Delineating major and trace element hotspots in groundwater through spatial autocorrelations and a double-clustering approach: The role of geologic and anthropogenic factors
Academic Article in Scopus
The use of geospatial statistical techniques for mapping groundwater quality has increased rapidly, as they provide powerful tools for delineating hotspots of potentially toxic elements. This study assesses the application of a novel double-clustering approach to delineate the spatial distribution of major and heavy metal(loid)s in groundwater of the semi-arid Meoqui-Delicias aquifer in northcentral Mexico, comparing outcomes with well-known geostatistical analysis such as the Moran index and LISA. Thirty-eight wells were sampled for the analysis of physicochemical parameters and major and trace metal(loid)s. The double-clustering approach delineated hotspots of As, F¿, Sb, Ge, Mo, Zr, W, V, U, Y, and Si in the northeastern sector of the alluvial aquifer, an area surrounded by ignimbrites and rhyolites where the weathering of volcanic lithics, alkaline conditions, and desorption processes from oxyhydroxides phases favor their mobilization. It also delineated areas where weathering of evaporites, evaporative concentration, and irrigation return flow enhance the concentrations of Na, Li, Cl¿, Br¿, and Se in groundwater, as well as southern hotspot areas of Ca, Mg, Sr, Ba, K, Rb, and NO3¿ surrounded by limestones and basalts, which were linked to weathering and nitrification processes. In contrast, although the Moran index determined that several variables show significant spatial autocorrelations, LISA detected fewer significant cluster analysis, identifying high-high clusters (hotspots) only for Sb and Li. Overall, these results demonstrate the effectiveness of double-clustering for delineating groundwater contamination hotspots, offering a robust tool for managing water quality in vulnerable regions where geogenic and anthropogenic factors may pose health risks.
