Search Results (1,083)

Aluminum–chromium slag (ACS), a by-product of aluminothermic reduction, which is used to produce metallic chromium and its alloys, contains toxic, carcinogenic hexavalent chromium (Cr(VI)). Therefore, improper ACS utilization may severely harm human health and the environment. This study analyzed the Cr(VI) contents, leaching characteristics, and surface concentrations in ACS and four industrially utilized products derived from it (fused alumina for refractories, ferrochromium, aluminum–chromium bricks, and high-chromium bricks). A risk assessment framework was established to evaluate their human health and environmental risks. Results showed 111 mg/kg Cr(VI) in the ACS, with its leaching concentration (7.8 mg/L) exceeding China’s hazardous waste standard. The Cr(VI) contents in the products were low (from <2 mg/kg to 16 mg/kg), and their maximum leaching concentration was below the detection limit (<0.004 mg/L). Furthermore, the four products were found to have acceptable levels of human health risk (<10⁻⁵ carcinogenic risk and <1 noncarcinogenic hazard quotient) under two risk assessment methods (particle-contact- and surface-contact-based methods). Additionally, the predicted concentration of leached Cr(VI) in groundwater (0.008 mg/L) was below the drinking water standard (0.05 mg/L). Cr(VI) limit standards for the products were then proposed based on the risk assessment (≤31 mg/kg content, ≤0.189 mg/m² surface concentration, and ≤0.259 mg/L leaching concentration). Overall, these results may provide a reference for the safe utilization and risk management of ACS and other solid wastes. Full article

The long-term stability of grout-reinforced fractured rock masses in acidic groundwater environments after tunnel fires is critical for the safe operation of underground engineering. In this study, grouting reinforcement tests were performed on fractured diorite specimens using a high-strength fast-anchoring agent (HSFAA), and their mechanical degradation and microstructural evolution mechanisms were investigated under coupled high-temperature (25–1000 °C) and acidic corrosion (pH = 2) conditions. Multi-scale characterization techniques, including uniaxial compression strength (UCS) tests, X-ray computed tomography (CT), scanning electron microscopy (SEM), three-dimensional (3D) topographic scanning, and X-ray diffraction (XRD), were employed systematically. The results indicated that the synergistic thermo-acid interaction accelerated mineral dissolution and induced structural reorganization, resulting in surface whitening of specimens and decomposition of HSFAA hydration products. Increasing the prefabricated fracture angles (0–60°) amplified stress concentration at the grout–rock interface, resulting in a reduction of up to 69.46% in the peak strength of the specimens subjected to acid corrosion at 1000 °C. Acidic corrosion suppressed brittle disintegration observed in the uncorroded specimens at lower temperature (25–600 °C) by promoting energy dissipation through non-uniform notch formation, thereby shifting the failure modes from shear-dominated to tensile-shear hybrid modes. Quantitative CT analysis revealed a 34.64% reduction in crack volume (V_ca) for 1000 °C acid-corroded specimens compared to the control specimens at 25 °C. This reduction was attributed to high-temperature-induced ductility, which transformed macroscale crack propagation into microscale coalescence. These findings provide critical insights for assessing the durability of grouting reinforcement in post-fire tunnel rehabilitation and predicting the long-term stability of underground structures in chemically aggressive environments. Full article

Because of its specific nature, mining activity causes numerous negative impacts on the environment, both during the exploitation phase and after it has ended. An important source of income in the Jiu Valley is represented by the Lupeni Mining Exploitation. Like any mining activity, coal exploitation causes various negative effects on the environment. The subsidence phenomenon represents a significant issue associated with coal mining in the Jiu Valley. Underground extraction of mineral deposits induces displacement of the overburden strata. Such displacements result in ground subsidence and modifications of the surface topography. The larger the voids created following the exploitation of useful mineral deposits, the more they affect the surface of the land above the exploitation through sinking, displacement, deformation, and even cracks. Secondary deformations refer to post-mining surface movements induced by delayed rock mass adjustment, manifesting as ground collapse, localized subsoil failure, or uplift driven by groundwater rebound after drainage cessation. In this paper, we aim to study the subsidence phenomenon produced by coal mining at the Lupeni Mining Exploitation using the COMSOL simulation software and applying the Barcelona Basic Model (BBM) and Modified Cam-Clay (MCC) models. Following the simulation, the behavior of the rocks could be observed in order to improve prediction accuracy to support sustainable land management in post-mining areas. 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

