Zhang Qi, Li Zhongwu, Zeng Guangming, Li Jianbing, Fang Yong, Yuan Qingshui, Wang Yamei, Ye Fangyi
College of Environment Science and Engineering, Hunan University, Changsha, 410082, People's Republic of China.
Environ Monit Assess. 2009 May;152(1-4):123-31. doi: 10.1007/s10661-008-0301-y. Epub 2008 Jun 6.
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.
在该研究中，运用了包括因子分析、主成分分析和聚类分析在内的多元统计方法，对从湘江流域获取的地表水水质数据集进行分析。这些数据集是在1994 - 2000年的7年间，对34个不同断面的12个参数进行监测而生成的。层次聚类分析将34个采样点分为三类，包括污染相对较轻（LP）、中度污染（MP）和高度污染（HP）的采样点，并根据水质特征的相似性，将该流域划分为三个区域。对聚类分析得到的三个不同组的数据集进行因子分析/主成分分析，结果显示四个潜在因子分别解释了LP、MP和HP区域水质数据集中总方差的71.62%、71.77%和72.01%。从因子分析中得到的主成分表明，水质变化的参数主要与溶解态重金属有关。因此，这些方法被认为有助于水资源管理者了解水质问题的复杂本质，并确定改善水质的优先事项。
