# Economic Analysis for the Final Stage 2 Disinfectants and Disinfection Byproducts Rule

### Office of Water (4606-M) EPA 815-R-05-010 December 2005 www.epa.gov/safewater

### Final Economic Analysis for the Stage 2 DBPR December 2005 i 

# Contents

Executive Summary

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-1 ES.1 Need for the Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-1 ES.2 Consideration of Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-2 ES.3 Summary of the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-3 ES.4 Systems Subject to the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-8 ES.5 National Benefits and Costs of the Stage 2 DBPR Preferred Regulatory Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-10 ES.5.1 Derivation of the Stage 2 DBPR Compliance Forecast and Consequent Reductions in DBPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-13 ES.5.2 Derivation of Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-18 ES.5.3 Derivation of Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-22 ES.6 Estimated Impacts on Household Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-23 ES.7 Comparison of Costs and Benefits for Four Regulatory Alternatives . . . . . . . . . . . ES-24 ES.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-29

Chapter 1. Introduction

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.1 Summary of the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 1.2 Document Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7 1.3 Calculations and Citations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-8



## Chapter 2. Need for the Rule

2. Need for the Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.1.1 Description of the Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 2.2 Public Health Concerns to Be Addressed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 2.3 Regulatory History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 2.3.1 Statutory Authority for Promulgating the Rule . . . . . . . . . . . . . . . . . . . . . . . . 2-2 2.3.2 1979 Total Trihalomethane Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 2.3.3 1989 Total Coliform Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.3.4 1989 Surface Water Treatment Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.3.5 1996 Information Collection Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 2.3.6 1998 Interim Enhanced Surface Water Treatment Rule . . . . . . . . . . . . . . . . . . 2-5 2.3.7 1998 Stage 1 Disinfectants and Disinfection Byproducts Rule . . . . . . . . . . . . 2-6 2.3.8 2000 Proposed Ground Water Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6 2.3.9 2001 Arsenic Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 2.3.10 2001 Filter Backwash Recycling Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 2.3.11 2002 Long Term 1 Enhanced Surface Water Treatment Rule . . . . . . . . . . . . . 2-7 2.3.12 2005 Long Term 2 Enhanced Surface Water Treatment Rule . . . . . . . . . . . . . 2-7 2.4 Economic Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8



## Chapter 3. Baseline Conditions

3. Baseline Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

### Final Economic Analysis for the Stage 2 DBPR December 2005 ii 

### 3.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.3 Surface Water Analytical Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 3.4 Industry Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5 3.4.1 Public Water System Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7 3.4.2 Systems, Plants, and Population Subject to the Stage 2 DBPR . . . . . . . . . . . . 3-9 3.4.2.1 Plant Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 3.4.2.2 Population Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-17 3.4.3 Water Treatment Plant Design and Average Daily Flows . . . . . . . . . . . . . . . 3-21 3.4.4 Number of Households Served . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23 3.5 Influent Water Quality Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24 3.5.1 Summary of Available Influent Water Quality Data . . . . . . . . . . . . . . . . . . . 3-24 3.5.2 Regional Differences in Water Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-26 3.6 Treatment Characterization for the Pre-Stage 1 Baseline . . . . . . . . . . . . . . . . . . . . . . 3-31 3.7 DBP Occurrence for the Pre-Stage 1 Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-36 3.7.1 Description of ICR and SWAT DBP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-36 3.7.1.1 ICR DBP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-36 3.7.1.2 SWAT DBP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-37 3.7.2 Pre-Stage 1 DBP Occurrence for Large Surface Water Plants . . . . . . . . . . . . 3-37 3.7.3 Pre-Stage 1 DBP Occurrence in Large Ground Water Plants . . . . . . . . . . . . . 3-38 3.7.4 Pre-Stage 1 DBP Occurrence for Medium Surface and Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-42 3.7.5 Pre-Stage 1 DBP Occurrences for Small Surface and Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-42 3.8 Uncertainties in Development of the Pre-Stage 1 Baseline . . . . . . . . . . . . . . . . . . . . . 3-43



## Chapter 4. Consideration of Regulatory Alternatives

4. Consideration of Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 4.2 Process for Development of Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2 4.3 Regulatory Alternatives Considered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3



## Chapter 5. Compliance Forecast and Predicted Changes in DBP Levels

5. Compliance Forecast and Consequent Reduction in Chlorination DBPs . . . . . . . . . . . . . . . . . . . . . . 5-1 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 5.2 Overview of Methodologies used in the Primary Analysis . . . . . . . . . . . . . . . . . . . . . . 5-1 5.3 Compliance Forecast Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.3.1 Tools for Surface and Ground Water Systems . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 5.3.2 Accounting for the Stage 1 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6 5.3.3 Operational Safety Margins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10 5.3.4 Accounting for the IDSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11 5.3.4.1 Analysis of Spatial Variability in Large and Medium Surface Water Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12 5.3.4.2 Modifying the Operational Safety Margin . . . . . . . . . . . . . . . . . . . . . 5-16 5.3.4.3 Incorporating Potential Impacts of the IDSE into the Compliance Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17 5.3.5 Methodology for Incorporating SWAT and ICR Matrix Method Results into the Compliance Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18 5.3.6 Compliance Forecast Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-20 5.4 Compliance Forecast Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-21 5.5 Reduction in National Average TTHM and HAA5 Levels . . . . . . . . . . . . . . . . . . . . . 5-35

### Final Economic Analysis for the Stage 2 DBPR December 2005 iii 

### 5.5.1 Overview of Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35 5.5.2 Reductions for Large and Medium Surface Water Systems . . . . . . . . . . . . . . 5-35 5.5.2.1 SWAT Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35 5.5.2.2 The ICR Matrix Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-36 5.5.2.3 Combining SWAT and ICR Matrix Method Results . . . . . . . . . . . . 5-43 5.5.3 Reductions for Small Surface Water Systems . . . . . . . . . . . . . . . . . . . . . . . . 5-45 5.5.4 Reductions for Large and Medium Ground Water Systems . . . . . . . . . . . . . . 5-46 5.5.5 Reductions for Small Ground Water Systems . . . . . . . . . . . . . . . . . . . . . . . . 5-46 5.5.6 Results for All Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-51 5.6 Reduction in Frequency of P eak TTHM and HAA5 Concentrations . . . . . . . . . . . . . 5-57 5.6.1 Methodology and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-57 5.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-61 5.7 Uncertainties in the Compliance Forecast and Subsequent DBP Reduction . . . . . . . . 5-61 5.7.1 Uncertainty in DBP data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-64 5.7.1.1 Representativeness of the ICR data . . . . . . . . . . . . . . . . . . . . . . . . . . 5-64 5.7.1.2 Uncertainty in the subset of ICR data used for the ICR Matrix Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-65 5.7.2 Uncertainty in the Delta Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-65



## Chapter 6. Benefits Analysis

6. Benefits Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1 6.1.1 Overview of Methodology for Quantifying Stage 2 DBPR Benefits . . . . . . . . 6-2 6.1.2 Summary of National Benefits of the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . 6-6 6.2 Problem Identification and Assessment of Potential Hazard . . . . . . . . . . . . . . . . . . . . . 6-7 6.2.1 Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7 6.2.1.1 Epidemiological Evidence of DBP Carcinogenicity . . . . . . . . . . . . . . 6-7 6.2.1.2 Toxicological Evidence of DBP Carcinogenicity . . . . . . . . . . . . . . . 6-26 6.2.1.3 Issues with Human and Animal Cancer Data Concordance . . . . . . . 6-32 6.2.1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-33 6.2.2 Reproductive and Developmental Health Effects . . . . . . . . . . . . . . . . . . . . . . 6-34 6.2.2.1 Epidemiological Evidence of A dverse Reproductive and Developmental Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-35 6.2.2.2 Toxicological Evidence of Adverse Reproductive and Developmental Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-53 6.2.2.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-59 6.3 Exposure Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-60 6.3.1 Population Exposed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-60 6.3.2 Routes of Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-61 6.3.2.1 Special Exposure Issues for Pregnant Women . . . . . . . . . . . . . . . . . 6-62 6.3.3 Exposure Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-62 6.3.3.1 Reducing Exposure to All Levels of DBPs . . . . . . . . . . . . . . . . . . . . 6-63

6.3.3.2 Reducing Exposure to Peak DBP Occurrences . . . . . . . . . . . . . . . . . 6-64 6.4 Benefits of the Stage 2 DBPR: Reduced Incidence of Adverse Effects . . . . . . . . . . . 6-65 6.4.1 Reduced Incidence of Bladder Cancer Cases . . . . . . . . . . . . . . . . . . . . . . . . . 6-65 6.4.1.1 Annual Cancer Cases Ultimately Avoidable . . . . . . . . . . . . . . . . . . . 6-66 6.4.1.2 Annual Cancer Cases Avoided Accounting for Cessation Lag . . . . . 6-72 6.4.1.3 Adjustments in Annual Cancer Cases Avoided to Account for the Rule Implementation Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-74 6.4.2 Reduced Incidence of Reproductive and Developmental Effects . . . . . . . . . . 6-76

### Final Economic Analysis for the Stage 2 DBPR December 2005 iv 

### 6.4.3 Other Health-Related Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-77 6.4.4 Non-Health-Related Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-77 6.4.5 Potential Increases in Health Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-77 6.5 Valuation of Health Benefits for the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . 6-81 6.5.1 Value of Reductions in Potentia l Adverse Reproductive and Developmental Health Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-81 6.5.2 Value of Reductions in Bladder Cancer Cases . . . . . . . . . . . . . . . . . . . . . . . . 6-82 6.5.3 Value of Benefits Resulting from the Stage 2 DBPR for the Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-87 6.5.4 Comparison of the Value of Benefits for Regulatory Alternatives . . . . . . . . . 6-91 6.6 Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-93 6.7 Sensitivity Analysis for Other Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-94 6.8 Potential Fetal Losses Avoided . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-97 6.8.1 Reproductive Effects Illustrative Calculation . . . . . . . . . . . . . . . . . . . . . . . . . 6-98 6.8.2 Value of Potential Reduc tions in Fetal Losses Avoided . . . . . . . . . . . . . . . . 6-100



