Partitioning of Organonitrates in the Production of Secondary Organic Aerosols from α-Pinene Photo-Oxidation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The chemical pathways for the production of secondary organic aerosols (SOA) are influenced by the concentration of nitrogen oxides (NO x ), including the production of organonitrates (ON). Herein, a series of experiments conducted in an environmental chamber investigated the production and partitioning of total organonitrates from α-pinene photo-oxidation from <1 to 24 ppb NO x . Gas-phase and particle-phase organonitrates (gON and pON, respectively) were measured by laser-induced fluorescence (LIF). The composition of the particle phase and the particle mass concentration were simultaneously characterized by online aerosol mass spectrometry. The LIF and MS measurements of pON concentrations had a Pearson correlation coefficient of 0.91 from 0.3 to 1.1 μg m –3 . For 1–6 ppb NO x, the yield of SOA particle mass concentration increased from 0.02 to 0.044 with NO x concentration. For >6 ppb NO x, the yield steadily dropped, reaching 0.034 at 24 ppb NO x . By comparison, the yield of pON steadily increased from 0.002 to 0.022 across the range of investigated NO x concentrations. The yield of gON likewise increased from 0.005 to 0.148. The gas-to-particle partitioning ratio (pON/(pON + gON)) depended strongly on the NO x concentration, changing from 0.27 to 0.13 as the NO x increased from <1 to 24 ppb. In the atmosphere, there is typically a cross-over point between clean and polluted conditions that strongly affects SOA production, and the results herein quantitatively identify 6 ppb NO x as that point for α-pinene photo-oxidation under these study conditions, including the production and partitioning of organonitrates. The trends in SOA yield and partitioning ratio as a function of NOx occur because of the changes in pON volatility.
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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) | 0.001 | 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".