The effect of anode potential on electrogenesis, methanogenesis and sulfidogenesis in a simulated sewer condition

Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network

Deciphering and mitigating dynamic greenhouse gas (GHG) emissions under environmental fluctuation in urban drainage systems (UDGSs) is challenging due to the absence of a high-prediction model that accurately quantifies the contributions of biological production pathways. Here we infused biological production pathways into the graph neural network (GNN) model architecture, developing ecological knowledge-infused GNN (EcoGNN-GHG) models to evaluate methane (CH4) and nitrous oxide (N2O) production in sewers and wastewater treatment plants (WWTPs). The EcoGNN-GHG model demonstrated high predictive accuracy, achieving an R2 of 0.96 for CH4 in sewers and 0.82 for N2O in WWTPs. Model interpretability analysis revealed fluctuations in contributions of the anaerobic hydrolysis acidification pathway to CH4 production and the nitrification-denitrification pathway to N2O production under dynamic environmental conditions, guiding the formulation of a precise dissolved oxygen control strategy targeting critical water quality parameters (acetate for CH4 production and nitrite for N2O production). Implementing this strategy to control DO thereby regulating biological production pathway contributions, CH4 production in sewers and N2O production in WWTPs were reduced by 35.50% and 29.94%, respectively. Our findings offer a robust, accurate method for predicting GHG emissions, quantifying production pathway contributions, and developing effective control strategies in UDGSs.

Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning

Accurate modeling of methane (CH4) and sulfide (H2S) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML) framework (Micro-ML), which integrates microbial process representations from mechanistic models (microbial information) with ML models. Results indicate that Micro-ML model enhanced predictions of CH4 and H2S production, where microbial information provides more information for model optimization. The feature importance of microbial information performed comparable weightings for 58.12 % and 55.16 %, respectively, but their relative significance in influencing Micro-ML model performance varies considerably. The application of Micro-ML performed great potential in reducing CH4 and H2S production (decreased ∼ 80 % and 90 %). The integrated model not only improves the accuracy of CH4 and H2S predictions but also offers a valuable tool for effective management strategies for sewer systems.

Anode potential regulates gas composition and microbiome in anaerobic electrochemical digestion

Anaerobic electrochemical digestion (AED) is an effective system for recovering biogas from organic wastes. However, the effects of different anode potentials on anaerobic activated sludge remain unclear. This study confirmed that biofilms exhibited the best electroactivity at -0.2 V (vs. Ag/AgCl) compared to -0.4 V and 0 V. Gas was further regulated, with the highest hydrogen content (47 ± 7 %) observed at -0.2 V. The 0 V system produced the largest amount of methane (70 ± 8 %) and exhibited the greatest presence of hydrogen-utilizing microorganisms. The gas yield at -0.4 V was the lowest, with no hydrogen detected. Excess bioelectrohydrogen at -0.2 V and 0 V caused the co-enrichment of Methanobacterium and Acetoanaerobium, establishing a thermodynamically feasible current-acetate-hydrogen electron cycle to improve electrogenesis. These results provide insights into the regulatory strategies of MEC technology during anaerobic digestion, which play a decisive role in determining the composition of biogas.

Unravelling the mechanisms of organic micropollutant removal in bio-electrochemical systems: Insights into sorption, electrochemical degradation, and biodegradation processes

Bio-electrochemical systems (BESs) have recently been proposed as an efficient treatment technology to remove organic micropollutants from water treatment plants. In this study, we aimed to differentiate between sorption, electrochemical transport/degradation, and biodegradation. Using electro-active microorganisms and electrodes, we investigated organic micropollutant removal at environmentally relevant concentrations, clarifying the roles of sorption and electrochemical and biological degradation. The role of anodic biofilms on the removal of 10 relevant organic micropollutants was studied by performing separate sorption experiments on carbon-based electrodes (graphite felt, graphite rod, graphite granules, and granular activated carbon) and electrochemical degradation experiments at two different electrode potentials (-0.3 and 0 V). Granular activated carbon showed the highest sorption of micropollutants; applying a potential to graphite felt electrodes increased organic micropollutant removal. Removal efficiencies >80 % were obtained for all micropollutants at high anode potentials (+0.955 V), indicating that the studied compounds were more susceptible to oxidation than to reduction. All organic micropollutants showed removal when under bio-electrochemical conditions, ranging from low (e.g. metformin, 9.3 %) to exceptionally high removal efficiencies (e.g. sulfamethoxazole, 99.5 %). The lower removal observed under bio-electrochemical conditions when compared to only electrochemical conditions indicated that sorption to the electrode is key to guarantee high electrochemical degradation. The detection of transformation products of chloridazon and metformin indicated that (bio)-electrochemical degradation occurred. This study confirms that BES can treat some organic micropollutants through several mechanisms, which merits further investigation.

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.

Bioremediation of hydrocarbon by co-culturing of biosurfactant-producing bacteria in microbial fuel cell with Fe2O3-modified anode

The most common type of environmental contamination is petroleum hydrocarbons. Sustainable and environmentally friendly treatment strategies must be explored in light of the increasing challenges of toxic and critical wastewater contamination. This paper deals with the bacteria-producing biosurfactant and their employment in the bioremediation of hydrocarbon-containing waste through a microbial fuel cell (MFC) with Pseudomonas aeruginosa (exoelectrogen) as co-culture for simultaneous power generation. Staphylococcus aureus is isolated from hydrocarbon-contaminated soil and is effective in hydrocarbon degradation by utilizing hydrocarbon (engine oil) as the only carbon source. The biosurfactant was purified using silica-gel column chromatography and characterised through FTIR and GCMS, which showed its glycolipid nature. The isolated strains are later employed in the MFCs for the degradation of the hydrocarbon and power production simultaneously which has shown a power density of 6.4 W/m3 with a 93% engine oil degradation rate. A biogenic Fe2O3 nanoparticle (NP) was synthesized using Bambusa arundinacea shoot extract for anode modification. It increased the power output by 37% and gave the power density of 10.2 W/m3. Thus, simultaneous hydrocarbon bioremediation from oil-contamination and energy recovery can be achieved effectively in MFC with modified anode.