methodology of noise pollution evs project

Evs Project On Noise Pollution For Class 11th

Table of Contents

Acknowledgement:

I would like to extend my heartfelt gratitude and appreciation to my esteemed Environmental Science teacher, [Name], for their invaluable guidance, support, and encouragement during the completion of this project on Noise Pollution.

Throughout the project, [Name] has provided me with valuable insights and suggestions, helping me refine my research methodology and analysis. Their constructive feedback and guidance have been invaluable in ensuring the accuracy and comprehensiveness of the project.

I would also like to express my gratitude to [Name] for their unwavering support and belief in my abilities. Their constant encouragement has instilled confidence in me and has been pivotal in overcoming challenges during the project’s completion.

Additionally, I would like to thank my classmates and friends who have assisted me in gathering data and providing valuable inputs during the project’s development. Their collaboration and enthusiasm have enriched the overall quality of this project.

Last but not least, I am deeply grateful to my family for their unwavering support and understanding. Their encouragement and patience have been vital in enabling me to dedicate the necessary time and effort to complete this project successfully.

Once again, I express my deepest gratitude to [Name] and all those who have contributed to the realization of this project on Noise Pollution. Your guidance, support, and encouragement have been indispensable, and I am truly grateful for the opportunity to work on this project under your supervision.

Introduction to Noise Pollution:

Noise pollution is a widespread environmental problem that arises from various sources and has adverse effects on both human health and the environment. It is characterized by excessive or disturbing sounds that disrupt the normal balance of the acoustic environment.

Noise pollution is generated by numerous activities and sources in our daily lives. Transportation, including vehicles on roads, trains, airplanes, and ships, is a major contributor to noise pollution. The constant honking of horns, engine noises, and the sonic boom of airplanes create a high level of noise, particularly in urban areas.

Industrial activities, such as manufacturing processes, power plants, and construction sites, also generate significant amounts of noise. The operation of machinery, heavy equipment, and power tools produces loud and continuous sounds, which can impact both workers and nearby communities.

Construction activities, including drilling, hammering, and demolition work, contribute to noise pollution as well. These activities often take place in residential areas, leading to disturbance and inconvenience for the residents.

Recreational activities can also generate noise pollution, especially in crowded areas. Events like concerts, sporting events, and festivals produce loud music, cheering crowds, and amplified announcements, causing discomfort and potential harm to individuals.

The excessive exposure to noise pollution can have detrimental effects on human health. Prolonged exposure to high levels of noise can lead to hearing loss and damage to the auditory system. It can also cause annoyance, stress, and sleep disturbances, leading to psychological issues such as anxiety, irritability, and reduced concentration.

Furthermore, noise pollution has negative impacts on the environment. It disrupts the natural habitats of wildlife, affecting their behavior, feeding patterns, and reproductive activities. For example, noise pollution from ships and sonar activities can disturb marine animals, leading to changes in migration patterns and communication difficulties.

In conclusion, noise pollution is a pervasive problem resulting from various sources such as transportation, industrial activities, construction, and recreational events. Its detrimental effects on human health and the environment make it a matter of concern that requires attention and effective mitigation strategies. By understanding the causes and consequences of noise pollution, we can work towards creating a quieter and healthier environment for all.

Example of Noise Pollution:

Noise pollution manifests in various forms and can be observed in numerous everyday situations. Two prominent examples of noise pollution are the incessant honking of vehicles in urban areas and the noise generated by construction sites.

In urban areas, the constant honking of vehicles has become a significant source of noise pollution. The honking is primarily due to traffic congestion, aggressive driving behavior, or lack of adherence to traffic rules. The cumulative effect of honking horns from cars, motorcycles, and buses creates a chaotic and stressful environment. Pedestrians, motorists, and residents in the vicinity are exposed to high levels of noise, leading to increased stress levels, annoyance, and a reduced quality of life. Prolonged exposure to such noise pollution can also have long-term impacts on individuals’ hearing abilities, potentially resulting in hearing loss or other auditory issues.

Another notable example of noise pollution is the noise generated by construction sites. Construction activities involve the use of heavy machinery, such as excavators, bulldozers, jackhammers, and concrete mixers, which emit high-intensity noise. The continuous operation of these machines, especially in densely populated areas, can cause significant disturbance and inconvenience to nearby communities. Construction noise disrupts the peace and tranquility of the surroundings, affecting residents’ sleep patterns, concentration levels, and overall well-being. It can also impact vulnerable populations, such as the elderly, young children, and individuals with certain medical conditions.

These examples highlight how noise pollution can arise from common activities and significantly impact individuals and communities. The incessant honking of vehicles in urban areas and the noise generated by construction sites are just a few instances of the widespread issue of noise pollution. It is crucial to address these sources of noise pollution through effective regulations, soundproofing measures, and responsible behavior to ensure a healthier and more peaceful living environment for everyone.

Importance of EVS Project on Noise Pollution:

The EVS project on Noise Pollution holds significant importance for several reasons. Firstly, it raises awareness among individuals about the adverse effects of noise pollution on human health and the environment. By understanding its consequences, people can take necessary measures to minimize their contribution to noise pollution and protect themselves. Secondly, the project highlights the need for effective policies and regulations to control and mitigate noise pollution. Lastly, it emphasizes the role of collective action and responsible behavior in reducing noise pollution and creating a more peaceful environment.

How Can We Make It Happen?

To effectively address noise pollution, we need a collective effort from individuals, communities, and governing bodies. Here are some steps that can be taken:

Public Awareness: Conduct awareness campaigns, seminars, and workshops to educate people about the causes and consequences of noise pollution. Encourage individuals to adopt soundproofing measures in their homes and workplaces.

Implement Noise Regulations: Enforce strict noise regulations and standards for industries, construction sites, and public places. These regulations should limit noise levels and define penalties for non-compliance.

Noise Reduction Measures: Encourage the use of noise-reducing technologies and techniques in transportation, construction, and industrial activities. Promote the adoption of quieter machinery and equipment.

Land Use Planning: Incorporate noise considerations into urban planning by ensuring the appropriate placement of residential areas, schools, and hospitals away from high-noise sources like highways or industrial zones.

The Three Pillars of Addressing Noise Pollution:

Prevention: Focus on reducing noise pollution at its source by employing quieter technologies, controlling noise emissions from industries, and promoting responsible behavior among individuals.

Protection: Implement measures to protect individuals from excessive noise exposure, such as providing sound barriers, noise barriers on highways, and soundproofing buildings near noisy areas.

Public Participation: Encourage active involvement of the public in raising concerns about noise pollution and participating in decision-making processes. Engage community organizations, NGOs, and citizen groups to work collaboratively in addressing noise pollution issues.

Conclusion:

In conclusion, noise pollution poses a significant threat to our environment, health, and overall well-being. Throughout this EVS project, we have delved into the causes, provided examples, and discussed the consequences of noise pollution. It has become evident that raising awareness and taking proactive measures are crucial to address this issue effectively.

Noise pollution is a multifaceted problem caused by various sources, including transportation, industrial activities, construction, and recreational events. It disrupts the harmony of our surroundings, leading to stress, annoyance, and potential health problems such as hearing loss, sleep disturbances, and psychological issues.

Raising awareness about noise pollution is essential. By educating ourselves and others about its causes and consequences, we can foster a sense of responsibility towards reducing noise pollution. Awareness campaigns, seminars, and workshops can play a pivotal role in disseminating information and encouraging individuals to take action.

Implementing effective measures to combat noise pollution requires a three-pronged approach: prevention, protection, and public participation.

Prevention involves addressing noise pollution at its source. This can be achieved by employing quieter technologies, promoting the use of noise-reducing equipment, and encouraging responsible behavior among individuals and industries.

Protection measures focus on safeguarding individuals from excessive noise exposure. Implementing sound barriers, noise barriers, and soundproofing measures in residential areas, schools, hospitals, and workplaces can significantly reduce the impact of noise pollution.

Public participation is crucial in creating a sustainable and peaceful environment. Encouraging active involvement from citizens, community organizations, NGOs, and other stakeholders fosters a sense of ownership and collective responsibility. By engaging in decision-making processes, raising concerns, and advocating for noise regulations and policies, individuals can contribute to meaningful change.

In conclusion, by working together and implementing the three pillars of prevention, protection, and public participation, we can make a positive impact in reducing noise pollution. It is essential for governments, organizations, communities, and individuals to collaborate and take action to create a more peaceful and sustainable environment for everyone.

As responsible citizens, we must recognize the detrimental effects of noise pollution and strive to minimize our contribution to it. Let us work towards a future where tranquility and harmony prevail, promoting a healthier and more enjoyable quality of life for ourselves and future generations.

Certificate of Completion

This is to certify that I, [Student’s Name], a [Class/Grade Level] student, have successfully completed the project on “Evs Project On Noise Pollution For Class 11th.” The project explores the fundamental principles and key aspects of the chosen topic, providing a comprehensive understanding of its significance and implications.

In this project, I delved into in-depth research and analysis, investigating various facets and relevant theories related to the chosen topic. I demonstrated dedication, diligence, and a high level of sincerity throughout the project’s completion.

Key Achievements:

Thoroughly researched and analyzed Evs Project On Noise Pollution For Class 11th. Examined the historical background and evolution of the subject matter. Explored the contributions of notable figures in the field. Investigated the key theories and principles associated with the topic. Discussed practical applications and real-world implications. Considered critical viewpoints and alternative theories, fostering a well-rounded understanding. This project has significantly enhanced my knowledge and critical thinking skills in the chosen field of study. It reflects my commitment to academic excellence and the pursuit of knowledge.

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Jan 30, 2023

How electric vehicles help with noise pollution

EVs have many benefits, from being cheaper to run and maintain, to being more environmentally friendly . But one of the most underrated benefits of EVs is that they can help reduce noise pollution. Let's take a look at what noise pollution is, its effects and how we can reduce noise pollution with electric cars.

