Synergy between quantitative microbial source tracking (qMST) and quantitative microbial risk assessment (QMRA): A review and prospectus

Abstract

The use of microbial source tracking (MST) marker genes has grown in recent years due to the need to attribute point and non-point fecal contamination to specific sources. Quantitative microbial risk assessment (QMRA) is a modeling approach used to estimate health risks from exposure to feces-contaminated water and associated pathogens. A combination of these approaches [quantitative MST (qMST) and QMRA] can provide additional pathogen-related information for prioritizing and addressing health risks, compared to reliance on conventional fecal indicator bacteria (FIB). To inform expansion of this approach, a review of published qMST-QMRA studies was conducted to summarize the state of the science and to identify research needs. The reviewed studies primarily aimed to identify what levels of MST marker genes in hypothetical recreational waterbodies would exceed the United States Environmental Protection Agency (USEPA) risk benchmarks for primary contact recreators. The QMRA models calculated relationships between MST marker gene(s) and reference pathogens based on published data in the literature. The development of a robust, accurate relationship was identified as an urgent research gap for qMST-QMRA. This metric requires additional knowledge to quantify the relationship between MST marker genes and the degree of variability in decay of pathogens as a dynamic function of environmental conditions and combinations of fecal sources at multiple spatial and temporal scales. Improved characterization of host shedding rates of host-associated microorganisms (i.e., MST marker genes), as well as fate and transport of these microorganisms and their nucleic acids, would facilitate expansion of this approach to other exposure pathways. Incorporation of information regarding the recovery efficiency, and host-specificity of MST marker genes into QMRA model parameters, and the sensitivity analysis, would greatly improve risk management and site-specific water monitoring criteria.