Potential contamination of drinking water in private wells during floods in southern Quebec, Canada: an integration of water geochemistry, risk perception and behavioural changes
Bibliographic record
Abstract
Floods are recurring natural disasters in the province of Quebec, with recent major events in the springs of 2017, 2019, and 2023, when snowmelt and heavy rainfall converged. These events not only caused significant property damage and population displacement but also posed serious risks to public health, especially in areas where drinking water is sourced from private wells that may be vulnerable to contamination. Critical risk factors include the proximity of wells to rivers, the presence of contaminants in floodwaters, and surface pollutants on flood-prone lands, which can infiltrate drinking water sources during floods. This interdisciplinary project evaluates the spatial risk of potable water contamination and consumption in the Stoneham-et-Tewkesbury region, QC, through a combined approach involving natural and social sciences.The natural science component involves assessing the water quality of residential wells during baseline and flood periods, and conducting spatio-temporal analyses to: 1) identify factors influencing contamination risk; 2) assess duration of contamination post-flood; and 3) determine the lateral extent of contamination. To do so, water samples collected over 15 field campaigns were analyzed for a variety of geochemical, isotopic and microbiological parameters. Although the chemical quality of well water was generally acceptable, microbiological contamination (e.g., total coliforms and E. coli) frequently exceeded safety thresholds.The social dimension of the project explores: 1) riverside residents' risk perception in relation to their well water quality during floods; 2) their water consumption practices during floods; and 3) the views of various stakeholders (riverside residents, municipality, regional water agencies) regarding roles, responsibilities and approaches to promote safe water consumption. This was achieved through semi-directed interviews conducted with seven residents participating in the well sampling campaigns, and three organization representatives.The results of this study aim to strengthen the resilience of flood-prone communities by integrating scientific data, local knowledge and community feedback to develop practical recommendations to reduce the contamination risks and promote safe water use during flood events. The results will be shared through workshops organized with residents and the municipality of Stoneham-et-Tewkesbury, as well as local water organizations. Results will also be shared with the Quebec Department of Environment to provide feedback on adequacy of the current government guidelines regarding well water consumption during floods.Keywords: Floods, human health, contamination, interdisciplinary, social, drinking water, groundwater, community, spatial assessment, risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".