Water Science and Technology最新文献

As urbanization and extreme stormwater events increase, sustainable stormwater management becomes vital. Sponge city green infrastructure offers a solution to flooding and pollution, providing additional benefits like recreation and clean air. However, understanding public preferences for sponge city green infrastructure's ecosystem services and balancing its co-benefits poses challenges, especially regarding financial sustainability. This study used a discrete choice experiment with 1,154 residents from three Chinese sponge cities to assess the monetary value of five sponge city green infrastructure benefits: improving black and malodorous water bodies, flood prevention, mitigating urban heat island effects, rainwater reuse, and landscape recreation. Analyzing data via the mixed logit model, distinct preferences for sponge city green infrastructure benefits emerged. Residents across all cities were significantly willing to pay for rainwater reuse, indicating a gap in existing research. Preferences varied for other benefits, with positive responses for water body improvement and flood prevention but indifference towards heat island effect and mixed views on landscape recreation. This information is crucial for stormwater managers to optimize sponge city green infrastructure design and fee evaluation.

This review hypothesizes that polymer composites, due to their unique adsorption capacity and chemical tunability, can provide a cost-effective, efficient solution for recovering nutrients from wastewater, thereby addressing the urgent global challenge of nutrient pollution while producing potential composites for agriculture. Confirming or rejecting this hypothesis is critical, given the escalating economic and environmental costs of nutrient-induced ecosystem imbalances which amount to billions annually in water treatment, agricultural losses, and biodiversity decline worldwide. This review achieves its breakthrough by synthesizing insights from academic papers, patents, and industry reports, uncovering interdisciplinary links between polymer chemistry, environmental engineering, and agricultural science that enable novel modifications to polymer composites. The growing market for next-generation adsorbents and fertilizers, driven by stricter nutrient discharge regulations and demand for sustainable inputs, presents profit-driven business opportunities. Future research should focus on enhancing composite selectivity and regeneration, scaling production, and evaluating environmental impacts, while overcoming challenges like economic competitiveness, regulatory approval, and wastewater variability.

The present paper reports the energy and environmental impacts of integrating a column of activated alumina (AA) for fluoride adsorption with a humidification-dehumidification (HDH) desalination system. The effects of fluoride concentration (C_i), the mass of AA (m_aa), and the mass flow rate ratio (MR) on the yield and gained-output ratio (GOR) of the considered system have been investigated using an experimental approach. The experiments are conducted for 5 to 30 mg/L, 1 to 2 kg, and 1 to 6 for C_i, m_aa, and MR, respectively. The effect of brine recirculation on the investigated system is also reported in this paper. The obtained results have shown an enhancement of up to 21% in the value of GOR with this system. Moreover, nearly 80% of the fluoride disposal to nature as concentrated brine can be prevented using the adsorption unit. The current study is carried out to prevent fluoride disposal in the reject stream. However, the proposed system can obtain freshwater at the community level without disposing of harmful contaminants in rejected water by incorporating other adsorption materials. The results presented in this paper may be useful for the stakeholders working on sustainable freshwater production.

Flash floods are among the most devastating natural disasters, as climate change and human activities increase their risks. This study focused on the development of a reliable flash flood map for the city of Amman, Jordan, using the integration of geographic information systems with the hierarchical analysis method and multi-criteria decision analysis. The consistency of the weights was checked using the consistency index of 0.0547, indicating the results' reliability. Eleven main criteria for assessing flood risk have been identified, the most prominent of which are rainfall, elevation, subsidence, and land uses, which are the most influential factors in determining flood-prone areas. The authors used high-resolution data, such as LiDAR data and supervised classification, to improve the accuracy of the assessment. The results revealed that 43% of the study area is considered high to very high risk, while 29% is moderate, and 28% is low to very low.

This study presents a protocol for the enrichment and immobilization of anaerobic ammonium oxidation (anammox) bacteria using local natural resources to facilitate nitrogen removal in wastewater treatment, particularly in regions where anammox seed biomass and advanced treatment facilities are not readily available. Anammox bacteria were successfully enriched from river sediments in a fixed-bed reactor using a nonwoven fabric as a support medium. Subsequently, the bacteria were immobilized onto porous polyvinyl alcohol carriers, demonstrating stable attachment and enhanced nitrogen removal efficiency. Performance evaluation experiments confirmed a high nitrogen conversion rate of 4.3 kg-N m^-3 d^-1 with stable long-term operation even under increasing nitrogen loading rates. This approach eliminates the need for external bacterial sources, enabling cost-effective and sustainable nitrogen treatment in regions where anammox technology has not yet been applied. The findings highlight the potential for localized wastewater treatment using indigenous microbial communities, with microbial analysis revealing the predominance of Candidatus Brocadia on the carriers, supporting stable nitrogen removal even under increasing loading rates. These insights emphasize the value of exploring adaptations that can enable effective operation under a broad range of temperature conditions.

