Search Results (819)

It is well known that the operation of a Borehole Heat Exchanger (BHE) can thermally induce groundwater convection in aquifers, enhancing the thermal performance of the BHE. However, the effect on the thermal performance of BHEs installed in low-permeable rock formations remains unclear. In this study, two BHEs were installed in a silty sandstone formation, one backfilled with high-permeable materials and the other grouted with sand–bentonite slurry. A Thermal Response Test (TRT) showed that the fluid outlet temperature of the high-permeable-material backfilled BHE was about 2.5 °C lower than that of the BHE refilled with sand–bentonite slurry, implying a higher thermal efficiency. The interpreted borehole thermal parameters also show a lower borehole thermal resistance in the high-permeable-material backfilled BHE. Physical model tests reveal that groundwater convective flow was induced in the high-permeable-material backfilled BHE. A test of BHEs with different borehole diameters shows that the larger the borehole diameter, the higher the thermal efficiency is. Thus, the thermal performance enhancement was attributed to two factors. First, the induced groundwater flow accelerates heat transfer by convection. Additionally, the increment of the thermal volumetric capacity of the groundwater stored inside a high-permeable-material refilled borehole stabilized the borehole’s temperature, which is key to sustaining high thermal efficiency in a BHE. The thermal performance enhancement demonstrated here shows potential for reducing reliance on fossil-fuel-based energy resources in challenging geological settings, thereby contributing to developing more sustainable geothermal energy solutions. Further validation in diverse field conditions is recommended to generalize these findings. Full article

Assessing the quality of groundwater, a primary source of water in many sectors, is of paramount importance. To this end, modeling the geospatial distribution of chemical contaminants in groundwater can be of great utility. Machine learning (ML) models are being increasingly used to overcome the shortcomings of conventional predictive techniques. We report here a systematic review of the nature and utility of various supervised and unsupervised ML models during the past two decades of machine learning groundwater hazard mapping (MLGHM). We identified and reviewed 284 relevant MLGHM journal articles that met our inclusion criteria. Firstly, trend analysis showed (i) an exponential increase in the number of MLGHM studies published between 2004 and 2025, with geographical distribution outlining Iran, India, the US, and China as the countries with the most extensively studied areas; (ii) nitrate as the most studied target, and groundwater chemicals as the most frequently considered category of predictive variables; (iii) that tree-based ML was the most popular model for feature selection; (iv) that supervised ML was far more favored than unsupervised ML (94% vs. 6% of models) with tree-based category—mostly random forest (RF)—as the most popular supervised ML. Secondly, compiling accuracy-based comparisons of ML models from the explored literature revealed that RF, deep learning, and ensembles (mostly meta-model ensembles and boosting ensembles) were frequently reported as the most accurate models. Thirdly, a critical evaluation of MLGHM models in terms of predictive accuracy, along with several other factors such as models’ computational efficiency and predictive power—which have often been overlooked in earlier review studies—resulted in considering the relative merits of commonly used MLGHM models. Accordingly, a flowchart was designed by integrating several MLGHM key criteria (i.e., accuracy, transparency, training speed, number of hyperparameters, intended scale of modeling, and required user’s expertise) to assist in informed model selection, recognising that the weighting of criteria for model selection may vary from problem to problem. Lastly, potential challenges that may arise during different stages of MLGHM efforts are discussed along with ideas for optimizing MLGHM models. Full article

