Core activated sludge communities are influenced little by immigration: Case study of a membrane bioreactor plant

Microbial community assembly in engineered bioreactors

Microbial community assembly (MCA) processes that shape microbial communities in environments are being used to analyze engineered bioreactors such as activated sludge systems and anaerobic digesters. The goal of studying MCA is to be able to understand and predict the effect of design and operation procedures on bioreactor microbial composition and function. Ultimately, this can lead to bioreactors that are more efficient, resilient, or resistant to perturbations. This review summarizes the ecological theories underpinning MCA, evaluates MCA analysis methods, analyzes how these MCA-based methods are applied to engineered bioreactors, and extracts lessons from case studies. Furthermore, we suggest future directions in MCA research in engineered bioreactor systems. The review aims to provide insights and guidance to the growing number of environmental engineers who wish to design and understand bioreactors through the lens of MCA.

Accounting for the microbial assembly of each process in wastewater treatment plants (WWTPs): study of four WWTPs receiving similar influent streams

We evaluated a unique model in which four full-scale wastewater treatment plants (WWTPs) with the same treatment schematic and fed with similar influent wastewater were tracked over an 8-month period to determine whether the community assembly would differ in the activated sludge (AS) and sand filtration (SF) stages. For each WWTP, AS and SF achieved an average of 1-log10 (90%) and <0.02-log10 (5%) reduction of total cells, respectively. Despite the removal of cells, both AS and SF had a higher alpha and beta diversity compared to the influent microbial community. Using the Sloan neutral model, it was observed that AS and SF were individually dominated by different assembly processes. Specifically, microorganisms from influent to AS were predominantly determined by the selective niche process for all WWTPs, while the microbial community in the SF was relatively favored by a stochastic, random migration process, except two WWTPs. AS also contributed more to the final effluent microbial community compared with the SF. Given that each WWTP operates the AS independently and that there is a niche selection process driven mainly by the chemical oxygen demand concentration, operational taxonomic units unique to each of the WWTPs were also identified. The findings from this study indicate that each WWTP has its distinct microbial signature and could be used for source-tracking purposes.IMPORTANCEThis study provided a novel concept that microorganisms follow a niche assembly in the activated sludge (AS) tank and that the AS contributed more than the sand filtration process toward the final microbial signature that is unique to each treatment plant. This observation highlights the importance of understanding the microbial community selected by the AS stage, which could contribute toward source-tracking the effluent from different wastewater treatment plants.

Mass-immigration shapes the antibiotic resistome of wastewater treatment plants

Wastewater treatment plants (WWTPs) are the hotspots for the spread of antibiotic resistance genes (ARGs) into the environment. Nevertheless, a comprehensive assessment of the city-level and short-term daily (inter-day) variations of ARG profiles in the whole process (influent-INF, activated sludge-AS and effluent-EF) of WWTPs is still lacking. Here, 285 ARGs and ten mobile gene elements were monitored in seven WWTPs in Xiamen for seven days via high-throughput qPCR. The average daily load of ARGs to WWTPs was about 1.32 × 1020 copies/d, and a total of 1.56 × 1018 copies/d was discharged to the environment across the entire city. Stochastic processes were the main force determining the assembly of ARG communities during sampling campaign, with their relative importance ranked in the order of INF > EFF > AS. There're little daily variations in ARG richness, abundance, β-diversity composition as well as assembly mechanisms. The results of SourceTracker, variation partitioning analysis, and hierarchical partitioning analysis indicated that bacteria and ARGs from upstream treatment processes played an increasingly dominant role in shaping ARG communities in AS and EFF, respectively, suggesting the importance of mass-immigration of bacteria and ARGs from the source on ARG transport in wastewater treatment processes. This emphasizes the need to revise the way we mitigate ARG contamination but focus on the source of ARGs in urban wastewater.

