The rapid expansion of spent lithium-ion battery (LIB) recycling has led to the generation of saline, organic-rich discharging wastewater, raising urgent demands for effective treatment. This study investigated the performance of the membrane bioreactor (MBR) in treating such wastewater, focusing on the effects of salinity stress on pollutant removal, membrane fouling, and microbial community dynamics. Results showed that increased salinity initially inhibited COD and NH4+-N removal, but the system recovered through acclimation and achieved high removal efficiencies of 90.74 % and 99.23 % at 1.3 % salinity, respectively. At low salinity levels (0.1 %- 0.6 %), transmembrane pressure (TMP) gradually increased, leading to severe membrane fouling. In contrast, at higher salinity (0.9 %-1.3 %), TMP remained stable with negligible fouling observed. Sludge characterization revealed that smaller flocs under low salinity contributed to pore clogging. Microbial analysis showed that salinity stress reduced species richness and promoted the enrichment of salt-tolerant taxa. Methyloversatilis, capable of degrading methylated compounds under saline conditions, became the dominant functional genus in later stages. Additionally, Pseudonocardia, a filamentous bacterium, gradually accumulated and likely enhanced sludge granulation, which may have contributed to fouling mitigation. These findings clarify the adaptive mechanisms of MBRs under high salinity and demonstrate their potential for treating complex LIB discharging wastewater.

Sludge gasification technology has emerged as a promising method for sludge treatment, offering significant advancements in resource recovery, pollution reduction and carbon mitigation. However, the generated air pollutants limit the widespread adoption of this technology. We construct a timeline-based framework using actual production data with minute-level resolution. This framework integrates historical emission concentrations, operational parameters (e.g., temperature, pH), input materials (e.g., gasifying agents, steam), as well as predetermined operational settings and inputs (e.g., steam flow rate, gasification agent pressure and fan frequency) to construct multi-source features, while constantly rolling the lookback and forecasting window to dynamically predict the SO2 concentrations, oxygen content and particulate matter. On this basis, we systematically evaluate four machine learning models and four deep learning models using the Python-based Darts time series library, and use interpretability including SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) in conjunction with process knowledge to analyze the underlying causes of SO2 fluctuations. The results suggest that model performance is closely related to the generation mechanisms of pollutants: TiDE performs optimally in predicting SO2, which has strong nonlinearity and hysteresis characteristics, while the timeline-based Light Gradient Boosting Machine (LightGBM) model demonstrates robust performance across multi-tasks. Additionally, the gasifier outlet temperature, pressure and steam flow rate are key factors for SO2 fluctuation, with their effects exhibiting a lag window of 5-8 min. Notably, the synergistic effect of low steam flow rate (<300 kg/h) and high outlet pressure (>4.0 kPa) contribute to a steep increase in SO2. By integrating data science with process mechanisms, this study aims to provide accurate and reliable decision support for proactive control and parameter optimization, thus enhance the environmental friendliness and economic feasibility of sludge gasification.

The rapid expansion of lithium-ion battery applications has led to the generation of substantial discharging wastewater (DW) during recycling processes, presenting both an environmental challenge and a potential resource opportunity. This study demonstrates, for the first time, the anaerobic bioconversion of DW into medium-chain fatty acids (MCFAs) and butanol. We conducted the process in batch serum bottles with open-culture inocula at 32 degrees C and pH 6.5-7.0, employing a staged ethanol addition strategy over 40 days. Under these conditions, approximately 70% of the organic carbon in DW was microbially processed, yielding 7.88 g/L butyrate, 2.11 g/L caproate, and 3.79 g/L butanol. Notably, this butanol concentration represents the highest value reported to date in chain elongation (CE) systems, likely attributable to elevated hydrogen partial pressure and high ethanol availability. Microbial community analysis revealed that Lachnoclostridium and Anaerostignum were the dominant hydrolytic genera, while Clostridium kluyveri and g_Haloimpatiens emerged as key species during the chain elongation phase. Co-occurrence network and correlation analyses indicated a highly cooperative microbial community, with significant overlap between microbial taxa capable of producing MCFAs and those producing butanol. This work presents a sustainable strategy for resource recovery from high-salinity lithium-ion battery DW and provides insights into metabolic regulation for steering product spectra in CE-based bioprocesses.

