Microplastics shape microbial interactions and affect the dissemination of antibiotic resistance genes in different full-scale wastewater treatment plants.

Wastewater treatment plants (WWTPs) pose a potential threat to the environment because of the accumulation of antibiotic resistance genes (ARGs) and microplastics (MPs). However, the interactions between ARGs and MPs, which have both indirect and direct effects on ARG dissemination in WWTPs, remain unclear. In this study, spatiotemporal variations in different types of MPs, ten ARGs (sul1, sul2, tetA, tetO, tetM, tetX, tetW, qnrS, ermB, and ermC), class 1 integron integrase (intI1) and transposon Tn916/1545 in three typical WWTPs were characterized. Sul1, tetO, and sul2 were the predominant ARGs in the targeted WWTPs, whereas the intI1 and transposon Tn916/1545 were positively correlated with most of the targeted ARGs. Saccharimonadales (4.15 %), Trichococcus (2.60 %), Nitrospira (1.96 %), Candidatus amarolinea (1.79 %), and SC-I-84 (belonging to phylum Proteobacteria) (1.78 %) were the dominant genera. Network and redundancy analyses showed that Trichococcus, Faecalibacterium, Arcobacter, and Prevotella copri were potential hosts of ARGs, whereas Candidatus campbellbacteria and Candidatus kaiserbacteria were negatively correlated with ARGs. The potential hosts of ARGs had a strong positive correlation with polyethylene terephthalate, silicone resin, and fluor rubber and a negative correlation with polyurethane. Candidatus campbellbacteria and Candidatus kaiserbacteria were positively correlated with polyurethane, whereas potential hosts of ARGs were positively correlated with polypropylene and fluor rubber. Structural equation modeling highlighted that intI1, transposon Tn916/1545 and microbial communities, particularly microbial diversity, dominated the dissemination of ARGs, whereas MPs had a significant positive correlation with microbial abundance. Our study deepens the understanding of the relationships between ARGs and MPs in WWTPs, which will be helpful in designing strategies for inhibiting ARG hosts in WWTPs.

Phosphorus recovery from synthetic anaerobic fermentation supernatant via vivianite crystallization: Coupling effects of various physicochemical process parameters.

Recovery of phosphorus (P) via vivianite crystallization is an effective strategy to recycle resources from the anaerobic fermentation supernatant. However, the presence of different components in the anaerobic fermentation supernatant (e.g., polysaccharides and proteins) might alter conditions for optimal growth of vivianite crystals, resulting in distinct vivianite characteristics. In the present study, the effect of different components on vivianite crystallization was explored. Then, the reaction parameters (pH, Fe/P, and stirring speed) for P recovery from synthetic anaerobic fermentation supernatant as vivianite were optimized using response surface methodology, and the relationship between crystal properties and supersaturation was elucidated using a thermodynamic equilibrium model. The optimized values for pH, Fe/P, and stirring speed were found to be 7.8, 1.74, and 500 rpm respectively, resulting in 90.54 % P recovery efficiency. Moreover, the variation of reaction parameters did not change the crystalline structure of the recovered vivianite but influenced its morphology, size, and purity. Thermodynamic analysis suggested the saturation index (SI) of vivianite increased with increasing pH and Fe/P ratio, leading to a facilitative effect on vivianite crystallization. However, when the SI was >11, homogenous nucleation occurred so that the nucleation rate was much higher than the crystal growth rate, causing a smaller crystal size. The findings presented herein will be highly valued for the future large-scale application of the vivianite crystallization process for wastewater treatment.

Metagenomic insight into the prevalence and driving forces of antibiotic resistance genes in the whole process of three full-scale wastewater treatment plants.

