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Spatiotemporal Distribution of Continuous Air Pollution and Its Relationship with Socioeconomic and Natural Factors in China

Author

Listed:
  • Dongsheng Zhan

    (School of Management, Zhejiang University of Technology, Hangzhou 310023, China)

  • Qianyun Zhang

    (School of Management, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xiaoren Xu

    (Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276012, China)

  • Chunshui Zeng

    (College of Tourism, Fujian Normal University, Fuzhou 350117, China)

Abstract

Continuous air pollution (CAP) incidents last even longer and generate greater health hazards relative to conventional air pollution episodes. However, few studies have focused on the spatiotemporal distribution characteristics and driving factors of CAP in China. Drawing on the daily reported ground monitoring data on the ambient air quality in 2019 in China, this paper identifies the spatiotemporal distribution characteristics of CAP across 337 Chinese cities above the prefecture level using descriptive statistics and spatial statistical analysis methods, and further examines the spatial heterogeneity effects of both socioeconomic factors and natural factors on CAP with a Multiscale Geographically Weighted Regression (MGWR) model. The results show that the average proportion of CAP days in 2019 reached 11.50% of the whole year across Chinese cities, a figure equaling to about 65 days, while the average frequency, the maximum amount of days and the average amount of days of CAP were 8.02 times, 7.85 days and 4.20 days, respectively. Furthermore, there was a distinct spatiotemporal distribution disparity in CAP in China. Spatially, the areas with high proportions of CAP days were concentrated in the North China Plain and the Southwestern Xinjiang Autonomous Region in terms of the spatial pattern, while the proportion of CAP days showed a monthly W-shaped change in terms of the temporal pattern. In addition, the types of regions containing major pollutants during the CAP period could be divided into four types, including “Composite pollution”, “O 3 + NO 2 pollution”, “PM 10 + PM 2.5 pollution” and “O 3 + PM 2.5 pollution”, while the region type “PM 10 + PM 2.5 pollution” covered the highest number of cities. The MGWR model, characterized by multiple spatial scale impacts among the driving factors, outperformed the traditional OLS and GWR model, and both socioeconomic factors and natural factors were found to have a spatial non-stationary relationship with CAP in China. Our findings provide new policy insights for understanding the spatiotemporal distribution characteristics of CAP in urban China and can help the Chinese government make prevention and control measures of CAP incidents.

Suggested Citation

  • Dongsheng Zhan & Qianyun Zhang & Xiaoren Xu & Chunshui Zeng, 2022. "Spatiotemporal Distribution of Continuous Air Pollution and Its Relationship with Socioeconomic and Natural Factors in China," IJERPH, MDPI, vol. 19(11), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6635-:d:827199
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    References listed on IDEAS

    as
    1. Dongsheng Zhan & Mei-Po Kwan & Wenzhong Zhang & Shaojian Wang & Jianhui Yu, 2017. "Spatiotemporal Variations and Driving Factors of Air Pollution in China," IJERPH, MDPI, vol. 14(12), pages 1, December.
    2. Chunshan Zhou & Shijie Li & Shaojian Wang, 2018. "Examining the Impacts of Urban Form on Air Pollution in Developing Countries: A Case Study of China’s Megacities," IJERPH, MDPI, vol. 15(8), pages 1-18, July.
    3. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    4. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
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    1. Mengge Zhou & Yonghua Li & Fengying Zhang, 2022. "Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China," IJERPH, MDPI, vol. 19(15), pages 1-19, August.

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