Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment


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.

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@inproceedings{Whelan2014UsingIE, title={Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment}, author={Gene Whelan and Keewook Kim and Rajbir Parmar and Mike Galvin and Paul Duda and Marirosa Molina and Richard Zepp and Brenda Kitchens and G. Whelan}, year={2014} }