Within the framework of the contract of Sourou River, a survey of the groundwater quality was performed through 7 campaigns of water sampling and analysis from 2006 till 2012. The water samples resulted from 23 drilli...Within the framework of the contract of Sourou River, a survey of the groundwater quality was performed through 7 campaigns of water sampling and analysis from 2006 till 2012. The water samples resulted from 23 drillings and 9 wells located in the Sourou Valley. Among the analyzed physico-chemical parameters, the nitrates concentrations observed were worrisome. Out of 32 water sources, 14 (44%) supplied a nitrates content superior to the WHO threshold value for drinking water (50 mg NO3/L). Very high concentrations, superior to 500 mg NO3/L with a peak in 860 mg/L, were observed. Given the important variations observed from a sampling point to another, a generalized contamination of the total aquifer was not possible. An individual diagnosis allowed to identify the possible causes of this degradation. Several sources of contamination, in connection with the anthropological activities, were observed near the water facilities (drillings/wells): animal and human wild defecation, presence of nontight latrines, solid waste, wastewater discharges. It is also advisable to wonder about the impact of the dynamite use for digging wells, this one being able to leave nitrates in the water. With regard to the intensive use of water from the strongly contaminated wells and drillings by the rural populations of Sourou, implementing protection areas within which would be eliminated the sources of contamination in addition to health education among populations could improve the situation. Care should also be taken in the use of nitrates explosives for digging new wells or drillings.展开更多
Rare earth element （REE） concentrations of two different types of groundwaters （high SO42–water-SW and high alkaline waterCW） from coal bearing aquifer （–400~–280 m） in Renlou coal mine,northern Anhui Provinc...Rare earth element （REE） concentrations of two different types of groundwaters （high SO42–water-SW and high alkaline waterCW） from coal bearing aquifer （–400~–280 m） in Renlou coal mine,northern Anhui Province,China were measured.The results indicated that they had different REE characteristics： the total concentrations of REEs （ΣREE） of SW were lower than those of CW in general although they all had heavy REEs enriched relative to light REEs.The dissolved REE inorganic species of SW included Ln3＋,LnCO3＋,LnSO4＋,Ln（CO3）2– and Ln（SO4）2–,whereas the CW are Ln（CO3）2– and LnCO3＋ dominant,and the proportions of Ln（CO3）2– increased while other species decreased with pH increasing.Combined with correlation analysis,the enrichment and fractionation of SW （low alkaline water） were considered to be affected by alkaline concentrations via affecting the types and proportions of REE inorganic species.However,the effect of alkaline concentrations to the enrichment and fractionation of REEs of CW （high alkaline water） was less important than total dissolved solids and pH,which reflected the contribution from different rocks they flowed over,different degrees of water-rock interactions and/or REE solid-liquid partition coefficients.展开更多
The agricultural production on the irrigated grounds can not carry on without mineral fertilizers,pesticides and herbicides.Especially it is shown in Uzbekistan, in cultivation of cotton.There is an increase in minera...The agricultural production on the irrigated grounds can not carry on without mineral fertilizers,pesticides and herbicides.Especially it is shown in Uzbekistan, in cultivation of cotton.There is an increase in mineralization,rigidity,quantity of heavy metals,phenols and other pollutions in the cotton fields.Thus there is an exhaustion of stocks of fresh underground waters.In the year 2003 we were offered to create展开更多
The problem of arsenic(As)poisoning in the upper deltaic plains of the Ganges-Bhagirathi river system of West Bengal(WB),India,is terrifying. Elevated As(】50 ppb)in well water was observed within a depth range of 10-...The problem of arsenic(As)poisoning in the upper deltaic plains of the Ganges-Bhagirathi river system of West Bengal(WB),India,is terrifying. Elevated As(】50 ppb)in well water was observed within a depth range of 10-30 m in older grey terraces of abandoned fluvial channel deposits in the Murshidabad and Malda districts in WB.Both surface and cored(2-20 m)sediment samples from banks of the river Ganges and along a north-south transect of the main tributary Bhagirathi-Hooghly river展开更多
Major ions and stable isotopes of groundwater in the Cape Coast granitoid complex (G1) and Lower Birimian (LB) formations in the Eastern Region of Ghana were evaluated to establish the source of recharge to the ground...Major ions and stable isotopes of groundwater in the Cape Coast granitoid complex (G1) and Lower Birimian (LB) formations in the Eastern Region of Ghana were evaluated to establish the source of recharge to the groundwater system. Five major hydrochemical facies were identified in the various rocks in the study area. They are calcium-magnesium-bicarbonate, sodium bicarbonate, sodium chloride and calcium chloride waters and mixed or non dominant water type. Sodium chloride and calcium chloride waters dominate aqui-fers of the Cape Coast granitoid complex whereas calcium-magnesium-bicarbonate is the dominant hydro-chemical facies in the Lower Birimian aquifers. The most probable geochemical process responsible for the evolution of these hydrochemical facies is dissolution of minerals in the various rock types. Stable isotope composition of the groundwaters established that the recharge to the groundwater system is derived from rainfall.展开更多