The role of the core microorganisms in the microbial interactions in activated sludge

In order to gain a deeper understanding of the microbial interactions in wastewater treatment plants (WWTPs) in China and clarify the role of the core community in the microbial interactions in activated sludge (AS), this study used a molecular ecological network approach based on random matrix theory to construct co-occurrence networks of the core microorganisms (CoreN), the whole AS community (WholeN) and the microbial communities without the core microorganisms (OtherN), respectively. It was shown that the WholeN had more complex and tighter connections compared with the OtherN, because of its higher total number of nodes, higher average clustering coefficient, and shorter average geodesic distance. The proportions of positive links in the CoreN, WholeN and OtherN were gradually decreased, indicating that the core microorganisms promoted cooperation between AS microorganisms. Moreover, higher robustness after random removal of 50% of the nodes of the WholeN (0.2836 ± 0.0311) was observed than the robustness of the OtherN (0.1152 ± 0.0263). In addition, the vulnerability of OtherN (0.0514) is significantly higher than WholeN (0.0225). Meanwhile, the average ratio of negative/positive cohesion, was significantly decreased when the core microorganisms were removed. These results demonstrated that core community could strengthen the stability of the ecological network in AS. By discerning the key factors affecting ecological network, AS temperature was observed to have a strong correlation with all three networks. Moreover, pollutants in wastewater shown stronger correlations with the CoreN and WholeN, supporting the point that core community play a critical role in pollutant removal in WWTPs to a certain extent.

Microbial community-based production of single cell protein from soybean-processing wastewater of variable chemical composition

The use of food-processing wastewaters to produce microbial biomass-derived single cell protein (SCP) is a sustainable way to meet the global food demand. Microbial community-based approaches to SCP production have the potential benefits of lower costs and greater resource recovery compared to pure cultures, yet they have received scarce attention. Here, SCP production from soybean-processing wastewaters using their existent microbial communities was evaluated. Six sequencing batch reactors of 4.5-L working volume were operated at 30 °C for 34 d in cycles consisting of 3-h anaerobic and 9-h aerobic phases. Four reactors received no microbial inoculum and the remaining two were amended with 1.5 L of a mixed culture from a prior SCP production cycle. Reactors produced more SCP when fed with wastewaters of higher soluble total Kjeldahl nitrogen (sTKN) content. The protein yield in biomass ranged from 0.53 to 3.13 g protein/g sTKN, with a maximum protein content of 50 %. The average removal of soluble chemical oxygen demand (sCOD) and soluble total nitrogen (sTN) was 92 % and 73 %, respectively. Distinct microbial genera were enriched in all six bioreactors, with Azospirillum, Rhodobacter, Lactococcus, and Novosphingobium dominating. The study showed that constituents in soybean wastewater can be converted to SCP and demonstrated the effect of variable influent wastewater composition on SCP production.

The positive roles of influent species immigration in mitigating membrane fouling in membrane bioreactors treating municipal wastewater

The influence of influent species immigration (ISI) on membrane fouling behaviors of membrane bioreactors (MBRs) treating municipal wastewater remains elusive, leading to an incomprehensive understanding of fouling ecology in MBRs. To address this issue, two anoxic/aerobic MBRs, which were fed with raw (named MBR-C) and sterilized (MBR-E) municipal wastewater, were operated. Compared with the MBR-E, the average fouling rate of the MBR-C was lowered by 30% over the long-term operation. In addition, the MBR-E sludge had significantly higher unified membrane fouling index and biofilm formation potential than the MBR-C sludge. Considerably larger flocs size and lower soluble microbial products (SMP) concentrations were observed in the MBR-C than in the MBR-E. Moreover, the 16S rRNA gene sequencing results showed that highly diverse and abundant populations responsible for floc-forming, hydrolysis/fermentation and SMP degradation readily inhabited the influent, shaping a unique microbial niche. Based on species mass balance-based assessment, most of these populations were nongrowing and their relative abundances were higher in the MBR-C than in the MBR-E. This suggested an important contribution of the ISI on the assemblage of these bacteria, thus supporting the increased flocs size and lowered SMP concentrations in the MBR-C. Moreover, the SMP-degrading related bacteria and functional pathways played a more crucial role in the MBR-C ecosystem as revealed by the bacterial co-occurrence network and Picrust2 analysis. Taken together, this study reveals the positive role of ISI in fouling mitigation and highlights the necessity for incorporating influent wastewater communities for fouling control in MBR plants.

Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant

Membrane bioreactor (MBR) systems are one of the most widely used wastewater treatment processes for various municipal and industrial waste streams. The present study aimed to advance the understanding of ecologically important keystone taxa that play an important role in full-scale MBR systems. A machine-learning (ML) modeling framework based on microbiome data was developed to successfully predict, with an average accuracy of >91.6%, the operational characteristics of three representative full-scale wastewater systems: an MBR, a conventional activated sludge system, and a sequencing batch reactor. ML-based feature-importance analysis identified Ferruginibacter as a keystone organism in the MBR system. The phylogeny and known ecophysiology of members of Ferruginibacter supported their role in metabolizing complex organic polymers (e.g., extracellular polymeric substances) in MBR systems characterized by high concentrations of mixed liquor suspended solids and a high solid retention time. ML regression modeling also revealed temporal patterns of Ferruginibacter in response to water temperature. ML modeling was thus successfully employed in the present study to investigate complex/non-linear relationships between keystone taxa and environmental conditions that cannot be detected using conventional approaches. Overall, our microbiome-data-enabled ML modeling approach represents a methodological advance for identifying keystone taxa and their complex ecological interactions, which has implications for the sustainable and predictive management of MBR systems.

Phylogenetic group-based assembly and co-occurrence pattern of the microbial community in full-scale wastewater treatment plants during the Chinese spring festival

The quality and quantity of domestic sewage discharge vary significantly during the Chinese Spring Festival due to the huge population shift. The dynamics of microbial community traits during the Spring Festival, particularly the phylogenetic group-based assembly and co-occurrence patterns, are however little understood. Here, influent and activated sludge samples from 2 full-scale wastewater treatment plants were collected bi-daily throughout a 20-day Spring Festival period and subjected to high-throughput Illumina-MiSeq sequencing. The findings revealed that the microbial communities in the activated sludge displayed a comparatively stable pattern, and that the influent communities experienced significant temporal fluctuations in terms of diversity and composition. The characterization by "Infer Community Assembly Mechanisms by Phylogenetic-bin based null model" demonstrated that for Competibacter glycogen-accumulating organisms, the assembly mechanism shifted from deterministic process (HoS = 69.5%) before the Spring Festival to stochastic process (DR = 65.9%) after the Spring Festival. The network analysis revealed that the network structure of sludge communities was more stable before the Spring Festival than that after the Spring Festival. Additionally, sludge communities had no keystone species in common with the influent before the Spring Festival, while the sludge and influent communities shared two keystone taxa after the Spring Festival (Sebaldella and Candidatus Competibacter). This study would deepen our understanding of the microbial ecology in biological wastewater treatment systems, which also aids in managing wastewater treatment plants.

Effects of influent immigration and environmental factors on bacterial assembly of activated sludge microbial communities

The functional mechanism of microbial assembly of activated sludge (AS) in urban wastewater treatment plants (UWTPs) remains unclear. A comprehensive quantitative evaluation of the contribution of influent immigration and environmental factors to AS community composition requires investigation. In this study, the microbial characteristics of six full-scale UWTPs with different influent compositions and environmental factors (altitude, temperature, dissolved oxygen (DO), pH, chemical oxygen demand (COD), total nitrogen (TN), ammonia nitrogen (NH₄⁺-N), and total phosphorus (TP)) were analyzed to determine the main forces affecting the bacterial assembly of AS microbial communities. Abundant and core taxa were screened out based on the abundance and frequency of operational taxonomic units (OTUs) occurrence in all samples. Abundant OTUs (18.7% occurrence) accounted for 87.7% of the total 16S rRNA sequences, while rare OTUs (71.7% occurrence) accounted for only 7.8% of the total 16S rRNA sequences. A total of 135 OTUs were identified as core taxa, accounting for 14.6-26.2% of the total reads, of which 83 OTUs belonged to abundant taxa. The richness and uniformity of the influent community were significantly lower than those of the AS system. The community composition in influent varied from that in AS. Moreover, about 89.7% (86.5% of 16S rRNA sequences) OTUs in AS samples showed positive growth rates, indicating that immigration of influent communities had a limited effect on the microbial composition of AS. Redundancy analysis (RDA) combined with co-occurrence network showed that the bacterial assembly of microbial communities was significantly correlated with altitude, pH, and TN (P < 0.05), and these three parameters could explain 23.3%, 21.1%, and 17.7% of the bacterial assembly of AS microbial communities in UWTPs, respectively.