Spatial prediction of ozone concentration profiles

Chivalai Temiyasathit, Seoung Bum Kim, Sun Kyoung Park

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3892-3906
Number of pages15
JournalComputational Statistics and Data Analysis
Volume53
Issue number11
DOIs
Publication statusPublished - 2009 Sep 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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