{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T04:32:37Z","timestamp":1777782757278,"version":"3.51.4"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T00:00:00Z","timestamp":1556064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871351"],"award-info":[{"award-number":["41871351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471376"],"award-info":[{"award-number":["41471376"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","award":["P30ES007048"],"award-info":[{"award-number":["P30ES007048"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016225","name":"Southern California Environmental Health Sciences Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016225","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004800","name":"California Air Resources Board","doi-asserted-by":"publisher","award":["04-323"],"award-info":[{"award-number":["04-323"]}],"id":[{"id":"10.13039\/100004800","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environment International"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1016\/j.envint.2019.04.057","type":"journal-article","created":{"date-parts":[[2019,5,8]],"date-time":"2019-05-08T03:10:45Z","timestamp":1557285045000},"page":"310-323","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"special_numbering":"C","title":["Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions"],"prefix":"10.1016","volume":"128","author":[{"given":"Lianfa","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariam","family":"Girguis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frederick","family":"Lurmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2693-7112","authenticated-orcid":false,"given":"Jun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Urman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7044-3515","authenticated-orcid":false,"given":"Edward","family":"Rappaport","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beate","family":"Ritz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meredith","family":"Franklin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carrie","family":"Breton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Gilliland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rima","family":"Habre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.envint.2019.04.057_bb0010","doi-asserted-by":"crossref","first-page":"262","DOI":"10.3390\/rs8030262","article-title":"A geographically and temporally weighted regression model for ground-level PM2.5 estimation from satellite-derived 500 m resolution AOD","volume":"8","author":"Bai","year":"2016","journal-title":"Remote Sens."},{"key":"10.1016\/j.envint.2019.04.057_bb0015","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.atmosenv.2013.02.037","article-title":"Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe e the ESCAPE project","volume":"72","author":"Beelen","year":"2013","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0020","series-title":"Random Search for Hyper-Parameter Optimization Machine Learning Research","first-page":"281","volume":"13","author":"Bergstra","year":"2012"},{"key":"10.1016\/j.envint.2019.04.057_bb0025","series-title":"Pattern Recognition and Machine Learning: Springer","author":"Bishop","year":"2006"},{"key":"10.1016\/j.envint.2019.04.057_bb0030","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1136\/bmj.308.6942.1499","article-title":"Statistic notes: regression towards the mean","volume":"308","author":"Bland","year":"1994","journal-title":"Br. Med. J."},{"key":"10.1016\/j.envint.2019.04.057_bb0035","article-title":"Introductory Biostatistics for the Health Sciences","volume":"272","author":"Chernick","year":"2003","journal-title":"In.: Wiley-Interscience"},{"key":"10.1016\/j.envint.2019.04.057_bb0040","series-title":"Hyperparameter Search in Machine Learning","author":"Claesen","year":"2015"},{"key":"10.1016\/j.envint.2019.04.057_bb0045","author":"CRAN Task View"},{"issue":"9","key":"10.1016\/j.envint.2019.04.057_bb0050","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.2019.04.057_bb0055","series-title":"Workshop on Multiple Classifier Systems: 2000","first-page":"1","article-title":"Ensemble methods in machine learning","author":"Dietterich","year":"2000"},{"key":"10.1016\/j.envint.2019.04.057_bb0060","doi-asserted-by":"crossref","DOI":"10.1186\/s12940-016-0137-9","article-title":"Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions","volume":"15","author":"Eeftens","year":"2016","journal-title":"Environ. Health"},{"key":"10.1016\/j.envint.2019.04.057_bb0065","series-title":"How Nitrogen Oxides Affect the Way we Live and Breathe","author":"Environmental Protection Agency","year":"2008"},{"key":"10.1016\/j.envint.2019.04.057_bb0070","series-title":"Cluster Analysis","author":"Everitt","year":"2011"},{"key":"10.1016\/j.envint.2019.04.057_bb0075","series-title":"Statistical Methods for Spatio-Temporal Systems","author":"Finkenstadt","year":"2007"},{"key":"10.1016\/j.envint.2019.04.057_bb0080","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2017.06.001","article-title":"Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model","volume":"198","author":"Guo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0085","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.2019.04.057_bb0090","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2016.07.005","article-title":"Development of west-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data","volume":"151","author":"de Hoogh","year":"2016","journal-title":"Environ. Res."},{"key":"10.1016\/j.envint.2019.04.057_bb0095","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2013.08.032","article-title":"Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model","volume":"140","author":"Hu","year":"2014","journal-title":"Remote Sens. Environ."},{"issue":"12","key":"10.1016\/j.envint.2019.04.057_bb0100","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.2019.04.057_bb0105","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/sj.jea.7500388","article-title":"A review and evaluation of intraurban air pollution exposure models","volume":"15","author":"Jerrett","year":"2005","journal-title":"J. Expo. Anal. Environ. Epidemiol."