Search Results (2,034)

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning models across multiple soil and groundwater prediction tasks. This study systematically evaluated the interactions between 10 metaheuristic feature-selection algorithms and 13 predictive models, including random forest (RF), convolutional neural network (CNN), recurrent architectures, CNN–recurrent neural network (RNN) hybrids, squeeze-and-excitation (SE)-enhanced hybrids, and iTransformer-based hybrids, across four prediction tasks involving soil organic carbon (SOC), soil–water extract electrical conductivity (ECe), apparent electrical conductivity (ECa), and groundwater level (GWL) in Xinjiang, China. A total of 149 candidate environmental covariates were considered for ECe, SOC, and ECa, whereas 122 candidate covariates were considered for GWL. The results showed that no single feature-selection method consistently performed best across all four targets; instead, predictive performance depended on the interaction among the feature-selection strategy, predictive architecture, and target variable. CNN–RNN hybrid architectures generally achieved higher predictive performance than standalone models, although their benefits varied among prediction targets. The best-performing combinations yielded coefficient of determination (R²) values of 0.9826, 0.6981, 0.8429, and 0.8085 for GWL, SOC, ECe, and ECa, respectively. These findings indicate that target-specific compatibility, rather than aggressive dimensionality reduction or a universally superior algorithm, is a key determinant of predictive performance in high-dimensional DSM. By demonstrating that feature-selection effectiveness is jointly influenced by model architecture and target characteristics, this study provides a methodological reference for developing target-specific digital soil mapping models in arid regions. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

Coastal dune fields provide important ecological and geomorphic functions, while their evolution is strongly influenced by groundwater-controlled surface moisture. However, the effects of seasonal groundwater-level fluctuations and moisture-affected sand on long-term dune development remain insufficiently represented in numerical models. In this paper, a time-varying groundwater-level field and moisture-dependent entrainment thresholds are incorporated into a real-space cellular automaton model to investigate the coupled evolution of coastal dunes and interdune lakes. The results show that rising groundwater levels reduce the wind-erodible surface area, inundate interdune depressions, and delay the growth of peak dune height. Periodic water-level fluctuations also produce a sediment storage–release cycle, in which sand is temporarily stored on inundated interdune surfaces during high-water stages and is progressively remobilized during subsequent low-water stages, while newly exposed moisture-affected sand remains subject to an elevated entrainment threshold. In addition, increasing the critical threshold shear stress within the groundwater-controlled moisture-affected layer suppresses dune development and reduces final peak dune height by approximately 8–17%. Comparison with the observed wet-season water-pond area further shows that incorporating the moisture-affected layer brings the simulated relative water-pond area closer to the observed value in the Lençóis Maranhenses dune field. Overall, the results demonstrate that groundwater regulates dune evolution through both direct inundation and an enhanced entrainment resistance of moisture-affected sand above the water table. Full article

Heavy metal contamination in mining-affected groundwater may pose potential risks to drinking water safety and human health. In this study, 25 groundwater samples, including 18 from Aquifer IV of the Cenozoic unconsolidated porous aquifer system and 7 from the Taiyuan Formation limestone karst aquifer, were collected from the Zhuxianzhuang Coal Mine, Huaibei Coalfield, China. The Heavy Metal Pollution Index (HPI), Metal Index (MI), Positive Matrix Factorization (PMF), and oral-ingestion health risk model were applied to assess metal contamination, source contributions, and human health risks. HPI values ranged from 8.0 to 37.5, with a mean of 21.5, indicating a low overall pollution level. In contrast, MI values suggested cumulative metal contamination, with higher mean values in Taiyuan Formation limestone karst groundwater (5.60) than in Aquifer IV groundwater (2.38). PMF identified two major factors, interpreted as a natural water–rock interaction source and a combined anthropogenic and mining-related source. Health risks were consistently higher in Taiyuan Formation limestone karst groundwater. Non-carcinogenic risks were higher for children and mainly controlled by As, whereas carcinogenic risks were higher for adults and mainly associated with Cr. These findings highlight the need for aquifer-specific monitoring and source control to reduce toxic metal exposure in coal mining areas. Full article

