A chemical transport model study of plume rise and particle size distribution for the Athabasca oil sands
Bibliographic record
Abstract
Abstract. We evaluate four high-resolution model simulations of pollutant emissions, chemical transformation and downwind transport for the Athabasca oil sands using the Global Environmental Multiscale – Modelling Air-quality and Chemistry (GEM-MACH) model using surface monitoring network and aircraft observations of multiple pollutants, for simulations spanning a time period corresponding to an aircraft measurement campaign in the region in summer 2013. We have focussed here on the impact of different representations of the model's aerosol size distribution and plume-rise parameterization on model results. The use of a more finely resolved representation of the aerosol size distribution was found to have a significant impact on model performance, reducing the magnitude of the original surface PM2.5 negative biases by 32 %. We compared model predictions of SO2, NO2, and speciated particulate matter concentrations from simulations employing the commonly-used Briggs (1984) plume-rise algorithms to redistribute emissions from large stacks with stack plume observations. As in our companion paper (Gordon et al., 2018), we found these algorithms resulted in under-predictions of plume rise, with 39 to 60 % of predicted plume heights falling below half of the observed plume heights. However, we found here that a layered buoyancy approach for stable to neutral atmospheres, coupled with the assumption of free rise in convectively unstable atmospheres, resulted in much better model performance, both for atmospheric constituent concentrations and the predicted height of the plumes. Persistent issues with over-fumigation of plumes in the model were linked to positive biases in the predicted temperatures between the surface and 1km elevation. These in turn may lead to overestimates of near-surface diffusivity, resulting in excessive fumigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".