## Chapter 7. Cost Analysis

7. Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7.1.1 Overview of Methodology for Quantifying Stage 2 DBPR Costs . . . . . . . . . . 7-1 7.1.2 Cost Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3 7.2 Labor Rates and Laboratory Fees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11 7.3 Non-Treatment Costs for Systems and States/Primacy Agencies . . . . . . . . . . . . . . . . 7-13 7.3.1 Rule Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 7.3.2 Initial Distribution System Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14 7.3.3 Monitoring Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-15 7.3.4 Additional Routine Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16 7.3.5 Operational Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17 7.3.6 Results (One-Time and Yearly Costs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17 7.4 Technology Unit Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18 7.4.1 Treatment Technologies Used to Estimate Costs . . . . . . . . . . . . . . . . . . . . . . 7-19 7.4.2 Alternatives to Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-30 7.4.3 Uncertainty in Unit Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-31 7.5 The Stage 2 DBPR Cost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-31 7.5.1 Probability Analysis to Estimate Nominal Treatment Costs . . . . . . . . . . . . . . 7-31 7.5.2 Projections and Discounting to Produce Annualized Costs . . . . . . . . . . . . . . 7-34 7.5.3 Methodology for Estimating Household Costs . . . . . . . . . . . . . . . . . . . . . . . . 7-35 7.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-36 7.6.1 Number of Plants Making Treatment Technology Changes . . . . . . . . . . . . . . 7-36 7.6.2 One-Time Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-36 7.6.3 Total Annual Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-36 7.6.4 Household Cost Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-41 7.7 Non-Quantified Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-47 7.8 Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-47 7.9 Comparison of Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-50

Chapter 8. Economic Impact Analysis

8. Economic Impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.2 Regulatory Flexibility Act and Small Business Regulatory Enforcement Fairness Act 8-1

### Final Economic Analysis for the Stage 2 DBPR December 2005 v 

### 8.2.1 Determining Significant Impacts on Small Entities . . . . . . . . . . . . . . . . . . . . . 8-2 8.2.2 Summary of the SBREFA Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-6 8.3 Small-System Affordability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7 8.3.1 Affordability Threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-8 8.3.2 Affordable Compliance Treatment Technologies . . . . . . . . . . . . . . . . . . . . . . . 8-9 8.3.3 Funding Options for Disadvantaged Systems . . . . . . . . . . . . . . . . . . . . . . . . . 8-13 8.4 Feasible Treatment Technologies for All Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-14 8.4.1 ICR Treatment Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15 8.4.2 BAT Evaluation Using SWAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16 8.4.3 BATs for Consecutive Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 8.5 Effect of Compliance with the Stage 2 DBPR on the Technical, Managerial, and Financial Capacity of Public Water Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-18 8.5.1 Requirements of the Stage 2 DPBR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19 8.5.2 Systems Subject to the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-20 8.5.3 Impact of the Stage 2 DBPR on System Capacity . . . . . . . . . . . . . . . . . . . . . 8-20 8.5.4 Rationale for Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-20 8.5.5 Derivation of Stage 2 DBPR Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-23 8.5.5.1 Familiarization with the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . 8-23 8.5.5.2 Conducting an Initial Distribution System Evaluation . . . . . . . . . . . 8-24 8.5.5.3 Compliance with MCLs for TTHM and HAA5 . . . . . . . . . . . . . . . . . 8-24 8.5.5.4 Stage 2 Monitoring Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 8.5.5.5 Additional Routine Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 8.5.5.6 Operational Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 8.5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27 8.6 Paperwork Reduction Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-27 8.7 Unfunded Mandates Reform Act Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-28 8.7.1 UMRA Requirements and their Impact on the Stage 2 DBPR . . . . . . . . . . . . 8-28 8.7.2 Social Benefits and Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-30 8.7.3 Disproportionate Budgetary Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-31 8.7.4 Macroeconomic Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-37 8.7.5 Consultation with Small Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-37 8.7.6 Consultation with State, Local, and Tribal Governments . . . . . . . . . . . . . . . . 8-37 8.7.7 Regulatory Alternatives Considered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-38 8.7.8 Impacts on Small Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-38 8.8 Indian Tribal Governments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-38 8.9 Impacts on Sensitive Subpopulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43 8.9.1 Protecting Children from Environmental Health Risks and Safety Risks . . . 8-43 8.10 Environmental Justice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-44 8.11 Federalism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-45 8.12 Actions Concerning Regulations That Signi ficantly Affect Energy Supply, Distribution, or Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-45

Chapter 9. Comparison of Benefits and Costs of the Stage 2 DBPR

9. Comparison of Benefits and Costs of the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 9.2 Summary of National Benefits, Costs, a nd Net Benefits of the Stage 2 Preferred Regulatory Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 9.2.1 National Benefits Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4 9.2.2 National Cost Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-8 9.2.3 National Net Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-11 9.3 Comparison of Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-14

### Final Economic Analysis for the Stage 2 DBPR December 2005 vi 

### 9.3.1 Comparison of Reductions in DBP Occurrence . . . . . . . . . . . . . . . . . . . . . . . 9-14 9.3.2 Comparison of Benefits and Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-15 9.3.3 Cost-Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-20 9.4 Effect of Uncertainties on the Estimation of Net National Benefits . . . . . . . . . . . . . . 9-23 9.5 Summary of Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-27

Chapter 10. References

### Final Economic Analysis for the Stage 2 DBPR December 2005 vii 

# Appendices

Appendix A: Surface Water Compliance Forecasts Using SWAT

Appendix B: Ground Water Plant Compliance Forecasts

Appendix C: Supplemental Compliance Forecasts

Appendix D: Rule Activity Schedule

Appendix E: Annual Cancer Cases Avoided as a Result of the Stage 2 DBPR

Appendix E2:Calculation of PAR, Attributab le Cases and Cases Avoided for the Colon and Rectal Cancer Sensitivity Analyses

Appendix F: Valuation of Stage 2 DBPR Benefits

Appendix G: Illustrative Calculation for Quantifying Potential Reproductive and Developmental Benefits of the Stage 2 DBPR

Appendix H: National Costs for Non- Treatment Related Rule Activities

Appendix I: Unit Costs for Technologies Considered in the Stage 2 DBPR

Appendix J: Stage 2 DBPR Cost Projections

Appendix K: Benefit and Cost Models

Appendix L: Quality Assurance Supplemental Information

Appendix M: Ground Water Systems Adding Di sinfection Under the Ground Water Rule

Appendix N: Cost Effectiveness Analysis Us ing a Quality-Adjusted Life Years Approach

### Final Economic Analysis for the Stage 2 DBPR December 2005 viii 

## Exhibits

Executive Summary

Exhibit ES.1 Summary of Stage 2 DBPR Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-4 Exhibit ES.2 Implementation Timeline for the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-5 Exhibit ES.3 Stage 2 DBPR Population-Based Compliance Monitoring Requirements . . . . . . . . ES-7 Exhibit ES.4a Number of Disinfecting Systems Subject to Non-Treatment-Related Rule Activities ES-9 Exhibit ES.4b Non-Treatment Rule Activities for Systems Installing Disinfection to Comply with the Ground Water Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-10 Exhibit ES.5 Summary of Estimated National Benefits and Costs of the Stage 2 DBPR Preferred Regulatory Alternative ($ Million / Year) . . . . . . . . . . . . . . . . . . . . . . . . ES-12 Exhibit ES.6 Tools Used to Develop the Stage 2 DBPR Compliance Forecasts . . . . . . . . . . . . . ES-14 Exhibit ES.7a Plants Making Treatment Technology Changes, Preferred Regulatory Alternative ES-17 Exhibit ES.7b Estimated Reduction in Average TTHM and HAA5 from Pre-Stage 2 to Post-Stage 2, Preferred Regulatory Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-18 Exhibit ES.8 Summary of Annual Household Cost Increases . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-23 Exhibit ES.9 Comparison of Benefits fo r All Regulatory Alternatives ($Millions) . . . . . . . . . . . ES-25 Exhibit ES.10 Comparison of Costs for All Regulatory Alternatives ($Millions) . . . . . . . . . . . . . ES-25 Exhibit ES.11 Comparison of Annualized Mean Net Benefits for All Regulatory Alternatives ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-26 Exhibit ES.12 Cost Per Discounted Case Avoided, by Discount Rate and Regulatory Alternative ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES-28 Exhibit ES.13 Cost Effectiveness Analysis Using MILYs Saved from Cases of Bladder Cancer Avoided,by Rule Alternative, 3 and 7 Percent Discount Rates . . . . . . . . . . . . . . . . ES-29

Chapter 1. Introduction

Exhibit 1.1 Comparison of Stage 1 and Stage 2 DBPR Compliance Calculations . . . . . . . . . . . . . . 1-3 Exhibit 1.2 IDSE Standard Monitoring Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4 Exhibit 1.3 Stage 2 Population-Based Monitoring Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5 Exhibit 1.4 Stage 2 DBPR Implementation Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-6

Chapter 3. Baseline Conditions

Exhibit 3.1a SWAT Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 Exhibit 3.1b SWAT Inputs and Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6 Exhibit 3.2 Derivation of the Stage 2 DBPR Plant Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13 Exhibit 3.3 Derivation of the Stage 2 DBPR Population Baseline . . . . . . . . . . . . . . . . . . . . . . . . . 3-18 Exhibit 3.4 Design Flows and Average Daily Flows per Plant (MGD) . . . . . . . . . . . . . . . . . . . . . 3-22 Exhibit 3.5 Number of Households Subject to the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . 3-23 Exhibit 3.6 ICR Large System Influent Water Quality Parameters—Summary of Pre-Stage 1 Plant-Mean Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25 Exhibit 3.7 Cumulative Distribution of TOC in Influent Water of Large System ICR Plant-Mean Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27 Exhibit 3.8 Cumulative Distribution of Bromide in Influent Water of Large System ICR Plant-Mean Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28 Exhibit 3.9 Medium and Small System Influent Water Quality Parameters– Summary of Pre-Stage 1 Plant-Mean Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-29 Exhibit 3.10 Influent Water TOC Distribution for ICR Surface Water Systems . . . . . . . . . . . . . . . 3-30 Exhibit 3.11 Influent Water TOC Distribution for ICR Ground Water Systems . . . . . . . . . . . . . . . 3-30 Exhibit 3.12 Influent Water TOC Distribution for Ground Water Systems Derived from the Ground

