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
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
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
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
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 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
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
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 is the major source of fresh water,and it performs a crucial role in maintaining ecosystems and adapting humans to climate variation.Due to excessive reliance on groundwater in some regions,the amount of g...Groundwater is the major source of fresh water,and it performs a crucial role in maintaining ecosystems and adapting humans to climate variation.Due to excessive reliance on groundwater in some regions,the amount of groundwater being consumed is higher than the recharge,which leads to a durative decline of groundwater level.This study analyzed the spatiotemporal variability in groundwater storage(GWS)in China.And the possible drivers of observed GWS changes were also identified.GWS level displayed large regional disparities with higher reserves in the Yangtze River Basin and Songhua River Basin.Temporally,GWS level showed decreasing trends in the North China Plain region,Yellow River Basin,Inner Mongolia Plateau and Junggar Basin.And,GWS showed a significant increase in the Tibetan Plateau and Songhua River Basin.Without considering the impact of human activities,groundwater reserves are also showing a decreasing trend in future climate scenarios in most of the 15 zones.Contribution analysis of driving forces on the basis of the percentages of standardized coefficient(r)suggested that the variations of GWS were largely controlled by anthropogenic activities with the contribution proportions of 35.43%-73.37%.And the contribution proportions of natural drivers accounted for 26.63%-64.62%,with the key factors of precipitation,temperature and vegetation cover.The results would help to formulate sustainable strategies for managing groundwater resource.展开更多
Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base ...Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base in China with significant strategic importance,has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation.This study integrated data from the Gravity Recovery and Climate Experiment Satellite(GRACE)and Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS)models,combined with weighted downscaling methodology and water balance principles,to reconstruct high-resolution(0.01°)terrestrial water storage(TWS)and GWS changes in the Ordos Mining Region,China from April 2002 to December 2021.The accuracy of GWS variations were validated through pumping test measurements.Subsequently,Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics.Key findings include:1)TWS in the study area showed a fluctuating but overall decreasing trend,with a total reduction of 8901.11 mm during study period.The most significant annual decrease occurred in 2021,reaching 1696.77 mm.2)GWS exhibited an accelerated decline,with an average annual change rate of 44.35 mm/yr,totaling a decrease of 887.05 mm.The lowest annual groundwater storage level was recorded in 2020,reaching 185.69 mm.3)Precipitation(PRE)contributed the most to GWS variation(q=0.52),followed by coal mining water consumption(MWS)(q=0.41).The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes(0.54).The synergistic effect of natural hydrological factors has a great influence on the change of GWS,but coal mining water consumption will continue to reduce GWS.These findings provide critical references for the management and regulation of groundwater resource in mining regions.展开更多
Water scarcity in Khuzestan Province,Iran,has attracted growing concerns despite the region's abundant water resources.The province predominantly relies on surface water,prompting an assessment of groundwater'...Water scarcity in Khuzestan Province,Iran,has attracted growing concerns despite the region's abundant water resources.The province predominantly relies on surface water,prompting an assessment of groundwater's potential to supplement water supplies during surface water shortages.This study assesses the province's groundwater availability and quality under increased exploitation conditions.Between 2008 and 2018,data on groundwater quantity and quality were collected from 204 exploration wells and 70 piezometric wells across 19 aquifers.The analysis revealed that 53%of aquifers in the eastern and northeastern regions experienced declining groundwater levels.Hydrochemical assessments indicated low concentrations of major ions in the northeastern,while high levels were observed from the central region towards the southeast.These variations were attributed to agricultural and industrial activities,seawater intrusion,and the influences of evaporation and geological factors.The dominant hydrochemical facies identified were of the Ca-Cl type.Water quality classification showed that 48%of groundwater samples fell within the C4S4-C4S1 category,primarily in the western,central,and southern regions,while 27%were classified as C3S2,C3S1,and 25%as C2S1,mainly in the northern and eastern regions.The Irrigation WWater Quality(IWQ)index indicated that many samples were suitable for irrigation.Additionally,the analysis potable groundwater was primarily found in the northern,northeastern,and eastern aquifers,with quality declining toward the south.The study highlights that certain aquifers in the northern and eastern regions offer greater potential for sustainable groundwater exploitation during water shortages.These findings provide valuable insights for on how to implement effective land and water management strategies to mitigate future water crises.展开更多
