Using SWAT, Bacteroidales microbial source tracking markers, and fecal indicator bacteria to predict waterborne pathogen occurrence in an agricultural watershed.
Frey, S.K., Topp, E., Edge, T.A., Fall, C., Gannon, V.P.J., Jokinen, C.C., Marti, R., Neumann, N.F., Ruecker, N.J., Wilkes, G.A., and Lapen, D.R. (2013). "Using SWAT, Bacteroidales microbial source tracking markers, and fecal indicator bacteria to predict waterborne pathogen occurrence in an agricultural watershed.", Water Research, 47(16), pp. 6326-6337. doi : 10.1016/j.watres.2013.08.010 Access to full text
Developing the capability to predict pathogenic microorganism presence in surface water is important for reducing the risk that such organisms pose to human health. In this study, three primary data availability scenarios (measured stream flow and water quality, modelled stream flow and water quality, and host-associated Bacteroidales) are investigated within a Classification and Regression Tree Analysis (CART) framework for classifying pathogen (E. coli 0157:H7, Salmonella, Campylobacter, Cryptosporidium, and Giardia) presence and absence (P/A) in the outlet of a 178 km2 agricultural watershed. To provide modelled data, a Soil Water Assessment Tool (SWAT) model was developed to predict stream flow, total suspended solids (TSS), nutrient, and indicator bacteria loads; however, the model was only successful for flow and nutrient simulations, and did not accurately simulate TSS and indicator bacteria transport. Also, the SWAT model was not sensitive to an observed reduction in the cattle population within the watershed that may have resulted in significant reduction in E. coli concentrations and Salmonella detections. Results show that when combined with air temperature and precipitation, SWAT modelled stream flow and nutrient concentrations were useful for classifying pathogen P/A using CART methodology. From the host-associated Bacteroidales markers employed in CART analysis, ruminant was found to be most important for pathogen P/A classification; while from the measured data, air temperature, stream flow, and total P were most important. Results indicate a close relationship between cattle pollution and pathogen occurrence in this watershed, and an especially strong link between the cattle population and Salmonella detections.
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