A Multicompartment Assessment of Microplastic Contamination in Semi-remote Boreal Lakes
Bibliographic record
Abstract
Abstract Microplastic contamination is ubiquitous across the globe, even in remote locations. Still, the sources and pathways of microplastics to such locations are largely unknown. To investigate microplastic contamination in a semi-remote location, we measured microplastic concentrations in nine oligotrophic lakes within and around the International Institute for Sustainable Development—Experimental Lakes Area in northwestern Ontario, Canada. Our first objective was to establish ambient concentrations of microplastics in bottom sediments, surface water, and atmospheric deposition in semi-remote boreal lakes. Across all lakes, mean shallow and deep sediment microplastic concentrations, near-surface water microplastic concentrations from in situ filtering, and dry atmospheric microplastic deposition rates were 551 ± 354 particles kg−1, 177 ± 103 particles kg−1, 0.2 ± 0.3 particles L−1, and 0.4 ± 0.2 particles m−2 day−1, respectively. Our second objective was to investigate whether microplastic contamination of these lakes is driven by point sources including local runoff and direct anthropogenic inputs or nonpoint sources such as atmospheric deposition. Lakes were selected based on three levels of anthropogenic activity—low, medium, and high—though activity levels were minimal across all study lakes compared with highly populated areas. Whereas a positive correlation would indicate that point sources were a likely pathway, we observed no relationship between the level of anthropogenic activity and microplastic contamination of surface water. Moreover, the composition of microplastics in surface water and atmospheric deposition were similar, comprising mostly polyester and acrylic fibers. Together, these results suggest that atmospheric deposition may be the main pathway of microplastics to these remote boreal lakes. Environ Toxicol Chem 2024;43:999–1011. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".