{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:36:49Z","timestamp":1765438609612,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T00:00:00Z","timestamp":1568246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China &quot;Development of Meteorological Satellite Remote Sensing Technology and Platform for Global Monitoring, Assessments and Applications&quot;","award":["2018YFC1506500"],"award-info":[{"award-number":["2018YFC1506500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 \u03bcg m\u22123. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.<\/jats:p>","DOI":"10.3390\/rs11182120","type":"journal-article","created":{"date-parts":[[2019,9,12]],"date-time":"2019-09-12T10:56:06Z","timestamp":1568285766000},"page":"2120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8747-3334","authenticated-orcid":false,"given":"Jianjun","family":"Liu","sequence":"first","affiliation":[{"name":"Laboratory of Environmental Model and Data Optima (EMDO), Laurel, MD 20707, USA"}]},{"given":"Fuzhong","family":"Weng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Severe Weather, Beijing 100081, China"}]},{"given":"Zhanqing","family":"Li","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"},{"name":"Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Maureen C.","family":"Cribb","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1289\/ehp.1307049","article-title":"An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure","volume":"122","author":"Burnett","year":"2014","journal-title":"Environ. Health Perspect."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8057","DOI":"10.1021\/acs.est.5b01236","article-title":"Addressing global mortality from ambient PM2.5","volume":"49","author":"Apte","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_3","first-page":"422","article-title":"Meta-analysis of exposure\u2013response functions of air particulate matter and adverse health outcomes in China","volume":"19","author":"Kan","year":"2002","journal-title":"J. Environ. Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/nature15371","article-title":"The contribution of out-door air pollution sources to premature mortality on a global scale","volume":"525","author":"Lelieveld","year":"2015","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1289\/ehp.1409481","article-title":"Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004\u20132013","volume":"124","author":"Ma","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1038\/s41598-017-07478-0","article-title":"Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting","volume":"7","author":"Yu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_7","first-page":"D22206","article-title":"Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States","volume":"109","author":"Liu","year":"2004","journal-title":"J. Geophys. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2015.05.016","article-title":"Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model","volume":"166","author":"Geng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","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":"Martin","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5621","DOI":"10.1002\/jgrd.50479","article-title":"Optimal estimation for global ground-level fine particulate matter concentrations","volume":"118","author":"Martin","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","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 PM2.5","volume":"156","author":"Lin","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2015.02.005","article-title":"Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation","volume":"160","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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":"Holloman","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.5194\/acp-11-7991-2011","article-title":"A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations","volume":"11","author":"Lee","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7436","DOI":"10.1021\/es5009399","article-title":"Estimating ground-level PM2.5 in China using satellite remote sensing","volume":"48","author":"Ma","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_16","first-page":"6","article-title":"Monitoring PM2.5 from space for health: Past, present, and future directions","volume":"6","author":"Liu","year":"2014","journal-title":"Environ. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11985","DOI":"10.1002\/2017GL075710","article-title":"Estimating ground level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5314","DOI":"10.1021\/es9007504","article-title":"Atmospheric particulate matter pollution during the 2008 Beijing Olympics","volume":"43","author":"Wang","year":"2009","journal-title":"Environ. Sci. Technol."},{"key":"ref_19","first-page":"D14205","article-title":"Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach","volume":"114","author":"Gupta","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.3155\/1047-3289.59.11.1358","article-title":"The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: A geographical comparison by U.S. Environmental Protection Agency regions","volume":"59","author":"Zhang","year":"2009","journal-title":"J. Air Waste Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2012.11.003","article-title":"Estimating ground-level PM2.5 concentrations in the southeastern US using geographically weighted regression","volume":"121","author":"Hu","year":"2013","journal-title":"Environ. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.rse.2014.08.008","article-title":"A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China","volume":"154","author":"Song","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8327","DOI":"10.1007\/s11356-015-6027-9","article-title":"Estimating national-scale ground-level PM2.5 concentration in China using geographically weighted regression based on MODIS and MISR AOD","volume":"23","author":"You","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.envres.2012.06.011","article-title":"Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations","volume":"118","author":"Lee","year":"2012","journal-title":"Environ. Res."},{"key":"ref_25","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."