Transport, Fate, and Effects of Microplastics in a Rapidly Changing Arctic
Bibliographic record
Abstract
Microplastics (<5 mm) have been deemed a contaminant of emerging Arctic concern. Microplastics are multi-dimensional in that they range in morphology, size, and chemical cocktail (e.g., additives, sorbed contaminants) and are hypothesized to serve as an important transport mechanism for chemical contaminants to and within northern ecosystems. While microplastics and plastic additives have been identified across Arctic ecosystems, there is a need to better understand the transport, fate, and effects of microplastics and their additive chemicals. Here, I explore these three themes through a series of field observations and laboratory experiments. First, in Chapter 2 of this dissertation, I show that while seabirds may be an important transport pathway of microplastics to the Arctic, there are other regional sources that need to be considered. In Chapters 3 and 4, I explored the role of long-range atmospheric transport of microplastics via atmospheric deposition. In Chapter 3, using a high-Arctic ice core, I show an increasing trend of microplastics over time. I also observed a positive relationship between microplastics and organophosphate esters (a plastic additive), suggesting microplastics may act as a long-range transport pathway. In Chapter 4, I sampled snow from five sites at increasing distances from a local community. I observed a change in microplastic and polymer composition in sites closer to the local community suggesting that local sources of microplastics may also be important. In Chapter 5, I investigated the presence of microplastics and additive chemicals in wild-caught Arctic char (Salvelinus alpinus) and their habitat. Here, I show elevated concentrations of microplastics in Arctic char and their summer feeding habitat compared to previously reported observations. I also demonstrate translocation of microplastics to Arctic char. And finally, in Chapter 6, informed by observations in the previous chapters, I exposed larval fish to polyurethane, with and without chemical additives to assess the mechanisms of toxicity. Here, I show that the physical and chemical components of polyurethane affect growth and development, and that the physical particles may facilitate the bioaccumulation of plastic additive chemicals. Throughout my thesis, I highlight the importance of evaluating microplastics as a multi-dimensional contaminant - the physical and chemical properties may influence their fate, transport, and biological effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".