### Final Economic Analysis for the Stage 2 DBPR December 2005 ix 

### Water Supply Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-31 Exhibit 3.13a Pre-Stage 1 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants 3-33 Exhibit 3.13b Pre-Stage 1 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-34 Exhibit 3.14a Pre-Stage 1 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants 3-35 Exhibit 3.14b Pre-Stage 1 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-35 Exhibit 3.15 Summary of Pre-Stage 1 DBP Occurre nce for Large Surface Water Plants, DS Average Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-39 Exhibit 3.16 Cumulative Distributions of TTHM Data Predicted by SWAT, Pre-Stage 1 (DS Average) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-40 Exhibit 3.17 Cumulative Distributions of HAA5 Data Predicted by SWAT, Pre-Stage 1 (DS Average) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-40 Exhibit 3.18 Cumulative Distributions of Bromate Da ta Predicted by SWAT, Pre-Stage 1 (Finished Water) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-41 Exhibit 3.19 Cumulative Distributions of Chlorite Data Predicted by SWAT (Finished Water) . . . 3-41 Exhibit 3.20 Summary of Pre-Stage 1 DBP Occurrence for Large Ground Water Plants, ICR Data 3-42 Exhibit 3.21 Summary of Pre-Stage 1 DBP Occu rrence Data for Small Systems, DS Average Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-43 Exhibit 3.22 Summary of Uncertainties Affecting Stage 2 DBPR Baseline Estimates . . . . . . . . . . 3-44

Chapter 4. Consideration of Regulatory Alternatives

Exhibit 4.1 Comparison of Hypothetical Complia nce Calculations for Stage 1 and Stage 2 Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7 Chapter 5. Compliance Forecast and Predicted Changes in DBP Levels

Exhibit 5.1 Tools Used to Develop the Stage 2 DBPR Compliance Forecasts . . . . . . . . . . . . . . . . 5-2 Exhibit 5.2 Compliance Evaluation of Screened ICR Surface and Ground Water Plants . . . . . . . . 5-9 Exhibit 5.3 Predicted Increase in Percent Making Treatment Technology Changes based on Spatial Variability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-13 Exhibit 5.4a Analysis of Variability for Stage 2 Non-Compliant Plants . . . . . . . . . . . . . . . . . . . . . 5-13 Exhibit 5.4b Cumulative Distribution of ICR LRAA MAX 

### - ICR LRAA 2ndHI 

### for Stage 2 Non-Compliant Plants (TTHM data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 Exhibit 5.4c Cumulative Distribution of ICR LRAA MAX 

### - ICR LRAA 2ndHI 

### for Stage 2 Non-Compliant Plants (HAA5 Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15 Exhibit 5.5 Compliance Analysis of ICR Screen ed Plants at Different Operational Safety Margins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-17 Exhibit 5.6 Predicted Percent of Plants Making Treatment Technology Changes to Meet Stage 1 and Stage 2 Regulatory Alternatives for the ICR Matrix Method and SWAT . . . . . . . . . . 5-19 Exhibit 5.7 Uniform Distributions for Incorpora ting Results from SWAT and the ICR Matrix Method into the Compliance Forecast for Surface Water Systems . . . . . . . . . . . . . . . . . . . . . . 5-20 Exhibit 5.8 Compliance Forecast Exhibits for the Stage 2 DBPR Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-22 Exhibit 5.9 Plants in CWSs and NTNCWSs Making Treatment Technology Changes From Stage 1 For Stage 2 DBPR Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-24 Exhibit 5.10a Pre-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants 5-25 Exhibit 5.10b Pre-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25 Exhibit 5.11a Treatment Technology Selection De ltas for CWS Surface Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-26

### Final Economic Analysis for the Stage 2 DBPR December 2005 x 

### Exhibit 5.11b Treatment Technology Selection Deltas for CWS Surface Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-26 Exhibit 5.11c Treatment Technology Selection De ltas for NTNCWS Surface Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-27 Exhibit 5.11d Treatment Technology Selection Deltas for NTNCWS Surface Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-27 Exhibit 5.12a Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-28 Exhibit 5.12b Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-28 Exhibit 5.12c Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-29 Exhibit 5.12d Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-29 Exhibit 5.13a Pre-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants 5-30 Exhibit 5.13b Pre-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-30 Exhibit 5.14a Treatment Technology Selection Deltas for CWS Ground Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-31 Exhibit 5.14b Treatment Technology Selection Deltas for CWS Ground Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-31 Exhibit 5.14c Treatment Technology Selection Deltas for NTNCWS Ground Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-32 Exhibit 5.14d Treatment Technology Selection Deltas for NTNCWS Ground Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-32 Exhibit 5.15a Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-33 Exhibit 5.15b Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-33 Exhibit 5.15c Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants, Percentage of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-34 Exhibit 5.15d Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants, Number of Plants, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-34 Exhibit 5.16a ICR Matrix Method for the Stage 2 DBPR Preferred Alternative (80/60 LRAA, IDSE)- 20 Percent Safety Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-38 Exhibit 5.16b ICR Matrix Method for a Stage 2 DB PR Preferred Alternative (80/60 LRAA, IDSE)- 25 Percent Safety Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39 Exhibit 5.16c ICR Matrix Method fo r Regulatory Alternative 2 (80/60 SH) . . . . . . . . . . . . . . . . . . 5-40 Exhibit 5.16d ICR Matrix Method for Regulatory Alternative 3 (40/30 RAA) . . . . . . . . . . . . . . . . . 5-41 Exhibit 5.17 TTHM and HAA5 Levels for Stage 2- Compliant Plants Using Chloramines and/or an Advanced Treatment Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-42 Exhibit 5.18 Inputs to Monte Carlo Simulation Model: Estimated DBP Reduction from SWAT and ICR Matrix Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-44 Exhibit 5.19 Inputs to the Monte Carlo Simulation Model: Uniform Distributions Based on ICR Matrix Method-to-SWAT Multiplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-45 Exhibit 5.20 TTHM and HAA5 Levels for St age 2-Compliant Ground Water Plants Using Chloramines and/or an Advanced Treatment Technology . . . . . . . . . . . . . . . . . . . . . . 5-47 Exhibit 5.21a ICR Matrix Method for Ground Wa ter Plants for the Stage 2 DBPR Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-48 Exhibit 5.21b ICR Matrix Method for Ground Wa ter Plants for Regulatory Alternative 2 . . . . . . . 5-49 Exhibit 5.21c ICR Matrix Method for Ground Water Plants for Regulatory Alternative 3 . . . . . . . . 5-50 Exhibit 5.22 Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 1 to

### Final Economic Analysis for the Stage 2 DBPR December 2005 xi 

### Pre-Stage 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-52 Exhibit 5.23 Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2, Preferred Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-53 Exhibit 5.24a Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2, Regulatory Alternative 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54 Exhibit 5.24b Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2, Regulatory Alternative 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-55 Exhibit 5.24c Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2, Regulatory Alternative 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-56 Exhibit 5.25a ICR Matrix Method for Peak Locations for the Stage 2 DBPR, 20 Percent Safety Margin, Large Surface and Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58 Exhibit 5.25b ICR Matrix Method for Peak Locations for the Stage 2 DBPR, 25 Percent Safety Margin, Large Surface and Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-59 Exhibit 5.26 Frequency of Occurrence of Peak Locations for ICR Surface and Ground Water Plants Using Chloramines and/or Advanced Treatment Technologies . . . . . . . . . . . . . . . . . 5-60 Exhibit 5.27 Predicted Percent of Distribution System Sampling Locations with Peaks for Pre-Stage 1, Pre-Stage 2, and Post-Stage 2 Conditions, 20 Percent safety Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-61 Exhibit 5.28 Summary of Uncertainties in the Compliance Forecast . . . . . . . . . . . . . . . . . . . . . . . . 5-63 Exhibit 5.29 Potential Impact of Uncertainti es on the Compliance Forecast and DBP Reduction Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-64

Chapter 6. Benefits Analysis

Exhibit 6.1 Summary of Quantified Benefits for the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . 6-6 Exhibit 6.2 Venn Diagram of Bladder Cancer in the U.S. Population . . . . . . . . . . . . . . . . . . . . . . 6-10 Exhibit 6.3 Summary of Epidemiology Studies fo r Bladder Cancer Associated with Chlorinated Drinking Water and EPA Calculated PAR Values . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12 Exhibit 6.4 Summary of Epidemiology Studies from Villanueva et al. (2003) for Bladder Cancer Associated with Chlorinated Drinking Wa ter used in Developments of the PAR Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-14 Exhibit 6.5 Estimated OR for Ever-Exposed, Both Sexes Category from Villanueva et al. (2003) Meta-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15 Exhibit 6.6 Summary of Epidemiology Studies from Villanueva et al. (2004) for Bladder Cancer Associated with Chlorinated Drinking Water used in Developments of the PAR Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-15 Exhibit 6.7 Summary of Estimated OR Valu es Associated with Average TTHM Exposures for Both Sexes from Villanueva et al. (2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-17 Exhibit 6.8 Detailed Data on OR as a Functi on of Average TTHM Exposure Level Provided by Kogevinas and Villanueva (2005) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-18 Exhibit 6.9 Estimates of Pre-Stage 1 Annual Bladder Cancer Cases Attributable to DBPs . . . . . . 6-18 Exhibit 6.10 Summary of Bladder Cancer Epidemiology Studies and Review/Meta-analysis Studies Reviewed for Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-20 Exhibit 6.11 Summary of EPA’s Cancer Risk A ssessments as currently presented on IRIS for Specific DBPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27 Exhibit 6.12 Quantification of Cancer Risk for BDCM, Bromoform, DBCM, and DCAA, Pre-Stage 2 Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-29 Exhibit 6.13 Summary of Reproductive/Developmental Epidemiology Studies . . . . . . . . . . . . . . . 6-37 Exhibit 6.14 Odds Ratios (and 95 Percent Confidence Intervals 1 

