{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T20:03:50Z","timestamp":1789502630656,"version":"build-2803163510"},"reference-count":25,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["EGP 534232-18"],"award-info":[{"award-number":["EGP 534232-18"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001160","name":"Health Effects Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001160","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science of The Total Environment"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.scitotenv.2025.179286","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T21:06:37Z","timestamp":1743973597000},"page":"179286","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Traffic-related air pollution backcasting using convolutional neural network and long short-term memory approach"],"prefix":"10.1016","volume":"976","author":[{"given":"Arman","family":"Ganji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marshall","family":"Lloyd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junshi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Weichenthal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianne","family":"Hatzopoulou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.scitotenv.2025.179286_bb0005","doi-asserted-by":"crossref","DOI":"10.1289\/ehp.10952","article-title":"A cohort study of traffic-related air pollution impacts on birth outcomes","volume":"116","author":"Brauer","year":"2008","journal-title":"Environ. Health Perspect."},{"issue":"10","key":"10.1016\/j.scitotenv.2025.179286_bb0010","doi-asserted-by":"crossref","DOI":"10.1136\/oem.2007.037846","article-title":"Comparison between various indices of exposure to traffic-related air pollution and their impact on respiratory health in adults","volume":"65","author":"Cesaroni","year":"2008","journal-title":"Occup. Environ. Med."},{"issue":"15","key":"10.1016\/j.scitotenv.2025.179286_bb0015","doi-asserted-by":"crossref","first-page":"3859","DOI":"10.3390\/rs15153859","article-title":"Application of deep learning in multitemporal remote sensing image classification","volume":"15","author":"Cheng","year":"2023","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.scitotenv.2025.179286_bb0025","doi-asserted-by":"crossref","first-page":"31191","DOI":"10.1109\/ACCESS.2021.3060290","article-title":"On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach","volume":"9","author":"Farsi","year":"2021","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.scitotenv.2025.179286_bb0030","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1111\/mice.12508","article-title":"Methodology for spatio-temporal predictions of traffic counts across an urban road network and generation of an on-road greenhouse gas emission inventory","volume":"35","author":"Ganji","year":"2020","journal-title":"Comput. Aided Civ. Inf. Eng."},{"key":"10.1016\/j.scitotenv.2025.179286_bb0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2022.103739","article-title":"Traffic volume prediction using aerial imagery and sparse data from road counts","volume":"141","author":"Ganji","year":"2022","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.scitotenv.2025.179286_bb0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2022.120720","article-title":"Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: the urban scanner platform","volume":"317","author":"Ganji","year":"2023","journal-title":"Environ. Pollut."},{"key":"10.1016\/j.scitotenv.2025.179286_bb0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2024.170075","article-title":"Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories","volume":"915","author":"Ganji","year":"2024","journal-title":"Sci. Total Environ."},{"issue":"10","key":"10.1016\/j.scitotenv.2025.179286_bb0050","doi-asserted-by":"crossref","DOI":"10.1164\/rccm.200901-0117OC","article-title":"Influence of ambient air pollutant sources on clinical encounters for infant bronchiolitis","volume":"180","author":"Karr","year":"2009","journal-title":"Am. J. Respir. Crit. Care Med."},{"issue":"6","key":"10.1016\/j.scitotenv.2025.179286_bb0060","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"10.1016\/j.scitotenv.2025.179286_bb0065","author":"Liaw"},{"key":"10.1016\/j.scitotenv.2025.179286_bb0070","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."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.179286_bb0075","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1097\/EDE.0b013e3181fdb60f","article-title":"Residential air pollution and otitis media during the first two years of life","volume":"22","author":"MacIntyre","year":"2011","journal-title":"Epidemiology"},{"issue":"1","key":"10.1016\/j.scitotenv.2025.179286_bb0080","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.csda.2006.12.019","article-title":"Relaxed Lasso","volume":"52","author":"Meinshausen","year":"2007","journal-title":"Computational Statistics & Data Analysis"},{"issue":"1","key":"10.1016\/j.scitotenv.2025.179286_bb0085","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.scitotenv.2010.10.005","article-title":"Modelling air pollution for epidemiologic research \u2013 part II: predicting temporal variation through land use regression","volume":"409","author":"M\u00f6lter","year":"2010","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.scitotenv.2025.179286_bb0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.166168","article-title":"Beyond here and now: evaluating pollution estimation across space and time from street view images with deep learning","volume":"903","author":"Nathvani","year":"2023","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.scitotenv.2025.179286_bb0100","series-title":"inrix global traffic scorecard.<\/i>","author":"Pishue","year":"2023"},{"issue":"9","key":"10.1016\/j.scitotenv.2025.179286_bb0105","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1289\/ehp.10047","article-title":"Traffic-related atmospheric pollutants levels during pregnancy and offspring\u2019s term birth weight: A study relying on a land-use regression exposure model","volume":"115","author":"Slama","year":"2007","journal-title":"Environ. Health Perspect."},{"issue":"1","key":"10.1016\/j.scitotenv.2025.179286_bb0110","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/j.1467-9868.2005.00490.x","article-title":"Sparsity and smoothness via the fused lasso","volume":"67","author":"Tibshirani","year":"2005","journal-title":"J. R. Stat. Soc. Series B Stat. Methodology"},{"issue":"9","key":"10.1016\/j.scitotenv.2025.179286_bb0115","doi-asserted-by":"crossref","first-page":"5104","DOI":"10.3390\/su14095104","article-title":"A hybrid spatiotemporal deep model based on CNN and LSTM for air pollution prediction","volume":"14","author":"Tsokov","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.scitotenv.2025.179286_bb0120","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2019.05.012","article-title":"Using deep learning for multivariate mapping of soil with quantified uncertainty","volume":"351","author":"Wadoux","year":"2019","journal-title":"Geoderma"},{"issue":"39","key":"10.1016\/j.scitotenv.2025.179286_bb0125","doi-asserted-by":"crossref","first-page":"55129","DOI":"10.1007\/s11356-021-14687-8","article-title":"A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism","volume":"28","author":"Yang","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"issue":"23","key":"10.1016\/j.scitotenv.2025.179286_bb0130","doi-asserted-by":"crossref","first-page":"16621","DOI":"10.1021\/acs.est.2c04320","article-title":"Fifteen years of community exposure to heavy-duty emissions: capturing disparities over space and time","volume":"56","author":"Zalzal","year":"2022","journal-title":"Environ. Sci. Technol."},{"issue":"18","key":"10.1016\/j.scitotenv.2025.179286_bb0135","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.3390\/rs14184441","article-title":"A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based Phenological variables","volume":"14","author":"Zhang","year":"2022","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.scitotenv.2025.179286_bb0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2019.04.034","article-title":"Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm","volume":"232","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Science of The Total Environment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969725009222?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969725009222?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T08:20:33Z","timestamp":1779265233000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0048969725009222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":25,"alternative-id":["S0048969725009222"],"URL":"https:\/\/doi.org\/10.1016\/j.scitotenv.2025.179286","relation":{},"ISSN":["0048-9697"],"issn-type":[{"value":"0048-9697","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Traffic-related air pollution backcasting using convolutional neural network and long short-term memory approach","name":"articletitle","label":"Article Title"},{"value":"Science of The Total Environment","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scitotenv.2025.179286","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"179286"}}