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Endogenous System Microbes as Treatment Process Indicators for Decentralized Non-potable Water Reuse
Citation:
Brinkman, N., S. Keely, E. Anneken, M. Jahne, AND J. Garland. Endogenous System Microbes as Treatment Process Indicators for Decentralized Non-potable Water Reuse. 11th IWA International Conference on Water Reclamation and Reuse, Long Beach, CA, July 23 - 27, 2017.
Impact/Purpose:
Presentation to deliver research results to the water reuse community.
Description:
Monitoring the efficacy of treatment strategies to remove pathogens in decentralized systems remains a challenge. Evaluating log reduction targets by measuring pathogen levels is hampered by their sporadic and low occurrence rates. Fecal indicator bacteria are used in centralized systems to indicate the presence of fecal pathogens, but are ineffective decentralized treatment process indicators as they generally occur at levels too low to assess log reduction targets. System challenge testing by spiking with high loads of fecal indicator organisms, like MS2 coliphage, has limitations, especially for large systems. Microbes that are endogenous to the decentralized system, occur in high abundances and mimic removal rates of bacterial, viral and/or parasitic protozoan pathogens during treatment could serve as alternative treatment process indicators to verify log reduction targets. To identify abundant microbes in wastewater, the bacterial and viral communities were examined using deep sequencing. Building infrastructure-associated bacteria, like Zoogloea, were observed as dominant members of the bacterial community in graywater. In blackwater, bacteriophage of the order Caudovirales constituted the majority of contiguous sequences from the viral community. This study identifies candidate treatment process indicators in decentralized systems that could be used to verify log removal during treatment. The association of the presence of treatment process indicators with real-time, continuous measurements made with on-line physical and chemical sensors will be discussed as a framework to monitor treatment integrity in decentralized systems.
