{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:07:49Z","timestamp":1761196069632,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T00:00:00Z","timestamp":1583712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013282","name":"National Magnetic Confinement Fusion Program of China","doi-asserted-by":"publisher","award":["No. 2017YFB0503901","No. 2016YFC0200404"],"award-info":[{"award-number":["No. 2017YFB0503901","No. 2016YFC0200404"]}],"id":[{"id":"10.13039\/501100013282","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aerosol optical depth (AOD) has been widely used to estimate near-surface particulate matter (PM). In this study, ground-measured data from the Campaign on Atmospheric Aerosol Research network of China (CARE-China) and the Aerosol Robotic Network (AERONET) were used to evaluate the accuracy of Visible Infrared Imaging Radiometer Suite (VIIRS) AOD data for different aerosol types. These four aerosol types were from dust, smoke, urban, and uncertain and a fifth \u201ctype\u201d was included for unclassified (i.e., total) aerosols. The correlation for dust aerosol was the worst (R2 = 0.15), whereas the correlations for smoke and urban types were better (R2 values of 0.69 and 0.55, respectively). The mixed-effects model was used to estimate the PM2.5 concentrations in Beijing\u2013Tianjin\u2013Hebei (BTH), Sichuan\u2013Chongqing (SC), the Pearl River Delta (PRD), the Yangtze River Delta (YRD), and the Middle Yangtze River (MYR) using the classified aerosol type and unclassified aerosol type methods. The results suggest that the cross validation (CV) of different aerosol types has higher correlation coefficients than that of the unclassified aerosol type. For example, the R2 values for dust, smoke, urban, uncertain, and unclassified aerosol types BTH were 0.76, 0.85, 0.82, 0.82, and 0.78, respectively. Compared with the daily PM2.5 concentrations, the air quality levels estimated using the classified aerosol type method were consistent with ground-measured PM2.5, and the relative error was low (most RE was within \u00b120%). The classified aerosol type method improved the accuracy of the PM2.5 estimation compared to the unclassified method, although there was an overestimation or underestimation in some regions. The seasonal distribution of PM2.5 was analyzed and the PM2.5 concentrations were high during winter, low during summer, and moderate during spring and autumn. Spatially, the higher PM2.5 concentrations were predominantly distributed in areas of human activity and industrial areas.<\/jats:p>","DOI":"10.3390\/rs12050881","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T11:59:36Z","timestamp":1583841576000},"page":"881","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth"],"prefix":"10.3390","volume":"12","author":[{"given":"Qiaolin","family":"Zeng","sequence":"first","affiliation":[{"name":"The College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065; China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"Chongqing Institute of Meteorological Sciences, Chongqing 401147, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhua","family":"Tao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangfu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Meteorological Sciences, Chongqing 401147, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SongYan","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Department of Geography, University of Exeter, Rennes Drive, Exeter EX4 4RJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography, Fujian Normal University, Fujian 350007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10695","DOI":"10.1007\/s11356-017-8673-6","article-title":"Estimation of the PM2.5 health effects in China during 2000\u20132011","volume":"24","author":"Wu","year":"2017","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1289\/ehp.0901623","article-title":"Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application","volume":"118","author":"Donkelaar","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8852","DOI":"10.1016\/j.atmosenv.2008.09.011","article-title":"Chemical characteristics of fine particles emitted from different gas cooking methods","volume":"42","author":"See","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1080\/10473289.2006.10464485","article-title":"Health effects of fine particulate air pollution: Lines that connect","volume":"56","author":"Pope","year":"2006","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3762","DOI":"10.1021\/acs.est.5b05833","article-title":"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors","volume":"50","author":"Donkelaar","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11109","DOI":"10.1021\/es502113p","article-title":"Fifteen-year global time series of satellite-derived fine particulate matter","volume":"48","author":"Boys","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2012.11.003","article-title":"Estimating ground-level PM (2.5) concentrations in the southeastern U.S. using geographically weighted regression","volume":"121","author":"Hu","year":"2013","journal-title":"Environ. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2009.09.011","article-title":"A semi-empirical model for predicting hourly ground-level fine particulate matter (PM 2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements","volume":"114","author":"Tian","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4295","DOI":"10.5194\/acp-7-4295-2007","article-title":"Sensitivity of PM 2.5 to climate in the Eastern U.S.: A modeling case study","volume":"7","author":"Dawson","year":"2007","journal-title":"Atmos. Chem. Phys. Discuss."