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ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Environmentally Integrated Dairy Management Research » Research » Publications at this Location » Publication #376565

Research Project: Improving Nutrient Use Efficiency and Mitigating Nutrient and Pathogen Losses from Dairy Production Systems

Location: Environmentally Integrated Dairy Management Research

Title: Assessing the risk of acute gastrointestinal illness (AGI) acquired through recreational exposure to combined sewer overflow-impacted waters in Philadelphia: A quantitative microbial risk assessment

Author
item MCGINNIS, SHANNON - TEMPLE UNIVERSITY
item Burch, Tucker
item MURPHY, HEATHER - TEMPLE UNIVERSITY

Submitted to: Microbial Risk Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/21/2021
Publication Date: 3/19/2022
Citation: McGinnis, S.M., Burch, T.R., Murphy, H.M. 2022. Assessing the risk of acute gastrointestinal illness (AGI) acquired through recreational exposure to combined sewer overflow-impacted waters in Philadelphia: A quantitative microbial risk assessment. Microbial Risk Analysis. 20:100189. https://doi.org/10.1016/j.mran.2021.100189.
DOI: https://doi.org/10.1016/j.mran.2021.100189

Interpretive Summary: Zoonotic gastrointestinal pathogens (e.g., Cryptosporidium and Salmonella) can be transported via point and non-point processes to surface water from both human and livestock fecal material, and when present in large enough quantities, represent a public health risk for swimming and other recreational activities. However, the relative risks of human-impacted versus livestock-impacted surface waters have not been well-defined in the scientific literature. This study examines surface water contamination caused by human fecal material in combined sewer overflows (CSOs) impacting sites used for recreational water activities. Quantitative microbial risk assessment (QMRA) was used with site-specific water quality data in Philadelphia to estimate the risk of acute gastrointestinal illness (AGI) due to a variety of pathogens present in both human and livestock fecal material and for both CSO-impacted and non-impacted conditions. Results indicate that risk estimates may exceed USEPA standards at the sites studied, though risk estimates were also sensitive to assumed inputs. This study provides valuable estimates of public health risks associated with surface waters impacted by human fecal material, which can be used to place corresponding risks associated with livestock-impacted surface water in context and help delineate the relative contributions of each fecal source to overall public health impacts of recreational water use.

Technical Abstract: Combined sewer overflows (CSOs) are known contributors of human fecal pollution in urban waterways. Exposure to these waterways can occur during recreational activities, including swimming, wading, playing, boating, and fishing. This study uses quantitative microbial risk assessment (QMRA) to estimate the risk of acute gastrointestinal illness (AGI) among those who recreate along CSO-impacted surface water bodies in Philadelphia. Data were collected from two creeks and one river in Philadelphia from June-August 2017-2019. In total 69 water samples were collected and analyzed for concentrations of HF183. Observational data on the types of recreational activities that occurred and the number of individuals exposed per day were also collected. QMRA models were developed for each exposure scenario using data collected during CSO-impacted (within 24 hours of a CSO) and non-impacted (more than 24 hours after a CSO) conditions. In addition, the number of disease cases expected due to these activities during the June-August summer months were estimated. Results found that the estimated risk of illness due to recreational exposures at all three sites may exceed the US EPA’s acceptable risk threshold of 30 illnesses per 1,000 exposed under both CSO-impacted and non-impacted conditions. Results also highlight the importance of the norovirus dose-response model in QMRA estimates.