Elucidating emissions control strategies for ozone to protect human health and public welfare within the continental United States
Bibliographic record
Abstract
Abstract Ground-level ozone, which forms photochemically in the atmosphere from precursor emissions of oxides of nitrogen (NO x ) and volatile organic compounds, is a criteria pollutant that harms human health and public welfare. For a representative summer episode, premature mortality and potential productivity losses (PPLs) of selected crops and trees attributable to ozone exposure have been quantified using ozone fields from the Community Multiscale Air Quality (CMAQ) model. We applied exposure-response models for the increased risk of premature mortality due to long-term exposure to ozone over a theoretical minimum risk exposure level (TMREL) and for the reduced accumulation of vegetative biomass for four crop species and eleven tree species using the W126 metric designed to capture impacts on plants. To elucidate which emissions contributed to these disbenefits, we applied adjoint-based sensitivity analysis, which efficiently estimates sensitivities of concentration-based metrics with respect to numerous emissions parameters simultaneously. The adjoint of CMAQ was applied to the continental US to calculate the influence of spatially-resolved ozone precursor emissions on the annual average, domain-wide daily maximum 8 h average over the TMREL (elevated MDA8), premature mortality attributable to exposure to ozone above the TMREL, and PPLs. These quantities provide the impact in terms of the percent reduction in precursor emissions. Additionally, locations where similar percent reductions in ozone precursor emissions would impact one or more endpoints greater than average have been identified. NO x emissions were found to contribute most to the three metrics. The distinct spatial patterns of emissions influences on public welfare disbenefits as compared to the elevated MDA8 and premature mortality suggest that the current regulatory averaging time motivates different emissions control strategies than those that could most directly protect public welfare.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".