Source-oriented health risk assessment of groundwater nitrate by using EMMTE coupled with HHRA model.

Conventional concentration-oriented approaches for nitrate risk diagnosis only provide overall risk levels without identifying risk values of individual sources or sources accountable for potential health risks. Therefore, a hybrid model combining the end-member mixing model tool on Excel™ (EMMTE) with human health risk assessment (HHRA) was developed to assess the source-oriented health risks for groundwater nitrate, particularly in the Poyang Lake Plain (PLP) region. The results indicated that the EMMTE and the Bayesian stable isotope mixing model (MixSIAR) exhibited remarkable consistency in source apportionment of groundwater nitrate. The source contribution of groundwater nitrate in PLP was related to land use types, hydrogeological conditions, and soil properties. Notably, manure and sewage sources, contributing up to 53.4 %, represented the largest nitrate pollution sources, with a significant contribution of soil nitrogen and nitrogen fertilizers. The non-carcinogenic risk for four potential sources was below the acceptable threshold of 1. Given the factors including rainfall dilution and economic development, attention should be directed towards mitigating the health risks posed by manure and sewage. This study can verify the efficacy of EMMTE in source apportionment and offer valuable insights for decision-makers to regulate the largest sources of nitrate contamination and enhance groundwater management efficiency.

Exploring dissolved N2O characteristics and unearthing indirect N2O emission factors in the shallow groundwater of paddy and upland fields.

Indirect emissions of nitrous oxide (N2O) stemming from nitrogen (N) leaching in agricultural fields constitute a significant contributor to atmospheric N2O. Groundwater nitrate (NO3--N) pollution is severe in the Ningxia Yellow River Irrigation Area (NYRIA), coupled with high NO3--N leaching, exacerbates the risk of indirect N2O emissions from groundwater. Over two years of field observations, this study investigated the characteristics and interannual variations of dissolved N2O (dN2O) concentrations and indirect N2O emission factors (EF5g) in shallow groundwater. The research focused on three typical farmlands in the NYRIA, each subjected to six levels of N fertilizer application. The mean dN2O concentrations in the groundwater of paddy, corn and vegetable fields were 5.17, 8.40 and 16.35 µg N·L-1, respectively. Notably, the dN2O concentrations in the shallow groundwater of upland fields exceeded those in paddy fields, with maximum levels in vegetable fields nearly an order of magnitude higher. Elevated N application significantly increased dN2O concentrations across various farmlands, showing statistically significant variation. However, differences in EF5g-A and EF5g-B within the same farmland were negligible. Denitrification was the primary process contributing to N2O production in groundwater, with nitrification also played a crucial role in upland fields. Factors such as NO3--N, NH4+-N, dissolved oxygen (DO), and pH critically influenced N2O production. EF5g-B, which considers the NO3--N consumption during denitrification processes in groundwater, was deemed more appropriate than EF5g-A for assessing the indirect N2O emission in the NYRIA. The EF5g of agricultural fields exhibited minimal sensitivity to N input but was significantly affected by other factors, such as the planting pattern. The study revealed the rationality of adopting EF5g-B in assessing indirect N2O emissions, providing valuable insights for N management strategies in regions with high NO3--N leaching. Minimizing N fertilizer application while ensuring crop yield, especially in upland fields, is beneficial for reducing N2O emissions.

Design optimization of groundwater circulation well based on numerical simulation and machine learning.

The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi'an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.

Geospatial analysis of toxic metal contamination in groundwater and associated health risks in the lower Himalayan industrial region.