The spread of antibiotic resistance genes (ARGs) is an emerging global health concern, and wastewater treatment plants (WWTPs), as an essential carrier for the occurrence and transmission of ARGs, deserves more attention. Based on the Illumina NovaSeq high-throughput sequencing platform, this study conducted a metagenomic analysis of 18 samples from three full-scale WWTPs to explore the fate of ARGs in the whole process (influent, biochemical treatment, advanced treatment, and effluent) of wastewater treatment. Total 70 ARG subtypes were detected, among which multidrug, aminoglycoside, tetracycline, and macrolide ARGs were most abundant. The different treatment processes used for three WWTPs were capable of reducing ARG diversity, but did not significantly reduce ARG abundance. Compared to that by denitrification filters, the membrane bioreactor (MBR) process was advantageous in controlling the prevalence of multidrug ARGs in WWTPs. Linear discriminant analysis Effect Size (LEfSe) suggested g_Nitrospira, g_Curvibacter, and g_Mycobacterium as the key bacteria responsible for differential ARG prevalence among different WWTPs. Meanwhile, adeF, sul1, and mtrA were the persistent antibiotic resistance genes (PARGs) and played dominant roles in the prevalence of ARGs. Proteobacteria and Actinobacteria were the host bacteria of majority ARGs in WWTPs, while Pseudomonas and Nitrospira were the most crucial host bacteria influencing the dissemination of critical ARGs (e.g., adeF). In addition, microbial richness was determined to be the decisive factor affecting the diversity and abundance of ARGs in wastewater treatment processes. Overall, regulating the abundance of microorganisms and key host bacteria by selecting processes with microbial interception, such as MBR process, may be beneficial to control the prevalence of ARGs in WWTPs.

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion.

Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.

Sludge reduction and microbial community evolution of activated sludge induced by metabolic uncoupler o-chlorophenol in long-term anaerobic-oxic process.

Excess sludge management is a restrictive factor for the development of municipal wastewater treatment plants. The addition of metabolic uncouplers has been proven to be effective in sludge reduction. However, the long-term effect of metabolic uncoupler o-chlorophenol (oCP) on the biological wastewater treatment system operated in anaerobic-oxic mode is still unclear. To this end, two parallel reactors operated in anaerobic-oxic mode with and without 10 mg/L of oCP addition were investigated for 91 days. The results showed that 56.1 ± 2.3% of sludge reduction was achieved in the oCP-added system, and the nitrogen and phosphorus removal ability were negatively affected. Dosing oCP stimulated the formation of microbial products and increased the DNA concentration, but resulted in a decrease in the electronic transport activity of activated sludge. Microbial community analysis further demonstrated that a significant reduction of bacterial richness and diversity occurred after oCP dosing. However, after stopping oCP addition, the pollutant removal ability of activated sludge was gradually increased, but the sludge yield, as well as species richness and diversity, did not recover to the previous level. This study will provide insightful guidance on the long-term application of metabolic uncouplers in the activated sludge system.

Rapid quantification of intracellular polyhydroxyalkanoates via fluorescence techniques: A critical review.

Polyhydroxyalkanoates (PHA) are promising bioplastics with excellent physicochemical properties and biodegradability, whereas PHA products suffer from high manufacturing costs. To reduce costs of PHA production, experiments with mixed microbial cultures and low-cost substrates have been conducted widely, where rapid and robust PHA quantification methods are necessary. Compared with traditional gas chromatography methods, PHA fluorescence quantification (PHA-FQ) methods may be quicker, safer and more suitable for modern experiments with high throughput requirements. However, practical applications of PHA-FQ methods are still limited. Therefore, this review provides a comprehensive understanding of PHA-FQ methods. Performance of PHA-staining fluorochromes, relevant spectral properties, and important staining procedures are summarized. Current developments of PHA-FQ protocols are critically reviewed. Main considerations needed to make PHA-FQ protocol reliable are comprehensively discussed. Finally, potential improvements in various aspects of PHA-FQ methods are highlighted. This review could help researchers develop more effective PHA-FQ methods and facilitate future experiments related to PHA.

The response of sediment microbial communities to temporal and site-specific variations of pollution in interconnected aquaculture pond and ditch systems.

