Continental and Ecoregion‐Specific Drivers of Atmospheric NO<sub>2</sub> and NH<sub>3</sub> Seasonality Over Africa Revealed by Satellite Observations
Bibliographic record
Abstract
Abstract Ammonia (NH 3 ) and nitrogen oxides (NO x : nitrogen dioxide [NO 2 ] + nitric oxide [NO]) play important roles in atmospheric chemistry. Throughout most of Africa, emissions of these gases are predominantly from soils and biomass burning. Here we use observations of tropospheric NO 2 vertical column densities (VCDs) from the Ozone Monitoring Instrument from 2005 through 2017 and atmospheric NH 3 VCDs from the Infrared Atmospheric Sounding Interferometer from 2008 through 2017 to evaluate seasonal variation of NO 2 and NH 3 VCDs across Africa and in seven African ecoregions. In regions where mean annual precipitation (MAP) is under 500 mm yr −1 , we find that NO 2 and NH 3 VCDs are positively related to monthly precipitation, and where MAP is between 500 and 1,750 mm yr −1 or higher, NO 2 VCDs are negatively related to monthly precipitation. In dry ecoregions, temperature and precipitation were important predictors of NH 3 and NO 2 VCDs, likely related to variation in soil emissions. In mesic ecoregions, monthly NO 2 VCDs were strongly related to burned area, suggesting that biomass burning drives seasonality. NH 3 VCDs in mesic ecoregions were positively related to both monthly temperature and monthly carbon monoxide (CO) VCDs, suggesting that a mixture of soil and biomass burning emissions influenced NH 3 seasonality. In northern mesic ecoregions, monthly temperature explained most of the variance in monthly NH 3 VCDs, suggesting that soil sources, including animal excreta, determined NH 3 seasonality. In southern mesic ecoregions, monthly CO VCDs explained more variation in NH 3 VCDs than temperature, suggesting that biomass burning may have greater influence over NH 3 seasonality.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".