干旱区生态环境知识资源中心(Arid): 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

Arid

DOI	10.1029/2021JD036393
	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
	Bin Chen; Song, Zhihao; Huang, Jianping; Zhang, Peng; Hu, Xiuqing; Zhang, Xingying; Guan, Xiaodan; Ge, Jinming; Zhou, Xingzhao
通讯作者	Bin C
来源期刊	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
ISSN	2169-897X
EISSN	2169-8996
出版年	2022
卷号	127 期号:9
英文摘要	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, mu g/m(3)) 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.
英文关键词	PM10 top-of-the-atmosphere reflectance FY-4A deep forest model long-range transport dust
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000788037400001
WOS关键词	MATTER AIR-POLLUTION ; USE REGRESSION-MODEL ; YANGTZE-RIVER DELTA ; GROUND-LEVEL PM10 ; PM2.5 CONCENTRATIONS ; LAND-USE ; MASS CONCENTRATION ; NORTHERN CHINA ; DUST ; AEROSOL
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393456
推荐引用方式 GB/T 7714	Bin Chen,Song, Zhihao,Huang, Jianping,et al. 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[J],2022,127(9).
APA	Bin Chen.,Song, Zhihao.,Huang, Jianping.,Zhang, Peng.,Hu, Xiuqing.,...&Zhou, Xingzhao.(2022).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.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,127(9).
MLA	Bin Chen,et al."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".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 127.9(2022).