The raw data obtained from the experiments were subjected to statistical analysis to determine various descriptive statistics. Pearson correlation coefficient was used to determine the interrelationships between the metals.
Environmetrics, also called multivariate statistical techniques, like Principal component analysis (PCA)/factor analysis (FA) and agglomerative hierarchal cluster analysis (AHCA), were performed to determine the sources of heavy metals. KMO and Barlett’s test of sphericity were initially performed to confirm the appropriateness of water quality data for PCA. The major aim of the PCA is data reduction to better describe the relationship among the variables. PCA was performed with correlation matrix among the variables and VARIMAX normalised rotation to make the results more interpretable [5, 6]. Cluster analysis was done for identifying relatively homogeneous groups of variables based on their similarities. In agglomerative hierarchal cluster method each variables first forms a separate cluster which combine repeatedly until all the variables come under a single cluster. A dendrogram is constructed where cohesiveness and correlations among the variables can be clearly observed [5].