Satellite constraints of Nitrogen Oxide (NOX) emissions from India based on OMI observations and WRF-Chem simulations
Bibliographic record
Abstract
[1] In this work, we map and develop for the first time an independent satellite constrained NOx emission inventory for India for 2005 using an inverse technique and iterative procedure. We used OMI tropospheric NO2 column retrievals over the Indian region, with tropospheric NO2 columns simulated by the WRF-Chem model using the INTEX-B emission inventory. We determined the local relationship between modeled emissions and tropospheric columns and iteratively apply this relationship to OMI observations to derive an optimized NOx emission inventory on a 0.5° × 0.5° grid. The optimized total NOx emissions for India amount to 1.9 TgN/y and agree within 25% with EDGARv4.1 and the INTEX-B estimate. Our top-down inventory captures many of the missing hotspots in the original inventory and suggests that the INTEX-B inventory overestimates emissions over the Western and Eastern Indo-Gangetic region and underestimates point sources. We further evaluate the effect of the top-down inventory on surface ozone, which clearly indicates significant changes in spatial distribution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".