4.7 Article

Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 676, 期 -, 页码 535-544

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2019.04.299

关键词

Himawari-8 AOD; Hourly PM2.5; Linear mixed-effects model; Spatiotemporal variation

资金

  1. National Key Research and Development Program of China [2016YFC0200900, 2017YFC0212600]

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High concentrations of particulate matter with diameter of <2.5 mu m (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 (R-2) 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 mu g/m(3) 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. (C) 2019 Elsevier B.V. All rights reserved.

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