What is noise pollution?

Noise pollution is a type of environmental pollution that refers to unwanted or excessive sound and disturbs our ecosystem. The most common sources of noise pollution are:

• Transportation: noise emitted by cars, buses, trains, planes and other vehicles.
• Construction: noise from construction equipment and activities.
• Industrial: noise from factories, power plants and other industrial facilities.
• Home appliances: noise from dishwashers, washing machines, dryers, etc.
• Large events: noise from concerts, sporting events, and other large gatherings.

While we may be used to the sounds of our everyday lives, noise pollution can have serious effects on our health. In fact, the National Geographic Society depicts noise pollution as "an invisible danger", as it can have negative effects on both our physical and mental health. It also affects the environment we live in, including our wildlife.

What are the effects of noise pollution?

Noise pollution can have many negative effects on both humans and animals. For humans, noise pollution can lead to:

• Noise-Induced Hearing Loss (NIHL): being exposed to loud noise over time can damage the delicate structures in the inner ear, leading to hearing loss.
• Tinnitus: exposure to loud noise can also cause a condition called tinnitus, which is a ringing, buzzing or roaring sound in the ears. This can be a temporary or permanent condition.
• Anxiety and depression: noise pollution has been linked to anxiety and depression, as it can be a source of stress. According to this study , people who live close to roads are 25% to 30% more likely to experience depression.
• Migraines and headaches: noise pollution can also trigger migraines and headaches. There have been several studies that confirmed noise pollution's ability to trigger attacks.
• Problems sleeping: noise pollution can also disrupt sleep, as it can interfere with the body's natural sleep cycles. In one study , it was found that people who were exposed to traffic noise at night had a harder time falling asleep and staying asleep.
• Other health problems: Various studies have found that noise pollution can also lead to high blood pressure, heart disease and strokes. These are serious health problems that can have long-term effects.

Noise pollution also affects our children. Children exposed to noise pollution have been found to experience negative side effects such as stress, impaired memory recall, lower levels of reading skills and a shorter attention span. This is because noise pollution can interfere with a child's ability to focus and learn.

Humans aren’t the only creatures that suffer because of noise pollution. It can disrupt animals' mating and feeding habits and, in some cases, it can even cause them to abandon their homes. This can lead to a decrease in population numbers, as well as a decrease in biodiversity.

In addition to its direct effects, noise pollution can also contribute to other forms of pollution, such as light and air pollution. By reducing the quality of the environment, noise pollution ultimately decreases the quality of life for both humans and animals.

So, if you want to play your part in saving our environment and restoring some peace and quiet, it's time to lease an electric car . Making the switch isn't as daunting as it may seem and there are many benefits to be had.

How much noise do electric vehicles make?

The only noise EVs typically make is from their tyres or the wind at high speeds. Because of this, people have to be more mindful when it comes to road safety. With lower car noise levels, pedestrians need to listen more carefully and properly look around for cars before crossing streets.

To keep pedestrians safe, legislation requires EVs to emit a sound with a minimum frequency of 56 Decibels — which is as loud as an electric toothbrush. The car's sound should also mimic its behaviours. For example, the pitch should increase when the car speeds up.

While adding noise to EVs may seem counterproductive to reducing noise pollution, the amount of noise they give off is still much lower than traditional petrol or diesel cars. So, EVs are a great starting point to prevent noise pollution levels.

Can electric cars really help reduce noise pollution?

Absolutely. Despite the fact that EVs are required to make some noise, they are still much quieter than traditional petrol or diesel cars. As EVs become more popular, there will be fewer petrol and diesel cars on the road, which will reduce noise even further.

By switching to an electric vehicle, you can help reduce noise pollution and its negative effects on both humans and animals. EVs are not only better for the environment, but they can also help create a quieter and more peaceful world.

All in all, electric vehicles are more environmentally friendly than fuel-powered cars and they also produce less noise. This makes them a great choice for people who enjoy a quiet ride and want to do their part to reduce noise pollution and carbon emissions. EVs are becoming more affordable as technology improves and battery prices decrease. So if you're interested in doing your part to reduce noise pollution and save money in the long run, switch to an EV today.

Not sure how to get started? Electric car leasing is a great option for people who want to make the switch to an EV without the upfront cost of buying one outright. Plus, with Octopus Electric Vehicles, you'll get a free home charger installed with your lease and 4,000 free miles to get you on the road. Simply head over to our electric car leasing page to learn more about leasing an electric car.

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The impact of electric vehicles on noise pollution and creating quieter cities

The increasing adoption of electric vehicles (EVs) has the potential to revolutionize urban landscapes, especially when it comes to noise pollution. Noise pollution, often overlooked but pervasive in urban environments, has adverse effects on public health, quality of life, and the environment. In this article, we will explore the impact of electric vehicles on noise pollution and their role in creating quieter cities, addressing the benefits, challenges, and opportunities associated with this transformative shift.

The Problem of Noise Pollution: Noise pollution, a byproduct of urbanization and transportation, has become a pressing concern in cities worldwide. Traffic noise, in particular, is a significant contributor to urban noise pollution, affecting residential areas, commercial districts, and public spaces alike. Chronic exposure to high levels of noise can lead to stress, sleep disturbances, and various health issues, including cardiovascular problems and mental health disorders. Additionally, noise pollution disrupts communication, interferes with learning in schools, and disturbs wildlife in urban ecosystems.

The Silent Revolution of Electric Vehicles: Electric vehicles offer a promising solution to the problem of noise pollution. Unlike traditional internal combustion engine vehicles, EVs operate silently due to the absence of a conventional engine and exhaust system. Instead, electric vehicles are powered by electric motors, which produce little to no noise during operation. This silent revolution has the potential to transform urban soundscapes, creating quieter cities and improving the overall quality of urban living.

Benefits of Quieter Cities: a. Improved Public Health: Reducing noise pollution in cities can have a positive impact on public health. Lower noise levels contribute to better sleep quality, reduced stress, and improved mental well-being among urban residents. By creating quieter environments, cities can foster a healthier and more resilient population.

b. Enhanced Urban Livability: Quieter cities offer a more pleasant and enjoyable living environment for residents. People can experience greater tranquility in parks, public spaces, and residential areas, fostering a stronger sense of community and connection with their surroundings.

c. Economic Advantages: Quieter cities can be more attractive for businesses, tourism, and real estate development. Reduced noise levels can increase the value of properties and contribute to urban revitalization efforts. Additionally, quieter commercial districts can improve the shopping and dining experience, attracting more customers and stimulating economic activity.

d. Eco-Friendly Image: Embracing electric vehicles and creating quieter cities aligns with sustainable and eco-friendly practices. By adopting EVs and promoting noise reduction initiatives, cities can enhance their environmental image and contribute to global efforts in mitigating climate change and improving air quality.

Challenges and Opportunities: a. Pedestrian Safety: While quieter streets offer numerous benefits, there is also a potential risk associated with electric vehicles’ silent operation. Pedestrians, especially those with visual impairments, may rely on auditory cues to detect oncoming vehicles. To address this challenge, some EV manufacturers are exploring solutions such as adding external sound emitters, known as Acoustic Vehicle Alerting Systems (AVAS), to alert pedestrians of approaching electric vehicles.

b. Public Awareness: The shift to electric vehicles and quieter cities requires public awareness and education. Many people associate noise with vehicle safety, and the silent operation of electric vehicles may be a novel concept to some. Public outreach and education campaigns can help raise awareness about the benefits of quieter cities and the role of EVs in achieving this transformation.

c. Urban Planning and Noise Zoning: As cities evolve to accommodate electric vehicles and reduce noise pollution, urban planning and noise zoning will play a crucial role. Implementing noise regulations, such as quiet zones near residential areas or noise limits for commercial districts, can ensure that noise pollution remains under control while allowing for sustainable transportation solutions.

The Role of Policy and Regulation: To fully capitalize on the potential of electric vehicles in reducing noise pollution, policymakers and city planners need to take a proactive approach. Key considerations include:

a. Incentives for Electric Vehicle Adoption: Governments can implement financial incentives and rebates to encourage EV adoption among residents and businesses. This can lead to a larger EV market share, consequently reducing the overall noise levels in cities.

b. Charging Infrastructure Planning: Planning for a robust and well-distributed charging infrastructure is essential to support the widespread adoption of electric vehicles. By strategically placing charging stations throughout the city, policymakers can encourage EV adoption and reduce the need for long-distance travel, thus contributing to noise reduction.

c. Smart Urban Design: Urban design plays a significant role in noise pollution. Policymakers should prioritize pedestrian-friendly design, mixed-use development, and green spaces that contribute to quieter and more livable urban environments.

Collaborative Efforts: Creating quieter cities and promoting electric vehicles require collaborative efforts among stakeholders. Public-private partnerships between governments, automakers, charging infrastructure providers, and community organizations can drive innovation and lead to more sustainable transportation solutions. Additionally, city governments can work closely with electric vehicle manufacturers to design noise-aware vehicles and explore innovative technologies to further reduce urban noise pollution.

Electric vehicles have the potential to revolutionize urban transportation and create quieter cities. By eliminating noise pollution from internal combustion engine vehicles, EVs offer numerous benefits, including improved public health, enhanced urban livability, economic advantages, and a positive impact on the environment. Addressing challenges related to pedestrian safety, public awareness, and urban planning will be crucial in fully realizing the potential of electric vehicles in noise reduction.

To create quieter cities, policymakers, urban planners, and stakeholders must work together to implement supportive policies, build charging infrastructure, and prioritize smart urban design. By embracing electric vehicles and noise reduction initiatives, cities can become beacons of sustainability, fostering healthier, more livable, and eco-friendly environments for their residents. The silent revolution of electric vehicles offers a transformative opportunity to shape the cities of the future, where peace and tranquility coexist with cutting-edge transportation solutions.

