Sunlight can convert atmospheric aerosols into a glassy solid state and modify their environmental impacts
Bibliographic record
Abstract
Secondary organic aerosol (SOA) plays a critical, yet uncertain, role in air quality and climate. Once formed, SOA is transported throughout the atmosphere and is exposed to solar UV light. Information on the viscosity of SOA, and how it may change with solar UV exposure, is needed to accurately predict air quality and climate. However, the effect of solar UV radiation on the viscosity of SOA and the associated implications for air quality and climate predictions is largely unknown. Here, we report the viscosity of SOA after exposure to UV radiation, equivalent to a UV exposure of 6 to 14 d at midlatitudes in summer. Surprisingly, UV-aging led to as much as five orders of magnitude increase in viscosity compared to unirradiated SOA. This increase in viscosity can be rationalized in part by an increase in molecular mass and oxidation of organic molecules constituting the SOA material, as determined by high-resolution mass spectrometry. We demonstrate that UV-aging can lead to an increased abundance of aerosols in the atmosphere in a glassy solid state. Therefore, UV-aging could represent an unrecognized source of nuclei for ice clouds in the atmosphere, with important implications for Earth's energy budget. We also show that UV-aging increases the mixing times within SOA particles by up to five orders of magnitude throughout the troposphere with important implications for predicting the growth, evaporation, and size distribution of SOA, and hence, air pollution and climate.
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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".