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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Published:2022-10-14
Issue:19
Volume:22
Page:13183-13200
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Hu Yiwen, Zang Zengliang, Ma XiaoyanORCID, Li Yi, Liang Yanfei, You Wei, Pan Xiaobin, Li Zhijin
Abstract
Abstract. 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
42.2×106, 40.1×106, and 36.4×106 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.
Funder
National Natural Science Foundation of China
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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