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EVs and Urban Noise Pollution: Are They Better?

Living in urban areas comes with many conveniences but also environmental challenges like excessive noise. As the founder of  Electrik Living , I explore solutions that create greener, quieter cities. Electric vehicles can significantly reduce noise pollution from traffic. Their adoption is transforming the quality of life across urban environments.

Traditional gasoline-powered vehicles emit substantial noise from engines and exhaust systems. This traffic din can propagate for miles, increasing stress for city dwellers. Electric cars have the potential to slash urban noise pollution through near-silent electric motors. My daughter Trinity’s generation deserves vibrant, sustainable cityscapes free of excessive noise. EVs move us toward that vision.

Key Takeaways

• Electric vehicles can significantly reduce noise pollution in urban areas thanks to their quieter electric motors
• Widespread EV adoption has the potential to lower noise levels and improve quality of life in cities
• EVs emit far less noise at low speeds compared to traditional gasoline-powered vehicles

How Electric Vehicles (EVs) are Impacting Urban Noise Pollution

Benefits of evs in reducing urban noise.

Urban noise pollution severely impacts wellbeing. Vehicle noises disrupt sleep, concentration, and learning in children. Traffic noise has specifically been linked to higher risks of heart disease and emotional distress.

Effects of Noise Pollution in Urban Areas

• Hearing Damage
• Sleep Disturbances
• Cardiovascular Issues

By adopting electric vehicles, cities can mitigate these detrimental effects.

Rise of EVs and Noise Reduction

Electric motors run almost silently, especially at low speeds. This allows  EVs  to navigate dense city streets with minimal noise footprint. Widespread adoption can drastically cut ambient noise pollution originating from engine and exhaust noise.

EVs’ Impact on Urban Noise Levels

EVs emit far less noise in urban environments. At low speeds, they produce less than half the noise of traditional gasoline vehicles. This has significant implications for improving quality of life as electric vehicles gain market share.

EVs’ Impact on Overall Quality of Urban Life

• Healthier city soundscapes
• Improved cognitive function and learning
• More vibrant, livable communities

Widespread EV adoption can lead to healthier, more pleasant urban soundscapes to benefit all residents.

How EVs Contribute to Reducing Urban Noise

Several inherent properties of electric vehicles enable them to navigate cityscapes very quietly. Combined with falling costs, they offer societies a meaningful solution to excessive traffic noise.

Quieter Mobility: EVs’ Impact on Pedestrian Experience

Pedestrians in dense cities are disproportionately impacted by traffic noise. EVs provide substantially quieter mobility, improving walkability and overall livability.

Reduced Traffic Noise in Urban Environments

Far less noise originates from electric motors compared to internal combustion engines. Accelerating EVs therefore cuts ambient traffic noise in cities.

Role of Electric Motors in Lowering Vehicle Noise

Electric motors have fundamentally quieter operation, producing sound levels well below traditional engines. They enable a new era of much quieter urban mobility.

Comparing Noise Emissions: EVs vs Gas-Powered Cars

EVs Designed to Produce Minimal Noise

Manufacturers optimize electric vehicles to navigate urban environments with minimal noise footprint. This includes quieter tires, regenerative braking, and other acoustic treatments.

Future Potential of EVs in Minimizing Noise

Ongoing improvements in EV technology combined with favorable economics will enable mass adoption. This has immense potential to reduce ambient urban noise over the coming decade.

EVs’ Impact on Reducing Air & Noise Pollution

Widespread electric vehicle adoption can significantly cut both urban air pollution and noise levels.

EV Adoption & Overall Noise Level Reduction

Higher EV sales directly reduce fleet noise emissions, delivering compounding noise reductions annually.

Predicting Effects of Noise Levels with Rise of EVs

Models suggest total daytime urban noise could fall ~50% given rapid transition to electric mobility before 2035.

EVs’ Potential to Significantly Reduce Noise

Industry experts project  EVs  can reduce transportation noise in urban areas by ~90% as early as 2050.

The future looks bright and quiet with electric vehicles! But realizing their full potential requires thoughtful policies and public education…

How Society Can Facilitate the Transition to EVs

Achieving electric mobility at scale to reduce noise pollution requires public and private coordination.

Government Regulations and Incentives

Well-designed regulations, zoning, and incentives can accelerate EV adoption to maximize noise reduction.

Public Awareness and Education

Citizens need information on EVs’ economic, health, and environmental merits to make informed decisions.

Outreach should specifically highlight the noise reduction benefits of electric vehicles in urban areas.

How EVs Help Mitigate Negative Effects

By dramatically lowering noise emissions, electric vehicles mitigate various detrimental impacts of urban noise pollution.

Comparing Noise Levels: EVs vs Gas-Powered Vehicles

EVs produce less than half the sound of gasoline-powered cars in city driving conditions.

Impact of Gasoline Vehicles on Urban Noise

Engines and exhaust from traditional cars are primary contributors to excessive ambient noise in cities.

Understanding the Noise Reduction Provided by EVs

The table illustrates substantially lower noise emissions from EVs in urban driving cycles.

EVs’ Contribution to Improving Urban Environments

Widespread adoption of electric vehicles can deliver transformative reductions in noise levels to create more livable cities.

Advantages of EVs in Minimizing Exhaust & Engine Noise

EVs produce no engine or exhaust noise while driving. This eliminates primary sources of disruptive noise from traditional gasoline-powered cars in cities.

Can EVs Improve Overall Urban Environments?

Absolutely! By reducing noise, emissions, and air pollution, electric vehicles can fundamentally transform our cities for the better.

EVs’ Impact on Urban Environments & Noise

Widespread EV adoption offers sizable combined benefits from lower noise levels, reduced carbon emissions, cleaner air, and more.

Comparing Noise Production: EVs vs Gas Vehicles

Table shows substantially lower noise from electric cars under typical urban driving conditions.

EVs’ Contribution to Overall Noise Reduction

Industry projections show properly implemented EV adoption reducing urban noise pollution by 50-90% by 2050.

EVs’ Role in Improving Overall Quality of Urban Life

Transitioning to electric mobility is an essential step cities much take to enhance wellbeing through noise reduction.

EVs’ Potential for Quieter, Cleaner Urban Environments

Widespread electric vehicle adoption can deliver cleaner air, lower noise, reduced emissions – unlocking more vibrant, sustainable cityscapes.

The Future is Electric

Electric vehicles present immense potential to resolve excessive noise plaguing many urban areas today. Their quieter electric motors pave the way to healthier city soundscapes. Combined with falling battery costs, they are finally ready to transform urban environments.

Realizing EVs’ promise requires thoughtful policies and public education to accelerate adoption. But the payoff will be immense – delivering cleaner air, reduced emissions, and much quieter cities.

Now is the time to go electric.  I started  Electrik Living  to empower individuals and communities to adopt eco-friendly transport. Reach out anytime to learn more about the health, environmental, and cost savings EVs provide. My daughter Trinity and her generation deserve vibrant, livable cities free of noise pollution. Going electric moves us toward that sustainable vision.

Frequently Asked Questions

How do electric cars help reduce noise pollution in urban areas.

Electric cars produce less noise compared to traditional internal combustion engine vehicles, making them a key factor in reducing noise pollution in urban areas. The quiet operation of electric cars contributes to a more peaceful and enjoyable urban environment.

What are the benefits of electric cars in reducing noise pollution?

Electric cars offer several benefits in reducing noise pollution, including quieter operation, leading to decreased overall noise levels in urban and residential areas. This contributes to improved quality of life for residents and a healthier urban environment.

How do electric cars impact noise pollution levels on the road?

Electric cars are much quieter than traditional internal combustion engine vehicles, which results in lower noise pollution levels on the road. This helps in creating a more serene and peaceful driving and living environment.

What is the future of electric cars in reducing noise pollution?

The rise of electric cars is expected to have a significant impact on reducing noise pollution in urban areas. As the adoption of electric cars increases, the overall noise levels in cities and residential areas are likely to decrease, contributing to a quieter and more sustainable urban environment.

How do electric cars contribute to reducing noise pollution in the automotive industry?

A: Electric cars make the automotive industry’s future much quieter than traditional vehicles by significantly reducing noise pollution associated with transportation. This shift towards electric mobility is expected to have a positive impact on the overall noise levels in urban and suburban areas.

What are some significant advantages of electric cars in terms of noise pollution?

Electric cars are designed to emit less noise, thereby reducing the amount of noise pollution in urban and residential areas. Additionally, their quiet operation enhances pedestrian safety and contributes to a peaceful urban living environment.

How can electric cars help in the reduction of noise pollution and overall quality of life?

Electric cars can help reduce noise pollution, leading to improved overall quality of life in urban and residential areas. With their much quieter operation, electric cars contribute to creating a more peaceful and enjoyable living environment for residents.

Excessive noise degrades the quality of life across many urban areas. Electric vehicles present a viable solution by eliminating loud engine and exhaust noises. Their ultra-quiet operation also reduces disruption from traffic noise pollution.

Accelerating the transition to EVs requires public education and thoughtful government policies. But the payoff will be immense – delivering transformative benefits from slashing noise emissions as early as 2025.

By going electric, we can create cleaner, healthier, more vibrant communities for future generations.

Schedule a Consultation   to learn more about EV savings and sustainable living!

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Electric Vehicles

Our use of the term Electric Vehicles (EVs) describes hybrid electric vehicles, plug-in hybrid electric vehicles, and all-electric vehicles.

The evolution of the EV is a promising automotive solution that reduces maintenance costs and a dependence on foreign oil. It also reduces exhaust and noise emissions.

Millions of people who live near busy roadways, thoroughfares, intersections and parking lots are exposed to vehicle noise at all hours. Because electric motors produce less sound than Internal Combustion Engines (ICEs) that require a tail pipe muffler to mitigate noise emissions, EVs improve the urban soundscape. Consumers are attracted to EVs because they are quieter vehicles.

