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Physics Theses, Dissertations, and Masters Projects

Theses/dissertations from 2024 2024.

Experimental Studies Of Neutral Particles And The Isotope Effect In The Edge Of Tokamak Plasmas , Ryan Chaban

Extensions Of The Standard Model With Improved Ultraviolet Behavior , Mikayla Anderson

Transitions To Two-Hadron States From Quantum Chromodynamics , Felipe Ortega Gama

Theses/Dissertations from 2023 2023

Ab Initio Computations Of Structural Properties In Solids By Auxiliary Field Quantum Monte Carlo , Siyuan Chen

Constraining Of The Minerνa Medium Energy Neutrino Flux Using Neutrino-Electron Scattering , Luis Zazueta

From The Hubbard Model To Coulomb Interactions: Quantum Monte Carlo Computations In Strongly Correlated Systems , Zhi-Yu Xiao

Theses/Dissertations from 2022 2022

Broadband Infrared Microspectroscopy and Nanospectroscopy of Local Material Properties: Experiment and Modeling , Patrick McArdle

Edge Fueling And Neutral Density Studies Of The Alcator C-Mod Tokamak Using The Solps-Iter Code , Richard M. Reksoatmodjo

Electronic Transport In Topological Superconducting Heterostructures , Joseph Jude Cuozzo

Inclusive and Inelastic Scattering in Neutrino-Nucleus Interactions , Amy Filkins

Investigation Of Stripes, Spin Density Waves And Superconductivity In The Ground State Of The Two-Dimensional Hubbard Model , Hao Xu

Partial Wave Analysis Of Strange Mesons Decaying To K + Π − Π + In The Reaction Γp → K + Π + Π − Λ(1520) And The Commissioning Of The Gluex Dirc Detector , Andrew Hurley

Partial Wave Analysis of the ωπ− Final State Photoproduced at GlueX , Amy Schertz

Quantum Sensing For Low-Light Imaging , Savannah Cuozzo

Radiative Width of K*(892) from Lattice Quantum Chromodynamics , Archana Radhakrishnan

Theses/Dissertations from 2021 2021

AC & DC Zeeman Interferometric Sensing With Ultracold Trapped Atoms On A Chip , Shuangli Du

Calculation Of Gluon Pdf In The Nucleon Using Pseudo-Pdf Formalism With Wilson Flow Technique In LQCD , Md Tanjib Atique Khan

Dihadron Beam Spin Asymmetries On An Unpolarized Hydrogen Target With Clas12 , Timothy Barton Hayward

Excited J-- Resonances In Meson-Meson Scattering From Lattice Qcd , Christopher Johnson

Forward & Off-Forward Parton Distributions From Lattice Qcd , Colin Paul Egerer

Light-Matter Interactions In Quasi-Two-Dimensional Geometries , David James Lahneman

Proton Spin Structure from Simultaneous Monte Carlo Global QCD Analysis , Yiyu Zhou

Radiofrequency Ac Zeeman Trapping For Neutral Atoms , Andrew Peter Rotunno

Theses/Dissertations from 2020 2020

A First-Principles Study of the Nature of the Insulating Gap in VO2 , Christopher Hendriks

Competing And Cooperating Orders In The Three-Band Hubbard Model: A Comprehensive Quantum Monte Carlo And Generalized Hartree-Fock Study , Adam Chiciak

Development Of Quantum Information Tools Based On Multi-Photon Raman Processes In Rb Vapor , Nikunjkumar Prajapati

Experiments And Theory On Dynamical Hamiltononian Monodromy , Matthew Perry Nerem

Growth Engineering And Characterization Of Vanadium Dioxide Films For Ultraviolet Detection , Jason Andrew Creeden

Insulator To Metal Transition Dynamics Of Vanadium Dioxide Thin Films , Scott Madaras

Quantitative Analysis Of EKG And Blood Pressure Waveforms , Denise Erin McKaig

Study Of Scalar Extensions For Physics Beyond The Standard Model , Marco Antonio Merchand Medina

Theses/Dissertations from 2019 2019

Beyond the Standard Model: Flavor Symmetry, Nonperturbative Unification, Quantum Gravity, and Dark Matter , Shikha Chaurasia

Electronic Properties of Two-Dimensional Van Der Waals Systems , Yohanes Satrio Gani

Extraction and Parametrization of Isobaric Trinucleon Elastic Cross Sections and Form Factors , Scott Kevin Barcus

Interfacial Forces of 2D Materials at the Oil–Water Interface , William Winsor Dickinson

Scattering a Bose-Einstein Condensate Off a Modulated Barrier , Andrew James Pyle

Topics in Proton Structure: BSM Answers to its Radius Puzzle and Lattice Subtleties within its Momentum Distribution , Michael Chaim Freid

Theses/Dissertations from 2018 2018

A Measurement of Nuclear Effects in Deep Inelastic Scattering in Neutrino-Nucleus Interactions , Anne Norrick

Applications of Lattice Qcd to Hadronic Cp Violation , David Brantley

Charge Dynamics in the Metallic and Superconducting States of the Electron-Doped 122-Type Iron Arsenides , Zhen Xing

Dynamics of Systems With Hamiltonian Monodromy , Daniel Salmon

Exotic Phases in Attractive Fermions: Charge Order, Pairing, and Topological Signatures , Peter Rosenberg

Extensions of the Standard Model Higgs Sector , Richard Keith Thrasher

First Measurements of the Parity-Violating and Beam-Normal Single-Spin Asymmetries in Elastic Electron-Aluminum Scattering , Kurtis David Bartlett

Lattice Qcd for Neutrinoless Double Beta Decay: Short Range Operator Contributions , Henry Jose Monge Camacho

Probe of Electroweak Interference Effects in Non-Resonant Inelastic Electron-Proton Scattering , James Franklyn Dowd

Proton Spin Structure from Monte Carlo Global Qcd Analyses , Jacob Ethier

Searching for A Dark Photon in the Hps Experiment , Sebouh Jacob Paul

Theses/Dissertations from 2017 2017

A global normal form for two-dimensional mode conversion , David Gregory Johnston

Computational Methods of Lattice Boltzmann Mhd , Christopher Robert Flint

Computational Studies of Strongly Correlated Quantum Matter , Hao Shi

Determination of the Kinematics of the Qweak Experiment and Investigation of an Atomic Hydrogen Møller Polarimeter , Valerie Marie Gray

Disconnected Diagrams in Lattice Qcd , Arjun Singh Gambhir

Formulating Schwinger-Dyson Equations for Qed Propagators in Minkowski Space , Shaoyang Jia

Highly-Correlated Electron Behavior in Niobium and Niobium Compound Thin Films , Melissa R. Beebe

Infrared Spectroscopy and Nano-Imaging of La0.67Sr0.33Mno3 Films , Peng Xu

Investigation of Local Structures in Cation-Ordered Microwave Dielectric a Solid-State Nmr and First Principle Calculation Study , Rony Gustam Kalfarisi

Measurement of the Elastic Ep Cross Section at Q2 = 0.66, 1.10, 1.51 and 1.65 Gev2 , YANG WANG

Modeling The Gross-Pitaevskii Equation using The Quantum Lattice Gas Method , Armen M. Oganesov

Optical Control of Multi-Photon Coherent Interactions in Rubidium Atoms , Gleb Vladimirovich Romanov

Plasmonic Approaches and Photoemission: Ag-Based Photocathodes , Zhaozhu Li

Quantum and Classical Manifestation of Hamiltonian Monodromy , Chen Chen

Shining Light on The Phase Transitions of Vanadium Dioxide , Tyler J. Huffman

Superconducting Thin Films for The Enhancement of Superconducting Radio Frequency Accelerator Cavities , Matthew Burton

Theses/Dissertations from 2016 2016

Ac Zeeman Force with Ultracold Atoms , Charles Fancher

A Measurement of the Parity-Violating Asymmetry in Aluminum and its Contribution to A Measurement of the Proton's Weak Charge , Joshua Allen Magee

An improved measurement of the Muon Neutrino charged current Quasi-Elastic cross-section on Hydrocarbon at Minerva , Dun Zhang

Applications of High Energy Theory to Superconductivity and Cosmic Inflation , Zhen Wang

A Precision Measurement of the Weak Charge of Proton at Low Q^2: Kinematics and Tracking , Siyuan Yang

Compton Scattering Polarimetry for The Determination of the Proton’S Weak Charge Through Measurements of the Parity-Violating Asymmetry of 1H(E,e')P , Juan Carlos Cornejo

Disorder Effects in Dirac Heterostructures , Martin Alexander Rodriguez-Vega

Electron Neutrino Appearance in the Nova Experiment , Ji Liu

Experimental Apparatus for Quantum Pumping with a Bose-Einstein Condensate. , Megan K. Ivory

Investigating Proton Spin Structure: A Measurement of G_2^p at Low Q^2 , Melissa Ann Cummings

Neutrino Flux Prediction for The Numi Beamline , Leonidas Aliaga Soplin

Quantitative Analysis of Periodic Breathing and Very Long Apnea in Preterm Infants. , Mary A. Mohr

Resolution Limits of Time-of-Flight Mass Spectrometry with Pulsed Source , Guangzhi Qu

Solving Problems of the Standard Model through Scale Invariance, Dark Matter, Inflation and Flavor Symmetry , Raymundo Alberto Ramos

Study of Spatial Structure of Squeezed Vacuum Field , Mi Zhang

Study of Variations of the Dynamics of the Metal-Insulator Transition of Thin Films of Vanadium Dioxide with An Ultra-Fast Laser , Elizabeth Lee Radue

Thin Film Approaches to The Srf Cavity Problem: Fabrication and Characterization of Superconducting Thin Films , Douglas Beringer

Turbulent Particle Transport in H-Mode Plasmas on Diii-D , Xin Wang

Theses/Dissertations from 2015 2015

Ballistic atom pumps , Tommy Byrd

Determination of the Proton's Weak Charge via Parity Violating e-p Scattering. , Joshua Russell Hoskins

Electronic properties of chiral two-dimensional materials , Christopher Lawrence Charles Triola

Heavy flavor interactions and spectroscopy from lattice quantum chromodynamics , Zachary S. Brown

Some properties of meson excited states from lattice QCD , Ekaterina V. Mastropas

Sterile Neutrino Search with MINOS. , Alena V. Devan

Ultracold rubidium and potassium system for atom chip-based microwave and RF potentials , Austin R. Ziltz

Theses/Dissertations from 2014 2014

Enhancement of MS Signal Processing for Improved Cancer Biomarker Discovery , Qian Si

Whispering-gallery mode resonators for nonlinear and quantum optical applications , Matthew Thomas Simons

Theses/Dissertations from 2013 2013

Applications of Holographic Dualities , Dylan Judd Albrecht

A search for a new gauge boson , Eric Lyle Jensen

Experimental Generation and Manipulation of Quantum Squeezed Vacuum via Polarization Self-Rotation in Rb Vapor , Travis Scott Horrom

Low Energy Tests of the Standard Model , Benjamin Carl Rislow

Magnetic Order and Dimensional Crossover in Optical Lattices with Repulsive Interaction , Jie Xu

Multi-meson systems from Lattice Quantum Chromodynamics , Zhifeng Shi

Theses/Dissertations from 2012 2012

Dark matter in the heavens and at colliders: Models and constraints , Reinard Primulando

Measurement of Single and Double Spin Asymmetries in p(e, e' pi(+/-,0))X Semi-Inclusive Deep-Inelastic Scattering , Sucheta Shrikant Jawalkar

NMR study of paramagnetic nano-checkerboard superlattices , Christopher andrew Maher

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Waste Management, Waste Indicators and the Relationship with Sustainable Development Goals (SDGs): A Systematic Literature Review

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W&M Launches 5-Year Applied Research Pilot With $1M Gift

The applied research & innovation initiative will advance vision 2026 goals, promote multidisciplinary collaborations and increase experiential learning opportunities.

January 26, 2023 By Tina Eshleman

William & Mary is embarking on a five-year pilot initiative this spring that will support multidisciplinary applied research — with increased student internships and work experiences as a primary goal – in an effort to better solve pressing local, national and global problems. The initiative will encourage collaborations throughout the university and engage outside partners.

Made possible with a $1-million gift from former W&M Foundation Chair Michael Foradas ’78 and Valerie Foradas HON ’21, the Applied Research & Innovation Initiative (ARII) will be anchored by three core William & Mary units with proven success at working across disciplines: the Global Research Institute, the Institute for Integrative Conservation and the Office of Career Development & Professional Engagement.

The Innovation Initiative’s coordinators will seek proposals from across campus to develop or expand experiential learning projects that incorporate at least two of the four W&M strategic initiatives identified in the university’s Vision 2026 strategic plan — data, water, democracy and careers. Projects will address high-priority issues such as health care, education, economic resilience and biodiversity loss.  

“We are grateful for Mike and Valerie Foradas’ vision and partnership that spur investment in the preparation of our society’s next generation of leaders,” W&M President Katherine Rowe said. “The Innovation Initiative will build on William & Mary’s strong traditions of teaching and student-centered research as we work toward solutions to some of the world’s most urgent challenges. By expanding our reach across disciplines to form new collaborations, we will accelerate our ability to make a positive impact far beyond our campus.”

