Analysis reveals urban morphology and green spaces influence PM2.5 and O3 levels in Guiyang, suggesting urban planning can mitigate air pollution.
PM2.5 and O3 are now the primary air pollutants in Chinese cities and pose serious risks to human health. In particular, the two- and three-dimensional patterns of urban buildings and green spaces play a crucial role in governing the dispersion of air pollutants. Using multi-source geospatial data and 2D/3D morphology metrics, this study employs an Extreme Gradient Boosting (XGBoost) model coupled with Shapley Additive Explanations (SHAP) to analyze the nonlinear effects of 2D/3D landscape and green space patterns on PM2.5 and O3 concentrations in the central urban area of Guiyang City. The results indicate the following findings: (1) PM2.5 exhibits a U-shaped seasonal pattern, being higher in winter and spring and lower in summer and autumn, whereas O3 displays an inverted U-shaped pattern, being higher in spring and summer and lower in autumn and winter. (2) PM2.5 concentrations are higher in suburban and industrial zones and lower in central residential areas, while O3 concentrations increase from the urban core toward the suburbs. (3) MV, BSI, BSA, BEL, BD, FAR, and BV show significant positive correlations with both PM2.5 and O3 (p < 0.001), whereas TH shows a significant negative correlation with PM2.5 (p < 0.001). (4) High-density and complex building-edge patterns intensify both PM2.5 and O3 pollution by hindering urban ventilation and enhancing pollutant accumulation, whereas moderate vertical heterogeneity and greater tree height effectively reduce PM2.5 concentrations but simultaneously increase O3 concentrations due to enhanced VOC emissions. Urban form and vegetation jointly regulate air quality, highlighting the need for integrated urban planning that balances building structures and green infrastructure. The findings of this study provide practical implications for urban design and policymaking aimed at the coordinated control of PM2.5 and O3 pollution through the optimization of urban morphology.
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Lu et al. (2025) studied this question.
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