Composting is an effective strategy for organic waste recycling, however, the frequent coexistence of heavy metals and antibiotics in mixed feedstocks introduces substantial challenges. These co-contaminants can impair microbial activity and raise ecological risks in end products. This study investigated the effects of biochar amendment on composting under combined heavy metal and antibiotic contamination. Biochar significantly improved composting performance by extending the thermophilic phase and enhancing system stability. It promoted nitrogen stabilization, as indicated by increased organic-to-inorganic nitrogen ratios from 24:76 in the control to 33:67, 43:57, and 51:49 under 5%, 10%, and 15% biochar treatments, respectively. Humification was also enhanced, with the CHA/CFA ratio increasing from 1.0 to 1.5 at the highest biochar dosage. In addition, biochar facilitated stabilization of heavy metal, achieving high removal efficiencies of sulfamethazine (98.2%) and ofloxacin (99.3%). Microbial community analysis revealed sustained dominance of Firmicutes and enrichment of Actinobacteria up to 23.2% with higher biochar doses. These results indicate that biochar improved compost maturity and contaminant stabilization under co-contaminated conditions, providing a feasible strategy for safe and efficient recycling of organic wastes.

Sudden shock loads in wastewater influent can severely disrupt biological treatment processes and cause effluent quality exceedances in wastewater treatment plants, particularly in domestic-industrial integrated facilities. Timely and reliable early warning of such disturbances is critical for enabling proactive intervention and minimizing environmental and operational risks. This study develops a COD-centric closed-loop early warning framework that integrates a machine-learning-based soft-sensing module with a multi-step effluent prediction module, in which anomaly tagging is automatically triggered by effluent discharge limit thresholds. Two coupling architectures were evaluated: a serially coupled architecture (SCA) that sequentially connects influent sensing and effluent prediction, and a jointly coupled architecture (JCA) that enables end-to-end learning within a unified model. Feature importance was interpreted using SHAP analysis and validated through ablation studies to identify the key process variables that govern predictive performance and early-warning responsiveness. In a full-scale integrated wastewater treatment plant (IWTP) case study, the proposed framework achieved 95.0% accuracy and 87.2% anomaly detection precision for 12 h ahead warnings, with JCA outperforming SCA. These results demonstrate that backward inference from predicted effluent compliance risk enables timely identification of upstream disturbances before limit violations occur. This integrated and interpretable framework provides a novel, real-time, and cost-effective solution for linking effluent-risk forecasting with influent-anomaly diagnosis, substantially enhancing the operational resilience and proactive management of IWTPs.

The stringent effluent standards rise strong demand for efficient wastewater treatment, particularly for the superior removal of nitrogen and phosphorus. To address the issues of membrane fouling and insufficient phosphorus removal in the internal circulation aerated membrane bioreactor (ICAMBR) treating municipal wastewater, this study achieved significantly improved system performance and effective membrane fouling control through the addition of polyaluminum chloride (PAC). The findings showed that the addition of PAC resulted in average removal efficiencies of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) of 98.87%, 98.46%, and 94.83%, respectively, while the effluent TP level was reduced to 0.26 mg/L. Additionally, PAC effectively reduced the extracellular polymeric substances (EPS) content and enhanced sludge settleability, which led to a decrease in membrane fouling from 1.42 × 1012 m−1d−1 to 0.54 × 1012 m−1d−1. The irreversible membrane resistance decreased from 1.98 × 108 m−1 to 0.35 × 108 m−1, thereby effectively alleviating membrane fouling in the system. Microbial analysis revealed that PAC fostered the enrichment of denitrifying and ammonia-oxidizing bacteria, which in turn enhanced the abundance of key nitrogen metabolism genes, thereby reinforcing the nitrogen removal pathways in the system. This study provides both theoretical insights and technical support for the enhanced operation of ICAMBR through PAC addition in municipal wastewater treatment.

