Based on the first linearized Boussincsq equation, the analytical solution of the transient groundwater model, which is used for describing phreatic flow in a semiinfinite aquifer bounded by a linear stream and subjec...Based on the first linearized Boussincsq equation, the analytical solution of the transient groundwater model, which is used for describing phreatic flow in a semiinfinite aquifer bounded by a linear stream and subjected to time-dependent vertical seepage, is derived out by Laplace transform and the convolution integral. According to the mathematical characteristics of the solution, different methods for estimating aquifer parameters are constructed to satisfy different hydrological conditions. Then, tile equation for estimating water exchange between stream and aquifer is proposed, and a recursion equation or estimating the intensity of phreatic evaporation is also proposed. A phreatic aquifer stream system located in Huaibei Plain, Anhui Province, China, is taken as an example to demonstrate tile estimation process of the methods stated herein.展开更多
Effective groundwater management is crucial for economic sustainable development,particularly as climate change and population growth increase the uncertainty of aquifer dynamics.Due to limited geological data,Punjab&...Effective groundwater management is crucial for economic sustainable development,particularly as climate change and population growth increase the uncertainty of aquifer dynamics.Due to limited geological data,Punjab's complex hydrogeological conditions and Quaternary alluvial deposits present significant challenges for groundwater management.This study employs cost-effective numerical techniques as alternatives to traditional methods to safeguard groundwater quality,quantity,and accessibility.It introduces an edit-embedded transition frequency model that integrates regional datasets and utilizes algorithms such as GAMEAS,MCMOD,and TSIM to evaluate aquifer heterogeneity and simulate spatial variations using one-dimensional and three-dimensional Markov chains.Findings show that sand exhibits the highest self-transition(33.112 m),indicating strong stability,followed by silt,clay,and gravel,suggesting overall hydrofacies stability both horizontally and vertically.The model's predictions are largely consistent with actual material distribution,with a slight under-prediction of clay(-0.750%)and an over-prediction of sand(2.985%),which accounts for 58.77%of the aquifer material.It also highlights significant heterogeneity in the northern mountainous regions and minor variations in the south.The study emphasizes Punjab's severe water crisis,with groundwater reserves of 3502.3 BCM,declining water levels(0.38–33.62 m),and low hydraulic conductivity,urging government action on rainwater harvesting and sustainable groundwater management policies.展开更多
The transport behavior of pollutants under dynamic groundwater conditions has attracted significant attention recently.However,there is limited research on the simultaneous effects of groundwater table fluctuations on...The transport behavior of pollutants under dynamic groundwater conditions has attracted significant attention recently.However,there is limited research on the simultaneous effects of groundwater table fluctuations on the transport of co-existing pollutants,especially combined dense and light non-aqueous phase liquids(DNAPLs and LNAPLs).In this study,column experiments investigated toluene and dichloromethane transport in a controlled water table system with varying fluctuation conditions.Results showed that both dichloromethane and toluene accumulated near the groundwater table under static water table conditions,but the concentration of dichloromethane declined more rapidly than toluene due to differences in their physicochemical properties,such as solubility,density,and hydrophobicity.Groundwater fluctuations facilitated pollutants transportation towards deeper layers,potentially resulting in unforeseen increases in pollutant volatilization and downward fluxes.The interactions between dichloromethane and toluene,including competitive adsorption,enhanced dissolution,and altered kinematic viscosities,resulted in the reduced transport potential of dichloromethane while enhancing that of toluene.Furthermore,compared to dichloromethane,the initial upward fluctuation of the water table had a more pronounced impact on toluene due to its lower solubility and volatility.The downward transport risk index assessment indicated that among various factors considered,groundwater fluctuation amplitude exerted the most significant influence on pollutant migration risk.These new findings will provide important insights into understanding and assessment of the potential transport risk associated with combined LNAPLs and DNAPLs in the natural environment.展开更多
