Photolysis‐driven indoor air chemistry following cleaning of hospital wards
Bibliographic record
Abstract
Effective cleaning techniques are essential for the sterilization of rooms in hospitals and industry. No-touch devices (NTDs) that use fumigants such as hydrogen peroxide (H2O2), formaldehyde (HCHO), ozone (O3), and chlorine dioxide (OClO) are a recent innovation. This paper reports a previously unconsidered potential consequence of such cleaning technologies: the photochemical formation of high concentrations of hydroxyl radicals (OH), hydroperoxy radicals (HO2), organic peroxy radicals (RO2), and chlorine radicals (Cl) which can form harmful reaction products when exposed to chemicals commonly found in indoor air. This risk was evaluated by calculating radical production rates and concentrations based on measured indoor photon fluxes and typical fumigant concentrations during and after cleaning events. Sunlight and fluorescent tubes without covers initiated photolysis of all fumigants, and plastic-covered fluorescent tubes initiated photolysis of only some fumigants. Radical formation was often dominated by photolysis of fumigants during and after decontamination processes. Radical concentrations were predicted to be orders of magnitude greater than background levels during and immediately following cleaning events with each fumigant under one or more illumination condition. Maximum predicted radical concentrations (1.3 × 107 molecule cm−3 OH, 2.4 ppb HO2, 6.8 ppb RO2 and 2.2 × 108 molecule cm−3 Cl) were much higher than baseline concentrations. Maximum OH concentrations occurred with O3 photolysis, HO2 with HCHO photolysis, and RO2 and Cl with OClO photolysis. Elevated concentrations may persist for hours after NTD use, depending on the air change rate and air composition. Products from reactions involving radicals could significantly decrease air quality when disinfectants are used, leading to adverse health effects for occupants.
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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".