Ammonia-laden wastewater containing residual tetracycline (TC) and insufficient readily biodegradable carbon poses a challenge for stable biological nitrogen removal. In this study, a microaerophilic anoxic/oxic process based on the Biological Low Oxygen and High Activated Sludge concentration (Bio-LOHAS) strategy was applied to treat synthetic high-ammonia, low-C/N wastewater under stepwise TC loading (0-5.0 mg/L). Compared with conventional AO operation, Bio-LOHAS combines low dissolved oxygen and high sludge concentration to promote simultaneous nitrification and denitrification (SND) and improve tolerance to antibiotic stress. At TC <= 1.0 mg/L, the system maintained stable COD and total nitrogen (TN) removal above 90% and 85%, respectively. At 5.0 mg/L TC, nitrite accumulated because nitrite-oxidizing bacteria were more strongly inhibited than ammoniaoxidizing bacteria, shifting nitrogen conversion toward partial nitrification and denitrification. However, relatively high TN removal was still retained because denitrification was less affected than nitrification. Recovery was achieved after influent TC reduction or withdrawal, indicating improved tolerance of acclimated sludge during re-exposure rather than unrestricted stable operation under continuously high TC loading. Overall, BioLOHAS is a promising strategy for improving nitrogen-removal resilience in antibiotic-impacted, carbon-limited ammonia-rich wastewater.

Iron oxides can facilitate anaerobic digestion but are faced with electron competition between methanogens and iron-reducing bacteria. In this study, we developed a highly crystalline iron oxide material via the pyrolytic synthesis of metal-organic framework (MOF) precursors and systematically investigated its mechanistic role in improving anaerobic digestion. The results showed that 10 g/L of the MOF-derived iron oxide (MDF) enhanced the methane yield to 547.91 mL/g volatile solids. Characterisation of the MDF material revealed that it had a highly ordered crystalline structure (95.56 % crystallinity) and superior electrochemical activity, with a positively charged surface facilitating microbial adhesion. Microbial analysis revealed that MDF selectively enriched Methanosarcina (methanogens) and Clostridium_sensu_stricto_1 (syntrophic bacteria), while up-regulating genes associated with conductive pili (PilA) and quorum-sensing signalling molecules. This shift established a direct interspecies electron transfer-driven metabolic network. Furthermore, the high crystallinity of MDF suppressed the enrichment of iron-reducing bacteria (Trichococcus abundance, < 2.5 %), thereby mitigating iron reduction competition for acetate/hydrogen and overcoming the limitations of conventional Fe2O3. This superior stability was further confirmed in a long-term experiment, where MDF exhibited both higher methane yield and greater resistance to reduction than Fe2O3. This study provides fundamental insights into crystallinity-dependent electron transfer pathways for methanogenesis and a strategic framework for engineering stable conductive materials to enhance the efficiency and stability of anaerobic digestion.

Commercial adsorption resins are widely used for heavy metals removal from industrial wastewater, yet their selection still depends on labor-intensive and time-consuming experiments. Machine learning (ML) offers a promising alternative, but its predictive power is often constrained by insufficient mechanistic understanding. In this study, a hybrid grid search-extreme gradient boosting (GS-XGBoost) model was developed by integrating adsorption mechanisms into a data-driven framework. Key physicochemical descriptors, including chemical structure, atomic number, valence, electronegativity, atomic radius, as well as environmental conditions including temperature and pH, are incorporated, achieving high predictive accuracy (R2 = 0.915). SHAP analysis identified chemical structure as the most critical factor, where nitrogen-containing functional groups (excluding symmetrical N-containing motifs) enhanced heavy metal adsorption, whereas long-chain structures suppressed it. To further improve generalization, Density Functional Theory (DFT) were employed to identify actual adsorption sites, thereby refining the model's prediction of intramolecular adsorption configurations. This approach reduced the average relative error from 57.13 % to 24.27 %. This study establishes a robust framework for rapid adsorbent screening and provides a novel strategy for integrating mechanistic insights into data-driven models, advancing the intelligence design of adsorbents for environmental remediation.

