High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China

• DOI: 10.1016/j.atmosres.2022.106199
• Publication Date: 2022-04-15T15:06:10Z
• Authors: Zhihao Song; Bin Chen; Peng Zhang; Xiaodan Guan; Xin Wang; Jinming Ge; Xiuqing Hu; Xingying Zhang; Yixuan Wang
• Keywords: Particulate Pollution

Due to urbanization and industrialization, PM2.5 (particulate matter with a diameter less than 2.5 μm) pollution has become serious environmental problem. The low spatial resolution insufficient coverage of observation stations affect research on causes human health risks. With the launch FY-4A, new generation Chinese geostationary weather satellites, it is possible obtain high temporal covering all China. In this study, FY-4A top-of-the-atmosphere reflectance data, meteorological factors, geographic information were input into deep forest (DF) model hourly in samples based 10-fold cross validation DF an R2 0.83–0.88, root mean square error 8.81–14.7 μg/m3, while result sites was 0.77. monthly (R2 = 0.98) seasonal 0.99) estimated results showed consistency observations. Feature importance that contribution features varies regions seasons. Estimation indicated substantial spatiotemporal differences PM2.5, highest between 09:00–10:00 then gradually decreased. Regions China mainly distributed Tarim Basin Central assessment that: 1) more 80% winter days higher World Health Organization interim target 3 (37.5 μg/m3); 2) bimodal distribution there are obvious cities suburbs; 3) autumn winter, where population-weighted IT-3 Beijing-Tianjin-Hebei, China, Guanzhong Plain, Sichuan Basin, Yangtze River Delta. Our advantages thus shows great potential for estimating pollutants.