Constraints on ship NO <sub>x</sub> emissions in Europe using GEOS-Chem and OMI satellite NO <sub>2</sub> observations
Bibliographic record
Abstract
Abstract. We present a top-down ship NOx emission inventory for the Baltic Sea, North Sea, Bay of Biscay and Mediterranean Sea, based on satellite observed tropospheric NO2 columns of the Ozone Monitoring Instrument (OMI) for 2005–2006. We improved the representation of ship emissions in the GEOS-Chem chemistry transport model, and compared simulated NO2 columns to consistent satellite observations. Relative differences between simulated and observed NO2 columns have been used to constrain ship emissions in four European seas (Baltic Sea, North Sea, Bay of Biscay and Mediterranean Sea). The constrained ship tracks account for 39% of total top-down European ship NOx emissions, which amounts to 0.96 Tg N for 2005, and 1.0 Tg N for 2006 (11–15% lower than the bottom-up EMEP ship emission inventory). Our results indicate that EMEP emissions in the Mediterranean Sea are too high (by 60%) and misplaced by up to 150 km, which can have important consequences for local air quality simulations. In the North Sea, our top-down emissions amount to 0.05 Tg N for 2005 (35% lower than EMEP). Increased top-down emissions were found for the Baltic Sea and Bay of Biscay, with emission totals of 0.05 Tg N (131% higher than EMEP) and 0.08 Tg N for 2005 (128% higher than EMEP), respectively. Our study explicitly accounts for the (non-linear) sensitivity of satellite retrievals to changes in the a priori NO2 profiles. Although the effect of this sensitivity might be minor for small emission increments, our findings stress the need for consistent information in satellite retrieval and model, as satellite observations are never fully independent of model information (i.e. assumptions on vertical NO2 profiles). Our study provides for the first time a space-based top-down ship NOx emission inventory, and can serve as a framework for future studies to constrain ship emissions using satellite NO2 observations in other seas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".