Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 633, Issue -, Pages 1667-1678Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2018.02.288
Keywords
Unregulated water sources; Metal and metalloid mixtures; Spatial clustering
Categories
Funding
- National Institute for Environmental Health Sciences [RO1 ES014565, R25 ES013208, P30 ES-012072]
- NIGMS ASERT IRACDA postdoctoral fellowship [K12 GM088021]
- UNM Center for Native Environmental Health Equity Research- A Center of Excellence In Environmental Health Disparities Research - NIEHS [1P50ES026102]
- UNM Center for Native Environmental Health Equity Research- A Center of Excellence In Environmental Health Disparities Research - NIMHD [1P50ES026102]
- UNM Center for Native Environmental Health Equity Research- A Center of Excellence In Environmental Health Disparities Research - USEPA [83615701]
- National Institute of Environmental Health Sciences Superfund Research Program [1 P42 ES025589]
- U.S. Environmental Protection Agency [83615701]
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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. (c) 2018 The Authors. Published by Elsevier B.V.
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