Estimation of Atmospheric PM10 Concentration in China Using an Interpretable Deep Learning Model and Top‐of‐the‐Atmosphere Reflectance Data From China's New Generation Geostationary Meteorological Satellite, FY‐4A. Issue 9 (27th April 2022)

• Record Type: Journal Article
• Authors: Chen, Bin; Song, Zhihao; Huang, Jianping; Zhang, Peng; Hu, Xiuqing; Zhang, Xingying; Guan, Xiaodan; Ge, Jinming; Zhou, Xingzhao
• Is Part Of: Journal of geophysical research. Volume 127:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-27
• Subjects: PM10 -- top‐of‐the‐atmosphere reflectance -- FY‐4A -- deep forest model -- long‐range transport dust; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD036393
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The rapid urbanization in China and the long‐range transport dust (LRTD) from arid and semi‐arid areas has resulted in an increase of PM10 concentration. In this study, an interpretable deep learning model [deep forest (DF)] with FY‐4A top‐of‐the‐atmosphere reflectance (TOAR) data were used to obtain the hourly PM10 in China. The optimal hourly average R 2 of 10‐fold cross validation can achieve 0.85 (13:00 Beijing time); The R 2 (RMSE, μg/m³) of the daily, monthly, and annual averages were 0.82 (24.16), 0.97 (6.53), and 0.99 (2.30), respectively. Using TOAR data, the DF model performed better than other machine learning models. The feature importance of the TOAR‐PM10 model showed that TOAR and meteorological elements both contributed significantly to the model. In spring, the PM10 in northern China was greater than that in southern China, which may be related to the LRTD. Excluding the dust weather periods, the areas with high PM10 values in China were mainly in cities and their suburbs, where were correlated with human activities. During a dust weather process, LRTD increased PM10 in northern China by 80.4%. During a mixture haze and dust weather process, the PM10 increased by 130.2% in northern China, of which LRTD led to an increase of 73.7%. The sources (from the Taklimakan Desert in China) and transmission paths of these two LRTD processes were similar. The contribution of LRTD to PM10 was related to dust intensity and meteorological conditions. The results showed that LRTD and local pollution to PM10 was both important in haze periods. Key Points: Hourly PM10 in China was estimated using interpretable deep forest and FY‐4A top‐of‐the‐atmosphere reflectance, and the daily and monthly mean R 2 were 0.82 and 0.97 Excluding dust weather periods, the areas with high PM10 values in China were mainly in cities and suburbs related to human activities The contribution of long‐range transport dust and local pollution in China to PM10 was both important during haze periods