Mobilization mechanisms and spatial distribution of arsenic in groundwater of western Bangladesh: Evaluating water quality and health risk using EWQI and Monte Carlo simulation.

Arsenic (As) contamination in groundwater is emerging as a significant global concern, posing serious risks to the safety of drinking water and public health. To understand the release mechanisms, mobilization processes, spatial distribution, and probabilistic health risks of As in western Bangladesh, forty-seven samples were collected and analyzed using an atomic absorption spectrometer (AAS). The As concentrations in groundwater ranged from 1.97 to 697.4 µg L⁻1 (mean: 229.9), significantly exceeding recommended levels. The dominant hydrochemistry of As-enriched groundwater was Ca-Mg-HCO3, with the primary sources of arsenic in groundwater being the dissolution of arsenic-bearing minerals in sediment and the recharge of aquifers from the Ganges River Basin. The assessment using the Entropy Water Quality Index revealed that the groundwater is unsuitable for drinking, with 89.36% (n = 42) of the samples surpassing the WHO's limit for arsenic. Rock-water interactions, including calcite dissolution and silicate weathering within the confined aquifer, predominantly influenced hydrochemical properties. The significant relationships among Fe, Mn, and As indicate that the reductive dissolution of FeOOH and/or MnOOH considerably contributes to the release of As from sediment into groundwater. Geochemical modeling analysis revealed that siderite and rhodochrosite precipitate into aquifer solids, suggesting a weak to moderate relationship among As, Fe, and Mn. The long residence time of groundwater, combined with the presence of a clayey aquitard, likely controls the mobilization of arsenic in the aquifer. For the first time, Monte Carlo simulations have been used in arsenic-prone areas to assess the severity of arsenic contamination in western Bangladesh. The analysis indicates that out of 100,000 people, 10 may develop cancer as a result of drinking arsenic-contaminated water, with children being more susceptible than adults.

Patterns of spatiotemporal variations in the hydrochemistry and controlling factors of bedrock aquifers in the northern region of the Linhuan mining area.

Systematically studying the hydrochemical evolution of bedrock groundwater in mining areas during mining process is crucial for effective groundwater resource management and coal mine production. The spatiotemporal characteristics and hydrochemical evolution patterns of the Permian fractured sandstone aquifer (PA) and the Carboniferous Taiyuan Formation limestone aquifer (CTA), both of which are directly associated with coal mining in the northern Linhuan mining area, China, were investigated using multivariate statistical analyses, hydrochemical graphical methods, ion ratio analysis, and a conceptual model. 72 groundwater samples, collected before and after mining, were classified into four groups by hierarchical cluster analysis (HCA). Principal component analysis (PCA) and ion ratio analysis indicated that water-rock interactions involve mineral dissolution (carbonates, gypsum, dolomite, silicates), cation exchange, and common ion effects. Hydrochemical evolution is influenced by bedrock paleotopography, aquifer hydraulic conductivity, and mining drainage. Paletopographic differences significantly influence water-rock interactions and spatial variability in hydrochemistry, with ion concentrations in groundwater increasing as paleotopographic elevation decreases. The pattern of hydraulic conductivity reflects the control exerted by variations in aquifer characteristics on mineral dissolution, leading to minor changes in hydrochemical characteristics. Mining activities disrupt the aquifer's reducing environment, resulting in a significant increase in groundwater SO42- concentration. These findings provide insights and a solid theoretical foundation for studying the hydrochemical variations patterns of groundwater and these control mechanisms in the hidden coal fields of North China.

Evaluation of drinking water quality and associated health risks in Adama City, Ethiopia.

