Polycyclic aromatic compounds (PACs) in the Canadian environment: Sources and emissions
Bibliographic record
Abstract
Twenty-five years after the first look at polycyclic aromatic compounds (PACs) in Canada, this article presents current knowledge on Canadian PAC emission sources. The analysis is based on national inventories (the National Pollutant Release Inventory (NPRI) and the Air Pollutant Emissions Inventory (APEI)), an analysis of Canadian forest fires, and several air quality model-ready emissions inventories. Nationally, forest fires continue to dominate PAC emissions in Canada, however there is uncertainty in these estimates. Though forest fire data show a steady average in the total annual area burned historically, an upward trend has developed recently. Non-industrial sources (home firewood burning, mobile sources) are estimated to be the second largest contributor (∼6-8 times lower than forest fires) and show moderate decreases (25%-65%) in the last decades. Industrial point sources (aluminum production, iron/steel manufacturing) are yet a smaller contributor and have seen considerable reductions (90% +) in recent decades. Fugitive emissions from other industrial sources (e.g. disposals by the non-conventional oil extraction and wastewater sectors, respectively) remain a gap in our understanding of total PAC emissions in Canada. Emerging concerns about previously unrecognized sources such as coal tar-sealed pavement run-off, climate change are discussed elsewhere in this special issue. Results affirm that observations at the annual/national scale are not always reflective of regional/local or finer temporal scales. When determining which sources contribute most to human and ecosystem exposure in various contexts, examination at regional and local scales is needed. There is uncertainty overall in emissions data stemming in part from various accuracy issues, limitations in the scope of the various inventories, and inventory gaps, among others.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".