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Date Permissions Signed


Date of Award

Summer 2022

Document Type

Masters Thesis

Department or Program Affiliation

Environmental Science

Degree Name

Master of Science (MS)


Environmental Sciences

First Advisor

Landis, Wayne G.

Second Advisor

Montaño, Manuel D.

Third Advisor

Harper, Stacey L.


Here we present an ecological risk assessment for a specific type of microplastic in the San Francisco Bay. There has been an increased interest in understanding and managing the impacts that microplastics may have on ecological systems because recent studies have shown that plastic particles are widespread in the environment and that exposure to these particles has toxicological effects. Until now, an ecological risk assessment for microplastics that meets the current standards for risk assessment, has not been completed. This study lays the groundwork for future ecological risk assessments of microplastics and identifies key uncertainties that need to be addressed. Using a Bayesian network relative risk model (BN-RRM), we determined the risk tire wear particles present to juvenile Chinook salmon and Northern anchovy. In past studies, BN-RRM has been a successful framework for regional scale ecological risk assessments of multi-stressor systems, allowing for the creation of a model with predictive capability and adaptive potential as new data become available. The BN-RMM is parameterized for each risk region with tire wear particle environmental concentration data collected by the San Francisco Estuary Institute, plastic particle toxicity data generated by Oregon State University, and site-specific water quality, chemical, and land use data from regional databases. Relative risk was then calculated for each risk region and spatial gradients of risk were determined. Results indicate a relatively low risk for juvenile Chinook salmon and Northern anchovy at current tire wear particle concentrations in the San Francisco Bay. This risk assessment confirms that, with the data that is currently available, a quantitative, spatially specific risk assessment is possible. Additionally, Bayesian networks are an excellent tool for modeling the complex and uncertain nature of microplastics. This study is funded by the National Science Foundation Growing Convergence Research Grant (1935018) program.




microplastic, tire wear particles, ecological risk assessment, risk assessment, San Francisco Bay


Western Washington University

OCLC Number


Subject – LCSH

Tires--Erosion--California--San Francisco Bay; Microplastics--Environmental testing--California--San Francisco Bay; Microplastics--Toxicology--California--San Francisco Bay; Fishes--Effect of contaminated sediments on--California--San Francisco Bay; Bayesian statistical decision theory

Geographic Coverage

San Francisco Bay (Calif.)




masters theses




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