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 O3 and PM2.5 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 NOX and SO2 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 (M8hO3) over the United States. Mean annual PM2.5 concentrations are estimated to be 23% lower (varies from −9% to −32%). Major reductions in sulfate, nitrate and ammonium PM2.5 components combined with the limited reduction in organic carbon suggests that organic carbon will be the dominant component of PM2.5 mass in the future. Regionally, the eastern United States benefits more than the rest of the regions from reductions in both M8hO3 and PM2.5, because of both spatial variations in the meteorological and emissions changes. Reduction in the higher M8hO3 concentrations is also estimated for all subregions and fewer days with M8hO3 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".