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

ABSTRACT: 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

SUBMITTER: Zhan D 

PROVIDER: S-EPMC11825043 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature