Quantifying trace gas uptake to tropospheric aerosol: recent advances and remaining challenges
Bibliographic record
Abstract
The interactions of trace gases with tropospheric aerosol can have significant effects on both gas phase and aerosol composition. In turn, this may affect the atmospheric oxidizing capacity, aerosol hygroscopicity and optical properties, and the lifetimes of trace aerosol species. Through the detailed description of specific reaction systems, this review article illustrates how detailed experimental studies of gas-particle interactions lead to both a comprehensive understanding of the underlying physical chemistry as well as accurate parameterizations for atmospheric modeling. The reaction systems studied illustrate the complexity in the field: (i) N(2)O(5) uptake, presented as a benchmark multiphase system, can lead to both NO(x) loss and halogen activation, (ii) loss of HO(2) on aqueous particles is surprisingly poorly studied given its potential importance for HO(x) loss, (iii) uptake of HNO(3) by marine aerosol and heterogeneous oxidation of organic-bearing particles are examples of how gas-particle interactions can lead to substantial alteration of aerosol composition, and (iv) the uptake of glyoxal to ammonium sulfate aerosol leads to highly complex particle-phase chemistry. In addition, for the first time, this article presents the challenges that must be addressed in the design and interpretation of atmospheric gas-to-particle uptake experiments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".