Simulating the Influence of Snow on the Fate of Organic Compounds
Bibliographic record
Abstract
Snow scavenging, a seasonal snowpack, and a dynamic water balance are incorporated in a non-steady-state generic multimedia fate model in order to investigate the effect of snow on the magnitude and temporal variability of organic contaminant concentrations in various environmental media. Efficient scavenging of large nonpolar organic vapors and particle-bound organic chemicals by snow can lead to reduced wintertime air concentrations and incorporation in the snowpack. The snow cover functions as a temporary storage reservoir that releases contaminants accumulating over the winter during a short melt period, resulting in temporarily elevated concentrations in air, water, and soil. The intensity of these peaks increases with the length of the snow accumulation period. Organic chemicals of sufficient volatility (log KOA < 9; e.g., light polychlorinated biphenyls) can volatilize from the snowpack, resulting in springtime concentration maxima in the atmosphere. The behavior of fairly water-soluble chemicals during snowmelt depends on their relative affinity for the newly formed liquid water phase and the rapidly diminishing ice surface-quantitatively expressed by their interface-water partition coefficient (KIW). Chemicals with a preference for the dissolved phase (low KIW; e.g., pentachlorophenol) can become enriched in the first meltwater fractions and experience a temporary concentration peak in lakes and rivers. Organic chemicals that are neither volatile enough to evaporate from the snowpack nor sufficiently water soluble to dissolve in the meltwater (e.g., polybrominated diphenyl ethers) sorb to the particles in the snowpack. These particles may be sufficiently contaminated to constitute the major input route to the terrestrial environment upon release during snowmelt. Because wintertime deposition to the snowpack may be higher than to a non-snow covered surface, this can result in higher soil concentrations of persistent organic contaminants in the long term. The potential ecotoxicological significance of peak exposures demands a better understanding of the role of snow in the fate of organic contaminants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".