Heavy metals in the near-road environment: Results of semi-continuous monitoring of ambient particulate matter in the greater Toronto and Hamilton area
Bibliographic record
Abstract
Six heavy metals - Mn, Fe, Cu, Zn, Se, and Pb among other elemental species were monitored in ambient PM 2.5 at three near-road ambient air monitoring locations in the Greater Toronto and Hamilton Area (GTHA) with semi-continuous X-ray fluorescence (XRF) instrumentation over a period spanning January 1st, 2014 to June 30th, 2017. Land use in these air monitoring locations includes residential, institutional and industrial, thus, air monitoring is representative of typical urban areas. Ambient metal concentrations were found below Ontario's ambient air quality criteria. Temporal trends however indicated that high concentrations of Fe and Cu correlated with peak commuting and working hours on weekdays. To further understand the potential sources of these metals, scatterplots of metal concentrations and criteria pollutant gases were made on weekdays and weekends. These scatterplots reveal edges that are due to multiple sources of these metals. When these scatterplots are colour-coded by the hour of day, edges associated with the morning rush hour on weekdays for Fe and Cu (also Mn and Zn to a lesser extent) likely due to traffic-related emissions are more clearly-delineated from other edges arising from industrial or regional sources that were prevalent during other times of the day. Finally, an auxiliary receptor model was used to explore the potential source regions of these metals. It was observed that Mn, Fe and Cu had intense potential source regions within the GTHA on weekdays that diminished on the weekends, and in the case of Fe, the potential source regions in the GTHA were sensitive to the morning rush hour period, indicating that traffic-related emissions are a major source of Fe. Other metals, especially Zn, Se and Pb have source regions that are less sensitive to the morning rush hour period and are usually situated outside the GTHA.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".