},{"issue":"21","key":"10.1016\/j.envint.2019.04.057_bb0110","doi-asserted-by":"crossref","first-page":"11913","DOI":"10.1021\/es302673e","article-title":"Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the mid-atlantic states","volume":"46","author":"Kloog","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2019.04.057_bb0115","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.atmosenv.2012.03.035","article-title":"Use of generalized additive models and cokriging of spatial residuals to improve land-use regression estimates of nitrogen oxides in Southern California","volume":"55","author":"Li","year":"2012","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0120","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.atmosenv.2013.01.038","article-title":"Estimating spatiotemporal variability of ambient air pollutant concentrations with a hierarchical model","volume":"71","author":"Li","year":"2013","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0125","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.atmosenv.2014.12.004","article-title":"The application of semicircular-buffer-based land use regression models incorporating wind direction in predicting quarterly NO2 and PM10 concentrations","volume":"103","author":"Li","year":"2015","journal-title":"Atmos. Environ."},{"issue":"17","key":"10.1016\/j.envint.2019.04.057_bb0130","doi-asserted-by":"crossref","first-page":"9920","DOI":"10.1021\/acs.est.7b01864","article-title":"Constrained mixed-effect models with ensemble learning for prediction of nitrogen oxides concentrations at high spatiotemporal resolution","volume":"51","author":"Li","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envint.2019.04.057_bb0135","series-title":"SpatioTemporal: An R Package for Spatio-Temporal Modelling of Air-Pollution","author":"Lindstrom","year":"2013"},{"key":"10.1016\/j.envint.2019.04.057_bb0140","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.atmosenv.2015.06.056","article-title":"Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China","volume":"116","author":"Liu","year":"2015","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0145","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.atmosenv.2012.10.019","article-title":"Short-term effects of daily air pollution on mortality","volume":"65","author":"Mahiyuddin","year":"2013","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2017.12.001","article-title":"Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation","volume":"101","author":"Meyer","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.envint.2019.04.057_bb0155","series-title":"MS: Computer Assisted Cartography Principles and Prospects","author":"Monmonier","year":"1982"},{"key":"10.1016\/j.envint.2019.04.057_bb0160","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1559\/152304075784447289","article-title":"Cartographic data structures","volume":"2","author":"Peucker","year":"1975","journal-title":"American Cartographer"},{"issue":"7","key":"10.1016\/j.envint.2019.04.057_bb0165","doi-asserted-by":"crossref","first-page":"A288","DOI":"10.1289\/ehp.116-a288","article-title":"Air pollution: salt mist is the right seasoning for ozone","volume":"116","author":"Potera","year":"2008","journal-title":"Environ. Health Perspect."},{"key":"10.1016\/j.envint.2019.04.057_bb0170","unstructured":"Root-mean-square deviation."},{"key":"10.1016\/j.envint.2019.04.057_bb0175","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/08958370701495998","article-title":"A review of land-use regression for characterizing intraurban air models pollution exposure","volume":"19","author":"Ryan","year":"2007","journal-title":"Inhal. Toxicol."},{"issue":"75","key":"10.1016\/j.envint.2019.04.057_bb0180","first-page":"383","article-title":"A regionalized national universal kriging model using partial least squares regression for estimating annual PM2.5 concentrations in epidemiology","volume":"2013","author":"Sampson","year":"1994","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envint.2019.04.057_bb0185","series-title":"Spatial Modeling Principles in Earth Sciences","author":"Sen","year":"2016"},{"key":"10.1016\/j.envint.2019.04.057_bb0190","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.envres.2009.06.001","article-title":"Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy","volume":"109","author":"Su","year":"2009","journal-title":"Environ. Res."},{"key":"10.1016\/j.envint.2019.04.057_bb0195","series-title":"Introduction to Algorithms","author":"Thomas","year":"2009"},{"issue":"5","key":"10.1016\/j.envint.2019.04.057_bb0205","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1097\/EDE.0000000000000686","article-title":"Trade-offs of personal versus more proxy exposure measures in environmental epidemiology","volume":"28","author":"Weisskopf","year":"2017","journal-title":"Epidemiology"},{"key":"10.1016\/j.envint.2019.04.057_bb0210","series-title":"WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen, Dioxide and Sulfur Dioxide","author":"World Health Organization","year":"2005"},{"key":"10.1016\/j.envint.2019.04.057_bb0215","series-title":"Review of Evidence on Health Aspects of Air Pollution\u2014REVIHAAP Project: Final Technical Report","author":"World Health Organization","year":"2013"},{"key":"10.1016\/j.envint.2019.04.057_bb0220","series-title":"Health Effects of Particular Matter: Policy Implications for Countries in Eastern Europe, Caucasus and Central Asia","author":"World Health Organization","year":"2013"},{"key":"10.1016\/j.envint.2019.04.057_bb0225","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.rse.2017.07.023","article-title":"Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China","volume":"199","author":"Xiao","year":"2017","journal-title":"Remote Sens. Environ."},{"issue":"20","key":"10.1016\/j.envint.2019.04.057_bb0230","doi-asserted-by":"crossref","first-page":"12280","DOI":"10.1021\/acs.est.5b01413","article-title":"Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD","volume":"49","author":"Xie","year":"2015","journal-title":"Environ. Sci. Technol."}],"container-title":["Environment International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0160412018331441?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0160412018331441?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:35:04Z","timestamp":1777491304000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0160412018331441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":44,"alternative-id":["S0160412018331441"],"URL":"https:\/\/doi.org\/10.1016\/j.envint.2019.04.057","relation":{},"ISSN":["0160-4120"],"issn-type":[{"value":"0160-4120","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions","name":"articletitle","label":"Article Title"},{"value":"Environment International","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envint.2019.04.057","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}]}}