The 1996 amendments to the US Safe Drinking Water Act (SDWA) mandate revision of current maximum contaminant levels (MCLs) for various harmful substances in public drinking water supplies. The determination of a revised MCL for any contaminant must reflect a judicious compromise between the potential benefits of lowered exposure and the feasibility of obtaining such levels. This evaluation is made as part of a regulatory impact assessment (RIA) requiring detailed information about the occurrence of the contaminant and the costs and efficiencies of the available treatment technologies. our work focuses on the first step of this process, using a collection of data sources to model arsenic occurrence in treatment facility source waters as a function of system characteristics such as source water type, location and size. We fit Bayesian hierarchical models to account for the spatial aspects of arsenic occurrence as well as to characterize uncertainty in our estimates. After model selection based on cross-validation predictive densities, we use a national census of treatment systems and their associated covariates to predict the national distribution of raw water arsenic concentrations. We then examine the relationship between proposed MCLs and the number of systems requiring treatment and identify classes of systems which are most likely to be problematic. The posterior distribution of the model parameters, obtained via Markov Chain Monte Carlo, allows us to quantify the uncertainty in our predictions.