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Record W4232234127 · doi:10.5194/acp-2020-1008

10–year satellite–constrained fluxes of ammonia improve performance of chemistry transport models

2020· preprint· en· W4232234127 on OpenAlexaff
Nikolaos Evangeliou, Yves Balkanski, Sabine Eckhardt, Anne Cozic, Martin Van Damme, Pierre‐François Coheur, Lieven Clarisse, Mark W. Shephard, Karen Cady‐Pereira, Didier Hauglustaine

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsEnvironment and Climate Change Canada
FundersNorges ForskningsrådFonds De La Recherche Scientifique - FNRS
KeywordsAmmoniaEnvironmental scienceSatelliteAtmospheric sciencesAgricultureChemistryGeographyEngineeringPhysics

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.213
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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