Tropical and temperate wastewater treatment plants assemble different and diverse microbiomes

Deep insights into the assembly mechanisms, co-occurrence patterns, and functional roles of microbial community in wastewater treatment plants

The understanding of activated sludge microbial status and roles is imperative for improving and enhancing the performance of wastewater treatment plants (WWTPs). In this study, we conducted a deep analysis of activated sludge microbial communities across five compartments (inflow, effluent, and aerobic, anoxic, anaerobic tanks) over temporal scales, employing high-throughput sequencing of 16S rRNA amplicons and metagenome data. Clearly discernible seasonal patterns, exhibiting cyclic variations, were observed in microbial diversity, assembly, co-occurrence network, and metabolic functions. Notably, summer samples exhibited higher α-diversity and were distinctly separated from winter samples. Our analysis revealed that microbial community assembly is influenced by both stochastic processes (66%) and deterministic processes (34%), with winter samples demonstrating more random assembly compared to summer. Co-occurrence patterns were predominantly mutualistic, with over 96% positive correlations, and summer networks were more organized than those in winter. These variations were significantly correlated with temperature, total phosphorus and sludge volume index. However, no significant differences were found among microbial community across five compartments in terms of β diversity. A core community of keystone taxa was identified, playing key roles in eight nitrogen and eleven phosphorus cycling pathways. Understanding the assembly mechanisms, co-occurrence patterns, and functional roles of microbial communities is essential for the design and optimization of biotechnological treatment processes in WWTPs.

Using machine learning to identify environmental factors that collectively determine microbial community structure of activated sludge

Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we employed advanced machine learning techniques to elucidate the collective effects of environmental variables on the structure and function of AS microbial communities. Applying Dirichlet multinomial mixtures analysis to 311 global AS samples, we identified four distinct microbial community types (AS-types), each characterized by unique microbial compositions and metabolic profiles. We used 14 classical linear and nonlinear machine learning methods to select a baseline model. The extremely randomized trees demonstrated optimal performance in learning the relationship between environmental factors and AS types (with an accuracy of 71.43%). Feature selection identified critical environmental factors and their importance rankings, including latitude (Lat), longitude (Long), precipitation during sampling (Precip), solids retention time (SRT), effluent total nitrogen (Effluent TN), average temperature during sampling month (Avg Temp), mixed liquor temperature (Mixed Temp), influent biochemical oxygen demand (Influent BOD), and annual precipitation (Annual Precip). Significantly, Lat, Long, Precip, Avg Temp, and Annual Precip, influenced metabolic variations among AS types. These findings emphasize the pivotal role of environmental variables in shaping microbial community structures and enhancing metabolic pathways within activated sludge. Our study encourages the application of machine learning techniques to design artificial activated sludge microbial communities for specific environmental purposes.

Comparative analyses on nitrogen removal microbes and functional genes within anaerobic-anoxic-oxic and deoxidation ditch sewage-treating processes in Wuhan and Xi'an cities, China

Introduction

Anaerobic-anoxic-oxic (A2/O) and deoxidation ditch (DOD) processes are being increasingly preferred owing to their effectiveness in treating various wastes in wastewater treatment plants (WWTPs). Considering seasonal variations is crucial in optimizing treatment processes, ensuring compliance with regulations, and maintaining the overall efficiency and effectiveness of WWTPs. This study aimed to determine the influence of seasonality on nitrogen removing microbes and functional genes within A2/O and DOD processes in the humid Wuhan and semi-arid Xi'an cities, China.

Methods

The physicochemical parameters of water quality were determined, and molecular and bioinformatic analyses of the bacterial community and nitrogen metabolism functional genes in the two different treatment processes of two WWTPs were performed over four seasons.

Results and discussion

Our analyses revealed a significant difference in all physicochemical parameters across all experimental groups (p < 0.05). At the genus level, the abundance of Dokdonella, one unidentified genus of Nitrospiraceae, Terrimonas, and one unidentified genus of Chloroflexi was the highest in all groups. Generally, warmer seasons exhibited higher biodiversity indices. The A2/O system exhibited higher values in terms of most nitrogen metabolism functional genes than those of the DOD sewage treatment system. In both WWTPs, the abundance of most genes in spring and summer were higher than that of autumn and winter seasons. Taken together, changes in temperature, caused by seasonal changes, may contribute to changes in abundance of nitrogen metabolic functional genes.

