Reaction of HOCl with Wood Smoke Aerosol: Impacts on Indoor Air Quality and Outdoor Reactive Chlorine
Bibliographic record
Abstract
High loadings of biomass burning (BB) aerosol particles from wildfire or residential heating sources can be present in both outdoor and indoor environments, where they deposit onto surfaces such as walls and furniture. These pollutants can interact with oxidants in both the aerosol and deposited forms. Hypochlorous acid (HOCl), a strong oxidant emitted during cleaning with chlorine-cleaning agents such as bleach, can attain mixing ratios of hundreds of ppbv indoors; moreover, lower mixing ratios are naturally present outdoors. Here, we report the heterogeneous reactivity of HOCl with wood smoke aerosol particles. After exposure to gas-phase HOCl, the particle chlorine content increased reaching chlorine-to-organic mass ratios of 0.07 with the chlorine covalently bound as organochlorine species, many of which are aromatic. Investigating individual potential BB components, we observed that unsaturated species such as coniferaldehyde and furfural react efficiently with HOCl. These observations indicate that organochlorine pollutants will form indoors when bleach cleaning a wildfire impacted space. The chlorine component of particles internally mixed with BB material and chloride initially increased, upon HOCl exposure, indicating that active chlorine recycling in the outdoor environment will be suppressed in the presence of BB emissions.
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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".