Isolating the Industrial Contribution of PM2.5 in Hamilton and Burlington, Ontario
Bibliographic record
Abstract
Abstract Hourly measurements of particulate matter that is smaller than 2.5 μm in diameter (PM2.5) have been made at air-monitoring sites in Hamilton and Burlington, Ontario, Canada, since 2003. These sites are separated by ~6 km; Burlington is right on Lake Ontario while Hamilton has, directly to the east, very heavy industry between it and Lake Ontario. Hence, by taking the difference between measurements at Hamilton and Burlington, it is possible to isolate, during east-wind conditions, PM2.5 that result from emissions from the industrial sectors (primarily steel mills) located in Hamilton’s northeast end. After screening the data for east winds off Lake Ontario, it was found that median background values of PM2.5, of 5–10 μg m−3 are increased by an additional 5–10 μg m−3 by emissions from local sources. On the contrary, however, industrial contributions to PM2.5 in Burlington during south winds are much smaller at ~3 μg m−3 (industrial sectors are due south of Burlington). This difference is likely due either to wind direction–dependent local circulation patterns or to alignment of sources that can concentrate PM2.5 into Hamilton. It was also found that throughout much of 2009, but especially during spring and early summer, the industrial contribution of PM2.5 at Hamilton was reduced relative to other years by amounts that are statistically significant at the 95% confidence level, even when measurements are augmented with large amounts of Gaussian noise. These reductions are consistent with documented reductions in steel production during the global economic crisis that peaked in the first half of 2009.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".