### ) Calculated by Reif et al. (2000) for Reproductive a nd Developmental Health Endpoints at TTHM Levels of > 80 μg/L versus < 80 μg/L and > 60 μg/L versus < 60 μg/L . . . . . 6-50 Exhibit 6.15 PAR Values (and 95 Percent Confidence Intervals 1 

### ) Calculated by

### Final Economic Analysis for the Stage 2 DBPR December 2005 xii 

### Reif et al. (2000) for Reproductive a nd Developmental Health Endpoints at TTHM Levels of > 80 μg/L versus < 80 μg/L and > 60 μg/L versus < 60 μg/L (Values are Percentages) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51 Exhibit 6.16 Availability of Reproductive and Developmental Toxicology Studies for Specific DBPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-55 Exhibit 6.17 Reproductive and Developmental H ealth Effects Associated with DBPs in Toxicological Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-56 Exhibit 6.18 Estimated Population Exposed to DBPs in Drinking Water . . . . . . . . . . . . . . . . . . . . 6-60 Exhibit 6.19 National Average TTHM 1 

### Reduction Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-63 Exhibit 6.20 Comparison of Range of Estimates of Stage 2 Cases Ultimately Avoidable for Three PAR Approaches and DBP Reductions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-71 Exhibit 6.21a Comparison of Alternative C essation Lag Models: Estimates of Annual Cases Avoided by Year Following Exposure Reduction (TTHM as an indicator) . . . . . . . . 6-75 Exhibit 6.21b Estimates of Annual Cases Avoide d by Year For Three Cessation Lag Models, Considering Rule Implementation Schedule (TTHM as an Indicator) . . . . . . . . . . . . 6-76 Exhibit 6.22a Predicted Chlorite Plant-Mean Concentration for Pre-Stage 2 and Post-Stage 2 . . . . 6-79 Exhibit 6.22b Predicted Chlorite Monthly Average Concentrations for Pre-Stage 2 and Post-Stage 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-79 Exhibit 6.23a Predicted Bromate Plant-Mean Concen trations for Pre-Stage 2 and Post-Stage 2 . . . 6-80 Exhibit 6.23b Predicted Bromate Monthly Average Concentrations for Pre-Stage 2 and Post-Stage 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-80 Exhibit 6.24 VSL, WTP, and Morbidity Increment Price Level Updates . . . . . . . . . . . . . . . . . . . . 6-84 Exhibit 6.25 Value of Morbidity Increment, VSL, and WTP by Year, Adjusted for Income Elasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-86 Exhibit 6.26a Non-Discounted Stream of Benef its from the Stage 2 DBPR Preferred Regulatory Alternative, All Systems, WTP Curable Lymphoma, TTHM as Indicator . . . . . . . . . 6-88 Exhibit 6.26b Non-Discounted Stream of Benef its from the Stage 2 DBPR Preferred Regulatory Alternative, All Systems, WTP Chronic Bronchitis, TTHM as Indicator . . . . . . . . . . 6-89 Exhibit 6.27 Benefits Summary for the Stag e 2 DBPR, Preferred Regulatory Alternative (Millions, 2003$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-90 Exhibit 6.28 Benefits Summary for the Stag e 2 DBPR, Preferred Regulatory Alternative (Millions, 2003$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-91 Exhibit 6.29 Number and Annualized Value of Estimated Bladder Cancer Cases Avoided for All Stage 2 DBPR Regulatory Alternatives, Villanueva et al. (2003) for Baseline Risk (Millions, 2003$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-92 Exhibit 6.30 Uncertainties and Possible Effect on Estimate of Benefits . . . . . . . . . . . . . . . . . . . . . 6-94 Exhibit 6.31 Annualized Value 1 

### of Estimated Bladder Cancer Cases Avoided for the Primary Analysis, and Estimated Colon and Rectal Cancer Cases Avoided for the Sensitivity Analysis (Millions, 2003$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-97 Exhibit 6.32 Summary of the Fetal Loss Human Epidemiology Studies . . . . . . . . . . . . . . . . . . . . . 6-99

Chapter 7. Cost Analysis

Exhibit 7.1 Stage 2 DBPR Cost Model Inputs and Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 Exhibit 7.2a Baseline Systems Subject to Non-Treatment-Related Rule Activities . . . . . . . . . . . . . 7-4 Exhibit 7.2b Non-Treatment-Related Rule Activities for Systems Installing Disinfection to Comply with the Ground Water Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5 Exhibit 7.3 Number and Percent of Plants Making Treatment Technology Changes for the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7 Exhibit 7.4 Initial Capital and One-Time Costs for the Stage 2 DBPR ($Millions) . . . . . . . . . . . . . 7-8 Exhibit 7.5a Total Annualized Costs for Stage 2 DBPR Rule Activities ($Millions/Year, 3 Percent Discount Rate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

### Final Economic Analysis for the Stage 2 DBPR December 2005 xiii 

### Exhibit 7.5b Total Annualized Costs for Stage 2 DBPR Rule Activities ($Millions/Year, 7 Percent Discount Rate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10 Exhibit 7.6a System Wage Rates by Standard Size Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11 Exhibit 7.6b System Wage Rates by Monitoring Size Categories . . . . . . . . . . . . . . . . . . . . . . . . . . 7-12 Exhibit 7.7 Summary of System Costs for Non-Treatment Related Stage 2 DBPR Rule Activities (One-Time and Yearly) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18 Exhibit 7.8a Treatment Technologies for Surface Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-21 Exhibit 7.8b Treatment Technologies for Disinfecting Ground Water Plants . . . . . . . . . . . . . . . . . 7-23 Exhibit 7.9 Household Cost Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-25 Exhibit 7.10a Capital Unit Costs ($/Plant) for CWS Surface Water Plants . . . . . . . . . . . . . . . . . . . . 7-27 Exhibit 7.10b Annual O&M Unit Costs ($/Plant/Year) for CWS Surface Water Plants . . . . . . . . . . 7-27 Exhibit 7.10c Household Unit Treatment Costs ($/Household/Year) for CWS Surface Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-28 Exhibit 7.11a Capital Cost ($/Plant) for CWS Disinfecting Ground Water Plants . . . . . . . . . . . . . . 7-29 Exhibit 7.11b Annual O&M Costs ($/Plant/Y ear) for CWS Disinfecting Ground Water Plants . . . . 7-29 Exhibit 7.11c Household Unit Treatment Costs ($/Household/Year) for CWS Disinfecting Ground Water Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-30 Exhibit 7.12 Uniform Distributions for Incorporating the ICR Matrix Method-to-SWAT Multiplier into the Compliance Fo recasts for Surface Water Systems . . . . . . . . . . . . 7-33 Exhibit 7.13 Total Initial Capital Costs ($Milli ons) and Yearly O&M Costs ($Millions/Year) . . . 7-38 Exhibit 7.14a Total Annualized Costs at 3 Percent Social Discount Rate ($Millions) . . . . . . . . . . . 7-39 Exhibit 7.14b Total Annualized Costs at 7 Percent Social Discount Rate ($Millions) . . . . . . . . . . . 7-40 Exhibit 7.15 Annual Household Cost Increases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-42 Exhibit 7.16a Household Cost Distributions, All Surface Water Systems Subject to the Rule . . . . . 7-43 Exhibit 7.16b Household Cost Distributions, All Ground Water Systems Subject to the Rule . . . . . 7-44 Exhibit 7.17a Household Cost Distributions, Surface Water Systems Making Treatment Technology Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-45 Exhibit 7.17b Household Cost Distributions, Ground Water Systems Making Treatment Technology Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-46 Exhibit 7.17c Household Cost Distributions, Small Systems Making Treatment Technology Changes (Surface and Ground) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-47 Exhibit 7.18 Cost Uncertainty Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-49 Exhibit 7.19 Total Annualized Cost for the Stag e 2 DBPR Regulatory Alternatives ($Millions) . . 7-50

Chapter 8. Economic Impact Analysis

Exhibit 8.1 Annualized Compliance Cost as a Percentage of Revenues for All Small Entities . . . . 8-5 Exhibit 8.2 Derivation of Available Expenditure Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9 Exhibit 8.3 Affordability Analysis Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-10 Exhibit 8.4a Affordable Compliance Treatment Technologies and Household Unit Treatment Costs ($/HH/Year) for Surface Water Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11 Exhibit 8.4b Affordable Compliance Treatment Technologies and Household Unit Treatment Costs ($/HH/Year) for Ground Water Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11 Exhibit 8.4c Distribution of Household Unit Treatment Costs for Plants Adding Treatment . . . . . 8-12 Exhibit 8.5 SWAT Model Predictions of Percent of Large Plants in Compliance with TTHM and HAA5 Stage 2 MCLs after Application of Specified Treatment Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 Exhibit 8.6 Estimated Impact of the Stage 2 DBPR on Small System Capacity (0 = no impact, 1 = minimal impact, and 5 = very significant impact) . . . . . . . . . . . . 8-21 Exhibit 8.7 Estimated Impact of the Stage 2 DBPR on Large System Capacity (0 = no impact, 1 = minimal impact, and 5 = very significant impact) . . . . . . . . . . . . 8-22 Exhibit 8.8 Summary of Average Annual Burden Hours and Labor Costs . . . . . . . . . . . . . . . . . . 8-28