This study examines the impact of physicochemical and geological factors on radon concentrations in groundwater throughout the Mexican Altiplano. Geological diversity, uranium deposits, seismic zones, and geothermal areas with high heat flow are all potential factors contributing to the presence of radon in groundwater. To move beyond local-scale assessments, this research employs spatial prediction methodologies that incorporate geological and geochemical variables recognized for their role in radon transport and geogenic potential. Certain properties of radon enable it to serve as an ideal tracer, viz., short half-life, inertness, and higher incidence in groundwater than surface water. Twenty-five variables were analyzed in samples from 135 water wells. Geostatistical techniques, including inverse distance weighted interpolation and kriging, were used in conjunction with multivariate statistical analyses. Salinity and geothermal heat flow are key indicators for determining groundwater origin, revealing a dynamic interplay between geothermal activity and hydrogeochemical evolution, where high temperatures do not necessarily correlate with increased solute concentrations. The occurrence of toxic trace elements such as Cd, Cr, and Pb is primarily governed by lithogenic sources and proximity to mineralized zones. Radon levels in groundwater are mainly influenced by geological and structural features, notably rhyolitic formations and deep hydrothermal systems. These findings underscore the importance of site-specific groundwater examination, combined with spatiotemporal models, to account for uranium–radium dynamics and flow paths, thereby enhancing radiological risk assessment. Full article

Groundwater seepage plays a critical role in the long-term safety of high-level radioactive waste (HLW) disposal, yet its characterization remains challenging due to the complexity of fractured rock media. This study introduces the Double-Layered Seismo-Electric Method (DSEM) for imaging groundwater seepage fields with enhanced sensitivity and spatial resolution. By integrating elastic wave propagation with electrokinetic coupling in a stratified framework, DSEM improves the detection of hydraulic gradients and preferential flow pathways. Application at a representative disposal site demonstrates that the method effectively delineates seepage channels and estimates hydraulic conductivity, providing reliable input parameters for groundwater flow modeling. These results highlight the potential of DSEM as a non-invasive geophysical technique to support safety assessments and long-term monitoring in deep geological disposal of high-level radioactive waste. Full article

Heavy metal contamination poses significant environmental risks to groundwater and soil, necessitating efficient early-warning technologies for leakage detection. This study proposes a novel early-warning approach for heavy metal leakage using the self-potential (SP) method. A coupled numerical model integrating seepage, ion diffusion, and electric potential fields was developed within the COMSOL Multiphysics platform in order to elucidate the dynamic response mechanism of SP signals to advancing seepage fronts. Key findings reveal that the SP signal responds 1.5 h earlier than the contaminant diffusion front (Case 1), providing a critical early-warning window. The leakage process exhibits a distinct bipolar SP anomaly pattern (negative upstream/positive downstream), with the most significant response observed at the downstream toe area. Consequently, an optimized monitoring strategy prioritizing downstream deployment is proposed and validated using a representative landfill model. This SP-based technology offers a promising solution for real-time environmental risk monitoring, particularly in ecologically sensitive zones. Full article

The Niland Moving Mud Spring, located near the southeastern margin of the Salton Sea, represents a rare and evolving geotechnical hazard. Unlike the typically stationary mud pots of the Salton Trough, this spring is a CO₂-driven mud spring that has migrated southwestward since 2016, at times exceeding 3 m per month, posing threats to critical infrastructure including rail lines, highways, and pipelines. Emergency mitigation efforts initiated in 2018, including decompression wells, containment berms, and route realignments, have since slowed and recently almost halted its movement and growth. This study integrates hydrochemical, temperature, stable isotope, and tritium data to propose a refined conceptual model of the Moving Mud Spring’s origin and migration. Temperature data from the Moving Mud Spring (26.5 °C to 28.3 °C) and elevated but non-geothermal total dissolved solids (~18,000 mg/L) suggest a shallow, thermally buffered groundwater source influenced by interaction with saline lacustrine sediments. Stable water isotope data follow an evaporative trajectory consistent with imported Colorado River water, while tritium concentrations (~5 TU) confirm a modern recharge source. These findings rule out deep geothermal or residual floodwater origins from the great “1906 flood”, and instead implicate more recent irrigation seepage or canal leakage as the primary water source. A key external forcing may be the 4.1 m drop in Salton Sea water level between 2003 and 2025, which has modified regional groundwater hydraulic head gradients. This recession likely enhanced lateral groundwater flow from the Moving Mud Spring area, potentially facilitating the migration of upwelling geothermal gases and contributing to spring movement. No faults or structural features reportedly align with the spring’s trajectory, and most major fault systems trend perpendicular to its movement. The hydrologically driven model proposed in this paper, linked to Salton Sea water level decline and correlated with the direction, rate, and timing of the spring’s migration, offers a new empirical explanation for the observed movement of the Niland Moving Mud Spring. Full article

