Geostatistical assessment of groundwater arsenic contamination in the Padana Plain.

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.

A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture.

The rapid decline in both quality and availability of freshwater resources on our planet necessitates their thorough assessment to ensure sustainable usage. The growing demand for water in industrial, agricultural, and domestic sectors poses significant challenges to managing both surface and groundwater resources. This study tests and proposes a hybrid evaluation approach to determine Groundwater Quality Indices (GQIs) for irrigation (IRRI), seawater intrusion (SWI), and potability (POT), finalized to the spatial distribution of groundwater suitability involving water quality indicator along with hydrogeological and socio-economic factors. Mean Decrease Accuracy (MDA) and Information Gain Ratio (IGR) were used to state the importance of chosen factors such as level of groundwater above the sea, thickness of the aquifer, land cover, distance from coastline, silt soil content, recharge, distance from river and lagoons, depth to water table from ground, distance from agricultural wells, hydraulic conductivity, and lithology for each quality index, separately. The results of both methods showed that recharge is the most important parameter for GQIIRRI and GQIPOT, while the distance from the coastline and the rivers, are the most important for GQISWI. The spatial modelling of GQIIRRI and GQIPOT in the study area has been achieved applying three machine learning (ML) algorithms: the Boosted Regression Tree (BRT), the Random Forest (RF), and the Support Vector Machine (SVM). Validation results showed that RF has the highest prediction for GQIIRRI, while the SVM model has the highest prediction for the GQIPOT index. It is worth to mention that the future utilization and testing of new algorithms could produce even better results. Finally, GQIIRRI and GQIPOT were combined and compared using two combine and overlay methods to prepare a hybrid map of multi-GQIs. The results showed that 69% of the study area is suitable for irrigation and potable use, due to both geogenic and anthropogenic activities which contribute to make some water resources unsuitable for either use. Specifically, the northern, western, and eastern portions of the study area are in the "very high and high quality" classes while the southern portion shows "very low and low quality" classes. In conclusion, the developed map and approach can serve as a practical guide for enhancing groundwater management, identifying suitable areas for various uses and pinpointing regions requiring improved management practices.

Salinity origin in the coastal aquifer of the Southern Venice lowland.

Groundwater salinization can be natural and anthropogenic in origin, although it often results from a combination of both, especially in low-lying coastal regions that are hydraulically controlled. This study proposes a method to assess the origin of salinity using environmental tracers in porewater, like Cl- and Br-, combined with depositional facies associations detected in sediment cores. Such integrated approach was tested in a target area south of the Venice Lagoon (Italy), where groundwater salinization is triggered by multiple mechanisms due to the complexity of the hydro-geomorphological environment. Batch tests were performed on sediment core samples from boreholes to quantify major anions and total inorganic N. Cl- and Br- porewater concentrations coupled with sedimentary facies association provided insights into the origin of groundwater salinity from a variety of sources, including past and present seawater intrusion, agricultural leaching, and evaporites. The strengths and limitations of the integrated approach are discussed to provide a pathway for improving water resource management and planning measures to prevent groundwater salinization in coastal areas.

Modelling biogeochemical reactions triggered by graphene's addition in a fertilized calcareous sandy soil.

Graphene production has dramatically increased in the last years and new ways to recycle this engineered material need to be investigated. To this purpose, a reactive model network was developed using PHREEQC-3 code to quantify the relevant biogeochemical reactions induced by graphene scraps' incorporation in a calcareous sandy soil. The numerical model was calibrated versus a complete dataset of column experiments in water saturated conditions using two different fertilizers, a synthetic NPK fertilizer and fertigation water produced in a wastewater treatment plant. Column experiments consisted of 50 cm columns filled with a mixture of graphene scraps (0.015 % dry weight) and soil in the first 10 cm, while the remaining 40 cm had only soil. The model performance was tested using classical statistical indices (R2, Modelling Efficiency, and Index of Agreement), resulting to be satisfactory. Besides, a simple sensitivity analysis via the perturbation of relevant parameters showed a low degree of uncertainty. The main outcome of this study was the quantification of the increased denitrification rate triggered by graphene incorporation into the soil. Moreover, graphene incorporation substantially increased soil CEC and DOC sorption capacity, demonstrating a good adsorption capacity for ammonium and organic compounds, thus decreasing nutrients leaching that represents a major concern related to agricultural practice. Indeed, Graphene incorporation increased by 40 % the CEC in the first 10 cm of the CSG_NPK column (2.50e-02 mol/L) respect to the CS_NPK column (1.75e-02 mol/L) and increased it by 150 % in the first 10 cm of the CSG_FW column (2.50e-02 mol/L) in comparison with the CS_FW column 1.00e-02 (mol/L). pH fluctuations were most likely due to the precipitation of Ca5(PO4)3OH, indeed the consumption of H+ ions could have triggered the pH lowering during the experiment. These results could be relevant for future graphene applications as a soil improver or as suitable material to enhance soil bioremediation in order to include graphene in a circular economy loop.

