In recent years, regulators have devoted increasing attention to health risks from exposure to multiple chemicals. In 1996, the US Congress directed the US Environmental Protection Agency (EPA) to study mixtures of chemicals in drinking water, with a particular focus on potential interactions affecting chemicals' joint toxicity. The task is complicated by the number of possible mixtures in drinking water and lack of toxicological data for combinations of chemicals. As one step toward risk assessment and regulation of mixtures, the EPA and the Agency for Toxic Substances and Disease Registry (ATSDR) have proposed to estimate mixtures' toxicity based on the interactions of individual component chemicals. This approach permits the use of existing toxicological data on individual chemicals, but still requires additional information on interactions between chemicals and environmental data on the public's exposure to combinations of chemicals.
Large compilations of water-quality data have recently become available from federal and state agencies. This chapter demonstrates the use of these environmental data, in combination with the available toxicological data, to explore scenarios for mixture toxicity and develop priorities for future research and regulation. Occurrence data on binary and ternary mixtures of arsenic, cadmium, and manganese are used to parameterize the EPA and ATSDR models for each drinking water source in the dataset. The models' outputs are then mapped at county scale to illustrate the implications of the proposed models for risk assessment and rulemaking. For example, according to the EPA's interaction model, the levels of arsenic and cadmium found in US groundwater are unlikely to have synergistic cardiovascular effects in most areas of the country, but the same mixture's potential for synergistic neurological effects merits further study. Similar analysis could, in future, be used to explore the implications of alternative risk models for the toxicity and interaction of complex mixtures, and to identify the communities with the highest and lowest expected value for regulation of chemical mixtures.