The present paper,which is part of the implementation of the Project“Evaluation of the Groundwater Resources of Peru”,reports methodologies and techniques developed for on-site artificial tracer aided measurements o...The present paper,which is part of the implementation of the Project“Evaluation of the Groundwater Resources of Peru”,reports methodologies and techniques developed for on-site artificial tracer aided measurements of groundwater flow velocities.Horizontal flows are computed through labeling of the whole water column which is coated with a holed pipe in its entire length,below the piezometric level.Concentration monitoring inside the well,is performed prior to the experiment.The injection of a tracer in a borehole located in the influence area of the project,allowed the determination of velocity of ground water flow.The basis of the technique relates to the application of a relationship existing between the observed concentration decreases of a tracer solution released into the borehole.Changes in the position of the tracer as a function of time,allow us to draw some conclusions about the direction of flow as well.Satisfactory results show that techniques applied herein are cheap,simple and rapid methods for the determination of groundwater flow characteristics.展开更多
Groundwater is a vital drinking water source for populations in remote karst regions. However, the highly developed karst tube systems facilitate the infiltration of surface wastewater containing N-nitrosamines, raisi...Groundwater is a vital drinking water source for populations in remote karst regions. However, the highly developed karst tube systems facilitate the infiltration of surface wastewater containing N-nitrosamines, raising concerns about groundwater safety. To assess the safety of groundwater and identify which types are safer for consumption, this study investigated N-nitrosamines in various groundwater types, including ground river, karst cavern, well, and mountain spring waters, in Guangxi, a typical karst region in southwestern China. The total concentrations of eight N-nitrosamines in groundwater ranged from 5.1 to 70.3 ng/L, with N-nitrosodiethylamine (NDEA), N-nitrosodimethylamine (NDMA), and N-nitrosopyrrolidine (NPYR) being the dominant species. Ground river water exhibited significantly higher N-nitrosamine concentrations than karst cavern, well, and mountain spring waters. Significant correlations between N-nitrosamines and dissolved inorganic nitrogen suggested their co-emissions from domestic wastewater and the secondary formation potential of N-nitrosamines in groundwater. Redundancy analysis further identified domestic and swine wastewater as the primary sources. Ground river and mountain spring waters posed the highest risks among the four groundwater types, with 30 % and 20 % of sites, respectively, exceeding acceptable cancer risk thresholds. These findings underscore the importance of thorough water treatment before groundwater is used for drinking. Strict livestock farming and domestic wastewater discharge regulations are essential to mitigate contamination risks, particularly in karst areas.展开更多
The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims ...The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims to develop a hybrid model that combines the Fracture Aquifer Index(FAI)with the conventional GOD(Groundwater occurrence,Overall lithology,Depth to water table)method,to assess groundwater vulnerability in fractured aquifer.To develop the hybrid model,the classical GOD method was integrated with FAI to produce a single composite index.Each parameter within both GOD and FAI was scored,and a final index was calculated to delineate vulnerable areas.The results show that the study area can be classified into four vulnerability levels:Very low,low,moderate,and high,indicating that approximately 8%of the area exhibits very low vulnerability,29%has low vulnerability,25%falls into the moderate category,and 38%is considered highly vulnerable.The FAI-GOD model further incorporates fracture network characteristics.This refinement reduces the classification to three vulnerability classes:Low,medium,and high.The outcomes demonstrate that 46%of the area is highly vulnerable due to a dense concentration of fractures,while 17%represents an intermediate zone characterized by either shallow or deeper fractures.In contrast,37%corresponds to areas with lightly fractured rock,where the impact on vulnerability is minimal.Multivariate statistical analysis was employed using Principal Components Analysis(PCA)and Hierarchical Cluster Analysis(HCA)on 24 samples across six variables.The first three components account for over 76%of the total variance,reinforcing the significance of fracture dynamics in classifying vulnerability levels.The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low-and high-vulnerability zones,reflecting the dominant influence of fracture networks on aquifer sensitivity.While both indices use a five-class system,FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories,highlighting the critical role of fractures.A strong correlation(R2=0.94)between the GOD and FAI-GOD indices,demonstrated through second-order polynomial regression,confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution.This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.展开更多
Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai C...Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.展开更多
Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study...Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.展开更多
Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.Th...Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.The aim of this study was to assess impact of 15 diverse oils on groundwater quality and environmental forensics based on oil-water equilibrium experiments.Our results indicate that contamination of groundwater by gasoline and naphtha is primarily attributed to volatile hydrocarbons,while pollution from diesel,kerosene,and crude oil is predominantly from non-hydrocarbons.Rapid determination of the extent of non-hydrocarbon pollution in WSFs was achieved through a new quantitative index.Gasoline and naphtha exhibited the highest groundwater contamination potential while kerosene and light crude oils were also likely to cause groundwater contamina-tion.Although volatile hydrocarbons in the WSFs of diesel and jet fuel do not easily exceed current regulatory standards,unregulated non-hydrocarbons may pose a more severe contamination risk to groundwater.Notably,the presence of significant benzene and toluene,hydrogenation and alkylation products(e.g.,C4-C5 alkylben-zenes,alkylindenes,alkyltetralins,and dihydro-indenes),cycloalkanes in WSFs can effectively be utilized for preliminary source identification of light distillates,middle distillates,and crude oils,respectively.展开更多
The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This rev...The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.展开更多
Access to clean drinking water is essential for human health,economic development,and environmental sustain-ability.To effectively preserve water quality and ensure a safe and stable water supply,it is essential to de...Access to clean drinking water is essential for human health,economic development,and environmental sustain-ability.To effectively preserve water quality and ensure a safe and stable water supply,it is essential to determine the priority control factors of potentially hazardous elements in water.This study focused on public drinking wa-ter fountains in Zaječar City(Serbia),examining water hydrochemistry,quality,potential sources of hazardous elements,and the health risks associated with consumption or dermal exposure.Among all potentially hazardous elements,iron showed a deviation from the limit in drinking water prescribed by the World Health Organization,reaching 631μg/L.However,all samples were categorized as excellent quality for drinking.Water composition was governed by water-rock interactions,distinguishing Na-HCO_(3)as the dominant water type.A total of 3.3%and 6.6%of samples exceeded the threshold of 1 for non-carcinogenic health risk for adults and children,re-spectively,with the mean HIa value of 0.35 and the mean HIc value of 0.57.However,the carcinogenic risk was within the allowable limits for children,whereas it surpassed the threshold of 1.0×10^(–4)for adults in 10%of the samples.The positive matrix factorization model identified four sources responsible for water quality,i.e.,natural source,industrial source,sewage source,and agricultural source,with contributions of 37.1%,35.0%,17.8%,and 10.1%,respectively.The Monte Carlo simulation of source-specific health risks revealed that the industrial source was the main contributor to both non-carcinogenic and carcinogenic risks,attributed to its high arsenic load.展开更多
Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have n...Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.展开更多
This paper reports our work on the strontium hydrogeochemistry of thermal groundwa-ters in the Baikal Rift System (BRS) in Russia and Mongolia and the Xinzhou basin of the Shanxi Rift System (SRS) in northern China. T...This paper reports our work on the strontium hydrogeochemistry of thermal groundwa-ters in the Baikal Rift System (BRS) in Russia and Mongolia and the Xinzhou basin of the Shanxi Rift System (SRS) in northern China. Though similar in geological background, groundwaters from the BRS and the Xinzhou basin have different strontium isotope compositions. Both the strontium contents and the 87Sr/86Sr ratios of thermal water samples from Xinzhou are higher than those of most samples from Baikal. The major reason is the difference in hostrock geochemistry. The hos-trocks of the Xinzhou waters are Archaean metamorphic rocks, while those of the Baikal waters except the Kejielikov spring are Proterozoic or younger rocks. In the study areas, cold groundwaters usually show lower 87Sr/86Sr ratio due to shorter water-rock interaction history and lower equilibration degree. Strontium hydrogeochemistry often provides important information about mixing processes. Ca/Sr ratio can be used as an important hydrogeochemical parameter. Case studies at Xinzhou show that thermal waters with lowest Ca/Sr ratios are most weakly affected by mixing with shallow groundwaters, as supported by our hydrochemical and sulfur isotope studies.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Groundwater and surface water contamination by PTE(Potentially Toxic Elements)was assessed in Ruashi and Annexe municipalities of Lubumbashi city.Analyses of seventy water samples collected from six drilled wells,eigh...Groundwater and surface water contamination by PTE(Potentially Toxic Elements)was assessed in Ruashi and Annexe municipalities of Lubumbashi city.Analyses of seventy water samples collected from six drilled wells,eight spade-sunk wells,one river and one spring in both municipalities in 2017 and 2018 were carried out by ICP-SF-MS(Inductively Coupled Plasma-Sector Field Mass Spectrometry).Twenty PTEs including aluminum,arsenic,barium,bismuth,cadmium,cesium,chromium,cobalt,copper,iron,lead,manganese,molybdenum,nickel,strontium,thallium,tungsten,uranium,vanadium and zinc were detected at various concentrations in each one of the samples.Many samples had concentrations and mean concentrations of PTEs,such as aluminum,cadmium,copper,iron,lead,manganese,nickel and zinc,higher than the respective acceptable limits set for drinking water by the EU(European Union),the USEPA(United States Environmental Protection Agency),and the WHO(World Health Organization)standards.Most PTEs being deleterious to human health even at very low concentrations,people who use the groundwater and surface water to meet their water needs in both Ruashi and Annexe municipalities are at risk.展开更多
