On-line Analysis of Urban Particulate Matter Focusing on Elevated Wintertime Aerosol Concentrations
Bibliographic record
Abstract
A 10-day winter sampling campaign was conducted in downtown Toronto for particulate matter (PM) air pollution in the fine (<2.5 microm) size range. An aerosol laser ablation mass spectrometer (LAMS), a tapered-element oscillating microbalance (TEOM), and an aerodynamic particle sizer (APS) were operated in parallel to characterize the PM on-line. In this study, the LAMS observed differences in the chemical composition between three separate episodes with higher PM2.5 mass and APS counts. LAMS results showed that in one instance of elevated PM, organic amines were present in the particulates. Temporal analyses of this episode revealed chemical transformations as the amines, characterized by m/z peaks 58(C3HeN)+, 86(C5H2N)+, and nitrates, increased in number concentration while Ca and hydrocarbon particle classes concurrently decreased. On another day, sulfates were found to have increased significantly. The third event was only 4 h in duration and exhibited an increase in the number of submicron-sized K/hydrocarbons and sulfate-containing particles. In this last event, the hydrocarbons and a K to Fe ratio enrichment indicated there was likely a contribution from a combustion source. This work offers some of the first insights into single particle size and chemistry in a cold winter climate.
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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 | 0.000 | 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".