Search Results (203)

Understanding groundwater and surface water interaction is critical for managing water resources, particularly in water-stressed and rapidly urbanizing areas, such as many parts of Africa. A survey was conducted of borehole, spring, seep and river water radon, δ²H, δ¹⁸O and field parameters in the Jukskei River catchment, Johannesburg. Average values of electrical conductivity (EC) were 274 and 411 μS·cm⁻¹ for groundwater and surface water, and similarly for radon, 37,000 and 1100 Bq·m⁻³, with a groundwater high of 196,000 Bq·m⁻³ associated with a structural lineament. High radon was a good indicator of baseflow, highest at the end of the rainy season (March) and lowest at the end of the dry season (September), with the FINIFLUX model computing groundwater inflow as 2.5–4.7 L·m⁻¹s⁻¹. High EC was a poorer indicator of baseflow, also considering the possibility of wastewater with high EC, typical in urban areas. Groundwater δ²H and δ¹⁸O values are spread widely, suggesting recharge from both normal and unusual rainfall periods. A slight shift from the local meteoric water line indicates light evaporation during recharge. Surface water δ²H and δ¹⁸O is clustered, pointing to regular groundwater input along the stream, supporting the findings from radon. Given the importance of groundwater, further study using the same parameters or additional analytes is advisable in the urban area of Johannesburg or other cities. Full article

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (initial support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

The treatment of contaminants from road infrastructure poses significant challenges due to their variable composition and the high concentrations of chloride ions, heavy metals, and oil-derived substances. Traditional methods for protecting groundwater environments are often insufficient. A promising alternative is permeable reactive barrier (PRB) technology, which utilizes recycled materials and construction waste as reactive components within the treatment zone of the ground. This paper delves into the potential of employing a composite (MIX) consisting of modified construction aggregate (as recycled material) and activated carbon (example of reactive material) to address environmental contamination from a mixture of heavy metals and chloride. The research involved chemical modifications of the road aggregate, activated carbon, and their composite, followed by laboratory tests in glass reactors and non-flow batch tests to evaluate the kinetics and chemical equilibrium of the reactions. The adsorption process was stable and conformed to the pseudo-second-order kinetics and Langmuir, Toth, and Redlich–Peterson isotherm models. Studies using MIX from a heavy metal model solution showed that monolayer adsorption was a key mechanism for removing heavy metals, with strong fits to the Langmuir (R² > 0.80) and Freundlich models, and optimal efficiencies for Cd and Ni (R² > 0.90). The best fit, at Cd, Cu, Ni = 0.96, however, was with the Redlich–Peterson isotherm, indicating a mix of physical and chemical adsorption on heterogeneous surfaces. The Toth model was significant for all analytes, fitting Cl and Cd well and Pb and Zn moderately. The modifications made to the composite significantly enhanced its effectiveness in removing the contaminant mixture. The test results demonstrated an average reduction of chloride by 85%, along with substantial removals of heavy metals: lead (Pb) by 90%, cadmium (Cd) by 86%, nickel (Ni) by 85%, copper (Cu) by 81%, and zinc (Zn) by 79%. Further research should focus on the removal of other contaminants and the optimization of magnesium oxide (MgO) dosage. Full article

Traditional numerical models have been widely employed to simulate the transport of multispecies reactive contaminants in groundwater systems; however, their high computational cost limits their applicability in real-time or large-scale scenarios. Recent advances in artificial intelligence (AI) offer promising alternatives, particularly data-driven machine learning techniques, for accelerating such simulations. This study presents the development of a surrogate model based on artificial neural networks (ANNs) to simulate the transport and decay of interacting multispecies contaminants in groundwater. High-fidelity training datasets are generated through finite difference-based reactive transport simulations across a wide range of environmental and geochemical conditions. The ANN model is trained to learn the complex nonlinear relationships governing the multispecies transport and transformation processes. Model validation reveals that the ANN surrogate accurately reproduces the spatial–temporal concentration profiles of both original and degradation species, capturing key dynamic behaviors with high precision. Notably, the ANN model achieves up to a 100-fold reduction in computational time compared to traditional analytical or semi-analytical solutions. These results highlight the ANN’s potential as an efficient and accurate surrogate modeling tool for groundwater contamination assessment, offering a valuable advancement for decision-making in environmental risk analysis and remediation planning. Full article

