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Peng Z, Zhang B, Wang D, Niu X, Sun J, Xu H, Cao J, Shen Z. Application of machine learning in atmospheric pollution research: A state-of-art review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 910:168588. [PMID: 37981149 DOI: 10.1016/j.scitotenv.2023.168588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/07/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.
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Affiliation(s)
- Zezhi Peng
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Bin Zhang
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Diwei Wang
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xinyi Niu
- School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jian Sun
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Hongmei Xu
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Junji Cao
- Key Lab of Aerosol Chemistry & Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710049, China
| | - Zhenxing Shen
- Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
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Gong X, Liu L, Huang Y, Zou B, Sun Y, Luo L, Lin Y. A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1183. [PMID: 37695355 PMCID: PMC10829730 DOI: 10.1007/s10661-023-11814-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/30/2023] [Indexed: 09/12/2023]
Abstract
Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman's rank correlation coefficients for tenfold cross-validation (mean ± standard deviation: 0.906 ± 0.028) and for random cross-validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufficiently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a flexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.
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Affiliation(s)
- Xi Gong
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Lin Liu
- Department of Computer Science, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yanhong Huang
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
| | - Bin Zou
- School of Geosciences and Info-Physics, Central South University, Changsha, 410083, Hunan, China
| | - Yeran Sun
- Department of Geography, University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, UK
| | - Li Luo
- Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yan Lin
- Department of Geography & Environmental Studies, UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, NM, 87131, USA
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Lim S, Bassey E, Bos B, Makacha L, Varaden D, Arku RE, Baumgartner J, Brauer M, Ezzati M, Kelly FJ, Barratt B. Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM 2.5 exposure. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 833:155207. [PMID: 35421472 PMCID: PMC7615091 DOI: 10.1016/j.scitotenv.2022.155207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 04/02/2022] [Accepted: 04/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure. OBJECTIVES The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income. METHODS This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents). RESULTS From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 μg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources. CONCLUSION This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.
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Affiliation(s)
- Shanon Lim
- MRC Centre for Environment and Health, Imperial College London, UK.
| | - Eridiong Bassey
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Brendan Bos
- MRC Centre for Environment and Health, Imperial College London, UK
| | - Liberty Makacha
- MRC Centre for Environment and Health, Imperial College London, UK; Place Alert Labs, Department of Surveying and Geomatics, Faculty of Science and Technology, Midlands State University, Zimbabwe; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King's College London, UK
| | - Diana Varaden
- MRC Centre for Environment and Health, Imperial College London, UK; NIHR-HPRU Environmental Exposures and Health, School of Public Health, Imperial College London, UK
| | - Raphael E Arku
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, USA
| | - Jill Baumgartner
- Institute for Health and Social Policy, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada; Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - Majid Ezzati
- MRC Centre for Environment and Health, Imperial College London, UK; Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, UK; Regional Institute for Population Studies, University of Ghana, Legon, Ghana
| | - Frank J Kelly
- MRC Centre for Environment and Health, Imperial College London, UK; NIHR-HPRU Environmental Exposures and Health, School of Public Health, Imperial College London, UK
| | - Benjamin Barratt
- MRC Centre for Environment and Health, Imperial College London, UK; NIHR-HPRU Environmental Exposures and Health, School of Public Health, Imperial College London, UK
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Johnson M, Piedrahita R, Pillarisetti A, Shupler M, Menya D, Rossanese M, Delapeña S, Penumetcha N, Chartier R, Puzzolo E, Pope D. Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya. INDOOR AIR 2021; 31:1441-1457. [PMID: 33655590 DOI: 10.1111/ina.12790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/24/2020] [Indexed: 06/12/2023]
Abstract
This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM2.5 , black carbon, CO); household air pollution (PM2.5 , CO); personal exposure (PM2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM2.5 (R2 = 0.45) and CO (R2 = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R2 = 0.76) and a root mean-squared error of 85 µg/m3 , and the survey-only-based regression model was able to predict PM2.5 exposures with an R2 of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R2 of 0.57 and RMSE of 98 µg/m3 .
