Transformative Removal of Aqueous Micropollutants into Polymeric Products by Advanced Oxidation Processes.

This perspective presents the latest advancements in selective polymerization pathways in advanced oxidation processes (AOPs) for removal of featured organic pollutants in wastewater. In radical-based homogeneous reactions, SO4• --based systems exhibit superior oxidative activity toward aromatics with electron-donating substituents via single electron transfer and radical adduct formation (RAF). The produced organic radical cations subsequently undergo coupling and polymerization reactions to produce polymers. For •OH-based oxidation, metal ions facilitate the production of monomer radicals via RAF. Additionally, heterogeneous catalysts can mediate both coupling and polymerization reactions via persulfate activation without generating inorganic radicals. Metal-based catalysts will mediate a direct oxidation pathway toward polymerization. In contrast, carbon-based catalysts will induce coupling reactions to produce low-molecular-weight oligomers (≤4 units) via an electron transfer process. In comparison to mineralization, polymerization pathways remarkably reduce peroxide usage, quickly separate pollutants from the aqueous phase, and generate polymeric byproducts. Thus, AOP-driven polymerization systems hold significant promise in reducing carbon emission and realizing carbon recycling in water treatment processes.

Smoothing the Phosphorus Resource Stress under the Socioeconomic Development in China.

Phosphorus (P) is the key in maintaining food security and ecosystem functions. Population growth and economic development have increased the demand for phosphate rocks. China has gradually developed from zero phosphate mining to the world's leading P miner, fertilizer, and agricultural producer since 1949. China released policies, such as designating phosphate rock as a strategic resource, promoting eco-agricultural policies, and encouraging the use of solid wastes produced in mining and the phosphorus chemical industry as construction materials. However, methodological and data gaps remain in the mapping of the long-term effects of policies on P resource efficiency. Here, P resource efficiency can be represented by the potential of the P cycle to concentrate or dilute P as assessed by substance flow analysis (SFA) complemented by statistical entropy analysis (SEA). P-flow quantification over the past 70 years in China revealed that both resource utilization and waste generation peaked around 2015, with 20 and 11 Mt of mined and wasted P, respectively. Additionally, rapidly increasing aquaculture wastewater has exacerbated pollution. The resource efficiency of the Chinese P cycle showed a U-shaped change with an overall improvement of 22.7%, except for a temporary trough in 1975. The driving force behind the efficiency decline was the roaring phosphate fertilizer industry, as confirmed by the sharp increase in P flows for both resource utilization and waste generation from the mid-1960s to 1975. The positive driving forces behind the 30.7% efficiency increase from 1975 to 2018 were the implementation of the resource conservation policy, downstream pollution control, and, especially, the circular agro-food system strategy. However, not all current management practices improve the P resource efficiency. Mixing P industry waste with construction materials and the development of aquaculture to complement offshore fisheries erode P resource efficiency by 2.12% and 9.19%, respectively. With the promotion of a zero-waste society in China, effective P-cycle management is expected.

Understanding the Ecological Robustness and Adaptability of the Gut Microbiome in Plastic-Degrading Superworms (Zophobas atratus) in Response to Microplastics and Antibiotics.

Recent discoveries indicate that several insect larvae are capable of ingesting and biodegrading plastics rapidly and symbiotically, but the ecological adaptability of the larval gut microbiome to microplastics (MPs) remains unclear. Here, we described the gut microbiome assemblage and MP biodegradation of superworms (Zophobas atratus larvae) fed MPs of five major petroleum-based polymers (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) and antibiotics. The shift of molecular weight distribution, characteristic peaks of C═O, and metabolic intermediates of residual polymers in egested frass proved depolymerization and biodegradation of all MPs tested in the larval intestines, even under antibiotic suppression. Superworms showed a wide adaptation to the digestion of the five polymer MPs. Antibiotic suppression negatively influenced the survival rate and plastic depolymerization patterns. The larval gut microbiomes differed from those fed MPs and antibiotics, indicating that antibiotic supplementation substantially shaped the gut microbiome composition. The larval gut microbiomes fed MPs had higher network complexity and stability than those fed MPs and antibiotics, suggesting that the ecological robustness of the gut microbiomes ensured the functional adaptability of larvae to different MPs. In addition, Mantel's test indicated that the gut microbiome assemblage was obviously related to the polymer type, the plastic degradability, antibiotic stress, and larval survival rate. This finding provided novel insights into the self-adaptation of the gut microbiome of superworms in response to different MPs.

