Mixotrophic denitrification of waste activated sludge fermentation liquid as an alternative carbon source for nitrogen removal: Reducing N2O emissions and costs.

Heterotrophic-sulfur autotrophic denitrification (HAD) has been proposed to be a prospective nitrogen removal process. In this work, the potential of fermentation liquid (FL) from waste-activated sludge (WAS) as the electron donor for denitrification in the HAD system was explored and compared with other conventional carbon sources. Results showed that when FL was used as a carbon source, over 99% of NO3--N was removed and its removal rate exceeded 14.00 mg N/g MLSS/h, which was significantly higher than that of methanol and propionic acid. The produced sulfate was below the limit value and the emission of N2O was low (1.38% of the NO3--N). Microbial community analysis showed that autotrophic denitrifiers were predominated in the HAD system, in which Thiobacillus (16.4%) was the dominant genus. The economic analysis showed the cost of the FL was 0.062 €/m3, which was 30% lower than that in the group dosed with methanol. Our results demonstrated the FL was a promising carbon source for the HAD system, which could reduce carbon emission and cost, and offer a creative approach for waste-activated sludge resource reuse.

Revealing the mechanism of quartz sand seeding in accelerating phosphorus recovery from anaerobic fermentation supernatant through vivianite crystallization.

The recovery of phosphorus (P) through vivianite crystallization offers a promising approach for resource utilization in wastewater treatment plants. However, this process encounters challenges in terms of small product size and low purity. The study aimed to assess the feasibility of using quartz sand as a seed material to enhance P recovery and vivianite crystal characteristics from anaerobic fermentation supernatant. Various factors, including seed dosage, seed size, Fe/P ratio, and pH, were systematically tested in batch experiments to assess their influence. Results demonstrated that the effect of seed enhancement on vivianite crystallization was more pronounced under higher seed dosages, smaller seed sizes, and lower pH or Fe/P ratio. The addition of seeds increased P recovery by 4.43% in the actual anaerobic fermentation supernatant and also augmented the average particle size of the recovered product from 19.57 to 39.28 µm. Moreover, introducing quartz sand as a seed material effectively reduced co-precipitation, leading to a notable 12.5% increase in the purity of the recovered vivianite compared to the non-seeded process. The formation of an ion adsorption layer on the surface of quartz sand facilitated crystal attachment and growth, significantly accelerating the vivianite crystallization rate and enhancing P recovery. The economic analysis focused on chemical costs further affirmed the economic viability of using quartz sand as a seed material for P recovery through vivianite crystallization, which provides valuable insights for future research and engineering applications.

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.

Recovery of phosphorus from wastewater containing humic substances through vivianite crystallization: Interaction and mechanism analysis.

Vivianite crystallization has been regarded as a suitable option for recovering phosphorus (P) from P-containing wastewater. However, the presence of humic substances (HS) would inevitably affect the formation of vivianite crystals. Therefore, the influences of HS on vivianite crystallization and the changes in the harvested vivianite crystals were investigated in this study. The results suggested the inhibition effect of 70 mg/L HS on vivianite crystallization reached 12.24%, while it could be attenuated by increasing the pH and Fe/P ratio of the solution. Meanwhile, the addition of HS altered the size, purity, and morphology of recovered vivianite crystals due to the blockage of the growth sites on the crystal surface. Additionally, the formation of phosphate ester group, hydrogen bonding, and COOH-Fe2+ complexes are the potential mechanisms of HS interaction with vivianite crystals. The results obtained herein will help to elucidate the underlying mechanism of HS on vivianite crystallization from P-containing wastewater.

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.

Metagenomic insights into the spatiotemporal responses of antibiotic resistance genes and microbial communities in aquaculture sediments.

The dissemination of antibiotic resistance genes (ARGs) in aquaculture systems is a potential threat to environmental safety and human health. However, the spatiotemporal distribution pattern of ARGs and key factors associated with their dissemination in aquaculture sediments remain unclear. In this study, ARGs, mobile genetic elements, microbial community composition, heavy metal contents, and nutrient contents of samples collected from a whole culture cycle of fish in a representative aquaculture farm were characterized. The distribution patterns of nine subtypes of ARGs (tetW, tetM, tetA, ermC, ermB, sul1, sul2, floR, and qnrS) showed clear spatiotemporal differences. The absolute abundance of ARGs in aquaculture sediments was higher in winter and in rivers of the aquaculture farm. Proteobacteria was the dominant phylum in all sediment samples. The results of network and redundancy analyses confirmed that the Dechloromonas, Candidatus Accumulibacter, Smithella, Geobacter, and Anaeromyxobacter belonging to Proteobacteria were positively correlated with ARGs, suggesting that these microbial species are potential hosts of corresponding ARGs. Our study highlights that the microbial community is the determining factor for ARG dissemination. Strategies for inhibiting these potential hosts of ARGs should be developed based on controllable factors.

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.