Coupling SWAT and DPSIR models for groundwater management in Mediterranean catchments.

Groundwater is an essential natural resource and has a significant role in human and environmental health as well as in the economy. Management of subsurface storage remains an important option to meet the combined demands of humans and ecosystems. The increasing need to find multi-purpose solutions to address water scarcity is a global challenge. Thus, the interactions leading to surface runoff and groundwater recharge have received particular attention over the last decades. Additionally, new methods are developed to incorporate the spatial-temporal variation of recharge in groundwater modeling. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in the Upper Volturno-Calore hydrological basin in Italy and the results were compared with other two basins in Greece (Anthemountas and Mouriki). SWAT model was applied in actual and future projections (2022-2040) using the Representative Concentration Pathway (RCP) 4.5 emissions scenario to evaluate changes in precipitation and assess the future hydrologic conditions, along with, the Driving Force-Pressure-State-Impact-Response (DPSIR) framework that was applied in all the basins as a low-cost analysis of integrated physical, social, natural, and economic factors. According to the results, no significant variations in runoff are predicted in the Upper Volturno-Calore basin for the period 2020-2040 while the potential evapotranspiration percentage varies from 50.1% to 74.3% and infiltration around 5%. The limited primary data constitutes the main pressure in all sites and exaggerates the uncertainty of future projections.

Modeling stochastic saline groundwater occurrence in coastal aquifers.

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.

Performance of graphene and traditional soil improvers in limiting nutrients and heavy metals leaching from a sandy Calcisol.

Given the large amount of Graphene produced in the last years, there is the need to introduce this new material into a green and circular economy loop. In this study, for the first time, the fate of nutrients and heavy metals in a sandy Calcisol amended with Graphene was monitored and compared to other traditional improvers such as Compost, Zeolites, and Biochar. This was performed via saturated and unsaturated columns' experiments with two different fertilization regimes: one with NPK fertilizer and one with an innovative fertigation water (FW) produced from a pilot wastewater treatment plant. The breakthrough curves of each nutrient and heavy metal were analysed to understand the main processes occurring in saturated and unsaturated conditions, comparing the columns amended with the improvers versus the unamended Controls. Mass balances for each nutrient and heavy metal were developed to infer whether the different soil improvers were effective in minimizing leaching. Graphene, for most cases, behaved as the Control in nutrients' leaching for all the saturated and unsaturated experiments, both with NPK and FW. Biochar increased EC, K+, and pH of the leaching water, which could be an issue for the growth of some plants. Compost increased NO3- leaching in all the experiments. Zeolites showed the best N compounds retention, but great PO43- leaching in saturated conditions. Heavy metals leachates were analysed only for unsaturated columns (as more representative of field conditions) and found at concentrations well below the limits suggested by the U.S. Environmental Protection Agency. Overall, Graphene performed well in minimizing nutrients and heavy metals leaching, respect to classical improvers. This study is a starting point for field studies that will be critical to have a clear understanding of how Graphene behaves in the environment.

Modeling groundwater and surface water interaction: An overview of current status and future challenges.