Sediment microbial communities play critical roles in the health of fish and the biogeochemical cycling of elements in aquaculture ecosystems. However, the response of microbial communities to temporal and spatial variations in interconnected aquaculture pond and ditch systems remains unclear. In this study, 61 sediment bacterial samples were collected over one year from 11 sites (including five ponds and six ditches) in a 30-year-old fish aquaculture farm. The 16S rRNA approach was used to determine the relative abundances of microbial communities in the sediment samples. The relationships among nutrients, heavy metals, and abundant microorganisms were analyzed. Our results showed that Proteobacteria, Bacteroides and Chloroflexi were the predominant phyla in the sediments of aquaculture pond, with average abundances of 36.33%, 18.60%, and 14.58%, respectively. The microbial diversity in aquaculture sediments was negatively correlated (P < 0.05) with the concentrations of total nitrogen and total phosphorus in sediments, indicating that the microbial diversity is highly associated with the remediation of nutrients in sediments. The sediment samples with high similarities were discovered by the t-distributed stochastic neighbor embedding (t-SNE) method. The site-specific correlations between specific microorganisms and heavy metals were explored. The network analysis revealed that the microbial diversities in aquaculture ponds were more stable than that in aquaculture ditches. The network analysis also illustrated that the microbial genera with low relative abundances may become key groups of microbial communities in sediment ecosystems. Our work deepens the understanding of the relationships between microbial communities and the spatiotemporal characteristics of surface water and sediments in aquaculture farms.

Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures.

The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovatively to accurately predict the dynamic enrichment of PAB. Well-calibrated activated sludge models (ASM) of the PAB enrichment process provide time-dependent data under different operating conditions. Recurrent neural network (RNN) models are trained and tested based on the time-dependent dataset generated by ASM. The accurate prediction performance is achieved (R2 > 0.991) for three different PAB enrichment datasets by the optimized RNN model. The optimized RNN model can also predict the equilibrium concentration of PAB (R2 = 0.944) and corresponding time, which represents the end of the PAB enrichment process. This study demonstrates the strength of integrating mechanistic and deep learning models to predict long-term variations of specific microbes, helping to optimize their selection process for PHA production.

Exploring the feasibility of nitrous oxide reduction and polyhydroxyalkanoates production simultaneously by mixed microbial cultures.

Nitrous oxide (N2O), as a powerful greenhouse gas, has drawn increasing attention in recent years and different strategies for N2O reduction were explored. In this study, a novel strategy for valuable polyhydroxyalkanoates (PHA) production coupling with N2O reduction by mixed microbial cultures (MMC) using different substrates was evaluated. Results revealed that N2O was an effective electron acceptor for PHA production. The highest PHA yield (0.35 Cmmol PHA/Cmmol S) and PHA synthesis rate (227.47 mg PHA/L/h) were obtained with acetic acid as substrate. Low temperature (15℃) and pH of 8.0 were beneficial for PHA accumulation. Results of the thermogravimetric analysis showed that PHA produced with N2O as electron acceptor has better thermal stability (melting temperature of 99.4℃ and loss 5% weight temperature of 211.4℃). Our work opens up new avenues for simultaneously N2O reduction and valuable bioplastic production, which is conducive to resource recovery and climate protection.

Distribution patterns of functional microbial community in anaerobic digesters under different operational circumstances: A review.

Anaerobic digestion (AD) processes are promising to effectively recover resources from organic wastes or wastewater. As a microbial-driven process, the functional anaerobic species played critical roles in AD. However, the lack of effective understanding of the correlations of varying microbial communities with different operational factors hinders the microbial regulation to improve the AD performance. In this paper, the main anaerobic functional microorganisms involved in different stages of AD processes were first demonstrated. Then, the response of anaerobic microbial community to different operating parameters, exogenous interfering substances and digestion substrates, as well as the digestion efficiency, were discussed. Finally, the research gaps and future directions on the understanding of functional microorganisms in AD were proposed. This review provides insightful knowledge of distribution patterns of functional microbial community in anaerobic digesters, and gives critical guidance to regulate and enrich specific functional microorganisms to accumulate certain AD products.

An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process.

