Mass absorption cross-section of flare-generated black carbon: Variability, predictive model, and implications
Bibliographic record
Abstract
Global gas flaring is an important source of black carbon (BC) emissions with uncertain climate impacts. The link between atmospheric concentration and direct radiative forcing (DRF) by BC is its mass absorption cross-section (MAC). MAC data for flare-generated BC are lacking in the literature and the only known data conflict with generally-accepted BC MAC values, which are assumed to be source-independent. This paper presents the first measurements of BC MAC for large-scale flares, burning globally-representative, industry-relevant flare gas compositions in a controlled facility. BC MAC was calculated with precisely-quantified uncertainties using photoacoustic and thermal-optical instruments. Flare-generated carbon was found to be primarily elemental in composition (typically >92%), and most probably externally-mixed based on detailed analysis of attenuation vs. evolved carbon data and consideration of flare-specific mechanisms for organic carbon emissions. Flare BC MAC was generally larger than well-cited literature values and had statistically significant variations with fuel and operating conditions. Variability in BC MAC was well-predicted by a novel phenomenological model based on flame radiative characteristics and relative BC production. The derived model consolidates previously-unreconciled disparate data from different sources and suggests that flare BC MAC is likely >1.3–2 times standard values, implying an underestimation of DRF by flare-generated BC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".