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, the North Sea, the Bay of Biscay and the 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 (the Baltic Sea, the North Sea, the Bay of Biscay and the Mediterranean Sea) using a mass-balance approach, and accounting for non-linear sensitivities to changing emissions in both model and satellite retrieval. These constraints are applied to 39 % of total top-down European ship NOx emissions, which amount 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 ship track, 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 the Bay of Biscay ship tracks, with totals in these tracks 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, 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 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) | 0.000 | 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) | 0.000 | 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".