Addressing the global challenge of uranium(U)-contaminated groundwater requires innovative bioremediation strategies.This study investigates Desulfovibrio desulfuricans,a neutrophilic and mesophilic sulfate-reducing b...Addressing the global challenge of uranium(U)-contaminated groundwater requires innovative bioremediation strategies.This study investigates Desulfovibrio desulfuricans,a neutrophilic and mesophilic sulfate-reducing bacteria(SRB)strain optimized for lowtemperature(15℃)and acidic(initial pH 4)conditions,to validate its bioaugmentation potential for uranium decontamination in groundwater.Our research aimed to assess its efficacy in treating U-contaminated groundwater and elucidate the optimal growth conditions for this strain in acidic and sulfate-enriched environments.We found that D.desulfuricans was phylogenetically distinct from the native microbial community in acidic Ucontaminated groundwater,while it maintained appreciable activity in sulfate reduction under contaminated groundwater conditions after accumulation.Acid-tolerant D.desulfuricans removed 75.87%of uranium and 30.64%of sulfate from acidic U-contaminated groundwater(pH 4.0)at 15℃ within 14 days.Furthermore,we explored the optimal sulfate concentration for bacterial growth,which was found to be 2000 mg/L,and an elevated Fe^(2+) concentration from 100 to 1000 mg/L increasingly stimulated sulfate-reducing activity.These findings provide a novel insight into the application of neutrophilic and mesophilic SRB in bioremediation of acidic and low-temperature groundwater after accumulation and underscore the feasibility of bioremediation by using exogenously pure SRB.展开更多
Understanding the levels,causes,and sources of fluoride in groundwater is critical for public health,effective water resource management,and sustainable utilization.This study employs multivariate statistical methods,...Understanding the levels,causes,and sources of fluoride in groundwater is critical for public health,effective water resource management,and sustainable utilization.This study employs multivariate statistical methods,hazard quotient assessment,and geochemical analyses,such as mineral saturation index,ionic activities,and Gibbs diagrams,to investigate the hydrochemical characteristics,causes,and noncarcinogenic risks of fluoride in Red bed groundwater and geothermal water in the Guang'an area and neighboring regions.Approximately 9%of the Red bed groundwater samples contain fluoride concentrations exceeding 1 mg·L^(-1).The predominant water types identified are Cl-Na and HCO_(3)-Na,primarily influenced by evapotranspiration.Low-fluoride groundwater and high-fluoride geothermal water exhibit distinct hydrochemical types HCO_(3)-Ca and SO_(4)-Ca,respectively,which are mainly related to the weathering of carbonate,sulfate,and fluorite-containing rocks.Correlation analysis reveals that fluoride content in Red bed groundwater is positively associated with Na^(+),Cl^(-),SO_(4)^(2-),and TDS(r^(2)=0.45-0.64,p<0.01),while in geothermal water,it correlates strongly with pH,K^(+),Ca^(2+),and Mg^(2+)(r^(2)=0.52-0.80,p<0.05).Mineral saturation indices and ionic activities indicate that ion exchange processes and the dissolution of minerals such as carbonatite and fluorite are important sources of fluoride in groundwater.The enrichment of fluorine in the Red bed groundwater is linked to evaporation,cation exchange and dissolution of fluorite,caused by the lithologic characteristics of the red bed in this area.However,it exhibits minimal correlation with the geothermal water in the adjacent area.The noncarcinogenic health risk assessment indicates that 7%(n=5)of Red bed groundwater points exceed the fluoride safety limit for adults,while 12%(n=8)exceed the limit for children.These findings underscore the importance of avoiding highly fluoridated red bed groundwater as a direct drinking source and enhancing groundwater monitoring to mitigate health risks associated with elevated fluoride levels.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
文摘Within the framework of the contract of Sourou River, a survey of the groundwater quality was performed through 7 campaigns of water sampling and analysis from 2006 till 2012. The water samples resulted from 23 drillings and 9 wells located in the Sourou Valley. Among the analyzed physico-chemical parameters, the nitrates concentrations observed were worrisome. Out of 32 water sources, 14 (44%) supplied a nitrates content superior to the WHO threshold value for drinking water (50 mg NO3/L). Very high concentrations, superior to 500 mg NO3/L with a peak in 860 mg/L, were observed. Given the important variations observed from a sampling point to another, a generalized contamination of the total aquifer was not possible. An individual diagnosis allowed to identify the possible causes of this degradation. Several sources of contamination, in connection with the anthropological activities, were observed near the water facilities (drillings/wells): animal and human wild defecation, presence of nontight latrines, solid waste, wastewater discharges. It is also advisable to wonder about the impact of the dynamite use for digging wells, this one being able to leave nitrates in the water. With regard to the intensive use of water from the strongly contaminated wells and drillings by the rural populations of Sourou, implementing protection areas within which would be eliminated the sources of contamination in addition to health education among populations could improve the situation. Care should also be taken in the use of nitrates explosives for digging new wells or drillings.
