Effect of industrialization on the differences in sources and composition of ambient PM2.5 in two Southern Ontario locations
Bibliographic record
Abstract
PM2.5 was sampled over a seven-year period (2013–2019) at two locations ∼50 km apart in Southern Ontario (concurrently for five years: 2015–2019). One is a heavily industrialized site (Hamilton), while the other was a rural site (Simcoe). To assess the impact of industrialization on the composition and sources of PM affecting air quality in these two locations, Positive matrix factorization coupled with dispersion normalization (DN-PMF) was used to identify six and eight factors at Simcoe and Hamilton, respectively. The Simcoe factors in order of diminishing PM mass contribution were: particulate sulphate (pSO4), secondary organic aerosol (SOA), crustal matter, particulate nitrate (pNO3), biomass burning, and vehicular emissions. At Hamilton, the effects of industrialization were observed by the ∼36% higher average ambient PM2.5 concentration for the study period as well as the presence of factors unique to metallurgy, i.e., coking and steelmaking, compared to Simcoe. The coking and steelmaking factors contributed ∼15% to the PM mass at Hamilton. Seasonal variants of appropriate nonparametric trend tests with the associated slopes (Sen's) were used to assess statistically significant changes in the factor contributions to PM2.5 over time. Specifically at Hamilton, a significant decline in PM contributions was noted for coking (−0.03 μg/m³/yr or −4.1%/yr) while steelmaking showed no statistically significant decline over the study period. Other factors at Hamilton that showed statistically significant declines over the study period were: pSO4 (−0.27 μg/m³/yr or −12.6%/yr), biomass burning (−0.05 μg/m³/yr or −9.02%/yr), crustal matter (−0.03 μg/m³/yr or −5.28%/yr). These factors mainly accounted for the significant decline in PM2.5 over the study period (−0.35 μg/m³/yr or −4.24%/yr). This work shows the importance of long-term monitoring in assessing the unique contributions and temporal changes of industrialization on air quality in similarly affected locations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".