Spatially Heterogeneous Effects of Microscale Built Environments on PM2.5 Concentrations Based on Street View Imagery and Machine Learning

PM2.5 pollution is a significant environmental problem in global urbanization. However, traditional macro-scale studies are constrained by data resolution limitations, failing to accurately characterize the microscale built environment or thoroughly investigate its spatially heterogeneous effects on PM2.5 concentrations. To address this gap, this study constructs a multidisciplinary framework of “Street View Imagery element extraction–spatial heterogeneity modeling–planning strategy optimization” with Xi’an as the case. Leveraging machine learning techniques, the study employs the ResNet50 deep learning model and the ADE20K dataset to precisely extract ten microscale built environment factors from tens of thousands of street view images. Combined with the High-resolution and High-quality Ground-level PM2.5 Dataset for China, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models were used to systematically reveal the impacts of the microscale built environment on PM2.5 concentrations. Ten built environment factors were identified with varying spatial heterogeneity in their effects on the PM2.5 concentrations, as follows: (1) factors with positive effects, in descending order of strength, include building, wall, fence, tree, sky, and grass; (2) factors with negative effects, in descending order of strength, include sidewalk, plant, and car; (3) compared with other factors, the road factor showed a relatively weaker effect. This research provides decision-making support for targeted urban planning and environmental protection, while offering valuable references for air pollution control in other cities.