A comprehensive analysis of microbial community differences in four morphologies of mainstream anaerobic ammonia oxidation systems using big-data mining and machine learning

Abstract

Achieving carbon neutrality in wastewater treatment plants relies heavily on mainstream anaerobic ammonia oxidation. However, the stability of this process is often compromised, largely due to the significant influence of microbial morphology. This study analyzed 208 microbial samples using bioinformatics and machine learning (ML) across four different morphologies: Suspended Sludge (SS), Biofilm, Granular Sludge (GS) and the Integrated Fixed-film Activated Sludge process (IFAS). The results revealed IFAS’s notably complex and stable community structure, along with the identification of endemic genera and common genera among the four microbial morphologies. Through co-occurrence network analysis, the interaction between microorganisms of various genera was displayed. Utilizing the Extreme Gradient Boosting (XGBoost) model, a ML modeling framework based on microbiome data was developed. The ML-based feature importance analysis identified LD-RB-34 as a key organism in SS and BSV26 was an important bacterium in IFAS. Additionally, functional bacteria KF-JG30-C25 occupied a higher proportion in GS, and Unclassified Brocadiaceae occupied a higher proportion in Biofilm. Furthermore, dissolved oxygen, temperature and pH were identified as the primary factors determining microbial communities and influencing anammox activity. Overall, this study deepens our understanding of bacterial communities to enhance the mainstream anammox nitrogen removal.