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Sustainability 2016, 8(3), 243; doi:10.3390/su8030243

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

1
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
2
Institution of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310058, China
3
College of Resource and Environmental Science, Xinjiang University, Urumqi 830046, China
4
Institute of Rural Development and Information, Ningbo Academy of Agricultural Sciences, Ningbo 315040, China
*
Author to whom correspondence should be addressed.
Academic Editor: Arjen Y. Hoekstra
Received: 19 October 2015 / Revised: 23 February 2016 / Accepted: 2 March 2016 / Published: 5 March 2016
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Abstract

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. View Full-Text
Keywords: reservoir; water quality; spatial pattern; temporal variation; source apportionment; multivariate methods reservoir; water quality; spatial pattern; temporal variation; source apportionment; multivariate methods
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MDPI and ACS Style

Gu, Q.; Zhang, Y.; Ma, L.; Li, J.; Wang, K.; Zheng, K.; Zhang, X.; Sheng, L. Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China. Sustainability 2016, 8, 243.

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