{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T04:13:46Z","timestamp":1791260026323,"version":"4.1.0"},"reference-count":38,"publisher":"American Geophysical Union (AGU)","issue":"5","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFE0115300"],"award-info":[{"award-number":["2018YFE0115300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82003549"],"award-info":[{"award-number":["82003549"]}],"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":["1R01ES032140"],"award-info":[{"award-number":["1R01ES032140"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["agupubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["GeoHealth"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Despite the recent development of using satellite remote sensing to predict surface NO\n                    <jats:sub>2<\/jats:sub>\n                    levels in China, methods for estimating reliable historical NO\n                    <jats:sub>2<\/jats:sub>\n                    exposure, especially before the establishment of NO\n                    <jats:sub>2<\/jats:sub>\n                    monitoring network in 2013, are still rare. A gap\u2010filling model was first adopted to impute the missing NO\n                    <jats:sub>2<\/jats:sub>\n                    column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO\n                    <jats:sub>2<\/jats:sub>\n                    concentrations at 0.05\u00b0 spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO\n                    <jats:sub>2<\/jats:sub>\n                    associated mortality burdens in China. The coverage of satellite NO\n                    <jats:sub>2<\/jats:sub>\n                    column densities increased from 46.9% to 100% after gap\u2010filling. The ensemble model predictions had good agreement with observations, and the sample\u2010based, temporal and spatial cross\u2010validation (CV)\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO\n                    <jats:sub>2<\/jats:sub>\n                    concentrations, with both by\u2010year CV\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    and external separate year validation\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    achieving 0.80. The estimated national NO\n                    <jats:sub>2<\/jats:sub>\n                    levels showed a increasing trend during 2005\u20132011, then decreased gradually until 2020, especially in 2012\u20132015. The estimated annual mortality burden attributable to long\u2010term NO\n                    <jats:sub>2<\/jats:sub>\n                    exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite\u2010based ensemble model could provide reliable long\u2010term NO\n                    <jats:sub>2<\/jats:sub>\n                    predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO\n                    <jats:sub>2<\/jats:sub>\n                    and call for more targeted policies to reduce the emission of nitrogen oxides in China.\n                  <\/jats:p>","DOI":"10.1029\/2023gh000798","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T08:12:14Z","timestamp":1684311134000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Satellite\u2010Based Long\u2010Term Spatiotemporal Trends in Ambient NO\n                    <sub>2<\/sub>\n                    Concentrations and Attributable Health Burdens in China From 2005 to 2020"],"prefix":"10.1029","volume":"7","author":[{"given":"Keyong","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Epidemiology Fuwai Hospital, National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"},{"name":"Key Laboratory of Cardiovascular Epidemiology Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Gangarosa Department of Environmental Health Rollins School of Public Health Emory University  Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangfeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Fuwai Hospital, National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"},{"name":"Key Laboratory of Cardiovascular Epidemiology Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongfeng","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Fuwai Hospital, National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"},{"name":"Key Laboratory of Cardiovascular Epidemiology Chinese Academy of Medical Sciences  Beijing China"},{"name":"School of Medicine Southern University of Science and Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5477-2186","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Gangarosa Department of Environmental Health Rollins School of Public Health Emory University  Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"13","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2021.105821"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(17)30505-6"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04229-0"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.9b03107"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.9b03358"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1289\/ehp.1409567"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-16-0758.1"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/abc7df"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2021.118285"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","unstructured":"Huang 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