### Final Economic Analysis for the Stage 2 DBPR December 2005 xiv 

### Exhibit 8.9 Public and Private Costs for the Stage 2 DBPR (Annualized at 3 and 7 Percent, $Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-30 Exhibit 8.10 Total Annualized Benefits and Costs of Regulatory Alternatives ($Millions, 2003$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-31 Exhibit 8.11a Number of Small Disinfecting Systems by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-33 Exhibit 8.11b Percent of Small Disinfecting Systems by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-34 Exhibit 8.12a Total Annualized Cost of Complia nce for CWSs (3 and 7 Percent Discount Rates) ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-35 Exhibit 8.12b Annualized Cost of Compliance for NTNCWSs (3 and 7 Percent Discount Rates) ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-35 Exhibit 8.13 Percentages and Costs by Public and Private Sector (Costs Annualized at 3 and 7 Percent) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-36 Exhibit 8.14 Annual Cost of Compliance for Tribal Systems by System Type and Size (Annualized at 3 Percent) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-41 Exhibit 8.15 Increase in Energy Usage as a Result of the Stage 2 DBPR . . . . . . . . . . . . . . . . . . . . 8-48 Exhibit 8.16 Sample Calculation for Determ ining Increase in Energy Usage:Chloramines . . . . . . 8-49

Chapter 9. Comparison of Benefits and Costs of the Stage 2 DBPR

Exhibit 9.1a Summary of Benefit and Cost Estim ates by Year for the Stage 2 Preferred Regulatory Alternative Using Lymphoma WTP ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2 Exhibit 9.1b Summary of Benefit and Cost Estimates by Year for the Stage 2 Preferred Regulatory Alternative Using Bronchitis WTP ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-3 Exhibit 9.2 Summary of Nonquantified National Benefits of the Stage 2 DBPR . . . . . . . . . . . . . . 9-5 Exhibit 9.3 Summary of Annual Bladder Cancer Cases Ultimately Avoidable for the Stage 2 DBPR Preferred Regulatory Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6 Exhibit 9.4 Estimated Annualized National Benefits for the Stage 2 Preferred Regulatory Alternative ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7 Exhibit 9.5a Annualized Costs for Stage 2 DBPR Preferred Regulatory Alternative Rule Activities ($Millions/Year, 3% Discount Rate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-9 Exhibit 9.5b Annualized Costs for Stage 2 DBPR Preferred Regulatory Alternative Rule Activities ($Millions/Year, 7 Percent Discount Rate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-10 Exhibit 9.6 Annualized Mean Net Benefits fo r the Stage 2 Preferred Regulatory Alternative ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-12 Exhibit 9.7 Estimated Annualized National Costs a nd Benefits for the Stage 2 Preferred Regulatory Alternative with Uncertainty Measured as a Percent of the Mean ($Millions) . . . . . . 9-13 Exhibit 9.8 Estimated Breakeven Points (Number of Bladder Cancer Cases Avoided) for the Stage 2 Preferred Regulatory Alternative . . . . . . . . . . . . . . . 9-13 Exhibit 9.9 Comparison of DBP Reduction (of Annual Plant Mean TTHM Data) . . . . . . . . . . . . 9-15 Exhibit 9.10 Comparison of Number and Annuali zed Value of Estimated Bladder Cancer Cases Avoided for All Regulatory Alternatives ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . 9-16 Exhibit 9.11 Comparison of Costs for All Regulatory Alternatives ($Millions) . . . . . . . . . . . . . . . 9-17 Exhibit 9.12 Comparison of Mean Net Benef its for All Regulatory Alternatives ($Millions) . . . . 9-18 Exhibit 9.13 Incremental Net Benefits fo r All Regulatory Alternatives ($ Millions) . . . . . . . . . . . 9-19 Exhibit 9.14 Incremental Cost Per Case Avoided 1 

### for All Regulatory Alternatives by Discount Rate ($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-22 Exhibit 9.15 Benefit Cost Ratios for All Regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . 9-23 Exhibit 9.16 Effects of Uncertainties on National Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-25

### Economic Analysis for the Stage 2 DBPR December 2005 xv 

# Acronyms and Notations

AAM Annual Average of the Maximum ACS American Cancer Society AIPC All Indian Pueblo Council AMWA Association of Metropolitan Water Agencies ARBRP Arsenic Rule Benefits Review Panel ASDWA Association of State Drinking Water Administrators AD Advanced Disinfectants AO Advanced Oxidants ATSDR Agency for Toxic Substances and Disease Registry AUX1 Auxiliary Database 1 AUX8 Auxiliary Database 8 AVG 1 Average sample point number 1 AVG 2 Average sample point number 2 AWWA American Water Works Association AWWARF American Water Works Association Research Foundation BAT Best Available Technology BCAA Bromochloroacetic Acid BCAN Bromochloroacetonitrile BDCAA Bromodichloroacetic Acid BDCM Bromodichloromethane BLS Bureau of Labor Statistics CATT Cases Attributable to DBPs CCR Consumer Confidence Report Rule (1998) CDBG Community Development Block Grant CDC Centers for Disease Control and Prevention CDHS California Department of Health Services CI Confidence Interval CKA Chernoff-Kavlock Assay CL2 Chlorine CLM Chloramines CLO2 Chlorine Dioxide COI Cost of Illness CPI Consumer Price Index CWS Community Water System CWSS Community Water Systems Survey DBAA Dibromoacetic Acid DBAN Dibromoacetonitrile DBCAA Dibromochloroacetic Acid DBCM Dibromochloromethane DBPR Disinfectants and Disinfection Byproducts Rule DBP Disinfection Byproduct DCAA Dichloroacetic Acid DCAN Dichloroacetonitrile DNA Deoxyribonucleic Acid DOC Dissolved Organic Carbon DS Average Distribution System Average Sample Point DSE Distribution System Equivalent Sample Point DS Maximum Distribution System Maximum Sample Point DWSRF Drinking Water State Revolving Fund

### Economic Analysis for the Stage 2 DBPR December 2005 xvi 

### EA Economic Analysis EBCT Empty Bed Contact Time EC Enhanced Coagulation ECI Employment Cost Index Information ED 10 

### Effective Dose for 10% response EPA Environmental Protection Agency ES Enhanced Softening FACA Federal Advisory Committees Act FBRR Filter Backwash Recycling Rule (2001) FLR Full Liter Resorption FR Federal Register FRFA Final Regulatory Flexibility Analysis FTE Full-Time Equivalent GAC Granular Activated Carbon GAC10 Granular Activated Carbon—10-Minute Contact Time GAC20 Granular Activated Carbon—20-Minute Contact Time GDP Gross Domestic Product GIS Geographical Information System GW Ground Water GWR Ground Water Rule GWSS Ground Water Supply Survey GWUDI Ground Water Under the Direct Influence of Surface Water HAA5 Haloacetic Acids [five][sum of monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, monobromoacetic acid, and dibromoacetic acid] HAA6 Haloacetic Acids [total of six] HAA9 Haloacetic Acids [total of nine] HAN Haloacetonitrile ICMA International City/County Management Association ICR Information Collection Rule (1996) ICRSS Information Collection Rule Supplemental Survey ILSI International Life Sciences Institute IDSE Initial Distribution System Evaluation IDSE SMP Initial Distribution System Evaluation Standard Monitoring Program IESWTR Interim Enhanced Surface Water Treatment Rule (1998) IPCS International Programme on Chemical Safety IRFA Initial Regulatory Flexibility Analysis IRIS Integrated Risk Information System kg Kilogram KWh/y Kilowatt Hours per Year LED 10 

### Lower Bound on the Effective Dose for 10% response LF Lag Function LH Luteinizing Hormone LOAEL Lowest-Observed-Adverse-Effect-Level LRAA Locational Running Annual Average LT1ESWTR Long Term 1 Enhanced Surface Water Treatment Rule (2002) LT2ESWTR Final Long Term 2 Enhanced Surface Water Treatment Rule MBAA Monobromoacetic Acid MCAA Monochloroacetic Acid MCAN Monochloroacetonitrile MCL Maximum Contaminant Level MCLG Maximum Contaminant Level Goal M-DBP Microbial-Disinfectants/Disinfection Byproducts [Advisory Committee]

### Economic Analysis for the Stage 2 DBPR December 2005 xvii 

### MF Microfiltration MG Million Gallon MGD Million Gallons per Day

: 

## g/L Micrograms per Liter mg/kg-day Milligrams per Kilogram per Day mg/L Milligrams per Liter MHI Median Household Income mJ/cm 2 

### Millijoules per centimeter square MLE Maximum Likelihood Estimation MRDL Maximum Residual Disinfectant Level MRDLG Maximum Residual Disinfectant Level Goal MRRR Maximum Relative Risk Reduction MW Megawatt mWh Megawatt Hours MX 3-Chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone NAICS North American Industry Classification System NCI National Cancer Institute NCSL National Conference of State Legislatures NCWS Noncommunity Water System NDMA N-nitrosodimethylamine NDWAC National Drinking Water Advisory Council NF Nanofiltration ng Nanograms NGA National Governors’ Association NHEERL National Health and Environmental Effects Research Laboratory (EPA) NLC National League of Cities NOAEL No-Observed-Adverse-Effect-Level NODA Notice of Data Availability NOM Natural Organic Matter NPDWR National Primary Drinking Water Regulations NRWA National Rural Water Association NTNCWS Nontransient Noncommunity Water System NTU Nephelometric Turbidity Unit O 3 

### Ozone OGWDW Office of Ground Water and Drinking Water OR Odds Ratio OMB Office of Management and Budget O&M Operations and Maintenance PAR Population Attributable Risk POE Point-of-Entry POU Point-of-Use ppb Parts per Billion ppm Parts per Million PUC Public Utilities Commission PSC Public Services Commission PV Present Value PWS Public Water System PWSID Public Water System Identification RAA Running Annual Average RFA Regulatory Flexibility Act RfD Reference Dose RIA Regulatory Impact Analysis

