Application of Geostatistical Tools to the Geochemical Characterization of the Peloritani Mts (Sicily, Italy) Aquifers

Sources of groundwater contaminants in inhabited areas, located in complex geo-tectonic contexts, are often deeply interlocked, thus, making the discrimination between anthropic and natural origins difficult. In this study, we investigate the Peloritani Mountain aquifers (Sicily, Italy), using the c...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Marianna Cangemi, Valentina Censi, Paolo Madonia, Rocco Favara
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
Acceso en línea:https://doaj.org/article/ae816cc0afa14d40a5287d31ddf0b2e6
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Sources of groundwater contaminants in inhabited areas, located in complex geo-tectonic contexts, are often deeply interlocked, thus, making the discrimination between anthropic and natural origins difficult. In this study, we investigate the Peloritani Mountain aquifers (Sicily, Italy), using the combination of probability plots with concentration contour maps to retrieve an overall view of the groundwater geo-chemistry with a special focus on the flux of heavy metals. In particular, we present a methodology for integrating spatial data with very different levels of precision, acquired before and during the “geomatic era”. Our results depict a complex geochemical layout driven by a geo-puzzle of rocks with very different lithological natures, hydraulically connected by a dense tectonic network that is also responsible for the mixing of deep hydrothermal fluids with the meteoric recharge. Moreover, a double source, geogenic or anthropogenic, was individuated for many chemicals delivered to groundwater bodies. The concentration contour maps, based on the different data groups identified by the probability plots, fit the coherency and congruency criteria with the distribution of both rock matrices and anthropogenic sources for chemicals, indicating the success of our geostatistical approach.