Anaerobic bacteria require specific concentrations of nutrients (nitrogen and phosphorus) and micronutrients (minerals) to function efficiently. Many industries produce waste that can be treated anaerobically for the improvement of waste characteristics or the generation of biogas. Anaerobic biomasses will adapt to the feedstock conditions and reflect the nutrient and mineral content of the material they are digesting. This paper used a one-way analysis of variance (ANOVA) test with Tukey's post hoc tests to compare the nutrient and mineral content of biomass from five different industries (beverage, brewery, dairy, food, and pulp-paper). The complete dataset contains nutrient/mineral compositions of over 300 anaerobic biomass samples from these five industries. The analyses showed that there were substantial differences in the concentration of key micronutrients contained in the anaerobic biomasses of the five industries. Results of the analysis can serve as a resource for operators and engineers to anticipate nutrient and mineral needs for a digester or to diagnose the cause of poor reactor performance related to nutrient and trace element deficiencies. A recommended minimum concentration for cells grown in an anaerobic reactor is provided.HIGHLIGHTSOver 300 anaerobic biomass samples analyzed across five major industries showed significant variation in nutrient and mineral content found between industries. ANOVA and Tukey HSD tests identify statistically distinct nutrient profiles between industries. Results provide practical guidance for digester nutrient management, with 10th percentile values proposed as minimum thresholds for successful digestion.
This review assessed two cross-cutting layers integrating the impacts of climate change and water availability, with five dimensions: governance, technical performance, environmental and health aspects, social acceptance, and economic feasibility. The analysis aimed to evaluate their influence on treated wastewater (TWW) reuse in Tunisia and Jordan. Although Tunisia produces 293 Mm 3/year of TWW and operates 127 wastewater treatment plants, only 3.4% (10.0 Mm 3/year) is reused, whereas Jordan achieves a higher reuse rate of 90% (178.2 Mm 3/year), supplying 98.4% of irrigation water in the Jordan Valley. This difference is attributed to variability in treatment technologies, quality compliance with standards, and institutional regimes. Jordan's application of tertiary treatment and its adherence to the JS 893/2022 standard enabled a higher reuse rate. In contrast, Tunisia still relies heavily on secondary treatment (77.2%) with limited tertiary treatment coverage, and it is under NT 106.03/1989 application for agricultural reuse. Tunisian farmers exhibited a greater level of acceptability, but lower institutional trust than their Jordanian counterparts. Financial advantages, regulatory coherence, and awareness campaigns are the major factors influencing farmer acceptance. The framework clarifies why TWW reuse adoption varies between both countries, highlighting the importance of a practical strategy for addressing long-term water security and agricultural development.
Wastewater generated from animal slaughtering is discharged into rivers without adequate treatment, causing diseases, as it is used for agricultural irrigation and as drinking water for animals. In this context, this research proposed two treatment steps to evaluate the reduction of the high bacterial load of Escherichia coli in slaughterhouse wastewater using titania nanoparticles (TiO2-NPs) synthesized by chemical precipitation, evaluating the dose and contact time. TiO2-NPs, characterized by scanning electron microscopy, exhibited sizes from 16 to 100 nm in diameter, with vibrational bands of 429.01 cm(-1) before and 420.25 cm(-1) after photocatalytic treatment (O-Ti-O mode) according to Fourier transform infrared analysis and a BET surface area of 64.719 m(2) g(-1). The first stage consisted of pretreating wastewater with H2O2 and FeCl3, which contained 2.0 x 10(4) CFU mL(-1) (5.35 McFarland) of E. coli, reducing the bacterial density by 28.22%. In the second stage, photocatalysis with TiO2-NPs was applied using UV lamps (253.80, 313.04, and 365.44 nm) on a synthetic matrix with a concentration of E. coli equivalent to that of the water after pretreatment (1.44 x 10(4) CFU mL(-1) or 3.84 McFarland), achieving 97.31% removal using 1 g of TiO2-NPs in 25 mL of sample for 120 min.