Structural uncertainty limits the reliability of conceptual hydrological models, yet the effect of pairing runoff-generation and routing structures remains poorly understood. We evaluated 46 models from the Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) in the Xunhe and Hanzhong catchments of the upper Han River Basin and recombined the modules of five representative models into 20 off-diagonal configurations. Validation KGE values for the 46 base models ranged from 0.166 to 0.806 in Xunhe and from 0.043 to 0.830 in Hanzhong. Only M58 and M60 improved KGE in both catchments: M58 increased KGE by 1.68% in Xunhe and 5.98% in Hanzhong, whereas M60 increased it by 5.05% and 3.72%, respectively. Most other recombinations preserved or reduced performance. The improved combinations retained contributions from runoff pathways with different response timescales and showed greater sensitivity to routing parameters; degraded combinations tended to be dominated by groundwater runoff and showed weak routing-parameter sensitivity. These convergent patterns provide indirect evidence that correspondence between runoff composition and routing response may influence recombined-model performance. Because structural compatibility was not measured directly and only two catchments were examined, the proposed relationship requires validation across broader hydroclimatic and structural settings. Full article

This study proposes an innovative framework that combines hydroclimatic and agro-environmental predictors, including nitrate concentration and NDVI, with climatic factors such as Rainfall, temperature, and evapotranspiration to forecast piezometric level variations in the Saïss Basin, Morocco. Eight Machine Learning (ML) models were benchmarked, and feature importance was assessed using Shapley Additive exPlanations (SHAP) to ensure model transparency and interpretability. In parallel, the PELT algorithm was applied to detect structural change points, while Sen’s slope estimator and the Mann–Kendall test were used to quantify long-term trends. The Extra Trees (ET) model achieved the best performance (R² = 0.92), with Rainfall emerging as the most influential predictor, followed by nitrate concentration, confirming the added value of hydrochemical indicators for groundwater forecasting. Change-point analysis revealed significant declines during the 1980s and 1990s, followed by lower-amplitude fluctuations since the late 2000s. Projections toward 2050 suggest partial stabilization in the central part of the basin under favorable recharge conditions, whereas persistent declines are expected to continue in peripheral areas subjected to sustained groundwater abstraction pressure. These findings provide a robust and transferable decision-support tool for the sustainable management of groundwater resources in semi-arid agricultural area. Full article

Earthquake-induced fracturing and microcrack development in subsurface strata are widely recognized as important processes influencing seepage and the hydrological behaviour of surface water bodies, particularly in tectonically active mountainous terrains. However, the hydrogeological response to repeated low-magnitude (<4) seismic events remains poorly understood. This study presents an integrated geoelectrical and remote sensing investigation of the Nagi Lake region in the Sikkim Himalaya, India, based on Vertical Electrical Sounding (VES) surveys conducted in May 2022 and March 2026, following a seismic sequence of 74 low-magnitude earthquakes recorded during February 2026. Comparative analysis of four VES profiles (VES1–VES4), supported by validatory factor analysis, reveals spatially heterogeneous changes in subsurface electrical characteristics between the two survey periods. VES1, VES2, and VES3 indicate reduced signatures of pre-existing microcracks that are consistent with sediment densification and partial sealing, whereas VES4 suggests localized development or persistence of microfractures. Because the surveys span approximately four years, these changes likely reflect the combined influence of long-term hydrogeological, environmental, and geomorphic processes, with the February 2026 seismic sequence representing one potential contributing factor rather than the sole driver. To further evaluate ground deformation, Sentinel-1A Synthetic Aperture Radar (SAR) data acquired between January 2019 and March 2026 were analysed using Persistent Scatterer Interferometric SAR (PS-InSAR). The results indicate cumulative Line-of-Sight (LOS) displacements ranging from −17.9 cm (movement away from the satellite) to +3.5 cm (movement toward the satellite) in the vicinity of Nagi Lake, reflecting localized surface deformation with millimetre-scale precision. These observations provide complementary evidence of ongoing subsurface adjustment that may promote sediment compaction and microcrack modification. Overall, the study demonstrates measurable temporal changes in the subsurface structure of the Nagi Lake area and suggests that repeated low-magnitude seismicity may contribute to subsurface restructuring alongside other environmental processes. The findings highlight the value of integrating geophysical monitoring and satellite-based deformation analysis for understanding groundwater–surface water interactions and supporting the sustainable management of vulnerable Himalayan water bodies. Full article

