Date of Thesis
Spring 2026
Description
Nearly half of all surface waters in the United States are currently classified as impaired, rendering them unsuitable for fundamental uses such as swimming or fishing (Erickson et al., 2025). As the state with the second-highest total waterway length in the nation – spanning over 83,000 miles – Pennsylvania is a large contributor to watersheds, most notably the Chesapeake Bay. Given the Bay's 17:1 land-to-water ratio – the highest of any estuary in North America – local land-use practices directly influence downstream water quality (Quick Reference Guide for Best Management Practices, 2022). Despite the ambitious goals of the Clean Water Act (1972), regulatory gaps and the persistence of non-point source pollution have left many waterways unsafe decades later. In Union County, Pennsylvania, the Buffalo Creek and Limestone Run watersheds are currently designated as impaired for primary contact recreation due to 'Bacteria and Other Microbes' (US EPA, 2020). While traditional Fecal Indicator Bacteria (FIB), such as E. coli, confirm the presence of contamination, they lack the specificity required to distinguish between human and animal origins. This study addresses these monitoring gaps by (1) utilizing Microbial Source Tracking (MST) to pinpoint host-specific fecal contributors and (2) employing Quantitative Microbial Risk Assessment (QMRA) to model the probabilistic risk of human illness associated with these sources.
In this study, 66 water samples were collected across eleven sites in Union County to assess physicochemical parameters and fecal source markers. The study utilized four quantitative polymerase chain reaction (qPCR) assays targeting human, bovine, swine, and avian sources, alongside a general E. coli qPCR assay. The results revealed a majority of sites experiencing mixed-source pollution, with over 50% of samples containing both human- and animal-associated markers. Bovine and human markers emerged as the primary drivers of contamination; the bovine marker represented the highest host-associated concentration at the majority of sites, while human markers were a secondary but significant contributor. Statistical analysis revealed a strong correlation between precipitation and marker abundance, indicating that non-point source runoff is the primary transport mechanism for both agricultural discharge and effluent from suspected septic system failures. Furthermore, the detection of the E. coli qPCR assay in 98% of samples validates the potential for molecular qPCR methods to detect and quantify bacteria in Union County waterways.
Also in this study, quantitative microbial risk assessment (QMRA) – a probabilistic framework to simulate risk scenarios – was utilized to better understand exposure events for recreators across the watersheds. By converting pathogen doses into probabilities of infection, the model provided a site-specific risk profile compared against the EPA’s risk-based threshold of 32 illnesses per 1,000 events (Recreational Water Quality Criteria, 2012). The findings revealed a discrepancy between bacteria counts and quantitative microbial risk assessment; while QMRA models suggested that median risk levels are generally "acceptable" for primary contact, the 95th percentile risk estimates – representing high-risk, storm-driven scenarios – consistently exceeded risk-based thresholds. These results demonstrate that relying solely on median values can be misleading, as they fail to capture pathogen loads that may occur during storm events. Ultimately, the host-specificity provided by MST techniques allows for better suited engineering designs and targeted Best Management Practices (BMPs). By pinpointing specific sources, Union County can transition toward effective management strategies that prioritize both infrastructure maintenance for human waste and improved manure management for agricultural runoff. This research establishes the foundational data necessary to safeguard Union County’s waterways for the farmers and recreators who depend on them.
Keywords
microbial source tracking, quantitative microbial risk assessment
Access Type
Masters Thesis
Degree Type
Master of Science in Environmental Engineering
Major
Environmental Engineering
First Advisor
Deborah Sills
Recommended Citation
Dunn, Tyler, "Coupling Microbial Source Tracking with Quantitative Microbial Risk Assessment to Evaluate Fecal Bacterial Pollution in Union County" (2026). Master’s Theses. 313.
https://digitalcommons.bucknell.edu/masters_theses/313