Regional sources drive atmospheric microplastic deposition at rural background sites
Bibliographic record
Abstract
) using pleurocarpous moss collected from 33 background rural sites across Tuscany, Central Italy. A total of 288 MPs (>50-5000 μm) were found across all sites, dominated by fibres at 86.8 % and tire wear particles at 4.9 %. Given the dominance of textile fibres, polyethylene terephthalate was the dominant polymer at 29.2 %; nonetheless, the diversity of polymers also suggested local agricultural sources, such as plastic mulch (polyethylene and copolyester, both at 12.5 %) and agricultural superabsorbent hydrogel polymers (polyacrylic acid at 16.7 %). The accumulation of MPs ranged from 1.3 to 11.6 MPs per gram of moss dry weight (median 4.8 ± 2.3 MP/g) and estimated mass concentration ranged from 0.3 to 116.8 μg/g (median 2.9 ± 2.1 μg/g). Median particle length was 650 μm and median particle mass was 0.5 μg, suggesting that atmospheric transport was the primary pathway for these small lightweight particles. The population within a 10 km buffer, distance to urban centres, and moss tissue content of chromium (Cr) and nickel (Ni) were significantly associated with airborne MPs, suggesting that MP concentrations were primarily influenced by local and regional-scale anthropogenic factors within a range of 10-100 km, rather than long-range sources. The sources of Cr and Ni are primarily geogenic, originating from ultramafic rocks, particularly ophiolites, which are a unique indicator of Tuscan aeolian dust emissions from agricultural fields or wind-blown soil particles. These findings highlight the potential of moss biomonitoring as a practical and scalable tool for the source assessment of atmospheric MP contamination on a regional scale. Further, our results identify agricultural plastics and urban centres as important regional sources of microplastics.
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".