10–year satellite–constrained fluxes of ammonia improve performance of chemistry transport models
Bibliographic record
Abstract
Abstract. In recent years, ammonia emissions have been continuously increasing being almost four times higher than in the 20th century. Although an important species as its use as a fertilized sustains human living, ammonia has major consequences both for humans and the environment, because of its reactive gas phase chemistry that makes it easily convertible to particles. Despite its pronounced importance, yet, ammonia emissions are highly uncertain in most emission inventories. However, the great development of satellite remote sensing nowadays provides the opportunity for more targeting research in constraining ammonia emissions. Here, we used satellite measurements to calculate global ammonia emissions over the period 2008–2017. Then, the calculated ammonia emissions were fed to a chemistry transport model and ammonia concentrations were simulated for the period 2008–2017. The simulated concentrations of ammonia were compared with ground measurements from Europe, North America and Southeastern Asia, as well as with satellite measurements. The satellite-constrained ammonia emissions represent global concentrations more accurately than state-of-the-art emissions, which underestimate ammonia with a factor of two. Calculated fluxes in the North China Plain were increased after 2015, not due to emission changes, but due to changes in sulfate emissions that resulted in less ammonia neutralization and hence in larger atmospheric loads. Emissions over Europe were also twice as much as those in traditional datasets with dominant sources to be industrial and agricultural applications. Four hot-spot regions of high ammonia emissions were seen in North America characterized by large agricultural activity (Colorado), animal breeding (Iowa, northern Texas and Kansas), animal farms (Salt Lake, Cache, and Utah) and animal breeding and agricultural practices (California). South America is dominated by ammonia emissions from biomass burning, which cause a strong seasonality. In Southeastern Asia, ammonia emissions from fertilizer plants in China, Pakistan, India and Indonesia are the most important, while a strong seasonality was observed with a spring and late summer peak due to rice and wheat cultivation. Modelled concentrations from the satellite-constrained ammonia emissions are overestimated in Eastern Europe, where state-of-the-art emissions capture observations better. Measurements of ammonia concentrations in North America were better reproduced with satellite-constrained emissions, while all emissions generally underestimate station concentrations in Southeastern Asia. The calculated ammonia emissions also reproduce global CrIS (Cross-track Infrared Sounder) observations more effectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".