Impact of smoking intensity and device cleaning on IQOS emissions: comparison with an array of cigarettes
Bibliographic record
Abstract
SIGNIFICANCE: IQOS is a heated tobacco product that has been widely advertised by Philip Morris International (PMI) as a reduced-exposure product compared with cigarettes. Reduced exposure results from reduced emission of toxicants which could be influenced by product constituents and user behaviour. This study aims to assess the influence of user behaviour, including device cleaning and puffing parameters, on toxicant emissions from IQOS. METHODS: IQOS aerosols were generated by a smoking machine using the combination of two cleaning protocols (after 1 stick vs 20 sticks) and five puffing regimes (including standard cigarette puffing regimes and IQOS-tailored regimes). The generated aerosols were analysed by targeted methods for phenol and carbonyl quantification, and by chemical screening for the identification of unknown compounds. RESULTS: Puffing parameters significantly affected phenol and carbonyl emissions while device cleaning had no effect. Harsher puffing conditions like more, longer, and larger puffs yielded higher levels for most toxicant emissions. Comparing the obtained data with data reported by PMI on 50 cigarette brands smoked under different puffing regimes showed various trends for phenol and carbonyl emissions, with IQOS emissions sometimes higher than cigarettes. Also, the chemical screening resulted in the tentative identification of ~100 compounds in the IQOS aerosols (most of limited toxicity data). CONCLUSION: This study showed that puffing parameters, but not device cleaning, have significant effects on carbonyl, phenol and other emissions. Data analysis highlighted the importance of comparing IQOS emissions with an array of commercial cigarettes tested under different puffing regimes before accepting reduced exposure claims.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".