Rate of atmospheric brown carbon whitening governed by environmental conditions
Bibliographic record
Abstract
Biomass burning organic aerosol (BBOA) in the atmosphere contains many compounds that absorb solar radiation, called brown carbon (BrC). While BBOA is in the atmosphere, BrC can undergo reactions with oxidants such as ozone which decrease absorbance, or whiten. The effect of temperature and relative humidity (RH) on whitening has not been well constrained, leading to uncertainties when predicting the direct radiative effect of BrC on climate. Using an aerosol flow-tube reactor, we show that the whitening of BBOA by oxidation with ozone is strongly dependent on RH and temperature. Using a poke-flow technique, we show that the viscosity of BBOA also depends strongly on these conditions. The measured whitening rate of BrC is described well with the viscosity data, assuming that the whitening is due to oxidation occurring in the bulk of the BBOA, within a thin shell beneath the surface. Using our combined datasets, we developed a kinetic model of this whitening process, and we show that the lifetime of BrC is 1 d or less below ∼1 km in altitude in the atmosphere but is often much longer than 1 d above this altitude. Including this altitude dependence of the whitening rate in a chemical transport model causes a large change in the predicted warming effect of BBOA on climate. Overall, the results illustrate that RH and temperature need to be considered to understand the role of BBOA in the atmosphere.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".