China has been under threat of severe haze in recent years, particularly that caused by fine particulate matter (PM2.5). Exploring the determinants of PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 pollution influencing mechanism from physical and perspective and provided effective strategies for restricting air pollution.
Keywords: China; Multilevel regression analysis; PM(2.5); Physical and anthropogenic factors.
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