Secondary Organic Aerosol Formation Enhanced by Organic Seeds of Similar Polarity at Atmospherically Relative Humidity
Bibliographic record
Abstract
Secondary Organic Aerosol (SOA) forms in the atmosphere when semi-volatile oxidation products from biogenic and anthropogenic hydrocarbons condense onto atmospheric particulate matter. Climate models assume that oxidation products and preexisting organic aerosol form a well-mixed particle and enhance condensation, and, as a result, predict that future increases in anthropogenic primary organic aerosol (POA) will cause a significant increase in SOA. However, recent experiments performed at low humidity (<10%) demonstrate a single-phase particle does not always form, challenging the validity of model assumptions. In this work, we investigate the formation of SOA at atmospherically relevant humidities (55 - 65%) and examine this mixing assumption. We hypothesized that humidity leads to decreased viscosity and shorter mixing timescales, which is favorable for aerosol mixing. Here, α-pinene, a biogenic volatile organic compound is oxidized with ozone in a flow tube reactor in the presence of different organic aerosol seeds. Increased humidity did not enhance SOA formation with erythritol or squalane seed as hypothesized, implying that these compounds do not mix with α-pinene SOA in the range of humidities studied (55 – 65%). Yield enhancements were observed with tetraethylene glycol seed, demonstrating interaction between the SOA and seed. These observations suggest increased humidity does not promote mixing between the oxidation products and POA and highlight the need to fully understand the aerosol phase state in the atmosphere in order to better parameterize SOA formation and accurately predict future changes in air quality.
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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".