Search Results (3,750)

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Reliable forecasting of karst spring discharge is critical for sustainable groundwater resource management under the dual pressures of climate change and intensified anthropogenic activities. This study proposes a Heterogeneous Spatiotemporal Graph Attention Network (H-STGAT) to predict spring discharge dynamics at Shentou Spring, Shanxi Province, China. Unlike conventional spatiotemporal networks that treat all relationships uniformly, our model derives its heterogeneity from a graph structure that explicitly categorizes spatial, temporal, and periodic dependencies as unique edge classes. Specifically, a dual-layer attention mechanism is designed to independently extract hydrological features within each relational channel while dynamically assigning importance weights to fuse these multi-source dependencies. This architecture enables the adaptive capture of spatial heterogeneity, temporal dependencies, and multi-year periodic patterns in karst hydrological processes. Results demonstrate that H-STGAT outperforms both traditional statistical and deep learning models in predictive accuracy, achieving an RMSE of 0.22 m³/s and an NSE of 0.77. The model reveals a long-distance recharge pattern dominated by high-altitude regions, a finding validated by independent isotopic evidence, and accurately identifies an approximately 4–6 month lag between precipitation and spring discharge, which is consistent with the characteristic hydrological lag identified through statistical cross-covariance analysis. This research enhances the understanding of complex mechanisms in karst hydrological systems and provides a robust predictive tool for sustainable groundwater management and ecological conservation, while offering a generalizable methodological framework for similar complex karst hydrological systems. Full article

Horizontal well air sparging (HAS) technology provides a promising approach for pollution remediation. In this study, a model experiment assessed the airflow distribution characteristics of HAS under varying air sparging (AS) pressure, tube burial depth, and groundwater flow conditions, while evaluating the retardation effects of HAS on dissolved groundwater contaminants. The results indicated that airflow velocity and diffusion range increased markedly with elevated AS pressure. Deeper AS tube burial depths resulted in more uniform airflow distribution and broader coverage. Groundwater flow significantly affected airflow distribution, as greater water head differences induced a downstream shift in the airflow pattern, resulting in an asymmetric diffusion range. Regarding pollutant retardation, airflow created a physical barrier by reducing permeability and interfacial resistance, effectively hindering pollutant diffusion. Airflow from the AS tube aligned parallel to the flow direction reduced Rhodamine B concentration by 53.1% over 300 min, preventing deeper pollutant migration into the sand layer. Conversely, airflow from the AS tube oriented perpendicular to the flow direction reduced Rhodamine B concentration by 84.38% over the same period, demonstrating superior effectiveness in limiting horizontal pollutant diffusion. These findings provide valuable theoretical insights and practical guidance for implementing HAS technology in groundwater pollution management. Full article

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping. Full article

Interactions between surface water and groundwater in arid regions regulate their response to climate and human impacts. In the salar systems of the Altiplano-Puna plateau (Bolivia, Chile, Argentina), understanding how surface waters connect to groundwater is crucial for accurate modeling and assessment. This study introduces new data and analysis using subsurface thermal profiles and modeling to identify flow patterns and possible surface water links. We document, to our knowledge, for the first time in the literature, deep-seated cooling of the subsurface caused by extreme evaporation rates. The subsurface is cooled by 4–5 degrees Celsius below the mean annual air temperature to depths greater than 50 m, even though groundwater inflow waters are elevated by 10 degrees °C due to geothermal heating. Three thermal zones are observed along the southern edge of Salar de Atacama, with temperature dropping from 28 °C to about 12 °C over 2.5 km. A 2D numerical model of groundwater and heat flow was developed to test various hydrological scenarios and understand the factors controlling the thermal regime. Two flow scenarios at the southern margin were examined: a diffuse flow model with uniform flow and flux to the surface and a focused flow model with preferential discharge at a topographic slope break. Results indicate that the focused flow scenario matches thermal data, with warm inflow water discharging into a transition zone between freshwater and brine, cooling through evaporation, re-infiltration, and surface flow, then re-emerging near lagoons at the halite nucleus margin. This research offers valuable insights into the groundwater hydraulics in the Salar de Atacama and can aid in monitoring environmental changes causally linked to lithium mining and upgradient freshwater extraction. Full article

