid adaptive forecasting models combining SVM and ANN for predicting PM 10 and SO 2 , demonstrating superior performance compared to individual models. FL and SVM in the forecasting air pollution levels have proven to be highly effective in addressing the complexities and uncertainties associated with predicting pollutant concentrations.

While still in its early stages, the potential of DNNs in this domain is evident from a review of various applications such as forecasting of variables in earth and atmospheric sciences. Early on, Freeman et al. (2018) employed a combination of Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict ozone concentrations in an urban area. While showing strong predictability in 8 h average ozone concentrations, various model runs revealed overfitting concerns, underscoring the necessity for further refinement. Wang and Song 125 introduced an ensemble method using a deep LSTM network with fuzzy c-means clustering for air quality forecasting. This ensemble approach outperformed individual models, showcasing its efficacy in both short-term and long-term predictions. Zhou et al. 126 explored the application of LSTM and deep learning algorithms for multi-step ahead forecasting of PM 2.5 , PM 10 , and NO x . Their deep learning architecture, integrating dropout neurons and L 2 regularization, demonstrated exceptional capabilities in capturing variations in the processes of air pollutant generation. Recent research highlights the growing preference for employing deep neural networks to capture dynamic spatiotemporal features from historical air quality and climatological datasets. Fan et al. 91 introduced stacked LSTM (LSTME), spatiotemporal deep learning (STDL), time delay neural network (TDNN), autoregressive moving average (ARMA), and support vector regression (SVR) for modelling of air pollutants over different spatiotemporal resolutions. The inclusion of auxiliary inputs resulted in a model with exceptional performance, outshining other machine learning techniques. Soh et al. 127 proposed a STDL integrating ANN, CNN, and LSTM for PM 2.5 prediction. The model exhibited stability over extended time periods, with noise reduction achieved through Airbox sensor source models, further enhancing prediction accuracy. Qi et al. 128 presented a novel forecasting approach employing a fusion of Graph Convolutional and LSTM (GC-LSTM) neural networks, aiming to investigate spatial interdependence within air quality data. The spatial correlation modelling highlighted the consistency of the GC-LSTM model for short-term forecasting, suggesting potential improvements for long-term predictions with enhanced spatiotemporal considerations. Fan et al. 91 developed a LSTM-based deep–RNN for predicting PM 2.5 for different spatiotemporal frames showcasing superior specificity measures compared to baseline models. In a novel approach, Li et al. 129 and Zhang et al. 130 incorporated large-scale datasets of graphical images for air pollution estimation, utilizing CNN. The models, trained on images capturing various atmospheric conditions, demonstrated improved prediction accuracy, emphasizing the adaptability of deep learning to diverse data types. These models offer robust solutions, demonstrating superior performance in various studies and showcasing their potential to contribute significantly to the field of environmental monitoring and public health.

Performance analysis

The evaluation is based on the comparison of their performances using statistical measures such as RMSE and R 2 , widely accepted metrics in air pollution forecasting studies. Previous research, utilizing a range of datasets, has yielded disparate results 134 . While certain studies advocate for ensemble methods, others find negligible disparities in the overall accuracy of the outcomes. The efficacy of AI and ML-driven methodologies relies heavily on the precise curation of influential parameters, especially when addressing various pollutants such as PM, O 3 , NO 2 , SO 2 , and CO 29 . For example, for PM forecasting, critical elements such as precipitation, pressure, humidity, land utilization, wind speed and direction, traffic flow on roads, and population density exert significant influence. Similarly, different influential parameters are identified for SO 2 , NO 2 , O 3 , and CO, emphasizing the importance of tailoring models to specific pollutants. The precision of the methods is notably impacted by the direct correlation between these factors and forecasted levels of pollutants. Additionally, the efficacy of AI&ML models hinges upon variables including network structure, intricacy, learning algorithms, correspondence between input and output information, and the presence of data interference. A comprehensive analysis shows the varying performances of DNN, SVM, ANN, and Fuzzy techniques across different pollutants. DNNs emerge as particularly effective in forecasting PM concentrations, outperforming other techniques with R 2 and mean RMSE values of 0.96 and 7.27 μg/m 3 , respectively 91 , 126 , 133 . In O 3 prediction, SVM, FL and DNN exhibit superior accuracy, with DNNs once again leading with R 2 and mean RMSE values of 0.92 and 3.51 μg/m 3 , respectively 119 , 120 . SVM excels in forecasting NO 2 concentrations, although Fuzzy and DNN techniques also demonstrate reasonable accuracy 116 , 118 , 131 . Notably, the DNN approach consistently stands out, showcasing the best statistical performance for O 3 and CO categories. For CO, DNN achieves an exceptional RMSE of 0.69 × 10 –5  ppm and an R 2 of 0.95 119 , 120 , 124 , 125 . The overall analysis represents the superiority of DNN across all pollutants, with the lowest overall RMSE score of 5.68. However, despite DNN's dominance, it is crucial to note the underdeveloped application of ensemble methodologies based on DL models for the forecasting of air pollution 131 , 135 , 136 . These approaches, involving multiscale spatiotemporal predictions, have untapped potential to further advance the field, incorporating more explanatory variables to represent air pollution episodes with robust dynamical forcing. The DNN emerges as the leading AI&ML system for the forecasting and prediction of air pollution based on statistical evidence, the exploration of ensemble approaches presents an avenue for future developments in enhancing predictive accuracy.

Prediction of PM 2.5 concentrations

The study used a convolutional autoencoder (CA) for analysing PM 2.5 concentrations. The dataset was divided into training (70%), testing (20%), and validation (10%) sets, trained over 30 epochs (Fig.  3 ). This PM 2.5 -focused CA processes sequences of ten consecutive images, using acquired features to reconstruct subsequent images. The visual representation of the model's capabilities includes sequences of 10 input images, their corresponding 11 th ground truth, and the model's predictions (Fig.  4 ). The model demonstrates promising performance in predicting PM 2.5 concentration patterns across India. Comparing the actual 11 th image with the predicted one reveals that the model successfully captures the broad spatial distribution of PM 2.5 concentrations. Key findings show that the model accurately predicts high concentration areas in the northern regions, particularly in the IGP (Fig.  4 ). It also effectively represents lower concentrations in southern and eastern coastal areas. The model captures the general gradient from northwest to southeast quite effectively. The prediction tends to slightly overestimate PM 2.5 levels in the northwestern region. Additionally, some localized high-concentration areas in central India are not fully captured in the prediction. Furthermore, the model's prediction shows a smoother distribution compared to the more granular actual data. (Fig.  4 ). Performance evaluation employed established image quality metrics: Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) (Fig.  5 ). SSIM, which assesses image similarity, predominantly ranged from 0.50 to 0.70 during training, slightly lowering to 0.45 to 0.55 during testing, and stabilizing at 0.50 to 0.60 in validation. PSNR peaked at 25 to 30 dB during training, followed by 24 to 28 dB in testing, and 28 to 30 dB in validation. Lower MSE values (10 to 15 µg/m 3 in training, 10 to 20 µg/m 3 in testing, and 8 to 11 µg/m 3 in validation) signify improved accuracy at the pixel level.

RMSE loss during the training, testing and validation phase.

Example set for predicting the 11th image of PM 2.5 by providing a batch of 10 images of concentration and comparing with the 11th actual image. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries ( https://matplotlib.org/ and https://matplotlib.org/basemap/ ).

Model evaluation parameters used for prediction the PM 2.5 concentrations.

These metrics offer insights into image quality, indicating some variation between training, testing, and validation, yet within acceptable ranges. Consistently higher SSIM and PSNR values and lower MSE values highlight the model's exceptional precision compared to benchmarks. The model's excellence traces back to its ability to capture complex spatio-temporal features through Autoencoder-based models and strategic integration of Conv2d, Batch Normalization, and Upsampling layers. The model outperforms prior methodologies in predicting PM 2.5 concentrations, achieving precise and high-quality predictions across phases. Attempting to forecast PM 2.5 levels for the next 4 days led to efficiency parameter decreases (SSIM, PSNR, MSE) with increased time frames, suggesting the need for more parameters for model efficiency improvement (Fig.  6 ). Predicting PM 2.5 concentrations remains challenging due to intricate spatiotemporal features, where DL models offer promise. Leveraging deep learning architectures and transfer learning, this study fine-tuned models, achieving promising PM 2.5 prediction results. Despite ongoing challenges in precise location predictions due to PM 2.5 's dynamic nature, the model demonstrated spatial distribution prediction abilities, evident in visual comparisons between predicted and actual PM 2.5 concentration maps.

Example set of predictions of PM 2.5 for next 4 days compared with their actual images. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries ( https://matplotlib.org/ and https://matplotlib.org/basemap/ ).

Challenges and limitations

Technological barriers.

One of the primary challenges lies in overcoming technological barriers. While advanced pollution control technologies exist, their widespread adoption is hindered by factors such as high costs and limited access to cutting-edge solutions. Many regions, particularly in rural areas, lack the infrastructure necessary to deploy and maintain sophisticated air quality monitoring and purification systems. Bridging this technological divide is essential for comprehensive pollution control.

Regulatory and enforcement challenges

India grapples with the challenge of implementing and enforcing air quality regulations consistently. While the country has established regulatory frameworks to curb emissions from industries, vehicles, and other pollution sources, enforcement remains uneven. This inconsistency is often compounded by resource constraints, bureaucratic hurdles, and the need for stronger mechanisms to penalize non-compliance. Strengthening regulatory frameworks and enhancing enforcement mechanisms are critical steps in addressing this challenge.

Public awareness and participation

Creating widespread awareness and fostering public participation are essential components of any successful pollution control strategy. However, there is a considerable gap in public awareness regarding the causes and consequences of air pollution. Engaging citizens in proactive measures, such as adopting sustainable practices and reducing individual carbon footprints, requires comprehensive educational campaigns and community involvement. Overcoming societal inertia and instigating behavioral change are significant challenges in this regard.

Agricultural practices and crop burning

Agricultural practices, particularly the prevalent practice of crop burning, contribute significantly to air pollution. The burning of crop residues releases substantial amounts of particulate matter and pollutants into the air. Farmers resort to this practice due to a lack of viable alternatives and time constraints between harvest seasons. Developing and promoting sustainable agricultural practices, coupled with providing farmers with effective alternatives to crop burning, is a complex challenge that requires a holistic approach.

Urbanization and infrastructure development

Rapid urbanization and infrastructure development, while essential for economic growth, often contribute to increased pollution levels. The construction industry, in particular, releases pollutants into the air. Balancing the need for development with sustainable and environmentally conscious practices poses a significant challenge. Implementing green building technologies, stringent emission norms for construction activities, and incorporating urban planning strategies that prioritize air quality are vital steps in addressing this challenge.

Cross-border pollution

Air pollution knows no boundaries, and India contends with the impact of cross-border pollution. Transboundary movement of pollutants, especially during crop burning seasons, contributes to elevated pollution levels in various regions. Collaborative efforts with neighbouring countries are necessary to address this challenge effectively. Developing joint strategies, sharing data, and fostering regional cooperation are imperative for tackling the transboundary dimension of air pollution.

Climate change interlinkages

The interlinkages between air pollution and climate change present a complex challenge. Mitigating air pollution often aligns with climate action goals, but there are trade-offs and synergies that need careful consideration. Striking a balance between addressing immediate air quality concerns and contributing to long-term climate resilience requires integrated policies and strategic planning.

Socio-economic disparities

Air pollution disproportionately affects vulnerable communities, exacerbating existing socio-economic disparities. The challenge lies in designing interventions that address environmental concerns and promote social equity. Ensuring that pollution control measures do not inadvertently burden marginalized communities and providing equitable access to clean technologies are critical to overcoming this challenge.

Future prospects

India stands at the cusp of a pivotal moment in its battle against air pollution, with promising avenues emerging on both technological and collaborative fronts.

Emerging technoloagies

The integration of cutting-edge technologies offers hope for India's future in pollution control. Advancements in AI&ML, when coupled with sophisticated numerical weather prediction models, present a potent toolset for predicting and managing air pollution. These technologies can enhance real-time monitoring, improve predictive capabilities, and facilitate data-driven decision-making, allowing for more precise and targeted interventions. Additionally, the fusion of AI&ML with numerical weather prediction (NWP) models can refine pollution control strategies by providing a deeper understanding of atmospheric dynamics and pollutant dispersion patterns. Furthermore, exploring potential breakthroughs in sustainable energy sources offers a transformative pathway. Shifting from traditional, pollutant-intensive energy sources to sustainable alternatives is crucial for reducing the overall carbon footprint. Investments in research and development, coupled with policy incentives, can accelerate the adoption of clean and renewable energy solutions, fostering a paradigm shift in India's energy landscape.

Global collaborations

Recognizing that air pollution transcends national boundaries, India looks toward global collaborations as a key driver for progress. International efforts in combating air pollution gain significance as countries join forces to address shared challenges. Collaborative platforms provide opportunities for knowledge sharing, exchange of best practices, and collective research initiatives. India's participation in these global endeavours not only enriches its own understanding of air pollution dynamics but also contributes to the global pool of knowledge. By fostering partnerships with other nations, India can access expertise, technologies, and resources that augment its capacity to implement effective pollution control measures. Knowledge sharing and collaborative research initiatives form the cornerstone of global efforts. Platforms that facilitate the exchange of data, research findings, and innovative solutions enable nations to collectively tackle the intricate and interconnected challenges of air pollution. As India engages in these collaborative endeavours, it not only benefits from the collective wisdom of the global community but also contributes its unique insights and experiences, enriching the collective understanding of air pollution dynamics.

India's strategic focus on emerging technologies and global collaborations holds immense promise in navigating the future. By harnessing the power of advanced technologies and participating in international initiatives, India can chart a course toward a cleaner, more sustainable future where the skies are clear, and the air is a testament to the collective commitment to environmental well-being.

Materials and methods

Maintaining fresh air quality is a complex undertaking influenced by various factors over time. These elements encompass air pollutant emissions, deposition, weather patterns, traffic dynamics, and human activities, among others 8 , 64 . The complexity of these interrelated factors makes it challenging for traditional shallow models to offer precise portrayals of air quality attributes. Based on the above review, deep learning algorithms were found most suitable for predicting air quality variables without needing prior knowledge. This capability enhances the potential for more accurate predictions regarding air quality, signifying a valuable contribution to addressing the intricacies associated with sustaining optimal air quality levels.

The case study utilized MERRA-2 reanalysis data from the NASA GESDISC DATA ARCHIVE application 137 , 138 . This dataset, spanning from January 1, 2015, to December 31, 2022, features a spatial resolution of 0.5° × 0.625° and a temporal resolution of 1 h (Fig.  7 ). It includes five key variables: black carbon surface mass concentration (BCSMASS), dust surface mass concentration—PM 2.5 (DUSMASS25), organic carbon surface mass concentration (OCSMASS), sea salt surface mass concentration—PM 2.5 (SSSMASS25), and SO 4 surface mass concentration (SO 4 SMASS). These variables are analysed across three dimensions: latitude, longitude, and time. The concentration of the PM 2.5 (µg/m 3 ) for each grid cell was computed as 139 , 140 :

Surface PM 2.5 concentration over India during ( a ) Winters and ( b ) Summers; Maps were generated using R Studio (v4.3.3, https://www.rstudio.com/ ).

