Assessment of Lake Water Quality in Gudiyattam Region using Multivariate Statistical Techniques

Brijesh Nair; Krishnan Shankar Hemath Naveen

doi:10.47277/JETT/10(1)102

Brijesh Nair Professor, Department of Environmental and Water Resource Engineering School of Civil Engineering, Vellore Institute of Technology, Vellore-632014. Tamilnadu, India
Krishnan Shankar Hemath Naveen Professor, Department of Environmental and Water Resource Engineering School of Civil Engineering, Vellore Institute of Technology, Vellore-632014. Tamilnadu, India

DOI: https://doi.org/10.47277/JETT/10(1)102

Keywords: Lake water Quality, PCA, CA, Gudiyattam, Heavy metals

Abstract

Nowadays, the quality of water is adversely affected by both natural as well as anthropogenic activities taking place in the surrounding area of the water resources. The present study deals with the assessment of lake water quality in the Gudiyattam region, Vellore district, Tamilnadu, India. Which, the eight significant lakes in this region were selected and monitored for their quality from May-2018 to Apil-2019. The water samples were analyzed for various significant parameters and the mean value was considered for the water quality assessment purpose during the Pre-Monsoon, Monsoon, and Post-Monsoon season. Finally, the results were interpreted as variables using Multivariate statistical techniques – Principal Component Analysis (PCA) /Factor Analysis and Cluster Analysis (CA). The results indicated the existence of a lot of industrial as well as agricultural activity near Site 2 (Nellorepet) and Site 3 (Eripattrai). Especially, Site 2 revealed the highest mean value for Chromium, Lead, Copper, and Zinc compared to other sites in all three seasons owing to the agricultural runoff, dumping of leather finishing waste, and discharge of effluent from Leather processing industries, in that region. Through PCA, the complex data obtained from pre-monsoon, monsoon, and post-monsoon seasons were converted into 8,8, and 5 components. Based on the PCA score, CA has been performed to group the parameters based on the similarity of a cluster. Accordingly, the data were grouped into five, six, and four clusters respectively, for pre-monsoon, monsoon, and post-monsoon. Overall, the results acquired from the study recognized the possible pollution source of the lakes.

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