Molecular-Weight-Dependent Degradation of Plastics: Deciphering Host-Microbiome Synergy Biodegradation of High-Purity Polypropylene Microplastics by Mealworms.

The biodegradation of polypropylene (PP), a highly persistent nonhydrolyzable polymer, by Tenebrio molitor has been confirmed using commercial PP microplastics (MPs) (Mn 26.59 and Mw 187.12 kDa). This confirmation was based on the reduction of the PP mass, change in molecular weight (MW), and a positive Δδ13C in the residual PP. A MW-dependent biodegradation mechanism was investigated using five high-purity PP MPs, classified into low (0.83 and 6.20 kDa), medium (50.40 and 108.0 kDa), and high (575.0 kDa) MW categories to access the impact of MW on the depolymerization pattern and associated gene expression of gut bacteria and the larval host. The larvae can depolymerize/biodegrade PP polymers with high MW although the consumption rate and weight losses increased, and survival rates declined with increasing PP MW. This pattern is similar to observations with polystyrene (PS) and polyethylene (PE), i.e., both Mn and Mw decreased after being fed low MW PP, while Mn and/or Mw increased after high MW PP was fed. The gut microbiota exhibited specific bacteria associations, such as Kluyvera sp. and Pediococcus sp. for high MW PP degradation, Acinetobacter sp. for medium MW PP, and Bacillus sp. alongside three other bacteria for low MW PP metabolism. In the host transcriptome, digestive enzymes and plastic degradation-related bacterial enzymes were up-regulated after feeding on PP depending on different MWs. The T. molitor host exhibited both defensive function and degradation capability during the biodegradation of plastics, with high MW PP showing a relatively negative impact on the larvae.

Sustainable efficient utilization of magnetic porous biochar for adsorption of orange G and tetracycline: Inherent roles of adsorption and mechanisms.

Iron-doped biochar has been widely used as an adsorbent to remove contaminants due to the high adsorption performance, but it still suffers from complicated preparation methods, unstable iron loading, unsatisfactory specific surface area, and uneven distribution of active sites. Here, a novel magnetic porous biochar (FeCS800) with nanostructure on surface was synthesized by one-pot pyrolysis method of corn straw with K2FeO4, and used in orange G (OG) and tetracycline (TC) adsorption. FeCS800 exhibited outstanding adsorption capacities for OG and TC after K2FeO4 activation and the adsorption data were fitted satisfactorily to Langmuir isotherm and Pseudo-second-order kinetic model. The maximum adsorption capacities of FeCS800 for OG and TC were around 303.03 mg/g and 322.58 mg/g, respectively, at 25 °C and pH 7.0, which were 16.27 and 24.61 times higher than that before modification. Thermodynamic studies showed that the adsorption of OG/TC by FeCS800 were thermodynamically favorable and highly spontaneous. And the adsorption capacity of OG and TC by FeCS800 remained 77% and 81% after 5 cycles, respectively, indicating that FeCS800 had good stability. The outstanding adsorption properties and remarkable reusability of FeCS800 show its great potential to be an economic and environmental adsorbent in contaminants removal.

Deciphering the formation of granules by n-DAMO and Anammox microorganisms.

Nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) process is a promising wastewater treatment technology, but the slow microbial growth rate greatly hinders its practical application. Although high-level nitrogen removal and excellent biomass accumulation have been achieved in n-DAMO granule process, the formation mechanism of n-DAMO granules remains unresolved. To elucidate the role of functional microbes in granulation, this study attempted to cultivate granules dominated by n-DAMO microorganisms and granules coupling n-DAMO with anaerobic ammonium oxidation (Anammox). After long-term operation, dense granules were developed in the two systems where both n-DAMO archaea and n-DAMO bacteria were enriched, whereas granulation did not occur in the other system dominated by n-DAMO bacteria. Extracellular polymeric substances (EPS) measurement indicated the critical role of EPS production in the granulation of n-DAMO process. Metagenomic and metatranscriptomic analyses revealed that n-DAMO archaea and Anammox bacteria were active in EPS biosynthesis, while n-DAMO bacteria were inactive. Consequently, more EPS were produced in the systems containing n-DAMO archaea and Anammox bacteria, leading to the successful development of n-DAMO granules. Furthermore, EPS biosynthesis in n-DAMO systems is potentially regulated by acyl-homoserine lactones and c-di-GMP. These findings not only provide new insights into the mechanism of granule formation in n-DAMO systems, but also hint at potential strategies for management of the granule-based n-DAMO process.

Deep insights into the biofilm formation mechanism and nitrogen-transformation network in a nitrate-dependent anaerobic methane oxidation biofilm.

Nitrate/nitrite-dependent anaerobic methane oxidation (n-DAMO) process offers a promising solution for simultaneously achieving methane emissions reduction and efficient nitrogen removal in wastewater treatment. Although nitrogen removal at a practical rate has been achieved by n-DAMO biofilm process, the mechanisms of biofilm formation and nitrogen transformation remain to be elucidated. In this study, n-DAMO biofilms were successfully developed in the membrane aerated moving bed biofilm reactor (MAMBBR) and removed nitrate at a rate of 159 mg NO3--N L-1 d-1. The obvious increase in the content of extracellular polymeric substances (EPS) indicated that EPS production was important for biofilm development. n-DAMO microorganisms dominated the microbial community, and n-DAMO bacteria were the most abundant microorganisms. However, the expression of biosynthesis genes for proteins and polysaccharides encoded by n-DAMO archaea was significantly more active compared to other microorganisms, suggesting the central role of n-DAMO archaea in EPS production and biofilm formation. In addition to nitrate reduction, n-DAMO archaea were revealed to actively express dissimilatory nitrate reduction to ammonium and nitrogen fixation. The produced ammonium was putatively converted to dinitrogen gas through the joint function of n-DAMO archaea and n-DAMO bacteria. This study revealed the biofilm formation mechanism and nitrogen-transformation network in n-DAMO biofilm systems, shedding new light on promoting the application of n-DAMO process.

Biodegradation of various grades of polyethylene microplastics by Tenebrio molitor and Tenebrio obscurus larvae: Effects on their physiology.

Polyethylene (PE) is the most productive plastic product and includes three major polymers including high-density polyethylene (HDPE), linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE) variation in the PE depends on the branching of the polymer chain and its crystallinity. Tenebrio obscurus and Tenebrio molitor larvae biodegrade PE. We subsequently tested larval physiology, gut microbiome, oxidative stress, and PE degradation capability and degradation products under high-purity HDPE, LLDPE, and LDPE powders (<300 µm) diets for 21 days at 65 ± 5% humidity and 25 ± 0.5 °C. Our results demonstrated the specific PE consumption rates by T. molitor was 8.04-8.73 mg PE ∙ 100 larvae-1⋅day-1 and by T. obscurus was 7.68-9.31 for LDPE, LLDPE and HDPE, respectively. The larvae digested nearly 40% of the ingested three PE and showed similar survival rates and weight changes but their fat content decreased by 30-50% over 21-day period. All the PE-fed groups exhibited adverse effects, such as increased benzoquinone concentrations, intestinal tissue damage and elevated oxidative stress indicators, compared with bran-fed control. In the current study, the digestive tract or gut microbiome exhibited a high level of adaptability to PE exposure, altering the width of the gut microbial ecological niche and community diversity, revealing notable correlations between Tenebrio species and the physical and chemical properties (PCPs) of PE-MPs, with the gut microbiome and molecular weight change due to biodegradation. An ecotoxicological simulation by T.E.S.T. confirmed that PE degradation products were little ecotoxic to Daphnia magna and Rattus norvegicus providing important novel insights for future investigations into the environmentally-friendly approach of insect-mediated biodegradation of persistent plastics.

Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals.

Machine learning (ML) provides an efficient manner for rapid prediction of the life-cycle environmental impacts of chemicals, but challenges remain due to low prediction accuracy and poor interpretability of the models. To address these issues, we focused on data processing by using a mutual information-permutation importance (MI-PI) feature selection method to filter out irrelevant molecular descriptors from the input data, which improved the model interpretability by preserving the physicochemical meanings of original molecular descriptors without generation of new variables. We also applied a weighted Euclidean distance method to mine the data most relevant to the predicted targets by quantifying the contribution of each feature, thereby the prediction accuracy was improved. On the basis of above data processing, we developed artificial neural network (ANN) models for predicting the life-cycle environmental impacts of chemicals with R2 values of 0.81, 0.81, 0.84, 0.75, 0.73, and 0.86 for global warming, human health, metal depletion, freshwater ecotoxicity, particulate matter formation, and terrestrial acidification, respectively. The ML models were interpreted using the Shapley additive explanation method by quantifying the contribution of each input molecular descriptor to environmental impact categories. This work suggests that the combination of feature selection by MI-PI and source data selection based on weighted Euclidean distance has a promising potential to improve the accuracy and interpretability of the models for predicting the life-cycle environmental impacts of chemicals.

Prospective Life Cycle Assessment for the Electrochemical Oxidation Wastewater Treatment Process: From Laboratory to Industrial Scale.

Electrochemical oxidation (EO) is a promising technology for water purification, but indirect environmental burdens may arise in association with consumption of materials and energy during electrode preparation and process operation. This study evaluated the life cycle environmental impacts of emerging EO technology from laboratory scale to industrial scale using prospective life cycle assessment (LCA) on a quantitative basis. Environmental impacts of EO technology were assessed at laboratory scale by comparing three representative anode materials (SnO2, PbO2, and boron-doped diamond) and other two typical processes (adsorption and Fenton method), which verified the competitiveness of the EO process and identified the key factors to environmental hotspots. Thereafter, LCA of scale-up EO was performed to offer guidance for practical application, and the life cycle inventory was compiled upon thermodynamic and kinetic simulations, empirical calculation rules, and similar technical information. Results demonstrated EO to be effective for destructing recalcitrant organic pollutants, but visible direct benefits might be outweighed by increased indirect environmental burdens associated with the preparation of anode materials, use of electrolytes, and energy consumption during the operation stage at both laboratory scale and larger scale. This necessitated attention to overall life cycle profiles by taking into account reactor design, anode materials, electrolyte and flow pattern, and decentralized location with a large share of renewable power station and rigorous contamination control strategies for wastewater treatment plants.

Density Functional Theory and Machine Learning-Based Quantitative Structure-Activity Relationship Models Enabling Prediction of Contaminant Degradation Performance with Heterogeneous Peroxymonosulfate Treatments.

