{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T04:35:46Z","timestamp":1792211746370,"version":"4.4.0"},"reference-count":85,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environment International"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1016\/j.envint.2024.108992","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T08:18:39Z","timestamp":1725610719000},"page":"108992","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"special_numbering":"C","title":["Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment"],"prefix":"10.1016","volume":"191","author":[{"given":"Peng","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Anand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyuan","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.envint.2024.108992_b0005","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1021\/acs.est.7b00891","article-title":"High-resolution air pollution mapping with google street view cars: exploiting big data","volume":"51","author":"Apte","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0010","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1021\/acssensors.7b00620","article-title":"Amperometric gas sensors as a low cost emerging technology platform for air quality monitoring applications: a review","volume":"2","author":"Baron","year":"2017","journal-title":"ACS Sensors"},{"key":"10.1016\/j.envint.2024.108992_b0015","doi-asserted-by":"crossref","unstructured":"Briggs, D., 2006. The Role of Gis: Coping With Space (And Time) in Air Pollution Exposure Assessment. http:\/\/dx.doi.org\/10.1080\/15287390590936094 68, 1243\u20131261. https:\/\/doi.org\/10.1080\/15287390590936094.","DOI":"10.1080\/15287390590936094"},{"key":"10.1016\/j.envint.2024.108992_b0020","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2109249118","article-title":"Local- And regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring","volume":"118","author":"Chambliss","year":"2021","journal-title":"Proc. Natl. Acad. Sci. u. s. a."},{"key":"10.1016\/j.envint.2024.108992_b0025","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.jes.2022.02.041","article-title":"Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data","volume":"125","author":"Che","year":"2023","journal-title":"J. Environ. Sci."},{"key":"10.1016\/j.envint.2024.108992_b0030","doi-asserted-by":"crossref","unstructured":"Chen, D., Hu, F., Nian, G., Yang, T., 2020. Deep Residual Learning for Nonlinear Regression. Entropy 2020, Vol. 22, Page 193 22, 193. https:\/\/doi.org\/10.3390\/E22020193.","DOI":"10.3390\/e22020193"},{"key":"10.1016\/j.envint.2024.108992_b0035","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/S0140-6736(16)32399-6","article-title":"Living near major roads and the incidence of dementia, Parkinson\u2019s disease, and multiple sclerosis: a population-based cohort study","volume":"389","author":"Chen","year":"2017","journal-title":"Lancet"},{"key":"10.1016\/j.envint.2024.108992_b0040","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1037\/0096-3445.136.1.23","article-title":"Mapping air pollution with google street view cars: efficient approaches with mobile monitoring and land use regression(SI)","volume":"136","author":"Dalgleish","year":"2018","journal-title":"J. Exp. Psychol. Gen."},{"key":"10.1016\/j.envint.2024.108992_b0045","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1289\/ehp.10911","article-title":"Personal and ambient air pollution exposures and lung function decrements in children with asthma","volume":"116","author":"Delfino","year":"2008","journal-title":"Environ. Health Perspect."},{"key":"10.1016\/j.envint.2024.108992_b0050","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1021\/acs.est.5b04235","article-title":"Investigating the use of portable air pollution sensors to capture the spatial variability of traffic-related air pollution","volume":"50","author":"Deville Cavellin","year":"2015","journal-title":"Environ. Sci. & Technol."},{"key":"10.1016\/j.envint.2024.108992_b0055","doi-asserted-by":"crossref","first-page":"4712","DOI":"10.1021\/acs.est.5b06121","article-title":"Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States","volume":"50","author":"Di","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0060","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1021\/acs.est.9b03358","article-title":"Assessing NO2 concentration and model uncertainty with high spatiotemporal resolution across the contiguous United States using ensemble model averaging","volume":"54","author":"Di","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2021.118212","article-title":"A CatBoost approach with wavelet decomposition to improve satellite-derived high-resolution PM2.5 estimates in Beijing-Tianjin-Hebei","volume":"249","author":"Ding","year":"2021","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2024.108992_b0070","unstructured":"Emporis, 2018. Hong Kong building types [WWW Document]. URL https:\/\/www.emporis.com\/city\/101300\/hong-kong-china."