Search Results (1,127)

Improving the effluent quality of municipal wastewater treatment plants (WWTPs) is essential for sustainable water management and water quality protection in the Yellow River Basin. Many existing WWTPs in northern China were constructed under earlier discharge requirements and now face dual challenges of stricter effluent standards and poor low-temperature performance in winter. In this study, a municipal WWTP with a design capacity of 5 × 10⁴ m³/d in northern China was upgraded to improve winter treatment performance and support stable compliance with the discharge requirements of the Yellow River Basin. The original anaerobic + oxidation ditch process suffered from unstable effluent quality, excessive sludge loading, and insufficient pollutant removal under low-temperature conditions. A land-saving retrofit strategy was therefore proposed, involving oxidation ditch wall-height raising to extend the hydraulic retention time (HRT) and membrane bioreactor (MBR) integration to increase the mixed liquor suspended solids (MLSS) concentration. After the retrofit, the total HRT increased to 19.82 h, and the average MLSS concentration reached 7050 mg/L. The relative abundances of key nitrogen-removing bacteria, including Nitrospiraceae, Nitrosomonadaceae, and Rhodocyclaceae, increased markedly. Meanwhile, denitrification sludge loading and BOD₅ sludge loading decreased to 0.030 and 0.033 kg/(kg·d), respectively. Under low-temperature conditions, the theoretical removal capacities of total nitrogen (TN) and BOD₅ reached 44.32 and 286.19 mg/L, respectively, enabling stable effluent compliance. The results show that this retrofit strategy can improve WWTP effluent quality while avoiding large-scale land expansion, providing a practical and sustainable solution for upgrading cold-region WWTPs along the Yellow River Basin. Full article

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article

Reservoir characterization of the Abu Roash “G” (AR/G) Member in the Karama Field, Abu Gharadig Basin, Western Desert of Egypt, is complicated by structural deformation, facies variability, and lithologic heterogeneity, which introduce uncertainties in reservoir evaluation and hydrocarbon estimation. This study aims to provide a comprehensive reservoir assessment through an integrated three-dimensional (3D) static modeling workflow. Well-log data from four wells were combined with the interpretation of seventeen seismic lines to construct structural, stratigraphic, and petrophysical models of the AR/G reservoir. The results indicate that reservoir thickness ranges from 9 to 14 ft and is structurally controlled by nine normal faults forming a horst–graben configuration that significantly influences compartmentalization and hydrocarbon distribution. Petrophysical modeling reveals favorable reservoir quality, with effective porosity ranging from 14% to 20%, an average shale volume of approximately 19%, and hydrocarbon saturation averaging 56%. Two prospective zones were identified, with estimated original oil in place (OOIP) of 10.76 MMSTB and 3.23 MMSTB, respectively, representing recoverable volumes within structurally defined closures rather than the entire field volume. The model also explains the relatively poor performance of Karama-5 and Karama-11 wells due to their peripheral structural positions outside the main closures and their higher water saturation (44–53%). These findings demonstrate that integrated structural and petrophysical modeling improves reservoir understanding and helps identify optimal drilling targets in structurally complex reservoirs of the Abu Gharadig Basin and comparable North African settings. Although the estimated volumes correspond to relatively small accumulations, they are considered economically viable within mature basins such as the Abu Gharadig Basin, where existing infrastructure and optimized development strategies enable efficient exploitation of marginal reserves. Full article

Physicochemical and microbiological parameters are important indicators of drinking water quality. This study assessed the quality of groundwater used for drinking in four regions of Kosovo at 16 locations using an integrated assessment framework that combined physicochemical, microbiological, and Water Quality Index (WQI) approaches. The results reveal substantial spatial variability in water quality. While most physicochemical parameters remained within recommended limits, elevated values of total dissolved solids (up to 2792.5 mg/L), electrical conductivity (up to 2768.5 µS/cm), nitrate (up to 60.75 mg/L), and phosphate (up to 0.875 mg/L) were observed at several locations, indicating localized hydrogeochemical and anthropogenic influences. Dissolved oxygen levels were generally low (0.68–5.49 mg/L), reflecting limited aeration conditions in groundwater systems. Microbiological analysis revealed critical contamination, with Escherichia coli concentrations up to 299.9 CFU/100 mL, and all sampling sites exceeded permissible limits, indicating widespread fecal pollution and rendering the groundwater unsafe for direct consumption. WQI assessment further confirmed this condition, where 93.75% of locations were classified as medium quality using the NSF-WQI method, whereas the WA-WQI method categorized 68.75% of samples as poor and 6.25% as very poor. The novelty of this study lies in the integrated evaluation of hydrogeochemical processes and microbiological contamination using dual WQI methods and multivariate statistical analysis, providing a comprehensive understanding of groundwater degradation pathways. The findings are significant for policymakers, environmental managers, and public health authorities, highlighting the urgent need for groundwater treatment, improved sanitation infrastructure, and sustainable water resource management strategies in vulnerable regions. Full article

