Water Research X最新文献

The rapid expansion of coastal aquaculture has led to an increase in the coverage of aquaculture ponds, where intense feed-derived nitrogen is causing significant emissions of nitrous oxide (N₂O). Multiple N₂O production pathways and the relative importance of water column vs. sedimentary production in aquaculture ponds remain uncertain. Clarifying these pathways is vital for sustainable aquaculture development. Using ¹⁵N-labeled dissolved inorganic nitrogen, the pathways and rates of N₂O production in subtropical aquaculture ponds located in south China, cultivating whiteleg shrimp, Japanese seabass, and giant river prawn, were successfully characterized. Total N₂O production rates ranged from 6 to 70 µmol-N m⁻² d⁻¹, with the shrimp pond exhibiting the highest total N₂O production rates, followed by ponds for seabass and prawn. These differences are primarily due to varying feed amounts causing differences in dissolved nutrients in water column and sediment. Particularly, nutrient and organic matter accumulation at the surface sediment stimulated N₂O production. The oxygenated sediment on a centimeter scale could produce substantially more N₂O compared to the water column above on a meter scale. Partial denitrification, i.e., nitrate and nitrite reduction to N₂O, was more important (> 60 %) for N₂O production in aquaculture ponds. The availability of nitrite is likely a major factor driving partial denitrification for both sedimentary and water column N₂O production.

In the field of water supply management, multi-steps water demand forecasting plays a crucial role. While there have been many studies related to multi-steps water demand forecasting based on deep learning, little attention has been paid to the interpretability of forecasting models. Aiming to improve both the forecasting accuracy and interpretability of the model, a novel urban water demand forecasting neural network (UWDFNet) was presented in this paper. Compared with traditional deep learning models, it innovatively considered domain-specific prior knowledge from water supply management and incorporated the correlation relationship between different input variables into the design of the neural network structure, and verified the consistency between the knowledge learned by the model and prior knowledge through interpretability analysis. Additionally, a systematic performance evaluation was conducted and proved that UWDFNet possesses better accuracy and stability compared to other baseline models(e.g., gated recurrent unit network (GRUN), GRUN with a corrected Network (GRUN+CORRNet), GRUN+PID, GRUN+Kmeans).

The use of polymers for water storage or distribution is closely monitored, especially with regard to the possible contamination with substances coming from the material's surfaces. Different standards are practiced across countries according to type of applied materials and such test methods are prevalently based on constant temperature conditions. However, these polymers systems could be located in diverse environment which does not necessarily provide constant conditions. Experimental findings show that exposure of liquid inside polymeric materials to specific temperature gradients, and consequently to emerging organized flows, can result in an accelerated leaching of undesirable substances from the solid surface. In presented work model steady-state and organized flow conditions are used to compare release of contaminates from polyethylene by measuring of surface tension, UV–Vis spectroscopy, FTIR, scanning electron microscopy and elemental analysis of polymer surfaces and water leachates. The pilot study shows that convective flow generated via temperature gradient significantly affects contaminant release in comparison to a steady state and mixing flow conditions.

Aquaculture, producing half of global fish production, offers a high-quality protein source for humans. Improving nitrogen use efficiency (NUE) through microbial protein recovery is crucial for increasing fish production and reducing environmental footprint. However, the poor palatability and high moisture content of microbial protein make its utilization challenging. Here, a biofloc-worm reactor was integrated into a recirculating aquaculture system (BW_RAS) for the first time to convert microbial protein into Tubificidae (Oligochaeta) biomass, which was used as direct feed for culturing fish. Batch experiments indicated that an aeration rate of 0.132 m³ L⁻¹ h⁻¹ and a worm density of 0.3 g cm⁻² on the carrier were optimal for microbial biomass growth and worm predation, respectively. Compared to the biofloc reactor-based recirculating aquaculture system (B_RAS), the BW_RAS improved water quality, NUE, and fish production by 17.1 % during a 120-day aquaculture period. The abundance of heterotrophic aerobic denitrifier Deinococcus in BW_RAS was one order of magnitude higher than in B_RAS, while heterotrophic bacteria Mycobacterium was more abundant in B_RAS. Denitrifiers cooperated with organic matter degraders and nitrogen assimilation bacteria for protein recovery and gaseous nitrogen loss while competing with predatory bacteria. Function prediction and qPCR indicated greater aerobic respiration, nitrate assimilation, nitrification (AOB-amoA), and denitrification (napA, nirK, nirS, nosZI), but lower fermentation in BWR compared to BR. This study demonstrated that BW_RAS increased microbial protein production and aerobic nitrogen cycling through ongoing worm predation, further enhancing fish production to a commercially viable level.

Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration ($S_{NH}$) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the $S_{NH}$ with nitrate nitrogen ($S_{NO}$) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.

Per- and polyfluoroalkyl substances (PFASs) are emerging contaminants detected ubiquitously and have negative impacts on human health and ecosystem; thus, developing in-situ sensing technique is important to ensure safety. Herein, we report a novel colorimetric-based sensor with perfluoroalkyl receptor attached to citrate coated gold nanoparticles (Citrate-Au NPs) that can detect several PFASs including perfluorocarboxylates with different chain lengths (PFHxA, PFOA, PFNA, PFDA), perfluorooctanoic sulfonate (PFOS), and perfluorooctanoic phosphonate (PFOPA). The sensor detects PFASs utilizing fluorous interaction between PFASs and the perfluoroalkyl receptor of Citrate-Au NPs in a solution at a fixed salt concentration, inducing changes in nanoparticle dispersity and the solution color. The rate of spectrum shift was linearly dependent on PFASs concentrations. Citrate-Au NPs with size between 29 – 109 nm were synthesized by adjusting citrate/Au molar ratios, and 78 nm showed the best sensitivity to PFOA concentration (with level of detection of 4.96 µM). Citrate-Au NPs only interacted with PFASs with perfluoroalkyl length > 4 and not with non-fluorinated alkyl compound (nonanoic acid). The performance of Citrate-Au NP based sensor was strongly dependent on the chain length of the perfluoroalkyl group and the head functional group; higher sensitivity was observed with longer chain over shorter chain, and with sulfonate functional group over carboxylate and phosphonate. The sensor was tested using real water samples (i.e., tap water, filtered river water), and it was found that the sensor is capable of detecting PFASs in these conditions if calibrated with the corresponding water matrix. While further optimization is needed, this study demonstrated new capability of Citrate-Au NPs based sensor for detection of PFASs in water.

