Global inventory of nitrogen oxide emissions constrained by space‐based observations of NO<sub>2</sub> columns
Bibliographic record
Abstract
We use tropospheric NO 2 columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument to derive top‐down constraints on emissions of nitrogen oxides (NO x ≡ NO + NO 2 ), and combine these with a priori information from a bottom‐up emission inventory (with error weighting) to achieve an optimized a posteriori estimate of the global distribution of surface NO x emissions. Our GOME NO 2 retrieval improves on previous work by accounting for scattering and absorption of radiation by aerosols; the effect on the air mass factor (AMF) ranges from +10 to −40% depending on the region. Our AMF also includes local information on relative vertical profiles (shape factors) of NO 2 from a global 3‐D chemical transport model (GEOS‐CHEM); assumption of a globally uniform shape factor, as in most previous retrievals, would introduce regional biases of up to 40% over industrial regions and a factor of 2 over remote regions. We derive a top‐down NO x emission inventory from the GOME data by using the local GEOS‐CHEM relationship between NO 2 columns and NO x emissions. The resulting NO x emissions for industrial regions are aseasonal, despite large seasonal variation in NO 2 columns, providing confidence in the method. Top‐down errors in monthly NO x emissions are comparable with bottom‐up errors over source regions. Annual global a posteriori errors are half of a priori errors. Our global a posteriori estimate for annual land surface NO x emissions (37.7 Tg N yr −1 ) agrees closely with the GEIA‐based a priori (36.4) and with the EDGAR 3.0 bottom‐up inventory (36.6), but there are significant regional differences. A posteriori NO x emissions are higher by 50–100% in the Po Valley, Tehran, and Riyadh urban areas, and by 25–35% in Japan and South Africa. Biomass burning emissions from India, central Africa, and Brazil are lower by up to 50%; soil NO x emissions are appreciably higher in the western United States, the Sahel, and southern Europe.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".