A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region

Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO(2)), fine particulate matter (PM(2.5)) and ozone (O(3)) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO(2) concentrations varies at different stages of the lockdown. Variation between simulated and observed O(3) and PM(2.5) concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO(2) concentrations decreased significantly with a maximum decrease of 65.28 %, and O(3) concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO(2) concentrations, leading to the redistribution of Ox (NO(2) + O(3)) in the atmosphere and eventually to the production of O(3) and secondary PM(2.5). The effect of traffic restrictions on the reduction of NO(2) concentrations is significant. However, it is also important to consider the increase in O(3) due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO(2)). Traffic restrictions had a limited effect on improving PM(2.5) pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.