### Economic Analysis for the Stage 2 DBPR December 2005 xviii 

### RR Relative Risk RUS Rural Utility Service RSI Risk Sciences Institute SAB Science Advisory Board SBA Small Business Administration SBAR Small Business Advocacy Review SBREFA Small Business Regulatory Enforcement Fairness Act SCADA Supervisory Control and Data Acquisition SD Sprague-Dawley SDS Simulated Distribution System SDWA Safe Drinking Water Act (1974) SDWIS Safe Drinking Water Information System SEER Surveillance, Epidemiology, and End Results SER Small Entity Representatives SH Single Highest SIC Standard Industrial Codes SMP Standard Monitoring Program SSS System-Specific Study SW Surface Water SWAT Surface Water Analytical Tool Stage 1 DBPR Stage 1 Disinfectants and Disinfection Byproducts Rule (1998) Stage 2 DBPR Final Stage 2 Disinfectants and Disinfection Byproducts Rule SWTR Surface Water Treatment Rule (1989) TBAA Tribromoacetic Acid TCAA Trichloroacetic Acid TCAN Trichloroacetonitrile TCR Total Coliform Rule (1989) THM Trihalomethane TMF Technical, Managerial, and Financial TNCWS Transient Noncommunity Water System TOC Total Organic Carbon TOX Total Organic Halides TTHM Total Trihalomethanes [four] [sum of chloroform, bromodichloromethane, dibromochloromethane, and bromoform] TWG Technical Workgroup T&C Technology and Cost UF Ultrafiltration UMRA Unfunded Mandates Reform Act USC United States Code USDA United States Department of Agriculture USEPA United States Environmental Protection Agency UV Ultraviolet [Light Disinfection] UVA Ultraviolet-254 Absorbance μg/L Micrograms per Liter VSL Value of a Statistical Life WEC Whole embryo culture WHO World Health Organization WITAF Water Industry Technical Action Fund w(t) cessation lag weighting factor WTP Willingness to Pay

### Economic Analysis for the Stage 2 DBPR December 2005 xix 

# Health Risk Reduction and Cost Analysis (HRRCA)

Under the Safe Drinking Water Act (SDWA) Amendments of 1996, when proposing a national primary drinking water regulati on that includes an maximum contaminant level (MCL), the Environmental Protection Agency (EPA ) must conduct a health risk reduction and cost analysis (HRRCA). A HRRCA contains seven require ments, all of which are addressed in this Economic Analysis (EA) for the Stage 2 Disinfectants and Di sinfection Byproducts Rule (DBPR). The table below shows where the HRRCA requirements are discussed in this document.



## HRRCA Crosswalk to the Economic Analysis for the Stage 2 DBPR

HRRCA Requirement Addressed in Economic Analysis
Quantifiable and nonquantifiable health risk reduction benefits Chapter 6 (All sections and exhibits) Chapter 9 (Sections 9.2.2; Exhibits 9.1-9.4, 9.6, 9.7-9.10, and 9.12)
Quantifiable and nonquantifiable health risk reduction benefits from co-occurring contaminants Chapter 6 (Section 6.4.3 and 6.4.4)

Quantifiable and nonquantifiable costs Chapter 7 (All sections and exhibits) Chapter 8 (Sections 8.2, 8.3, and 8.6-8.8; Exhibits 8.1, 8.7-8.9 and 8.11-8.13) Chapter 9 (Sections 9.1.2 and 9.1.3, 9.4.2; Exhibits 9.2-9.3, 9.5, 9.7, and 9.11)

Incremental costs and benefits associated with MCL alternatives Chapter 6 (Section 6.5.4; Exhibit 6.28) Chapter 7 (Section 7.9; Exhibit 7.19) Chapter 8 (Section 8.7.2; Exhibit 8.9) Chapter 9 (Section 9.3; Exhibits 9.9-9.15)
Effects of the contaminants on the general population and sensitive subpopulations Chapter 6 (Sections 6.2 and 6.3.2.1; Exhibits 6.2- 6.17) Chapter 8 (Sections 8.9 and 8.10)
Increased health risk that may occur as a result of compliance Chapter 6 (Section 6.4.5)

Other relevant factors (quality and uncertainty of information) Chapter 3 (Section 3.8; Exhibit 3.22) Chapter 5 (Section 5.7; Exhibit 5.28) Chapter 6 (Section 6.6; Exhibit 6.29) Chapter 7 (Section 7.8; Exhibit 7.18) Chapter 9 (Section 9.4; Exhibits 9.16)1 

### The key outcomes of that regulatory negotiation effo rt were recommendations to proceed with rules addressing DBPs and microbial pathogens in two stages and to collect relevant information from public water supplies for use in the development of these rules and the an alysis of their impacts. This two-stage approach was subsequently incorporated into the 1996 Safe Drinking Wa ter Act (SDWA) Amendments. The first stage of the M-DBP rulemaking process culminated with the joint prom ulgation of the Stage 1 DBPR and the Interim Enhanced Surface Water Treatment Rule (IESWTR) by EPA in December 1998.
Final Economic Analysis for the Stage 2 DBPR ES-1 December 2005 

# Executive Summary

This document presents the Economic Analysis (EA), prepared by the U.S. Environmental Protection Agency (EPA), of the benefits and costs of the final Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR). Executive Order 12866 requires federal agencies to conduct an analysis of the benefits and costs of proposed and final rul es that cost over $100 million annually. Although EPA's analysis of the Stage 2 DBPR has determined that its annual costs are most likely below this threshold, EPA has chosen to publish a complete EA for this rule.



## ES.1 Need for the Rule

Over 48,000 public water systems (PWSs), serving more than 260 million people in the United States, chemically disinfect their water to kill or inactivate microbial contaminants (USEPA 2001c). This is an essential public health measure. Chemical disinf ection, however, may pose health risks of its own. Disinfection byproducts (DBPs) result from reactions between chemical disinfectants and naturally occurring compounds in source waters. Research has shown that chlorinated waters and DBPs may be associated with increased risk of bladder and othe r cancers. While there are uncertainties in the quantitative relationship between the incidence of th ese cancers and the occurrence of DBPs in drinking water, EPA believes that additional reductions in these DBP levels in drinking water will reduce the incidence of bladder cancer and, possibly, other cancers.

In addition, results from toxicology and, particularly, epidemiology studies published in the last several years suggest a potential increased risk for pregnant women and their fetuses who are exposed to DBPs in drinking water. The studies have shown that early-term miscarriage, stillbirth, low birth weight, and some birth defects may be associated with drinking water containing DBPs. (These studies are discussed in detail in Chapter 6.) Uncertainties pertain to the extent to which reproductive and developmental effects may be associated with DBP exposure, which DBPs may be of greatest concern, what levels of DBPs may pose a risk, and at what period of development fetuses may be at the greatest risk. Although the levels of DBPs potentially associated with specific adverse reproductive and developmental effects are not known and no causal li nk has been established, EPA believes the evidence supports concern for these potential h azards and warrants regulatory action.

In a separate but concurrent action, EPA is promulgating the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) to improve c ontrol of microbial contaminants, particularly Cryptosporidium , in surface water and to ensure that microbial protection is not compromised by efforts to reduce exposure to DBPs. Together, the Stage 2 DBPR and LT2ESWTR represent the final stage of a two-stage strategy that was developed in a regulatory negotiation effort in 1992 and 1993. 1 

### They reflect recommendations presented by the Stage 2 Microbial and Disinfection Byproducts (M-DBP) Federal Advisory Committee Agreement in Principle, signed in September 2000 (USEPA 2000n).

### Final Economic Analysis for the Stage 2 DBPR ES-2 December 2005 

## ES.2 Consideration of Regulatory Alternatives

The M-DBP Advisory Committee met from March 1999 to December 2000 to evaluate whether and to what degree EPA should promulgate revised or additional DBP standards to protect public health. The Advisory Committee carefully considered extensiv e new data on the occurrence and health effects of DBPs, as well as costs and potential impacts on PWSs, and concluded that a targeted protective public health approach should be taken to address exposure to DBPs beyond the requirements of the Stage 1 DBPR. While there had been substantial research to date, the Advisory Committee also concluded that significant uncertainty remained regarding the risk associated with DBPs in drinking water.

After extensive deliberations, the Advisory Committee recommended maintaining the MCLs for total trihalomethanes (TTHM) and haloacetic acids [t otal of five] (HAA5) at 0.080 mg/L (80 μg/L) and 0.060 mg/L (60 μg/L) respectively, but changing the compliance calculation from the system-wide running annual average (RAA) calculation to a loca tional running annual average (LRAA) calculation. Systems would also be required to conduct an Initial Distribution System Evaluation (IDSE) to select compliance monitoring sites that reflect high TTHM and HAA5 concentrations. The revised compliance determination would require MCLs of 0.080 mg/L for TTHM and 0.060 mg/L for HAA5 calculated as LRAAs to be met at individual monitoring sites identified through the IDSE. The committee also provided recommendations for simultaneous complian ce with the LT2ESWTR so that the reduction of DBPs would not compromise microbial protection. The M-DBP Agreement in Principle (available on the web at http://www.epa.gov/safewater/disinfection/ st2agreement.html) summarizes the recommendations from the Advisory Committee (USEPA 2000m).

This EA considers four regulatory alternatives derived from a larger number discussed by the MDBP Advisory Committee, including the Preferred Alte rnative that EPA is promulgating in the Stage 2 DBPR:

• Preferred Alternative: MCLs of 80 micrograms per liter ( : 

### g/L) for TTHM and 60 : 

### g/L for HAA5, measured as an LRAA. MCL of 10 : 

### g/L for bromate. Compliance monitoring is preceded by the IDSE.

• Alternative 1: MCLs of 80 : 

### g/L for TTHM and 60 : 

### g/L for HAA5, measured as an LRAA. MCL of 5 : 

### g/L for bromate.

• Alternative 2: MCLs of 80 : 

### g/L for TTHM and 60 : 

### g/L for HAA5, measured as a single highest (SH) value. MCL of 10 : 

### g/L for bromate.

• Alternative 3: MCLs of 40 : 

### g/L for TTHM and 30 : 

### g/L for HAA5, measured as an RAA. MCL of 10 : 

### g/L for bromate.

For comparison with the Preferred Alternative, EPA also developed estimates of the benefits and costs for Alternatives 1, 2, and 3 in this document.2 

### For the purposes of this EA, “surface water” is equi valent to the definition of subpart H systems used in the Stage 2 DBPR rule language and includes systems that provide ground water under the direct influence of surface water (GWUDI). Final Economic Analysis for the Stage 2 DBPR ES-3 December 2005 

## ES.3 Summary of the Stage 2 DBPR

The requirements of the Stage 2 DBPR apply to all community water systems (CWSs) and nontransient noncommunity water systems (N TNCWSs)—both ground and surface water systems 2 

### —that add a disinfectant other than ultraviolet light (UV), or that deliver water that has been treated with a disinfectant other than UV. New since the Stag e 1 DBPR, the Stage 2 DBPR formally defines consecutive systems and includes provisions specific to consecutive systems to ensure the same level of heath protection for people served in consecutive systems as people served by non-consecutive systems.

