Water Science and Technology最新文献

The bacterial groups responsible for nitrogen elimination in biological wastewater treatment are diverse and ubiquitous. This diversity implies the possibility for the enhancement of biotechnological processes by natural selection (adaptation and community shift) in the activated sludge under conditions usually considered unfavourable. The 1-year operation of a partial nitritation reactor - seeded with sludge from a municipal single-stage deammonification reactor and fed with liquid pig slurry (high NH₄-N and salinity) - revealed a combined functional and structural change in the activated sludge and the ammonia-oxidizing bacteria (AOB) community to cope with the process's conditions. The relative abundance of AOB within the living microbial biomass increased from 22 to 35%. Initially, the AOB community consisted of Nitrosomonas europaea/eutropha and Nitrosomonas communis. Following adaptation, members of the Nitrosomonas marina lineage and Nitrosococcus mobilis were also identified within the sludge. AOB maximum activity (expressed as autotrophic specific oxygen uptake rate, SOUR) increased from 66.3 to 139.2 mgO₂/gVSS/h, while the FA concentration associated with half-maximum SOUR (IC₅₀) rose from 21.7 to 25.4 mg/L NH₃ and 62.0 to 73.7 mg/L NH₃. Natural selection acting on the microorganisms in the nitritation reactor helped to form a highly specialized and productive activated sludge.

Global water scarcity underscores the growing importance of rainwater harvesting systems (RWHS) as a sustainable supplement to conventional water supplies. This study proposes the 'required roof area per capita' as a new quantitative indicator for evaluating the long-term performance and economic feasibility of RWHS in subtropical metropolitan residential environments. Using 40 years of daily rainfall records from 12 representative meteorological stations across Taiwan, system performance was simulated through a water-balance model, and regional characteristics were further classified using K-means clustering. The results identify three distinct potential categories. (1) High-potential regions can maximize water savings by expanding storage capacity and improving substitute water yield ratios. (2) Mid-potential regions benefit from optimally scaled tank volumes - typically five to ten times the baseline demand - combined with adequate roof catchment areas to achieve cost-effective outcomes. (3) Low-potential regions should prioritize increasing roof collection area while limiting tank size to meet essential water-saving targets. By incorporating roof area as a cost component, the study emphasizes the need for region-specific RWHS design strategies that respond to local rainfall patterns and building conditions. These tailored approaches can enhance both system efficiency and overall economic viability, providing practical guidance for sustainable urban water management.

The recovery of ammonium nitrogen (NH₄⁺-N) from human urine offers a sustainable strategy to mitigate eutrophication and enhance nutrient recovery for agricultural use. Despite numerous adsorbents being explored, low-cost and eco-friendly options remain limited, prompting investigation into locally available lateritic soils (LS) and the fungus Agaricus impudicus as alternative materials. This study evaluated the potential of LS and fungal biomass as adsorbents for NH₄⁺-N recovering from human urine. The materials were characterized using FTIR, SEM, and elemental analysis to determine surface functional groups and morphological features relevant to adsorption. Batch experiments were conducted to assess the adsorption capacity and desorption properties of the adsorbents. The equilibrium data were analyzed using Dubinin-Radushkevich (D-R), Freundlich, and Langmuir isotherm models to determine the adsorption mechanisms and capacities. The D-R model best described NH₄⁺-N adsorption on LS (R² = 0.968), indicating a predominantly physical adsorption process, while the Freundlich model best fitted the fungal biomass data (R² = 0.813), suggesting multilayer adsorption on heterogeneous surfaces. LS exhibited a higher adsorption capacity (16.314 mg/g) than fungal biomass, which could be attributed to its mineralogical composition. Overall, both adsorbents demonstrated effective NH₄⁺-N removal and moderate nutrient release, highlighting their potential for sustainable nutrient recovery and reuse in agriculture.

Promoting industrial green water resource efficiency (IGWRE) is critical for reconciling economic growth with water sustainability in rapidly industrializing basins. This study proposes an integrated 'ecological-economic' evaluation framework within a Super-SBM-DEA model that incorporates undesirable outputs to assess IGWRE across 11 provinces in China's Yangtze River Economic Belt (YREB) from 2014 to 2023. Combining kernel density estimation, ArcGIS spatial mapping, and spatial Durbin models (SDM), we reveal three key findings: (1) IGWRE exhibits a significant long-term upward trend but with persistent regional heterogeneity and recent short-term fluctuations, with downstream regions (Shanghai, Jiangsu, Zhejiang) maintaining efficiency leadership while upstream provinces such as Chongqing and Sichuan demonstrate remarkable catch-up effects. (2) Spatial correlation analysis confirms strong spatial dependence, with technology diffusion generating stronger positive spillovers than pollution transfer. (3) SDM decomposition identifies technological progress as the primary driver, while economic development inhibits local efficiency due to the 'resource curse.' Environmental regulation enhances local IGWRE but triggers negative spillovers, and industrial structure optimization uniformly benefits efficiency. Social investment boosts local efficiency yet suppresses neighboring regions. These findings underscore the necessity for differentiated regional policies, inter-provincial technology transfer mechanisms, and basin-wide industrial water rights trading to reconcile economic growth with water sustainability.

