Size‐resolved fluxes of sub‐100‐nm particles over forests
Bibliographic record
Abstract
Dry deposition of atmospheric particles is critically dependent on particle size and plays a key role in dictating the mass and number distributions of atmospheric particles. However, modeling dry deposition is constrained by a lack of understanding of controlling dependencies and accurate size‐resolved observations. We present size‐resolved particle number fluxes for sub‐100‐nm particle diameters (Dp) over a deciduous forest derived using eddy covariance applied to data from a fast mobility particle sizer. The size‐resolved particle number fluxes in 18 diameters between 8 and 100 nm were collected during leaf‐on and are statistically robust. Particle deposition velocities normalized by friction velocity (vd+) are approximately four times smaller than comparable values for coniferous forests reported elsewhere. Comparison of the data with output from a new one‐dimensional mechanistic particle deposition model designed for broadleaf forest exhibits greater accord with the measurements than two previous analytical models, but modeled vd+ underestimate observed values by at least a factor of two for all Dp between 6 and 100 nm. When size‐resolved particle deposition velocities for Dp < 100 nm are normalized by friction velocity, the key controlling role of particle diffusivity is strongly manifest. On the basis of analyses of these new measurements and recently published size‐resolved particle number fluxes from a conifer forest, we present working parameterizations for size‐resolved particle deposition velocities over forests that could reasonably be applied in regional and global atmospheric chemistry transport models.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".