Search Results (377)

Machine learning (ML) is increasingly applied to geochemical baseline estimation and anomaly detection in soils and sediments, yet the methodological conditions under which machine learning outperforms traditional approaches—and which preprocessing and validation decisions most consequentially determine that advantage—remain incompletely characterized across environmental and mineral exploration domains. A structured systematic scoping review of 146 records from the Web of Science Core Collection applied sequential filtering to yield 78 thematically eligible studies, from which 20 were prioritized through a composite index integrating age-adjusted citation impact, platform usage, and semantic relevance. Four cross-cutting findings emerge. First, performance gains in environmental applications were driven primarily by spatial model structure rather than algorithm selection: incorporating a spatial covariate derived from geographically weighted regression raised test-set explained variance from $R^2=0.80$ to $R^2=0.96$ for cadmium mobility prediction in a geochemically heterogeneous karst setting, a gain the source study supported with a held-out test set and a Monte Carlo analysis of sensitivity to data size. Second, isometric or centered log-ratio preprocessing was applied in the majority of mineral exploration studies (three of five classical and hybrid studies and four of five deep-learning studies) but in none of the seven environmental studies, representing a systematic methodological gap with direct consequences for covariate importance estimates under compositional closure. Third, Shapley additive explanations and accumulated local effects functioned as instruments of operational value, enabling element-specific anomaly threshold derivation, training sample diagnosis, and grid-cell anomaly type classification; this evidence demonstrates that the accuracy–interpretability trade-off commonly assumed in the machine learning literature is not fundamental in geochemical applications but contingent on algorithm selection. Fourth, 90% of the 20 synthesized studies (18 of 20 by study-area location—13 in China and five in Iran) were evaluated under within-domain validation designs, and the consistently high performance metrics reported should be interpreted as interpolation estimates rather than evidence of transferable predictive capability. Geographic diversification of training datasets and spatially explicit cross-regional validation are identified as structural prerequisites for regulatory-grade applicability. Full article

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields. Full article

Tight oil reservoir quality and development effectiveness are highly dependent on microscopic pore structure characteristics and spatial heterogeneity. In this study, tight sandstones from the Chang 3, Chang 6, Chang 7, and Chang 8 members of the Triassic Yanchang Formation in the Xunyi exploration area, southern Ordos Basin, were selected as research objects. By integrating X-ray diffraction (XRD), cast thin sections, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI) experiments, and multifractal theory, the multi-scale heterogeneity characteristics of pore structures in different layers were quantitatively characterized. The response relationships between multifractal parameters, macroscopic physical properties, and pore size distributions were discussed, and the geological control mechanisms of sedimentation and diagenesis on heterogeneity were revealed. The results indicate that the sedimentary environment plays a fundamental role in controlling reservoir physical properties. The Chang 3 and Chang 8 members, deposited in underwater distributary channels, are dominated by primary and dissolution pores, with physical properties significantly superior to the gravity flow-deposited Chang 6 and Chang 7 members. Multifractal analysis shows that the Chang 3 member has the largest singularity spectrum width (Δα =1.943 ± 0.56) and heterogeneity index (R_d = 1.782 ± 0.99), reflecting its broadest pore size distribution, strongest heterogeneity, and significant intra-layer differences; while the pore structures from Chang 6 to Chang 8 are relatively stable, with the Chang 8 member exhibiting high spatial connectivity. This study demonstrates that the quantitative evaluation method based on multifractal theory can effectively identify microscopic structural differences in tight sandstones, providing a critical supporting basis for reservoir classification characterization and favorable layer selection in the Yanchang Formation of the Ordos Basin. Full article

