Machine learning for high-precision simulation of dissolved organic matter in sewer: Overcoming data restrictions with generative adversarial networks.

Understanding the transformation process of dissolved organic matter (DOM) in the sewer is imperative for comprehending material circulation and energy flow within the sewer. The machine learning (ML) model provides a feasible way to comprehend and simulate the DOM transformation process in the sewer. In contrast, the model accuracy is limited by data restriction. In this study, a novel framework by integrating generative adversarial network algorithm-machine learning models (GAN-ML) was established to overcome the drawbacks caused by the data restriction in the simulation of the DOM transformation process, and humification index (HIX) was selected as the output variable to evaluate the model performance. Results indicate that the GAN algorithm's virtual dataset could generally enhance the simulation performance of regression models, deep learning models, and ensemble models for the DOM transformation process The highest prediction accuracy on HIX (R2 of 0.5389 and RMSE of 0.0273) was achieved by the adaptive boosting model which belongs to ensemble models trained by the virtual dataset of 1000 samples. Interpretability analysis revealed that dissolved oxygen (DO) and pH emerge as critical factors warranting attention for the future development of management strategies to regulate the DOM transformation process in sewers. The integrated framework proposed a potential approach for the comprehensive understanding and high-precision simulation of the DOM transformation process, paving the way for advancing sewer management strategy under data restriction.

Intermittent electrostimulation-modified direct interspecies electron transfer for enhanced methanogenesis in anaerobic digestion of sulfate-rich wastewater.

Methane recovery and organics removal in sulfate (SO42-)-rich wastewater anaerobic digestion are hindered by electron competition between methanogenesis and sulfidogenesis. Here, intermittently electrostimulated bioelectrodes were developed to facilitate direct interspecies electron transfer (DIET)-driven syntrophic methanogenesis, increasing substrate competition among methanogenic archaea (MA). By optimising the electrochemical environment, MA was able to employ electron transfer more efficiently than sulfate-reducing bacteria (SRB), resulting in significant methane accumulation (58.1 ± 1.0 mL-CH4/m3reactor) and COD removal (90.5 ± 0.5 %) at lower COD/SO42- ratio. Intermittent electrostimulation improved the metabolic pathway for electroactive bacteria to utilize acetate and direct electrons to electrotrophic MA, decreasing SRB abundance and affecting the sulfate reduction pathway. Intermittently electrostimulated biofilms significantly increased gene levels of key enzymes in electron transport for cytochrome and e-pili biosynthesis, crucial for DIET, demonstrating enhanced DIET-driven syntrophic methanogenesis. This study provides a strategic approach to optimize methanogenesis in sulfate-rich wastewater anaerobic digestion.

Frequency-modulated alternating current-driven bioelectrodes for enhanced mineralization of Alizarin Yellow R.

The alternating current (AC)-driven bioelectrochemical process, in-situ coupling cathodic reduction and anodic oxidation in a single electrode, offers a promising way for the mineralization of refractory aromatic pollutants (RAPs). Frequency modulation is vital for aligning reduction and oxidation phases in AC-driven bioelectrodes, potentially enhancing their capability to mineralize RAPs. Herein, a frequency-modulated AC-driven bioelectrode was developed to enhance RAP mineralization, exemplified by the degradation of Alizarin Yellow R (AYR). Optimal performance was achieved at a frequency of 1.67 mHz, resulting in the highest efficiency for AYR decolorization and subsequent mineralization of intermediates. Performance declined at both higher (3.33 and 8.30 mHz) and lower (0.83 mHz) frequencies. The bioelectrode exhibited superior electron utilization, bidirectional electron transfer, and redox bifunctionality, effectively aligning reduction and oxidation processes to enhance AYR mineralization. The 1.67 mHz frequency facilitated the assembly of a collaborative microbiome dedicated to AYR bio-mineralization, characterized by an increased abundance of functional consortia proficient in azo dye reduction (e.g., Stenotrophomonas and Shinella), aromatic intermediates oxidation (e.g., Sphingopyxis and Sphingomonas), and electron transfer (e.g., Geobacter and Pseudomonas). This study reveals the role of frequency modulation in AC-driven bioelectrodes for enhanced RAP mineralization, offering a novel and sustainable approach for treating RAP-bearing wastewater.

Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics.

As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage systems, analyzing the complex effects between water quality, hydrodynamics, and sewer infrastructure on GHG production and emission processes. It reveals significant spatiotemporal heterogeneity in GHG emissions, particularly under long-term scenarios where flow rates and temperatures exhibit strong impacts and correlations. Given the presence of fugitive and dissolved potential GHGs, standardized monitoring and accounting methods are deemed essential. Advanced modeling techniques emerge as crucial tools for large-scale carbon emission prediction and management. The review identifies that traditional definitions and computational frameworks for carbon emission boundaries fail to fully consider the inherent heterogeneity of sewers and the dynamic changes and impacts of multi-source pollution within the sewer system during the urban water cycle. This includes irregular fugitive emissions, the influence of stormwater systems, climate change, geographical features, sewer design, and the impacts of food waste and antibiotics. Key strategies for emission management are discussed, focusing on the need for careful consideration of approaches that might inadvertently increase global emissions, such as ventilation, chemical treatments, and water management practices. The review advocates for an overarching strategy that encompasses a holistic view of carbon emissions, stressing the importance of refined emission boundary definitions, novel accounting practices, and comprehensive management schemes in line with the water treatment sector's move towards carbon neutrality. It champions the adoption of interdisciplinary, technologically advanced solutions to mitigate pollution and reduce carbon emissions, emphasizing the importance of integrating cross-scale issues and other environmentally friendly measures in future research directions.

Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning.

Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.

Automated machine learning-based models for predicting and evaluating antibiotic removal in constructed wetlands.

Machine learning models can improve antibiotic removal performance in constructed wetlands (CWs) by optimizing the operation process. However, robust modeling approaches for revealing the complex biochemical treatment process of antibiotics in CWs are still lacking. In this study, two automated machine learning (AutoML) models achieved good performance with different sizes of the training dataset (mean absolute error = 9.94-13.68, coefficient of determination = 0.780-0.877), demonstrating the ability to predict antibiotic removal performance without human intervention. Explainable analysis results (the variable importance and Shapley additive explanations) revealed that the variable substrate type was more influential than the variables of influent wastewater quality and plant type. This study proposed a potential approach to comprehensively understanding the complex effects of key operational variables on antibiotic removal, which serve as a reference for optimizing operational adjustments in the CW process.

Reinforced nitrite supplement by cathode nitrate reduction with a bio-electrochemical system coupled anammox reactor.

Anammox has been widely used for the treatment of nitrogen wastewater. However, the problem of stable NO2- supplement becomes one of the limiting factors. It is an effective method to obtain NO2- by denitrifying the NO3-, including the by-product of Anammox. In this study, NO2- was reinforced by bio-electrochemical system (BES) through the reaction of partial denitrification in situ in an Anammox reactor. Our results showed that both NO3- and NO2- can be reduced on the cathode with different Coulombic efficiencies. The reduction of NO3- amount increased with an increase in Inf-NO3-, which was greater than that of NO2-. The conversion amount of NO3- was 2.50% ± 17.25% to the theoretical Eff-NO3-, and the maximum reduction amount was 23.24% with the highest Coulombic efficiency of 3.56%. High throughput results showed that denitrifying bacteria, such as Limnobacter, Thauera, Denitratisoma, Nitrosomonas and Nitrospira, were attached to the cathode surface and in Anammox granular sludge. This study showed that NO2- can be supplied by reducing the by-product NO3- with denitrification cathode at Anammox environment in-situ.