The high concentration of salt ions in saline organic wastewater poses significant challenges for wastewater treatment technologies, particularly impacting the stability of anaerobic digesters. Aceticlastic methanogenesis is a crucial pathway for converting acetate into methane through methanoarchaea whose metabolism is adversely impacted by salt stress. To address this, long-term adaptive laboratory evolution (ALE) was conducted to cultivate halotolerant aceticlastic methanoarchaea, incorporating metagenomics, metatranscriptomic sequencing, metabolomics, and metabolic modeling to delineate genetic and metabolic responses. The evolved microbiome achieved a substantial increase in methanogenic activity at 5% sodium chloride, reaching 82.25% theoretical conversion of acetate to methane, significantly outperforming the original microbiome. This ALE process overcame the natural scarcity of aceticlastic methanogens in hypersaline environments. Key adaptation mechanisms were confirmed at the transcriptional level, primarily involving the upregulation of genes for inorganic ion transport, compatible solute uptake, and de novo biosynthesis. Horizontal gene transfer also contributed significantly through the transfer of osmoregulation genes, particularly those for compatible solute transport, suggesting an energy-efficient adaptation strategy of accumulating rather than synthesizing solutes. Metabolic flux analysis revealed that adjustments in energy distribution under salt stress are driven by the energetic cost of synthesizing compatible solutes, which highlights the importance of solute transporters for energy conservation. This study elucidates the complex interplay between metabolic reprogramming and gene transfer in enhancing microbial resilience under salt stress, thereby deepening our understanding of microbial adaptations in extreme environments and advancing biotechnological approaches for saline wastewater treatment.

Phenolics and nitrogenous heterocyclic (NHC) compounds are the primary recalcitrant pollutants in coal chemical wastewater (CCW), posing challenges to conventional biological treatment processes. This study systematically evaluated the enhanced anaerobic removal of five representative phenolic and NHC compounds (phenol, o-cresol, hydroquinone, quinoline, and pyridine) using zero-valent iron (ZVI), cementite (Fe3C), magnetite (Fe3O4), hematite (Fe2O3), and ferrous chloride in semi-continuous reactors. The results showed that ZVI and Fe3C provided the strongest enhancement, increasing the chemical oxygen demand (COD) degradation capacity of anaerobic sludge by 33.9 % and 28.8 %, respectively. Phenol degradation was improved by 21.5 % and 18.3 %, o-cresol by 46.3 % and 33.3 %, and hydroquinone by 40.1 % and 46.5 %, respectively. In contrast, Fe3O4 supplementation moderately improved COD, phenol, and hydroquinone degradation, whereas Fe2O3 provided a slight but statistically significant improvement in COD removal. High-throughput 16S rRNA gene sequencing revealed that ZVI and Fe3C increased species diversity, richness, and evenness within the sludge community. They also raised the relative abundance of aromatic degraders such as Syntrophus, Sphingomonas, Bacillus, Chryseobacterium, and Thermomonas, while concurrently increasing the predicted abundance of functional genes associated with xenobiotic biodegradation and metabolic pathways. In contrast, Fe3O4 and Fe2O3 favored different degraders like Longilinea, Comamonas, and Ottowia. Conductive Fe3O4 also stimulated electroactive genera such as Brooklawnia and Geobacter. This study elucidates how iron additives influence microbial structure and function. ZVI and Fe3C are identified as optimal for enhancing anaerobic CCW treatment by enriching key degraders and functional metabolic pathways.

In this research, typical industrial scenarios were analyzed optimized by machine learning algorithms, which fills the gap of massive data and industrial requirements in ultrasonic sludge treatment. Principal component analysis showed that the ultrasonic density and ultrasonic time were positively correlated with soluble chemical oxygen demand (SCOD), total nitrogen (TN), and total phosphorus (TP). Within five machine learning models, the best model for SCOD prediction was XG-boost (R2 = 0.855), while RF was the best for TN and TP (R2 = 0.974 and 0.957, respectively). In addition, SHAP indicated that the importance feature for SCOD, TN, and TP was ultrasonic time, and sludge concentration, respectively. Finally, the typical industrial scenario of ultrasonic pretreatment of sludge was analyzed. In the secondary sludge, treatment volume at 0.6 L, the pH at 7.0, and the ultrasonic time at 20 min was best to improve the SCOD. In the ultrasonic pretreatment primary sludge, treatment volume of 0.3 L, pH of 7.0, and ultrasonic time of 15 min was best to improve the SCOD. Furthermore, the ultrasonic power at 700 W and ultrasonic time at 20 min were best to improve the C/N and C/P in the secondary sludge. In the primary sludge, the ultrasonic power at 600 W, and the ultrasonic time at 15 min were best to improve C/N and C/P. This study lays a foundation for the practical application of ultrasonic pretreatment of sludge and provides basic information for typical industrial scenarios.

