Identifying the socioeconomic determinants of population exposure to particulate matter (PM2.5) in China using geographically weighted regression modeling

Abstract

Air pollution contributes significantly to premature death in China. However, only a limited number of studies have identified the potential determinants of population exposure to PM2.5 from a socioeconomic perspective. This paper analyses the socioeconomic determinants of population exposure at the city level in China. We first estimated population exposure to PM2.5 by integrating high resolution spatial distribution maps of PM2.5 concentrations and population density, using data for 2013. Then, geographically weighted regression (GWR) modeling was undertaken to explore the strength and direction of relationships between the selected socioeconomic factors and population exposure. The results indicate that approximately 75% of the population of China lived in an area where PM2.5 concentrations were over 35 μg/m3 in 2013. From the GWR models, we found that the percentages for cities that showed a statistically significant relationship (p < 0.05) between population exposure and each of the six factors were: urbanization, 91.92%; industry share, 91.58%; construction level, 88.55%; urban expansion, 73.40%; income disparity, 64.98%; and private vehicles, 27.27%. The R-squared value for the six factors in the multivariable GWR model was 0.88, and all cities demonstrated a statistically significant relationship. More importantly, the association between the six factors and population exposure was found to be spatially heterogeneous at the local geographic level. Consideration of these six drivers of population exposure can help policy makers and epidemiologists to evaluate and reduce population exposure risks.