The multi-cycle high-rate injection and production operations in Underground Gas Storage (UGS) facilities converted from depleted fracture-pore carbonate gas reservoirs induce complex rock–fluid interactions that threaten long-term integrity and performance. This study experimentally investigates the petrophysical responses of the Xiangguosi (XGS) UGS carbonate reservoirs in China using multi-cycle stress sensitivity tests, fines migration experiments, and water evaporation–salt precipitation analyses. SEM observations distinguish the contributions of crack closure and matrix compression to permeability evolution. Results show a sharp contrast in mechanical damage: high-quality rocks present negligible permanent deformation (<8% Young’s modulus reduction), whereas poor-quality rocks suffer catastrophic deterioration (>60%). Fines migration exhibits a three-stage behavior under cyclic flow, with water saturation significantly aggravating permeability impairment. A critical salinity threshold (220,000 ppm) is identified for the transition between drying-enhanced storage and salt plugging. Permeability declines sharply despite a slight porosity increase due to selective salt clogging of key pore throats, revealing a clear porosity–permeability decoupling. Salt deposition under movable water conditions can reduce UGS capacity by up to 1.45%. Reservoir heterogeneity, microfractures, karst structures, and initial petrophysical properties dominate the storage and flow space evolution. This work provides a predictive framework for optimizing injection–production strategies and improving the performance of complex carbonate UGS. Full article

Wheat bran, as a major nutrient-rich agricultural by-product, is underutilized due to poor functional properties. This study investigated the synergistic effects of steam explosion (SE), enzymatic hydrolysis (EH), and SE combined with EH (SE-EH) on wheat bran to improve the dough properties, bread quality, and in vitro starch digestion. Results showed that SE destroyed the dense structure of wheat bran to form a porous surface morphology and promoted the conversion of insoluble dietary fiber (IDF) to soluble dietary fiber (SDF). This structural loosening facilitated further fiber degradation for subsequent EH and achieved the obvious improvements in hydration properties after combined treatment. For the dough system, the addition of SE-EH bran increased the water absorption, hardness, and viscosity, but reduced the development and stability time of the dough, in comparison with the control dough. These changes suggested that the modified bran altered dough hydration behavior and gluten network continuity, contributing to the increment of bread’s specific volume. The starch hydrolysis rate of bread adding SE-EH wheat bran was decreased; the slowly digestible starch (SDS) and resistant starch (RS) contents were 2.59-fold and 1.31-fold higher than the control group, respectively. Additionally, the incorporation of modified wheat bran delayed bread hardening during storage, with the SE-modified group showing the best effect. Wheat bran modification enhanced its processing functionality, providing a feasible approach for bread production to improve storage stability and nutritional quality. Full article

Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important nut crops worldwide, valued for its nutritional properties and adaptability to diverse agroecological environments. This review summarizes current knowledge on almond domestication, genetic diversity, production trends, and improvement strategies, with a focus on drought tolerance under climate change. Archaeobotanical and molecular evidence indicate central Asia and the eastern Mediterranean as key centers of origin, where recurrent introgression from wild Prunus species contributed to the high genetic variability of cultivated almond. Global production trends reveal increasing challenges due to prolonged drought, climate variability, and rising water and energy costs, particularly affecting major producers such as the United States. Mediterranean regions are transitioning from traditional low-density orchards to intensive systems, where cultivar and rootstock choice are crucial for sustainability. Self-fertile and late-blooming cultivars improve yield stability, while interspecific hybrid rootstocks enhance water use efficiency and tolerance to drought and poor soils. Drought stress impacts almond physiology and yield, although moderate deficit irrigation can maintain productivity and improve kernel quality. Future improvement relies on germplasm conservation, marker-assisted selection, and genomic tools to develop climate-resilient cultivars integrated with sustainable water management strategies. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Surface defects in high-temperature continuous cast billets are critical factors affecting the quality of steel products. Owing to high-temperature radiation, heavy dust contamination, varying billet specifications, and background interference from oxide scales and water stains, existing online surface defect detection technologies for high-temperature continuous cast billets still suffer from limitations including high false-positive rates, inefficient identification of pseudo-defects, and the inability to simultaneously detect three-dimensional (3D) depth information alongside two-dimensional (2D) features. To solve these problems, this paper proposes a multi-dimensional online detection technology for surface defects in high-temperature continuous cast billets based on multi-information fusion. A four-channel multispectral image sensor and a corresponding three-light-source imaging system were developed. Furthermore, a defect sample augmentation method, a deep learning-based 2D recognition method, and a photometric stereo-based 3D reconstruction method were designed to mitigate problems of low detection accuracy and poor robustness caused by sample imbalance among different defect types. Finally, industrial applications were conducted on large-section continuous cast billets, beam blanks, and billets during the grinding process. According to the surface defect detection requirements of different continuous cast billets, multispectral multi-information fusion and traditional 2D defect imaging methods were adopted respectively. The results demonstrate high-precision online detection of surface defects in continuous cast billets, with favorable practical application effects. Full article

