Assessing human health risks associated with wastewater flooding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it