Are northern communities an overlooked source of microplastics and tire wear particles in the Arctic?
Bibliographic record
Abstract
Microplastic particles (plastic 1 µm to 5 mm in length) are a contaminant of emerging concern in Arctic environments; nonetheless, few studies have evaluated atmospheric microplastics in Arctic communities. This study investigated microplastics and tire wear particles across 16 sites in the community of Iqaluit, Nunavut (population = 7,429) using road dust as an indicator of atmospheric microplastic deposition (size detection limit >50 µm). The mean concentration of microplastics (excluding tire wear particles), ranged from 36.5 ± 68.4 µg/g (5.41 ± 4.69 n/g) in industrial sites and 73.4 ± 121 µg/g (6.21 ± 4.46 n/g) in commercial sites and non-fibrous microplastics ( i.e ., fragments, films, and foams) were dominant across the study area. Various polymers were identified using Fourier-Transform Infrared spectroscopy in Attenuated Total Reflectance down to a particle size of 100 µm. The dominant polymers being polyethylene terephthalate (15%), polyester (15%), polymethyl acrylate (15%), and polystyrene (15%). Further, based on the results of the microplastic diversity integrated index, commercial and industrial regions were composed of unique microplastic communities. The mean concentration of tire wear particles (dominated by rubber; 27%) in road dust was significantly greater than other microplastics, ranging from 83.2 ± 49.1 µg/g (49.3 ± 30.0 n/g) in industrial sites to 481 ± 514 µg/g (102 ± 132 n/g) in commercial sites. The concentration of microplastics and tire wear particles in Iqaluit was consistent with observations from metropolitan cities, suggesting Arctic communities may be a substantial local source of atmospheric microplastics and tire wear particles to surrounding Arctic ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".