Natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United States: implications for policy
Bibliographic record
Abstract
We use a global 3-D coupled oxidant-aerosol model (GEOS-CHEM) to quantify natural and transboundary pollution influences on sulfate-nitrate-ammonium aerosol concentrations in the United States. This work is motivated in part by the Regional Haze Rule of the U.S. Environmental Protection Agency (EPA), which requires immediate action to improve visibility in U.S. wilderness areas along a linear trajectory towards an endpoint of natural visibility conditions by 2064. We present full-year simulations for 1998 and 2001 and evaluate them with nationwide networks of observations in the United States and Europe (IMPROVE, CASTNET, NADP, EMEP) and with Asian outflow observations from the NASA TRACE-P aircraft mission. Model results for sulfate are highly correlated with observations and generally unbiased across all seasons. For ammonium and nitrate but the model is too high in fall, likely because of excessive ammonia emissions. Shutting off U.S. anthropogenic emissions in the model defines background aerosol concentrations representing contributions from both natural and transboundary pollution sources. We find that transboundary transport of pollution from Canada, Mexico, and Asia dominates over natural influences for both sulfate and nitrate. Transpacific transport of Asian pollution accounts for 30% of background sulfate in both the western and eastern United States. Our best estimates of natural concentrations for ammonium sulfate and ammonium nitrate in the United States are either consistent with or lower than the default values recommended by EPA for natural visibility calculations. However, the large transboundary pollution influence means that a natural visibility objective cannot be approached without international emission controls. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".