Each Stage 2 DBPR rule activity for the Preferre d Regulatory Alternative is described below and illustrated in the flow chart in Exhibit ES.1. Exhi bit ES.2 displays the compliance schedule for each rule activity. Note that consecutive systems of any size must comply with the requirements of the Stage 2 DBPR on the same schedule as required for the largest system in the combined distribution system.

Initial Distribution System Evaluations

For many systems, compliance monitoring will be preceded by an Initial Distribution System Evaluation (IDSE) to identify Stage 2 DBPR complian ce monitoring locations that represent distribution system sites with high TTHM and HAA5 levels. The ID SE consists of either standard monitoring or a system specific study (SSS), unless a system meets the criteria for a 40/30 certification or a very small system waiver. To meet the criteria for a 40/30 cer tification, systems must have low Stage 1 monitoring results (every individual compliance sample is less than or equal to 40 : 

### g/L and 30 : 

### g/L for TTHM and HAA5, respectively) and no TTHM or HAA5 monitoring violations during a 2-year eligibility period. Systems can qualify for a very small systems waiver if they serve fewer than 500 people and have TTHM and HAA5 data. In addition, NTNCWSs serving fewe r than 10,000 people are not required to conduct an IDSE.

Compliance with Stage 2 DBPR MCLs

The Stage 2 DBPR changes the way sampling results are averaged to determine compliance. The determination for the Stage 2 DBPR is based on an LRAA (i.e., compliance must be met at each monitoring location) instead of the syst em-wide RAA used under the Stage 1 DBPR.

Monitoring Plans

Systems must develop a Stage 2 DBPR monitoring plan that includes monitoring locations, monitoring dates, and compliance calculation procedures. States have the option to implement a procedure for addressing modifications to wholesale system and consecutive system monitoring plans on a case-by-case basis.

### Final Economic Analysis for the Stage 2 DBPR ES-4 December 2005 

## Systems Subject to the Stage 2 DBPR (All surface water and ground water CWSs and NTNCWSs that apply a chemical disinfectant to their water, or deliver such water, including consecutive systems.)



## Rule Implementation (All systems subject to the rule must perform rule implementation activities such as reading the rule, training, etc.)



## Initial Distribution System Evaluation (IDSE) (All systems subject to the rule must perform an IDSE, or meet the criteria not to perform an IDSE.)

Systems not performing an IDSE Systems performing an IDSE and submitting an IDSE plan

Systems receiving a 40/30 certification Systems conducting Standard Monitoring

Systems may or may not have to select new Stage 2 DBPR monitoring sites Systems submit an IDSE report recommending

Stage 2 monitoring sites Systems conducting a System Specific Study Systems receiving

a very small system waiver NTNCWSs serving <10,000 people



## Ensure Compliance with Stage 2 DBPR MCLs (All systems subject to the rule must meet Stage 2 DBPR MCLs. Systems may or may not have to make treatment or operational changes.)



## Routine Monitoring Requirements (Monitoring requirements for the Stage 2 DBPR are based on system type and population served [not number of plants per

system, as for the Stage 1 DBPR]. Systems subject to the Stage 2 DBPR may have fewer or more routine monitoring requirements compared to those already required by the Stage 1 DBPR.)



## Operational Evaluations (All systems subject to the rule that exceed the operational evaluation level must perform an operational evaluation and submit a report to the State within 90 days.) 

## Stage 2 DBPR Monitoring Plans (All systems subject to the rule must develop a Stage 2 DBPR monitoring plan that includes monitoring locations, monitoring dates, and compliance calculation procedures) 

## Systems Subject to the Stage 2 DBPR (All surface water and ground water CWSs and NTNCWSs that apply a chemical disinfectant to their water, or deliver such water, including consecutive systems.)



## Rule Implementation (All systems subject to the rule must perform rule implementation activities such as reading the rule, training, etc.)



## Initial Distribution System Evaluation (IDSE) (All systems subject to the rule must perform an IDSE, or meet the criteria not to perform an IDSE.)

Systems not performing an IDSE Systems performing an IDSE and submitting an IDSE plan

Systems receiving a 40/30 certification Systems conducting Standard Monitoring

Systems may or may not have to select new Stage 2 DBPR monitoring sites Systems submit an IDSE report recommending

Stage 2 monitoring sites Systems conducting a System Specific Study Systems receiving

a very small system waiver NTNCWSs serving <10,000 people



## Ensure Compliance with Stage 2 DBPR MCLs (All systems subject to the rule must meet Stage 2 DBPR MCLs. Systems may or may not have to make treatment or operational changes.)



## Routine Monitoring Requirements (Monitoring requirements for the Stage 2 DBPR are based on system type and population served [not number of plants per

system, as for the Stage 1 DBPR]. Systems subject to the Stage 2 DBPR may have fewer or more routine monitoring requirements compared to those already required by the Stage 1 DBPR.)



## Operational Evaluations (All systems subject to the rule that exceed the operational evaluation level must perform an operational evaluation and submit a report to the State within 90 days.) 

## Stage 2 DBPR Monitoring Plans (All systems subject to the rule must develop a Stage 2 DBPR monitoring plan that includes monitoring locations, monitoring dates, and compliance calculation procedures) 

## Exhibit ES.1 Summary of Stage 2 DBPR RequirementsFinal Economic Analysis for the Stage 2 DBPR ES-5 December 2005Schedule 1Systems serving> 100,0001IDSE Plan Due IDSE Report Due Begin ComplianceOctober 1, 2006 January 1, 2009 April 1, 2012Schedule 2Systems serving50,000 to 99,9991IDSE Plan Due IDSE Report Due Begin ComplianceApril 1, 2007 July 1, 2009 October 1, 2012Schedule 3Systems serving10,000 to 49,9991IDSE Plan Due IDSE Report Due Begin ComplianceOctober 1, 2007 January 1, 2010 October 1, 2013Schedule 4Systems serving< 10,0001IDSE Plan Due IDSE Report DueApril 1, 2008 July 1, 2010 Begin ComplianceOctober 1, 20132010 2011 20122006 2007 2008 2009Cryptomon.3Treatment InstallationPossible Extension22014 20152013Treatment InstallationPossible Extension2Treatment InstallationPossible Extension22006 2007 2008 2009 2014 20152010 2011 2012 2013Treatment InstallationPossible Extension2LT2CryptomonitoringIDSE mon.LT2CryptomonitoringLT2CryptomonitoringIDSE mon.IDSE mon.IDSE mon.E. Colimon.Exhibit ES.2 Implementation Timeline for the Stage 2 DBPR1Includes all systems that are part of a combined distribution system that have a largest system with this population.2A State may grant up to an additional 2 years for systems to complyif the State determines thatadditional time is necessary for capital improvements.3Subpart H systems that must conductCryptosporidiummonitoring have an additional12 months to comply with the Stage 2 DBPR MCLs.The IDSE plan is either a Standard Monitoring plan or an SSS plan. The IDSE report iseither for Standard Monitoring or the SSS.

### Final Economic Analysis for the Stage 2 DBPR ES-6 December 2005 

### Routine Monitoring Requirements

EPA has adopted a population-based monitori ng approach for the Stage 2 DBPR, where compliance and IDSE monitoring requirements are b ased only on source water type and retail population served. This is a change from the plant-based a pproach used in the 1979 TTHM rule and the Stage 1 DBPR. EPA’s decision to use a population-based a pproach for all systems is based on improved public health protection, flexibility, and simplified implem entation. Exhibit ES.3 presents the new, populationbased Stage 2 DBPR compliance monitoring requirements.

Operational Evaluations

Because Stage 2 DBPR MCL compliance is based on individual DBP measurements at a location averaged over a four-quarter period, a system c ould find higher TTHM or HAA5 levels than the MCL values, while at the same time maintaining compliance with the Stage 2 DBPR. This is because the high concentration could be averaged with lower concentrati ons at a given location. For this reason, the Stage 2 DBPR includes a provision for “operational evaluations” as follows:

• A system has exceeded an operational evaluati on level at any monitoring location when the sum of the two previous quarters’ compliance monitoring results plus twice the current quarters result, divided by 4, exceeds 80 : 

### g/L for TTHM or 60 : 

### g/L for HAA5.

If an operational evaluation level is exceeded , the system must conduct an “operational evaluation” and submit a written report of the evaluation to the State/Primacy Agency no later than 90 days after being notified of the analy tical results that caused the excursion.

Requirements for Consecutive Systems

The Stage 2 DBPR includes provisions for con secutive systems, which are PWSs that receive finished water from another public water system (a wholesale system). Consecutive systems face particular challenges in providing water that meets regulatory standards for DBPs and other contaminants whose concentration can increase in the distribution system. Moreover, previous regulation of DBP levels in consecutive systems varies widely among States. In consideration of these factors, the Stage 2 DBPR provides monitoring, compliance schedule, a nd other requirements specifically for consecutive systems. These requirements are intended to facilitate compliance by consecutive systems with MCLs for TTHM and HAA5 under the Stage 2 DBPR and help to ensure that consumers in consecutive systems receive equivalent public health protection.

