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 microbubble ozonation effect on treatment efficiency and acute toxicity of industrial effluents was evaluated. Eight effluents were sampled from two gold mines and one Ni-Cu mine in northern Canada, as well as a smelter from Abitibi-Temiscamingue region of Canada. Effluents showed contrasted salinities (from <0.05 mg/L to >50 g/L of Cl-) and ammonia nitrogen (from <0.1 mg/L to >475 mg/L of NH3-N). Treatability tests used microbubble ozonation, in an 18 L laboratory reactor, for 150 to 200 min, with the pH adjusted to 9. Physicochemical parameters and NH3-N concentrations were measured before, during, and after treatment, while toxicity on Daphnia magna was evaluated before and after the treatment. Results showed variable initial NH3-N concentrations (0.06-482 mg/L) and a sharp decrease to below the detection limit (0.003 mg/L) after treatment, corresponding to removal efficiencies exceeding 99%. Before treatment, acute toxicity ranged from 0.46 to 16.2 toxic units (TU) and decreased after ozonation to 0.14-10.9 TU (-33% to-73%, depending on the effluent). Fe and Mn concentrations decreased in most of the treated effluents, suggesting the precipitation of oxyhydroxides. Conversely, Zn concentrations slightly increased in some effluents, suggesting its potential mobilization from fine particles (<0.45 mu m), most likely ZnS. The microbubble ozonation had significant effect on treatment efficiency and the reduction of toxicity to D. magna in all tested effluents. Following treatment, only the highest saline effluent (Cl-> 50 g/L) showed toxicity, indicating that residual toxicity was likely driven primarily by salinity rather than oxidizable contaminants.

The long-term performance of a woodchip-based field-scale biofilter (50 m x 57 m x 1 m) treating As-rich neutral mine drainage (As-NMD) at the restored Wood-Cadillac mine site (Québec, Canada) was assessed. Operated for over 22 years, the biofilter showed up to 80 % As removal efficiency, decreasing As influent concentration from 0.35 to < 0.1 mg/L in the effluent, and complying with Canadian regulation. Speciation of As revealed depth-dependent redox processes, with As(V) as dominant As species in the surface layer (0-30 cm), As(III) proportions increased in the middle layer (30-60 cm), while in the deepest layer (60-90 cm), monomethylarsonic acid becomes the predominant methylated form. Isotopic analyses ( [Formula: see text] ) and sulfate concentration profiles suggest localized hotspots of microbial sulfate reduction in anoxic layers, associated with higher [Formula: see text] (> 12.0 ‰) and lower sulfate concentrations (61 to 10 mg/L). These spots aligned with high relative abundances of sulfate-reducing bacteria, suggesting As immobilization via As-S complexes and FeAsS/AsS precipitates. Microbial analyses showed stable α-diversity of prokaryotic communities, but significant variation among fungal populations. β-diversity differed significantly within the vertical profile of the biofilter, suggesting depth-dependent shifts in the composition of both prokaryotic and fungal communities. Functional taxa involved in S, Fe, and As cycling showed depth-dependent distributions, reflecting stratified redox conditions and biogeochemical processes. These findings highlight the role of coupled microbial and geochemical processes in sustaining As immobilization over decades and support the use of engineered organic biofilters as long-term passive treatment systems for As-NMD.

Effective mitigation of As-rich neutral mine drainage (As-NMD) is critical for responsible mine site rehabilitation. This study evaluated the performance of pilot-scale field biofilters for As removal from As-NMD. Two duplicate biofilters were filled with a mixture of organic material (peat) and Fe-rich sludge from acid mine drainage treatment, and set-up at an active gold mine in northern Québec, Canada. The biofilters, designed with an upward vertical flow and a volume of 1 m3, were operated for three months with a 1-day hydraulic retention time. An As removal efficiency of 96 ± 2.9 % was maintained for 3 months, with final As concentrations of 0.04 ± 0.02 mg/L, in compliance with regulations. At the onset of the test, a first flush effect was noticed, resulting in the leaching of As, Fe, and dissolved organic carbon. However, the performance of biofilters stabilized within 5-10 days, with As removal primarily driven by sorption to Fe-(oxy)hydroxides and organic matter. The presence of neutrophilic prokaryotes that catalyze transformations of S and As at circumneutral pH indicated the occurrence of complex biogeochemical cycling. The mean abundance of As-metabolizers was 4.6 % of total reads in the biofilter samples, while sulfur-oxidizers (highly dominated by Thiobacillus spp.) and sulfate reducers accounted for 5.6 %, and 4.3 %, respectively. While the design of the pilot-scale field biofilter showed promising results for As removal, further studies are necessary to assess its long-term performance including the influence of fluctuations in temperature, hydraulic conductivity, and mineral phase stability.

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.

