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A Clustering Framework to Reveal the Structural Effect Mechanisms of Natural and Social Factors on PM 2.5 Concentrations in China

Author

Listed:
  • Wentao Yang

    (National-Local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China)

  • Zhanjun He

    (School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Huikun Huang

    (School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China)

  • Jincai Huang

    (Shenzhen Key Laboratory of Spatial Smart Sensing and Service, Shenzhen University, Shenzhen 518060, China)

Abstract

Understanding the mechanisms of various factors that affect PM 2.5 can assist in the development of scientific measures to improve air quality. Nevertheless, existing research has concentrated on exploring local effect mechanisms, while structural effect mechanisms at regional or national scales have scarcely been analysed. Consequently, this study presents an analytical framework for elucidating the structural effect mechanisms of associated factors on PM 2.5 . Geographically and temporally weighted regression was used to explore the local effect mechanisms. This was followed by spatial clustering analysis to reveal these mechanisms by detecting their aggregation patterns. In the analysis, datasets for annual mean PM 2.5 and socio-economic factors in China from 1999 to 2016 were employed. Urban population, gross industrial output, and sulphur dioxide emissions were identified as factors affecting changes in PM 2.5 concentrations. These three factors had both negative and positive effects, while the gross industrial output had the largest coefficient variation degree. Three geographically related factors exhibited different impacts on PM 2.5 concentrations in most of mainland China. These factors were the urban population roughly west of the Heihe-Tengchong line, gross industrial output primarily in southwestern China, and sulphur dioxide emissions primarily in southern China.

Suggested Citation

  • Wentao Yang & Zhanjun He & Huikun Huang & Jincai Huang, 2021. "A Clustering Framework to Reveal the Structural Effect Mechanisms of Natural and Social Factors on PM 2.5 Concentrations in China," Sustainability, MDPI, vol. 13(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1428-:d:489661
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    References listed on IDEAS

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    1. Shen Zhao & Yong Xu, 2019. "Exploring the Spatial Variation Characteristics and Influencing Factors of PM 2.5 Pollution in China: Evidence from 289 Chinese Cities," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    2. מחקר - ביטוח לאומי, 2006. "Summary for 2005," Working Papers 29, National Insurance Institute of Israel.
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    Cited by:

    1. Tingchen Wu & Xiao Xie & Bing Xue & Tao Liu, 2021. "A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM 2.5 Removal Modes on a Micro-Temporal Scale," Sustainability, MDPI, vol. 13(19), pages 1-17, October.
    2. Bin Zhao & Xuexi Yang & Qianhong Wu & Weifeng Xiao & Wentao Yang & Min Deng, 2022. "Uncovering the Structural Effect Mechanisms of Natural and Social Factors on Land Subsidence: A Case Study in Beijing," Sustainability, MDPI, vol. 14(16), pages 1-18, August.

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