Multiple environmental tracers combined with a constrained Bayesian isotope mixing model to elucidate nitrate and sulfate contamination in a coastal groundwater system

Several groundwater quality investigations have been conducted in coastal regions that are commonly exposed to multiple anthropogenic stressors. Nonetheless, such studies remain challenging because they require focused-diagnostic approaches for a comprehensive understanding of groundwater contamination. Therefore, this study integrates a multi-tracer approach to acquire comprehensive information allowing for an improved understanding of the origins of groundwater contamination, the relative contribution of contaminants, and their biogeochemical cycling within a coastal groundwater system. This multi-tracer approach, focusing on nitrate (NO₃) and sulfate (SO₄) groundwater contamination, is applied to a Mediterranean coastal aquifer underlying an important economically strategic agricultural area. Dissolved NO₃ in groundwater has concentrations up to 89 mg/L, whereas SO₄ concentrations in groundwater are up to 458 mg/L. By integrating isotope tracers (i.e., δ¹⁵N_NO3, δ¹⁸O_NO3, δ¹¹B, δ³⁴S_SO4, and δ¹⁸O_SO4), NO₃ and SO₄ in the groundwater are found to have originated from multiple anthropogenic and natural sources including synthetic fertilizers, manure, sewage, atmospheric deposition, and marine evaporites. Chemical and isotopic data are coupled to identify the dominant hydro(geo)logic processes and the major subsurface biogeochemical reactions that govern the NO₃ and SO₄ occurrences. Nitrate and SO₄ concentrations are identified to be respectively controlled by nitrification/denitrification and by bacterial dissimilatory SO₄ reduction. Identifying these subsurface biogeochemical processes constrained the Bayesian isotope MixSIAR model, that is used for apportioning the relative contributions of the identified groundwater contamination sources, by informed site-specific isotopic fractionation effects. Results from MixSIAR indicate that manure is distinguished as the predominant source for NO₃ (61 %), whereas SO₄ in groundwater is mostly supplied from two sources (i.e., synthetic fertilizers and soil-derived sulfate) identified with similar contributions (30 %). This study particularly demonstrates the utility of initially describing the subsurface processes, not only to predict the fate of NO₃ and SO₄ concentrations within the groundwater system, but also to constrain the MixSIAR model with justified site-specific isotopic fractionation effects for subsurface transformation processes affecting NO₃ and SO₄.