Airborne measurements of fire Emission Factors for African biomass burning sampled during the MOYA Campaign
Bibliographic record
Abstract
Abstract. Airborne sampling of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and nitrous oxide (N2O) mole fractions was conducted during field campaigns targeting fires over Senegal in February and March 2017, and Uganda in January 2019. The majority of fire plumes sampled were close to, or directly over burning vegetation, with the exception of two longer-range flights over the West African Atlantic seaboard, (100–300 km from source) where the continental outflow of biomass burning emissions from a wider area of West Africa was sampled. Fire Emission Factors (EFs) and modified combustion efficiencies (MCEs) were estimated from the enhancements in measured mole fractions. For the Senegalese fires, mean EFs and corresponding one-standard deviation variabilities, in units of g per kg of dry fuel were 1.8 (± 0.06) for CH4, 1633 (± 56.4) for CO2 and 679 (± 1.6) for CO, with a mean MCE of 0.94 (± 0.005). For the Ugandan fires, mean EFs (in units of g kg−1) were 3.1 (± 0.1) for CH4, 1610 (± 54.9) for CO2 and 78 (± 1.9) for CO, with a mean modified combustion efficiency of 0.93 (± 0.004). A mean N2O EF of 0.08 (± 0.002) g kg−1 is also reported for one flight over Uganda; issues with temperature control of the instrument optical bench prevented N2O EFs from being obtained for other flights over Uganda. This study has provided new datasets of African biomass burning EFs and MCEs for two distinct study regions, in which both have been studied little by aircraft measurement previously. These results highlight the important intracontinental variability of biomass burning trace gas emissions, and can be used to better constrain future biomass burning emission budgets. More generally, these results highlight the importance of regional and fuel-type variability when attempting to spatially scale biomass burning emissions. Further work to constrain EFs at more local scales and for more specific (and quantifiable) fuel types will serve to improve global estimates of biomass burning emissions of climate-relevant gases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".