Real-time and reliable monitoring of nitrogen in wastewater treatment plants (WWTPs) is essential for effluent quality control and mitigation of environmental risks such as eutrophication. However, conventional online analyzers for total nitrogen (TN) and related nitrogen indicators remain costly, maintenance-intensive and difficult to deploy at high temporal resolution, especially under data-scarce conditions. To address this limitation, a hybrid soft sensing framework was developed by integrating a simplified prior model with a calibration model through a bagging ensemble strategy, based on 1068 laboratory-simulated datasets and 576 real-world observations from a WWTP. The prior model, derived from controlled water-mixing experiments, provided a constrained reference representation, while the calibration model learned the system-level discrepancy between this reference system and real wastewater observations. Using pH, electrical conductivity (EC), oxidationreduction potential (ORP), and dissolved oxygen (DO) as inputs, the proposed framework improved both robustness and predictive accuracy under data-scarce conditions. Compared with purely data-driven models, it reduced MSE, MAE, and RMSE by 41.21%, 24.31%, and 23.58% for ammonia nitrogen (NH4+-N), and by 45.79%, 25.74%, and 26.36% for TN, respectively. Reliable prediction was maintained with as little as 10% and 40% of the original training data for NH4+-N and TN, respectively. In addition, lifecycle monitoring cost was reduced by 91.34% relative to conventional online analyzers. These results demonstrate that the proposed framework offers a cost-effective and practical solution for nitrogen monitoring in WWTPs under data-scarce conditions.

The efficient recovery of tin (Sn) from acidic steel wastewater remains a critical challenge for resource sustainability and environmental protection. Herein, epichlorohydrin (ECH) cross-linked alginate fibers (ECHalginate fibers) were developed via covalent bonding between the C3-OH groups of alginate and epoxide/C-Cl groups of ECH. Unlike conventional Ca(II)-cross-linked alginate prepared in CaCl2 solutions, this ethanol-based solidification strategy avoids ion-exchange consumption of carboxyl groups, thereby preserving active adsorption sites. The resultant fibers exhibited excellent structural integrity and a high Sn(II) uptake of 497.6 +/- 92.6 mg/g at pH 2.3 within 180 min, 7.4-48.5 times higher than that of commercial carbons and ion exchange resins. Comprehensive characterization (FE-SEM, BET, XRD, FT-IR, XPS) and DFT calculations confirmed that ECH crosslinking occurred predominantly at C3-OH sites, while Sn(II) adsorption proceeded through chelation with-COO-groups and Na(I)/Sn(II) exchange, followed by oxidation to Sn(IV). A speciation-weighted adsorption energy model Eads,adj was established, showing a strong linear correlation (R2 = 0.995) with experimental capacities, enabling quantitative prediction of adsorption performance under variable pH. Moreover, the ECH-alginate fibers demonstrated rapid regeneration in 0.5 mol/L HCl with complete desorption within 5 min and minimal capacity loss after five cycles. The practical application of ECH-alginate fibers yielded a remarkable recovery efficiency of 99.7 +/- 1.0% for Sn from real Sn-plating wastewater. This work provides a mechanistically guided and scalable approach for designing durable, high performance biopolymer adsorbents for sustainable metal resource recovery.

