Nonlinear influence of winter meteorology and precursor on PM(2.5) based on mathematical and numerical models: A COVID-19 and Winter Olympics case study

Air pollution during the COVID-19 epidemic in Beijing and its surrounding regions has received substantial attention. We collected observational data, including air pollutant concentrations and meteorological parameters, during January and February from 2018 to 2021. A statistical and a numerical model were applied to identify the formation of air pollution and the impact of emission reduction on air quality. Relative humidity, wind speed, SO(2), NO(2), and O(3) had nonlinear effects on the PM(2.5) concentration in Beijing, among which the effects of relative humidity, NO(2), and O(3) were prominent. During the 2020 epidemic period, high pollution concentrations were closely related to adverse meteorological conditions, with different parameters having different effects on the three pollution processes. In general, the unexpected reduction of anthropogenic emissions reduced the PM(2.5) concentration, but led to an increase in the O(3) concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM(2.5) concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O(3) enhances the PM(2.5) levels, to achieve the collaborative improvement of PM(2.5) and O(3) concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NO(x) to achieve the collaborative improvement of PM(2.5) and O(3) concentrations. The conclusions of this study provide a basis for designing a plan that guarantees improved air quality for the 2022 Winter Olympics and other international major events in Beijing.