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

Author

Listed:
  • Qing Gu

    (Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China)

  • Yao Zhang

    (Institution of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310058, China)

  • Ligang Ma

    (College of Resource and Environmental Science, Xinjiang University, Urumqi 830046, China)

  • Jiadan Li

    (Institute of Rural Development and Information, Ningbo Academy of Agricultural Sciences, Ningbo 315040, China)

  • Ke Wang

    (Institution of Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310058, China)

  • Kefeng Zheng

    (Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China)

  • Xiaobin Zhang

    (Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China)

  • Li Sheng

    (Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China)

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.

Suggested Citation

  • Qing Gu & Yao Zhang & Ligang Ma & Jiadan Li & Ke Wang & Kefeng Zheng & Xiaobin Zhang & Li Sheng, 2016. "Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China," Sustainability, MDPI, vol. 8(3), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:3:p:243-:d:65175
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    References listed on IDEAS

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    Cited by:

    1. Mariela González-Narváez & María José Fernández-Gómez & Susana Mendes & José-Luis Molina & Omar Ruiz-Barzola & Purificación Galindo-Villardón, 2021. "Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    2. Can Bülent Karakuş, 2020. "Evaluation of water quality of Kızılırmak River (Sivas/Turkey) using geo-statistical and multivariable statistical approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4735-4769, June.
    3. Rong-Song Chen & Chan-Ming Tsai, 2017. "Development of an Evaluation System for Sustaining Reservoir Functions—A Case Study of Shiwen Reservoir in Taiwan," Sustainability, MDPI, vol. 9(8), pages 1-18, August.

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