Comparison of Lichen and Moss Transplants for Monitoring the Deposition of Airborne Microfibers
Bibliographic record
Abstract
Interest in using lichens and mosses to monitor airborne microplastics is growing, but few studies have thoroughly compared their effectiveness as biomonitors. Here, we directly compare the ability of lichen and moss transplants collected from a rural area to accumulate microfibers (MFs) and Potentially Toxic Elements (PTEs) under the same deployment conditions. Transplants (n = 60; triplicates for both lichen and moss) were co-deployed on tree branches across a range of urban exposure sites (e.g., commercial and residential areas and urban parks) for 77 days in Siena, Italy. The results showed that both biomonitors accumulated similar amounts of MFs, in terms of counts and on a mass basis, but when expressed on a surface area basis, lichens showed significantly higher values. Irrespective of the metric, lichen and moss MF accumulation data were strongly correlated. In contrast, there was no correlation between MFs and PTEs, suggesting that their sources were different. MFs accumulated by lichen and moss transplants were dominated by polyethylene terephthalate (PET) and polypropylene polymers, suggesting that the main source of airborne MFs is synthetic textiles. Our results suggest that both lichen and moss transplants can be effectively used as low-cost monitors of atmospheric MFs in urban areas in support of the sustainable development goal of clean air.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".