IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i2p1183-d1030159.html
   My bibliography  Save this article

Quantifying the Spatiotemporal Heterogeneity of PM 2.5 Pollution and Its Determinants in 273 Cities in China

Author

Listed:
  • Li Yang

    (College of Tourism, Hunan Normal University, Changsha 410081, China)

  • Chunyan Qin

    (College of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Ke Li

    (College of Mathematics & Statistics, Hunan Normal University, Changsha 410081, China)

  • Chuxiong Deng

    (College of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Yaojun Liu

    (College of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

Abstract

Fine particulate matter (PM 2.5 ) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM 2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM 2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed “U”-shaped, pulse-shaped and “W”-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM 2.5 pollution; (3) The determinants showed significant heterogeneity on PM 2.5 pollution—specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM 2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM 2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Suggested Citation

  • Li Yang & Chunyan Qin & Ke Li & Chuxiong Deng & Yaojun Liu, 2023. "Quantifying the Spatiotemporal Heterogeneity of PM 2.5 Pollution and Its Determinants in 273 Cities in China," IJERPH, MDPI, vol. 20(2), pages 1-17, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1183-:d:1030159
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/2/1183/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/2/1183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marian R. Chertow, 2000. "The IPAT Equation and Its Variants," Journal of Industrial Ecology, Yale University, vol. 4(4), pages 13-29, October.
    2. Wenju Cai & Ke Li & Hong Liao & Huijun Wang & Lixin Wu, 2017. "Weather conditions conducive to Beijing severe haze more frequent under climate change," Nature Climate Change, Nature, vol. 7(4), pages 257-262, April.
    3. Claudia Ghisetti & Susanna Mancinelli & Massimiliano Mazzanti & Mariangela Zoli, 2017. "Financial barriers and environmental innovations: evidence from EU manufacturing firms," Climate Policy, Taylor & Francis Journals, vol. 17(0), pages 131-147, June.
    4. Song, Malin & Fisher, Ron & Kwoh, Yusen, 2019. "Technological challenges of green innovation and sustainable resource management with large scale data," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 361-368.
    5. Yu, Xiaoman & Geng, Yong & Dong, Huijuan & Ulgiati, Sergio & Liu, Zhe & Liu, Zuoxi & Ma, Zhixiao & Tian, Xu & Sun, Lu, 2016. "Sustainability assessment of one industrial region: A combined method of emergy analysis and IPAT (Human Impact Population Affluence Technology)," Energy, Elsevier, vol. 107(C), pages 818-830.
    6. Ji, Xi & Yao, Yixin & Long, Xianling, 2018. "What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective," Energy Policy, Elsevier, vol. 119(C), pages 458-472.
    7. Yang Yang & Lynn M. Russell & Sijia Lou & Hong Liao & Jianping Guo & Ying Liu & Balwinder Singh & Steven J. Ghan, 2017. "Dust-wind interactions can intensify aerosol pollution over eastern China," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    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. Peiqi Hu & Kai Zhou & Haoxi Zhang & Zhong Ma & Jingyuan Li, 2023. "The Cause and Correlation Network of Air Pollution from a Spatial Perspective: Evidence from the Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 15(4), pages 1-21, February.

    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. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    2. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    3. Zhao, Jun & Shahbaz, Muhammad & Dong, Kangyin, 2022. "How does energy poverty eradication promote green growth in China? The role of technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Tan, Xiujie & Yan, Yaxue & Dong, Yuyang, 2022. "Peer effect in green credit induced green innovation: An empirical study from China's Green Credit Guidelines," Resources Policy, Elsevier, vol. 76(C).
    5. Casey, Gregory & Galor, Oded, 2017. "Is faster economic growth compatible with reductions in carbon emissions? The role of diminished population growth," MPRA Paper 76164, University Library of Munich, Germany.
    6. D’Orazio, Paola & Valente, Marco, 2019. "The role of finance in environmental innovation diffusion: An evolutionary modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 417-439.
    7. Wang, Xianzhu & Huang, He & Hong, Jingke & Ni, Danfei & He, Rongxiao, 2020. "A spatiotemporal investigation of energy-driven factors in China: A region-based structural decomposition analysis," Energy, Elsevier, vol. 207(C).
    8. Hwang, In Chang, 2013. "Stochastic Kaya model and its applications," MPRA Paper 55099, University Library of Munich, Germany.
    9. Francesco Aiello & Paola Cardamone & Lidia Mannarino & Valeria Pupo, 2021. "Green patenting and corporate social responsibility: Does family involvement in business matter?," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(4), pages 1386-1396, July.
    10. Vera A. Barinova & John A. “Skip” Laitner, 2019. "Examining the critical role of institutions and innovations in shaping productive energy policy for Russia," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 9(1), pages 54-66, March.
    11. Gudipudi, Ramana & Rybski, Diego & Lüdeke, Matthias K.B. & Zhou, Bin & Liu, Zhu & Kropp, Jürgen P., 2019. "The efficient, the intensive, and the productive: Insights from urban Kaya scaling," Applied Energy, Elsevier, vol. 236(C), pages 155-162.
    12. Jose García‐Quevedo & Elisenda Jové‐Llopis & Ester Martínez‐Ros, 2020. "Barriers to the circular economy in European small and medium‐sized firms," Business Strategy and the Environment, Wiley Blackwell, vol. 29(6), pages 2450-2464, September.
    13. Jun Bai & Shixiang Li & Nan Wang & Jianru Shi & Xianmin Li, 2020. "Spatial Spillover Effect of New Energy Development on Economic Growth in Developing Areas of China—An Empirical Test Based on the Spatial Dubin Model," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    14. Gebara, C.H. & Laurent, A., 2023. "National SDG-7 performance assessment to support achieving sustainable energy for all within planetary limits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    15. Torres-Brito, David Israel & Cruz-Aké, Salvador & Venegas-Martínez, Francisco, 2023. "Impacto de los contaminantes por gases de efecto invernadero en el crecimiento económico en 86 países (1990-2019): Sobre la curva inversa de Kuznets [Impact of the Effect of Greenhouse Gas Pollutan," MPRA Paper 119031, University Library of Munich, Germany.
    16. Qianqian Yang & Qiangqiang Yuan & Tongwen Li & Huanfeng Shen & Liangpei Zhang, 2017. "The Relationships between PM 2.5 and Meteorological Factors in China: Seasonal and Regional Variations," IJERPH, MDPI, vol. 14(12), pages 1-19, December.
    17. Song, Yanwu & Zhang, Jinrui & Song, Yingkang & Fan, Xinran & Zhu, Yuqing & Zhang, Chen, 2020. "Can industry-university-research collaborative innovation efficiency reduce carbon emissions?," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    18. Nicholas Z. Muller, 2020. "Long-Run Environmental Accounting in the US Economy," Environmental and Energy Policy and the Economy, University of Chicago Press, vol. 1(1), pages 158-191.
    19. Li, Guangpei & Wang, Xiaoyu & Wu, Jinhua, 2019. "How scientific researchers form green innovation behavior: An empirical analysis of China's enterprises," Technology in Society, Elsevier, vol. 56(C), pages 134-146.
    20. Felipe Vásquez & Gibran Vita & Daniel B. Müller, 2018. "Food Security for an Aging and Heavier Population," Sustainability, MDPI, vol. 10(10), pages 1-19, October.

    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:20:y:2023:i:2:p:1183-:d:1030159. 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.