Determination of Selected Chemical Levels in Room Air and on Surfaces after the Use of Cartridge- and Tank-Based E-Vapor Products or Conventional Cigarettes
Bibliographic record
Abstract
There is an ongoing debate regarding the potential of secondhand exposure of non-users to various chemicals from use of e-vapor products (EVPs). Room air levels of 34 chemicals (nicotine, propylene glycol (PG), glycerol, 15 carbonyl chemicals, 12 volatile organic chemicals (VOCs), and four selected trace elements) were measured where EVPs and cigarettes were used by n = 37 healthy adult tobacco users in an exposure chamber. The products used were MarkTen® 2.5% Classic (Group I), a Prototype GreenSmoke® 2.4% (Group II), Ego-T® Tank with subjects’ own e-liquids (Group III) and subjects’ own conventional cigarettes (Group IV). Products were used under controlled conditions and 4-h ad libitum use. Background (without subjects) and baseline levels (with subjects) were measured. Cumulative 4-h. levels of nicotine, PG and glycerol measured were several-fold below the time-weighted average limits used in workplace exposure evaluation. Most the other chemicals (>75%) were at or below the limit of quantification during EVP use. Significant levels of chemicals (17 out of 34) were observed in Group IV. Overall, our results indicate that under the study conditions with the products tested, cumulative room air levels of the selected chemicals measured over 4-h were relatively small and were several-fold below the current occupational regulatory and consensus limits.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".