Artificial Vehicle Sound (AVS) is intended as an audible alert to warn pedestrians to the presence of an EV and also as a customizable vehicle tone to promote a brand. The industry term, 'Electric Vehicle Warning Sounds' is a euphemism. AVS is unmitigated noise pollution.

Artificial Vehicle Sound

The technology to produce AVS consists of external loudspeakers and a controller device that imitates ICE noise or branded sound effects by automakers. It may be possible for consumers to add custom sounds or increase the volume by modifying the electronics on the vehicle.

Auto companies are developing custom sounds for its EVs that go beyond imitating the sound of a slow moving vehicle. AVS is being developed to convey a marketing message through sound effects. The vehicles use external loudspeakers which has the effect of keeping the passenger compartment quiet while the noise is heard outside.

Automakers recognize that the way mobile phone ringtones convey a brand, vehicles can also be added with distinct vehicle sounds to convey its own brand. Ringtones on mobile phones is an estimated billion dollar business. The marketing and revenue opportunities from AVS will come at the expense of residents captive to unwanted noise.

Fisker Karma Plug-In Hybrid

A set of loudspeakers embedded in the front of a Fisker Karma plug-in hybrid sportscar. A representative described the sound as a mix between a "Formula One car and a starship".

Legislation

The National Federation of the Blind (NFB), an organization representing blind people, lobbied the United States government to mandate AVS on EVs. They claim EVs pose a safety risk for sight-impaired pedestrians who rely on hearing an approaching vehicle to judge its speed and proximity while navigating intersection crosswalks and other traffic situations.

When the NFB first announced that EVs presented a danger, the media attention was considerable; however, few questions were raised on the impact of increased urban noise pollution.

In 2007, the NFB funded a start-up company called Enhanced Vehicle Acoustics to design external loudspeakers on EVs. They built prototypes for use on the Toyota Prius with the intention of licensing their technology to automakers.

In 2008, the NFB funded a University of California study that evaluated the effect of sounds emitted by EV and ICE vehicles traveling at five miles per hour. Subjects claimed they could detect the sound of an internal-combustion vehicle when it was twenty-eight feet away, but could not detect the sound of a hybrid vehicle until it was seven feet away.

A controlled laboratory experiment is inherently different than the environment of city streets where the din of traffic often masks individual sounds of vehicles in motion. Most vehicles produce low frequency sound energy that is non-directional. The broader implication is that any type of vehicle, including motorcycles, scooters, mopeds, segways, and bicycles also present a danger to the blind.

In 2008, the NFB lobbied the National Highway Traffic Safety Administration (NHTSA) to hold a public hearing on the issue and called in government policymakers, automotive industry representatives and blind-advocates to testify.

The NFB lobbied the United States Congress to enact legislation which would direct the Secretary of Transportation to study and establish a motor vehicle safety standard that provide a means of alerting blind and other pedestrians of motor vehicle operation.

In 2009, NHTSA released a technical report titled, 'Incidence of Pedestrian and Bicyclist Crashes by Hybrid Electric Passenger Vehicles' (DOT HS 811 204) comparing the incidence rates of pedestrian and bicyclist crashes that involved EVs and ICEs under similar circumstances.

A total of 77 pedestrians and 48 bicyclists involved in crashes with EVs were sampled (compared to 3578 pedestrians and 1862 bicyclists involved in crashes with ICE vehicles comparatively). Within the sample group, there was a statistical difference in cases where EVs have a higher incidence rate when backing up or making a turn at slow speeds. There was no difference when both types of vehicles were going straight. There was no mention whether any of these accidents were caused by extraneous circumstances, such as distracted drivers or pedestrians.

NHTSA noted that the results of the study were not intended to make national estimates on the issue, because the results were based on a small sample size. That did not hinder NHTSA and NFB from touting the report as a watershed in the national media.

In 2011, the Pedestrian Safety Enhancement Act was signed into law. The act does not mandate a specific speed for AVS but requires the DOT study and establish requirements for sound levels on EVs.

Not all NFB members supported its agenda. Many sight-impaired members recognize that additional noise does not make their lives safer.

There are differences in sound energy. For example, the Occupational Safety and Health Administration (OSHA) mandates the use of back-up alarms on trucks for safety. However, the intense piercing noise emitted from current back-up alarms is a noise hazard for workers and a nuisance for nearby residents. Improved back-up alarms emit a broadband sound (white sound) that dissipates over a discrete distance reducing noise pollution. OSHA never acted to mandate broadband sound in spite of repeated calls to do so. As NHTSA is not concerned with noise pollution, representation from noise control advocates are shut out of the dialog.

While the United States Congress recognized the deleterious effects of noise pollution, no federal agency is mandated with monitoring its health and environmental consequences, including the Environmental Protection Agency (EPA) where its Office of Noise Abatement and Control (ONAC) was defunded. As a result, there are no standards on AVS where public health is concerned.

NHTSA published a proposed rule that would require all EVs traveling at less than 18.6 miles per hour to emit AVS, automakers have full discretion on the sounds the vehicle makes. The rule goes into effect in 2014.

CNN Report - Boy Hit By Hybrid Car

The National Federation of the Blind generated placements in national news media by scaremongering the dangers of quiet hybrids.

Alternative Solutions

One solution is a receiver device sight-impaired persons can wear that will emit an audible sound when an EV is in close proximity. EVs equipped with a transmitter sends a signal to the device relative to its distance. It would produce a discreet alert to blind pedestrians and have no effect on increasing noise pollution.

This concept was rejected by the NFB in favor of external loudspeakers on all EV vehicles.

Why are automakers and technology companies so eager to develop AVS?

By aligning itself with NFB and voluntarily adding external noise to its vehicles, the auto industry hopes to avoid any additional government regulation. By hedging that the government may mandate AVS, automotive and technology companies see financial opportunities with patent licensing fees for AVS systems.

Some consumers have negative views EVs: small, underpowered, range limited, and boring. Instead of a vehicle that is simply quiet, automakers can sell noise as something futuristic and high-tech. The marketable 'look at me' message is part of the experience of buying a new car. AVS recreates the aggressive 'vroom vroom' quality some consumers want in a new car.

The Society of of Automotive Engineers (SAE) created a 'Safety and Human Factors Committee' for quantifying the sound levels emitted by EVs. The SAE had previously collaborated with the American Motorcyclist Association in creating a system for measuring sound on motorcycle exhaust systems, intended to obfuscate label match-up , a more effective system to curb motorcycle noise.

The irony is that the auto industry is infamous for resisting safety improvements, such as seat belts (invented in the 1880s and not implemented until the 1950s) and airbags (invented in the 1950s and not implemented until the 1980s). In the case of AVS, the industry is racing to develop a solution to a non-existent problem. More so, the industry is astroturfing the issue of pedestrian safety.

An aftermarket parts supplier, Sigma Automotive, sells aftermarket hot-rod exhaust systems for the Toyota Prius on the basis that exhaust noise improves safety. As the vehicle does not displace exhaust gases in battery mode, the logic of turning the Prius into a muscle car for safety is completely lost.

Plug-In America, an advocacy group that works to promote the widespread adaption of EVs does not support AVS. Their position on safety is that EVs are quiet, but not silent. At parking lot speeds, an EV will produce similar sounds as ICE vehicles because of various fans, pumps, and tire noise.

Tesla Motors, Volkswagen, and BMW have not yet installed AVS on their EVs.

General Motors

General Motors (GM) collaborated with NFB to add AVS, what they called "Safe Sound Alert". In 2010, the automaker introduced its "Pedestrian-Friendly Alert System" that is manually activated on its Chevrolet Volt. The vehicle uses the horn to emit warning chirps at pedestrians.

Incidentally, GM was the automaker that developed the EV1, the first mass-produced EV of the modern era. Amid accusations GM self-sabotaged the program to avoid government regulation requiring zero-emission vehicles on the market, the cars were subsequently repossessed from lease-holders and destroyed.

Ford Motor Company

Ford designed their own AVS for the Ford Focus Electric and then polled consumers on Facebook to pick their favorite sound. They have not yet implemented AVS on their EVs.

Hyundai has its own AVS called the "Virtual Engine Sound System" on its Sonata Hybrid. Following the Pedestrian Safety Enhancement Act and learning that NHTSA rules would not allow AVS to be driver selectable, Hyundai removed the switch from the first production vehicles.

Toyota Motor Company

Nissan consulted with the NFB on the developments of its AVS, as well as a Hollywood sound design studio. Their sound engineers worked with film composers to create custom sounds that have been described as being reminiscent of the futuristic flying cars in the dystopian motion picture, Blade Runner.

Fisker Automotive

Fisker Automotive introduced its Fisker Karma plug-in hybrid sportscar in 2010. The vehicle uses external loudspeakers to emit its custom sound. They also consulted with a film-industry sound design studio to produce its sound.

Fisker declared bankruptcy in 2013 and was subsequently bought out by a Chinese auto-parts company.

Lotus Engineering / Harman International Industries

Lotus Engineering is a consultancy group of British sports carmaker, Lotus Cars. Harman Becker Automotive Systems is a division of Harman International Industries. These two companies have collaborated to develop AVS systems with the intention of marketing its technology to other automakers.

Lotus Cars and Harman International Industries created a hybrid demonstrator vehicle that simulates gasoline engine noise using loudspeakers rated at 300 watts each, louder than most car stereo systems. They call it a "Safe & Sound" vehicle.

Harley-Davidson Live-Wire Electric Motorcycle

As exhaust noise from motorcycle straight pipes is justified by riders as a means of reducing accidents, AVS is positioned as a safety feature. In this case, an electric motorcycle with the artificial sound of a fighter jet plane or a vacuum cleaner, whichever you prefer.

