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The accelerating pace of urbanization has markedly increased the risk of humid-heat exposure, posing serious threats to public health and urban ecosystems. Taking the central urban area of Nanjing as a case study, this research employs a downscaling approach based on the Extreme Gradient Boosting machine learning model. By integrating 1 km resolution relative humidity data with 30 m resolution remote sensing data, a high-precision relative humidity dataset at 30 m resolution was constructed. This dataset was further combined with conventional heat exposure assessment methods to develop a novel Population-Weighted Humid Heat Exposure Index (PWHHEI). The spatial distribution patterns between PWHHEI and urban blue-green spaces (UBGS) were analyzed using the bivariate Local Moran’s I statistic, and a Geographically Weighted Random Forest model was applied to assess the regulatory effects of UBGS morphological characteristics on humid-heat exposure and their spatial heterogeneity. The results indicated that high-risk humid-heat exposure areas were mainly concentrated in densely populated urban centers with fragmented UBGS; green spaces played a dominant role in regulating humid-heat exposure, with green area size being the most influential factor, while the regulatory effect of existing blue spaces was relatively limited; and the effectiveness of UBGS was significantly influenced by its spatial configuration, with complex-shaped and clustered green patches enhancing cooling efficiency. This study provides a novel perspective on the dynamics and driving mechanisms of urban UBGS and offers a scientific foundation for its future planning, conservation, and sustainable development.
Ma et al. (Thu,) studied this question.