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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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    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.

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