Organic Emissions of Volatile Chemical Products in Canada: Emission Inventories, Indoor-to-Outdoor Transfer, and Regional Impacts
Bibliographic record
Abstract
The contribution of volatile chemical products (VCPs) to ambient air pollution has increased following decades of regulating combustion sources. There is a research gap concerning the impact of indoor physicochemical phenomena on VCP emissions. In this work, a bottom-up speciated VCP emission inventory with indoor–outdoor resolution was developed for Canada, an industrialized country with low air pollution levels, whose major cities are among the largest urban areas in North America. VCPs were estimated to account for about 290 kilotons of gaseous organic emissions for a typical year in the 2010s, with more than 60% of emissions occurring indoors. Coatings and cleaners were the most emissive VCP categories. Oxygenated species and saturated aliphatics dominated the chemical profiles of most emissions. Less than 5% of VCP emissions were impacted by indoor physicochemical phenomena. VCP emissions were predicted to account for 0.8–3.2 s –1 of OH reactivity and 0.22–0.52 μg/m 3 of secondary organic aerosol formation potential in major urban areas in Canada. Our predictions aligned with previous measurements concerning indoor and outdoor organic pollutant levels, underscoring the important air quality impacts of VCPs relative to other sources. Our results provide helpful insights for future research regarding VCP emissions, especially from indoor spaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".