This bibliometric study examines artificial intelligence’s impact on sustainable water management through systematic analysis of 424 publications from Scopus, Web of Science, and IEEE Xplore following the 2020 PRISMA guidelines. Four analytical approaches were implemented: descriptive bibliometric characterization, VOSviewer network visualization, principal component analysis with Ward’s hierarchical clustering (86.58% variance explained, cophenetic correlation = 0.951), and qualitative synthesis. The results reveal exponential growth from 4 publications (2018) to 167 (2025) with geographic concentration in China (30.2%), the USA (9.7%), and India (8.0%). Collaboration networks exhibit pronounced fragmentation (density = 0.04, modularity = 0.78) with minimal North–South partnerships (12%). Critically, keyword analysis identifies five thematic clusters dominated by machine learning methodologies, whereas governance and equity dimensions appear fewer than eight times, revealing a fundamental gap wherein technical optimization proceeds without the institutional frameworks necessary for equitable water access. Multivariate analysis suggests that technological infrastructure capacity is a stronger correlate of research output than geographic water stress, based on the observed geographic distribution of high-output nations rather than direct operationalization of scarcity indicators. The qualitative synthesis revealed that 68% of the studies remained pilot-scale studies, 82% were concentrated in developed nations, and 66% cited data quality as the primary constraint. The bibliometric patterns suggest a pronounced orientation toward computational approaches, alongside paradoxical AI infrastructure water consumption that may partially offset conservation benefits. (Note: 2025 figures reflect early-access articles retrieved before the November 2024 search date and should be interpreted as partial-year estimates.) Achieving sustainable water management requires a reorientation emphasizing measurement infrastructure in data-poor contexts, North–South partnerships, and the integration of socioinstitutional dimensions as constitutive elements within technical development frameworks. Full article

Remote-sensing inversion of water-quality parameters is a critical interdisciplinary field, integrating remote-sensing technology, environmental science, and water resources management, providing key technical support for precise water resources monitoring and ecological governance. To address the lack of comprehensive systematic reviews in this field, this study conducted a bibliometric-based narrative review, selecting 2812 valid English studies published during 1980–2026 from the Web of Science Core Collection (WOSCC) and performing in-depth knowledge mapping analysis via CiteSpace software. The results showed that global research in this field has gone through three stages: initial exploration (1980–2000), slow growth (2001–2015), and rapid explosion (2016–2026). China ranks first in publication volume worldwide, with a collaborative research pattern dominated by core institutions, including the Chinese Academy of Sciences, Wuhan University, and the National Aeronautics and Space Administration (NASA). The core research hotspots focus on multi-source data fusion, AI-driven inversion-model optimization, and the research shift from coastal to inland water bodies. Current research faces three key challenges: poor adaptability of multi-source data-fusion technologies to water-quality monitoring, inadequate integration of geospatial and thematic factors in inversion models, and an insufficient systematic approach of inland-water-body research. Accordingly, future research should focus on advancing remote-sensing data-fusion methods, further optimizing water-quality inversion models, and strengthening inland-water-body studies. This study clarifies the field’s development context and research characteristics, providing valuable references for subsequent academic exploration and practical applications in water resources management. Full article

