Water Research X最新文献

Solar evaporation exhibits significant potential for the treatment of high-salt organic wastewater. However, it's also confronted with challenges due to the accumulation of organic pollutants and salts in the concentrated wastewater following evaporation, which compromises the long-term stability of evaporation unit and complicates subsequent treatment processes. To address these challenges, a volumetric solar interfacial evaporation (V-SIE) system by integrating Fe₃O_{4 -}H₂O nanofluids and peroxydisulfate (PDS) were proposed in this study. In V-SIE system, Fe₃O₄ magnetic nanoparticles (NPs) were prepared as solar receivers to form a volume-absorbing solar energy interface and enhance evaporation efficiency. The results show that the evaporation rate was 1.412 kg/(m²·h) and the solar efficiency reached 93.75 % as the temperature rose to 57.2 ℃. Additionally, the high thermal conductivity of Fe₃O₄ facilitated the effective heat transfer to the fluid and provided sufficient thermal energy to activate PDS, thereby removing 99.3 % of Rhodamine B (RhB). Fe₃O₄ NPs effectively promoted the generation of reactive species including SO₄ ^·-, ·OH, O₂ ^·- and ¹O₂ from PDS and the four main stages including N-de-ethylation, chromophore cleavage, ring-opening, and mineralization were proposed as the possible degradation pathway of RhB. This study provides a reference for developing V-SIE system and highlights the positive effect of nanofluids in advanced oxidation processes.

Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment of treated effluents to discharge quality standards. To aid in real-time decision-making for stable WWTP operations, this study developed a probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity of producing probability predictions, to enhance the robustness of real-time WWTP effluent quality prediction under such events. The developed probabilistic encoder-decoder LSTM (P-ED-LSTM) model was tested in an actual WWTP, where bihourly effluent quality prediction of total nitrogen was performed and compared with classical deep learning models, including LSTM, gated recurrent unit (GRU) and Transformer. It was found that under shocking load events, the P-ED-LSTM could achieve a 49.7% improvement in prediction accuracy for bihourly real-time predictions of effluent concentration compared to the LSTM, GRU, and Transformer. A higher quantile of the probability data from the P-ED-LSTM model output, indicated a prediction value more approximate to real effluent quality. The P-ED-LSTM model also exhibited higher predictive power for the next multiple time steps with shocking load scenarios. It captured approximately 90% of the actual over-limit discharges up to 6 hours ahead, significantly outperforming other deep learning models. Therefore, the P-ED-LSTM model, with its robust adaptability to significant fluctuations, has the potential for broader applications across WWTPs with different processes, as well as providing strategies for wastewater system regulation under emergency conditions.

Hydrophobic organic pollutants in aqueous environments are challenging to biodegrade due to limited contact between microorganisms, the pollutants and the electron acceptor, particularly under anaerobic or anoxic conditions. Here, we propose a novel strategy that uses inexpensive, dual-function elemental sulfur (S⁰) to enhance biodegradation. Using petroleum hydrocarbons as the target pollutants, we demonstrated that hydrophobic and nonpolar S° can concentrate hydrocarbons while simultaneously serving as an electron acceptor to enrich hydrocarbon-degrading bacteria. The permeable reactive barrier filled with S⁰ effectively removed petroleum hydrocarbons. In addition to rapid adsorption, we discovered, for the first time, that petroleum hydrocarbons underwent efficient biodegradation through the reduction of S⁰. Specifically, n-alkanes were degraded by 80 % to 90 % and polycyclic aromatic hydrocarbons by 40 % to 95 %. These degradation rates were 17 % to 30 % and 26 % to 43 % higher, respectively, compared to those observed without S⁰. Consecutive subcultures combined with untargeted metabolomics analysis revealed that bacteria capable of dissimilatory sulfur reduction enhanced the fermentation process. These bacteria provided electrons to the metabolic network, which facilitated the mineralization of petroleum hydrocarbons. Our findings highlight the significant potential of S° for removing hydrophobic organic pollutants in oxygen-free environments, demonstrate the feasibility of integrating adsorption, biodegradation, and electron supply to enhance pollutant removal.

A full-scale simultaneous partial nitrification, anaerobic ammonia oxidation (anammox), and denitrification (SNAD) reactor was initiated to address the problem of high energy consumption for the treatment of low C/N wastewater. The SNAD system achieved a nitrogen removal rate of 0.9 kg/(m³·d) at an influent NH₄⁺-N concentration of 500 mg/L after 450 days of stable operation. Partial nitrification was achieved by maintaining free ammonia levels at 0.8 ± 0.3 mg/L and dissolved oxygen concentrations between 0.3 mg/L and 1.2 mg/L, which resulted in synergistic nitrogen removal, with anammox contributing 61 % and denitrification contributing 39 %. Microbiological analyses indicated that the dominant microorganisms were Candidatus Brocadia, Thauera, Denitratisoma, and Nitrosomonas. In conclusion, study provides a solid foundation for the broader implementation of the SNAD process in wastewater treatment systems.

Due to accelerating climate change and the need for new development to accommodate population growth, adaptation of urban drainage systems has become a pressing issue in cities. Questions arise whether decentralised urban drainage systems are a better alternative to centralised systems, and whether Nature Based Solutions' (NBS) multifunctionality also brings economic benefits. This research aims to develop spatio-economic scenarios to support cities in increasing their resilience to urban flooding with NBS. The novelty of our work lies in the automated routines to assess the potential for decentralised NBS within the existing urban catchment. The identification of locations and dimensioning is based on open, publicly available geospatial data. Moreover, a block-based decentralization potential metric is developed to indicate stormwater mitigation potential in any urban setting. The Ecully catchment, Lyon metropolitan area (France), is presented as a case study to achieve zero combined sewer overflow (CSO) for specific design storm events. This planning workflow enables project cost savings through the most suitable allocation of distributed interventions, with cost functions also incorporating scaling effects. By reducing the number of decentralised NBS sites compared to smaller, wide-distributed interventions up to 34 % of project costs are saved when planning for a 5-year design storm and up to 7 % for a 100-year design storm. When the decentralised NBS scenario is analysed alongside other urban stormwater management practices, the centralised constructed wetland for CSO results to be the most economical solution due to the higher retention capacity and scaling effect, significantly outperforming the grey alternatives.