People spend most of their time indoors and are exposed to numerous contaminants in the built environment. Water management plans implemented in buildings are designed to manage the risks of preventable diseases caused by drinking water contaminants such as opportunistic pathogens (e.g., Legionella spp.), metals, and disinfection by-products (DBPs). However, specialized training required to implement water management plans and heterogeneity in building characteristics limit their widespread adoption. Implementation of machine learning and artificial intelligence (ML/AI) models in building water settings presents an opportunity for faster, more widespread use of data-driven water quality management approaches. We demonstrate the utility of Random Forest and Long Short-Term Memory (LSTM) ML models for predicting a key public health parameter, free chlorine residual, as a function of data collected from building water quality sensors (ORP, pH, conductivity, and temperature) as well as WiFi signals as a proxy for building occupancy and water usage in a “green” Leadership in Energy and Environmental Design (LEED) commercial and institutional building. The models successfully predicted free chlorine residual declines below 0.2 ppm, a common minimum reference level for public health protection in drinking water distribution systems. The predictions were valid up to 5 min in advance, and in some cases reasonably accurate up to 24 h in advance, presenting opportunities for proactive water quality management as part of a sense-analyze-decide framework. An online data dashboard for visualizing water quality in the building is presented, with the potential to link these approaches for real-time water quality management.

Given that many organic pollutants have been reported to facilitate the plasmid-mediated conjugative transfer of antibiotic resistance genes (ARGs), it was naturally deduced that nonylphenol (NP) can also have this kind of effect. Whereas, this study demonstrates an entirely different result that environmentally relevant concentrations of NP attenuate plasmid-mediated ARGs conjugative transfer (maximum inhibition rate 64 %), further study show that NP exposure had no significant effect on bacterial growth, cell vitality, oxidative stress response, and expression of conjugation-relevant genes, which were reported to closely relate to the conjugative transfer in numerous studies. Conclusively, it was found that the dispersant function of NP impeded the occurrence of cell mating, thus was responsible for the decline of conjugative transfer. This study shows a new perspective on understanding the effect of organic pollutants like NP on the ARGs horizontal dissemination in environment.

The goal of this study was to assess the impacts or benefits of sludge in situ reduction (SIR) within wastewater treatment processes with relation to global warming potential in wastewater treatment plants, with a comprehensive consideration of wastewater and sludge treatment. The anaerobic side-stream reactor (ASSR) and the sludge process reduction activated sludge (SPRAS), two typical SIR technologies, were used to compare the carbon footprint analysis results with the conventional anaerobic - anoxic - oxic (AAO) process. Compared to the AAO, the ASSR with a typical sludge reduction efficiency (SRE) of 30 % increased greenhouse gas (GHG) emissions by 1.1 - 1.7 %, while the SPRAS with a SRE of 74 % reduced GHG emissions by 12.3 - 17.6 %. Electricity consumption (0.025 - 0.027 kg CO_2-eq/m³), CO₂ emissions (0.016 - 0.059 kg CO_2-eq/m³), and N₂O emissions (0.009 - 0.023 kg CO_2-eq/m³) for the removal of secondary substrates released from sludge decay in the SIR processes were the major contributor to the increased GHG emissions from the wastewater treatment system. By lowering sludge production and the organic matter content in the sludge, the SIR processes significantly decreased the carbon footprints associated with sludge treatment and disposal. The threshold SREs of the ASSR for GHG reduction were 27.7 % and 34.6 % for the advanced dewatering - sanitary landfill and conventional dewatering - drying-incinerating routes, respectively. Overall, the SPRAS process could be considered as a cost-effective and sustainable low-carbon SIR technology for wastewater treatment.

Wastewater treatment plants produce large amounts of sludge requiring stabilization before safe disposal. Traditional biological stabilization approaches are cost-effective but generally require either an extended retention time (10–40 days), or elevated temperatures (40–80 °C) for effective pathogens inactivation. This study overcomes these limitations via a novel acidic aerobic digestion process, leveraging an acid-tolerant ammonia-oxidizing bacterium (AOB) Candidatus Nitrosoglobus. To retain this novel but slowly growing AOB, we proposed the first-ever application of a classical wastewater configuration—moving bed biofilm reactor (MBBR)—for sludge treatment. The AOB in biofilm maintains acidic pH and high nitrite levels in sludge, generating free nitrous acid in situ to expedite sludge stabilization. This process was tested in two laboratory-scale aerobic digesters processing full-scale anaerobically digested sludge. At an ambient temperature of 20 °C, pathogens were reduced to levels well below the threshold specified for the highest stabilization level (Class A), within a retention time of 3.5 days. A high volatile solids reduction of 27.4 ± 5.2% was achieved. Through drastically accelerating stabilization and enhancing reduction, this process substantially saves capital and operational costs for sludge disposal.