Global inventory of nitrogen oxide emissions constrained by space‐based observations of NO<sub>2</sub> columns
Why this work is in the frame
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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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it