Major chemical ionic components in water serve as indicators of natural factors in the areas traversed by water bodies,and are thus widely used to elucidate key hydrogeochemical processes,including rock weathering,aqu...Major chemical ionic components in water serve as indicators of natural factors in the areas traversed by water bodies,and are thus widely used to elucidate key hydrogeochemical processes,including rock weathering,aquatic evaporation-crystallization,and the input of precipitation-derived materials into river basins.A total of 208 water samples were collected between August 2021 and August 2022 to investigate the hydrochemical characteristics and their influencing factors of the surface water and the groundwater in the Mingyong River Basin.To systematically analyze the data,we combined hydrogeochemical and statistical methods:descriptive statistics characterized ion concentration and physicochemical parameter distributions;Piper trilinear diagrams classified hydrochemical types;Pearson correlation analysis assessed ion-ion and ionTDS dependencies;Gibbs diagrams and ion ratio analysis identified solute sources;and the absolute principal component score-multiple linear regression(APCS-MLR)model quantified the contribution rates of different influencing factors.The results revealed that the dominant cations in the surface water and groundwater are Ca^(2+)and Mg^(2+),while the dominant anions are HCO_(3)^(-)and SO_(4)^(2-).The groundwater exhibits an extended residence time within rock strata,facilitating prolonged interaction with soluble minerals and intensifying the water-rock reaction process,thereby resulting in higher levels of electrical conductivity(EC),pH,and total dissolved solids(TDS)than those in the surface water.Secondly,the parameters of the surface water and groundwater indicate positive correlation.The weathering of rocks constitutes the primary solute source in the water of the basin.The hydrochemical composition of the basin water is primarily influenced by both carbonate and silicate rocks,with a minor contribution from evaporite rocks.The water bodies in the basin are affected by anthropogenic activities.The surface water is influenced by four sources,namely lixiviation-enrich,human activities,geological environmental,and unknown sources.The groundwater is influenced by five sources,namely lixiviation-enrich,primary geological,human activities,geological environmental,and unknown sources.展开更多
Using unscientific agricultural methods can harm human health by increasing harmful nitrate(NO_(3)−)levels in groundwater,as observed in the Yinchuan Plain.This research utilized hydrochemical data,dual isotopic data,...Using unscientific agricultural methods can harm human health by increasing harmful nitrate(NO_(3)−)levels in groundwater,as observed in the Yinchuan Plain.This research utilized hydrochemical data,dual isotopic data,the MixSIAR model,and the uncertainty index(UI90)to detect the potential sources of groundwater NO_(3)−,track NO_(3)−conversion processes,and calculate the apportionment of each groundwater NO_(3)−source in the agricultural lands of the Yinchuan Plain.The results show that soil organic nitrogen accounted for 49.4%,and N-fertilizers contributed 30.4%,making them the two main contributors to NO_(3)-contamination in groundwater.Long-term N-fertilization enhances soil organic nitrogen accumulation,resulting in NO_(3)−leaching into groundwater during irrigation.The highest uncertainty regarding soil organic nitrogen and N-fertilizers may stem from changes in groundwater flow patterns,unbalanced N-fertilization,irrigation,and precipitation.Denitrification is the dominant process,resulting in lower NO_(3)−concentrations in groundwater in most areas.As a result,most groundwater in the Yinchuan Plain is generally safe for human consumption,except the specific areas in Qingtongxia City and Wuzhong City.Flood irrigation can increase the leaching of NO_(3)−into groundwater,and the repeated recharge of groundwater contaminated with high NO_(3)−levels could also be a potential source of NO_(3)−contamination in agricultural areas.This research provides scientific guidance for sustainable groundwater management in the Yinchuan Plain,mitigating the risk of groundwater NO_(3)−pollution.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
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
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
Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC rec...Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.展开更多
Groundwater is a key part of the terrestrial ecosystem,but it is vulnerable to pollution in the context of chemical industry development.Treating contaminated groundwater is challenging due to its stable water quality...Groundwater is a key part of the terrestrial ecosystem,but it is vulnerable to pollution in the context of chemical industry development.Treating contaminated groundwater is challenging due to its stable water quality,hidden contamination,and complex treatment requirements.Current research focuses on advanced treatment technologies,among which the advanced oxidation process(AOPs) of peroxomonosulfate(PMS) has great potential.Although there are many reviews of PMS-based AOP,most of them focus on surface water.This review aims to explore the activation reaction of PMS to groundwater by in-situ chemical oxidation(ISCO) technology,further study the reaction mechanism,compare the treatment effect of characteristic pollutants in the groundwater of the chemical industry park,propose new activation methods and catalyst selection,and provide guidance for future groundwater treatment research.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
