Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202604008
Municipal wastewater treatment plant (WWTP) effluent in China typically exhibits a low carbon-to-nitrogen (C/N) ratio, necessitating substantial external carbon addition for conventional heterotrophic denitrification, which incurs high costs and secondary pollution risks. Nitrate-dependent ferrous oxidation (NDFO) offers a promising alternative, yet suffers from unsustainable iron sources and surface passivation. This study constructed a pilot-scale electrochemical-biological coupled system (CF-Fe-CS/NAFO) with an effective volume of 200 L and a treatment capacity of 100 L/d, incorporating a composite cathode (5% apparent filling rate of carbon felt-iron-chitosan, CF-Fe-CS). A constant potential of -1.0 V (vs. Ag/AgCl) was applied to the cathode to achieve in-situ electrochemical reduction of Fe(III). During stable operation from day 16 to 60 with a hydraulic retention time (HRT) of 48 h and synthetic influent containing 15 mg/L NO3-N, effluent NO3-N remained below 6.5 mg/L, achieving total nitrogen (TN) removal of 50-60%, whereas the control reactor (no applied potential) exhibited effluent NO3-N above 12 mg/L and TN removal below 20%. From day 61 to 74, treating real secondary sedimentation tank effluent (influent NO3-N: 15.29 mg/L), the system reduced effluent NO3-N to 6.06 mg/L, maintaining TN removal above 50%. From day 74 to 98, as HRT was sequentially reduced from 48 h to 24, 12, 6, and 3 h, effluent NO3-N increased to 9.5, 11.9, 13.6, and 14.6 mg/L, with TN removal efficiencies of 32.6%, 19.9%, 10.9%, and 5.8%, respectively. These results demonstrate that the CF-Fe-CS/NAFO system achieves long-term, stable, advanced nitrogen removal from WWTP secondary effluent without external organic carbon.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202608001
The discharging wastewater from spent lithium-ion batteries is characterized by complex composition, high salinity, and substantial organic load, making its efficient treatment and resource recovery a critical challenge in the lithium battery recycling chain. This study investigated the feasibility of applying anaerobic chain elongation technology for resource recovery from such wastewater. The results showed that the reactor could tolerate up to 40% discharge wastewater in the feed, with caproate production reaching 6.38 g/L. However, when the wastewater proportion increased to 60%, system performance declined sharply, and the synthesis of butyrate and caproate ceased. Batch screening experiments ruled out the influence of high salinity (TDS ≈ 12 g/L) and metal ions such as Li+, Ni2+, Co2+, and Mn2+, identifying fluoride (F-) as the dominant inhibitory factor leading to functional failure. Concentration gradient experiments further quantified the inhibitory effect of F-. At concentrations below 600 mg/L, butyrate production remained largely unaffected; at 900 mg/L, substrate metabolism was severely inhibited, with only slight recovery observed at the final stage; and at 1200 mg/L, chain elongation metabolism was completely blocked. Microbial community analysis revealed that the chain elongation function was undertaken by different taxonomic groups at different stages. Initially, Clostridium kluyveri dominated, followed by a shift to Caproicibacterium and Thermocaproicibacter during the mid-phase. In the recovery phase, a synergistic consortium of Oscillibacter valericigenes and Caproicibacterium sp. emerged. Furthermore, after introducing actual discharging wastewater, microbial groups such as Brevundimonas diminuta and Clostridium ljungdahlii, which are likely involved in degrading complex organics, gradually became enriched, providing the substrate foundation for chain elongation. This study offers a feasible strategy and mechanistic insights for the high-value bioconversion of wastewater from lithium battery recycling.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202608006
Organic waste is a potential phosphorus reservoir, and understanding the dynamics of available phosphorus (AP) during its resource utilization is critical for efficient phosphorus recovery. Composting, a key route for organic waste valorization, involves complex transformations of phosphorus alongside organic matter degradation and humification. However, the long duration and high cost of composting experiments, coupled with multifactorial influences, hinder efficient elucidation of AP dynamics via conventional methods. This study compiled data from 33 publications, constructing a dataset of 647 samples. Data preprocessing included iterative imputation, one-hot encoding, and standardization. A stacking ensemble learning model was developed to predict AP generation during composting. The optimal ensemble comprised XGBoost and SVR as base learners and ElasticNet as the meta-learner, achieving R² values of 0.954 and 0.928 on training and test sets, respectively, with low overall error. SHAP analysis revealed that key factors influencing AP content, in descending order of importance, were feedstock type, bulking agent type, turning interval, pH, electrical conductivity (EC), and C/N ratio. Notably, livestock manure as feedstock and straw-based bulking agents contributed positively to AP predictions. Partial dependence plots indicated that lower pH and C/N ratios generally favored AP accumulation throughout composting. During the initial stage, higher moisture content and lower EC enhanced AP; in the thermophilic phase, higher temperatures corresponded to higher AP; and during cooling and maturation, maintaining moisture below 48% and C/N below 14, while extending composting beyond 43 days, promoted AP accumulation. This study demonstrates accurate AP prediction via stacking ensemble learning and identifies critical factors, offering support for optimizing phosphorus management in composting engineering.