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• W2941635653 abstract "High concentrations of particulate matter with diameter of <2.5 μm (PM2.5) demonstrate severe effects on human health, especially in the metropolitan agglomerations of China. Estimating PM2.5 based on satellite aerosol optical depth (AOD) is a widely used method. AOD data from Himawari-8, a geostationary satellite, enable improvement of the temporal resolution of PM2.5 estimates to the hourly level, thereby reflecting diurnal variations of pollutants compared with AOD products from polar orbit satellites, which only have one value per day. In this study, PM2.5 concentrations are estimated based on Himawari-8 AOD and other ancillary data by constructing spatiotemporal linear mixed effects model in Central China (CCH), Beijing-Tianjin-Henan (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. The determination coefficient (R2) between the measurements and estimates of PM2.5 calculated with the tenfold cross-validation method are 0.82, 0.84, 0.80 and 0.74 in CCH, BTH, YRD and PRD, respectively. The spatial distributions of PM2.5 present large regional variation, which is highly correlated with land-use type. Heavily polluted zones are mainly located in urban or rural areas, which have dense population and high anthropogenic emissions. Comparisons among different seasons show that particle pollution during the cold seasons (autumn and winter) is relatively severe with an average PM2.5 of >60 μg/m3 in CCH, BTH and YRD, whereas the level does not greatly change throughout the year in the PRD region. During the daytime, particulate pollution levels are generally high in the morning." @default.
• W2941635653 created "2019-05-03" @default.
• W2941635653 creator A5023683595 @default.
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• W2941635653 creator A5046597133 @default.
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• W2941635653 date "2019-08-01" @default.
• W2941635653 modified "2023-10-17" @default.
• W2941635653 title "Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8" @default.
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• W2941635653 doi "https://doi.org/10.1016/j.scitotenv.2019.04.299" @default.
• W2941635653 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31051362" @default.
• W2941635653 hasPublicationYear "2019" @default.
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