National and transboundary contributions to surface ozone concentration across European countries
Bibliographic record
Abstract
Tropospheric ozone impacts health, climate, and ecosystems. Effective ozone mitigation policies are challenged by limited quantitative understanding of national versus transboundary contributions to surface ozone. This study uses a chemical transport model with a source apportionment algorithm to analyze ozone contributions across Europe from 2015 to 2017 during peak ozone season. We quantify country-level ozone production and imported ozone, distinguishing contributions from 35 European countries, neighboring countries, seas, and hemispheric influences. Results show substantial contributions from outside the 35 European countries, with hemispheric contributions playing a significant role. European contributions are crucial during high ozone episodes, especially from Germany, France, Italy, the UK, Poland, and Spain. Spain, northern Italy, and northwest France are identified as areas where national precursor reductions would be more effective in improving national air quality. Furthermore, 25 of the 35 European countries studied are net importers of cumulative ozone mass, with the Netherlands, Belgium, and the UK acting as major exporters. These findings highlight the need for comprehensive air quality policies and cross-border cooperation. The hemispheric transport of air pollutants is a critical external contributor to surface ozone concentration in Europe, and national emissions are key during high concentration episodes, according to an analysis using regional air quality modeling and source apportionment tagging method.
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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 | 0.001 |
| 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 | 0.001 | 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".