Urban and power plant NOx emissions in Sub-Saharan Africa inferred from TROPOMI
Bibliographic record
Abstract
Nitrogen oxides (NO x ) emissions are increasing rapidly in Sub-Saharan Africa, affecting local air quality. Outside South Africa, most hotspots are relatively small, posing a challenge for traditional top-down inversions. We tailor an existing top-down wind rotation and Gaussian plume fit inversion to suit the relatively small NO x hotspots for most of Sub-Saharan Africa. We apply the customised inversion to three years of nitrogen dioxide (NO 2 ) observations from the TROPospheric Monitoring Instrument (TROPOMI) to derive annual NO x emissions for 24 isolated hotspots (21 urban, 3 power plants) compared to at most 5 in past studies. Annual hotspot emissions total 207.3 kilotonnes NO. Urban hotspot emissions range from <2 mol/s for Antananarivo, Madagascar, to 27.7±11.7 mol/s for the megacity Lagos in Nigeria. Coal-fired power plant emissions are 2.7±0.9 mol/s for Hwange, Zimbabwe, and similar (~70 mol/s) for Lethabo and combined Medupi and Matimba plumes in South Africa. Top-down estimates are 8-20% less than Continuous Emissions Monitoring Systems emissions. We conduct a quasi-independent evaluation of urban top-down emissions by assessing improved agreement between the GEOS-Chem model and TROPOMI NO 2 after updating modelled emissions to match the top-down estimates. The inventory hotspot emissions decline from an annual total of 176 kt NO to 133 kt NO and the model root mean squared error more than halves from 1.2 Pmolecules/cm 2 to 0.48 Pmolecules/cm 2 . Our top-down emissions exhibit large, up to 6-fold, systematic differences with contemporary global and regional inventories.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".