Modeling trace elements over Athabasca oil sands region in Alberta, Canada using WRF-Chem
Bibliographic record
Abstract
The Athabasca Oil Sands Region (AOSR) in northern Alberta, Canada is a significant source of particulate elements, which may cause negative effects on human and ecosystem health. This study simulates the transport and deposition of eight elements (Al, Ca, Fe, K, Mn, Si, Ti, and Zn) in the AOSR during 2016–2017 using WRF-Chem with a recently developed regional-scale emission database of elements as model input. Point and area emissions of the elements were gridded in the model domain, with stack emissions also considering the plume rise. The model-measurement differences in annual concentrations of the sum of the eight elements were 23 % at AMS1, 25 % at AMS17, and − 56 % at AMS18. Modeled annual average concentrations and atmospheric deposition of individual elements ranged from 0.016 to 2.67 μg m −3 (the sum total of 5.98 μg m −3 ) and from 2.62 to 385 mg m −2 yr −1 (862 mg m −2 yr −1 ), respectively, in the central industrial area of the AOSR. The concentration and deposition decreased rapidly with distance from the center industrial area, e.g., by three orders of magnitude in areas 150 km away. Adding the three sites together, modeled total concentrations of the eight elements were 110 % higher during the cold season and 29 % lower during the warm season than the measured values, noting that constant emission rates were used throughout the years of 2016–2017. Two model sensitivity tests were conducted, with the first one using seasonally varying emissions and the second one replacing the default dry and wet deposition schemes in WRF-Chem with different ones found in literature, to demonstrate the magnitudes of the uncertainties in the model simulated ambient concentrations and atmospheric deposition of particulate elements and major causing factors of the uncertainties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".