**4. Conclusions**

This paper constructed a prediction model of PM2.5 increase and decrease dynamic changes in neighborhoods based on 22 factors of green space, gray space, and meteorological factors, revealing the comprehensive impact mechanism of neighborhood-level built environments on PM2.5 and laying a foundation for proposing specific optimization strategies. The adj\_*R*<sup>2</sup> of these models was concentrated in 0.6~0.8, with the highest value of 0.836, indicating that it can better fit the existing indicators. P1, P3, P4, and P16 were the most important factors that significantly affected the increase and decrease in PM2.5 at the same time, which reflected the characteristics of the green–gray space difference, building height and its difference, relative humidity, and openness, respectively. Among the many indicators of green space, green space coverage was significantly conducive to PM2.5 reduction. The green corridor connecting green space internally would greatly influence the change of PM2.5 increase. For gray space, the openness of the neighborhood was important to PM2.5 reduction. In addition, relative humidity and wind speed contributed more to the change of both the decrease and increase of PM2.5 than temperature.

There are some issues that need to be further explored. This study used PM2.5 monitoring data for analysis, lacking exploration of PM2.5 sources. Further study requires discussion on the emissions in the urban zones because of the different pollution sources in the five cities [46]. Data from more days, especially heavy pollution levels, can be included for more accurate research.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/atmos13010115/s1, Table S1: Detailed information for 37 neighborhoods, Table S2: The date of different pollution level in 2016 and 2017 for the five cities, Table S3: Statistical of green space indicators, Table S4: Statistical of gray space indicators, Table S5: Statistical of meteorological factors, Table S6: Regression models of principal factors at different pollution levels.

**Author Contributions:** Conceptualization, methodology, software, validation, formal analysis, investigation, writing—original draft preparation, visualization, M.C.; writing—review and editing, supervision, project administration, funding acquisition, F.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (grant number 2020kfyXJJS104) and the National Natural Science Foundation of China (grant number 51778254).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data used in this study are not currently publicly available.

**Acknowledgments:** The authors would like to thank all the participants for their time and effort to participate in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.