The interaction between surface water and groundwater constitutes a critical process to understand the quantitative and qualitative regime of dependent hydrosystems. A multi-scale approach combining cross-disciplinary techniques can considerably reduce uncertainties and provide an optimal understanding of groundwater and surface water exchanges. The simulation process constitutes the most effective tool for such analysis; however, its implementation requires a variety of data, a detailed analysis of the hydrosystem, and time to finalize a reliable solution. The results of the simulation process contribute to the raising of awareness for water protection and the application of better management strategies. Knowledge of models' parameters has great importance to ensure reliable results in the modeling process. In this study, a literature overview of modeling applications in groundwater - surface water interaction is provided. In this context, a comprehensive and holistic approach to groundwater and surface water simulation codes is here presented; results, case studies, and future challenges are also discussed. The main finding of the analysis highlights uncertainties and gaps in the modeling process due to the lack of high frequency and depth dependent field measurements. In many studies, authors underestimate the importance of the hydrogeological regime, and the discretization of hydraulic parameters is often lumped in a simplified manner. The modeling ethics in terms of data transparency and openness should be widely considered to improve the modeling results. The current study contributes to overcome common weaknesses of model applications, fulfils gaps in the existing literature, and highlights the importance of the modeling process in planning sustainable management of water resources.

Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the drastic framework: The case of South America.

An assessment of the intrinsic aquifer vulnerability of South America is presented. The outcomes represent the potential sensitivity of natural aquifers to leaching of dissolved compounds from the land surface. The study, developed at continental scale but retaining regionally a high resolution, is based on a critical application of the DRASTIC method. The biggest challenge in performing such a study in South America was the scattered and irregular nature of environmental datasets. Accordingly, the most updated information on soil, land use, geology, hydrogeology, and climate at continental, national, and regional scale were selected from international and local databases. To avoid spatial discrepancy and inconsistency, data were integrated, harmonized, and accurately cross-checked, using local professional knowledge where information was missing. The method was applied in a GIS environment to allow spatial analysis of raw data along with the overlaying and rating of maps. The application of the DRASTIC method allows to classify South America into five vulnerability classes, from very low to very high, and shows an overall medium to low vulnerability at continental scale. The Amazon region, coastal aquifers, colluvial Andean valleys, and alluvial aquifers of main rivers were the areas classified as highly vulnerable. Moreover, countries with the largest areas with high aquifer vulnerability were those characterized by extended regions of rainforest. In addition, a single parameter sensitivity analysis showed depth to water table to be the most significant factor, while a cross-validation using existing vulnerability assessments and observed concentrations of compounds in groundwater confirmed the reliability of the proposed assessment, even at regional scale. Overall, although additional field surveys and detailed works at local level are needed to develop effective water management plans, the present DRASTIC map represents an essential common ground towards a more sustainable land-use and water management in the whole territory of South America.

In situ arsenic immobilisation for coastal aquifers using stimulated iron cycling: Lab-based viability assessment.

Arsenic (As) is one of the most harmful and widespread groundwater contaminants globally. Besides the occurrence of geogenic As pollution, there is also a large number of sites that have been polluted by anthropogenic activities, with many of those requiring active remediation to reduce their environmental impact. Cost-effective remedial strategies are however still sorely needed. At the laboratory-scale in situ formation of magnetite through the joint addition of nitrate and Fe(II) has shown to be a promising new technique. However, its applicability under a wider range of environmental conditions still needs to be assessed. Here we use sediment and groundwater from a severely polluted coastal aquifer and explore the efficiency of nitrate-Fe(II) treatments in mitigating dissolved As concentrations. In selected experiments >99% of dissolved As was removed, compared to unamended controls, and maintained upon addition of lactate, a labile organic carbon source. Pre- and post experimental characterisation of iron (Fe) mineral phases suggested a >90% loss of amorphous Fe oxides in favour of increased crystalline, recalcitrant oxide and sulfide phases. Magnetite formation did not occur via the nitrate-dependent oxidation of the amended Fe(II) as originally expected. Instead, magnetite is thought to have formed by the Fe(II)-catalysed transformation of pre-existing amorphous and crystalline Fe oxides. The extent of amorphous and crystalline Fe oxide transformation was then limited by the exhaustion of dissolved Fe(II). Elevated phosphate concentrations lowered the treatment efficacy indicating joint removal of phosphate is necessary for maximum impact. The remedial efficiency was not impacted by varying salinities, thus rendering the tested approach a viable remediation method for coastal aquifers.

