biostudies-literatureRen YNational Natural Science Foundation of China163190https://www.ebi.ac.uk/biostudies/studies/S-EPMC10102532biostudies-literatureUnknown884Large-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 (NO₂), fine particulate matter (PM_2.5) and ozone (O₃) 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 NO₂ concentrations varies at different stages of the lockdown. Variation The Science of the total environmentA machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region.PMC1010253222236004Wang QGuan XRen YTao CWang WLi LRen SZhang QfalseA 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 (NO₂), fine particulate matter (PM_2.5) and ozone (O₃) 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 NO₂ concentrations varies at different stages of the lockdown. Variation 2023-01-01T00:00:00Z2023 Apr2025-04-26T14:34:03.735Z2025-02-19T03:55:57.77ZS-EPMC101025323706105110.1016/j.scitotenv.2023.163190