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On the density estimation of air pollution in Beijing

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  • Fan, Yanqin
  • Hou, Lei
  • Yan, Karen X.

Abstract

We apply both the kernel method and the k-nearest neighbor (k-nn) method to estimate the density of air pollutant PM2.5 in Beijing. We find that the k-nn method accommodates the data better and delivers a more reasonable density estimate than the kernel method. Then we compare the density estimates between summer and winter, rush and non-rush hours. Results suggest that the air pollution is more serious in winter and rush hours.

Suggested Citation

  • Fan, Yanqin & Hou, Lei & Yan, Karen X., 2018. "On the density estimation of air pollution in Beijing," Economics Letters, Elsevier, vol. 163(C), pages 110-113.
    • Handle: RePEc:eee:ecolet:v:163:y:2018:i:c:p:110-113
    DOI: 10.1016/j.econlet.2017.12.020
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    References listed on IDEAS

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    4. Mack, Y. P. & Rosenblatt, M., 1979. "Multivariate k-nearest neighbor density estimates," Journal of Multivariate Analysis, Elsevier, vol. 9(1), pages 1-15, March.
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    Cited by:

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    2. 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).
    3. 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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    More about this item

    Keywords

    Density estimation; k-nn method; Air pollution;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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