Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ.

In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM2.5 and O3 and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM2.5 and O3 changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO2, NO2, and O3, among which NO2 tended to be the dominant influencing factor. O3 pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM2.5 and low concentration of NO2, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM2.5 and O3, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM2.5 pollution but aggravated O3 pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM2.5 and O3. This research may help understand the influencing factors of PM2.5 and O3 and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.