Evaluation of space‐based constraints on global nitrogen oxide emissions with regional aircraft measurements over and downwind of eastern North America
Bibliographic record
Abstract
We retrieve tropospheric nitrogen dioxide (NO2) columns for May 2004 to April 2005 from the SCIAMACHY satellite instrument to derive top‐down emissions of nitrogen oxides (NOx = NO + NO2) via inverse modeling with a global chemical transport model (GEOS‐Chem). Simulated NO2 vertical profiles used in the retrieval are evaluated with airborne measurements over and downwind of North America (ICARTT); a northern midlatitude lightning source of 1.6 Tg N yr−1 minimizes bias in the retrieval. Retrieved NO2 columns are validated (r2 = 0.60, slope = 0.82) with coincident airborne in situ measurements. The top‐down emissions are combined with a priori information from a bottom‐up emission inventory with error weighting to achieve an improved a posteriori estimate of the global distribution of surface NOx emissions. Our a posteriori NOx emission inventory for land surface NOx emissions (46.1 Tg N yr−1) is 22% larger than the GEIA‐based a priori bottom‐up inventory for 1998, a difference that reflects rising anthropogenic emissions, especially from East Asia. A posteriori NOx emissions for East Asia (9.8 Tg N yr−1) exceed those from other continents. The a posteriori inventory improves the GEOS‐Chem simulation of NOx, peroxyacetylnitrate, and nitric acid with respect to airborne in situ measurements over and downwind of New York City. The a posteriori is 7% larger than the EDGAR 3.2FT2000 global inventory, 3% larger than the NEI99 inventory for the United States, and 68% larger than a regional inventory for 2000 for eastern Asia. SCIAMACHY NO2 columns over the North Atlantic show a weak plume from lightning NOx.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".