Rapid urbanization and inadequate sanitation infrastructure threaten water security in many sub-Saharan African cities. This study aimed to provide an integrated assessment of groundwater, surface water, and wastewater quality in Bangui by characterizing physicochemical parameters, trace metals, and microbiological indicators, and by identifying potential contamination sources and pathways among these water compartments. A total of 28 water samples were collected from groundwater, surface water, and wastewater sites. Physicochemical parameters, major ions, trace metals, and microbiological indicators were analyzed using standardized methods, including ion chromatography, ICP-OES, ICP-MS, and membrane filtration. Results revealed a clear contamination gradient. Wastewater showed the highest electrical conductivity, turbidity, chloride concentrations, and microbial loads, reaching 2.41 × 10⁶ CFU/100 mL for total coliforms and 1.93 × 10⁶ CFU/100 mL for fecal coliforms. Groundwater exhibited high nitrite levels and low dissolved oxygen, indicating vulnerability to sewage infiltration. Surface waters were characterized by high turbidity and widespread fecal contamination despite relatively good oxygenation. In contrast, trace metal concentrations generally remained below World Health Organization guideline values. Geochemical analyses identified distinct elemental signatures for each water type. Microbiological contamination emerged as the dominant water quality concern. High fecal coliform/fecal streptococci ratios (13.08-22.16) indicated predominantly human-derived pollution linked to untreated wastewater and inadequate sanitation systems. The association between elevated nitrite concentrations and fecal indicators suggests active contamination pathways connecting wastewater, surface water, and shallow aquifers. These findings highlight the urgent need for improved wastewater management, groundwater protection, and long-term monitoring to ensure sustainable urban water security in Bangui. Full article

The leaching behavior of bisphenol A (BPA) remains a significant environmental concern. Despite EU regulatory restrictions and the World Health Organization (WHO) establishing precautionary values for human exposure to this monomer, BPA is still widely used in polymer-based products, including materials for sewer rehabilitation. Standard leaching assessments typically employ deionized water, although natural waters represent actual field conditions. This study investigates the release of BPA and its substitute, bisphenol F (BPF), from an epoxy resin system and a vinyl ester resin system. Sample discs (0.5 cm in height and 11 cm in diameter) were subjected to dynamic surface leaching tests using groundwater and tap water as leaching media. Eluate replacement intervals followed the standardized Dynamic Surface Leaching Test (DSLT; EN 16637-2), marking the first application of natural waters within this standardized test procedure. The detection limit for the gas chromatography analyses was 0.001 µg/L. Compared with the deionized water prescribed by the standard, natural waters resulted in significantly higher cumulative releases of BPA and BPF, with cumulative BPA release increasing by factors of 5.2–9.2 to a maximum of 0.412 mg/m². These findings indicate that the standardized 64-day DSLT may underestimate environmentally relevant emissions and that permissible cumulative release values, derived from modeling and regulatory thresholds, may not be maintained under environmentally relevant conditions. GC–MS screening and the Umu test showed no evidence of genotoxic effects in the leachates. Exploratory analysis suggested a positive relationship between BPA release and pH (R² = 0.79). Overall, the results demonstrate that the choice of leaching medium strongly influences measured emissions and should therefore be reconsidered in assessment frameworks for polymer-based construction materials to better reflect environmentally relevant conditions. Full article