Plans for the Innovation Initiative include:

• The development of three projects that bring disparate researchers together, have the potential to generate positive change in the world and the ability to attract or increase external funding.
• Two years of funding for each project, supported by three faculty fellowships, three undergraduate fellowships and $25,000 annually for research spending.
• The creation of 50 student internships, funded through the initiative and other resources secured by the core partners.
• A collaborative working environment and technology for multidisciplinary research teams.
• A universitywide speaker series and visiting scholars in connection with ARII projects.

Michael and Valerie Foradas played a significant role in William & Mary’s For the Bold fundraising campaign as steering committee members and hosts of W&M Weekend in Chicago and are members of the James Blair Society, which recognizes donors who have contributed $1 million or more to the university. Their prior philanthropy at William & Mary focused mainly on scholarships. To support undergraduate students with financial need, they established the Peter and Diane Foradas Scholarship Endowment in 1993 in honor of Michael Foradas’ parents.

The couple said the Applied Research & Innovation Initiative combines their interests in broadening access to education and providing opportunities for students to gain meaningful work experience. The Innovation Initiative ties in well with the Vision 2026 goals of educating for impact and expanding William & Mary’s reach, Michael Foradas said.

“This pilot, if successful, will help put William & Mary on the map and help reach out outside the boundaries of the university,” he said. “That's always been, in my view, something that is the essential next step for us as a great global university.”

Valerie Foradas said an internship provided a pivotal experience in her own life. Financial circumstances had not allowed her to pursue higher education full time immediately after graduating from high school, and when she and Michael were married, she worked while he attended law school. Then when he graduated, she became a full-time student at Northwestern University at age 23. One of her professors there recommended her for an opening for a student researcher at the Art Institute of Chicago’s Department of Prints and Drawings.

“The opportunity to have that internship turned into a full-time job for me,” she said. “It’s hard to express my level of gratitude for having that kind of exposure. I still get emotional about it because it opened the door. Education does that.”

(Editor's note: The article "Gaining Experience" in our spring 2023 issue of the W&M Alumni Magazine includes news about the couple's gift creating the Applied Research & Innovation Initiative as well as a $1 million gift from Darpan Kapadia ’95 to the Career Center Student Internship Fund. Also see our online exclusive article " Funding for William & Mary Internships and Applied Learning Increases Tenfold With New Leadership Gift." )

William & Mary’s Institute for Integrative Conservation (IIC) already provides opportunities for students to work with partners within and outside the university. For one such project, Bibiana Mirones ’23 and other students traveled last summer to Milpa Alta, Mexico, to work with community and university partners there on evaluating and monitoring plant species to better manage natural areas . Mirones, a first-generation student whose family emigrated from Bolivia to the United States, is a double major in environmental policy and gender, sexuality and women’s studies who plans to become an environmental lawyer.

Another project, which involved developing a business model for sustainable oyster aquaculture , teamed biology and environmental science major Grace Dho ’23 with biology professor and IIC Faculty Director John Swaddle and Bay McLaughlin ’06, M. Acc. ‘07, founder of the First Landing Seafood Co. William & Mary faculty experts from the Alan B. Miller Entrepreneurship Center at the Raymond A. Mason School of Business and the Virginia Institute of Marine Science and W&M Law School’s Virginia Center for Coastal Policy also took part in the effort.

“The Innovation Initiative will offer mechanisms and support for multidisciplinary teams of faculty and students to deliver on W&M’s vision — to think boldly and act humanely to address the most pressing issues of our time,” Swaddle said. “It is extremely exciting that we can bring people together across schools, departments and programs to work on societally important applied research while also launching undergraduates on to a broad diversity of career tracks.”

IIC Executive Director Robert Rose said that in addition to the potential for addressing global challenges, the ARII pilot program will allow William & Mary to rethink and redesign how multidisciplinary research can be motivated, conducted and improved.

“Built into the pilot is a careful examination of the process of conducting research, and we intend to take an entrepreneurial approach to improving the research and use the outcomes to further improve the way global challenges are addressed at William & Mary,” Rose said.

Like the IIC, William & Mary’s Global Research Institute engages collaborators on campus and around the world to solve pressing global challenges. GRI’s AidData research lab, for example, is working with two external partners on a new project called GeoField that will broaden the use of satellite imagery for studying agriculture and climate resiliency in developing countries.

Across the Institute’s 10 labs, student researchers such as Jiaya Echevarria M.P.P. ’22 help advance research with real-world consequences.

“You really learn a lot about how the world works, and also how to make changes in the world,” said Echevarria, a former research assistant for AidData’s Research and Evaluation Unit, where she analyzed gender roles within Ghanaian households. “The younger you are, the more you want to see the world change, and knowing that you can use a lab to help change the world is really important.”

As a GRI Summer Fellow, Henry Young ’20 collaborated with fellow research assistants and Professor Philip Roessler, director of the institute’s Africa Research Center and Digital Inclusion & Governance Lab, to conduct an impact evaluation for an academy in Liberia that combines high-quality education with professional football training and courses in life skills and leadership.

“GRI provides students unparalleled real-world experience dealing with pressing challenges. It not only gives students a taste of what is to come after college but instills students with the skills, network and confidence they need to go out and pursue a career,” said Young, who went on to work as a senior research assistant at the Federal Reserve Board and is now a Ph.D. candidate in economics and public policy at the University of Michigan.

Global Research Institute Director Mike Tierney, ’87, M.A. ’88, P ’15, who is also the George & Mary Hylton Professor of Government, said he is energized by ARII’s potential to integrate faculty from other William & Mary departments and schools into projects with GRI and IIC.

“We each have our strengths that can complement each other,” he said. “When we bring students and faculty together from across the university, we enhance our ability to conduct research that matters in the world. As we widen our network of external partners, we also attract new resources to support this work. ”

The Applied Research & Innovation Initiative will also leverage existing corporate, professional and alumni networks cultivated by the Office of Career Development & Professional Engagement.

“The Innovation Initiative will expand paid internship programs where students work with W&M researchers and external partners on activities identified during the project selection process,” said W&M Chief Career Officer Kathleen Powell. “This aligns well with our Vision 2026 goal of ensuring applied learning opportunities that will prepare our students for success in the workplace. We anticipate that the impact of this initiative will extend beyond the five-year pilot and become a model for collaborations that impact career readiness for other students well into the future. ”

For information on how to contribute to the Applied Research & Innovation Initiative, visit give.wm.edu  or contact Suzie Armstrong ’93, assistant vice president for development for campus initiatives, at [email protected] or 757-221-7647.

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Artificial intelligence for waste management in smart cities: a review

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• Published: 09 May 2023
• Volume 21 , pages 1959–1989, ( 2023 )

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• Bingbing Fang 1   na1 ,
• Jiacheng Yu 1   na1 ,
• Zhonghao Chen 1 ,
• Ahmed I. Osman   ORCID: orcid.org/0000-0003-2788-7839 2 ,
• Mohamed Farghali 3 , 4   na1 ,
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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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Introduction

Rapid urbanization, population growth, and economic development have increased waste generated worldwide in recent years. According to the latest statistics, 2.01 billion tonnes of municipal solid waste was generated globally in 2016. This figure is expected to increase to 3.4 billion tonnes by 2050 (Kaza et al. 2018 ). Unfortunately, 33% of solid waste is managed correctly and disposed of in illegal dumpsites or unmonitored landfills (Kaza et al. 2018 ). Improper waste disposal poses many environmental and health risks, such as groundwater contamination, land degradation, increased cancer incidence, child mortality, and congenital disabilities (Triassi et al. 2015 ). In the past, waste management practices were more rudimentary, with a small group of individuals collecting garbage from the streets and depositing it in designated areas (Brancoli et al. 2020 ). Once the trucks were full, the waste was left in these designated areas. However, with the advent of artificial intelligence, the waste management industry is experiencing significant transformation toward achieving sustainability and profitability.

Artificial intelligence is a rapidly advancing technology that is gaining popularity in various industries, particularly waste management (Abdallah et al. 2020 ). The incorporation of artificial intelligence and robotics in the design and operation of urban waste treatment plants can revolutionize how solid waste is managed, leading to increased operational efficiency and more sustainable waste management practices (Goutam Mukherjee et al. 2021 ; Yigitcanlar and Cugurullo 2020 ). Several developed countries, including Austria, Germany, New Zealand, the USA, the UK, Japan, Singapore, Switzerland, South Korea, and Canada, have already begun to adopt artificial intelligence technologies to maximize resource utilization, efficiency, and recycling opportunities throughout the solid waste management cycle (Soni et al. 2019 ). Artificial intelligence technologies, particularly for sorting and treating solid waste, are increasingly critical in waste management (Andeobu et al. 2022 ; Wilts et al. 2021 ).

Therefore, artificial intelligence is critical in developing sustainable waste management models, particularly for transitioning to a “zero waste circular economy” while considering social, economic, and environmental factors (Osman et al. 2022 ). Waste management should be considered when examining the problems facing different geographic areas and economic sectors, including smart cities. For instance, researchers have proposed various models for sustainable waste management, such as a model for megacities that considers waste treatment, recycling, and reuse options (Liamputtong 2009 ). To choose the best location for solid waste management system components, another model was developed by researchers that take into account uncertain waste generation rates, facility running costs, transportation costs, and revenue (Chadegani et al. 2013 ). Based on the England panel data, Liu et al. ( 2017 ) investigated data on landfill, waste management, and environmental safety in England, including the reasons for illegal dumping (Goutam Mukherjee et al. 2021 ; Yigitcanlar and Cugurullo 2020 ). In addition, Zhang et al. ( 2019 ) emphasized the need for a new school of management thought to transition to a “zero waste circular economy.”

To summarize, this paper provides an overview of waste types, their generation, and associated issues, as well as explores various applications of artificial intelligence in waste management. These applications include intelligent bin systems, waste-sorting robots, sensor-based waste monitoring, and predictive models of waste generation. Additionally, the paper discusses how artificial intelligence can help monitor and track waste materials throughout the recycling process, optimize the logistics and transportation of recycled waste, identify and reduce illegal dumping and waste treatment practices, and analyze the chemical composition of waste. One of the unique contributions of this paper is the combination of artificial intelligence and waste chemical analysis to improve the process of converting waste into energy. Figure  1 shows the key concepts of artificial intelligence in waste management and the content of this article.

Application of artificial intelligence in waste management. The figure illustrates five key aspects: waste type and generation, the use of artificial intelligence in waste management, artificial intelligence-based optimization of waste transportation, the role of artificial intelligence in detecting and reducing illegal dumping and waste treatment practices, and the use of artificial intelligence to analyze the chemical composition of waste. This optimized representation provides a clear and concise overview of the main themes discussed in this review, highlighting the potential of artificial intelligence to revolutionize waste management practices

Waste types and production

Waste is a major environmental issue due to its potential to contaminate air, water, and soil. Its generation is mainly attributed to human activities such as industrial production, construction, agricultural activities, pollution emissions, consumption, and waste disposal (Chen et al. 2023 ; Ukaogo et al. 2020 ). This can lead to environmental pollution, health risks, economic losses, and the loss of resources and costs associated with waste management. To address these issues, governments and organizations have implemented waste management strategies such as recycling, composting, reusing, using renewable energy, and adopting green technologies. Moreover, public education and awareness campaigns can help reduce the amount of waste generated and encourage individuals to make more sustainable choices (Osman et al. 2023 ).

Waste classification principles vary and may involve categorization based on the waste’s material, state, or source (Peng et al. 2023 ). Based on the waste sources, research has demonstrated that industrial waste is the primary source of waste (Gaur et al. 2020 ). This waste is mainly composed of volatile compounds, wastewater, slag, and scrap generated during industrial production, which contain many hazardous substances, such as heavy metals, organic pollutants, and radioactive materials, leading to severe environmental pollution (Patel et al. 2022 ; Tawfik et al. 2022 ). Additionally, organic waste is generated by agricultural, animal, and wastewater treatment waste and the food industry. This type of waste can be used for value-added purposes, composting, or sent to landfills (Ren et al. 2018 ). Organic waste from natural environments may also be hazardous, as it contains toxic substances, such as ammonia and chlorine, which can cause air, water, and soil pollution.

According to the waste states, waste can be divided into solid waste (Jha et al. 2022 ), hazardous waste (Jha et al. 2022 ), liquid waste (Mekonnen 2012 ), organic waste (Sharma et al. 2019 ), and recyclable waste (Ren et al. 2018 ). Solid waste is mainly generated from human activities such as manufacturing, agriculture, and mining, and the treatment methods include recycling, incineration, and landfill (Bhatt et al. 2018 ). Solid waste types include construction, household, industrial, hazardous, electronic, medical, and agricultural waste (Peng et al. 2023 ; Vyas et al. 2022 ). Hazardous waste contains toxic, flammable, combustible, radioactive, and corrosive waste, mostly from electronic and biomedical waste (Akpan and Olukanni 2020 ). Hexavalent chromium liquid, mercury liquid waste, corrosive and alkaline liquid waste, cyanide liquid waste, and heavy metal liquid waste are all examples of liquid waste. In particular, corrosive and alkaline liquid waste makes up 12.4%, organic liquid waste 32.2%, and heavy metal liquid waste 47.9% (Ho and Chen 2018 ). If appropriate control measures are not taken, most liquid waste is hazardous industrial waste, which can significantly negatively affect the environment and public health (Tong and Elimelech 2016 ). Recyclable waste is refuse that can be removed from the waste stream and used as a raw material to create new products like paper, glass bottles, and ceramics (Fenta 2017 ). Some refuse recycling techniques include biological re-treatment, energy recovery, and physical re-treatment (Waheeg et al. 2022 ).