Drinking water deterioration causes to risk of public health which is essential to supply safe water to the public. This study assessed groundwater quality and health risks in Adama City by analyzing groundwater and chlorine samples. Ion photometry techniques detected anions and cations, ensuring accuracy with quality control protocols. Water Quality Index (WQI) and chlorine decay modeling via WaterGEMs assessed water quality. Hazard index (HI) calculations evaluated exposure risks; Pearson correlation analyzed physicochemical relationships. Findings highlighted water quality and hazards. Aquachem software analyzed Adama's groundwater, revealing high total alkalinity and potassium exceeding WHO limits. Other parameters (nitrate, nitrite, chloride, fluoride, and sulfate) met WHO standards. Sodium, calcium, magnesium, iron, manganese, and boron also complied. Multivariate analysis showed significant parameter associations. Water types included Ca-Na-HCO3 (27.27 %), Na-Ca-HCO3 (36.36 %), Na-Ca-Mg-HCO3, Na-HCO3 (9.09 % each), and Na-Mg-HCO3 (18.18 %). Drinking Water Quality Index rated boreholes as "Good." Health risk assessments found no significant fluoride, iron, or manganese risks across ages. Chlorine residual analysis indicated 74 % had levels below WHO recommendations, prompting chlorine dosing adjustments. Findings inform groundwater management in Adama City.

Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An exploratory study.

Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. In the current study, groundwater quality was thoroughly appraised using various indexing methods, including the drinking water quality index (DWQI), pollution index of heavy metals (HPI), pollution index (PI), metal index (MI), degree of contamination (Cd), and risk indicators, like hazard quotient (HQ) and total hazard indicator (HI). The assessments were augmented through multivariate analytical techniques, models based on recurrent neural networks (RNNs), and integration of geographic information system (GIS) technology. The analysis measured physicochemical parameters across 48 groundwater wells from El-Menoufia region, revealing distinct water types influenced by ion exchange, rock-water interactions, and silicate weathering. Notably, the groundwater showed elevated levels of certain metals, particularly manganese (Mn) and lead (Pb), exceeding the drinking water limits. The DWQI deemed the bulk of the tested samples suitable for consumption, assigning them to the "good" category, whereas a small number were considered inferior quality. The HPI, MI, and Cd indices indicated significant pollution in the central study region. The PI revealed that Pb, Mn, and Fe were significant contributors to water pollution, falling between classes IV (strongly affected) and V (seriously affected). HQ and HI analyses identified the central area of the study as particularly prone to metal contamination, signifying a high risk to children via oral and dermal routes and to adults through oral exposure alone (non-carcinogenic risk). The adults had no health risks due to dermal contact. Finally, the RNN simulation model effectively predicted the health and water quality indices in training and testing series. For instance, the RNN model excelled in predicting the DWQI, with three key parameters being crucial. The model demonstrated an excellent fit on the training set, achieving an R2 of 1.00 with a very low root mean of squared error (RMSE) of 0.01. However, on the testing set, the model's performance slightly decreased, showing an R2 of 0.96 and an RMSE of 2.73. Regarding HPI, the RNN model performed exceptionally well as the primary predictor, with R2 values of 1.00 (RMSE = 0.01) and 0.93 (RMSE = 27.35) for the training and testing sets, respectively. This study provides a unique perspective for improving the integration of various techniques to gain a more comprehensive understanding of groundwater quality and its associated health risks, with a strong focus on feature selection strategies to enhance model accuracy and interpretability.

Quality and health impact of groundwater in a coastal region: a case study from west coast of southern India.

Seawater intrusion seriously threatens the quality of coastal groundwater, affecting nearly 40% of the world's population in coastal areas. A study was conducted in the Kamini watershed situated in the Udupi district of Karnataka to assess the groundwater quality and extent of seawater intrusion. During the pre-monsoon period, 57 groundwater and 3 surface water samples were analyzed to understand the impact of seawater on the groundwater and surface water. The analysis revealed that the groundwater in the study area is slightly alkaline. The weighted overlay analysis map indicated that 11% of the study area is unsuitable for drinking water due to the influence of seawater. The Piper plot analysis revealed that the groundwater is predominantly CaMgCl facies. The hydrogeochemical facies evolution diagram (HFED) showed that 62% of the groundwater is affected by seawater. The HFED and Piper plots also indicate that the surface water is also affected by seawater. These results are also supported by various molar ratios such as Cl- vs. Cl⁻/HCO3⁻, Cl⁻ vs. Na⁺/Cl⁻, Cl- vs. SO42-/Cl-, and Cl⁻/HCO3- vs. Mg2+/Ca2+, suggesting that the majority of the water sample has been affected by seawater. The saturation indices indicated that mineral dissolution has significantly contributed to groundwater salinization. The correlation between sulfate concentration and calcite and dolomite dissolution suggested the influence of seawater intrusion in the coastal aquifer. The process of reverse ion exchange mainly influences the groundwater chemistry according to chloroalkali indices. The total hazard index (THI) values of nitrate and fluoride exceeded limits, posing health risks to adults and children. Studies suggest that with time and space, seawater intrusion is increasing in some pockets of the study area, especially along the west coast.

