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Excess of arsenic in groundwater is a worldwide problem threatening the health of the millions of people directly exposed to As-rich water intake. The problem is particularly acute in naturally occurring unconsolidated aquifers where As-rich groundwater is an easily-accessible resource of drinking water, such as in India, Bangladesh and Vietnam. Naturally occurring high concentrations of As in groundwater also notoriously mark the Venetian Alluvial Plain (VAP) in Italy, affecting in particular the shallower confined aquifers. The VAP is characterized by a patchy As distribution with variable extensions and concentrations. Although empirical evidences exist about the relationship between As occurrence and other factors controlling As, several aspects regarding the physical and geochemical processes controlling As in the VAP aquifers remain unclear. This study aims to elucidate the geochemical conditions that control As mobilization in the VAP, in order to improve the knowledge about As-controlling processes in the study area and use them as a proxy to evaluate occurrence of As in other world regions characterized by a similar hydrogeological and geochemical settings, such as the Bengal Delta Plain. To this end, we focused in detail on an agricultural zone nearby the Venice lagoon, affected by As contamination (called “Aree Agricole West”, AAW). The available data, collected by several hydrogeological surveys, show a spatial and temporal variability of As concentration, which can be associated to a variety of hydro-geochemical processes such as redox variations, sorption or reductive dissolution of As-rich iron oxy-hydroxides. Multicomponent 1D reactive transport modeling is applied to elucidate the leading As-controlling processes and their relevance on the AAW and VAP in general. The results provide the initial input for a 2D/3D reactive transport model aiming at highlighting As control by other mechanisms, including influence of recharge due to agricultural activities, supporting regional-scale environmental management and future stakeholders’ decision procedures.

Arsenic groundwater contamination affects worldwide shallower groundwater bodies. Starting from the actual knowledges around arsenic origin into groundwater, we know that the major part of dissolved arsenic is naturally occurring through the dissolution of As-bearing minerals and ores. Several studies on the shallow aquifers of both the regional Venetian Plain (NE Italy) and the local Drainage Basin to the Venice Lagoon (DBVL) show local high arsenic concentration related to peculiar geochemical conditions, which drive arsenic mobilization. The uncertainty of arsenic spatial distribution makes difficult both the evaluation of the processes involved in arsenic mobilization and the stakeholders’ decision about environmental management. Considering the latter aspect, the present study treats the problem of the Natural Background Level (NBL) definition as the threshold discriminating the natural contamination from the anthropogenic pollution. Actually, the UE’s Directive 2006/118/EC suggests the procedures and criteria to set up the water quality standards guaranteeing a healthy status and reversing any contamination trends. In addition, the UE’s BRIDGE project proposes some criteria, based on the 90th percentile of the contaminant’s concentrations dataset, to estimate the NBL. Nevertheless, these methods provides just a statistical NBL for the whole area without considering the spatial variation of the contaminant’s concentration. In this sense, we would reinforce the NBL concept using a geostatistical approach, which is able to give some detailed information about the distribution of arsenic concentrations and unveiling zones with high concentrations referred to the Italian drinking water standard (IDWS = 10 g/liter). Once obtained the spatial information about arsenic distribution, we can apply the 90th percentile methods to estimate some Local NBL referring to every zones with arsenic higher than IDWS. The indicator kriging method was considered because it estimates the spatial distribution of the exceedance probabilities respect some pre-defined thresholds. This approach is largely mentioned in literature to face similar environmental problems. To test the validity of the procedure, we used the dataset from “A.Li.Na” project (founded by the Regional Environmental Agency) that defined regional NBLs of As, Fe, Mn and NH4+ into DBVL’s groundwater. Primarily, we defined two thresholds corresponding respectively to the IDWS and the median of the data over the IDWS. These values were decided basing on the dataset’s statistical structure and the quality criteria of the GWD 2006/118/EC. Subsequently, we evaluated the spatial distribution of the probability to exceed the defined thresholds using the Indicator kriging. The results highlight different zones with high exceedance probability ranging from 75% to 95% respect both the IDWS and the median value. Considering the geological setting of the DBVL, these probability values correspond with the occurrence of both organic matter and reducing conditions. In conclusion, the spatial prediction of the exceedance probability could be useful to define the areas in which estimate the local NBLs, enhancing the procedure of NBL definition. In that way, the NBL estimation could be more realistic because it considers the spatial distribution of the studied contaminant, distinguishing areas with high natural concentrations from polluted ones.