Evaluating crop water consumption through the water footprint (WF) offers a valuable approach to optimizing water use. The study proposes a remote sensing method to quantify blue WF (BWF), green WF (GWF), and total WF (TWF) for crops in the upper Cauvery River Basin (UCB) using evapotranspiration (ET_a) datasets derived from the vegetation coefficients (K_v), rather than relying on generalized FAO K_c tables. Using a stacked ensemble algorithm, study downscaled ET_o and rainfall to a 500 m. Fine-scale seasonal BWF, GWF, and TWF for selected crops were mapped for the study period from 2015 to 2019. Our results show that while most crops predominantly depend on GW with minimal blue water extraction, paddy (Summer-IRR) and ragi (Rabi-RF) exhibit disproportionately high BWF, indicating critical hotspots of irrigation pressure and groundwater stress. Limited rainfall makes summer paddy strongly reliant on irrigation (BW), whereas Kharif maize utilizes GW, underscoring significant implications for water management policy. The high BWF and GWF values for ragi in 2016 highlight the impact of drought on WF, representing a distinction. This work provides a reference regional dataset of BWF, GWF, and TWF for the UCB, supporting improved understanding of water consumption dynamics, thereby informing future agricultural water management planning.

Water quality prediction is crucial for efficient operation in wastewater treatment plants (WWTPs). This paper presents a novel soft sensor framework based on an adaptive long short-term memory (LSTM) ensemble for influent water quality prediction. The framework integrates a temporal pattern attention LSTM (TPA-LSTM) model with preprocessing techniques, including the Hodrick-Prescott Filter (HP) for trend extraction and density-based spatial clustering of applications with noise (DBSCAN) for anomaly detection. Missing data are addressed using Newton's Interpolation Method. This combined HP_DBSCAN approach extracts informative features and enhances data quality. The TPA-LSTM model is trained on the preprocessed water quality dataset, enabling accurate prediction of key parameters (e.g., BOD, COD, NH3_N). Comparative analysis demonstrates the superior performance of this hybrid-algorithm model over a single LSTM, highlighting the benefits of the adaptive ensemble approach. The hybrid-LSTM model, in comparison to the single LSTM, has improved the average fitting correlation for seven water quality indicators by 52.7% and reduced the prediction error by 62.1%. Experimental results validate the effectiveness and applicability of the proposed soft sensor for influent quality prediction in WWTPs. This framework offers insights for advancing the use of LSTM networks within wastewater infrastructure, ultimately contributing to improved operational efficiency and reduced costs.

Safe drinking water remains a critical challenge in Ethiopian towns due to rapid urbanization, poor sanitation, and limited treatment. In Injibara Town, this study assessed the physicochemical and bacteriological quality of drinking water across 18 sites, including boreholes, springs, reservoirs, and distribution points, using 268 triplicate samples collected from March to August 2024. Analyses followed the WHO and Ethiopian standards, with spatial-temporal variations evaluated via one-way ANOVA and Pearson correlation. The results were compared with the WHO and Ethiopian standards, and residual chlorine was monitored at 2-h intervals (four times per sampling day) during monthly sampling events conducted over three consecutive months in both dry and wet seasons. Most parameters met standards; however, iron in boreholes, TDS in BH2, and turbidity in the spring (wet season) exceeded permissible limits. Total coliforms occurred in all sources, while chlorine levels were excessive in reservoirs (up to 8.7 mg/L) but low in distribution, revealing rapid decay and heightened contamination risk. Water quality declined from source to distribution due to weak chlorination, poor sanitation, and aging infrastructure. Strengthening chlorination, protecting springs, improving drainage and waste management, and regular monitoring are essential to ensure safe and sustainable drinking water for the community.

This study presents one of the first integrated numerical and experimental studies focusing on the effect of pipeline vibrations on canal bed scour for suspended pipelines through extensive analysis, encompassing 300 numerical simulations and 25 complementary laboratory experiments. Numerical outcomes were validated against experimental measurements, demonstrating strong conformity with discrepancies consistently within ±10%, therefore emphasizing the reliability of our numerical modelling approach. The study findings explicitly reveal that scour depth and morphology were substantially affected by pipeline height, vibration amplitude, pipeline diameter, and flow conditions (represented by the Froude number). Specifically, temporal evolution significantly influences scour development, with vibration amplitude playing a particularly decisive role. Increasing the vibration amplitude from a baseline value of (A/D = 0.2) to double, triple, and quadruple this value resulted in respective increases in scour depth of approximately 12, 47, and 71%. A 27% reduction in scour depth when the pipeline diameter is reduced by 40%. The critical influence of Froude number and vibration frequency. Consequently, these quantitative insights underscore the critical necessity of accurately considering pipeline-induced vibrations in the hydraulic design and protection strategies of river-crossing pipelines to effectively mitigate associated scour risks.