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The Relationships between PM 2.5 and Meteorological Factors in China: Seasonal and Regional Variations

Author

Listed:
  • Qianqian Yang

    (School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China)

  • Qiangqiang Yuan

    (School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Tongwen Li

    (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Huanfeng Shen

    (Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China)

  • Liangpei Zhang

    (Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
    State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

Abstract

The interactions between PM 2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM 2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM 2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM 2.5 exist. Spatially, RH is positively correlated with PM 2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM 2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM 2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM 2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM 2.5 in autumn and the opposite in winter. PS is more positively correlated with PM 2.5 in autumn than in other seasons. Our study investigated the relationships between PM 2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM 2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM 2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:12:p:1510-:d:121694
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    References listed on IDEAS

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    1. 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.
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    2. Jiajia Chen & Huanfeng Shen & Tongwen Li & Xiaolin Peng & Hairong Cheng & Chenyan Ma, 2019. "Temporal and Spatial Features of the Correlation between PM 2.5 and O 3 Concentrations in China," IJERPH, MDPI, vol. 16(23), pages 1-17, November.
    3. Xiangxue Zhang & Changxiu Cheng, 2022. "Temporal and Spatial Heterogeneity of PM 2.5 Related to Meteorological and Socioeconomic Factors across China during 2000–2018," IJERPH, MDPI, vol. 19(2), pages 1-15, January.
    4. Yi Yang & Jie Li & Guobin Zhu & Qiangqiang Yuan, 2019. "Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM 2.5 in China During 1998–2016," IJERPH, MDPI, vol. 16(7), pages 1-24, March.
    5. Abdulmuhsin S. Shihab, 2024. "Meteorological effects on particulate matter PM10, PM2.5 concentrations with diurnal and seasonal variations in cities neighboring desert lands," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 11293-11321, September.
    6. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    7. Xiao Gong & Jianing Mi & Chunyan Wei & Ruitao Yang, 2019. "Measuring Environmental and Economic Performance of Air Pollution Control for Province-Level Areas in China," IJERPH, MDPI, vol. 16(8), pages 1-19, April.
    8. Zhiyu Fan & Qingming Zhan & Chen Yang & Huimin Liu & Meng Zhan, 2020. "How Did Distribution Patterns of Particulate Matter Air Pollution (PM 2.5 and PM 10 ) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    9. Huanfeng Shen & Man Zhou & Tongwen Li & Chao Zeng, 2019. "Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM 2.5 Mapping," IJERPH, MDPI, vol. 16(21), pages 1-18, October.
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