This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural fa...This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity.Incorporating factors such as population growth,agricultural practices,and groundwater extraction enhances the framework’s ability to capture multi-dimensional,spatiotemporal changes in groundwater vulnerability.Additional improvements include refined weighting and rating scales for thematic layers based on available observational data,and the inclusion of distributed recharge.We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin,a major groundwater-dependent agricultural area in South Asia.Results indicate that between 2005 and 2020,54%of the study area became highly vulnerable to pollution.The MHGI revealed a 13%decline in potential groundwater storage and a 25%increase in groundwater-stressed zones,driven primarily by population growth and intensive agriculture.Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19%decline in areas of low to very low vulnerability and a 6%reduction in medium vulnerability zones by 2020.Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge(42%)and renewable groundwater stress(38%).Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI.The framework provides valuable insights to guide sustainable groundwater management,safeguarding both environmental integrity and human well-being.展开更多
The diversity and discontinuity of plant communities in the oasis–desert ecotone are largely shaped by variations in groundwater depth,yet the relationships between spatial distribution patterns and ecological niches...The diversity and discontinuity of plant communities in the oasis–desert ecotone are largely shaped by variations in groundwater depth,yet the relationships between spatial distribution patterns and ecological niches at a regional scale remain insufficiently understood.This study examined the oasis–desert ecotone in Qira County located in the Tarim Basin of China to investigate the spatial distribution of plant communities and groundwater depth as well as their relationships using an integrated approach that combined remote sensing techniques,field monitoring,and numerical modeling.The results showed that vegetation distribution exhibits marked spatial heterogeneity,with coverage ranked as follows:Tamarix ramosissima>Phragmites australis>Populus euphratica>Alhagi sparsifolia.Numerical simulations indicated that groundwater depths range from 2.00 to 65.00 m below the surface,with the system currently in equilibrium,sustaining an average annual recharge of 1.06×10^(8) m^(3) and an average annual discharge of 1.01×10^(8) m^(3).Groundwater depth strongly influences vegetation composition and structure:Phragmites australis dominates at average groundwater depth of 5.83 m,followed by Populus euphratica at average groundwater depth of 7.05 m.As groundwater depth increases,the community is initially predominated by Tamarix ramosissima(average groundwater depth of 8.35 m),then becomes a mixture of Tamarix ramosissima,Populus euphratica,and Karelinia caspia(average groundwater depth of 10.50 m),and finally transitions to Alhagi sparsifolia(average groundwater depth of 14.30 m).These findings highlight groundwater-dependent ecological thresholds that govern plant community composition and provide a scientific basis for biodiversity conservation,ecosystem stability,and vegetation restoration in the arid oasis–desert ecotone.展开更多
[Objective]The paper aimed to study effect of shallow groundwater at different depths on crop water requirement and crop evaporation in spring wheat field.[Method]Five treatments of shallow groundwater table at differ...[Objective]The paper aimed to study effect of shallow groundwater at different depths on crop water requirement and crop evaporation in spring wheat field.[Method]Five treatments of shallow groundwater table at different depth were designed to do evaporation experiment for spring wheat in 2008-2009.[Result]The groundwater at different depths had great impact on crop growth and field evaporation;its supply accounted for 0-52% of actual evapotranspiration.Atmospheric evaporation and crop rooting depth were the major factors to affect the uptake of groundwater at shallow table,and the supply of deep groundwater was controlled by groundwater table.[Conclusion]The study reveled the pattern of evapotranspiration of spring wheat and evaporation of shallow groundwater at different depth,in order to supply basis for the rational and effective utilization of shallow groundwater as well as optimization of the irrigation scheduling for spring wheat.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
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 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 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.
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 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 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.
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funded by the China Geological Survey Program(No.DD20230075)the key project supported by the National Natural Science Foundation of China(No.U21A20155)。
文摘Groundwater is the major source of fresh water,and it performs a crucial role in maintaining ecosystems and adapting humans to climate variation.Due to excessive reliance on groundwater in some regions,the amount of groundwater being consumed is higher than the recharge,which leads to a durative decline of groundwater level.This study analyzed the spatiotemporal variability in groundwater storage(GWS)in China.And the possible drivers of observed GWS changes were also identified.GWS level displayed large regional disparities with higher reserves in the Yangtze River Basin and Songhua River Basin.Temporally,GWS level showed decreasing trends in the North China Plain region,Yellow River Basin,Inner Mongolia Plateau and Junggar Basin.And,GWS showed a significant increase in the Tibetan Plateau and Songhua River Basin.Without considering the impact of human activities,groundwater reserves are also showing a decreasing trend in future climate scenarios in most of the 15 zones.Contribution analysis of driving forces on the basis of the percentages of standardized coefficient(r)suggested that the variations of GWS were largely controlled by anthropogenic activities with the contribution proportions of 35.43%-73.37%.And the contribution proportions of natural drivers accounted for 26.63%-64.62%,with the key factors of precipitation,temperature and vegetation cover.The results would help to formulate sustainable strategies for managing groundwater resource.