Arsenic and fluoride occurrence in groundwater of an alluvial fan-delta junction zone in an arid climate: Implication for potential health risk and irrigation water quality.

Alluvial fans and deltas are two environments with different hydrochemical conditions. Their junction zones, as mixing environments, are variably influenced by different processes, leading to variable environmental conditions. The purpose of this study is to investigate groundwater quality in the junction zone of these environments in the northern part of the Jazmourian depression (known as the Rudbar plain) in southeastern Iran to determine the dominant processes, assess arsenic and fluoride health risks, and evaluate irrigation water quality. A total of 33 samples from deep drilled wells were taken, and the concentrations of major ions and elements were determined. Additionally, statistical and hydrochemical analyses were undertaken. The dominant processes in the delta are evaporation and ion exchange, while the dominant process in the fan environment is silicate hydrolysis. Among the samples, 26.7% were mainly affected by the delta, and 73.3% were mainly affected by fan conditions. Although the majority of groundwater samples were suitable for irrigation based on quality standards, a significant portion exceeded the acceptable level for Na%. Non-carcinogenic health risk assessments indicated that arsenic hazard risks exceeded thresholds in 63.3% of cases for children and 36% for adults. Carcinogenic health risks associated with arsenic and fluoride exceeded acceptable levels in 4 and 2 stations, respectively. Elevated As concentrations contribute to a greater average health risk in parts of fans environment.

Hydrogeochemical characteristics and agricultural suitability of shallow groundwater quality in a concentrated coalfield area of Huaibei Plain, China.

Groundwater is one of the chief water sources for agricultural activities in an aggregation of coal mines surrounded by agricultural areas in the Huaibei Plain. However, there have been few reports on whether mining-affected groundwater can be adopted for agricultural irrigation. We attempted to address this question through collecting 71 shallow groundwater samples from 12 coal mining locations. The Piper trilinear chart, the Gibbs diagram, the proportional coefficient of major ions, and principal component analysis were examined to characterize the source, origin, and formation process of groundwater chemical composition. The suitability for agricultural irrigation was evaluated by a final zonation map that establishes a comprehensive weighting model based on analytic hierarchy process and criteria importance though the intercriteria correlation (AHP-CRITIC). The results revealed that the groundwater was classified as marginally alkaline water with a predominant cation of HCO3- and anion of Na+. Total hardness, total dissolved solids, sulfate (SO42-), sodium (Na+), and fluoride (F-) were the primary ions that exceeded the standard. The results also indicated that the dominant hydrochemical facies were Ca-HCO3 and Na-Cl. The dissolution of carbonate, silicate, sulfate minerals, along with cation exchange, were the main natural drivers controlling the hydrogeochemical process of groundwater. The zonation map suggested that 43.17%, 18.85%, and 37.98% of the study area were high, mediate, and low suitability zones, respectively. These results from this study can support policymakers for better managing groundwater associated with a concentration of underground coal mines.

New insights on the hydrochemistry, geothermometry, and isotopic characteristics of the hydrothermal groundwater of the SASS basin: case study of the Jérid geothermal field, Southern Tunisia.

Chemical and isotopic indicators were used to recognize the origin of hydrothermal groundwater, to assess the mineralization processes and groundwater quality, to identify the source of solutes and the likely mixing with cold, and elucidate the fluid geothermometry in the Jérid field of Southern Tunisia. The results show that the geothermal groundwater is neutral to slightly alkaline. They are characterized by SO4-Cl-Na-Ca water type. The dissolution of evaporates and pyrite-bearing rocks is the dominant mineralization process. The groundwater quality index indicates that the majority of samples are very hard and belong to poor to unsuitable for drinking classes. Applications and calculations of hydrogeochemical parameters, including SAR, %Na, PI, Kr, and MAR, showed that the majority of samples are unsuitable for agricultural practices. The human health risk was assessed based on hazard quotient and total hazard index through ingestion and dermal contact with iron-rich groundwater. The consumption of CI groundwaters does not present non-carcinogenic risk to adults and children. The δ18 O and δ2H signatures indicate that the geothermal groundwater was recharged by ocean precipitation during cold and wet paleoclimatic periods. The slight enrichment of oxygen-18 and deuterium contents suggests a limited mixing effect between geothermal water and cold groundwater within the same aquifer. This mixing effect is confirmed by the Na-K-Mg and the chloride-enthalpy diagrams. The K-Mg and SiO2 geothermometers provided fairly reliable reservoir temperature values, ranging between 69.6 and 99 °C. Calculated geothermal potential values, varying between 469 and 16987 kWth, which allow several applications such as domestic and agricultural heating.