Heavy metal contamination of aquatic systems represents a critical environmental and public health concern due to the persistence, toxicity, and bioaccumulative potential of these elements. Geographic information systems (GISs) have emerged as indispensable tools for the spatial assessment and management of heavy metals (HMs) in water resources. This review systematically synthesizes current research on GIS applications in detecting, monitoring, and modeling heavy metal pollution in surface and groundwater. A bibliometric analysis highlights five principal research directions: (i) global research trends on GISs and heavy metals in water, (ii) occurrence of HMs in relation to World Health Organization (WHO) permissible limits, (iii) GIS-based modeling frameworks for contamination assessment, (iv) identification of pollution sources, and (v) health risk evaluations through geospatial analyses. Case studies demonstrate the adaptability of GISs across multiple spatial scales, ranging from localized aquifers and river basins to regional hydrological systems, with frequent integration of advanced statistical techniques, remote sensing data, and machine learning approaches. Evidence indicates that concentrations of some HMs often surpass WHO thresholds, posing substantial risks to human health and aquatic ecosystems. Furthermore, GIS-supported analyses increasingly function as decision support systems, providing actionable insights for policymakers, environmental managers, and public health authorities. The synthesis presented herein confirms that the GIS is evolving beyond a descriptive mapping tool into a predictive, integrative framework for environmental governance. Future research directions should focus on coupling GISs with real-time monitoring networks, artificial intelligence, and transdisciplinary collaborations to enhance the precision, accessibility, and policy relevance of heavy metal risk assessments in water resources. Full article

Groundwater fluoride contamination poses a significant threat to sustainable water resources and public health, yet conventional water quality analysis is both time-consuming and costly, making large-scale, sustainable monitoring challenging. Machine learning methods offer a promising, cost-effective, and sustainable alternative for assessing the spatial distribution of fluoride. This study aimed to develop and compare the performance of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models for predicting groundwater fluoride contamination in the Datong Basin with the help of satellite embeddings from the AlphaEarth Foundation. Data from 391 groundwater sampling points were utilized, with the dataset partitioned into training (80%) and testing (20%) sets. The ANOVA F-value of each feature was calculated for feature selection, identifying surface elevation, pollution, population, evaporation, vertical distance to the rivers, distance to the Sanggan river, and nine extra bands from the satellite embeddings as the most relevant input variables. Model performance was evaluated using the confusion matrix and the area under the receiver operating characteristic curve (ROC-AUC). The results showed that the SVM model demonstrated the highest ROC-AUC (0.82), outperforming the RF (0.80) and MLP (0.77) models. The introduction of satellite embeddings improved the performance of all three models significantly, with the prediction errors decreasing by 13.8% to 23.3%. The SVM model enhanced by satellite embeddings proved to be a robust and reliable tool for predicting groundwater fluoride contamination, highlighting its potential for use in sustainable groundwater management. Full article

