Greenspaces can reduce the level of airborne microplastic contamination in urban environments: Evidence from a lichen biomonitoring study
Bibliographic record
Abstract
Microplastics (MPs) have been found across a variety of environments, nonetheless few studies have evaluated atmospheric MPs. In this study, airborne MP contamination was investigated using transplants of the fruticose lichen Evernia prunastri in urban sites. Lichen transplants were exposed for seven weeks (April to June 2023) in parking lots (n = 9) and urban parks (n = 9) in the city of Pisa (Tuscany, Central Italy); in parallel, native samples from rural areas (n = 4) were also investigated. The overall aim was the characterization of MPs in terms of number, shape, size and polymer composition (via Fourier Transform Infrared spectroscopy) under different environmental conditions. Further, the positive role of green urban areas in buffering atmospheric MPs was assessed. We found MPs, including fragments, fibres and tyre wear particles, across all sites. The average number of MPs (per gram dry weight of lichen) significantly increased from rural areas (2 ± 0.4 MPs/g dw) to urban parks (7 ± 1.1 MPs/g dw) and parking lots (16 ± 4.1 MPs/g dw). Average daily MP deposition rates across sites in urban areas was in the range of 12–143 MPs/m 2 /d, suggesting that inhabitants are exposed to varying levels of airborne MPs. There was no difference in the length of the fibres between parking lots and urban parks; however, longer fragments and shorter tyre wear particles were found in parking lots. Polyethylene terephthalate was the dominant polymer detected across sites. The transplants maintained their overall vitality after the exposure (assessed by chlorophyll a fluorescence emission analysis), similar to native samples from rural areas, suggesting that the exposure had a negligible effect on lichen metabolism. Overall, our results suggest that lichen transplants are effective biomonitors of atmospheric MPs in urban areas, and that the presence of greenspaces (parks) in urban environments can significantly buffer the level of atmospheric MPs.
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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 | 0.000 | 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) | 0.001 | 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".