To summarize, waste types mainly include solid waste, hazardous waste, liquid waste, organic waste, and recyclable waste. The main sources are individuals, industry, agriculture, and transportation. Treatment methods include recollection, incineration, landfill, biological, and pyrolysis.

Artificial intelligence in waste management

The utilization of artificial intelligence has the potential to bring about a revolution in municipal waste management by enhancing the effectiveness of waste collection, processing, and classification. Artificial intelligence-based technologies like intelligent garbage bins, classification robots, predictive models, and wireless detection enable the monitoring of waste bins, predict waste collection, and optimize the performance of waste processing facilities. The details are shown in Table 1 . By leveraging artificial intelligence, municipalities bin reduce costs, improve safety, and reduce environmental impacts associated with waste management.

Smart bin systems

Conventional garbage bins solely collect waste, and sanitation workers must carry out manual inspections to assess the trash level in the bins. This approach is not efficient for routine waste disposal inspections. Moreover, due to the frequent filling of the containers, disease-causing organisms and insects tend to breed on them (Noiki et al. 2021 ). Therefore, designing intelligent garbage bin monitoring systems to manage garbage is essential in constructing smart cities.

Numerous research studies on intelligent garbage bins have focused on two key functions: automatic waste classification and monitoring. These studies offer a potential solution for cities to achieve an effective garbage collection system. An intelligent garbage bin can be created by utilizing a system on a chip produced by the Espressif systems (ESP 8266) module, automatically detecting objects and setting thresholds within the bin. The information gathered can then be transmitted to another node for further analysis and processing (Praveen et al. 2020b ). For example, Praveen et al. ( 2020a ) designed a garbage bin with two main pins: the trigger pin connected to the sensor and the echo pin. An ultrasonic sensor is placed at the top and bottom of the cover. Rajathi G et al. ( 2020 ) designed a robot garbage bin with two sensors installed at the bottom, which moves along a straight line. An obstacle sensor is embedded on one side of it, which can sense black and emit a buzzer sound to indicate that the garbage has stopped storing for some time. In addition, an ultrasonic sensor can be placed at the bin’s edge to detect the waste level (Mbom et al. 2022 ). The status of the container will be updated on the web page via the wireless fidelity module, showing whether it is full or empty. Some researchers design bins that separate and monitor garbage using Arduino and wireless fidelity (Samann 2017 ). It has an automatic metal and non-metal separator. Using NodeMCU, the bin’s water level can be monitored in real time and sent to the cloud for further analysis and processing (Saranya et al. 2020 ).

In summary, the research on smart garbage bins mainly focuses on automatically monitoring the garbage filling level and notifying users in time. The information is primarily received by sensors and transmitted through the network. Intelligent bin systems can potentially increase the efficiency of garbage collection, reduce the spread of diseases, and enhance the city’s overall environment. However, the cost of implementing smart garbage bins is relatively high, making it challenging to promote them widely. To address this issue, the government could consider funding policies to reduce the cost of smart garbage bins, making them more accessible to the general public. Furthermore, the regular operation of these bins can be affected by environmental factors such as temperature and humidity. Thus, dedicated personnel must regularly check and maintain the garbage bins. Therefore, it is crucial to focus on developing and promoting smart garbage bins in the future.

Waste-sorting robots

Garbage classification is strongly recommended for municipal solid waste management, and using robots can substantially enhance the efficiency of garbage classification. However, robots require advanced visual and operational skills to function in highly heterogeneous, complex, and unpredictable industrial environments for garbage classification (Koskinopoulou et al. 2021 ). Recent research has focused on improving the accuracy and efficiency of garbage classification robots, which requires the development of better sensors and cameras to identify different types of waste, as well as improved artificial intelligence algorithms for classifying waste. Utilizing hyperspectral images to locate the target region of interest is a promising approach (Xiao et al. 2020 ). Based on previous research, robots can cope with complex field conditions by adding simultaneous localization and mapping technology and instance segmentation methods. They can automatically collect construction and demolition waste (Wang et al. 2020 ). Deep learning technology, such as instance segmentation, can accurately detect the contours of all objects in an image, including construction and demolition waste (Chen et al. 2022b ). Given the complexity of construction sites and the large amounts of construction waste generated, manual collection and classification are often inefficient and pose safety risks. Consequently, the recovery of construction waste has become a research focus. (Yang et al. 2023b ).

Researchers are currently investigating methods of integrating waste-sorting robots into existing waste management systems, such as utilizing robots to sort waste before it is sent to landfills. In this regard, studies have suggested a parallel robot model, with the primary concept revolving around a gripper that is fully integrated into a 4-degree-of-freedom similar robot structure (Leveziel et al. 2022 ). Researchers are also exploring using visual sensors to improve the performance of waste-sorting robots. For instance, a waste-sorting robot has been developed using deep learning techniques and optical sensors that can accurately identify and classify different types of waste (Mao et al. 2022 ).

In conclusion, trash-sorting robots have the potential to significantly increase waste management effectiveness, decrease labor expenses, and boost refuse classification precision. However, some argue that waste-sorting robots are impractical due to their high installation and maintenance costs compared to traditional waste-sorting methods. Nonetheless, researchers are exploring more affordable ways of creating waste-sorting robots, such as utilizing less expensive materials or designing robots operating in diverse settings. Additionally, efforts are being made to improve the robot’s structure, sensors, waste classification algorithms, and robotic arms to make them more effective and efficient. Waste-sorting machines will continue to be of great interest and play a significant part in real-world uses in the future.

Sensor-based waste monitoring

Sensor-based waste monitoring is a technology that utilizes sensors to track the amount of waste generated, identify the sources of waste, and measure the effectiveness of waste management strategies in a specific area. Wireless sensor network is a network composed of many self-organized wireless sensors installed in the network to monitor the physical or environmental parameters of the system (Gurram et al. 2022 ). As illustrated in Fig.  2 , a typical wireless sensor network architecture for solid waste treatment systems includes various sensors, such as temperature, humidity, odor, infrared, gas, and sound sensors. Increase waste management efficacy.

Uses of artificial intelligence in the garbage bin and waste robotic sorting. These include real-time garbage bin monitoring to optimize waste collection routes and prevent bin overflows. Additionally, intelligent garbage sorting can improve recycling efficiency and reduce contamination. In contrast, robotic waste sorting can utilize robotic arms to sort waste in recycling facilities, increasing the speed and accuracy of sorting while reducing the need for manual labor. Artificial intelligence can also be used for predictive maintenance to anticipate when waste-sorting equipment will require maintenance, reducing downtime and extending equipment lifespans. Lastly, waste management optimization using artificial intelligence can consider factors such as traffic, weather, and population density to enhance the efficiency of waste collection and processing

These sensors can be used to monitor parameters in real time, thus better controlling the waste treatment process. For instance, an electronic nose can be developed using sensors to quantify odor concentration in real time to help treat wastewater (Burgués et al. 2021 ). Additionally, Sivaprakasam et al. ( 2020 ) proposed using a non-contact microwave sensor for in situ process monitoring of nuclear waste glass melts in cold crucible induction melting furnaces. In addition, they designed infrared sensors to determine the filling level of the carriages, gas sensors to detect hazardous gases, temperature, humidity sensors, and sound sensors to monitor noise pollution.

An intelligent garbage bin monitoring system based on a Zigbee network structure was suggested by Karthikeyan et al. ( 2017 ), in which the terminal nodes installed on the garbage bins detect the level of unfilled. Raaju et al. ( 2019 ) suggested using a solar energy collection device to power the wireless sensor network to increase the nodes’ lifespan. The major drawback of this system is its inability to display real-time filling levels of the garbage bin. Jino Ramson et al. ( 2017 ) suggested a solar-powered wireless monitoring unit with a sensor to measure the level of the can when it is empty and transmits the information to solar-powered wireless monitoring unit to address this problem. A self-powered, direct-connection wireless sensor network solid waste management system will be created by sending the data collected from numerous sensor nodes to the central monitoring station for analysis and visualization (Ramson et al. 2021 ). Additionally, a progressive bar is created in the graphical user interface to symbolize the garbage bin’s dynamic unfilled level.

To summarize, the development of wireless sensor networks has been rapid, and there have been numerous studies on the application of sensors in waste monitoring, mainly through monitoring the level of garbage and then utilizing the network to notify users (Joshi et al. 2022 ). Many studies have reported applying different machine learning-based methods in waste management to predict and optimize municipal solid waste generation, detection, collection, classification, and properties. The mechanism is shown in Fig.  3 .

A typical wireless sensor network structure for a solid waste management system. A sensor is installed on the garbage bin. When garbage enters, the sensor can obtain information such as smell, weight, and humidity to classify the trash. At the same time, it can detect the environment of garbage bins and monitor the filling level of garbage bins. Users can monitor the status of garbage bins on the platform in real time as the information is uploaded through the internet

Models to predict waste generation

Research on waste generation prediction models has recently gained increasing attention, and various models have been proposed to predict better the amount of waste generated (Kolekar et al. 2016 ). These models include statistical, machine learning, deep learning, and fuzzy models. Artificial intelligence algorithms are considered the most advanced models for reliable waste generation prediction, as they possess unique capabilities (e.g., data input, learning, and prediction) (Coskuner et al. 2020 ).

Artificial neural networks are one of the nonlinear models widely used for modeling various urban waste management processes due to their robustness, fault tolerance, and ability to describe the complex relationships between variables in multi-variable systems (Cha et al. 2020 ). Machine learning algorithms such as artificial neural networks multilayer perceptron, support vector regression algorithms, linear regression algorithms, decision tree algorithms, and genetic algorithms can be used to develop models with better predictive performance on small datasets composed mainly of categorical variables (Cha et al. 2022 , 2017 ; Golbaz et al. 2019 ). For cities with historical records, past historical data can be referred to and integrated with multiple datasets to establish a gradient boosting regression model. For example, Johnson et al. ( 2017 ) developed a short-term prediction model for garbage generation in New York, achieving an average accuracy of 88%. To analyze urban waste prediction models in cities with limited historical data, a method that includes all predictive factors can be utilized to evaluate regional differences and their influence on waste prediction. By assessing the model’s dependence on predictive factors, the impact of regional differences on urban waste prediction can be analyzed (Wu et al. 2020 ).

In conclusion, due to the rapid development of artificial intelligence, it has been widely used in waste generation prediction models. Artificial intelligence systems commonly used in waste management include artificial neural networks, support vector regression, linear regression, decision trees, and genetic algorithms. Among these, artificial neural networks have been widely implemented in waste generation prediction applications, followed by support vector machines.

Monitoring and tracking waste materials

Artificial intelligence technologies can be used to facilitate more efficient and effective waste classification and recycling. Machine learning techniques can be employed to identify the type of waste, such as plastics, metals, paper, and other materials, for more accurate and efficient recycling (Chen 2022 ). Artificial intelligence-based systems can also monitor the recycling process for anomalies, such as incorrect material classification or material contamination, and alert the relevant personnel to take corrective measures.

Furthermore, artificial intelligence can optimize the recycling process by analyzing the data from the recycling process and suggesting improvements (Pouyanfar et al. 2022 ). Additionally, artificial intelligence can be essential in measuring and tracking waste (Ponis et al. 2023 ). This can help to ensure that materials are recycled most efficiently and effectively.

Artificial intelligence can significantly improve the efficiency of environmental pollution information acquisition (Liu et al. 2021b ). With the development of big data technology, the application of artificial intelligence can quickly improve the efficiency of information acquisition. Artificial intelligence has powerful perception capabilities, which can more effectively identify the source of environmental information and make basic judgments on the current environmental situation. For example, artificial intelligence can identify the location and size of noise pollution sources through sound recognition and present the noise situation in the area through spectrum analysis, so decision-makers can intuitively understand the noise distribution (Pan et al. 2018 ). Moreover, artificial intelligence can also achieve good environmental management (Zhu et al. 2022 ). By installing intelligent terminals in each garbage bin, various state information can be obtained in real time and further analyzed and processed. For example, it can detect whether the garbage bin is complete and use gas sensors to classify it into recyclable and non-recyclable categories (Rabano et al. 2018 ). This can reduce labor costs and improve classification efficiency, thus achieving good environmental management.