Once known for its clean and natural environment, the lower Himalayan region is now no exception to human-induced disturbances. Rapid industrial growth in Baddi-Barotiwala (BB) industrial region has led to degradation of groundwater resources in the area. Groundwater samples were collected from 37 locations to study the groundwater chemistry, geospatial variation of 15 toxic metals in groundwater, source apportionment, metals of concern and associated health risks in the region. The results showed rock dominated hydrogeology with decreasing order of anion and cation abundance as HCO3- > Cl- > SO42- > NO3- > Br- > F- and Ca+ > Na+ > Mg2+ > K+ > Li+ respectively. Concentrations of Iron (BDL-3.6 mg/l), Nickel (BDL-0.023 mg/l), Barium (0.22-0.89 mg/l), Lead (0.0001-0.085 mg/l) and Zinc (0.006-21.4 mg/l) were found above the permissible limits at few locations. Principal component analysis (PCA) and coefficient of variance (CV) showed both geogenic and anthropogenic origin of metals in groundwater of the BB industrial region. A consistent concentration of Uranium was detected at all the sampling locations with an average value of 0.0039 mg/l and poor spatial variation indicating its natural presence. Overall, non-carcinogenic (N-CR) risk in the study area via oral pathway was high for adults and children (Hazard Index > 1) with geogenic Uranium as the major contributor (Hazard Quotient > 1) followed by Zinc, Lead and Cobalt. Carcinogenic (CR) risk in the region was high for adults having mean value above the threshold (1E-04) with Nickel and Chromium as the metals of major concern. Spatial variation of health risks was overlayed on village boundaries of the region to identify the potential industrial sources of the metals of major concern. The results highlight the need for immediate remediation of groundwater resources in order to achieve a harmonious coexistence between industrialization and human well-being.

Geochemical evaluation and the mechanism controlling groundwater chemistry using chemometric approach and groundwater pollution index (GPI) in the Kishangarh city of Rajasthan, India.

This study is primarily focused on delving into the geochemistry of groundwater in the Kishangarh area, located in the Ajmer district of Rajasthan, India. In pursuit of this research goal, the sampling locations were divided into three parts within the Kishangarh region: Badgaon Rural (KSGR), Kishangarh Urban (KSGU), and the Kishangarh RIICO marble industrial area (KSGI). Various analytical methods have been executed to assess the suitability of groundwater for various purposes based on pH, electric conductivity, total dissolved solids, hardness, salinity, major anions, and cations. The ionic trend of anions and cations was found as HCO3- > Cl- > SO42- > NO3- > Br- > NO2- > F- and Na+ > Ca2+ > Mg2+ > K+, respectively. Applying statistical techniques such as principal component analysis (PCA) and Pearson correlation matrix analysis (PCMA) makes it evident that the physicochemical attributes of water sourced from the aquifers in the study area result from a blend of diverse origins. In addition, Gibbs, Piper, Durov, and scatter plots were used to assess groundwater's geochemical evolution. Piper plot demonstrated the two types of groundwater facies, Na-HCO3- and Na-Cl, implying significant contributions from evaporitic dissolution and silicate weathering. Also, the scatter plots have evaluated the impression of mine acid leachate, evaporitic dissolution, and silicate weathering to upsurge salt formation in the groundwater. The pollution risk evaluation within the study area was conducted using the groundwater pollution index (GPI). This index revealed a prominent concern for pollution, particularly in the northern segment of the study region. As a result, it can be inferred that the fine aeolian sand and silt formations in the northern part are relatively more vulnerable to contamination.

Leaching of antibiotic resistance genes and microbial assemblages following poultry litter applications in karst and non-karst landscapes.

Antibiotic resistance is increasingly recognized as a critical challenge affecting human, animal, and environmental health. Yet, environmental dynamics and transport of antibiotic resistance genes (ARGs) and microbial communities in karst and non-karst leachate following poultry litter land applications are not well understood. This study investigates impacts of broiler poultry litter application on the proliferation of ARGs (tetW, qnrS, ermB, sulI, and blaCTX-M-32), class 1 integron (intI1 i), and alterations in microbial communities (16S rRNA) within karst derived soils, which are crucial and under-researched systems in the global hydrological cycle, and non-karst landscapes. Using large, intact soil columns (45 cm diam. × 100 cm depth) from karst and non-karst landscapes, the role of preferential flow and ARG transport in leachate was enumerated following surface application of poultry litter and simulated rain events. This research demonstrated that in poultry litter amended karst soils, ARG (i.e., ermB and tetW) abundance in leachate increased 1.5 times compared to non-karst systems (p < 0.05), highlighting the influence of geological factors on ARG proliferation. Notably, microbial communities in karst soil leachate exhibited increased diversity and abundance, suggesting a potential linkage between microbial composition and ARG presence. Further, our correlation and network analyses identified relationships between leachate ARGs, microbial taxa, and physicochemical properties, underscoring the complex interplay in these environmentally sensitive areas. These findings illuminate the critical role of karst systems in shaping ARG abundance and pollutant dispersal and microbial community dynamics, thus emphasizing the need for landscape-specific approaches in managing ARG dissemination to the environment. This study provides a deeper understanding of hydrogeological ARG dynamics but also lays the groundwork for future research and strategies to mitigate ARG dissemination through targeted manure applications across agricultural landscapes.

