Contrasting Chemical Complexity and the Reactive Organic Carbon Budget of Indoor and Outdoor Air
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Reactive organic carbon (ROC) comprises a substantial fraction of the total atmospheric carbon budget. Emissions of ROC fuel atmospheric oxidation chemistry to produce secondary pollutants including ozone, carbon dioxide, and particulate matter. Compared to the outdoor atmosphere, the indoor organic carbon budget is comparatively understudied. We characterized indoor ROC in a test house during unoccupied, cooking, and cleaning scenarios using various online mass spectrometry and gas chromatography measurements of gaseous and particulate organics. Cooking greatly impacted indoor ROC concentrations and bulk physicochemical properties (e.g., volatility and oxidation state), while cleaning yielded relatively insubstantial changes. Additionally, cooking enhanced the reactivities of hydroxyl radicals and ozone toward indoor ROC. We observed consistently higher median ROC concentrations indoors (≥223 μg C m –3 ) compared to outdoors (54 μg C m –3 ), demonstrating that buildings can be a net source of reactive carbon to the outdoor atmosphere, following its removal by ventilation. We estimate the unoccupied test house emitted 0.7 g C day –1 from ROC to outdoors. Indoor ROC emissions may thus play an important role in air quality and secondary pollutant formation outdoors, particularly in urban and suburban areas, and indoors during the use of oxidant-generating air purifiers.
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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 | 0.001 | 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".