ABSTRACT The diagram summarizes bilgewater’s environmental and societal threats, oil weathering and emulsion formation, and electrocoagulation, membrane, biological, and hybrid treatments. Bilge water is estimated to account for a substantial fraction of oily wastewater discharged from marine vessels. It is a complex effluent composed of seawater, lubricants, fuels, detergents, solvents, and solid particles. This review provides a comprehensive analysis of bilge water characteristics, environmental and socioeconomic impacts, and current treatment technologies. Emphasis is placed on quantifying pollutants, with reported oil content ranging from 36 to 976 ppm, pH values between 6.8 and 9.0, and chemical oxygen demand (COD) levels reaching up to 12,800 ppm. The environmental hazards of untreated bilge water include severe ecological harm, risks to seabirds and marine biodiversity, and contamination of fisheries and tourism sectors, contributing to economic losses in coastal communities. Treatment strategies are categorized into electrochemical, membrane-based technologies, and biological processes. Hybrid systems, combining techniques such as electrocoagulation and reverse osmosis, have demonstrated superior pollutant removal efficiencies exceeding 99% for oil, turbidity, and total dissolved solids. Furthermore, although AI use in bilge water treatment remains underexplored, AI-based image analysis and machine learning models have shown potential to predict emulsion stability, thereby optimizing treatment design and monitoring. Hybrid systems achieved the highest removal efficiencies; however, several limitations still require further investigation, including cost, energy consumption, membrane lifetime and fouling in membrane-based systems, and regulatory compliance for marine oily wastewater remediation.
ABSTRACT Membrane fouling remains a major bottleneck for seawater reverse osmosis (SWRO) desalination, and microfiltration/ultrafiltration (MF/UF) pretreatment is increasingly adopted to mitigate it. However, the relative importance of membrane material versus pore size has not been clarified through systematic comparison. Here we evaluated three hollow-fiber membranes – MF1 (PVDF, 100 nm), UF1 (PVDF, 150 kDa) and UF2 (PAN, 80 kDa) — operated under matched transmembrane pressure on polluted seawater (Fukuura coast, Japan), quantifying the colloidal fouling potential of permeates by the modified fouling index in ultrafiltration mode (MFI-UF). MFI-UF decreased monotonically with pore size: the largest reduction occurred between the 1 μm cartridge and the 100 nm MF membrane, and tightening pore size further into the UF range lowered it more, indicating pore size as a primary factor and membrane material as secondary – though not negligible – in this range. LC-OCD and ICP-OES revealed strong biopolymer–Ca covariation (R2 = 0.95), suggesting biopolymer–Ca complexes as a principal colloidal foulant, while silica appeared to associate with low-molecular-weight substances. NaOH extraction with EEM fluorescence identified protein-like organic matter as the main NaClO-recoverable foulant of PVDF membranes, whereas HCl-extractable iron was implicated in inorganic fouling. These findings provide a quantitative basis for MF/UF membrane selection in SWRO pretreatment.
ABSTRACT Sequence of text and pictures showing Tanfloc with alum or pH adjustment applied to liquid manure, indicating that combining Tanfloc with alum improves turbidity removal, reduces costs, and increases phosphorus separation. Tannin-based coagulant-flocculants, such as Tanfloc, are proposed as environmentally friendly alternatives to clarify water and recover nutrients from farm dairy effluent. However, cost and dosage requirements limit their use, and their performance when combined with metal salts remains poorly investigated. This study evaluated the performance and cost-effectiveness of combining Tanfloc with aluminium sulphate (alum) for turbidity removal and nutrient separation from manure (0.5–3.0% total solids). We used jar testing and response surface methods to compare performance with Tanfloc alone (pH 4.0–8.0). Although pH influenced turbidity removal (>99%), and separation of TN (77–79%) and TP (63–75%), alum inclusion enhanced turbidity removal at sub-optimal Tanfloc dosages with similar TN separation across treatments, and increased TP separation to 94–97%. Minimizing residual turbidity (6 NTU) using Tanfloc with pH adjustment cost $23.6 AUD/m3, while combining Tanfloc with alum reduced it to $15.6 AUD/m3. Aiming for moderate clarification (∼50 NTU) did not affect nutrient separation and reduced chemical costs to $2.9 AUD/m3. This study provides a cost-effective strategy to improve nutrient recovery and effluent quality, which may support future reuse on dairy farms. Pathogen analysis, field validation, and assessment of long-term alum effects in soil are required before on-farm implementation.
Water eutrophication prediction remains challenging due to poor long-term feature retention, susceptibility to local optima, and difficulties in balancing smooth and abrupt time-series patterns. To address these issues, this study develops a two-stage reinforcement-learning forecasting framework in which Transformer-based temporal representation, auxiliary replay learning, and error-aware Q-value selection are jointly organized for multivariate eutrophication prediction. In the first stage, the TDDPG model replaces the conventional actor representation in DDPG with a Transformer-based temporal feature extractor and uses an auxiliary replay buffer to reduce the effect of strongly correlated sequential samples during policy learning. In the second stage, the DDPG-Double 3Q model treats the outputs and errors of several DDPG-based predictors as decision states, allowing the final prediction policy to select and refine candidate predictions under both gradual and abrupt water-quality variations. Experimental validation using multi-factor water quality monitoring data demonstrates that the proposed framework achieves an average improvement of 35% across key evaluation metrics - Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) compared to baseline models such as ADDPG and RDPG. The results indicate that the framework improves prediction accuracy and training stability in the tested dataset, suggesting that reinforcement learning can provide a useful sequential decision-making formulation for multivariate eutrophication forecasting.
ABSTRACT Graphical abstract showing the study workflow for planned treated wastewater reuse in Vietnam. A vertical flowchart on the left moves from 'International literature review' to 'Vietnam assessment (2,511 projects right arrow 118 selected+stakeholders),' then to 'Key drivers and barriers (PESTEL),' and finally to 'Strategic recommendations for planned treated wastewater reuse in Vietnam.' A map of Vietnam appears on the right. A red boxed list highlights the main recommendation areas: policy and standards, institutional coordination, economic and finance, technology, and capacity building. Vietnam faces growing pressure on water resources due to rapid urbanization, industrial expansion, climate change, and declining water quality. Wastewater reuse offers a promising pathway to enhance water security, reduce pollution, and advance circular-economy objectives. This study assessed wastewater reuse potential in Vietnam through a mixed qualitative approach: a systematic review of international experience, targeted review of Vietnamese policies and sectoral evidence, screening of 2,511 wastewater-generating projects reported on the national environmental consultation portal, detailed analysis of 118 selected projects, and validation through stakeholder consultation and questionnaire survey. Results show that wastewater reuse in Vietnam is driven mainly by rising water demand, environmental pressure, and regulatory interest in resource circulation, and emerging market incentives linked to green production and supply chains. However, implementation remains limited and uneven across sectors. Manufacturing and tourism projects show the strongest uptake, while industrial park infrastructure, urban systems, and livestock applications face greater institutional, technical, and economic constraints. Main barriers are the absence of fit-for-purpose reclaimed-water standards, fragmented institutional responsibilities, high treatment and monitoring costs, limited financial incentives, and low implementation confidence. Scale-up will require clearer regulation, stronger inter-agency coordination, targeted incentives, improved technical capacity, and explicit integration into long-term water and urban development strategies.
