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The Natural Background Level (NBL), suggested by UE BRIDGE project, is suited for spatially distributed datasets providing a regional value that could be higher than the Threshold Value (TV) set by every country. In hydro-geochemically dis-homogeneous areas, the use of a unique regional NBL, higher than TV, could arise problems to distinguish between natural occurrences and anthropogenic contaminant sources. Hence, the goal of this study is to improve the NBL definition employing a geostatistical approach, which reconstructs the contaminant spatial structure accounting geochemical and hydrogeological relationships. This integrated mapping is fundamental to evaluate the contaminant's distribution impact on the NBL, giving indications to improve it. We decided to test this method on the Drainage Basin of Venice Lagoon (DBVL, NE Italy), where the existing NBL is seven times higher than the TV. This area is notoriously affected by naturally occurring arsenic contamination. An available geochemical dataset collected by 50 piezometers was used to reconstruct the spatial distribution of arsenic in the densely populated area of the DBVL. A cokriging approach was applied exploiting the geochemical relationships among As, Fe and NH4+. The obtained spatial predictions of arsenic concentrations were divided into three different zones: i) areas with an As concentration lower than the TV, ii) areas with an As concentration between the TV and the median of the values higher than the TV, and iii) areas with an As concentration higher than the median. Following the BRIDGE suggestions, where enough samples were available, the 90th percentile for each zone was calculated to obtain a local NBL (LNBL). Differently from the original NBL, this local value gives more detailed water quality information accounting the hydrogeological and geochemical setting, and contaminant spatial variation. Hence, the LNBL could give more indications about the distinction between natural occurrence and anthropogenic contamination.

This work proposed a novel method to elucidate the controls of As mobility in complex aquifers based on an unsupervised machine learning algorithm, Self-Organizing Map (SOM), and process-based geochemical modeling. The approach is tested in the shallow aquifers of the Venetian Alluvial Plain (VAP) near Venice, Italy, where As concentrations seasonally and locally exceed recommended drinking water limits. SOM was fed using information from two geochemical surveys on eight VAP boreholes, and continuous reading of hourly groundwater head levels and weekly geochemical analyses from three VAP boreholes between mid-October 2017 and end of January 2018. The SOM analysis is consistent with redox-controlled dissolution-precipitation hydrous ferric oxides (HFOs) as a key control of As mobility in the aquifer. Dissolved As is positively correlated to Fe and NH þ 4 and negatively to the oxidizing-reducing potential (ORP). Negative correlation between As and groundwater head levels suggests a redox control by rainfall-driven recharge, which adds oxidants to the aquifer while progressively attenuating As. This mechanism is tested using process-based geochemical modeling, which simulates different transport modalities of oxidants entering the aquifer. Starting from reducing aquifer conditions, the model reproduces correctly the observed ORP and the trends in As and Fe, when the function describing the occurrence of oxidizing events scales according to the temporal occurrence of rainfall events. Heterogeneity can strongly control the local-scale effectiveness of recharge as a natural As attenuating factor, requiring a different model analysis to be properly assessed and to be developed in a follow-up study.

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