Selective Nox Control for Exposure and Attainment- Based Policy Metrics
Bibliographic record
Abstract
Selective Nox Control for Exposure and Attainment- Based Policy MetricsAbstract Number:2553 Amir Hakami* and Amanda Pappin Amir Hakami* Carleton University, Canada, E-mail Address: [email protected] and Amanda Pappin Carleton University, Canada, E-mail Address: [email protected] AbstractCost-effective strategies for reducing the public health and environmental effects of ozone would preferentially target sources whose emissions are most damaging. Adjoint or reverse sensitivity analysis in air quality models allows for estimating the response of a single exposure-based metric (such as nation-wide mortality) to small changes in emissions in each location and thus differentiates between influences exerted by various sources. Metrics related to attainment of standards and those quantifying health impacts of population exposures are of significant policy relevance. While ozone standards are informed by epidemiological findings, they do not directly account for the magnitudes of adverse health effects themselves. Further, the extreme-value nature of standards (thresholds pertaining to the fourth-highest concentration in a year) is such that attainment metrics are more heavily influenced by (few) days of extreme pollution episodes rather than the entire distribution of ozone concentrations that forms the basis for health impact assessments. We use adjoint sensitivity analysis to compare responses of (1) nationwide mortality from short-term ozone exposure and (2) attainment of a 65 ppb standard by means of a model- based probability-weighted fourth-highest concentration, both in Canada and the U.S., to NOx emission reductions on a source-by-source basis. We find a strong spatial component to both metrics: location-specific 10% reductions in NOx emissions reduce probability-weighted concentrations by up to a 73 ppb (Los Angeles) and mortality by 4 deaths/summer (Atlanta). We discuss policy insights of the effectiveness spectrum of NOx controls, with specific regard for dense urban areas where disbenefits exist (e.g., -5.3 deaths/summer for NY). We also discuss how the duality between attainment and exposure metrics, and inclusion of aerosol health metrics, can shift the air quality management paradigm into a 'multi-pollutant/multi-objective' framework.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".