},{"key":"10.1016\/j.envint.2024.108992_b0075","doi-asserted-by":"crossref","DOI":"10.1021\/acs.est.1c01739","article-title":"Selecting data analytic and modeling methods to support air pollution and environmental justice investigations: a critical review and guidance framework","author":"Gardner-Frolick","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0080","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.envpol.2017.05.091","article-title":"Urban emissions hotspots: Quantifying vehicle congestion and air pollution using mobile phone GPS data","volume":"229","author":"Gately","year":"2017","journal-title":"Environ. Pollut."},{"key":"10.1016\/j.envint.2024.108992_b0085","doi-asserted-by":"crossref","DOI":"10.1021\/acs.est.4c00783","article-title":"Weather and the city: machine learning for predicting and attributing fine scale air quality to meteorological and urban determinants","volume":"58","author":"Gerges","year":"2024","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0090","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.buildenv.2018.02.042","article-title":"Mapping sky, tree, and building view factors of street canyons in a high-density urban environment","volume":"134","author":"Gong","year":"2018","journal-title":"Build. Environ."},{"key":"10.1016\/j.envint.2024.108992_b0095","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1038\/s41561-017-0009-0","article-title":"Lower vehicular primary emissions of NO2 in Europe than assumed in policy projections","volume":"10","author":"Grange","year":"2017","journal-title":"Nat. Geosci."},{"key":"10.1016\/j.envint.2024.108992_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/S0140-6736(14)60617-6","article-title":"Outdoor air pollution and asthma","author":"Guarnieri","year":"2014","journal-title":"Lancet"},{"key":"10.1016\/j.envint.2024.108992_b0105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: an interdisciplinary review","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.envint.2024.108992_b0110","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1289\/EHP442","article-title":"Population-level exposure to particulate air pollution during active travel: planning for low-exposure","volume":"125","author":"Hankey","year":"2017","journal-title":"Health-Promoting Cities. Environ. Health Perspect."},{"key":"10.1016\/j.envint.2024.108992_b0115","doi-asserted-by":"crossref","first-page":"9194","DOI":"10.1021\/acs.est.5b01209","article-title":"Land use regression models of on-road particulate air pollution (particle number, black carbon, PM2.5, particle size) using mobile monitoring","volume":"49","author":"Hankey","year":"2015","journal-title":"Environ. Sci. & Technol."},{"key":"10.1016\/j.envint.2024.108992_b0120","doi-asserted-by":"crossref","first-page":"4305","DOI":"10.1021\/acs.est.8b05249","article-title":"Using mobile monitoring to develop hourly empirical models of particulate air pollution in a rural appalachian community","volume":"53","author":"Hankey","year":"2019","journal-title":"Environ. Sci. & Technol."},{"key":"10.1016\/j.envint.2024.108992_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2020.106329","article-title":"More mileage in reducing urban air pollution from road traffic","volume":"149","author":"Harrison","year":"2021","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0130","doi-asserted-by":"crossref","unstructured":"Hatzopoulou, M., Weichenthal, S., Dugum, H., Pickett, G., Miranda-Moreno, L., Kulka, R., Andersen, R., Goldberg, M., 2012. The impact of traffic volume, composition, and road geometry on personal air pollution exposures among cyclists in Montreal, Canada. J. Expo. Sci. Environ. Epidemiol. 2013 231 23, 46\u201351. https:\/\/doi.org\/10.1038\/jes.2012.85.","DOI":"10.1038\/jes.2012.85"},{"key":"10.1016\/j.envint.2024.108992_b0135","doi-asserted-by":"crossref","first-page":"3938","DOI":"10.1021\/acs.est.7b00366","article-title":"Robustness of land-use regression models developed from mobile air pollutant measurements","volume":"51","author":"Hatzopoulou","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0140","unstructured":"HEI, 2010. Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects. Spec. Rep."},{"key":"10.1016\/j.envint.2024.108992_b0145","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.envint.2019.03.026","article-title":"A new approach for inferring traffic-related air pollution: Use of radar-calibrated crowd-sourced traffic data","volume":"127","author":"Hilpert","year":"2019","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0150","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/S0140-6736(02)11280-3","article-title":"Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study","volume":"360","author":"Hoek","year":"2002","journal-title":"Lancet"},{"key":"10.1016\/j.envint.2024.108992_b0155","doi-asserted-by":"crossref","first-page":"7561","DOI":"10.1016\/j.atmosenv.2008.05.057","article-title":"A review of land-use regression models to assess spatial variation of outdoor air pollution","volume":"42","author":"Hoek","year":"2008","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2024.108992_b0160","doi-asserted-by":"crossref","first-page":"6936","DOI":"10.1021\/acs.est.7b01210","article-title":"Estimating PM2.5 concentrations in the conterminous United States using the random forest approach","volume":"51","author":"Hu","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2019.106424","article-title":"Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning","volume":"167","author":"Hu","year":"2020","journal-title":"Build. Environ."