Attainment vs Exposure: Ozone Metric Responses to Source-Specific NO<i><sub>x</sub></i> Controls Using Adjoint Sensitivity Analysis
Bibliographic record
Abstract
We establish linkages between sources of NOx emissions and two types of national ozone metrics in Canada and the U.S. using the adjoint of an air quality model. We define an attainment-based metric using probabilistic design values (PDVs) exceeding 65 ppb to represent polluted regions and define an exposure-based metric as the premature mortality count related to short-term ozone exposure, both in Canada and the U.S. Our results reveal differences in both temporally averaged and day-specific influences of NOx emission controls across source locations. We find NOx emission reductions in California and the eastern U.S. to be most effective for reducing attainment- and exposure-based metrics, amounting to a total reduction of 6500 ppb in PDVs and 613 deaths/season nationally from a 10% reduction in NOx emissions from those source locations. While source controls in the remainder of the western U.S. are beneficial at reducing nonattainment, these reductions are less influential on ozone mortality. We also find that while exposure-based metrics are sensitive to daily emission reductions, much of the reduction in PDVs arises from controlling emissions on only a fraction of simulation days. We further illustrate the dependency of adjoint estimates of emission influences on the choice of averaging period as a follow-up to previous work.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 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".