The Amazonian açaí waste is promising for producing charcoal through pyrolysis and bioenergy through combustion, but the property losses from its poor disposal in the environment remain unknown. Therefore, this work aimed to analyze how different storage conditions of the açaí waste over time, which mimic the reality throughout the Amazon, modify its bioenergetic properties. The samples were stored in a covered greenhouse for nine months in the following conditions: immersed in water, on the soil, and in open plastic bags. The biomass was analyzed by Fourier-transformed near-infrared spectroscopy, physical properties, stereomicroscopy, proximate composition, and thermogravimetry. The degraded waste showed endocarp attack and fungi proliferation. The chemical groups of primary cell wall components were concentrated, unlike water-soluble materials, raising the fixed carbon from 22% to 25% after 30 days. Consequently, higher heating values were kept (≈19 MJ/kg). However, water immersion storage sharply decreased the waste basic density from 0.81 g/cm³ to 0.56 g/cm³, dropping the energy density from 12 GJ/m³ to 8 GJ/m³. Moreover, storage raised ash content from 1.1% up to 1.9%. The storage hindered the start of the main phases of combustion and pyrolysis, which were later intensified, especially for soil-stored waste. Therefore, more stable combustion and pyrolysis require fresh waste. Besides natural drying, plastic bag storage over time kept the waste quality closer to that of the fresh waste. Full article

Recycled concrete aggregate (RC A) is considered a sustainable material; however, its porosity and interfacial properties are poor due to adhering mortar. This study investigates the influence of synergistic surface treatments in terms of improving RCA quality and the resulting compressive strength of recycled aggregate concrete (RAC). A machine learning (ML) model was also developed to predict the compressive strength of recycled aggregate concrete (RAC) with different surface treatments, not just untreated RCA. In this study, three different RCA surface treatments were investigated. In this regard, acetic acid, silica fume, and sodium silicate treatments were combined. The properties of concrete and fresh concrete were investigated using slump and compressive tests at 28 and 90 days. The performance of various ML models, incorporating Gradient Boosting, Random Forest, XGBoost, and Extra Trees, was investigated. The performance of different models was also evaluated using R², MAE, and RMSE. SHAP analysis was used to evaluate the performance of different models. It has been observed that the use of surface treatment leads to lower water absorption values and higher interfacial bonding, as well as substantial improvements in compressive strength. Specifically, the use of acetic acid and silica fume for treating RCA produced compressive strengths similar to those achieved from natural aggregates at lower costs. XGBoost has the highest accuracy among all models. The R² value of XGBoost was 0.909. The SHAP analysis indicates that cement and curing age are the main features. RCA treatment parameters are considered modifiers. A user-friendly online tool was created to estimate compressive strength using different types of RCA treatment. The RCA treatment with sodium silicate and silica fume performed best in terms of embodied carbon among the treated mixes; it was deemed the best alternative from an environmental standpoint. Full article

Sustainable groundwater management in hyper-arid regions requires accurate water quality assessments, yet remote desert environments present major challenges due to data scarcity, high sampling costs, and limited laboratory infrastructure. This study proposes a framework integrating the Water Quality Index (WQI) with Inverse Distance Weighting (IDW)-based spatial data augmentation and machine learning classification for groundwater quality assessment in the Tabelbala region, southwestern Algeria. Three classifiers were evaluated, Random Forest (RF), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), and trained on an augmented dataset generated from 178 original groundwater samples using IDW interpolation with a sensitivity-optimized 150 m radius, producing 2779 augmented training points. RF achieved the highest predictive accuracy (85.9%), followed by ANNs (84.7%) and SVMs (83.1%), with all models demonstrating excellent discriminative performances (area under the receiver operating characteristic curve > 0.96). Permutation Feature Importance analysis identified total dissolved solids (TDS), sulfates (SO₄²⁻), total hardness (TH), and chlorides (Cl⁻) as the most influential parameters, consistent with World Health Organization (WHO) guidelines. Spatial distribution maps revealed that the majority of groundwater sources exhibited poor to very poor quality, highlighting the urgent need for local water management interventions. The proposed framework offers a replicable decision-support tool for water resource managers in data-scarce arid environments, supporting SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action). Full article

Lebanon, active on the international environmental front, paradoxically faces a national water crisis: shortages and degrading quality. The issue lies not in the lack of environmental policies, but in their implementation. The ‘whole-of-society’ (WoS) approach, which emphasises collaboration among all actors, is recognised as a potential solution. However, it remains untransposed to the national context and poor policy implementation is explained in a linear logic, rather than as interconnected variables forming feedback loops. This research argues that water management, policy implementation, and actor involvement form a triangular system that must be addressed holistically. Adopting the system-thinking approach explains the complexity by the principle that observed behaviours result from causal loops created by the interdependence of variables. Through actor mapping and the building of a causal loop diagram based on literature review and textual analysis, it reveals that actors are organised as a top-down hierarchy that struggles to function, with limited cooperation and coordination and overlapping responsibilities, so local-level action is the only balancing force with regard to the water deficit. The research highlights the application of WoS in developing countries where implementation issues in relation to actors’ interaction is rarely addressed. It provides recommendations for connecting relevant actors within a fragmented system. Full article