基金Project supported by the National Natural Science Foundation of China (40873015)the Eleventh Five-Year Scientific and Technological Project of Anhui Province,China (08010302062)
文摘Rare earth element （REE） concentrations of two different types of groundwaters （high SO42–water-SW and high alkaline waterCW） from coal bearing aquifer （–400~–280 m） in Renlou coal mine,northern Anhui Province,China were measured.The results indicated that they had different REE characteristics： the total concentrations of REEs （ΣREE） of SW were lower than those of CW in general although they all had heavy REEs enriched relative to light REEs.The dissolved REE inorganic species of SW included Ln3＋,LnCO3＋,LnSO4＋,Ln（CO3）2– and Ln（SO4）2–,whereas the CW are Ln（CO3）2– and LnCO3＋ dominant,and the proportions of Ln（CO3）2– increased while other species decreased with pH increasing.Combined with correlation analysis,the enrichment and fractionation of SW （low alkaline water） were considered to be affected by alkaline concentrations via affecting the types and proportions of REE inorganic species.However,the effect of alkaline concentrations to the enrichment and fractionation of REEs of CW （high alkaline water） was less important than total dissolved solids and pH,which reflected the contribution from different rocks they flowed over,different degrees of water-rock interactions and/or REE solid-liquid partition coefficients.
文摘The agricultural production on the irrigated grounds can not carry on without mineral fertilizers,pesticides and herbicides.Especially it is shown in Uzbekistan, in cultivation of cotton.There is an increase in mineralization,rigidity,quantity of heavy metals,phenols and other pollutions in the cotton fields.Thus there is an exhaustion of stocks of fresh underground waters.In the year 2003 we were offered to create
文摘The problem of arsenic(As)poisoning in the upper deltaic plains of the Ganges-Bhagirathi river system of West Bengal(WB),India,is terrifying. Elevated As(】50 ppb)in well water was observed within a depth range of 10-30 m in older grey terraces of abandoned fluvial channel deposits in the Murshidabad and Malda districts in WB.Both surface and cored(2-20 m)sediment samples from banks of the river Ganges and along a north-south transect of the main tributary Bhagirathi-Hooghly river
文摘Major ions and stable isotopes of groundwater in the Cape Coast granitoid complex (G1) and Lower Birimian (LB) formations in the Eastern Region of Ghana were evaluated to establish the source of recharge to the groundwater system. Five major hydrochemical facies were identified in the various rocks in the study area. They are calcium-magnesium-bicarbonate, sodium bicarbonate, sodium chloride and calcium chloride waters and mixed or non dominant water type. Sodium chloride and calcium chloride waters dominate aqui-fers of the Cape Coast granitoid complex whereas calcium-magnesium-bicarbonate is the dominant hydro-chemical facies in the Lower Birimian aquifers. The most probable geochemical process responsible for the evolution of these hydrochemical facies is dissolution of minerals in the various rock types. Stable isotope composition of the groundwaters established that the recharge to the groundwater system is derived from rainfall.
文摘The present paper,which is part of the implementation of the Project“Evaluation of the Groundwater Resources of Peru”,reports methodologies and techniques developed for on-site artificial tracer aided measurements of groundwater flow velocities.Horizontal flows are computed through labeling of the whole water column which is coated with a holed pipe in its entire length,below the piezometric level.Concentration monitoring inside the well,is performed prior to the experiment.The injection of a tracer in a borehole located in the influence area of the project,allowed the determination of velocity of ground water flow.The basis of the technique relates to the application of a relationship existing between the observed concentration decreases of a tracer solution released into the borehole.Changes in the position of the tracer as a function of time,allow us to draw some conclusions about the direction of flow as well.Satisfactory results show that techniques applied herein are cheap,simple and rapid methods for the determination of groundwater flow characteristics.
