North American influence on tropospheric ozone and the effects of recent emission reductions: Constraints from ICARTT observations
Bibliographic record
Abstract
We use observations from the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) campaign over eastern North America in summer 2004, interpreted with a global 3‐D model of tropospheric chemistry (GEOS‐Chem), to improve and update estimates of North American influence on global tropospheric ozone and the effect of recent U.S. anthropogenic reductions on surface ozone pollution. We find that the 50% decrease in U.S. stationary NOx sources since 1999 has decreased mean U.S. boundary layer ozone concentrations by 6–8 ppbv in the southeast and 4–6 ppbv in the Midwest. The observed dO3/dCO molar enhancement ratio in the U.S. boundary layer during ICARTT was 0.46 mol mol−1, larger than the range of 0.3–0.4 from studies in the early 1990s, possibly reflecting the decrease in the NOx/CO emission ratio as well as an increase in the ozone production efficiency per unit NOx. North American NOx emissions during summer 2004 as constrained by the ICARTT observations (0.72 Tg N fossil fuel, 0.11 Tg N biomass burning, 0.28 Tg N lightning for 1 July to 15 August) enhanced the hemispheric tropospheric ozone burden by 12.4%, with comparable contributions from fossil fuel and lightning (5–6%), but only 1% from biomass burning emissions despite 2004 being a record fire year over Alaska and western Canada.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".