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Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment
Whelan, G., K. Kim, R. Parmar, K. Wolfe, M. Galvin, P. Duda, M. Gray, M. Molina, R. Zepp, Y. Pachepsky, J. Ravenscroft, L. Prieto, AND B. Kitchens. Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment. In Proceedings, 7th International Congress on Environmental Modelling and Software (iEMSs), San Diego, CA, June 15 - 19, 2014. International Environmental Modelling and Software Society, Manno, Switzerland, 1500-1507, (2014).
Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and human health effects relationships) to characterize potential health impacts/risks from exposure to pathogenic microorganisms. We demonstrate loosely connected IEM legacy technologies (SDMProjectBuilder, Microbial Source Module, HSPF, and BASINS) to support watershed-scale microbial source-to-receptor modeling, focusing on animal-impacted catchments. The coupled models automate manual steps in standard watershed assessments to expedite the process, minimize resources, increase ease of use, and introduce more science-based processes to the analysis. SDMProjectBuilder accesses, retrieves, analyzes, and caches web-based data. The Microbial Source Module provides estimates of microbial loading rates within a watershed; HSPF simulates flow and microbial fate/transport within a watershed; and BASINS provides a user interface to access/modify HSPF files and provide visualization tools. The assessment performs HUC-12 or pour point analyses; automates watershed delineation and data-collection; pre-populates HSPF input requirements, accounting for snow accumulation/melt, microbial fate/transport, and different time increments (hourly, daily, etc.); assigns NLDAS radar meteorological data automatically to individual HUC-12s when observed data are scarce, incorrect, or insufficient; and processes manure-based source terms to estimate manure/microbial loads on subwatersheds automatically, based on number of animals, septic systems, etc. that correlate to land-use patterns.
Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs), San Diego, CA, June 15-19, 2014.
Record Details:Record Type: DOCUMENT (PAPER IN NON-EPA PROCEEDINGS)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LAB
ECOSYSTEMS RESEARCH DIVISION