A Systematic Analysis of Dysregulated Long Non-Coding RNAs/microRNAs/mRNAs in Lung Squamous Cell Carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) accounts up for approximately 30% of all lung cancers with a high mortality. The study was aimed at finding genes critical in the diagnosis and prognosis of LUSC. MATERIALS AND METHODS: The differentially expressed (DE) genes (DEGs) and DE lncRNAs (DELs) from 501 LUSC and 49 normal lung tissues, and DE miRNAs (DEMs) from 478 LUSC and 45 normal lung tissues were respectively obtained via the TCGA database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and co-expression network analyses were performed. Survival analysis and receiver operating characteristic curve of hub mRNAs were also analyzed. Competitive endogenous RNA networks of lncRNAs, miRNAs and mRNAs were constructed. RESULTS: A total of 5747 DEGs, 378 DEMs and 3141 DELs in LUSC were identified in LUSC. The DEGs including AUARK, CDK1, KIF11 and EXO1 were proven to be significant metastatic indicators in LUSC, and 2 DEGs were significantly associated with the survival in LUSC patients. Some genes might have connections with many other gene nodes through a co-expression network. Four lncRNAs, 2 mRNAs and 2 miRNAs were identified as the candidates for the competitive miRNA-mRNA-lncRNA network and might serve as prognostic markers in LUSC. CONCLUSIONS: We identified the differentially expressed lncRNAs, miRNAs and mRNAs in LUSC, providing further insights into the molecular mechanism of LUSC tumorigenesis and the potential prognostic biomarkers or therapeutic targets for LUSC.

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.

Impacts of environmental factors on AHL-producing and AHL-quenching activities of aerobic granules.

Aerobic granule is widely recognized as a promising biological wastewater treatment technique. Acyl-homoserine lactone (AHL)-mediated quorum sensing and quenching are reported to be involved in the formation of aerobic granules. However, little is known about how environmental factors affect the AHL-producing and AHL-quenching communities and their activities in aerobic granules. Therefore, in this work, the bacterial community of aerobic granules was explored and the impacts of substrate, electron acceptor, sludge concentration, pH, and temperature on the AHL-related communities and activities of aerobic granules were examined. These factors were found to affect the AHL-related activities, and thereby change the AHL level. The AHL-producing activities were observed to be more sensitive to the variation of these factors than the AHL-quenching activities. These findings help to establish the links between environmental factors and AHL-related activities and thus provide useful guides for the operation of aerobic granule systems.

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.

Augmentation of acyl homoserine lactones-producing and -quenching bacterium into activated sludge for its granulation.

Quorum sensing (QS), especially acyl homoserine lactone (AHL)-mediated QS, in activated sludge arouses great interests because of its vital role in the formation of biofilm and aerobic granules (AG). Although QS is reported to be largely related to the properties of activated sludge, it is not economically feasible to tune QS in an activated sludge reactor through dosing pure AHL or AHL hydrolase. A more reasonable way to tune QS is to augment reactors with AHL-producing or -quenching bacteria. In this work, the impacts of continuous dose of AHL-producing or -quenching strains on the activated sludge during its granulation process were explored. Augmentation of AHL-producing or -quenching strains resulted in up- or down-regulation of the AHL concentration in the reactors. Granulation of activated sludge was also accomplished in all reactors, but the granules showed negligible or slight differences in the physicochemical properties of sludge, such as nutrients removal, biomass concentration, extracellular polymeric substances, and zeta potential. Interestingly, a smaller granule size was observed for both the reactor augmented with either an AHL-quenching strain or an AHL-producing strain, suggesting that the AHL augmentation suppressed the biofilm development. Pyrosequencing analysis reveals that the granules cultured in the reactors varied widely in bacterial community structure, indicating that the AHL augmentation had a greater impact on the bacterial community structure, rather than on the physicochemical properties of activated sludge. These results demonstrate that the role of QS in the biofilm formation in complex wastewater treatment bioreactors should be re-evaluated.

Quantitative evaluation on the characteristics of activated sludge granules and flocs using a fuzzy entropy-based approach.

Activated sludge granules and flocs have their inherent advantages and disadvantages for wastewater treatment due to their different characteristics. So far quantitative information on their evaluation is still lacking. This work provides a quantitative and comparative evaluation on the characteristics and pollutant removal capacity of granules and flocs by using a new methodology through integrating fuzzy analytic hierarchy process, accelerating genetic algorithm and entropy weight method. Evaluation results show a higher overall score of granules, indicating that granules had more favorable characteristics than flocs. Although large sized granules might suffer from more mass transfer limitation and is prone to operating instability, they also enable a higher level of biomass retention, greater settling velocity and lower sludge volume index compared to flocs. Thus, optimized control of granule size is essential for achieving good pollutant removal performance and simultaneously sustaining long-term stable operation of granule-based reactors. This new integrated approach is effective to quantify and differentiate the characteristics of activated sludge granules and flocs. The evaluation results also provide useful information for the application of activated sludge granules in full-scale wastewater treatment plants.