Water scarcity, soil salinization, and desertification threaten sustainable agricultural ecosystems of Hetao irrigation district, Yellow River Basin (YRB). Precise quantification of soil water dynamics and plant water consumption processes is essential for the agricultural sustainability of the irrigation district. Therefore, this study mainly focused on the crop coefficients and water consumption processes of three representative plant types in the Hetao irrigation district, each corresponding to a specific land system: Helianthus annuus (cultivated land), Tamarix chinensis (wasteland), and Phragmites australis (lake). The SIMDualKc model was calibrated and validated based on situ observation data (soil water content and yield) during 2018 (conventional conditions), 2023 and 2024 (deep water-saving conditions). Results show strong agreement between simulated and observed soil moisture and crop yields. The results indicate that the process curves of K_cb (basal crop coefficient) and K_cbadj (adjusted crop coefficient) nearly overlapped for the three plant types in 2018 and 2023. However, under the deep water-saving project implemented in 2024, the K_cbadj process curves for all three plant types exhibited a significant reduction (approximately 15%). Soil evaporation fractions (E/ET_cadj) were stable at 19–30% during the 2018, 2023, and 2024. The contribution of capillary rise to ET reached 38.61–43.18% in cultivated land (Helianthus annuus), 41.52–48.93% in wasteland (Tamarix chinensis), and 38.08–46.57% in lake boundary areas (Phragmites australis), which underscores the significant role of groundwater recharge in sustaining plant water consumption. Actual-to-potential transpiration ratios (T_a/T_p) during 2023–2024 decreased by 3–11% for Helianthus annuus, 5–12% for Tamarix chinensis, and 23% for Phragmites australis compared to T_a/T_p values in 2018. Capillary rise decreased approximately 10% during the whole system. Deep water-saving practices increased the groundwater depth and restricted groundwater recharge to plants via capillary rise, thereby impairing plant transpiration and growth. These findings provide scientific support for sustainable agriculture and ecological security in the Yellow River Basin. Full article

Compared to shallow geothermal systems, coaxial medium-deep and deep borehole heat exchangers (MDBHE and DBHE) offer higher temperatures and heat extraction rates while requiring less surface area, making them attractive options for sustainable heat supply in combination with ground-source heat pumps (GSHP). However, existing simulation tools for such systems are often limited in computational efficiency or open-source availability. To address this gap, we propose a rapid modeling approach using the open-source Python package “pygfunction” (v2.3.0). Its workflow was adjusted to accept the fluid inlet temperature as input. The effective undisturbed ground temperature and ground thermophysical properties were weight-averaged considering stratified ground layers. Validation of the approach was conducted by comparing simulation results with 12 references, including established models and experimental data. The proposed method enables fast estimation of fluid temperatures and heat extraction rates for single boreholes and small-scale bore fields in both homogeneous and heterogeneous geological conditions at depths of 700–3000 m, thus supporting rapid assessments of the coefficient of performance (COP) of GSHP. The approach systematically underestimates fluid outlet temperatures by up to 2–3 °C, resulting in a maximum underestimation of COP of 4%. Under significant groundwater flow or extreme geothermal gradients, these errors may increase to 4 °C and 6%, respectively. Based on the available data, these discrepancies may result in errors in GSHP electric power estimation of approximately ±10%. The method offers practical value for GSHP performance evaluation, geothermal potential mapping, and district heating network planning, supporting geologists, engineers, planners, and decision-makers. Full article

The precise and accurate use of aquifer hydraulic parameters for assessing local and regional recharge potential for enhancing groundwater allocation planning is vital for many hydrogeological studies. The conventional approach for allocating groundwater presents a challenging scenario, as it remains uncertain whether the applied recharge estimate is local or regional recharge. The approach does not account for the extent of the aquifer recharge in terms of local and regional scale; instead, it assumes that recharge is distributed across the catchment. This study aimed to demonstrate the use of aquifer hydraulic parameters (transmissivity and storativity) to explain areas of potential recharge (local and regional) for enhancing groundwater allocation planning with a specific case study of De Aar, Northern Cape, South Africa. It argues that not integrating local and regional recharge potentials in planning for groundwater allocations can result in over- or under-allocation of groundwater resources to users. A constant discharge pumping test and recovery test matching the duration of pumping were conducted for data collection. The Flow Characteristics method was used as a diagnostic tool to understand the different aquifer flow regimes in the study area. To develop an integrated understanding of the groundwater system, a hydrogeological conceptual model was used to visualize areas with higher or lower recharge potential across local and regional scales. Results showed significant variability in transmissivity, ranging from 213 to 596 m²/d, and storativity, ranging from 0.0000297 to 0.000185. The transmissivity values suggest that groundwater moves faster; meanwhile, the storativity values suggest that the aquifer system has high water storage capacity. These results will assist water resource planners in making informed decisions on how to allocate groundwater to users. This study demonstrated that aquifer hydraulic parameters are a valuable tool for improving groundwater allocations, thereby highlighting the importance of considering areas for potential recharge, both local and regional, in planning groundwater allocation. Full article

