Chemical Characteristics and Origins of Nitrogen-Containing Organic Compounds in PM<sub>2.5</sub> Aerosols in the Lower Fraser Valley
Bibliographic record
Abstract
Some nitrogen-containing organic compounds (NOCs) in PM2.5 aerosols in forest, tunnel, urban, rural, and mixed forest/ urban areas in the Lower Fraser Valley (LFV), British Columbia, Canada, were measured to assess their chemical characteristics, temporal and spatial distributions, and origins. The levels of E-caprolactam, isoindole-1,3-dione, benzothiazolone, and N-butyl-benzensulfonamide showed significant differences among the sites, with the highest level at the mixed forest/urban site, indicating that aerosols at this site were impacted by chemical manufacturing activities. N,N-diethyl-m-toluamide (deet) was detected at all locations but was highest in the forest area, demonstrating a widespread usage as an insect repellent in the LFV and at camps at the forest site. Alkyl amides, tracers from wood burning and cooking, ranging from C6 to C20 including two unsaturated amides, hexadecenamide, and 9-octa-decenamide, were detected at all sites. Three patterns of carbon number distributions of alkyl amides varied with location and time, and were mainly impacted by biomass burning or cooking compared to levoglucosan and cholesterol in the LFV. Ratio of oleamide to stearamide (C18:1/C18:0) was discussed as a potential indicator for determining "age" or transport range of biomass combustion plumes.
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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 | 0.001 | 0.001 |
| 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".