Thirdhand smoke from tobacco, e-cigarettes, cannabis, methamphetamine and cocaine: Partitioning, reactive fate, and human exposure in indoor environments
Bibliographic record
Abstract
A source of chemical exposure to humans, thirdhand smoke (THS) refers to the contamination that persists indoors following the cessation of a smoking event. The composition of thirdhand smoke depends on the type of substance from which it originates. Although past studies have investigated the effects of tobacco THS on indoor air quality and human health, few have focused on the chemical composition and health impacts of other sources and components of THS. Here we review the state of knowledge of the composition and partitioning behavior of various types of indoor THS, with a focus on THS from tobacco, e-cigarettes, cannabis, and illicit substances (methamphetamine and cocaine). The discussion is supplemented by estimates of human exposure to THS components made with a chemical fate and exposure model. The modeling results show that while very volatile THS compounds (i.e., aromatics) are likely to be taken up by inhalation, highly water-soluble compounds tended to be dermally absorbed. Conversely, minimally volatile THS compounds with low solubility are predicted to be ingested through hand-to-mouth and object-to-mouth contact.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".