Investigating the dispersion and transport of carbon monoxide in West Africa with a focus on biomass burning and gas flaring sources
Bibliographic record
Abstract
This study presents an in-depth analysis of the dispersion and transport of carbon monoxide (CO) in West Africa, with a specific focus on the contributions from biomass burning and gas flaring activities. Utilizing data from the Global Fire Emissions Database (GFED4s) for biomass burning and the Infrared Atmospheric Sounding Interferometer (IASI) for CO concentrations, the research examines the seasonality of air pollution. Multi-seasonal 5-day trajectories of the NAME atmospheric dispersion model were employed to trace seasonal CO sources and their movement patterns. The findings highlight the dual impact of biomass burning inland and gas flaring offshore, particularly in Angola, on CO levels. Notably, the dispersion modelling process showed that emissions from gas flaring activities affect CO concentrations along the West African coast, especially during periods of lower wind speeds and dispersion rates. The study underscores the necessity of incorporating gas flaring emissions into atmospheric models to accurately simulate air pollution in the region. This comprehensive approach enhances the understanding of CO distribution and its environmental impacts, emphasizing the critical need for improved emission inventories and modeling techniques to address air pollution in West Africa.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".