Conceptual Model of Arsenic Mobility in the Shallow Alluvial Aquifers Near Venice (Italy) Elucidated Through Machine Learning and Geochemical Modeling

Abstract

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.

Probability of Non-Exceedance of Arsenic Concentration in Groundwater Estimated Using Stochastic Multicomponent Reactive Transport Modeling

“…Our stochastic analysis revealed that the shallow aquifers in the WAA are more sensitive to rainfall-controlled reactions than deeper parts of the aquifers. On the one hand, this corroborates and extends the validity of the conclusions by Dalla Libera et al [29]. On the other hand, the results are well aligned with the conclusions from other studies that already identified a link between As mobility and aquifer recharge.…”

“…This site is a well-characterized subset of the Venetian Alluvial Plain in Northern Italy (Figure 1). Previous works on the area [26][27][28][29] have generally concluded that As concentrations can be considered a random spatiotemporal variable, amenable to be modelled using probabilistic and geostatistical analysis. This makes the WAA an appealing case study to evaluate the actual usefulness of the stochastic model to predict the PNE of arsenic concentrations above the 10 μgL −1 and 50 μgL −1 critical thresholds.…”

“…This densely populated region near Venice (Italy) is composed of 108 municipalities, covering an area of about 2000 km 2 and with a population close to 1 million inhabitants (Italian National Institute of Statistics, 2001). Arsenic concentrations in the VAP shallow groundwater can exceed the Italian maximum concentration limit and the WHO-recommended threshold of 10 µgL −1 [26][27][28][29]. Following the EU Water and Groundwater Directive, implemented in Italy mainly through the National Decree D.Lgs 152/2006, an excess of As concentration in groundwater implies that local administration should properly monitor and characterize the extension of the contamination, the natural background levels and thresholds values that can potentially entail a risk for the groundwater consumers.…”

“…In a recent study, Dalla Libera et al [29] (hereafter, "DL2020") proposed a conceptual model that mechanistically explains the spatiotemporal As variations in the VAP aquifer. The study focused on the Western Agricultural Areas (WAA), a well characterized area of the distal portion of the VAP towards the Adriatic Sea.…”

Fingerprinting sources of salinity in a coastal chalk aquifer in Denmark using trace elements

“…Detailed information on SOM can be found in Kohonen (1982Kohonen ( , 1990Kohonen ( , 2001Kohonen ( , 2013 and Kalteh et al (2008). The SOM method has recently been used in the analysis of groundwater geochemical data in a coastal aquifer near Venice (Dalla Libera et al 2020) and in geochemical pattern recognitions of deep thermal groundwater in South Korea (Kim et al 2020).…”