This research paper presents an advanced water demand forecasting model through CaffeNet deep-learning architecture as well as a developed student psychology-based optimizer (DSPBO), aiming to improve the predictability of water consumption for the domestic, industrial, and agricultural sectors. The combined CaffeNet-DSPBO model has performed well in the performance evaluation to capture the complex nonlinear relationships caused by weather conditions, seasonality, and sector-specific patterns, and is trained using real data from the Yangtze River Delta of China. The main findings show a model with low RMSE values of 0.25 (domestic), 0.40 (industrial), and 0.58 (agricultural) and high correlation coefficients of 0.87, 0.75, and 0.62, respectively. This indicates that the domestic consumption sector, in particular, can be considered a reliable and accurate forecasting model. Also, the model demonstrated superior performance compared to other meta-heuristic algorithms in terms of convergence stability and solution accuracy. Another performance advantage is the training time of less than an hour and the inference latency of less than 10 ms. The results show how important this can be in combining deep-learning and better optimization techniques for predicting multi-sector water needs, paving the way for sustainable yet efficient management of this precious resource.

Ultrafiltration (UF) membranes are widely used in potable water reuse, but their virus removal capabilities can be underestimated due to operational variability and membrane damage over time. This study evaluates the log reduction values (LRVs) of a pilot-scale UF system continuously processing tertiary treated wastewater, focusing on a compromised membrane. Virus removal was assessed under various operational states, including physical backwash (PBW) and chemically enhanced backwash (CEB). Samples were collected after CEB, before PBW, and after PBW. Indigenous viruses such as AiV, NoVGII, enteric AdV, PMMoV, CGMMV, and crAssphage were quantified using (RT-)qPCR, alongside spiked MS2 bacteriophage. A laboratory-scale study examined the synergistic effects of hydraulic and chemical stresses, with deteriorated membrane fibers analyzed through field emission scanning electron microscope (FE-SEM), SEM equipped with energy-dispersive X-ray spectroscopy (SEM-EDS), and liquid-liquid displacement porometry (LLDP). Despite structural damage and fouling observed in compromised fibers, the Kruskal-Wallis test revealed no significant differences ( > 0.05) in virus removal across operational states, indicating consistent UF performance. Laboratory-scale MS2 filtration studies showed a significant effect of water quality on increasing LRV ( < 0.05) in compromised fibers. This study underscores UF systems' robustness in virus removal and highlights membrane integrity loss pathways in real-world applications.

This study presents a comprehensive analysis of the distribution and performance of advanced anaerobic digestion (AD) technologies across the United States and Canada. The study reveals that temperature-phased anaerobic digestion is the most prevalent technology, with 20 water resource recovery facilities (WRRFs) adopting it, followed by acid-methane AD and thermal hydrolysis process. The distribution analysis indicates that 59% of the projects have a plant capacity of 40-400 million liters per day, and 30% of the projects have more than 20 AD reactors. The biosolids classification shows that Class A biosolids constitute 45%, while Class B biosolids make up 51% of these projects. Case studies from Madison Metropolitan Sewerage District, City of St Petersburg, City of Montpelier WRRF, Metro Water Recovery, and DC Water highlight the financial impacts, including cost savings and increased revenue from high-strength biosolids. The findings underscore the variability in the effectiveness of AD technologies and the importance of cost and operational efficiencies in technology selection.

Combined sewer overflow (CSO) screens are critical components of sewer and drainage networks, separating sewer solids from overflow spills before they reach receiving waters. Selecting suitable and sustainable CSO screening devices, however, remains a complex task. This process has traditionally depended on conventional design calculations, technical guidance from screen manufacturers and precedents from past projects. Inappropriate screen selections have led to adverse effects on water quality and public health, due to insufficient screening capacity, the unpredictable behaviour of sewer solids of varying densities, low trapping efficiency, frequent screen blinding or high equipment failure rates, particularly at unmanned or remote sites. This paper presents a design methodology for screen selection and formulates an input-output relationship model. Using 50 screen project data, a framework has been proposed to construct a predictive model that integrates sustainability criteria, lessons learnt from historical applications and artificial neural network (ANN) techniques. A Levenberg-Marquardt-based ANN was developed and trained to identify optimal selection between 2 categories of screen solutions, encompassing 12 screen types - 3 within non-powered self-cleaning and 9 within the powered screen category. The framework aims to provide an initial proof-of-concept evidence with a supplementary decision-support tool, enabling design engineers to make intelligent, resilient and sustainable choices in screen application.

