Regional and intercontinental pollution signatures on modeled and measured PAN at northern mid-latitude mountain sites
Bibliographic record
Abstract
Abstract. Peroxy acetyl nitrate (PAN) is the most important reservoir species for nitrogen oxides (NOx) in the remote troposphere. Upon decomposition in remote regions, PAN promotes efficient ozone production. We evaluate monthly mean PAN abundances from global chemical transport model simulations (HTAP1) for 2001 with measurements from five northern mid-latitude mountain sites (four European and one North American). The multi-model mean generally captures the observed monthly mean PAN but individual models simulate a factor of ~ 4–8 range in monthly abundances. We quantify PAN source-receptor relationships at the measurement sites with sensitivity simulations that decrease regional anthropogenic emissions of PAN (and ozone) precursors by 20 % from North America (NA), Europe (EU), and East Asia (EA). The HTAP1 models attribute more of the observed PAN at Jungfraujoch (Switzerland) to emissions in NA and EA, and less to EU, than a prior trajectory-based estimate. The trajectory-based and modeling approaches agree that EU emissions play a role in the observed springtime PAN maximum at Jungfraujoch. The signal from anthropogenic emissions on PAN is strongest at Jungfraujoch and Mount Bachelor (Oregon, U.S.A.) during April. In this month, PAN source-receptor relationships correlate both with model differences in regional anthropogenic volatile organic compound (AVOC) emissions and with ozone source-receptor relationships. PAN observations at mountaintop sites can thus provide key information for evaluating models, including links between PAN and ozone production and source-receptor relationships. Establishing routine, long-term, mountaintop measurements is essential given the large observed interannual variability in PAN.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".