Assessment of Surface Water Quality by using Multivariate Statistical Techniques of Various Lakes of Kancheepuram District in Tamil Nadu, India.

Abstract

Multivariate statistical methods such as Principal Component Analysis/Factor analysis (PCA/FA), and Cluster Analysis (CA) were used to characterize spatial and temporal variations and to find out the pollution source of 11 lakes of the Kancheepuram district, a northeast district of Tamilnadu in India. Sampling events were conducted during the northeast monsoon (Dec 2015 & 2016) and Pre-monsoon (Oct 2016). The 20 physicochemical water quality parameters were used to analyze the collected water samples. In PCA, four components were demonstrated up to 73.12% of the total variance in the data set. It clarifies that variations in water quality parameters concentration are mainly identified with agricultural, industrial, domestic wastes, and natural factors. The CA showed the formation of three clusters based on the water quality variation at different locations. From the CA data, sampling sites were classified into high, moderate, and least polluted. Among these sites, Kolavai, Nattapettai and Uthiramerur were highly polluted because of rapid urbanization and industrialization. Therefore, these areas need some effective measures to enhance the quality of water.