The classification position of substrates forming on the beds of aquaculture ponds remains a poorly resolved issue at the intersection of pedology, limnology, and aquaculture science. We examine how major international and national soil classification systems—the USDA Soil Taxonomy, the World Reference Base for Soil Resources (WRB), the German Bodenkundliche Kartieranleitung, the Australian Soil Classification (ASC), the Russian Soil Classification, and the classification systems of Brazil and China—approach the systematics of subaqueous soils and their aquaculture analogues. A systematic literature search was conducted across the Web of Science, Scopus, and Google Scholar databases covering the period from 1953 to 2025. Our analysis reveals that Soil Taxonomy provides the most developed taxonomic framework through specialized suborders (Wassents and Wassists), while the WRB offers the greatest flexibility via its qualifier system (subaquatic, limnic, and gleyic). The German classification uniquely assigns subaqueous soils to the highest taxonomic level (division) with a substantive typology that is directly applicable to pond substrates. The Australian classification contributes a three-part sulfidic material typology of practical significance for pond management. The Russian and Brazilian systems currently lack formal taxa for subaqueous soils, although recent proposals (e.g., Aquazems) may address this gap. The Chinese paddy soil model offers a conceptual bridge between subaqueous pedology and aquaculture. No existing system adequately addresses the specific anthropogenic impacts of aquaculture management on pond soil formation. Permanently inundated little-disturbed ponds fall within the subaqueous soil concept, whereas intensively managed, frequently drained or dredged ponds are better treated as anthropogenic soils with a subaqueous phase. We recommend the WRB (4th edition, 2022) as the most suitable framework for current classification of aquaculture pond soils while acknowledging that a multi-system approach may ultimately prove most effective. These findings carry particular relevance for countries of the former Soviet Union (CIS), where extensive pond aquaculture is practiced but pond substrates remain outside formal pedological classification. Full article

A novel wide-neck classifier (WNC) was designed to address the problem that thickeners cannot achieve classification prior to flocculation in a single unit. Using the computational fluid dynamics-discrete phase method and PIV experimental method, the reliability of the model was validated. We studied the motion characteristics of different particles within the novelty-designed WNC. The primary forces acting on coal slime particles in the composite force field were gravity, drag force, pressure gradient force, and virtual mass force. Drag force dominated the classification and sedimentation processes. In contrast, gravity, pressure gradient, and virtual mass forces promoted downward sedimentation but hindered upward overflow. The classification of slime particles in WNC was divided into initial classification after tangential feeding and centrifugal classification in a cone. Both simulation and experimental results demonstrate that, under consistent feed conditions, mineral density significantly affected the distribution of particles at the classification underflow and classification overflow. Among the three minerals, kaolinite has the highest classification effect, followed by quartz, while coal has the lowest classification effect. Full article

Microplastics (MPs; <5 mm) represent a growing environmental concern that increasingly challenges environmental monitoring, governance, and evidence-based decision-making. This review critically examines how current scientific understanding of microplastic sources, classification, occurrence, and environmental behavior can support environmental governance. MPs are classified as primary and secondary particles; however, persistent inconsistencies in size definitions, shape descriptors, and polymer identification limit the comparability of monitoring data and constrain the development of coherent regulatory frameworks. Evidence on the occurrence of MPs in surface waters and sediments highlights widespread contamination and pronounced spatial variability, raising challenges for risk assessment and policy harmonization across regions. Key transport pathways, including atmospheric deposition, terrestrial runoff, and riverine fluxes, are analyzed to illustrate how local emissions translate into large-scale environmental impacts. Rivers emerge as key components linking sources to receptors, offering relevant points for policy intervention and management measures. The review evaluates current policy responses to microplastic pollution, identifying significant gaps in standardized monitoring, data integration, and risk assessment approaches. It emphasizes the need for stronger alignment between scientific outputs and policy requirements, including the co-production of knowledge involving scientists, regulators, and stakeholders. By outlining pathways through which scientific evidence can inform regulatory design and environmental management, this study provides actionable insights for improving policy effectiveness. Advancing harmonized methodologies and integrating science into decision-making processes are essential steps toward mitigating microplastic pollution and supporting sustainable environmental governance. Full article