Sulfonamide Per- and Polyfluoroalkyl Substances Can Impact Microorganisms Used in Aromatic Hydrocarbon and Trichloroethene Bioremediation.

Per- and polyfluoroalkyl substances (PFASs) from aqueous film forming foams (AFFFs) can hinder bioremediation of co-contaminants such as trichloroethene (TCE) and benzene, toluene, ethylbenzene, and xylene (BTEX). Anaerobic dechlorination can require bioaugmentation of Dehalococcoides, and for BTEX, oxygen is often sparged to stimulate in situ aerobic biodegradation. We tested PFAS inhibition to TCE and BTEX bioremediation by exposing an anaerobic TCE-dechlorinating coculture, an aerobic BTEX-degrading enrichment culture, and an anaerobic toluene-degrading enrichment culture to n-dimethyl perfluorohexane sulfonamido amine (AmPr-FHxSA), perfluorohexane sulfonamide (FHxSA), perfluorohexanesulfonic acid (PFHxS), or nonfluorinated surfactant sodium dodecyl sulfate (SDS). The anaerobic TCE-dechlorinating coculture was resistant to individual PFAS exposures but was inhibited by >1000× diluted AFFF. FHxSA and AmPr-FHxSA inhibited the aerobic BTEX-degrading enrichment. The anaerobic toluene-degrading enrichment was not inhibited by AFFF or individual PFASs. Increases in amino acids in the anaerobic TCE-dechlorinating coculture compared to the control indicated stress response, whereas the BTEX culture exhibited lower concentrations of all amino acids upon exposure to most surfactants (both fluorinated and nonfluorinated) compared to the control. These data suggest the main mechanisms of microbial toxicity are related to interactions with cell membrane synthesis as well as protein stress signaling.

Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence.

Groundwater models are essential for understanding aquifer systems behavior and effective water resources spatio-temporal distributions, yet they are often hindered by challenges related to model assumptions, parametrization, uncertainty, and computational efficiency. Machine intelligence, especially deep learning, promises a paradigm shift in overcoming these challenges. A critical examination of existing machine-driven methods reveals the inherent limitations, particularly in terms of the interpretability and the ability to generalize findings. To overcome these challenges, we develop a ternary framework that synergizes the valuable insights from multisource data, human expertise, and machine intelligence. This framework capitalizes on the distinct strengths of each element: the value and relevance of multisource data, the innovative capacity of human expertise, and the analytical efficiency of machine intelligence. Our goal is to conceptualize sustainable water management practices and enhance our understanding and predictive capabilities of groundwater systems. Unlike approaches that rely solely on abundant data, our framework emphasizes the quality and strategic use of available data, combined with human intellect and advanced computing, to overcome current limitations and pave the way for more realistic groundwater simulations.

Long-term impact of wastewater effluent discharge on groundwater: Identification of contaminant plume by geochemical, isotopic, and organic tracers' approach.

