Surface water extracted from rivers is the main source of drinking water of many major cities worldwide. In wet, temperate climates, groundwater often contributes substantially to river discharge, yet quantifying these inputs remains challenging - particularly in heterogeneous, urbanised, mesoscale catchments. This study focuses on the Saint-Charles River catchment (344 km(2)), Quebec City, Canada, a partially urbanised catchment which supplies drinking water to over 300 000 people from surface water. Long-term stream monitoring of major ions and trace elements (2003 - 2023) was used to identify water quality end-members and their temporal variability. Hydrograph separation based on major ions concentrations, enabled the estimation of monthly groundwater inflows to the river between 2013 and 2023. A mathematical filter was calibrated against the tracer-based approach results to determine the daily groundwater contribution between 2013 and 2023. Results show that groundwater is the major contributor to the river annually with a BFI between 0.59 and 0.63. In addition, up to 87 % of the year is classified as baseflow-dominated. The highest monthly average contribution are found in summer (>80 % of total river flow from July to September), while the lowest are found in spring (35-45 % of total river flow in April). Groundwater also controls the river total mineralization. While electrical conductivity proved unreliable due to anthropogenic inputs, alkalinity emerged as a robust tracer of groundwater contribution. River silica dynamics were modelled using a lumped parameter approach, yielding estimates of mean transit times ranging from a few days during high flows to several months or years during low flows. These findings improve the understanding of river-aquifer interactions and have significant implications for the vulnerability assessment of surface water intakes under climate change and urbanization in similar hydrological settings.

The use of organic covers is a promising approach for rehabilitating sulfidic tailings in abandoned mine sites. However, their performance over periods exceeding 50 years require a thorough evaluation of their resilience to climate change. In the present study, a 25-year monitoring dataset (2000-2024) from the East-Sullivan mine site (Val-d'Or, Quebec, Canada), was used to evaluate the effectiveness of an organic cover (residual wood and sewage sludge) coupled with constructed wetlands in improving the quality of drainage from acid-generating tailings. Surface water samples were collected from a network of 13 monitoring points located around the covered tailings, including a point where mine-impacted waters flow into a nearby river. The study focused on the evolution of pH, electrical conductivity (EC), total suspended solids (TSS), sulfate, and iron concentrations. The results show significant improvement in water quality over time. The pH was maintained at circumneutral (6-8), with EC <2 mS/cm. The concentrations of TSS decreased from 217 mg/L to <1 mg/L, while sulfate and iron showed a notable decline as the cover completion progressed, aside from some fluctuations during the disposal of new, non-acid generating tailings in a nearby mine pit. Vegetation recovery at the East Sullivan site has improved since the installation of the organic cover, reflecting chemical stabilization and the effectiveness of the organic cover as a rehabilitation measure. Multivariate statistics further revealed weak correlations between hydroclimatic and physicochemical factors, suggesting that variations in temperature, precipitation, and river flow have little direct impact on the organic cover performance or the site water quality.

The acute toxicity of binary and ternary combinations of Cu, Zn, As and Se to Daphnia magna was investigated. The aim was to provide a detailed characterization of the interactive effects of these elements in relation to aquatic toxicity. The binary and ternary combinations were evaluated using isobolograms, the concentration addition (CA) model, the independent action (IA) model, and the Hewlett model. They were also implemented using the MixModel package in R. This approach facilitated the identification of potential synergism, additivity, and antagonism, in addition to the characterization of binary and ternary interactions within a mixture composition space. For the binary combinations, Cu-Zn and Zn-Se had the most robust evidence for synergism with EC50 estimates <1 toxicity unit (TU), as low as 0.27 TU for Cu-Zn, and 0.30 TU for Zn-Se. Combinations of As-Cu, As-Se, Cu-Se, and As-Zn were mostly additive, with EC50 estimates ∼1 TU for all evaluated ratios. In all of the tested ternary combinations, Cu was identified the most toxic component. Emergent effects, which were defined as toxicity responses arising from combinations of toxicity which were not dependent on binary combinations, were observed in some ternary combinations. Synergism was observed in the As-Cu-Se and As-Cu-Zn mixtures, suggesting that binary combinations alone are insufficient for predicting interactions involving more than two components. Ternary diagrams also showed potential areas of high toxicity (TU>3), mostly in the Cu-dominated mixtures. These observations suggest that the toxicity assessment of trace metals and metalloids commonly found in mining and metallurgical effluents (in this case Cu, Zn, As, and Se) must take into account the effect of mixtures and not rely on substance-by-substance assessments.