Data-driven models are suitable for simulating biological wastewater treatment processes with complex intrinsic mechanisms. However, raw data collected in the early stage of biological experiments are normally not enough to train data-driven models. In this study, an integrated modeling approach incorporating the random standard deviation sampling (RSDS) method and deep neural networks (DNNs) models, was established to predict volatile fatty acid (VFA) production in the anaerobic fermentation process. The RSDS method based on the mean values (x¯) and standard deviations (α) calculated from multiple experimental determination was initially developed for virtual data augmentation. The DNNs models were then established to learn features from virtual data and predict VFA production. The results showed that when 20000 virtual samples including five input variables of the anaerobic fermentation process were used to train the DNNs model with 16 hidden layers and 100 hidden neurons in each layer, the best correlation coefficient of 0.998 and the minimal mean absolute percentage error of 3.28% were achieved. This integrated approach can learn nonlinear information from virtual data generated by the RSDS method, and consequently enlarge the application range of DNNs models in simulating biological wastewater treatment processes with small datasets.

Formation of microbial products by activated sludge in the presence of a metabolic uncoupler o-chlorophenol in long-term operated sequencing batch reactors.

Metabolic uncouplers are widely used for reducing excess sludge in biological wastewater treatment systems. However, the formation of microbial products, such as extracellular polymeric substances, polyhydroxyalkanoate and soluble microbial products by activated sludge in the presence of metabolic uncouplers remains unrevealed. In this study, the impacts of a metabolic uncoupler o-chlorophenol (oCP) on the reduction of activated sludge yield and formation of microbial products in laboratory-scale sequencing batch reactors (SBRs) were evaluated for a long-term operation. The results show the average reduction of sludge yield in the four reactors was 17.40%, 25.80%, 33.02% and 39.50%, respectively, when dosing 5, 10, 15, and 20 mg/L oCP. The oCP addition slightly reduced the pollutant removal efficiency and decreased the formation of soluble microbial products in the SBRs, but stimulated the productions of extracellular polymeric substances and polyhydroxyalkanoate in activated sludge. Furthermore, the significant reduction of electronic transport system activity occurred after the oCP addition. Microbial community analysis of the activated sludge indicates dosing oCP resulted in a decrease of sludge richness and diversity in the SBRs. Hopefully, this study would provide useful information for reducing sludge yield in biological wastewater treatment systems and behaviors of activated sludge in the presence of uncouplers.

Production of polyhydroxyalkanoates and enrichment of associated microbes in bioreactors fed with rice winery wastewater at various organic loading rates.

This study aimed to explore the production of polyhydroxyalkanoates (PHA) and selection of PHA-accumulating microorganisms in bioreactors fed with rice winery wastewater at various organic loading rates (OLRs). The substrate utilization, sludge properties, PHA synthesis and microbial community structure of three sequencing batch reactors were monitored. The results show the highest PHA yield (0.23 g/g) was achieved in one of the three reactors with an OLR of 2.4 g COD/L/d, in which Zoogloea was the most dominant PHA-accumulating microorganism. To quantify the PHA production and track the population changing profiles of the PHA-accumulating microorganisms in the long-term reactor operation, the Activated Sludge Model No. 3 was modified with two different heterotrophic microorganisms responding differently with the same substrate. The modeling results indicate that a moderate OLR (>2.4 gCOD/L/d) was beneficial for PHA production. The results are useful for understanding the PHA production from industrial wastewaters and selection of PHA-accumulating microorganisms.

Characterization of interactions between a metabolic uncoupler O-chlorophenol and extracellular polymeric substances of activated sludge.

Metabolic uncouplers are widely used for the in-situ reduction of excess sludge from activated sludge systems. However, the interaction mechanism between the metabolic uncouplers and extracellular polymeric substances (EPS) of activated sludge is unknown yet. In this study, the interactions between a typical metabolic uncoupler, o-chlorophenol (oCP), and the EPS extracted from activated sludge were explored using a suite of spectral methods. The binding constants calculated for the four peaks of three-dimensional excitation-emission matrix fluorescence were in a range of 1.24-1.76 × 103 L/mol, implying that the tyrosine protein-like substances governed the oCP-EPS interactions. Furthermore, the results of Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and 1H nuclear magnetic resonance indicated that the carboxyl, carbonyl, amine, and hydroxyl groups of EPS were the main functional groups involved in the formation of the oCP-EPS complex. The results of this study are useful for understanding the interactions between metabolic uncouplers and the EPS of activated sludge as well as their fates in biological wastewater treatment systems.