Infiltration of effluents from wastewater treatment plants (WWTP) into groundwater can be a source of Contaminants of Emerging Concern (CECs), such as pharmaceutical compounds, that are not fully removed during the treatment processes. A multi-tracer approach, based on hydrogeochemical, isotopic, and organic tracers, is applied in the Vistrenque Aquifer (Gard, France) to assess the dispersion of such unintentional plumes and its potential implication on groundwater quality for CECs in a small catchment area. In this area, a point source of WWTP effluent causes contaminant infiltration and unintentional transfer to the aquifer. This strong impact of an urban effluent was revealed from the Br/Cl ratio, boron concentrations and δ11B isotopic signature of the groundwater in the direct vicinity of the infiltration point. With increasing distance from that point, dilution with groundwater rapidly attenuates the urban signal from these hydrogeochemical and isotopic tracers. Nevertheless, a gadolinium anomaly, resulting from discharges of urban wastewater containing the contrast agents used for magnetic resonance imaging (MRI), highlights the presence of a wastewater plume further along the flow line, that comes with a series of organic molecules, including pharmaceutical residues. Monitoring persistent or reactive molecules along the plume provides a more detailed understanding of the transfer of CECs into groundwater bodies. This highlights the relevance of pharmaceutical compounds as co-tracers for WWTP plume delineation. The present multi-tracer approach for groundwater resource vulnerability towards CECs allows a more in-depth understanding of contaminant transfer and their fate in groundwater.

Factors Influencing Microbial Contamination of Groundwater: A Systematic Review of Field-Scale Studies.

Pathogenic microorganisms released onto the soil from point or diffuse sources represent a public health concern. They can be transported by rainwater that infiltrates into subsoil and reach the groundwater where they can survive for a long time and contaminate drinking water sources. As part of the SCA.Re.S. (Evaluation of Health Risk Related to the Discharge of Wastewater on the Soil) project, we reviewed a selection of field-scale studies that investigated the factors that influenced the fate of microorganisms that were transported from the ground surface to the groundwater. A total of 24 studies published between 2003 and 2022 were included in the review. These studies were selected from the PubMed and Web of Science databases. Microbial contamination of groundwater depends on complex interactions between human activities responsible for the release of contaminants onto the soil, and a range of environmental and biological factors, including the geological, hydraulic, and moisture characteristics of the media traversed by the water, and the characteristics and the viability of the microorganisms, which in turn depend on the environmental conditions and presence of predatory species. Enterococci appeared to be more resistant in the underground environment than thermotolerant coliforms and were suggested as a better indicator for detecting microbial contamination of groundwater.

Approaches for the Efficient Removal of Fluoride from Groundwater: A Comprehensive Review.

Contamination of groundwater with fluoride represents a significant global issue, with high concentrations posing serious public health threats. While fluoride is a critical element in water, excessive levels can be detrimental to human health and potentially life-threatening. Addressing the challenge of removing fluoride from underground water sources via nanotechnological approaches is a pressing concern in environmental science. To collate relevant information, extensive literature searches were conducted across multiple databases, including Google Scholar, PubMed, Scopus, Web of Science, the American Chemical Society, Elsevier, Springer, and the Royal Society of Chemistry. VOS Viewer software version 1.6.20 was employed for a systematic review. This article delivers an exhaustive evaluation of various groundwater fluoride removal techniques, such as adsorption, membrane filtration, electrocoagulation, photocatalysis, and ion exchange. Among these, the application of nanoparticles emerges as a notable method. The article delves into nano-compounds, optimizing conditions for the fluoride removal process and benchmarking their efficacy against other techniques. Studies demonstrate that advanced nanotechnologies-owing to their rapid reaction times and potent oxidation capabilities-can remove fluoride effectively. The implementation of nanotechnologies in fluoride removal not only enhances water quality but also contributes to the safeguarding of human health.

Integrated monitoring and modeling to disentangle the complex spatio-temporal dynamics of urbanized streams under drought stress.

We have a poor understanding of how urban drainage and other engineered components interact with more natural hydrological processes in green and blue spaces to generate stream flow. This limits the scientific evidence base for predicting and mitigating the effects of future development of the built environment and climate change on urban water resources and their ecosystem services. Here, we synthesize > 20 years of environmental monitoring data to better understand the hydrological function of the 109-km2 Wuhle catchment, an important tributary of the river Spree in Berlin, Germany. More than half (56%) of the catchment is urbanized, leading to substantial flow path alterations. Young water from storm runoff and rapid subsurface flow provided around 20% of stream flow. However, most of it was generated by older groundwater (several years old), mainly recharged through the rural headwaters and non-urban green spaces. Recent drought years since 2018 showed that this base flow component has reduced in response to decreased recharge, causing deterioration in water quality and sections of the stream network to dry out. Attempts to integrate the understanding of engineered and natural processes in a traditional rainfall-runoff model were only partly successful due to uncertainties over the catchment area, effects of sustainable urban drainage, adjacent groundwater pumping, and limited conceptualization of groundwater storage dynamics. The study highlights the need for more extensive and coordinated monitoring and data collection in complex urban catchments and the use of these data in more advanced models of urban hydrology to enhance management.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India.

