Role of Aerosol Liquid Water in Secondary Organic Aerosol Formation from Volatile Organic Compounds
Bibliographic record
Abstract
A key mechanism for atmospheric secondary organic aerosol (SOA) formation occurs when oxidation products of volatile organic compounds condense onto pre-existing particles. Here, we examine effects of aerosol liquid water (ALW) on relative SOA yield and composition from α-pinene ozonolysis and the photooxidation of toluene and acetylene by OH. Reactions were conducted in a room-temperature flow tube under low-NO x conditions in the presence of equivalent loadings of deliquesced (∼20 μg m –3 ALW) or effloresced (∼0.2 μg m –3 ALW) ammonium sulfate seeds at exactly the same relative humidity (RH = 70%) and state of wall conditioning. We found 13% and 19% enhancements in relative SOA yield for the α-pinene and toluene systems, respectively, when seeds were deliquesced rather than effloresced. The relative yield doubled in the acetylene system, and this enhancement was partially reversible upon drying the prepared SOA, which reduced the yield by 40% within a time scale of seconds. We attribute the high relative yield of acetylene SOA on deliquesced seeds to aqueous partitioning and particle-phase reactions of the photooxidation product glyoxal. The observed range of relative yields for α-pinene, toluene, and acetylene SOA on deliquesced and effloresced seeds suggests that ALW plays a complicated, system-dependent role in SOA formation.
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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".