Digital twin models offer great potential for process improvements in wastewater treatment plants (WWTPs). Such models require a constant real-time input data feed from the physical process. Collecting these data is challenging, especially in the harsh conditions in the headworks of the process. In this study, data-driven models and process and sewer system expertise were combined to design soft-sensors for primary effluent COD and NH-N prediction. Ordinary least squares regression and the seasonal autoregressive integrated moving average model with exogenous variables were tested using flow rate and suspended solids concentration as model input. An excellent NH-N prediction was achieved, and the prediction accuracy was further improved by implementing process-insight-driven weights. The tested models were able to achieve either good COD estimation accuracy or effectively capture the variability in the target data. However, achieving both simultaneously remained challenging, with or without weights. Simulation tests using the calibrated process model demonstrated that the developed soft-sensors were able to provide real-time predictions leading to goodness-of-fit in simulations comparable to or better than that achieved using laboratory data influent quality.

To develop a more cost-effective nitrogen removal strategy, this study investigated the impact of low-temperature storage methods on nitrifying bacterial activity. Sludge was stored under laboratory-scale static batch conditions in three media: (1) distilled water, (2) nutrient solution, and (3) nutrient solution supplemented with hydroxylamine (NHOH). Ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) activity, sludge properties, and microbial characteristics were examined. Results revealed that all storage methods inhibited both AOB and NOB activity. Notably, nutrient solution storage demonstrated the most significant effect: it suppressed NOB activity by 86.6% and reduced its relative abundance by 20%, while maintaining high extracellular polymeric substance content (43.5 mg/g VSS) and AOB relative abundance (0.18%). This method substantially shortened the required storage duration (from 8 months to 60 days) and better preserved AOB activity and sludge stability. Metagenomic analysis indicated strong inhibition of the NOB functional gene nitrite oxidoreductase across all methods, while nutrient solution storage specifically elevated the abundance of the AMO gene. Although NHOH supplementation exhibited inhibitory effects on microorganisms, the concurrent addition of nutrient solution effectively mitigated this impact. Consequently, sludge properties and functional microbiota abundance showed no significant difference between the NHOH-supplemented nutrient solution method and distilled water storage.

The inhibition of denitrification in low-temperature environments poses challenges for wastewater treatment plants in cold regions to achieve compliance and control costs. The cold tolerance mechanisms of existing technologies remain unclear, limiting their engineering stability and widespread adoption. Simultaneously, the lack of systematic evaluations balancing technical efficacy and economic viability hinders the selection of optimal technologies. Through bibliometrics analysis, mechanism comparison and multidimensional evaluation, this paper outlines trends in low-temperature denitrification technologies. It indicates that research focus has shifted from traditional methods like constructed wetlands and activated sludge to novel technologies such as biofilms, anammox and solid-phase denitrification (SPD). Among these, SPD and partial denitrification/anammox (PD/A) show promise as advanced solutions combining environmental effectiveness and economic sustainability. SPD achieves a high nitrate removal rate of 91 ± 4% by enriching functional microorganisms, enhancing enzyme activity and accelerating electron transfer, demonstrating outstanding environmental effectiveness. PD/A constructs a more efficient denitrification pathway, circumventing low-temperature limitations on Nir and Nos activity, holding potential for energy conservation and emission reduction. Future priorities should focus on leveraging artificial intelligence to optimize the composite carbon source ratios in SPD for enhanced economic efficiency and employing biofilm/granular sludge to enrich aerobic ammonium-oxidizing bacteria for scalable PD/A.

Large Language Models (LLMs) have shown strong performance across natural language processing tasks, yet their general-purpose embeddings often fall short in domains with specialized terminology and complex syntax, such as hydrology and environmental science. This study introduces HydroEmbed, a suite of open-source sentence embedding models fine-tuned for four QA formats: multiple-choice (MCQ), true/false (TF), fill-in-the-blank (FITB), and open-ended questions. Models were trained on the HydroLLM Benchmark, a domain-aligned dataset combining textbook and scientific article content. Fine-tuning strategies included MultipleNegativesRankingLoss, CosineSimilarityLoss, and TripletLoss, selected to match each task's semantic structure. Evaluation was conducted on a held-out set of 400 textbook-derived QA pairs, using top-k similarity-based context retrieval and GPT-4o-mini for answer generation. Results show that the fine-tuned models match or exceed performance of strong proprietary and open-source baselines, particularly in FITB and open-ended tasks, where domain alignment significantly improves semantic precision. The MCQ/TF model also achieved competitive accuracy. These findings highlight the value of task- and domain-specific embedding models for building robust retrieval-augmented generation (RAG) pipelines and intelligent QA systems in scientific domains. This work represents a foundational step toward HydroLLM, a domain-specialized language model ecosystem for environmental sciences.

