Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Evaluation of the Geum River by Multivariate Analysis: Principal Component Analysis and Factor Analysis

다변량분석법을 이용한 금강 유역의 수질오염특성 연구

  • Kim, Mi-Ah (Department of Environmental Diagnostics Research, National Institute of Environmental Research) ;
  • Lee, Jae-kwan (Department of Environmental Diagnostics Research, National Institute of Environmental Research) ;
  • Zoh, Kyung-Duk (Department of Environmental Health, School of Public Health, Seoul National University)
  • 김미아 (국립환경과학원 환경진단연구부) ;
  • 이재관 (국립환경과학원 환경진단연구부) ;
  • 조경덕 (서울대학교 보건대학원 환경보건학과)
  • Received : 2006.10.19
  • Accepted : 2006.11.28
  • Published : 2007.01.30
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Abstract

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

Keywords

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