Air monitoring of tire-derived chemicals in global megacities using passive samplers
Bibliographic record
Abstract
Pollution from vehicle tires has received world-wide research attention due to its ubiquity and toxicity. In this study, we measured various tire-derived contaminants semi-quantitatively in archived extracts of passive air samplers deployed in 18 major cities that comprise the Global Atmospheric Passive Sampling (GAPS) Network (GAPS-Megacities). Analysis was done on archived samples, which represent one-time weighted passive air samples from each of the 18 monitoring sites. The target analytes included cyclic amines, benzotriazoles, benzothiazoles, and p-phenylenediamine (PPD) derivatives. Of the analyzed tire-derived contaminants, diphenylguanidine was the most frequently detected analyte across the globe, with estimated concentrations ranging from 45.0 pg/m3 in Beijing, China to 199 pg/m3 in Kolkata, India. The estimated concentrations of 6PPD-quinone and total benzothiazoles (including benzothiazole, 2-methylthio-benzothiazole, 2-methyl-benzothiazole, 2-hydroxy-benzothiazole) peaked in the Latin American and the Caribbean region at 1 pg/m3 and 100 pg/m3, respectively. In addition, other known tire-derived compounds, such as hexa(methoxymethyl)melamine, phenylguanidine, and various transformation products of 6PPD, were also monitored and characterized semi-quantitatively or qualitatively. This study presents some of the earliest data on airborne concentrations of chemicals associated with tire-wear and shows that passive sampling is a viable techniquefor monitoring airborne tire-wear contamination. Due to the presence of many tire-derived contaminants in urban air across the globe as highlighted by this study, there is a need to determine the associated exposure and toxicity of these chemicals to humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".