Heterogeneous peroxymonosulfate (PMS) treatment is recognized as an effective advanced oxidation process (AOP) for the treatment of organic contaminants. Quantitative structure-activity relationship (QSAR) models have been applied to predict the oxidation reaction rates of contaminants in homogeneous PMS treatment systems but are seldom applied in heterogeneous treatment systems. Herein, we established QSAR models updated with density functional theory (DFT) and machine learning approaches to predict the degradation performance for a series of contaminants in heterogeneous PMS systems. We imported the characteristics of organic molecules calculated using constrained DFT as input descriptors and predicted the apparent degradation rate constants of contaminants as the output. The genetic algorithm and deep neural networks were used to improve the predictive accuracy. The qualitative and quantitative results from the QSAR model for the degradation of contaminants can be used to select the most appropriate treatment system. A strategy for selection of the optimum catalyst for PMS treatment of specific contaminants was also established according to the QSAR models. This work not only increases our understanding of contaminant degradation in PMS treatment systems but also highlights a novel QSAR model to predict the degradation performance in complicated heterogeneous AOPs.

Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification.

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.

Copper Nanowire Networks: An Effective Electrochemical Peroxymonosulfate Activator toward Nitrogenous Pollutant Abatement.

Herein, we developed an electrochemical filtration system for effective and selective abatement of nitrogenous organic pollutants via peroxymonosulfate (PMS) activation. Highly conductive and porous copper nanowire (CuNW) networks were constructed to serve simultaneously as catalyst, electrode, and filtration media. In one demonstration of the CuNW network's capability, a single pass through a CuNW filter (τ < 2 s) degraded 94.8% of sulfamethoxazole (SMX) at an applied potential of -0.4 V vs SHE. The exposed {111} crystal plane of CuNW triggered atomic hydrogen (H*) generation on sites, which contributed to effective PMS reduction. Meanwhile, with the involvement of SMX, a Cu-N bond was formed by the interactions between the -NH2 group of SMX and the Cu sites of CuNW, accompanied by the redox cycling of Cu2+/Cu+, which was facilitated by the applied potential. The different charges of the active Cu sites made it easier to withdraw electrons and promote PMS oxidation. Theoretical calculations and experimental results were combined to suggest a mechanism for pollution abatement with CuNW networks. The results showed that system efficacy for the degradation of a wide array of nitrogenous pollutants was robust across a broad range of solution pH and complex aqueous matrices. The flow-through operation of the CuNW filter outperformed conventional batch electrochemistry due to convection-enhanced mass transport. This study provides a new strategy for environmental remediation by integrating state-of-the-art material science, advanced oxidation processes, and microfiltration technology.

Enhancing Biochar-Based Nonradical Persulfate Activation Using Data-Driven Techniques.

Converting biomass into biochar (BC) as a functional biocatalyst to accelerate persulfate activation for water remediation has attracted much attention. However, due to the complex structure of BC and the difficulty in identifying the intrinsic active sites, it is essential to understand the link between various properties of BC and the corresponding mechanisms promoting nonradicals. Machine learning (ML) recently demonstrated significant potential for material design and property enhancement to help tackle this problem. Herein, ML techniques were applied to guide the rational design of BC for the targeted acceleration of nonradical pathways. The results showed a high specific surface area, and O% values can significantly enhance nonradical contribution. Furthermore, the two features can be regulated by simultaneously tuning the temperatures and biomass precursors for efficient directed nonradical degradation. Finally, two nonradical-enhanced BCs with different active sites were prepared based on the ML results. This work serves as a proof of concept for applying ML in the synthesis of tailored BC for persulfate activation, thereby revealing the remarkable capability of ML for accelerating bio-based catalyst development.

How ß-Cyclodextrin-Functionalized Biochar Enhanced Biodenitrification in Low C/N Conditions via Regulating Substrate Metabolism and Electron Utilization.