Olive mill wastewater (OMW) poses a serious environmental challenge, specifically in the Mediterranean region, due to its high content of phenolic compounds (PCs). In this study, eco-friendly nanocomposites made of chitosan, kaolinite, and iron oxide nanoparticles were prepared, characterized, and tested for their removal efficiency (RE) of PCs from OMW. The removal efficiencies of seven targeted PCs and the overall removal for the total phenolic content were evaluated. The nanocomposite powder cross-linked with glutaraldehyde exhibited the highest RE of 91% for the sum of the seven target PCs (Σ7PCs) using a 10 g/L of adsorbent dose, pH = 4.8, at a temperature of 25 °C within 2 h. Desorption studies showed that up to 85% of the adsorbed PCs were desorbed, allowing the efficient regeneration of the adsorbent for at least four cycles with RE exceeding 50%. These promising results suggest the potential of the large-scale utilization of the developed process for large-scale remediation of OMW.

The evaluation of water quality constitutes a critical aspect of water management strategies, particularly in arid and semi-arid environments, where the use and protection of sustainable resources are crucial. This study focuses on assessing and predicting water quality in three Jordanian dams using advanced data mining techniques. Physical, chemical, and biological water quality parameters were collected and analyzed over a four-year period. The Weighted Arithmetic Water Quality Index (WA-WQI) was used to evaluate the overall water quality. Various data mining algorithms, including generalized linear models, decision trees, random forests, gradient-boosted trees, and support vector machine (SVM), were employed to predict WQI and understand the seasonal and annual variations. Key findings highlight significant fluctuations in water quality, influenced by parameters such as pH, conductivity, nutrients, and microbial contamination. The study emphasizes the importance of continuous monitoring and predictive modeling for effective water resource management. It also demonstrates the effectiveness of using SVM for water quality prediction in arid regions. The models were evaluated using different performance metrics. The SVM outperformed other employed models. This study provides a critical benchmark and a robust predictive framework for water resource management in Jordan and semi-arid areas, addressing a significant gap in regional environmental monitoring.

Urban waterlogging presents a significant menace to urban operations and the livelihoods of residents. It is of the utmost necessity to establish an accurate and efficient early warning system. This research focuses on multi-source data fusion and intelligent models, and conducts a comprehensive exploration of the integration of data from meteorology, hydrology, geospatial information, and drainage systems. It processes multi-source data in real time through a distributed computing architecture. By applying methods such as the Horton infiltration formula, the isochron method, the Saint-Venant equations, and the Hazen-Williams formula, precise simulation of surface runoff and monitoring of urban drainage capacity are realized. Furthermore, the waterlogging risk level is dynamically adjusted according to real-time data. The experimental findings suggest that, when compared with AquaTalk, MIKE FLOOD, CAE S.p.A., and FIEDLER, the urban waterlogging early warning model proposed in this paper shows improvements in the accuracy, reliability, timeliness, and spatial precision of early warning. This offers a reference for urban waterlogging prevention and disaster relief.

Using opportunistic sensors, such as commercial microwave links from mobile networks, to estimate precipitation is an innovative and promising approach to improving hydrometeorological monitoring in urban areas. As part of the TOPRAINCELL project in Burkina Faso, a real-time system for collecting transmitted and received power data was deployed in collaboration with the national operator Telecel Faso. This study is based on data acquired in 2022 in the country's two main cities, Ouagadougou and Bobo-Dioulasso. These cities have different climatic contexts, yet they both have limited conventional rainfall coverage. Cross-analyzing data from opportunistic sensors, ground-based rain gauges, and satellites reveals a strong correlation between microwave link estimates and reference measurements, with Pearson coefficients reaching 0.97 in Ouagadougou and 0.94 in Bobo-Dioulasso. Spatial precipitation maps have been produced to demonstrate the ability of this multi-source approach to reproduce the spatial variability of urban rainfall. These results confirm the potential of opportunistic sensors as a complementary and adaptable solution for rainfall monitoring in West Africa.

