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The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM 2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas

Author

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  • Wenting Wang

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
    College of Environmental and Planning, Henan University, Kaifeng 475004, China
    South-to-North Water Diversion Middle Route Information Technology Co., Ltd., Beijing 100038, China
    Both authors contributed equally to this work.)

  • Lijun Zhang

    (College of Environmental and Planning, Henan University, Kaifeng 475004, China
    Both authors contributed equally to this work.)

  • Jun Zhao

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
    College of Environmental and Planning, Henan University, Kaifeng 475004, China)

  • Mengge Qi

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
    College of Environmental and Planning, Henan University, Kaifeng 475004, China)

  • Fengrui Chen

    (Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education/Collaborative Innovation Center of Yellow River Civilization, Henan University, Kaifeng 475004, China
    College of Environmental and Planning, Henan University, Kaifeng 475004, China)

Abstract

The study investigated the spatiotemporal evolution of PM 2.5 concentration in the Beijing–Tianjin–Hebei region and surrounding areas during 2015–2017, and then analyzed its socioeconomic determinants. First, an estimation model considering spatiotemporal heterogeneous relationships was developed to accurately estimate the spatial distribution of PM 2.5 concentration. Additionally, socioeconomic determinants of PM 2.5 concentration were analyzed using a spatial panel Dubin model, which aimed to improve the robustness of the model estimation. The results demonstrated that: (1) The proposed model significantly increased the estimation accuracy of PM 2.5 concentration. The mean absolute error and root-mean-square error were 9.21 μg/m 3 and 13.10 μg/m 3 , respectively. (2) PM 2.5 concentration in the study area exhibited significant spatiotemporal changes. Although the PM 2.5 concentration has declined year by year, it still exceeded national environmental air quality standards. (3) The per capita GDP, urbanization rate and number of industrial enterprises above the designated size were the key factors affecting the spatiotemporal distribution of PM 2.5 concentration. This study provided scientific references for comprehensive PM 2.5 pollution control in the study area.

Suggested Citation

  • Wenting Wang & Lijun Zhang & Jun Zhao & Mengge Qi & Fengrui Chen, 2020. "The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM 2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas," IJERPH, MDPI, vol. 17(9), pages 1-16, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3014-:d:350721
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    References listed on IDEAS

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    Cited by:

    1. Mei Yang & Hong Fan & Kang Zhao, 2020. "Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution," IJERPH, MDPI, vol. 17(13), pages 1-22, July.
    2. Pengzhi Wei & Shaofeng Xie & Liangke Huang & Lilong Liu, 2021. "Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM 2.5 Concentration in Central and Southern China," IJERPH, MDPI, vol. 18(15), pages 1-26, July.

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