Impact of the 2016 Fort McMurray wildfires on atmospheric deposition of polycyclic aromatic hydrocarbons and trace elements to surrounding ombrotrophic bogs
Bibliographic record
Abstract
Fort McMurray and the Athabasca oil sands region (AOSR) experienced major wildfires in 2016, but the impact of these on regional deposition of polycyclic aromatic hydrocarbons (PAHs) and trace elements has not been reported nor compared to industrial sources of these pollutants in the region. Living moss (Sphagnum fuscum) was collected in triplicate from five ombrotrophic bogs in the AOSR after the wildfires, and analyzed for PAHs and trace elements. These post-wildfire data were compared to data from previous years at the same sites, and also to remote reference bogs in Alberta and Ontario. Elevated post-wildfire concentrations and flux of naphthalene and fluorene were observed at all five bogs in the AOSR, but no consistent trend was evident for higher molecular weight PAHs or the sum of priority PAHs (∑13PAH). Trace elements at most AOSR bogs were not elevated post-wildfire, except at one bog in the burned area (MIL), but even here the elements that were increased (1.7–5.6 × ) were likely of bitumen-origin (i.e., V, Ni, Se, Mo and Re). Significant post-wildfire correlations between PAHs and most trace elements suggested a common source, and few significant correlations were observed with retene, suggesting that wildfires were not the dominant source of most contaminants detected. Mass balance receptor models were used to apportion sources, indicating that the major sources of trace elements among five AOSR bogs post-wildfire were oil sands ore (mean 42%), haul road dust (17%), and petcoke (11%), whereas wildfire was always a minor source (3–4%). For PAHs at the most contaminated site (MIL), delayed petcoke (27%) and wildfire (25%) were the major sources, but the contribution of wildfire to PAHs at other sites was less or not discernable. Impacts of the 2016 wildfires on regional atmospheric deposition of major pollutants was less than from ongoing deposition of anthropogenic dust from oil sands activities.
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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 | 0.000 |
| 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".