Cloud processing of nitrate
Bibliographic record
Abstract
The role of clouds in the transport and transformation of tropospheric pollutants was investigated through airborne measurements made out of Cleveland, Ohio, from 21 July to 18 August 2004, as part of the International Consortium for Atmospheric Research on Transport and Transformation 2004 program. Observations of gas‐phase nitrate, size‐resolved particulate nitrate, cloud water nitrate, and size‐distributed cloud residual nitrate are used to examine changes in the partitioning of nitrate from precloud to postcloud as a function of particle size. The [NO3−]/[SO42−] ratio was highest in the bulk cloud water and higher in the cloud droplet residuals compared with the below‐cloud aerosols. Most of the nitrate entered the cloud water as HNO3, and in 30% of 43 size distributions examined, the nitrate in the cloud droplets was found in residual particle sizes smaller than those of sulfate. Simulations from a trace gas‐aerosol‐cloud parcel model show that this size difference results from differences in the processes by which nitrate and sulfate enter cloud water. The transfer of HNO3 to cloud droplets is governed primarily by gas‐phase mass transfer to the droplets, leading to greater accumulation in the smaller, more numerous droplets with higher total surface area. In contrast, much of the sulfate in the cloud water is the result of nucleation scavenging, which distributes the sulfate mass toward slightly larger sizes. The extent of separation between nitrate and sulfate is dependent on the cloud base sulfate size distribution and the factors that govern both HNO3 and SO2 uptake, with subsequent S(IV) oxidation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".