Linking local natural background levels in groundwater to their generating hydrogeochemical processes in Quaternary alluvial aquifers

Calculating natural background levels (NBLs) in groundwater is vital for supporting a sustainable use of groundwater resources. Although NBLs are often assessed through a unique concentration value per groundwater body, where hydrogeochemical features are highly variable, spatial heterogeneity needs to be accounted for, leading to the calculation of so-called “local” NBLs. Despite much research devoted to the identification of the best performing techniques for local NBLs spatialization, a deep understanding of the link between local NBL values and their generating hydrogeochemical processes is often lacking and so is addressed here for the redox-sensitive species As, NH4, Fe and Mn in the groundwater bodies of Lombardy region, N Italy. Local NBLs were calculated by a tired approach involving the hybridization of preselection and probability plot methods. Since the spatial variability of the target species depends mainly on redox conditions, a redox zonation was performed using multivariate statistical analysis. A conceptual model was developed and improved combing factor and cluster analysis. Results showed that NBLs for arsenic were up to 291 μg/L, reached in groundwaters under methanogenesis, a condition related to the prolonged degradation of peat buried in aquifer sediments. Ammonium NBLs up to 6.62 mg/L were generated by the upwelling of fluids from deep sediments hosting petroleum systems; ammonium NBLs up to 4.48 mg/L were generated as the accumulation of by-products of peat degradation. Iron and manganese NBLs up to, respectively, 6.0 and 1.51 mg/L were generated by the oxidation of younger and less stable Mn and Fe oxides within river valleys, mostly the Po River valley. The evaluation of local NBLs, and their association to generating natural hydrogeochemical processes/conditions, achieves a step forward from the commonly used approach of a single NBL per groundwater body, improving decision-support tools for sustainable groundwater management and protection.