The utilization of artificial intelligence in waste recycling has gained widespread application. This includes optimizing waste collection truck routes, identifying waste management facilities, simulating the waste transformation process, and integrating various technologies such as radio frequency identification (Zhou and Piramuthu 2013 ), a global positioning system (Hidalgo-Crespo et al. 2022 ), and geographic information system (Zewdie and Yeshanew 2023 ) to monitor solid waste collection trucks and containers. Moreover, machine learning and image processing techniques have been combined with these technologies to automatically detect the level of containers (Vitorino de Souza Melaré et al. 2017 ). However, further research is still needed to explore integrating remote sensing technologies and artificial intelligence. On the one hand, it is necessary to use remote sensing technologies to quickly update the data of domestic waste and realize the dynamic planning of urban household waste management with a business system that can be continuously updated and run for a long time. In addition, it is crucial to further incorporate the expertise accumulated by specialists in this field into the system to achieve intelligent decision support for garbage management.

In summary, artificial intelligence’s rapid advancement has enabled its waste monitoring application to become a new research direction. Developing artificial intelligence platforms for waste monitoring is a highly sought-after research topic. The artificial intelligence platform receives data collected by monitors and sensors, which is then transmitted to the artificial intelligence server. The data train, optimize, and predict on the platform, generating intelligent waste management prediction models. These models can improve the quality and efficiency of pollution tracing and environmental problem-solving, providing effective solutions to environmental challenges.

Chemical analysis of waste using artificial intelligence

Recent applications of machine learning have attracted considerable interest in areas including waste-to-energy conversion (Ahmad et al. 2015 ), biochar for metal and organic compound adsorption (Ascher et al. 2022 ; Dubdub and Al-Yaari 2021 ), municipal solid waste treatment (Goutam Mukherjee et al. 2021 ), and the oxidation of micropollutants (Ascher et al. 2022 ). Table 2 summarizes the latest applications of artificial intelligence in waste chemistry.

Prediction of pyrolysis conditions for plastic recycling

There is currently insufficient capability in the world’s waste management systems to securely dispose of or recycle all waste plastics, which inevitably increases the number of waste plastics dumped into the environment (Osman et al. 2023 ). Each year, the seas are thought to receive 8 million tonnes of microplastics and 1.5 million tonnes of main microplastics (Lau et al. 2020 ). For billions of years, waste polymers can deteriorate in the environment. Due to ineffective pre- and/or post-user management and widespread landfilling of refuse plastics, pyrolysis as a conversion technique can overcome severe ecological and environmental obstacles. Machine learning methods can be used to forecast the continuous and non-catalytic process products of refuse plastic pyrolysis.

Through data ingestion and experience, machine learning can instantly improve to correlate inputs to matching reactions and comprehend the mathematical relationships between intricate processes and algorithms (Ascher et al. 2022 ). Several algorithms have been recently reported to simulate pyrolysis or gasification processes. These include artificial neural networks, tree-based algorithms such as decision trees and random forests, and support vector machines (Mutlu and Yucel 2018 ; Yaka et al. 2022 ). Artificial neural network methods are the most popular machine learning techniques, for example, in the context of pyrolysis simulation by using tar, coke, and permanent gas interactions to model biomass gasification. Artificial neural network methods outperform realistic gas balance models that predict gasification products (Cheng et al. 2020 ). Tree-based methods and support vector machines have been successfully implemented in waste management. Cheng et al. ( 2020 ) proposed combining random forest-based predictive models with life cycle assessment and economic analysis to evaluate different pyrolysis feedstocks comprehensively. In addition, support vector machines have been widely used in pyrolysis prediction tasks. For example, support vector machines perform better than artificial neural networks at R 2 , and root means a squared error in predicting pyrolysis biochar yield (Cao et al. 2016 ).

The input factors of particle size (millimeters) allow for the classification of the composition of various plastic types, such as polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate, in the research (Osman et al. 2023 ). Additionally, the factors included the ashless chemical components of carbon, hydrogen, oxygen, nitrogen, and chlorine. Sulfur was not chosen, however, because it is typically insignificant compared to the elements mentioned above. As an additional reactor, working factors, feed capacity (kilograms/hour), pyrolysis temperature (Celsius), and steam residence time (seconds) were taken into account. Since they were only mentioned in a few carefully chosen sources, heating and carrier gas flow rates were not chosen as input factors. Where ranges were reported in the authorities, the mean values of the input variables were used in the study (Cheng et al. 2023 ).

In the hydrothermal gasification of waste biomass, recent research suggested a machine learning model for filtering and choosing catalysts. The writers used principal component analysis and split the dataset into three subcategories: non-catalysts, alkali metal catalysts, and transition metal catalysts (Li et al. 2021a ). The created model yielded encouraging results when selecting and screening catalysts to maximize hydrogen production and decrease carbon dioxide production during hydrothermal gasification of discarded biomass. Comparable research used machine learning and a technology decision support system to optimize heterogeneous catalyst input during hydrothermal gasification (Gopirajan et al. 2021 ). The model demonstrated that catalyst addition had a favorable impact on hydrogen generation.

Artificial neural network techniques employing Levenberg–Marquardt and Bayesian regularization algorithms were utilized to analyze the environmental effects of waste biomass hydrothermal gasification with sodium hydroxide as a catalyst. Fózer et al. ( 2021 ) showed that a machine learning-based model could optimize and predict catalyst composition with a variance value of 0.965. Using sodium catalysts also increases the process’s ability to cause global warming. Future studies should focus on the effectiveness of catalysts in hydrothermal gasification processes and their effects on the atmosphere.

In conclusion, machine learning methods are often considered “black boxes,” making it challenging to apply them to study pyrolysis mechanisms and pathways comprehensively. Therefore, future research should integrate machine learning models with traditional modeling methods, such as kinetic studies, to provide more comprehensive information on reaction processes and routes. Some authors have suggested using feature alignment to evaluate the behavior and applicability of various input factors (Ascher et al. 2022 ). Additionally, future studies should clarify the opaque nature of machine learning algorithms to make them more accessible and facilitate the quick learning of the relationships between input and goal variables. To create more complete models, integrated predictive models should be developed to forecast critical aspects of relevant variables.

Identifying modern and fossil carbon

Accurately measuring carbon emissions is essential to distinguish between the number of carbon sources in solid waste, including biogenic and fossil carbon. To this end, the three groups of carbon sources, namely the “biogenic carbon group,” “fossil carbon group,” and “inert carbon group,” are commonly used. Carbon dioxide outputs from paper, food refuse, and timber by the Bureau of Coast and Geodetic Survey are typically considered carbon neutral. However, carbon dioxide emissions from the Freight Classification Guide System are linked to climate change (Schwarzböck et al. 2018 ). Therefore, the findings may be overestimated if biomass-derived carbon emissions are not considered when calculating greenhouse gas emissions from solid refuse incineration. Hence, the Freight Classification Guide System and the Bureau of Coast and Geodetic Survey shares are crucial markers to evaluate how well solid refuse burning reduces carbon emissions. Machine learning, a tool for data extraction that identifies patterns, has shown promise in resolving challenging environmental issues (He et al. 2021 ). Machine learning algorithms such as random forests and support vector machines have become powerful tools for identifying hidden relationships that can predict the characteristics of different solid waste groups using established datasets and literature data. However, there are three main reasons why these methods may not be widely adopted: (1) limited data size and quality can hamper performance (Li et al. 2021b ); (2) poor interpretability can make it challenging to extract relevant information (Visser et al. 2022 ); and (3) heavy computation requirements can result in extended processing times (Yan et al. 2021 ).

Moreover, many studies have focused on only one or, at most, two types of models, providing limited data for comparing the prediction accuracy of various models based on the same waste dataset (Wang et al. 2021 ). In the case of separately weighted garbage, the reduced total reflection infrared spectra to determine the mass-based amounts of biogenic and fossil carbon, Fourier-transform infrared, can be used with a machine learning method. This information can then be used to determine how solid refuse burning will affect the reduction of carbon emissions. This can save a lot of labor and chemicals while producing quick and precise results. From this viewing point, this strategy has much to offer regarding environmental and commercial fiscal benefits. By separating contaminants like plastics and stones from feedstock, compost, and solid digestate, the machine learning algorithm can be used in bioprocessing to lower possible environmental risks and enhance the profitability of composting and anaerobic digestion products (Guo et al. 2021 ).

In summary, accurately measuring carbon emissions from solid waste requires identifying the sources of biogenic and fossil carbon. Machine learning algorithms such as random forests and support vector machines can be used to extract data to identify carbon sources. Furthermore, combining Fourier-transform infrared spectroscopy with machine learning methods can determine the quantity of biogenic and fossil carbon. This approach can save resources and produce fast and accurate results.

Waste to energy

Biogas for electricity generation is a renewable energy source that takes its input from organic waste produced by humans and animals and is part of a circular economy (Farghali et al. 2022 ; Salguero-Puerta et al. 2019 ). According to the International Energy Agency, this century will see a two- to threefold rise in energy consumption, using up many resources. According to data from 2020, the global energy supply was 584,523,552 exajoules. Of this supply, 29.47% was derived from oil, 26.80% from coal, 23.68% from natural gas, 9.84% from biofuels, and 5.21% from renewable sources (Farghali et al. 2023 ). Encouraging renewable energy to generate power is one of the primary growth areas. Among the various methods of energy production, biogas power generation is considered one of the most energy-efficient and ecologically favorable bioenergy production technologies. Biogas resources have high utilization rates, especially in the circular economy. For example, in Europe, biogas energy production has exceeded 6 million tonnes of oil, with a yearly growth rate of over 20% (Chiu et al. 2022 ). However, producing biogas needs complicated inputs and slow reaction times. Although researchers have recently begun using predictive models to analyze the input factors for biogas production, key factors have not been adequately analyzed, resulting in inconsistent output. To maximize biogas output, machine learning was incorporated to analyze and identify the key variables that significantly influence results, obviating the need for intricate computations and lowering the risk of mistakes (Farghali et al. 2023 ; Pence et al. 2023 ).

Currently, there are limited approaches that combine deep learning, machine learning, and neural networks for producing methane, as illustrated in Fig.  4 . Some ongoing research is focused on developing a deep learning model for predicting faults in internal combustion engine power production, which can aid biogas plants in adjusting their maintenance and troubleshooting plans (Liu and Karimi 2020 ). Additionally, several studies have worked to develop an artificial neural networks model to forecast the optimum biogas output using particular sources like sugarcane bagasse and bovine manure (Ghatak and Ghatak 2018 ) and anaerobic co-digestion of palm mill wastewater (Zaied et al. 2023 ). Biogas generation studies and study findings use deep learning to analyze important waste and output forecasts compared to other renewable energy sources. Studies have looked into and made predictions about refuse gathering (Huang and Koroteev 2021 ) and some on output prediction, but waste inputs and outputs are rarely combined. Previous research focused on predictive models using machine learning techniques like artificial neural networks, k -nearest neighbors, and support vector machines. However, the effect of time on capability has not been considered. To make predictions, this research uses a time series model. According to a literature study, extended short-term memory deep neural networks can handle numerous variables, which makes them helpful for resolving issues with time series forecasting (Bouktif et al. 2018 ).

A neural network for predicting biogas volume using four input attributes. The diagram has eight hidden layers and one output layer to induce biogas prediction. It consists of three layers: input, hidden, and output

In conclusion, methane gas power generation is a renewable energy source whose input comes from organic waste produced by humans and animals and is part of the circular economy. Machine learning and deep learning models are used to analyze and identify key variables that significantly affect methane output. Extended short-term memory deep neural networks are used to predict waste input and output.

Logistics, transportation and recycling

Logistics and transportation of refuse are essential components of waste management. Moreover, the waste’s logistics and transportation system is a critical hub connecting the waste source and treatment (Xia et al. 2022 ). However, the current waste logistics and transportation systems suffer from several shortcomings. Firstly, the costs associated with waste logistics and transport are prohibitively high, particularly during the collection phase. According to Sulemana et al. ( 2018 ), transportation costs incurred during waste collection account for approximately 70–80% of the total waste management costs. Secondly, personnel constraints lead to inefficiencies, such as disorganized collection plans and inadequate vehicles (Andeobu et al. 2022 ). Therefore, solutions based on artificial intelligence have been developed and implemented to optimize waste logistics and transportation processes (Abdallah et al. 2020 ). This involves optimizing waste transportation and logistics from four perspectives: transportation distance, transportation cost, transportation time, and efficiency.

Akdaş et al. ( 2021 ) introduced a method for vehicle routing using an ant colony optimization algorithm. First, collect 110 points of a particular city in Turkey. Then, convert the coordinates of the points and import them into the database. Visualize these points on a map and create a distance matrix. Finally, the ant colony optimization algorithm determines the shortest route on the distance matrix. Researchers found that the 10th iteration of the ant colony optimization algorithm can reduce the transportation distance by 13% to reach the optimal solution. In addition, other studies have shown that using Dijkstra and Tabu search algorithms can also reduce the distance of waste transportation (Rızvanoğlu et al. 2019 ). First, it used the Dijkstra algorithm to calculate the shortest distance between two coordinates. The Tabu search algorithm is to determine the fastest path between two coordinates. In the subsequent experiment, there were 200 waste collection points in a certain turkey area, 16,106 m. The two cases of transportation distance and 80 waste collection points, 5497 m transportation distance, are optimized. Rızvanoğlu et al. ( 2019 ) confirmed that Dijkstra and Tabu search algorithms could reduce transportation distance by 28%. In general, upon implementing the ant colony optimization algorithm, waste transport distances were reduced by 13% on average. Additionally, utilizing the Dijkstra-Tabu search algorithm reduced the waste transport distance by 28%. 

