The benefit of kitchen exhaust fan use after cooking - An experimental assessment
Bibliographic record
Abstract
Cooking is one of the main sources of indoor air pollutants, and may even exceed the contribution from outdoor sources. This pilot study examines the use of different flow-rate fans during cooking and tests whether continuing to run the fan after cooking significantly improves pollutant removal rates and integrated exposures. Tests were carried out in the Canadian Centre for Housing Technology's twin research houses, in Ottawa, Ontario. We completed the same cooking protocol 60 times on a gas stove, testing 6 different flow rates on three different over-the-range exhaust fans, while continuously measuring UFP, PM2.5, NO2, and NO. The fan was operated during cooking for all tests and then either turned off or left on after cooking for the duration of the three hour test. We estimated decay rates, source emission rates, and integrated exposures to measured pollutants following the cooking test. The results showed that while leaving the fan on after cooking generally increased decay rates, it had a relatively small effect on integrated exposures compared to the effects of fan flow rate and the specific fan used during cooking. For PM2.5, the effect of running an exhaust fan for 15 min after cooking was similar in magnitude to the impact of a 100 cfm increase in the flow rate used while cooking: both were associated with a decrease in 15-min integrated exposure of roughly 3 μg m−3. This suggests that one can partially compensate for a low flow rate exhaust fan by continuing to run the fan after cooking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".