Monitoring of total and off-road NO <sub> <i>x</i> </sub> emissions from Canadian oil sands surface mining using the Ozone Monitoring Instrument
Bibliographic record
Abstract
The oil sands in Alberta, Canada, are a significant source of air pollution. Observations from the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite have been used to quantify NO x emissions from the surface mining region of the oil sands. Two related emissions methods were utilized, one for point and one for area sources, where OMI vertical column densities of NO 2 were combined with winds from a meteorological reanalysis and a two-dimensional exponentially modified Gaussian (EMG) plume model. This work better connects the two (point and area) emissions methods and discusses the interpretation of fit parameters and the ability of OMI (and other sensors) to resolve emissions between neighbouring sources. The two methods employed, in good agreement with each other, indicated an increase in emissions from about 55 to 80 kt [NO 2 ] yr −1 between 2005–2011 and a flat trend thereafter. Reported emissions were within 15 % of reported emissions, consistent to within uncertainties. In an extension of this methodology, OMI observations were combined with reported point source emissions to derive the more uncertain emissions component from the large off-road mining fleet. These were found to make up about 60 % of total NO x emissions, also consistent with reported emissions. The OMI-derived 0.9 % yr −1 increase in fleet emissions and the 5.5 % yr −1 increase in bitumen mined, generally a good proxy for fleet emissions, can be reconciled by considering the evolution of the mine fleet over this period. OMI is therefore able to track the transition from US EPA Tier 1 standards, through Tier 4 standards, to the present and in doing so demonstrates the efficacy of this policy. Furthermore, this analysis shows that had the fleet remained at Tier 1, this source would currently be emitting an additional 40 kt [NO 2 ] yr −1 .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".