Treffer: Interactive Mechanisms and Pathways of Meteorology and Blue-Green Space on PM 2.5 : An Empirical Study Integrating XGBoost-SHAP and SEM.

Blue-green space patterns and meteorological conditions jointly influence PM<subscript>2.5</subscript> concentrations. However, the nonlinear mechanisms and interactions among these key drivers remain insufficiently studied. To address this gap, this study applied an interpretable machine learning approach (XGBoost-SHAP) to detect seasonal nonlinearities, thresholds, and interaction effects of meteorological and landscape metrics on PM<subscript>2.5</subscript> distribution in Jiangsu Province, China. Structural Equation Model was further employed to quantify the direct and indirect effect pathways among these factors. Model explanatory power showed distinct seasonal variations, with the highest performance in summer (R² = 0.615) and the lowest in winter (R² = 0.316). Meteorological factors exerted stronger influences than blue-green space pattern metrics, with wind speed being the most critical meteorological factor across all seasons. Among landscape metrics, the proportion of green space and water body (G_PLAND and W_PLAND) was the key driver of PM<subscript>2.5</subscript> concentrations in spring, autumn, and winter, while its influence became insignificant in summer, replaced by the number and shape complexity of green space patches. This study further revealed that in spring, autumn, and winter, G_PLAND and W_PLAND not only exerted direct effects on PM<subscript>2.5</subscript> but also significantly influenced it indirectly by modulating land surface temperature. Additionally, green space shape complexity and land surface temperature were found to interact with other meteorological and landscape factors during these seasons; once exceeding specific thresholds, they reversed the direction of other factors' effects on PM<subscript>2.5</subscript>. No significant interactions were detected in summer, indicating that dominant factors primarily exerted independent effects during this season. Collectively, our findings provide important insights for formulating seasonally adaptive planning strategies to advance sustainable urban development and long-term air quality management. [ABSTRACT FROM AUTHOR]

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