Abstract
The COVID-19 led to a stringent lockdown in most cities in China to effectively prevent the spread of the epidemic, resulting in reductions in air pollutant emissions. A high-resolution data assimilation experiment was carried out to invetigate the pollution emissions changes before and during the COVID-19 lockdown by adjusting the chemical initial conditions and the pollutant emissions simultaneously through the use of the online regional chemical transport model, WRF-Chem and an ensemble square-root system. Numerical results showed that PM2.5 emissions and NO emissions in February fell by about 4.4% and 30% respectively, compared to the emissions before the COVID-19 lockdown in Jan 2020. This work show that data assimilation has the potential to inverse emission with high-resolution. © 2021, Science Press. All right reserved.
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