Atmospheric transport of persistent semi-volatile organic chemicals to the Arctic and cold condensation at the mid-troposphere – Part 1: 2-D modeling in mean atmosphere
Bibliographic record
Abstract
Abstract. In the first part of this study for revisiting the cold condensation effect on global distribution of semi-volatile organic chemicals (SVOCs), the atmospheric transport of SVOCs to the Arctic at the mid-troposphere in a mean meridional atmospheric circulation over Northern Hemisphere was simulated by a two-dimensional atmospheric transport model. Results show that under the mean meridional atmosphere the long-range atmospheric transport of SVOCs from warm latitudes to the Arctic occurs primarily at the mid-troposphere. Accordingly, the cold condensation of the chemicals is likely also to take place at the mid-troposphere over a source region of the chemicals in warm low latitudes. We demonstrate that the temperature dependent vapour pressure and atmospheric degradation rate of SVOCs exhibit similarities between lower atmosphere over the Arctic and the mid-troposphere over a tropical region. Frequent occurrence of atmospheric ascending motion and convection over warm latitudes carry the chemicals to a higher altitude where some of these chemicals may condense/partition to particle or aqueous phase through the interaction with atmospheric aerosols, cloud water droplets and ice particles, and become more persistence in the lower temperatures. Stronger winds at the mid-troposphere then convey the condensed chemicals to the Arctic where they are brought down to the surface by large-scale descending motion and wet deposition. Using calculated water droplet-air partitioning coefficient of several persistent organic semi-volatile chemicals under a mean air temperature profile from the equator to the North Pole we propose that clouds are likely important sorbing media for SVOCs and pathway of the cold condensation effect and poleward atmospheric transport. The role of deposition and atmospheric descending motion in the cold condensation effect over the Arctic was also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".