We introduce an innovative machine learning (ML)-enhanced method to assess groundwater vulnerability in coastal regions, with a specific focus on the Azarshahr plain near Urmia Lake in Northwestern Iran. Our methodology integrates the traditional DRASTIC and GALDIT frameworks to surpass their limitations (e.g. subjectivity) in varied contexts such as coastal and agricultural-industrial environments. The traditional frameworks including the DRASTIC framework form the core of our approach, featuring seven key layers: Depth to water [D], Net Recharge [R], Aquifer Media [A], Soil Media [S], Topography [T], Impact of Vadose Zone [I], and Hydraulic Conductivity [C], each meticulously developed with specific ratings and weights according to DRASTIC standards. Similarly, the GALDIT framework contributes a six-layer map, including Groundwater Occurrence [G], Aquifer Hydraulic Conductivity [A], Height of Groundwater Level [L], Distance from the Shore [D], Impact of Existing Seawater Intrusion Status [I], and Aquifer Thickness [T], each layer uniquely rated and weighted. To address the limitations of these traditional frameworks, our study integrates an advanced ML recalibration of the GALDIT and DRASTIC indices, using the maximum concentrations of Total Dissolved Solids (TDS) and Nitrate (NO3) in the study area as proxies. We employed a range of decision tree-based ML models, including Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF), to predict the adjusted vulnerability indices, applying six predictors for GALDIT and seven for DRASTIC. These models were trained and validated on a dataset split into 70% for training and 30% for validation. Our results indicate that the traditional DRASTIC indices correlate weakly with NO3 concentrations. However, the ML-augmented models, particularly AdaBoost, significantly improved predictive accuracy. Likewise, GALDIT results were greatly enhanced by incorporating the AdaBoost model. A key innovation in our research is the development of a sophisticated meta-ensemble ML model. This model, based on the most effective AdaBoost applications in the DRASTIC and GALDIT assessments, marks a significant methodological advancement. It integrates vulnerabilities from both frameworks using a Fuzzy operation and then redeveloping a meta-ensemble ML model. This comprehensive model demonstrated exceptional performance, highlighting the effectiveness of our integrated ML approach in providing a more detailed, accurate, and robust assessment of coastal aquifer vulnerability. Moreover, our study includes an extensive spatial analysis of groundwater vulnerability in the Azarshahr plain. The DRASTIC model indicated varying vulnerability levels, with heightened susceptibility in central and southern regions, albeit showing a weaker correlation with NO3 concentrations. Conversely, AdaBoost exhibited a strong correlation with actual NO3 levels, showcasing its predictive capability. The GALDIT index identified several high-risk areas, particularly those vulnerable to seawater intrusion, with the AdaBoost-enhanced model outperforming other ML approaches. Our comprehensive AdaBoost meta-ensemble model merges insights from both NO3 and TDS evaluations, offering a holistic groundwater vulnerability. This model is crucial for informed decision-making, identifying areas where NO3 and TDS risks converge. Its spatial analysis strongly correlates 'Very High' vulnerability zones with high NO3 and TDS concentrations, confirming its integrative efficiency in environmental risk assessment.

This study focuses on evaluating the relationship between the co-occurrence and speciation of trace metallic elements with reference to the acute toxicity observed to Daphnia magna. Calculations were performed on data from the regular monitoring of an industrial effluent. The effluent generally met regulatory discharge criteria for metal(loid)s (Fe > Zn > Al > Cu > Ni > As > Cd > Pb) concentrations, but sporadic toxicity was observed, indicating that the interactions between trace metallic elements might affect toxicity. The methodological approaches include correlation analyses (CA), one-way analyses of variance (ANOVA), principal component analyses (PCA), hierarchical cluster analyses (HCA), and geochemical calculations performed for the purpose of assessing trace metallic elements speciation. The results suggest that Cd and Cu are the primary contributors to toxicity while Fe could inhibit toxicity. Moreover, speciation calculations suggest that the bioavailable forms of Cd2+and Cu2+, even at sublethal levels, could play a pivotal role in the observed toxicity. The analyses of changes in correlations between pairs of elements in non-toxic versus toxic effluents further suggest synergistic Cu-Cd and antagonistic Fe effects on toxicity. The approach developed in the present study has the potential for wider implementation. The identification of statistical links between the concentrations of different contaminants and toxicity could facilitate toxicants identification, particularly for effluents that meet regulatory standards in terms of contaminant concentrations.

