NO at low concentration can enhance the formation of highly oxygenated biogenic molecules in the atmosphere
Bibliographic record
Abstract
Abstract The interaction between nitrogen monoxide (NO) and organic peroxy radicals (RO2) greatly impacts the formation of highly oxygenated organic molecules (HOM), the key precursors of secondary organic aerosols. It has been thought that HOM production can be significantly suppressed by NO even at low concentrations. Here, we perform dedicated experiments focusing on HOM formation from monoterpenes at low NO concentrations (0 – 82 pptv). We demonstrate that such low NO can enhance HOM production by modulating the RO2loss and favoring the formation of alkoxy radicals that can continue to autoxidize through isomerization. These insights suggest that HOM yields from typical boreal forest emissions can vary between 2.5%-6.5%, and HOM formation will not be completely inhibited even at high NO concentrations. Our findings challenge the notion that NO monotonically reduces HOM yields by extending the knowledge of RO2-NO interactions to the low-NO regime. This represents a major advance towards an accurate assessment of HOM budgets, especially in low-NO environments, which prevails in the pre-industrial atmosphere, pristine areas, and the upper boundary layer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".