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Analysis of Spatiotemporal Variation and Influencing Factors of PM 2.5 in China Based on Multisource Data

Author

Listed:
  • Xi Kan

    (School of Internet of Things, Wuxi University, Wuxi 214105, China)

  • Xu Liu

    (School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Zhou Zhou

    (School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yonghong Zhang

    (School of Internet of Things, Wuxi University, Wuxi 214105, China
    School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Linglong Zhu

    (School of Internet of Things, Wuxi University, Wuxi 214105, China)

  • Kenny Thiam Choy Lim Kam Sian

    (School of Atmospheric Science and Remote Sensing, Wuxi University, Wuxi 214105, China)

  • Qi Liu

    (School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

The urbanization process over the past decades has resulted in increasing attention being paid to atmospheric pollution by researchers, especially changes in PM 2.5 concentration. This study attempted to explore the spatiotemporal changes in PM 2.5 concentration in China from 2000 to 2021, as well as their interaction patterns and intensities with temperature, precipitation, vegetation coverage, and land use types. This was carried out by analyzing monthly average PM 2.5 concentration data and various meteorological and geographical factors. Suggestions have also been made to reduce PM 2.5 concentration and improve air quality. The results show that in the past 22 years, the overall concentration of PM 2.5 in China has shown a downward trend, with an average annual rate of 1.42 μg/m 3 from 2013 to 2021, accompanied by a clear spatial pattern and significant seasonal changes. The high pollution areas are mainly concentrated in the Tarim Basin, Sichuan Basin, North China Plain, and the Middle and Lower Yangtze Valley Plain, where the PM 2.5 concentration in autumn and winter is significantly higher than that in spring and summer. In addition, based on the national spatial scale, PM 2.5 concentration is negatively correlated with precipitation and vegetation coverage, while it is significantly positively correlated with arable land and impervious surfaces. Strengthening the control of farmland pollution, accelerating urban greening construction, further expanding the scale of forests and grasslands, and enriching vegetation types will help reduce PM 2.5 concentration and improve air quality.

Suggested Citation

  • Xi Kan & Xu Liu & Zhou Zhou & Yonghong Zhang & Linglong Zhu & Kenny Thiam Choy Lim Kam Sian & Qi Liu, 2023. "Analysis of Spatiotemporal Variation and Influencing Factors of PM 2.5 in China Based on Multisource Data," Sustainability, MDPI, vol. 15(19), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14656-:d:1256391
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    References listed on IDEAS

    as
    1. Yongxia Ding & Shouzhang Peng, 2020. "Spatiotemporal Trends and Attribution of Drought across China from 1901–2100," Sustainability, MDPI, vol. 12(2), pages 1-17, January.
    2. Dongdong Ma & Guifang Li & Feng He, 2021. "Exploring PM2.5 Environmental Efficiency and Its Influencing Factors in China," IJERPH, MDPI, vol. 18(22), pages 1-15, November.
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