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1289\/ehp.0800123","article-title":"Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information","volume":"117","author":"Liu","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2016.08.027","article-title":"Satellite-based ground PM2.5 estimation using timely structure adaptive modeling","volume":"186","author":"Fang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.scitotenv.2016.12.049","article-title":"Daily estimation of ground-level PM2.5 concentrations at 4-km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations","volume":"580","author":"Lv","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4752","DOI":"10.1021\/acs.est.5b05940","article-title":"Improving the accuracy of daily PM2.5 distributions derived from the fusion of ground-level measurements with aerosol optical depth observations, a case study in North China","volume":"50","author":"Lv","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical modeling: The two cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.atmosenv.2017.02.023","article-title":"Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm","volume":"155","author":"Zhan","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"012268","DOI":"10.1088\/1755-1315\/17\/1\/012268","article-title":"Using support vector regression to predict PM10 and PM2.5","volume":"17","author":"Hou","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_33","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":"ref_34","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_35","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 PM2.5 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_36","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.rse.2016.07.015","article-title":"VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing\u2212Tianjin\u2212Hebei: A spatiotemporal statistical model","volume":"184","author":"Wu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.5194\/acp-6-4843-2006","article-title":"Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data","volume":"6","author":"Kacenelenbogen","year":"2006","journal-title":"Atmos. Chem. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1002\/qj.828","article-title":"The ERA-Interim reanalysis: Configuration and performance of the data assimilation system","volume":"137A","author":"Dee","year":"2011","journal-title":"Quart. J. R. Meteorol. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity analysis of k-fold cross validation in prediction error estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local indicators of spatial association\u2014LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xin, J.Y., Gong, C.S., Liu, Z.R., Cong, Z.Y., Gao, W.K., Song, T., Pan, Y.P., Sun, Y., Ji, D.S., and Wang, L.L. (2016). The observation-based relationships between PM2.5 and AOD over China. J. Geophys. Res. Atmos., 121.","DOI":"10.1002\/2015JD024655"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6546","DOI":"10.1021\/acs.est.6b01438","article-title":"Enhancing the applicability of satellite remote sensing for PM2.5 estimation using MODIS deep blue AOD and land use regression in California, United States","volume":"50","author":"Lee","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_44","first-page":"D00K38","article-title":"Seasonal variations of aerosol optical properties, vertical distribution and associated radiative effects in the Yangtze Delta region of China","volume":"117","author":"Liu","year":"2012","journal-title":"J. Geophys. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.4209\/aaqr.2015.01.0009","article-title":"Estimating ground-level PM2.5 using fine-resolution satellite data in the megacity of Beijing, China","volume":"15","author":"Li","year":"2015","journal-title":"Aerosol Air Qual. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.atmosenv.2015.06.046","article-title":"Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements","volume":"124","author":"Zheng","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, W., Mao, F., Du, L., Pan, Z., Gong, W., and Fang, S. (2017). Deriving hourly PM2.5 concentrations from Himawari-8 AODs over Beijing\u2013Tianjin\u2013Hebei in China. Remote Sens., 9.","DOI":"10.3390\/rs9080858"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.atmosenv.2019.04.002","article-title":"Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm","volume":"208","author":"Liu","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3528","DOI":"10.1002\/2016JD025659","article-title":"Development of a daytime cloud and haze detection algorithm for Himawari-8 satellite measurements over central and eastern China","volume":"122","author":"Shang","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"15921","DOI":"10.5194\/acp-18-15921-2018","article-title":"Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: Regional pattern and influencing factors","volume":"18","author":"Su","year":"2018","journal-title":"Atmos. Chem. Phys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6215","DOI":"10.5194\/acp-17-6215-2017","article-title":"Technical note: Boundary layer height determination from lidar for improving air pollution episode modeling: Development of new algorithm and evaluation","volume":"17","author":"Yang","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zang, Z., Wang, W., Cheng, X., Yang, B., Pan, X., and You, W. (2017). Effects of boundary layer height on the model of ground-level PM2.5 concentrations from AOD: Comparison of stable and convective boundary layer heights from different methods. Atmosphere, 8.","DOI":"10.3390\/atmos8060104"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.atmosenv.2017.07.054","article-title":"An inter-comparison of AOD-converted PM2.5 concentrations using different approaches for estimating aerosol vertical distribution","volume":"166","author":"Su","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, D., Qin, K., Wu, L., Xu, J., Letu, H., Zou, B., He, Q., and Li, Y. (2019). Evaluation of JAXA Himawari-8-AHI level-3 aerosol products over Eastern China. Atmosphere, 10.","DOI":"10.3390\/atmos10040215"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6225","DOI":"10.1016\/j.atmosenv.2011.07.068","article-title":"Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010","volume":"45","author":"Martin","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_56","first-page":"929","article-title":"Estimation of PM2.5 from fine-mode aerosol optical depth","volume":"17","author":"Zhang","year":"2013","journal-title":"J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.atmosenv.2017.09.023","article-title":"Satellite-based PM2.5 estimation using fine-mode aerosol optical depth thickness over China","volume":"170","author":"Yan","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rse.2017.02.005","article-title":"An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 1: Algorithm development","volume":"192","author":"Yan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.envpol.2018.01.053","article-title":"Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model","volume":"236","author":"He","year":"2018","journal-title":"Environ. Pollut."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:19:14Z","timestamp":1760188754000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,12]]},"references-count":59,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11182120"],"URL":"https:\/\/doi.org\/10.3390\/rs11182120","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,9,12]]}}}