{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T20:10:13Z","timestamp":1793391013142,"version":"4.5.0"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"name":"The Research Grants Council of Hong Kong","award":["GRF 16202120"],"award-info":[{"award-number":["GRF 16202120"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3711700"],"award-info":[{"award-number":["2024YFC3711700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007121","name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["E53HDDBR"],"award-info":[{"award-number":["E53HDDBR"]}],"id":[{"id":"10.13039\/501100007121","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSFC\/RGC Joint Research Project","award":["N_HKUST609\/21"],"award-info":[{"award-number":["N_HKUST609\/21"]}]},{"name":"NSFC\/RGC Joint Research Project","award":["42161160329"],"award-info":[{"award-number":["42161160329"]}]},{"DOI":"10.13039\/501100010700","name":"National Institute of Environmental Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010700","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The National Key R&D Program of China"},{"name":"The Hefei Institutes of Physical Science"},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["16302623"],"award-info":[{"award-number":["16302623"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Pollution"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.envpol.2025.126592","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T11:05:34Z","timestamp":1748862334000},"page":"126592","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning"],"prefix":"10.1016","volume":"381","author":[{"given":"Naveed","family":"Ahmad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2326-7889","authenticated-orcid":false,"given":"Changqing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianshu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1508-9218","authenticated-orcid":false,"given":"Jhoon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.envpol.2025.126592_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2023.140615","article-title":"Effects of meteorological conditions on the mixing height of nitrogen dioxide in China using new-generation geostationary satellite measurements and machine learning","volume":"346","author":"Ahmad","year":"2024","journal-title":"Chemosphere"},{"issue":"16","key":"10.1016\/j.envpol.2025.126592_bib2","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.5194\/acp-24-9645-2024","article-title":"Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model","volume":"24","author":"Ahmad","year":"2024","journal-title":"Atmos. Chem. Phys."},{"key":"10.1016\/j.envpol.2025.126592_bib3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.atmosenv.2012.11.046","article-title":"Remote sensing of exposure to NO2: satellite versus ground-based measurement in a large urban area","volume":"69","author":"Bechle","year":"2013","journal-title":"Atmos. Environ."},{"issue":"7893","key":"10.1016\/j.envpol.2025.126592_bib4","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1038\/s41586-021-04229-0","article-title":"Global fine-scale changes in ambient NO2 during COVID-19 lockdowns","volume":"601","author":"Cooper","year":"2022","journal-title":"Nature"},{"issue":"17","key":"10.1016\/j.envpol.2025.126592_bib5","doi-asserted-by":"crossref","first-page":"10279","DOI":"10.1021\/acs.est.9b03107","article-title":"Predicting fine-scale daily NO2 for 2005\u20132016 incorporating OMI satellite data across Switzerland","volume":"53","author":"De Hoogh","year":"2019","journal-title":"Environ. Sci. Technol."},{"issue":"11","key":"10.1016\/j.envpol.2025.126592_bib6","doi-asserted-by":"crossref","first-page":"7563","DOI":"10.1007\/s00521-019-04287-6","article-title":"A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks","volume":"32","author":"Eslami","year":"2020","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"10.1016\/j.envpol.2025.126592_bib7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"10.1016\/j.envpol.2025.126592_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2024.108818","article-title":"Deep learning bias correction of GEMS tropospheric NO2: a comparative validation of NO2 from GEMS and TROPOMI using pandora observations","volume":"190","author":"Ghahremanloo","year":"2024","journal-title":"Environ. Int."},{"issue":"21","key":"10.1016\/j.envpol.2025.126592_bib9","doi-asserted-by":"crossref","DOI":"10.1029\/2021JD034925","article-title":"Deep learning estimation of daily ground\u2010level NO2 concentrations from remote sensing data","volume":"126","author":"Ghahremanloo","year":"2021","journal-title":"J. Geophys. Res. Atmos."