Convolutional Autoencoder model

Air quality monitoring and predicting PM 2.5 concentrations accurately stands crucial for public health and environmental management 8 . The case study explores an innovative approach employing an Autoencoder-based DL model for forecasting PM 2.5 concentrations from spatiotemporal data over India. The study begins by complexly handling the datasets, leveraging PyTorch's Dataset and data loader classes. The ATMriver Dataset class is crafted to capture the dataset, enabling sequential data handling 9 . The data, formatted into tensors and split into training, testing, and validation subsets in a ratio of 70, 20 and 10, respectively, undergoes a custom transformation via the tensor class, ensuring compatibility with the neural network model 8 , 141 . The core of this methodology lies in the architecture of the Autoencoder, a neural network comprising convolutional and transposed convolutional layers. Specifically, the model comprises convolutional layers (conv1, conv2, conv3) responsible for feature extraction and transposed convolutional layers (conv1_d, conv2_d, conv3_d) for data reconstruction (Fig.  8 ). Each convolutional layer is paired with batch normalization and dropout (set at 25%) to regularize the network and prevent overfitting. The use of five layers in this Autoencoder architecture allows for hierarchical feature extraction and reconstruction, enhancing the model's ability to learn complex representations. The learning rate, a critical hyperparameter governing the magnitude of parameter updates during optimization, is set to 0.0025 for the Adam optimizer. This value influences the convergence speed and stability of the training process. A higher learning rate might lead to faster convergence but risks overshooting the optimal parameters, while a lower rate might result in slower convergence. The chosen learning rate balances the trade-off between convergence speed and stability, aiming to facilitate efficient model training while preventing divergence or oscillation in the optimization process.

Convolution autoencoder architecture for PM 2.5 data processing with model features an encoding phase with three autoencoder stages, followed by a decoding phase with two transpose convolution stages; structure enables dimensionality reduction and subsequent reconstruction of PM 2.5 concentration maps.

To train the Autoencoder, a custom root mean squared error (RMSE) loss function is defined. This loss function quantifies the disparity between predicted and actual PM 2.5 concentrations, guiding the model toward more accurate predictions. The training process iterates through the dataset multiple times (epochs), optimizing the model parameters using the Adam optimizer. The evaluation phase of the model involves assessing its predictive capabilities on separate testing and validation sets. The model's outputs are compared against the original images PM 2.5 concentrations, and the RMSE loss is computed. The best-performing model, based on its performance on the testing set, is identified and saved for the prediction. Further the records and reports the losses incurred during training, testing, and validation across epochs, providing insights into the model's loss curve and performance stability. Additionally, the best model's loss metric is highlighted, signifying its capability to accurately predict PM 2.5 concentrations. The evaluation of the trained model's predictive capability in this study primarily relied on two widely accepted image quality metrics: Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The Structural Similarity Index (SSIM) serves as a measure to assess the similarity between the predicted and actual images 142 . SSIM evaluates the perceived change in structural information, including luminance, contrast, and structure, between the predicted and actual images. A higher SSIM score, closer to 1, indicates a greater similarity between the two images, implying better predictive performance of the model. Peak Signal-to-Noise Ratio (PSNR) is another commonly used metric for quantifying the quality of reconstructed or predicted images. PSNR measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values signify lower image distortion or higher image fidelity, implying better prediction accuracy in capturing the details of the actual images.

Addressing the complex challenges of air pollution in India necessitates a multifaceted and technically informed approach. The existing impediments, including technological barriers and limited access to advanced pollution control technologies, underline the urgency of bridging the technological divide, particularly in rural areas. While regulatory frameworks are in place, inconsistent enforcement due to resource constraints and bureaucratic hurdles requires strategic strengthening. Public awareness and participation, integral components of effective pollution control, demand targeted educational campaigns to instigate behavioural change. Agricultural practices, notably crop burning, pose a significant challenge, and resolving this requires not only viable alternatives but a holistic approach that integrates sustainable agricultural practices. Rapid urbanization and infrastructure development, while essential for economic growth, necessitate the incorporation of green building technologies, stringent emission norms, and urban planning strategies prioritizing air quality. Cross-border pollution adds a transboundary dimension, demanding collaborative efforts with neighbouring countries. The intricate interlinkages between air pollution and climate change underscore the need for carefully balanced policies that address immediate air quality concerns while contributing to long-term climate resilience. Moreover, the disproportionate impact of air pollution on vulnerable communities emphasizes the importance of interventions that promote social equity alongside environmental considerations. Looking towards the future, the convergence of emerging technologies offers a beacon of hope. The integration of AI&ML with numerical weather prediction models presents a potent toolset for real-time monitoring, precise predictive capabilities, and data-driven decision-making. This amalgamation not only enhances our understanding of atmospheric dynamics and pollutant dispersion patterns but also refines pollution control strategies. Exploring breakthroughs in sustainable energy sources becomes imperative for reducing the overall carbon footprint. Shifting from traditional, pollutant-intensive energy sources to clean and renewable alternatives require concerted efforts through research, development, and policy incentives.

Furthermore, global collaborations stand out as a key driver for progress, given the transboundary nature of air pollution. Participating in international efforts fosters knowledge sharing, exchange of best practices, and collective research initiatives. By engaging in these collaborative activities, India not only enriches its understanding of air pollution dynamics but contributes to the global pool of knowledge. Platforms facilitating data exchange, research findings, and innovative solutions enable nations to collectively tackle the complex challenges of air pollution. In navigating the future, India's strategic focus on emerging technologies and global collaborations holds immense promise. The careful harnessing of advanced technologies and participation in international initiatives can chart a course toward a cleaner, more sustainable future. The fusion of AI&ML with numerical weather prediction (NWP) models positions India to proactively manage air quality, with the skies serving as a testament to the collective commitment to environmental well-being. As India progresses, the synergy of technological advancements and global cooperation emerges as the cornerstone for effective, informed, and sustainable solutions to combat air pollution.

Data availability

Data will be made online on a reasonable request to the corresponding author.

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Acknowledgements

We would like to express our sincere gratitude to the Department of Civil Engineering, Indian Institute of Technology, Indore for their support and resources, which have been instrumental in the successful completion of the present study.

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Your environment. your health., air pollution and your health, introduction.

Air Pollution

Air pollution is a familiar environmental health hazard. We know what we’re looking at when brown haze settles over a city, exhaust billows across a busy highway, or a plume rises from a smokestack. Some air pollution is not seen, but its pungent smell alerts you.

It is a major threat to global health and prosperity. Air pollution, in all forms, is responsible for more than 6.5 million deaths each year globally , a number that has increased over the past two decades.

What Is Air Pollution?

Air pollution is a mix of hazardous substances from both human-made and natural sources.

Vehicle emissions, fuel oils and natural gas to heat homes, by-products of manufacturing and power generation, particularly coal-fueled power plants, and fumes from chemical production are the primary sources of human-made air pollution.

Nature releases hazardous substances into the air, such as smoke from wildfires, which are often caused by people; ash and gases from volcanic eruptions; and gases, like methane, which are emitted from decomposing organic matter in soils.

Traffic-Related Air Pollution (TRAP), a mixture of gasses and particles, has most of the elements of human-made air pollution: ground-level ozone, various forms of carbon, nitrogen oxides, sulfur oxides, volatile organic compounds, polycyclic aromatic hydrocarbons, and fine particulate matter.

Ozone , an atmospheric gas, is often called smog when at ground level. It is created when pollutants emitted by cars, power plants, industrial boilers, refineries, and other sources chemically react in the presence of sunlight.

Noxious gases , which include carbon dioxide, carbon monoxide, nitrogen oxides (NOx), and sulfur oxides (SOx), are components of motor vehicle emissions and byproducts of industrial processes.

Particulate matter (PM) is composed of chemicals such as sulfates, nitrates, carbon, or mineral dusts. Vehicle and industrial emissions from fossil fuel combustion, cigarette smoke, and burning organic matter, such as wildfires, all contain PM.

A subset of PM, fine particulate matter (PM 2.5) is 30 times thinner than a human hair. It can be inhaled deeply into lung tissue and contribute to serious health problems. PM 2.5 accounts for most health effects due to air pollution in the U.S.

Volatile organic compounds (VOC) vaporize at or near room temperature—hence, the designation volatile. They are called organic because they contain carbon. VOCs are given off by paints, cleaning supplies, pesticides, some furnishings, and even craft materials like glue. Gasoline and natural gas are major sources of VOCs, which are released during combustion.

Polycyclic aromatic hydrocarbons (PAH) are organic compounds containing carbon and hydrogen. Of more than 100 PAHs known to be widespread in the environment, 15 are listed in the Report on Carcinogens . In addition to combustion, many industrial processes, such as iron, steel, and rubber product manufacturing, as well as power generation, also produce PAHs as a by-product. PAHs are also found in particulate matter.

Air Pollution and Climate Change

Air pollution and climate change affect each other through complex interactions in the atmosphere. Air pollution is intricately linked with climate change because both problems come largely from the same sources, such as emissions from burning fossil fuels. Both are threats to people’s health and the environment worldwide. Read more: Health Impacts of Air Quality .

What is NIEHS Doing?

Over its 50-plus year history, NIEHS has been a leader in air pollution research. The institute continues to fund and conduct research into how air pollution affects health and the population groups who are most affected.

How does air pollution affect our health?

When the National Ambient Air Quality Standards were established in 1970, air pollution was regarded primarily as a threat to respiratory health. In 1993, NIEHS researchers published the landmark Six Cities Study , which established an association between fine particulate matter and mortality.

Air pollution exposure is associated with oxidative stress and inflammation in human cells, which may lay a foundation for chronic diseases and cancer. In 2013, the International Agency for Research on Cancer of the World Health Organization (WHO) classified air pollution as a human carcinogen .

Many studies have established that short-term exposure to higher levels of outdoor air pollution is associated with reduced lung function, asthma, cardiac problems, emergency department visits, and hospital admissions . Mortality rates related to air pollution are also a concern. Exposure to the air pollutant PM2.5 is associated with an increased risk of death .

A team of researchers, partially funded by NIEHS, found that deaths decreased after air pollution regulations were implemented and coal-powered plants were retired. The study data covered 21 years. More specifically, they found exposure to PM2.5 from coal was associated with a mortality risk that was twice as high as the risk from exposure to PM2.5 from all sources. PM2.5 from coal is high in sulfur dioxide, black carbon, and metals.

Public health concerns related to high air pollution exposures include cancer, cardiovascular disease, respiratory diseases, diabetes mellitus, obesity, and reproductive, neurological, and immune system disorders.

Research on air pollution and health effects continually advances.

• A large study of more than 57,000 women found living near major roadways may increase a woman’s risk for breast cancer .
• Occupational exposure to benzene, an industrial chemical and component of gasoline, can cause leukemia and is associated with non-Hodgkin’s Lymphoma .
• A long-term study, 2000-2016, found an association between lung cancer incidence and increased reliance on coal for energy generation.
• Using a national dataset of older adults, researchers found that 10-year long exposures to PM2.5 and NO2 increased the risks of colorectal and prostate cancers .

Cardiovascular Disease

• Fine particulate matter can impair blood vessel function and speed up calcification in arteries .
• NIEHS researchers established links between short-term daily exposure by post-menopausal women to nitrogen oxides and increased risk of hemorrhagic stroke .
• For some older Americans, exposure to TRAP can result in lowered levels of high-density lipoprotein , sometimes called good cholesterol, increasing their risk for cardiovascular disease.
• According to a National Toxicology Program (NTP) report , TRAP exposure also increases a pregnant woman’s risk for dangerous changes in blood pressure, known as hypertensive disorders, which are a leading cause of pre-term birth, low birth weight, and maternal and fetal illness and death.

Respiratory Disease

• Air pollution can affect lung development and is implicated in the development of emphysema , asthma, and other respiratory diseases, such as chronic obstructive pulmonary disease (COPD).
• Increases in asthma prevalence and severity are linked to urbanization and outdoor air pollution. Children living in low-income urban areas tend to have more asthma cases than others. Research published in 2023 tied two air pollutants, ozone and PM2.5, to asthma-related changes in children’s airways.
• In a study of 50,000 women across the country, long-term exposure to PM2.5, PM10, and nitrogen dioxide were linked to chronic bronchitis .
• In 2020, a major public health challenge was confluence of the COVID-19 pandemic and wildfires across the western U.S. Building on a well-established connection between air pollution and respiratory-tract infections, a study linked exposure to wildfire smoke with more severe cases of COVID-19 and deaths .

Whom does air pollution affect the most?

Air pollution affects everyone’s health, but certain groups may be harmed more. Almost 9 out of 10 people who live in urban areas worldwide are affected by air pollution.

NIEHS-funded research indicates there are racial or ethnic and socioeconomic disparities in air pollution emissions. Air pollution emissions have decreased over past decades but the changes vary by demographics . This research found that people with annual incomes above $70,000 generally experience greater declines in industry, energy, transportation, residential, and commercial-related emissions than do people with lower incomes.

The NIEHS-funded Children’s Health Study at the University of Southern California is one of the largest studies of the long-term effects of air pollution on children’s respiratory health. Among its findings:

• Higher air pollution levels increase short-term respiratory infections, which lead to more school absences.
• Children who play several outdoor sports and live in high ozone communities are more likely to develop asthma.
• Children living near busy roads have an increased chance of developing asthma.
• Children who were exposed to high levels of air pollutants were more likely to develop bronchitis symptoms in adulthood .
• Living in communities with higher pollution levels can cause lung damage .

Other studies on women and children

• Breathing PM 2.5, even at relatively low levels, may alter the size of a child's developing brain , which may ultimately increase the risk for cognitive and emotional problems later in adolescence.
• In a large-scale study that looked at more than 1 million birth records, prenatal PM2.5 exposure was associated with an increased risk of cerebral palsy . While this finding adds to knowledge about environmental risk factors for cerebral palsy development and how to reduce the chance of it developing, further studies are needed. Prenatal exposure to PAHs was associated with brain development effects, slower processing speed, attention-deficit and hyperactivity disorder (ADHD) symptoms, and other neurobehavioral problems in urban youth .
• Prenatal exposure to air pollution may play a role in the development of ADHD-related behavior problems in childhood.
• Prenatal exposure to particulate matter was associated with low birth weight .
• Women exposed to high levels of fine particulate matter during pregnancy, particularly in the third trimester, may have up to twice the risk of having a child with autism .
• Second and third trimester exposure to PM2.5 might increase the chance of those children having high blood pressure in early life .
• A large study of more than 300,000 women found long-term exposure to air pollution, especially ozone and PM2.5, during and after pregnancy increases the risk of postpartum depression .
• The study with data on more than 5 million babies assessed associations between prenatal exposure to wildfire smoke and the risk of preterm birth. The researchers found that exposure to high levels of wildfire particulate matter during any period of pregnancy was associated with a greater chance of preterm birth .

Older adults

• Alzheimer’s disease and related dementias are a public health challenge for aging populations. NIEHS-funded researchers at the University of Washington identified a link between air pollution and dementias. This well-conducted study adds considerable evidence that ambient air fine particles increase risk of dementias . Conversely, a multi-year study published in 2022 shows improved air quality is associated with lower risk of dementia in older women. The researchers also stated this decline in dementia risk was equivalent to taking nearly 2 1/2 years off the age of the women studied.
• A large, nationally representative study looked at PM2.5 from many sources and incident dementia. Emissions from agriculture, traffic, coal combustion, and wildfires, in particular, were associated with increased rates of dementia .
• Air pollution was linked to a greater chance of developing several neurological disorders , including Parkinson's disease, Alzheimer's disease, and other dementias. Hospital admissions data from 63 million older adults in the U.S., obtained over 17 years (2000-2016), was analyzed along with estimated PM2.5 concentrations by zip code to conduct the study. Another study with data from 10-year long exposures also found a relationship between CO and PM2.5 and an increased chance of developing Parkinson’s disease .
• Osteoporosis affects women more than men. A large study associated high levels of air pollutants with bone damage , particularly in the lumbar spine, among postmenopausal women. This study expands previous findings linking air pollution and bone damage.
• Nutrients may counter some harmful effects from air pollution. A 2020 study found omega-3 fatty acids , obtained by eating certain fish, may protect against PM2.5-associated brain shrinkage in older women.

