Water Science and Technology最新文献

Microplastic particles floating in rivers, lakes and seawater are one of the factors that threaten human health and the environment. In this study, the process of dissolved air flotation (DAF) to remove microplastics from various wastewaters was simulated by the computational fluid dynamics (CFD) method. First, a solution with a plastic particle concentration of 0.2 g/L was injected at a flow rate of 200 mL/min. Then, a microbubble water with a water-to-microbubble volume ratio of 4:1 was injected at a flow rate of 300 mL/min and the simulated results showed that the kinematic and distribution characteristics of the plastic particles and microbubbles were in good agreement with the experimental results. Based on the simulation system to analyze the behavior of particles in flotation solutions, the flotation performance was evaluated by varying the concentration of NaCl solution from 0 to 12 g/L and the pH value of 10^-2 M salt solution from 4 to 9. The calculation results showed that the flotation performance improved with increasing NaCl concentration and pH, and the concentration of NaCl had a greater effect. These results provide some basic information for designing industrial wastewater treatment processes for the removal of microplastics in wastewater.

Water scarcity is intensifying worldwide due to climate change and poor resource management, while rising sea levels threaten coastal regions and ecosystems. Addressing these interconnected challenges requires sustainable solutions to secure freshwater supplies and protect vulnerable communities. Solar-powered desalination has emerged as a promising approach for converting seawater and brackish water into potable water. This review critically examines recent advancements in solar desalination technologies, focusing on improvements in performance, efficiency, and sustainability. Solar desalination systems offer a clean, noise-free, and cost-effective alternative to conventional methods, particularly in arid and remote regions. The study highlights the benefits of integrating waste heat into desalination processes, which can reduce costs and enhance overall efficiency. Additionally, the review finds that incorporating batteries into photovoltaic (PV) reverse osmosis (RO) desalination is impractical due to high capital and maintenance costs. However, energy recovery devices eliminate the need for preheating feed water, particularly in PV RO systems. Currently, most operational RO plants rely on PV energy, while solar thermal desalination - using parabolic trough collectors (PTCs) with organic Rankine cycle (ORC) technology - remains in the research phase. Although the PTC-ORC-RO system presents a promising solution, large-scale implementation has yet to be realised.

This study investigates the optimal conditions of microbial fuel cells (MFCs) for ammonium transport and chemical oxygen demand (COD) removal, and their potential for electricity generation. Batch experiments were conducted using external resistors of 1,500; 200; and 0 Ω, with synthetic domestic wastewater containing an initial NH₄⁺ concentration of 20 mg N/L. Continuous flow experiments were also conducted to assess and compare the performance of MFC systems in terms of electricity generation, COD removal, and ammonium recovery. The results of the batch experiments showed that increasing the external resistance up to 1,500 Ω enhanced the concentration of NH₄⁺ in the cathode chamber. Moreover, higher external resistance contributed to greater voltage stability. The continuous flow experiments revealed that electricity generation did not affect COD removal efficiency. The maximum COD removal efficiencies for the 1,500 Ω and control MFCs were 80.98 and 76.65%, respectively. The concentration of NH₄⁺ in the cathode chamber of the 1,500 Ω MFC was higher than that of the control MFC. Struvite precipitation was used to recover ammonium from the cathode chamber in the continuous experiment. The crystallinity of the precipitate was confirmed by XRD analysis.

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 Chlorella vulgaris 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 Proteobacteria was the predominant phylum in MFC-A, whereas Bacteroidota 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.