The origin of Uranium in groundwater of the eastern Halkidiki region, northern Greece.

Uranium (U) pollution in groundwater has become a serious problem worldwide. Even in low concentrations, U has both radiological and toxicological impacts on human health. In this study an integrated hydrogeological approach was applied to conceptualize an aquifer system, and determine the origin of U detected in the aquifer of the eastern Halkidiki region in northern Greece. Data from measurements of groundwater level and hydrochemical and stable isotope analyses of groundwater samples were applied to perform geochemical modeling and multivariate statistical analysis. The modeling and statistical analysis identified three hydrogeochemical groups within the studied hydro-system, and U(VI) as the dominant U species. The first group is linked to the deeper aquifer which is characterized by water-rock interactions with weathering products of granodiorite. In this group the dominant U species is uranyl phosphate and U concentration is 3.7 µg/L. The upper aquifer corresponds to the second hydrogeochemical group where U concentrations are mainly influenced by high concentrations of nitrogen species (NO3- and NO2-). Factor analysis further discriminated the upper aquifer into a saline coastal zone and an inland zone impacted by agricultural activities. The third hydrogeochemical group presents the highest concentration of U (up to 15 µg/L) in groundwater and corresponds to the internal aquifer system. The U within this system is triggered by the presence of Mn2+, while the long residence time of the groundwater contributes synergistically to the hydrogeochemical process. Manganese triggers U oxidation in parallel with Fe2+ precipitation that acts as a regulator of U concentration. Groundwater depletion of the upper aquifers promotes the up-coning of geothermal fluids from fault zones leading to increased concentrations of U in the mid-depth aquifers.

Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer.

Trace element (TE) pollution in groundwater resources is one of the major concerns in both developing and developed countries as it can directly affect human health. Arsenic (As), Barium (Ba), and Rubidium (Rb) can be considered as TEs naturally present in groundwater due to water-rock interactions in Campania Plain (CP) aquifers, in South Italy. Their concentration could be predicted via some readily available input variables using an algorithm like the iterative classifier optimizer (ICO) for regression, and novel hybrid algorithms with additive regression (AR-ICO), attribute selected classifier (ASC-ICO) and bagging (BA-ICO). In this regard, 244 groundwater samples were collected from water wells within the CP and analyzed with respect to the electrical conductivity, pH, major ions and selected TEs. To develop the models, the available dataset was divided randomly into two subsets for model training (70% of the dataset) and evaluation (30% of the dataset), respectively. Based on the correlation coefficient (r), different input variables combinations were constructed to find the most effective one. Each model's performance was evaluated using common statistical and visual metrics. Results indicated that the prediction of As and Ba concentrations strongly depends on HCO3-, while Na+ is the most effective variable on Rb prediction. Also, the findings showed that the most powerful predictive models were those that used all the available input variables. According to models' performance evaluation metrics, the hybrid ASC-ICO outperformed other hybrid (BA- and AR-ICO) and standalone (ICO) algorithms to predict As and Ba concentrations, while both hybrid ASC- and BA-ICO models had higher accuracy and lower error than other algorithms for Rb prediction.

Critical range of soil organic carbon in southern Europe lands under desertification risk.

