Citation:

Diedrich, A., M. Sivaganesan, J. Willis, A. Shrifi, AND O. Shanks. Genetic Fecal Source Identification in Urban Streams Impacted by Municipal Separate Storm Sewer System Discharges. PLOS ONE . Public Library of Science, San Francisco, CA, 18(1):e0278548, (2023). https://doi.org/10.1371/journal.pone.0278548

Impact/Purpose:

Fecal pollution is the number one biological contaminant in U.S. surface waters with polluted sites in every state. When present, fecal waste can pose a serious public health risk and can lead to severe economic burdens, especially in communities that rely on clean and safe water. Fecal pollution can originate from untreated sewage due to combined sewer overflows, spills, and leaky infrastructure. In response to this nationwide need, the U.S. EPA ORD maintains an active research program to develop, validate, implement, and provide technical support for tools to characterize fecal pollution sources in environmental waters. Information covered in this manuscript was prepared based on Region 3 RARE project objectives (SORD.0.0.0 - Temporary ORD Product for all non-RAP and Ancillary Research, ORD.0.0.0.300).

Description:

Municipal stormwater systems are designed to collect, transport, and discharge precipitation from a defined catchment area into local surface waters. However, these discharges may contain unsafe levels of fecal waste. Paired measurements of Escherichia coli, precipitation, three land use metrics determined by geographic information system (GIS) mapping, and host-associated genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), dog (DG3), and avian (GFD) fecal sources were assessed in 231 urban stream samples impacted by two or more municipal stormwater outfalls. Receiving water samples were collected twice per month (n = 24) and after rain events (n = 9) from seven headwaters of the Anacostia River in the District of Columbia (United States) exhibiting a gradient of impervious surface, residential, and park surface areas. Almost 50% of stream samples (n = 103) were impaired, exceeding the local E. coli single sample maximum assessment level (410 MPN/100 ml). Fecal scores (average log10 copies per 100 ml) were determined to prioritize sites by pollution source and to evaluate potential links with land use, rainfall, and E. coli levels using a recently developed censored data analysis approach. Dog, ruminant, and avian fecal scores were almost always significantly increased after rain or when E. coli levels exceeded the local benchmark. Human fecal pollution trends showed the greatest variability with detections ranging from 9.1% to 96.7% across sites. Avian fecal scores exhibited the closest connection to land use, significantly increasing in catchments with larger residential areas after rain events (p = 0.038; R2 = 0.62). Overall, results demonstrate that combining genetic fecal source identification methods with GIS mapping complements routine E. coli monitoring to improve management of urban streams impacted by stormwater outfalls.

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