Water Science and Technology最新文献

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.

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.

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.

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.

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.

This study explores how quantitative image analysis can enhance dewatering efficiency of stabilized biosolids at a major industrial wastewater treatment plant in Northern Europe. The aim is to develop a transparent and systematic analysis workflow encompassing data integration from various sources to predict decanter organic solids recovery. During two campaigns, data were collected from operational conditions and laboratory measurements. In addition, data were collected from image analysis and generated by transfer learning techniques using a readily available online database. Partial Least Squares (PLS) and Random Forest (RF) models were tested using different combinations of data sources. The best recovery prediction was obtained using a RF model utilizing both process and laboratory data in combination with transfer learning, improving the prediction by 14% as compared to baseline prediction (using average values). In addition, clustering of segmented particle images and RF-based recovery prediction revealed a strong dependency on specific crystalline particles. In general, the RF model outperformed the PLS model in predicting recovery, although both models lack consistency in prediction across the organic solids concentration range. This study offers operators insight into factors affecting dewatering efficiency and provides a diagnostic workflow transferable to other systems with heterogeneous particle mixtures.

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 m³/s. Annual runoff volumes ranged from 18,440.35 m³ in 1993 to 72,553.7 m³ 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 m³/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.

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 R² > 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 (P < 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.

The effects of two post-treatments with CaCl₂ and NaOH on nanofiltration (NF) membranes were investigated according to the physicochemical properties and antifouling performance. The membrane post-treated with NaOH (NF-OH^-) exhibited enhanced hydrophilicity and greater negative surface charge, accompanied by collapsed nodular structures. Compared with the pristine NF membrane, NF-OH^- demonstrated a 50% increase in permeance, but showed compromised separation performance, with SO₄^2- rejection of 97.3% and Cl^-/SO₄^2- separation factor of 46. In contrast, the CaCl₂-treated membrane (NF-Ca²⁺) displayed improved hydrophilicity with reduced negative charge and denser nodular morphology. While NF-Ca²⁺ achieved 25% higher permeance than the pristine membrane, it maintained comparable SO₄^2- rejection and Cl^-/SO₄^2- separation factor. Fouling experiments using biological treatment effluent of leachate revealed that NF-OH^- showed greater fouling propensity and lower cleanability than the other two membranes. This is likely due to the stronger negative charge and smoother surface morphology of the NF-OH^- membrane, which promoted foulant adhesion while limiting surface turbulence. Conversely, the NF-Ca²⁺ membrane demonstrated superior antifouling characteristics and higher permeance recovery, attributable to its reduced negative surface charge and denser nodular morphology. These findings underscore the critical influence of surface charge and nanostructure on the filtration and antifouling behavior of NF membrane.