Recently, the growing capacity of silicone monomers directly lead to a large accumulation of associated high-boiling residues (HBRs), which has become an urgent issue for limiting the sustainably development in industry. Dimethyldichlorosilane (M2) is an essential intermediate in silicone industry, which have been extensively applied in kinds of fields like architecture, electronics, new energy, medicine, transportation and plastic rubber. Consequently, the catalytic cracking of HBRs to produce value-added M2 is considered a promising strategy. However, most cracking catalysts exhibit low M2 selectivity in batch reactors. In this perspective, we initially provide an overview of the HBRs current cracking system. Moreover, the influence factors of M2 selectivity during HBRs-catalytic cracking system through machine learning (ML) methods are systematically calculated and analyzed. The latest findings regarding the actual components of HBRs feeding and their effluxes of HBRs-tri-n-butylamine cracking system are presented, as well as their distribution of final products. Finally, the existing challenges and suggestions for HBRs to M2, are given with the aim of providing novel research ideas and stimulating inspiration to drive toward a sustainable development for minimizing pollution and enhancing cost-effectiveness.

Deoxygenationis an essential link for upgrading bio-based low-valuephenolics to aromatics, which is achieved through the catalytic hydrogenationway. Herein, we designed and synthesized kinds of Ni-confined catalystsvia tailoring the preparation procedures. The highest hydroxyl hydrogenolysisperformance (r[g(aromatics)center dot g(Ni) (-1)center dot h(-1)] = 103.5 at 290 degrees C)in vapor m-cresol conversion so far was obtainedover Ni@Silicalite-1 prepared via the in situ encapsulation methoddue to the average 2.5 nm Ni nanoparticles uniformly encapsulatedwithin silicalite-1 crystals for improving the shape selectivity withthe vertical adsorption mode of phenolics. The appropriate porositiesare proven to play a crucial role in shape-selective catalysis forhydroxyl hydrogenolysis of m-cresol. In addition,Ni@Silicalite-1 showed outstanding stability in 200 h long run with95.5% conversion and 74.2% aromatics yield without obvious deactivation.This work provided a novel insight into tuning hydroxyl hydrogenolysisof phenolics by designing metal@zeolite catalysts with different microenvironmentsand ultrasmall metal nanoparticles. Ni@Silicalite-1catalyzed vapor m-cresolto toluene through hydroxyl hydrogenolysis rather than phenyl ringhydrogenation even below 300 degrees C.

Removal of low-carbon fatty amines (LCFAs) in wastewater treatment poses a significant technical challenge due to their small molecular size, high polarity, high bond dissociation energy, electron deficiency, and poor biodegradability. Moreover, their low Bronsted acidity deteriorates this issue. To address this problem, we have developed a novel base-induced autocatalytic technique for the highly efficient removal of a model pollutant, dimethylamine (DMA), in a homogeneous peroxymonosulfate (PMS) system. A high reaction rate constant of 0.32 min-1 and almost complete removal of DMA within 12 min are achieved. Multi-scaled characterizations and theoretical calculations reveal that the in situ constructed C=N bond as the crucial active site activates PMS to produce abundant 1O2. Subsequently, 1O2 oxidizes DMA through multiple H-abstractions, accompanied by the generation of another C=N structure, thus achieving the autocatalytic cycle of pollutant. During this process, base-induced proton transfers of pollutant and oxidant are essential prerequisites for C=N fabrication. A relevant mechanism of autocatalytic degradation is unraveled and further supported by DFT calculations at the molecular level. Various assessments indicate that this self-catalytic technique exhibits a reduced toxicity and volatility process, and a low treatment cost (0.47 $/m3). This technology has strong environmental tolerance, especially for the high concentrations of chlorine ion (1775 ppm) and humic acid (50 ppm). Moreover, it not only exhibits excellent degradation performance for different amine organics but also for the coexisting common pollutants including ofloxacin, phenol, and sulforaphane. These results fully demonstrate the superiority of the proposed strategy for practical application in wastewater treatment. Overall, this autocatalysis technology based on the insitu construction of metal-free active site by regulating proton transfer will provide a brand-new strategy for environmental remediation.

