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Bayesian Prediction of National Multi-Contaminant Trends in Community Water System Sources

J.R. Lockwood, Mark J. Schervish, Patrick Gurian and Mitchell J. Small

Abstract:

The current framework for U.S. Environmental Protection Agency regulation of water quality in community drinking water supplies consists of sequential rules for either single contaminants or small groups of similar contaminants. For both substantive and pragmatic reasons, promulgating less frequent rules for larger contaminant classes may be desirable. Such a change would require the expansion of existing regulatory evaluation technologies to account for joint occurrence distributions of the contaminants. This paper extends existing methods for modeling the distributions of a single contaminant in community water system source waters to the simultaneous consideration of multiple contaminants. It considers alternatives for addressing the implementation difficulties inherent in the multivariate setting, providing solutions of general methodological interest. Through case studies involving arsenic, sulfate, magnesium and calcium, it shows how jointly modeling contaminants provides better fit and predictive power than marginal models, and emphasizes how inference about critical regulatory quantities can be improved through joint modeling. The methods presented in this paper make significant progress in redressing several shortcomings of existing analyses.



Keywords: water quality regulation; regulatory impact assessment; Markov Chain Monte Carlo; multivariate spatial data; data augmentation.



Heidi Sestrich
1/2/2002
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