Predicting Drinking Water Contamination from Extreme Weather to Reduce Early Life Contaminant Exposures

Private wells are the drinking water source for over 43 million U.S. households but remain unregulated federally and are vulnerable to contamination from environmental hazards. Over 2 million North Carolina residents use wells in areas prone to hurricanes and floods, which can increase chemical and biological contamination (e.g., pathogenic bacteria). Households near contaminated sites, such as animal feeding operations, hazardous waste and coal ash facilities are especially vulnerable to upgradient contaminants, which can be intensified by air temperature and precipitation trends. No mechanism exists for predicting contamination events and informing a cost-effective and human health-preserving intervention. Yet, large-scale and fine-resolution datasets, from federal, state and local agencies, have the potential to capture the complex human and physico-chemical interactions that predict contamination. Developing novel quantitative approaches that synthesize large and disparate data sets is critical to guiding interventions that could be used by local health offices to prevent early life exposures that impair cognitive development.

Scholars @ ECU