Babaee Tirkolaee et al. ( 2019 ) presented the simulated annealing algorithm for generating initial values based on a random algorithm. The simulated annealing algorithm is used for optimization based on obtaining the initial value. An area in Iran with 330 square kilometers and 43 recycling nodes is optimized using the simulated annealing algorithm (Babaee Tirkolaee et al. 2019 ). The simulated annealing algorithm reduced the total cost by 13.3%.

Amal et al. ( 2018 ) introduced genetic algorithms for optimizing vehicle routing. First, use a geographic information system to get the solution, including the vehicle’s route, subsequently utilizing the genetic algorithm to optimize the vehicle’s route. Finally, use ArcGIS and Python scripts to represent the optimal solution. Later, in an experiment in a city in Tunisia, after ten iterations of the genetic algorithm, the running time was reduced from 15.2 to 10.91 h. After optimizing the genetic algorithm, the running time of the vehicle was reduced by 28.22% (Amal et al. 2018 ). In addition, parallel annealing algorithms are also used to optimize vehicle collection paths (Zhang et al. 2020 ). Using the parallel annealing algorithm to optimize the waste collection path in Xuanwu District, Beijing, Zhang et al. ( 2020 ) found that the optimized scheme of the parallel annealing algorithm can reduce the time by 12% compared with the original scheme. However, this research also has certain limitations. For example, the collection of vehicle speed is a fixed value, which is impossible. Overall, the genetic and parallel annealing algorithms can reduce the transportation time by 28.22% and 12%, respectively. However, the optimization of the parallel annealing algorithm is limited by a fixed vehicle speed.

Akhtar et al. ( 2017 ) proposed an improved backtracking search algorithm on capable vehicle routing problems modeled under smart bins. Use the backtracking search algorithm to optimize based on the original route. At the same time, the data provided by the smart garbage bin is used to find the optimal range to reduce the number of garbage bins, thereby minimizing the distance. After four days of simulation experiments, Akhtar et al. ( 2017 ) found that the efficacy of waste collection increased by 36.78%. Algorithms and models can be developed further if more constraints and uncertainties are considered.

Furthermore, Nowakowski et al. ( 2020 ) proposed a harmony search algorithm for optimizing vehicle collection routes. After applying the harmony search algorithm to optimize vehicle routes for collecting electronic waste in a region of Poland, Nowakowski et al. ( 2020 ) found that the harmony search algorithm could increase the number of collection points visited by 5.4%. In summary, the backtracking search algorithm can increase efficiency by 36.78%, and the harmony search algorithm can increase the number of access points by 5.4%. Table 3 summarizes the optimization of artificial intelligence for waste logistics and transportation.

Illegal dumping and waste disposal

Illegal dumping refers to disposing of waste and garbage in areas not designated for waste disposal, including private and public areas (Liu et al. 2017 ; Lu 2019 ; Niyobuhungiro and Schenck 2022 ). Illegal waste dumping can significantly impact the surrounding ecosystem, create social problems, and pose risks to human health (Lu 2019 ; Niyobuhungiro and Schenck 2021 ). With the rapid growth of the global economy, the quantity of waste generated annually is rising, and crimes leading to illegal dumping are also increasing (Du et al. 2021 ). Hence, governments worldwide must address the problem of illegal dumping as a critical waste management issue (Du et al. 2021 ; Niyobuhungiro and Schenck 2021 ). Identifying illegal dumping is an integral part of the process of dealing with illegal dumping. Municipalities have also adopted various methods to identify illegal dumping. For example, the South Korean government has installed cameras in areas where illegal dumping is concentrated to monitor and arranged for supervisors to patrol. However, installing cameras and arranging personnel to patrol requires many human and material resources, but it has not achieved good results (Kim and Cho 2022 ). As artificial intelligence technology advances, researchers have explored its potential to detect illegal dumping. The following section will discuss artificial intelligence’s use in identifying illegal dumping cases.

Shahab and Anjum ( 2022 ) used a multipath convolutional neural network model to identify and localize waste areas to identify illegal dumping. Because no data set for illegal dumping was identified in this model, Shahab and Anjum ( 2022 ) devised a quantitative investigation procedure. After training a multipath convolutional neural network model with 9000 pictures, the researchers used 3000 additional pictures to test the model’s classification accuracy, which was found to be 98.33%. Additionally, Thotapally ( 2022 ) proposed a method that utilizes the faster regions with a convolutional neural network features target detection framework and the residual network algorithm as a convolutional layer to identify street photos captured by surveillance cameras to determine the cleanliness of the street. Facts have proved that using the residual network algorithm as a convolutional layer method improves the accuracy of target detection and positioning. To sum up, both multipath convolutional neural network mode and residual network algorithm can judge whether there is waste dumping behavior through pictures. Among them, the accuracy rate of multipath convolutional neural network mode recognition is 98.33%.

Based on deep neural networks, Kim and Cho ( 2022 ) proposed a technique for monitoring illicit dumping that measures the distance between dumpers and garbage bags. The illegal dumping monitoring technology uses an openpose algorithm to identify the joints of the dumper and type of trash bag according to the “you only look once” (YOLO) model by detecting the distance between the dumper and trash bag to determine whether there is illegal dumping. After a series of experiments, the accuracy rate of the illegal dumping monitoring system is 93%. Additionally, since illegally dumped waste is often transported by trucks, Du ( 2020 ) suggested capturing images of waste-carrying trucks through monitoring systems and then using the YOLO algorithm to detect and determine whether they are involved in illegal dumping by comparing shape, color, and movement changes. In summary, the “you only look once” (YOLO) model algorithm can be used to distinguish whether there is an illegal dumping of humans and vehicles. The accuracy rate, when used to identify humans, is 93%.

Takahashi et al. ( 2022 ) used drones to collect river channel images and the faster regions with convolutional neural network features (faster r-convolutional neural network) model to train these images. Artificial intelligence can effectively identify garbage in pictures. However, using the faster r-convolutional neural network model to identify garbage in the city requires more training for a faster r-convolutional neural network model. Moreover, Youme et al. ( 2021 ) proposed using the single-shot detector algorithm (SSDA) to identify the pictures collected by drones. The SSDA can identify the location of garbage.

Nevertheless, the SSDA also has certain limitations. For example, only less litter can be identified in some covered regions such as wood areas. In addition, Padubidri et al. ( 2022 ) demonstrated using a basic convolutional neural network classification model and a residual block classification model to identify and report illegal dumping sites in high-resolution aerial imagery. With some limitations, recognition errors may occur and may not be suitable for recognizing low-resolution aerial images. In short, a faster r-convolutional neural network model and SSDA can identify garbage in the drone to shoot images. Deep learning models identify illegal leaning points in high-resolution aerial imagery. Nevertheless, there are some shortcomings in the above three methods.

Furthermore, Ulloa-Torrealba et al. ( 2023 ) introduced a method of illegal dumping detection using the random forest algorithm on the segmented high-resolution earth observation. However, this method has shortcomings, such as the inability to identify wastes smaller than 64 square centimeters and the high-resolution images not being real time. Torres and Fraternali ( 2021 ) presented residual network 50 and feature pyramid network to identify illegal dumps in aerial images. After testing, the method can reach 88% accuracy. Devesa and Brust ( 2021 ) proposed a method based on neural networks to identify illegal dumping sites in satellite images. However, this method has some disadvantages, such as the inability to identify small areas and obtain clear satellite images when there are clouds.

To sum up, the random forest algorithm, residual network 50, feature pyramid network, and neural network are all three methods that can be used to identify illegal dumping, but there are some shortcomings. Table 4 summarizes eleven methods for artificial intelligence to identify illegal dumping.

Waste disposal reduces waste volume and accelerates waste purification through physical, biochemical, and pyrolysis gasification methods. Waste disposal methods can be categorized into four main types: waste recycling, waste incineration, waste composting, and waste landfilling (Chen 2022 ). Waste recycling involves collecting, treating, and reusing human-generated waste (Erkinay Ozdemir et al. 2021 ). Waste incineration uses high-temperature and high-pressure pyrolysis oxidation to reduce the volume of waste and eliminate hazardous materials (Chen et al. 2022a ). Waste composting involves the controlled decomposition of organic matter in waste and its conversion into substrates and fertilizers (Aydın Temel et al. 2023 ; Wei et al. 2022 ). Waste landfilling involves filling waste into depressions or large pits, followed by anti-seepage, drainage, and air-guiding treatments.

The following sections will discuss using artificial intelligence in waste disposal concerning these four methods. The treatment and reuse of waste are two parts of waste recycling, and some researchers also focus on applying artificial intelligence in treatment and reuse.

Regarding waste treatment, Ziouzios et al. ( 2020 ) trained a convolutional neural network model to identify different wastes in waste recycling. The convolutional neural network model can classify wastes into five categories. After testing, the accuracy of the convolutional neural network model is as high as 96.57%. Next, the researchers will further experiment to improve the accuracy of the convolutional neural network model (Ziouzios et al. 2020 ). Moreover, the artificial intelligence technology using transfer learning realized the classification of 12 different models of smartphones (Abou Baker et al. 2021 ).

Regarding waste reuse, Qi et al. ( 2018 ) proposed a model for predicting strength that incorporated boosted regression tree and particle swarm optimization. The experimental results show that the strength prediction model can accurately predict the strength and reduce the required mechanical tests. In general, the convolutional neural network model and transfer learning can be applied to waste classification, while boosted regression trees and particle swarm optimization are applied to waste reuse.

Waste composting is widely used as a waste treatment method for organic matter. However, some problems in actual operation exist, such as maturity, heavy metals, and carbon dioxide emissions (Guo et al. 2022 ; Li et al. 2022 ; Sharma et al. 2021 ; Yang et al. 2023c ). Therefore, artificial intelligence-based solutions are developed. Sharma et al. ( 2021 ) proposed using artificial neural network modeling to improve the maturity parameters of flower waste and cow manure in vermicomposting. After experiments, Sharma et al. ( 2021 ) found that the fertilizer optimized by artificial neural network modeling has enough nutrients to benefit the growth of plants.

Regarding risk control of heavy metals in livestock and poultry manure composting, researchers combined machine learning models such as layer perceptron regression and support vector regression to predict and optimize heavy metals in pig manure composting (Guo et al. 2022 ). After the experiment, Guo et al. ( 2022 ) found that the machine learning model and genetic algorithm can effectively reduce the risk of heavy metal pollution in livestock and poultry manure composting. In addition, Li et al. ( 2022 ) created a new machine learning model to predict carbon dioxide from green waste composting. A total of six different algorithms were used to predict carbon dioxide, with the random forest algorithm achieving the maximum prediction precision of 88%.

In summary, artificial neural networks can optimize compost maturity, and machine learning models can be used to predict heavy metals and carbon dioxide.

The waste landfill has become the most critical waste disposal method in most countries because of its large-scale operation and simple management (Mehrdad et al. 2021 ). However, landfills also present problems with siting, leachate, and odors (Abunama et al. 2019 ; Mohsin et al. 2022 ; Xu et al. 2022 ). Abunama et al. ( 2019 ) presented several models for forecasting leachate production rates in landfills. These models included single- and double-hidden layer artificial neural networks, multilinear perceptron, and support vector machine regression time series algorithms. After testing, Abunama et al. ( 2019 ) proved that the method of artificial neural network-multilinear perceptron with double hidden layers is optimal.

Meanwhile, regarding site selection for waste landfills, the fuzzy analytic hierarchy process–support vector machine and fuzzy analytic hierarchy process–random forest integrated models based on a geographic information system were developed Mohsin et al. ( 2022 ). Three landfills were selected to apply the model to siting landfills in a region of India. Xu et al. ( 2022 ) also constructed an artificial neural network model for ethanol, methyl sulfide, and dimethyl disulfide. They used a genetic algorithm to predict the odor emission rate of the landfill’s working face. Experiments show that the prediction accuracy is satisfactory. Overall, artificial neural networks can predict landfill leachate generation rates, vector machines and random forest models can be used for landfill siting, and genetic algorithms can be used to predict odor emission rates.

Although waste incineration is a common method of waste disposal, an improper operation can lead to adverse effects and problems. The complexity of waste incineration modeling arises from its nonlinear nature, strong coupling, significant delays, and high inertia. To overcome these difficulties, Chen et al. ( 2022a ) developed an intelligent modeling approach based on deep learning models, which proved more accurate and effective in simulating waste incineration power plants. Wajda and Jaworski ( 2021 ) used an ant optimization algorithm after conducting laboratory experiments and practical tests, achieving satisfactory results. In addition, Cho et al. ( 2021 ) employed artificial neural networks to optimize the conditions of an incinerator, and their findings indicated that the model could reduce nitrogenous gas emissions by 34%. These optimization methods can help improve waste incineration processes’ efficiency and environmental impact.

