Evolution of Atmospheric Total Organic Carbon from Petrochemical Mixtures
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Reactive organic compounds play a central role in the formation of ozone and secondary organic aerosols. The ability to accurately predict their fate, in part, relies upon quantitative knowledge of the chemical and physical parameters associated with the total organic carbon (TOC), which includes both precursors and oxidation products that evolve in the atmosphere over short to long time scales. However, such knowledge, obtained via limited carbon closure experiments, has not been attained for complex anthropogenic emissions. Here we present the first comprehensive characterization of TOC in the atmospheric oxidation of organic vapors from light and heavy oil mixtures associated with oil sand operations. Despite the complexity of the investigated oil mixtures, we are able to achieve carbon closure (83–116%) within the uncertainties (±20%), with the degree of the closure being dependent upon the vapor composition and NO x levels. In contrast to biogenic precursors (e.g., α-pinene), the photochemical time scale required for a largely complete oxidation and evolution of chemical parameters is very long for the petrochemical vapors (i.e., ∼7–10 days vs ∼1 day), likely due to the lower initial precursor reactivity. This suggests that petrochemical emissions and their impacts are likely to extend further spatially than biogenic emissions, and retain more of their complex composition and reactivity for many days. The results of this work provide key parameters to regional models for further improving the representation of the chemical evolution of petrochemical emissions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".