Substantial secondary organic aerosol formation in a coniferous forest: observations of both day- and nighttime chemistry
Bibliographic record
Abstract
Abstract. Substantial biogenic secondary organic aerosol (BSOA) formation was investigated in a coniferous forest mountain region in Whistler, British Columbia. A largely biogenic aerosol growth episode was observed, providing a unique opportunity to investigate BSOA formation chemistry in a forested environment with limited influence from anthropogenic emissions. Positive matrix factorization of aerosol mass spectrometry (AMS) measurement identified two types of BSOA (BSOA-1 and BSOA-2), which were primarily generated by gas-phase oxidation of monoterpenes and perhaps sesquiterpenes. The temporal variations of BSOA-1 and BSOA-2 can be explained by gas–particle partitioning in response to ambient temperature and the relative importance of different oxidation mechanisms between day and night. While BSOA-1 arises from gas-phase ozonolysis and nitrate radical chemistry at night, BSOA-2 is likely less volatile than BSOA-1 and consists of products formed via gas-phase oxidation by OH radical and ozone during the day. Organic nitrates produced through nitrate radical chemistry can account for 22–33 % of BSOA-1 mass at night. The mass spectra of BSOA-1 and BSOA-2 have higher values of the mass fraction of m/z 91 (f91) compared to the background organic aerosol. Using f91 to evaluate BSOA formation pathways in this unpolluted, forested region, heterogeneous oxidation of BSOA-1 is a minor production pathway of BSOA-2.
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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 | 0.001 | 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".