Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements

doi:10.3390/rs14020419

	Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements
	Zhao, Chunhui 2,3; Zhang, Chengxin 3; Lin, Jinan 2; Wang, Shuntian 2; Liu, Hanyang 4; Wu, Hongyu 5; Liu, Cheng 1,2,3,6,7
刊名	REMOTE SENSING
	2022
卷号	14
关键词	NO2 COVID-19 air pollution satellite remote sensing TROPOMI
DOI	10.3390/rs14020419
通讯作者	Zhang, Chengxin(zcx2011@ustc.edu.cn)
英文摘要	Since the COVID-19 outbreak in 2020, China's air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2-71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53-69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42-64.96% and 1.05-34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China's urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production.
WOS关键词	AIR-QUALITY ; ANTHROPOGENIC EMISSIONS ; PM2.5 ; TROPOMI ; TRENDS ; SATELLITE ; IMPACTS ; MODEL
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000746186000001
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/127323]
专题	中国科学院合肥物质科学研究院
通讯作者	Zhang, Chengxin
作者单位	1.Univ Sci & Technol China, Key Lab Precis Sci Instrumentat Anhui Higher Educ, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China 3.Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei 230026, Peoples R China 4.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei 230026, Peoples R China 5.Univ Sci & Technol China, Sch Environm Sci & Optoelect Technol, Hefei 230026, Peoples R China 6.Univ Sci & Technol China, Anhui Prov Key Lab Polar Environm & Global Change, Hefei 230026, Peoples R China 7.Chinese Acad Sci, Inst Urban Environm, Ctr Excellence Reg Atmospher Environm, Xiamen 361021, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Chunhui,Zhang, Chengxin,Lin, Jinan,et al. Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements[J]. REMOTE SENSING,2022,14.
APA	Zhao, Chunhui.,Zhang, Chengxin.,Lin, Jinan.,Wang, Shuntian.,Liu, Hanyang.,...&Liu, Cheng.(2022).Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements.REMOTE SENSING,14.
MLA	Zhao, Chunhui,et al."Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements".REMOTE SENSING 14(2022).