Discharge of saline organic wastewater is increasing worldwide, yet how salt stress disrupts the microbial community's structure and metabolism in bioreactors has not been systematically investigated. The non-adapted anaerobic granular sludge was inoculated into wastewater with varying salt concentration (ranging from 0% to 5%) to examine the effects of salt stress on the structure and function of the anaerobic microbial community. Result indicated that salt stress had a significant impact on the metabolic function and community structure of the anaerobic granular sludge. Specifically, we observed a notable reduction in methane production in response to all salt stress treatments (r =-0.97, p < 0.01), while an unexpected increase in butyrate production (r = 0.91, p < 0.01) under moderate salt stress (1-3%) with ethanol and acetate as carbon sources. In addition, analysis of microbiome structures and networks demonstrated that as the degree of salt stress increased, the networks exhibited lower connectance and increased compartmentalization. The abundance of interaction partners (methanogenic archaea and syntrophic bacteria) decreased under salt stress. In contrast, the abundance of chain elongation bacteria, specifically Clostridium kluyveri, increased under moderate salt stress (1-3%). As a conse-quence, the microbial carbon metabolism patterns shifted from cooperative mode (methanogenesis) to inde-pendent mode (carbon chain elongation) under moderate salt stress. This study provides evidence that salt stress altered the anaerobic microbial community and carbon metabolism characteristics, and suggests potential guidance for steering the microbiota to promote resource conversion in saline organic wastewater treatment.

Ochrobactrum sp. XKL1, previously found to have the ability to efficiently degrade quinoline, was bioaugmented into a lab-scale A/O/O system to treat real coking wastewater. During the bioaugmentation stage, the removal of quinoline and pyridine of the O1 tank could be enhanced by 9.88% and 7.96%, respectively. High-throughput sequencing analysis indicated that the addition of XKL1 could significantly affect the alteration of microbial community structure in the sludge. In addition, the relative abundance of Ochrobactrum has demonstrated a trend of increasing first followed by decreasing with the highest abundance of 7.87% attained on the 94th day. The bioaugmentation effects lasted for about 14 days after the strains was inoculated into the reactor. Although a decrease in the relative abundance of XKL1 was observed for a rather short period of time, the bioaugmented A/O/O system has been proven to be more effective in the removal of organic pollutants than the control. Hence, the results of this study indicated that the bioaugmentation with XKL1 is a feasible operational strategy that would be able to enhance the removal of NHCs in the treatment of coking wastewater with complex composition and high organic concentrations.

The carboxylate platform-based bioprocess for medium-chain carboxylic acid (MCCA) production from waste biomass via mixed culture has been the subject of extensive research because of the high economic value of MCCA and potential environmental benefits. However, modeling the conversion process using mechanistic models is challenging due to the complex and unclear interactions and metabolic pathways of the system. Herein, four data-driven machine learning (ML) algorithms, including random forest (RF), extreme gradient boosting (Xgboost), k-nearest neighbor (KNN), and artificial neural network (ANN), were employed to predict the MCCA concentration and production rate based on data (environmental and operational parameters and corresponding genomic data) collected under 94 experiment conditions from 8 research groups. It was found that all selected ML algorithms achieved prediction accuracy higher than 0.7 using operational parameters only. A significant improvement in the predictive efficacy (ranging from 0.83 to 0.87) was observed when incorporating the genomic data with environmental and operational parameters. The prediction of MCCA production by the random forest (RF) model had the highest prediction accuracy of 0.83, 0.87, and 0.89 when the operational parameters, genomic data, and combined dataset were used as input parameters, respectively. Hydraulic retention time (HRT) and organic loading rate (OLR) were identified as the dominant operational parameters that affect the MCCA concentration and rate based on the feature importance generated by RF. The key microbes that affected the MCCA concentration and MCCA production rate were different. Bacteroidales and Coriobacteriales were the only orders sensitive to both the MCCA concentration and rate, with feature importance weights of 6.71% and 6.97%, respectively, and could be potential universal biomarkers for process monitoring. The results demonstrated that the proposed ML models could be used as a means of simulating the carboxylate platform for MCCA production from waste feedstock, enhancing the understanding of the behavior of microorganisms in the process, and providing guidance for further optimization.

