Slower CCN growth kinetics of anthropogenic aerosol compared to biogenic aerosol observed at a rural site
Bibliographic record
Abstract
Abstract. Growth rates of water droplets were measured with a static diffusion cloud condensation chamber in May–June 2007 at a rural field site in Southern Ontario, Canada, 70 km north of Toronto. Observations were made during periods when the winds were from the south and the site was impacted by anthropogenic air from the US and Southern Ontario as well as during a 5-day period of northerly wind flow when the aerosol was dominated by biogenic sources. The growth of droplets on anthropogenic size-selected particles centred at 0.1 μm diameter and composed of approximately 40% organic and 60% ammonium sulphate (AS) by mass, was delayed on the order of 1 second compared to a pure AS aerosol. Simulations of the growth rate indicate that a lowering of the water mass accommodation coefficient from αc=1 to an average of αc=0.044 is needed (assuming an insoluble organic with hygroscopicity parameter, κorg, of zero). In contrast, the growth rate of the aerosol of biogenic character, consisting of >80% organic, was similar to that of pure AS. Simulations of the predominantly biogenic aerosol show agreement between the observations and simulations when κorg=0.05–0.2 and αc=1. Inhibition of water uptake by the anthropogenic organic applied to an adiabatic cloud parcel model in the form of a constant low αc increases the number of droplets in a cloud compared to pure AS. If the αc is assumed to increase with increasing liquid water on the droplets, then the number of droplets decreases which could diminish the indirect effect. The slightly lower κorg in the biogenic case decreases the number of droplets in a cloud compared to pure AS.
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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".