Biodenitrification plays a vital role in the remediation of nitrogen-contaminated water. However, influent with a low C/N ratio limits the efficiency of denitrification and causes the accumulation/emission of noxious intermediates. In this study, ß-cyclodextrin-functionalized biochar (BC@ß-CD) was synthesized and applied to promote the denitrification performance of Paracoccus denitrificans when the C/N was only 4, accompanied by increased nitrate reduction efficiency and lower nitrite accumulation and nitrous oxide emission. Transcriptomic and enzymatic activity analyses showed BC@ß-CD enhanced glucose degradation by promoting the activities of glycolysis (EMP), the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle. Notably, BC@ß-CD drove a great generation of electron donors by stimulating the TCA cycle, causing a greater supply of substrate metabolism to denitrification. Meanwhile, the promotional effect of BC@ß-CD on oxidative phosphorylation accelerates electron transfer and ATP synthesis. Moreover, the presence of BC@ß-CD increased the intracellular iron level, causing further improved electron utilization in denitrification. BC@ß-CD helped to remove metabolites and induced positive feedback on the metabolism of P. denitrificans. Collectively, these effects elevated the glucose utilization for supporting denitrification from 36.37% to 51.19%. This study reveals the great potential of BC@ß-CD for enhancing denitrification under low C/N conditions and illustrates a potential application approach for ß-CD in wastewater bioremediation.

Gut Microbiome Associating with Carbon and Nitrogen Metabolism during Biodegradation of Polyethene in Tenebrio larvae with Crop Residues as Co-Diets.

Tenebrio molitor and Tenebrio obscurus (Coleoptera: Tenebrionidae) larvae are two commercial insects that eat plant and crop residues as diets and also biodegrade synthetic plastics polyethylene (PE). We examined biodegradation of low-density PE (LDPE) foam (Mn = 28.9 kDa and Mw = 342.0 kDa) with and without respective co-diets, i.e., wheat brain (WB) or corn flour (CF), corn straw (CS), and rice straw (RS) at 4:1 (w/w), and their gut microbiome and genetic metabolic functional groups at 27.0 ± 0.5 °C after 28 days of incubation. The presence of co-diets enhanced LDPE consumption in both larvae and broad-depolymerized the ingested LDPE. The diet type shaped gut microbial diversity, potential pathways, and metabolic functions. The sequence of effectiveness of co-diets was WB or CF > CS > RS for larval development and LDPE degradation. Co-occurrence networks indicated that the larvae co-fed with LDPE displayed more complex correlations of gut microbiome than the larvae fed with single diets. The primary diet of WB or CF and crop residues CS and RS provided energy and nitrogen source to significantly enhance LDPE biodegradation with synergistic activities of the gut microbiota. For the larvae fed LDPE and LDPE plus co-diets, nitrogen fixation function was stimulated compared to normal diets and associated with LDPE biodegradation.

Microbial Niche Differentiation during Nitrite-Dependent Anaerobic Methane Oxidation.

Nitrite-dependent anaerobic methane oxidation (n-DAMO) has been demonstrated to play important roles in the global methane and nitrogen cycle. However, despite diverse n-DAMO bacteria widely detected in environments, little is known about their physiology for microbial niche differentiation. Here, we show the microbial niche differentiation of n-DAMO bacteria through long-term reactor operations combining genome-centered omics and kinetic analysis. With the same inoculum dominated by both species "Candidatus Methylomirabilis oxyfera" and "Candidatus Methylomirabilis sinica", n-DAMO bacterial population was shifted to "Ca. M. oxyfera" in a reactor fed with low-strength nitrite, but shifted to "Ca. M. sinica" with high-strength nitrite. Metatranscriptomic analysis showed that "Ca. M. oxyfera" harbored more complete function in cell chemotaxis, flagellar assembly, and two-component system for better uptake of nitrite, while "Ca. M. sinica" had a more active ion transport and stress response system, and more redundant function in nitrite reduction to mitigate nitrite inhibition. Importantly, the half-saturation constant of nitrite (0.057 mM vs 0.334 mM NO2-) and inhibition thresholds (0.932 mM vs 2.450 mM NO2-) for "Ca. M. oxyfera" vs "Ca. M. sinica", respectively, were highly consistent with genomic results. Integrating these findings demonstrated biochemical characteristics, especially the kinetics of nitrite affinity and inhibition determine niche differentiation of n-DAMO bacteria.

