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Record W3089022107 · doi:10.1080/10962247.2020.1823525

Residential cooking and use of kitchen ventilation: The impact on exposure

2020· article· en· W3089022107 on OpenAlexaff
Liu Sun, Lance Wallace

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2020
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsHealth Canada
Fundersnot available
KeywordsVentilation (architecture)Environmental scienceEnvironmental healthWaste managementEnvironmental engineeringMedicineEngineeringMeteorologyGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.017
GPT teacher head0.259
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations61
Published2020
Admission routes1
Has abstractyes

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