This study proposes a hybrid computational model for diagnosing such biases using groundwater observation data across Kazakhstan. The analytical dataset included 2402 georeferenced observations, including 492 natural springs from OpenStreetMap (OSM), 109 boreholes from OSM, and 1801 spatially filtered pseudo-absence observations. Springs and boreholes together formed 601 positive groundwater observations, while pseudo-absence samples represented a spatially filtered background level rather than confirmed groundwater absence. Each observation was characterized by 89 environmental predictors extracted from Google Earth Engine. The proposed hybrid observation source bias index (HOSBI) combines a normalized robust effect size based on the median absolute value of the Cliff delta, multivariate distribution divergence quantified using RBF-MMD, and spatially confirmed source distinctiveness. These components were assigned fixed weights of 0.40, 0.35, and 0.25 to emphasize statistical and distributional data while maintaining spatial validation. Spatial cross-validation achieved a balanced accuracy of 0.855 for distinguishing OSM sources from OSM wells and 0.846 for separating positive observations from background pseudo-absences. Climate showed the strongest source-related bias (HOSBI = 0.923), while Sentinel-1 SAR contributed the most to the contrast between positive and background data (HOSBI = 0.923). The proposed framework provides an interpretable and replicable preliminary assessment of source bias in heterogeneous geospatial datasets. Full article

A systematic evaluation of salt tolerance at the seedling stage was conducted using 143 maize inbred lines under a 150 mM mixed-salt solution (NaCl:Na₂SO₄ = 9:1, EC = 16.78 dS/m) that mirrors the ionic composition of saline groundwater in the Yellow River Delta. The comprehensive salt tolerance index (D value) ranged from 0.15 to 0.85 across the population, with the elite line B114 exhibiting the highest D value (0.835) and the sensitive line PHT55 ranking near the bottom. Under salt stress, B114 displayed remarkable growth stability, with plant height decreasing by only 25.9%, fresh weight by 13.3%, and dry weight remaining unchanged, whereas PHT55 suffered severe growth inhibition (plant height: 61.5% decrease; fresh weight: 63.2% decrease; dry weight: 33.3% decrease). Time-series RNA-seq of root tissues across four time points (5, 8, 11, and 14 days) revealed markedly distinct transcriptional dynamics: B114 exhibited relatively stable temporal regulation (2261–9124 DEGs), whereas PHT55 showed a pronounced early transcriptional burst that progressively intensified (3728–10,108 DEGs). Using random forest-based machine learning, 50 core salt tolerance-related genes were unbiasedly identified from 16,194 significantly differentially expressed genes. Functional enrichment analysis revealed that these genes were primarily involved in redox regulation, ion homeostasis maintenance, and stress signal transduction pathways. qRT-PCR validation confirmed biphasic expression patterns, with Zm00001d024160 showing the strongest early induction (48-fold at 5 h). This study established a maize salt tolerance evaluation system closely aligned with field conditions and demonstrated that coordinated temporal transcriptional regulation represents a core molecular mechanism underlying high salt tolerance in maize. The elite salt-tolerant germplasm and key candidate genes identified here provide valuable genetic resources and a theoretical foundation for molecular breeding of salt-tolerant maize adapted to saline-alkaline soils. Full article

Cow milk and dairy products may reflect environmental elemental inputs transferred through the feed–animal–milk chain. This study investigated the elemental traceability, regional differentiation, dairy processing effects, and dietary exposure associated with milk and dairy products from two contrasting Romanian dairy production systems: the traditional Vaideeni region and the industrial Ibănești region. A total of 88 samples (water, feed, raw milk, and dairy products) were seasonally collected during 2024–2025 and analysed for trace elements, geochemical fingerprint elements, and macroelements using ICP-OES and atomic absorption spectrometry. The strongest regional elemental contrasts were observed in groundwater for V (Δlog₁₀ = +1.30), U (+0.82), Sr (+0.61), and Mn (+0.60), but these differences were substantially attenuated during biological transfer. Nevertheless, Li, U, Sr, Mn, and Cu retained measurable regional differences in milk. Regional elemental contrasts within the feed indicated an important inverse relationship (Pearson r = −0.60, p = 0.018), whereas no significant correlation was found between water and milk (Pearson r = −0.19, p = 0.493), suggesting that the milk elemental patterns may be influenced by biological regulatory processes in addition to the environmental signatures. Dairy processing further modified the elemental distribution, with the trace elements showing increased redistribution compared to the macroelements, while the geochemical fingerprint elements preserved regional information. The dietary exposure estimates for Cd and Ni remained below the corresponding toxicological reference values, whereas the highest estimated Pb exposure from hard cheese reached 0.977 μg/kg body weight/day for children and 0.558 μg/kg body weight/day for adults, exceeding the EFSA BMDL₀₁ reference point. The results indicate that multielement fingerprinting may provide promising information for investigating elemental traceability and supporting dairy product authenticity assessment within the investigated production systems, although extensive geographical validation will be required before application as a general authentication tool. Full article

