Search Results (490)

Freshwater demand for cementing operations in the Delaware Basin continues to increase with expanding unconventional development, creating a high demand for an alternative source of water. This study develops a chemistry screening and operational framework to evaluate the reusability potential in cementing operations in the Delaware Basin. A three-tier screening system for the produced-water samples was established by using the major-ion chemistry, total dissolved solids (TDS), pH, and saturation index (SI) thresholds derived from the cement literature and American Petroleum Institute (API) guidelines. The results of the geochemical screening aid in classifying the water samples into four suitability categories: Excellent/Preferred, Good/Suitable, Moderate/Marginal, and Poor/Unsuitable. The results suggest that the samples obtained from the Loving, Pecos, Reeves, Eddy and Lea counties meet the criteria for reuse in cementing operations with minimal conditioning. To assess the feasibility of operational use, a probabilistic forecasting model was developed to predict the cement water demand in 2026 for the basin. Linear regression of historical drilling trends between 2015 and 2025 showcased that approximately 3595 new wells will be drilled, with an average well depth of 21,778 ft. To evaluate whether the produced-water volumes in the basin are adequate for reuse in cementing, a Monte Carlo simulation (10,000 iterations) estimated an annual cementing water requirement centered at 6.16 MMbbl/year (P50). Produced-water availability from wells classified as Excellent/Preferred was also modeled probabilistically, using uncertainty in the water–oil ratio (WOR), estimated ultimate recovery (EUR), and forecast duration. These results demonstrate the potential for produced-water reuse to reduce freshwater demand for cementing operations in the Delaware Basin. Full article

The Beishan area of Gansu, China, is the primary candidate site for the geological disposal of China’s high-level radioactive waste (HLW). To assess the long-term safety of this repository, the evolutionary patterns of groundwater and the primary migration vector of radionuclides must be understood. Through experiments and hydrogeochemical simulations of snowmelt samples from the Qilian Mountains and deep rock samples from Beishan, we reveal different hydrochemical compositions and types of the snowmelt and deep groundwater. The results show that the hydrochemical type of Qilian Mountain snowmelt is SO₄–Na·Ca, whereas that of the deep groundwater in the Beishan is Cl·SO₄–Na, indicating substantial differences in the hydrochemical characteristics of the two samples. The water–rock interactions between snowmelt and granite are dominated by the dissolution of silicate minerals and the precipitation of carbonate minerals, accompanied by cation exchange and adsorption. After the interaction, the hydrochemical type of the snowmelt becomes SO₄–Na, with total dissolved solids (TDS) consistently maintained at ~500 mg/L, which is distinct from the TDS range of 1540–2045 mg/L observed for the deep groundwater in the Beishan. Under the experimental and simulation conditions set in this study, the water–rock interactions between Qilian Mountain snowmelt and Beishan granite cannot reproduce the hydrochemical characteristics of the deep groundwater in the Beishan. This study provides theoretical support for the hydrogeological safety assessment of HLW geological repositories. Full article