Soil quality is fundamental for ecosystem long term functionality, productivity and resilience to current climatic changes. Despite its importance, soil is lost and degraded at dramatic rates worldwide. In Europe, the Mediterranean areas are a hotspot for soil erosion and land degradation due to a combination of climatic conditions, soils, geomorphology and anthropic pressure. Soil organic carbon (SOC) is considered a key indicator of soil quality as it relates to other fundamental soil functions supporting crucial ecosystem services. In the present study, the functional relationships among SOC and other important soil properties were investigated in the topsoil of 38 sites under different land cover and management, distributed over three Mediterranean regions under strong desertification risk, with the final aim to define critical SOC ranges for fast loss of important soil functionalities. The study sites belonged to private and public landowners seeking to adopt sustainable land management practices to support ecosystem sustainability and productivity of their land. Data showed a very clear relationship between SOC concentrations and the other analyzed soil properties: total nitrogen, bulk density, cation exchange capacity, available water capacity, microbial biomass, C fractions associated to particulate organic matter and to the mineral soil component and indirectly with net N mineralization. Below 20 g SOC kg-1, additional changes of SOC concentrations resulted in a steep variation of all the analyzed soil indicators, an order of magnitude higher than the changes occurring between 50 and 100 g SOC kg-1 and 3-4 times the changes observed at 20-50 g SOC kg-1. About half of the study sites showed average SOC concentration of the topsoil centimetres <20 g SOC kg-1. For these areas the level of SOC might hence be considered critical and immediate and effective recovery management plans are needed to avoid complete land degradation in the next future.

Developing a SINTACS-based method to map groundwater multi-pollutant vulnerability using evolutionary algorithms.

In this study, the modified SINTACS method, a rating-based groundwater vulnerability approach, was applied to data from the Campanian Plain, southern Italy, to identify groundwater vulnerable areas accurately. To mitigate the subjectivity of SINTACS rating and weighting schemes, a modified SINTACS model was formulated by optimizing parameter ratings using the Wilcoxon rank-sum test, and the weight scores using the evolutionary algorithms including artificial bee colony (ABC) and genetic algorithm (GA) methods. The validity of the models was verified by analyzing the correlation coefficient between the vulnerability index and nitrate (NO3) and sulfate (SO4) concentrations found in the groundwater. The correlation coefficients between the pollutant concentrations and the relevant vulnerability index increased significantly from - 0.35 to 0.43 for NO3 and from - 0.28 to 0.33 for SO4 after modifying the ratings and weights of typical SINTACS. Besides, a multi-pollutant vulnerability map considering both NO3 and SO4 pollutants was produced by amalgamating the best calibrated vulnerability maps based on the obtained correlation values (i.e., the Wilcoxon-ABC-based SINTACS vulnerability map for NO3 and the Wilcoxon-GA-based SINTACS vulnerability map for SO4). The resultant multi-pollutant vulnerability map coincided significantly with a land use map of the study area, where anthropogenic activities represented the main sources of pollution.

Evaluating SWAT model performance, considering different soils data input, to quantify actual and future runoff susceptibility in a highly urbanized basin.

The Soil and Water Assessment Tool (SWAT) is a physical model designed to predict the hydrological processes that could characterize natural and anthropized watersheds. The model can be forced using input data of climate prediction models, soil characteristics and land use scenarios to forecast their effect on hydrological processes. In this study, the SWAT model has been applied in the Aspio basin, a small watershed, highly anthropized and characterized by a short runoff generation. Three simulations setup, named SL1, SL2 and SL3, were investigated using different soil resolution to identify the best model performance. An increase of space requirement and calibration time has been registered in conjunction with the increasing soil resolution. Among all simulations, SL1 has been chosen as the best one in describing watershed streamflow, despite it was characterized by the lower soil resolution. A map of susceptibility to runoff for the entire basin was so created reclassifying the runoff amount of four years in five classes of susceptibility, from very low to very high. Eleven sub-basins, coinciding with the main urban settlements, were identified as highly susceptible to runoff generation. Considering future climate predictions, a slight increase of runoff has been forecasted during summer and autumn. The map of susceptibility successfully identified as highly prone to runoff those sub-basins where extreme flood events were yet recorded in the past, remarking the reliability of the proposed assessment and suggesting that this methodology could represent a useful tool in flood managing plan.

Soil conditioners effects on hydraulic properties, leaching processes and denitrification on a silty-clay soil.

