Particle dose estimation from frying in residential settings
Bibliographic record
Abstract
UNLABELLED: Fumes produced during frying have been implicated as a potential cause for the increased incidence of adenocarcinoma. Particulate matter exposure has also been linked with other pulmonary and coronary disease. This study investigated the contribution of frying in residential settings to ultrafine and fine particulate matter (UFP, PM2.5, respectively) exposure in homes. Production rates of 44 +/- 26 particles (pt)/cm3 s (mean +/- standard deviation) and 0.13 +/- 0.12 microg/m3 s were found for UFP and PM2.5, respectively, from frying a variety of foods at medium heat in a loft-style apartment. Rates of 290 +/- 150 pt/cm3 s and 3.5 +/- 4.9 microg/m3 s were found for UFP and PM2.5, respectively, from frying with vegetable oil alone in five homes; the higher rates were ascribed to differences between the homes rather than the absence of food. The elimination of UFP and PM2.5 was found to be primarily through exhaust fans in these homes, and it was found to follow a first-order process with an elimination rate constant of 6.1 x 10(-4) +/- 2.5 x 10(-4) s(-1). The dose to an individual from frying was estimated based on the measured production and elimination rates and found to be significant when compared with the typical daily dose incurred within a home because of outside sources. PRACTICAL IMPLICATIONS: The contribution of indoor sources to particulate matter exposure in homes remains poorly understood. Yet common household activities such as frying may produce substantial concentrations of potentially toxic particles. Because of the potential adverse health impacts associated with exposure to air pollution, potentially vulnerable individuals may be advised to remain indoors at certain times so as to reduce their overall exposure. Such interventions can be negated without proper guidance regarding the exposure involved in various indoor activities such as cooking. This paper outlines a methodology to estimate the dose to particulate matter incurred during frying and shows that this can represent a significant source of daily exposure.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".