Spatial prediction of ozone concentration profiles
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Temiyasathit, Chivalai | - |
dc.contributor.author | Kim, Seoung Bum | - |
dc.contributor.author | Park, Sun-Kyoung | - |
dc.date.accessioned | 2021-09-08T13:40:57Z | - |
dc.date.available | 2021-09-08T13:40:57Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2009-09-01 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/119330 | - |
dc.description.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. (C) 2009 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | AIR-QUALITY | - |
dc.subject | MODEL | - |
dc.subject | REGRESSION | - |
dc.subject | MAXIMUM | - |
dc.subject | SITE | - |
dc.title | Spatial prediction of ozone concentration profiles | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Seoung Bum | - |
dc.identifier.doi | 10.1016/j.csda.2009.03.027 | - |
dc.identifier.scopusid | 2-s2.0-65749103838 | - |
dc.identifier.wosid | 000267505600016 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.53, no.11, pp.3892 - 3906 | - |
dc.relation.isPartOf | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.citation.title | COMPUTATIONAL STATISTICS & DATA ANALYSIS | - |
dc.citation.volume | 53 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 3892 | - |
dc.citation.endPage | 3906 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | AIR-QUALITY | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | MAXIMUM | - |
dc.subject.keywordPlus | SITE | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.