Linking recharge water sources to groundwater composition in the Hindon subbasin of the Ganges River, India.

Groundwater resources of the densely populated Indo-Gangetic Basin are under increasing pressure, not only from extensive groundwater abstraction, but also from contamination. In this study we aim to better understand how different recharge sources affect the hydrochemical and isotope composition of groundwater. We used the Hindon subbasin in Northern India as a case study. Recharge water sources and groundwater were analysed for hydrochemical variables and stable isotopes along a 50 km transect between the Yamuna and Ganges rivers. Groundwater samples were statistically clustered based on hydrochemical variables, and the spatial variation of the groundwater clusters was compared with recharge sources. Groundwater quality could be linked to both recharge from irrigation canal water as well as recharge from polluted river water of the Hindon and its tributaries. We could not directly link groundwater outside these related zones to their recharge source. However, we suspect that shallow polluted groundwater (< 40 m depth) is affected by recharge from agricultural areas and infiltration of municipal wastewater, whereas deeper unpolluted groundwater (40-80 m depth) originates from recharge by rain and river water under more pristine conditions. Our findings show that human activities significantly impact the quality of groundwater, as we found vertical recharge of clean irrigation canal water and polluted municipal, agricultural and river surface water (up to 40 m depth). At one location, groundwater at 75 m depth shows increased Cl, NO3 and SO4 concentrations, suggesting accelerated downward displacement of polluted shallow groundwater by pumping. Limited horizontal displacement was found. We present a conceptual model demonstrating the evolution from a previously unpolluted groundwater system discharging to the river, to a contemporary system with infiltration dominance of polluted river, municipal and agricultural water and local clean irrigation canal water. This model may be relevant for large parts of the Indo-Gangetic Basin.

Groundwater-rock interactions and mixing in fault-controlled karstic aquifers: A structural, hydrogeochemical and multi-isotopic review of the Pontina Plain (Central Italy).

Karstic aquifers represent crucial water resources and are categorized as either stratigraphically or fault-controlled. This study investigates groundwater-rock interactions and mixing processes within one of the largest fault-controlled karstic aquifers in Central Italy, adjacent to the Pontina plain, which is a highly populated area where agricultural activities and climate change challenge the groundwater assessment of a complex aquifer. We conducted structural, hydrogeochemical, and multi-isotopic screening of ten selected springs with different degrees of mineralization (ranging from Ca-HCO3 to Na-Cl hydrofacies), incorporating new analyses and modeling of δ34S(SO4), δ18O(SO4), 87Sr/86Sr, and δ11B. Additionally, the reinterpretation of a seismic section provides a more detailed framework extending to depths of approximately 5-7 km that allows the identification of the geometry of normal faults, which act as pathways for upwelling fluids. Our findings reveal that hydrogeochemical compositions result from multiple interactions between karstic water and deeper fluids that have interacted with different rocks. Concentration (Na/Li) and isotope (SO4-H2O) geothermometers, coupled with geochemical modeling and trace element analysis, enabled the estimation of a water temperature equilibrium of approximately 95.5 °C, with Triassic evaporites generally corresponding to a depth of approximately 3 km and a temperature of 40 °C with magmatic rocks at approximately 1 km depth, which is likely associated with ongoing tectonics and the Quaternary tectonically controlled Volsci Volcanic Field. To obtain the latter estimate, we used a new geothermometer activity based on the equilibrium between analcime and pollucite. Furthermore, this multidisciplinary approach enhances the understanding of groundwater behavior in fault-controlled karstic aquifers, where mantle-derived CO2 dissolved in groundwater is the driving force behind water-rock interactions. Given the potential for further variations in mixing, which may worsen water quality and increase aquifer vulnerability, periodic monitoring of these processes is essential in a human-impacted environment amidst ongoing climate change.

