Grantee Research Project Results
1999 Progress Report: Statistical Models for the Concentrations of Chemicals in Source and Treated Drinking Water Supplies
EPA Grant Number: R826890Title: Statistical Models for the Concentrations of Chemicals in Source and Treated Drinking Water Supplies
Investigators: Schervish, Mark J. , Small, Mitchell J.
Institution: Carnegie Mellon University
EPA Project Officer: Hahn, Intaek
Project Period: September 1, 1998 through August 31, 2001 (Extended to September 30, 2002)
Project Period Covered by this Report: September 1, 1998 through August 31, 1999
Project Amount: $250,000
RFA: Environmental Statistics (1998) RFA Text | Recipients Lists
Research Category: Human Health , Aquatic Ecosystems , Environmental Statistics
Objective:
The objective is to develop statistical models for the concentrations of multiple contaminants in both raw and finished drinking water sources. These models will be used to quantify the variability across the United States of the concentrations of these contaminants as well as the uncertainty associated with estimates of concentration. The key components will include models for concentrations in raw water, models for the choice of treatment to be used, and models for the efficiency of the treatments. These models can be combined to produce a model for concentrations in finished water.The project has interacted closely with a smaller, ongoing project with the U.S. Environmental Protection Agency (EPA) Office of Ground Water and Drinking Water to develop more effective methods for benefit-cost analysis and uncertainty analysis, and for conducting Regulatory Impact Assessments (RIAs) of proposed drinking water maximum contaminant levels (MCLs). As such, our research team has focused both on the development of new, fundamental approaches in statistics, and the direct translation and application of these methods into regulatory policy and decisionmaking.
Progress Summary:
We have completed a manuscript describing a model for a single contaminant, arsenic, in source waters. The model assumes spatial dependence between arsenic levels at different sources. It allows different amounts of variability in different locations and in different source types. It also allows dependence on system size, but the analysis of the data that we had available did not allow any clear conclusions concerning the dependence on system size. We also have begun work on a manuscript that develops methodology for estimating treatments that different facilities already have in place. Such information is not publicly available for all water treatment facilities. This will help us to predict levels of arsenic (or another contaminant) in finished waters. We also are developing a model for estimating the costs to treatment facilities associated with different possible MCLs that might be set for arsenic.Future Activities:
We plan to complete the manuscript on costs of compliance in the near future. We are beginning to develop an occurrence model for multiple contaminants. We also are developing methods for dealing with multiple datasets that are similar, but were collected under differing conditions.Journal Articles:
No journal articles submitted with this report: View all 6 publications for this projectSupplemental Keywords:
Bayesian analysis, geographic variation, water treatment, drinking water., RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Toxics, Water, POLLUTANTS/TOXICS, Environmental Chemistry, Arsenic, Chemistry, Contaminant Candidate List, Environmental Statistics, Water Pollutants, Drinking Water, Environmental Engineering, monitoring, public water systems, data synthesis, CCL, Bayesian space-time model, co-pollutant effects, exposure and effects, Bayesian hierarchical statistical models, environmental risks, predictive distributors, exposure, other - risk assessment, community water system, drinking water supplies, treatment, statistical models, maximum contaminant levels, data analysis, drinking water contaminants, water treatment, data models, hierarchical statistical analysis, innovative statistical models, chemical concentrations, drinking water treatment, drinking water system, multiple contaminantsRelevant Websites:
http://lib.stat.cmu.edu/cmu-stats/tr/tr700/tr700.html![Exit EPA icon](https://www.epa.gov/ncer/images/exit.gif)
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.