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
Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especially...Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios.展开更多
Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi...Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi-head Self-attention mechan-ism(MSAM-GRU)to simulate GWLs in both confined and unconfined aquifers simultaneously.The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation,as well as between GWLs in the confined aquifer and the upper aquifer.We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain,China from January 2005 to December 2020.With the consideration of lag times,the results indicated that the MSAM-GRU model exhibits a maximum 67%and 73%reduction in RMSE compared to the Attention mechanism-GRU(AM-GRU)and GRU model,respectively.MSAM-GRU model exhibited a 31%reduction in RMSE and a 0.12 increase in R^(2) compared to the same model that do not account for lag time.In Region I,the shortest lag time of GWL in the unconfined aquifer was two months,while that in the confined aquifer was three months,indicating a longer delayed response in the confined aquifer.MSAM-GRU model considering lag time,was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations.The simulation res-ults showed that under the scenario 1,the GWL in the unconfined aquifer would rise above the depth of subway station floor,threaten-ing the operation of subways.This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.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
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
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
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
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
Rapid screening of inorganic arsenic(iAs)in groundwater used for drinking by hundreds of millions of mostly rural residents worldwide is crucial for health protection.Most commercial field test kits are based on the G...Rapid screening of inorganic arsenic(iAs)in groundwater used for drinking by hundreds of millions of mostly rural residents worldwide is crucial for health protection.Most commercial field test kits are based on the Gutzeit reaction that uses mercury-based reagents for color development,an environmental concern that increasingly limits its utilization.This study further improves the Molybdenum Blue(MB)colorimetric method to allow for faster screening with more stable reagents.More importantly,a portable three-channel colorimeter is developed for screening iAs relative to the WHO drinking water guideline value(10μg/L).Adding the reducing reagents in sequence not only prolongs the storage time to>7 days,but also accelerates the color development time to 6 min in conjunction with lowering the H_(2)SO_(4) concentration in chromogenic reagents.The optimal pH ranges from 1.2 to 1.3 and is achieved by acidifying groundwater to 1%(V/V)HCl.With detection limits of 3.7μg/L for inorganic arsenate(iAs(V))and 3.8μg/L for inorganic arsenite(iAs(Ⅲ)),testing groundwater with-10μg/L of As has a precision<20%.The method works well for a range of phosphate concentrations of 48-950μg/L(0.5-10μmol/L).Concentrations of total_iAs(6-300μg/L),iAs(V)(6-230μg/L)and iAs(Ⅲ)(0-170μg/L)for 14 groundwater samples from Yinchuan Plain,Pearl River Delta,and Jianghan Plain,are in excellent agreements(linear regression slope:0.969-1.029)with the benchmark methods.The improved chemistry here lays the foundation for the MB colorimetric method to become a commercially viable screening tool,with further engineering and design improvement of the colorimeter.展开更多
基金National Natural Science Foundation of China(No.40474065)the National TCM Project in the 11th Five-Year Plan Period of China(No.2006BAB01B01)
文摘Based on the first linearized Boussincsq equation, the analytical solution of the transient groundwater model, which is used for describing phreatic flow in a semiinfinite aquifer bounded by a linear stream and subjected to time-dependent vertical seepage, is derived out by Laplace transform and the convolution integral. According to the mathematical characteristics of the solution, different methods for estimating aquifer parameters are constructed to satisfy different hydrological conditions. Then, tile equation for estimating water exchange between stream and aquifer is proposed, and a recursion equation or estimating the intensity of phreatic evaporation is also proposed. A phreatic aquifer stream system located in Huaibei Plain, Anhui Province, China, is taken as an example to demonstrate tile estimation process of the methods stated herein.
