Determining air pollutant emission rates based on mass balance using airborne measurement data over the Alberta oil sands operations
Bibliographic record
Abstract
Abstract. Top-down approaches to measure total integrated emissions provide verification of bottom-up, temporally resolved, inventory-based estimations. Aircraft-based measurements of air pollutants from sources in the Canadian oil sands were made in support of the Joint Canada–Alberta Implementation Plan for Oil Sands Monitoring during a summer intensive field campaign between 13 August and 7 September 2013. The measurements contribute to knowledge needed in support of the Joint Canada–Alberta Implementation Plan for Oil Sands Monitoring. This paper describes the top-down emission rate retrieval algorithm (TERRA) to determine facility emissions of pollutants, using SO2 and CH4 as examples, based on the aircraft measurements. In this algorithm, the flight path around a facility at multiple heights is mapped to a two-dimensional vertical screen surrounding the facility. The total transport of SO2 and CH4 through this screen is calculated using aircraft wind measurements, and facility emissions are then calculated based on the divergence theorem with estimations of box-top losses, horizontal and vertical turbulent fluxes, surface deposition, and apparent losses due to air densification and chemical reaction. Example calculations for two separate flights are presented. During an upset condition of SO2 emissions on one day, these calculations are within 5 % of the industry-reported, bottom-up measurements. During a return to normal operating conditions, the SO2 emissions are within 11 % of industry-reported, bottom-up measurements. CH4 emissions calculated with the algorithm are relatively constant within the range of uncertainties. Uncertainty of the emission rates is estimated as less than 30 %, which is primarily due to the unknown SO2 and CH4 mixing ratios near the surface below the lowest flight level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".