Simulation Analysis of the Impact of COVID-19 Pandemic Control on Air Quality in Henan Province based on Machine Learning Algorithm

Abstract

To reduce the risks of COVID-19 on society and the health of the general public, necessary prevention and control measures were implemented throughout China in 2020. Consequently, air quality was greatly improved due to lower emissions. However, the improvement of air quality could also be closely related to meteorological conditions. During quarantine (January 27 to February, 2020), reductions were observed in the concentration of all air pollutants in Henan Province (PM2.5, PM10, SO2, CO, and NO2 decreased by 36.89%, 34.18%, 19.43%, 29.85%, and 58.51%, respectively) relative to measurements taken from January 1 to 26, 2020. The only exception was for the concentration of O3, which increased by 69.64%. This study evaluates the importance of meteorological conditions in air pollution, through simulation with a long-and-short-term memory network (LSTM) and a machine learning algorithm. Results show that meteorological conditions play a crucial role in air pollutant formation. Given favorable meteorological factors, the concentrations of pollutants could be reduced by 15%-30%, while the reduction due to anthropogenic emission control ranges from 6%-40%. During the epidemic, meteorological conditions and human emissions accounted for 34.84% and 34.81% of the increase in O3 concentration, respectively. The results show that primary pollutant concentrations are more sensitive to the intensity of anthropogenic emissions. However, secondary pollutants are more dependent on meteorological factors. Furthermore, a nonlinear relationship has been identified between O3 concentration and to emission intensity. Further investigation into the causes of the rise in O3 concentration is necessary to gain a greater understanding and better control of particulate matter and O3 pollution.