On-site wastewater treatment systems (OWTS), including soil treatment units (STUs), rely on effluent infiltration into local soils for treatment. However, in regions with impervious soils, this process becomes challenging. As a result, borrow sand is necessary for STU construction. For example, in the Abitibi-Temiscamingue region (western Quebec, Canada), impervious sediments from the Clay Belt necessitate the use of such systems. This study evaluates the hydraulic performance and phosphorus retention capacity of borrow sand-based STUs through field inspections, laboratory experiments, and numerical modelling. Field inspections of 26 septic systems revealed that only 2 systems exhibited surface flooding, suggesting minimal hydraulic failure. However, sampling from 12 systems showed that 9 released phosphorus into the environment, with phosphorus concentrations exceeding 35 mg/L in the downstream ditches in some cases, which were far above Quebec's surface water limit of 0.03 mg/L. Laboratory sorption tests demonstrated that the sands used in STUs had limited maximum P sorption capacities (88.49 and 17.12 mg/kg). Numerical simulations using COMSOL Multiphysics further indicated that P retention in the sand layer is likely to be exhausted within a year, leading to P migration into the environment. Simulations also indicated that systems with properly designed outlets to a drainage ditch could maintain hydraulic performance during extreme rainfall events. These findings highlight the inadequacy of current borrow sand-based STU designs in ensuring long-term phosphorus retention. To mitigate environmental contamination, improved design strategies and management practices should be considered.

In passive biofilters, sorption is the main mechanism of As removal from As-rich neutral mine drainage (As-NMD), but biochemical processes contributing to their performance needs to be better documented. This study aims to evaluate the suitability of two locally available materials (peat and Fe-rich acid mine drainage treatment sludge (AMD-S)) for As-NMD treatment at an operating gold mine located in northern Quebec, Canada. First, the efficiency of different mixtures of peat and AMD-S was evaluated. Batch tests were conducted at 22 and 5 degrees C using synthetic (pH 7.16; 2.0 mg/L As) and real (pH 7.63; 0.91 mg/L As) As-NMD. Results revealed that the P50/ AMD-S50 mixture (50 wt% peat +50 wt% AMD-S) was the most efficient for As immobilization. Further tests with P50/AMD-S50 showed that As removal was higher for synthetic As-NMD (88 % at 22 degrees C, 82 % at 5 degrees C) than for real As-NMD (77 % at 22 degrees C, 48 % at 5 degrees C), which was ascribed to residual salinity. P50/AMD-S50 was then used in a lab-scale biofilter system operated in a continuous mode for 3 months (22 degrees C; hydraulic retention time 1 day). A maximum of As removal efficiency of 98 % was obtained within the first week while As leaching occurred after 27 days. A mixture of aerobic and anaerobic bacteria was observed in the biofilter samples, including Fe-reducers (11 % of total reads), S-oxidizers (6.1 %), SO42--reducers (6.0 %), and As-metabolizing prokaryotes (3.5 %). The remobilization of As from the sorbent remains to be addressed to ensure long-term stability of contaminant treatment systems under real mine site conditions.

Despite the efficiency and ease of implementation of electrocoagulation (ECG), its industrial application for the treatment of contaminated mine water is limited due to high energy and electrode consumption. The main objective of the present study was to identify the most performant ECG conditions using Fe-electrodes for the simultaneous removal of As(III) and Mn(II) from synthetic and surrogate neutral mine water, while limiting energy and electrode consumption. Treatability tests were performed on i) single synthetic effluents containing only 3.5 mg/L As (S-As) or 4.5 mg/L Mn (S-Mn), ii) binary synthetic effluents containing both As and Mn (SAs+Mn), and iii) surrogate mine water (E-low and E-high). The effect of current density (CD), ionic strength, and retention time were first evaluated for the treatment of S-As, S-Mn, and SAs+Mn. Then, the most performant conditions were used to treat E-low and E-high. The toxicity to Daphnia magna of E-low and E-high was evaluated before and after ECG treatment. The results showed that the increase of CD from 0.25 to 10 mA/cm(2) greatly improved the removal of Mn and to a lesser extent of As, as well as energy and electrode consumption. Despite a negligible effect on the removal efficiency of As and Mn, the ionic strength (from 0.25 to 2.5 mS/cm) and inter-electrode distance (from 10 to 50 mm) strongly affected the energy consumption. Results showed that ECG efficiently removed As (>97 %) from S-As at low CD (0.5 mA/cm(2)), while Mn required a 3.3-fold higher CD to achieve satisfactory removal (>60 %) from S-Mn. For the treatment of SAs+Mn, As removal was more efficient and faster than Mn removal (99 vs. 60 % and 20 vs. 60 min, respectively) at a CD of 2.0 mA/cm(2). Moreover, the simultaneous removal of As and Mn at 2.0 mA/cm(2) produced a volume of sludge 3.3 times greater than at 0.5 mA/cm(2). Lastly, high As (99 %) and variable Mn (40-57 %) removal efficiencies were obtained after E-low and E-high treatment, with potential positive effect of the presence of Ca2+ and Cl- on the removal of As, while SO42- may have negatively affected the removal of Mn. After ECG treatment, no addition of toxicity to D. magna was observed for E-low and E-high. The outcomes of this study may provide promising insights for the application of ECG for simultaneous removal of metal(loid)s from contaminated mine water.

