ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Marine Pollution
Assessing Shellfish Bed Water Exposure to Fecal Bacteria Pollution in Salish Sea: Three-Dimensional Modeling and Implications for Monitoring
Provisionally accepted
- 1Coastal Sciences Division, Pacific Northwest National Laboratory (DOE), Sequim, United States
- 2University of Washington Tacoma, Tacoma, United States
- 3US Environmental Protection Agency Region 10 Pacific Northwest, Seattle, United States
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Fecal bacteria (FB) contamination poses significant risks to shellfish safety and management in coastal and estuarine waters. Despite extensive pollution identification and correction efforts, FB contamination in shellfish-growing areas persists in the Salish Sea, highlighting the need to identify overlooked sources and better understand FB transport from riverine and shoreline inputs to shellfish beds. To address this, a high-resolution three-dimensional hydrodynamic model coupled with FB kinetics was developed and applied to a case study site in Salish Sea—Portage Bay—to simulate freshwater plume circulation, flushing dynamics, and bacterial transport. Daily FB loading from the major freshwater inflow— Nooksack River was generated by both linear interpolation and integrating a machine learning approach (XGBoost), trained on historical hydrological and meteorological data. The model successfully reproduced both the magnitude and seasonal variation of FB concentrations in Portage Bay for the year of 2021, demonstrating that simplified FB kinetics with first-order decay due to mortality was effective in this dynamic coastal environment with short flushing time. Model results identified the Nooksack River as the dominant far-field FB source, while scenario simulations showed that near-field coastal stormwater outfalls substantially elevated local FB levels following rainfall, particularly under low-flow conditions. The XGBoost prediction provided comparable or superior accuracy to linear interpolation, particularly during periods of missing observational data, by capturing short-term variability and event-driven loading more effectively. Integrating data-driven riverine FB inputs with mechanistic coastal numerical modeling provides a robust framework for operational forecasting of shellfish bed exposure risk and supports adaptive monitoring and management of shellfish growing areas in the Salish Sea and similar coastal systems.
Keywords: Fecal bacteria, machine learning, Salish Sea, Shellfish, three-dimensional numerical model
Received: 03 Nov 2025; Accepted: 10 Feb 2026.
Copyright: © 2026 Ni, Premathilake, Khangaonkar and Gockel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wenfei Ni
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