In this research, experiments were conducted to quantify how turbulence affects the initiation of three groups of quartz sands with consecutively increasing particle diameter. In the experimental setup, controlled acrylic paddles, a camera equipped with a macro lens and an acoustic Doppler velocimeter (ADV) were applied to generate turbulence, to record the precise moments of sediment initiation and to measure flow velocity and turbulence properties, respectively. Two categories of sediment initiation were characterized: sliding and suspension, with the onset of suspension occurring at higher turbulence intensities when compared to sliding. In addition, turbulent kinetic energy and Reynolds shear stress were identified as the most critical parameters for sediment initiation. The results suggested that the first and third groups required higher turbulent levels compared with the second group, due to variations in particle size and weight. Quadrant analysis showed that sliding events predominantly occurred during ejection and up-acceleration phases, while suspension events were mostly associated with ejections, which confirmed that initiation tends to occur in specific turbulent structures. Last, a single dimensionless parameter (J) that expresses the probability of sediment initiation under turbulent flow conditions, based on the ratio of turbulent kinetic energy to critical incipient shear stress, was proposed.

Natural flood management (NFM) is a sustainable and increasingly popular approach for riverine flooding mitigation, often used in conjunction with traditional flood protection solutions. This investigation focuses on timber bunds, a relatively novel NFM technique. Timber bunds are designed large in-stream dams made of wood sourced on site. Having been installed in only two trial locations in the United Kingdom so far, there is still a limited understanding of their performance at local and catchment scales. In this study, the timber bunds effectiveness in reducing flood peak magnitude and delaying flood arrival, when deployed as individual assets or in a network, is quantified for rainfall events of different return periods. Modelling results indicate positive effects in reducing peak flows, with more favourable results for large return period events and when timber bunds are deployed individually at the sub-basin scale. When deployed in a network, benefits may decrease due to peak synchronisation and the larger runoff volumes. The remarkable cost-effectiveness of timber bunds adds to their potential for successful wider use.

In this paper, an optimization model is developed for the synthesis of water reuse networks in industrial processes, considering scenarios with and without pretreatment units. The model has a mixed-integer nonlinear programming (MINLP) formulation containing bilinear terms. The objectives are to reduce freshwater consumption in water networks, effluent generation, and total annual costs. The optimization model was coded in GAMS, and the bound contraction technique, which involves contracting the limits of one variable at a time, was employed. This technique enables the reduction of the distance between the lower and upper bounds obtained by solving the original problem. Two case studies, one involving a single contaminant and the other multiple contaminants, were used to test the applicability of the developed model and the optimization method, considering regeneration and pretreatment units. Results indicate that global optimal solutions are found for all the cases studied. The strategy employed contributes to both a reevaluation of existing water networks and the design of new ones, resulting in more efficient configurations.

The minimum navigation flow discharge (MNFD) required to maintain waterway dimensions in basins affected by water transfer projects is often subject to strategic interactions over ecological compensation among stakeholders, making its rapid determination highly important. However, in backwater zones of small to medium rivers, water level is jointly controlled by topographic variations and downstream backwater effects, complicating its estimation. This study develops a rapid calculation procedure for MNFD in such zones through theoretical analysis and numerical simulation, introducing a water surface slope correction into the Manning equation. Taking the Shiguan River, affected by the Huai River, as a case study, a two-dimensional hydrodynamic model was constructed to compare the simulated MNFD with the proposed method's result. Results indicate that, for a Class IV waterway depth of 3.10 m, direct application of the Manning equation yields an MNFD of approximately 1,529 m³/s, while the proposed rapid procedure gives 616 m³/s, and the numerical simulation indicates 600 m³/s. Compared with the direct Manning estimate, the proposed method substantially improves agreement with the numerical result, reducing the relative deviation from 155% to 3%.

A dam is constructed across a river, which stores and supplies water for various purposes. Though the dams have many benefits, there is always a threat of a break. The research is aimed at developing a dam break simulation model for the Tiga dam and producing an inundation map of affected areas. The study conducted a dam break analysis for the Tiga dam in Kano State, Nigeria, using HEC-RAS and HEC-HMS. The analysis simulated dam failure scenarios to predict breach parameters and flood hydrograph downstream. The results showed that 213 communities would be affected, with 122 in Jigawa State and 91 in Kano, covering an area of 4,397.06 km². Breach peak flow was estimated as 117,000 m³/s, arriving in 31.3 min. Validation of the hydrodynamic breach model performance was done using the observed annual outflow and simulated results, employing the Nash -Sutcliffe efficiency (NSE) statistical analysis. The NSE score of 0.71 indicates a decent fit of the HEC-RAS model to the data. The study recommends that the Hadejia Jama'are River Basin Development Authority implement the findings to develop emergency response plans and flood mitigation strategies to safeguard lives and property downstream. Future studies should conduct socio-economic impacts on affected areas.

Accurate reservoir water level forecasting is crucial for effective water resource management, flood control, and irrigation planning. This study investigates the application of deep learning methods to forecast daily water elevation in the Nesa Dam, located in southeastern Iran. A 15-year dataset comprising daily hydrometeorological variables - including rainfall, temperature, evaporation, inflow, and outflow - was used to train and evaluate three models: CNN + BiLSTM + Attention, Encoder-Decoder LSTM with Attention, and ConvLSTM2D.Using a sliding window approach, the models were trained on 80% of the data and tested on the remaining 20%. Performance was evaluated using RMSE, MAE, and R². The Encoder-Decoder LSTM with Attention model achieved the best results, with the lowest prediction errors and highest generalization performance. The CNN-based model showed moderate accuracy, while ConvLSTM2D exhibited noisy outputs and limited predictive capability. The study demonstrates that attention-based architectures are highly effective in modelling temporal dependencies in hydrological time series. The novelty lies in the comparative analysis of these models under identical experimental conditions using real-world data. The findings offer practical insights for developing intelligent forecasting systems in water resource management.