On the role of monoterpene chemistry in the remote continental boundary layer
Bibliographic record
Abstract
Abstract. The formation of organic nitrates (RONO2) represents an important NOx (NOx = NO + NO2) sink in the remote and rural continental atmosphere, thus impacting ozone production and secondary organic aerosol (SOA) formation. In these remote and rural environments, the organic nitrates are primarily derived from biogenic volatile organic compounds (BVOCs) such as isoprene and monoterpenes. Although there are numerous studies investigating the formation of SOA from monoterpenes, there are few studies investigating monoterpene gas-phase chemistry. Using a regional chemical transport model with an extended representation of organic nitrate chemistry, we investigate the processes controlling the production and fate of monoterpene nitrates (MTNs) over the boreal forest of Canada. MTNs account for 5–12% of total oxidized nitrogen over the boreal forest, and production via NO3 chemistry is more important than production via OH when the NOx mixing ratio is greater than 75 pptv. The regional responses are investigated for two oxidation pathways of MTNs: one that returns NOx to the atmosphere and one that converts MTNs into a nitrate that behaves like HNO3. The likely situation is in between, and these two assumptions bracket the uncertainty about this chemistry. In the case where the MTNs return NOx after oxidation, their formation represents a net chemical NOx loss that exceeds the net loss to peroxy nitrate formation. When oxidation of MTNs produces a molecule that behaves like HNO3, HNO3 and MTNs are nearly equal chemical sinks for NOx. This uncertainty in the oxidative fate of MTNs results in changes in NOx of 8–14%, in O3 of up to 3%, and in OH of 3–6% between the two model simulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".