The influence of African air pollution on regional and global tropospheric ozone
Bibliographic record
Abstract
Abstract. We investigate the influence of African biomass burning, biogenic, lightning and anthropogenic emissions on the tropospheric ozone over Africa and globally using a coupled global chemistry climate model. Our model studies indicate that surface ozone concentration may rise by up to 50 ppbv in the burning region during the biomass burning seasons. Biogenic emissions yield between 5–30 ppbv increase in the near surface ozone concentration over tropical Africa. The impact of lightning on surface ozone is negligible, while anthropogenic emissions yield a maximum of 7 ppbv increase in the annual-mean surface ozone concentration over Nigeria, South Africa and Egypt. Our results show that biogenic emissions are the most important African emission source affecting total tropospheric ozone. The influence of each of the African emissions on the global tropospheric ozone burden (TOB) of 384 Tg yields about 9.5 Tg, 19.6 Tg, 9.0 Tg and 4.7 Tg for biomass burning, biogenic, lightning and anthropogenic emissions emitted in Africa respectively. The impact of each of these emission categories on African TOB of 33 Tg is 2.5 Tg, 4.1 Tg, 1.75 Tg and 0.89 Tg respectively, which together represents about 28% of the total TOB calculated over Africa. Our model calculations also suggest that more than 70% of the tropospheric ozone produced by each of the African emissions is found outside the continent, thus exerting a noticeable influence on a large part of the tropical troposphere. Apart from the Atlantic and Indian Ocean, Latin America experiences the largest impact of African emissions, followed by Oceania, the Middle East, Southeast and south-central Asia, northern North America (i.e. the United States and Canada), Europe and north-central Asia, for all the emission categories.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".