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

Author

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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)

  • Rongping Lin

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 .

Suggested Citation

  • Jie Song & Suhong Zhou & Yinong Peng & Jianbin Xu & Rongping Lin, 2022. "Relationship between neighborhood land use structure and the spatiotemporal pattern of PM2.5 at the microscale: Evidence from the central area of Guangzhou, China," Environment and Planning B, , vol. 49(2), pages 485-500, February.
    • Handle: RePEc:sae:envirb:v:49:y:2022:i:2:p:485-500
    DOI: 10.1177/23998083211007866
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    References listed on IDEAS

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    1. Jing Ma & Yinhua Tao & Mei-Po Kwan & Yanwei Chai, 2020. "Assessing Mobility-Based Real-Time Air Pollution Exposure in Space and Time Using Smart Sensors and GPS Trajectories in Beijing," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(2), pages 434-448, March.
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    Cited by:

    1. Kai Ren & Jin Xu, 2024. "Formation Process and Spatial Representation of Tourist Destination Personality from the Perspective of Cultural Heritage: Application in Traditional Villages in Ancient Huizhou, China," Land, MDPI, vol. 13(4), pages 1-27, March.

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