The Kundulun River Basin is the most prominent branch of the Yellow River system within the jurisdiction of Baotou City. As an important water source and ecological barrier, its ecological quality is directly related to the ecological security and sustainable development of the surrounding areas. This study selected the Kundulun River Basin in Baotou City as the research area. On the basis of collecting relevant information, a field investigation was conducted on the ecological and geological conditions of the atmospheric surface subsurface Earth system, using the watershed as the survey scope and water as the carrier for the transfer and conversion of materials and energy in the watershed. This study selected the main factors that affect the ecological geological quality of the watershed and established an evaluation model using the analytic hierarchy process, the coefficient of variation method, and the comprehensive analysis method. We have established an ecological geological quality evaluation index system for the Kundulun River Basin. We conducted quantitative evaluation and comprehensive analysis of regional ecological and geological environment quality. The results indicate that ecological environment indicators contribute the most to the ecological quality of the study area, while the impact of human activities on ecological quality is relatively small. From the perspective of evaluation indicators, grassland has the highest weight, followed by precipitation, groundwater depth, forest land, and cultivated land. Approximately 30.26% of the land in the research area is in a state of high or relatively high ecological and geological–environmental quality risk. It can be seen that the overall quality of the ecological geological environment is not optimistic and needs further protection. Full article

This study was conducted in the Harran Plain within the framework of the Southeastern Anatolia Project (GAP) in Türkiye to evaluate the vulnerability of groundwater to contamination, with a special emphasis on the high salinity conditions attributed to agricultural and rural practices. The region is notably challenged by salinization resulting from intensive irrigation and insufficient drainage systems. The DRASTIC framework was used to assess groundwater contamination vulnerability. The DRASTIC framework parameters were numerically integrated using both the original DRASTIC framework and its modified version, serving as the basis for subsequent predictive analytics and soft computing model development. The primary aim was to determine the most effective predictive model for groundwater contamination vulnerability in salinity-affected areas. In this context, various models were implemented and evaluated, including artificial neural networks (ANNs) with varied hidden layer configurations, four different regression-based methods (MARS, TreeNet, GPS, and CART), and three classical regression analysis approaches. The modeling process utilized 24 adjusted vulnerability indices (AVIs) as target variables, with the dataset partitioned into 58.34% for training, 20.83% for validating, and 20.83% for testing. Model performance was rigorously assessed using various statistical indicators such as mean absolute error, root mean square error, and the Nash–Sutcliffe efficiency coefficient, in addition to evaluating the predictive AVIs through spatial mapping. The findings revealed that the ANNs and TreeNet models offered superior performance in accurately predicting groundwater contamination vulnerability, particularly by delineating the spatial distribution of risk in areas experiencing intensive agricultural pressure. Full article

In the context of progressing climate change and the increasing frequency of extreme weather events, there is a growing need for effective strategies to mitigate their impacts. One such strategy involves the implementation of tools aimed at sustainable rainfall management at the site of precipitation. This study focuses on assessing the state of the water environment as a prerequisite for introducing sustainable Managed Aquifer Recharge (MAR) practices in urban areas. The research was conducted in the historic district of Warsaw, Poland. A comprehensive methodological approach was employed, including field and laboratory measurements of soil moisture and electrical conductivity (EC), vadose zone hydraulic conductivity, spring discharge rates, and analytical calculations based on climatic data. These were supplemented by groundwater flow modeling to estimate infiltration rates. The study showed that the infiltration rate in the aquifer is low—only 4.4% of the average annual precipitation. This is primarily due to limited green space coverage and high surface runoff, as well as high potential evaporation rates and low soil permeability in the vadose zone. A positive water balance and infiltration were observed only in December and January, as indicated by increased soil moisture and decreased EC values. A multi-criteria spatial analysis identified priority zones for the installation of retention infrastructure aimed at enhancing effective infiltration and improving the urban water balance. These findings underscore the need for targeted interventions in urban water management to support climate resilience and sustainable development goals. Full article

Groundwater constitutes the primary source of liquid freshwater on Earth and is essential for ecosystems, agriculture, and human consumption. However, rising demand, urbanization, and climate change have intensified groundwater depletion, particularly in semi-arid regions. Therefore, assessing groundwater recharge zones is essential for sustainable water resource management in vulnerable areas such as Dera Ismail Khan, Pakistan. This study aims to delineate groundwater potential zones (GWPZs), using an integrated approach combining the Geographic Information System (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). Twelve factors were identified in a study conducted using GIS-based AHP to determine the groundwater recharge zones in the region. These include land use/land cover (LULC), rainfall, drainage density, soil type, slope, road density, water table depth, and remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Moisture Stress Index (MSI), Worldview Water Index (WVWI), and Land Surface Temperature (LST). The results show that 17.52% and 2.03% of the area have “good” and “very good” potential for groundwater recharge, respectively, while 48.63% of the area has “moderate” potential. Furthermore, gentle slopes (0–2.471°), high drainage density, shallow water depths (20–94 m), and densely vegetated areas (with a high NDVI) are considered important influencing factors for groundwater recharge. Conversely, areas with steep slopes, high temperatures, and dense built-up areas showed “poor” potential for recharge. This approach demonstrates the effectiveness of integrating advanced remote sensing indices with the AHP model in a semi-arid context, validated through high-accuracy field data (Kappa = 0.93). This methodology offers a cost-effective decision support tool for sustainable groundwater planning in similar environments. Full article