Groundwater in Pakistan, especially in the 6,498 km(2) area between the Chenab and Sutlej Rivers, is a critical yet stressed resource. Levels decline by 0.08-0.6 m annually due to increasing domestic, agricultural demands, urbanization, climate change, and poor groundwater management governance. Using the MODFLOW model, this study simulates groundwater dynamics over a 15-year historical period and projects trends up to 2045. Projections indicate that, under current abstraction rates, groundwater levels could decline by 0.84-0.97 m/year by 2045. Mitigation via artificial recharge, like constructing six cascade dams on the Sutlej River, could boost recharge from 80 to 103.3 MCM daily, but abstraction may stay high at 117.8 MCM/day. On the supply side, supplementary interventions such as recharge lakes and infiltration wells can further enhance recharge potential. Climate projections also indicate a favorable 17-20% increase in rainfall from 2015 to 2064 compared with 1965 to 2014, supporting recharge initiatives. Sustainable abstraction is estimated at 0.12684 MCM/day for urban areas and 55 MCM/day across the study area. To ensure long-term water security and sustainable groundwater management, the study emphasizes policy actions such as integrating recharge strategies, issuing permits, enforcing safe yield limits, and community engagement.
The increasing scarcity of potable water resources, exacerbated by climate change and aging urban infrastructure, has intensified the demand for real-time monitoring and detection of water losses in distribution networks. Traditional leak detection methods often rely on single-point analysis and lack detailed information on the hardware and software architecture of proposed systems, limiting their scalability and applicability. In this study, a wireless sensor network (WSN) based on FSR sensors was designed and implemented, with a gateway forwarding data to a cloud platform for remote monitoring, to address these limitations. The proposed system integrates both hardware and software components, providing secure wireless communication and real-time monitoring of pressure variations along the pipeline. Experimental validation was conducted using a laboratory-scale prototype pipeline where multiple simultaneous leakage scenarios were created. Results demonstrated that the developed device reliably detected and differentiated leaks at varying flow rates and multiple points, with sensors closest to leakage sites exhibiting rapid pressure drops, while downstream sensors showed delayed but converging responses. These findings not only validate the effectiveness of FSR-based sensing for multi-leak detection but also highlight the advantages of combining IoT and WSN architectures for scalable and low-cost monitoring solutions.
This study aimed to predict daily runoff using three machine learning models: artificial neural networks (ANNs), random forests (RFs), and extreme gradient boosting (XGBoost). Thirty-five years of discharge records and remote-sensing climate data were used to develop and validate the models, demonstrating the potential of satellite data for machine-learning-based runoff prediction, especially in data-poor regions like Nepal. The study identified 1-day lagged discharge (lag_1), average temperature, and maximum temperature as the three most important input variables. The developed ANN model demonstrates high accuracy in streamflow prediction, achieving an R-2 of 0.928, a root-mean-square error (RMSE) of 435.43 m(3)/s, a Nash-Sutcliffe efficiency (NSE) of 0.93, and a Kling-Gupta efficiency (KGE) of 0.944 for the Narayani River Basin. When applied to the Trishuli River, a major tributary of the Narayani, the model demonstrated strong performance, with an R2 of 0.895, an RMSE of 425.92 m(3)/s, an NSE of 0.89, and a KGE of 0.908. Nonetheless, RF outperformed ANN in capturing low-flow conditions in the Narayani River. Overall, the models provide valuable tools for retrospective streamflow prediction, gap-filling in hydrological records, and supporting effective and sustainable water management.
