Assessing human health risks associated with wastewater flooding
Bibliographic record
Abstract
Exposure to wastewater, resulting from flooding of sanitary sewer systems during extreme weather events, presents a critical public health challenge, exacerbated by climate change and population growth. Wastewater contains a mixture of biological and chemical contaminants, posing significant health risk to communities, and leading to lingering risks of mould growth in flooded buildings. The health risks associated with exposure to contaminated wastewater during flooding events are particularly acute for vulnerable populations, including children (<5 years), the elderly (>65 years), and individuals with chronic obstructive pulmonary disease (COPD), asthma, mobility and visual impairments, mental health disorders, and high blood pressure. In this study, scenario-based wastewater modeling is used to estimate the population of vulnerable individuals and buildings at-risk during flood events, focusing on Charlottetown, Prince Edward Island as a case study. The modeling estimates that by 2023, approximately 3225 individuals and 6.4 % of total buildings are at risk from wastewater flooding under a 2-year scenario, increasing to 9479 individuals and 11.6 % of buildings by 2060. For a 100-year scenario, the risk rises from 8170 individuals and 17 % of buildings in 2023 to over 16,708 individuals and 21.5 % of buildings by 2060. The study also proposes detailed exposure pathways and introduces a collaborative planning framework to support adaptive wastewater management. The results highlight increasing vulnerabilities, with severe consequences such as exposure to aerosolized pathogens, heavy metals, and mould growth. By addressing health risks and advocating for socially equitable flood risk mitigation, the study offers actionable insights to support sustainable and resilient communities. This study aligns with the goals of good health and wellbeing (SDG3), and clean water and sanitation (SDG6), both of which are essential for achieving sustainable cities and communities (SDG11).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".