To prevent the spread of COVID-19 (2019 novel coronavirus), from January 23 to April 8 in 2020, the highest Class 1 Response was ordered in Wuhan, requiring all residents to stay at home unless absolutely necessary. This action was implemented to cut down all unnecessary human activities, including industry, agriculture and transportation. Reducing these activities to a very low level during these hard times meant that some unprecedented naturally occurring measures of controlling emissions were executed. Ironically, however, after these measures were implemented, ozone levels increased by 43.9%. Also worthy of note, PM 2.5 decreased 31.7%, which was found by comparing the observation data in Wuhan during the epidemic from 8 th Feb. to 8 th Apr. in 2020 with the same periods in 2019. Utilizing CMAQ (The Community Multiscale Air Quality modeling system), this article investigated the reason for these phenomena based on four sets of numerical simulations with different schemes of emission reduction. Comparing the four sets of simulations with observation, it was deduced that the emissions should decrease to approximately 20% from the typical industrial output, and 10% from agriculture and transportation sources, attributed to the COVID-19 lockdown in Wuhan. More importantly, through the CMAQ process analysis, this study quantitatively analyzed differences of the physical and chemical processes that were affected by the COVID-19 lockdown. It then examined the differences of the COVID-19 lockdown impact and determined the physical and chemical processes between when the pollution increased and decreased, determining the most affected period of the day. As a result, this paper found that (1) PM 2.5 decreased mainly due to the reduction of emission and the contrary contribution of aerosol processes. The North-East wind was also in favor of the decreasing of PM 2.5 . (2) O 3 increased mainly due to the slowing down of chemical consumption processes, which made the concentration change of O 3 pollution higher at about 4 P.M.∼7 P.M. of the day, while increasing the concentration of O 3 at night during the COVID-19 lockdown in Wuhan. The higher O 3 concentration in the North-East of the main urban area also contributed to the increasing of O 3 with unfavorable wind direction.

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Anthropogenic emissions play an important role in air quality forecasting. To improve the forecasting accuracy, the use of nudging as the data assimilation method, combined with extremely randomized trees (ExRT) as the machine learning method, was developed and applied to adjust the anthropogenic emissions in the Community Multiscale Air Quality modeling system (CMAQ). This nudging–ExRT method can iterate with the forecast and is suitable for linear and nonlinear emissions. For example, an episode between 15 and 30 January 2019 was simulated for China’s Beijing–Tianjin–Hebei (BTH) region. For PM2.5, the correlation coefficient of the site averaged concentration (Ra) increased from 0.85 to 0.94, and the root mean square error (RMSEa) decreased from 24.41 to 9.97 µg/m3. For O3, the Ra increased from 0.75 to 0.81, and the RMSEa decreased from 13.91 to 12.07 µg/m3. These results showed that nudging–ExRT can significantly improve forecasting skills and can be applied to routine air quality forecasting in the future.

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