Source apportionment of ambient fine and coarse particulate matter at the Fort McKay community site, in the Athabasca Oil Sands Region, Alberta, Canada
Bibliographic record
Abstract
An ambient air particulate matter sampling study was conducted at the Wood Buffalo Environmental Association (WBEA) AMS-1 Fort McKay monitoring station in the Athabasca Oil Sand Region (AOSR) in Alberta, Canada from February 2010 to July 2011. Daily 24 h integrated fine (PM 2.5 ) and coarse (PM 10–2.5 ) particulate matter was collected using a sequential dichotomous sampler. Over the duration of the study, 392 valid daily dichotomous PM 2.5 and PM 10–2.5 sample pairs were collected with concentrations of 6.8 ± 12.9 μg m − 3 (mean ± standard deviation) and 6.9 ± 5.9 μg m − 3 , respectively. A subset of 100 filter pairs was selected for element analysis by energy dispersive X-ray fluorescence and dynamic reaction cell inductively coupled plasma mass spectrometry. Application of the U.S. EPA positive matrix factorization (PMF) receptor model to the study data matrix resolved five PM 2.5 sources explaining 96% of the mass including oil sands upgrading (32%), fugitive dust (26%), biomass combustion (25%), long-range Asian transport lead source (9%), and winter road salt (4%). An analysis of historical PM 2.5 data at this site shows that the impact of smoke from wildland fires was particularly high during the summer of 2011. PMF resolved six PM 10–2.5 sources explaining 99% of the mass including fugitive haul road dust (40%), fugitive oil sand (27%), a mixed source fugitive dust (16%), biomass combustion (12%), mobile source (3%), and a local copper factor (1%). Results support the conclusion of a previous epiphytic lichen biomonitor study that near-field atmospheric deposition in the AOSR is dominated by coarse fraction fugitive dust from bitumen mining and upgrading operations, and suggest that fugitive dust abatement strategies targeting the three major sources of PM 10–2.5 (e.g., oil sand mining, haul roads, bulk material stockpiles) would significantly reduce near-field atmospheric deposition gradients in the AOSR and reduce ambient PM concentrations in the Fort McKay community.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".