Measurement constrained emission estimates of alkylated polycyclic aromatic hydrocarbons in the Canadian Athabasca oil sands region
Bibliographic record
Abstract
Alkylated polycyclic aromatic hydrocarbons (APAH) are important contaminants of crude oil production and exhibit similar toxicity to their parent compounds. This study developed an emission inventory of APAH in a major oil sands development region of Alberta, Canada, and validated the inventory with ambient concentration measurements through dispersion modeling. The initial estimate of regional total annual emissions of 21 APAH species was 362 tonnes/year in the last decade, of which 309 and 53 tonnes/year were in particle-bound and gas-phase APAH, respectively. Fugitive dust from oil sands mining activities is the primary source of particle-bound APAH, emitting 274 tonnes/year. Other major sources of APAH include point sources (31), tailings ponds (21), anthropogenic fuel consumption from mine fleet (17), and local transportation (13). The group of species with highest emissions was C1–C4 alkylnaphthalenes (53%), followed by C1–C4 alkylphenanthrenes/anthracenes (19%), C1–C4 fluorenes (13%), and C1–C4 fluoranthenes/pyrenes and C1–C4 benz[a]anthracenes/chrysene/triphenylenes (7% each). CALPUFF dispersion modeling was performed using the APAH emissions as model input. The model-predicted annual average ambient APAH concentrations at 17 monitoring sites were 1%–52% (19% on average) lower than the measurements. Inverse dispersion modeling was then applied to adjust APAH emissions higher by 19% for each of the 21 APAH species, which resulted in a revised estimate of APAH emissions to 431 tonnes/year. With the revised emissions as model input, model bias in the predicted ambient concentration was reduced from −19% to −8%. The model results showed the highest concentrations of APAH near tailings ponds and open mining faces and downwind areas, with total APAH concentrations being higher than 50 ng/m3.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".