Comparison of Lichens and Mosses as Biomonitors of Airborne Microplastics
Bibliographic record
Abstract
The atmosphere is an important pathway for microplastic (MP) transport; however, observations are limited, as traditional sampling methods are generally labor-intensive. Biological monitors (biomonitors) have been widely used as a simple alternative to determine the abundance or presence of anthropogenic pollutants. Here, we compared the effectiveness of co-located lichen and moss species as biomonitors of the atmospheric deposition of microplastics. Samples of the epiphytic lichen Evernia prunastri and the epigeic moss Pseudoscleropodium purum were collected from five remote areas of central Italy. A total of 154 MPs were found across all samples, 93.5% of which were fibers and 6.5% were fragments. The accumulation of MPs for lichens (range of 8–12 MP/g) was significantly lower than for mosses (12–17 MP/g), which might be related to their structural characteristics or habitat positions (epiphytic versus epigeic). Nonetheless, higher accumulation facilitates analytical determination and provides greater separation from the limit of detection, suggesting that mosses are preferred over lichens for studying the deposition of airborne MPs. This study further suggests that biomonitoring may be an effective tool to assess the spatial distribution of atmospheric microplastics, which is a key requirement for the development of waste mitigation policies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".