Exploring the spatiotemporal pattern of PM_2.5 distribution and its determinants in Chinese cities based on a...

China has been under threat of severe haze in recent years, particularly that caused by fine particulate matter (PM_2.5). Exploring the determinants of PM_2.5 concentration is critical for improving air quality. The influencing mechanism of smog pollution is a comprehensive and systematic process affected by multiple driving factors. In this research, we collected PM_2.5 monitoring data from 292 cities across China in 2015 and employed multilevel regression models constructed using three levels to detect the physical and socioeconomic driving forces behind the PM_2.5 concentration at monthly, seasonal and spatial scales, which captured random effects both varied by season and region. The results indicated significant spatiotemporal heterogeneity in the PM_2.5 distribution, with the pollution core located in central China and northern China. The most severely haze episodes occurred in winter. Multilevel models showed that 46.40% of the variance was derived from the seasonal and spatial levels, and the models could explain a maximum of 90.7% of the PM_2.5 concentration variance. The multilevel model identified more determinant influences varying by time and region. The outcomes suggested that the impacts of temperature and relative humidity on PM_2.5 were of significant spatiotemporal heterogeneity due to the influencing mechanism differing from season and station. The variation of anthropogenic activities led to the socioeconomic influences featured a significant spatiotemporal heterogeneity. This research revealed the spatiotemporal characteristic of PM_2.5 pollution influencing mechanism from physical and perspective and provided effective strategies for restricting air pollution.