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