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 level (GWL) prediction in arid regions faces two fundamental challenges in conventional numerical modeling: (i) irreducible parameter uncertainty, which systematically reduces predictive accuracy; (ii) oversimplification of nonlinear process interactions, which leads to error propagation. Although machine learning (ML) methods demonstrate strong nonlinear mapping capabilities, their standalone applications often encounter prediction bias and face the accuracy–generalization trade-off. This study proposes a hybrid TCN–Transformer–LSTM (TTL) model designed to address three key challenges in groundwater prediction: high-frequency fluctuations, medium-range dependencies, and long-term memory effects. The TTL framework integrates TCN layers for short-term features, Transformer blocks to model cross-temporal dependencies, and LSTM to preserve long-term memory, with residual connections facilitating hierarchical feature fusion. The results indicate that (1) at the monthly scale, TTL reduced RMSE by 20.7% (p < 0.01) and increased R² by 0.15 compared with the Groundwater Modeling System (GMS); (2) during abrupt hydrological events, TTL achieved superior performance (R² = 0.96–0.98, MAE < 0.6 m); (3) PCA revealed site-specific responses, corroborating the adaptability and interpretability of TTL; (4) Grad-CAM analysis demonstrated that the model captures physically interpretable attention mechanisms—particularly evapotranspiration and rainfall—thereby providing clear cause–effect explanations and enhancing transparency beyond black-box models. This transferable framework supports groundwater forecasting, risk warning, and practical deployment in arid regions, thereby contributing to sustainable water resource management. 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

Soil secondary salinization is a major limiting factor of sustainable agricultural production in arid and semi-arid irrigation zones, yet predictive tools for regional water–salt dynamics remain limited. The Yichang Irrigation District, located within the Hetao Irrigation Area, has experienced persistent salinity challenges due to shallow groundwater tables and intensive irrigation. In this study, we aimed to simulate long-term soil water–salt dynamics in the Yichang Irrigation District and evaluate the effectiveness of different engineering and management scenarios using the SaltMod model. Field monitoring of soil salinity and groundwater levels during summer and fall (2022–2024) was used to calibrate and validate SaltMod parameters, ensuring accurate reproduction of seasonal soil salinity fluctuations. Based on the calibrated model, ten-year scenario simulations were conducted to assess the effects of changes in soil texture, irrigation water quantity, water quality, rainfall, and groundwater table depth on root-zone salinity. Our results show that under baseline management, soil salinity is projected to decline by 5% over the next decade. Increasing fall autumn leaching irrigation further reduces salinity by 5–10% while conserving 50–300 m³·ha⁻¹ of water. Sensitivity analysis indicated groundwater depth and irrigation water salinity as key drivers. Among the engineering strategies, drainage system improvement and groundwater regulation achieved the highest salinity reduction (15–20%), while irrigation regime optimization provided moderate benefits (~10%). This study offers a quantitative basis for integrated water–salt management in the Hetao Irrigation District and similar regions. Full article

Triclosan (TCS), a persistent antimicrobial and endocrine-disrupting compound, is commonly found in surface and groundwater due to incomplete removal by conventional wastewater treatment. This study evaluated its fate in authentic rainwater runoff collected from a state road using rubber tiles made from recycled tires that were either uncoated (RRT) or coated with TiO₂ via the sol–gel method (SGT). Pollutants were analyzed by a high-resolution liquid chromatography–quadrupole time-of-flight mass spectrometry system (LC/MS QTOF) before and after treatment in a flat-plate cascade reactor under UV-A irradiation. After 120 min SGT achieved >50% TCS removal, while RRT achieved ~44%. Further analysis identified degradation products (chlorocatechole, quinone, and transient dioxin-like species). ECOSAR predictions indicated moderate to high toxicity for some degradation products, but their transient and low-abundance detection suggests that photocatalysis suppresses accumulation, ultimately yielding less harmful products such as benzoic acid. These findings highlight the dual role of TiO₂-coated rubber tiles: improving material durability while enabling photocatalytic degradation. Full article