Rural dwellers

• NIEHS supported a translational research project,  Addressing Air Pollution and Asthma (1MB) , that may lead to improved health for children suffering from asthma. They found that certain agricultural practices contribute to poor air quality and asthma among children. The team combined high-efficiency particulate air (HEPA) cleaners and a home-based education program to reduce children’s exposure to pollutants in the home.
• Exposure to smoke from agricultural burns for as little as two weeks per year may worsen children's respiratory health outcomes, according to research supported by NIEHS. The study was conducted in response to community concerns about children's heath in Imperial Valley, a rural, agricultural area in southern California. Such agricultural burning is done to clear post-harvest crop remnants. This form of clearing is inexpensive, and farmers in the area do not have other economical methods for disposing of waste.
• In the rural U.S., large-scale animal feeding operations might compromise regional air quality through emission of pollutants, such as ammonia gas. A study found acute lung function problems in children with asthma in such areas.

NIEHS and community involvement

NIEHS supports community participation in the research process and encourages collaborative approaches that build capacity in communities to address environmental health concerns. Community-engaged research and citizen science are two types of collaborative research approaches.

For example, NIEHS grant recipients developed community-level tactics and public policies for reducing exposure to TRAP:

• Using high-efficiency particulate air (HEPA) filtration.
• Building land-use buffers and vegetation barriers.
• Improving urban design with gardens, parks, and street-side trees.
• Creating active-travel options, such as bicycling and walking paths.

Why improving air quality matters

• Air pollution and birth outcomes are linked as global public health concerns. Researchers analyzed indoor and outdoor air pollution data from all inhabited continents along with key pregnancy outcomes. Their findings indicate efforts to reduce PM2.5 exposure could lead to significant reductions in the number of low-birth weight and pre-term birth infants worldwide . Air pollution reduction would be especially beneficial for children born in low- and middle-income countries.
• Among children in Southern California, decreases in ambient nitrogen dioxide and PM 2.5 were associated with fewer cases of asthma .
• Bronchitis symptoms declined as pollution levels dropped in the Los Angeles region.
• Improving air quality may improve cognitive function and reduce dementia risk, according to studies supported in part by NIH and the Alzheimer's Association.
• When fossil-fuel power plants close, nearby air pollution is reduced. A study found the incidence of preterm births went down within 5 kilometers of retired coal and oil-powered plant locations.

Further Reading

Stories from the environmental factor (niehs newsletter).

• Wildfire Smoke: Effects on Male Fertility, Offspring Studied by Expert (August 2024)
• Air Pollution May Trigger DNA Modifications Tied to Alzheimer’s Disease (April 2024)
• Scientific Journeys: Using AI to Track a Major Source of Pollution (March 2024)
• Burn Pits’ Complex Emissions Simulated in NIEHS Grantee’s Laboratory (December 2023)
• Indoor Wood-burning May Be Linked to Lung Cancer in U.S. Women (September 2023)
• Everyday Air Pollution Can Harm Brain Development in Adolescents (September 2023)
• Wildfire Smoke, Other Air Pollution Can Harm Brain Health, Expert Says (August 2023)
• Burning Plastic Can Affect Air Quality, Public Health (August 2022)
• Interventions Needed to Slow Climate-driven Air Pollution, Researchers Note (March 2022)
• Air Pollution and Forever Chemicals Continue to Pose Health Risks (March 2022)
• Air Pollution Affects Children’s Brain Structure (February 2022)
• Increasing Evidence Links Air Pollution With Breast Cancer (November 2021)
• Fine Particulate Air Pollution Associated With Higher Dementia Risk (September 2021)

Printable Fact Sheets

Fact sheets.

Breast Cancer: Why the Environment Matters

Climate Change and Human Health

Lung Health and Your Environment

Partnerships for Environmental Public Health (PEPH)

• When Wildfires Hit Close to Home is about NIEHS-funded research on the complexity of urban wildfires and how they may affect human health.
• Wildfire Smoke and Children's Health

Additional Resources

• Air Pollution Linked to Dementia Cases (September 2023) – In this edition of NIH Research Matters, read about findings from the Health and Retirement Study, funded by the National Institute on Aging, that showed higher air pollution exposure was linked to an increased risk of dementia. After consideration of all sources, fine particulate matter, or PM2.5, from agriculture and wildfires were specifically associated with an increased risk of dementia. Reducing such exposures might help lower the incidence of dementia. The study was published in JAMA Internal Medicine.
• AirNow , a tool developed in partnership by several government agencies, allows you to monitor air quality in real time anywhere in the U.S. Simply enter your zip code as indicated on the website.
• EPA's Air Sensor Toolbox provides information on the operation and use of air-sensor monitoring systems for technology developers, air-quality managers, citizen scientists, and the public.
• NIH Climate Change and Health Initiative – This solutions-focused research initiative aims to reduce the health consequences associated with extreme weather events and evolving climate conditions. NIH has a strong history of creating innovative tools, technologies, and data-driven solutions to address global environmental problems.
• Smoke-ready Toolbox for Wildfires is a compendium of resources from the EPA to help educate you about the risks of smoke exposure and actions that protect your health.

Related Health Topics

• Exposure Science
• Gene and Environment Interaction
• Lung Diseases

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Air Pollution: Everything You Need to Know

How smog, soot, greenhouse gases, and other top air pollutants are affecting the planet—and your health.

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What is air pollution?

What causes air pollution, effects of air pollution, air pollution in the united states, air pollution and environmental justice, controlling air pollution, how to help reduce air pollution, how to protect your health.

Air pollution  refers to the release of pollutants into the air—pollutants that are detrimental to human health and the planet as a whole. According to the  World Health Organization (WHO) , each year, indoor and outdoor air pollution is responsible for nearly seven million deaths around the globe. Ninety-nine percent of human beings currently breathe air that exceeds the WHO’s guideline limits for pollutants, with those living in low- and middle-income countries suffering the most. In the United States, the  Clean Air Act , established in 1970, authorizes the U.S. Environmental Protection Agency (EPA) to safeguard public health by regulating the emissions of these harmful air pollutants.

“Most air pollution comes from energy use and production,” says  John Walke , director of the Clean Air team at NRDC. Driving a car on gasoline, heating a home with oil, running a power plant on  fracked gas : In each case, a fossil fuel is burned and harmful chemicals and gases are released into the air.

“We’ve made progress over the last 50 years in improving air quality in the United States, thanks to the Clean Air Act. But climate change will make it harder in the future to meet pollution standards, which are designed to  protect health ,” says Walke.

Air pollution is now the world’s fourth-largest risk factor for early death. According to the 2020  State of Global Air  report —which summarizes the latest scientific understanding of air pollution around the world—4.5 million deaths were linked to outdoor air pollution exposures in 2019, and another 2.2 million deaths were caused by indoor air pollution. The world’s most populous countries, China and India, continue to bear the highest burdens of disease.

“Despite improvements in reducing global average mortality rates from air pollution, this report also serves as a sobering reminder that the climate crisis threatens to worsen air pollution problems significantly,” explains  Vijay Limaye , senior scientist in NRDC’s Science Office. Smog, for instance, is intensified by increased heat, forming when the weather is warmer and there’s more ultraviolet radiation. In addition, climate change increases the production of allergenic air pollutants, including mold (thanks to damp conditions caused by extreme weather and increased flooding) and pollen (due to a longer pollen season). “Climate change–fueled droughts and dry conditions are also setting the stage for dangerous wildfires,” adds Limaye. “ Wildfire smoke can linger for days and pollute the air with particulate matter hundreds of miles downwind.”

The effects of air pollution on the human body vary, depending on the type of pollutant, the length and level of exposure, and other factors, including a person’s individual health risks and the cumulative impacts of multiple pollutants or stressors.

Smog and soot

These are the two most prevalent types of air pollution. Smog (sometimes referred to as ground-level ozone) occurs when emissions from combusting fossil fuels react with sunlight. Soot—a type of  particulate matter —is made up of tiny particles of chemicals, soil, smoke, dust, or allergens that are carried in the air. The sources of smog and soot are similar. “Both come from cars and trucks, factories, power plants, incinerators, engines, generally anything that combusts fossil fuels such as coal, gasoline, or natural gas,” Walke says.

Smog can irritate the eyes and throat and also damage the lungs, especially those of children, senior citizens, and people who work or exercise outdoors. It’s even worse for people who have asthma or allergies; these extra pollutants can intensify their symptoms and trigger asthma attacks. The tiniest airborne particles in soot are especially dangerous because they can penetrate the lungs and bloodstream and worsen bronchitis, lead to heart attacks, and even hasten death. In  2020, a report from Harvard’s T.H. Chan School of Public Health showed that COVID-19 mortality rates were higher in areas with more particulate matter pollution than in areas with even slightly less, showing a correlation between the virus’s deadliness and long-term exposure to air pollution. 

These findings also illuminate an important  environmental justice issue . Because highways and polluting facilities have historically been sited in or next to low-income neighborhoods and communities of color, the negative effects of this pollution have been  disproportionately experienced by the people who live in these communities.

Hazardous air pollutants

A number of air pollutants pose severe health risks and can sometimes be fatal, even in small amounts. Almost 200 of them are regulated by law; some of the most common are mercury,  lead , dioxins, and benzene. “These are also most often emitted during gas or coal combustion, incineration, or—in the case of benzene—found in gasoline,” Walke says. Benzene, classified as a carcinogen by the EPA, can cause eye, skin, and lung irritation in the short term and blood disorders in the long term. Dioxins, more typically found in food but also present in small amounts in the air, is another carcinogen that can affect the liver in the short term and harm the immune, nervous, and endocrine systems, as well as reproductive functions.  Mercury  attacks the central nervous system. In large amounts, lead can damage children’s brains and kidneys, and even minimal exposure can affect children’s IQ and ability to learn.

Another category of toxic compounds, polycyclic aromatic hydrocarbons (PAHs), are by-products of traffic exhaust and wildfire smoke. In large amounts, they have been linked to eye and lung irritation, blood and liver issues, and even cancer.  In one study , the children of mothers exposed to PAHs during pregnancy showed slower brain-processing speeds and more pronounced symptoms of ADHD.

Greenhouse gases

While these climate pollutants don’t have the direct or immediate impacts on the human body associated with other air pollutants, like smog or hazardous chemicals, they are still harmful to our health. By trapping the earth’s heat in the atmosphere, greenhouse gases lead to warmer temperatures, which in turn lead to the hallmarks of climate change: rising sea levels, more extreme weather, heat-related deaths, and the increased transmission of infectious diseases. In 2021, carbon dioxide accounted for roughly 79 percent of the country’s total greenhouse gas emissions, and methane made up more than 11 percent. “Carbon dioxide comes from combusting fossil fuels, and methane comes from natural and industrial sources, including large amounts that are released during oil and gas drilling,” Walke says. “We emit far larger amounts of carbon dioxide, but methane is significantly more potent, so it’s also very destructive.” 

Another class of greenhouse gases,  hydrofluorocarbons (HFCs) , are thousands of times more powerful than carbon dioxide in their ability to trap heat. In October 2016, more than 140 countries signed the Kigali Agreement to reduce the use of these chemicals—which are found in air conditioners and refrigerators—and develop greener alternatives over time. (The United States officially signed onto the  Kigali Agreement in 2022.)

Pollen and mold

Mold and allergens from trees, weeds, and grass are also carried in the air, are exacerbated by climate change, and can be hazardous to health. Though they aren’t regulated, they can be considered a form of air pollution. “When homes, schools, or businesses get water damage, mold can grow and produce allergenic airborne pollutants,” says Kim Knowlton, professor of environmental health sciences at Columbia University and a former NRDC scientist. “ Mold exposure can precipitate asthma attacks  or an allergic response, and some molds can even produce toxins that would be dangerous for anyone to inhale.”

Pollen allergies are worsening  because of climate change . “Lab and field studies are showing that pollen-producing plants—especially ragweed—grow larger and produce more pollen when you increase the amount of carbon dioxide that they grow in,” Knowlton says. “Climate change also extends the pollen production season, and some studies are beginning to suggest that ragweed pollen itself might be becoming a more potent allergen.” If so, more people will suffer runny noses, fevers, itchy eyes, and other symptoms. “And for people with allergies and asthma, pollen peaks can precipitate asthma attacks, which are far more serious and can be life-threatening.”

More than one in three U.S. residents—120 million people—live in counties with unhealthy levels of air pollution, according to the  2023  State of the Air  report by the American Lung Association (ALA). Since the annual report was first published, in 2000, its findings have shown how the Clean Air Act has been able to reduce harmful emissions from transportation, power plants, and manufacturing.

Recent findings, however, reflect how climate change–fueled wildfires and extreme heat are adding to the challenges of protecting public health. The latest report—which focuses on ozone, year-round particle pollution, and short-term particle pollution—also finds that people of color are 61 percent more likely than white people to live in a county with a failing grade in at least one of those categories, and three times more likely to live in a county that fails in all three.

In rankings for each of the three pollution categories covered by the ALA report, California cities occupy the top three slots (i.e., were highest in pollution), despite progress that the Golden State has made in reducing air pollution emissions in the past half century. At the other end of the spectrum, these cities consistently rank among the country’s best for air quality: Burlington, Vermont; Honolulu; and Wilmington, North Carolina. 

No one wants to live next door to an incinerator, oil refinery, port, toxic waste dump, or other polluting site. Yet millions of people around the world do, and this puts them at a much higher risk for respiratory disease, cardiovascular disease, neurological damage, cancer, and death. In the United States, people of color are 1.5 times more likely than whites to live in areas with poor air quality, according to the ALA.

Historically, racist zoning policies and discriminatory lending practices known as  redlining  have combined to keep polluting industries and car-choked highways away from white neighborhoods and have turned communities of color—especially low-income and working-class communities of color—into sacrifice zones, where residents are forced to breathe dirty air and suffer the many health problems associated with it. In addition to the increased health risks that come from living in such places, the polluted air can economically harm residents in the form of missed workdays and higher medical costs.

Environmental racism isn't limited to cities and industrial areas. Outdoor laborers, including the estimated three million migrant and seasonal farmworkers in the United States, are among the most vulnerable to air pollution—and they’re also among the least equipped, politically, to pressure employers and lawmakers to affirm their right to breathe clean air.

Recently,  cumulative impact mapping , which uses data on environmental conditions and demographics, has been able to show how some communities are overburdened with layers of issues, like high levels of poverty, unemployment, and pollution. Tools like the  Environmental Justice Screening Method  and the EPA’s  EJScreen  provide evidence of what many environmental justice communities have been explaining for decades: that we need land use and public health reforms to ensure that vulnerable areas are not overburdened and that the people who need resources the most are receiving them.

In the United States, the  Clean Air Act  has been a crucial tool for reducing air pollution since its passage in 1970, although fossil fuel interests aided by industry-friendly lawmakers have frequently attempted to  weaken its many protections. Ensuring that this bedrock environmental law remains intact and properly enforced will always be key to maintaining and improving our air quality.