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2013.08.032","article-title":"Estimating ground-level PM 2.5 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."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1016\/j.atmosenv.2004.01.039","article-title":"Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality","volume":"38","author":"Hoff","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1029\/2003GL018174","article-title":"Intercomparison between satellite-derived aerosol optical thickness and PM 2.5 mass: Implications for air quality studies","volume":"30","author":"Wang","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.scitotenv.2017.08.209","article-title":"A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM 2.5 concentrations over a heavily polluted region in China","volume":"618","author":"Yao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1016\/j.asr.2017.08.008","article-title":"Satellite Remote Sensing of Fine Particulate air pollutants over Indian Mega Cities","volume":"60","author":"Sreekanth","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1289\/ehp.1409481","article-title":"Satellite-Based Spatiotemporal Trends in PM 2.5 Concentrations: China, 2004\u20132013","volume":"124","author":"Ma","year":"2016","journal-title":"Env. Health Perspect."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2015.02.005","article-title":"Remote sensing of atmospheric fine particulate matter (PM 2.5) mass concentration near the ground from satellite observation","volume":"160","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9769","DOI":"10.5194\/acp-11-7991-2011","article-title":"A novel calibration approach of MODIS AOD data to predict PM 2.5 concentrations","volume":"11","author":"Lee","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1016\/j.scitotenv.2014.11.024","article-title":"Estimating PM 2.5 in Xi\u2019an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models","volume":"505","author":"You","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2006.05.022","article-title":"Using aerosol optical thickness to predict ground-level PM 2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1289\/ehp.0800123","article-title":"Estimating regional spatial and temporal variability of PM 2.5 concentrations using satellite data, meteorology, and land use information","volume":"117","author":"Liu","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Hu, H. (2017). Improving Satellite-Driven PM 2.5 Models with VIIRS Nighttime Light Data in the Beijing\u2013Tianjin\u2013Hebei Region, China. Remote Sens., 9.","DOI":"10.3390\/rs9090908"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.atmosenv.2015.11.013","article-title":"Potential application of VIIRS Day\/Night Band for monitoring nighttime surface PM 2.5 air quality from space","volume":"124","author":"Wang","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_23","unstructured":"Li, W., Zeng, R., and Wu, J. (2014, January 8\u201312). Remote sensing monitoring of ground-level PM 2.5 concentrations in China: A comparison between VIIRS and MODIS. Proceedings of the Association of American Geographers (AAG) Annual Meeting 2014, Tampa, FL, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, W., Mao, F., Du, L., Pan, Z., Gong, W., and Fang, S. (2017). Deriving Hourly PM 2.5 Concentrations from Himawari-8 AODs over Beijing\u2013Tianjin\u2013Hebei in China. Remote Sens., 9.","DOI":"10.3390\/rs9080858"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1289\/ehp.1408646","article-title":"Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter","volume":"123","author":"Van","year":"2015","journal-title":"Environ. Health Perspect."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Y., Park, R.J., Jacob, D.J., Li, Q., Kilaru, V., and Sarnat, J.A. (2004). Mapping annual mean ground\u2013level PM 2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States. J. Geophys. Res. Atmos., 109.","DOI":"10.1029\/2004JD005025"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.atmosres.2015.11.011","article-title":"Spatial distribution of aerosol hygroscopicity and its effect on PM 2.5 retrieval in East China","volume":"170","author":"He","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2016.03.023","article-title":"Estimation of long-term population exposure to PM 2.5 for dense urban areas using 1-km MODIS data","volume":"179","author":"Lin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.rse.2017.07.023","article-title":"Full-coverage high-resolution daily PM 2.5 estimation using MAIAC AOD in the Yangtze River Delta of China","volume":"199","author":"Xiao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.atmosenv.2017.05.027","article-title":"Spatio-temporal distribution of localized aerosol loading in China: A satellite view","volume":"163","author":"Sun","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.atmosenv.2017.03.050","article-title":"Estimation of surface-level PM 2.5 concentration using aerosol optical thickness through aerosol type analysis method","volume":"159","author":"Chen","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.rse.2014.09.015","article-title":"Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM 2.5","volume":"156","author":"Lin","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gao, C., Zhang, X.L., Wang, W.Y., Xiu, A.J., Tong, D.Q., and Chen, W.W. (2018). Spatiotemporal Distribution of satellite-retrieved ground-level PM 2.5 and near real-time daily retrieval algorithm development in Sichuan Basin, China. Atmosphere, 9.","DOI":"10.20944\/preprints201801.0083.v1"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.scitotenv.2018.04.251","article-title":"A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information","volume":"636","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_35","first-page":"1703","article-title":"AOD and Angstrom parameters of aerosols observed by the Chinese Sun Hazemeter Network from August to December 2004","volume":"27","author":"Wang","year":"2006","journal-title":"Environ. Sci."