Unraveling the association between the built environment and air pollution from a geospatial perspective

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Fine particulate matter poses a negative effect on air quality and public health. The formation and dispersion of PM2.5 are often affected by the urban built environment. Previous literature has made great efforts to study the relationship between the built environment and air pollution, and propose corresponding policy implications for reducing air pollution. Most proposed policies are instructive but lack spatial heterogeneity, which is essential because the degree of air pollution and measures in each specific place is different. In this study, the built environment was depicted with multisource data and methods such as street view imagery (SVI) and deep learning, and the integration of geographically weighted regression (GWR) and k-means clustering was applied to explore the spatially varying impacts of the built environment factors on air pollution, and classify the study area into different groups with zonal policies and mitigation measures for PM2.5 reduction. Results show that parking lots significantly influenced PM2.5, followed by floor area ratio, road density, street vehicle volume, industrial facility, and street view greenery. Street view greenery was found to be negatively related to PM2.5 concentration in most of Xiamen Island. The study area was categorized into four groups with tailor-made policy implications considering spatial heterogeneity. For example, zone 1 is in the south of Xiamen Island with the highest coefficients of the parking lot and floor area ratio and lowest coefficient of street view greenery. For zone 1, greenery maintenance in the mountains, restricted high-density development, and improvement of greenery around the parking lot may be effective for this zone. These empirical results provide a new perspective for exploring the spatially heterogeneous mechanism of air pollution and a new approach to propose targeted and zonal policy implications for PM2.5 mitigation.