Pharmaceuticals and pesticides in rural community drinking waters of Quebec, Canada – a regional study on the susceptibility to source contamination
Bibliographic record
Abstract
Abstract In Canada, the presence of pharmaceuticals and pesticides in municipal drinking water has been examined primarily in larger urban centres which draw their supplies from surface water. However, few studies have examined this issue in smaller and rural communities, which represent nearly one-third of the Canadian population and which draw their drinking water mainly from groundwater. This study presents a regional-scale assessment of the presence of these contaminants in the drinking waters of 17 smaller rural communities, compared with two larger urban communities, in south-central Quebec. From a total of 70 chemicals examined, 15 compounds (nine pharmaceuticals and six pesticides) were detected. The three most frequently detected contaminants were caffeine, atrazine and naproxen, respectively, in 29%, 24% and 21% of the samples. Detections reported here for the first time in Quebec drinking water include the known human carcinogen cyclophosphamide and the fungicide thiabendazole. Maximum concentrations of pharmaceuticals ranged from 30 to 1,848 ng L−1 and of pesticides from 21 to 856 ng L−1. This study provides direct evidence that drinking water in smaller, rural communities of Quebec, Canada, whether sourced from groundwater or surface water, can contain measurable levels of pharmaceuticals and pesticides, indicative of their susceptibility to source contamination. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".