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Record W2767000979 · doi:10.5194/acp-18-7361-2018

Quantification of methane sources in the Athabasca Oil Sands Region of Alberta by aircraft mass balance

2018· article· en· W2767000979 on OpenAlexafffundabout
Sabour Baray, Andrea Darlington, Mark Gordon, Katherine Hayden, Amy Leithead, Shao‐Meng Li, Peter S. K. Liu, R. L. Mittermeier, Samar G. Moussa, Jason O’Brien, Ralf M. Staebler, Mengistu Wolde, Doug Worthy, R. McLaren

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNational Research Council CanadaEnvironment and Climate Change CanadaYork University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsOil sandsTailingsEnvironmental scienceSurface miningFugitive emissionsMining engineeringPollutantHydrology (agriculture)BTEXStockpileMethaneEnvironmental engineeringAsphaltWaste managementGeologyCoal miningCoalGreenhouse gasEngineeringGeotechnical engineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

Aircraft-based measurements of methane (CH 4 ) and other air pollutants in the Athabasca Oil Sands Region (AOSR) were made during a summer intensive field campaign between 13 August and 7 September 2013 in support of the Joint Canada–Alberta Implementation Plan for Oil Sands Monitoring. Chemical signatures were used to identify CH 4 sources from tailings ponds (BTEX VOCs), open pit surface mines (NO y and r BC) and elevated plumes from bitumen upgrading facilities (SO 2 and NO y ). Emission rates of CH 4 were determined for the five primary surface mining facilities in the region using two mass-balance methods. Emission rates from source categories within each facility were estimated when plumes from the sources were spatially separable. Tailings ponds accounted for 45 % of total CH 4 emissions measured from the major surface mining facilities in the region, while emissions from operations in the open pit mines accounted for ∼ 50 %. The average open pit surface mining emission rates ranged from 1.2 to 2.8 t of CH 4 h −1 for different facilities in the AOSR. Amongst the 19 tailings ponds, Mildred Lake Settling Basin, the oldest pond in the region, was found to be responsible for the majority of tailings ponds emissions of CH 4 (> 70 %). The sum of measured emission rates of CH 4 from the five major facilities, 19.2 ± 1.1 t CH 4 h −1 , was similar to a single mass-balance determination of CH 4 from all major sources in the AOSR determined from a single flight downwind of the facilities, 23.7 ± 3.7 t CH 4 h −1 . The measured hourly CH 4 emission rate from all facilities in the AOSR is 48 ± 8 % higher than that extracted for 2013 from the Canadian Greenhouse Gas Reporting Program, a legislated facility-reported emissions inventory, converted to hourly units. The measured emissions correspond to an emissions rate of 0.17 ± 0.01 Tg CH 4 yr −1 if the emissions are assumed as temporally constant, which is an uncertain assumption. The emission rates reported here are relevant for the summer season. In the future, effort should be devoted to measurements in different seasons to further our understanding of the seasonal parameters impacting fugitive emissions of CH 4 and to allow for better estimates of annual emissions and year-to-year variability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.197
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations123
Published2018
Admission routes3
Has abstractyes

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