基金supported by the K.C.Wong Education Foundation(GJTD-2020-14)the National Natural Science Foundation of China(42071245)+3 种基金Third Xinjiang Scientific Expedition Program(2021XJKK1400)the China-Pakistan Joint Research Center on Earth Sciences that supported the implementation of this studythe Chinese Academy of Sciences(CAS)the CSC Scholarship for Young Talents(Doctor Program)for the financial support of this study。
文摘Effective groundwater management is crucial for economic sustainable development,particularly as climate change and population growth increase the uncertainty of aquifer dynamics.Due to limited geological data,Punjab's complex hydrogeological conditions and Quaternary alluvial deposits present significant challenges for groundwater management.This study employs cost-effective numerical techniques as alternatives to traditional methods to safeguard groundwater quality,quantity,and accessibility.It introduces an edit-embedded transition frequency model that integrates regional datasets and utilizes algorithms such as GAMEAS,MCMOD,and TSIM to evaluate aquifer heterogeneity and simulate spatial variations using one-dimensional and three-dimensional Markov chains.Findings show that sand exhibits the highest self-transition(33.112 m),indicating strong stability,followed by silt,clay,and gravel,suggesting overall hydrofacies stability both horizontally and vertically.The model's predictions are largely consistent with actual material distribution,with a slight under-prediction of clay(-0.750%)and an over-prediction of sand(2.985%),which accounts for 58.77%of the aquifer material.It also highlights significant heterogeneity in the northern mountainous regions and minor variations in the south.The study emphasizes Punjab's severe water crisis,with groundwater reserves of 3502.3 BCM,declining water levels(0.38–33.62 m),and low hydraulic conductivity,urging government action on rainwater harvesting and sustainable groundwater management policies.
基金supported by the National Key Research and Development Project of China(Nos.2024YFC3713802 and 2023YFC3709700)the Strategic Priority Research Program of the Chinese Academy of Sciences,China(No.XDA23030202)the Science and Technology Program for Foreign Cooperation in Fujian Province of China(No.2022I0040).
文摘The transport behavior of pollutants under dynamic groundwater conditions has attracted significant attention recently.However,there is limited research on the simultaneous effects of groundwater table fluctuations on the transport of co-existing pollutants,especially combined dense and light non-aqueous phase liquids(DNAPLs and LNAPLs).In this study,column experiments investigated toluene and dichloromethane transport in a controlled water table system with varying fluctuation conditions.Results showed that both dichloromethane and toluene accumulated near the groundwater table under static water table conditions,but the concentration of dichloromethane declined more rapidly than toluene due to differences in their physicochemical properties,such as solubility,density,and hydrophobicity.Groundwater fluctuations facilitated pollutants transportation towards deeper layers,potentially resulting in unforeseen increases in pollutant volatilization and downward fluxes.The interactions between dichloromethane and toluene,including competitive adsorption,enhanced dissolution,and altered kinematic viscosities,resulted in the reduced transport potential of dichloromethane while enhancing that of toluene.Furthermore,compared to dichloromethane,the initial upward fluctuation of the water table had a more pronounced impact on toluene due to its lower solubility and volatility.The downward transport risk index assessment indicated that among various factors considered,groundwater fluctuation amplitude exerted the most significant influence on pollutant migration risk.These new findings will provide important insights into understanding and assessment of the potential transport risk associated with combined LNAPLs and DNAPLs in the natural environment.