},{"key":"10.1016\/j.envint.2024.108992_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2021.116971","article-title":"A review of strategies for mitigating roadside air pollution in urban street canyons","volume":"280","author":"Huang","year":"2021","journal-title":"Environ. Pollut."},{"key":"10.1016\/j.envint.2024.108992_b0175","doi-asserted-by":"crossref","first-page":"8641","DOI":"10.1021\/acs.est.1c02653","article-title":"Spatial modeling of daily PM2.5, NO2, and CO concentrations measured by a low-cost sensor network: comparison of linear, machine learning, and hybrid land use models","volume":"55","author":"Jain","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.168984","article-title":"Traffic-related air pollution, chronic stress, and changes in exhaled nitric oxide and lung function among a panel of children with asthma living in an underresourced community","volume":"912","author":"Ji","year":"2024","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0185","doi-asserted-by":"crossref","DOI":"10.1021\/acs.est.8b06038","article-title":"Performance of prediction algorithms for modeling outdoor air pollution spatial surfaces","volume":"53","author":"Kerckhoffs","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0190","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1164\/rccm.200403-281OC","article-title":"Traffic-related air pollution near busy roads","volume":"170","author":"Kim","year":"2004","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"10.1016\/j.envint.2024.108992_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2014.11.019","article-title":"The rise of low-cost sensing for managing air pollution in cities","author":"Kumar","year":"2015","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0200","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.scitotenv.2017.03.094","article-title":"Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong","volume":"592","author":"Lee","year":"2017","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.landurbplan.2018.07.011","article-title":"Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model","volume":"191","author":"Li","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"10.1016\/j.envint.2024.108992_b0210","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.landurbplan.2017.08.011","article-title":"Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View","volume":"169","author":"Li","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"10.1016\/j.envint.2024.108992_b0215","doi-asserted-by":"crossref","first-page":"11985","DOI":"10.1002\/2017GL075710","article-title":"Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.envint.2024.108992_b0220","doi-asserted-by":"crossref","first-page":"13841","DOI":"10.5194\/acp-19-13841-2019","article-title":"Using wavelet transform to analyse on-road mobile measurements of air pollutants: a case study to evaluate vehicle emission control policies during the 2014 APEC summit","volume":"19","author":"Li","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"10.1016\/j.envint.2024.108992_b0225","article-title":"Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul","volume":"131","author":"Lim","year":"2019","journal-title":"South Korea. Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0230","first-page":"13387","article-title":"Novel application of machine learning algorithms and model-agnostic methods to identify factors influencing childhood blood lead levels","volume":"55","author":"Liu","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2023.108106","article-title":"Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models","volume":"178","author":"Lloyd","year":"2023","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0240","doi-asserted-by":"crossref","first-page":"15519","DOI":"10.1021\/acs.est.1c04047","article-title":"National empirical models of air pollution using microscale measures of the urban environment","volume":"55","author":"Lu","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0245","unstructured":"Lundberg, S., Lee, S.-I., 2017. A Unified Approach to Interpreting Model Predictions."},{"key":"10.1016\/j.envint.2024.108992_b0250","doi-asserted-by":"crossref","first-page":"7306","DOI":"10.1021\/acs.est.9b01117","article-title":"Fusion Method Combining ground-level observations with chemical transport model predictions using an ensemble deep learning framework: application in China to estimate spatiotemporally-resolved PM2.5 exposure fields in 2014\u20132017","volume":"53","author":"Lyu","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0255","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.scitotenv.2019.05.408","article-title":"A site-optimised multi-scale GIS based land use regression model for simulating local scale patterns in air pollution","volume":"685","author":"Ma","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0260","unstructured":"Google Maps, 2009. Official Google Blog: The bright side of sitting in traffic: Crowdsourcing road congestion data [WWW Document]. URL https:\/\/googleblog.blogspot.com\/2009\/08\/bright-side-of-sitting-in-traffic.html (accessed 6.23.21)."