The Influence of Meteorology and Air Transport on CO2 Atmospheric Distribution over South Africa
Bibliographic record
Abstract
This paper demonstrates the role of meteorology and air transport in influencing the South African atmospheric CO2 distribution. CO2 data from December 2004 to December 2009 acquired by the Tropospheric Emission Spectrometer (TES) instrument onboard the Aura satellite were used to establish the CO2 vertical distribution at selected regions in South Africa. The Hybrid Single-Particle Lagrangian Integrated Trajectories (HYSPLIT) atmospheric model backward trajectories were used to determine the long-range air transport impacting on South African CO2 atmospheric distribution and to detect the source areas of air masses impacting on South Africa’s atmosphere. The study found that long-range air transport can result in the accumulation or dilution of atmospheric CO2 at various sites in South Africa, depending on the source region and type of air flow. The long-range air transport from different source regions at the upper air level between the 700 and 500 hPa stable layers and the layer above 500 hPa strengthens the inhomogeneity in the vertical distribution of CO2, which is caused by the decoupling effect of the upper air stable layers. This long-range air transport also involves intercontinental air transport.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".