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The impact of wind and non-wind factors on PM2.5 levels

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  • Xu, Bing
  • Lin, Weiran
  • Taqi, Syed Ali

Abstract

Humankind is confronting a critical challenge in the reduction of PM2.5 pollution. In this paper, a new non-parametric path recognition framework is established to identify whether the decrease of PM2.5 levels is mainly due to wind (taken as a proxy for natural factors) or government action (taken as a proxy for non-natural factors). Based on PM2.5 and wind data for Beijing from 2010 to 2014, the reasons for the annual decrease of PM2.5 levels in Beijing are identified. The decline of PM2.5 levels in 2011 and 2012 was mainly due to wind, while in 2013 and 2014, it was mainly due to government action. From the point of view of seasonal changes, neither wind nor government action has systematically improved PM2.5 pollution in winter. From the perspective of pollution levels, from 2010 to 2014, the level of heavy pollution continued to improve, and the four harmful levels from the summer of 2010 to 2014 likewise continued to improve, mainly due to government action. The bootstrap method shows that the results are robust. This study not only provides empirical evidence for governments to improve PM2.5 levels, but also provides a new alternative method for assessing government behavior.

Suggested Citation

  • Xu, Bing & Lin, Weiran & Taqi, Syed Ali, 2020. "The impact of wind and non-wind factors on PM2.5 levels," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    • Handle: RePEc:eee:tefoso:v:154:y:2020:i:c:s0040162519316841
    DOI: 10.1016/j.techfore.2020.119960
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    4. Ning Xu & Fan Zhang & Xin Xuan, 2021. "Impacts of Industrial Restructuring and Technological Progress on PM 2.5 Pollution: Evidence from Prefecture-Level Cities in China," IJERPH, MDPI, vol. 18(10), pages 1-17, May.
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    7. Weiran Lin & Qiuqin He, 2021. "The Influence of Potential Infection on the Relationship between Temperature and Confirmed Cases of COVID-19 in China," Sustainability, MDPI, vol. 13(15), pages 1-11, July.

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    Keywords

    PM2.5; Path recognition; Kernel density; Wind; Government action;
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