Residential cooking and use of kitchen ventilation: The impact on exposure
Bibliographic record
Abstract
Cooking is one of the most significant indoor sources of particles. This study investigated residential cooking and kitchen ventilation behaviors in Canadian homes, using data from 132 households in Halifax and Edmonton. Only 27% of the cooking activities were conducted with added ventilation (range hood use 10%, window opening 15%, and both 2%). The use pattern of the range hood was associated with mealtime and cooking method/device. The frequency of window opening was influenced by season and did not show a clear linkage to ventilation for cooking. Fine particle (PM2.5) decay rates, source strengths, emission masses, and exposure levels were estimated for cooking activities under different ventilation conditions. The results demonstrated the effect of kitchen ventilation on PM2.5 removal. Using a range hood and (or) opening kitchen windows increased the geometric mean (GM) decay rate by a factor of two. The GM source strength from cooking was 0.73 mg min−1 (geometric standard deviation (GSD) = 4.3) over an average cooking time of 17 minutes (GSD = 2.6). The GM emission mass was 12.6 mg (GSD = 5.3). The GM exposure from a single cooking event was 12 µg m−3 h (GSD = 6.6). The average number of cooking events per day was 2.4 (SD = 1.5) times. Cooking contributed about 22% to the total daily PM2.5 exposure in participating homes. The frequency and duration of cooking conducted at various temporal scales (mealtime, weekday/weekend, and season), as well as the use of different methods and devices, can support more accurate modeling of the impact of cooking on indoor air quality and human exposure.Implications: The inadequate use of ventilation during cooking highlights the need for educational programs on cooking exposures and ventilation strategies, such as running a range hood fan or opening kitchen windows when possible. Exposures in newly built homes might be a bigger concern than older homes if not providing sufficient ventilation during cooking, due to the tighter building envelopes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".