Hoover, and four co-authors, employed by community health programs reported in 2018 that Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity.
These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures.
The authors concluded that drinking water is an important exposure pathway and identifying common contaminant mixtures in water supplies is a critical challenge to address. There are few examples that use multivariate statistical methods to identify representative combinations of metals and metalloids in water sources. To address this gap Bayesian Profile Regression was used to identify subgroups of unregulated water sources that had similar contaminant profiles.
A human health toxicity metric was then employed to identify contaminant combinations that might warrant additional investigation. Lastly, a spatial analysis method was applied to evaluate the spatial clustering of unregulated water sources to inform public health officials and policy makers. Five metal combinations that may contribute to negative human health consequences were identified: arsenic and uranium; arsenic, uranium, and selenium; arsenic, uranium, and manganese.
For a regional study, such as the study presented here, the ability to visualize the spatial distribution of clusters may aid in outreach and public policy decisions. More generally, this investigation demonstrated that a multivariate classification technique may be used to identify and characterize contaminant mixtures in water supplies. These methods may be applied in the future to inform public policy makers, community outreach, and future research efforts to reduce exposure to multiple contaminants from unregulated water sources.
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