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Zhao, Y., Guo, L., Liang, J. and Zhang, M. (2016). Seasonal artificial neural network model for water quality prediction via a clustering analysis method in a wastewater treatment plant of China. Desalination and Water Treatment, 57, 3452-3465.

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Article

Water Quality Assessment Using Water Quality Index and Principal Component Analysis: A Case Study of Historically Important Lakes of Guwahati City, North-East India

1Department of Environmental Science, Gauhati University, Jalukbari, Guwahati-781014, Assam, India

2Material Science and Technology Division, CSIR-North East Institute of Science and Technology, Jorhat-785006, Assam, India


Applied Ecology and Environmental Sciences. 2020, Vol. 8 No. 5, 207-217
DOI: 10.12691/aees-8-5-4
Copyright © 2020 Science and Education Publishing

Cite this paper:
Pallavi Sharma, Priyam Jyoti Bora. Water Quality Assessment Using Water Quality Index and Principal Component Analysis: A Case Study of Historically Important Lakes of Guwahati City, North-East India. Applied Ecology and Environmental Sciences. 2020; 8(5):207-217. doi: 10.12691/aees-8-5-4.

Correspondence to: Pallavi  Sharma, Department of Environmental Science, Gauhati University, Jalukbari, Guwahati-781014, Assam, India. Email: pallavi.sharma.env@gmail.com

Abstract

This study evaluates the water quality of ten ancient lakes of Guwahati city, located in North east India, using water quality index (WQI) and multivariate statistical methods.The surface water samples were subjected to comprehensive physico-chemical analysis involving important physical parameters (pH, EC, TDS, alkalinity, total hardness, DO, BOD, COD, turbidity); major cations (Ca2+, Mg2+, Na+, K+), and major anions (HCO3-, Cl-, SO42-, NO3-, F-, PO43). Principal component analysis (PCA) has been used to assess the factors which influence the quality of water. The results revealed that water quality variations are mostly affected by dissolved mineral salts along with anthropogenic activities in the areas contiguous to the lakes. The present study points out that pH, DO and BOD played a central role in affecting the WQI of these lakes. The WQI values range from 74.78 to 178.55, indicating that majority of the lakes fall in “very poor” and “unsuitable” category. It also reveals an alarming fact that none of the water tanks fall under good category. Hence, the water is not fit for drinking, and is also becoming toxic for the aquatic fauna. The analysis of hydro chemical facies of the pond water shows that most of the water samples belong to Ca-Mg-HCO3 type of water. These findings will be useful for decisions making regarding water quality management and can also be applied in water modelling for better environmental management and planning perspective.

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