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Assessment of hydraulic and thermal properties of the Antarctic active layer: Insights from laboratory column experiments and inverse modeling.

To better understand the changes in the hydrologic cycle caused by global warming in Antarctica, it is crucial to improve our understanding of the groundwater flow system, which has received less attention despite its significance. Both hydraulic and thermal properties of the active layer, through which groundwater can flow during thawing seasons, are essential to quantify the groundwater flow system. However, there has been insufficient information on the Antarctic active layer. The goal of this study was to estimate the hydraulic and thermal properties of Antarctic soils through laboratory column experiments and inverse modeling. The column experiments were conducted with sediments collected from two lakes in the Barton Peninsula, Antarctica. A sand column was also operated for comparison. Inverse modeling using HydroGeoSphere (HGS) combined with Parameter ESTimation (PEST) was performed with data collected from the column experiments, including permeameter tests, saturation-drain tests, and freeze-thaw tests. Hydraulic parameters (i.e., Ks, θs, Swr, α, ß, and Ss) and thermal diffusivity (D) of the soils were derived from water retention curves and temperature curves with depth, respectively. The hydraulic properties of the Antarctic soil samples, estimated through inverse modeling, were 1.6 × 10-5-3.4 × 10-4 cm s-1 for Ks, 0.37-0.42 for θs, 6.62 × 10-3-1.05 × 10-2 for Swr, 0.53-0.58 cm-1 for α, 5.75-7.96 for ß, and 5.11 × 10-5-9.02 × 10-5 cm-1 for Ss. The thermal diffusivities for the soils were estimated to be 0.65-4.64 cm2 min-1. The soil hydraulic and thermal properties reflected the physical and ecological characteristics of their lake environments. The results of this study can provide a basis for groundwater-surface water interaction in polar regions, which is governed by variably-saturated flow and freeze-thaw processes.

Spectral and Molecular Insights into the Characteristics of Dissolved Organic Matter in Nitrate-Contaminated Groundwater.

The characteristics of dissolved organic matter (DOM) serves as indicators of nitrate pollution in groundwater. However, the specific DOM components associated with nitrate in groundwater systems remain unclear. In this study, dual isotopes of nitrate, three-dimensional Excitation emission matrices (EEMs) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was utilized to uncover the sources of nitrate and their associations with DOM characteristics. The predominant nitrate in the targeted aquifer was derived from soil organic nitrogen (mean 46.0%) and manure &sewage (mean 34.3%). The DOM in nitrate-contaminated groundwater (nitrate-nitrogen > 20mg/L) exhibited evident exogenous characteristics, with a bioavailable content 2.58 times greater than that of uncontaminated groundwater. Regarding the molecular characteristics, DOM molecules characterized by CHO + 3N, featuring lower molecular weights and H/C ratios, indicated potential for mineralization, while CHONS formulas indicated the exogenous features, providing the potential for accurate traceability. These findings provide insights at the molecular level into the characterization of DOM in nitrate-contaminated groundwater and offer scientific guidance for decision-making regarding the remediation of groundwater nitrate pollution.

Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan.

