Field Measurements of Black Carbon Yields from Gas Flaring
Bibliographic record
Abstract
Black carbon (BC) emissions from gas flaring in the oil and gas industry are postulated to have critical impacts on climate and public health, but actual emission rates remain poorly characterized. This paper presents in situ field measurements of BC emission rates and flare gas volume-specific BC yields for a diverse range of flares. Measurements were performed during a series of field campaigns in Mexico and Ecuador using the sky-LOSA optical measurement technique, in concert with comprehensive Monte Carlo-based uncertainty analyses. Parallel on-site measurements of flare gas flow rate and composition were successfully performed at a subset of locations enabling direct measurements of fuel-specific BC yields from flares under field conditions. Quantified BC emission rates from individual flares spanned more than 4 orders of magnitude (up to 53.7 g/s). In addition, emissions during one notable ∼24-h flaring event (during which the plume transmissivity dropped to zero) would have been even larger than this maximum rate, which was measured as this event was ending. This highlights the likely importance of superemitters to global emission inventories. Flare gas volume-specific BC yields were shown to be strongly correlated with flare gas heating value. A newly derived correlation fitting current field data and previous lab data suggests that, in the context of recent studies investigating transport of flare-generated BC in the Arctic and globally, impacts of flaring in the energy industry may in fact be underestimated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".