In this study, two biological processes-a novel two-stage integrated biological reactor (TS-IBR) and a conventional two-stage anoxic/aerobic system (TS-A/O)-were applied to the treatment of piggery wastewater. The performance of the two systems was systematically evaluated in terms of pollutant removal efficiency, effluent refractory organic matter, functional microbial community composition, and metabolite profiles. Both systems achieved comparable removal efficiencies for COD (88.3-88.7%) and NH4+-N (95.8-96.7%); however, TS-IBR exhibited superior TN removal (83.6%) relative to TS-A/O (75.5%). Effluent analysis revealed that the predominant refractory organics consisted of fulvic and humic acid-like substances (FA/HA-1), humic acid-like substances (HA-2), and tryptophan-like compounds (TP-3). Proteobacteria and Bacteroidetes were identified as the dominant phyla across both systems, accounting for 41.3-61.3% and 26.6-49.3% in TS-IBR, and 42.6-67.5% and 23.9-38.8% in TS-A/O, respectively. Nitrosomonas was the principal ammonia-oxidizing bacterium, with abundances of 1.5-1.8% in TS-IBR and 0.1-0.2% in TS-A/O. The heterotrophic nitrification aerobic denitrification (HNAD) microorganisms (5.38-13.8%) are likely to play a more pivotal role in nitrogen removal. Metabolite profiling further indicated that the seed sludge primarily generated small molecules such as ketones, amines, acids, and esters, whereas both TS-IBR and TS-A/O were enriched in amino acids, carbohydrates, and alcohols. Considering pollutant removal performance, energy demand, and operational feasibility, TS-IBR demonstrated distinct advantages over the conventional TS-A/O process. These findings underscore the potential of TS-IBR as an effective and practical technology for the treatment of swine farm wastewater characterized by high organic and nitrogen loads.

Biochar, acting as a conductive medium, is widely used to stabilize anaerobic digestion (AD) systems for improved efficiency. However, the crucial characteristics of biochar affecting distinct processes of AD remain unclear. In this study, biochar with various typical characteristic functional group (hydroxyl and carboxyl groups) was purposefully synthesized to explore its specific impact on the AD process. Compared with the control, a substantial increase by 14.72 % in methane production (454.04 +/- 7.50 mL/g) was achieved with NaOH ball-milled biochar (BMB-NaOH), while the H2SO4 ball-milled biochar (BMB-H2SO4) showed a facilitating effect on organic matter decomposition. Subsequently, a low correlation coefficient value (0.211) ruled out specific surface area as a determining factor of methane production efficiency. Nonetheless, the presence of hydroxyl and carboxyl groups showed positive correlations with methane production and organic matter decomposition efficiency, respectively. The microbial community and metabolic pathways identified electronrich hydroxyl groups in the biochar as electron donors to enrich methanogen (i.e., Methanobacterium and Methanosaeta), 11.25 % for BMB-NaOH vs. 8.14 % for BMB-H2SO4. Meanwhile, fermentative bacteria (Georgenia 28.28 % for BMB-NaOH vs. 38.82 % for BMB-H2SO4) could exploit the electron-deficient carboxyl groups, thereby facilitating the breakdown of large molecules. Together, the diverse surface functional groups primarily affected electron transmission capabilities. The findings of this study illuminate the unique roles of functional groups in microbial electron transfer processes, providing insights for targeted modifications of conductive media.

The aerobic granular sludge (AGS) process has emerged as a viable alternative to landfill leachate treatment. The mechanisms by which dissolved organic matter (DOM) in landfill leachate, a potential stimulant, is utilized during treatment with AGS systems remain unclear. In this study, we revealed DOM-mediated nitrogen removal in AGS receiving the effluent from up-flow anaerobic sludge blanket (UASB). The results showed that granules were successfully formed with real fresh leachate contents increasing from 10% to 30%. The established AGS bioreactor could achieve good nitrogen removal (76.16% on average) through partial nitrification-denitrification (PND), effectively reducing carbon demand and selectively utilizing DOM from leachate. Fourier transform ion cyclotron resonance mass spectrometry analysis showed the utilization of DOM of aliphatic, protein-like and amino sugar-like compounds with low saturation in situ of leachate. Microbial analysis identified that Thauera and Rhodobacter belong to Proteobacteria as the dominant nitrogen-removing bacteria, and OLB13, OLB12, and Devosia, accounting for 16.11% of the microbial community, are the primary DOM degraders, with significant correlation (p < 0.05). High-throughput analysis showed that protein-like and amino sugar-like compounds were the main DOM components used to facilitate PND via diverse metabolic pathways. This study suggests that the critical role of DOM interactions with AGS microbial consortia could shed light on the regulation of nitrogen removal in landfill leachate treatment.

