ABSTRACT
Arsenic (As) in groundwater from natural and anthropogenic sources is one of the most common pollutants worldwide affecting people and ecosystems. A large dataset from >3600 wells is employed to spatially simulate the depth-averaged As concentration in phreatic and confined aquifers of the Padana Plain (Northern Italy). Results of in-depth geostatistical analysis via PCA and simulations within a Monte Carlo framework allow the understanding of the variability of As concentrations within the aquifers. The most probable As contaminated zones are located along the piedmont areas in the confined aquifers and in the lowland territories in the phreatic aquifers. The distribution of the As contaminated zones has been coupled with hydrogeological, geological, and geochemical information to unravel the sources and mechanisms of As release in groundwater. The reductive dissolution of Fe oxyhydroxides and organic matter mineralization under anoxic conditions resulted to be the major drivers of As release in groundwater. This phenomenon is less evident in phreatic aquifers, due to mixed oxic and reducing conditions. This large-scale study provides a probabilistic perspective on As contamination, e.g. quantifying the spatial probability of exceeding national regulatory limits, and to outline As major sources and drivers.
ABSTRACT
The issue of freshwater salinization in coastal areas has grown in importance with the increase of the demand of groundwater supply and the more frequent droughts. However, the spatial patterns of salinity contamination are not easy to be understood, as well as their numerical modeling is subject to various kinds of uncertainty. This paper offers a robust, flexible, and reliable geostatistical methodology to provide a stochastic assessment of salinity distribution in alluvial coastal areas. The methodology is applied to a coastal aquifer in Campania (Italy), where 83 monitoring wells provided depth-averaged salinity data. A Monte Carlo (MC) framework was implemented to simulate depth-averaged groundwater salinity fields. Both MC stochastic fields and the mean across MC simulations enabled to the delineation of which areas are subject to high salinity. Then, a probabilistic approach was developed setting up salinity thresholds for agricultural use to delineate the areas with unsuitable groundwater for irrigation purposes. Furthermore, steady spatial patterns of saline wedge lengths were unveiled through uncertainty estimates of seawater ingression at the Volturno River mouth. The results were compared versus a calibrated numerical model with remarkable model fit (R2=0.96) and versus an analytical solution, obtaining similar wedge lengths. The results pointed out that the high groundwater salinities found inland (more than 2 km from the coastline) could be ascribed to trapped paleo-seawater rather than to actual seawater intrusion. In fact, the inland high salinities were in correspondence of thick peaty layers, which can store trapped saline waters because of their high porosity and low permeability. Furthermore, these results are consistent with the recognition of depositional environments and the position of ancient lagoon alluvial sediments, located in the same areas where are the highest (simulated) salinity fields. This robust probabilistic approach could be applied to similar alluvial coastal areas to understand spatial patterns of present salinization, to disentangle actual from paleo-seawater intrusion, and more in general to delineate zones with unsuitable salinity for irrigation purposes.