In the semi-arid regions of Northwest China, research on phytoremediation technology has focused predominantly on soil matrices, with few studies investigating its efficacy in removing heavy metals from aqueous solutions. Four plants were selected for formal hydroponic experiments: Iris sibirica L. (I. sibirica), Alternanthera philoxeroides (A. philoxeroides), Sedum aizoon L. (S. aizoon) and Hydrocotyle vulgaris L. (H. vulgaris). The results revealed that S. aizoon presented the highest bioaccumulation ability for Cu, whereas H. vulgaris presented the strongest bioaccumulation of Zn, Ni and Pb. S. aizoon had a maximum removal rate of 90% for Cu. The removal rate of Ni by A. philoxeroides was greater than 61%. The removal rate of Zn by H. vulgaris and S. aizoon was greater than 69%. The highest removal rate of Pb by I. sibirica was 93.96%. These plants can serve as candidate species for the phytoremediation of specific heavy metals in river or tailings pond waters in Northwest China. This study holds practical significance for the remediation of water bodies contaminated with heavy metals in the semi-arid regions of Northwest China.

The distribution of water resources determines the pattern of economic development, making the strategic layout of China's water resources crucial. This study focuses on the water-receiving area of the South-to-North Water Diversion Project in Henan Province, addressing issues related to water resource scheduling and allocation. This paper constructs an evaluation model using the Gini coefficient and the multi-correlation coefficient method to analyse the development levels of three types of water resource matching relationships. It then employs kernel density estimation, centroid, and standard deviation ellipse models to study the spatial impact of water resources, revealing the microstructure, central tendency, and dispersion characteristics of water resource distribution. The results indicate: (1) There is a correlation between the algorithms of the Gini coefficient and relational degree. The overall spatial equilibrium of the three matching relationships differs. (2) The spatial equilibrium within the same subregion varies across different matching relationships. (3) The total water resources in the South-to-North Water Diversion area of Henan Province have steadily increased, with rapid development across regions and narrowing disparities among cities. The centroid is located in Xuchang City, and the directional characteristics of total water resources are becoming less pronounced.

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.

This study develops an Interval Minimax Relative Regret Analysis (IMRA) method that combines interval linear programming (ILP) with minimax relative regret analysis (MRA) techniques to address growing challenges in water resources management under climate change. The proposed IMRA framework minimizes maximum potential regret among stakeholders while integrating multidimensional considerations of climate projections, risk management, and ecosystem protection, with particular emphasis on prioritizing ecological water requirements in allocation decisions. Applied to Sri Lanka's Mahaweli River Basin, the methodology employs multi-objective optimization to reconcile competing water demands across sectors and resolve disparities among diverse water users, systematically evaluating irrigation benefits and associated economic losses across 23 regions with varying crop patterns. The investigation reveals critical interactions between water resources and key socioeconomic and ecological determinants, enabling scientifically-grounded regulation and efficient utilization of water sources. Through absolute and relative regret criteria, the IMRA model identifies optimal solutions, determining an ideal irrigation water supply range of [174.09, 216.62] 10⁶ m³. Results demonstrate substantial ecological water demands in Matale and Pussellawa, constituting 36 and 40% of total regional water needs respectively under varying supply conditions, while maintaining allocation efficiency and ecosystem integrity. This study provides a robust framework for sustainable water management amidst environmental uncertainties.

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.

This article presents an evaluation of a model predictive control (MPC) strategy that employs long short-term memory (LSTM) networks as internal predictive models for anaerobic digestion (AD) processes. The primary objective was to develop and validate a data-driven control approach utilizing readily available online measurements. The strategy was tested in two simulated AD environments: the simplified Anaerobic Model No. 2 (AM2) and the detailed Anaerobic Digestion Model No. 1 (ADM1). LSTM networks were effectively trained to predict methane flow rates from simulated data, including stochastic disturbances. The integrated LSTM-MPC framework demonstrated robust methane flow rate setpoint tracking and ensured process stability in both environments, even amidst nonlinear operating conditions and influent disturbances. Importantly, the computational requirements remained feasible for real-time applications in these typically slow processes. The findings suggest that the LSTM-MPC strategy is a promising and computationally efficient alternative for controlling AD processes, providing a practical solution compared with traditional mechanistic model-based approaches that often rely on more complex and less accessible measurements.