Quantification of Hydrocarbon Contaminants in Meltwater and Sediment in a City Snow Pile
Bibliographic record
Abstract
Snow collected from roadways in northern climates is not pristine and may contain sand or gravel, deicing agents, litter, and airborne pollutants. Although fate and transport of contaminants in snowmelt has been studied under laboratory conditions, it is unknown whether the same mechanisms apply to a heterogeneous snow pile. This case study sought to identify and quantify organic species in snow pile meltwater in Edmonton, Canada, and estimate the total potential hydrocarbon loading bound to sediment. Analysis of meltwater and sediment samples revealed that most hydrocarbons were present in sediments, with meltwater containing low concentrations (<1 mg L−1) of F2 (>C10–C16) and F3 (>C16–C34) fraction hydrocarbons. Sediments showed detectable levels of F2 and F3 hydrocarbons in ∼21% of the samples tested, with concentrations ranging from approximately 600 to 7,800 mg·kg−1, with average F3 concentrations (the main source of hydrocarbons) well below Canadian guidelines for most samples. The estimated total hydrocarbon loading of the snow pile studied was ∼5.1 t of hydrocarbons. Findings are anticipated to be of value in the establishment of water quality monitoring programs, environmental policy and strategies involving the treatment, disposal, or reuse of residual snow pile sediments.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".