How does urban structure affect concentrations of fine particulate matter (PM2.5) and components? Evidence from 260 Chinese cities

Abstract

PM2.5, as a key atmospheric pollutant severely threatening human health and sustainable development, has drawn increasing attention regarding the influence of urban structure on its concentration. This study focuses on 260 cities in China from 2011 to 2021. By integrating multi-source remote sensing data, reanalysis data, and statistical records, a high-resolution PM2.5 concentration dataset based on aerosol optical depth was constructed. By coupling the light gradient boosting machine machine learning model with the geographically and temporally weighted regression (GTWR) model, the study systematically reveals the influence mechanisms and spatiotemporal heterogeneity of urban spatial, economic, and social structures on PM2.5 concentrations. The results indicate that population density (POP) in the social structure is the most critical factor influencing PM2.5, contributing 23.2%, with a notable threshold effect (586 persons/km2). Air pollutant emission (APE) and the night light index (NL) exhibit significant positive and negative influences, contributing 17.65% and 14.18%, respectively. An increase in the separation degree of construction land (IJI) contributes to reducing PM2.5 concentrations. The GTWR model further reveals significant spatiotemporal heterogeneity among the influencing factors. The impact intensities of POP and IJI show a weakening trend over time, while those of APE and NL display fluctuating characteristics of first decreasing and then increasing. Spatially, the influences of POP and APE exhibit a pattern of increasing intensity from south to north and from central regions toward coastal and northeastern areas.