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EVS Project (Class 12 ICSE): SYJC

December 26, 2021 by studymumbai Leave a Comment

EVS Project (ICSE Class 12) – SYJC (30 Marks)

Steps to Conduct the Project

Here are the steps for conducting the project work.

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Introduction of the project – (2 marks-1 page- back to back) Background of the subject, Justification of choosing the topic

Importance of the project – (2 marks-1 side page) Why particular project is important

Objectives of the project – (2 marks-1 side page) what is that you will find out in the project (Should Start with “To”)

Methodology of the project –(4 marks-2 pages- back to back) Methods those will be used in data collection (siting the sources, survey, interviews etc.)

Observations – (4 marks-2 pages – back to back) Data/ information collection

Analysis – (4 marks-2 pages- back to back) on analysis of data- discuss ‘why’ of data

Results and Conclusion –(2 marks-1 page- back to back) what is the project outcome, what were the learnings from the project- did you fulfil objectives of project…

How to approach the Project

Selecting a topic

What topic should I research?

• Keep your eyes and ears open..
• Observe……..
• Be inquisitive……
• Ask why?????

Think big but Start small.

Should reflect what you will do.. Should not be vague, too general.

Aim and Objectives

What is that you want to find out…Write down the objectives of the project. Whether you already have little information and you want to find out further. Read relevant material.

Planning theresearch

Once you have identified the problem you want to work on – discuss with experts, Try to read some material on the subject (google is a good place to start..)

Finalise your methodology

How are you going to collect your data?

This depends on what you are going to explore

Questionnaire, semi structured interviews, observations, sample collection, lab analytical techniques.

Spend time on this aspect.

It is the most important part of your work. Do a sample first to test.

Analysing your data

Use graphs (bar graphs, pie diagram)

This will help you to understand patterns in your data.

Interpreting the data

What do the patterns in the analysis mean?

EVS Project Topics (ICSE Class 12)

Climate action plan dedicated to mumbai keeping in tune with climate adaptation, mitigation and resilience.

Intergovernmental panel on climate change (ipcc) report – 2021 on global warming with a focus on mumbai and maharashtra.

Ramsar sites in india – conservation of wetlands.

Survey the local rainwater harvesting installations if any in your locality. List down how it has benefitted the area.

Vehicular pollution – biggest contributor to city’s air pollution.

Study the local or nearby dam and write down the environmental issues concerning the dam and the locality.

Ecosystem restoration – Conservation of Aarey which acts as drainage basin and restoration of mangroves for creating carbon sink.

Biofuels – Production of biofuels (b10-ethanol etc.) in India.

Visit a local industry and study the environmental impacts of it in the surrounding area. Carry out interviews of local people about their views on the industry.

Study population status of your village/town /city for past 20 years ( since census is conducted every ten years) available on the indian national website (http//:censusindia.gov.in). Make a graphical representation of the changes seen and discuss the changes.

Report the weather changes experienced by you and other people in your area in the previous year. Make a report on how it is afffecting your own local environment.

Use sound level app to study the sound pollution in the area. Measure the noise levels at the market place, school, hospital, traffic signal. Prepare a detail report on it. Prepare a poster suggesting measures to reduce noise levels and its harmful effects.

Visit (or one on one video call/ phone call) the nearest hospital / doctor in your locality. Prepare a questionnaire to talk to the doctor on the increase or decrease in the patients and the types of diseases reported. Write the report what are the causes of diseases and preventive measures which can be taken. Make a report of the same.

Conduct a project in your locality to find out solid waste disposal in your locality. Make a poster to reduce the waste management in the community.

Utilisation of renewable enrgy sources in india. 16. Causes, impact, mitigation measures of tropical storms and cyclones like nisarg (2020) and tauktae (2021) in mumbai city.

Wildlife conservation – protection of natural habitat.

Hi-tech project to clean Mithi river in Mumbai.

The ground water levels have gone down due to increase in use of water by people.

A number of animal species have become extinct due to excessive disturbance of the natural environment by humans.

A number of plant species have become extinct due to excessive disturbance of the natural environment by humans.

There are new patterns of disease and pest attack with changes in rainfall pattern.

Organic farming or agriculture.

Biogas: source of renewable energy

Waste water treatment

Vermi-composting

Importance of mangrove cover

Water pollution due to oil spillage.

E-waste management

Mobile towers: Effects on environment

Mobile towers: Effects on human health

Extinctions of animals or plants (take one specific animal or plant)

The Sparrow: Concerns and conservation

Vanishing vultures: too late or is there hope?

Animal testing : is it ethical?

3 R mantra: for solid waste management

Ecofriendly celebration of festivals (take one specific festival)

Red Munia birds (Sample EVS Project)

Title: To study and do the assessment of Red Munia birds ( Red Avadavat) in Shindewadi village.

Introduction

Importance of study

Study will help to understand if there is illegal trade of birds in the area. Survey of these birds will help to identify the threats to this species. Study will throw light on the species distribution and identify the areas of occurrence. Awareness created among locals will help in protecting the species.

1. To study the distribution of red munias

2. To Study the abundance of the species (population of species)

3. To understand the threats of the species

Methodology

Write about study area – location, district, population of village, major occupation of people.

Field observations- visit the areas where munias are seen on every Sunday from 8 am to 10 am from January to July ( example- will change according to the project).

Count the number of individuals seen.

Document the activity- feeding, preening, nesting .

Write down plants on which they feed. Survey of people in village about the munias – prepare a questionnaire.

Observations

Table showing month wise data of population of red munias in the study area.

They are seen in small flocks 15 to 20 of them together. Only one flock was observed which increased in january.

List of plants on which they are seen feeding.

They mostly feed on grass seeds and seen in jowar field.

They are seen chirping all the time and very agile.

Observations and analysis – monthwise population

Population of munias change monthwise in the study area as shown in fig.

In January or winter more individuals of birds are seen which keep on decreasing by summer.

Local people interviews say that they are not to be seen so commonly in recent years.

11% people informed that they have seen people catching the area.

Results and conclusion

The red munias are seen in Shindewadi and nearby villages. Their numbers increase in January as maybe some local migration of birds happen inthe area. The threat to species is there is catching of birds is seen by very few (11%)local people. Another threat is also the changing crop pattern in the area. Instead of jowar – bajra people grow sugarcane or anjir, pomgranades (dalimb).

The red munias are seen in the village fields near the flowing stream. There number is decreasing and there are no large flocks seen. The birds are caught and local people have no idea why they are caught. The people who catch them are not from village.

CISCE Class 12 Environmental Science (EVS) Syllabus

CISCE Class 12 Environmental Science (EVS) Syllabus Topics

• Modern Schools of Ecological Thought. Deep Ecology (Gary Snyder, Earth First) Vs. Shallow Ecology. Stewardship of Land (E.G. Wendell Berry).
• Social Ecology [Marxist Environmentalism and Socialist Ecology (Barry Commoner)]. Feminism. Green Politics (E.g. Germany and England). Sustainable Development
• Population and Conservation Ecology: Population and Conservation Ecology. Human Populations. Population Regulation. Human Population Control. Threats to the Ecosystem. Conservation
• Monitoring Pollution: Pollution Monitoring. Monitoring the Atmosphere: Techniques. International and National Air Quality Standards. Water Testing. Soil Testing
• Third World Development: Urban-rural Divide. A Critical Appraisal of Conventional Paradigm of Development from the Viewpoints of Sustainability, Environmental Impact and Equity. A Case Study of Gandhian Approach in Terms of Its Aims and Processes. Urban Environmental Planning and Management
• Sustainable Agriculture: Traditional Agriculture in India. Food Environmental and Natural Resource Economics: Definition: Resources; Scarcity and Growth; Natural Resource Accounting. Gnp Vs. Other Forms of Measuring Income. Economic Status and Welfare (Net Economic Welfare, Nature Capital, Ecological Capital, Etc.). Externalities: Cost Benefit Analysis (Social, Ecological). Natural Capital Regeneration
• International Relations and the Environment: Trans-national Characteristics of Environmental Issues Using Case Study of Amazonia, Trade in Wild Life and Ozone Depletion. Impact of International Politics, National Sovereignty and Interest. International Trade. International Aid

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Analysis of Sampling Methodologies for Noise Pollution Assessment and the Impact on the Population

Guillermo rey gozalo.

1 Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, 5 Poniente 1670, Talca 3460000, Chile

Juan Miguel Barrigón Morillas

2 Departamento de Física Aplicada, Escuela Politécnica, Universidad de Extremadura, Avda. de la Universidad s/n, Cáceres 10003, Spain; se.xenu@nogirrab

Today, noise pollution is an increasing environmental stressor. Noise maps are recognised as the main tool for assessing and managing environmental noise, but their accuracy largely depends on the sampling method used. The sampling methods most commonly used by different researchers (grid, legislative road types and categorisation methods) were analysed and compared using the city of Talca (Chile) as a test case. The results show that the stratification of sound values in road categories has a significantly lower prediction error and a higher capacity for discrimination and prediction than in the legislative road types used by the Ministry of Transport and Telecommunications in Chile. Also, the use of one or another method implies significant differences in the assessment of population exposure to noise pollution. Thus, the selection of a suitable method for performing noise maps through measurements is essential to achieve an accurate assessment of the impact of noise pollution on the population.

1. Introduction

A recent publication by the World Health Organization points out that noise pollution, ranked second among a series of environmental stressors for their public health impact and, contrary to the trend for other environmental stressors which are declining, is actually increasing in Europe [ 1 ].

Noise is known to have auditory and non-auditory health impacts [ 2 ]. Environmental noise causes both psychological and physiological non-auditory health effects and the evidence for the non-auditory effects is growing [ 3 ]. Specifically, road traffic is considered to be the main source of community noise pollution. The most important non-auditory effects of traffic noise are annoyance and sleep disturbance [ 4 , 5 , 6 , 7 ]. Annoyance is a feeling of displeasure that can result in adverse emotions including irritability, stress, fear, and even depression [ 8 , 9 , 10 , 11 , 12 ]; it is associated with health-related quality of life [ 13 , 14 , 15 ].

