Urban–Suburban PM2.5 Trends in China Under Different Urban Classification Methods

Urban–suburban PM2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM2.5 monitoring data and 1 km × 1 km gridded population density data to analyze the sensitivity of urban–suburban PM2.5 trends to spatial structure-based and population-density-based classification (300, 1500, 2200, 2500 people km−2) at national, Eastern and Western China scales. Results showed significant national PM2.5 decline, with urban reduction rates of −3.1 to −3.3 µg m−3 yr−1 in summer and −6.0 to −6.3 µg m−3 yr−1 in winter, and faster air quality improvement in winter. Urban–suburban PM2.5 differences were highly sensitive to classification methods: the spatial structure-based framework showed minimal differences (0.09 µg m−3 in summer, 5 µg m−3 in winter), while the 300 people km−2 threshold yielded much larger ones (11 µg m−3 in summer, 29 µg m−3 in winter) with faster urban declines. Higher population density thresholds narrowed such differences and converged trends with the spatial structure-based results. Pronounced spatial heterogeneity existed: Eastern China had larger PM2.5 declines with consistent response patterns to national trends, while Western China showed weaker declines, with urban–suburban differences highly sensitive to classification methods and opposite temporal evolution trends. This study confirms that urban definition is a critical methodological factor for interpreting China’s long-term urban–suburban PM2.5 trends, as different methods cause notable inferential deviations. Future air pollution spatial heterogeneity studies should carefully select and specify urban classification methods to ensure comparable, scientifically rigorous findings.