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Spatiotemporal Patterns and Dominant Factors of Urban Particulate Matter Islands: New Evidence from 240 Cities in China

Author

Listed:
  • Ziqiang Peng

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Shisong Cao

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Mingyi Du

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Meizi Yang

    (School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Linlin Lu

    (Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Yile Cai

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • You Mo

    (China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China)

  • Wenji Zhao

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

Abstract

With rapid urbanization and industrialization, PM 2.5 pollution exerts a significant negative impact on the urban eco-environment and on residents’ health. Previous studies have demonstrated that cities in China are characterized by urban particulate matter island (UPI) phenomena, i.e., higher PM 2.5 concentrations are observed in urban areas than in rural settings. How, though, nature and socioeconomic environments interact to influence UPI intensities is a question that still awaits a general explanation. To fill this knowledge gap, this study investigates spatiotemporal variations in UPI effects with respect to different climatic settings and city sizes in 240 cities in China from 2000 to 2015 using remotely sensed data and explores the effective mechanism of human–environmental factors on UPI dynamics based upon the Geographically Weighted Regression (GWR) model. In particular, a conceptual framework that considers natural environments, technology, population, and economics is proposed to explore the factors influencing UPIs. The results show (1) that about 70% of the cities in China selected exhibited UPI effects from 2000 to 2015. In addition, UPI intensities and the number of UPI-related cities decreased over time. It is noteworthy that PM 2.5 pollution shifted from urban to rural areas. (2) Elevation was the most efficient driving factor of UPI variations, followed by precipitation, population density, NDVI, per capita GDP, and PM 2.5 emission per unit GDP. (3) Climatic backgrounds and city sizes influenced the compositions and performance of dominant factors regarding UPI phenomena. This study provides valuable a reference for PM 2.5 pollution mitigation in cities experiencing global climate change and rapid urbanization and thus can help sustainable urban developments.

Suggested Citation

  • Ziqiang Peng & Shisong Cao & Mingyi Du & Meizi Yang & Linlin Lu & Yile Cai & You Mo & Wenji Zhao, 2022. "Spatiotemporal Patterns and Dominant Factors of Urban Particulate Matter Islands: New Evidence from 240 Cities in China," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6117-:d:818097
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    References listed on IDEAS

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    1. Feili Wei & Shuang Li & Ze Liang & Aiqiong Huang & Zheng Wang & Jiashu Shen & Fuyue Sun & Yueyao Wang & Huan Wang & Shuangcheng Li, 2021. "Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM 2.5 Concentrations in Major Chinese Cities between 2005 and 2015," Energies, MDPI, vol. 14(11), pages 1-20, June.
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    Cited by:

    1. Yong Ma & Hang Li & Yun Tong, 2022. "Distribution Differentiation and Influencing Factors of the High-Quality Development of the Hotel Industry from the Perspective of Customer Satisfaction: A Case Study of Sanya," Sustainability, MDPI, vol. 14(11), pages 1-20, May.

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