Constraints on Asian ozone using Aura TES, OMI and Terra MOPITT
Bibliographic record
Abstract
Abstract. Rapid industrialization in Asia in the last two decades has resulted in a significant increase in Asian ozone (O3) pre-cursor emissions with likely a corresponding increase in the export of O3 and its pre-cursors. However, the relationship between this increasing O3, the chemical environment, O3 production efficiency, and the partitioning between anthropogenic and natural precursors is unclear. In this work, we use satellite measurements of O3, CO and NO2 from TES (Tropospheric Emission Spectrometer), MOPITT (Measurement of Pollution In The Troposphere) and OMI (Ozone Monitoring Instrument) to quantify O3 pre-cursor emissions for 2006 and their impact on free-tropospheric O3 over North-East Asia, where pollution is typically exported globally due to strong westerlies. Using the GEOS-Chem global chemical transport model, we show that the modeled seasonal variation of O3 based on these updated O3 pre-cursor emissions is consistent with the observed O3 variability and amount, after accounting for known biases in the TES O3 data. Using the adjoint of GEOS-Chem we then partition the relative contributions of natural and anthropogenic sources to free troposphere O3 in this region. We find that the influence of lightning NOx is important in summer. The contribution from anthropogenic NOx is dominant in other seasons. China is the major contributor of anthropogenic VOCs (Volatile Organic Compounds), whereas the influence of biogenic VOCs is mainly from Southeast Asia. Our result shows that the influence of India and Southeast Asia emissions on O3 pollution export is significant, comparable with Chinese emisisons in winter and about 50% of Chinese emissions in other seasons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".