IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i11p6635-d827199.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/11/6635/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/11/6635/
    Download Restriction: no
    ---><---

    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 1-18, 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A. Stewart Fotheringham & M. Sachdeva, 2022. "Scale and local modeling: new perspectives on the modifiable areal unit problem and Simpson’s paradox," Journal of Geographical Systems, Springer, vol. 24(3), pages 475-499, July.
    2. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    3. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    4. Liv Osland & Inge Thorsen, 2013. "Spatial Impacts, Local Labour Market Characteristics and Housing Prices," Urban Studies, Urban Studies Journal Limited, vol. 50(10), pages 2063-2083, August.
    5. Breisinger, Clemens & Ecker, Olivier & Funes, Jose & Yu, Bingxin, 2010. "Food as the basis for development and security: A strategy for Yemen," IFPRI discussion papers 1036, International Food Policy Research Institute (IFPRI).
    6. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    7. repec:rri:wpaper:200711 is not listed on IDEAS
    8. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    9. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    10. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    11. Fang Di & Richards Timothy J. & Grebitus Carola, 2019. "Modeling Product Choices in a Peer Network," Forum for Health Economics & Policy, De Gruyter, vol. 22(1), pages 1-13, June.
    12. Montmartin, Benjamin & Herrera, Marcos & Massard, Nadine, 2018. "The impact of the French policy mix on business R&D: How geography matters," Research Policy, Elsevier, vol. 47(10), pages 2010-2027.
    13. Stoop, Nik & Verpoorten, Marijke & van der Windt, Peter, 2019. "Artisanal or industrial conflict minerals? Evidence from Eastern Congo," World Development, Elsevier, vol. 122(C), pages 660-674.
    14. Huang, Wei, 2019. "Forest condition change, tenure reform, and government-funded eco-environmental programs in Northeast China," Forest Policy and Economics, Elsevier, vol. 98(C), pages 67-74.
    15. K.P. Gluschenko (glu@nsu.ru ), 2010. "Income inequality in Russian regions: comparative analysis," Journal "Region: Economics and Sociology", Institute of Economics and Industrial Engineering of Siberian Branch of RAS, vol. 4.
    16. Nik Stoop & Marijke Verpoorten & Peter van der Windt, 2018. "More Legislation, More Violence? The Impact of Dodd-Frank in the DRC," SALDRU Working Papers 231, Southern Africa Labour and Development Research Unit, University of Cape Town.
    17. repec:asg:wpaper:1048 is not listed on IDEAS
    18. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    19. Roger Bivand, 2008. "Implementing Representations Of Space In Economic Geography," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 1-27, February.
    20. repec:asg:wpaper:1006 is not listed on IDEAS
    21. Tony H. Grubesic & Kelly M. Durbin, 2022. "Breastfeeding, Community Vulnerability, Resilience, and Disasters: A Snapshot of the United States Gulf Coast," IJERPH, MDPI, vol. 19(19), pages 1-14, September.
    22. Licia Ferranna & Margherita Gerolimetto & Stefano Magrini, 2016. "The effect of immigration on convergence dynamics in the US," Working Papers 2016:27, Department of Economics, University of Venice "Ca' Foscari".
    23. Lin, Joanne Yuh-Jye & Jenelius, Erik & Cebecauer, Matej & Rubensson, Isak & Chen, Cynthia, 2023. "The equity of public transport crowding exposure," Journal of Transport Geography, Elsevier, vol. 110(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6635-:d:827199. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.