Estimation of the Volatility and Apparent Activity Coefficient of Levoglucosan in Wood-Burning Organic Aerosols
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Biomass burning (BB) is a major source of aerosols and black carbon, thereby exerting an important impact on climate and air quality. Levoglucosan is the most well-recognized organic marker compound of BB and has been used to quantitatively assess BB’s contribution to ambient aerosols. However, little is known about levoglucosan’s evaporation under atmospheric conditions, primarily due to the uncertainty of its effective saturation vapor concentration ( C *) and its unknown activity coefficient (γ), in the complex BB emission matrix. Here, we utilized a thermodenuder to investigate the evaporation of levoglucosan from mixtures with polyethylene glycol (PEG) or BB primary organic aerosol (BBPOA) matrices, respectively. We estimate a pure component log 10 ( C */[μg m –3 ]) of levoglucosan of 1.1 ± 0.1 at 298 K. We reveal that levoglucosan mixed with PEG or BBPOA becomes more volatile than when treated as a single component due to nonideal molecular interactions. Considering that phase separation might occur in such systems, we term γ apparent activity coefficient (γ a ). We estimate log 10 C * and γ a of levoglucosan in BBPOA of 1.8 ± 0.1 and 3.8 ± 0.3, assuming a liquid phase state. Consequently, γ a must be considered to avoid significant underestimation of levoglucosan evaporation via gas–particle partitioning during transport.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".