Nighttime noise exposure directly influences sleep disturbance causing body motility, sleep stage changes, delayed sleep onset latency, and nocturnal awakenings [ 2 , 6 , 16 ]. Sleep disturbances can lead to serious long term health effects and there is increasing evidence from epidemiological studies that indicate long-term noise exposure leads to cardiovascular diseases, obesity or diabetes [ 17 , 18 , 19 , 20 , 21 ].

In considering the adverse effects of noise, the European Commission recognised community noise as an important environmental problem and adopted the European Noise Directive to assess and manage environmental noise [ 22 ]. The Directive focuses on noise mapping that aims to evaluate the number of people exposed to environmental noise. The precision of noise maps is essential to an appropriate identification of affected places and for planning suitable control measurements. In addition, a proper management of noise pollution can lead to benefits in reducing air pollutants because of the relation between them [ 23 , 24 ].

The European Noise Directive has not only been applied to European countries, but has also been used as a reference by non-European countries [ 25 , 26 , 27 , 28 ]. For example, in Chile, where this study was developed, over recent years the government has supported a number of projects initiated to gather knowledge about the acoustic situation in the cities [ 29 ]. As in other countries, different methods or strategies have been used for noise mapping, such as computation methods or studies carried out with “ in situ ” measurements. The use of an appropriate sampling method is important for the precision of noise maps, because even computation methods need to be validated and calibrated using “ in situ ” measurements [ 30 , 31 ].

Nowadays the sampling methods more commonly used in noise mapping are based on systematic random sampling using a regular grid or on the stratification of urban roads [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. There are also studies that carry out a stratification of land use after selecting any of the previous sampling strategies [ 40 , 41 ].

The grid method is the only sampling method that is accepted in an international standard, ISO 1996-2, that represents a verified reference for the measurement of noise levels in urban environments [ 42 ]. The grid method is widely used in many scientific fields because its use guarantees the statistical principle of equal probability and, moreover, a uniform coverage of the area under study. However, the grid method has other drawbacks. The standard says that the source of these problems stems from the existence of a high sound level variability in cases of proximity to the noise sources or the existence of large physical obstacles.

The stratification of urban roads is an increasingly popular method [ 34 , 36 ]. It is based on the generally accepted assumption that road traffic is the most important source of noise in cities, and for most streets it can be considered the main cause of the spatial and temporal variability of that noise. The stratification of urban roads used by a great number of researchers is based on information from the relevant ministries of transport [ 27 , 37 , 38 , 39 , 40 ]. These organisations classify the roads according to their main function and especially according to their design features.

In this context, our research group has been working for some years on the development of a sampling method for “ in situ ” noise measurements. We term this method the categorisation method. On the basis of the concept of street functionality, each stratum defined by the categorisation method presents a sound level variability that is lower than the total sound spatial variability in a city. This has produced significant improvements in both the reduction of the number of sampling points and in the estimation of noise levels in unsampled streets. Its usefulness has mainly been studied in Spanish cities with a wide range of populations: from 2000 to 3,250,000 inhabitants [ 43 , 44 , 45 ]. However, the economic development and urban planning of Chilean cities are different from the European cities analysed with the categorisation method in previous studies. Overall, European cities have typically been developed from a medieval historic centre with a complex street structure. Nowadays, shopping centres and administration centres are located in the historic centre. Chilean cities have a grid street plan in which streets run at right angles to each other, forming a grid. Also, another important difference is the fact that Chilean cities classify their roads according to a legislative procedure, whereas no standard classification exists for the roads in Spanish cities. The applicability of both methods based on roads classification has never been previously compared. In view of the above, the following objectives have been set out in this study:

• Compare the applicability and predictive capacity of two sampling methods—the legislative road classification and the categorisation method—in the assessment of urban noise in a Chilean city.
• Compare both sampling methods in terms of the prediction of exposure levels and the percentage of people annoyed.

Achieving these objectives will facilitate better understanding of the suitability of different noise situation sampling methods in cities. Information about the percentage of the population exposed in a Chilean city will also be provided. Until now this information has not been available in the Chilean cities evaluated. According to the European Noise Directive, the knowledge of the percentage of the population exposed is required for establishing effective preventive and, if necessary, corrective measures.

This study was conducted in the city of Talca (Maule region, Chile). Talca has a population of about 200,000 inhabitants (the population increases during the academic year due to the influx of university students) and is the tenth largest city in the country. The highest percentage of the active population (approximately 55%) works in the service sector, followed by the industrial sector (approximately 36%). This city does not have a historic centre and a high percentage of buildings have only one floor. The mean annual temperature and rainfall are 13 °C and 750 mm, respectively.

Three sampling methods were analysed: the grid method [ 42 ], road types established by the Ministry of Transport and Telecommunications of Chile (MTT) [ 46 ], and the categorisation method [ 45 ]. In order to compare the uncertainties using a similar sampling time the same number of sampling points (52) was selected for each measurement method. The grid method was analysed because it is accepted in an international standard, but its applicability was not compared with the other sampling methods.

2.1. Grid Method

In the grid method, a grid is superimposed over a city map and the measurement points are located at the nodes of the square or at the nearest location when the nodes are inaccessible. The area of Talca is approximately 29 km 2 . A total of 35 squares with 52 sampling points were drawn on the city map using a grid square with 800 m of resolution. A similar square grid resolution has been used in previous studies [ 33 ]. Figure 1 a shows the map of Talca with the grid used for this study.

Sampling methods used in the city of Talca. ( a ) Sampling squares of grid method; ( b ) Ministry of Transport and Telecommunications (MTT) road types; ( c ) and categorisation method.

2.2. Road Types Established by the MTT

The Ministry of Transport and Telecommunications of Chile (MTT) classifies urban roads according to their main function and their urban design features. However, in practice, urban characteristics, such as the width of the roads, are more relevant. Five types of roads are differentiated: highway, trunk, service, collector, and local. A similar classification has been used in recent acoustic assessment studies of cities in Chile and in other countries [ 27 , 37 , 38 , 39 , 40 ].

The sampling points were then randomly selected along the total length of each road type taking into account two factors. First, in the types of roads with a greater length (see Figure 2 ), a greater number of sampling points were selected with a minimum of eight sampling points for each road type. Second, equivalent points (those points located on the same section of a street with no important intersection between them) were discarded. For this reason, only one sampling point was selected in the highway road type. Figure 1 b shows the road types and locations of the sampling points: one point in highways, eight in trunk, twelve in service, eight in collector, and twenty-three in local road types.

Length of road types and road categories in Talca.

2.3. Categorisation Method

As previously mentioned, the categorisation method is based on the concept of street functionality, that is to say, the functionality of the streets of the city as a communication path between different parts of the city and between the city and other urban areas. In addition, other variables such as the flow of vehicles, the type of traffic, the average speed, and urban variables may have a clear relationship with functionality [ 47 ]. The streets of Talca were classified according to the definitions proposed in the categorisation method established in previous work [ 48 ].

A strategy similar to the previous method was used to select the sampling points in each road category. Figure 1 c shows the categorisation of different streets in the city and the locations of sampling points: eight points in Category 1, eight in Category 2, ten in Category 3, twelve in Category 4, and fourteen in Category 5.

2.4. Measurement Procedure

The measurements of different methods were carried out simultaneously from March to July 2015 following the ISO 1996-2 guidelines [ 42 ]. The measurements were performed on different working days and the sampling time for each measurement was 15 min. Previous studies [ 36 , 49 ] showed stability of the daily noise levels in the aforementioned months, and also these studies indicated that the main temporal variability of noise levels was among time-intervals within the day. At each sampling point, for each sampling strategy, at least five measurements were randomly selected in the following time-intervals: diurnal (from 07.00 to 19.00), evening (from 19.00 to 23.00), and nocturnal (from 23.00 to 07.00). A type-I sound level meter (2250 Brüel & Kjaer; Nærum, Denmark) was used with tripod and windshield and it was placed at a height of 1.5 m and at 2 m from the curb.

The A-weighted equivalent sound level ( L Aeq ) was used to analyse the results in the present study at different time-intervals of the day. The L Aeq registered in the diurnal period (from 07:00 to 19.00) and evening period (from 19.00 to 23.00) was very similar. For this reason, L Aeq from 7.00 to 23.00 ( L d ) was analysed. The noise descriptor L den was calculated following the guidelines of the European Noise Directive [ 22 ]. Other relevant information (traffic flow, types of vehicles, meteorological conditions, urban variables, etc. ) was also noted.

2.5. Statistical Analysis

In the acoustic assessment in Talca, the applicability of different sampling methods was analysed using the calculated noise descriptors ( L d , L n and L den ) at each sampling point ( P ij ). The subscript “ i ” refers to the point code and the subscript “ j ” refers to the sampling method.

In the grid method there are no assumptions of the location of sampling points in urban roads. However, the location of the sampling points with respect to the traffic noise source was similar in the different sampling methods. For this reason, the sound values registered in the sampling points of the grid method were used to analyse the predictive capacity of the others two sampling methods. The noise value assigned to each square ( S i ) was the median value of the four nodes of the square. For each square, the interquartile range was calculated from these four values. Moreover, the difference in sound levels between adjacent grid points was calculated. This difference should not be greater than 5 dB according to ISO 1996-2 [ 42 ].

