Impacts of global climate change and emissions on regional ozone and fine particulate matter concentrations over the United States
Bibliographic record
Abstract
Simulated future summers (i.e., 2049–2051) and annual (i.e., 2050) average regional O 3 and PM 2.5 concentrations over the United States are compared with historic (i.e., 2000–2002 summers and all of 2001) levels to investigate the potential impacts of global climate change and emissions on regional air quality. Meteorological inputs to the CMAQ chemical transport model are developed by downscaling the GISS Global Climate Model simulations using an MM5‐based regional climate model. Future‐year emissions for North America are developed by growing the U.S. EPA CAIR inventory, Mexican and Canadian emissions and by using the IMAGE model with the IPCC A1B emissions scenario that is also used in projecting future climate. Reductions of more than 50% in NO X and SO 2 emissions are forecast. Impacts of global climate change alone on regional air quality are small compared to impacts from emission control‐related reductions, although increases in pollutant concentrations due to stagnation and other factors are found. The combined effect of climate change and emission reductions lead to a 20% decrease (regionally varying from −11% to −28%) in the mean summer maximum daily 8‐hour ozone levels (M8hO 3 ) over the United States. Mean annual PM 2.5 concentrations are estimated to be 23% lower (varies from −9% to −32%). Major reductions in sulfate, nitrate and ammonium PM 2.5 components combined with the limited reduction in organic carbon suggests that organic carbon will be the dominant component of PM 2.5 mass in the future. Regionally, the eastern United States benefits more than the rest of the regions from reductions in both M8hO 3 and PM 2.5 , because of both spatial variations in the meteorological and emissions changes. Reduction in the higher M8hO 3 concentrations is also estimated for all subregions and fewer days with M8hO 3 above the air quality standards in urban sites with Atlanta in the southeast benefiting most.
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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) | 0.001 | 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".