基金supported by the National Natural Science Foundation of China(No.42203002)Guangxi Science and Technology Program(Guike AD25069074)+1 种基金Guangzhou Municipal Science and Technology Bureau(No.2023A04J0948)Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology(No.PM-zx960-202305-154).
文摘Groundwater is a vital drinking water source for populations in remote karst regions. However, the highly developed karst tube systems facilitate the infiltration of surface wastewater containing N-nitrosamines, raising concerns about groundwater safety. To assess the safety of groundwater and identify which types are safer for consumption, this study investigated N-nitrosamines in various groundwater types, including ground river, karst cavern, well, and mountain spring waters, in Guangxi, a typical karst region in southwestern China. The total concentrations of eight N-nitrosamines in groundwater ranged from 5.1 to 70.3 ng/L, with N-nitrosodiethylamine (NDEA), N-nitrosodimethylamine (NDMA), and N-nitrosopyrrolidine (NPYR) being the dominant species. Ground river water exhibited significantly higher N-nitrosamine concentrations than karst cavern, well, and mountain spring waters. Significant correlations between N-nitrosamines and dissolved inorganic nitrogen suggested their co-emissions from domestic wastewater and the secondary formation potential of N-nitrosamines in groundwater. Redundancy analysis further identified domestic and swine wastewater as the primary sources. Ground river and mountain spring waters posed the highest risks among the four groundwater types, with 30 % and 20 % of sites, respectively, exceeding acceptable cancer risk thresholds. These findings underscore the importance of thorough water treatment before groundwater is used for drinking. Strict livestock farming and domestic wastewater discharge regulations are essential to mitigate contamination risks, particularly in karst areas.
文摘The Gabes aquifer system,located in southeastern Tunisia,is a crucial resource for supporting local socio-economic activities.Due to its dual porosity structure,is particularly vulnerable to pollution.This study aims to develop a hybrid model that combines the Fracture Aquifer Index(FAI)with the conventional GOD(Groundwater occurrence,Overall lithology,Depth to water table)method,to assess groundwater vulnerability in fractured aquifer.To develop the hybrid model,the classical GOD method was integrated with FAI to produce a single composite index.Each parameter within both GOD and FAI was scored,and a final index was calculated to delineate vulnerable areas.The results show that the study area can be classified into four vulnerability levels:Very low,low,moderate,and high,indicating that approximately 8%of the area exhibits very low vulnerability,29%has low vulnerability,25%falls into the moderate category,and 38%is considered highly vulnerable.The FAI-GOD model further incorporates fracture network characteristics.This refinement reduces the classification to three vulnerability classes:Low,medium,and high.The outcomes demonstrate that 46%of the area is highly vulnerable due to a dense concentration of fractures,while 17%represents an intermediate zone characterized by either shallow or deeper fractures.In contrast,37%corresponds to areas with lightly fractured rock,where the impact on vulnerability is minimal.Multivariate statistical analysis was employed using Principal Components Analysis(PCA)and Hierarchical Cluster Analysis(HCA)on 24 samples across six variables.The first three components account for over 76%of the total variance,reinforcing the significance of fracture dynamics in classifying vulnerability levels.The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low-and high-vulnerability zones,reflecting the dominant influence of fracture networks on aquifer sensitivity.While both indices use a five-class system,FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories,highlighting the critical role of fractures.A strong correlation(R2=0.94)between the GOD and FAI-GOD indices,demonstrated through second-order polynomial regression,confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution.This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.
基金supported by the Northeast Geological Science and Technology Innovation Center of China Geological Survey(Grant NO.QCJJ2022-43)the Natural Resources Comprehensive Survey Project(Grant Nos.DD20230470,DD20230508)the National Groundwater Monitoring Network Operation and Maintenance Program(Grant No.DD20251300109).
文摘Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.
基金supported by the Iran National Science Foundation(INSF)the University of Birjand under grant number 4034771.
文摘Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.
基金supported by the National Science Foundation of China(Nos.42177042,and 42477051)the National Key R&D Program of China(No.2023YFC3708700)the Science Foundation of China University of Petroleum-Beijing(No.2462022QNXZ006).