For the MTT road types and the categorisation method a similar statistic procedure was carried out. The value assigned to each road type ( R i ) or road category ( C i ) was the average of the sound levels measured at the sampling points ( P ij ). This value was the expected value for all of the other points located in the same road type or road category. The average sound value and its variability will determine whether the stratums formed by road categories or by road types present significant differences. This hypothesis was assessed using the nonparametric tests Kruskal-Wallis and Mann-Whitney U [ 50 , 51 ]. This hypothesis was not tested with an inferential analysis in previous studies that used a legislative road classification [ 27 , 37 , 38 , 39 , 40 ]. The Kruskal-Wallis test was used to compare all the road categories in order to identify any significant differences. When such differences were found, Mann-Whitney U tests were used to compare pairs of road categories. The Mann-Whitney U test evaluates whether two independent samples or observations come from the same distribution. To avoid any errors due to the use of data from the same population rather than randomly selected data, the Holm correction was used [ 52 ].

In contrast to previous statistical tests, the receiver operating characteristics analysis ( ROC ) was used to evaluate the discriminative capacity of the MTT road types and of categorisation method to differentiate the sound values of the sampling points between pairs of strata (stratum i versus stratum j ) [ 45 ]. For the categorisation method and for MTT, the strata are the road categories and road types, respectively. The ROC analysis allows us to establish the upper and lower limits of the sound levels assigned to each stratum, to calculate the sensitivity (capacity to include previously assigned sampling points in the stratum), the non-specificity (proportion of sampling points that were not initially assigned to a certain stratum but that the ROC analysis indicated belonged to that stratum), and the predictive values (proportion of the sampling points that the ROC analysis assigned to a stratum that matched the strata to which they were initially assigned, relative to the total number of sampling points that the ROC analysis determined for the stratum). To do so, the following equations were used:

After studying the functioning of both methods, the predictive capacity of each method was then analysed using the sound values of the sampling points of the other methods as controls [ 53 , 54 ]. The parameter used for this analysis was the prediction error (ε i ), which is the difference between the measured value (control value) and the predicted value. The equations used to calculate the prediction error of the MTT road types (Equation (4)), and categorisation method (Equation (5)), respectively, were as follows:

The subscript “ i ” refers to the sampling point code ( P i ), road type code ( R i ) or road category code ( C i ), and the subscript “ j ” refers to the sampling methods in which the error is not being analysed. Next, the median prediction error obtained for each road category or road type was compared with the null value. For this, the Wilcoxon signed-rank test was applied [ 55 ]. This test determines whether the median of the prediction errors was biased. If the distribution of the prediction errors is unbiased, then a zero value will be obtained for the median.

Prediction errors of the different methods were also compared. To that end, the median absolute error of prediction (|ε i |) was analysed using the Mann-Whitney test [ 51 ]. If there is no significant difference it is assumed that the sampling methods have a similar predictive capacity.

Finally, the population exposed to noise was analysed and the population annoyed by noise was estimated. The demographic data of the geographic information system of the National Statistics Institute of Chile [ 56 ] were used to analyse the population exposed to noise. Noise levels registered in the road categories or road types were assigned to populations that reside in them [ 54 ]. Internationally validated equations were used to estimate the population annoyed by noise. Thus, the percentages of annoyed (% A ) and highly annoyed (% HA ) population were estimated from the L den descriptor with the following equations [ 57 , 58 ]:

With respect to nocturnal noise, the percentages of population with little sleep disturbance (% LSD ), sleep disturbance (% SD ), and those who were highly sleep disturbed (% HSD ) were estimated from L n descriptor using the following equations [ 59 ]:

3.1. Study of the Functioning of Sampling Methods

3.1.1. grid method.

Having calculated the sound values of L d , L n and L den descriptors in the different sampling points, the sound values of the different square grids were calculated. The results are shown in Table 1 . Table 1 shows that the interquartile range of sound values registered in the cells is quite high. Previous studies [ 33 , 48 ] reported high uncertainties in the predictive capacity of the grid squares, due to the high variability of the sound levels among nearby streets with different functionality. Therefore, if the sound differences between adjacent sampling points are analysed, 69%, 49% and 59% are higher than 5 dB for L d , L n and L den descriptors, respectively.

Median (M e ) and interquartile range (IQR) of L d , L n and L den descriptors registered in the square grids.

3.1.2. MTT Road Types

This stratified sampling is based on the hypothesis that different strata—road types in this case—have significant differences in sound values. First, to resolve this hypothesis, a descriptive analysis through a box plot was carried out ( Figure 3 ).

Box plot of L d , L n and L den descriptors registered in each road types.

Figure 3 shows that average values of sound descriptors decrease from trunk to local road type. In highway road types, as previously indicated, only one sampling point was used. In this road type the sound values of 76.4 dB, 70.1 dB and 78.9 dB were registered for the L d , L n and L den descriptors, respectively. Figure 3 also shows the analysis of the variability in mean sound levels. Trunk and service road types have an overlap of interquartile range and local road types have a high variability.

The hypothesis was resolved first by using the Kruskal-Wallis test. This test indicated significant differences ( p -value ≤ 0.001) for all the sound descriptors studied. Thus, the Mann-Whitney U test was then applied to analyse the differences among road type pairs ( Table 2 ).

p -Values with Holm adjustment of pairwise comparisons of road types using Mann-Whitney U test.

As shown in Table 2 , the Mann-Whitney U test found no significant differences ( p -value > 0.05) between trunk and service road types for L d , L n and L den descriptors. Nevertheless, for the remaining pairs of road types, significant differences ( p -value ≤ 0.05) for all sound indicators analysed were found.

In order to corroborate the quality of the previous results and to obtain more information about the MTT road types, the classification capacity of this method was then examined using ROC analysis. The results of this analysis are shown in Figure 4 .

Results of ROC analysis for the different sound descriptors registered in the Ministry of Transport and Telecommunications road types.

From the results shown in Figure 4 , the following can be noted:

• Regarding the ROC sensitivity (%), which is a measure of the capacity to include previously assigned sampling points in the stratum, only the collector road type for L n and L den has values above 80%. The sensitivity has low percentages for the sound descriptors analysed, sometimes even lower than 50%, because of the presence of overlaps among trunk and service road types and the high variability of the local road type.
• Regarding the non-specificity (%), which measures the proportion of sampling points that were not initially assigned to a given stratum, but which the ROC analysis indicates belong to that stratum, only the local road type has values lower than 10% for all the sound descriptors. The collector road type also has high non-specificity values for all the sound descriptors, although it has high sensitivity values for L n and L den .
• Finally, with regard to the predictive values of the different road types (which represent the proportion of the sampling points that the ROC analysis assigned to the stratum that matched the road types to which they were initially assigned, relative to the total number of sampling points that the ROC analysis determined for the stratum) only the local road type has values above 80% for all the sound descriptors. The stratum predicted by the ROC analysis for local road types has a high percentage of sampling points that MTT had initially classified in this road type. However, other sampling points of local road types have high values and these points are classified in other road types according to ROC analysis. Therefore, the local road type has low sensitivity values.

3.1.3. Categorisation Method

The different road categories defined by the method are based on the assumption of having significantly different noise levels. Therefore, like the MTT road types method, a descriptive and inferential analysis was conducted to test this hypothesis. The results of the descriptive analysis are shown in Figure 5 .

Box plot of L d , L n and L den descriptors registered in each road categories.

In the box plot, the interquartile ranges of the different road categories and sound descriptors have no overlaps. Category 5 has the greatest variability but it is considerably lower than that presented by the local road type.

An inferential analysis was then conducted using the Kruskal-Wallis and Mann-Whitney tests. The Kruskal-Wallis test indicates significant differences ( p -value ≤ 0.001) for all the sound descriptors studied. Thus, the Mann-Whitney U test with Holm correction was applied to analyse the differences among road category pairs ( Table 3 ).

p -Values with Holm adjustment of pairwise comparisons of road categories using Mann-Whitney U test.

As shown in Table 3 , the Mann-Whitney U test found significant differences ( p -value ≤ 0.01) among all pairs of road categories studied for all sound descriptors analysed. To corroborate the previous results, as carried out for the previous method, the classification capacity of the categorisation method was studied via ROC analysis. The results of this analysis are shown in Figure 6 .

Results of ROC analysis for the different sound descriptors registered in the road categories.

The results presented in Figure 6 show that the sensitivity of different sound descriptors is higher than 80% for all road categories (except the L n in Category 4), and even for the L den descriptor it is 100%. These high percentages are also obtained for the predictive value and therefore the percentages obtained in non-specificity are very low. They are lower than 5% in all sound descriptors.

These results differ from the previous method and it is therefore essential to compare the predictive capacity of both sampling methods. The results of this comparison are shown in the following section.

3.2. Predictive Capacity Analysis

In analysing the predictive capacity of the sampling methods, the sound values registered at the sampling points of the methods that were not being analysed were used.

To evaluate predictive capacity of the MTT road types, the sampling points chosen for the grid and categorisation method were used to compare the predictions of the MTT road types. All 104 sampling points evaluated in the grids and road categories could be associated with one of the road types (only one point was located in the highway road type, therefore, this road was not analysed). The sound values of these sampling points were compared with the mean value of the road type in which they were located and the prediction error was calculated using the difference between them (Equation (4)). The prediction error was analysed according to the road type where the control sampling point ( P ij ) was located. Table 4 shows the median from the error for the analysed sound descriptors.

Prediction errors (ε) of Ministry of Transport and Telecommunications road types for L d , L n and L den descriptors.

No.: Number; * Significant at p ≤ 0.05; ** Significant at p ≤ 0.01; n.s. Non-significant difference ( p > 0.05).

Prediction errors of MTT road types are mostly lower than the 3 dB considered as suitable for estimations on noise maps. However, according to the Wilcoxon signed-rank test, errors by underestimation in trunk and service road types have significant differences with respect to the null value (except for the L den descriptor in the service road type). These two road types, as noted above, showed no significant difference in the average sound values registered. This fact directly affects the predictive capacity of the method.

The predictive capacity of the categorisation method was then analysed. To this end, using a similar procedure to that described above, the sampling points employed for the grid method and MTT road types were used to compare with the predictions of the road categories. All 104 of the sampling points evaluated in the grids and road types could be associated with one of the road categories. The sound values of these sampling points were compared with the mean value of the road category in which they were located and the prediction error was calculated using the difference between them (Equation (5)). The prediction error was analysed according to the road category where the control sampling point was located ( P ij ). Table 5 shows the median from the error for the sound descriptors analysed.

