Relationship between neighborhood land use structure and the spatiotemporal pattern of PM2.5 at the microscale: Evidence from the central area of Guangzhou, China

Jie Song, Suhong Zhou, Yinong Peng, Jianbin Xu and Rongping Lin
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Jie Song: School of Geography and Planning, Sun Yat-sen University, China; Guangdong Key Laboratory for Urbanization and Geo-Simulation, China
Suhong Zhou: School of Geography and Planning, Sun Yat-sen University, China; Guangdong Key Laboratory for Urbanization and Geo-Simulation, China; Guangdong Provincial Engineering Research Center for Public Security and Disaster, China
Yinong Peng: Guangzhou Urban Planning and Design Survey Research Institute, China
Jianbin Xu: School of Geography and Planning, Sun Yat-sen University, China

Environment and Planning B, 2022, vol. 49, issue 2, 485-500

Abstract: Fine particulate matter (PM 2.5 ) is harmful to human health. Although the relationship between urban land use and PM 2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM 2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM 2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM 2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM 2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM 2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM 2.5 . The higher the PM 2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM 2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM 2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM 2.5 .

Keywords: PM2.5; spatiotemporal pattern; neighborhood; land use structure; microscale (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:49:y:2022:i:2:p:485-500

DOI: 10.1177/23998083211007866

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