This study evaluates the salinity vulnerability of the Urmia coastal aquifer in northwestern Iran using the DRASTIC index and proposes an enhanced framework tailored for coastal settings. To address the limitations of the typical DRASTIC method, two coastal-specific parameters—hydraulic gradient (i) and distance to the shoreline (d)— were incorporated into the framework. Pearson’s correlation between vulnerability indices and water quality indicators (Cl, EC, and TDS) showed notable improvements with the extended framework, increasing from 0.46 to 0.75 for Cl, from 0.35 to 0.82 for EC, and from 0.33 to 0.79 for TDS. The highest correlation was observed between EC and the extended “DRASTIC-id” index, highlighting its enhanced predictive ability. The extended vulnerability map indicated very high to high vulnerability in the eastern and central parts of the Urmia aquifer and along the coastal margin, with lower values in the western zones farther from the lake. Sensitivity analysis of parameter removal revealed that the net recharge (R) parameter is among the most influential factors in the “Typical DRASTIC”, “DRASTIC-i”, and “DRASTIC-d” frameworks, while ‘i’ is most critical in the “DRASTIC-id”. Single-parameter sensitivity analysis confirmed ‘R’ as consistently important across all frameworks, with ‘i’ also emerging as a key driver in the “DRASTIC-i” and “DRASTIC-id”. The findings demonstrate that incorporating coastal-specific parameters significantly enhances the accuracy of vulnerability mapping and provides a more robust tool for sustainable groundwater management in salinity-prone aquifers.

The critical role of groundwater in meeting diverse needs, including drinking, industrial and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion – groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T) – merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the 'i' and 'P' parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with chloride concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the 'i' parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of 'G', 'L', 'D', and 'i' parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion—groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)—merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the “i” and “P” parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the “i” parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of “G,” “L,” “D,” and “i” parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

Assessing the vulnerability of groundwater in coastal aquifers is crucial for mitigating risks associated with seawater intrusion and anthropogenic impacts. This study introduces an innovative machine learning (ML)-enhanced methodology that synergizes the strengths of two established vulnerability assessment frameworks, DRASTIC and GALDIT. The hybrid approach overcomes the limitations of each framework-DRASTIC's inadequacies in coastal settings and GALDIT's limited consideration of agricultural and industrial impacts. Utilizing advanced decision tree-based ML algorithms-Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF)-this research was conducted in the Azarshahr Plain, NW Iran. Model efficacy was validated using Pearson's correlation coefficient (r) and distance correlation (DC), with nitrate (NO3-) and total dissolved solids (TDS) serving as proxies for evaluating the DRASTIC and GALDIT models, respectively. The original DRASTIC indices exhibited weak correlations with NO3- (r = 0.24, DC = 0.25), but ML-enhanced models, particularly AdaBoost, showed significant improvements (r =0.78, DC = 0.79). Similar enhancements were observed with GALDIT, where correlations improved markedly with AdaBoost integration. A sophisticated second-level AdaBoost meta-ensemble was developed to integrate enhanced DRASTIC and GALDIT assessments, achieving superior correlation metrics (r = 0.80, DC = 0.84 for NO3-; r = 0.82, DC = 0.83 for TDS). These results underscore the effectiveness of an integrated ML-based approach in advancing beyond traditional vulnerability assessment methods, providing a more comprehensive, accurate, and robust evaluation of coastal aquifer vulnerability.

