Identification of the Major Sources Contributing to PM<sub>2.5</sub>Observed in Toronto
Bibliographic record
Abstract
The chemical composition of Toronto PM2.5 was measured daily from Feb 2000 to Feb 2001, and source apportionment was undertaken using positive matrix factorization (PMF). In Toronto, PM2.5 levels were influenced both by local urban activities and also by regional-scale transport. Although several PMF solutions were possible, an eight-source model for explaining the observed Toronto PM2.5 was found to provide realistic results and interesting insights into sources. The four main sources were coal combustion related to regional transport and secondary sulfate (26%), secondary nitrate related to both local and upwind sources of NOx and NH3 (36%), secondary organic aerosols (SOA) formed from a variety of precursor organic emissions (15%), and motor vehicle traffic (10%). The other detectable sources were road salt (winter) and three types of primary PM2.5 hypothesized to be associated with smelters, coal and oil combustion, industry, and local construction. Overall, motor vehicle-related emissions (including road salt and nitrate) were estimated to be responsible for about 40% of the PM2.5. In the summer, the SOA mass was estimated to contribute approximately 20% to the PM2.5. Inclusion of water-soluble, low-molecular-weight organic acids led to identification of this component, thus providing a significant improvement in PMF's ability to resolve sources. Without organic acid measurements the SOA portion of the observed PM2.5 was assigned to the secondary coal component, increasing its contribution and resulting in a source profile with an unrealistic amount of organic mass. This suggests that in the northeastern part of North America, there are physical and/or chemical processes that lead to close interaction between secondary organic and inorganic aerosols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".