Evidence for Nitrous Oxide Emissions by Nitrite-Dependent Anaerobic Methane Oxidizing Bacteria.

Nitrite-dependent anaerobic methane oxidizing (n-DAMO) bacteria generally convert nitrite to dinitrogen and bypass the nitrous oxide (N2O) formation step. However, N2O is often detected in n-DAMO bacteria dominated cultures and it remains an open question as to the microbial origin of N2O in these enrichments. Using a stable nitrite consuming microbial community enriched for n-DAMO bacteria, we demonstrated that N2O production was coupled to methane oxidation and the higher initial nitrite concentrations led to increased quantities of N2O being formed. Moreover, continuous exposure of the enrichment culture to about 5 mg of N L-1 nitrite resulted in constant N2O being produced (12.5% of nitrite was reduced to N2O). Metatranscriptomic analyses revealed that nitrite reductase (nirS) and nitric oxide reductase (norZ) transcripts from n-DAMO bacteria increased in response to nitrite exposure. No other bacteria significantly expressed nor genes under these conditions, suggesting n-DAMO bacteria are responsible for N2O being produced. In a 35-day bioreactor experiment, N2O produced by the n-DAMO bacteria accumulated when nitrite was in excess; this was found to be up to 3.2% of the nitrogen that resulted from nitrite removal. Together, these results suggested that excess nitrite is an important driver of N2O production by n-DAMO bacteria. To this end, proper monitoring and control of nitrite levels in wastewater treatment plants would be effective strategies for mitigating N2O emissions to the atmosphere.

Revisiting the Engineering Roadmap of Nitrate/Nitrite-Dependent Anaerobic Methane Oxidation.

Nitrate/nitrite-dependent anaerobic oxidation of methane (n-DAMO) is a recently discovered process, which provides a sustainable perspective for simultaneous nitrogen removal and greenhouse gas emission (GHG) mitigation by using methane as an electron donor for denitrification. However, the engineering roadmap of the n-DAMO process is still unclear. This work constitutes a state-of-the-art review on the classical and most recently discovered metabolic mechanisms of the n-DAMO process. The versatile combinations of the n-DAMO process with nitrification, nitritation, and partial nitritation for nitrogen removal are also clearly presented and discussed. Additionally, the recent advances in bioreactor development are systematically reviewed and evaluated comprehensively in terms of methane supply, biomass retention, membrane requirement, startup time, reactor performance, and limitations. The key issues including enrichment and operation strategy for the scaling up of n-DAMO-based processes are also critically addressed. Moreover, the challenges inherent to implementing the n-DAMO process in practical applications, including application scenario recognition, GHG emission mitigation, and operation under realistic conditions, are highlighted. Finally, prospects as well as opportunities for future research are proposed. Overall, this review provides a roadmap for potential applications and further development of the n-DAMO process in the field of wastewater treatment.

A critical review on graphene oxide membrane for industrial wastewater treatment.

An important way to promote the environmental industry's goal of carbon reduction is to promote the recycling of resources. Membrane separation technology has unique advantages in resource recovery and advanced treatment of industrial wastewater. However, the great promise of traditional organic membrane is hampered by challenges associated with organic solvent tolerance, lack of oxidation resistance, and serious membrane fouling control. Moreover, the high concentrations of organic matter and inorganic salts in the membrane filtration concentrate also hinder the wider application of the membrane separation technology. The emerging cost-effective graphene oxide (GO)-based membrane with excellent resistance to organic solvents and oxidants, more hydrophilicity, lower membrane fouling, better separation performance has been expected to contribute more in industrial wastewater treatment. Herein, we provide comprehensive insights into the preparation and characteristic of GO membranes, as well as current research status and problems related to its future application in industrial wastewater treatment. Finally, concluding remarks and future perspectives have been deduced and recommended for the GO membrane separation technology application for industrial wastewater treatment, which leads to realizing sustainable wastewater recycling and a nearly "zero discharge" water treatment process.