This study aims at identifying the sources and fate of salinity in the Djebeniana basin (Tunisia) aquifers. Groundwater samples from the shallow Plio-Quaternary (PQ) phreatic aquifer and deep Miocene aquifer were analyzed for major/minor ions, trace elements, and stable isotopes of the water molecule (δ18OH2O - δ2HH2O), chlorine (δ37Cl) and bromide (δ81Br). Two clusters of samples with distinct chemical compositions were identified. Cluster I samples correspond to Na–Cl mineralized waters with δ37Cl from −0.3‰ to 0.16‰ and δ81Br from 0.21‰ to 0.39‰. Cluster II samples correspond to Ca–SO4 waters presenting a lower salinity than Cluster I, with δ37Cl from −0.3‰ to 0.09‰ and δ81Br from 0.28‰ to 0.54‰. When plotted in a δ2HH2O vs δ18OH2O graph, most of Cluster I groundwater samples tend to align on a mixing line with seawater whereas Cluster II samples appear to define a local evaporation line. The spatial distribution of samples associated with these clusters allows for evaluating the hydrogeochemical processes associated with groundwater salinization: 1) recent recharge from rainwater dissolving salts and entailing the downward migration of saltwater in the higher reaches of the groundwater flow system, 2) upward leakage of mineralized waters from the deep Miocene aquifer, 3) seawater intrusion in the lower reaches of the coastal aquifer, and 4) ion-exchange reactions prompted by seawater intrusion. A conceptual model of groundwater salinization is proposed as a tool for better managing groundwater resources in the Djebeniana basin.

This study aims at evaluating the hydrological balance of large watersheds of the Canadian Shield in the James Bay area in Northwestern Quebec, Canada. The focus is set on six rivers of the Canadian Shield altogether draining more than 185,000 km2 of the Boreal Shield, Taiga Shield and Hudson Plains ecozones of Canada. River discharge measurements, geochemical data (delta 2H, delta 18O and electrical conductivity [EC] of water), remote sensing, and GIS models are used jointly to calculate water balances. The approach allows for partitioning the influence of rainwater, snowmelt, surface runoff, evaporation, transpiration, and groundwater discharge to the hydrological balances of watersheds. On an annual basis, the results suggest that runoff from rainwater (30-61 % of total precipitation) and snowmelt (18-40 % of total precipitation) are the main contributions to river discharge, while the contribution of groundwater discharge to rivers represents < 12 % of the total precipitation. Over the study area, this contribution represents 2-5 km3 of water. The stable isotope mass balances allow for estimating watershed-scale evaporation over inflow ratios ranging between 2 and 10 % and suggest that transpiration has an isotopic composition close to summer rainwater. The hydrological balances further suggest that the total pool of water stored in the active portion of watersheds represents 10-20 % of the total annual precipitation, while the exports of groundwater beyond the limits of surface watersheds are negligible. The seasonal trends in the hydrological balances of monitored watersheds were further documented to provide insights into the sensitivity of watersheds as they face climate change. The observations are used to propose recommendations for monitoring of rivers in the Canadian Shield and to identify future research needs.

The contamination of mine water by nitrogen-based compounds represents an ongoing challenge for the mining industry. These contaminants, often present in concentrations that are toxic to aquatic life, need to be carefully managed. This study aims at developing hydrogeochemical mass balances and isotopic approaches to trace the sources, fluxes and transformations of nitrogen bearing species in a mining context. The work was carried out at an underground gold mine located in Quebec, Canada. The main sources of nitrogen at this mine site are explosive residues and cyanide derivatives. The management of ammoniacal nitrogen (NH3−NH4+) is of particular interest to the mining company operating the site. Analyses were carried out for concentrations of nitrite (NO2−), nitrate (NO3−), NH3−NH4+, total Kjeldahl nitrogen (TNK), total cyanide (CN−), cyanate (CNO−), thiocyanate (SCN−), chloride (Cl−) and sulfate (SO42−), as well as the isotopic composition of the water molecule (δ2HH2O,δ18OH2O), nitrate (δ15NNO3−,δ18ONO3−) and ammoniacal nitrogen (δ15NNH4+). Mass balance calculations performed using the δ2HH2O and δ18OH2O of water suggest evaporation over inflow (E/I) ratios ranging between 0% and 34% onsite. Mass balances using the δ2HH2O and δ18OH2O of water and dissolved Cl− concentrations allow for estimating that 65% of the water, 43% of the NH4+ and 84% of the NO3− in the final effluent originated from the dewatering of the underground workings. The chemical and isotopic analyses of nitrogen bearing species revealed that nitrification and denitrification are the most likely mechanisms transforming nitrogen onsite. From these findings, recommendations were elaborated to optimize the management of the water on the mine site, with a focus on promoting the reactions that lead to a decrease in the concentration of NH4+ prior to the final effluent discharge to the environment. The proposed approach is innovative for the mining context and has great potential for applications at other mine sites.