Biochemical oxygen demand (BOD5) is a key indicator of organic pollution in wastewater, but its conventional determination requires at least 5 days, limiting its use for real-time monitoring and process control. This study proposes a rapid alternative method based on measuring the oxygen uptake rate (OUR) to estimate BOD5. Approximately 100 tests were conducted to correlate BOD5 with the Delta OURs value, defined as the difference between exogenous and endogenous respiration. Samples were collected from 13 urban wastewater treatment plants (WWTPs) of different capacities, evaluating both raw influent and wastewater after pretreatment. For these conditions, the model estimated BOD5 as 11.172 times the Delta OURs. The accuracy of the correlation was also assessed, considering the percentage of industrial wastewater contribution. A stronger correlation was observed in WWTPs with an industrial load between 10 and 30%, where BOD5 was estimated as 10.544 times the Delta OURs. Overall, the results demonstrate that OUR measurement can serve as a reliable and fast alternative to standard BOD5 analysis, enabling real-time estimation of organic pollution in wastewater and helping overcome the limitations of conventional analytical methods.
Climate change, urbanization and increasing water demand cause serious pressure on water resources. Efficient use of water, planning and sustainable implementation of water efficiency practices are quite important for sustainable water management. The aim of this study is to develop a novel water efficiency sustainability index framework for evaluating and monitoring water efficiency practices in schools. A total of 123 components were defined under the sub-sustainability indexes of Education and Capacity Development (EKG-index) (47 components), Water Consumption Analysis and Monitoring (STA-index) (50) and Audit, Control and Maintenance (DKB-index) (26). The weights of indices and components were determined by the best worst method. The highest weight was calculated as 0.394 for EKG. The weights of the DKB and STA indices were 0.307 and 0.299, respectively. The scores of components were obtained from 35 schools based on field data. The highest and lowest OSVS index scores were obtained as 3.90 and 2.14, respectively. In pilot schools, the scores of the DKB sub-index were higher than those of the EKG and STA indices. This comprehensive framework for water efficiency with a hierarchical calculation methodology is the novelty of this study. This framework will make significant contributions to monitoring water efficiency in schools.
This study investigates the hydrochemical characteristics and controlling factors of karst groundwater in southern Sichuan, China, to assess its quality and identify the sources of major ions. A total of 19 groundwater samples were collected and analyzed using mathematical statistics, Piper and Gibbs diagrams, and ion correlation analysis. The results show that the groundwater is predominantly fresh and weakly acidic, with HCO3-Ca as the primary hydrochemical type. However, a key finding is the high spatial heterogeneity of the hydrochemical system. The relatively low variation in Ca2+ concentrations underscores the pervasive influence of carbonate rock dissolution as a regional background process. In contrast, the extremely high variability of Na+, Cl-, and SO42- highlights significant contributions from localized influences. Correlation and cluster analyses confirm that groundwater chemistry is predominantly controlled by water-rock interaction, but also identify a subset of samples distinctly affected by anthropogenic activities (e.g., domestic wastewater and agriculture). In summary, groundwater evolution is governed by a regional baseline of carbonate weathering, upon which are superimposed highly variable localized influences. This study provides a critical scientific basis for the differentiated protection and sustainable management of water resources in karst regions.
Dairy wastewater, a nutrient-rich resource, presents a viable strategy to boost agricultural productivity in regions facing severe water scarcity. The comprehensive study at Bangladesh Agricultural University evaluated the effects of conventional irrigation (CI) and water-saving alternate wetting and drying (AWD) practices using varying ratios of dairy wastewater and freshwater on wheat cultivation through a meticulously designed lysimeter experiment. The investigation encompassed six distinct treatments with three replications each, analyzing growth performance, yield components, water productivity, and soil microbiological properties. Results demonstrated that conventional irrigation with pure wastewater (CIW) produced the highest grain yield (4.76 t/ha) and straw yield (5.75 t/ha), along with superior growth parameters, including plant height and spike density. In contrast, alternate wetting and drying with wastewater (AWDW) achieved remarkable water savings of up to 49.78% compared to CIW and recorded the highest water productivity (1.08 kg/m3). Microbiological analysis revealed no Salmonella, and E. coli were detected in post-harvest soil samples under the tested conditions, though significantly higher total viable counts were observed in AWDW treatments, indicating enhanced microbial activity. These findings demonstrate that dairy farm wastewater irrigation significantly improves wheat yields and water use efficiency, offering a sustainable agricultural solution for water-scarce regions like Bangladesh.HIGHLIGHTSDairy wastewater boosts wheat growth and yield through conventional irrigation (CI). Alternate wetting and drying (AWD) increases water productivity significantly. Higher microbial activity in AWD improves soil health and nutrient content. CI with wastewater enhances plant height, spike length, and grain yield. AWD saves up to 49.78% water compared to CI with wastewater.
