Bibliographic record
Abstract
Most human exposure to air pollutants happen indoors, where people spend most of their time (~90%). In the ambient atmosphere, photolysis plays a major role in initiating chemical reactions. However, indoor photolysis is less well studied. Consequently, the role that photolysis plays in indoor chemical processing, particularly in the formation of harmful species, is unclear. The major aim of this thesis was, therefore, to improve the representation of indoor lighting and attenuated sunlight in the Indoor Detailed Chemical Model (INDCM). The improved model was then used to investigate the impacts of glass type, indoor artificial light, cloudiness, time of year and latitude on indoor photolysis rates and hence indoor air chemistry. \nThe results show that variations in glass composition produce the highest deviations (~71%) in predicted concentrations of key indoor species (ozone, nitrous acid, nitric oxide, hydroxyl radicals, hydroperoxy radicals, organic peroxy radicals, peroxyacetyl nitrates and organic nitrates), followed by cloud level (~53%) and proximity to artificial light source (~53%), when compared to baseline conditions. These impacts were greatest for predicted hydroxyl radical concentrations, which deviated by an average of ~142% from the baseline scenario depending on the conditions studied. \nEnhanced radical concentrations were found during two cleaning case studies (automated and traditional techniques), with predicted hydroxyl radical concentrations up to 1.3 × 107 and 1.5106 molecule/cm3 respectively. Furthermore, radical concentrations were found to be highest under stronger lighting conditions, persisting for several hours after the cleaning events. \nThis study provides a valuable contribution to the understanding of the impacts of photolysis on indoor air chemistry. Indoor artificial lights, such as LED, together with low cut-off wavelength glasses, will likely reduce the effects of photolysis indoors, but more research is needed on the health effects of different indoor air mixtures to confirm this recommendation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".