Agricultural landscapes are often affected by groundwater quality issues due to fertilizers leaching. To address this worldwide problem several agricultural best practices have been proposed, like limiting the amount of fertilizers and increasing soil organic matter content. To evaluate if these practices may promote groundwater quality enhancement, vadose zone retention time and complex biogeochemical processes must be known in detail. In this study, sequential undisturbed column experiments were performed to determine the amount of nutrients and heavy metals leached after simulated stormwater events. The column was amended with urea then flushed for two pore volumes, then straw residuals were incorporated and flushed for two pore volumes and finally compost was incorporated and flushed for six pore volumes. Dissolved ions, major gasses and heavy metals were determined in leachate samples. Nitrate and nitrite were leached in the urea treatment producing the highest concentrations, followed by compost and straw residuals. The redox conditions were aerobic in all treatments and pH was circumneutral or slightly basic. Denitrification was low but increased with the addition of straw residuals and compost. Heavy metals were all at very low concentrations except for lead and cadmium, which slightly exceeded threshold limits (10 and 1 µg/L, respectively) in all the treatments. The compost treatment, after three pore volumes, was affected by clay swelling due to sodium dispersion, which in turn provoked a reduction of porosity and hydraulic conductivity.

Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm.

Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.

A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses.

Groundwater resources are the main supply of freshwater for human activities. However, in the last fifty years aquifers have become more susceptible to chemical pollution due to human activities. The concept of groundwater vulnerability constitutes a worldwide accepted tool for water protection and planning. However, the existing methods and modified versions do not account for all the hydrogeochemical processes that drive anthropogenic pollution. The hydrogeochemical processes occurring within an aquifer can be determined using multivariate statistical analysis. In this study a specific vulnerability method named SVAP (Specific Vulnerability to Anthropogenic Pollution) is proposed. The index is based on seven quantitative parameters: depth to groundwater, recharge, nitrate losses, hydraulic resistance of the vadose zone, aquifer thickness, hydraulic conductivity of the aquifer, and slope. Weights of anthropogenic factors were determined by factor analysis and used to validate the SVAP methodology. The parameters' classification was selected according to the highest Pearson's correlation coefficient with factor weights and then grouped via a linear combination. The new index was applied in two watersheds: the Florina basin (Greece) and the Garigliano River basin (Italy), both of which possess complex hydrogeochemical regimes. The main hydrogeochemical processes acting in the study areas were identified via factor analysis, which revealed that the anthropogenic pollution in both sites was due mainly to chemical fertilizers and manure. Verification of the SVAP method produced correlation coefficients with nitrate concentrations of 0.75 and 0.62 in Florina and Garigliano, respectively. The proposed SVAP method is more reliable and flexible than standard vulnerability assessment methods and can be easily adapted for complex aquifers.

Contrasting biogeochemical processes revealed by stable isotopes of H2O, N, C and S in shallow aquifers underlying agricultural lowlands.

Lowland coastal areas as the Po Delta (Italy) are often intensively cultivated and affected by nitrogen imbalance due to fertilizers leaching to groundwater and export via run-off. To address this issue several agricultural best practices have been proposed, like limiting the amount of fertilizers and increasing soil organic matter content. In this study, groundwater samples were analysed for major ions and stable isotopes of H2O, C, N and S using multi-level sampler (MLS) from two contrasting depositional environments, one representative of alluvial plain (AP) and the other representative of a reclaimed coastal plain (RCP). In each site, controlled plots with different agriculture practice including fertilizers and tillage and compost amendment and no tillage were considered in the study. Tracer test results highlight that recharge water infiltrated at the start of the controlled study has not yet reached the saturated zone, thus current groundwater concentrations are representative of former agricultural practices. Stable isotopes show a clear distinction between different sources of nitrogen in both sites, from synthetic fertilizers to sedimentary nitrogen pool and atmospheric input. The main source of sulphate in groundwater is pyrite and fertilizers. Denitrification, sulphate reduction and methanogenesis were involved in the C, N and S cycle in the RCP site characterized by low hydraulic conductivity sediments and high SOM. These processes were not relevant in the AP site characterized by oxic condition and low SOM, but some evidence of denitrification was found in one of the AP sites. High resolution monitoring was a key tool to identify the different redox zones responsible for N, C and S cycling in these aquifers. This study shows that a clear understanding of transit times in the vadose zone is a key prerequisite to evaluate the effect of controlled agriculture practice on the quality of shallow groundwater.