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Record W4411370933 · doi:10.1007/s44292-025-00038-6

Investigating the dispersion and transport of carbon monoxide in West Africa with a focus on biomass burning and gas flaring sources

2025· article· en· W4411370933 on OpenAlexaff
Ajoke Ruth Onojeghuo, Zoë L. Fleming, Marios Panagi, Heiko Balzter, P. S. Monks

Bibliographic record

VenueDiscover Atmosphere · 2025
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsAlberta Environment and Protected Areas
Fundersnot available
KeywordsCarbon monoxideBiomass (ecology)Dispersion (optics)Biomass burningEnvironmental scienceFocus (optics)Atmospheric sciencesGeographyChemistryGeologyPhysicsMeteorologyOceanographyOpticsAerosol

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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 teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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