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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #303123
Title: Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

Author
item WHELAN, GENE - Environmental Protection Agency (EPA)
item KIM, KEEWOK - Environmental Protection Agency (EPA)
item PARMAR, RAJBIR - Environmental Protection Agency (EPA)
item WOLFE, KURT - Environmental Protection Agency (EPA)
item GALVIN, MIKE - Environmental Protection Agency (EPA)
item DUDA, PAUL - Aqua Terra Consultants
item GRAY, MARK - Aqua Terra Consultants
item MOLINA, MARIRISA - Environmental Protection Agency (EPA)
item ZEPP, RICHARD - Environmental Protection Agency (EPA)
item Pachepsky, Yakov
item RAVENSCROFT, JOHN - Environmental Protection Agency (EPA)
item PRIETO, LOURDES - Environmental Protection Agency (EPA)
item KITCHENS, BRENDA - Environmental Protection Agency (EPA)

Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: 2/25/2014
Publication Date: 2/25/2014
Citation: Whelan, G., Kim, K., Parmar, R., Wolfe, K., Galvin, M., Duda, P., Gray, M., Molina, M., Zepp, R., Pachepsky, Y.A., Ravenscroft, J., Prieto, L., Kitchens, B. 2014. Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment. Meeting Abstract. Proceedings, 7th International Congress on Environmental Modelling and Software (iEMSs), San Diego, CA, June 15 - 19, 2014. International Environmental Modeling and Software Society, Manno, Switzerland, (2014). 1500-1507.

Interpretive Summary: Quantitative Microbial Risk Assessment (QMRA) is a type of computer modeling that estimates the probability that a particular surface water will contain pathogenic microorganisms harmful to human health. A large volume of data from disparate sources have to be collected to apply QMRA at a specific site. Currently, the data collection is done manually; a process that is very slow, tedious, prone to errors, and devoid of quality assurance. This presents a substantial obstacle for evaluating microbial water quality at specific sites and assessing mitigation measures. This work describes a working system for data collection automation based on combining existing EPA software packages. The system automatically creates all inputs required for simulations of flow and microbial fate/transport within a watershed. Different levels of data availability can be accommodated. This work will be useful in large number environmental management and conservation projects, irrigation water microbial quality assessments, and hydroepidemiologcal forecasts.

Technical Abstract: 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 determines 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.