Estimation of Atmospheric Dry and Wet Deposition of Particulate Elements at Four Monitoring Sites in the Canadian Athabasca Oil Sands Region
Bibliographic record
Abstract
Abstract This study develops a framework for estimating atmospheric dry deposition using the inferential approach, and wet deposition using the scavenging ratio approach for particulate elements monitored in the Athabasca oil sands region (AOSR) of Canada. The framework was applied to four monitoring sites (AMS01, AMS04, AMS17, and AMS18) in the AOSR where ambient concentrations of particulate elements were collected in 2016–2017 to estimate atmospheric dry and wet deposition fluxes of 35 elements, including eight USEPA priority elements. Annual total (dry + wet) deposition of the individual elements in PM10 varied by about five orders of magnitudes, ranging from 3 (μg/m2/year) for Yb to 172,000 (μg/m2/year) for Si. Total deposition of the elements that are typically associated with bitumen (e.g., Mo, Ni, and V) did not show any statistically significant spatial variations (p < 0.05). However, S, which is significantly enriched in bitumen and its byproducts, had up to 38% higher fluxes at AMS01 and AMS17 (closer to the oil sands facilities) than at AMS18 (background site). Wet deposition fluxes dominated over the dry deposition fluxes for almost all elements due to efficient snow scavenging and prolonged winter season. Dry deposition fluxes of the crustal elements were higher in the summer season due to their elevated concentrations in PM2.5–10 during the warmer months, whereas most anthropogenic elements did not show any significant seasonal variations. The total deposition of the individual priority toxic elements considered in this study ranged from 6 (μg/m2/year) (Cd; AMS18) to 2,290 (µg/m2/year) (Zn; AMS04), and ranked as follows: Zn > Cu > Ni > Cr > Sb > As > Pb > Cd.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".