A MULTIVARIATE STATISTICAL ANALYSIS TO ASSESS THE GROUNDWATER QUALITY OF DELHI REGION, INDIA | Journal of Global Ecology and Environment

Pearson correlation matrix, hierarchical cluster, and principal component analysis (PCA) were used to analyse 22 groundwater hydrochemical data obtained in the Delhi region during the post-monsoon season of 2013. The principle component (PC) was extracted from the data using the Kaiser criterion and rotated using varimax normalisation for 22 locations. The concentrations of EC, TDS, Cl-, Mg2+, TH, Fe2+, F-, Na+, and K+ were found to be higher in the analysis. The aquifer is primarily governed by EC, TDS, Cl-, Mg2+, TH, Na+, SO42-, and K+, according to correlation analysis of hydrochemical data. The first two factors explain 85.67 percent of the total variation in principal component analysis. Sample sites were divided into four statistically significant clusters by HCA. The combined use of the PCA and HCA techniques resulted in a more trustworthy hydrochemistry interpretation. The findings of this study show that multivariate statistical techniques can be quite effective in hydrochemical examination.

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• Global Ecology and Environment,