How much microplastic reaches the stratosphere? The example of road traffic-related emissions
Bibliographic record
Abstract
Depending on their size and shape, microplastic particles have the potential to be transported over great distances in the atmosphere, both vertically and horizontally. Recent studies have shown that they can even reach the stratosphere. However, there is a lack of information on the distribution and amount of microplastics in the stratosphere.Here, we estimate how much of microplastics from the second largest primary source can be found in the stratosphere. To investigate this, we use global road traffic-related emissions of microplastics - from tyres, road markings and polymer-modified bitumen - to simulate the atmospheric transport of particles of different sizes and spherical and cylindrical shapes using the Lagrangian particle dispersion model FLEXPART (Pisso et. al, 2019).When exposed to the ultraviolet (UV) light, microplastic particles degrade and can release halogen-containing gases such as chlorine and bromine. Forexample, neoprene, aka polychloroprene, which is present in tyres, contains around 40% chlorine by weight.The released bromine and chlorine compounds could be involved in the catalytic destruction of ozone, similar to the release of chlorofluorocarbons andhalons under the Montreal Protocol. Therefore, in addition to quantifying the amount of microplastics reaching the stratosphere, we also estimate the amount of chlorine and bromine that can potentially be released during UV degradation of microplastics.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".