Sources of carbonaceous aerosols over the United States and implications for natural visibility
Bibliographic record
Abstract
We use a global three‐dimensional model (GEOS‐CHEM) to better quantify the sources of elemental carbon (EC) and organic carbon (OC) aerosols in the United States through simulation of year‐round observations for 1998 at a network of 45 sites (Interagency Monitoring of Protected Visual Environments (IMPROVE)). Simulation with our best a priori understanding of sources, including global satellite data to constrain fire emissions, captures most of the variance in the observations (R 2 = 0.84 for EC, 0.67 for OC) with a low bias of 15% for EC and 26% for OC. Multiple linear regression to fit the IMPROVE data yields best estimates of 1998 U.S. sources of 0.60 Tg year −1 EC and 0.52 Tg year −1 OC from fossil fuel; 0.07 Tg year −1 EC and 0.89 Tg year −1 OC from biofuel; 0.08 Tg year −1 EC and 0.60 Tg year −1 OC from wildfires; and 1.10 Tg year −1 OC from vegetation. We find that fires in Mexico and Canada contributed 40–70% of annual mean natural EC in the United States for 1998 and 20–30% of annual mean natural OC. Transpacific transport from Asian pollution sources amounted to less than 10% of the natural EC and less than 2% of the natural OC; in contrast to ozone, we find that intercontinental transport of anthropogenic carbonaceous aerosols does not enhance significantly the natural background. IMPROVE observations and model simulations for the summer of 1995 show that Canadian fire emissions can produce large events of elevated EC and OC in the southeastern United States. Our best estimates of mean natural concentrations of EC and OC in the United States, using a model simulation with climatological monthly mean fire emissions, are 2–3 times higher than the default values recommended by the U.S. Environmental Protection Agency for visibility calculations, except for OC in the eastern United States (16% lower).
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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 | 0.000 |
| 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".