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.

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.

Hydrogeochemical characteristics and health risk assessment of potentially toxic elements in groundwater and their relationship with the ecosystem: case study in Tunisia.

Sidi Bouzid basin knows for several decades a fast-growing anthropogenic activity and, consequently, an increase in groundwater pollution which attracted researcher attention. For this task, we performed an exhaustive study to evaluate groundwater geochemical evolution. Our research begins with analyzing the geochemical process, then determining the water quality indices and their impact on the ecosystem, and after that correlating between different compartments, and ends with the assessment of the human health risk toward NO3-, Fe, Mn, Zn, Cd, and Pb. The dominant facies of the groundwater in the study area are Ca-Mg-SO4 and Ca-Mg-HCO3 and are mainly influenced by evaporite deposits (CaSO4, CaSO4·2H2O, and NaCl). The pollution index of groundwater (PIG) displays values ranging between 0.5 and 4.5 indicating four classes of pollution (insignificant, low, moderate, and high). More than half of the samples (55%) belong to the low and moderate PIG classes. However, the results show elevated values of NO3- concentration; 76% of samples exceed 30 mg/L. Among the studied contaminants, the highest carcinogenic and non-carcinogenic risks in study areas were related to NO3-. For all water samples, the risk levels for children were greater than those for adults. Lastly, the partial least square-structural equation modelling (PLS-SEM) shows that the chemical elements do not have a short-term potential impact of pollutants on ecosystems.

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.

Hybridization of GALDIT method to assess actual and future coastal vulnerability to seawater intrusion.

In the recent years, the coastal aquifer of Jijel plain (North Algeria) located on the south of the Mediterranean Sea was utilized for cities growth and agricultural development of the region. Consequently, overexploitation and seawater intrusion were identified as major risks to the groundwater resource. In this work, a new approach integrating groundwater vulnerability method and numerical model for predicting the actual and future seawater is proposed. The groundwater vulnerability assessment has been performed by applying the GALDIT method using GIS and the MODFLOW model was used to simulate the actual and future groundwater level of the aquifer over the period 2020-2050. Three scenarios were simulated under water demand and climate conditions (drought, recharge) to obtain the changes in the groundwater level variation. The results of the GALDIT model application to the actual conditions (year 2020) showed that the high class of groundwater vulnerability is located in the coastal fringe and the terminal stretches of wadis where the seawater intrusion limit is located at a distance range between 840 and 1420 m from the shoreline. However, the results for predicting future groundwater vulnerability showed that the scenario which proposed the artificial recharge basins, although predicting a worrying situation compared to the actual condition, has the best figure of the groundwater vulnerability assessment and seawater intrusion despite the other two scenarios. In this case the limit in the year 2050 is located between distances of 850-1640 m from the shoreline with a forward speed of seawater intrusion of 1-8 m/year, compared to the reference year 2020. This showed that groundwater level variation and recharge were the key factors in controlling groundwater vulnerability to seawater intrusion. The presented new approach can be used to mapping the actual and future groundwater vulnerability assessment to seawater intrusion and groundwater resources management in any coastal areas worldwide.

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.

Geochemical characterization and health risk assessment in two diversified environmental settings (Southern Italy).

An integrated approach using chemical and microbial indicators has been tested in two different sites of the Campania Plain (Southern Italy) with different land use covering and different hydrogeological features in order: (1) to define the water-rock interaction processes, (2) to differentiate sources of pollution in a detailed way (3) to evaluate the degree of water quality in the studied alluvial aquifer and (4) to identify the most worrying elements for human's health. Groundwater have showed a HCO3-Ca signature for both investigated sites, and a progressive enrichment in alkali ions has been highlighted moving from the boundary of the plain toward the coastal areas, due to groundwater interaction with volcanic rocks along the flow path. The application of the Factor Analysis allowed to identify different sources of pollution, which were attributed to (a) leaks in the sewer system for the Agro-Aversano Area and also the spreading of manure as fertilizers in agricultural activities for the Caiazzo Plain. Furthermore, it has been highlighted that the use of major elements, trace elements and microbiological indicators, allows to accurately differentiate contamination processes in progress. In fact, from the results of the Factor Analysis applied in the Agro-Aversano area, no significant statistically relationships between major elements and microbiological indicators of fecal contamination were highlighted, unlike the Caiazzo plain where statistically significant correlations have been found between major and trace elements and microbiological indicators. The use of a Groundwater Quality Index has shown general poor water quality for the majority of analyzed samples due to the high amount of Nitrate and Fecal indicators. The use of a Health Risk Assessment highlighted that Nitrate coupled with Fluoride represent the most important concern for human health compared to the all investigated parameters in both sites.

