Impact of Human Presence on Secondary Organic Aerosols Derived from Ozone-Initiated Chemistry in a Simulated Office Environment
Bibliographic record
Abstract
Several studies have documented reductions in indoor ozone levels that occur as a consequence of its reactions with the exposed skin, hair and clothing of human occupants. One would anticipate that consumption of ozone via such reactions would impact co-occurring products derived from ozone's reactions with various indoor pollutants. The present study examines this possibility for secondary organic aerosols (SOA) derived from ozone-initiated chemistry with limonene, a commonly occurring indoor terpene. The experiments were conducted at realistic ozone and limonene concentrations in a 240 m(3) chamber configured to simulate a typical open office environment. During an experiment the chamber was either unoccupied or occupied with 18-20 workers. Ozone and particle levels were continuously monitored using a UV photometric ozone analyzer and a fast mobility particle sizer (FMPS), respectively. Under otherwise identical conditions, when workers were present in the simulated office the ozone concentrations were approximately two-thirds and the SOA mass concentrations were approximately one-half of those measured when the office was unoccupied. This was observed whether new or used filters were present in the air handling system. These results illustrate the importance of accounting for occupancy when estimating human exposure to pollutants in various indoor settings.
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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 | 0.001 |
| 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 | 0.001 |
| 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".