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Although water is considered essential to life and an important natural resource, disadvantaged communities, such as low-income and minority communities, are disproportionately burdened by lead exposure in drinking water. In this paper, we highlight case studies that have received national press coverage as well as recent examples of community lead poisoning hazards that are still ongoing across various regions of the US. We show through these three case studies of Flint, Michigan, Washington, D.C., and Birmingham, Alabama, that the severity and frequency of this pervasive public health problem are highly concentrated in minority and low-income populations, and thus they bear the brunt of the socio-economic impacts. We identify the use of sensors to improve detection of hazardous materials and decrease inequities in drinking water contamination. To address water-related equity issues, we call for a sustainable community capacity approach that consists of shared governance between those who live in a community and stakeholders, such as businesses and health services, who have vested interests in it. We conclude by highlighting ways that a community could build collective social capital, safeguarding its environment from lead poisoning through health literacy education. Promoting water literacy is highly significant since water knowledge is crucial towards achieving water sustainability and equity.
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This content will become publicly available on March 31, 2026
Graph neural network-based water contamination detection from community housing information
Introduction:Detecting water contamination in community housing is crucial for protecting public health. Early detection enables timely action to prevent waterborne diseases and ensures equitable access to safe drinking water. Traditional methods recommended by the Environmental Protection Agency (EPA) rely on collecting water samples and conducting lab tests, which can be both time-consuming and costly. Methods:To address these limitations, this study introduces a Graph Attention Network (GAT) to predict lead contamination in drinking water. The GAT model leverages publicly available municipal records and housing information to model interactions between homes and identify contamination patterns. Each house is represented as a node, and relationships between nodes are analyzed to provide a clearer understanding of contamination risks within the community. Results:Using data from Flint, Michigan, the model demonstrated higher performance compared to traditional methods. Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67. Discussion:In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. This approach offers a practical tool for policymakers and public health officials to assess and mitigate contamination risks, ultimately improving community health and safety.
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- Award ID(s):
- 2230180
- PAR ID:
- 10614653
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Environmental Engineering
- Volume:
- 4
- ISSN:
- 2813-5067
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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