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

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Antonio José Ferreira Gadelha
Clarice Oliveira da Rocha
José Germano Veras Neto
Mirelly Alexandre Gomes

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.

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How to Cite
Gadelha, A. J. F., Rocha, C. O. da, Veras Neto, J. G., & Gomes, M. A. (2023). Multivariate statistical analysis of physicochemical parameters of groundwater quality using PCA and HCA techniques. Eclética Química, 48(4), 37–47. https://doi.org/10.26850/1678-4618eqj.v48.4.2023.p37-47
Section
Original articles
Author Biography

Antonio José Ferreira Gadelha, Federal Institute of Paraíba, Campina Grande Campus, Campina Grande, Brazil.

Coordenação do Curso Técnico em Química

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