Knowledge of the natural background concentrations of groundwater constituents is important for the management of groundwater resources, particularly for the assessment of groundwater contamination and the establishment of clean-up goals and regulatory target levels. In recent years, an increasing number of studies have assessed the natural background concentrations of dissolved constituents in groundwater using a variety of different methods, each with its own assumptions, advantages and limitations. The objective of this paper is to provide a methodological basis for improving the estimation of natural background concentrations of groundwater constituents. To this end, this paper critically reviews the different approaches used to determine natural background concentrations of dissolved constituents in groundwater. In addition, two regional case studies of fluoride in Canadian groundwater are presented to illustrate the estimation of background concentrations for natural groundwater constituents. The review of existing methods shows that the use of pristine groundwater samples is not possible in many cases, due to the widespread influence of human activities. The widely used pre-selection method can provide misleading results because of inadequate selection criteria and poor statistical significance associated with the reduction of the dataset. A variety of model-based methods have been developed, but these methods are all based on assumptions that cannot be verified. Relying on the user's experience and previous knowledge of the groundwater system, exploratory data analysis has many advantages and can be applied for both anthropogenic and natural constituents. The case studies show that the exploratory data analysis approach provides critical information to determine the sources of groundwater constituents and to properly delineate groundwater bodies for which background values will be established. Natural background concentrations should always be considered as theoretical values due to their spatio-temporal variability and scale dependence, and thresholds as concentration values above which further investigation is required.

This study focuses on the development of two GIS-based approaches that are used jointly to evaluate the groundwater resources associated with granular aquifers in shield environments. The first approach is a multi-criteria analysis (MCA) using an analytical hierarchic process (AHP) based on geological and hydrogeological data for ranking the probability of finding readily available groundwater resources in a specific territory. The second approach relies on GIS-based geometric calculations that were developed for evaluating the extent and volume of aquifers. The approaches are applied on a 100 × 100 m grid in a 185,000-km2 area corresponding to watersheds of the James Bay area in Quebec, Canada. The MCA-AHP approach revealed that the unconfined granular aquifers that present the highest aquifer potential (AP) are sparsely distributed and mostly associated with glaciofluvial formations such as the Harricana and Sakami moraines. The geometric calculations approach allowed for estimating that the total volume of groundwater stored in the unconfined granular aquifers reaches approximately 40 km3. When used jointly, the two approaches reveal that the shallow unconfined aquifers that require increased groundwater protection account for approximately 5% of the territory. In areas of confined granular aquifers, the highest APs are located in river valleys and lowlands. A sensitivity analysis conducted on the MCA-AHP approach revealed that the grid size does not significantly affect the results. Therefore, the approach was expanded northward, to a 490,000-km2 territory reaching the Ungava Bay area. The proposed method could be adapted and applied in other shield areas.

This study focuses on the development of thermal remote sensing and modeling approaches for delineating groundwater discharge areas near eskers in a boreal region of the Canadian Shield, in north-western Quebec. The working hypothesis is that groundwater partly controls soil surface temperatures in groundwater discharge areas. Low-resolution (30 m x 30 m) satellite images are first used for identifying thermal anomalies at the regional scale. High-resolution (0,1 m x 0,1 m) thermal images are subsequently acquired locally in areas associated with thermal anomalies identified from satellite images. Coupled simulations of water and heat fluxes are then conducted using SEEP/W and TEMP/W in order to provide a quantitative interpretation of the influence of groundwater depth on soils surface temperatures in groundwater discharge areas. The approaches developed here provide tools for identifying the potential localization of groundwater dependent ecosystems where increased environmental protection could be relevant.