Climate change increases extreme weather events and ecosystem disruptions. It is imperative to estimate and further eliminate greenhouse gas (GHG) emissions in wastewater treatment sector. However, it lacks practical wastewater and sludge treatment and disposal (STD) GHG evaluation by using onsite datasets at a megacity level. This study proposes a comprehensive GHG evaluation approach, integrating the carbon footprint accounting, mass balance modeling, and IPCC-recommended first-order of decay method by using the most practical datasets to estimate Wuhan City's wastewater GHG emissions from 30 wastewater treatment plants (WWTPs) and 11 sludge treatment and disposal sites. It is concluded that 317832 t CO2eq GHG emissions are generated in WWTPs, and 588913.5 t CO2eq are generated in STD. Within WWTPs, indirect emissions from energy consumption constitute the highest emissions at 68 % of the total emissions. This is followed by onsite nitrous emissions with 23 % contribution, and methane emissions with a 9 % contribution. In WWTPs, sequencing batch reactors exhibited the highest GHG emission intensity with 1.63 kg CO2eq/m3, while in STD, building material application generated the highest contribution (52 %). Within the system boundary, direct GHG emissions accounted for 37 %, primarily stemming from onsite biological wastewater treatment and landfilling. Conversely, indirect GHG emissions were predominantly attributed to sludge disposal of building material applications. Sensitivity analysis identified the emissions factor in electricity is the most critical parameter, highlighting its significance towards carbon neutrality. The findings of this study highlight the significance of using practical datasets to develop city-specific strategies in mitigating GHG emissions.

Conventional biological treatment of low carbon-to-nitrogen (C/N) municipal wastewater is challenged by the need for supplemental carbon sources and high aeration energy. Here, we first introduce a Biological Low Oxygen and High Activated Sludge concentration (Bio-LOHAS) system and evaluate its performance under two low dissolved oxygen (DO) gradients strategies: a monotonically increasing DO profile (M-O) and a reverse profile (O-M). At an influent C/N ratio of 4.41 ± 0.92, the O-M strategy outperformed the M-O strategy, achieving total nitrogen (TN) and total phosphorus (TP) removal efficiency of 77.8 % and 95.8 %, respectively, compared with 64.3 % and 68.9 % under M-O. Integration of in-situ pathway profiling and batch experiments revealed that the O-M strategy facilitated carbon allocation and enhanced microbial synergy. Concurrently, 16S rRNA-based community analysis indicated that the O-M strategy favored the enrichment of denitrifying glycogen-accumulating organisms (DGAOs) and denitrifying phosphorus-accumulating organisms (DPAOs), thereby driving polyhydroxyalkanoates (PHAs) -mediated denitrifying phosphorus removal. Notably, it promoted a dynamic balance between DGAOs and DPAOs, optimized internal carbon source conversion efficiency, and increased microbial network complexity. Metagenomic analysis further confirmed the activation of endogenous denitrification and polyphosphate metabolic pathways, with increased abundance of key functional genes involved in PHAs-glycogen cycling and polyphosphate synthesis. More importantly, the Bio-LOHAS process reduced external carbon demand by 40 % and aeration energy input by 27-33 %. This study demonstrates the potential of the Bio-LOHAS process as a promising and energy-efficient strategy for low C/N municipal wastewater treatment and provides a rational basis for optimizing DO gradients in full-scale applications.

