Wind Tunnel‐Based Comparison of PM<sub>10</sub> Emission Rates for Volcanic Ash and Glaciogenic Aerosol Sources Within Iceland
Bibliographic record
Abstract
Abstract Iceland contributes 30–40 million tons of mineral dust to the atmosphere each year. Emission events are linked to exceedingly high concentrations of PM 10 , poor air quality, and respiratory disease, particularly in Reykjavík. Volcaniclastic aerosols have unique microphysical properties, and usually are porous and highly angular, with large surface areas bearing ultrafine, nanoscale dustcoats. The large internal void space contributes to low particle density, believed to affect emission and deposition rates in the atmospheric boundary layer, as well as the range of transport. However, the aerodynamic and sedimentological factors that govern dust entrainment in these high‐latitude settings are not well constrained empirically, providing little guidance for the parameterization of aerosol dispersion models. A series of laboratory wind tunnel simulations were carried out under full climate control to evaluate these effects for Icelandic samples collected from five active dust sources, inclusive of volcanic ash and glaciofluvial sediments. PM 10 emission rates measured in this study are similar in magnitude to those obtained in a small number of field studies within selected high‐latitude regions, and for volcanic ash using a PI‐SWERL. The scaling with friction velocity is well described by the 1988 dust emission model of Gillette and Passi, while the parameterization appears to be strongly dependent on the median particle size. Sediments from the coarsest Icelandic dust sources were found to be most emissive, owing to the importance of particle impact, while the proportionate amount of PM 10 within the test bed was not found to correlate with the emission rate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".