The Yilgarn Craton hosts some of the world’s largest orogenic gold deposits, yet discovery rates have declined sharply as near-surface resources approach exhaustion. Exploring deeper, covered terrains demands new predictive tools that transcend the limitations of conventional mineral prospectivity mapping (MPM). Here we integrate convolutional neural networks (CNNs) and Vision Transformers to construct a data-driven MPM framework trained on 6028 gold occurrences across 16 map sheets in the Yilgarn Craton. The CNN achieves 79.3% classification accuracy by capturing local structural features; the Vision Transformer attains 74.0% but identifies prospective zones in data-sparse regions that the CNN misses. An empirical test was conducted in the untrained Sandstone Greenstone Belt to verify the model’s generalization ability. The results reveal that most known gold deposits lie within the high metallogenic potential zones defined by the model. Meanwhile, three prospective targets are newly delineated in this area based on model prediction, including northwest-trending ultramafic units, a basalt-sediment transition zone and NW-SE trending amphibolite units along the Edale Shear Zone. These targets are hardly identifiable by conventional exploration techniques and merit further field investigation. These results demonstrate that CNN–Transformer integration provides a robust, complementary framework for orogenic gold exploration in covered terrains. Full article

The Mugan Syncline in northeastern Yunnan represents a significant relay area for shale gas exploration in China. However, due to the combined effects of tectonic superimposition and sedimentary heterogeneity, systematic investigations into the intervals hosting high-quality shales and the coupling relationships among microfacies, reservoir quality, and gas-bearing properties remain insufficient. The core objective of this study is to establish a high-resolution microfacies framework and to quantitatively elucidate the multi-parameter coupling mechanisms by which microfacies control organic matter enrichment, pore development, and gas storage capacity in this structurally complex, basin-margin setting. By integrating core observations, thin-section petrography, scanning electron microscopy (SEM), whole-rock X-ray diffraction (XRD), total organic carbon (TOC) analysis, trace-element geochemistry, and well-logging data, we establish a stratigraphic subdivision and cross-well correlation framework for the Wufeng (WF) Formation and the Long11 submember. Furthermore, a lithofacies (microfacies) identification scheme based on a “TOC + siliceous (quartz + feldspar)–carbonate–clay” ternary classification is applied. The results reveal the following: (1) Based on the locally developed erosional contact at the boundary between the Longmaxi (LMX) Formation and the underlying Guanyinqiao Formation, the WF Formation in the study area can be subdivided into two submembers, whereas the Long11 submember comprises four sublayers. The thicknesses of the Long11-1 through Long11-3 sublayers range from 21.42 to 25.47 m, exhibiting a subtle northward-thickening trend. In contrast, the Long11-4 sublayer displays a relatively uniform thickness and high stratigraphic continuity of shale deposition. (2) Based on TOC content and ternary mineral composition, the shales are classified into four lithofacies associations and sixteen lithofacies subtypes. The main favorable microfacies assemblages are identified as high-carbon siliceous/calcareous shale (C-1), high-carbon calcareous/siliceous mixed shale (M-1), carbon-rich argillaceous siliceous shale (S-3), and high-carbon siliceous/argillaceous mixed shale (M-2). (3) High-quality shales (TOC > 2%) are predominantly developed in the upper member of the WF Formation and in the Long11-1 through Long11-4 sublayers. Their lateral distribution is markedly controlled by variations in paleotopography and terrigenous sediment supply. (4) The microfacies exert a synergistic control on shale gas enrichment. Carbon-rich argillaceous siliceous and siliceous-rich microfacies generally correspond to higher TOC contents and better-developed organic-matter pores. Siliceous-rich and mixed microfacies exert a positive influence on pore preservation and rock brittleness. The gas-bearing properties are influenced not only by TOC content but also by pore structure, mineral composition, and tectonic preservation conditions. The findings of this study provide a scientific basis for the prediction of shale gas sweet spots and the optimization of target intervals in the Mugan Syncline and other structurally and sedimentologically complex regions of northeastern Yunnan. Full article