Effects of temperature and relative humidity on the COVID-19 pandemic in different climates: a study across some regions in Algeria (North Africa).

After more than a year from the first confirmed cases of coronavirus (COVID-19) disease, the role of meteorological factors in the transmission of the virus still needs to be correctly determined. In this scenario of deep uncertainty, the present study aims to investigate the effects of temperature and relative humidity on daily new cases of COVID-19. For this purpose, the COVID-19's development of infection in fourteen Algerian cities characterized by different climatic conditions, during the period from April 1, 2020, to August 31, 2020, has been investigated. A detailed time series analysis along with linear regression was used to state a possible correlation among some climate's factor variability (temperature and relative humidity) and daily new confirmed cases of COVID-19. The results showed a weak correlation between daily new cases of COVID-19 and meteorological factors throughout the selected regions. In addition, we concluded that the COVID-19 could fit to high or low values of temperature and relative humidity, and other factors not climates could affect the spreading of the virus like demography and human contact. So, after the discovery of the vaccine and before vaccination of 70% of the world's population, living with the virus has become an inevitable reality, and it is mandatory to apply the sanitary procedures to slow down the COVID-19 transmission.

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.

Deep carbon degassing in the Matese massif chain (Southern Italy) inferred by geochemical and isotopic data.

The Italian Apennines are among the most important sources of freshwater for several Italian regions. With evidences of deep CO2-rich fluids intruding into aquifers in the nearby central-southern Apennines, a thorough investigation into the geochemistry of groundwater became critical to ensure the water quality in the area. Here, we show the main hydrogeochemical processes occurring in the Matese Massif (MM) aquifer through the investigation of 98 water samples collected from springs and water wells. All waters were classified as HCO3 type with Ca dominance (from 50% up to 97%) and variable amount of Mg (from 1% up to 49%). A multivariate statistical approach through the application of the factor analysis (FA) highlighted three main hydrogeochemical processes: (i) water-carbonate rock interactions mostly enhanced in peripheral areas of the MM by CO2 deep degassing; (ii) addition of NaCl-rich components linked to recharging process and to water mixing processes of the groundwater with a thermal component relatively rich in Cl, Na, and CO2; (iii) anthropogenic activities influencing groundwater composition at the foothills of MM. Furthermore, the first detailed TDIC, pCO2, and δ13C-TDIC distribution maps of the MM area have been created, which track chemical and isotopic anomalies in several peripheral areas (Pratella, Ailano, and Telese) throughout the region. These maps systematically highlight that the greater the amount of dissolved carbon occurs the heavier the C isotope enrichment, especially in the peripheral areas. Conversely, spring waters emerging at higher altitudes within MM are only slightly mineralized and associated with δ13C-TDIC values mainly characterized by recharging processes with the addition of biogenic carbon during the infiltration process through the soil.

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.

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.

Evaluating the suitability of urban groundwater resources for drinking water and irrigation purposes: an integrated approach in the Agro-Aversano area of Southern Italy.

Deterioration of groundwater quality due to the introduction of pollutants from natural and anthropic sources has become a major environmental issue. We tested three methodologies in assessing groundwater quality and intrinsic aquifer vulnerability in the Agro-Aversano area (Southern Italy). A geographic information system (GIS)-based groundwater quality index (GQI) was realized to assess groundwater quality for drinking and irrigation use and, in parallel, standard SINTACS was applied to evaluate the intrinsic vulnerability of the aquifer. Nitrate concentrations and sodium absorption ratio (SAR) in groundwater samples were used to verify the reliability of vulnerability data. GQI analysis pointed to a general poor quality of groundwater both for drinking and irrigation use, especially in sub-urban areas. The spatial pattern of water quality from GQI analysis was positively related to nitrate and fluoride concentrations for drinking use and to bicarbonate and sodium concentrations for irrigation use, whose levels exceeded the WHO and FAO recommended thresholds, respectively. Standard SINTACS was found to be inadequate for describing the aquifer state, its results showing no correlation with nitrate concentration or SAR. Because of this inconsistency, we tested a novel approach combining GQI with SINTACS analysis. Results showed positive correlation with nitrate (r = 0.63) and SAR (r = 0.64) contents, thus pointing to combined SINTACS-GQI as a more reliable approach than standard methodologies.

