Open Collections

Sampara, Pranav

University of British Columbia

Biological nutrient removal within water resource recovery facilities (WRRFs) is driven by diverse microbes that transform carbon, phosphorus, and nitrogen present in untreated wastewater into environmentally safe concentrations. However, current bioprocess models rely on homogenous biomass measures and typically do not account for metabolic diversity. Thus, they are inadequate to accurately predict microbial and nutrient dynamics, and consequently the processes have reduced efficiency and sustainability. In contrast, trait-based frameworks can incorporate metabolic diversity by aggregating microbial populations based on their physiological traits, and could be used to improve mechanistic prediction and diagnosis of WRRF bioprocesses. To address this need, I presented a proof-of-concept trait-based process model for nitrification in wastewater systems based on highly-replicated respirometry, activity-based cell sorting, and metagenomics for trait-inference. I demonstrated its efficacy in predicting nitrogen and nitrifying community dynamics, outperforming conventional methods. This framework can thus aid in devising operational strategies for sustainable nutrient removal. While the above framework incorporates microbial community structure and potential function, inclusion of only the metabolically active microbes can further refine the trait-based bioprocess models. Here, I introduced a standardized framework for quantifying metabolic activity using quantitative stable isotope probing (qSIP) metagenomics, based on absolute concentrations of genomic features. By comparing existing approaches, I found that my proposed framework had higher accuracy, specificity, and sensitivity to quantify metabolic activity. I discussed strategies for optimal utilization of this framework, which can be applicable for various mixed microbiomes. I leveraged this qSIP framework to quantify metabolic activity of microbes participating in carbon and phosphorus removal in two full-scale WRRFs. This analysis revealed the interplay between carbon utilization and dynamics of active microbes, informing operational strategies for optimal phosphorus removal. Additionally, leveraging functional traits and absolute concentrations of active microbes, I identified several novel microbes potentially participating in phosphorus removal. Overall, the findings in this dissertation contribute to advancing sustainable wastewater treatment. Building upon this work, developments in correlative and confirmative multi-omic and physiological assessments could further improve formulation and calibration of ‘functional response-inferred’ (not just ‘functional potential-inferred’) trait-based models, and could be foundational in managing WRRF microbiomes for efficient and sustainable nutrient removal.

Text

2024-09-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89135

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/