The electrocoagulation (ECG) and ferrate (Fe(VI))-based processes are increasingly acknowledged as efficient for the simultaneous removal of As and Mn from synthetic and real mine effluents. Prior to design of full-scale applications, more information on the physicochemical, mineralogical, and environmental characterization of the produced sludge is required. The main objective of this study was to characterize and evaluate the leaching potential of problematic elements in As- and Mn-rich sludge produced during ECG or Fe(VI) treatment of circumneutral surrogate mine water. To do so, PHREEQC modelling was carried out on the effluents, before and after ECG or Fe(VI) treatment, to calculate the saturation index of dissolved As, Fe, and Mn species. A physicochemical and mineralogical characterization of the sludge was also performed using powder X-ray diffraction (PXRD) and a scanning electron microscope equipped with an energy dispersive spectrometer (SEM-EDS). Then, a non-sequential selective extraction procedure (N-SEP) combined with a USGS field leaching test (FLT) were conducted to evaluate the environmental behaviour of the As- and Mn-rich sludge. Geochemical modelling indicated that the Fe(VI) and ECG processes favor the precipitation of Fe-(oxy)hydroxides (lepidocrocite, schwertmannite, ferrihydrite). Chemical characterization showed that the Fe(VI)-sludge contained higher As and Mn concentrations and lower Fe concentrations than the ECG-sludge (3.8% As, 5.3% Mn, and 34% Fe for the Fe(VI)-sludge vs 1.2% As, 0.77% Mn, and 52% Fe for the ECG-sludge). These findings can be explained by the smaller amount of sludge produced during the Fe(VI) treatment and the higher removal efficiency of this method, especially for Mn. The PXRD patterns suggested the formation of poorly crystalline Fe-(oxy)hydroxides (lepidocrocite or βFeO(OH) in the ECG-sludge vs ferrihydrite in the Fe(VI)-sludge); however, no As- or Mn-bearing minerals were identified. Findings from N-SEP tests showed different speciation of As and Mn in the sludge, with a higher proportion of As bound to poorly crystalline Fe-(oxy)hydroxides in the Fe(VI) sludge than the ECG-sludge (97% and 71%, respectively), and higher proportion of Mn associated with the residuals in the Fe(VI)-sludge than the ECG-sludge (57% and 5.7%, respectively). Finally, FLT results indicated that very low concentrations of As (<0.05 mg/L) and Mn (<0.5 mg/L) were leached from the ECG- and Fe(VI)-sludge, with the Fe(VI) treatment resulting in slightly better As and Mn immobilization in the sludge relative to the ECG process. Nevertheless, both treatment processes were satisfactory in terms of efficient removal of As and Mn and their immobilization in the produced sludge.

This study focuses on assessing the hydrogeochemical processes influencing the mobility of dissolved metal and metalloid species during mine effluent mixing. Field samples were collected to characterize effluents at an active gold mine located in the Abitibi Greenstone belt in western Quebec, Canada. Controlled laboratory mixing experiments were further performed with real effluents. In situ physicochemical parameters, concentrations of major dissolved ions and trace elements were analyzed. Mineralogical analyses were also performed on precipitates from the laboratory mixtures. The data were used for statistical analyses and for modeling the geochemical evolution of effluents using PHREEQC with the wateq4f.dat database (with modifications). The results suggest that the formation of secondary minerals such as schwertmannite, Fe(OH)3, and jarosite could significantly affect the concentrations of trace elements in effluents. The precipitation of secondary minerals immobilized trace elements through coprecipitation and sorption processes. The main limitations of the modeling approach used here include the evaluation of the ion balance for low pH samples with high Fe and Al concentrations and the omission of biological processes. The approach provides insights into the geochemical evolution of mine effluents and could be adapted to several mining sites as a tool for improving water management.

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.