Geophysical and remote sensing observations offer powerful means to monitor large-scale hydrological changes, particularly in regions where in situ data are scarce. In this study, we integrate satellite-derived water storage from the Gravity Recovery and Climate Experiment (GRACE) with land surface variables from the Global Land Data Assimilation System (GLDAS) to assess and forecast groundwater storage (GWS) dynamics across eight major regions in Sudan. Missing GRACE observations of terrestrial water storage (TWS) were first reconstructed using a Random Forest machine learning model, after which GWS anomalies were estimated by subtracting GLDAS-based surface and root-zone components from TWS. The resulting GWS time series was decomposed into trend, seasonal, and residual components, and the trend signals were used to train a bootstrapped Bidirectional Long Short-Term Memory (BiLSTM) model. This framework generated probabilistic forecasts accompanied by confidence intervals, which were generally narrow and consistent with the historical range. The forecasted GWS anomalies indicate positive recovery across all regions, with Sen’s slope values ranging from 0.014 to 0.051 per month. The strongest recoveries are evident in the southern and southwestern regions, while northern and eastern areas display more modest gains. This work represents one of the first applications of deep learning with uncertainty quantification for GRACE-based groundwater analysis in Sudan, demonstrating the potential of such an integrated approach to support informed and sustainable groundwater management in data-limited environments. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

The Gravity Recovery and Climate Experiment (GRACE) mission, operational from 2002 to 2017, provided critical measurements of Earth’s gravity field anomalies which have been extensively used to study groundwater and terrestrial water storage (TWS) dynamics. In this research, we utilize GRACE data to identify, model, and analyze potential climate parameters contributing to the reconstruction of TWS variability in the Aral Sea Basin region (ASB). We assess the impact of climate change and anthropogenic nature management on TWS change using a quantitative method. Our analysis reveals a significant decline in the TWS at a rate of 0.44 cm year⁻¹ during the 2005–2009 period, primarily attributed to the prevailing drought conditions in the region. Notably, the estimated impact of anthropogenic influence on TWS during the same period of −1.39 cm year⁻¹ is higher than the influence of climatic variables, indicating that anthropogenic activity was the dominant factor in water resource depletion. In contrast, we observed an increase in TWS at a rate of 0.82 cm year⁻¹ during the 2013–2017 period, which can be attributed to the implementation of more effective water resource management practices in the ASB. Full article

This study has successfully developed a practical and straightforward approach for synthesizing nanocomposites with excellent dye removal potential. The SEM inspection of γ-Al₂O₃ (γ-Al) and MgO/MgAl₂O₄ (Mg/MgAl) nanocomposite showed a mean size of 59.0 and 46.4 nm, respectively, while the TEM results show particles with an average size of 26.63 and 13.4 nm, respectively. The surface area of γ-Al and Mg/MgAl was 50.0 and 69.5 m² g⁻¹, respectively. The study of adsorbing indigo carmine (IC) sorption onto γ-Al and Mg/MgAl presented sorption capacities of 41.6 and 55.9 mg g⁻¹, respectively, and both adsorbents attained equilibrium at 90 min. The highest IC sorption onto Mg/MgAl took place at pH 6.0. It is worth mentioning that raising the IC solution’s temperature from 20 °C to 50 °C increased the q_t to 268 mg g⁻¹. The IC sorption onto γ-Al and Mg/MgAl agreed with the pseudo-second-order model, and the liquid-film diffusion controlled the IC sorption. The Mg/MgAl showed an average removal of 98.2% when tested for removing six dyes at 10 mg L⁻¹, particularly malachite green, methylene blue, fast green, methyl orange, rhodamine B, and basic fuchsin, and the removal efficiency was 93.3% when their concentration increased to 20 mg L⁻¹. The Mg/MgAl nanocomposite performed exceptionally well in natural water samples (seawater and groundwater), indicating its potential applicability in managing water contamination. Full article