Towards Cleaner Air in Urban Areas: The Dual Influence of Urban Built Environment Factors and Regional Transport

Li Han; Yongjie Qi; Dong Liu; Feiyue Liu; Yuejing Gao; Wenjing Ren; Jingyuan Zhao

doi:10.1016/j.envpol.2024.125584

Towards Cleaner Air in Urban Areas: The Dual Influence of Urban Built Environment Factors and Regional Transport

Environ Pollut. 2024 Dec 31:125584. doi: 10.1016/j.envpol.2024.125584. Online ahead of print.

Authors

Li Han¹, Yongjie Qi², Dong Liu², Feiyue Liu², Yuejing Gao², Wenjing Ren³, Jingyuan Zhao⁴

Affiliations

¹ School of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi, China; Geological resources and geological engineering postdoctoral research mobile station, Xi'an University of Science and Technology, Xi'an, Shaanxi, China. Electronic address: hanl@xust.edu.cn.
² School of Architecture and Civil Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
³ Department of Fine Arts and Craft Design, Yuncheng University, Yuncheng, Shanxi, China.
⁴ College of Architecture, Chang'an University, Xi'an 710061, Shaanxi, China.

PMID: 39746635
DOI: 10.1016/j.envpol.2024.125584

Abstract

Exposure to air pollution significantly elevates the risk of disease among urban populations. Improving city air quality requires not only traditional emission reduction strategies but also a focus on the intricate impacts of the urban built environment and meteorological elements. The complexity and diversity of factors within the urban built environment pose significant challenges to pollution control. This study employs machine learning to predict the spatial distribution of inhalable particulate matter (PM₁₀) and fine particulate matter (PM_2.5), integrating the clustering of pollutant-emitting enterprises and prevailing wind direction to trace pollutant sources. The results indicate that, compared to the multiple linear regression model, the R² of the PM₁₀ random forest prediction model improved from 0.64 to 0.88, while the RMSE decreased from 48.63 to 27.34. Similarly, the R² of the PM_2.5increased from 0.70 to 0.92, and the RMSE decreased from 30.85 to 15.31. High concentrations of PM₁₀ and PM_2.5 in Xi'an are primarily concentrated in the northeast and southwest of the central urban area. By integrating a kernel density analysis of polluting enterprises with the analysis of prevailing wind patterns, it is evident that particulate matter in Xi'an is substantially influenced by regional urban transport. Therefore, pollution control efforts must be enhanced through coordinated regional governance. According to the analysis results of the partial dependence plot, reducing winter temperature proves beneficial in reducing PM₁₀ and PM_2.5 levels. Effective measures encompass sprinkling and humidifying, reducing traffic emissions, and controlling various dust sources to lower PM₁₀. Enhancing ventilation, increasing green spaces, and regulating vehicle and industrial emissions effectively reduce PM_2.5. The study's findings offer a scientific foundation for administrative authorities to craft pollution reduction management policies and create adaptable territorial spatial planning. Moreover, they contribute to diminishing public exposure to pollution and improving the quality of public environmental health.

Keywords: Particulate matter; Pollution control; Random forest; Urban built environment.