The removal of oxygen is an essential link for upgrading biomass phenolics to aromatics, which is achieved through catalytic hydrodeoxygenation way. Here, we designed and synthesized four encapsulated Ni@S-1 catalysts with similar sizes of S-1 crystals and Ni nanoparticles via tailoring the encapsulation methods. The highest hydrogenolysis activity of hydroxyl and hydrocarbons selectivity were obtained over Ni@S-1 via the in-situ encapsulation method (r[g aromatics •g Ni -1 •h -1 ] = 103.5 at 290 °C) in vapor hydrogenation of m -cresol as a model due to the average 2.5 nm Ni nanoparticles uniformly encapsulated within S-1 crystals for improving the shape selectivity with the vertical adsorption mode of phenolics. The appropriate porosities of Ni@S-1 catalysts are proven to play a crucial role in shape selective-catalysis for hydroxyl hydrogenolysis of m -cresol. Significantly, the outstanding stability of Ni@S-1 via the in-situ encapsulation method has been proven by the hydroxyl hydrogenolysis of vapor m -cresol for 200 h long-run without obvious deactivation. This work provided an insight into the tuning hydroxyl hydrogenolysis of phenolics by designing metal@zeolite catalysts with different microenvironment and well-dispersion of small metal nanoparticles.

喹啉、吡啶等含氮杂环化合物是焦化废水中主要的难降解有机物.以喹啉、吡啶作为目标污染物,研究了筛选出的高效降解菌红球菌(Rhodococcus sp.)KDPy1对焦化废水A/O2生物处理工艺的强化作用.结果表明,与对照组相比,红球菌的添加使O1池的COD、喹啉、吡啶去除率分别增加了11.4％、17.3％、14.0％.经生物强化后,系统内微生物群落多样性增加,且有机污染物降解菌如Stenotrophomonas和Ochrobactrum等更具优势,证实了红球菌(Rhodococcus sp.)KDPy1在焦化废水处理中的巨大应用前景.

The use of zero-valent iron (ZVI) to enhance anaerobic digestion (AD) systems is widely advocated as it improves methane production and system stability. Accurate modeling of ZVI-based AD reactor is conducive to predicting methane production potential, optimizing operational strategy, and gathering reference information for industrial design in place of time-consuming and laborious tests. In this study, three machine learning (ML) algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and deep learning (DL), were evaluated for their feasibility of predicting the performance of ZVI-based AD reactors based on the operating parameters collected in 9 published articles. XGBoost demonstrated the highest accuracy in predicting total methane production, with a root mean squared error (RMSE) of 21.09, compared to 26.03 and 27.35 of RF and DL, respectively. The accuracy represented by mean absolute percentage error also showed the same trend, with 14.26%, 15.14% and 17.82% for XGBoost, RF and DL, respectively. Through the feature importance generated by XGBoost, the parameters of total solid of feedstock (TSf), sCOD, ZVI dosage and particle size were identified as the dominant parameters that affect the methane production, with feature importance weights of 0.339, 0.238, 0.158, and 0.116, respectively. The digestion time was further introduced into the above-established model to predict the cumulative methane production. With the expansion of training dataset, DL outperformed XGBoost and RF to show the lowest RMSEs of 11.83 and 5.82 in the control and ZVI-added reactors, respectively. This study demonstrates the potential of using ML algorithms to model ZVI-based AD reactors.

Coking wastewater (CWW) has long been a serious challenge for anaerobic treatment due to its high concentrations of phenolics and nitrogen-containing heterocyclic compounds (NHCs). Herein, we proposed and validated a new strategy of using zero-valent iron (ZVI) to strengthen the anaerobic treatment of CWW. Results showed that COD removal efficiencies was increased by 9.5-13.7% with the assistance of ZVI. GC-MS analysis indicated that the removal of phenolics and NHCs was improved, and the intermediate 2(1H)-Quinolinone of quinoline degradation was further removed by ZVI addition. High-throughput sequencing showed that phenolics and NHCs degraders, such as Levilinea and Sedimentibacter were significantly enriched, and the predicted gene abundance of xenobiotic degradation and its downstream metabolic pathways was also increased by ZVI. Network and redundancy analysis indicated that the decreased oxidation-reduction potential (ORP) by ZVI was the main driver for microbial community succession. This study provided an alternative strategy for strengthening CWW anaerobic treatment.

