Ozone Loss on Painted Surfaces: Dependence on Relative Humidity, Aging, and Exposure to Reactive SVOCs
Bibliographic record
Abstract
Ozone and its oxidation products result in negative health effects when inhaled. Despite painted surfaces being the most abundant surface in indoor spaces, surface loss remains one of the largest uncertainties in the indoor ozone budget. Here, ozone uptake coefficients (γ O3 ) on painted surfaces were measured in a flow-through reactor where 79% of the inner surfaces were removable painted glass sheets. Flat white paint initially had a high uptake coefficient (8.3 × 10 –6 ) at 20% RH which plateaued to 1.1 × 10 –6 as the paint aged in an indoor office over weeks. Increasing the RH from 0 to 75% increased γ O3 by a factor of 3.0, and exposure to 134 ppb of α-terpineol for 1 h increased γ O3 by a factor of 1.6 at 20% RH. RH also increases α-terpineol partitioning to paint, further increasing ozone loss, but the type of paint (flat, eggshell, satin, semigloss) had no significant effect. A kinetic multilayer model captures the dependence of γ O3 on RH and the presence of α-terpineol, indicating the reacto-diffusive depth for O 3 is 1 to 2 μm. Given the similarity of the kinetics on aged surfaces across many paint types and the sustained reactivity during aging, these results suggest a mechanism for catalytic loss.
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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".