基金funded by the National Natural Science Foundation of China(No.42061012)the Open Project of Yunnan Province’s First Class Discipline of Soil and Water Conservation and Desertification Control(SBK20240019)。
文摘Major chemical ionic components in water serve as indicators of natural factors in the areas traversed by water bodies,and are thus widely used to elucidate key hydrogeochemical processes,including rock weathering,aquatic evaporation-crystallization,and the input of precipitation-derived materials into river basins.A total of 208 water samples were collected between August 2021 and August 2022 to investigate the hydrochemical characteristics and their influencing factors of the surface water and the groundwater in the Mingyong River Basin.To systematically analyze the data,we combined hydrogeochemical and statistical methods:descriptive statistics characterized ion concentration and physicochemical parameter distributions;Piper trilinear diagrams classified hydrochemical types;Pearson correlation analysis assessed ion-ion and ionTDS dependencies;Gibbs diagrams and ion ratio analysis identified solute sources;and the absolute principal component score-multiple linear regression(APCS-MLR)model quantified the contribution rates of different influencing factors.The results revealed that the dominant cations in the surface water and groundwater are Ca^(2+)and Mg^(2+),while the dominant anions are HCO_(3)^(-)and SO_(4)^(2-).The groundwater exhibits an extended residence time within rock strata,facilitating prolonged interaction with soluble minerals and intensifying the water-rock reaction process,thereby resulting in higher levels of electrical conductivity(EC),pH,and total dissolved solids(TDS)than those in the surface water.Secondly,the parameters of the surface water and groundwater indicate positive correlation.The weathering of rocks constitutes the primary solute source in the water of the basin.The hydrochemical composition of the basin water is primarily influenced by both carbonate and silicate rocks,with a minor contribution from evaporite rocks.The water bodies in the basin are affected by anthropogenic activities.The surface water is influenced by four sources,namely lixiviation-enrich,human activities,geological environmental,and unknown sources.The groundwater is influenced by five sources,namely lixiviation-enrich,primary geological,human activities,geological environmental,and unknown sources.
基金supported by the National Natural Science Foundation of China(Nos.42472316,42072286 and 41761144059)the National Key Research and Development Program of China(No.2023YFC3706901)Qinchuangyuan"Scientist+Engineer"Team Development Program of the Shaanxi Provincial Department of Science and Technology(No.2022KXJ-005).
文摘Using unscientific agricultural methods can harm human health by increasing harmful nitrate(NO_(3)−)levels in groundwater,as observed in the Yinchuan Plain.This research utilized hydrochemical data,dual isotopic data,the MixSIAR model,and the uncertainty index(UI90)to detect the potential sources of groundwater NO_(3)−,track NO_(3)−conversion processes,and calculate the apportionment of each groundwater NO_(3)−source in the agricultural lands of the Yinchuan Plain.The results show that soil organic nitrogen accounted for 49.4%,and N-fertilizers contributed 30.4%,making them the two main contributors to NO_(3)-contamination in groundwater.Long-term N-fertilization enhances soil organic nitrogen accumulation,resulting in NO_(3)−leaching into groundwater during irrigation.The highest uncertainty regarding soil organic nitrogen and N-fertilizers may stem from changes in groundwater flow patterns,unbalanced N-fertilization,irrigation,and precipitation.Denitrification is the dominant process,resulting in lower NO_(3)−concentrations in groundwater in most areas.As a result,most groundwater in the Yinchuan Plain is generally safe for human consumption,except the specific areas in Qingtongxia City and Wuzhong City.Flood irrigation can increase the leaching of NO_(3)−into groundwater,and the repeated recharge of groundwater contaminated with high NO_(3)−levels could also be a potential source of NO_(3)−contamination in agricultural areas.This research provides scientific guidance for sustainable groundwater management in the Yinchuan Plain,mitigating the risk of groundwater NO_(3)−pollution.
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 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.
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 Natural Science Foundation of China(Grant Nos.42077242 and 42171407)the Graduate Innovation Fund of Jilin University.
文摘Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.
