Identifying the spatiotemporal patterns and natural and socioeconomic influencing factors of PM2.5 and O3 pollution in China.

To promote collaborative governance of PM2.5 and O3 pollution, understanding their spatiotemporal patterns and determining factors is crucial to control air pollution in China. Using the ground-monitored data encompassing PM2.5 and O3 concentrations in 2019 across 337 Chinese cities, this study explores the spatiotemporal patterns of PM2.5 and O3 concentrations, and then employed the Multi-scale Geographically Weighted Regression (MGWR) model to examine the socioeconomic and natural factors affecting PM2.5 or O3 concentrations. The results show that PM2.5 and O3 concentrations exhibit distinct monthly U-shaped and inverted U-shaped temporal fluctuation patterns across Chinese cities, respectively. Spatially, both pollutants manifest spatial clustering characteristic and a certain degree of bivariate spatial correlation. Elevated PM2.5 concentrations are predominantly concentrated on north and central China, as well as the Xinjiang Autonomous Region, whereas higher O3 concentrations are distributed widely across north, east, and northwest China. The MGWR model outperforms traditional OLS and global spatial regression models, evidenced by its enhanced goodness-of-fit metrics. Specifically, the R2 values for the PM2.5 and O3 MGWR models are notably high, at 0.842 and 0.861, respectively. Socioeconomic and natural factors are found to have multi-scale spatial effects on PM2.5 and O3 concentrations in China. On average, PM2.5 concentrations show positively correlations with population density, the proportion of the added value of secondary industry in GDP, wind speed, relative humidity, and atmospheric pressure, but negatively relationship with per capita GDP, road density, urban greening, air temperature, precipitation, and sunshine duration. In contrast, O3 concentrations are also positively associated with population density, the proportion of the added value of secondary industry in GDP, energy consumption, precipitation, wind speed, atmospheric pressure, and sunshine duration, but negatively correlated with per capita GDP, road density, and air temperature. Our findings offer valuable insights to inform the development of comprehensive air pollution management policies in in developing countries.