Mitigating nitrogen dioxide (NO2) pollution is a critical objective for enhancing urban environmental quality. The spatial structure of urban tree canopies plays a crucial role in influencing NO2 diffusion and deposition. However, previous studies have focused mainly on the linear relationships between two-dimensional green spaces and NO2, while the associated nonlinear relationships and threshold effects of three-dimensional urban tree canopy (UTC) spatial structure remain underexplored. To address this gap, we leveraged 1 m resolution satellite-derived data and explainable machine learning (XGBoost, SHAP, PDP) to examine the nonlinear influences and threshold effects of three-dimensional UTC spatial structures on NO2 in Shenzhen. The results revealed that urban tree canopy spatial structure is associated with NO2 concentrations. Among the key metrics, the two-dimensional canopy coverage ratio (CCR) emerged as the primary canopy-related correlate of lower NO2 concentrations, while three-dimensional vertical structure metrics, particularly canopy height variability (CHV) and standard deviation of canopy height (SDCH), acted as critical secondary correlates in modulating the spatial distribution of pollutants. Based on these relationships, we identified potential threshold ranges for key metrics by comparing mathematically identified inflection points with practical urban planning constraints. In summary, this study advances the spatial analysis of “green spaces-NO2” interactions from a two-dimensional to a three-dimensional perspective. Our findings could provide quantitative guidance for optimizing green space structure in high-density urban areas to inform strategies potentially associated with improved NO2 outcomes.
Liufu et al. (Sun,) studied this question.