Change of climate and land use can drive and change the frequency of flooding. Moreover, the ensemble method demonstrates reliable efficiency in preparing flood vulnerability maps when integrated with climate and land-use data. In exploring the impact of climate change and land-use changes in the future (2050) on future flood risk, the general circulation model (GCM) with representative concentration pathways of the 2.6 and 8.5 scenarios by 2050 was adopted to understand the impact on eight variable rainfall. The Cellular Automata (CA)-Markov model was also applied to future land use in 2050. To validate it, the receiver operating characteristic-area under the curve (AUC) statistical analysis and other statistical analyses were carried out. The ensemble model showed a good AUC value (0.99) and consistent results across other statistical validation indices, outperforming standalone models. The areas with moderate to very high risk of flooding will increase in 2050. The proportion from the current distribution to 2050 in the Representative Concentration Pathways (RCP) 2.6 scenario changes in the ensemble model. However, this change is more significant in the RCP 8.5 than the current scenario. The integration of ensemble modeling, climate projections, and land-use simulations provides a novel framework for improving future flood assessment and management strategies.HIGHLIGHTSVarious conditioning factors have been considered to estimate flood vulnerability. Various machine learning algorithms are considered for flood vulnerability assessment. The impact of climate change on flooding is considered based on rainfall scenarios. The CA-Markov model is used to simulate future land use and land cover (LULC) scenarios. The rainfall and LULC scenarios are associated with an increasing trend in flooding.
The Niger Delta in southern Nigeria faces growing groundwater stress due to industrial expansion and land-use changes. Understanding aquifer behaviour and water quality is critical for sustainable management in this vulnerable region. This study aims to evaluate the geo-electrical and hydrochemical characteristics of unconfined aquifers to support sustainable groundwater use in line with SDG 6 (Clean Water and Sanitation). Water samples from eight boreholes were analysed for pH, conductivity, major ions, and heavy metals. Vertical electrical sounding (VES) and pump tests assessed subsurface resistivity, transmissivity, and aquifer thickness. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to identify pollution sources and water quality trends. Groundwater showed strong acidity (pH 3.2-4.6), elevated conductivity (up to 940 mu S/cm), and chloride (6.08-85.08 mg/L). Lead and mercury exceeded WHO limits. VES revealed resistivity of 97-722 Omega m and transmissivity of 0.92-30.33 m(2)/day. WQI classified BH 1-5 as 'Good' and BH 6-8 as 'Poor'. Contamination likely stems from industrial effluents and agricultural runoff. Aquifer variability suggests the need for localized interventions. This integrated hydrochemical-geophysical approach offers a robust framework for groundwater monitoring and informed management in complex deltaic systems.