But the best, most effective way to control air pollution is to speed up our transition to cleaner fuels and industrial processes. By switching over to renewable energy sources (such as wind and solar power), maximizing fuel efficiency in our vehicles, and replacing more and more of our gasoline-powered cars and trucks with electric versions, we'll be limiting air pollution at its source while also curbing the global warming that heightens so many of its worst health impacts.

And what about the economic costs of controlling air pollution? According to a report on the Clean Air Act commissioned by NRDC, the annual  benefits of cleaner air  are up to 32 times greater than the cost of clean air regulations. Those benefits include up to 370,000 avoided premature deaths, 189,000 fewer hospital admissions for cardiac and respiratory illnesses, and net economic benefits of up to $3.8 trillion for the U.S. economy every year.

“The less gasoline we burn, the better we’re doing to reduce air pollution and the harmful effects of climate change,” Walke explains. “Make good choices about transportation. When you can, ride a bike, walk, or take public transportation. For driving, choose a car that gets better miles per gallon of gas or  buy an electric car .” You can also investigate your power provider options—you may be able to request that your electricity be supplied by wind or solar. Buying your food locally cuts down on the fossil fuels burned in trucking or flying food in from across the world. And most important: “Support leaders who push for clean air and water and responsible steps on climate change,” Walke says.

• “When you see in the news or hear on the weather report that pollution levels are high, it may be useful to limit the time when children go outside or you go for a jog,” Walke says. Generally, ozone levels tend to be lower in the morning.
• If you exercise outside, stay as far as you can from heavily trafficked roads. Then shower and wash your clothes to remove fine particles.
• The air may look clear, but that doesn’t mean it’s pollution free. Utilize tools like the EPA’s air pollution monitor,  AirNow , to get the latest conditions. If the air quality is bad, stay inside with the windows closed.
• If you live or work in an area that’s prone to wildfires,  stay away from the harmful smoke  as much as you’re able. Consider keeping a small stock of masks to wear when conditions are poor. The most ideal masks for smoke particles will be labelled “NIOSH” (which stands for National Institute for Occupational Safety and Health) and have either “N95” or “P100” printed on it.
• If you’re using an air conditioner while outdoor pollution conditions are bad, use the recirculating setting to limit the amount of polluted air that gets inside. 

This story was originally published on November 1, 2016, and has been updated with new information and links.

This NRDC.org story is available for online republication by news media outlets or nonprofits under these conditions: The writer(s) must be credited with a byline; you must note prominently that the story was originally published by NRDC.org and link to the original; the story cannot be edited (beyond simple things such as grammar); you can’t resell the story in any form or grant republishing rights to other outlets; you can’t republish our material wholesale or automatically—you need to select stories individually; you can’t republish the photos or graphics on our site without specific permission; you should drop us a note to let us know when you’ve used one of our stories.

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ENCYCLOPEDIC ENTRY

Air pollution.

Air pollution consists of chemicals or particles in the air that can harm the health of humans, animals, and plants. It also damages buildings.

Biology, Ecology, Earth Science, Geography

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Air pollution consists of chemicals or particles in the air that can harm the health of humans, animals, and plants. It also damages buildings. Pollutants in the air take many forms. They can be gases , solid particles, or liquid droplets. Sources of Air Pollution Pollution enters the Earth's atmosphere in many different ways. Most air pollution is created by people, taking the form of emissions from factories, cars, planes, or aerosol cans . Second-hand cigarette smoke is also considered air pollution. These man-made sources of pollution are called anthropogenic sources . Some types of air pollution, such as smoke from wildfires or ash from volcanoes , occur naturally. These are called natural sources . Air pollution is most common in large cities where emissions from many different sources are concentrated . Sometimes, mountains or tall buildings prevent air pollution from spreading out. This air pollution often appears as a cloud making the air murky. It is called smog . The word "smog" comes from combining the words "smoke" and " fog ." Large cities in poor and developing nations tend to have more air pollution than cities in developed nations. According to the World Health Organization (WHO) , some of the worlds most polluted cities are Karachi, Pakistan; New Delhi, India; Beijing, China; Lima, Peru; and Cairo, Egypt. However, many developed nations also have air pollution problems. Los Angeles, California, is nicknamed Smog City. Indoor Air Pollution Air pollution is usually thought of as smoke from large factories or exhaust from vehicles. But there are many types of indoor air pollution as well. Heating a house by burning substances such as kerosene , wood, and coal can contaminate the air inside the house. Ash and smoke make breathing difficult, and they can stick to walls, food, and clothing. Naturally-occurring radon gas, a cancer -causing material, can also build up in homes. Radon is released through the surface of the Earth. Inexpensive systems installed by professionals can reduce radon levels. Some construction materials, including insulation , are also dangerous to people's health. In addition, ventilation , or air movement, in homes and rooms can lead to the spread of toxic mold . A single colony of mold may exist in a damp, cool place in a house, such as between walls. The mold's spores enter the air and spread throughout the house. People can become sick from breathing in the spores. Effects On Humans People experience a wide range of health effects from being exposed to air pollution. Effects can be broken down into short-term effects and long-term effects . Short-term effects, which are temporary , include illnesses such as pneumonia or bronchitis . They also include discomfort such as irritation to the nose, throat, eyes, or skin. Air pollution can also cause headaches, dizziness, and nausea . Bad smells made by factories, garbage , or sewer systems are considered air pollution, too. These odors are less serious but still unpleasant . Long-term effects of air pollution can last for years or for an entire lifetime. They can even lead to a person's death. Long-term health effects from air pollution include heart disease , lung cancer, and respiratory diseases such as emphysema . Air pollution can also cause long-term damage to people's nerves , brain, kidneys , liver , and other organs. Some scientists suspect air pollutants cause birth defects . Nearly 2.5 million people die worldwide each year from the effects of outdoor or indoor air pollution. People react differently to different types of air pollution. Young children and older adults, whose immune systems tend to be weaker, are often more sensitive to pollution. Conditions such as asthma , heart disease, and lung disease can be made worse by exposure to air pollution. The length of exposure and amount and type of pollutants are also factors. Effects On The Environment Like people, animals, and plants, entire ecosystems can suffer effects from air pollution. Haze , like smog, is a visible type of air pollution that obscures shapes and colors. Hazy air pollution can even muffle sounds. Air pollution particles eventually fall back to Earth. Air pollution can directly contaminate the surface of bodies of water and soil . This can kill crops or reduce their yield . It can kill young trees and other plants. Sulfur dioxide and nitrogen oxide particles in the air, can create acid rain when they mix with water and oxygen in the atmosphere. These air pollutants come mostly from coal-fired power plants and motor vehicles . When acid rain falls to Earth, it damages plants by changing soil composition ; degrades water quality in rivers, lakes and streams; damages crops; and can cause buildings and monuments to decay . Like humans, animals can suffer health effects from exposure to air pollution. Birth defects, diseases, and lower reproductive rates have all been attributed to air pollution. Global Warming Global warming is an environmental phenomenon caused by natural and anthropogenic air pollution. It refers to rising air and ocean temperatures around the world. This temperature rise is at least partially caused by an increase in the amount of greenhouse gases in the atmosphere. Greenhouse gases trap heat energy in the Earths atmosphere. (Usually, more of Earths heat escapes into space.) Carbon dioxide is a greenhouse gas that has had the biggest effect on global warming. Carbon dioxide is emitted into the atmosphere by burning fossil fuels (coal, gasoline , and natural gas ). Humans have come to rely on fossil fuels to power cars and planes, heat homes, and run factories. Doing these things pollutes the air with carbon dioxide. Other greenhouse gases emitted by natural and artificial sources also include methane , nitrous oxide , and fluorinated gases. Methane is a major emission from coal plants and agricultural processes. Nitrous oxide is a common emission from industrial factories, agriculture, and the burning of fossil fuels in cars. Fluorinated gases, such as hydrofluorocarbons , are emitted by industry. Fluorinated gases are often used instead of gases such as chlorofluorocarbons (CFCs). CFCs have been outlawed in many places because they deplete the ozone layer . Worldwide, many countries have taken steps to reduce or limit greenhouse gas emissions to combat global warming. The Kyoto Protocol , first adopted in Kyoto, Japan, in 1997, is an agreement between 183 countries that they will work to reduce their carbon dioxide emissions. The United States has not signed that treaty . Regulation In addition to the international Kyoto Protocol, most developed nations have adopted laws to regulate emissions and reduce air pollution. In the United States, debate is under way about a system called cap and trade to limit emissions. This system would cap, or place a limit, on the amount of pollution a company is allowed. Companies that exceeded their cap would have to pay. Companies that polluted less than their cap could trade or sell their remaining pollution allowance to other companies. Cap and trade would essentially pay companies to limit pollution. In 2006 the World Health Organization issued new Air Quality Guidelines. The WHOs guidelines are tougher than most individual countries existing guidelines. The WHO guidelines aim to reduce air pollution-related deaths by 15 percent a year. Reduction Anybody can take steps to reduce air pollution. Millions of people every day make simple changes in their lives to do this. Taking public transportation instead of driving a car, or riding a bike instead of traveling in carbon dioxide-emitting vehicles are a couple of ways to reduce air pollution. Avoiding aerosol cans, recycling yard trimmings instead of burning them, and not smoking cigarettes are others.

Downwinders The United States conducted tests of nuclear weapons at the Nevada Test Site in southern Nevada in the 1950s. These tests sent invisible radioactive particles into the atmosphere. These air pollution particles traveled with wind currents, eventually falling to Earth, sometimes hundreds of miles away in states including Idaho, Utah, Arizona, and Washington. These areas were considered to be "downwind" from the Nevada Test Site. Decades later, people living in those downwind areascalled "downwinders"began developing cancer at above-normal rates. In 1990, the U.S. government passed the Radiation Exposure Compensation Act. This law entitles some downwinders to payments of $50,000.

Greenhouse Gases There are five major greenhouse gases in Earth's atmosphere.

• water vapor
• carbon dioxide
• nitrous oxide

London Smog What has come to be known as the London Smog of 1952, or the Great Smog of 1952, was a four-day incident that sickened 100,000 people and caused as many as 12,000 deaths. Very cold weather in December 1952 led residents of London, England, to burn more coal to keep warm. Smoke and other pollutants became trapped by a thick fog that settled over the city. The polluted fog became so thick that people could only see a few meters in front of them.

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Air pollution linked to higher risk of infertility in men

by British Medical Journal

Long term exposure to fine particulate matter (PM2.5) air pollution is linked to a higher risk of infertility in men, whereas road traffic noise is linked to a higher risk of infertility in women over 35, finds a Danish study published by The BMJ .

If these findings are confirmed in future studies, they could help guide strategies to regulate noise and air pollution to protect the general population from these exposures, say the researchers.

Infertility is a major global health problem affecting one in seven couples trying to conceive.

Several studies have found negative links between particulate air pollution and sperm quality and success after fertility treatment, but results on fecundability (the likelihood of conceiving) are inconsistent, and no studies have investigated the effects of transport noise on infertility in men and women .

To address this uncertainty, researchers set out to investigate whether long term exposure to road traffic noise and fine particulate matter (PM2.5) air pollution was associated with a higher risk of infertility in men and women.

Their findings are based on national registry data for 526,056 men and 377,850 women aged 30–45 years, with fewer than two children, cohabiting or married, and residing in Denmark between 2000 and 2017.

This group was selected to include a high proportion of people actively trying to become pregnant, and thus at risk of an infertility diagnosis. Individuals with an existing infertility diagnosis were excluded, as were women who had undergone surgery that prevents pregnancy and men who were sterilized.

Yearly average PM2.5 concentrations and road traffic noise levels at each participant's address (1995–2017) were calculated, and infertility diagnoses were recorded from the national patient register.

Infertility was diagnosed in 16,172 men and 22,672 women during an 18-year follow-up period (average of just over 4 years).

After adjusting for several potentially influential factors including income, education level , and occupation, exposure to 2.9 µg/m 3 higher average levels of PM2.5 over five years was associated with a 24% increased risk of infertility in men aged 30-45 years. PM2.5 was not associated with infertility in women.

Exposure to 10.2 decibels higher average levels of road traffic noise over five years was associated with a 14% increased risk of infertility among women older than 35 years. Noise was not associated with infertility among younger women (30–35 years).

In men, road traffic noise was associated with a small increased risk of infertility in the 37-45 age group, but not among those aged 30-37 years.

The higher risk of noise related infertility in women and PM2.5 related infertility in men was consistent across people living in rural, suburban, and urban areas as well as across people with low, medium, and high socioeconomic status.

This is an observational study, so it can't establish cause, and the researchers acknowledge that couples not trying to conceive may have been included, and that information on lifestyle factors and exposure to noise and air pollution at work and during leisure activities was lacking.

However, this was a large study based on reliable health and residential data that used validated models to assess pollution and noise levels, and the researchers were able to account for a range of important social and economic factors.

As such, they conclude, "If our results are confirmed in future studies, it suggests that political implementation of air pollution and noise mitigations may be important tools for improving birth rates in the Western world."

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Gaps and future directions in research on health effects of air pollution

Ruzmyn vilcassim.

a Department of Environmental Health Sciences, The University of Alabama at Birmingham, School of Public Health, USA

George D. Thurston

b Departments of Medicine and Population Health, New York University School of Medicine, USA

Despite progress in many countries, air pollution, and especially fine particulate matter air pollution (PM 2.5 ) remains a global health threat: over 6 million premature cardiovascular and respiratory deaths/yr. have been attributed to household and outdoor air pollution. In this viewpoint, we identify present gaps in air pollution monitoring and regulation, and how they could be strengthened in future mitigation policies to more optimally reduce health impacts. We conclude that there is a need to move beyond simply regulating PM 2.5 particulate matter mass concentrations at central site stations. A greater emphasis is needed on: new portable and affordable technologies to measure personal exposures to particle mass; the consideration of a submicron (PM 1 ) mass air quality standard; and further evaluations of effects by particle composition and source. We emphasize the need to enable further studies on exposure–health relationships in underserved populations that are disproportionately impacted by air pollution, but not sufficiently represented in current studies.

Introduction

Since the early establishment of air quality regulations in the United Kingdom in 1956 and the 1970 Clean Air Act in the United States, followed by similar governmental legislations across Europe and the rest of the world, air pollution levels have decreased considerably in most major cities in high-income countries that used to be primary hubs of industrialization and poor air quality not so long ago. Six ‘Criteria’ or ‘classical’ air pollutants were targeted by the United States Environmental Protection Agency (US EPA) and the World Health Organization (WHO): Ground-level ozone (O 3 ), particulate matter (PM), carbon monoxide (CO), lead, sulfur dioxide (SO 2 ) and nitrogen dioxide (NO 2 ). Standards and guidelines were imposed for each pollutant 1 , 2 initiating mitigatory measures. However, controlling air pollution has been more challenging globally, and levels of air pollutants have worsened in most large cities in low and low-middle income countries, 3 at times leading to historic air pollution episodes in cities such as New Delhi, Beijing, and Karachi. 4 , 5 In addition, as levels have declined in high income countries, new evidence has documented severe adverse health effects still occur even at their now lowered exposure levels. 6 , 7 Concentrations previously considered ‘healthy’ may now exceed the newer more stringent guidelines by the WHO, and frequent short-term excursions are observed even in usually ‘low-pollution’ cities. Thus, air pollution remains a major global environmental concern impacting human health, particularly among vulnerable groups and socioeconomically disadvantaged communities. 8 , 9 The Lancet Commission's 2019 report and the WHO have estimated that some 6.7 million premature deaths can be attributed to the combined impact of household and outdoor air pollution, primarily from increased mortality from cardiovascular and respiratory diseases. 10

Over the past half century, exposure scientists, epidemiologists, and researchers of various related disciplines have made significant contributions in developing methods for monitoring and controlling airborne pollutants and investigating the harmful effects of exposure to air pollution. However, the chemistry of air pollutants, their behavior in the atmosphere/environment, and their interactions with biological systems are complex and, despite major strides in research, many unknowns persist. In 2010, an international specialty conference sponsored by the American Association for Aerosol Research (AAAR) titled “ Air Pollution and Health: Bridging the Gap from Sources to Health Outcomes ” 11 identified key needs to improve our understanding of air pollution related adverse effects: 1) a greater focus on multipollutant science that includes studies on mixtures and pollutant sources, 2) a better understanding of biological mechanism and associations of various health effects with sub-components of PM (e.g., submicron particles, elemental carbon, trace elements, and source-specific mixtures); 3) a further understanding of susceptibility of populations - including the role of genetics/epigenetics, the influence of socioeconomic and other confounding factors, and; 4) the addition of new technologies, such as ‘microsensors’, hybrid air quality modeling, and remote (e.g., satellite) sensing data. 11 While there have been significant improvements in addressing some of these concerns, many gaps identified at that time still persist.