Coastal environments are becoming more susceptible to enrichment of trace elements from human activities and natural processes. This research presents a detailed assessment of heavy metal pollution and radiological risks in coastal sediments from the Ras Mohamed area, South Sinai, at the northern Red Sea. Fifteen surface sediment samples were examined for nine trace metals and naturally occurring radionuclides (²²⁶Ra, ²³²Th, and ⁴⁰K) using ICP-OES and gamma spectrometry techniques, respectively. Geochemical analyses showed the concentration sequence Fe > Ba > V > Cr > Zn > Co > Ni > Cu > Pb, where average levels of Cr, V, and Co were higher than Canadian Soil Quality Guidelines (CSQGs) and global crustal background values. Environmental evaluation using the pollution load index = 2.16 reflected ongoing contamination, and the Geo-Accumulation Index indicated low to moderate polluted sediment conditions. Nevertheless, ecological risk results (PERI = 87.21) together with toxicity indicators pointed to low to moderate biological effects. Human exposure assessments for adults and children revealed no significant non-carcinogenic risk (HI < 1), and the Total Cancer Risk remained below the acceptable regulatory threshold (1 × 10⁻⁴). From the other side, all recorded radiation activities were low, falling below internationally recognized safety limits. An evaluation of radiological hazard indices further confirmed that the sediments present no significant radiation risk, as all measurements remain within the low-level classification of international standards. Overall, the results indicate that although localized sediment transport and tourism-related pressures have increased certain metal levels, the region is radiologically secure and currently presents negligible risk to human health. Full article

Spain’s extensive marine jurisdiction—comprising a continental shelf of approximately 100,000 km² and an Exclusive Economic Zone approaching one million km²—requires robust geospatial frameworks to support ecosystem assessment and marine policy implementation. This study presents GIS-based methodologies developed by the Spanish Oceanographic Institute (IEO-CSIC) within national initiatives such as LIFE IP INTEMARES project and the implementation of Marine Strategy Framework Directive (European Directive 2008/56/EC). The geospatial workflows developed for these initiatives integrates heterogeneous spatial datasets—such as multibeam bathymetry, acoustic backscatter, Remote Operated Vehicle (ROV) and towed-camera transects, sediment samples, oceanographic profiles, and species-habitat occurrence records—into a unified spatial analysis environment. Applied methods include digital terrain modeling, derivation of geomorphometric indices (e.g., slope, rugosity, curvature), image classification, and spatial statistics to quantify habitat extent, condition, and anthropogenic pressures. An integrated spatial analysis framework combining environmental and anthropogenic data is used to support zoning and management decisions within Marine Protected Areas (MPAs). Additionally, the deployment of WebGIS platforms facilitates data dissemination, iterative review, and stakeholder engagement, thereby enhancing transparency and accessibility. The resulting high-resolution maps, harmonized datasets, and computed spatial indicators—aligned with Marine Strategy Framework Directive (MSFD) descriptors such as habitat distribution (D1C4–C5) and seafloor integrity (D6C2–C3)—demonstrate how GIScience methods provide reproducible, decision-ready information to support the monitoring and management of Spain’s diverse marine ecosystems. Full article

The sustainable management of dredged sediments poses a major environmental and economic challenge, particularly in Morocco, where large quantities are annually discarded as waste. Contributing to resource efficiency and circular economy objectives, this study represents the first systematic application research of Moroccan Mehdia Harbor sediments in mortar formulations. Three substitution strategies were investigated at substitution rates of 5–30%: (i) replacement of cement with fine sediments (series MA); (ii) replacement of sand with intermediate sediments (series MB); (iii) replacement of sand with sandy sediments (series MC). Mechanical testing at 28 days showed that both compressive and flexural strengths remained comparable to the reference mortar for substitution levels up to 10–15%, depending on sediment type. Beyond these limits, a marked strength reduction was observed, particularly for cement replacement with fine, clay-rich sediments. Mortars incorporating sandy sediments (MC) exhibited the best performance, maintaining over 80% of the reference compressive strength up to 15%. Leaching tests confirmed the environmental stability of all formulations, which remained within the “inert” waste classification up to 15% substitution. These findings demonstrate that dredged sediment incorporation in mortar is both technically and environmentally feasible for non-structural applications, promoting sustainable materials within a circular economy framework. Full article

