Four-dimensional variational assimilation for SO<sub>2</sub> emission and its application around the COVID-19 lockdown in the spring 2020 over China
<p>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 inve...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-10-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/13183/2022/acp-22-13183-2022.pdf |
| Summary: | <p>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 (SO<span class="inline-formula"><sub>2</sub>)</span> emissions by
assimilating the data of SO<span class="inline-formula"><sub>2</sub></span> concentrations from surface observational
stations. The 4DVAR system was then applied to obtain the SO<span class="inline-formula"><sub>2</sub></span> 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
<span class="inline-formula">42.2×10<sup>6</sup></span>, <span class="inline-formula">40.1×10<sup>6</sup></span>, and <span class="inline-formula">36.4×10<sup>6</sup></span> kg d<span class="inline-formula"><sup>−1</sup></span>. 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 SO<span class="inline-formula"><sub>2</sub></span> emissions related to the COVID
lockdown, and it can thus be used to improve the accuracy of forecasts.</p> |
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| ISSN: | 1680-7316 1680-7324 |