Exposure to secondhand and thirdhand smoke in private vehicles: Measurements in air and dust samples
Bibliographic record
Abstract
This study aimed to estimate airborne nicotine concentrations and nicotine, cotinine, and tobacco-specific nitrosamines (TSNAs) in settled dust from private cars in Spain and the UK. We measured vapor-phase nicotine concentrations in a convenience sample of 45 private cars from Spain (N = 30) and the UK (N = 15) in 2017–2018. We recruited non-smoking drivers (n = 20), smoking drivers who do not smoke inside the car (n = 15), and smoking drivers who smoke inside (n = 10). Nicotine, cotinine, and three TSNAs (NNK, NNN, NNA) were also measured in settled dust in a random subsample (n = 20). We computed medians and interquartile ranges (IQR) of secondhand smoke (SHS) and thirdhand smoke (THS) compounds according to the drivers’ profile. 24-h samples yielded median airborne nicotine concentrations below the limit of quantification (<LOQ) (IQR:<LOQ- < LOQ) in non-smokers’ cars, 0.23 μg/m3 (IQR:0.18–0.45) in cars of smokers not smoking inside, and 3.53 μg/m3, (IQR:1.74–6.38) in cars of smokers smoking inside (p < 0.001). Nicotine concentrations measured only while travelling increased to 21.44 μg/m3 (IQR:6.60–86.15) in cars of smokers smoking inside. THS concentrations were higher in all cars of smokers, and specially in cars of drivers smoking inside (nicotine: 38.9 μg/g (IQR:19.3–105.7); NNK: 28.5 ng/g (IQR:26.6–70.2); NNN: 23.7 ng/g (IQR:14.3–55.3)), THS concentrations being up to six times those in non-smokers’ cars. All cars of smokers had measurable SHS and THS pollution, the exposure levels were markedly higher in vehicles of drivers where smoking took place. Our results evidence the need for policies to prohibit smoking in vehicles, but also urge for more comprehensive strategies aiming towards the elimination of tobacco consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".