Urban coastal wetlands along the Peruvian Pacific coast are increasingly affected by urban expansion, pollution, and hydrological alterations, compromising their ecological integrity. In this context, the spatiotemporal variation of the aquatic macrophyte community and its relationship with limnological conditions and drivers of change were evaluated in the Santa Rosa wetland (Chancay, Lima). The objective is to evaluate the spatiotemporal variation of the aquatic macrophyte community in the Santa Rosa wetland and analyze its relationship with physicochemical limnological variables and drivers of change. Sampling was conducted during two contrasting hydrological seasons in 2022: T1 (low-water season) and T2 (high-water season), at six sampling points (P1–P6). Physicochemical variables (water depth, temperature, pH, conductivity, total dissolved solids—TDS, total suspended solids—TSS, dissolved oxygen—DO, turbidity, nitrate—NO₃⁻, ammonium—NH₄⁺, phosphate—PO₄³⁻, and dissolved organic matter—DOM) were measured, and the relative abundance of aquatic macrophytes was evaluated. Drivers of change were identified through direct observation and a structured matrix, with phosphate a PCoA performed to summarize spatiotemporal trends. Data were analyzed using Principal Component Analysis (PCA), Co-inertia analysis, and Multi-Response Permutation Procedures (MRPP). Significant spatiotemporal variation was observed in physicochemical parameters (p < 0.05), with moderate covariation between the two matrices (RV = 0.47). A total of ten aquatic macrophyte species were recorded, with higher abundance of Pontederia crassipes and Pistia stratiotes in T1, and Hydrocotyle ranunculoides and Bacopa monnieri in T2. The most relevant drivers of change were solid waste, livestock grazing, organic contamination, and urban expansion. Spatial heterogeneity was observed in the drivers of change affecting the Santa Rosa wetland, forming a mosaic of areas with different impact profiles. Despite multiple anthropogenic pressures, the Santa Rosa wetland maintains a limnological structure and a functionally coupled macrophyte community, suggesting that essential ecological processes are maintained within the temporal scope of this study. The observed covariation between physicochemical conditions and vegetation confirms the persistence of essential ecological processes, even within an altered urban context. This study demonstrates that integrating biotic components, limnological variables, and drivers of change is fundamental to understanding and monitoring the ecological dynamics of urban wetlands along the Peruvian coast. 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

The Saoura Valley (southwestern Algeria) hosts14 oases that primarily depend on groundwater in an endorheic basin. The hydrogeological system is bisected by the Saoura Wadi into two distinct compartments: an active, interconnected eastern compartment (Mio–Plio–Quaternary alluvial aquifer and terraces of the Great Western Erg) and a passive, fossil western compartment (Guir Hamada and Cambro–Ordovician aquifers). In September 2024, 51 groundwater samples were collected from nine oases. Temperature ranged from 16.2 to 31.4 °C and pH ranged from 7.1 to 7.85. Total dissolved solids (TDS) varied widely (179–4480 mg/L; median of 454 mg/L), with electrical conductivity between 280 and 7000 µS/cm. Three main hydrochemical facies were identified: Ca–Mg–SO₄–Cl (30%), Na–Cl–SO₄ (55%), and hypersaline types in the terminal inferoflux zone. Nitrate concentrations exceeded the WHO guideline (50 mg/L) in 22% of samples, attributed to localized agricultural and domestic inputs. Geochemical evolution is controlled by evaporite dissolution (gypsum, halite), cation exchange, and evaporative concentration, with a downstream salinity gradient from freshwaters near the Great Western Erg toward hypersaline inferoflux. Comparison with historical data (1941, 1963, and earlier studies) indicates a trend of increasing salinization since the 1990s, associated with intensive borehole pumping and irrigation return flow. These findings suggest risks to the long-term sustainability of the Saoura oases. Full article

Precise water quality forecasting is vital for sustainable resource management and public health, especially in semi-arid environments. This study investigates the predictive capabilities of ten Machine Learning (ML) algorithms using a dataset of 308 drinking water samples collected from various districts in Şanlıurfa Province, Türkiye. We evaluated ten predictive models, including Support Vector Regressor (SVR) and Extreme Gradient Boosting (XGBoost), both integrated with dimensionality reduction and hyperparameter optimization. Nineteen physicochemical and microbiological parameters—Temperature, chlorine (Cl⁻), pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), sulfate (SO₄²⁻), Free Chlorine (Cl₂), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), fluoride (F⁻), trihalomethanes (THMs), Escherichia coli, Enterococci, Total Coliform—were used as input features. The dataset was split into training (75%) and testing (25%) subsets, and model performance was assessed through 10-fold cross-validation and hold-out testing procedures. To improve model generalization and mitigate the effects of class imbalance, we implemented the Adaptive Synthetic Sampling (ADASYN) technique. ML algorithms were evaluated using standard regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The LSTM model optimized using Randomized Search outperformed the SVR and XGBoost models, demonstrating the highest accuracy and generalization capability, as evidenced by the superior R² value of 0.999 following ADASYN balancing and the lowest RMSE (1.206). These findings underscore the effectiveness of the LSTM framework in modeling the complex variance of the Weighted Arithmetic Water Quality Index (WAWQI). The findings of this study are expected to support future water quality monitoring strategies, inform policy development, and contribute to sustainable water resource management in arid and semi-arid regions. Full article