Identifying and recovering valuable resources

The rapid development of the global economy and urbanization has increased waste production, posing a serious problem for modern society (Chen et al. 2020 ). Governments mainly rely on landfill and waste incineration to manage waste, especially in developing countries, but improper disposal can cause environmental issues (Ferronato and Torretta 2019 ). However, the waste contains many recyclable materials, and recycling can reduce environmental impact and enable waste reuse (Zhang et al. 2021b ). To promote waste recycling, many countries are implementing waste classification. However, manual classification is inefficient and prone to errors, hindering progress. Researchers are applying artificial intelligence to waste identification and classification to overcome these obstacles, proposing more reliable methods (Zhang et al. 2021b ).

Zhang et al. ( 2021c ) proposed a two-stage recognition–retrieval algorithm for waste classification. The first stage involves constructing a recognition model to sort waste into thirteen categories, and the second stage trains a recognition–retrieval model to classify waste into four categories. However, the algorithm has limitations, such as only identifying one waste type in mixed waste and low accuracy in classifying paper, tissue, and fabric. Moreover, Sousa et al. ( 2019 ) introduced a hierarchical deep learning method for sorting and identifying waste in food trays, which classifies waste into four categories based on material or ten categories based on shape using faster regions with convolutional neural network features. Similarly, Melinte et al. ( 2020 ) demonstrated a deep convolutional neural network that can classify municipal waste into five categories with an accuracy rate of 97.63% using single-shot detectors and faster regions with convolutional neural network features.

Based on the image classification model of deep learning, Zhang et al. ( 2021b ) developed a method for adding a self-monitoring module to the residual network model. This model divides waste into six types according to the material. After experiments, it was found that the model’s accuracy was 95.87%. However, the model also has some limitations, such as the small amount of data in the data set, it is not realistic enough, and the actual situation in real life is quite different. Furthermore, Fahmi and Lubis ( 2022 ) developed a waste recognition system using a convolutional neural network to classify waste into inorganic and organic substances, with each group further subdivided into five subclasses. After conducting experiments, the waste recognition system achieved an average accuracy rate of 90%. Zhang et al. ( 2021a ) proposed a waste identification and classification method using transfer learning and convolutional neural networks. The method categorized waste into five classes based on different materials, and the model’s classification accuracy was 82% through testing. However, the transfer learning and convolutional neural network models have some limitations, such as the simplicity of waste pictures used in the experiments and the gap between these pictures and real-life waste.

Shi et al. ( 2021 ) proposed a multilayer hybrid convolution neural network for waste classification, achieving an accuracy rate of 92.6% for classifying six waste categories based on material. Meanwhile, Na et al. ( 2022 ) employed image data augmentation and transfer learning to classify construction waste into five categories based on material. However, the model’s quality may be affected by increased data, and collecting pictures during sunrise and sunset should be avoided. Ziouzios et al. ( 2022 ) developed a waste detection and classification system using convolutional neural networks, achieving a 92.43% accuracy rate for classifying four waste categories based on material. However, the system has high hardware costs and energy consumption.

In order to better classify and identify textile waste, Du et al. ( 2022 ) have developed a deep learning model based on a convolutional neural network to better classify textile waste. The model can accurately classify textile waste into 13 categories based on material with a recognition time of fewer than two seconds and an accuracy rate of 95.4%. Bobulski and Kubanek ( 2021a ) have demonstrated a deep learning-based classification system that uses a convolutional neural network to classify plastic waste into four categories based on different materials. Both factories and households can use the system. Toğaçar et al. ( 2020 ) have proposed a waste classification method based on a convolutional neural network that divides waste into recyclable and non-recyclable categories with a precision rate of 99.5%. Furthermore, Thumiki and Khandelwal ( 2022 ) have developed a real-time mobile application that uses image recognition and a convolutional neural network to classify waste into six categories based on material and determine whether it is recyclable or non-recyclable.

In summary, artificial intelligence, particularly deep learning models based on convolutional neural networks, has been shown to be effective in classifying waste according to their material and shape. These models can accurately identify and classify different types of waste, which can help with waste management and recycling efforts, as listed in Table 5 .

Improving public health and quality of life

Implementing artificial intelligence technology to improve sustainable waste management can help reduce the use of natural resources without compromising the standard of living. This ensures a reduction in the generation of solid waste and its disposal to minimize its impact on health and the environment (Yigitcanlar et al. 2021 ; Yusoff 2018 ). The amount of solid waste generated worldwide is much greater than that of recyclables, and this trend is expected to continue (Kabirifar et al. 2021 ; Khudyakova and Lyaskovskaya 2021 ). By incorporating artificial intelligence technology for intelligent recycling, waste classification, and disposal in developed and developing countries, the municipal solid waste process can be strengthened, leading to more sustainable recycling methods (Tanveer et al. 2020 ).

To effectively manage solid waste generation, creating and implementing a strategic roadmap is important (Wath et al. 2010 ; Williams 2019 ). Additionally, accurately predicting the age of solid waste is crucial for achieving efficient municipal solid waste management, which can be accomplished through artificial intelligence (Yigitcanlar and Cugurullo 2020 ). Traditional waste-sorting techniques are being replaced by automated intelligent machines capable of multitasking and sorting large amounts of solid refuse. These machines are powered by artificial intelligence, can distinguish between different types of solid waste, and exhibit a high degree of autonomy in computer vision programs (Wirtz et al. 2019 ).

Urban waste is commonly known as “municipal waste,” and certain types of waste within this category require special handling and management due to their explosive, toxic, or polluting nature that poses a risk to public health and living conditions (Olugboja and Wang 2019 ). Municipal-level hazardous, toxic, or detrimental waste to the quality of life and living conditions must be managed precisely. This includes waste generated from operational processes such as combustion ash, raw sewage, toxic enzyme oil, waste material, scrap metal, asphalt waste, ceramic waste, slag, gravel, animal manure, animals, grains, ashes, artificial waste, materials used in urban waste management, menstrual waste, and the generation of menstrual waste, all of which have the potential to cause harm, toxicity, or infection to public health and the environment.

Intelligent bins exemplify how artificial intelligence is employed in municipal solid waste management. Waste management companies can utilize artificial intelligence technology to monitor garbage bins’ fill levels throughout a city. Municipalities and recycling companies can optimize their trash collection schedules, routes, and frequencies (Brynjolfsson and Mcafee 2017 ; Ortega-Fernández et al. 2020 ). This optimization reduces the time required to empty the bins while also reducing labor and fuel costs. Furthermore, artificial intelligence technology can detect when a bin is full and distinguish between various waste types. For example, smart bins can rapidly classify and sort garbage using machine learning algorithms (Yigitcanlar and Cugurullo 2020 ).

Artificial intelligence is critical in solid waste management by facilitating classification (Gundupalli et al. 2017 ). Artificial intelligence is used for intelligent classification by using cameras to automatically scan and analyze items on a conveyor belt using deep learning algorithms, similar to how it is used in manufacturing (Gundupalli et al. 2017 ; McKinnon et al. 2017 ). Recent studies have demonstrated that artificial intelligence-powered machines can process up to 160 recyclable materials per minute, compared to 30 to 40 materials per minute for human workers (Andeobu et al. 2022 ). Moreover, artificial intelligence-powered machines can operate continuously, highlighting deficiencies in classification and recycling facilities (McKinnon et al. 2017 ). Traditionally, solid waste management has been a labor-intensive process. However, thanks to advances in artificial intelligence, computer vision, robotics, and other cutting-edge technologies, municipalities can now improve public health and quality of life while reducing costs and eliminating the need for manual labor.

In summary, using artificial intelligence in waste management is becoming increasingly popular. An example is developing an artificial intelligence-based hybrid intelligent framework, which optimizes waste management and improves urban environment monitoring using graph theory and artificial intelligence technologies (Ihsanullah et al. 2022 ). By employing different approaches and algorithms based on artificial intelligence, this system can better accommodate various demographic groups, enhance environmental planning, and improve urban management’s efficiency, accuracy, and performance. Results show that the method improves the efficiency and accuracy of waste processing compared to other existing methods (Yu et al. 2021 ).

Since the outbreak of the novel coronavirus (COVID-19) pandemic, the rapidly increasing amount of medical waste has posed more significant challenges to various regions’ disposal facilities and management capabilities. Medical waste has the characteristics of spatial pollution, acute infection, and latent infection, which can directly endanger human health. It can also cause severe consequences through soil pollution, water bodies, and the atmosphere (Yang et al. 2022a ). As shown in Fig.  5 , coronavirus disease 2019 (COVID-19) changes the composition of waste and the efficiency of waste disposal and increases the risk of infection in the population. Human intervention in solid waste management can be reduced through advanced intelligent waste management technologies, such as machine learning-based image classification and reliable item detection. Specific materials that improve ecological sustainability after a significant outbreak can be effectively recycled. These technological interventions will reduce the risk of human factor contamination in the waste management cycle, thus breaking the potential transmission chain of COVID-19 and similar viruses (Rubab et al. 2022 ).

Impact of the coronavirus disease 2019 (COVID-19) on waste management. The COVID-19 pandemic has significantly affected the composition, timing, and frequency of waste disposal. It has also increased the risk of infection for the public due to the production of masks and medical waste that require manual handling. These changes in waste volumes have complex and interrelated impacts on municipal waste management, as depicted in the chart

The proposed medical waste monitoring and assistance system utilizes artificial intelligence to perform deep mining and analysis of personnel disposal behavior in the temporary storage of medical waste and working areas of medical and health institutions. The system can intelligently identify illegal and irregular behaviors on the scene and synchronously push alarm information to medical institutions and law enforcement personnel (Yu et al. 2021 ). The aim is to improve the management and disposal of medical waste, reduce the risks of infectious disease transmission and environmental pollution caused by medical waste, and ensure public health and safety (Lakshmi et al., 2015 ). By utilizing the technology of “internet + video surveillance and data tracking,” artificial intelligence can intelligently recognize illegal behaviors related to waste management without the need for the physical presence of law enforcement personnel. This can help reduce the risk of infection and transmission of diseases while also improving the overall efficiency of waste management supervision.

In summary, improper disposal of solid waste can adversely impact human health and the environment. However, current solid waste management systems struggle to keep up with the ever-increasing amount of waste produced globally (Popa et al. 2017 ). There are certain recyclable materials that some municipal solid waste and recycling services cannot process. The integration of artificial intelligence in solid waste management is a growing trend and has the potential to significantly improve sustainable waste management practices (Khudyakova and Lyaskovskaya 2021 ). Adopting artificial intelligence-driven automation in the management process of municipal solid waste will provide a sustainable approach to recycling and disposal (Popa et al. 2017 ). Thus, implementing these advancements in solid waste management can promote a healthier and better quality of life for all.

Designing waste management systems for smart cities

In the pursuit of creating environmentally friendly cities, waste management plays a critical role. Ensuring sustainable and livable urban areas requires improving solid waste management services and reducing waste, as hazardous solid waste can negatively impact air quality and soil safety (Herath and Mittal 2022 ). With the rise of smart cities, artificial intelligence technology in waste management has become more prevalent, primarily as a modeling and prediction tool for simulation and optimization. The most recent applications have primarily focused on modeling and optimizing solid waste generation, as well as predictive recycling processes, as demonstrated in Table 6 .

Studies have suggested that integrating waste management into future smart cities with entire product lifecycles could be a potential step toward achieving “zero waste” (Lee et al. 2021 ). To reach this goal, three stages must be undertaken: Waste prevention, precise refuse collection, and functional value recovery from collected waste are all priorities (Yang et al. 2023a ). Furthermore, the Internet of things waste management networks should be encouraged to improve the life cycle of products and their recycling value (Shukla and Hait 2022 ).

To effectively implement and manage waste models for smart cities, it is crucial to intelligently estimate solid waste generation. In this regard, artificial intelligence technologies such as artificial neural networks, support vector machines, decision trees, and adaptive neuro-fuzzy inference systems have been increasingly used due to their practical predictive capabilities for modeling the production of municipal solid waste (Ihsanullah et al. 2022 ). Artificial intelligence-based models in waste management research are commonly categorized by the forecast period duration: short term (days to months), medium term (up to 3–5 years), and long term (years in advance). Recent studies have demonstrated promising results in utilizing artificial intelligence with historical data, such as sociodemographic, economic, and management-focused data. Additionally, combining artificial intelligence with conventional waste management systems can be achieved by integrating the Internet of things technology (Ijemaru et al. 2022 ).

Artificial intelligence techniques have been used in multiple studies to predict specific types of solid waste generation, such as plastic waste (Kumar et al. 2018 ) and household packaging waste (Oliveira et al. 2019 ), and have proven to be efficient and feasible. However, analyzing and selecting key metrics is crucial to achieving accurate prediction performance and ensuring the dataset’s completeness and adequacy. In addition to waste forecasting, automated waste sorting and management are essential for better waste recycling. Advanced technologies, particularly the idea of smart cities, require this model. Artificial waste separation is inappropriate for smart cities, so smart waste classification models are typically multilayer convolutional deep learning models with some physical requirements. These requirements include a system with a conveyor belt, a pusher, and a garbage basket that will collect the garbage pushed by the hammer according to the waste category (Gondal et al. 2021 ). Therefore, artificial intelligence-based models can accurately forecast and evaluate solid waste generation and automate waste management and sorting for better recycling, which is crucial for cutting-edge technologies like smart cities.

