First Advisor

Elise Granek

Date of Award

5-31-2020

Document Type

Thesis

Degree Name

Bachelor of Science (B.S.) in Environmental Science and University Honors

Department

Environmental Science

Language

English

Subjects

Sea surface microlayer, Microplastics -- Environmental aspects -- Washington (State) -- Gray's Harbor, Microplastics -- Morphology, Marine pollution -- Pacific Ocean

DOI

10.15760/honors.926

Abstract

The lack of baseline data regarding plastic debris moving through and accumulating in various environmental compartments creates a vast gap in the literature for a number of reasons. These include a current lack of codified methodology to characterize microplastics both in morphology and polymer type, as well as gaps regarding contamination control & validation techniques. In September of 2019, sea surface sampling took place off the Olympic Peninsula, WA with the goal of addressing the following three research questions. First, is variability in microplastic abundance driven by transect location, distance from shore, or both? Second, how does microplastic type, in morphology and polymer composition, vary relative to transect location and distance from shore? Third, how do environmental variables contribute to the patterns found in microplastic variability in abundance and type? The project goal is to provide a distribution-abundance baseline for sea surface microplastics (2-3m deep) along two transects: La Push & Grays Harbor in Washington, USA. Organic matter digestion was performed using a KOH solution & incubation procedure. After measuring, photographing and counting via a dissection microscope, lipophilic Nile Red dye (10µg/mL) was applied, and the MPs were fluoresced at 455nm wavelength & counted under the microscope again. We use the following metrics to count & characterize the MPs found: size (MP>63um), color, morphology (fragment, film, fiber, fiber bundle, foam, microbead, other), and plastic polymer type via micro-FTIR analysis. Microplastics count and type, independent environmental conditions, and geospatial location data will be analyzed using a t-test to compare transects. Multiple regressions will be used to identify relationships to environmental variables and an NMDS ordination to compare dissimilarity within the multivariate characterization data.

Rights

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Persistent Identifier

https://archives.pdx.edu/ds/psu/33191

Available for download on Friday, May 31, 2024

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