Gas−Particle Partitioning of Organic Compounds and Its Interpretation Using Relative Solubilities
Bibliographic record
Abstract
A review is presented of the current understanding of the partitioning phenomena of relatively low vapor pressure organic chemicals between the gas phase and atmospheric particulates. The phenomena have been interpreted as adsorption, absorption, or a combination of both and corresponding theoretical equations suggested to quantify partitioning. Empirical correlations involving the chemical's vapor pressure and octanol--air partition coefficient are discussed. It is suggested that insights into selecting preferred correlations and into the nature of the partitioning phenomena can be enhanced by treating the partition coefficient as a ratio of a solubility or pseudo-solubility of the chemical in the aerosol particle to that in air. Such particle solubilities when calculated for PCBs, alkanes, and PAHs show remarkable constancy and are generally consistent with near-ideal absorption into organic matter. An exception occurs when the PAH is generated simultaneously with the aerosol and unusually high solubilities are observed, indicative of adsorption to active carbon surfaces. Recommendations are made for interpreting experimental partitioning data and for the use of correlations for predictive purposes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".