*Article* **PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities**

**Ming Chen and Fei Dai \***

School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China; chen\_m@hust.edu.cn

**\*** Correspondence: daifei@hust.edu.cn; Tel.: +86-139-8620-3411

**Abstract:** Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj\_*R*<sup>2</sup> of most models was between 0.6 and 0.8, and the highest adj\_*R*<sup>2</sup> was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM2.5 based on these built environment factors.

**Keywords:** PM2.5; principal component analysis; green space; gray space; neighborhood