For studies utilizing methods such as water color parameter inversion and algal bloom classification, abundant spectral bands and high spectral resolution are of great significance. However, for multispectral satellite sensors that are not designed for water color studies (e.g., Sentinel-2 MSI), the number of bands in the visible–near-infrared range is limited, and lacks specific spectral bands with rich spectral information. Hyperspectral reconstruction of multispectral data based on hyperspectral remote sensing reflectance ($R_{rs}$) databases and machine learning algorithms have been proven to be a feasible solution. Based on the in situ measured $R_{rs}$ data, this study constructed a large-sample hyperspectral $R_{rs}$ database covering various optical water types using two Chinese hyperspectral satellites, and compared the spectral reconstruction accuracy of six machine learning algorithms. The results show that expanding the $R_{rs}$ database for model training by integrating hyperspectral satellite data can effectively improve the reconstruction accuracy in waters of different optical types. Comparisons with in situ measured hyperspectral $R_{rs}$ indicate that the reconstructed Sentinel-2 hyperspectral data achieve high accuracy, with the Spectral Angle Mapper (SAM) less than 5° and the correlation coefficient ($r$) higher than 0.7. Furthermore, the reconstructed data can effectively restore spectral information not captured by the original multispectral data, such as the suspended sediment $R_{rs}$ peak at 580 nm and the chlorophyll $R_{rs}$ valley at 680 nm. Through spectral reconstruction, the spectral resolution of Sentinel-2 can be maximized while retaining its advantages of fast revisit capability and high spatial resolution, thereby expanding its application potential in water color remote sensing. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

Soil erosion assessment in Southeast Europe’s mountainous regions often lacks systematic field validation, limiting confidence in model-based predictions. This study integrates the Erosion Potential Model (EPM) with remote sensing and field verification across 26,570 km² in the Federation of Bosnia and Herzegovina (FBiH) and Brčko District (BD). We developed a two-stage framework: initial GIS-based assessment using digital elevation models, soil maps, climate data, CORINE Land Cover, and Landsat imagery, followed by field calibration at 190 representative sites. Spectral indices (NDVI, BSI) provided dynamic corrections for vegetation cover and visible erosion features. Field validation significantly improved model performance; the erosion coefficient increased from Z = 0.21 to Z = 0.24, while discriminatory power improved AUC from 0.82 to 0.85, with corresponding gains in overall accuracy from 0.78 to 0.84 and F1-score from 0.78 to 0.85. The field-validated model estimated mean annual sediment production of 546.60 m³·km⁻²·year⁻¹, with total erosion material production of 14,074,940.2 m³·year⁻¹. Field calibration revealed substantial spatial redistribution, with medium-to-excessive erosion categories expanding by 30.37%, affecting 1319.12 km² requiring priority intervention. The Kappa coefficient (0.81) confirms high classification reliability. This field-validated framework enables evidence-based identification of degradation hotspots and provides actionable guidance for soil conservation planning in geomorphologically heterogeneous, data-limited regions. Full article

Background: Fatigue is a common and distressing symptom in inflammatory bowel disease (IBD), yet it is rarely addressed in routine care. Most available evidence comes from Western and East Asian populations, with limited data from the Middle East. Objectives: To estimate the prevalence of fatigue in Saudi patients with IBD, using the Arabic-validated Brief Fatigue Inventory (BFI-A), and to examine associations with demographic, clinical, treatment, and laboratory factors. Methods: This cross-sectional study was conducted at King Abdulaziz University Hospital, Saudi Arabia, between March and December 2025. Patients aged ≥12 years with histologically confirmed IBD completed a structured telephone interview. Demographic characteristics, comorbidities, IBD control scores, Montreal classification, medication history, and laboratory results were collected. Patients experiencing severe flares, hospitalization, or another primary condition likely to explain fatigue were excluded. Fatigue severity was classified as none, mild, moderate, or severe. Associations were tested using chi-square and Kruskal–Wallis tests. Results: Among 286 patients (mean age, 30.8 ± 9.1 years; 57.7% male), 23.1% reported mild fatigue, 36.4% moderate fatigue, and 19.2% severe fatigue on the BFI-A. Fatigue severity was not associated with demographic factors, IBD type or phenotype, treatment exposure, or most laboratory parameters. Only serum iron (p = 0.011) and erythrocyte sedimentation rate (p = 0.023) differed across fatigue categories, without a clear dose–response pattern. Conclusions: Fatigue affects more than half of Saudi patients with IBD and is not explained by routine clinical or laboratory factors. Routine fatigue assessment and attention to biopsychosocial contributors may improve IBD care. Full article