Chemical Characterization and Evolution of Cannabis Smoke in Indoor Environments
Bibliographic record
Abstract
The legalization of recreational cannabis use in several countries has led to an increased visibility of cannabis products and consumption methods. Cannabis smoke is chemically distinct from tobacco smoke, in that it contains many terpenes and cannabinoids which contribute to its unique aroma and physiological properties. Despite cannabis’ legal status in Canada and the rapid expansion of the commercial market, the chemical composition and fate of cannabis smoke remains poorly characterized relative to tobacco smoke. A better understanding of cannabis smoke emissions is necessary to mitigate unwanted exposure to smoke components and potentially harmful reaction products. The work presented herein aims to fill knowledge gaps regarding the chemical characterization and evolution of cannabis smoke through various computational and experimental methods. To begin, the fate of various cannabis smoke components was simulated using partitioning models, and a chemical fate and human exposure model. Cannabinoid components of cannabis smoke were predicted to favor organic surface films in indoor environments. Passive exposure to cannabinoids predominantly occurs by non-dietary ingestion, through contact with contaminated surfaces and subsequent hand-to-mouth contact. Next, the chemical evolution of cannabis smoke was experimentally observed in several multiphase oxidation studies conducted in a flow reactor, an environmental chamber, and a genuine indoor environment. Ozonolysis was identified as a major loss mechanism for delta-9-tetrahydrocannabinol (THC) in genuine smoke deposits, occurring on a timescale of hours to days. Further, ozone oxidation of cannabis smoke aerosol was identified as a source of ultrafine particle formation. Finally, the first measurements of free radicals in mainstream cannabis smoke using electron paramagnetic spectroscopy (EPR) and nitrone spin traps were performed, indicating that cannabis smoke is a source of both short-lived and environmentally persistent free radicals (EPFRs). Findings from this work emphasize the impacts of cannabis smoking on the amount of organic pollutants, particulate matter, and free radicals present in an indoor environment. Given that habitual cannabis use has been associated with an increased risk of major adverse cardiovascular events, further toxicological study of these pollutants may help to elucidate the biochemical mechanisms implicated in cannabis-associated pathology.
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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 | 0.000 | 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".