Ultrafine Particle Generation from Ozone Oxidation of Cannabis Smoke
Bibliographic record
Abstract
Cannabis smoke is a complex aerosol mixture, featuring characteristic monoterpenes and sesquiterpenes which are susceptible to reaction with ozone and other oxidants. These reactions form less-volatile species which can contribute to secondary organic aerosol (SOA) and ultrafine particle (UFP) formation. In this work, the reaction of ozone with cannabis smoke was observed in an environmental chamber. Particle size distribution, and gas-phase and particle-phase composition were monitored in real time. The diameter of primary particles ranged from 10 –1 to 1 μm. Ultrafine particle formation occurred when cannabis smoke was exposed to ozone levels greater than 10 ppb, over the entire observed primary particle concentration range (1030–4580 μg m –3 ). Gas-phase measurements indicate that monoterpene and sesquiterpene levels decayed rapidly upon ozone exposure, while oxygen-containing species were formed during oxidation. On the other hand, measurements of particle composition showed an increase in nitrogen-containing species during oxidation. Although ozone was the only oxidant added to cannabis smoke in the chamber, it is believed that the OH radical plays an important role in the oxidation mechanism, where OH results from the reaction of ozone with terpenes and sesquiterpenes. Overall, smoking cannabis in ozone-rich environments, both indoors and outdoors, will likely lead to UFP formation.
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 | 0.000 |
| 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".