A novel modified oyster shell (MOS-800) was developed to enhance phosphorus sequestration and recovery from wastewater. Approximately 33.3% of phosphate was eliminated by the MOS-800, which also exhibited excellent pH regulation capabilities. In semicontinuous tests, a synergistic phosphorus separation was achieved through the coupling process of CaCl2/MOS-800 and a circulating fluidized bed (CFB), resulting in an 86.5% phosphate separation. In continuous flow experiments, phosphorus elimination reached 98.2%. Material characterization revealed that hydroxyapatite (HAP) was the primary component of the crystallized products. Additionally, MOS-800 released 506.5–572.2 mg/g Ca2+ and 98.1 mg/g OH−. A four-stage heterogeneous crystallization mechanism was proposed for the coupling process. In the first stage, Ca2+ quickly reacted with phosphate to form Ca-P ion clusters, etc. In the second stage, these clusters packed randomly to form spherical amorphous calcium phosphate (ACP). In the third stage, the ACP spheres were transformed and rearranged into sheet-like HAP crystallites, Finally, in the fourth stage, the HAP crystallites aggregated on the surface of crystal seeds, also with the addition of crystal seeds and undissolved MOS-800, potentially catalyzing the heterogeneous crystallization. These findings suggest that the CaCl2/MOS-800/CFB system is a promising technique for phosphate recovery from wastewater.

Membrane bioreactors (MBRs), renowned for their high effluent quality and ability to completely retain biomass, encounter limitations in wider applications due to membrane fouling. This study evaluated the performance of a reciprocation MBR system at frequencies of 0.22, 0.33, and 0.45 Hz. The system achieved a superior removal of nitrogen (over 96 %) and phosphate (over 95 %) compared with the aeration (87 %). Compared to the filtration cycle duration at 0.22 Hz (13.67 +/- 0.58 days), the durations at 0.33 Hz (22.33 +/- 0.76 days) and 0.45 Hz (23.90 +/- 0.85 days) increased by 63.35 % and 74.84 %, respectively. Higher frequencies reduced polysaccharide and protein concentrations while promoting functional bacteria in foulants. The economic analysis revealed that the water production cost using reciprocation was $0.003/m3, which was 74.35 % lower than that of aeration ($0.0117/m3). Overall, reciprocation MBR technology effectively controls fouling, enhances nutrient removal, and reduces operational costs, offering a sustainable alternative for wastewater treatment.

Traditional methods for developing adsorbents often rely on trial-and-error methods in the laboratory, which are not only time-consuming and labor-intensive but also increase reagent waste. In this study, to efficiently design a high-performance adsorbent for silver (Ag(I)) recovery, a series of functionalized chitosan structures were constructed through density functional theory (DFT). Among the designed structures, thiourea-modified chitosan emerged as the most promising candidate due to its lowest adsorption energy for Ag(I). Based on this designed structure, thiourea-modified chitosan fibers (CFs-TU) was prepared. The resulting CFs-TU exhibited excellent stability and selective capture performance for Ag(I) across a broad pH range. The characteristics of CFs-TU and the underlying adsorption mechanism for Ag(I) were investigated using characterization analysis and DFT calculations. The analysis revealed that at pH 6, the adsorption was primarily governed by the coordination between Ag(I) and -SH/C=S/C-N moieties. However, at pH 2, a synergistic effect was observed, involving electrostatic attraction between NO3- adsorbed on the protonated amino group in the CFs-TU and Ag(I) cations, alongside coordination between Ag(I) and -SH/C=S, which facilitated Ag(I) adsorption. During the adsorption process, Ag(I) was generally reduced to Ag(0) owing to electron transfer as the -NH2/-NH- and -SH functional groups of CFs-TU were oxidized to =N- and C=S. Furthermore, the exhausted CFs-TU could be efficiently regenerated using an acidic thiourea solution. The CFs-TU prepared through this DFT-based approach demonstrated significant potential for the recovery of Ag(I) from aqueous solutions, highlighting a novel and effective method for adsorbent development.

