气候变化领域集成服务门户(CCPortal): Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China

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DOI	10.5194/acp-22-13183-2022
	Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China
	Hu, Yiwen; Zang, Zengliang; Ma, Xiaoyan; Li, Yi; Liang, Yanfei; You, Wei; Pan, Xiaobin; Li, Zhijin
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	13183
结束页码	13200
卷号	22 期号:19 页码:18
英文摘要	Emission inventories are essential for modelling studies and pollution control, but traditional emission inventories are usually updated after a few years based on the statistics of bottom-up approach from the energy consumption in provinces, cities, and counties. The latest emission inventories of multi-resolution emission inventory in China (MEIC) was compiled from the statistics for the year 2016 (MEIC_2016). However, the real emissions have varied yearly, due to national pollution control policies and accidental special events, such as the coronavirus disease (COVID-19) pandemic. In this study, a four-dimensional variational assimilation (4DVAR) system based on the top-down approach was developed to optimise sulfur dioxide (SO2) emissions by assimilating the data of SO2 concentrations from surface observational stations. The 4DVAR system was then applied to obtain the SO2 emissions during the early period of COVID-19 pandemic (from 17 January to 7 February 2020), and the same period in 2019 over China. The results showed that the average MEIC_2016, 2019, and 2020 emissions were 42.2 x 10(6), 40.1 x 10(6), and 36.4 x 10(6) kg d(-1). The emissions in 2020 decreased by 9.2 % in relation to the COVID-19 lockdown compared with those in 2019. For central China, where the lockdown measures were quite strict, the mean 2020 emission decreased by 21.0 % compared with 2019 emissions. Three forecast experiments were conducted using the emissions of MEIC_2016, 2019, and 2020 to demonstrate the effects of optimised emissions. The root mean square error (RMSE) in the experiments using 2019 and 2020 emissions decreased by 28.1 % and 50.7 %, and the correlation coefficient increased by 89.5 % and 205.9 % compared with the experiment using MEIC_2016. For central China, the average RMSE in the experiments with 2019 and 2020 emissions decreased by 48.8 % and 77.0 %, and the average correlation coefficient increased by 44.3 % and 238.7 %, compared with the experiment using MEIC_2016 emissions. The results demonstrated that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000867648800001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273452
作者单位	China Meteorological Administration; Nanjing University of Information Science & Technology; National University of Defense Technology - China; Fudan University; University of California System; University of California Los Angeles
推荐引用方式 GB/T 7714	Hu, Yiwen,Zang, Zengliang,Ma, Xiaoyan,et al. Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China[J],2022,22(19):18.
APA	Hu, Yiwen.,Zang, Zengliang.,Ma, Xiaoyan.,Li, Yi.,Liang, Yanfei.,...&Li, Zhijin.(2022).Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(19),18.
MLA	Hu, Yiwen,et al."Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.19(2022):18.