Human Exposure and Vulnerability to Manganese Contaminated Groundwater

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Project Summary / Abstract Manganese (Mn) is a naturally-occurring metal contaminant found in water supplies throughout the world. Although research using small samples (< 400 observations) from Bangladesh, China and Canada suggests that excessive consumption of Mn is neurotoxic in infants and young children, the United States Environmental Protection Agency (US EPA) classifies Mn only as a secondary contaminant. Thus, Mn is not subject to mandatory monitoring in public water systems, let alone private wells, which serve over 2 million Californians while remaining unregulated and untreated. To reconcile the gap between epidemiological evidence and regulatory action, we propose this timely research. Our project, which takes California’s private wells as a focal point for its investigation, has three specific aims: to identify (1) areas at high risk of exposure to Mn in drinking water (2) the socioeconomic characteristics of communities at high-risk of Mn exposure, and (3) the degree of association between Mn exposure and the health outcomes of infants and young children. While treatment for Mn-contaminated water is technologically straightforward, access may depend on a community’s socioeconomic and political status; thus, there is danger of Mn silently amplifying health inequalities. The ​broader, long-term objectives of this research are (I) to encourage further analyses of Mn exposure in California by developing a statewide publicly-available dataset from groundwater Mn data, and (II) to develop a ​causal ​framework for a primary-sample study of Mn effects on infant and child health. California enforces the US EPA’s secondary contaminant standard of 50 µg L​-1 but only in community water systems. Non-transient, non-community water systems (e.g. schools and hospitals) and private wells are exempt. In recent years, the US Geological Survey (USGS) has found Mn concentrations above the health-based target of 300 µg L​-1 in California’s wells. ​The proposed research will indicate whether and to what extent Mn affects child health and contributes to health disparities. As such, it is aligned with the NIEHS mission to determine how environmental exposures affect humans in order to promote healthier lives. To achieve the first aim of identifying areas at high risk of exposure to unsafe concentrations of Mn, we will use a machine learning model on subsurface flow and groundwater chemistry to predict Mn concentrations in the Central Valley, Los Angeles, Central Coast and West Coast Basins; then, we will overlay the Mn prediction grid with ZIP code and school district polygons to identify the areas that emerge with more than a 50% chance of exposure to Mn at the secondary standard as high-risk. For the second aim, we will determine if high-risk areas are distinguished from low-risk areas by their socioeconomic status and demographics using two-sample t-tests. For the third aim, we will run least squares regressions of birth weight, grade-by-school test scores and chronic absenteeism rates on predicted Mn exposure with controls for chemical confounds, socioeconomic status and demographic characteristics.