文摘Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.The aim of this study was to assess impact of 15 diverse oils on groundwater quality and environmental forensics based on oil-water equilibrium experiments.Our results indicate that contamination of groundwater by gasoline and naphtha is primarily attributed to volatile hydrocarbons,while pollution from diesel,kerosene,and crude oil is predominantly from non-hydrocarbons.Rapid determination of the extent of non-hydrocarbon pollution in WSFs was achieved through a new quantitative index.Gasoline and naphtha exhibited the highest groundwater contamination potential while kerosene and light crude oils were also likely to cause groundwater contamina-tion.Although volatile hydrocarbons in the WSFs of diesel and jet fuel do not easily exceed current regulatory standards,unregulated non-hydrocarbons may pose a more severe contamination risk to groundwater.Notably,the presence of significant benzene and toluene,hydrogenation and alkylation products(e.g.,C4-C5 alkylben-zenes,alkylindenes,alkyltetralins,and dihydro-indenes),cycloalkanes in WSFs can effectively be utilized for preliminary source identification of light distillates,middle distillates,and crude oils,respectively.
文摘The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.
基金supported by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia(No.451-03-136/2025-03/200135)。
文摘Access to clean drinking water is essential for human health,economic development,and environmental sustain-ability.To effectively preserve water quality and ensure a safe and stable water supply,it is essential to determine the priority control factors of potentially hazardous elements in water.This study focused on public drinking wa-ter fountains in Zaječar City(Serbia),examining water hydrochemistry,quality,potential sources of hazardous elements,and the health risks associated with consumption or dermal exposure.Among all potentially hazardous elements,iron showed a deviation from the limit in drinking water prescribed by the World Health Organization,reaching 631μg/L.However,all samples were categorized as excellent quality for drinking.Water composition was governed by water-rock interactions,distinguishing Na-HCO_(3)as the dominant water type.A total of 3.3%and 6.6%of samples exceeded the threshold of 1 for non-carcinogenic health risk for adults and children,re-spectively,with the mean HIa value of 0.35 and the mean HIc value of 0.57.However,the carcinogenic risk was within the allowable limits for children,whereas it surpassed the threshold of 1.0×10^(–4)for adults in 10%of the samples.The positive matrix factorization model identified four sources responsible for water quality,i.e.,natural source,industrial source,sewage source,and agricultural source,with contributions of 37.1%,35.0%,17.8%,and 10.1%,respectively.The Monte Carlo simulation of source-specific health risks revealed that the industrial source was the main contributor to both non-carcinogenic and carcinogenic risks,attributed to its high arsenic load.
基金supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China(No.2023QN04011)the National Natural Science Foundation of China(Nos.42307092 and 52279067)+1 种基金Ordos Science and Technology Major Project(No.ZD20232303)Project of Key Laboratory of River and Lake in Inner Mongolia Autonomous Region(No.2022QZBZ0003).
文摘Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.
基金The research work was financially supported by the National Natural Science Foundation of China(Grant No. 49832005) Ministry of Science and Technology of China (Grant No. 95-pre-39) The Higher Education Ministry of Russia.
文摘This paper reports our work on the strontium hydrogeochemistry of thermal groundwa-ters in the Baikal Rift System (BRS) in Russia and Mongolia and the Xinzhou basin of the Shanxi Rift System (SRS) in northern China. Though similar in geological background, groundwaters from the BRS and the Xinzhou basin have different strontium isotope compositions. Both the strontium contents and the 87Sr/86Sr ratios of thermal water samples from Xinzhou are higher than those of most samples from Baikal. The major reason is the difference in hostrock geochemistry. The hos-trocks of the Xinzhou waters are Archaean metamorphic rocks, while those of the Baikal waters except the Kejielikov spring are Proterozoic or younger rocks. In the study areas, cold groundwaters usually show lower 87Sr/86Sr ratio due to shorter water-rock interaction history and lower equilibration degree. Strontium hydrogeochemistry often provides important information about mixing processes. Ca/Sr ratio can be used as an important hydrogeochemical parameter. Case studies at Xinzhou show that thermal waters with lowest Ca/Sr ratios are most weakly affected by mixing with shallow groundwaters, as supported by our hydrochemical and sulfur isotope studies.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金funding received from UNESCO-SIDA Project as well as Professor Martine Leermakers and Professor Willy Baeyens for their financial help to analyze the water samples in their laboratory at VUB.Acknowledgements
文摘Groundwater and surface water contamination by PTE(Potentially Toxic Elements)was assessed in Ruashi and Annexe municipalities of Lubumbashi city.Analyses of seventy water samples collected from six drilled wells,eight spade-sunk wells,one river and one spring in both municipalities in 2017 and 2018 were carried out by ICP-SF-MS(Inductively Coupled Plasma-Sector Field Mass Spectrometry).Twenty PTEs including aluminum,arsenic,barium,bismuth,cadmium,cesium,chromium,cobalt,copper,iron,lead,manganese,molybdenum,nickel,strontium,thallium,tungsten,uranium,vanadium and zinc were detected at various concentrations in each one of the samples.Many samples had concentrations and mean concentrations of PTEs,such as aluminum,cadmium,copper,iron,lead,manganese,nickel and zinc,higher than the respective acceptable limits set for drinking water by the EU(European Union),the USEPA(United States Environmental Protection Agency),and the WHO(World Health Organization)standards.Most PTEs being deleterious to human health even at very low concentrations,people who use the groundwater and surface water to meet their water needs in both Ruashi and Annexe municipalities are at risk.
