Measuring the Release of Organic Contaminants from Melting Snow under Controlled Conditions
Bibliographic record
Abstract
The release of organic contaminants from a melting snowpack may result in temporary concentration peaks in receiving water bodies and respective pulse exposure of aquatic organisms. It is thus of considerable interest to gain a mechanistic and quantitative understanding of the processes determining the dynamic behavior of organic chemicals during snowmelt. Uniformly structured and contaminated snow was produced with the help of a newly designed snow gun and exposed to predetermined temperature conditions in a temperature-controlled cold room. The dry density and liquid water content during four freeze-thaw cycles was recorded continuously at different layers within the snowpack using time domain reflectometry, providing information on meltwater production and propagation as well as snow metamorphism. Fractionated meltwater samples were filtered and the dissolved and particle phase analyzed for five polycyclic aromatic hydrocarbons (PAHs) using gas chromatography/ mass spectrometry. The distribution of the PAHs between the dissolved and particulate fractions of the meltwater was strongly related to their hydrophobicity. Particle-bound PAHs were released late during the snowmelt, whereas PAHs in the dissolved phase were released uniformly during a two day melting period. Even though conductivity measurements indicated a preferential early elution of ions in the first meltwater fractions, no such "first flush" behavior was observed for soluble PAH. The developed laboratory-based approach opens up for the first time the possibility of reproducible experiments on organic contaminant behavior in snow. Future experiments will explore, in detail, how the properties of organic chemicals, the physical and chemical properties of the snowpack, and the temperature variations before and during the time of melting interact to determine the timing of chemical release from a snowpack.
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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 | 0.000 |
| 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".