On the Mechanism of Mountain Cold-Trapping of Organic Chemicals
Bibliographic record
Abstract
The preferential accumulation of selected organic pollutants at higher altitude has been observed in a number of mountain regions. It is proposed that this phenomenon is due to differences in the efficiency of precipitation scavenging at various elevations, which, in turn, is due to the temperature dependence of organic vapor partitioning into rain, snow, and aerosols. The occurrence and extent of enrichment with elevation depends on whether the scavenging efficiency of a chemical is sensitive to temperature within the range encountered along a mountain slope. A multicompartment fate and transport model parameterized for mountain systems suggeststhat substances with equilibrium partitioning coefficients at 25 degrees C between water and air from 10(3.5) to 10(5.5) and between atmospheric particles and air from 10(9) to 10(11) are most likely to be subject to mountain cold-trapping. Such substances remain in the atmospheric vapor phase at higher valley temperatures, but are scavenged efficiently at the lower temperatures prevailing at higher altitudes. This implies that substances subject to mountain cold-trapping are approximately 2 orders of magnitude less volatile than substances that experience global cold-trapping. For example, while lighter PCBs get preferentially trapped at higher latitudes, the heavier PCBs are predicted to experience the strongest mountain cold-trapping. These model results agree with the results of field studies, with the exception of those studies that rely on sample media such as plant foliage for which precipitation is not the dominant deposition pathway. It appears that very fast deposition processes are required to trap contaminants along mountain slopes, whereas such processes reduce contaminant transport to remote polar regions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".