Using measurements of the aerosol charging state in determination of the particle growth rate and the proportion of ion-induced nucleation
Bibliographic record
Abstract
Abstract. The fraction of charged nucleation mode particles as a function of particle diameter depends on the particle growth rate and the proportion of particles formed via ion-induced nucleation. In this study we have tested the applicability of recent data analysis methods to determine the growth rate and the proportion of ion-induced nucleation from the measured charged fractions. For this purpose we have conducted a series of aerosol dynamic simulations covering a wide range of atmospheric conditions. The growth rate and initial fraction of charged particles were estimated from simulated data using these methods and compared with the values obtained directly from the simulations. We found that the data analysis methods used in this study should not be used when the nuclei growth rate is less than ~3 nm h−1, or when charged particles grow much more rapidly than neutral ones. Furthermore, we found that the difference in removal rates of neutral and charged particles should be taken into account when estimating the proportion of ion-induced nucleation. Neglecting the higher removal rate of charged particles compared with that of neutral ones could result in an underestimation of the proportion of ion-induced nucleation by up to a factor of 2. This underestimation is further increased if charged particles grow more rapidly than neutral ones. We also provided a simple way of assessing whether these methods are suitable for analyzing data measured under specific conditions. The assessment procedure was illustrated using a few examples of actual measurement sites with a more detailed examination of the typical conditions observed at the SMEAR II station in Hyytiälä, Finland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".