Using SWAT, Bacteroidales microbial source tracking markers, and fecal indicator bacteria to predict waterborne pathogen occurrence in an agricultural watershed

• Published in: Water research (Oxford) Vol. 47; no. 16; pp. 6326 - 6337
• Main Authors: Frey, Steven K., Topp, Edward, Edge, Thomas, Fall, Claudia, Gannon, Victor, Jokinen, Cassandra, Marti, Romain, Neumann, Norman, Ruecker, Norma, Wilkes, Graham, Lapen, David R.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15-10-2013; Elsevier
• Subjects: Agricultural watershed water quality; agricultural watersheds; Agriculture; air temperature; Applied sciences; Bacteria; Bacteria - isolation & purification; Bacteroidales; Campylobacter; cattle; Classification; Cryptosporidium; Environmental Monitoring; Escherichia coli; Escherichia coli O157; Exact sciences and technology; Feces - microbiology; Fresh Water - microbiology; Fresh Water - parasitology; Giardia; human health; indicator species; Indicators; Mathematical models; Microbial source tracking; Models, Theoretical; MST; nitrogen content; pathogen occurrence; Pathogens; Pollution; regression analysis; risk; Ruminantia; Salmonella; Soil and water assessment tool; Soil and Water Assessment Tool model; stream flow; Streams; surface water; SWAT; total suspended solids; Water borne pathogen prediction; Water Microbiology; Water quality; Water Supply - standards; Water treatment and pollution; Watershed modelling; Watersheds; SWAT; Bacteroidales; Agricultural watershed water quality; Microbial source tracking; Water quality; Watershed modelling; Water borne pathogen prediction; Soil and water assessment tool; MST; Salmonella; Microbiology; Campylobacteraceae; Escherichia coli; Identification; Modeling; Interstitial water; Transport process; Water borne pathogen prediction Watershed modelling; Pollution; Pathogenic; Watershed; Classification; Bacteria; Agriculture; Stream; Feces; Biological contamination; Public health; Enterobacteriaceae; Gram negative bacteria; Campylobacter; Protozoa; Cryptosporidium; Apicomplexa; Farming animal; Prediction; Biological indicator; Regression analysis; Marker; Suspended particle; Surface water; Pollution source
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2013.08.010

Developing the capability to predict pathogens in surface water is important for reducing the risk that such organisms pose to human health. In this study, three primary data source 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 (Escherichia coli 0157:H7, Salmonella, Campylobacter, Cryptosporidium, and Giardia) presence and absence (P/A) for 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), total N and total P, and fecal indicator bacteria loads; however, the model was only successful for flow and total N and total P 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 total P concentrations were useful for classifying pathogen P/A using CART methodology. From a suite of host-associated Bacteroidales markers used as independent variables in CART analysis, the ruminant marker was found to be the best initial classifier of pathogen P/A. Of the measured sources of independent variables, air temperature, precipitation, stream flow, and total P were found to be the most important variables for classifying pathogen P/A. 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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•SWAT modelled total N and total P and stream discharge were useful for pathogen classification.•Ruminant Bacteroidales were the most important MST markers for pathogen classification.•Air temperature, precipitation, streamflow, and total P were useful measured data for pathogen classification.•Reduced cattle population may have resulted in lowered Escherichia coli concentrations.•Modelled E. coli was not sensitive to land use change reflective of reduced cattle.