Predicting Secondary Organic Aerosol Enhancement in the Presence of Atmospherically Relevant Organic Particles
Bibliographic record
Abstract
Secondary organic aerosol (SOA) produced from atmospheric oxidation of organic vapors comprises a large portion of ambient particulate matter. Currently, SOA models typically assume that all organic species form a well-mixed phase as a simplification, which follows that SOA formation is enhanced in the presence of pre-existing organic aerosol (OA) according to Raoult’s Law. In this work, we show through experiments with atmospherically relevant OA that not all organic species are equally miscible, and the thermodynamics of mixing are composition dependent. SOA formation from α-pinene ozonolysis was investigated in the presence of OA that was collected from Toronto ambient air and other sources including biomass burning, meat-cooking emissions, and diesel exhaust. Compared to experiments with ammonium sulfate seed particles, enhanced SOA yields were observed with particles from biomass burning and meat cooking but not with diesel exhaust and concentrated ambient particles. We demonstrate that both kinetic (bulk diffusion-limitation) and thermodynamic (miscibility-limitation) factors are important in determining atmospheric organic aerosol partitioning. We develop parametrization methods using bulk elemental ratios (H/C and O/C) and functional group abundance (R OH and R COOH ) to estimate average intermolecular interactions, which allow us to use Hansen Solubility Framework we had previously developed to predict atmospheric organic aerosol miscibility and SOA yield enhancements in these complex mixtures. The framework has also been utilized to better understand the liquid–liquid phase separation between organic aerosol and inorganic salts. Our results show that a molecular description of thermodynamic forces is needed to describe aerosol mixing in the atmosphere and accurately parametrize 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".