The Optical Properties of Limonene Secondary Organic Aerosols: The Role of NO<sub>3</sub>, OH, and O<sub>3</sub> in the Oxidation Processes
Bibliographic record
Abstract
Abstract Limonene, a typical proxy of monoterpenes emitted from biogenic sources, plays an important role in secondary organic aerosol (SOA) formation. However, the optical properties of SOA generated from limonene under various oxidation pathways remain poorly understood. In this study, we investigate the refractive index (RI) of limonene SOA produced from four oxidation conditions with cavity ring‐down spectrometer (CRDS) and photoacoustic extinctiometer operated at 532 and 375 nm. Our results show that there is a significant difference in RI values of SOA produced from NO 3 oxidation compared to other oxidation pathways. The mean values of RI of SOA produced from NO 3 oxidation, NO x oxidation, OH oxidation with NO x ‐free, and O 3 oxidation experiments are 1.578, 1.469, 1.495, and 1.494 at 532 nm; and 1.591, 1.527, 1.513, and 1.537 at 375 nm, respectively, while no detectable absorption is found in all oxidation conditions. We attribute the high RI values of SOA by NO 3 oxidation to two factors: a large proportion of organic nitrates and high‐molecular‐weight dimers/oligomers in the SOA. Our study results indicate that the nighttime chemistry may significantly influence the optical properties of limonene oxidation products. The RI values of limonene SOA generated under various oxidation conditions at different wavelengths retrieved in our laboratory experiments could help improve the model predictions for evaluating the effect of biogenic SOA on the global radiative forcing as well as climate change.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".