Cooking, Bleach Cleaning, and Air Conditioning Strongly Impact Levels of HONO in a House
Bibliographic record
Abstract
Abstract The relative importance of common activities on indoor nitrous acid (HONO) mixing ratios was explored during high time resolution, month-long measurements by chemical ionization mass spectrometry in a previously unoccupied house. Indoor HONO varied from 0.2 to 84.0 ppb (mean: 5.5 ppb; median 3.8 ppb), an order of magnitude higher than simultaneously measured outdoor values, indicating important indoor sources. They agree well with simultaneous measurements of HONO by Laser-Photofragmentation/Laser-Induced Fluorescence. Before any combustion activities, the mixing ratio of 3.0 ± 0.3 ppb is indicative of secondary sources such as multiphase formation from NO2. Cooking (with propane gas), especially the use of an oven, led to significant enhancements up to 84 ppb, with elevated mixing ratios persisting for a few days due to slow desorption from indoor surface reservoirs. Floor bleach cleaning led to prolonged, substantial decreases of up to 71–90% due to reactive processes. Air conditioning modulated HONO mixing ratios driven by condensation to wet surfaces in the AC unit. Enhanced ventilation also significantly lowered mixing ratios. Other conditions including human occupancy, ozone addition, and cleaning with terpene, natural product, and vinegar cleaners had a much smaller influence on HONO background levels measured following these activities.
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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".