Microaerophilic Activated Sludge System for Ammonia Retention toward Recovery from High-Strength Nitrogenous Wastewater: Performance and Microbial Communities

A transition to ammonia recovery from wastewater has started; however, a technology for sustainable nitrogen retention in the form of ammonia and organic carbon removal is still in development. This study validated a microaerophilic activated sludge (MAS) system to efficiently retain ammonia from high-strength nitrogenous wastewater. The MAS is based on conventional activated sludge (CAS) with aerobic and settling compartments. Low dissolved oxygen (DO) concentrations (<0.2 mg/L) and short solids retention times (SRTs) (<5 days) eliminated nitrifying bacteria. The two parallel MASs were successfully operated for 300 days and had ammonia retention of 101.7 ± 24.9% and organic carbon removal of 85.5 ± 8.9%. The MASs mitigated N2O emissions with an emission factor of <0.23%, much lower than the default value of CAS (1.6%). A short-term step-change test demonstrated that N2O indicated the initiation of nitrification and the completion of denitrification in the MAS. The parallel MASs had comparable microbial diversity, promoting organic carbon oxidation while inhibiting ammonia-oxidizing microorganisms (AOMs), as revealed by 16S rRNA gene amplicon sequencing, the quantitative polymerase chain reaction of functional genes, and fluorescence in situ hybridization of β-proteobacteria AOB. The microbial analyses also uncovered that filamentous bacteria were positively correlated with effluent turbidity. Together, controlling DO and SRT achieved organic carbon removal and successful ammonia retention, mainly by suppressing AOM activity. This process represents a new nitrogen management paradigm.

Changes in the composition of bacterial communities and pathogen levels during wastewater treatment

Wastewater treatment plants have been described as a potential source of spreading pathogens to the receiving water. However, few studies are reporting the presence and concentration changes of pathogens in these matrices. High-throughput sequencing provides new insights into understanding the changes of bacterial communities throughout wastewater treatment plants (WWTPs). In this study, the changes in microbial community composition and the levels of representative pathogens of effluents during the wastewater treatment process in two municipal WWTPs (A and B) were analyzed using Illumina NovaSeq sequencing and qPCR. Proteobacteria was the most abundant phylum in all samples, accounting for 45.0-75.2% of the bacterial community, followed by Firmicutes, Bacteroidetes, Actinobacteria, and Nitrospirae. A slight difference was observed between the bacterial community compositions of WWTPs A and B. However, a significant difference in the community compositions of effluent samples at different treatment stages was observed. Nutrients had a more substantial impact on bacterial community composition than physicochemical factors. Most human-associated Bacteroides and Mycobacterium were eliminated during the wastewater treatment process in both WWTPs. The bacterial community richness in WWTP A was significantly higher than that in WWTP B. The results of this study will provide insights into the potential problems that exist in WWTPs. In turn, these insights can enable the efficient and stable operation of WWTPs and help prevent the spread of pathogens.

Bacterial Community Structure and Dynamic Changes in Different Functional Areas of a Piggery Wastewater Treatment System

Chemicals of emerging concern (CEC) in pig farm breeding wastewater, such as antibiotics, will soon pose a serious threat to public health. It is therefore essential to consider improving the treatment efficiency of piggery wastewater in terms of microorganisms. In order to optimize the overall piggery wastewater treatment system from the perspective of the bacterial community structure and its response to environmental factors, five samples were randomly taken from each area of a piggery’s wastewater treatment system using a random sampling method. The bacterial communities’ composition and their correlation with wastewater quality were then analyzed using Illumina MiSeq high-throughput sequencing. The results showed that the bacterial community composition of each treatment unit was similar. However, differences in abundance were significant, and the bacterial community structure gradually changed with the process. Proteobacteria showed more adaptability to an anaerobic environment than Firmicutes, and the abundance of Tissierella in anaerobic zones was low. The abundance of Clostridial (39.02%) and Bacteroides (20.6%) in the inlet was significantly higher than it was in the aerobic zone and the anoxic zone (p < 0.05). Rhodocyclaceae is a key functional microbial group in a wastewater treatment system, and it is a dominant microbial group in activated sludge. Redundancy analysis (RDA) showed that chemical oxygen demand (COD) had the greatest impact on bacterial community structure. Total phosphorus (TP), total nitrogen (TN), PH and COD contents were significantly negatively correlated with Sphingobacteriia, Betaproteobacteria and Gammaproteobacteria, and significantly positively correlated with Bacteroidia and Clostridia. These results offer basic data and theoretical support for optimizing livestock wastewater treatment systems using bacterial community structures.