GALDIT-SUSI a modified method to account for surface water bodies in the assessment of aquifer vulnerability to seawater intrusion.

The salinization of coastal aquifers is one of the major environmental issue worldwide. Overexploitation is the most common reason of salinization, since it generates a piezometric inversion, which in turn leads to groundwater flow from the coast towards inland. This also occurs in water bodies connected to the sea like lagoons, rivers, torrents and wetlands. In this study, a modification of the GALDIT method including "SUperficial Seawater Intrusion (SUSI)" is proposed. Six new parameters were added to the classical ones. The analytical hierarchy process and the sensitivity analysis were performed for weights definition and validation of the proposed GALDIT-SUSI method. Two study areas, with different characteristics were chosen for the application of both methods: the coastal area of Epanomi (Greece) and the Po River lowland (Italy). The application of the standard GALDIT in both sites showed a poor discrimination of the vulnerability to seawater intrusion, confining it only in proximity to the coastline. Conversely, GALDIT-SUSI divided the two sites in five classes of vulnerability ranging from very low to very high, stressing the higher vulnerability of lagoons and wetland for Epanomi and lagoons and rivers for the Po River lowland. GALDIT-SUSI is easy to apply and versatile, since it can be adapted to the specific hydrogeological setting of the area of interest. Moreover, GALDIT-SUSI can be further improved to deal with other salinization mechanisms.

Multivariate statistical analysis to characterize/discriminate between anthropogenic and geogenic trace elements occurrence in the Campania Plain, Southern Italy.

Shallow aquifers are the most accessible reservoirs of potable groundwater; nevertheless, they are also prone to various sources of pollution and it is usually difficult to distinguish between human and natural sources at the watershed scale. The area chosen for this study (the Campania Plain) is characterized by high spatial heterogeneities both in geochemical features and in hydraulic properties. Groundwater mineralization is driven by many processes such as, geothermal activity, weathering of volcanic products and intense human activities. In such a landscape, multivariate statistical analysis has been used to differentiate among the main hydrochemical processes occurring in the area, using three different approaches of factor analysis: (i) major elements, (ii) trace elements, (iii) both major and trace elements. The elaboration of the factor analysis approaches has revealed seven distinct hydrogeochemical processes: i) Salinization (Cl-, Na+); ii) Carbonate rocks dissolution; iii) Anthropogenic inputs (NO3-, SO42-, U, V); iv) Reducing conditions (Fe2+, Mn2+); v) Heavy metals contamination (Cr and Ni); vi) Geothermal fluids influence (Li+); and vii) Volcanic products contribution (As, Rb). Results from this study highlight the need to separately apply factor analysis when a large data set of trace elements is available. In fact, the impact of geothermal fluids in the shallow aquifer was identified from the application of the factor analysis using only trace elements. This study also reveals that the factor analysis of major and trace elements can differentiate between anthropogenic and geogenic sources of pollution in intensively exploited aquifers.

A modified SINTACS method for groundwater vulnerability and pollution risk assessment in highly anthropized regions based on NO3- and SO42- concentrations.

Groundwater vulnerability and risk assessment are worldwide tools in supporting groundwater protection and land planning. In this study, we used three of these different methodologies applied to the Campanian Plain located in southern Italy: SINTACS, AVI and LOS. However, their capability to describe the observed chemical pollution of the area has resulted quite poor. For such a reason, a modified SINTACS method has been then implemented in the area in order to get a more reliable view of groundwater vulnerability. NO3- and SO42- from more than 400 monitoring wells were used for specific vulnerability assessment. Land use was chosen as key parameter to infer the risk of groundwater pollution in our area. The new methodology seems to show a higher correlation with observed NO3- concentrations and a more reliable identification of aquifer's pollution hot spots. The main sources of NO3- were found in sub-urban areas, where vulnerability and risk are higher than in other areas. Otherwise due to reducing conditions triggered by the presence of elevated sedimentary organic matter and peat, concentrations below agricultural areas were lower than in sub-urban areas. The SO42- specific vulnerability map showed a positive correlation with observed concentrations, due to geogenic and anthropogenic SO42- sources present in the area. The combination of both NO3- and SO42- derived risk maps becomes essential to improve the conceptual model of aquifer pollution in this severely anthropized area. The application of this new and original approach shed light on the strengths and weaknesses of each of the described previous methods and clearly showed how anthropogenic activities have to be taken into account in the assessment process.