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Affiliation(s)
| | | | - Ajay Pillarisetti
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Matthew Shupler
- Department of Public Health and Policy, University of Liverpool, Liverpool, UK
| | - Diana Menya
- Department of Epidemiology and Medical Statistics, School of Public Health, College of Health Sciences, Moi University, Eldoret, Kenya
| | | | | | | | - Ryan Chartier
- RTI International, Research Triangle Park, North Carolina, USA
| | - Elisa Puzzolo
- Department of Public Health and Policy, University of Liverpool, Liverpool, UK
- Global LPG Partnership, London, UK
| | - Daniel Pope
- Department of Public Health and Policy, University of Liverpool, Liverpool, UK
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Ravindra K, Kaur-Sidhu M, Mor S. Transition to clean household energy through an application of integrated model: Ensuring sustainability for better health, climate and environment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 775:145657. [PMID: 33621873 DOI: 10.1016/j.scitotenv.2021.145657] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 06/12/2023]
Abstract
Sustained use and adoption of clean cooking fuels have become an important concern for developing countries due to the enormous burden of diseases attributable to household air pollution (HAP). The transition and adoption of clean household energy involve various socio-economic, behavioral, and technological barriers at different community levels. Hence, the present paper aims to scrutinize the factors, key determinants, and other interventions among rural households that limit clean cookstoves' sustained uses. The study proposes an integrated model to enhance clean cooking fuel uptake and uses based on the available evidence. The health, climate and environmental factors were identified as the key to trigger the adoption of clean cooking fuel alternatives. The model comprises the integration of components for targeted clean fuel policy interventions and promotes green recovery. The elements include Knowledge, Housing characteristics, Awareness, Interventions, Willingness to pay, Adoption, Lower emissions and Gender Equality (THE KHAIWAL model) to ascertain the intervention focus regions. Integration of model components in policy implementation will promote clean household energy to reduce emissions, leading to improve quality of life, good health, women empowerment, better air quality and climate.
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Affiliation(s)
- Khaiwal Ravindra
- Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India.
| | - Maninder Kaur-Sidhu
- Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India
| | - Suman Mor
- Department of Environment Studies, Panjab University, Chandigarh 160014, India
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Gao S, Zhao H, Bai Z, Han B, Xu J, Zhao R, Zhang N, Chen L, Lei X, Shi W, Zhang L, Li P, Yu H. Combined use of principal component analysis and artificial neural network approach to improve estimates of PM 2.5 personal exposure: A case study on older adults. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 726:138533. [PMID: 32320881 DOI: 10.1016/j.scitotenv.2020.138533] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 04/05/2020] [Accepted: 04/05/2020] [Indexed: 06/11/2023]
Abstract
Accurate exposure estimate of the air pollutant PM2.5 is required to evaluate its health impacts in epidemiological studies, due to its adverse effects on human's respiratory and cardiovascular systems. However, traditional personal sampling is time and cost consuming. Thus, modeling techniques are needed to accurately predict the personal exposure level to PM2.5. In this study, a total of 117 older adults over 60 were recruited in Tianjin, a heavily polluted city in northern China, for indoor, outdoor and personal PM2.5 sampling. Eighteen variables which may increase the exposure level of older adults were recorded for artificial neural network (ANN) simulation. Four modeling techniques, including time-integrated activity modeling, Monte Carlo simulation, ANN modeling, and combined use of principal component analysis (PCA) and ANN model, were used to evaluate their ability for predicting real exposure values of PM2.5. The results of traditional time-weighted activity modeling showed the lowest correlation with measured values with R2 of 0.57 and 0.42 in winter and summer, respectively. For Monte Carlo simulation, high correlation was obtained (R2 of 0.93 and 0.92 in winter and summer, respectively) between percentiles of the predicted and the real exposure values. Compared with the simple ANN models, the combined use of PCA and ANN produced the most accurate results with R2 of 0.99 and RMSE lower than 15. Since the information of the input variables for the PCA-ANN model can be obtained from the questionnaire and fixed air quality monitoring sites, this technique shows a great potential in predicting personal exposure level to the air pollutant because no additional concentration measurement is needed.