},{"key":"10.1016\/j.envint.2024.108992_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.trd.2020.102536","article-title":"Modelling road transport emissions in Germany \u2013 Current day situation and scenarios for 2040","volume":"87","author":"Matthias","year":"2020","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"10.1016\/j.envint.2024.108992_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2012.11.060","article-title":"The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks","author":"Mead","year":"2013","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2024.108992_b0275","doi-asserted-by":"crossref","DOI":"10.1021\/acs.est.9b05523","article-title":"Characterizing elevated urban air pollutant spatial patterns with mobile monitoring in Houston Texas","author":"Miller","year":"2020","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0280","doi-asserted-by":"crossref","first-page":"9004","DOI":"10.1021\/acs.est.5b00476","article-title":"A Novel approach in quantifying the effect of urban design features on local-scale air pollution in central urban areas","volume":"49","author":"Miskell","year":"2015","journal-title":"Environ. Sci. & Technol."},{"key":"10.1016\/j.envint.2024.108992_b0285","first-page":"417","article-title":"Interpretable Machine Learning \u2013 A Brief History, State-of-the-Art and Challenges","volume":"1323","author":"Molnar","year":"2020","journal-title":"Commun. Comput. Inf. Sci."},{"key":"10.1016\/j.envint.2024.108992_b0290","unstructured":"Molnar, C., 2019. Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Book 247."},{"key":"10.1016\/j.envint.2024.108992_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2018.04.018","article-title":"Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?","author":"Morawska","year":"2018","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0300","doi-asserted-by":"crossref","first-page":"e0212845","DOI":"10.1371\/journal.pone.0212845","article-title":"Characterizing multicity urban traffic conditions using crowdsourced data","volume":"14","author":"Nair","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.envint.2024.108992_b0305","doi-asserted-by":"crossref","first-page":"12752","DOI":"10.1073\/pnas.1821667116","article-title":"Quantifying the sensing power of vehicle fleets","volume":"116","author":"O\u2019Keeffe","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.envint.2024.108992_b0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2019.07.367","article-title":"Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China","volume":"699","author":"Pak","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0315","doi-asserted-by":"crossref","first-page":"30039","DOI":"10.1073\/pnas.1907369117","article-title":"Theoretical issues in deep networks","volume":"117","author":"Poggio","year":"2020","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.envint.2024.108992_b0320","doi-asserted-by":"crossref","unstructured":"Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nat. 2019 5667743 566, 195\u2013204. https:\/\/doi.org\/10.1038\/s41586-019-0912-1.","DOI":"10.1038\/s41586-019-0912-1"},{"key":"10.1016\/j.envint.2024.108992_b0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2020.105827","article-title":"Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States","volume":"142","author":"Ren","year":"2020","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0330","doi-asserted-by":"crossref","first-page":"8178","DOI":"10.1021\/acs.est.6b01807","article-title":"Developing street-level PM2.5 and PM10 land use regression models in high-density hong kong with urban morphological factors","volume":"50","author":"Shi","year":"2016","journal-title":"Environ. Sci. & Technol."},{"key":"10.1016\/j.envint.2024.108992_b0335","article-title":"Estimating daily PM2.5 and PM10 over Italy using an ensemble model","author":"Shtein","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0340","doi-asserted-by":"crossref","first-page":"6985","DOI":"10.1021\/acs.est.8b00292","article-title":"Combining Measurements from Mobile Monitoring and a Reference Site To Develop Models of Ambient Ultrafine Particle Number Concentration at Residences","volume":"52","author":"Simon","year":"2018","journal-title":"Environ. Sci. & Technol."