Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Abstract

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM_2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM_2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry’s proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM_2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM_2.5 pollution; secondly, the spatial agglomeration state of PM_2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM_2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM_2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM_2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM_2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM_2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM_2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM_2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM_2.5 pollution in Chinese cities.