},{"key":"ref_36","unstructured":"Aeronet.gsfc.nasa.gov (2020, March 07). Aerosol Robotic Network (AERONET) Homepage, Available online: https:\/\/aeronet.gsfc.nasa.gov."},{"key":"ref_37","unstructured":"Bou.class.noaa.gov (2020, March 07). NOAA\u2019s Comprehensive Large Array-data Stewardship System, Available online: https:\/\/www.bou.class.noaa.gov\/saa\/products\/welcome."},{"key":"ref_38","first-page":"14","article-title":"Summarization on PM 2.5 Online Monitoring Technique","volume":"13","author":"Ouyang","year":"2012","journal-title":"China Environ. Prot. Ind."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5880","DOI":"10.1016\/j.atmosenv.2006.03.016","article-title":"Satellite remote sensing of particulate matter and air quality assessment over global cities","volume":"40","author":"Gupta","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/clen.200700047","article-title":"Fine Particles (PM 2.5) in Residential Areas of Lucknow City and Factors Influencing the Concentration","volume":"36","author":"Barman","year":"2010","journal-title":"Clean Soil Air Water"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.scitotenv.2011.12.064","article-title":"The role of meteorology on different sized aerosol fractions (PM 10, PM 2.5, PM 2.5\u201310)","volume":"419","author":"Pateraki","year":"2012","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.1021\/es049352m","article-title":"Estimating ground-level PM 2.5 in the eastern United States using satellite remote sensing","volume":"39","author":"Liu","year":"2005","journal-title":"Environ. Sci. Technol."},{"key":"ref_43","unstructured":"ECMWF (2020, March 07). ECMWF. Available online: http:\/\/www.ecmwf.int\/."},{"key":"ref_44","unstructured":"Srtm.csi.cgiar.org (2020, March 07). CGIAR-CSI SRTM\u2014SRTM 90m DEM Digital Elevation Database. Available online: http:\/\/srtm.csi.cgiar.org\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.08.008","article-title":"A satellite-based geographically weighted regression model for regional PM 2.5 estimation over the Pearl River Delta region in China","volume":"154","author":"Song","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Wang, Z., Tao, J., Wang, Y., Chen, L., Zhu, H., Yang, J., Wang, X., and Li, B. (2017). Estimation of Ground-Level PM 2.5 Concentrations in the Major Urban Areas of Chongqing by Using FY-3C\/MERSI. Atmosphere, 9.","DOI":"10.3390\/atmos9010003"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.scitotenv.2017.12.172","article-title":"Spatial patterns and temporal variations of six criteria air pollutants during 2015 to 2017 in the city clusters of Sichuan Basin, China","volume":"624","author":"Zhao","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_48","first-page":"4","article-title":"The ambient air quality and trends in cities of Sichuan province","volume":"29","author":"Zou","year":"2010","journal-title":"Sichuan Environ."},{"key":"ref_49","first-page":"3119","article-title":"Estimation of PM 2.5 concentration over the Yangtze Delta using remote sensing: Analysis of spatial and temporal variations","volume":"36","author":"Xu","year":"2015","journal-title":"Environ. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Wang, Y., Chen, L., Wang, Z., Zhu, H., and Li, B. (2018). Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013. Remote Sens., 10.","DOI":"10.3390\/rs10020168"},{"key":"ref_51","first-page":"946","article-title":"Analysis of a summertime PM 2.5 and haze episode in the mid-Atlantic region","volume":"53","author":"Chow","year":"2003","journal-title":"Air Repair"},{"key":"ref_52","first-page":"10","article-title":"Impact of the air mass trajectories on PM 2.5 concentrations and distribution in the Yangtze River Delta in December 2015","volume":"36","author":"Zhu","year":"2015","journal-title":"J. Environ. Sci."},{"key":"ref_53","first-page":"194","article-title":"Analysis on the pollution levels of atmospheric particles and the correlation of pollutants in hHubei province","volume":"39","author":"Chen","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_54","first-page":"5152","article-title":"Temporal and spatial variations characteristics of atmospheric particulate matter in Hubei province, China","volume":"11","author":"Deng","year":"2017","journal-title":"J. Environ. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.5194\/acp-17-2509-2017","article-title":"Aerosol vertical distribution and optical properties over China from long-term satellite and ground-based remote sensing","volume":"17","author":"Tian","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1175\/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2","article-title":"Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations","volume":"59","author":"Dubovik","year":"2002","journal-title":"J. Atmos. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/881\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:05:32Z","timestamp":1760173532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/881"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,9]]},"references-count":56,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050881"],"URL":"https:\/\/doi.org\/10.3390\/rs12050881","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,3,9]]}}}