Mountain wetlands in the Himalayas are under increasing anthropogenic and climatic stress. In this study, a combined approach using GIS, geostatistical, and machine learning was adopted to analyse water quality and spatio-temporal changes in the eco-hydrological state of Khecheopalri Lake, a RAMSAR wetland situated in Sikkim, India. Observed water parameters and calculated indices (pH: 5.3; nutrient index: 4.73; Organic Pollution Index: 1.49) indicated a Class II level of contamination. The results suggested that the lake was eutrophic, with organic pollution spreading throughout the system, prompting immediate intervention to protect the lake's water resources and the communities depend on them. The lake surface area decreased by approximately 3.8% (from 0.079 km(2) in 2013 to 0.076 km(2 )in 2023), and forecasting suggests a potential similar to 4% additional decline by 2033 under the same conditions. These findings revealed distinct transformations and highlighted areas within the lake vulnerable to external influences. The study provides valuable insights into the interplay of human interventions and natural processes in shaping the lake's water quality. Moreover, it contributes to the sustainable management of freshwater resources in alignment with SDG6, aiming to mitigate anthropogenic threats and ensure the long-term viability of the wetland as a vital ecological and economic resource. [GRAPHICS]
The accumulation of textile sludge from textile sharing companies poses a significant environmental challenge. This study aimed to identify and evaluate safe disposal and reuse options. Comprehensive sludge characterization revealed high concentrations of cadmium (Cd) and mercury (Hg), rendering traditional disposal methods like land application or incineration impractical due to associated health and environmental risks. To address this issue, the study explored the potential of compressed stabilized earth blocks (CSEBs) as a sustainable reuse option. CSEB production trials incorporating up to 15% sludge into blocks achieved acceptable quality standards, demonstrating the feasibility of this approach. However, the long-term behavior of heavy metals within the CSEBs requires further investigation. Economic analysis indicated that CSEB production could offer cost savings while contributing to waste reduction. The analysis shows promising results; however, rigorous monitoring and evaluation are essential to ensure its long-term sustainability and safety. This study provides valuable insights into managing textile sludges from the textile industry. Despite existing challenges, the findings highlight the potential of CSEB as a viable reuse option. Continued research and development are crucial for addressing the complex issues surrounding textile sludge management.
This investigation employed laboratory-controlled experiments to evaluate fog harvesting performance across multiple mesh configurations, integrating electrowetting techniques for enhanced fog water collection efficiency. In single-layer mesh, M1 (diamond shape) achieved optimal performance with 10.07 % collection efficiency and 39.21 ml of fog water yield. Multilayer configurations demonstrated superior performance, with M6 (M1 and M2) achieving 54.23 ml of collection attributed to increased surface area and optimized fiber architecture promoting hydrophilic interactions. The 3D mesh geometry yielded maximum fog water collection (99.43 ml), demonstrating the significance of structural complexity in fog capture mechanisms. Electrowetting led to substantial performance enhancement: mesh shown in Figure 8(a) (diamond) demonstrated voltage-dependent collection rates of 238 ml (24 V), 231.5 ml (12 V), versus 61.5 ml baseline. Similarly, mesh in Figure 8(b) (honeycomb) recorded 177.5 ml (24 V), 176 ml (12 V), compared to 50 ml without electrical activation. The hierarchical mesh design with active electrowetting created synergistic effects leading to enhanced fog water harvesting. The voltage-driven enhancement mechanism has significant potential for atmospheric water generation in water-scarce regions. Future development requires field validation, development of energy-efficient electrowetting protocols, durable hydrophobic coatings, and computational modeling for performance optimization of fog water harvesting in diverse environmental conditions.
Water stress is a significant concern as many cities worldwide face a rapidly depleting potable water supply. The prevailing water emergency requires a conscious effort to treat wastewater (WW) for reuse. The greatest challenge to accomplishing adequate WW remediation is maximising the overall efficiency of wastewater treatment (WWT) systems. If a suitable photocatalyst is considered, advanced oxidation processes (AOPs) possess major prospects in WWT settings. This study aimed to evaluate the performance of various semiconductor photocatalysts for treating municipal WW. The photocatalysts considered were titanium dioxide (TiO2), Iron III oxide (Fe2O3), zinc sulfate (ZnSO4), and copper sulphate (CuSO4). Also, two operating parameters, catalyst load (0.5-2.5 g/L) and mixing speed (30-150 rpm), were investigated at constant UV exposure time (45 min). The treated effluent's pH, color, turbidity, and chemical oxygen demand (COD) were monitored to ascertain photocatalytic efficiency. Catalyst loading (1.5 g/ L), mixing speed (90 rpm), and UV exposure time (45 min), CuSO4 displayed the best results overall for COD removal efficiency of 72.47%, while ZnSO4 was very efficient in removing turbidity and colour with removal efficiencies of 79 and 65.89%, respectively. This study considered CuSO4 as the most cost-effective (R-2 = 01) semiconductor photocatalyst to degrade the high organic content of WW.