基金Under the National Key R&D Program Key Project(No.2021YFC3201201)National Natural Science Foundation of China(No.52360032)+2 种基金Basic Scientific Research Business Fee Project of Colleges And Universities Directly Under the Inner Mongolia Autonomous Region(No.JBYYWF2022001)Development Plan of Innovation Team of Colleges And Universities in Inner Mongolia Autonomous Region(No.NMGIRT2313)the Innovation Team of‘Grassland Talents’。
文摘Clarifying the mechanisms through which coal mining affects groundwater storage(GWS)variations is crucial for water resource conservation and sustainable development.The Ordos Mining Region in China,a key energy base in China with significant strategic importance,has undergone intensive coal mining activities that have substantially disrupted regional groundwater circulation.This study integrated data from the Gravity Recovery and Climate Experiment Satellite(GRACE)and Famine Early Warning Systems Network(FEWS NET)Land Data Assimilation System(FLDAS)models,combined with weighted downscaling methodology and water balance principles,to reconstruct high-resolution(0.01°)terrestrial water storage(TWS)and GWS changes in the Ordos Mining Region,China from April 2002 to December 2021.The accuracy of GWS variations were validated through pumping test measurements.Subsequently,Geodetector analysis was implemented to quantify the contributions of natural and anthropogenic factors to groundwater storage dynamics.Key findings include:1)TWS in the study area showed a fluctuating but overall decreasing trend,with a total reduction of 8901.11 mm during study period.The most significant annual decrease occurred in 2021,reaching 1696.77 mm.2)GWS exhibited an accelerated decline,with an average annual change rate of 44.35 mm/yr,totaling a decrease of 887.05 mm.The lowest annual groundwater storage level was recorded in 2020,reaching 185.69 mm.3)Precipitation(PRE)contributed the most to GWS variation(q=0.52),followed by coal mining water consumption(MWS)(q=0.41).The interaction between PRE and MWS exhibited a nonlinear enhancement effect on GWS changes(0.54).The synergistic effect of natural hydrological factors has a great influence on the change of GWS,but coal mining water consumption will continue to reduce GWS.These findings provide critical references for the management and regulation of groundwater resource in mining regions.
文摘Water scarcity in Khuzestan Province,Iran,has attracted growing concerns despite the region's abundant water resources.The province predominantly relies on surface water,prompting an assessment of groundwater's potential to supplement water supplies during surface water shortages.This study assesses the province's groundwater availability and quality under increased exploitation conditions.Between 2008 and 2018,data on groundwater quantity and quality were collected from 204 exploration wells and 70 piezometric wells across 19 aquifers.The analysis revealed that 53%of aquifers in the eastern and northeastern regions experienced declining groundwater levels.Hydrochemical assessments indicated low concentrations of major ions in the northeastern,while high levels were observed from the central region towards the southeast.These variations were attributed to agricultural and industrial activities,seawater intrusion,and the influences of evaporation and geological factors.The dominant hydrochemical facies identified were of the Ca-Cl type.Water quality classification showed that 48%of groundwater samples fell within the C4S4-C4S1 category,primarily in the western,central,and southern regions,while 27%were classified as C3S2,C3S1,and 25%as C2S1,mainly in the northern and eastern regions.The Irrigation WWater Quality(IWQ)index indicated that many samples were suitable for irrigation.Additionally,the analysis potable groundwater was primarily found in the northern,northeastern,and eastern aquifers,with quality declining toward the south.The study highlights that certain aquifers in the northern and eastern regions offer greater potential for sustainable groundwater exploitation during water shortages.These findings provide valuable insights for on how to implement effective land and water management strategies to mitigate future water crises.
基金funding from the National Science Foundation(NSF Award 2114701)of the United States.