Prediction of geogenic source of groundwater fluoride contamination in Indian states: A comparative study of different supervised machine learning algorithms.

India has been dealing with fluoride contamination of groundwater for the past few decades. Long-term exposure of fluoride can cause skeletal and dental fluorosis. Therefore, an in-depth exploration of fluoride concentrations in different parts of India is desirable. This work employs machine learning algorithms to analyze the fluoride concentrations in five major affected Indian states (Andhra Pradesh, Rajasthan, Tamil Nadu, Telangana and West Bengal). A correlation matrix was used to identify appropriate predictor variables for fluoride prediction. The various algorithms used for predictions included K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector classifier (SVC), Gaussian NB, MLP classifier, decision tree classifier, gradient boosting classifier, voting classifier soft and voting classifier hard. The performance of these models is assessed over accuracy, precision, recall and error rate and receiver operating curve. As the dataset was skewed, the performance of models was evaluated before and after resampling. Analysis of results indicates that the RF model is the best model for predicting fluoride contamination in groundwater in Indian states.

A review of groundwater iodine mobilization, and application of isotopes in high iodine groundwater.

Excessive intake of iodine will do harm to human health. In recent years, high iodine groundwater has become a global concern after high arsenic and high fluorine groundwater. A deep understanding of the environmental factors affecting iodine accumulation in groundwater and the mechanism of migration and transformation is the scientific prerequisite for effective prevention and control of iodine pollution in groundwater. The paper comprehensively investigated the relevant literature on iodine pollution of groundwater and summarized the present spatial distribution and hydrochemical characteristics of iodine-enriched groundwater. Environmental factors and hydrogeological conditions affecting iodine enrichment in aquifers are systematically summarized. An in-depth analysis of the hydrologic geochemistry, physical chemistry, biogeochemistry and human impacts of iodine transport and transformation in the surface environment was conducted, the results and conclusions in the field of high iodine groundwater research are summarized comprehensively and systematically. Stable isotope can be used as a powerful tool to track the sources of hydrochemical components, biogeochemistry processes, recharge sources and flow paths of groundwater in hydrogeological systems, to provide effective research methods and means for the study of high iodine groundwater system, and deepen the understanding of the formation mechanism of high iodine groundwater, the application of isotopic technique in high iodine groundwater is also systematically summarized, which enriches the method and theory of high iodine groundwater research. This paper provides more scientific basis for the prevention and control of groundwater iodine pollution and the management of groundwater resources in water-scarce areas.

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region.

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML's superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Multi-method characterization of groundwater nitrate and sulfate contamination by karst mines in southwest China.

Groundwater contamination by nitrate and sulfate in mining areas is a significant challenge. Consequently, the inputs sources of these contaminants and their evolution have received considerable attention, with the knowledge gained critical for improved management of water quality. This study integrated data on multiple stable isotopes and water chemistry data and a Bayesian isotope mixing model to investigate the relative contributions of inputs sources of sulfate and nitrate sources to bodies of water in a karst mining area in southwest China. The outcomes indicated that hydrochemical component in the water bodies of the study area is mainly derived from the dissolution of silicate rocks, carbonate rocks and sulfate minerals as well as the oxidation of sulfides. The human and agricultural wastewater, soil nitrogen, and fertilizers were the predominant inputs sources of nitrate to the mine water environment; the predominant inputs sources of sulfide were mineral oxidation, evaporite dissolution, atmospheric deposition, and sewage. Groundwater is mainly recharged from atmospheric precipitation, and surface water is closely hydraulically connected to groundwater. Nitrogen and oxygen isotope composition and water chemistry indicative of nitrification dominate the nitrogen cycle in the study area. The oxidation of pyrite and bacterial sulfate reduction (SRB) had no significant impact on the stable isotopes of groundwater. The results of this study demonstrate the inputs of different sources to nitrate and sulfate in karst mines and associated transformation processes. The results of this study can assist in the conservation of groundwater quality in mining areas and can act as a reference for future related studies.

