Ozone export from East Asia: The role of PAN
Bibliographic record
Abstract
Abstract Peroxyacetyl nitrate (PAN) is an important ozone (O3) precursor. The lifetime of PAN is approximately 1 month in the free troposphere, and this allows O3 production to occur in pollution plumes at intercontinental distances from its source. In this study we use the Goddard Earth Observing System (GEOS)‐Chem global chemical transport model, new satellite measurements of PAN from the Aura Tropospheric Emission Spectrometer (TES), and data from the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign over North America, to study the role of natural and anthropogenic Asian emissions on free tropospheric (900–400 hPa) PAN distributions and subsequent O3 production. Using the ARCTAS data with GEOS‐Chem, we show that while GEOS‐Chem is unbiased with respect to the aircraft data, TES version 7 PAN data are biased high for regions with surface temperatures colder than 285 K. However, GEOS‐Chem and TES measurements provide a consistent representation (within 15% difference) of PAN abundance over East Asia. Because of the good agreement between model and observations, we use the GEOS‐Chem model to evaluate the sources of PAN precursors and the effect of free tropospheric PAN on the export of O3 from Asia to North America. The GEOS‐Chem model results show that the largest contributors to free tropospheric PAN over Asia and the northern Pacific are anthropogenic and soil NOx emissions. Biomass burning emissions have important contributions to free tropospheric PAN over northern Pacific (25% in April), while the contribution from lightning over northern Pacific is significant in July (40%). Strong springtime transport in April results in more export of free tropospheric PAN and O3 from East Asian emissions. This free tropospheric PAN contributes about 35% to the abundance of free tropospheric O3 over western North America in spring and 25% in summer.
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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 | 0.000 |
| 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 | 0.001 | 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".