Hydrogen Peroxide Emission and Fate Indoors during Non-bleach Cleaning: A Chamber and Modeling Study
Bibliographic record
Abstract
Activities such as household cleaning can greatly alter the composition of air in indoor environments. We continuously monitored hydrogen peroxide (H 2 O 2 ) from household non-bleach surface cleaning in a chamber designed to simulate a residential room. Mixing ratios of up to 610 ppbv gaseous H 2 O 2 were observed following cleaning, orders of magnitude higher than background levels (sub-ppbv). Gaseous H 2 O 2 levels decreased rapidly and irreversibly, with removal rate constants ( k H 2 O 2 ) 17–73 times larger than air change rate (ACR). Increasing the surface-area-to-volume ratio within the room caused peak H 2 O 2 mixing ratios to decrease and k H 2 O 2 to increase, suggesting that surface uptake dominated H 2 O 2 loss. Volatile organic compound (VOC) levels increased rapidly after cleaning and then decreased with removal rate constants 1.2–7.2 times larger than ACR, indicating loss due to surface partitioning and/or chemical reactions. We predicted photochemical radical production rates and steady-state concentrations in the simulated room using a detailed chemical model for indoor air (the INDCM). Model results suggest that, following cleaning, H 2 O 2 photolysis increased OH concentrations by 10–40% to 9.7 × 10 5 molec cm –3 and hydroperoxy radical (HO 2 ) concentrations by 50–70% to 2.3 × 10 7 molec cm –3 depending on the cleaning method and lighting conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".