The mining industry often must mix different kinds of water on the mine site during pre-treatment or post-treatment before the final discharge of the treated water to the environment. Microbubble ozonation has proven to be efficient in the removal of contaminants of concern from mine water, such as metals, metalloids, and nitrogen compounds, which can persist in the environment and entail toxicity issues. This study evaluated the efficiency of ozone microbubbles combined with lime precipitation on contaminant removal and its impact on toxicity for Daphnia magna with five different mine effluent mixes from an active mine site located in Abitibi-Témiscamingue, QC, Canada. For the non-acidic mixes, two scenarios were tested: first, pre-treatment of metals using lime precipitation and a flocculant was conducted prior to ozonation; and second, ozonation was conducted prior to metals post-treatment using the same precipitation and flocculation technique. Results showed that the NH3-N removal efficiency ranged from 90% for the lower initial concentrations (1.1 mg/L) to more than 99% for the higher initial concentrations (58.4 mg/L). Moreover, ozonation without metals pre-treatment improved NH3-N treatment efficiency in terms of kinetics but entailed abnormal toxicity issues. Results of bioassays conducted on water with metals pre-treatment did not show any toxicity events but showed abnormal toxicity patterns on the mixes treated without metals pre-treatment (diluted effluents were toxic, while undiluted were not). At 50% dilution, the water was toxic, probably due to the potential presence of metal oxide nanoparticles. The confirmation of the source of toxicity requires further investigation.

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.

Despite being efficient in removing (in)organic contaminants from effluents, ferrate (Fe(VI)) has scarcely been used for the removal of metal(loid)s from mine effluents. The present study evaluated the performance of wet vs. solid Fe(VI) to remove As(III) and Mn(II) from synthetic and surrogate neutral mine effluents. The experiments were done using single (SAs or SMn containing 3.5 mg As/L or 4.5 mg Mn/L, respectively) and binary (SAs+Mn containing 3.5 mg As/L and 4.5 mg Mn/L) synthetic effluents as well as surrogate mine effluents (Elow and Ehigh). Firstly, Fe(VI) type, optimal dose, adjusted pH (pHa), and retention time were assessed to evaluate their effect on the treatment of SAs and SMn. Then, the performance of Fe(VI)s, alone or combined with Fe(III), was tested for the removal of As and Mn from SAs+Mn. Lastly, the most performant conditions were applied to Elow and Ehigh; the toxicity to D. magna was evaluated before and after treatment. The results showed that at pHa of 5.5, Fe(VI)s efficiently removed As (> 98%) and Mn (> 97%) from SAs, SMn, and SAs+Mn within the first minute. The Fe(VI)s dose required had to be adjusted depending on the contaminant to be removed (SAs or SMn vs. SAs+Mn). The use of Fe(VI)s +Fe(III) showed improved removal efficiencies for As (99%) and Mn (> 99%), while requiring a lower Fe(VI) dose. Moreover, the Fe(VI) treatment of Ehigh, initially acutely toxic, entailed the elimination of D. magna toxicity. These findings provide new insights for the potential application of Fe(VI) in the removal of metal(loid)s from mine water.

Abandoned underground mine galleries or excavations in general pose important environmental and economic problems when a site is reactivated after being abandoned, because they might create risks for land stability. At the request of a need for a solution for the reopening of a mine (Horne 5 project of Falco Resources in Rouyn-Noranda, Quebec, Canada) future exploitation would start from 600 m deep. Before that reopening, it is very important to determine old mining excavation locations and conditions. The present study is targeting a technological challenge of detecting old and deep mining infrastructure. There is to date no application allowing the detection of underground tunnels or mining galleries at a depth of >80 m due to their depth and small size. Electrical Resistivity Tomography (ERT) is promising given its ability to image the contrasts between rocks and voids, whether these cavities are empty or filled with water or backfill materials. Several 2D and 3D ERT surveys were carried out to explore the abandoned old mining excavations. These ERT measurements highlighted the existence of old mining stopes, confirmed by the geotechnical boreholes performed on site, as well as two known galleries located at an approximate depth of 100 m. Overall, the results of this study highlight the potential of ERT approaches for the characterization of deep underground excavations.

EDITORIAL article Front. Earth Sci., 07 March 2023Sec. Hydrosphere Volume 11 - 2023 | https://doi.org/10.3389/feart.2023.1165061

Abstract. Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, for instance, the structure and properties of the different ice and snow layers dictate the quantity of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences as mild episodes and ROS events’ frequency increases. This study aims to develop a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repetitive and non-destructive way. Snowpack evolution was evaluated using a combination of drone-based GPR, photogrammetry surveys and time domain reflectometry (TDR) measurements, tested during the winter of 2020–2021 at the Sainte-Marthe experimental watershed, Quebec, Canada. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements, serve as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis are used to generate georeferenced snow depth, relative density, snow water equivalent and average liquid water content maps. In between site visits, snowpack properties are monitored using TDR probes. Despite some limitations, the results show that the approach is very promising in assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. Among others, results showed the prevalence of preferential pathways at the early stage of the ablation period, the difference in hydrological reaction to a ROS event between flat and sloped sections of the study area and the hydrological influence of solar radiation at the late stage of the ablation period.

Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.