Water uptake by biomass burning aerosol at sub- and supersaturated conditions: closure studies and implications for the role of organics
Bibliographic record
Abstract
Abstract. We investigate the CCN activity of freshly emitted biomass burning particles and their hygroscopic growth at a relative humidity (RH) of 85%. The particles were produced in the Mainz combustion laboratory by controlled burning of various wood types. The water uptake at sub- and supersaturations is parameterized by the hygroscopicity parameter, κ (c.f. Petters and Kreidenweis, 2007). For the wood burns, κ is low, generally around 0.06. The main emphasis of this study is a comparison of κ derived from measurements at sub- and supersaturated conditions (κG and κCCN), in order to see whether the water uptake at 85% RH can predict the CCN properties of the biomass burning particles. Differences in κGand κCCN can arise through solution non-idealities, the presence of slightly soluble or surface active compounds, or non-spherical particle shape. We find that κG and κCCN agree within experimental uncertainties (of around 30%) for particle sizes of 100 and 150 nm; only for 50 nm particles is κCCN larger than κG by a factor of 2. The magnitude of this difference and its dependence on particle size is consistent with the presence of surface active organic compounds. These compounds mainly facilitate the CCN activation of small particles, which form the most concentrated solution droplets at the point of activation. The 50 nm particles, however, are only activated at supersaturations higher than 1% and are therefore of minor importance as CCN in ambient clouds. By comparison with the actual chemical composition of the biomass burning particles, we estimate that the hygroscopicity of the water-soluble organic carbon (WSOC) fraction can be represented by a κWSOC value of approximately 0.2. The effective hygroscopicity of a typical wood burning particle can therefore be represented by a linear mixture of an inorganic component with κ ≅ 0.6, a WSOC component with κ ≅ 0.2, and an insoluble component with κ = 0.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".