Atmospheric deposition drives microplastic contamination in remote lakes of Newfoundland, Canada
Bibliographic record
Abstract
Microplastics (MPs) have been identified in virtually all environments around the globe, posing a threat to both humans and nature. Though there have been numerous studies on MPs in aquatic environments, there is still a lack of knowledge on MPs in freshwater lakes, especially small endorheic lakes in remote high-latitude regions. In this study, we present evidence of MP pollution in sediments from lakes across Newfoundland, Canada. We found between 6000 and 24,000 MP kg −1 wet weight in lake sediments, with most MP particles between 2 and 10 μm in size. Polyvinyl chloride and polyurethane were the most commonly identified polymers, and they were also found in atmospheric samples collected during a hurricane that made landfall in Newfoundland in 2021. Given that lakes were located in sparsely populated areas and were either endorheic or had minimal inflow and outflow, local sources of MPs are negligible. Therefore, the observed MPs likely originate from atmospheric transport, delivered via rain, snow, or wind from distant sources. Once deposited, the lakes' closed-basin hydrology promotes particle retention, allowing MPs to accumulate in sediments over time. This study provides a baseline of MP pollution in the lakes of Newfoundland, which can support future studies investigating how atmospheric events may redistribute microplastics across the globe, especially in remote regions. • Microplastics found in remote Newfoundland lakes at up to 24,000 MP kg −1 wet weight. • Dominant polymers were PVC and polyurethane, also detected in hurricane air samples. • Atmospheric transport is a key source of microplastics in remote high latitude lakes. • Endoriech Lakes act as a long-term sink for microplastics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".