It is well known that groundwater arsenic (As) contamination affects million(s) of people throughout the Indus flood plain, Pakistan. In this study, groundwater (n = 96) and drilled borehole samples (n = 87 sediments of 12 boreholes) were collected to investigate geochemical proxy-indicators for As release into groundwater across floodplains of the Indus Basin. The mean dissolved (µg/L) and sedimentary As concentrations (mg/kg) showed significant association in all studied areas viz.; lower reaches of Indus flood plain area (71 and 12.7), upper flood plain areas (33.7 and 7.2), and Thal desert areas (5.3 and 4.7) and are indicative of Basin-scale geogenic As contamination. As contamination in aquifer sediments is dependent on various geochemical factors including particle size (3-4-fold higher As levels in fine clay particles than in fine-coarse sand), sediment types (3-fold higher As in Holocene sediments of floodplain areas vs Pleistocene/Quaternary sediments in the Thal desert) with varying proportion of Al-Fe-Mn oxides/hydroxides. The total organic carbon (TOC) of cored aquifer sediments yielded low TOC content (mean = 0.13 %), which indicates that organic carbon is not a major driver (with a few exceptions) of As mobilization in the Indus Basin. Alkaline pH, high dissolved sulfate and other water quality parameters indicate pH-induced As leaching and the dominance of oxidizing conditions in the aquifers of upper flood plain areas of Punjab, Pakistan while at the lower reaches of the Indus flood plain and alluvial pockets along the rivers with elevated flood-driven dissolved organic carbon (exhibiting high dissolved Mn and Fe and a wide range of redox conditions). Furthermore, we also identified that paired dissolved AsMn values (instead of AsFe) may serve as a geochemical marker of a range of redox conditions throughout Indus flood plains.

Understanding the dynamics of enhanced light non-aqueous phase liquids (LNAPL) remediation at a polluted site: Insights from hydrogeophysical findings and chemical evidence.

This study intricately unfolds a pioneering methodology for remediating contaminants in a persistent light non-aqueous phase liquids (LNAPL)-contaminated site. The remediation strategy seamlessly integrates enhanced desorption and in-situ chemical oxidation (ISCO), orchestrating the injection of PetroCleanze® (a desorbent) and RegenOx® (an oxidizer) through meticulously designed wells. These injections, based on detailed geological and hydrogeological assessments, aim at mobilizing residual contaminants for subsequent extraction. Real-time subsurface dynamics are investigated through geophysical monitoring, employing electrical resistivity tomography (ERT) to trace reagent migration pathways via their effect on bulk electrical conductivity. The integration of groundwater sampling data aims at providing additional insights into the transformations of contaminants in the spatiotemporal context. Vivid two-dimensional time-lapse ERT sections showcase the evolution of resistivity anomalies, providing high-resolution evidence of the heterogeneity, dispersion pathways of desorbent and oxidant, and residual LNAPL mobilization. Hydrochemical analyses complement this, revealing effective mobilization processes with increasing aqueous concentrations of total petroleum hydrocarbons (TPH) over time. Speciation analysis unveils the intricate interplay of desorption and oxidation, portraying the dynamic fractionation of hydrocarbon components. The hydrogeophysical and data-driven framework not only delivers qualitative and quantitative insights into reagent and contaminant distribution but also enhances understanding of spatial and temporal physio-chemical changes during the remediation process. Time-lapse ERT visually narrates the reagent's journey through time, while chemical analyses depict the unfolding processes of desorption and oxidation across space and time. The coupling of hydrogeophysical and chemical findings pictures the transformations of pollutants following the sequence of product injection and the push and pull activities, capturing the removal of mobilized contaminants through hydraulic barrier wells. This enhanced understanding proves instrumental towards optimizing and tailoring remediation efforts, especially in heterogeneous environmental settings. This study establishes a new standard for a sophisticated and innovative contaminant remediation approach, advancing environmental practices through the harmonized analysis of geophysical and chemical data.

Saline and hydrocarbon-bearing fluids detected in shallow aquifers of southern New Brunswick, Canada: Natural occurrence, or deep migration along faults and industrial wellbores?

