Ambient Atmospheric Deposition of Anthropogenic Microfibers and Microplastics on the Western Periphery of Europe (Ireland)
Bibliographic record
Abstract
Microfibers (mf) are the most common type of microplastic in the environment. Few studies have focused on their abundance in atmospheric deposition in background environments. In the current study, we collected wet-only and bulk rainfall from four precipitation chemistry monitoring stations, primarily located in coastal areas around Ireland. Anthropogenic mf were observed in all samples; the average deposition across the four study sites was 80 mf m –2 day –1 . Wet-only mf deposition was 70 mf m –2 day –1 compared with bulk deposition of 100 mf m –2 day –1 . The wet-only collectors were estimated to capture ∼70% of the bulk collectors, suggesting that dry deposition makes up at least 30% of total deposition. Meteorological variables, i.e., relative humidity, rainfall volume, wind speed, and wind direction, were significantly related to mf abundance, suggesting that rainfall washout and air mass movement are important predictors of mf deposition in background regions. In total, 15% of all anthropogenic mf were identified as plastic. The most abundant polymer type was polyester or polyethylene terephthalate at 71%, followed by polyacrylonitrile at 11%, polyethylene at 11%, and polypropylene at 4%. The average deposition of plastic mf was 12 mf m –2 day –1 .
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".