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Water quality assessment of the Huaihe River segment of Bengbu (China) using multivariate statistical techniques

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Abstract

In the study, multivariate statistical methods including principal component analysis (PCA)/factor analysis (FA) and cluster analysis (CA) were applied to analyze surface water quality data sets obtained from the Huaihe River segment of Bengbu (HRSB) and generated during 2 years (2011–2012) monitoring of 19 parameters at 7 sampling sites. The results of PCA for 7 sampling sites revealed that the first four components of PCA showed 94.89% of the total variance in the data sets of HRSB. The Principal components (Factors) obtained from FA indicated that the parameters for water quality variations were mainly related to heavy metals (Pb, Mn, Zn and Fe) and organic related parameters (COD, PI and DO). The results revealed that the major causes of water quality deterioration were related to inflow of industrial, domestic and agricultural effluents into the Huaihe River. Three significant sampling locations—(sites 2, 3 and 4), (sites 1 and 5) and (sites 6 and 7)—were detected on the basis of similarity of their water quality. Thus, these methods were believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.

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Authors and Affiliations

  1. College of Life Science, Anhui Science and Technology University, Fengyang, 233100, China

    Mimgsong Xiao, Fangyin Bao, Song Wang & Feng Cui

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  1. Mimgsong Xiao
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  2. Fangyin Bao
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Correspondence to Mimgsong Xiao.

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Xiao, M., Bao, F., Wang, S. et al. Water quality assessment of the Huaihe River segment of Bengbu (China) using multivariate statistical techniques. Water Resour 43, 166–176 (2016). https://doi.org/10.1134/S0097807816010115

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