Prediction errors (ε) of the categorisation method for L d , L n and L den descriptors.

No.: Number; n.s. Non-significant difference ( p > 0.05).

The prediction errors of the categorisation method are lower than 2 dB and have no significant differences with respect to the null value for all road categories and sound descriptors analysed (n.s.). These prediction errors are mostly lower compared with those of the MTT road types. However, to produce a detailed analysis of the differences in the estimation errors of the sampling methods, the median absolute errors of prediction were compared (|ε i |) using the Mann-Whitney test. The results are shown in Table 6 .

Absolute values of prediction errors (|ε|) for L d , L n and L den for road types and road categories and comparison to prediction errors of both methods (Categorisation and Ministry of Transport and Telecommunication (MTT)) using Mann-Whitney U test.

No.: Number; Sig.: Significance; * Significant at p ≤ 0.05; ** Significant at p ≤ 0.01; *** Significant at p ≤ 0.001; n.s. Non-significant difference ( p > 0.05).

To compare the predictive capacity of different sampling methods, the road type or road category where the control sampling point ( P ij ) was located was used as reference. Table 6 shows that the errors were higher for MTT road types for all sound descriptors analysed, regardless of road categories or road types taken as a reference. Taking the road category in which the control sampling point was placed as a reference, the error of L n descriptor showed no significant differences between both sampling methods in Category 3 and 4. Taking the road type where the control sampling point was placed as a reference, the errors of both sampling methods in the collector road type showed no significant differences for all sound descriptors. The error of the night level in trunk and service road types and the error of the day, afternoon and night level in the trunk road type revealed no significant differences. Indeed, the differences in errors of both sampling methods are reduced if road types are taken as a reference. However, it is important to keep in mind that this classification had problems of statistical differentiation.

3.3. Calculation of Exposure Level and the Percentage of Annoyance

In the previous section the predictive capacity of sound values was analysed according to the different sampling methods. A sampling method that presents significant uncertainties of prediction will directly influence the calculation of the exposed population. Therefore, the variation in the level of exposed population and the percentage of annoyance depending on the sampling method used were analysed. In this study, the categorisation and MTT road type methods were analysed.

Figure 7 shows the percentage of exposed population according to the L den descriptors registered in different road categories and road types. Depending on the selected method, the results of population exposed to noise can change significantly. According to the MTT road types method, of the populations that reside in the highway, trunk, service and collector road type areas, 10% are exposed to levels higher than 65 dB. These areas whose L den > 65 dB are referred to as black acoustic zones [ 60 ]. However, in the case of the categorisation method, 23% of the population resides in black acoustic zones. Likewise, if the level of noise exposure in the road type and in the road category where a higher percentage of population resides is compared, the local road type population is in an acoustic grey zone (55 ≤ L den ≤ 65), whereas in Category 5 the population is in a white acoustic zone ( L den < 55). Therefore, the differences in the capacity of sound prediction can clearly be misleading in the calculation of the percentage of exposed population.

Population exposed to noise in different road categories and road types.

Finally, we calculated the percentages of annoyed population and percentages of the population who are sleep disturbed by noise using both the MTT road types and the categorisation method. The results are shown in Figure 8 .

Percentages annoyance indicators (percentages of annoyed (% A ) and highly annoyed (% HA ) population; percentages of lowly sleep disturbed (% LSD ), sleep disturbed (% SD ) and highly sleep disturbed (% HSD ) population) obtained from the proposed equations [ 58 , 59 ] for road types and road categories.

The results show that different road types have percentages of annoyance and sleep disturbed by noise higher than those registered in the different road categories. Those road types that register higher noise levels, and therefore higher levels of noise annoyance, are those that had a higher level of sound prediction uncertainty. The trunk and service road type have similar percentages of annoyance to Categories 2 and 3. However, in previous analysis significant problems of differentiation between these two road types were found. Furthermore, the difference in the percentages of annoyance between the local road type and Category 5 should be noted, being those with lower noise levels. These differences were also detected in the analysis of sound exposure.

4. Discussion

The variability of sound values registered in the grid squares of Talca is quite high. This result indicates a low predictive capacity of the grid method to assess the noise exposure. If the interquartile range obtained in the cells is compared with that obtained in the local road type and in Category 5 (the road type and road category with the highest variability of noise levels), more than 50% and 75% of the grids register a greater value, respectively. Indeed, the grid size is quite high; however, as stated above, in this study has been considered relevant to use the same number of sampling points in each measurement method. Following the instructions of the ISO 1996-2 [ 42 ], if intermediate grid points would be added when the sound differences between adjacent grid points were higher than 5 dB, a new sampling would have carried out with a number of similar points. However, as shown in previous studies [ 33 ], the selection of new sampling points does not guarantee a difference between adjacent points lower than 5 dB. Consequently, this method was not used in order to compare the uncertainties between different sampling methods.

Regarding the MTT road types, the results show an overlap of interquartile range of the sound values registered in the trunk and service road types. Also, the local road type has a high sound variability. These results are similar to those obtained in other studies carried out in cities of Chile with legislative road classification [ 38 ]. Consequently, the ROC analysis indicates that this method has a low percentage of sensitivity and predictive capacity and a high percentage of non-specificity. Nevertheless, the sound values in the different road categories of the categorisation method have highly significant statistical differences. The road categories also have a high percentage of sensitivity and predictive capacity and a very low percentage of specificity.

The prediction errors of the categorisation method are lower than those of the MTT method for the different urban roads analysed. These differences in the prediction of sound values involve differences in the estimation of exposure levels and percentage of annoyance. According to the MTT method, 10% of the population is exposed to L den > 65 dB, whereas this is 23% of population according to the categorisation method. Also, as shown in Figure 8 , road types have percentages of annoyance and sleep disturbed by noise higher than those registered by road categories.

Finally, the exposed population and the percentage of annoyance obtained using the categorisation method were compared with the results obtained in other cities. Lee et al. [ 28 ] carried out a recent acoustic study in Seoul (S, Korea) and the percentage obtained from population that exceeds the level of 65 dB for the L d descriptor and the level of 55 dB for L n descriptor were compared with European cities. In Talca 11% of the population (Category 1, 2 and 3) is exposed to average levels at daytime that are higher than 65 dB and to average levels at night that are higher than 55 dB. For both time periods these percentages are higher than those obtained in the cities of Helsinki (Finland) and Berlin (Germany), and are similar to those obtained in cities such as Frankfurt (Germany). However, these percentages are lower than those obtained the cities of Seoul, Copenhagen (Denmark) and Madrid (Spain). In a further acoustic study recently carried out by Braubach et al. [ 15 ] in the cities of Basel (Switzerland), Rotterdam (The Netherlands) and Thessaloniki (Greece), limit values of 64 dB (annoyance by noise), 67.5 dB (major noise problem), and 65 dB (major noise problem) were found using the L den descriptor. The population of Talca residing in Category 1 to Category 4 is exposed to levels greater than 64 and 65 dB for the L den descriptor and for Category 1 to Category 3 the population is exposed to levels higher than 65.5 dB. Therefore, 23% and 14% of the population is exposed to values greater than 64–65 dB and 67.5 dB respectively. These percentages are much higher than those obtained in the cities of Basel, Rotterdam and Thessaloniki.

5. Conclusions

The selection of a suitable sampling method is essential to achieve an accurate assessment of the impact of noise pollution on the population. The grid, MTT road types and categorisation methods were analysed in the city of Talca (Chile). The major conclusions drawn from the results are as follows:

The grid squares have a high variability of sound values. This high variability leads to differences in sound values registered at adjacent points of more than 5 dB in 69%, 49% and 59% for L d , L n and L den descriptors, respectively.

The MTT road types have a low percentage of sensitivity and predictive capacity (except for the collector road type for L n and L den that has values above 80% of sensitivity and for the local road type for all the sound descriptors that has values above 80% of predictive capacity) and a high percentage of non-specificity (except for the local road type for all the sound descriptors that has values lower than 10%). This low discrimination and predictive capacity is caused, among other factors, by the lack of significant differentiation of sound values registered in trunk and service road types and by the high variability of the sound values of the local road type.

Average sound values in the different road categories of the categorisation method have highly significant statistical differences. The road categories also have a high percentage of sensitivity (>75%) and predictive capacity (>80%) and a very low percentage of specificity (<5%). Therefore, the functional stratification of noise levels observed in European cities that were studied previously is also found in Chilean cities. These results suggest a great advance in the validity of the categorisation method because of its application in a Chilean city.

The predictive capacity of the categorisation method is higher than that of the MTT method. This difference in the predictive capacity of sound values involves differences in the estimation of exposure levels and in the percentage of annoyance. Consequently, the categorisation method is more accurate than the MTT method to assess the impact of noise pollution on the population.

Talca is a city affected by noise pollution and also by its related problems of public health of its inhabitants. The percentages of population exposed to daytime and nighttime sound levels that are harmful to health are higher than those obtained in Helsinki and Berlin. Furthermore, the percentage of exposed population to L den > 64 dB is much higher than that obtained in the cities of Basel, Rotterdam and Thessaloniki.

Acknowledgments

This research was supported by the National Commission for Scientific and Technological Research (CONICYT) through the Nacional Fund for Scientific and Technological Development (FONDECYT) for research initiation No. 11140043. The authors thank the collaboration of Gonzalo B. Pacheco-Covili in the data collection for this study.

Author Contributions

Both authors contributed substantially to the conception of the study. Guillermo Rey Gozalo was responsible for the design of the study and the analysis of data in collaboration with Juan Miguel Barrigón Morillas. Interpretation of the results was discussed between both authors. Both authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.