基金supported by the Centralized R&D Project of China National Nuclear Corporation(CNNC[2021]No.144)the Key Research and Development Program of Hunan Province(Nos.2022SK2076 and 2020WK2022)+2 种基金the Natural Science Foundation of Changsha(No.kq2202089)the Postdoctoral Fellowship Program of CPSF(No.BX20230437)the Natural Science Foundation of Hunan Province(No.2023JJ30658).
文摘Addressing the global challenge of uranium(U)-contaminated groundwater requires innovative bioremediation strategies.This study investigates Desulfovibrio desulfuricans,a neutrophilic and mesophilic sulfate-reducing bacteria(SRB)strain optimized for lowtemperature(15℃)and acidic(initial pH 4)conditions,to validate its bioaugmentation potential for uranium decontamination in groundwater.Our research aimed to assess its efficacy in treating U-contaminated groundwater and elucidate the optimal growth conditions for this strain in acidic and sulfate-enriched environments.We found that D.desulfuricans was phylogenetically distinct from the native microbial community in acidic Ucontaminated groundwater,while it maintained appreciable activity in sulfate reduction under contaminated groundwater conditions after accumulation.Acid-tolerant D.desulfuricans removed 75.87%of uranium and 30.64%of sulfate from acidic U-contaminated groundwater(pH 4.0)at 15℃ within 14 days.Furthermore,we explored the optimal sulfate concentration for bacterial growth,which was found to be 2000 mg/L,and an elevated Fe^(2+) concentration from 100 to 1000 mg/L increasingly stimulated sulfate-reducing activity.These findings provide a novel insight into the application of neutrophilic and mesophilic SRB in bioremediation of acidic and low-temperature groundwater after accumulation and underscore the feasibility of bioremediation by using exogenously pure SRB.
基金supported by the China Geological Survey Project(Nos.DD20220864 and DD20243077).
文摘Understanding the levels,causes,and sources of fluoride in groundwater is critical for public health,effective water resource management,and sustainable utilization.This study employs multivariate statistical methods,hazard quotient assessment,and geochemical analyses,such as mineral saturation index,ionic activities,and Gibbs diagrams,to investigate the hydrochemical characteristics,causes,and noncarcinogenic risks of fluoride in Red bed groundwater and geothermal water in the Guang'an area and neighboring regions.Approximately 9%of the Red bed groundwater samples contain fluoride concentrations exceeding 1 mg·L^(-1).The predominant water types identified are Cl-Na and HCO_(3)-Na,primarily influenced by evapotranspiration.Low-fluoride groundwater and high-fluoride geothermal water exhibit distinct hydrochemical types HCO_(3)-Ca and SO_(4)-Ca,respectively,which are mainly related to the weathering of carbonate,sulfate,and fluorite-containing rocks.Correlation analysis reveals that fluoride content in Red bed groundwater is positively associated with Na^(+),Cl^(-),SO_(4)^(2-),and TDS(r^(2)=0.45-0.64,p<0.01),while in geothermal water,it correlates strongly with pH,K^(+),Ca^(2+),and Mg^(2+)(r^(2)=0.52-0.80,p<0.05).Mineral saturation indices and ionic activities indicate that ion exchange processes and the dissolution of minerals such as carbonatite and fluorite are important sources of fluoride in groundwater.The enrichment of fluorine in the Red bed groundwater is linked to evaporation,cation exchange and dissolution of fluorite,caused by the lithologic characteristics of the red bed in this area.However,it exhibits minimal correlation with the geothermal water in the adjacent area.The noncarcinogenic health risk assessment indicates that 7%(n=5)of Red bed groundwater points exceed the fluoride safety limit for adults,while 12%(n=8)exceed the limit for children.These findings underscore the importance of avoiding highly fluoridated red bed groundwater as a direct drinking source and enhancing groundwater monitoring to mitigate health risks associated with elevated fluoride levels.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