The southern Junggar Basin in Xinjiang is rich in coalbed methane (CBM) resources. Large-scale development is underway in the Sigong River block (SGR block) of the Fukang West Block. Based on an integrated analysis of geological and hydrogeochemical characteristics, this study clarifies the key factors affecting CBM well productivity in the SGR block. Based on gas and water production performance, four distinct productivity types of CBM wells are identified, which are jointly controlled by burial depth, local structural and hydraulic disturbance, and also governed by synergistic interplay between gas content and permeability. The optimal geological combination—comprising the 700–1000 m burial depth, syncline core structure, stagnant hydrodynamic conditions, relatively high gas content, and favorable permeability—collectively contributes to the high-productivity Type I wells with low water production. In contrast, deep coal seams (>1400 m), characterized by reduced gas content and extremely low permeability, correspond to Type IV wells, which exhibit low gas and water production. Type II wells, located in the 1000–1400 m interval, exhibit moderate and variable productivity controlled by the interplay between high gas content and a wide range of permeability. Shallow margins (<700 m) affected by coal combustion and surface water influx produce high-water and low-gas wells (Type III). Geochemical signatures effectively differentiate between these types: closed, stagnant environments (Types I/II) are marked by a Na-Cl-HCO₃/Na-HCO₃-Cl water type, moderate total dissolved solids, and low sodium chloride coefficients, while open hydrodynamic conditions (Type III) are indicated by Na-SO₄-HCO₃ water with high sodium chloride coefficients. A δD-H₂O/δ¹⁸O-H₂O ratio of 7–9, combined with favorable TDS and water type, is identified as a key indicator of high productivity. Based on these relationships, a productivity response index model incorporating critical geological and geochemical parameters was developed. This model provides a practical tool for predicting CBM well performance and targeting sweet spots, offering significant value for exploring geologically and hydrologically complex basins. Full article

Groundwater in Al-Madinah Al-Munawwarah faces considerable challenges from high salinity, elevated TDS, and nitrate contamination, primarily due to urbanization and industrial activities, making ongoing monitoring and management essential for its sustainable use in both drinking water and agriculture. The assessment of groundwater quality was conducted on 44 wells tapping two major aquifers (basaltic and alluvial) in the region, utilizing various geochemical techniques, including ICP-MS, FAAS, and XRF, to evaluate hydrochemical characteristics and identify the primary controlling factors. Key physicochemical parameters, including total dissolved solids (TDSs), electrical conductivity (EC), pH, total hardness (TH), and major ion concentrations, were evaluated. The results indicate that several parameters exceed permissible limits established by Gulf and international standards, reflecting highly saline conditions that could adversely affect drinking water safety and agricultural practices. Elevated nitrate levels and other contaminants indicate a combination of geological processes, including mineral leaching, and anthropogenic activities, such as agricultural runoff. Correlations among various ions reveal complex interactions driven by both natural and human factors. High nitrate and potassium concentrations, particularly in the alluvial aquifer, combined with weak correlations with geogenic ions, indicate anthropogenic inputs. Heavy metals in groundwater were classified into two groups: those within permissible limits (Ag, Ba, Be, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Sb, and U) and those exceeding recommended limits (Zn, Al, As, Se, and Tl). Elevated metal concentrations are primarily attributed to water–rock interactions and the fertilizer use in surrounding agricultural areas. These findings highlight the urgent need for continuous monitoring and proactive groundwater to ensure sustainable and safe use of water resources. Full article

This study investigates how membrane transport characteristics affect permeate quality in a 2-pass reverse osmosis (RO) system for ultrapure water (UPW) production. Unlike conventional RO, UPW-RO operates in an ultra-low concentration range. Seven commercial 4-inch RO membrane modules spanning a wide range of water (A) and salt (B) permeability coefficients were evaluated under various second-pass RO feed concentrations (specifically, total dissolved solids (TDS) and total organic carbon (TOC)). Second-pass RO permeate TDS remained almost constant regardless of membrane specifications, whereas the permeate TOC was strongly membrane-dependent. RO permeates from high-permeability membranes showed significantly higher TOC than those from high-selectivity membranes. The experiments also revealed that a high-permeability membrane configuration for both RO passes resulted in excessive TOC leakage, while a high-selectivity membrane configuration mitigated TOC passage at the cost of a high operating pressure requirement. A combination of a high-permeability membrane (the first pass) and a high-selectivity membrane (the second pass) could achieve an acceptable TOC passage with a moderate operating pressure requirement in UPW-RO systems. Full article

