Snow as an indicator of atmospheric transport of anthropogenic particles (microplastics and microfibers) from urban to Arctic regions
Bibliographic record
Abstract
We report anthropogenic particles (APs) >100 µm, including microplastics and microfibers, from 70 surface snow samples collected from the urban Greater Toronto Area, remote and sparsely inhabited regions in the Yukon and Northwest Territories, and the unpopulated high Arctic. Concentrations and proportions of particles of anthropogenic origin were conservatively estimated after blank and “anthropogenic origin” corrections were performed based on visual analysis and micro-Fourier transform infrared spectroscopy (µFTIR). APs were dominated by microfibers (95%–100%), with variable concentrations across and within regions. Microfibers were distinguished as synthetic, regenerated semi-synthetic, anthropogenically modified cellulosic, and natural cellulosic or proteinaceous. Among microfibers with confirmed anthropogenic origin, most were polyester/PET (8%–22%) and semi-synthetic rayon (1%–18%), with anthropogenic cellulose comprising a small proportion (3%–7%) across all regional areas. Greater diversity of coloured nonfibrous particles (fragments, films, and foams) in settled regions (i.e., Greater Toronto Area (GTA) and Carcross, Yukon) suggests direct input from local sources. Back trajectory analyses performed for days leading up to sample collection showed high-frequency transport (>10%) from population centres exceeding 200 km distance. Our findings of APs in snow from uninhabited areas support the hypothesis that APs, especially microfibers, undergo long-range atmospheric transport whereby snow can scavenge and deposit APs in remote northern regions.
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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 | 0.000 | 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".