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation. Full article

Submarine groundwater discharge (SGD) is an important pathway of dissolved matter transport to coastal ecosystems, yet its identification in methane seep environments remains challenging because chemical signals are modified by sedimentary biogeochemical processes. This study evaluated hydrochemical tracers of SGD in methane seep sites, bacterial mat areas, and background sediments along the southern coast of Crimea (Black Sea). The studied settings exhibited distinct water chemical characteristics. Chloride concentrations decreased from 10.6 to 11.2 g L⁻¹ in background waters, to 8.7–9.3 g L⁻¹ in bacterial mat pore waters and to 7.7 g L⁻¹ in sediment–water interface waters, indicating the presence of a low-salinity water component. Dissolved silica increased by approximately one order of magnitude relative to background values at methane-associated sites. Methane concentrations ranged from 0.025 to 1058 μM, with the highest values occurring in bacterial mat areas. These zones were further characterized by sulfate depletion (down to 0.9 g L⁻¹), elevated normalized alkalinity, ammonium concentrations reaching 8000 μg L⁻¹, high sulfide contents, and low dissolved Fe concentrations consistent with iron sulfide precipitation. The results demonstrate that no single hydrochemical parameter is sufficient to identify SGD in methane-affected coastal sediments. Instead, the combined use of conservative tracers (Cl⁻ and DSi) and reactive constituents (SO₄²⁻, alkalinity, NH₄⁺, HS⁻, TDFe, and Mn²⁺) provides a robust hydrochemical framework for recognizing groundwater influence and evaluating associated biogeochemical transformations. Full article

Unreasonable nitrogen (N) management and the shortage of groundwater resources are prominent problems faced by intensive farmland in North China. The main processes to reduce N₂O emissions under drip irrigation still need to be further explored. This study conducted a three-year field experiment (2021–2024) of wheat–maize cropping system in Hebei Province to study the effects of four N levels under drip irrigation (i.e., N0: no N applied; N150: 150 kg N ha⁻¹; N210: 210 kg N ha⁻¹; N270: 270 kg N ha⁻¹) on yield, N₂O emission, soil carbon (C) and N fractions, enzymes, and N functional genes. The results showed that N rate significantly affected N₂O emissions (p < 0.05). The highest value of N₂O emissions (10.64 kg N ha⁻¹) over three years and yield-scale N₂O emissions (0.25 g kg⁻¹) were found under N270, which were 32.1% and 78.6% higher than those of N210. No significant difference in annual yields between N210 and N270 was found, but the annual yield of N150 was decreased by 14.4% as compared to N210. N rate significantly affected soil C-N fractions and enzymes, in which NO₃⁻-N, MBC, DON, amoA-AOB, and UE enzymes, as the major factors of soil properties, N functional genes, and enzymes affecting N₂O emissions, were more favorably regulated along with the increase in fertilizer N rate. Correlation analysis indicated that amoA-AOB was significantly correlated to N fractions (i.e., NH₄⁺-N, NO₃⁻-N, TN, DON and MBN) and UE enzymes. Structural equation modeling further proved that an increase in fertilizer N rate directly increased enzyme activities related to N transformation, which increased N fractions, thus promoting the abundance of amoA-AOB genes involved in nitrification that stimulate N₂O emissions. Therefore, these findings underscore that N210 was an optimal fertigation strategy for minimizing N₂O emissions without compromising yield, offering a practical pathway for sustainable intensification in water-limited regions. Full article