An emergency water pollution incident poses a significant risk to the proper functioning of wastewater treatment plants, particularly in domestic-industrial integrated facilities. Source tracing is recognized as an effective method to mitigate ongoing impacts. Machine learning-assisted traceability is emerging as a more efficient and faster method compared to traditional methods. In this study, a total of 712 sets of characterization wastewater information from effluent samples from14 discharge enterprises across 6 different sectors, as well as domestic wastewater was collected using 3-dimensional fluorescence spectroscopy. After data cleaning and augmentation, a feature fingerprint database of wastewater was constructed to train a traceability model. Several machine learning algorithms, including Back Propagation neural network (BP), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest Neighbors (KNN), were selected for constructing the traceability framework. Subsequently, an advanced Particle Swarm Optimization Random Forest model (PSO-RF), capable of automatically optimizing model parameters, was proposed and applied to trace the sources of wastewater in integrated wastewater treatment plant. The PSO-RF achieved and accuracy of 96.55 % in sector identification and 94.25 % in manufacturer identification. As part of the validation process, laboratory simulations were conducted using blended wastewater with different volume ratios of domestic and industrial wastewater to evaluated the potential application of PSO-RF. The results consistently demonstrated PSO-RF's effectiveness, particularly in tracing pharmaceutical wastewater sources, maintaining an accuracy of over 85 %. This work presents a novel strategy for tracing abnormal sources during emergency pollutant incidents, providing essential support for integrating artificial intelligence (AI) into meticulous wastewater management.

Conductive material-mediated anaerobic digestion (AD) systems offer a promising solution to enhance methane production, yet its sustainability and economic viability require holistic evaluation. In this study, we systematically assess the typical conductive materials, namely, biochar, iron-based material, and biochar-iron composites, through integrated life cycle and cost-benefit analyses of 219 experimental cases. Biochar-iron composites achieved the highest methane yield improvement (36 %), while iron-based materials posed significant carbon burdens (contributing up to 44 % of system emissions). Crucially, material recycling (five cycles) reduced iron's carbon footprint by 72 %, and digestate valorization into biochar further lowered net emissions by 113.8-184.9 %. Economically, iron-based materials outperformed biochar in profitability (220 USD/ton volatile solids), and combining material recovery with digestate valorization boosted net profits by 191.8-264.8 %. The findings demonstrate that prioritizing biochar-iron composites for performance and iron-based materials with recovery for cost-effectiveness, alongside closed-loop design, can reconcile environmental and economic goals. This work provides actionable pathways to optimize conductive material-enhanced AD systems for scalable, sustainable waste-to-energy conversion.

Membrane fouling control is crucial for the wide application of membrane bioreactors (MBR), highlighting the necessity for innovative strategies to reduce energy input and improve fouling control capability. In this study, three different membrane-fouling strategies, i.e., aeration, reciprocation, and reciprocation coupled with limited aeration (RecLA), were adopted and compared in MBR systems with a long-term investigation. Compared to the conventional aeration strategy, which achieved nitrogen and phosphorus removal efficiencies of 87.5 % ± 4.9 % and 30.2 % ± 4.3 % respectively, the reciprocation strategy demonstrated significantly higher removal efficiencies of 94.5 % ± 3.7 % for nitrogen and 94.3 % ± 3.7 % for phosphorus. More importantly, the filtration time was significantly extended from 4 days for aeration to 21.5 days for reciprocation and 26.7 days for RecLA. RecLA was effective in reducing cake layer thickness, enhancing foulant hydrophilicity, and decreasing the abundance of filamentous bacteria in the foulant. Particle image velocimetry analysis revealed that RecLA enhances bubble penetration into the module interior, increases the shear rate near the membrane surface, and mitigates foulant accumulation, thereby effectively alleviating membrane fouling. Therefore, the RecLA strategy achieves efficient membrane fouling control by enhancing hydrodynamic conditions and altering foulant properties, offering an innovative solution for the broader application of MBR systems.