Of the various air pollutants, greater importance has been attributed to the mass concentration of particulate matter (particularly PM with aerodynamic diameters smaller than 10 μm and 2.5 μm; PM 10 and PM 2.5 ), due to studies showing stronger links between fine PM concentration and adverse health effects. 12 While, even to-date, the mass concentration of PM is used as the standard and main exposure metric in many studies, the AAAR conference attendees raised concerns that mass concentration alone does not appear to be a metric sufficient to fully and effectively evaluate the health effects of PM exposure: the size, source, and composition of PM and other physical properties also need to be considered in evaluating health effects. More recently, Nicolaou and Chekley (2021) 13 discussed deficiencies in air quality monitoring including, research on the long-term effects of exposure, lack of knowledge in relative toxicities from different sources and the joint and independent effects of multipollutant exposures, the impacts of ultrafine particulate matter, and importantly, the need for more effort in research in low-and-middle-income countries (LMICs), where exposures are highest, but data are sparse. In addition, attention has been drawn to gaps in our understanding of air pollution control and health, particularly on diseases spread by airborne pathogens. 14 Thus, most knowledge gaps discussed in the past still persist, although insights into some have advanced significantly in recent years, such as studies on epigenetic factors associated with air pollution exposure, 15 , 16 as well as analyses of source mixtures and metals more strongly associated with health outcomes. 17 , 18 In addition, improved understanding of the biological mechanisms regarding how air pollutants affect various organ systems, including cardiovascular, neurological, developmental, and metabolic systems, provide vital insights for other aspects of research including identifying susceptibility and possible treatments. For example, recent research has pointed to oxidative stress from fine PM containing both transition metals and acidic sulfates, such as emitted by fossil fuel combustion, as a likely important health impact causal pathway. 19

Therefore, despite a long history of air pollution research, there is still much to learn about the interactions between air pollutants and human health systems, and the external modifying factors influencing this relationship. New challenges have emerged, in addition to the pre-existing issues and gaps in knowledge. In this viewpoint, we identify critical gaps in air pollution research/knowledge, and discuss future directions and their potential impact on air pollution related health risks along the following key themes: (1) Air pollution monitoring methods and technological limitations e.g. air pollution source and composition, number concentration vs. mass concentration, central vs. personal monitoring; (2) Exposure assessment uncertainties impacting health outcomes assessed and, (3) Regulatory standards and policies.

Gaps in monitoring methods and technological limitations

Pm mass, size, composition, and source.

While the U.S. EPA recognized the key role of fine particulate matter in the health effects of particles when it changed the U.S. ambient air quality standard from PM 10 to PM 2.5 in 1997, 20 further progress has been lagging in its regulation to better monitor and focus regulation on those fine particles that are most toxic, which varies within PM 2.5 depending on size, composition, and source. The growing evidence that the most toxic particles are among the sub-micron size (e.g., nanoparticles), and from sources emitting the most toxic mix of constituents (e.g. fossil fuel combustion), is yet to be addressed in regulations, or in most PM air pollution studies. 21 While some have called for the conduct of site-specific epidemiological studies of PM 2.5 health effects in every locality to address the variation in PM 2.5 toxicity per unit mass 22 , the development and application of source sector-specific and composition-specific health effect estimates (e.g., for those with the highest risk per μg/m 3 ) would more efficiently allow the derivation of more locally appropriate site-specific health effect coefficients, based on local measurements of PM 2.5 source and composition, sidestepping the need for multiple epidemiological studies in each locality. Thus, better quantifying source and composition-based air pollution associated health impacts needs to begin with more detailed particulate matter monitoring when evaluating air pollution levels over space and time.

In most countries and cities, air pollution concentrations are obtained via central fixed reference-grade ambient monitors. In the U.S., the EPA has established a large network of central ambient monitors, mainly to measure and meet federal regulatory NAAQ standards, which are based on either hourly, daily and/or annual averages of overall mass concentration. In addition, the U.S. EPA has established a more limited Chemical Speciation Network (CSN) that are useful in evaluating variations in PM 2.5 composition, as well as useful for the estimation of source-specific exposure levels at those sites and at intervening locales using land use regression methods (e.g., see Rahman and Thurston, 2021). 23 Such data have proved useful in discriminating the varying health effects of different PM 2.5 components, but more such composition-based analyses of PM 2.5 samples and their health effects at more sites around the world are needed to enable more location-specific health effects estimation, enabling more health benefit optimized PM 2.5 mitigation policies. The expansion and maintenance of a worldwide CSN will be financially and technically challenging, added by the complexity of chemical compositions of various PM components. However, the data generated from such methods are key to connect epidemiologic findings with toxicological findings, as demonstrated in the NPACT study in the USA. 18 , 24 The studies conducted under the NPACT initiative were key in identifying source components of PM which have greater potential to cause harm, as well as to identify the challenges and complexities that need to be addressed to understand the mechanisms of individual component toxicities.

Since particulate matter derived from sources most often associated with the adverse health effects of PM 2.5 (e.g., fossil fuel combustion particles) are found in the sub-micron part of PM 2.5 mass, we also recommend another, simpler, approach to focus mitigation on the most toxic particle sources: switch from monitoring and regulating PM 2.5 to PM 1 (particles less than 1 μm in aerodynamic diameter) mass. This is consistent with the past progression in particle mass regulation from Total Suspended Particulate Matter (TSP), to inhalable particulate matter (PM 10 ) to fine particulate matter (PM 2.5 ). While this concept has been in discussion among air pollution scientists in recent years, perhaps the main challenge for implementation of a PM 1 standard was the lack of evidence of associated health benefits in the past. PM 1 is not monitored in the U.S. and many other major cities, limiting the number of studies that investigate associations between PM 1 levels and health outcomes. However, in recent years there has been a growing body of epidemiology results finding stronger health associations with PM 1 mass than with PM 2.5 . For example, Yang et al. (2020) recently found that “Associations with lower lung function were consistently larger for PM 1 than for PM 2.5 . 25 Guo and colleagues (2022) evaluated the varying associations of the incidence rate of female lung cancer with PM 1 , PM 2.5 , and PM 10 in 436 Chinese cancer registries and demonstrated that the association with the incidence rate of female lung cancer was stronger for PM 1 than for PM 2.5 or PM 10 . 26 Similarly Hu et al. concluded that their mortality studies found greater PM 1 effects per μg/m 3 , and that “To effectively reduce the adverse health effects of PMs, more attention should be paid to fine and very fine particles”. 27 Clearly, further air pollution monitoring of PM 1 , and epidemiological studies comparing PM 2.5 vs. PM 1 associations with adverse health are needed in order to confirm the case for PM 1 based air pollution control and regulations.

Monitoring of personal exposures

While central monitors provide a very useful estimate of a region's typical pollution levels, they are of limited use in providing estimates of personal-level exposures.

• • First, the number of residents represented by a central monitor can vary significantly within a country and between countries. In Europe and North America, the estimates are about one monitor per 100,000–600,000 residents, while in contrast, across sub-Saharan Africa one ground-level monitor represents about 15.9 million residents. 28 , 29 , 30
• • Second, central site monitors do not represent concentrations in varying microenvironments and occupational settings, which may be higher. For example, it has been found that street level NO 2 exposures in a city can be significantly higher than measured at a regulation air monitoring site located just a few stories above. 31
• • Third, when the interest is to study the health effects of smaller targeted populations, including vulnerable communities that may live in areas that do not have central monitors, they provide little information on personal exposure levels in populations that may be more strongly linked to health outcomes.

However, it is important to note that, despite these limitations of stationary monitoring, consistent associations have still been found in epidemiological studies over large populations using central monitoring data in different geographical regions. More focused exposures are needed to consider more sensitive subpopulations.

Advanced modeling of higher spatial resolution exposures using central monitor data as inputs have provided more spatially detailed estimates, such as via Land Use Regression (LUR) models, and satellite estimates of surface PM concentrations. 23 , 32 However, LUR and air quality models require extensive monitoring, meteorological data, and built environment information, 33 , 34 , and may not be broadly applicable to other locations. Similarly, satellite estimates of PM, while more spatially comprehensive, may have errors in the range of 22–85% if they are not cross-validated by ground level monitoring data, and are also impacted by other atmospheric conditions and particles in the atmosphere. 28 Due to such limitations, accurately estimating air pollution exposures for epidemiological studies still remains a challenge, contributing to variations in the estimations of health effects per amount of exposure, particularly in LMICs and rural areas in high-income countries, where central monitor coverage is more sparse.

This brings us to a more accurate approach for the estimation of individual level exposures to air pollutants-personal monitoring. Personal monitor sampling at breathing level provides the most accurate time-integrated exposures and variations of an individual's exposure. 35 For example, van Nunen et al. (2021) successfully employed 24-h personal monitoring of PM 2.5 , ultrafine particles, and soot concentrations to study their associations with blood pressure and lung function changes. 36 Xie et al. (2021) simultaneously obtained PM measurements from personal monitors and regulatory monitors to study exposures in individuals with asthma, and demonstrated that the portable monitors were better able to capture personalized air quality information compared to the traditional method. 37 However, despite these advantages, the wide use of personal monitors for exposure studies is limited for several reasons. Personal monitors and methods that have been validated and are of research grade have been expensive and require initial training to use, particularly for monitoring of gases and volatile organic compounds (VOCs). Examples for PM personal monitoring methods and devices include gravimetric analysis using portable pumps and filters, as well as light scattering-based nephelometric devices, which can cost in the range of $7000 - $8000 per unit. Therefore, monitoring exposure concentrations of a group/population has been limited by the number and cost of research-grade personal monitors available. Thus, although personal monitoring can provide more accurate estimates of individual and sensitive subpopulation exposures, these limitations have prevented them from significantly advancing the field of air pollution and health studies, as compared to the contribution from studies that have used central-site monitoring data.

In recent years, however, the goal of higher spatial and time resolution individual level air pollution monitoring has been made more attainable by the introduction and rapid advancement of low-cost sensors . Low-cost sensors (LCS) are expected to be an important development in the future direction of more democratized, high resolution, and inter-connected air (and health) monitoring, generating ‘big data’ for complex, but more inclusive, research. In addition to being inexpensive, mobile, and light weight, currently available LCS are smartphone compatible, which has greatly increased their appeal among concerned citizens and environmental non-profits, allowing monitoring among those who could not previously afford the traditionally more expensive personal monitoring equipment. LCS are also typically linked via GPS, and are used for crowdsourcing and identifying air pollution ‘hotspots’ in cities. 38 Recognizing this, the U.S. EPA has developed a comprehensive program to test and validate currently available low-cost air monitoring devices against reference grade and/or more advanced instruments, which is a major step in testing their capabilities for research. 39 , 40 A significant body of research has now been done to test and use LCS for personal exposure monitoring, demonstrating their potential for use in research, with proper quality control. 38 , 39 , 41 , 42 Importantly, their advantages make low-cost sensors a strong candidate for studies in LMIC, where resources for environmental monitoring are more scarce.

Despite the numerous advantages of low-cost air monitoring sensors, their accuracy may be limited as measurements can be biased by variations in the ambient environment, inter-instrument variability, limitations in the range of concentrations that can be measured, and concentration plateauing due to signal saturation above certain levels - typically above 100 μg/m 3 . 41 , 43 They have also been found to underperform in lower pollution settings, demonstrating poor agreement with more advance instruments below 40 μg/m 3 . 44 Therefore, they are most accurate and have high agreement with reference instruments only within a particular range. 42 , 43 Sensor ‘aging’ drift is also a concern. 41 In very high concentration situation LCS may also become saturated, and fail to accurately assess extreme concentrations. Therefore, scientists and the U.S. EPA have recommended periodic calibration of low-cost devices with more advanced or reference instruments to achieve data quality and accuracy. 39 , 43 , 45 , 46 In addition, prior to use in studies, they require continuous development and evaluation of calibration protocols and algorithms, which, if not done, can lead to uncertainties in obtaining reliable and timely air quality data. 42 Indeed, monitoring data quality has been found to improve LCS performance significantly after calibration. 46 Another challenge in LCSs is the lack of a physical size ‘cut-point’ that are designed into advanced instruments. Sensors estimate particle size using an internal algorithm, which at times have been found to be different from reference instruments. 41 However, overall, LCS present a great potential to be a powerful tool for augmenting central site air quality monitoring data with higher resolution, particularly for research in communities in LMIC and other areas that are unable to afford central site reference monitors.

Other developments in methods and technologies for personal monitoring that have seen progress in recent years and have future potential include, low-cost wearable sensors to measure health biometrics, 47 , 48 and non-invasive health biomarker analysis methods, such as breath biopsies. 49 , 50 These methods, combined with low-cost air monitoring devices, could be used to generate high resolution exposure-health metrics for scientists and medical professionals in studying and mitigating the health impacts of air pollution.

Exposure assessment uncertainties and exposure misclassification due to movement between environments with varying conditions

Advancements in monitoring instrument technologies, statistical and modeling methods, and high-resolution geographical mapping have improved our ability to better estimate exposure concentrations of populations in regions of interest, such as in communities living close to a powerplant, or children exposed to vehicle emissions when they live near highways. Recent research indicates that epidemiological effect estimates of PM 2.5 health effects are robust to the choice of PM 2.5 exposure assessment spatial resolution. 51 , 52 , 53 However, individuals move between ‘microenvironments’ with varying sources and concentrations, and failing to incorporate these variations may still lead to exposure misclassification and/or exposure estimation errors. Exposure estimation errors may be exacerbated among those living outside an urban core, or when time is spent in microenvironments with higher than average air pollution within the urban core. 54 More epidemiological studies that incorporate study participant mobility into exposure assessments are needed, which may now be more practical, given the improvements and cost reductions in personal particulate matter monitoring equipment, and the common availability of cell phones for data storage and transmission.

We particularly note two venues of air pollution related health exposures that impact a large number of individuals, but have lacked sufficient attention and need further exposure - health effects investigations. They are: (a) when traveling to polluted cities abroad (particularly international travel) and, (b) when using major transit systems, especially in underground subway systems.