},{"key":"10.1016\/j.envint.2024.108992_b0345","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1016\/j.scitotenv.2009.01.061","article-title":"A distance-decay variable selection strategy for land use regression modeling of ambient air pollution exposures","volume":"407","author":"Su","year":"2009","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0350","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.envint.2014.12.007","article-title":"Populations potentially exposed to traffic-related air pollution in seven world cities","volume":"78","author":"Su","year":"2015","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0355","doi-asserted-by":"crossref","first-page":"11643","DOI":"10.1021\/es402156g","article-title":"Using building heights and street configuration to enhance intraurban PM10, NOX, and NO2 land use regression models","volume":"47","author":"Tang","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0360","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.teac.2016.06.001","article-title":"Crowd-sourced air quality studies: A review of the literature & portable sensors","volume":"11","author":"Thompson","year":"2016","journal-title":"Trends Environ. Anal. Chem."},{"key":"10.1016\/j.envint.2024.108992_b0365","doi-asserted-by":"crossref","first-page":"25370","DOI":"10.1073\/pnas.1921271117","article-title":"Health benefits of on-road transportation pollution control programs in China","volume":"117","author":"Wang","year":"2020","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.envint.2024.108992_b0370","article-title":"Key themes, trends, and drivers of mobile ambient air quality monitoring: a systematic review and meta-analysis","author":"Wang","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2021.118055","article-title":"Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network","volume":"290","author":"Wei","year":"2021","journal-title":"Environ. Pollut."},{"key":"10.1016\/j.envint.2024.108992_b0380","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.scitotenv.2017.11.028","article-title":"Development of a microscale land use regression model for predicting NO2 concentrations at a heavy trafficked suburban area in Auckland","volume":"619\u2013620","author":"Weissert","year":"2018","journal-title":"NZ. Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0385","doi-asserted-by":"crossref","unstructured":"World Health Organization, 2016. Ambient Air Pollution: A global assessment of exposure and burden of disease, World Health Organization.","DOI":"10.17159\/2410-972X\/2016\/v26n2a4"},{"key":"10.1016\/j.envint.2024.108992_b0390","doi-asserted-by":"crossref","DOI":"10.1021\/acs.est.8b02917","article-title":"An ensemble machine-learning model to predict historical PM2.5 concentrations in China from satellite data","author":"Xiao","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0395","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2020.106060","article-title":"New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data","volume":"144","author":"Yan","year":"2020","journal-title":"Environ. Int."},{"key":"10.1016\/j.envint.2024.108992_b0400","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.scitotenv.2019.01.123","article-title":"Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities","volume":"662","author":"Zalzal","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0405","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J., 2017. Pyramid scene parsing network, in: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.660.","DOI":"10.1109\/CVPR.2017.660"},{"key":"10.1016\/j.envint.2024.108992_b0410","first-page":"12741","article-title":"Machine learning: new ideas and tools in environmental science and engineering","volume":"55","author":"Zhong","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2024.108992_b0415","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1093\/nsr\/nwy108","article-title":"Deep forest","volume":"6","author":"Zhou","year":"2019","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.envint.2024.108992_b0420","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.147214","article-title":"Estimation of the losses in potential concentrated solar thermal power electricity production due to air pollution in China","volume":"784","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envint.2024.108992_b0425","doi-asserted-by":"crossref","DOI":"10.1289\/EHP11139","article-title":"Respiratory effects of traffic-related air pollution: a randomized, crossover analysis of lung function, airway metabolome, and biomarkers of airway injury","volume":"131","author":"Zhu","year":"2023","journal-title":"Environ. Health Perspect."}],"container-title":["Environment International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0160412024005786?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0160412024005786?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T16:38:22Z","timestamp":1740242302000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0160412024005786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":85,"alternative-id":["S0160412024005786"],"URL":"https:\/\/doi.org\/10.1016\/j.envint.2024.108992","relation":{},"ISSN":["0160-4120"],"issn-type":[{"value":"0160-4120","type":"print"}],"subject":[],"published":{"date-parts":[[2024,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment","name":"articletitle","label":"Article Title"},{"value":"Environment International","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envint.2024.108992","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108992"}}