Monitoring of urban drainage systems (UDS) is essential for operation, design, modelling, decision-making, and planning. However, it appears that metrology applied to urban drainage system is frequently of insufficient quality. To facilitate the adoption and application of best practices and advanced methods in metrology, the Urban Drainage Metrology Toolbox (UDMT) has been developed by European Project Co-UDlabs as a unique, free, online, and open-source software tool providing a set of coordinated functionalities including various methods for sensor calibration, data correction, uncertainty assessment, and data validation. This practice-oriented article presents the UDMT software, its main functionalities, and gives a detailed step-by-step training example of application to show its potential for practitioners, from raw measured water level and turbidity datasets to the calculated event pollutant load and its standard uncertainty during a storm event.
Hydraulic jumps are fundamental phenomena in open channel flows, playing a crucial role in energy dissipation, particularly downstream of structures such as block ramps and rock-filled chutes. This study presents an experimental investigation of hydraulic jump characteristics in a sloped compound rectangular channel with a rough minor bed. The research focuses on the effects of uniform bed roughness and channel slope on the sequent depth ratio. A series of experiments were conducted with four positive slopes and five roughness levels using artificially roughened beds made from uniform plastic pellets. Comparative analyses of hydraulic jumps over smooth and rough minor beds reveal that increased roughness significantly reduces the conjugate depth ratio while enhancing energy dissipation. Empirical relationships were developed, linking the incident Froude number, conjugate depth ratio, relative roughness (epsilon/b), and channel slope. The findings provide valuable insights for optimizing hydraulic structures, particularly in designing energy dissipation basins for dams and irrigation systems, where controlled flow management is essential.
Emerging pollutants (EPs) in aquatic environments have become a global environmental issue due to their toxicity, persistence, and non-biodegradablility, posing risks to both aquatic life and human health. This review focuses on several challenging issues for EPs in an aquatic environment, especially their monitoring program, analysis, risk assessment, and treatment processes, by reviewing more than 200 peer-reviewed articles published across the world in the past 20 years. Due to the limited effectiveness of traditional wastewater treatment plants (WWTPs), lack of adequate sanitation, and direct discharging of untreated wastewater, EPs originate from these sources, either directly discharging into water bodies continuously or slowly leaching via soils, posing a significant threat to aquatic life and human health. Current knowledge of EPs ecotoxicity is extremely insufficient because only a few of these compounds have been evaluated toxicologically. Due to the lack of reliable analytical and toxicity assessment methods, EPs are found to be in extremely low concentrations in aquatic systems and have widely varying physical and chemical properties; cost-effective techniques for the detection and removal of EPs are highly challenging. Therefore, comprehensive research and development are necessary for monitoring, risk assessment, and effective removal of EPs.
This study evaluates the impact of microirrigation technologies (MITs), primarily drip and microsprinkler systems, on financial resilience of smallholder horticultural farmers in northern Tanzania. A multistage sampling method was used to select 540 households, comprising 199 MITs adopters and 341 nonadopters. Data were collected through structured questionnaires capturing demographic, agroecological, and technical irrigation parameters, including emitter discharge rates (1.5-4.0 L/h), irrigation frequency two to three times per week, and water source quality (electrical conductivity, EC < 2 dS/m). To quantify MITs' contribution to financial resilience, a Financial Resilience Index (FRI) was constructed using both objective and subjective indicators. Propensity score matching was employed to calculate the average treatment effect on the treated (ATT) and the average treatment effect (ATE). The results indicate that MITs adoption significantly enhances both financial and production outcomes. Adopters experienced an increase of 549,515 Tanzania Shillings on FRI compared to nonadopters (ATT, p < 0.01) and achieved a 1.06 log-point increase in yield (t = 17.87). The ATE (0.6453) and ATT (0.7334) further confirmed MITs' significant impact across the sample. Policies facilitating adoption of MITs, including subsidies, technical training, and enhanced access to capital, are crucial for amplifying MITs adoption.