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R² ≥ 0.80 for all SWQPs) and demonstrated robust performance (R² ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems. Full article

Ammoniacal nitrogen (NH₃-N) is a major pollutant in municipal, industrial, and agricultural wastewaters and is a key driver of eutrophication and aquatic ecosystem degradation. This review paper assessed the potential of water hyacinth (Eichhornia crassipes) as a sustainable phytoremediation option for removing ammoniacal nitrogen from wastewater. This paper focused on the plant’s biological characteristics, nutrient uptake pathways, and adaptability to varying environmental conditions. Specific mechanisms examined include direct root uptake of ammonium, internal translocation, and microbial-assisted nitrification and denitrification within the rhizosphere. The influence of pH, temperature, salinity, retention time, and plant density on removal efficiency was also assessed in this study. Across laboratory, pilot, and field-scale studies, water hyacinth achieved ammoniacal nitrogen removal efficiencies ranging from 74% to 97% under favorable conditions, alongside significant reductions in biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TDS). Integration with constructed wetlands, microbial systems, and hybrid treatment approaches further enhanced nitrogen removal and process stability. This paper also highlighted opportunities for biomass valorization through biogas, bioethanol, and compost production while identifying challenges related to salinity sensitivity and biomass management. Overall, water hyacinth emerges as a cost-effective, nature-based solution for decentralized wastewater treatment, with strong potential to support sustainable water management and circular bioeconomy initiatives. Full article

This study examines the application of mining effluents as feed solutions in a bench scale pressure retarded osmosis (PRO) system for energy generation and the prospect of water recycling or safe discharge to the environment. Effluents were characterized and pretreated by ultrafiltration (UF) and nanofiltration (NF) prior to PRO. The PRO process was then conducted over 6 h in a cross flow flat plate cell with an effective membrane area of 34 cm², a hydraulic pressure of 12.4 bar and a 3M ammonium carbonate (NH₄)₂CO₃ as draw solution. Effluent 1 contained ions such as Cl⁻ (539 mg/L), NO₃⁻ (585 mg/L), SO₄²⁻ (3000 mg/L), Na⁺ (560 mg/L), and Mg²⁺ (656 mg/L), with a total dissolved solids (TDS) concentration of 5400 mg/L, chemical oxygen demand (COD) of 136 mg/L, total organic carbon (TOC) concentration of 3.5 mg/L, and acidic pH of 3.8, while effluent 2 was highly dominated by Cl⁻ (32,100 mg/L), NO₃⁻ (9720 mg/L), SO₄²⁻ (6512 mg/L), Na⁺ (14,306 mg/L), and Mg²⁺ (5336 mg/L), had a TDS concentration of 73,315 mg/L, COD of 8100 mg/L, TOC concentration of 10.2 mg/L, and pH of 7.4. These physiochemical properties indicated a significant potential of fouling and scaling which necessitated the appropriate pretreatments. It was shown that integrating UF and NF pretreatments was highly effective in refining the quality of effluents with a significant removal efficiency of above 90% for ions and heavy metals by NF, led to fouling mitigation, higher and more stable power density as well as potential water reuse or safe environmental discharge. The achieved water fluxes and power densities were 54 L/m²h and 18.6 W/m², for effluent 1, and 38 L/m²h and 13 W/m², for effluent 2, respectively. The outcome of this study is applicable for the mining sector especially in remote areas with the potential for water and energy recoveries to contribute to more sustainable mining operations. Full article