Air pollution health risks when travelling

Until the coronavirus disease (COVID-19) pandemic, international tourist arrivals had been steadily increasing with approximately 1.4 billion worldwide arrivals in 2019. 55 , 56 After a significant drop in 2020 and 2021, recent estimates show an increasing trend, and international tourism climbed to nearly 60% of pre-pandemic levels in January–July 2022. 56 During travel, a large population of individuals may be exposed to air pollution concentrations and compositions that significantly vary from their home city/country, especially when they travel to popular destinations in Asia, Africa and South America. Megacities in these regions have poor air quality which are known to exceed local and WHO guidelines by several levels of magnitude. 4 , 5 , 57 However, although billions of individuals travel internationally, there is very limited research addressing the impact of air pollution on travelers’ health. 58

Travelers may experience a large differential in ambient exposure concentrations and composition within a short time of air travel, increasing their risk of air pollution related injury compared to residents who are more likely to be adapted to local conditions and knowledge. Although limited in number, existing studies have indicated that exposure to elevated levels of PM 2.5 in cities abroad can be associated with adverse cardiopulmonary health impacts, including a reduction in lung function, increase in respiratory symptoms and, and impacting quality of life. 58 , 59 , 60 Importantly, most study participants recovered from symptoms after returning to home cities. Other studies provide evidence of systemic pro-oxidative and proinflammatory effects associated with travel-related exposure to air pollution, where the elevated levels of biomarkers were interestingly reversed after the participants returned to their home city. 61 In this study, exposure to Polycyclic Aromatic Hydrocarbons (PAHs) in cities traveled to altered oxidative metabolism, which can be attributable to ambient air pollution exposure.

In addition to air pollutant exposure related health risks, travelers may be unpredictably impacted significantly by climate-related events, which are expected to particularly affect vulnerable urban areas in South Asia, East Asia and the Pacific. 62 Rising global temperatures can increase the frequency of ‘extreme events’ such as floods, heatwaves, dust storms and wildfires, and increases in air or water pollution, thereby elevating health risks, and causing population displacement in affected regions. Thus, global warming is expected to contribute to human mobility, leading to increased migration and travel to regions that are perceived to be ‘safer’. 63 While studies on migrant health are emerging, there is a need for more studies linking previous and ‘new’ exposures of migrant populations to cardiovascular and respiratory health outcomes. 58 , 63

Despite these concerns faced by travelers and migrants, insufficient studies have further explored short and long-term health outcomes associated with visiting or temporarily migrating to polluted cities for work, safety, education, leisure etc., especially among vulnerable groups such as older, pregnant, and other susceptible travelers. 58 Adding to the difficulty of conducting such studies is the need to adjust for many confounders, such as stress, temperature changes, changes in diet and water intake, alterations in sleep and sleep patterns, effects of changing altitude, and infectious/transmissible diseases. Studies on physiological outcomes and biomarkers that can detect early cardiovascular effects due to air pollution exposure during international travel will be important to warn elderly and susceptible travelers of risks of traveling to polluted destination cities, prior to travel. Given that cities are increasingly connected via travel, their residents and visitors present dynamic interdependent systems in concert with variable air pollution profiles. Therefore, we suggest that future epidemiological studies that explore ambient PM associated all-cause, cardiovascular, and respiratory mortality not consider populations in individual cities as a static entity, but also strive to consider travel related exposures as a potentially significant component of disease risk when evaluating such outcomes. 64

Air pollution health risks in subways

Underground subway/metro systems move large numbers of people daily, and further growth in such systems are expected. 65 , 66 Although commuters spend a relatively shorter time on subway platforms, daily exposures to peak levels may significantly impact health. However, despite several studies documenting very high levels of PM exposure in underground systems, especially in North America, Europe, 65 , 67 , 68 , 69 , 70 we are unaware of studies that have yet comprehensively evaluated the health risks of inhalation of high levels of varying compositions in this unique environment. Subway PM 2.5 levels have shown to be elevated several fold over ambient levels even in busy cities, and contain higher proportions of iron and other metals, such as manganese and chromium. 65 , 68 High elemental carbon levels have also been reported in subways that utilize diesel-powered maintenance trains. 70 Except for some studies indicating that exposure to subway particles causes inflammation in lung epithelial cells and oxidative stress in exposed workers, 71 , 72 the health implications of repeated relatively brief, but very high, pollution exposure levels in subways are largely unknown. Further complicating the issue is the ambiguity of classifying the subway environment for regulatory purposes. Should outdoor ambient standards apply, and if so who has the authority to regulate pollution levels in subways? Or is it considered an ‘indoor’ environment? These legal questions remain unanswered, limiting our ability to evaluate the possible mitigatory options. Pollution mitigation approaches, such as improved ventilation in subway platforms and cars, and the use of electric/battery powered maintenance equipment for system maintenance, are suggested, and may also reduce virus transmission risks at the same time. 73 , 74 Further research on subway air quality is needed, especially as a large population of commuters around the world is expected to increasingly rely on these systems in the future.

Regulatory standards and policies impacting health

The establishment of ambient air quality standards around the world, particularly in North America and Europe, has greatly improved air quality in many regions compared to levels before they were established, and prompted improvements in air monitoring, technological advancements in emissions control technology, and more environmental friendly practices in industry. 75 , 76 , 77 In the U.S., these gains in air quality reduction benefits were made even as the economy has grown. 77 Legislation in Europe led to the rapid growth in monitoring stations, and progress was made towards improving air quality over time, despite some challenges such as rising O 3 levels in many European cities 76 In recent years, cities such as Beijing, which had extremely poor air quality in the past, has achieved sizable and steady declines in ambient air pollution levels due to stricter control measures on emissions, and particularly on coal burning. 78 Such significant reductions in PM 2.5 and PM 10 concentrations in 74 key cities in China (between 2013 and 2016) were shown to be associated with substantial reductions in mortality and years of life lost. 79 Thus, air quality regulations and action plans have overall reduced air pollutant levels and improved the lives of affected populations. However, there is still much to do on improving standards and policies, particularly considering the emerging knowledge on the complexity of particulate air pollutants and recent studies demonstrating inequalities in air pollution exposure and health disparities among historically disadvantaged and vulnerable (due to economic and environmental disasters) populations.

Recent research indicates that there is no known threshold of PM and other pollutants’ health effects (e.g., see US EPA, 2019 80 ), while reductions will likely become more challenging to implement as regulatory PM 2.5 mass concentration limits decrease. As a result, the focus on mass without consideration of variations in composition toxicity has the potential drawback that the fine mass constituents that contribute the most mass may become the focus of controls, even if they are not the most toxic constituents. For example, some have recently recommended focusing on controlling gaseous ammonia releases in order to lower PM 2.5 because it reacts with ambient sulfuric and nitric acid to form particulate matter, 81 but that step would lead to more acidic (less neutralized), and likely much more toxic, particulate matter that remains in the air, likely leading to increased toxicity per unit mass. 82 Therefore, it would likely be more health efficient to consider focus additional PM regulation on the most toxic constituents of PM 2.5 , or on the submicron subcomponent, of the mass PM 1 . As discussed above, this concept has been in discussion for many years, 83 but now may well be the time for its implementation.

The issue of varying PM 2.5 composition and toxicity also has implications to standard and Air Quality Index (AQI) interpretation. In contrast to the setting of a single AQI for individual gaseous pollutants, such as ozone, which is the same compound everywhere, the setting of a single world-wide AQI for particulate matter is less defensible, because PM 2.5 varies widely in its size distribution, composition, and dominant source, and likely in its toxicity to humans per μg/m 3 , from place to place. Thus, the above discussed need for the assessment of PM 2.5 exposures and health impacts as a function of size, composition, and source is directly relevant. Such studies would be useful for the setting of locality-specific PM 2.5 AQI values, For example, a recent study of pollution in Dhaka, Bangladesh found that the hospital admissions and mortality impacts of fossil-fuel combustion PM 2.5 has a much larger impact per unit mass than biomass related PM 2.5 in Dhaka. 84 Since biomass burning dominates the PM 2.5 mass in Dhaka, it may be that the overall health impacts of PM 2.5 are less per μg/m 3 than in the developed world cities where the WHO guideline studies were primarily conducted, and so it may well be that a higher AQI guideline would be appropriate in Bangladesh than in the US or Europe. Similarly, windblown sand is a large component of the PM 2.5 in the Middle East, unlike where PM 2.5 epidemiological studies have been conducted. Thus, it stands to reason that PM 2.5 AQI adjustments need to be made, depending on the region and particularly the primary sources of air pollution in that state or nation.

Environmental justice considerations make clear that the environmental health protection improvements suggested here for regulations and policies must most pressingly be applied to address those most affected by air pollution. Growing evidence has established that the burden of air pollution is not equally shared, and socioeconomically disadvantaged populations and certain racial and ethnic groups often face higher exposure to pollutants and greater responses from air pollution. 8 , 85 , 86 , 87 Thus, future research, education and air pollution control policies should consider their impact on groups most affected, and make an effort to mitigate inequities during the planning and implementation stages. For example, Wang Y et al. (2022) have shown that national inequalities in air pollution exposure can be eliminated with fewer emission reductions if those reductions target the most heavily burdened locations, rather than implementing across the board national standards ( Fig. 1 ). 88

PM 2.5 exposure-disparity and concentration-reduction curves . Each panel compares three approaches to emission reduction: location (green line), sector (blue line), and NAAQS-like (i.e., employing a concentration standard; here, 6 μg/m 3 ; orange line). An “equal reduction” approach, where all emissions are reduced proportionately, would be a straight line (black dotted line). The location approach (green line) can eliminate national disparities with modest total emission reductions. Fig. 1 was obtained from 88 with permission from the corresponding author.

The exposome and precision environmental health in air pollution research

Recent scientific discussions on the future of the field of environmental health have highlighted the importance of integrating knowledge from various related disciplines. Focus has been drawn to utilizing ‘exposomics’ which is based on the concept of the ‘exposome’-the totality of all exposures in an individual's life course. 89 Although the exposome is not a new concept, the realization that average exposures alone cannot explain disease spread or occurrence has highlighted the importance of considering the variations and complexities of the pollutants, and their interactions with individual and population characteristics over space and time. Thus, the concept is gaining increasing applications in environmental health and toxicology studies. Early prediction and avoidance of diseases has gained greater importance, combined with a push towards more precise individualized treatments for exposure associated diseases. 90

Precision environmental health, predictive and translational toxicology, social justice, and health disparities have been identified as key areas for future development of environmental health, as well as climate change and innovative computational methods for data analysis. For example, an expert panel from the National Academies sponsored by the National Institutes of Environmental Health Sciences (NIEHS) has identified areas that the biomedical community can use to integrate environmental health science into broader studies of human health. 91 Such integration of exposure data, ‘omics’ data, and personal health information will greatly improve our ability to predict air pollution related diseases (i.e. using predictively toxicology approaches) and implement more targeted early prevention strategies. However, for precision medicine to be effectively integrated with exposomics and to be utilized for predicting and preventing air pollution related diseases, the focus has to be expanded from genetic or molecular studies alone to also incorporate environmental factors that determine disease progression. Despite the available technologies, researchers have expressed concern that environmental or exposure related issues are rarely considered in current precision medicine programs. 90 Nevertheless, there is huge potential in integrating exposomics and precision medicine methods in future environmental health research, especially when combined with personal wearable monitors, advanced analytical methods, and modern artificial intelligence capabilities.

While acknowledging that the field of air pollution and associated health effects is robust and ever growing, and that scientists throughout the years have greatly contributed to the understanding and betterment of the science, we have identified key gaps and future directions especially needing attention in current and future studies and policies (as summarized in Table 1 ). Future directions will be influenced by technological developments and more advanced methods of particulate matter air quality measurement, modeling, analysis, and regulation, such as focusing future additional regulation on the most health threatening particles, such as PM 1 . On the other hand, other air pollutants, such as volatile organic compounds, nanoparticles, emissions from new technologies and industrial processes, emissions from e-waste disposal and burning also need attention and further investigation as to how more efficiently to mitigate their risks. Occupational exposures, medical exposures, and immune responses to ‘new’ and more toxic pollutants are other areas of research (among many others) that would also warrant attention and new methodologies for assessment.

Table 1

Summary of gaps and future directions in air pollution research and mitigation.

HICs, High-Income Countries; LMICs, Low- and Middle-Income Countries; PM, particulate matter; AQIs, Air Quality Indices.

Thus, the present and future of environmental health and air pollution research present many challenges, such as changing pollution source mixes and characteristics over space and time, but also new opportunities, as technology opens new exposure measurement possibilities. Strong international cooperation is needed between countries/communities with resources and those that do not, for more extensive and advanced exposure data collection and dissemination, research knowledge, and resource sharing, so that these new methods and technologies become accessible in LMICs and burdened communities, as well. In this way, there is the potential to achieve a world in which scientific collaborations, using more globally accessible methods-such as remote and low-cost sensors, open source data platforms, and capacity building programs, can greatly influence and mitigate air pollution related health risks, enabling better informed, fair, and more equitable environmental health solutions for all.

Contributors

Both authors (RV and GDT) contributed to the conceptualization, preparation of the original draft, and editing of the manuscript. Both authors read and approved the final version of this manuscript.

Data sharing statement

Not applicable.

Declaration of interests

The authors declare no completing interests.

Acknowledgements

This work was not funded by any specific funding agency. RV is partially supported by a JPB Environmental Health Fellowship award granted by The JPB Foundation and administered through the Harvard T.H. Chan School of Public Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of any funding agency.

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Air Pollution: Current and Future Challenges

Despite dramatic progress cleaning the air since 1970, air pollution in the United States continues to harm people’s health and the environment. Under the Clean Air Act, EPA continues to work with state, local and tribal governments, other federal agencies, and stakeholders to reduce air pollution and the damage that it causes.

• Learn about more about air pollution, air pollution programs, and what you can do.

Outdoor air pollution challenges facing the United States today include:

• Meeting health-based standards for common air pollutants
• Limiting climate change
• Reducing risks from toxic air pollutants
• Protecting the stratospheric ozone layer against degradation

Indoor air pollution, which arises from a variety of causes, also can cause health problems. For more information on indoor air pollution, which is not regulated under the Clean Air Act, see EPA’s indoor air web site .

Air Pollution Challenges: Common Pollutants

Great progress has been made in achieving national air quality standards, which EPA originally established in 1971 and updates periodically based on the latest science. One sign of this progress is that visible air pollution is less frequent and widespread than it was in the 1970s.

However, air pollution can be harmful even when it is not visible. Newer scientific studies have shown that some pollutants can harm public health and welfare even at very low levels. EPA in recent years revised standards for five of the six common pollutants subject to national air quality standards. EPA made the standards more protective because new, peer-reviewed scientific studies showed that existing standards were not adequate to protect public health and the environment.

Status of common pollutant problems in brief

Today, pollution levels in many areas of the United States exceed national air quality standards for at least one of the six common pollutants:

• Although levels of particle pollution and ground-level ozone pollution are substantially lower than in the past, levels are unhealthy in numerous areas of the country. Both pollutants are the result of emissions from diverse sources, and travel long distances and across state lines. An extensive body of scientific evidence shows that long- and short-term exposures to fine particle pollution, also known as fine particulate matter (PM 2.5 ), can cause premature death and harmful effects on the cardiovascular system, including increased hospital admissions and emergency department visits for heart attacks and strokes. Scientific evidence also links PM to harmful respiratory effects, including asthma attacks. Ozone can increase the frequency of asthma attacks, cause shortness of breath, aggravate lung diseases, and cause permanent damage to lungs through long-term exposure. Elevated ozone levels are linked to increases in hospitalizations, emergency room visits and premature death. Both pollutants cause environmental damage, and fine particles impair visibility. Fine particles can be emitted directly or formed from gaseous emissions including sulfur dioxide or nitrogen oxides. Ozone, a colorless gas, is created when emissions of nitrogen oxides and volatile organic compounds react.  
• For unhealthy peak levels of sulfur dioxide and nitrogen dioxide , EPA is working with states and others on ways to determine where and how often unhealthy peaks occur. Both pollutants cause multiple adverse respiratory effects including increased asthma symptoms, and are associated with increased emergency department visits and hospital admissions for respiratory illness. Both pollutants cause environmental damage, and are byproducts of fossil fuel combustion.  
• Airborne lead pollution, a nationwide health concern before EPA phased out lead in motor vehicle gasoline under Clean Air Act authority, now meets national air quality standards except in areas near certain large lead-emitting industrial facilities. Lead is associated with neurological effects in children, such as behavioral problems, learning deficits and lowered IQ, and high blood pressure and heart disease in adults.  
• The entire nation meets the carbon monoxide air quality standards, largely because of emissions standards for new motor vehicles under the Clean Air Act.

