Analysis of Contaminant Co-Occurrence in Community Water Systems

Abstract

The current framework for U.S. Environmental Protection Agency regulation of water quality in community drinking water supplies produces sequential rules for either single contaminants or small groups of similar contaminants. For both substantive and pragmatic reasons, some water industry experts have advocated the development of a more holistic regulatory process in which rules are promulgated less frequently but for larger contaminant classes. Such a framework would require the expansion of existing regulatory evaluation technologies to account for joint occurrence distributions of multiple contaminants. This article presents an analysis, using two national contaminant databases, of the joint distributions of seven contaminants (arsenic, nitrate, uranium, manganese, magnesium, calcium, and sulfate) in community water system source waters. Inferences are based on a flexible Bayesian hierarchical modeling structure with numerous features desirable for empirical exploration of multicontaminant regulations, including the simultaneous estimation of spatial heterogeneity in contaminant levels and covariations among contaminants, applicability to sparse data collected over a large spatial scale, and coherent assimilation of information provided by censored observations. The model is used to estimate a family of joint distributions for the contaminants indexed by water system characteristics, with empirically appropriate complexity given the resolution of the available data. The resulting distributions provide insights about the nature of, and uncertainty about, contaminant co-occurrence patterns, quantify the impact on national assessments of jointly modeling the contaminants, and facilitate identification of critical classes of water systems where uncertainty is highest.

KEY WORDS :

• Data augmentation
• Markov chain Monte Carlo
• Multivariate spatial data
• Regulatory impact assessment
• Water quality regulation