Exploring the ionic composition of the Asian Tropopause Aerosol Layer using medium duration balloon flights
Bibliographic record
Abstract
Satellite observations have revealed an enhanced aerosol layer near the tropopause over Asia during the summer monsoon, called the Asian Tropopause Aerosol Layer (ATAL). The chemical composition of the ATAL is investigated here using offline ionic analysis of aerosols collected with a balloon-borne impactor near the tropopause region over India onboard extended duration balloon flights in the summer of 2017 and winter 2018. We found NO3- and NO2- dominant among other ions with values ranging between 87-343 ng/m3 during the summer campaign. In contrast, SO4 levels were found above detection limit (>10 ng/m3) only in winter. In addition, we determined the origin of the air masses sampled during the flights through back trajectory analysis combined with convection. The results obtained therein were put into a context of large-scale transport and aerosol distribution with GEOS-Chem chemical transport model simulations. The first flight of summer 2017 sampled air mass within the Asian monsoon anticyclone (AMA), associated with smaller particle size found on stage 2 (particle size cut off > 0.15 microns) of the impactor, while the second flight sampled air mass at the edge of the AMA associated with larger particle size on stage 1 (particle size cut off between 2 and 0.5 microns). The sampled air masses in winter 2018 were affected by smoke from the Pacific Northwest fire event in Canada, which occurred 7 months prior to our campaign. Concentrations of SO42-, NH4+, and Ca2+ were enhanced. Overall, our results suggest that nitrogen- containing particles represent a large fraction of aerosols populating the ATAL in agreement with aircraft measurements during the StratoClim campaign. Furthermore, GEOS-chem model simulations suggest that lightning NOx emissions had a minimal impact on the production of nitrate aerosols sampled during the two flights.
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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) | 0.001 | 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".