Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater
DOI:
https://doi.org/10.11137/1982-3908_2024_47_49797Palavras-chave:
Factorial analysis, Principal component analysis, Hierarchical clusterResumo
The objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the composition of the percolating aquifer formations that can be associated with the sources of these components. For this purpose, multivariate statistical techniques were applied by means of a Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The PCA allowed a reduction of physicochemical parameters and determined the two components responsible for approximately 86% of total variance in the data for both sampling periods (rainy and dry). The first component is represented by variables that indicate natural rock weathering processes, and the second comprises seasonality and pollution indicators. Samples were also correlated through HCA according to compositional similarities, which were associated with possible natural or human sources.
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