Mapping trace element abundance in groundwater using a double-clustering approach

 

The main goal of this study was to determine the reliability of the double-clustering approach using hierarchical cluster analyses (HCA) to delineate the abundance of major and trace elements in groundwater of an arid watershed in northcentral Mexico, where the As geogenic contamination leads an important deterioration of the groundwater quality. For that, fifty-five groundwater samples were collected from wells within the watershed and physicochemical parameters such as pH, conductivity, temperature, and ORP were measured in situ. In the laboratory, major ions, metalloids, and trace elements were measured by ion chromatography and ICP-MS. For the development of the double-clustering approach, all the data were log-transformed and standardized to approximate normality. A first HCA was performed for clustering variables. This HCA produced six groups of variables. Then, an HCA of cases was applied to each group of variables, which delineated maps describing the magnitude of each group of variables in the aquifer. In general, the double-clustering approach was effective for identifying processes (lithogenic/anthropogenic) controlling the abundance of major and trace elements in groundwater of the above-mentioned watershed. This method identified hotspot of As, Sb, Ge, V, and W in the alluvial aquifer, suggesting a concomitant release to these elements to groundwater. In addition, the applied approach identified mountainous areas with high concentrations of HCO₃^-, Ca, Mg, K, Sr, Rb, Ga, Ba, Cs, Pb, Ni, Y, and U, indicating that the weathering of carbonate/silicate rocks plays an important role in the abundance of these ions/elements in groundwater. The double-clustering approach was also successful in delineating disperse areas where the salinity and the levels of Na, Cl^-, SO₄^2-, NO₃^-, B, Li, and the chalcophilic elements Cu, Re, and Se in groundwater were elevated, mainly related to processes such as evaporite dissolution and increasing concentrations due to the irrigation return flow. Overall, the double-clustering was also compared with spatial statistical techniques such as the Moran Index and the Local Indicator for Spatial Association (LISA), which demonstrated that the double-clustering is a powerful tool capable of visualizing zones where specific natural/anthropogenic processes may threaten the groundwater quality.