The presence of naturally occurring contaminants in groundwater is a public health concern in rural areas of northeastern North America, where public and private wells are important sources of drinking water. In southern Quebec (Canada), inorganic groundwater chemistry data have been recently collected following standard procedures in several regional hydrogeological projects implemented by the government of Quebec. In this study, a groundwater chemistry database was compiled from 16 regional projects altogether covering an area of approximately 100,000 km2. The database includes information on water supply infrastructures, geological settings, hydrogeological conditions and inorganic water chemistry for 2369 water samples. Samples were mostly collected from private domestic wells, and to a lesser extent from municipal and observation wells. The data revealed that fluoride, barium, manganese and arsenic are the most common elements exceeding Canadian drinking water guidelines. Exploratory data analysis techniques were applied to selected subsets of data to gain insight into the sources and distribution of these hazardous groundwater contaminants. These exploratory methods include graphical data analysis (maps, Piper and empirical cumulative distribution function plots), multivariate compositional data analysis (clustering and correlation analysis) and geochemical modeling (saturation index calculations). The results suggest that fluoride, barium, manganese and arsenic are all derived from natural sources. Elevated fluoride concentrations are mainly associated with dilute Ca–Na–HCO3 bedrock groundwaters from granitic areas (Grenville Province), and more geochemically evolved Na–HCO3 to Na–HCO3–Cl bedrock groundwaters from shale areas (St. Lawrence Platform). High-F groundwaters are generally characterized by low Ca concentrations (<30 mg/L) and alkaline pH (pH > 8), suggesting that F is mainly controlled by fluorite (CaF2) precipitation and anion exchange with OH−. Barium is present at elevated concentrations in mineralized Ca–Na–HCO3, Na–HCO3 to Na–HCO3–Cl waters from bedrock aquifers of the St. Lawrence Lowlands. These groundwaters are mainly chemically evolved, strongly reducing waters occurring in confined aquifers and near major faults, which appear to correspond to discharge areas for deep regional flow. High Ba concentrations are generally associated with very low SO4 concentrations (< 5 mg/L) resulting from sulfate reduction, suggesting a solubility control of Ba through barite (BaSO4) precipitation. Most high manganese concentrations occur in less chemically evolved, near-neutral Ca–HCO3 groundwaters from both granular and bedrock aquifers, particularly those associated with metasedimentary and metavolcanic lithologies (Superior Province, St. Lawrence Platform and Appalachian Province). The results suggest that dissolved Mn concentrations are limited by the precipitation of Mn carbonates under alkaline conditions, but increase under reducing conditions owing to the dissolution of Fe–Mn oxyhydroxides. Elevated arsenic concentrations were mostly found in Ca-(Na–Mg)–HCO3 bedrock groundwaters of the Superior and Appalachian Provinces. As-rich groundwaters are associated with the presence of As-bearing sulfides in weakly metamorphosed sedimentary rocks (shale, slate, phyllite) and hydrothermally altered rocks. Most high As concentrations do not appear to be directly derived from sulfide oxidation, but rather from secondary sources, in particular through the reductive dissolution of As-rich Fe–Mn oxyhydroxides. This work shows that the combination of graphical, multivariate statistical and geochemical modeling techniques is a powerful approach to explore large hydrochemical datasets. It also reveals the benefits of using multivariate compositional data analysis instead of classical approaches based on raw data and log-ratios for groundwater chemistry data. Compositional data analysis techniques improved the detection of multivariate outliers, the cluster reliability in cluster analysis, and removed the spurious positive correlation between hydrochemical variables associated with total mineralization.

Multivariate statistics are widely and routinely used in the field of hydrogeochemistry. Trace elements, for which numerous samples show concentrations below the detection limit (censored data from a truncated dataset), are removed from the dataset in the multivariate treatment. This study now proposes an approach that consists of avoiding the truncation of the dataset of some critical elements, such as those recognized as sensitive elements regarding human health (fluoride, iron, and manganese). The method aims to reduce the dataset to increase the statistical representativeness of critical elements. This method allows a robust statistical comparison between a regional comprehensive dataset and a subset of this regional database. The results from hierarchical Cluster analysis (HCA) and principal component analysis (PCA) were generated and compared with results from the whole dataset. The proposed approach allowed for improvement in the understanding of the chemical evolution pathways of groundwater. Samples from the subset belong to the same flow line from a statistical point of view, and other samples from the database can then be compared with the samples of the subset and discussed according to their stage of evolution. The results obtained after the introduction of fluoride in the multivariate treatment suggest that dissolved fluoride can be gained either from the interaction of groundwater with marine clays or from the interaction of groundwater with Precambrian bedrock aquifers. The results partly explain why the groundwater chemical background of the region is relatively high in fluoride contents, resulting in frequent excess in regards to drinking water standards.

