An integrated environmental modeling framework for performing Quantitative Microbial Risk Assessments

Standardized methods are often used to assess the likelihood of a human-health effect from exposure to a specified hazard, and inform opinions and decisions about risk management and communication. A Quantitative Microbial Risk Assessment (QMRA) is specifically adapted to detail potential human-health risks from exposure to pathogens; it can include fate and transport models for various media, including the source zone (initial fecal release), air, soil/land surface, surface water, vadose zone and aquifer. The analysis step of a QMRA can be expressed as a system of computer-based data delivery and modeling that integrates interdisciplinary, multiple media, exposure and effects models and databases. Although QMRA does not preclude using source-term and fate and transport models, it is applied most commonly where the source-term is represented by the receptor location (i.e., exposure point), so the full extent of exposure scenarios has not been rigorously modeled. An integrated environmental modeling infrastructure is, therefore, ideally suited to include fate and transport considerations and link the risk assessment paradigm between source and receptor seamlessly. A primary benefit of the source-to-outcome approach is that it allows an expanded view of relevant cause-and-effect relationships, which facilitate consideration of management options related to source terms and their fate and transport pathways. The Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) provides software technology for analysts to insert appropriate models and databases that fit the problem statement and design and construct QMRAs that are reproducible, flexible, transferable, reusable, and transparent. A sample application using different models and databases registered with FRAMES is presented. It illustrates how models are linked to assess six different manure-based contaminant sources, following three pathogens (Salmonella eterica, Cryptosporidium spp., and Escherichia coli O157:H7) to a receptor where exposures and health risk impacts are then evaluated. The modeling infrastructure demonstrates how analysts could use the system to discern which pathogens might be important and when, and which sources could contribute to their importance.