Analysis of activated sludge microbial communities in a full-scale activated sludge wastewater treatment system

Urban development relies on biological wastewater treatment to metabolize organic material, nutrients, and transform pollutants before entering the receiving environment. Activated sludge is a commonly used biological treatment process and hosts a complex and largely unidentified microbial community. Wastewater treatment operators utilize various monitoring parameters to optimize removal efficiencies in the activated sludge system; yet a knowledge gap exists regarding the specific roles certain microbes play in water treatment, and how microbial community data can be generated and used for routine process monitoring. This thesis studied the benefits of using microbial community composition as an additional monitoring parameter for wastewater operators to utilize for making informed decisions. Activated sludge samples were taken twice a week at a regional wastewater treatment plant for over a year and analyzed using a full-length 16S rRNA amplicon sequencing method with the Nanopore platform. To discern which microorganisms were active in the wastewater treatment process, an ecological genomics-based mass balance model (EGMB) was established using full-length 16S rRNA zero-radius operational taxonomic units (zOTU’s). Previous EGMB models utilized short read data that have lower taxonomic resolution than long-read data, presenting an opportunity here to develop a more accurate model. The EGMB model was able to accurately distinguish process-critical species from inactive immigrating species, find their seasonal differences, and predict an incoming process disturbance. An additional analysis was conducted on the activated sludge microbial community to determine which operational parameters and influent characteristics had the greatest influence on community composition and found temperature and stirred sludge volume index to have the highest association with the active microbial community.