Chemical characterization of the organic fraction of atmospheric aerosol at two sites in Ontario, Canada
Bibliographic record
Abstract
Atmospheric filter samples were collected during several field campaigns in 1998/1999 from two locations in and around the greater Toronto area. The objective of the campaigns was to investigate the difference in composition of the organic fraction of atmospheric aerosols at both an urban and rural site for different seasons. The composition of organic particulate matter, with an interest in organic carbon, elemental carbon, alkanoic acids, n‐alkanes, and polycyclic aromatic hydrocarbons (PAHs), was investigated using thermal desorption, solvent extraction, derivatization, and analysis by gas chromatography/mass spectroscopy. The concentrations measured were similar to other observations in urban and rural areas with seasonal differences observed at both sites. PM2.5 levels were larger in February at both urban and rural sites due to weather conditions favorable for buildup of particles. Organic carbon/elemental carbon ratios were typical of emission values except for the rural site where a greater potential for biogenic‐related particles, as well as secondary production, likely played a role. A comparison of the chemical species to the total organic carbon revealed that the acids, alkanes, and PAHs account for a very small portion of the mass, demonstrating the need for further analytical developments. Preliminary results of the tracer analyses indicated that at the urban site the distribution of alkanoic acids atmospheric concentrations was correlated to diesel emissions. The n‐alkanes distribution profile was representative of both anthropogenic sources for the urban site and biogenic sources for the rural site. At the rural site one sample exhibited a local source of atmospheric aerosol derived from vegetation.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".