},{"issue":"5","key":"10.1016\/j.envpol.2025.126592_bib10","doi-asserted-by":"crossref","DOI":"10.1029\/2023GH000798","article-title":"Satellite\u2010based long\u2010term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020","volume":"7","author":"Huang","year":"2023","journal-title":"GeoHealth"},{"issue":"6","key":"10.1016\/j.envpol.2025.126592_bib11","doi-asserted-by":"crossref","DOI":"10.4209\/aaqr.210398","article-title":"Retrieval of NO2 columns by exploiting MAX-DOAS observations and comparison with OMI and TROPOMI data during the time period of 2015\u20132019","volume":"22","author":"Iqbal","year":"2022","journal-title":"Aerosol Air Qual. Res."},{"issue":"1","key":"10.1016\/j.envpol.2025.126592_bib12","doi-asserted-by":"crossref","first-page":"6006","DOI":"10.1038\/s41467-024-50000-0","article-title":"Air pollution impacts from warehousing in the United States uncovered with satellite data","volume":"15","author":"Kerr","year":"2024","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.envpol.2025.126592_bib13","doi-asserted-by":"crossref","first-page":"E1","DOI":"10.1175\/BAMS-D-18-0013.1","article-title":"New era of air quality monitoring from space: geostationary environment monitoring spectrometer (GEMS)","volume":"101","author":"Kim","year":"2020","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"10.1016\/j.envpol.2025.126592_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2021.112573","article-title":"Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning","volume":"264","author":"Kim","year":"2021","journal-title":"Remote Sensing of Environment"},{"issue":"1","key":"10.1016\/j.envpol.2025.126592_bib15","first-page":"1","article-title":"Assessments of the GEMS NO2 products using ground-based pandora and In-Situ instruments over Busan, South Korea","volume":"40","author":"Kim","year":"2024","journal-title":"Korean Journal of Remote Sensin"},{"issue":"16","key":"10.1016\/j.envpol.2025.126592_bib16","doi-asserted-by":"crossref","first-page":"3959","DOI":"10.5194\/amt-16-3959-2023","article-title":"First-time comparison between NO2 vertical columns from geostationary environmental monitoring spectrometer (GEMS) and pandora measurements","volume":"16","author":"Kim","year":"2023","journal-title":"Atmos. Meas. Tech."},{"issue":"2","key":"10.1016\/j.envpol.2025.126592_bib17","doi-asserted-by":"crossref","first-page":"529","DOI":"10.5194\/essd-13-529-2021","article-title":"A 6-year-long (2013\u20132018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC","volume":"13","author":"Kong","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"issue":"21","key":"10.1016\/j.envpol.2025.126592_bib18","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.5194\/amt-17-6315-2024","article-title":"Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements","volume":"17","author":"Lange","year":"2024","journal-title":"Atmos. Meas. Tech."},{"key":"10.1016\/j.envpol.2025.126592_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2022.113152","article-title":"Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression","volume":"280","author":"Li","year":"2022","journal-title":"Remote Sensing of Environment"},{"issue":"2","key":"10.1016\/j.envpol.2025.126592_bib20","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1073\/pnas.1812168116","article-title":"Anthropogenic drivers of 2013\u20132017 trends in summer surface ozone in China","volume":"116","author":"Li","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.envpol.2025.126592_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.166762","article-title":"Long-term observations of NO2 using GEMS in China: validations and regional transport","volume":"904","author":"Li","year":"2023","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envpol.2025.126592_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.112246","article-title":"An assessment of emission characteristics of Northern hemisphere cities using spaceborne observations of CO2, CO, and NO2","volume":"254","author":"Park","year":"2021","journal-title":"Remote Sensing of Environment"},{"key":"10.1016\/j.envpol.2025.126592_bib23","series-title":"Differential Optical Absorption Spectroscopy","first-page":"135","article-title":"Differential absorption spectroscopy","author":"Platt","year":"2008"},{"key":"10.1016\/j.envpol.2025.126592_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2020.117322","article-title":"Satellite-based estimation of surface NO2 concentrations over east-central China: a comparison of POMINO and OMNO2d data","volume":"224","author":"Qin","year":"2020","journal-title":"Atmos. Environ."},{"issue":"9","key":"10.1016\/j.envpol.2025.126592_bib25","doi-asserted-by":"crossref","first-page":"950","DOI":"10.3390\/rs9090950","article-title":"Estimating ground level NO2 concentrations over central-eastern China using a satellite-based geographically and temporally weighted regression model","volume":"9","author":"Qin","year":"2017","journal-title":"Remote Sens."