Chemical and isotopic tracers combined with mixing models for tracking nitrate contamination in the Pampa de Pocho aquifer, Argentina.

In recent years, it has become evident that human activities have significantly disrupted the nitrogen cycle surpassing acceptable environmental thresholds. In this study, chemical and isotopic tracers were combined with a mathematical mass balance model (EMMA), PHREEQC inverse mixing model, and statistical analyses to evaluate groundwater quality, across an area experiencing substantial human activities, with a specific focus on tracing the origin of nitrate (NO3-) with potential water mixing processes. This multi-technique approach was applied to an unconfined aquifer underlying an agricultural area setting in an inter-mountain depression (i.e., the "Pampa de Pocho Plain" in Argentina). Here, the primary identified geochemical processes occurring in the investigated groundwater system include the dissolution of carbonate salts, cation exchange, and hydrolysis of alumino-silicates along with incorporating ions from precipitation. It was observed that the chemistry of groundwater, predominantly of sodium bicarbonate with sulfate water types, is controlled by the area's geology, recharge from precipitation, and stream water infiltration originating from the surrounding hills. Chemical results reveal that 60% of groundwater samples have NO3- concentrations exceeding the regional natural background level, confirming the impact of human activities on groundwater quality. The dual plot of δ15NNO3 versus δ18ONO3 values indicates that groundwater is affected by NO3- sources overlapping manure/sewage with organic-rich soil. The mathematical EMMA model and PHREEQC inverse modeling, suggest organic-rich soil as an important source of nitrogen in the aquifer. Here, 64 % of samples exhibit a main mixture of organic-rich soil with manure, whereas 36 % of samples are affected mainly by a mixture of manure and fertilizer. This study demonstrates the utility of combining isotope tracers with mathematical modeling and statistical analyses for a better understanding of groundwater quality deterioration in situations where isotopic signatures of contamination sources overlap.

Assessment of water supply to the east European arctic agglomeration from groundwater, taking into account their quality and health risks.

The purpose of the study is to assess the possibilities of using groundwater for water supply in the East European Arctic agglomeration based on an assessment of their quality and health risks. For this purpose, high-precision determinations of the complete macro- and microcomponent composition were carried out in sixty-six water samples taken from wells up to 180 m deep. It was found that in some samples the concentrations of Na+, Fe, B, Ba, Mn and U exceeded WHO standards. The least mineralized young waters are characterized by the processes of dissolution of carbonates with the transition of Ca, Mg, Ba, Sr into water, and the processes of leaching of Fe and Mn by acidic swamp waters from near-surface sediments. Waters of high mineralization, enriched in Na+, Cl-, B, Mo, Cd, Pb, were formed as a result of the dissolution of aluminosilicate rocks over thousands of years and mixing with relics of ancient and modern marine transgressions. An assessment of the average Water Quality Index value of the studied aquifer showed that, in general, the water is of excellent quality. Non-carcinogenic risks were determined primarily by uranium concentrations. The average danger index values for this element for children were 1.22. In adults it was slightly lower and amounted to 0.83. Carcinogenic risks are associated primarily with arsenic concentrations. The average total carcinogenic risk associated with this element was 3.8.10-5, which is acceptable, but samples from two wells showed total carcinogenic risk values above 10-4, which is in the high-risk area. For drinking purposes, it is preferable to use low-mineralized water with a minimum content of toxic elements. If necessary, preliminary aeration of the water is possible, during which precipitation of iron, arsenic and uranium occurs. Due to the typical nature of the problem under consideration for the Arctic regions, the results obtained can be used at other sites in the Subpolar zone.

Evaluating spatiotemporal groundwater quality changes in the Rhodope coastal aquifer system (NE Greece) employing a GIS-assisted hybrid approach of multivariate statistics and inverse geochemical modeling.

