Trends in peroxyacetyl nitrate (PAN) in the upper troposphere and lower stratosphere over southern Asia during the summer monsoon season: regional impacts
Bibliographic record
Abstract
Abstract. We analyze temporal trends of peroxyacetyl nitrate (PAN) retrievals from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) during 2002–2011 in the altitude range 8–23 km over the Asian summer monsoon (ASM) region. The greatest enhancements of PAN mixing ratios in the upper troposphere and lower stratosphere (UTLS) are seen during the summer monsoon season from June to September. During the monsoon season, the mole fractions of PAN show statistically significant (at 2σ) positive trends from 0.2 ± 0.05 to 4.6 ± 3.1 ppt yr−1 (except between 12 and 14 km) which is higher than the annual mean trends of 0.1 ± 0.05 to 2.7 ± 0.8 ppt yr−1. These rising concentrations point to increasing NOx (= NO + NO2) and volatile organic compound (VOC) emissions from developing nations in Asia, notably India and China. We analyze the influence of monsoon convection on the distribution of PAN in UTLS with simulations using the global chemistry–climate model ECHAM5-HAMMOZ. During the monsoon, transport into the UTLS over the Asian region primarily occurs from two convective zones, one the South China Sea and the other over the southern flank of the Himalayas. India and China host NOx-limited regimes for ozone photochemical production, and thus we use the model to evaluate the contributions from enhanced NOx emissions to the changes in PAN, HNO3 and O3 concentrations in the UTLS. From a set of sensitivity experiments with emission changes in particular regions, it can be concluded that Chinese emissions have a greater impact on the concentrations of these species than Indian emissions. According to SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) NO2 retrievals NOx emissions increases over India have been about half of those over China between 2002 and 2011.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".