Trace organic contaminants in lake waters: Occurrence and environmental risk assessment at the national scale in Canada
Bibliographic record
Abstract
Numerous contaminants are produced and used daily, a significant fraction ultimately finding their way into natural waters. However, data on their distribution in lakes is lacking. To address this gap, the presence of 54 trace organic contaminants (TrOCs), representative of various human activities, was investigated in the surface water of 290 lakes across Canada. These lakes ranged from remote to highly impacted by human activities. In 88% of the sampled lakes, contaminants were detected, with up to 28 detections in a single lake. The compounds most frequently encountered were atrazine, cotinine, and deethylatrazine, each of which was present in more than a third of the lakes. The range of detected concentrations was from 0.23 ng/L to about 2200 ng/L for individual compounds, while the maximum cumulative concentration exceeded 8100 ng/L in a single lake. A risk assessment based on effect concentrations for three aquatic species (Pimephales promelas, Daphnia magna, and Tetrahymena pyriformis) was conducted, revealing that 6% of lakes exhibited a high potential risk for at least one species. In 59% of lakes, some contaminants with potential sub-lethal effects were detected, with the detection of up to 17 TrOCs with potential impacts. The results of this work provide the first reference point for monitoring the evolution of contamination in Canadian lakes by TrOCs. They demonstrate that a high proportion of the sampled lakes bear an environmentally relevant anthropogenic chemical footprint.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".