文摘This study advances the DRASTIC groundwater vulnerability assessment framework by integrating a multi-hazard groundwater index(MHGI)to account for the dynamic impacts of diverse anthropogenic activities and natural factors on both groundwater quality and quantity.Incorporating factors such as population growth,agricultural practices,and groundwater extraction enhances the framework’s ability to capture multi-dimensional,spatiotemporal changes in groundwater vulnerability.Additional improvements include refined weighting and rating scales for thematic layers based on available observational data,and the inclusion of distributed recharge.We demonstrate the practical utility of this dynamic DRASTIC-based framework through its application to the agro-urban regions of the Irrigated Indus Basin,a major groundwater-dependent agricultural area in South Asia.Results indicate that between 2005 and 2020,54%of the study area became highly vulnerable to pollution.The MHGI revealed a 13%decline in potential groundwater storage and a 25%increase in groundwater-stressed zones,driven primarily by population growth and intensive agriculture.Groundwater vulnerability based on both groundwater quality and quantity dimensions showed a 19%decline in areas of low to very low vulnerability and a 6%reduction in medium vulnerability zones by 2020.Sensitivity analyses indicated that groundwater vulnerability in the region is most influenced by groundwater recharge(42%)and renewable groundwater stress(38%).Validation with in-situ data yielded area under the curve values of 0.71 for groundwater quality vulnerability and 0.63 for MHGI.The framework provides valuable insights to guide sustainable groundwater management,safeguarding both environmental integrity and human well-being.
基金financially supported by the Tianchi Talents Program of Xinjiang Uygur Autonomous Region(E5358525,2025–2026)the Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region(2024A03009-4)+4 种基金the Third Xinjiang Scientific Expedition Program(2022xjkk010402)the National Key Research and Development Program of China(2022FY202305-06)the Tianshan Talents Program of Xinjiang Uygur Autonomous Region(2022TSYCJU0002)the Outstanding Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences(20192024–2026).
文摘The diversity and discontinuity of plant communities in the oasis–desert ecotone are largely shaped by variations in groundwater depth,yet the relationships between spatial distribution patterns and ecological niches at a regional scale remain insufficiently understood.This study examined the oasis–desert ecotone in Qira County located in the Tarim Basin of China to investigate the spatial distribution of plant communities and groundwater depth as well as their relationships using an integrated approach that combined remote sensing techniques,field monitoring,and numerical modeling.The results showed that vegetation distribution exhibits marked spatial heterogeneity,with coverage ranked as follows:Tamarix ramosissima>Phragmites australis>Populus euphratica>Alhagi sparsifolia.Numerical simulations indicated that groundwater depths range from 2.00 to 65.00 m below the surface,with the system currently in equilibrium,sustaining an average annual recharge of 1.06×10^(8) m^(3) and an average annual discharge of 1.01×10^(8) m^(3).Groundwater depth strongly influences vegetation composition and structure:Phragmites australis dominates at average groundwater depth of 5.83 m,followed by Populus euphratica at average groundwater depth of 7.05 m.As groundwater depth increases,the community is initially predominated by Tamarix ramosissima(average groundwater depth of 8.35 m),then becomes a mixture of Tamarix ramosissima,Populus euphratica,and Karelinia caspia(average groundwater depth of 10.50 m),and finally transitions to Alhagi sparsifolia(average groundwater depth of 14.30 m).These findings highlight groundwater-dependent ecological thresholds that govern plant community composition and provide a scientific basis for biodiversity conservation,ecosystem stability,and vegetation restoration in the arid oasis–desert ecotone.
基金Supported by Science Research Project of Ningxia Higher Education~~
文摘[Objective]The paper aimed to study effect of shallow groundwater at different depths on crop water requirement and crop evaporation in spring wheat field.[Method]Five treatments of shallow groundwater table at different depth were designed to do evaporation experiment for spring wheat in 2008-2009.[Result]The groundwater at different depths had great impact on crop growth and field evaporation;its supply accounted for 0-52% of actual evapotranspiration.Atmospheric evaporation and crop rooting depth were the major factors to affect the uptake of groundwater at shallow table,and the supply of deep groundwater was controlled by groundwater table.[Conclusion]The study reveled the pattern of evapotranspiration of spring wheat and evaporation of shallow groundwater at different depth,in order to supply basis for the rational and effective utilization of shallow groundwater as well as optimization of the irrigation scheduling for spring wheat.
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.