Among emerging contaminants, pharmaceutical compounds have garnered significant scientific attention due to their presence in the environment and potential adverse effects on aquatic ecosystems and human health. The detection of pharmaceutical compounds, their ecological threat, and water quality was evaluated at six points along the Cauca River, Colombia's second most important river. The detected compounds included diclofenac, ibuprofen, naproxen, and paracetamol, with the latter presenting maximum concentrations of up to 4.20μg/L. Domestic wastewater discharges impacted the river's water quality, increasing the frequency and concentration of pharmaceutical contaminants. Ibuprofen and paracetamol were identified as high-risk compounds for aquatic biota, with Hazard Quotient (HQ) values between 190 and 250 in areas near urban wastewater discharges. This finding also indicated a high ecological risk due to the mixture of these pharmaceuticals. No single removal technology proved completely effective, highlighting the need for complementary treatments to conventional systems to ensure safe discharge into water bodies. Moreover, given the presence of these compounds in surface waters, drinking water treatment systems must be adapted to minimize health risks in distributed water. Finally, the study underscores the need for regulatory measures and continuous wastewater monitoring to protect both aquatic ecosystems and public health.

Digital twin (DT) technology is gaining attention for effective water quality management by integrating diverse data sources and enabling real-time insights. The practical implementation of DT technology for intelligent river water quality management requires extensive spatiotemporal big data, underscoring the critical need to integrate physical sensors, soft sensors, and remote sensing technologies. Here, we synthesized recent advancements in hybrid physical-soft sensing systems and highlighted their potential to address the inherent limitations of conventional water quality monitoring methods, such as limited spatiotemporal resolution and high operational costs. Soft sensors, driven by machine learning (ML), estimated difficult-to-measure water quality parameters by leveraging easily measurable variables from physical sensors. Therefore, soft sensors significantly expanded the range of measurable parameters and improved data collection frequency. In addition, remote sensing offers broad spatial coverage, enabling large-scale monitoring of optically active constituents, algal blooms, and sediment dynamics. We critically review methodologies and applications that integrate these sensing technologies into DT frameworks, and identify critical knowledge gaps, particularly the lack of a fully unified integration framework combining these technologies for next-generation DT systems. By assessing the strengths and limitations of each approach and proposing integration strategies, this study offers practical guidance and integration recommendations for DT-based river management.

In the study, a novel Fe/Al bimetal with a high specific surface area and electron transfer capacity was synthesized through ball milling Fe and Al powder. Importantly, this synthesis process did not consume Al powder and did not generate by-products that required treatment. Compared with Fe or Al powder and the ball-milled Fe or Al powder, Fe/Al could rapidly remove Cr(VI) through adsorption-reduction under near-neutral conditions. Nevertheless, the removal efficiency of Cr(VI) by Fe/Al was influenced by the initial pH of the solution and dissolved oxygen (DO). Kinetic studies and adsorption isotherm analysis demonstrated that the pseudo-second-order adsorption kinetic model and the Freundlich model could better describe the Cr(VI) removal data, and the maximum removal amount of Cr(VI) was 6.25 mg/g. Furthermore, based on the characterization analysis of XPS, the adsorbed Cr(VI) was reduced to Cr(III). SEM-EDS analysis revealed that Cr mainly overlapped with the Fe elemental distribution on the surface of Fe/Al particles, suggesting that Fe was the main reaction site. Consequently, the results indicated that highly active Fe/Al could be prepared by solid-solid blending for pollutant removal, which provided technological concepts for the waste utilization of scrap iron and aluminum.

Microbial fuel cells (MFCs) represent an advanced and environmentally friendly bioenergy technology with significant potential for simultaneous power generation and wastewater treatment. This study specifically compared the anodic performance of MFCs with versus those fed with acetate. Dual-chamber MFCs were constructed for simultaneous electricity generation and wastewater treatment. In addition, microbial communities of both the MFCs and the gene function of MFC-Ch were analyzed through metagenomic sequencing. When comparing all the electrochemical parameters produced from MFCs, MFC-Ch is slightly more efficient than MFC-A. Metagenomic analysis showed that was the predominant phylum in MFC-A, whereas was predominant in MFC-Ch. COG (Clusters of Orthologous Groups) analysis of the primary metabolic pathways in the anolyte of MFC-Ch revealed a relatively high abundance of genes associated with several metabolic pathways during MFC operation, including amino acid transport and metabolism, carbohydrate transport and metabolism, and coenzyme transport and metabolism. The study on carbohydrate and protein degradation indicated that protein metabolism occurred to a greater extent than carbohydrate metabolism. This aligns with the known ability of some bacteria present in the sludge to promote amino acid metabolism in MFCs, a finding further supported by the positive correlation observed in the COG analysis.

