An Environmental Chamber Study of the Characteristics of Air Pollutants Released from Environmental Tobacco Smoke
Bibliographic record
Abstract
Environment tobacco smoke (ETS) is an important source of anthropogenic pollution in indoor environments. This research reports an environmental chamber study of pollutants released from ETS generated by smoking cigarettes in the chamber. Six cigarettes samples sold in Hong Kong and China were characterized. Gaseous pollutants: carbon monoxide (CO), sulphur dioxide (SO2), nitric oxide (NO), nitrogen dioxide (NO2), methane (CH4), non-methane hydrocarbon (NMHC), carbonyls and volatile organic compounds (VOCs); and particulate matter (PM), including organic carbon (OC), elemental carbon (EC) and total carbon (TC), were determined using online and offline analytical methods during smoking and post-smoking periods. Acetaldehyde, acetone and formaldehyde were the three most abundant carbonyls. A total of 18 aromatic and chlorinated VOCs were quantified. Among these, benzene and toluene were the two most abundant VOCs. OC was more dominant (> 93% of TC) than EC. The amounts of tar and nicotine in the cigarettes could have a direct correlation with the PM emitted. Menthol, an additive in cigarettes, could also contribute to the ETS pollutants. The indoor ETS could be removed by a higher air exchange rate, which would also minimize secondary VOC formation.
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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 | 0.000 | 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 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".