Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network
2021
Wang, Bin | Yuan, Qiangqiang | Yang, Qian | Zhu, Liye | Li, Tongwen | Zhang, Liangpei
Fine particulate matter (PM₂.₅) has attracted extensive attention because of its baneful influence on human health and the environment. However, the sparse distribution of PM₂.₅ measuring stations limits its application to public utility and scientific research, which can be remedied by satellite observations. Therefore, we developed a Geo-intelligent long short-term network (Geoi-LSTM) to estimate hourly ground-level PM₂.₅ concentrations in 2017 in Wuhan Urban Agglomeration (WUA). We conducted contrast experiments to verify the effectiveness of our model and explored the optimal modeling strategy. It turned out that Geoi-LSTM with TOA reflectance, meteorological conditions, and NDVI as inputs performs best. The station-based cross-validation R², root mean squared error and mean absolute error are 0.82, 15.44 μg/m³, 10.63 μg/m³, respectively. Based on model results, we revealed spatiotemporal characteristics of PM₂.₅ in WUA. Generally speaking, during the day, PM₂.₅ concentration remained stable at a relatively high level in the morning and decreased continuously in the afternoon. While during the year, PM₂.₅ concentrations were highest in winter, lowest in summer, and in-between in spring and autumn. Combined with meteorological conditions, we further analyzed the whole process of a PM₂.₅ pollution event. Finally, we discussed the loss in removing clouds-covered pixels and compared our model with several popular models. Overall, our results can reflect hourly PM₂.₅ concentrations seamlessly and accurately with a spatial resolution of 5 km, which benefits PM₂.₅ exposure evaluations and policy regulations.
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