This study integrates 90 years of hydrometeorological observations (1930/31–2019/20) with end-century projections (2080–2099) to evaluate climate-driven changes in the water balance of the Nitra River basin (2094 km²), Slovakia. Despite a modest 2–3% increase in annual precipitation from 1930/31–1959/60 to 1990/91–2019/20, mean annual runoff declined from 229 mm to 201 mm (≈−12%), primarily due to enhanced evapotranspiration stemming from a +1.08 °C basin-wide temperature increase. An empirical regression from 90 hydrological years shows that +100 mm in precipitation boosts runoff by ≈41 mm, while +1 °C in temperature reduces it by ≈13 mm. The BILAN monthly water balance model was calibrated for 1930/31–2019/20 to decompose runoff components. Over the 90-year period, the modeled annual runoff averaged 222 mm, comprising a 112 mm baseflow (50.4%), a 91 mm interflow (41.0%), and a 19 mm direct runoff (8.6%), underscoring the key role of groundwater and subsurface flows in sustaining streamflow. In the second part of our study, the monthly water balance model BILAN was recalibrated for 1995–2014 to simulate future runoff under three CMIP6 Shared Socioeconomic Pathways. Under the sustainability pathway SSP1-1.9 (+0.88 °C; +1.1% precipitation), annual runoff decreases by 8.9%. The middle-of-the-road scenario SSP2-4.5 (+2.6 °C; +3.1% precipitation) projects a 17.5% decline in annual runoff, with particularly severe reductions in autumn months (September −32.3%, October −35.8%, December −40.4%). The high-emission pathway SSP5-8.5 (+5.1 °C; +0.4% precipitation) yields the most dramatic impact with a 35.2% decrease in annual runoff and summer deficits exceeding 45%. These results underline the extreme sensitivity of a mid-sized Central European basin to temperature-driven evapotranspiration and the critical importance of emission mitigation, emphasizing the urgent need for adaptive water management strategies, including new storage infrastructure to address both winter floods and intensifying summer droughts. Full article

Protecting groundwater against pollution from agricultural sources is a key aspect of sustainable management of soil and water resources. Implementation of sustainable strategies for agricultural production can be supported by modeling tools, which allow us to quantify the effects of different agricultural practices in the context of groundwater vulnerability to contamination. In this study we present a method to assess groundwater vulnerability to nitrate pollution based on a combination of the SWAT agro-hydrological model and the DRASTIC index method. SWAT modeling was applied to assess different scenarios of agricultural practices and identify solutions for sustainable management of soil and groundwater and reduction of nitrate pollution. The developed method was implemented for groundwater resources in a study area (Puck Bay region, southern Baltic coast), which represented a complex multi-aquifer system formed in Quaternary fluvioglacial deposits (sand and gravel) separated by moraine tills. In order to investigate the effects of different agricultural practices, 12 scenarios have been defined, which were grouped into four classes: crop type, fertilizer management, tillage, and grazing. An overlay index structure was applied, and ratings and weights to several factors were assigned. All analyses were processed using GIS tools, and the results are presented in the form of maps, which categorize groundwater vulnerability to nitrate pollution into five classes, ranging from very low to very high. The results reveal significant variability in groundwater vulnerability to nitrate pollution in the study area. Agricultural practices have a very strong influence on groundwater vulnerability by controlling both recharge rates and nitrogen losses from the soil profile. The most pronounced increases in vulnerability were associated with scenarios involving excessive fertilization and intensive grazing. Among crop types, potato cultivation appears to pose the greatest risk to groundwater quality. Full article

A nonlinear mathematical model is developed for unsteady groundwater flow in a three-layer heterogeneous aquifer system, comprising a confined aquifer, a covering layer, and a weakly permeable barrier. The model incorporates infiltration and evaporation governed by the M.M. Krylov–S.F. Averyanov law, where evaporation intensity depends on a critical groundwater level. Governing equations are nondimensionalized and solved using the alternating direction implicit (ADI) method with quasi-linearization to treat nonlinearities. Periodic variations in precipitation and evaporation are considered, alongside variable boundary permeabilities. The approach enables realistic simulation of multi-layer aquifer dynamics under diverse climatic and hydrogeological conditions, offering a robust tool for sustainable groundwater management, drought risk assessment, and aquifer protection strategies. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that persist in soil environments, necessitating reliable models to predict their fate and transport. This study evaluates the performance of three theoretical models in estimating the maximum adsorption capacity (Qmax) of perfluorooctanoic acid (PFOA) on kaolinite and montmorillonite clay minerals. The models assessed include a van der Waals interaction-based approach, a monolayer adsorption capacity model, and a surface site density model emphasizing reactive hydroxyl groups at mineral edges. Benzene, nitrogen, and glyphosate molecules were used as reference compounds for model validation. Results indicated that the van der Waals model significantly underestimated Qmax (0.0007 mg·g⁻¹ for kaolinite), while the monolayer capacity model produced substantial overestimations (17.51 mg·g⁻¹) compared to the experimental range (0.10–10.0 mg·g⁻¹). The surface site density model provided the most accurate predictions (3.39 mg·g⁻¹ for kaolinite), although it slightly underestimated values for montmorillonite (0.20 mg·g⁻¹) by excluding interlayer adsorption. These discrepancies demonstrate that simplified models cannot adequately capture the complex adsorption behavior of PFAS. Accurate prediction requires site-specific approaches incorporating electrostatic forces, hydrogen bonding, and steric effects. As PFAS accumulation in soil directly contributes to groundwater contamination, improving adsorption models is essential for accurate risk assessment and the development of effective remediation strategies. Full article

