Scenario Simulations for Estimating Environmental Impacts of Canadian Oil Sands Emissions
Bibliographic record
Abstract
Ten one-year simulations were conducted using a nested high-resolution air-quality model (Global Environmental Multiscale-Modelling Air-quality and CHemistry; GEM-MACH).  The model nesting is from a 10km grid cell size North American domain, to a 2.5km grid cell size domain covering the Canadian provinces of Alberta and Saskatchewan (1350 x 1345 km).  The simulation period was from October 1, 2017 through September 30, 2018.  In addition to a base case simulation (see Fathi et al., 2025, this session, for the evaluation of this base case), nine additional scenario simulations were carried out.  These included six “Zero-Out” scenarios, in which specific contributions to the base case emissions were removed – comparisons to the base case thus provide the relative impact of these emissions sources.  Specific Zero-Out scenarios included the removal of all emissions associated with Oil Sands activities, all anthropogenic emissions, emissions associated with the Oil Sands off-road mining vehicle fleet, emissions associated with large stack sources, emissions associated with tailings ponds, and emissions associated with Oil Sands fugitive dust.  Three additional scenarios examined the impact of converting mine fleet vehicles from the 2018 fleet to Tier 4 level emissions control vehicles, the impact of revised land use fields for deposition to wetlands, and the impact of co-deposition of base cations and SO2 on the latter’s deposition flux.Comparisons between the base case and the scenarios allow us to estimate the relative impact of the different emissions sources on air concentrations and deposition of pollutants of interest.  The zero-out scenarios thus give estimates of the relative impact of emissions from all Oil Sands sources, all anthropogenic sources, the Oil Sands off-road fleet, Oil Sands large stack sources, Oil Sands tailings ponds and Oil Sands fugitive dust on concentrations and deposition in the simulation area.  We also present the impact of a potential change in mine fleet emissions from the 2018 vehicle fleet composition to Tier 4 level vehicle emissions, of the land use data used as model input, and of co-deposition.   Two approaches will be used to investigate impacts:  in the first approach, the raw model output will be used for impact estimation; in the second approach, a simple form of model-measurement fusion will be applied to the gridded fields prior to impact estimation.   Ecosystem impacts will be assessed through applying model and model-measurement fusion deposition fields towards calculating exceedances of critical loads for forest, aquatic and bog ecosystems.  Human health impacts of the base case and scenarios will also be assessed using using a health impact function for fatal and non-fatal effects using the Air Quality Benefits Assessment Tool (AQBAT).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.004 | 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 teacher head, 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".