Air pollutant source characterization using the revised regional haze tracking metric and a photochemical grid model and implications for regional haze planning
Bibliographic record
Abstract
The 2017 revisions to the Regional Haze Rule clarify that visibility progress at Class I national parks and wilderness areas should be tracked on days with the highest anthropogenic contributions to haze (impairment). We compare the natural and anthropogenic contributions to haze in the western United States in 2011 estimated using the Environmental Protection Agency (EPA) recommended method and using model projections from the Comprehensive Air Quality Model with Extensions (CAMx) and the Particulate Source Apportionment Tool (PSAT). We do so because these two methods will be used by states to demonstrate visibility progress by 2028. If the two methods assume different natural and anthropogenic contributions, the projected benefits of reducing U.S. anthropogenic emissions will differ. The EPA method assumes that episodic elevated carbonaceous aerosols greater than an annual 95th percentile threshold are natural events. For western U.S. IMPROVE monitoring sites reviewed in this paper, CAMx-PSAT confirms these episodes are impacted by carbon from wildfire or prescribed fire events. The EPA method assumes that most of the ammonium sulfate is anthropogenic in origin. At most western sites CAMx-PSAT apportions more of the ammonium sulfate on the most impaired days to global boundary conditions and anthropogenic Canadian, Mexican, and offshore shipping emissions than to U.S. anthropogenic sources. For ammonium nitrate and coarse mass, CAMx-PSAT apportions greater contributions to U.S. anthropogenic sources than the EPA method assigns to total anthropogenic contributions. We conclude that for western IMPROVE sites, the EPA method is effective in selecting days that are likely to be impacted by anthropogenic emissions and that CAMx-PSAT is an effective approach to estimate U.S. source contributions. Improved inventories, particularly international and natural emissions, and further evaluation of global and regional model performance and PSAT attribution methods are recommended to increase confidence in modeled source characterization. Implications: The western states intend to use the CAMx model to project visibility progress by 2028. Modeled visibility response to changes in U.S. anthropogenic emissions may be less than estimated using the EPA assumptions based on total U.S. and international anthropogenic contributions to visibility impairment. Additional model improvements are needed to better account for contributions to haze from natural and international emissions in current and future modeling years. These improvements will allow more direct comparison of model and EPA estimates of natural and anthropogenic contributions to haze and future visibility progress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".