The influence of African air pollution on regional and global tropospheric chemistry
Bibliographic record
Abstract
Abstract. We investigate the relative importance of African biomass burning, biogenic volatile organic compounds (VOC), lightning and anthropogenic emissions to the tropospheric ozone budget over Africa and globally using a coupled global chemistry climate model. Our model studies indicate that the photochemical surface ozone concentration may rise by up to 50 ppbv in the burning region during the biomass burning seasons. Biogenic VOCs contribute between 5–20 ppbv to the near surface ozone concentration over the tropical African region. The impact of lightning on surface ozone is negligible, while anthropogenic emissions contribute a maximum of 10 ppbv to the surface ozone over Nigeria, South-Africa and Egypt. The annual average of the surface and column ozone over Africa shows that biomass burning is the single most important emission source affecting the African region, while biogenic emissions have the highest contribution during the rainy seasons. The contributions of African emissions to global tropospheric ozone burden (TOB) are about 9 Tg, 13 Tg, 8 Tg and 4 Tg for African biomass burning, biogenic VOC, lightning and anthropogenic emissions respectively. These correspond to 2.4%, 3.4%, 2.1% and 1% of the global tropospheric ozone budget respectively. Over Africa itself, the contribution of each of these emission types is only 2.4 Tg, 2.2 Tg, 1.4 Tg and 0.8 Tg respectively. Outside the continent, African biogenic VOC emissions yield the highest contribution to the TOB. Our model calculations suggest that about 70% of the tropospheric ozone produced from emissions in Africa is found outside the continent, thus exerting a noticeable influence on a large part of the tropical troposphere. Latin America experiences the highest impact of African emissions, followed by southeast and south-central Asia, Oceania, and the Middle East for all the emission categories; while Canada, the United States, Russia, Mongolia, China and Europe experience the least impact of African emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".