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

Fingerprint

Spatial Prediction
Ozone
Functional Data Analysis
Wavelet Transformation
Model Complexity
Kriging
Urban Areas
Pollutants
Ecosystem
Modeling
Ecosystems
Profile
Predictors
Data analysis
Health
Monitoring
Necessary
Experimental Results
Air

ASJC Scopus subject areas

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

Cite this

Spatial prediction of ozone concentration profiles. / Temiyasathit, Chivalai; Kim, Seoung Bum; Park, Sun Kyoung.

In: Computational Statistics and Data Analysis, Vol. 53, No. 11, 01.09.2009, p. 3892-3906.

Research output: Contribution to journalArticle

Temiyasathit, Chivalai ; Kim, Seoung Bum ; Park, Sun Kyoung. / Spatial prediction of ozone concentration profiles. In: Computational Statistics and Data Analysis. 2009 ; Vol. 53, No. 11. pp. 3892-3906.
@article{f9581754521a4a31830b1b4c311697f5,
title = "Spatial prediction of ozone concentration profiles",
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.",
author = "Chivalai Temiyasathit and Kim, {Seoung Bum} and Park, {Sun Kyoung}",
year = "2009",
month = "9",
day = "1",
doi = "10.1016/j.csda.2009.03.027",
language = "English",
volume = "53",
pages = "3892--3906",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "11",

}

TY - JOUR

T1 - Spatial prediction of ozone concentration profiles

AU - Temiyasathit, Chivalai

AU - Kim, Seoung Bum

AU - Park, Sun Kyoung

PY - 2009/9/1

Y1 - 2009/9/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=65749103838&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=65749103838&partnerID=8YFLogxK

U2 - 10.1016/j.csda.2009.03.027

DO - 10.1016/j.csda.2009.03.027

M3 - Article

AN - SCOPUS:65749103838

VL - 53

SP - 3892

EP - 3906

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

IS - 11

ER -