Semi-empirical parameterization of size-dependent atmospheric nanoparticle growth in continental environments
Bibliographic record
Abstract
Abstract. The capability to accurately yet efficiently represent atmospheric nanoparticle growth by biogenic and anthropogenic secondary organics is a challenge for current atmospheric large-scale models. It is, however, crucial to predict nanoparticle growth accurately in order to reliably estimate the atmospheric cloud condensation nuclei (CCN) concentrations. In this work we introduce a simple semi-empirical parameterization for sub-20 nm particle growth that distributes secondary organics to the nanoparticles according to their size and is therefore able to reproduce particle growth observed in the atmosphere. The parameterization includes particle growth by sulfuric acid, secondary organics from monoterpene oxidation (SORGMT) and an additional condensable vapor of non-monoterpene organics ("background"). The performance of the proposed parameterization was investigated using ambient data on particle growth rates in three diameter ranges (1.5–3 nm, 3–7 nm and 7–20 nm). The growth rate data were acquired from particle/air ion number size distribution measurements at six continental sites over Europe. The longest time series of 7 yr (2003–2009) was obtained from a boreal forest site in Hyytiälä, Finland, while about one year of data (2008–2009) was used for the other stations. The extensive ambient measurements made it possible to test how well the parameterization captures the seasonal cycle observed in sub-20 nm particle growth and to determine the weighing factors for distributing the SORGMT for different sized particles as well as the background mass flux (concentration). Besides the monoterpene oxidation products, background organics with a concentration comparable to SORGMT, around 6 × 107 cm−3 (consistent with an additional global SOA yield of 100 Tg yr−1) was needed to reproduce the observed nanoparticle growth. Simulations with global models suggest that the "background" could be linked to secondary biogenic organics that are formed in the presence of anthropogenic pollution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".