Biobutanol is acknowledged as a direct alternative of gasoline, which can meet the demand of sustainable economic development for renewable liquid fuel. Lignocellulosic biomass is an ideal raw material for the biobutanol production due to its merits of being renewable, cheap and easily accessible. However, the complex structure of lignocellulose hinders its direct hydrolysis, and efficient pretreatment is essential for its commercial application. As a novel and environmentally friendly solvent, deep eutectic solvents (DESs) have high potential for biomass pretreatment due to its advantages of low cost, low toxicity, strong solubility, excellent selectivity and biocompatibility. This article mainly focuses on the application of DES in lignocellulose pretreatment for biobutanol production. Firstly, the types and properties of DESs are introduced. Secondly, the dissolution efficiency of components in lignocellulose in DESs is summarized, and the effects of DESs pretreatment on enzymatic hydrolysis and butanol fermentation process are discussed. Thirdly, the application potential of consolidated bioprocessing in the production of biobutanol is reviewed by combing various bioprocessing processes. Finally, the prospects of DESs pretreatment lignocellulose for producing biobutanol are proposed.

Coal gasification wastewater (CGWW) is a typical toxic and refractory industrial wastewater. Here, a novel phenol and ammonia recovery process (IPE) was employed for CGWWpretreatment, and the coupled system assemble by the IPE process with A(2)/O system (IPE-A(2)/O) were operated to enhance the treatment performance of CGWW. The results showed that the IPE pre-treated effluent had a higher BOD5/COD ratio and lower refractory compounds compared to a typical process (MIBK). Subsequent A(2)/O biological treatment indicated that the A(2)/O-p system (A(2)/O system followed IPE process) obtained a higher average COD removal of 92% compared to 87.7% of the control (A(2)/O-m, A(2)/O system followed MIBK). The GC-MS analysis suggested that the content of alkanes in the IPE-A(2)/O effluent was lower than that of the MIBK-A(2)/O. The high-throughput sequencing revealed Levilinea, Alcaligenes, Acinetobacter, Thauera and Thiobacillus were the core genera in A(2)/O system. The genera Alcaligenes, Acinetobacter, Thauera and Thiobacillus in the degrading consortium were enriched in the A(2)/O-p system, leading to increased removals of organic pollutants and TN. These results suggested that the IPE process was a feasible pretreatment method, and the coupled IPE-A(2)/O system was an alternative technique for treating CGWW. (C) 2019 Published by Elsevier B.V.

Coal gasification wastewater is a typical high phenol-containing, toxic and refractory industrial wastewater. Here, lab-scale anaerobic-anoxic-oxic system was employed to treat real coal gasification wastewater, and methanol was added to oxic tank as the co-substrate to enhance the removal of refractory organic pollutants. The results showed that the average COD removal in oxic effluent increased from 24.9% to 36.0% by adding methanol, the total phenols concentration decreased from 54.4 to 44.9 mg/L. GC-MS analysis revealed that contents of phenolic components and polycyclic aromatic hydrocarbons (PAHs) were decreased compared to the control and their degradation intermediates were observed. Microbial community revealed that methanol increased the abundance of phenolics and PAHs degraders such as Comamonas, Burkholderia and Sphingopyxis. Moreover, functional analysis revealed the relative abundance of functional genes associated with toluene, benzoate and PAHs degradation pathways was higher than that of control based on KEGG database.

The bacterial strainRhodococcus sp. KDQ2 capable of utilizing quinoline as sole carbon and nitrogen source, and energy was isolated from the activated sludge of a coking wastewater treatment plant. The optimum temperature and initial pH for quinoline degradation were determined to be 37℃ and 6~9, respectively. KDQ2 degraded 96% of quinoline at a 200mg/L initial concentration within 24h, and its degradation kinetics could be described with Haldane's model. KDQ2 was shown to be able to also utilize pyridine but not phenol. The removal of quinoline (150mg/L) was not inhibited by the presence of pyridine (150mg/L) and phenol (400mg/L) in 1d. KDQ2 was able to adapt to real coking wastewater containing high concentrations of quinoline, pyridine and phenol, KDQ2 coexisted with other microbes of activated sludge in aeration tank and notably improved the removal of quinoline and TOC.