**1. Introduction**

Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. According to the 2020 World Air Quality Report published by IQ*Air*, China is 14th in the rankings for poor air quality among the 106 countries that have been given air quality monitoring stations by the WHO. In particular, the middle and lower reaches of the Yangtze River are among of the most polluted areas in China, where a large number of residents live. Serious PM2.5 pollution has resulted in respiratory issues, asthma, and even death [1]. To maintain the basic requirement of respiratory health, it is urgent to improve air quality.

Spatial-temporal variations in PM2.5 and the impact factors on PM2.5 have attracted much attention in recent years; among these studies, PM2.5 levels were mainly affected by socioeconomic environments [2], climatic conditions [3], and urban physical environments [4]. Human activities, such as traffic emissions, industrial activities, and coal consumption, cause severe PM2.5 pollution [5,6]. At the same time, the loss of natural land and increase in artificial ground cover exacerbate the problem [7]. In addition, the change in urban land cover and its spatial pattern leads to environmental problems, especially urban heat island effects that always contribute to gathering atmospheric pollutants or forming secondary pollutants, thereby strengthening PM2.5 pollution [8]. Investigating the

**Citation:** Chen, M.; Dai, F. PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities. *Atmosphere* **2022**, *13*, 115. https://doi.org/10.3390/ atmos13010115

Academic Editors: Duanyang Liu, Honglei Wang and Kai Qin

Received: 29 November 2021 Accepted: 10 January 2022 Published: 12 January 2022

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temporal variations in PM2.5 and its mitigation approach is essential to improve the human settlement environment.

The built environment, usually measured by factors from various perspectives including land cover type, land use, urban form at the urban scale and public space, layout of buildings and roads at the neighborhood scale [9], is one of the important factors affecting PM2.5; however, the key factors remain unclear. Obvious differences in PM2.5 levels are found across urban land cover patterns [10]. In addition, urban landscape patterns or structures, including the composition and configuration of built environments, have aroused interest by using a series of relevant metrics to investigate their effects on PM2.5 [11–14], although the influence of individual metrics may differ among studies. Moreover, urban morphology has attracted increasing attention because of its strong impacts on PM2.5. At the city scale, the spatial format of urban built-up areas, such as the size, compactness, fragmentation, and complexity of the morphology of urban areas, influences the city's average PM2.5 level [15–17]. At the local scale, the PM2.5 concentration varies from neighborhood to neighborhood [18]. The difference in street canyon characteristics, building layout, and spatial form is one of the important factors that significantly influence PM2.5 levels [19–21]. Local climate zones (LCZs), a concept aimed at classifying local built environment features, have been widely used in urban climate studies [22–24]. Ten built-up LCZs and seven land cover LCZs can be provided for measuring different built environments and natural environments, respectively. Indicators, such as the sky view factor, aspect ratio, impervious surface fraction, pervious surface fraction, and height of roughness elements, are frequently used for determining these LCZs [25–27]. However, they are rarely involved in the PM2.5 field. Neighborhood-level PM2.5 pollution should be a main concern because it is closely related to people's daily lives. In a high-density neighborhood environment, densely constructed buildings block air ventilation and consequently impede pollutant dispersion [28]. Particular attention should be given to the mitigation of PM2.5 by optimizing the neighborhood-level built environment, which constitutes the basic fabric in a city.

Previous studies have explored the relationship between the urban built environment and PM2.5 from different aspects, yet there are still shortcomings. First, the lack of systematic investigation of the built environment may result in the unclear key factors that significantly influence PM2.5. Second, most studies focus on individual cities instead of regions, which may limit the applicability of the research findings. Considering the complexity of the built environment in a neighborhood, traditional stepwise regression analysis has some limitations, including the potential collinearity among multiple variables and the possibility of removal of some predictive variables significantly related to dependent variables [29]. Principal component analysis (PCA) has been adopted to convert complex variables into new variables that contain most of the original information and are independent of each other, thereby reducing the predictors' collinearity in PM2.5 simulation [30].

For this, this study explored key built environment factors influencing PM2.5 in common neighborhoods that are abundant in cities. We focused on 37 neighborhoods located in five megacities in the middle and lower reaches of the Yangtze River to better understand the influence mechanism of the built environment on PM2.5 pollution. Principal component analysis (PCA) was conducted before regression analysis to increase its performance. All principal component variables were retained for regression analysis to establish the optimal influencing factors. Therefore, key factors can be obtained.