Chromium (VI) mobility in soils critically influenced groundwater contamination risks, but accurate predictions were hindered by the lack of systematic retardation factor (R) data across China’s diverse soils. This study combined Bromide anion (Br⁻)-tracer and Cr(VI) column leaching experiments with CXTFIT code to determine dispersion coefficients (D) and R values in seven representative Chinese soils (e.g., brown soil, black soil), with model validation against Br⁻ tracer data. By comparing deterministic equilibrium and two-region non-equilibrium models, the research demonstrated that the non-equilibrium approach better characterized Cr(VI) transport, revealing significant soil-dependent R variations (1.09–16.13). Particularly noteworthy was the exceptional Cr(VI) retention observed in Heilongjiang black soil (R > 10), which indicated strong immobilization capacity. As China’s first systematic Cr(VI) retardation database, these findings provided essential parameters for predicting Cr(VI) mobility at contaminated sites, refining risk assessment models, and designing soil-specific remediation strategies—particularly crucial for high-retention regions. Methodologically, this work established an integrated experimental-modeling framework that addressed soil heterogeneity, while its outcomes directly supported regulatory frameworks through updated soil screening values. These findings provided scientific support for formulating region-specific soil management policies, with particular implications for environmental protection and agricultural safety in Cr(VI)-contaminated black soil regions. Full article

Mining-induced surface subsidence is a typical geological hazard. Loose layer drainage disturbed by coal mining can exacerbate surface subsidence in terms of both the extent and amount, thereby increasing the risk of building deformation and environmental degradation in mining areas. However, currently the prediction results of surface subsidence considering these two factors are not precise enough, which contradicts the principles of green coal mining. Firstly, this paper introduces the probability integral method, which predicts mining-induced surface subsidence. Subsequently, based on the soil–water coupled theory and the derived characteristic curve of groundwater level decline, a surface subsidence prediction model that considers loose layer drainage is constructed using triple integral transformation. Finally, a more precise surface subsidence prediction model considering both factors is proposed based on the principle of superposition. The model is applied to the mining of working panel 1309 in Shanxi province, China, an area rich in coal yet scarce in water resources. When compared with the measured subsidence data, the proposed model achieves a root mean square error (RMSE) of 27 mm, while the RMSEs of existing models are 78 mm and 123 mm, respectively. The prediction accuracy has been significantly improved. In addition, the proposed model is further validated through fluid–solid coupling numerical calculations in FLAC3D. The subsidence results considering the single effect of each factor also demonstrated good validation accuracy. Overall, the proposed model can accurately describe the surface subsidence considering both factors. This research can provide a theoretical guide for assessing the environmental impact and building damage, while contributing to the sustainable development of land use and groundwater resource in mining areas. Full article

Microrefugia can be critical in mediating biological responses to climate change, but the location and characteristics of these habitats are often poorly understood. Groundwater-dependent ecosystems (GDEs) represent critical microrefugia for species dependent on cool, moist habitats. However, knowledge of the distribution and stability of GDE microrefugia remains limited. This challenge is typified in the Pacific Northwest, where poorly studied cliff-face seeps harbor exceptional biodiversity despite their diminutive size (e.g., ~1–10 m width). To improve knowledge about these microrefugia, we regionally modeled their distribution and stability. We searched for cliff-face seeps across 1608 km of roads, trails, and watercourses in Washington and Idaho, while monitoring water availability plus air and water temperatures at selected sites. We detected 457 seeps through an iterative process of surveying, modeling, ground-truthing, and then remodeling the spatial distribution of seeps using boosted regression trees. Additionally, we used linear and generalized linear models to identify factors linked to seep thermal and hydrologic stability. Seeps were generally most concentrated in steep and low-lying areas (e.g., edges of canyon bottoms), and were also positively associated with glacial drift, basalt or graywacke bedrock types, high average slope within 300 m, and low average vapor pressure deficit. North-facing slopes were the best predictor of stable air and water temperatures and perennial seep discharge; low-lying areas also predicted stable seep water temperatures. These findings improve possibilities to manage seep microrefugia in the Pacific Northwest and safeguard their associated biodiversity under climate change. Lastly, our iterative method adapts techniques commonly used in species distribution modeling to provide an innovative framework for identifying inconspicuous microrefugia. 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

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article