*3.3. The Influence of Built Environment on PM2.5*

Because the included principal factors varied from model to model (Table 2 and Table S6), the most important factors influencing PM2.5 were obtained by counting the times of principal factors that significantly influenced PM2.5 in 24 models of four pollution levels. As Figure 5 shows, the darker the color, the greater the frequency of a principal factor. First, Cin, Δtin, and Cin' were synthesized as the increase change of PM2.5. The decrease change of PM2.5 included Cde, Δtde, and Cde'. The principal factors that had the most significant impact on the increase change of PM2.5 were P1 and P3, which occurred seven times, followed by P4, P6, P16, and P17, which occurred six times. P7, P12, and P18 occurred five times. The most significant principal factor affecting the decrease change of PM2.5 was P5, which occurred 10 times, followed by P3 and P4, which occurred eight times. The frequency of P1, P12, P13, P15, and P16 was six times. Overall, P1, P3, P4, and P16 were important factors that significantly affected the growth and reduction of PM2.5 at the same time. These principal factors reflect the differences in green and gray space, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. In addition, these principal factors with high frequency were the factors that contributed the most to the corresponding dependent variable in the model. This further indicated their important role in PM2.5.

**Figure 5.** Frequency of principal factors that significantly influence PM2.5.

Second, each PM2.5 indicator was analyzed separately to find the differences in their principal factors. For Cin, P4 and P6 had the most contributions, indicating that meteorological factors and the green corridor connecting green space internally would greatly influence PM2.5 increase change. P1 contributed the most to Δtin. However, there were no obvious principal factors for Cin' compared with Cin and Δtin. As for Cde, P5 and P16 had the most contribution to it. Moreover, P5 showed a more important role in Δtde than Cde. Therefore, attention should be given to green space coverage and openness of neighborhood for PM2.5 reduction. P4 was the most important principal factor for Cde'.

To strengthen the application of the regression model based on the built environment of the neighborhood and provide a reference for the optimization strategy of the built environment, we selected several neighborhoods from five cities with strong and weak PM2.5 reduction effects. A neighborhood with strong PM2.5 reduction effects is characterized by a high value of Cde and Cde' or a low value of Δtde. Their effects on the increase and decrease of PM2.5 were analyzed through the analysis of the scale and spatial form of green and gray spaces in the neighborhood.

Neighborhoods with strong PM2.5 reduction capacity included WH6, HF5, NJ4, SH7, and HZ7 (Figure 6a). On the one hand, WH6 and NJ4 have large-scale green spaces, which play a great role in promoting the adsorption and reduction of PM2.5 [44]. Meanwhile, the lower building density contributes to the diffusion of PM2.5 in the neighborhood and promotes the decrease in PM2.5 [45]. HZ7 has a stable regulatory effect on PM2.5. It can both inhibit the increase in PM2.5 and promote the decrease in PM2.5. Although the building density of HF5 and SH7 is higher than that of other neighborhoods, the almost determinant building layout and height arrangement of HF5 are conducive to the formation of a ventilation corridor. The large building height difference and building shape uniformity index of SH7 promote the decline of PM2.5.

**Figure 6.** Neighborhoods with (**a**) strong and (**b**) weak PM2.5 reduction capacity.

Neighborhoods with weak PM2.5 reduction capacity included WH2, HF7, NJ1, SH3, and HZ3 (Figure 6b). Among these neighborhoods, WH2, NJ1, and HZ3 have a common feature of low-rise and high-density, which is not conducive to ventilation in the neighborhood. The building density of SH3 is high, and there are some high-rise buildings. However, the scale of green space in these neighborhoods is generally small, which reduces the active adsorption of PM2.5 by green space. HF7 is a high-rise, low-density neighborhood. Too many high-rise buildings aggravate the pollution of PM2.5 in the neighborhood and are difficult to evacuate.
