Characterization of secondary organic aerosol from heated-cooking-oil emissions: evolution in composition and volatility
Bibliographic record
Abstract
Cooking emissions account for a major fraction of urban organic aerosol. It is therefore important to understand the atmospheric evolution in the physical and chemical properties of organic compounds emitted from cooking activities. In this work, we investigate the formation of secondary organic aerosol (SOA) from oxidation of gas-phase organic compounds from heated cooking oil. The chemical composition of cooking SOA is analyzed using thermal desorption–gas chromatography–mass spectrometry (TD–GC–MS). While the particle-phase composition of SOA is a highly complex mixture, we adopt a new method to achieve molecular speciation of the SOA. All the GC-elutable material is classified by the constituent functional groups, allowing us to provide a molecular description of its chemical evolution upon oxidative aging. Our results demonstrate an increase in average oxidation state (from −0.6 to −0.24) and decrease in average carbon number (from 5.2 to 4.9) with increasing photochemical aging of cooking oil, suggesting that fragmentation reactions are key processes in the oxidative aging of cooking emissions within 2 d equivalent of ambient oxidant exposure. Moreover, we estimate that aldehyde precursors from cooking emissions account for a majority of the SOA formation and oxidation products. Overall, our results provide insights into the atmospheric evolution of cooking SOA, a majority of which is derived from gas-phase oxidation of aldehydes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".