Nitrate (NO₃⁻) pollution resulting from anthropogenic activities represents one of the most prevalent environmental issues in karst spring catchments of northern China. In June 2021, a comprehensive study was conducted in the Jinan Spring Catchment (JSC), where 30 groundwater and surface water samples were collected. The sources and spatial distribution of nitrate pollution were systematically investigated through hydrochemical analysis combined with dual-isotope tracing techniques (δ¹⁵N_NO3 and δ¹⁸O_NO3). Analytical results revealed that the predominant anion and cation sequences were HCO₃⁻ > SO₄²⁻ > Cl⁻ > NO₃⁻ and Ca²⁺ > Na⁺ > Mg²⁺ > K⁺, respectively, with HCO₃·SO₄-Ca identified as the primary hydrochemical type. Notably, the average NO₃⁻ concentration in groundwater (46.62 mg/L) significantly exceeded that in surface water (4.96 mg/L). Among the water samples, 11 locations exhibited substantial nitrate pollution, demonstrating an exceedance rate of 42%. Particularly, the NO₃⁻-N concentrations in both the upstream recharge area and downstream drainage area were markedly higher than those in the runoff area. The spatial distribution of NO₃⁻ concentrations was primarily influenced by mixing processes, with no significant evidence of denitrification observed. The isotopic compositions ranged from −1.42‰ to 12.79‰ for δ¹⁵N_NO3 and 0.50‰ to 15.63‰ for δ¹⁸O_NO3. Bayesian isotope mixing model (MixSIAR) analysis indicated that domestic sewage and manure constituted the principal nitrate sources, contributing 37.1% and 56.9% to groundwater and surface water, respectively. Secondary sources included soil organic nitrogen, rainfall and fertilizer NH₄⁺, and chemical fertilizers, while atmospheric deposition showed the lowest contribution rate. Additionally, potential mixing of soil organic nitrogen with chemical fertilizer was identified. Full article

Selecting the appropriate excavation support system (ESS) is critical for ensuring construction projects’ safety and cost-effectiveness. Several dynamic factors influence this decision-making process, such as the groundwater table, excavation depth, proximity to neighboring buildings, and soil characteristics. In practice, the selection often depends heavily on the subjective judgment of experienced professionals in the construction industry. Although the analytical hierarchy process (AHP) is frequently employed to evaluate alternatives using multiple criteria, it fails to adequately account for the subjectivity and uncertainty in converting the decision-maker’s intuition into exact numerical values. To overcome this challenge, this study proposes an enhanced method known as fuzzy AHP. This approach is designed to capture the subjective experiences of experts better and effectively incorporate the uncertainties present in the decision-making process, ultimately aiding in identifying the most suitable ESS. A case study of excavation projects is also included to demonstrate the practical application of the proposed model. By presenting this approach, the study aims to raise awareness within the construction industry about the critical factors to consider when selecting the best excavation support technique for specific projects. Full article

Dewatering and excavation are fundamental processes influencing soil deformation in deep foundation pit construction. Excavation causes stress redistribution through unloading, while dewatering lowers the groundwater level, increases effective stress, and generates seepage forces and compressive deformation in the surrounding soil. To systematically investigate their combined influence, this study conducted a scaled physical model test under staged excavation and dewatering conditions within a layered multi-aquifer–aquitard system. Throughout the experiment, soil settlement, groundwater head, and pore water pressure were continuously monitored. Two dimensionless parameters were introduced to quantify the contributions of dewatering and excavation: the total dewatering settlement rate ${\eta }_{dw}$ and the cyclic dewatering settlement rate ${\eta }_{dw,i}$. Under different experimental conditions, ${\eta }_{dw}$ ranges from 0.35 to 0.63, while ${\eta }_{dw,i}$ varies between 0.32 and 0.82. Both settlement rates decrease with increasing diaphragm wall insertion depth and increase with greater dewatering depth inside the pit and higher soil permeability. An analytical formula for dewatering-induced soil settlement was developed using a modified layered summation method that accounts for deformation coordination between soil layers and includes correction factors for unsaturated zones. Although this approach is limited by scale effects and simplified boundary conditions, the findings offer valuable insights into soil deformation mechanisms under the combined influence of excavation and dewatering. These results provide practical guidance for improving deformation control strategies in complex hydrogeological environments. Full article

