A MULTIVARIATE STATISTICAL ANALYSIS TO ASSESS THE GROUNDWATER QUALITY OF DELHI REGION, INDIA

Pearson correlation matrix, Hierarchical cluster and principal component analysis (PCA) were simultaneously applied to 22 groundwater hydrochemical data of Delhi region collected during post monsoon 2013. Using the Kaiser criterion, principle component (PC) was extracted from the data and rotated using varimax normalization for 22 locations. From the analysis, concentration of EC, TDS, Cl-, Mg2+, TH, Fe2+, F-, Na+ and K+ having higher values. Correlation analysis of hydro chemical data suggests that the aquifer is mainly controlled by EC, TDS, Cl-, Mg2+, TH, Na+, SO42- and K+. In principal component analysis, the first 2 factors explain 85.67 % of the total variance. HCA grouped sample sites into four statistically significant clusters. The combined use of PCA and HCA technique resulted in more reliable interpretation of the hydrochemistry. The results of this study clearly demonstrate the usefulness of multivariate statistical techniques in hydrochemical analysis.