*2.3. Built Environment Variables and Meteorological Factors*

Built environment factors, including green space and gray space, were included in this study, as well as three important meteorological factors: atmospheric temperature (Ta), relative humidity (RH), and wind velocity (V). A total of 22 variables were selected for analysis based on the quantity and spatial pattern of green and gray spaces (Table 1). Detailed information on the 22 variables is shown in Tables S3–S5. The determination of these variables also took into account their potential impacts on PM2.5 and the representativeness of built environment characteristics in neighborhoods.

**Table 1.** Indicators for model building.


The measurements and computations of built environment factors were based on a GIS vector dataset generated with a high-precision digital map (Google Earth, 2017) of each city, which ensured high accuracy of the calculation. On the one hand, the neighborhood green space cover ratio (GCR) and tree cover ratio (TCR) were two variables measuring the quantity of green space in the neighborhoods. The spatial pattern of green space was measured by morphological spatial pattern analysis (MSPA), which can provide seven types of green space patterns, including the core, islet, perforation, edge, loop, bridge, and branch [32].

On the other hand, the neighborhood hard space cover ratio (HSCR) was used as the quantity variable for gray space. Spatial pattern variables of the gray space were selected with a consideration of the density, vertical morphology, and spatial layout of gray spaces in neighborhoods. First, building density was considered one of the most important density variables, which was further classified into three categories, including building densities of one to three floors (BD\_1), four to nine floors (BD\_2), and more than nine floors (BD\_3) [35]. In addition, as one of the major pollutant sources in neighborhoods, roads are a special kind of gray space, which may reflect the degree of traffic emissions. Road density (RD) was calculated as in many previous studies [36,37]. Second, the floor area ratio (FAR), mean building height (H), and standard deviation of building height (Hσ) were adopted to measure the vertical morphology of gray space in neighborhoods. FAR reflects the development intensity of the neighborhood. The larger the FAR is, the

greater the development intensity and the higher the height of buildings. A neighborhood with a higher building height usually has a lower wind velocity near the ground, which results in better ventilation conditions [38]. Hσ represents the variation of building height. The higher the Hσ, the dispersion of the building height is greater. Third, the building evenness index (BEI) and sky view factor (SVF) were chosen to represent the layout of buildings in the neighborhood. BEI is a reflection of the difference in buildings' flat form. A neighborhood with a higher BEI value implies a more uneven flat form of buildings. SVF is an important index reflecting built environment geometry [39]. A lower value of SVF indicates a more closed neighborhood space. The 3D building models were adopted for the calculation of SVF based on ArcGIS software according to the method of Gal' et al. [40].

## *2.4. Analytical Methods*
