Examining the spatially varying effects of factors on PM2.5 concentrations in Chinese cities using geographically weighted regression modeling

Abstract

Whilst numerous studies have explored the spatial patterns and underlying causes of PM_2.5, little attention has been paid to the spatial heterogeneity of the factors affecting PM_2.5. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM_2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM_2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM_2.5 in 95.60% of the cities and it correlated negatively with PM_2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM_2.5. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM_2.5, revealing that increasing urban fragmentation was conducive to PM_2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM_2.5 governances.

Keywords: Geographically weighted regression; Natural conditions; PM(2.5); Socioeconomic determinants; Spatial heterogeneity.