Unconventional hydrocarbon production has sparked public concerns for several years, especially regarding potential potable groundwater contamination by hydrocarbons, brines, and various chemicals related to hydraulic fracturing operations. One possible contamination mechanism is upward migration of deep-seated contaminants over large vertical distances, through preferential pathways such as leaky well casings or permeable geological faults. In New Brunswick (Canada), thermogenic hydrocarbons and brackish water were previously reported in shallow water wells, some of them located close to unconventional gas wells or to major faults, but the exact origin of these fluids remained uncertain. The objective of this paper is to determine whether the presence of these fluids is the result of migration from the deep (>1 km) hydrocarbon bearing units (via natural or anthropogenic migration pathways), or whether they rather originate within the shallow aquifer (<100 m) or from intermediate zone. Tracking fluid origin was achieved by fingerprinting compositional and isotopic values of three indicators: 1) water isotopic signature (including tritium (3H), radiocarbon (14CDIC), δ18OH2O, δ2HH2O), 2) salinity (including Na, Ca, K, SO4, Cl, Br, 87Sr/86Sr), and 3) hydrocarbons (compositional data and δ13CCH4). These various analyses were conducted, when relevant, on samples of different matrices composing the hydrogeological system, namely shallow groundwater (12-90 m depth), shallow bedrock gas (8-131 m), and intermediate zone evaporitic rocks (173-332 m); they were compared with previously published values for deep basin brines and gases (1940-3168 m) from the hydrocarbon bearing Carboniferous Albert Formation. This unique suite of indicators, analytes and matrices allowed drawing the conclusion that thermogenic gas and high salinities present in the sampled wells were naturally occurring and originating from shallow and intermediate-zone bedrock units. Results obtained through this approach did not provide any evidence that hydrocarbon wells in this area have acted as preferential migration pathways for deep-seated fluids towards shallow aquifers.

Spatial health risk assessments of nickel in the groundwater sources of a mining-impacted area.

Mining activities have increased the potential risks of metal pollution to the groundwater resources in arid areas across the globe. Therefore, this study aimed to examine the health risk associated with nickel (Ni) in the groundwater sources of a mining-impacted area, South Khorasan, Eastern Iran. A total of 110 stations were included in the study, comprising 62 wells, 40 qanats, and 8 springs in summer, 2020. Initially, the collected samples were tested for temperature, pH, and electrical conductivity (EC). Subsequently, the samples were filtered and treated with nitric acid (HNO3) to measure the concentration of Ni using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Hazard quotient (HQ) and non-carcinogenic risk assessments were employed to evaluate the potential risks of Ni to the inhabitants. The findings revealed that the concentration of Ni ranged from 0.02 to 132.39 µg l-1, and only two stations exhibited Ni concentrations above the WHO standards (20 µg l-1). The results demonstrated that 98.21% of the sampled locations had HQ values below one, indicating negligible risk, while 1.78% of the stations exhibited HQ values of one or higher, representing a high non-carcinogenic risk for water consumers. Overall, the concentration of nickel in the groundwater of South Khorasan exceeded the World Health Organization (WHO) limit solely in the Halvan station, posing a non-carcinogenic risk for the residents in that area, and therefore, additional efforts should be made to provide healthier groundwater to consumers in this region.

Occurrence and geochemistry of altered radioactive accessory minerals as sources of radionuclide in Mesozoic granite aquifers (Korea).

Accessory minerals in granitic rocks are unlikely significant radionuclide contributions to groundwater due to their remarkable durability. However, accessory minerals incorporating U and Th may suffer structural damages due to the radioactivity and become highly susceptible to alteration. This study investigates geochemistry coupled with textural analysis of the U-Th bearing accessory minerals using a field emission scanning electron microscope and an electron probe micro-analyzer. Altered zircons with numerous open structures related to the radioactive decay show higher contents of U and Th and low analytical totals. Some thorites show high contents of U and non-formula elements due to the hydrothermal alteration in the metamicted thorite. The cerianite including U occurs as micro-veinlet in fracture with trace of Fe and Mn oxides, which indicates secondary phase formation from the decomposed accessory minerals in an oxidizing environment. Some accessory minerals with the high content of U and Th have been found in Mesozoic granite terrain in South Korea, where high concentration levels of radionuclide in groundwater were also reported. The leaching of U may be more likely when the accessory minerals are highly metamicted or altered as found in our samples. The altered zircon and thorite of the study area could be major carriers of radioelement in Mesozoic granitic aquifers where the occurrence of soluble U-minerals has not been reported.