Viscosity Variability from Smoldering Eucalyptus Smoke: From High Viscosity Tar Balls to Low Viscosity Organic Aerosol
Bibliographic record
Abstract
Uncontrolled wildfires in Australian eucalyptus forests emit large amounts of smoke, primarily composed of biomass burning organic aerosol (BBOA). Although BBOA viscosity has been studied for other fuels, it remains uncharacterized for eucalyptus. In this work, we generated BBOA by smoldering eucalyptus leaves and wood in a tube furnace and determined viscosities using optical microscopy, transmission electron microscopy, and rectangular fluorescence recovery after photobleaching. Early-stage burning of eucalyptus leaves produced nonhygroscopic tar balls with viscosities exceeding 8 × 10 10 Pa s. In contrast, late-stage leaf burning and both early- and late-stage wood burning produced hygroscopic BBOA with viscosities below 3 × 10 3 Pa s─over 7 orders of magnitude lower. These results show that BBOA viscosity is strongly influenced by both fuel type and burn stage, factors that should be considered in atmospheric models. Importantly, our findings demonstrate that smoldering eucalyptus leaves can directly produce tar balls without requiring atmospheric processing. These particles may act as ice-nucleating agents in mixed-phase and cirrus clouds. We further show that BBOA viscosity can strongly affect the atmospheric lifetime of brown carbon in eucalyptus smoke, potentially extending it by up to 4 orders of magnitude. This has important implications for evaluating the climate impact of eucalyptus wildfire emissions.
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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".