Oxidative Potential of Fresh and Aged Particles from Food-Cooking Emissions
Bibliographic record
Abstract
Particulate matter (PM) emitted from urban areas causes oxidative stress and is associated with adverse health effects. Cooking emissions represent one of the most important sources of organic PM in urban areas and have been shown to contribute 10-34% of total organic aerosol. Despite its importance, the chemical composition of fresh and aged PM from food cooking emissions and its oxidative potential (OP, or ability to cause oxidative stress) are poorly understood. In this work we use cooking oils (peanut oil, canola oil, olive oil) as a surrogate to investigate the chemical composition and its evolution upon simulated atmospheric aging. We determine the composition using thermal desorption-gas chromatography mass spectrometry (TD-GC/MS), and oxidative potential using the dithiothreitol (DTT) assay. Heated cooking oil particles were oxidised in a quartz flow tube reactor in presence of ozone or hydroxyl radicals to simulate atmospheric aging. Quartz filter samples were collected for particle-phase chemical characterization and OP evaluation. Our preliminary results show that the mass normalized OP of aged PM from cooking is similar to that of diesel exhaust particles (traffic emissions) reported in previous literature. We also observed different OPs from various types of cooking oils, among which the OP of canola oil ranks the highest. The unoxidized cooking emissions are found to be mainly comprised of saturated fatty acids ranging from C12-C20, along with palmitoleic acid, oleic acid, linoleic acid and sterols. Aged cooking emissions are comprised of dicarboxylic acids, hydroxy fatty acids, and short chain fatty acids. Overall, using oxidative potential as a screening tool, more toxicological studies are needed to understand the impact of cooking emissions on human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".