Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

文献类型： 外文期刊

第一作者： Gu, Qing

作者： Gu, Qing;Zheng, Kefeng;Zhang, Xiaobin;Sheng, Li;Zhang, Yao;Wang, Ke;Ma, Ligang;Li, Jiadan

作者机构：

关键词： multivariate methods;source apportionment;temporal variation;water quality;spatial pattern;reservoir

期刊名称：SUSTAINABILITY （ 影响因子：3.251； 五年影响因子：3.473 ）

ISSN： 2071-1050

年卷期： 2016 年 8 卷 3 期

页码：

收录情况： SCI

摘要： Qiandao Lake (Xin'an Jiang reservoir) plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010-2013 were obtained for analysis. Cluster analysis (CA) was applied to classify the 12 sampling sites into three groups (Groups A, B and C) and the 12 monitoring months into two clusters (April-July, and the remaining months). Discriminant analysis (DA) identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA) identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.

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