요인분석을 이용한 유해 중금속 복합 노출수준과 건강영향과의 관련성 평가
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김은섭 | - |
dc.contributor.author | 문선인 | - |
dc.contributor.author | 임동혁 | - |
dc.contributor.author | 최병선 | - |
dc.contributor.author | 박정덕 | - |
dc.contributor.author | 엄상용 | - |
dc.contributor.author | 김용대 | - |
dc.contributor.author | 김헌 | - |
dc.date.accessioned | 2023-06-22T02:43:37Z | - |
dc.date.available | 2023-06-22T02:43:37Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1738-4087 | - |
dc.identifier.issn | 2233-8616 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/66983 | - |
dc.description.abstract | Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals. | - |
dc.format.extent | 8 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 한국환경보건학회 | - |
dc.title | 요인분석을 이용한 유해 중금속 복합 노출수준과 건강영향과의 관련성 평가 | - |
dc.title.alternative | Evaluation of the Relationship between the Exposure Level to Mixed Hazardous Heavy Metals and Health Effects Using Factor Analysis | - |
dc.type | Article | - |
dc.identifier.doi | 10.5668/JEHS.2022.48.4.236 | - |
dc.identifier.bibliographicCitation | 한국환경보건학회지, v.48, no.4, pp 236 - 243 | - |
dc.identifier.kciid | ART002870242 | - |
dc.description.isOpenAccess | Y | - |
dc.citation.endPage | 243 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 236 | - |
dc.citation.title | 한국환경보건학회지 | - |
dc.citation.volume | 48 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Heavy metals | - |
dc.subject.keywordAuthor | smelter | - |
dc.subject.keywordAuthor | factor analysis | - |
dc.subject.keywordAuthor | multiple linear regression | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang 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.