title: Coupling a continuous watershed-scale microbial fate and transport model with a stochastic dose-response model to estimate risk of illness in an urban watershed.
abstract: Within the United States, elevated levels of fecal indicator bacteria (FIB) remain the leading cause of surface water-quality impairments requiring formal remediation plans under the federal Clean Water Act's Total Maximum Daily Load (TMDL) program. The sufficiency of compliance with numerical FIB criteria as the targeted endpoint of TMDL remediation plans may be questionable given poor correlations between FIB and pathogenic microorganisms and varying degrees of risk associated with exposure to different fecal pollution sources (e.g. human vs animal). The present study linked a watershed-scale FIB fate and transport model with a dose-response model to continuously predict human health risks via quantitative microbial risk assessment (QMRA), for comparison to regulatory benchmarks. This process permitted comparison of risks associated with different fecal pollution sources in an impaired urban watershed in order to identify remediation priorities. Results indicate that total human illness risks were consistently higher than the regulatory benchmark of 36 illnesses/1000 people for the study watershed, even when the predicted FIB levels were in compliance with the Escherichia coli geometric mean standard of 126CFU/100mL. Sanitary sewer overflows were associated with the greatest risk of illness. This is of particular concern, given increasing indications that sewer leakage is ubiquitous in urban areas, yet not typically fully accounted for during TMDL development. Uncertainty analysis suggested the accuracy of risk estimates would be improved by more detailed knowledge of site-specific pathogen presence and densities. While previous applications of the QMRA process to impaired waterways have mostly focused on single storm events or hypothetical situations, the continuous modeling framework presented in this study could be integrated into long-term water quality management planning, especially the United States' TMDL program, providing greater clarity to watershed stakeholders and decision-makers.
authors.affiliation: Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061, United States. Electronic address: hehuan86@vt.edu.
authors.forename: Hehuan
authors.initials: H
authors.lastname: Liao
authors.affiliation: Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061, United States.
authors.forename: Leigh-Anne H
authors.initials: LA
authors.lastname: Krometis
authors.affiliation: Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061, United States; Center for Watershed Studies, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA 24061, United States.
authors.forename: Karen
authors.initials: K
authors.lastname: Kline
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D004926:Escherichia coli; D006801:Humans; D008962:Models, Theoretical; D018570:Risk Assessment; D014481:United States; D014505:Urban Population; D014871:Water Microbiology; D014881:Water Supply / Q000706:statistics & numerical data*; D000069578:Waterborne Diseases / Q000453:epidemiology*
keywords: Fecal indicator bacteria; Human illness risks; Quantitative microbial risk assessment; Total maximum daily load; Waterborne diseases