Groundwater is a vital resource in arid and semi-arid regions, such as Ethiopia’s Jimma and Borena zones, where surface water availability is limited. This study employs Geographic Information Systems (GIS), Remote Sensing (RS), and the Analytical Hierarchy Process (AHP) to delineate groundwater potential zones. Key hydrogeological parameters, including lithology, slope, land use/land cover, drainage density, and recharge, were analyzed and weighted using the AHP to generate suitability maps. The findings indicate that in Jimma, 4.6% of the area is highly suitable for groundwater development, 24% is moderately suitable, and 70% has low suitability. In Borena, 6.2% of the area is highly suitable, 42.6% is moderately suitable, and 51.1% exhibits low suitability due to topographic and geological constraints. Validation using borehole data confirms the model’s reliability, demonstrating strong agreement with observed groundwater yields. These results provide a cost-effective approach for groundwater exploration and highlight the necessity of geophysical surveys in complex terrains to enhance mapping accuracy. This study offers valuable insights for water resource planners and policymakers, supporting sustainable groundwater management strategies in the region. Full article

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges. Full article

Overextraction of groundwater, as well as rapidly changing land use patterns, climatic change, and anthropogenic activities, in the densely populated Melur of Tamil Nadu state in India, has led to aquifer degradation. This study maps the groundwater potential (GWPZ) by evaluating 678 km² of this region in the Analytical Hierarchy Processes (AHP) and by using remote sensing and GIS tools as part of SDG 6 for the sustainable management of drinking, irrigation, and industrial uses for future generations. Data information layers, such as aquifer (a), topography (t), lineaments (l), land-use/land-cover (LuLc), soil (s), rainfall (r), and drainage (d) characteristics, separated the study area between poor and excellent groundwater potential zones with 361 km² or 53% of the study area remaining as low GWP and the prospective excellent groundwater potential zone covering only 9 km² (1.3% of total area). The integrated approach of the GWPZ and Water Quality Index (WQI) can effectively identify different zones based on their suitability for extraction and consumption for better understanding. This study also evaluates the performance of three machine learning models, such as Random Forest (RF), Gradient Boosting, and Support Vector Machine (SVM), based on a classification method using the same layers that govern the groundwater potential. The results indicate that both the RF model and Gradient Boosting achieved 100% accuracy, while SVM had a lower accuracy of 50%. Performance metrics such as precision, recall, and F1-score were analyzed to assess classification effectiveness. The findings highlight the importance of model selection, dataset size, and feature importance in achieving optimal classification performance. Results of this study highlight that the aquifer system of Melur has a low groundwater reserve, and it requires adequate water resource management strategies such as artificial recharge, pumping restriction, and implementation of groundwater tariffs for sustainability. Full article

This research focuses on groundwater mapping for the Çumra and Beyşehir Basins in Konya, a semi-arid region in Turkey that plays a crucial role in agriculture and the food industry. Geographic information systems (GIS), the analytical hierarchical process (AHP), and the multi-population-based differential evolution algorithm (MDE) were combined to identify potential groundwater zones. Since direct data on groundwater presence are costly to obtain, thematic maps created from groundwater conditioning factors (such as aquifer, slope, permeability, alluvial soil, soil quality, lithology, precipitation, temperature, salinity, and stone density) can be used to estimate groundwater potential. In this study, these factors were assigned weights using the AHP technique in Model 1 and the MDE technique in Model 2. The TOPSIS (technique for order preference by similarity to ideal solution) method was then employed to simulate groundwater potential, using weights from both techniques. The performance metrics of both models were as follows: Model 1 (RMSE: 114.219, MSE: 13,046.091, and MAE: 99.663) and Model 2 (RMSE: 114.209, MSE: 13,043.785, and MAE: 99.652). The proposed method addresses issues of consistency and bias that might arise from relying on expert opinions through the use of heuristic techniques. Moreover, this approach, which does not require direct data on groundwater availability, enables the creation of accurate predictions while overcoming the challenges of obtaining expensive data in underdeveloped and developing countries. It provides a scientifically sound way to identify and conserve water resources, reducing drilling and other related costs in watershed management and planning. Full article