Spatiotemporal Dynamic Evolution of PM2.5 Exposure from Land Use Changes: A Case Study of Gansu Province, China

Air pollution is a major trigger for chronic respiratory and circulatory diseases. As a key component of air pollution, fine particulate matter (PM2.5) exposure is largely determined by land use type and population density. However, simultaneous consideration of their spatiotemporal distribution is lacking in existing studies on PM2.5 exposure. In this paper, we first assess the dynamic evolution of land use patterns in Gansu Province, China, from 2000 to 2020, using a land use transfer matrix and dynamic degree. Population-weighted exposure (PWE) to PM2.5 is then evaluated for each land use type at provincial, city, and county levels, with seasonal variations analyzed. Spatial autocorrelation analysis is finally performed to explore the spatiotemporal evolution of PM2.5 exposure, whereas standard deviation ellipses and gravity center migration models highlight spatial distribution characteristics and shifting trends. Experimental results showed that 2010 was a turning point for annual PM2.5 exposure at the provincial level in Gansu Province, with an initial increase followed by a decrease. Construction land had the highest annual PM2.5 exposure, whereas forest had the lowest exposure (except in 2005). Exposure levels showed a seasonal pattern: higher in winter and spring and lower in summer and autumn. At city and county levels, southern Gansu indicated a continuous decline in annual PM2.5 exposure across all land use types since 2000. Exposure levels exhibited a strong spatial positive correlation, with a fluctuating spatial convergence. This study comprehensively analyzes the multi-scale differences and spatiotemporal evolution patterns of PM2.5 exposure across various land use types, contributing to provide scientific evidence and decision-making support for mitigating air pollution and enhancing coordinated air pollution control at multi-scale administrative levels.