Key Atmospheric Processes in The Canadian Oil Sands Identified through Model Evaluation
Bibliographic record
Abstract
We describe the current status of the ongoing model improvement and evaluation of the Oil Sands version of the Global Environmental Multiscale – Modelling Air-quality and CHemistry (GEM-MACH-OS) model. GEM-MACH-OS was designed to provide 2.5km horizontal grid cell size model predictions for the chemical processing of gases and particulate matter emitted from industrial activities in the Canadian Oil Sands and other sources in the Canadian provinces of Alberta, Saskatchewan and neighboring regions. Starting in 2022, a successive series of model updates and evaluations were carried out for the model simulation year October 1, 2017 through September 30, 2018. We report here on several of these simulations how the comprehensive dataset from different monitoring networks was used to improve GEM-MACH-OS predictions, and identify key processes for Oil Sands chemistry. The monitoring networks included the Wood Buffalo Environmental Association (WBEA, which provided hourly air concentration data for NO2, SO2, PM2.5, O3, NO and CO, daily intermittent total and speciated PM2.5 and PM10, and passive monthly to bimonthly SO2, NO2, HNO3, NH3 and O3), the National Trends Network (NTN, providing weekly precipitation totals and ions in precipitation for SO42-, NO3-, NH4+, Ca2+, Mg2+, K+, Na+ and Cl-), the National Air Pollution Surveillance program (NAPS, providing continuous hourly samples of NO2, SO2, PM2.5, O3, NO and CO, as well as daily intermittent samples of HNO3, SO2 speciated PM2.5, speciated total PM at CAPMoN stations), and the Canadian Air and Precipitation Monitoring Network (CAPMoN, providing daily intermittent samples of precipitation and ions in precipitation for the same species as NTN).Examples of evaluation over 5 consecutive model versions will be shown, demonstrating both the improvement in model performance over time, and identifying chemical species for which further improvement is desired. The evaluation also identified key processes governing chemical transformation in the region. These included: (1) O3: relatively little photochemical production from local emissions takes place, but down-mixing from the upper atmosphere creates a substantial seasonal signal; (2) SO2: mostly emitted from large stacks, with the plume heights depending on a parameterization including latent heat release from combustion water (Fathi et al., 2024), and co-deposition potentially has a significant influence on SO2 deposition; (3) NO2: a key reaction governing concentrations in the region is the reaction of NO2 on particle surfaces to form HONO and HNO3; (4) Forest fires in the region emit much lower levels of SO2 and NOx than standard inventory emission factors would suggest, and have a different particle speciation; (5) Particulate matter from Oil Sands fugitive dust sources is influenced both by vehicle-induced turbulence and meteorological modulation (with coarse mode emissions dropping off as temperatures drop below a fixed temperature when the ground is frozen, during rainfall and snowfall events, and as the surface soil water increases). Planned next steps in model improvement will also be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".