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Affiliation(s)
- Shuang Gao
- College of Computer Science, Nankai University, Tianjin, China; School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China; Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China; Postdoctoral Innovation Practice Base, Huafa Industrial Share Co., Ltd, Zhuhai, China.
| | - Hong Zhao
- College of Computer Science, Nankai University, Tianjin, China.
| | - Zhipeng Bai
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China.
| | - Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Jia Xu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Ruojie Zhao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Nan Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Li Chen
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, China
| | - Xiang Lei
- Postdoctoral Innovation Practice Base, Huafa Industrial Share Co., Ltd, Zhuhai, China
| | - Wendong Shi
- Postdoctoral Innovation Practice Base, Huafa Industrial Share Co., Ltd, Zhuhai, China
| | - Liwen Zhang
- Collage of Public Health, Tianjin Medical University, Tianjin, China
| | - Penghui Li
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin, China
| | - Hai Yu
- Commonwealth Scientific and Industrial Research Organization (CSIRO) Energy, North Ryde, Australia
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7
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Johnson MA, Steenland K, Piedrahita R, Clark ML, Pillarisetti A, Balakrishnan K, Peel JL, Naeher LP, Liao J, Wilson D, Sarnat J, Underhill LJ, Burrowes V, McCracken JP, Rosa G, Rosenthal J, Sambandam S, de Leon O, Kirby MA, Kearns K, Checkley W, Clasen T. Air Pollutant Exposure and Stove Use Assessment Methods for the Household Air Pollution Intervention Network (HAPIN) Trial. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:47009. [PMID: 32347764 PMCID: PMC7228125 DOI: 10.1289/ehp6422] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/27/2020] [Accepted: 04/01/2020] [Indexed: 05/05/2023]
Abstract
BACKGROUND High quality personal exposure data is fundamental to understanding the health implications of household energy interventions, interpreting analyses across assigned study arms, and characterizing exposure-response relationships for household air pollution. This paper describes the exposure data collection for the Household Air Pollution Intervention Network (HAPIN), a multicountry randomized controlled trial of liquefied petroleum gas stoves and fuel among 3,200 households in India, Rwanda, Guatemala, and Peru. OBJECTIVES The primary objectives of the exposure assessment are to estimate the exposure contrast achieved following a clean fuel intervention and to provide data for analyses of exposure-response relationships across a range of personal exposures. METHODS Exposure measurements are being conducted over the 3-y time frame of the field study. We are measuring fine particulate matter [PM < 2.5 μ m in aerodynamic diameter (PM 2.5 )] with the Enhanced Children's MicroPEM™ (RTI International), carbon monoxide (CO) with the USB-EL-CO (Lascar Electronics), and black carbon with the OT21 transmissometer (Magee Scientific) in pregnant women, adult women, and children < 1 year of age, primarily via multiple 24-h personal assessments (three, six, and three measurements, respectively) over the course of the 18-month follow-up period using lightweight monitors. For children we are using an indirect measurement approach, combining data from area monitors and locator devices worn by the child. For a subsample (up to 10%) of the study population, we are doubling the frequency of measurements in order to estimate the accuracy of subject-specific typical exposure estimates. In addition, we are conducting ambient air monitoring to help characterize potential contributions of PM 2.5 exposure from background concentration. Stove use monitors (Geocene) are being used to assess compliance with the intervention, given that stove stacking (use of traditional stoves in addition to the intervention gas stove) may occur. CONCLUSIONS The tools and approaches being used for HAPIN to estimate personal exposures build on previous efforts and take advantage of new technologies. In addition to providing key personal exposure data for this study, we hope the application and learnings from our exposure assessment will help inform future efforts to characterize exposure to household air pollution and for other contexts. https://doi.org/10.1289/EHP6422.
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Affiliation(s)
| | - Kyle Steenland
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | | | - Maggie L Clark
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Ajay Pillarisetti
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Kalpana Balakrishnan
- Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India
| | - Jennifer L Peel
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Luke P Naeher
- Department of Environmental Health Science, College of Public Health, University of Georgia, Athens, Georgia, USA
| | - Jiawen Liao
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | | | - Jeremy Sarnat
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Lindsay J Underhill
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Vanessa Burrowes
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - John P McCracken
- Center for Health Studies, Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Ghislaine Rosa
- Department of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Joshua Rosenthal
- Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Sankar Sambandam
- Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India
| | - Oscar de Leon
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Miles A Kirby
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Katherine Kearns
- Department of Environmental Health Engineering, ICMR Center for Advanced Research on Air Quality, Climate and Health, Sri Ramachandra Institute for Higher Education and Research (Deemed University), Chennai, India
| | - William Checkley
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas Clasen
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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