Using the chemical equilibrium partitioning space to explore factors influencing the phase distribution of compounds involved in secondary organic aerosol formation
Bibliographic record
Abstract
Abstract. Many atmospheric and chemical variables influence the partitioning equilibrium between gas phase and condensed phases of compounds implicated in the formation of secondary organic aerosol (SOA). The large number of factors and their interaction makes it often difficult to assess their relative importance and concerted impact. Here we introduce a two-dimensional space which maps regions of dominant atmospheric phase distribution within a coordinate system defined by equilibrium partition coefficients between the gas phase, an aqueous phase and a water-insoluble organic matter (WIOM) phase. Placing compounds formed from the oxidation of n-alkanes, terpenes and mono-aromatic hydrocarbons on the maps based on their predicted partitioning properties allows for a simple graphical assessment of their equilibrium phase distribution behaviour. Specifically, it allows for the simultaneous visualisation and quantitative comparison of the impact on phase distribution of changes in atmospheric parameters (such as temperature, salinity, WIOM-phase polarity, organic aerosol load, and liquid water content) and chemical properties (such as oxidation state, molecular size, functionalisation, and dimerisation). The graphical analysis reveals that the addition of hydroxyl, carbonyl and carboxyl groups increases the affinity of aliphatic, alicyclic and aromatic hydrocarbons for the aqueous phase more rapidly than their affinity for WIOM, suggesting that the aqueous phase may often be relevant even for substances that are considerably larger than the C2 and C3 compounds that are typically believed to be associated with aqueous SOA. In particular, the maps identify some compounds that contribute to SOA formation if partitioning to both WIOM and aqueous phase is considered but would remain in the gas phase if either condensed phase were neglected. For example, many semi-volatile α-pinene oxidation products will contribute to aqueous SOA under the conditions of high liquid water content encountered in clouds but would remain vapours in wet aerosol. It is conceivable to develop parameterisations of "partitioning basis sets" that group compounds with comparable partitioning properties, which – when combined with data on the abundance of those groups of compounds – could serve in the simulation of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".