},{"issue":"5","key":"10.1016\/j.envpol.2025.126592_bib26","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.5194\/amt-10-1957-2017","article-title":"In-flight performance of the ozone monitoring instrument","volume":"10","author":"Schenkeveld","year":"2017","journal-title":"Atmos. Meas. Tech."},{"issue":"20","key":"10.1016\/j.envpol.2025.126592_bib27","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.5194\/amt-17-6163-2024","article-title":"Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS","volume":"17","author":"Seo","year":"2024","journal-title":"Atmos. Meas. Tech."},{"key":"10.1016\/j.envpol.2025.126592_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2023.120111","article-title":"Estimation of daily NO2 with explainable machine learning model in China, 2007\u20132020","volume":"314","author":"Shao","year":"2023","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envpol.2025.126592_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2022.159767","article-title":"Increased diurnal difference of NO2 concentrations and its impact on recent ozone pollution in eastern China in summer","volume":"858","author":"Shen","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"9","key":"10.1016\/j.envpol.2025.126592_bib30","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/ac1df8","article-title":"Combined impacts of climate and air pollution on human health and agricultural productivity","volume":"16","author":"Sillmann","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"10.1016\/j.envpol.2025.126592_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2024.124562","article-title":"Global estimates of ambient NO2 concentrations and long-term health effects during 2000\u20132019","volume":"359","author":"Sun","year":"2024","journal-title":"Environmental Pollution"},{"key":"10.1016\/j.envpol.2025.126592_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.166340","article-title":"The impact of air pollution on respiratory diseases in an era of climate change: a review of the current evidence","volume":"898","author":"Tran","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"14","key":"10.1016\/j.envpol.2025.126592_bib33","doi-asserted-by":"crossref","first-page":"9988","DOI":"10.1021\/acs.est.2c03834","article-title":"Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence","volume":"56","author":"Wei","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envpol.2025.126592_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2021.116456","article-title":"Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals","volume":"273","author":"Wu","year":"2021","journal-title":"Environmental Pollution"},{"issue":"13","key":"10.1016\/j.envpol.2025.126592_bib35","doi-asserted-by":"crossref","first-page":"5349","DOI":"10.1021\/acs.est.2c08022","article-title":"Health impacts of long-term NO2 exposure and inequalities among the Chinese population from 2013 to 2020","volume":"57","author":"Xue","year":"2023","journal-title":"Environ. Sci. Technol."},{"issue":"7","key":"10.1016\/j.envpol.2025.126592_bib36","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1021\/acs.est.7b05669","article-title":"Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal kriging model","volume":"52","author":"Zhan","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envpol.2025.126592_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.envpol.2022.119510","article-title":"Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact","volume":"307","author":"Zhang","year":"2022","journal-title":"Environmental Pollution"},{"issue":"1","key":"10.1016\/j.envpol.2025.126592_bib38","article-title":"Fine particulate matter (PM2. 5) in China at a city level","volume":"5","author":"Zhang","year":"2015","journal-title":"Sci. Rep."}],"container-title":["Environmental Pollution"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0269749125009650?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0269749125009650?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T15:53:47Z","timestamp":1762358027000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0269749125009650"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":38,"alternative-id":["S0269749125009650"],"URL":"https:\/\/doi.org\/10.1016\/j.envpol.2025.126592","relation":{},"ISSN":["0269-7491"],"issn-type":[{"value":"0269-7491","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO2 via machine learning","name":"articletitle","label":"Article Title"},{"value":"Environmental Pollution","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envpol.2025.126592","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"126592"}}