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An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China

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  • Huisheng Wu

    (College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China)

  • Maogui Hu

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Yaping Zhang

    (College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China)

  • Yuan Han

    (College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China)

Abstract

Air pollution is a serious problem in China, and the government has taken a series of steps to solve it. However, it is still u2nclear how the situation has improved after years of atmospheric pollution control. Shandong Province, which has the second largest population and the highest coal consumption in China, was chosen to analyze the spatiotemporal air quality trends. We obtained daily air quality index (AQI) values from 91 stations in the province from 1 January 2014, to 31 December 2019, based on an adaptive data analysis method, empirical mode decomposition (EMD). The distribution of AQI in Shandong Province was heterogeneous at both spatial and temporal scales. All the stations could be divided into four clusters whose AQI trends decreased from 75 to 53, 95 to 68, 128 to 82, and 148 to 82, respectively. The overall trend of pollution became more serious from east to west in the province. The AQI is the largest in winter, followed by spring and autumn, and the smallest index occurs in summer. There are four types of annual trends of the AQI of each city. The overall downward trend indicates that the air quality of each city was improving annually.

Suggested Citation

  • Huisheng Wu & Maogui Hu & Yaping Zhang & Yuan Han, 2021. "An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China," Sustainability, MDPI, vol. 13(22), pages 1-10, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12901-:d:684704
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    1. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
    2. J. Lelieveld & J. S. Evans & M. Fnais & D. Giannadaki & A. Pozzer, 2015. "The contribution of outdoor air pollution sources to premature mortality on a global scale," Nature, Nature, vol. 525(7569), pages 367-371, September.
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    Cited by:

    1. Wen-Lu Wu & Chun-Yan Shan & Jing Liu & Jing-Lin Zhao & Jin-Yun Long, 2023. "Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China," IJERPH, MDPI, vol. 20(5), pages 1-19, February.

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