In Brief: How EPA is working with states and tribes to limit common air pollutants

• EPA's air research provides the critical science to develop and implement outdoor air regulations under the Clean Air Act and puts new tools and information in the hands of air quality managers and regulators to protect the air we breathe.  
• To reflect new scientific studies, EPA revised the national air quality standards for fine particles (2006, 2012), ground-level ozone (2008, 2015), sulfur dioxide (2010), nitrogen dioxide (2010), and lead (2008). After the scientific review, EPA decided to retain the existing standards for carbon monoxide.  EPA strengthened the air quality standards for ground-level ozone in October 2015 based on extensive scientific evidence about ozone’s effects.

EPA has designated areas meeting and not meeting the air quality standards for the 2006 and 2012 PM standards and the 2008 ozone standard, and has completed an initial round of area designations for the 2010 sulfur dioxide standard. The agency also issues rules or guidance for state implementation of the various ambient air quality standards – for example, in March 2015, proposing requirements for implementation of current and future fine particle standards. EPA is working with states to improve data to support implementation of the 2010 sulfur dioxide and nitrogen dioxide standards.

For areas not meeting the national air quality standards, states are required to adopt state implementation plan revisions containing measures needed to meet the standards as expeditiously as practicable and within time periods specified in the Clean Air Act (except that plans are not required for areas with “marginal” ozone levels).

• EPA is helping states to meet standards for common pollutants by issuing federal emissions standards for new motor vehicles and non-road engines, national emissions standards for categories of new industrial equipment (e.g., power plants, industrial boilers, cement manufacturing, secondary lead smelting), and technical and policy guidance for state implementation plans. EPA and state rules already on the books are projected to help 99 percent of counties with monitors meet the revised fine particle standards by 2020. The Mercury and Air Toxics Standards for new and existing power plants issued in December 2011 are achieving reductions in fine particles and sulfur dioxide as a byproduct of controls required to cut toxic emissions.  
• Vehicles and their fuels continue to be an important contributor to air pollution. EPA in 2014 issued standards commonly known as Tier 3, which consider the vehicle and its fuel as an integrated system, setting new vehicle emissions standards and a new gasoline sulfur standard beginning in 2017. The vehicle emissions standards will reduce both tailpipe and evaporative emissions from passenger cars, light-duty trucks, medium-duty passenger vehicles, and some heavy-duty vehicles. The gasoline sulfur standard will enable more stringent vehicle emissions standards and will make emissions control systems more effective. These rules further cut the sulfur content of gasoline. Cleaner fuel makes possible the use of new vehicle emission control technologies and cuts harmful emissions in existing vehicles. The standards will reduce atmospheric levels of ozone, fine particles, nitrogen dioxide, and toxic pollution.

Learn more about common pollutants, health effects, standards and implementation:

• fine particles
• ground-level ozone
• sulfur dioxide
• nitrogen dioxide
• carbon monoxide

Air Pollution Challenges: Climate Change

EPA determined in 2009 that emissions of carbon dioxide and other long-lived greenhouse gases that build up in the atmosphere endanger the health and welfare of current and future generations by causing climate change and ocean acidification. Long-lived greenhouse gases , which trap heat in the atmosphere, include carbon dioxide, methane, nitrous oxide, and fluorinated gases. These gases are produced by a numerous and diverse human activities.

In May 2010, the National Research Council, the operating arm of the National Academy of Sciences, published an assessment which concluded that “climate change is occurring, is caused largely by human activities, and poses significant risks for - and in many cases is already affecting - a broad range of human and natural systems.” 1 The NRC stated that this conclusion is based on findings that are consistent with several other major assessments of the state of scientific knowledge on climate change. 2

Climate change impacts on public health and welfare

The risks to public health and the environment from climate change are substantial and far-reaching. Scientists warn that carbon pollution and resulting climate change are expected to lead to more intense hurricanes and storms, heavier and more frequent flooding, increased drought, and more severe wildfires - events that can cause deaths, injuries, and billions of dollars of damage to property and the nation’s infrastructure.

Carbon dioxide and other greenhouse gas pollution leads to more frequent and intense heat waves that increase mortality, especially among the poor and elderly. 3 Other climate change public health concerns raised in the scientific literature include anticipated increases in ground-level ozone pollution 4 , the potential for enhanced spread of some waterborne and pest-related diseases 5 , and evidence for increased production or dispersion of airborne allergens. 6

Other effects of greenhouse gas pollution noted in the scientific literature include ocean acidification, sea level rise and increased storm surge, harm to agriculture and forests, species extinctions and ecosystem damage. 7 Climate change impacts in certain regions of the world (potentially leading, for example, to food scarcity, conflicts or mass migration) may exacerbate problems that raise humanitarian, trade and national security issues for the United States. 8

The U.S. government's May 2014 National Climate Assessment concluded that climate change impacts are already manifesting themselves and imposing losses and costs. 9 The report documents increases in extreme weather and climate events in recent decades, with resulting damage and disruption to human well-being, infrastructure, ecosystems, and agriculture, and projects continued increases in impacts across a wide range of communities, sectors, and ecosystems.

Those most vulnerable to climate related health effects - such as children, the elderly, the poor, and future generations - face disproportionate risks. 10 Recent studies also find that certain communities, including low-income communities and some communities of color (more specifically, populations defined jointly by ethnic/racial characteristics and geographic location), are disproportionately affected by certain climate-change-related impacts - including heat waves, degraded air quality, and extreme weather events - which are associated with increased deaths, illnesses, and economic challenges. Studies also find that climate change poses particular threats to the health, well-being, and ways of life of indigenous peoples in the U.S.

The National Research Council (NRC) and other scientific bodies have emphasized that it is important to take initial steps to reduce greenhouse gases without delay because, once emitted, greenhouse gases persist in the atmosphere for long time periods. As the NRC explained in a recent report, “The sooner that serious efforts to reduce greenhouse gas emissions proceed, the lower the risks posed by climate change, and the less pressure there will be to make larger, more rapid, and potentially more expensive reductions later.” 11

In brief: What EPA is doing about climate change

Under the Clean Air Act, EPA is taking initial common sense steps to limit greenhouse gas pollution from large sources:

EPA and the National Highway and Traffic Safety Administration between 2010 and 2012 issued the first national greenhouse gas emission standards and fuel economy standards for cars and light trucks for model years 2012-2025, and for medium- and heavy-duty trucks for 2014-2018.  Proposed truck standards for 2018 and beyond were announced in June 2015.  EPA is also responsible for developing and implementing regulations to ensure that transportation fuel sold in the United States contains a minimum volume of renewable fuel. Learn more about clean vehicles

EPA and states in 2011 began requiring preconstruction permits that limit greenhouse gas emissions from large new stationary sources - such as power plants, refineries, cement plants, and steel mills - when they are built or undergo major modification. Learn more about GHG permitting

• On August 3, 2015, President Obama and EPA announced the Clean Power Plan – a historic and important step in reducing carbon pollution from power plants that takes real action on climate change. Shaped by years of unprecedented outreach and public engagement, the final Clean Power Plan is fair, flexible and designed to strengthen the fast-growing trend toward cleaner and lower-polluting American energy. With strong but achievable standards for power plants, and customized goals for states to cut the carbon pollution that is driving climate change, the Clean Power Plan provides national consistency, accountability and a level playing field while reflecting each state’s energy mix. It also shows the world that the United States is committed to leading global efforts to address climate change. Learn more about the Clean Power Plan, the Carbon Pollution Standards, the Federal Plan, and model rule for states

The Clean Power Plan will reduce carbon pollution from existing power plants, the nation’s largest source, while maintaining energy reliability and affordability.  The Clean Air Act creates a partnership between EPA, states, tribes and U.S. territories – with EPA setting a goal, and states and tribes choosing how they will meet it.  This partnership is laid out in the Clean Power Plan.

Also on August 3, 2015, EPA issued final Carbon Pollution Standards for new, modified, and constructed power plants, and proposed a Federal Plan and model rules to assist states in implementing the Clean Power Plan.

On February 9, 2016, the Supreme Court stayed implementation of the Clean Power Plan pending judicial review. The Court’s decision was not on the merits of the rule. EPA firmly believes the Clean Power Plan will be upheld when the merits are considered because the rule rests on strong scientific and legal foundations.

On October 16, 2017, EPA  proposed to repeal the CPP and rescind the accompanying legal memorandum.

EPA is implementing its Strategy to Reduce Methane Emissions released in March 2014. In January 2015 EPA announced a new goal to cut methane emissions from the oil and gas sector by 40 – 45 percent from 2012 levels by 2025, and a set of actions by EPA and other agencies to put the U.S. on a path to achieve this ambitious goal. In August 2015, EPA proposed new common-sense measures to cut methane emissions, reduce smog-forming air pollution and provide certainty for industry through proposed rules for the oil and gas industry . The agency also proposed to further reduce emissions of methane-rich gas from municipal solid waste landfills . In March 2016 EPA launched the National Gas STAR Methane Challenge Program under which oil and gas companies can make, track and showcase ambitious commitments to reduce methane emissions.

EPA in July 2015 finalized a rule to prohibit certain uses of hydrofluorocarbons -- a class of potent greenhouse gases used in air conditioning, refrigeration and other equipment -- in favor of safer alternatives. The U.S. also has proposed amendments to the Montreal Protocol to achieve reductions in HFCs internationally.

Learn more about climate science, control efforts, and adaptation on EPA’s climate change web site

Air Pollution Challenges: Toxic Pollutants

While overall emissions of air toxics have declined significantly since 1990, substantial quantities of toxic pollutants continue to be released into the air. Elevated risks can occur in urban areas, near industrial facilities, and in areas with high transportation emissions.

Numerous toxic pollutants from diverse sources

Hazardous air pollutants, also called air toxics, include 187 pollutants listed in the Clean Air Act. EPA can add pollutants that are known or suspected to cause cancer or other serious health effects, such as reproductive effects or birth defects, or to cause adverse environmental effects.

Examples of air toxics include benzene, which is found in gasoline; perchloroethylene, which is emitted from some dry cleaning facilities; and methylene chloride, which is used as a solvent and paint stripper by a number of industries. Other examples of air toxics include dioxin, asbestos, and metals such as cadmium, mercury, chromium, and lead compounds.

Most air toxics originate from manmade sources, including mobile sources such as motor vehicles, industrial facilities and small “area” sources. Numerous categories of stationary sources emit air toxics, including power plants, chemical manufacturing, aerospace manufacturing and steel mills. Some air toxics are released in large amounts from natural sources such as forest fires.

Health risks from air toxics

EPA’s most recent national assessment of inhalation risks from air toxics 12 estimated that the whole nation experiences lifetime cancer risks above ten in a million, and that almost 14 million people in more than 60 urban locations have lifetime cancer risks greater than 100 in a million. Since that 2005 assessment, EPA standards have required significant further reductions in toxic emissions.

Elevated risks are often found in the largest urban areas where there are multiple emission sources, communities near industrial facilities, and/or areas near large roadways or transportation facilities. Benzene and formaldehyde are two of the biggest cancer risk drivers, and acrolein tends to dominate non-cancer risks.

In brief: How EPA is working with states and communities to reduce toxic air pollution

EPA standards based on technology performance have been successful in achieving large reductions in national emissions of air toxics. As directed by Congress, EPA has completed emissions standards for all 174 major source categories, and 68 categories of small area sources representing 90 percent of emissions of 30 priority pollutants for urban areas. In addition, EPA has reduced the benzene content in gasoline, and has established stringent emission standards for on-road and nonroad diesel and gasoline engine emissions that significantly reduce emissions of mobile source air toxics. As required by the Act, EPA has completed residual risk assessments and technology reviews covering numerous regulated source categories to assess whether more protective air toxics standards are warranted. EPA has updated standards as appropriate. Additional residual risk assessments and technology reviews are currently underway.

EPA also encourages and supports area-wide air toxics strategies of state, tribal and local agencies through national, regional and community-based initiatives. Among these initiatives are the National Clean Diesel Campaign , which through partnerships and grants reduces diesel emissions for existing engines that EPA does not regulate; Clean School Bus USA , a national partnership to minimize pollution from school buses; the SmartWay Transport Partnership to promote efficient goods movement; wood smoke reduction initiatives; a collision repair campaign involving autobody shops; community-scale air toxics ambient monitoring grants ; and other programs including Community Action for a Renewed Environment (CARE). The CARE program helps communities develop broad-based local partnerships (that include business and local government) and conduct community-driven problem solving as they build capacity to understand and take effective actions on addressing environmental problems.

Learn more about air toxics, stationary sources of emissions, and control efforts Learn more about mobile source air toxics and control efforts

Air Pollution Challenges: Protecting the Stratospheric Ozone Layer

The  ozone (O 3 ) layer  in the stratosphere protects life on earth by filtering out harmful ultraviolet radiation (UV) from the sun. When chlorofluorocarbons (CFCs) and other ozone-degrading chemicals  are emitted, they mix with the atmosphere and eventually rise to the stratosphere. There, the chlorine and the bromine they contain initiate chemical reactions that destroy ozone. This destruction has occurred at a more rapid rate than ozone can be created through natural processes, depleting the ozone layer.

The toll on public health and the environment

Higher levels of  ultraviolet radiation  reaching Earth's surface lead to health and environmental effects such as a greater incidence of skin cancer, cataracts, and impaired immune systems. Higher levels of ultraviolet radiation also reduce crop yields, diminish the productivity of the oceans, and possibly contribute to the decline of amphibious populations that is occurring around the world.

In brief: What’s being done to protect the ozone layer

Countries around the world are phasing out the production of chemicals that destroy ozone in the Earth's upper atmosphere under an international treaty known as the Montreal Protocol . Using a flexible and innovative regulatory approach, the United States already has phased out production of those substances having the greatest potential to deplete the ozone layer under Clean Air Act provisions enacted to implement the Montreal Protocol. These chemicals include CFCs, halons, methyl chloroform and carbon tetrachloride. The United States and other countries are currently phasing out production of hydrochlorofluorocarbons (HCFCs), chemicals being used globally in refrigeration and air-conditioning equipment and in making foams. Phasing out CFCs and HCFCs is also beneficial in protecting the earth's climate, as these substances are also very damaging greenhouse gases.

Also under the Clean Air Act, EPA implements regulatory programs to:

Ensure that refrigerants and halon fire extinguishing agents are recycled properly.

Ensure that alternatives to ozone-depleting substances (ODS) are evaluated for their impacts on human health and the environment.

Ban the release of ozone-depleting refrigerants during the service, maintenance, and disposal of air conditioners and other refrigeration equipment.

Require that manufacturers label products either containing or made with the most harmful ODS.

These vital measures are helping to protect human health and the global environment.

