Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil)

Abstract

The Marizal/São Sebastião aquifer system is the main water supply of the municipality of Alagoinhas in the state of Bahia. However, anthropic interventions contribute to soil and groundwater pollution, increasing the need for related research. Multivariate statistical analysis is a widely used tool, helping in the investigation of groundwater quality while being capable of simultaneously evaluating diverse variables of a sample set. In this study, factor analysis and multivariate cluster analysis methodologies were applied. Ten of the most influential variables for groundwater quality were selected and then grouped into two factors. The first factor included electrical conductivity, salinity, calcium, chloride, sulfate, manganese, and iron, which are indicators of water salinity. The second factor encompassed pH, bicarbonate, and phosphate, indicating anthropic interventions and alkalinity in the environment. The multivariate cluster analysis was applied to the parameters of both factors, resulting in dendrograms with four clusters. The present study showed that the multivariate statistical analysis is an efficient tool for monitoring and can contribute to the management of groundwater quality.