Asymmetric effects of natural and socioeconomic factors on PM2.5 pollution in Chinese counties

With rapid urbanization intensifying air pollution, especially PM_2.5, which poses a serious threat to public health, clarifying its spatial differentiation patterns and driving mechanisms is of great practical significance. As factors may exert different magnitudes and even opposite directions of influence across PM_2.5 levels, accounting for asymmetric effects is essential for designing targeted and effective governance strategies. We developed a spatial zoning plan for 2851 counties in China using annual average PM_2.5 remote sensing data from 2000 to 2015. Geographic detectors and quantile regression models were used to reveal the dominant factors and asymmetric effects affecting PM_2.5 pollution in 2015, respectively. The results show significant spatial agglomeration of PM_2.5 (Moran's I = 0.9387), with a pronounced divergence across the Hu Huanyong Line. Combining the spatial classification and temporal trends of PM_2.5 pollution, 84.5% of counties need to be treated (523 counties belong to key governance zone), and 15.5% of counties are classified as coexistence areas. The influences of socio-economic factors on PM_2.5 (additive q-value 0.7881) are greater than those of natural condition factors (additive q-value 0.7036), and land urbanization is the leading factor. The interesting findings are that the coefficient of population size declines with increasing pollution quantiles-its effect at the 10th quantile (0.1104) is nearly four times that at the 90th (0.0296), and that land urbanization showed an inverted U-shaped curve, while per capita GDP presented a U-shaped trend. Moreover, favorable natural conditions have mitigated local PM_2.5 pollution. These findings uncover how socio-economic and natural drivers perform differently at varying pollution levels, offering novel insights into targeted zoning regulation.