This research employs a GIS-assisted approach of multivariate statistics and inverse geochemical modeling to unravel the processes driving groundwater salinization in a complex aquifer system. Multivariate statistical methods define the end-member water groups, identifying dominant processes explaining hydrogeochemical variance in wet and dry season water chemistry datasets. Mineral saturation indices (SIs) and inverse geochemical modeling (IGM) investigate potential geochemical reactions and mixing processes responsible for the observed groundwater compositions and their spatiotemporal evolution along reversed flow paths caused by overexploitation in the Rhodope aquifer system. Results reveal that a concise set of reactant and product phases, including CO2(g), H2O, calcite, gypsum, halite, celestite, plagioclase, K-feldspar, illite, and Ca-montmorillonite, along with ion exchange processes (CaX2, MgX2, and NaX), explains the hydrogeochemical evolution of groundwater along reversed flow paths between genetically and compositionally different surface and groundwater bodies. Systematic changes in water chemistry along the flow paths are attributed to mixing of surface waters and/or different groundwater end-members, dilution by a freshwater component, water-rock interaction (WRI) processes, and ion exchange involving Ca/Mg- and/or Na-clays. The chemical evolution represented by IGMs initiates with the mixing of Aegean seawater and Aspropotamos River, incorporating WRI and ion exchange processes (Mg- and Na-clays) to produce the water chemistry of Vistonida Lake, the only surface water body with hydraulic interaction with the groundwater system in the study area. Statistically-defined end-member water groups effectively explain the groundwater flow system and evolutionary processes between hydraulically connected surface and groundwater bodies. Overall, the fusion of multivariate statistical analysis (MVSA), inverse geochemical modeling (IGM), and GIS techniques proves potent and comprehensive, enhancing understanding of groundwater dynamics, improving prediction accuracy, aiding proficient management, and facilitating data-driven decision-making within the realm of groundwater assessment and management.

Deciphering the geochemical influences on bacterial diversity and communities among two Algerian hot springs.

Northeastern Algeria boasts numerous hot springs, yet these hydrothermal sites remain largely unexplored for their microbial ecology. The present study explores the bacterial abundance and diversity within two distinct Algerian hot springs (Hammam Saïda and Hammam Debagh) and investigates the link between the prevailing bacteria with geochemical parameters. High-throughput 16S rRNA gene sequencing of water and sediment samples revealed a bacterial dominance of 99.85-91.16% compared to Archaea (0.14-0.66%) in both springs. Interestingly, Saïda hot spring, characterized by higher temperatures and sodium content, harbored a community dominated by Pseudomonadota (51.13%), whereas Debagh, a Ca-Cl-SO4 type spring, was primarily populated by Bacillota with 55.33%. Bacteroidota displayed even distribution across both sites. Additional phyla, including Chloroflexota, Deinococcota, Cyanobacteriota, and Chlorobiota, were also present. Environmental factors, particularly temperature, sodium, potassium, and alkalinity, significantly influenced bacterial diversity and composition. These findings shed light on the interplay between distinct microbial communities and their associated geochemical properties, providing valuable insights for future research on biogeochemical processes in these unique ecosystems driven by distinct environmental conditions, including potential applications in bioremediation and enzyme discovery.

Isolation and Characterization of Thermophilic Bacteria from a Hot Spring in the State of Hidalgo, Mexico, and Geochemical Analysis of the Thermal Water.

Hot springs worldwide can be a source of extremophilic microorganisms of biotechnological interest. In this study, samplings of a hot spring in Hidalgo, Mexico, were conducted to isolate, identify, and characterize morphologically, biochemically, and molecularly those bacterial strains with potential industrial applications. In addition, a physicochemical and geochemical examination of the hot spring was conducted to fully understand the study region and its potential connection to the strains discovered. The hot spring was classified as sulfate-calcic according to the Piper Diagram; the hydrogeochemical analysis showed the possible interactions between minerals and water. Eighteen bacterial strains were isolated with optimal growth temperatures from 50 to 55 °C. All strains are Gram-positive, the majority having a rod shape, and one a round shape, and 17 produce endospores. Hydrolysis tests on cellulose, pectin, and xylan agar plates demonstrated enzymatic activity in some of the strains. Molecular identification through the 16S rDNA gene allowed classification of 17 strains within the Phylum Firmicutes and one within Deinococcus-Thermus. The bacterial strains were associated with the genera Anoxybacillus, Bacillus, Anerunibacillus, Paenibacillus, and Deinococcus, indicating a diversity of bacterial strains with potential industrial applications.

Reservoir temperature prediction based on characterization of water chemistry data-case study of western Anatolia, Turkey.

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.