The Chi River Basin in northeastern Thailand is the country’s second-largest basin and a major tributary of the Mekong River, which is a regional hotspot for freshwater mollusc diversity. However, many of its sub-tributaries remain poorly studied. This study investigated molluscan diversity in the upper Chi River and examined relationships between assemblage structure and physicochemical habitat factors. Quantitative quadrat sampling was conducted at 11 stations along a 100 km reach, and community–environment linkages were analyzed using cluster analysis and canonical correspondence analysis (CCA). A total of 2734 individuals representing 25 taxa (12 gastropods, 13 bivalves) were recorded. Three distinct assemblages—Upstream, Midstream, and Downstream—were identified along the longitudinal gradient. CCA indicated that flow velocity, total dissolved solids (TDS), and dissolved oxygen (DO) were the primary predictors of assemblage structure (p < 0.01), jointly explaining 59.5% of community variation. Upstream reaches were dominated by Thiaridae (Tarebia, Brotia), midstream sections by Corbicula, and downstream areas exhibited the highest diversity, characterized by large unionid mussels. This study provides the first quantitative evidence of clear longitudinal zonation in the upper Chi River and establishes essential baseline data for conservation and management in this overlooked, biodiversity-rich basin. Full article

Millions of people across rural and peri-urban regions worldwide remain exposed to unsafe concentrations of naturally occurring fluoride in groundwater. In West Bengal, India, community-level water purification plants (CWPPs) have been widely installed to remove excess fluoride, yet their long-term operational performance remains minimally documented. This study assessed the pre-filter and post-filter water quality of 58 such groundwater-based CWPPs across the fluoride-affected districts of Bankura and Purulia in West Bengal, to evaluate in-field fluoride removal performance and potential hydrogeochemical, operational, and management drivers. Evaluation included fluoride concentration and key physicochemical parameters such as pH, temperature, electrical conductivity (EC), oxidation-reduction potential (ORP), total dissolved solids (TDS), and other anions including bromide, chloride, bicarbonate, nitrite, nitrate, phosphate, and sulphate. Fluoride concentration ranged from 1.7 mg/L to 8.2 mg/L and 1.6 mg/L to 3.9 mg/L in the sampled source water of Bankura and Purulia respectively, with both pre- and post-filter water of all the observed treatment units exceeding the WHO guideline of 1.5 mg/L. Potential contributors to underperformance may include inappropriate filter media selection, insufficient backwashing and regeneration, limited operational oversight and/or non-tailored treatment approaches. However, details on the adsorbent media and operational details were not available, and thus findings reflect observed field performance rather than necessarily causal relationships. These operational insights will contribute to the global discussion on improving decentralized groundwater treatment systems in resource-constrained settings. Full article

Caffeine is one of the most well-known biologically active compounds in coffee beans, and its content largely determines the taste and stimulating properties of the drink. However, the amount of caffeine in beans can vary significantly depending on growing conditions, even within the same coffee variety. The growing global demand for coffee and the current market dynamics emphasize the necessity to investigate how the origin of coffee beans influences beverage quality. Arabica beans, particularly the Red Bourbon variety, are known to exhibit variations in chemical composition, sensory characteristics, and technological behavior depending on their cultivation environment. The study aimed to evaluate the physicochemical and sensory properties of Arabica Red Bourbon beans sourced from distinct geographic regions, considering factors such as altitude and local environmental conditions. The sensory characteristics of the resulting beverages were evaluated using the capping method, and water activity, density, moisture content, color, pH, extractivity and caffeine content were determined. Roasted bean color ranged from 61.4 to 62.5, while ground coffee color was 72.5–75.4. Moisture content was highest in Col and R (3.4%) and lowest in Con (3.1%). The greatest moisture loss during roasting occurred in S and R (13.4%). Water activity decreased from 0.50–0.56 in green beans to 0.18–0.30 post-roasting. Extraction yield ranged from 20.03 to 21.21%, and total dissolved solids (TDS) varied at 1.23–1.30%. The least acidic sample was S (pH 5.04). Colombian beans contained unusually high caffeine. The conducted research confirmed that the geographical origin of Arabica Red Bourbon beans significantly impacts their physicochemical and sensory attributes. Variations in moisture, acidity, and caffeine content were observed among the samples, despite a consistent roasting profile. Full article