Photochemical Aging Enhances the Viscosity of Biomass Burning Organic Aerosol
Bibliographic record
Abstract
Biomass burning organic aerosols (BBOA) are a major contributor to organic aerosols in the atmosphere. Viscosity is an important property of BBOA, as it influences many of the processes it is involved in in the atmosphere; this includes but is not limited to particle growth rates, reaction and mixing rates, and cloud condensation nucleation. As BBOA is transported through the troposphere, it undergoes photochemical aging due to reactions with atmospheric oxidants such as OH and O3. Recently it has been shown that the viscosities of some aerosols can be enhanced through atmospheric aging processes. However, research on the influence of atmospheric aging on BBOA is still limited.We used a Potential Aerosol Mass oxidative flow reactor (185 nm mode) to expose BBOA to high concentrations of OH and O3, simulating the equivalent of 1 to 8 days in the troposphere. We measured the viscosity of the photochemically aged BBOA with the poke-flow viscometry technique, and found that aging increased the viscosity of BBOA. After 1 day of aging, the viscosity of BBOA increased by several orders of magnitude. However, further aging up to 8 days saw a less dramatic increase in viscosity, with no noticeable increase between 5 days and 8 days. We also measured the carbon oxidation state of the BBOA with high-resolution aerosol mass spectrometry, and the trend in increasing oxidation state reflected the trend in viscosity. This suggests that the most dramatic changes in the physicochemical properties of BBOA occur within the first days or hours of aging, after which oxidation becomes a less significant aging mechanism. These results have implications for how the aging and eventual fate of BBOA should be treated in models.
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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".