基金supported by the National Key Research and Development Program of China (No.2023YFC3708005)the National Natural Science Foundation of China (Nos.21872102,22172080)the Fundamental Research Funds for the Central Universities (Nankai University,No.63241208)。
文摘Groundwater is a key part of the terrestrial ecosystem,but it is vulnerable to pollution in the context of chemical industry development.Treating contaminated groundwater is challenging due to its stable water quality,hidden contamination,and complex treatment requirements.Current research focuses on advanced treatment technologies,among which the advanced oxidation process(AOPs) of peroxomonosulfate(PMS) has great potential.Although there are many reviews of PMS-based AOP,most of them focus on surface water.This review aims to explore the activation reaction of PMS to groundwater by in-situ chemical oxidation(ISCO) technology,further study the reaction mechanism,compare the treatment effect of characteristic pollutants in the groundwater of the chemical industry park,propose new activation methods and catalyst selection,and provide guidance for future groundwater treatment research.
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 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Fund for funding this research work under Research Support Program for Central labs at King Khalid University through the project number CL/CO/B/6.
文摘Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios.
基金Under the auspices of the National Key Research and Development Program of China(No.2024YFC3713102)。
文摘Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi-head Self-attention mechan-ism(MSAM-GRU)to simulate GWLs in both confined and unconfined aquifers simultaneously.The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation,as well as between GWLs in the confined aquifer and the upper aquifer.We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain,China from January 2005 to December 2020.With the consideration of lag times,the results indicated that the MSAM-GRU model exhibits a maximum 67%and 73%reduction in RMSE compared to the Attention mechanism-GRU(AM-GRU)and GRU model,respectively.MSAM-GRU model exhibited a 31%reduction in RMSE and a 0.12 increase in R^(2) compared to the same model that do not account for lag time.In Region I,the shortest lag time of GWL in the unconfined aquifer was two months,while that in the confined aquifer was three months,indicating a longer delayed response in the confined aquifer.MSAM-GRU model considering lag time,was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations.The simulation res-ults showed that under the scenario 1,the GWL in the unconfined aquifer would rise above the depth of subway station floor,threaten-ing the operation of subways.This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.
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 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 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.
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the National Key R&D Program of China(No.2021YFA0715900)the National Natural Science Foundation of China(No.41831279)+2 种基金the Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks(No.ZDSYS20220606100604008)the Guangdong Province Bureau of Education(No.2020KCXTD006)the Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control(No.2023B1212060002).
文摘Rapid screening of inorganic arsenic(iAs)in groundwater used for drinking by hundreds of millions of mostly rural residents worldwide is crucial for health protection.Most commercial field test kits are based on the Gutzeit reaction that uses mercury-based reagents for color development,an environmental concern that increasingly limits its utilization.This study further improves the Molybdenum Blue(MB)colorimetric method to allow for faster screening with more stable reagents.More importantly,a portable three-channel colorimeter is developed for screening iAs relative to the WHO drinking water guideline value(10μg/L).Adding the reducing reagents in sequence not only prolongs the storage time to>7 days,but also accelerates the color development time to 6 min in conjunction with lowering the H_(2)SO_(4) concentration in chromogenic reagents.The optimal pH ranges from 1.2 to 1.3 and is achieved by acidifying groundwater to 1%(V/V)HCl.With detection limits of 3.7μg/L for inorganic arsenate(iAs(V))and 3.8μg/L for inorganic arsenite(iAs(Ⅲ)),testing groundwater with-10μg/L of As has a precision<20%.The method works well for a range of phosphate concentrations of 48-950μg/L(0.5-10μmol/L).Concentrations of total_iAs(6-300μg/L),iAs(V)(6-230μg/L)and iAs(Ⅲ)(0-170μg/L)for 14 groundwater samples from Yinchuan Plain,Pearl River Delta,and Jianghan Plain,are in excellent agreements(linear regression slope:0.969-1.029)with the benchmark methods.The improved chemistry here lays the foundation for the MB colorimetric method to become a commercially viable screening tool,with further engineering and design improvement of the colorimeter.