Sustainable management of groundwater resources is essential for sound groundwater development, especially in sensitive salt-affected areas. In Northeast Thailand, the Central Huai Luang Basin, underlain by rock salt, is the source of groundwater and soil salinity. The future sustainable groundwater development yield was assessed under the plausible uncertainty of hydrogeological and projected climate scenarios that could impact the groundwater system. The SEAWAT and HELP3 models were used to simulate groundwater system. The four alternative scenarios of hydrogeological conceptual models were formulated to determine the impact on groundwater system and sustainable groundwater yield. In addition, impacts of projected climate conditions on each alternative model were explored. The results indicate that variable depths and thicknesses of rock salt layers have a higher impact on groundwater salinity distribution and sustainable yield estimations than model boundary conditions. Groundwater salinity, shallow water table areas, and sustainable yield projections vary substantially depending on the possible conceptual model scenarios. It is clear that the variable hydrogeological models affect groundwater sustainable yields.

Water quality evaluation is critically important for the protection and sustainable management of groundwater resources, which are variably vulnerable to ever-increasing human-induced physical and chemical pressures (e.g., overexploitation and pollution of aquifers) and to climate change/variability. Preceding studies have applied a variety of tools and techniques, ranging from conventional to modern, for characterization of the groundwater quality worldwide. Recently, geographic information system (GIS) technology has been successfully integrated with the advanced statistical/geostatistical methods, providing improved interpretation capabilities for the assessment of the water quality over different spatial scales. This review intends to examine the current standing of the GIS-integrated statistical/geostatistical methods applied in hydrogeochemical studies. In this paper, we focus on applications of the time series modeling, multivariate statistical/geostatistical analyses, and artificial intelligence techniques used for groundwater quality evaluation and aquifer vulnerability assessment. In addition, we provide an overview of salient groundwater quality indices developed over the years and employed for the assessment of groundwater quality across the globe. Then, limitations and research gaps of the past studies are outlined and perspectives of the future research needs are discussed. It is revealed that comprehensive applications of the GIS-integrated advanced statistical methods are generally rare in groundwater quality evaluations. One of the major challenges in future research will be implementing procedures of statistical methods in GIS software to enhance analysis capabilities for both spatial and temporal data (multiple sites/stations and time frames) in a simultaneous manner.

The groundwater geochemistry of the fractured rock aquifer system in the Montérégie Est region, southern Quebec, Canada, was studied as part of a regional groundwater resources assessment. The 9218 km² study area included three major watersheds that were divided into five hydrogeological contexts: Northern St. Lawrence Lowlands, Southern St. Lawrence Lowlands, Appalachian Uplands, Appalachian Piedmont and Monteregian Hills. A large part of this study area was invaded by the Champlain Sea from 13,000 to 11,000 years ago. Study objectives were to identify the mechanisms controlling groundwater composition and to support the understanding of the aquifer hydrodynamics. Groundwater from 206 wells drilled into the rock aquifer was sampled and analyzed for conventional parameters and isotopic analyses were also done on selected samples (δ2H, δ18O and 3H of water; δ13C and 14C of dissolved inorganic carbon). The interpretation of geochemical results was based on a multivariate statistical analysis, which led to the definition of eight water groups. The study allowed the delineation of a 2200-km² zone containing brackish groundwater of marine origin in the northwestern part of the study area. This zone is surrounded by sodic and alkaline groundwater originating from Na+-Ca2+ ionic exchange. Young groundwater and therefore recharge zones were only encountered in the southern part of the Lowlands, in the northern part of the Piedmont and in the Appalachian Uplands. In the southern part of Lowlands, recharge is presumed to be slow and water composition shows the influence of the former presence of the Champlain Sea. Relatively deep groundwater circulation was also inferred to occur from the Appalachian Uplands toward mixing zones mainly located to the west at the Appalachian frontal thrust faults and around the Monteregian Hills. The geochemical interpretation provided indications on regional recharge and discharge zones as well as groundwater flow, which could not have been determined otherwise.