The work of protecting the ozone layer is not finished. EPA plans to complete the phase-out of ozone-depleting substances that continue to be produced, and continue efforts to minimize releases of chemicals in use. Since ozone-depleting substances persist in the air for long periods of time, the past use of these substances continues to affect the ozone layer today. In our work to expedite the recovery of the ozone layer, EPA plans to augment CAA implementation by:

Continuing to provide forecasts of the expected risk of overexposure to UV radiation from the sun through the UV Index, and to educate the public on how to protect themselves from over exposure to UV radiation.

Continuing to foster domestic and international partnerships to protect the ozone layer.

Encouraging the development of products, technologies, and initiatives that reap co-benefits in climate change and energy efficiency.

Learn more About EPA’s Ozone Layer Protection Programs

Some of the following links exit the site

1 National Research Council (2010), Advancing the Science of Climate Change , National Academy Press, Washington, D.C., p. 3.

2 National Research Council (2010), Advancing the Science of Climate Change , National Academy Press, Washington, D.C., p. 286.

3 USGCRP (2009).  Global Climate Change Impacts in the United States . Karl, T.R., J.M. Melillo, and T.C. Peterson (eds.). United States Global Change Research Program. Cambridge University Press, New York, NY, USA.

4 CCSP (2008).  Analyses of the effects of global change on human health and welfare and human systems . A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Gamble, J.L. (ed.), K.L. Ebi, F.G. Sussman, T.J. Wilbanks, (Authors). U.S. Environmental Protection Agency, Washington, DC, USA.

5 Confalonieri, U., B. Menne, R. Akhtar, K.L. Ebi, M. Hauengue, R.S. Kovats, B. Revich and A. Woodward (2007). Human health. In:  Climate Change 2007: Impacts, Adaptation and Vulnerability  .  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change  Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, (eds.), Cambridge University Press, Cambridge, United Kingdom.

7 An explanation of observed and projected climate change and its associated impacts on health, society, and the environment is included in the EPA’s Endangerment Finding and associated technical support document (TSD). See EPA, “ Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act ,” 74 FR 66496, Dec. 15, 2009. Both the Federal Register Notice and the Technical Support Document (TSD) for Endangerment and Cause or Contribute Findings are found in the public docket, Docket No. EPA-OAR-2009-0171.

8 EPA, Endangerment Finding , 74 FR 66535.

9 . U.S. Global Change Research Program, Climate Change Impacts in the United States: The Third National Climate Assessment , May 2014.

10 EPA, Endangerment Finding , 74 FR 66498.

11 National Research Council (2011) America’s Climate Choices: Report in Brief , Committee on America’s Climate Choices, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., p. 2.

12 EPA, 2005 National-Scale Air Toxics Assessment (2011).

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• The Clean Air Act and the Economy

The University of Chicago The Law School

Abrams environmental law clinic—significant achievements for 2023-24, protecting our great lakes, rivers, and shorelines.

The Abrams Clinic represents Friends of the Chicago River and the Sierra Club in their efforts to hold Trump Tower in downtown Chicago accountable for withdrawing water illegally from the Chicago River. To cool the building, Trump Tower draws water at high volumes, similar to industrial factories or power plants, but Trump Tower operated for more than a decade without ever conducting the legally required studies to determine the impact of those operations on aquatic life or without installing sufficient equipment to protect aquatic life consistent with federal regulations. After the Clinic sent a notice of intent to sue Trump Tower, the State of Illinois filed its own case in the summer of 2018, and the Clinic moved successfully to intervene in that case. In 2023-24, motions practice and discovery continued. Working with co-counsel at Northwestern University’s Pritzker Law School’s Environmental Advocacy Center, the Clinic moved to amend its complaint to include Trump Tower’s systematic underreporting each month of the volume of water that it intakes from and discharges to the Chicago River. The Clinic and co-counsel addressed Trump Tower’s motion to dismiss some of our clients’ claims, and we filed a motion for summary judgment on our claim that Trump Tower has committed a public nuisance. We also worked closely with our expert, Dr. Peter Henderson, on a supplemental disclosure and on defending an additional deposition of him. In summer 2024, the Clinic is defending its motion for summary judgment and challenging Trump Tower’s own motion for summary judgment. The Clinic is also preparing for trial, which could take place as early as fall 2024.

Since 2016, the Abrams Clinic has worked with the Chicago chapter of the Surfrider Foundation to protect water quality along the Lake Michigan shoreline in northwest Indiana, where its members surf. In April 2017, the U. S. Steel plant in Portage, Indiana, spilled approximately 300 pounds of hexavalent chromium into Lake Michigan. In January 2018, the Abrams Clinic filed a suit on behalf of Surfrider against U. S. Steel, alleging multiple violations of U. S. Steel’s discharge permits; the City of Chicago filed suit shortly after. When the US government and the State of Indiana filed their own, separate case, the Clinic filed extensive comments on the proposed consent decree. In August 2021, the court entered a revised consent decree which included provisions advocated for by Surfrider and the City of Chicago, namely a water sampling project that alerts beachgoers as to Lake Michigan’s water quality conditions, better notifications in case of future spills, and improvements to U. S. Steel’s operations and maintenance plans. In the 2023-24 academic year, the Clinic successfully litigated its claims for attorneys’ fees as a substantially prevailing party. Significantly, the court’s order adopted the “Fitzpatrick matrix,” used by the US Attorney’s Office for the District of Columbia to determine appropriate hourly rates for civil litigants, endorsed Chicago legal market rates as the appropriate rates for complex environmental litigation in Northwest Indiana, and allowed for partially reconstructed time records. The Clinic’s work, which has received significant media attention, helped to spawn other litigation to address pollution by other industrial facilities in Northwest Indiana and other enforcement against U. S. Steel by the State of Indiana.

In Winter Quarter 2024, Clinic students worked closely with Dr. John Ikerd, an agricultural economist and emeritus professor at the University of Missouri, to file an amicus brief in Food & Water Watch v. U.S. Environmental Protection Agency . In that case pending before the Ninth Circuit, Food & Water Watch argues that US EPA is illegally allowing Concentrated Animal Feeding Operations, more commonly known as factory farms, to pollute waterways significantly more than is allowable under the Clean Water Act. In the brief for Dr. Ikerd and co-amici Austin Frerick, Crawford Stewardship Project, Family Farm Defenders, Farm Aid, Missouri Rural Crisis Center, National Family Farm Coalition, National Sustainable Agriculture Coalition, and Western Organization of Resource Councils, we argued that EPA’s refusal to regulate CAFOs effectively is an unwarranted application of “agricultural exceptionalism” to industrial agriculture and that EPA effectively distorts the animal production market by allowing CAFOs to externalize their pollution costs and diminishing the ability of family farms to compete. Attorneys for the litigants will argue the case in September 2024.

Energy and Climate

Energy justice.

The Abrams Clinic supported grassroots organizations advocating for energy justice in low-income communities and Black, Indigenous, and People of Color (BIPOC) communities in Michigan. With the Clinic’s representation, these organizations intervened in cases before the Michigan Public Service Commission (MPSC), which regulates investor-owned utilities. Students conducted discovery, drafted written testimony, cross-examined utility executives, participated in settlement discussions, and filed briefs for these projects. The Clinic’s representation has elevated the concerns of these community organizations and forced both the utilities and regulators to consider issues of equity to an unprecedented degree. This year, on behalf of Soulardarity (Highland Park, MI), We Want Green, Too (Detroit, MI), and Urban Core Collective (Grand Rapids, MI), Clinic students engaged in eight contested cases before the MPSC against DTE Electric, DTE Gas, and Consumers Energy, as well as provided support for our clients’ advocacy in other non-contested MPSC proceedings.

The Clinic started this past fall with wins in three cases. First, the Clinic’s clients settled with DTE Electric in its Integrated Resource Plan case. The settlement included an agreement to close the second dirtiest coal power plant in Michigan three years early, $30 million from DTE’s shareholders to assist low-income customers in paying their bills, and $8 million from DTE’s shareholders toward a community fund that assists low-income customers with installing energy efficiency improvements, renewable energy, and battery technology. Second, in DTE Electric’s 2023 request for a rate hike (a “rate case”), the Commission required DTE Electric to develop a more robust environmental justice analysis and rejected the Company’s second attempt to waive consumer protections through a proposed electric utility prepayment program with a questionable history of success during its pilot run. The final Commission order and the administrative law judge’s proposal for final decision cited the Clinic’s testimony and briefs. Third, in Consumers Electric’s 2023 rate case, the Commission rejected the Company’s request for a higher ratepayer-funded return on its investments and required the Company to create a process that will enable intervenors to obtain accurate GIS data. The Clinic intends to use this data to map the disparate impact of infrastructure investment in low-income and BIPOC communities.

In the winter, the Clinic filed public comments regarding DTE Electric and Consumers Energy’s “distribution grid plans” (DGP) as well as supported interventions in two additional cases: Consumers Energy’s voluntary green pricing (VGP) case and the Clinic’s first case against the gas utility DTE Gas. Beginning with the DGP comments, the Clinic first addressed Consumers’s 2023 Electric Distribution Infrastructure Investment Plan (EDIIP), which detailed current distribution system health and the utility’s approximately $7 billion capital project planning ($2 billion of which went unaccounted for in the EDIIP) over 2023–2028. The Clinic then commented on DTE Electric’s 2023 DGP, which outlined the utility’s opaque project prioritization and planned more than $9 billion in capital investments and associated maintenance over 2024–2028. The comments targeted four areas of deficiencies in both the EDIIP and DGP: (1) inadequate consideration of distributed energy resources (DERs) as providing grid reliability, resiliency, and energy transition benefits; (2) flawed environmental justice analysis, particularly with respect to the collection of performance metrics and the narrow implementation of the Michigan Environmental Justice Screen Tool; (3) inequitable investment patterns across census tracts, with emphasis on DTE Electric’s skewed prioritization for retaining its old circuits rather than upgrading those circuits; and (4) failing to engage with community feedback.

For the VGP case against Consumers, the Clinic supported the filing of both an initial brief and reply brief requesting that the Commission reject the Company’s flawed proposal for a “community solar” program. In a prior case, the Clinic advocated for the development of a community solar program that would provide low-income, BIPOC communities with access to clean energy. As a result of our efforts, the Commission approved a settlement agreement requiring the Company “to evaluate and provide a strawman recommendation on community solar in its Voluntary Green Pricing Program.” However, the Company’s subsequent proposal in its VGP case violated the Commission’s order because it (1) was not consistent with the applicable law, MCL 460.1061; (2) was not a true community solar program; (3) lacked essential details; (4) failed to compensate subscribers sufficiently; (5) included overpriced and inflexible subscriptions; (6) excessively limited capacity; and (7) failed to provide a clear pathway for certain participants to transition into other VGP programs. For these reasons, the Clinic argued that the Commission should reject the Company’s proposal.

In DTE Gas’s current rate case, the Clinic worked with four witnesses to develop testimony that would rebut DTE Gas’s request for a rate hike on its customers. The testimony advocated for a pathway to a just energy transition that avoids dumping the costs of stranded gas assets on the low-income and BIPOC communities that are likely to be the last to electrify. Instead, the testimony proposed that the gas and electric utilities undertake integrated planning that would prioritize electric infrastructure over gas infrastructure investment to ensure that DTE Gas does not over-invest in gas infrastructure that will be rendered obsolete in the coming decades. The Clinic also worked with one expert witness to develop an analysis of DTE Gas’s unaffordable bills and inequitable shutoff, deposit, and collections practices. Lastly, the Clinic offered testimony on behalf of and from community members who would be directly impacted by the Company’s rate hike and lack of affordable and quality service. Clinic students have spent the summer drafting an approximately one-hundred-page brief making these arguments formally. We expect the Commission’s decision this fall.

Finally, both DTE Electric and Consumers Energy have filed additional requests for rate increases after the conclusion of their respective rate cases filed in 2023. On behalf of our Clients, the Clinic has intervened in these cases, and clinic students have already reviewed thousands of pages of documents and started to develop arguments and strategies to protect low-income and BIPOC communities from the utility’s ceaseless efforts to increase the cost of energy.

Corporate Climate Greenwashing

The Abrams Environmental Law Clinic worked with a leading international nonprofit dedicated to using the law to protect the environment to research corporate climate greenwashing, focusing on consumer protection, green financing, and securities liability. Clinic students spent the year examining an innovative state law, drafted a fifty-page guide to the statute and relevant cases, and examined how the law would apply to a variety of potential cases. Students then presented their findings in a case study and oral presentation to members of ClientEarth, including the organization’s North American head and members of its European team. The project helped identify the strengths and weaknesses of potential new strategies for increasing corporate accountability in the fight against climate change.

Land Contamination, Lead, and Hazardous Waste

The Abrams Clinic continues to represent East Chicago, Indiana, residents who live or lived on or adjacent to the USS Lead Superfund site. This year, the Clinic worked closely with the East Chicago/Calumet Coalition Community Advisory Group (CAG) to advance the CAG’s advocacy beyond the Superfund site and the adjacent Dupont RCRA site. Through multiple forms of advocacy, the clinics challenged the poor performance and permit modification and renewal attempts of Tradebe Treatment and Recycling, LLC (Tradebe), a hazardous waste storage and recycling facility in the community. Clinic students sent letters to US EPA and Indiana Department of Environmental Management officials about how IDEM has failed to assess meaningful penalties against Tradebe for repeated violations of the law and how IDEM has allowed Tradebe to continue to threaten public and worker health and safety by not improving its operations. Students also drafted substantial comments for the CAG on the US EPA’s Lead and Copper Rule improvements, the Suppliers’ Park proposed cleanup, and Sims Metal’s proposed air permit revisions. The Clinic has also continued working with the CAG, environmental experts, and regulators since US EPA awarded $200,000 to the CAG for community air monitoring. The Clinic and its clients also joined comments drafted by other environmental organizations about poor operations and loose regulatory oversight of several industrial facilities in the area.

Endangered Species

The Abrams Clinic represented the Center for Biological Diversity (CBD) and the Hoosier Environmental Council (HEC) in litigation regarding the US Fish and Wildlife Service’s (Service) failure to list the Kirtland’s snake as threatened or endangered under the Endangered Species Act. The Kirtland’s snake is a small, secretive, non-venomous snake historically located across the Midwest and the Ohio River Valley. Development and climate change have undermined large portions of the snake’s habitat, and populations are declining. Accordingly, the Clinic sued the Service in the US District Court for the District of Columbia last summer over the Service’s denial of CBD’s request to have the Kirtland’s snake protected. This spring, the Clinic was able to reach a settlement with the Service that requires the Service to reconsider its listing decision for the Kirtland’s snake and to pay attorney fees.

The Clinic also represented CBD in preparation for litigation regarding the Service’s failure to list another species as threatened or endangered. Threats from land development and climate change have devastated this species as well, and the species has already been extirpated from two of the sixteen US states in its range. As such, the Clinic worked this winter and spring to prepare a notice of intent (NOI) to sue the Service. The Team poured over hundreds of FOIA documents and dug into the Service’s supporting documentation to create strong arguments against the Service in the imminent litigation. The Clinic will send the NOI and file a complaint in the next few months.

Students and Faculty

Twenty-four law school students from the classes of 2024 and 2025 participated in the Clinic, performing complex legal research, reviewing documents obtained through discovery, drafting legal research memos and briefs, conferring with clients, conducting cross-examination, participating in settlement conferences, and arguing motions. Students secured nine clerkships, five were heading to private practice after graduation, and two are pursuing public interest work. Sam Heppell joined the Clinic from civil rights private practice, bringing the Clinic to its full complement of three attorneys.