Multivariate statistical analysis of physicochemical parameters of groundwater quality using PCA and HCA techniques

Abstract

Multivariate analysis techniques are powerful tools in the study of groundwater quality, providing an expanded view of quality parameters. This work presents a multivariate analysis of groundwater quality in the city of Sousa, Paraíba state, through the techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Samples from 13 tubular wells were collected in different districts of the city of Sousa, during the rainy and dry seasons. For these samples, 11 parameters were analyzed: hydrogenic potential (pH), total dissolved solids, total alkalinity, carbonates, bicarbonates, total hardness, magnesium, calcium, sodium, potassium, and chlorides. PC1, PC2, PC3 and PC4 explain 87.48% of the total variance of the data. The PCA shows that there was a change in patterns between the analyzed periods. The correlation matrix corroborates the PCA data, showing the relationships between the physical-chemical variables evaluated. The HCA confirmed the correlations between the samples, making it possible to assess the degree of similarity between the composition of the wells and between the parameters evaluated.