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Combined Multivariate Statistical Techniques, Water Pollution Index (WPI) and Daniel Trend Test Methods to Evaluate Temporal and Spatial Variations and Trends of Water Quality at Shanchong River in the Northwest Basin of Lake Fuxian, China

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  • Quan Wang
  • Xianhua Wu
  • Bin Zhao
  • Jie Qin
  • Tingchun Peng

Abstract

Understanding spatial and temporal variations in river water quality and quantitatively evaluating the trend of changes are important in order to study and efficiently manage water resources. In this study, an analysis of Water Pollution Index (WPI), Daniel Trend Test, Cluster Analysis and Discriminant Analysis are applied as an integrated approach to quantitatively explore the spatial and temporal variations and the latent sources of water pollution in the Shanchong River basin, Northwest Basin of Lake Fuxian, China. We group all field surveys into 2 clusters (dry season and rainy season). Moreover, 14 sampling sites have been grouped into 3 clusters for the rainy season (highly polluted, moderately polluted and less polluted sites) and 2 clusters for the dry season (highly polluted and less polluted sites) based on their similarities and the level of pollution during the two seasons. The results show that the main trend of pollution was aggravated during the transition from the dry to the rainy season. The Water Pollution Index of Total Nitrogen is the highest of all pollution parameters, whereas the Chemical Oxygen Demand (Chromium) is the lowest. Our results also show that the main sources of pollution are farming activities alongside the Shanchong River, soil erosion and fish culture at Shanchong River reservoir area and domestic sewage from scattered rural residential area. Our results suggest that strategies to prevent water pollutionat the Shanchong River basin need to focus on non-point pollution control by employing appropriate fertilizer formulas in farming, and take the measures of soil and water conservation at Shanchong reservoir area, and purifying sewage from scattered villages.

Suggested Citation

  • Quan Wang & Xianhua Wu & Bin Zhao & Jie Qin & Tingchun Peng, 2015. "Combined Multivariate Statistical Techniques, Water Pollution Index (WPI) and Daniel Trend Test Methods to Evaluate Temporal and Spatial Variations and Trends of Water Quality at Shanchong River in th," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0118590
    DOI: 10.1371/journal.pone.0118590
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    References listed on IDEAS

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    1. Jiabo Chen & Jun Lu, 2014. "Effects of Land Use, Topography and Socio-Economic Factors on River Water Quality in a Mountainous Watershed with Intensive Agricultural Production in East China," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    2. Tao Tao & Kunlun Xin, 2014. "Public health: A sustainable plan for China's drinking water," Nature, Nature, vol. 511(7511), pages 527-528, July.
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    1. 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.
    2. Jiyu Seo & Jeongeun Won & Jeonghyeon Choi & Sangdan Kim, 2021. "Identification of the Relative Importance of Factors Affecting the Spatial Variability of River Water Quality," Sustainability, MDPI, vol. 13(20), pages 1-19, October.
    3. Ana Milanović Pešić & Jovana Brankov & Dragana Milijašević Joksimović, 2020. "Water quality assessment and populations’ perceptions in the National park Djerdap (Serbia): key factors affecting the environment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 2365-2383, March.
    4. O’Donoghue, Cathal & Buckley, Cathal & Chyzheuskaya, Aksana & Green, Stuart & Howley, Peter & Hynes, Stephen & Upton, Vincent & Ryan, Mary, 2021. "The spatial impact of rural economic change on river water quality," Land Use Policy, Elsevier, vol. 103(C).

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