Xiaofan Sun

Exposure to PM_2.5 poses severe risks to public health and sustainable development, with exposure inequalities exacerbated by variations in atmospheric activity and uneven regional development. However, the urban-rural inequalities and natural-human driving mechanisms underlying PM_2.5 exposure inequalities within urban agglomerations are poorly understood. Taking the Central Plains Urban Agglomeration (CPUA) in China as an example, this study investigated the spatio-temporal variations of PM_2.5 and considered its future trends. The Theil index was employed to quantify PM_2.5 exposure inequalities. An interpretable machine learning model (RF-SHAP) was applied to identify the raster natural and socioeconomic driving factors. We found that 99.68% of the CPUA exhibited a decreasing trend in ground-level PM_2.5. The overall Theil index decreased from 0.168 to 0.142, with a rural decline from 0.115 to 0.084, suggesting an overall reduction in air pollution inequalities, particularly in rural areas. Conversely, the urban Theil index increased from 0.096 to 0.208, highlighting an increasing inequality in urban PM_2.5 exposure. Resource-based cities, such as Changzhi, Jincheng, and Jiaozuo, exhibited the largest PM_2.5 exposure inequality. Elevation was identified as the dominant factor influencing overall and rural PM_2.5 exposure inequalities, while population density was the primary driver of urban inequalities. This study highlighted the differences in urban−rural PM_2.5 inequalities and their drivers at the city agglomeration scale. The aims were to mitigate PM_2.5 exposure inequalities through socio-environmental systems, provide evidence for the integrated management of PM_2.5 exposure inequalities in city agglomerations, and support regional sustainable development.