期刊
ENVIRONMENTAL MONITORING AND ASSESSMENT
卷 152, 期 1-4, 页码 123-131出版社
SPRINGER
DOI: 10.1007/s10661-008-0301-y
关键词
Water quality management; Principle component analysis; Cluster analysis; Xiangjiang watershed; Red soil hilly region
资金
- National 863 High Technologies Research Program of China [2007AA10Z222]
- Natural Science Foundation of China [50225926, 50425927]
In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.
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