Deep Learning to Evaluate US NO <sub>x</sub> Emissions Using Surface Ozone Predictions
Bibliographic record
Abstract
Abstract Emissions of nitrogen oxides (NO x = NO + NO 2 ) in the United States have declined significantly during the past three decades. However, satellite observations since 2009 indicate total column NO 2 is no longer declining even as bottom‐up inventories suggest continued decline in emissions. Multiple explanations have been proposed for this discrepancy including (a) the increasing relative importance of nonurban NO x to total column NO 2 , (b) differences between background and urban NO x lifetimes, and (c) that the actual NO x emissions are declining more slowly after 2009. Here, we use a deep learning model trained by NO x emissions and surface observations of ozone to assess consistency between the reported NO x trends between 2005 and 2014 and observations of surface ozone. We find that the satellite‐derived trends best reproduce ozone in low NO x emission (background) regions. The 2010–2014 trend from older satellite‐derived emission estimates produced at low spatial resolution results in the largest bias in surface ozone in regions with high NO x emissions, reflecting the blending of urban and background NO x in these low‐resolution top‐down analyses. In contrast, the trend from higher resolution satellite‐based estimates, which are more capable of capturing the urban emission signature, is in better agreement with ozone in high NO x emission regions, and is consistent with the trend based on surface observations of NO 2 . Our results confirm that the satellite‐derived trends reflect anthropogenic and background influences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".