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments. Full article

Shiqian County, located within a key geothermal fluids belt in Guizhou Province, China, has abundant underground hot water resources. Therefore, elucidating the hydrogeochemical characteristics and formation mechanisms of thermal mineral water in this area is essential for evaluating and sustainably utilizing regional geothermal fluids. This study focuses on the Shiqian Hot Spring Group and employs integrated analytical techniques, including rock geochemistry, hydrogeochemistry, isotope hydrology, digital elevation model (DEM) data analysis, remote sensing interpretation, geological surveys, mineral saturation index calculations, and PHREEQC-based inverse hydrogeochemical modeling, to elucidate its hydrogeochemical characteristics and formation mechanisms. The results show that strontium concentrations range from 0.06 to 7.17 mg/L (average 1.65 mg/L) and metasilicic acid concentrations range from 19.46 to 65.51 mg/L (average 33.64 mg/L). Most samples meet the national standards for natural mineral water and are classified as Sr-metasilicic acid type. Isotope analysis indicates that the geothermal water is recharged by meteoric precipitation at elevations between 911 m and 1833 m, mainly from carbonate outcrops and fracture zones on the southwestern slope of Fanjingshan, and discharges south of Shiqian County. The dominant hydrochemical types are HCO₃·SO₄-Ca·Mg and HCO₃-Ca·Mg. Strontium is primarily derived from carbonate rocks and celestite-bearing evaporites, whereas metasilicic acid mainly originates from quartz dissolution along the upstream groundwater flow path. PHREEQC-based inverse modeling indicates that, during localized thermal mineral water runoff in the middle-lower reaches or discharge areas, calcite dissolves while dolomite and quartz tend to precipitate, reflecting calcite dissolution-dominated water–rock interactions and near-saturation conditions for some minerals at late runoff stages. Full article

Groundwater constitutes a vital freshwater resource essential for sustaining agricultural productivity, industrial processes, and domestic water supply. Quantifying spatiotemporal dynamics of Groundwater Storage (GWS) across China provides a critical scientific basis for sustainable water resource management and conservation. Employing a unified methodology combining Gravity Recovery and Climate Experiment (GRACE) observations and global hydrological models (GLDAS, WGHM), this study investigates spatiotemporal variations in Groundwater Storage Anomalies (GWSA) across China and its nine major river basins from February 2003 to December 2019. The results indicate an overall declining trend in China’s GWSA at −2.27 to −0.38 mm/yr. Significant depletion hotspots are identified in northern Xinjiang, southeastern Tibet, and the Haihe River Basin. Conversely, statistically significant increasing trends are detected in the Endorheic Basin of the Tibetan Plateau and the middle reaches of the Yangtze River Basin. Although GWSA inversions derived from different Global Land Data Assimilation System (GLDAS) models show general consistency, there are still pronounced regional heterogeneities in model performance. The findings offer critical scientific foundations for water resources managers and policymakers to formulate sustainable groundwater management strategies in China. Full article

Groundwater–surface water interactions were investigated in the Highwood River (3952 km²) and Sheep River watersheds (1568 km²), originating in the Rocky Mountains headwaters of the South Saskatchewan River (Alberta, Canada), to improve understanding of hydrological processes that potentially influence water use and vulnerability to climatic change in representative, alpine-fed mixed-use watersheds. Similar to adjacent regions of the Bow, Red Deer and Oldman watersheds, the upper reaches of these watersheds are sparsely populated with significant seasonal glacier and snowmelt influence, while the lower watersheds are currently under increasing water supply pressure from competing agricultural–municipal interests, with notable risk of flooding during high-flow events and drought during the growing season. Investigations included mapping of hydrologic and hydrogeologic controls (aquifers, buried channels, colluvial deposits, etc.,) and synoptic geochemical and isotopic surveys (δ²H, δ¹⁸O, δ¹³C-DIC, ²²²Rn) to characterize evolution in water type and seasonal progression in streamflow sources and underlying mechanisms. Our findings confirm seasonal progression in streamflow water sources, characterized by a pronounced snowmelt-dominated spring freshet, but with a sustained recession fed by colluvial, moraine, fluvial, and fractured bedrock sources. Seasonal isotopic variations establish that shallow groundwater sources are actively maintained throughout the spring freshet, often accounting for a dominant portion of streamflow, which indicates active displacement of groundwater storage by snowmelt recharge during spring melt. The contrast in the proportion of alpine contributions in each watershed suggests these systems may respond very differently to climate change, which needs to be carefully considered in developing sustainable water-use strategies for each watershed. Full article

Time-Domain Electromagnetic (TDEM) soundings are widely recognized as an effective method for subsurface characterization at intermediate depths. This study applies TDEM surveying to the municipality of Alfaro (La Rioja, Spain), where ten stations with 200 × 200 m loops were acquired and processed using Occam 1D inversion. The resulting models were integrated into a 3D environment in geomatic software (Seequent, Oasis montaj 2025.1) to generate a continuous geoelectrical volume of the subsurface. Three major resistivity domains were identified: a shallow resistive unit (40–80 ohm·m), an intermediate unit (20–40 ohm·m), and a deep conductive domain (<20 ohm·m). The pseudo-3D model revealed a thickening of the intermediate–resistive domain toward the central and western sectors, interpreted as the most favorable zone for groundwater extraction. This workflow demonstrates that integrating 1D TDEM inversion results into a pseudo-3D geoelectrical framework improves the spatial interpretation of resistivity distributions and provides a practical decision-support framework for identifying optimal drilling sites. Full article