Wildfire Smoke, the Clean Air Act, and the Exceptional Events Rule: Implications and Policy Alternatives
Bibliographic record
Abstract
In recent years, increasing wildfire activity in the western US and Canada has driven declining air quality in some regions of the US. Under EPA's Exceptional Events Rule, states are allowed to exempt daily pollution monitor readings impacted by wildfire smoke from determinations of compliance with Clean Air Act air quality standards. As a result, wildfire smoke is leading to a growing divergence between actual and regulatory air quality. This paper reviews treatment of wildfire smoke under the Clean Air Act and the Exceptional Events Rule. It presents quantitative evidence on the effect of the rule on fulfillment of air quality standards, and an analysis of the degree to which smoke that currently leads to air quality violations is driven by out-of-state fires and fires on federal lands. We suggest a modification to the Exceptional Events Rule under which wildfire emissions would be excluded from air quality regulations only if states adopt government-defined best fire management policies, and we discuss the legal and practical feasibility of such a change.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".