Improved understanding of methane emissions from oil and gas industries of western Canada using aircraft and satellite data, and GEOS-Chem modeling
Bibliographic record
Abstract
The three western Canadian provinces (British Columbia, Alberta and Saskatchewan) are responsible for more than 90 % of the 2017 national total fugitive methane (CH4) emissions from the oil and gas sector. Several survey-based previous studies demonstrated that government estimated CH4 emissions from western Canada’s oil and gas sector are significantly underestimated due to large methodological uncertainties. Most of these survey-based studies were conducted for a limited number of days in a small area; scaling up the short-term results over a larger area for the whole year might lead to emission estimation errors. However, accurate quantification is required to inform effective emission reduction policies in Canada. To improve our understanding of CH4 emissions from the oil and gas sector in western Canada, this Ph.D work has addressed the above problem by using a combination of long-term satellite XCH4 data sets (SCIAMACHY and GOSAT; 2003-2017) and NOAA/ESRL aircraft CH4 measurements, as well as GEOS-Chem CH4 simulation (2010-2017). The overall XCH4 enhancement trend was found to be 6.71 ±4.84 %/yr in western Canada during 2009-2017, which is likely influenced by local oil and gas development activities. The oil and gas CH4 emissions estimation using an ensemble of GOSAT XCH4 data products in a mass balance method demonstrated a fluctuating pattern of emissions in these three provinces during 2009-2017, largely due to diminished oil and gas development activities particularly during 2014-2016. The satellite-based estimated oil and gas CH4 emissions in British Columbia and Saskatchewan are higher than the government inventory by ~200 %, but only higher by ~50 % in Alberta. Although the GOSAT products are suitable for quick and reasonably accurate annual CH4 emissions quantification in western Canada, the GOSAT product is limited in its ability to detect monthly emissions, which is required considering the seasonality of CH4 emissions from major sources. Therefore, further studies particularly with TROPOMI is strongly suggested. Furthermore, the GEOS-Chem based numerical experiments identified emissions from major sources such as wetlands and oil and gas sector are spatially overlapped in the oil and gas dominated areas, particularly during summer, suggesting further oil and gas CH4 emission studies should be carefully conducted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".