When Does Multiphase Chemistry Influence Indoor Chemical Fate?
Bibliographic record
Abstract
Human chemical exposure often occurs indoors, where large variability in contaminant concentrations and indoor chemical dynamics make assessments of these exposures challenging. A major source of uncertainty lies in the rates of chemical transformations which, due to high surface-to-volume ratios and rapid air change rates relative to rates of gas-phase reactions indoors, are largely gas-surface multiphase processes. It remains unclear how important such chemistry is in controlling indoor chemical lifetimes and, therefore, human exposure to both parent compounds and transformation products. We present a multimedia steady-state fugacity-based model to assess the importance of multiphase chemistry relative to cleaning and mass transfer losses, examine how the physicochemical properties of compounds and features of the indoor environment affect these processes, and investigate uncertainties pertaining to indoor multiphase chemistry and chemical lifetimes. We find that multiphase reactions can play an important role in chemical fate indoors for reactive compounds with low volatility, i.e., octanol-air equilibrium partitioning ratios ( K oa ) above 10 8, with the impact of this chemistry dependent on chemical identity, oxidant type and concentration, and other parameters. This work highlights the need for further research into indoor chemical dynamics and multiphase chemistry to constrain human exposure to chemicals in the built environment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".