The alluvial aquifers of the Po and Venetian Plain show high concentration of arsenic principally in reducing environments and it is often associated with the presence of organic matter. Our study concerns the Drainage Basin to the Venice Lagoon (DBVL) that is a densely populated area extending on the provinces of Venice, Padua and Treviso for approximately 2038 km2. Notoriously the arsenic contamination of groundwater affects the DBVL. The UE’s Groundwater Directive (GWD 2006/118/EC) suggests an arsenic Contamination Threshold Values (CTV) equal to 10 μg/l. In addition, the UE BRIDGE project proposes to use the 90th percentile of the concentration data to estimate the Natural Background Level (NBL) (Müller et al., 2006). Nevertheless, this method provides only a statistical NBL value for the whole area without considering the spatial variation of the As concentration. Thus, the aim of this study is to improve the NBL concept using a geostatistical approach on a sensitive case where the NBL is seven times higher than the CTV. The dataset used in this work comes from the “A.Li.Na” project (founded by the Regional Environmental Agency) that aimed to define the NBLs of As, Fe, Mn and NH4 + into the DBVL’s groundwater. Four seasonal surveys, from 2013 to 2014, collected the hydro-geochemical parameters in 50 piezometers. Primarily, we defined two thresholds that correspond respectively to the CTV and the median of the data over the CTV. These values were decided on the base of the dataset’s statistical structure and the GWD 2006/118/EC. Subsequently, we evaluated the spatial distribution of the probability to exceed the defined thresholds using a geostatistical approach based on Indicator kriging. The results highlight different zones with high exceeding probability respect both the CTV and the median value. In particular, some portions of the study area have an exceeding probability ranging from 75% to 95% for both thresholds. Considering the geological setting of the DBVL, these high exceeding probability values correspond with the occurrence of both organic matter and reducing conditions. In conclusion, the spatial prediction of the exceeding probability could be useful to define the regions in which estimate the local NBLs, improving the procedure of NBL definition. In this way, the estimation of the NBL could be more realistic because it considers the spatial distribution of the studied chemical element/compound, distinguishing areas with a high natural concentration from polluted areas.

In northern Italy, especially into the alluvial aquifers of the Po and Venetian Plain, high concentration of arsenic is principally found in reducing environments and it often is associated with the presence of organic matter. Our study is focused on the area of the Drainage Basin to the Venice Lagoon (DBVL) that is a densely populated area notoriously affected by the groundwater arsenic pollution extending on approximately 2038 km2 on the provinces of Venice, Padua and Treviso. The UE’s Groundwater Directive (GWD 2006/118/EC) suggests an arsenic Contamination Threshold Values (CTV) equal to 10 µg/l. In addition, the UE BRIDGE project proposes to use the 90th percentile of the concentration data for the estimation of the Natural Background Level (NBL). Nevertheless, this method provides only a NBL value for the whole area. The dataset used in this work comes from the “A.Li.Na” study (founded by the Regional Environmental Agency) that was aimed to define the NBLs of As, Fe, Mn and NH4+ into the DBVL’s groundwater. Hydro-geochemical parameters were collected by 50 piezometers during four seasonal surveys from 2013 to 2014. The aim of this study is to improve the NBL concept using a sensitive case where the NBL is higher than the CTV. A geostatistical prediction of the arsenic spatial distribution appears a good procedure to spatially highlight zones exceeding the CTV. The relations among As, Fe and NH4+ suggested a cokriging method to spatialize arsenic distribution in the DBVL. The results allow highlighting different zones of arsenic concentration: i) areas with values lower than the CTV ; ii) areas with values between the CTV and the median, calculated on the values over the CTV ; iii) areas exceeding the median. Subsequently, the 90th percentile is applied to calculate a local NBL for each zone following the BRIDGE suggestions. Hence, the spatial predictions could be useful to define local NBLs related to the different portions of the whole predicted area improving the procedure of NBL calculation. In addition, if necessary (n° pz < 30), in these three classed areas the “A.Li.Na” sampling network should be increased to calculate the LNBLs.