Estimation of Atmospheric Emissions of Six Semivolatile Polycyclic Aromatic Hydrocarbons in Southern Canada and the United States by Use of an Emissions Processing System
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are toxic compounds that are ubiquitous in the atmospheric environment. The input for an emissions processing system that was originally configured forthe study of criteria air pollutants was updated to calculate emissions of six semivolatile PAHs. The goal of the work was to produce emissions estimates with the spatial and temporal resolution needed to serve as input to a regional air quality model for southern Canada and the U.S. Such modeling is helpful in determining reductions in PAH emissions that may be necessary to protect human and ecosystem health. The total annual emission of the six PAHs (sigma6PAH) for both countries was estimated at 18 273 Mg/year. A total of 90% of these emissions arise from U.S. sources. The top six source types account for 73% of emissions and are related to metal production, open burning, incineration, and forest fires. The emission factors used in this study were derived from published compilations. Although this approach has the advantage of quality control during the compilation process, some compilations include factors from older studies that may overestimate emissions since they do not account for recent improvements in emission control technology. When compared to estimates published in the National Emissions Inventory (NEI) for 2002, the U.S. emissions in this study are higher by a factor of 4 (16 424 vs 4102 Mg/year). The cause of this difference has been investigated, and much of it is likely due to our use of data unavailable in the 2002 NEI but inferred here on the basis of the PAH emissions literature. Augmenting the 2002 NEI with this additional information would bring its reported annual emissions to 8213 Mg/year, which is within a factor of 2 of the estimates herein. The results presented for southern Canada are the first published values for all known PAH sources in that country.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".