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Spatial prediction of ozone concentration profiles

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  • Temiyasathit, Chivalai
  • Kim, Seoung Bum
  • Park, Sun-Kyoung

Abstract

Ground level ozone is one of the major air pollutants in many urban areas. Ozone formation affects ecosystems and is known to be associated with many adverse health issues in humans. Effective modeling of ozone is a necessary step to develop a system to warn residents of high ozone levels. In the present study we propose a statistical procedure that uses multiscale and functional data analysis to improve the spatial prediction of ozone concentration profiles in the Dallas Fort Worth (DFW) area of Texas. This study uses daily eight-hour ozone concentrations and meteorological predictors during a period between 2003 and 2006 at 14 monitoring sites in the DFW area. Wavelet transformation was used as a means of multiscale data analysis, followed by functional modeling to reduce model complexity. Kriging was then used for spatial prediction. The experimental results with real data demonstrated that the proposed procedures achieved acceptable accuracy of spatial prediction.

Suggested Citation

  • Temiyasathit, Chivalai & Kim, Seoung Bum & Park, Sun-Kyoung, 2009. "Spatial prediction of ozone concentration profiles," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3892-3906, September.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:11:p:3892-3906
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    References listed on IDEAS

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    1. Gabriel Huerta & Bruno Sansó & Jonathan R. Stroud, 2004. "A spatiotemporal model for Mexico City ozone levels," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 231-248, April.
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    Cited by:

    1. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    2. Longford, Nicholas T., 2010. "Small area estimation with spatial similarity," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1151-1166, April.

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