In general, the techniques used in waste management applications fall into the following four categories (Zhang et al. 2012 ):

Space technology includes using global navigation and geographic information systems to track waste and manage waste collection and disposal.

Identification techniques: such as radio frequency identification tags and bar codes which enable efficient waste tracking and monitoring

Data acquisition technology, such as sensors and imaging, provides valuable waste generation and composition data, enabling better waste management decisions.

Data communications technology, including wireless fidelity, Bluetooth, and global mobile communication systems, facilitates communication and data transfer between waste management systems and stakeholders

The steps to achieve “zero waste” include the following three main modules and components (Shukla and Hait 2022 ):

Establishing a framework for collating product life cycle data.

Encouraging responsible citizenship through innovative waste reduction ideas.

Developing an intelligent infrastructure with sensor-based technology for adequate garbage segregation, collection, and recycling.

In summary, smart cities have emerged as a global model that prioritizes sustainability. With the help of computing, networking, and data management advancements, institutions have successfully implemented efficient waste management systems that enhance the quality of life for citizens. However, the proposed model must be adapted to different demographics, waste types, and social needs and calibrated through real-world pilot studies. Extensive research into waste management in smart cities has produced numerous results.

Process efficiency and cost savings

There is great potential to automatically detect waste in natural settings to improve waste management efficiency. For instance, Zhou et al. ( 2023 ) proposed a few-shot waste detection framework that employs faster regions with convolutional neural network features (faster r-convolutional neural network) to detect waste automatically in natural settings, thereby improving waste management efficiency. Their experiments revealed that this framework outperformed state-of-the-art detectors with 1.68% accuracy. However, using a faster r-convolutional neural network in the framework resulted in high computational complexity and slow operation speed. Moreover, Alqahtani et al. ( 2020 ) presented an urban waste management system that employs the cuckoo search algorithm to analyze waste sources, types, and vehicle capacity to optimize waste collection.

Experimental results have shown that waste recycling systems can improve waste management efficiency and collect waste within 15 min. Akhtar et al. ( 2017 ) proposed an enhanced backtracking search algorithm for optimizing waste collection routes based on the smart bin concept. The algorithm uses data from the smart garbage bins to identify the optimal range and reduce the number of garbage bins, thereby minimizing distance. After four days of simulation experiments, they reported a 36.78% increase in the efficacy of waste collection. These algorithms and models can be further developed by considering more constraints and uncertainties.

Shreyas Madhav et al. ( 2022 ) have developed a convolutional neural network-based recognition system for e-waste classification. The system can classify e-waste into eight categories with 96% accuracy, potentially leading to a 20% cost reduction within five years if implemented as a replacement for manual classification. To optimize waste collection, Internet of things (IoT) -based waste management software can collect relevant information, and the route of waste collection vehicles can be optimized using an ant colony optimization algorithm. Experiments have reported a 30% reduction in the direct cost of waste collection using this algorithm (Oralhan et al. 2017 ). Meanwhile, Babaee Tirkolaee et al. ( 2019 ) proposed a simulated annealing algorithm for generating initial values based on a random algorithm, which was then used for optimization. The algorithm was applied to an area in Iran with 330 square kilometers and 43 recycling nodes, reducing the total cost by 13.3%.

In summary, developing the convolutional neural network-based recognition system for e-waste classification can improve accuracy and reduce costs. Optimizing waste collection routes using algorithms such as ant colony optimization or simulated annealing can lead to significant cost savings.

Over the years, the use of artificial intelligence technology in various waste management fields has risen. However, with its increasing use, some potential challenges have also emerged. Among these is the black box problem of artificial intelligence, which arises due to the complexity of internal structures of most artificial intelligence models, relatively independent operation processes, and difficulty in estimating the relative significance of each variable, thereby limiting manual intervention (Ihsanullah et al. 2022 ). These black box problems can lead to uncertainty in applying artificial intelligence models (Guo et al. 2021 ).

The training and testing of artificial intelligence models, especially those that utilize deep learning and machine learning, require significant data. However, the waste management industry, particularly in developing countries, suffers from data scarcity and incomplete data, which hinder current research (Abdallah et al. 2020 ). Insufficient and outdated data can result in overfitting and reduced model training efficiency (Guo et al. 2021 ). One example is the deep neural network, which relies heavily on extensive testing and experimentation using large datasets (Ihsanullah et al. 2022 ).

Artificial intelligence has been increasingly implemented in waste management, but researchers often rely on preexisting models like faster regions with convolutional neural network features or convolutional neural networks. However, a lack of customized artificial intelligence models designed specifically for waste management exists. This requires collaboration between waste management and computational technology teams to develop tailored models (Abdallah et al. 2020 ).

It can be concluded that implementing artificial intelligence in waste management has the potential to enhance efficiency and decrease costs. However, challenges like black box problems, inadequate data, and a lack of customized artificial intelligence models for waste management still exist. Figure  6 illustrates three potential obstacles that may arise in the application of artificial intelligence in waste management.

Three challenges of artificial intelligence in waste management. Implementing artificial intelligence in waste management can be summarized as black box problems, a lack of data, and a shortage of suitable models. Black boxes refer to the complexity of artificial intelligence models, which makes it difficult for researchers to understand their mechanisms. Lack of data refers to the scarcity and unreliability of data in the waste management industry, making it challenging to train artificial intelligence models. Finally, the lack of suitable models means that most existing applications of artificial intelligence in waste management rely on preexisting models rather than custom models explicitly developed for waste management

Limitation and prospects

In terms of artificial intelligence to optimize waste logistics and transportation, Zhang et al. ( 2020 ) pointed out that when the back-off algorithm optimizes the path of garbage collection vehicles, there is a limitation that the collection vehicles are at a fixed speed, which is impossible in real life.

In terms of artificial intelligence to identify illegal dumping, Takahashi et al. ( 2022 ) proposed that the faster regions with convolutional neural network features model can only be used to identify waste near rivers and cannot be used to identify waste in other complex areas such as cities. Moreover, there are also certain limitations in using the single-shot detector algorithm to identify waste in drone images. The algorithm cannot identify waste in covered areas such as woods (Youme et al. 2021 ). Additionally, Padubidri et al. ( 2022 ) proposed a method using a deep learning model to identify illegal dumping sites in high-resolution aerial images, which may suffer from identification errors and is not suitable for identifying low-resolution aerial images.

Regarding the use of artificial intelligence for waste identification and sorting, the waste images used to train transfer learning and convolutional neural network models for waste classification are much less complex than real-world waste, leading to reduced accuracy rates in practical applications (Zhang et al. 2021a ). Additionally, researchers have explored using image data augmentation and transfer learning to identify and classify construction waste. Increasing the amount of data reduces the quality of the model. Considering the picture quality, it is best to avoid sunrise and sunset when collecting pictures. In addition, waste detection and classification systems based on convolutional neural networks have high hardware costs and energy consumption limitations (Ziouzios et al. 2022 ).

Learn more about the mechanics of artificial intelligence models. Because of the black box characteristics of artificial intelligence models, it is difficult for people to understand the mechanism of artificial intelligence models (Guo et al. 2021 ). Now, there are some methods to explain the influence of input variables on artificial intelligence models, such as using visualization technology; Selvaraju et al. ( 2020 ) use class activation mapping to explain the mechanism of convolutional neural network. However, only a few methods have been applied to explain the mechanisms of artificial intelligence models. In the future, researchers could develop more ways to interpret artificial intelligence models for greater understanding (Lin et al. 2022 ).

Combine artificial intelligence models with other technologies (Guo et al. 2021 ). For example, combining artificial intelligence models and Internet of things (IoT) technology allows artificial intelligence to be better applied to waste management (Guo et al. 2021 ). In addition, it is also possible to combine artificial intelligence models and data science. This can provide more high-quality data for training artificial intelligence models to improve the quality of the model (Lin et al. 2022 ).

The combined use of multiple artificial intelligence models is an inevitable trend. Artificial intelligence models include but are not limited to convolutional neural networks, residual network models, and gradient enhancement regression models. Most existing research focuses on a single artificial intelligence model for waste management. Using a combination of multiple models for waste management has better accuracy than a single model and effectively prevents overfitting (Guo et al. 2021 ). This can better solve the service management problem.

In summary, the limitations of artificial intelligence in waste management are primarily related to its difficulty in practical operation compared to theory, the limited scope of application, and lower efficiency in practical use. The potential for future progress lies in comprehensively understanding the mechanisms behind artificial intelligence models, combining artificial intelligence with other technologies, and using multiple models, such as convolutional neural network, residual network model, and gradient enhancement regression model .

Waste disposal is inefficient, leading to severe environmental pollution, high costs, and a lack of leadership in the disposal process. Waste management is a challenge for both developed and developing countries. However, artificial intelligence can improve treatment efficiency, reduce environmental damage, and provide computational solutions for smarter waste management. This review is divided into nine sections, including definitions and the application of artificial intelligence in waste management. It highlights the potential impact of artificial intelligence on waste management, with practical applications such as smart bin systems, waste-sorting robots, and predictive waste tracking models. Artificial intelligence can also assist in managing hazardous waste, reducing illegal dumping, and recovering valuable resources from the waste stream. Additionally, artificial intelligence can aid public health interventions, including medical waste disposal and pandemic response.

The paper examines the impact of artificial intelligence on waste logistics and transportation, including reducing distance, cost, and collection time and improving collection efficiency. Although some algorithms have limitations, artificial intelligence can optimize waste treatment methods such as recycling, composting, landfill, and incineration. Machine learning, artificial intelligence, and deep learning techniques can improve waste classification, predict heavy metal levels in compost, and model waste incineration processes. Environmental variables such as temperature, humidity, and light can affect waste management artificial intelligence systems, causing fluctuations. Despite these challenges, artificial intelligence can change how people deal with waste, leading to a more sustainable future with efficient, economic, ecological, and intelligent waste management systems.

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Acknowledgements

Dr. Ahmed I. Osman and Prof. David W. Rooney wish to acknowledge the support of The Bryden Centre project (Project ID VA5048), which was awarded by The European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB), with match funding provided by the Department for the Economy in Northern Ireland and the Department of Business, Enterprise and Innovation in the Republic of Ireland.

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Bingbing Fang, Jiacheng Yu and Mohamed Farghali have contributed equally to this work.

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Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

Bingbing Fang, Jiacheng Yu, Zhonghao Chen & Pow-Seng Yap

School of Chemistry and Chemical Engineering, Queen’s University Belfast, David Keir Building, Stranmillis Road, Belfast, BT9 5AG, Northern Ireland, UK

Ahmed I. Osman & David W. Rooney

Department of Agricultural Engineering and Socio-Economics, Kobe University, Kobe, 657-8501, Japan

Mohamed Farghali & Ikko Ihara

Department of Animal and Poultry Hygiene & Environmental Sanitation, Faculty of Veterinary Medicine, Assiut University, Assiut, 71526, Egypt

Mohamed Farghali

Electric and Computer Engineering Department, Aircraft Armament (A/CA), Military Technical College, Cairo, Egypt

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Fang, B., Yu, J., Chen, Z. et al. Artificial intelligence for waste management in smart cities: a review. Environ Chem Lett 21 , 1959–1989 (2023). https://doi.org/10.1007/s10311-023-01604-3

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Waste Management & Research

The journal for a sustainable circular economy, iswa’s monthly scientific journal (if 3.9).

The Waste Management & Research (WM&R) is ISWA’s monthly peer reviewed scientific journal that satisfies the growing demand for new and scientific information for reference by waste management professionals in academia, government, industry, planning, engineering, management and operations. WM&R presents original work in the form of review articles, original articles, short articles, and letters to the editor. The detailed description, aims and scope of WM&R can be read here .

In the 2022 Journal Citation Report, published in June 2023, WM&R’s Impact Factor was reported at 3.9. The Impact Factor indicates the frequency on citations of articles published in the journal. Overall, the journal is continuing to see an increase in citations compared to the previous year.

WM&R can be freely accessed by ISWA members (excluding Online Members) at the right side of this page. Non-members can freely access Editor’s Choice papers , which consist of some of WM&R’s best papers, selected from each Issue by the Editor. Abstracts are freely accessible as well.

A Special Issue of WM&R has been published for every ISWA World Congress since 2012. These Special Issues include papers presented at the World Congress, please find an index of the ISWA World Congress and other Special Issues here .

The Editorial Group (EG) is responsible for the editorial management of WM&R and consists of the Senior Editor-in-Chief, two Editors-in-Chief and 6 Associate Editors. The WM&R Editorial Group is supported by Fran Saint-Geris (Senior Publishing Editor) at SAGE and Gazel de Klerk (Editorial Office Manager) at ISWA.

The Editorial Board supports WM&R through frequent review of papers and active contribution to WM&R’s strategy. WM&R’s International Advisory Board consists of senior experts who have taken an advisory role in shaping the journal’s development. For a complete list of the members of WM&R’s Editorial Group, International Advisory Board and Editorial Board please click here .

