The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2.5 Concentration in China?

被引:5
|
作者
Tan, Zhenyu [1 ]
Li, Xinghua [2 ]
Gao, Meiling [3 ]
Jiang, Liangcun [2 ]
机构
[1] Northwest Univ, Coll Urban & Environm Sci, Xian 710127, Peoples R China
[2] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
[3] Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Peoples R China
基金
中国国家自然科学基金;
关键词
Data models; Remote sensing; COVID-19; Aerosols; Training; Deep learning; Pollution; Absorbing Aerosol Index (AAI); Aerosol Optical Depth (AOD); deep learning; Google Earth Engine (GEE); PM25; remote sensing; GROUND-LEVEL PM2.5; AMBIENT PM2.5; PARTICULATE MATTER; HEALTH; PM10; URBAN; MODIS;
D O I
10.1109/LGRS.2020.3040435
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
At the end of 2019, the very first COVID-19 coronavirus infection was reported and then it spread across the world just like wildfires. From late January to March 2020, most cities and villages in China were locked down, and consequently, human activities decreased dramatically. This letter presents an "offline learning and online inference" approach to explore the variation of PM2.5 pollution during this period. In the experiments, a deep regression model was trained to establish the complex relationship between remote sensing data and in situ PM2.5 observations, and then the spatially continuous monthly PM2.5 distribution map was simulated using the Google Earth Engine platform. The results reveal that the COVID-19 lockdown truly decreased the PM2.5 pollution with certain hysteresis and the fine particle pollution begins to increase when advancing resumption of work and production gradually.
引用
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页数:5
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