Satellite based county- to provincial-level ammonia emissions estimates
Bibliographic record
Abstract
While ammonia (NH3) at its current levels is known to be a hazard to environmental and human health, the atmospheric budget is still quite uncertain. This can largely be attributed to the short lifetime of ammonia in combination with an overall lack of (dense) in-situ measurement networks. The capability to observe ammonia distributions with satellites has opened new ways to study the atmospheric ammonia budget. Previous studies have demonstrated the capability of current ammonia satellite sensors to resolve emissions from point like sources, biomass burning, and constraining emission sources at a regional level with methods involving the use of air quality models. In this study, we present the first spatially resolved ammonia emission estimates across the globe using a consistent methodology based solely on ammonia satellite observations from the Cross-track Infrared Sounder (CrIS) instrument and ECMWF ERA5 wind fields. The concept was evaluated for North Western Europe and demonstrated the ability to constrain annual emissions at county- to provincial-levels with most deviations within the bounds found in the error analysis. Furthermore, we show that for some regions the spatial patterns found in the satellite observations are consistent while others do not match the current inventories. Finally, the results indicate that the absolute emission levels tend to be underestimated for parts of the globe.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".