To submit an article, see the manuscript preparation and submission guidelines .

For any questions about WM&R, please contact the WM&R Editorial Office through the contact form below.

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WM&R Journal Access

ISWA Members (except Online) have full access to the Waste Management and Research Journal. If you would like to access the Journal, please log in or become a member.

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Collections & research, we are committed to making collections & research accessible, affordable, and sustainable.

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Accessible, Affordable, and Sustainable

William & Mary Libraries is committed to providing exceptional collections for research and teaching. The Libraries strive to support the evolving curricular and programmatic needs of the university, foster a broad and rigorous scholarship agenda, and develop collections that enable new and exciting research “aha” moments.

However, the university libraries’ collections budget is under enormous inflationary pressures due to large journal packages (“Big Deals”) that are unsustainable at their current pace. We are not alone. Universities and colleges worldwide are contending with the cost of the scholarly publishing industry and are increasingly taking action to signal their unwillingness to continue with business as usual. 

In 2021, the Virginia Research Libraries, of which W&M Libraries is a member, will be negotiating with Elsevier, a Big Deal scholarly publisher. The result of those negotiations will be made to the William & Mary community as soon as they are made clear, but efforts to make our research collections more affordable, sustainable, and equitable will not stop there. By helping scholars understand the true costs of paywalled scholarship, promoting affordable course materials like open textbooks, and supporting open access in research and education, we hope to truly abide by W&M’s values of curiosity, excellence, respect, and service.

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Paywall: The Business of Scholarship

Learn more about the for-profit academic publishing industry in this documentary.

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Virginia’s academic library consortium partners with knowledgeworks to develop open educational resources, virginia’s public academic research libraries welcome white house guidance to make taxpayer-funded research freely available without delay, two new oa publishing options available for w&m starting january 1, 2022, new read-and-publish agreement with rockefeller university press, virginia libraries negotiate new contract with elsevier .

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Renowned computer scientist, alumnus to lead W&M’s new School of Computing, Data Sciences & Physics

New school to speed the evolution of liberal arts and sciences through data fluency.

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Douglas “Doug” Schmidt ’84, M.A. ’86, an internationally recognized scholar with more than three decades of experience in academic and government leadership roles, has been named the inaugural dean of William & Mary’s School of Computing, Data Sciences & Physics . 

The new school – the university’s first in more than 50 years – will launch officially in fall 2025 and advance William & Mary’s efforts to prepare students for success in a data-driven world. 

Schmidt, a renowned computer scientist, currently serves as the director of Operational Test & Evaluation (DOT&E) for the U.S. Department of Defense. In that role, he is a senior advisor to the U.S. Secretary of Defense. A noted researcher and educator, Schmidt has served on the faculty at Vanderbilt University for more than two decades, holding such positions as associate provost and co-director of the Data Science Institute. 

Schmidt studied sociology as an undergraduate and graduate student at W&M before earning a master’s degree and doctorate in computer science at the University of California, Irvine. This trajectory honed his ability to integrate the liberal arts and sciences with rapid technology innovation. 

“Dr. Schmidt’s career exemplifies the success William & Mary aims for, for all our graduates,” said President Katherine A. Rowe. “From an exceptional liberal arts and sciences foundation he built the capacities needed for a rapidly changing data workforce. He has flourished as a respected leader in government, higher education and multiple industries. He understands at a personal and professional level what will differentiate our new school from the competition. We warmly welcome Dr. Schmidt back to William & Mary.”

Preparing students for success 

The idea for a new school at William & Mary to bring together computer science, data science, applied science and physics was initially raised by faculty members, spurred by an increased interest in those fields among students and employers. In just one decade, for example, the university’s combined total of bachelor’s degree graduates in computer science and physics grew by over 206%.

In spring 2022, the university began exploring the possibility of a school in consultation with faculty and staff around campus. William & Mary’s Board of Visitors voted unanimously in November 2023 to approve the proposed school’s administrative structure, and the State Council of Higher Education in Virginia approved the proposal for a new school in July.

As dean, Schmidt will be responsible for uniting the school’s four areas – computer science, data science, applied science and physics – into a cohesive academic unit and growing their research, grant funding and doctoral program enrollment. The school will be housed in the Integrated Science Center, which also holds such units as biology, chemistry, neuroscience and psychological sciences, creating increased opportunities for collaboration.

Schmidt will also be responsible for establishing a vision with strategic objectives that will ensure the school becomes nationally known as an inclusive and innovative leader in teaching and research. In addition, he will be charged with supporting faculty in their research, teaching and service work; leading fundraising efforts and the financial management of the school; and building or strengthening its strategic partnerships. 

“Doug is a pioneering scholar and a prolific author with a strong commitment to service. An innovator through and through, he brings an energetic and collaborative spirit to his work that is a natural fit for a new school,” said Provost Peggy Agouris. “He is also a two-time William & Mary alumnus, which is an especially meaningful testament to the power of a W&M education. I’m thrilled to welcome Doug back to campus as he helps prepare new generations of students for careers that make a difference.”

A leader across government, industry and higher ed

Schmidt was appointed to his current position by U.S. President Joe Biden last year and unanimously confirmed by the U.S. Senate this spring. As the DOT&E, he serves as senior advisor to the secretary of defense on testing and evaluating Department of Defense weapon systems. His office also provides independent reporting and unbiased assessments of system performance to Congress.

In his more than 20 years at Vanderbilt, he has held many roles, from associate chair of computer science and engineering through a number of leadership positions, including associate chair of the computer science department, Cornelius Vanderbilt Professor of Engineering in Computer Science and associate provost of research development and technologies. He was instrumental in building Vanderbilt’s digital learning initiatives, launching that school’s first massive open online course in 2013 and continually developing innovative programs to serve students. In 2022, Fortune magazine ranked Vanderbilt’s online Computer Science professional master’s degree number one in the nation.

Throughout his career, he has worked at a number of other leading universities, including the University of California, Irvine; Trinity College Dublin; and Washington University, Saint Louis. He was also visiting scientist and chief technology officer for the Software Engineering Institute at Carnegie Mellon University.

Schmidt and his family have remained closely connected to William & Mary since his graduation, and his parents created the Schmidt Family Cyber Technology Speaker Series Endowment. Last year, Schmidt gave a lecture on the future of artificial intelligence-augmented research and education as part of that series during Homecoming & Reunion Weekend.

“As an undergraduate and graduate student with degrees in sociology from William & Mary, I couldn’t have conceived of where I’d end up: coming back to alma mater to serve as the inaugural dean of its first new school in more than 50 years,” said Schmidt. “I am thrilled beyond words at this incredible opportunity, and my partner Karen Perry and I look forward to joining the entire community in January. My mother — who graduated from William & Mary in 1960 — and father are also looking forward to returning to Williamsburg, which is a place they love.”

Following his W&M education, Schmidt became an internationally respected researcher whose work focuses on patterns, optimization techniques and empirical analyses of frameworks and model-driven engineering tools that facilitate the development of distributed real-time and embedded (DRE) middleware frameworks and mobile cloud computing applications. His most recent research focuses on prompt engineering techniques and prompt patterns that enhance large language models (LLMs) and generative augmented intelligence platforms.

He is a highly cited author with almost 50,000 citations to date and has published more than 700 papers and co-authored more than 10 books. He has also developed widely-used open-source DRE middleware, model-driven engineering tools and mobile cloud computing apps.

In addition to higher education, Schmidt has served in a number of roles with government agencies. He worked as a program manager and deputy office director for the Defense Advanced Research Projects Agency (DARPA) and served as a member of the Air Force Scientific Advisory Board and an advisory board for the joint Navy/Army Future Airborne Capability Environment (FACE) initiative. He was also co-chair for the federal government’s Software Design and Productivity Coordinating Group, a collaborative IT research effort between major federal science and technology agencies. Schmidt has also worked for a number of private companies, including Prism Technologies and Zircon Computing.

William & Mary launched an international search for a dean of the School of Computing, Data Sciences & Physics last year. The search committee included faculty, staff and students and was chaired by Derek Aday, dean of the Batten School of Coastal & Marine Sciences and director of the Virginia Institute of Marine Science.

Staff , University News & Media

Science Activation’s PLACES Team Facilitates Third Professional Learning Institute

The NASA Science Activation program's Place-Based Learning to Advance Connections, Education, and Stewardship (PLACES) project supports middle and high school educators to engage students in data-rich Earth science learning through the integration of NASA data sets, images, classroom lessons, and other assets. This project draws on a place-based approach as a means to increase “data fluency” — the ability and confidence to make sense of and use data. This means knowing when, how, and why to use data for a specific purpose, such as solving problems and communicating ideas grounded in evidence.

As part of this effort, PLACES facilitated its third Professional Learning (PL) Summer Institute (SI) for 22 educators at the Gulf of Maine Research Institute (GMRI) in Portland, Maine the week of August 12th, 2024. This is the third PL Summer Institute the PLACES team has facilitated, each focusing on engaging educators in place-based, data-rich teaching and learning with NASA data and resources.

The GMRI PL development and facilitation was a collaborative co-design effort between two NASA Science Activation projects (PLACES led by WestEd and the Learning Ecosystems Northeast project led by GMRI) and colleagues from the Concord Consortium and NASA Langley Research Center. During this PL, teachers took part in community science projects developed by GMRI to incorporate youth in ongoing research projects, including a mix of field- and classroom-based experiences that explored the phenomena of Hemlock Woolly Adelgid (HWA) and the changes to intertidal crab populations - two invasive species that are proliferating as a result of climate change. During two field-based experiences, teachers gathered primary data using protocols from GMRI’s Ecosystem Investigation Network and the NASA-sponsored program, GLOBE (Global Learning and Observations to Benefit the Environment). Teachers then explored these primary data using Concord Consortium’s Common Online Data Analysis Platform (CODAP) to better understand the geographic and temporal spread of these species. To connect their local experiences to global happenings, teachers then explored secondary data sets, including those sourced from the My NASA Data (MND - also supported by NASA Science Activation as part of the GLOBE Mission Earth project) Earth System Explorer (e.g., Normalized Difference Vegetation Index, salinity, sea surface temperature). The facilitation team also used the MND Data Literacy Cubes to encourage teachers to consider a multitude of diverse questions about place, data, and the phenomena. The GLOBE protocols supplemented existing GMRI data collection protocols, presenting new opportunities for teachers already experienced with HWA and Green Crabs. The MND data and Data Literacy Cubes moved teachers from questions they generated as part of their primary data collection towards new knowledge.

Daily feedback from teachers highlighted their appreciation for the responsiveness of the facilitation team, as well as a growing curiosity and desire for using NASA resources such as protocols from GLOBE and data from MND’s Earth System Explorer. This is exciting to see as the teachers transition from the Summer Institute into a virtual Community of Practice during the school year. The Community of Practice engages them in peer-to-peer collaboration and dialogue as they develop, test, and give feedback on their own place-based, data-rich experiences using NASA data and resources. So far, teachers are planning to tackle a variety of topics ranging from ocean chemistry to human connections to the environment. Teachers indicated their interest in “making place-based experiences meaningful to our unique populations of students and having cultural representation in the classroom,” and focusing on “cross-school collaboration.” Preliminary evaluation data indicated that 76% of teachers thought their experiences with NASA resources during the SI helped them identify ways to bring data into their classroom. 85% of teachers indicated they feel a greater connection to NASA and knowledge of NASA resources for enhancing student understanding and engagement in science. Moving into the fall, teachers will take part in a Community of Practice, where they will work to implement a place-based, data-rich moment in their individual classrooms. In the summer of 2025, teachers will take part in a second summer institute where they will continue to learn more about implementing place-based, data-rich instruction.

The PLACES GMRI Summer Institute was made possible by a large co-design, collaborative effort across our partner organizations. This included:

• Facilitation Team: Catherine Bursk (GMRI), Meggie Harvey (GMRI), Sara Salisbury (GMRI), Daniel Damelin (Concord Consortium)
• In-person Facilitation Support Team: Leigh Peake (GMRI), Karen Lionberger (WestEd), Kristin Hunter-Thomson (Dataspire), Angela Rizzi (NASA Langley)
• In-Person Team Member Participants: Janet Struble and Kevin Czaikowski (GLOBE, University of Toledo), Svetlana Darche (WestEd)
• Virtual Observers: Kirsten Daehler, Nicole Wong, Leticia Perez (WestEd), Tracy Ostrom (GLOBE, UC Berkeley), Lori Rubino-Hare (NAU)
• Additional support: Frieda Reichsman (Concord Consortium), Barbie Buckner and Jessia Taylor (NASA Langley), Sean Ryan (NAU), Lauren Shollenberger (NAU)

PLACES is supported by NASA under cooperative agreement award number 80NSSC22M0005 and is part of NASA’s Science Activation Portfolio. Learn more about how Science Activation connects NASA science experts, real content, and experiences with community leaders to do science in ways that activate minds and promote deeper understanding of our world and beyond: https://science.nasa.gov/learn

Related Terms

• Earth Science
• Grades 5 - 8 for Educators
• Grades 9-12 for Educators
• Opportunities For Educators to Get Involved
• Science Activation

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