Odour emissions from passive area sources present a major challenge for environmental monitoring due to the complex chemical and physical mechanisms involved and the lack of standardized sampling methodologies. Wind tunnels (WTs) are widely adopted for this purpose, but significant methodological gaps remain, particularly concerning the gas sampling procedure at the outlet section of the hood. This study investigates the performance of two WTs, one optimized for fluid dynamics and mass transfer and one conventionally used in Italy, under both laboratory and field conditions. The optimized WT demonstrated greater stability and consistency in concentration measurements due to an improved outlet mixing system. To ensure representative sampling in cases where direct access to the WT outlet is limited, two different gas conveyance systems were tested: a Nalophan™ tubular and a Teflon grafted tube. Results showed that both configurations provided stable measurements when not occluded, but the Nalophan™ system was susceptible to wind-induced constrictions, leading to transient volatile organic compound accumulation. Field trials confirmed the laboratory findings, showing an optimal sampling time between 5 and 8 min. This study contributes to the development of standardized methodologies for odour sampling, addressing a critical operational gap and supporting recent regulatory advances in odour monitoring.

Distributed infrastructures play a key role in urban stormwater management by reducing flood risks. Over the past decade, green and blue roofs have emerged as effective distributed solutions, especially as roofs cover a large share of urban land and climate change intensifies storm events that centralized systems often struggle to manage. Designing these infrastructures poses challenges, particularly in selecting an appropriate design hyetograph based on rainfall duration and return period. Simulating water storage and release dynamics enables the optimal selection of outflow devices, ensuring compliance with maximum water levels and flow rates to prevent flooding and structural issues. To support this process, an Excel-based tool has been developed to simulate and select outflow devices for multiple blue roofs contributing to decentralized stormwater systems. The tool identifies which outflow devices meet performance requirements for different rainfall durations. A design case study in Norway demonstrated its application, illustrating how construction sector operators can use it to improve design practices and customer communication in the Norwegian context. Future advancements might consider different add-ons,including (a) green roofs and rain harvesting systems models, (b) datasets from different nations, (c) multiple hyetograph shapes, (d) different shapes and outflow devices curves, (e) future climatic scenarios.

Accurate estimation of nutrient contributions is essential for effective pollution control, yet remains challenging due to substantial uncertainties arising from limited sample sizes and dynamic hydrological regimes. This study employs a process-based load apportionment model (LAM), integrating daily flow records and high-resolution water quality data from 41 monitoring stations across the Pearl River Basin (PRB), to quantitatively distinguish point-source versus non-point-source contributions to total nitrogen (TN) and total phosphorus (TP) loads. Statistical T-tests were systematically applied to evaluate the sensitivity of source apportionment results to monitoring frequency and streamflow variability. The results indicate that: (1) Non-point sources dominate nutrient fluxes, contributing 85.95 and 92.13% of annual TN and TP loads respectively, acting as the largest sources averaging 83.41% (TN) and 90.88% (TP) of the period (average > 0.70); (2) Regional heterogeneity exists, with the Beijiang sub-basin exhibiting significantly lower non-point-source TN contributions (66.15%) compared to other sub-basins; (3) Monitoring frequency exerts greater influence on TN source partitioning ( < 0.05 at 65.85% stations) than TP (46.34% stations). These findings highlight the necessity of region-specific management strategies and underscore the value of high-frequency monitoring coupled with multi-source data fusion to enhance the robustness of pollution source identification.

This study evaluates flood frequency analysis (FFA) to support sand dam design and planning in the Dechatu catchment of Dire Dawa, Ethiopia, an area vulnerable to flash floods and irregular rainfall. Rainfall-runoff modeling was performed using the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) with the Soil Conservation Service Curve Number (SCS-CN) method, SCS Unit Hydrograph, and Muskingum routing. Curve numbers ranged from 78.5 to 83.07, while basin lag times varied between 8.69 and 57.34 h. Peak discharge rates fluctuated, with Kombolcha experiencing the highest at 214.4 m/s. Annual runoff volumes ranged from 18,440.35 m in 1993 to 72,553.7 m in 2006, reflecting heavy wet-season rainfall. FFA tested multiple distributions, Log-Pearson III, generalized Pareto (GPA), generalized extreme value (GEV), and normal with Log-Pearson III estimating a 100-year peak flow of 165.36 m/s, closely matching HEC-HMS results. In semi-arid regions, FFA is applied to optimize the design, planning, and implementation of sand dams. Sand dams are water storage structures built across seasonal streams to capture and store water during floods. The study underscores the importance of data-driven planning to improve sand dam resilience, water management, and flood preparedness, ensuring sustainable and safe water resources for vulnerable communities.