### Final Economic Analysis for the Stage 2 DBPR ES-7 December 2005 

## Exhibit ES.3 Stage 2 DBPR Populat ion-Based Compliance Monitoring Requirements

System Size (Population Served ) Distribution System Sample Locations 1



### Total Sample Locations per System 5 

### Monitoring Frequency 3 

### Highest TTHM Locations Highest HAA5 Locations Existing Stage 1 Compliance

Locations 2


### Systems Using Surface Water in Whole or in Part 4



### < 500 1 1 NA 2 per year
500-3,300 1 1 NA 2 per quarter
3,301-9,999 1 1 NA 2 per quarter
10,000-49,999 2 1 1 4 per quarter 50,000-249,999 3 3 2 8 per quarter
250,000-999,999 5 4 3 12 per quarter
1 Mil-4,999,999 6 6 4 16 per quarter

> 5,000,000 8 7 5 20 per quarter Systems Using Only Ground Water
< 500 1 1 NA 2 per year 500 - 9,999 1 1 NA 2 per year 10,000 - 99,999 2 1 1 4 per quarter 100,000 - 499,999 3 2 1 6 per quarter

> 500,000 3 3 2 8 per quarter

1 

### Locations must be based on the system ’s recommendations for Stage 2 DBPR compliance monitoring locations in

its report to the State/Primacy Agency, unless the State/Primac y Agency requires different or additional locations. Locations should be distributed throughout the distribution syst em to the extent possible.
2 

### Alternate between highest HAA5 LRAA and highest TTH M LRAA locations among the existing Stage 1 average

resident time compliance locations. If the number of existing Stage 1 compliance locations is fewer than the specified number for Stage 2, alte rnate between highest HAA5 LRAA locations and highest TTHM LRAA locations from the IDSE.
3 

### All systems must monitor during the month of highest DBP concentrations. Systems on quarterly monitoring must

take dual sample sets approximately every 90 days.

4 

### For the purposes of this EA, “surface water” system s are equivalent to “subpart H” systems and include systems

that use GWUDI.

5 

### Systems on quarterly monitoring must take dual sample se ts every 90 days at each monitoring location, except for

subpart H systems serving 500-3,300 people. Systems on annual monitoring and subpart H systems serving 500- 3,300 people are required to take individual TTHM and HAA5 samples (instead of a dual sample set) at the locations with the highest TTHM and HAA5 concentrations, re spectively. Only one location with a dual sample set per monitoring period is needed if the highest TTHM and HAA5 concentrations occur at a same location and month (if monitored annually). NA = Not Applicable3 

### The baseline number of plants–as opposed to syst ems–that may have to make treatment technology changes to meet rule requirements is different and discussed in section ES.5.1. Final Economic Analysis for the Stage 2 DBPR ES-8 December 2005 

## ES.4 Systems Subject to the Stage 2 DBPR

Exhibit ES.4a shows the baseline number of system s subject to the rule and the estimated number that will perform various rule activities (impleme ntation, IDSE monitoring, monitoring plans, and operational evaluations). 3 

### This baseline is derived from EPA’s Safe Drinking Water Information System (SDWIS) inventory, 4 th 

### quarter 2003 data. The systems are subdivided by type (CWS or NTNCWS), source water type (either disinfecting ground water only, or surface water and mixed-source) and size (small or large, based on population served). The number of ground water systems in column A represents the subset of all ground water systems that currently disinfect.

As shown in column B, EPA estimates that a ll disinfecting CWSs and NTNCWSs will have to perform at least minimal implementation activities (reading and understanding the rule, training, etc.). All systems will also have to develop Stage 2 monitoring plans as shown in column F. The number of systems performing IDSE monitoring (shown in column D), however, is only a fraction of all systems because some will choose to perform studies or will receive waivers from IDSE requirements.

EPA has established a population-based monitori ng approach for the Stage 2 DBPR, where monitoring requirements are no longer based on the number of plants per system as under the Stage 1 DBPR but rather on the population served. As a result, the number of Stage 2 compliance samples required per year for any particular system ma y stay the same, decrease, or increase from Stage 1 requirements. Appendix H includes information on changes in monitoring burden.

EPA expects that some number of Stage 2-compliant systems will find TTHM and HAA5 levels high enough to trigger the requirement for an opera tional evaluation. Column H shows the estimated number of systems that may require operational evaluations.

In addition to those ground water systems that currently disinfect, EPA predicts that some systems will install disinfection to comply with the anticipated Ground Water Rule (GWR). Exhibit ES.4b shows the number of systems predicted to install disinfection based on information in the GWR EA (USEPA 2000g) and system inventory data. Because th e GWR is expected to be promulgated within 8 months after the Stage 2 DBPR is promulgated, EPA expects new systems adding disinfection to meet GWR requirements to simultaneously achieve complian ce with Stage 2 MCLs. Therefore, these systems are not included in the treatment baseline. The IDSE will likely not apply to these systems because they are expected to install disinfection after the IDSE re quirement period is complete. Systems installing disinfection for the GWR will, however, be require d to prepare monitoring plans and in some cases, monitor DBPs for the first time under Stage 2. E xhibit ES.4b shows that all newly disinfecting ground water systems will prepare monitoring plans. All ne wly disinfecting systems will also need to conduct compliance monitoring for the first time.

### Final Economic Analysis for the Stage 2 DBPR ES-9 December 2005 A B C=B/A*100 D E=D/A*100 F G=F/A*100 H I=H/A*100

<10,000 9,397 9,397 100% 7,771 83% 8,160 87% 97 1%

> 10,000 2,406 2,406 100% 2,038 85% 2,406 100% 327 14% National Totals 11,803 11,803 100% 9,809 83% 10,566 90% 424 4%

<10,000 28,806 28,806 100% 2,707 9% 11,801 41% 0 0%

> 10,000 1,423 1,423 100% 258 18% 1,423 100% 0 0% National Totals 30,229 30,229 100% 2,966 10% 13,225 44% 0 0%

<10,000 771 771 100% 0 0% 0 0% 0 0%

> 10,000 6 6 100% 5 83% 6 100% 0 0% National Totals 777 777 100% 5 1% 6 1% 0 0%

<10,000 5,480 5,480 100% 0 0% 0 0% 0 0%

> 10,000 4 4 100% 1 24% 4 100% 0 0% National Totals 5,483 5,483 100% 1 0% 4 0% 0 0% GRAND TOTAL 48,293 48,293 100% 12,780 26% 23,800 49% 424 1%

Sources: (A), (B), (D), (F), and (H): Appendix H, Exhibit H.15a. Disinfecting Ground Water Only CWSs

Surface Water and Mixed NTNCWSs

Disinfecting Ground Water Only NTNCWSs Stage 2 Monitoring Plans IDSE Monitoring System Size (Population Served) Stage 2 DBPR System Baseline Number and Percent of Systems Performing Various Rule Activities

Implementation Operational Evaluations

Note: Detail may not add to totals due to independent rounding. Surface Water and Mixed CWSs

Non-treatment-related rule activities, in addition to those shown in the table, also include routine compliance monitoring. So me systems are expected to take more samples and some less from Stage 1 to Stage 2 depending on the number of plants in their systems. Ov erall the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. See Exhibit H.8 a, column H, for the change in total samples for the different size categories.



## Exhibit ES.4a Number of Di sinfecting Systems Subject to Non-Treatment-Related Rule Activities

### Final Economic Analysis for the Stage 2 DBPR ES-10 December 2005 A B C=B/A*100

<10,000 0 0 -

> 10,000 00 -
National Totals 0 0 -

<10,000 1,030 1,030 100%

> 10,000 13 13 100% National Totals 1,042 1,042 100%

<10,000 0 0 -

> 10,000 00 -
National Totals 0 0 -

<10,000 1,509 1,509 100%

> 10,000 1 1 100% National Totals 1,510 1,510 100% GRAND TOTAL 2,552 2,552 100%

Sources: Ground Water Only CWSs

Surface Water and Mixed NTNCWSs

Ground Water Only NTNCWSs

(A), (B), (D): Appendix H, Exhibit H.12b. Notes: Detail may not add to totals due to independent rounding. Non-Treatment-Related Rule Activities, in addition to those shown in the table, include routine compliance monitoring for all systems. Stage 2 Monitoring Plans

Surface Water and Mixed CWSs System Size (Population Served) Baseline No. of Systems Adding Disinfection for the GWR 

## Exhibit ES.4b Non-Treatment Rule Acti vities for Systems Installing Disinfection to Comply with the Ground Water Rule

ES.5 National Benefits and Costs of the St age 2 DBPR Preferred Regulatory Alternative

EPA has determined from its analysis of the available animal toxicological studies and human epidemiological studies that the Stage 2 DBPR could provide benefits resulting from reduced incidence of cancer, particularly bladder cancer, and reduced incidence of potential adverse reproductive and developmental effects. The main category of benef its that EPA has quantified is the expected range of avoided new cases of bladder cancer each year, includi ng both fatal and non-fatal cases. In addition, EPA has estimated the monetized value of avoidi ng these fatal and non-fatal bladder cancer cases.

The major steps in deriving and characteriz ing cancer cases avoided are the following: (1) Estimate the current and future annual bladder cancer cases from all causes; (2) Estimate how many cases can be attributed to DBP occurrence and exposur e; and (3) Estimate the reduction in future cases corresponding to anticipated reductions in DBP occurre nce and exposure due to the Stage 2 DBPR. For step 2, EPA has developed three approaches to es timating the number of bladder cancer cases attributable to DBPs. Taken together, the three approaches provi de a reasonable estimate of the range of potential risk. For simplicity’s sake, one estimate, based on a 2003 meta-analysis by Villanueva et al., is carried through the full benefits analysis.

To assign a monetary value to avoided bladder cancer cases, EPA used the value of a statistical life (VSL) for fatal cases and used two alternate es timates of willingness-to-pay to avoid non-fatal cases (one based on curable lymphoma and the other based on chronic bronchitis). Exhibit ES.5 summarizes4 

### For the Stage 2 DBPR cost and benefit analyses, cal culations are made using two discount rates–3 and 7 percent–to represent current policy evaluation methodologi es. Chapters 6 and 7 provide additional information on the derivation of these rates. Final Economic Analysis for the Stage 2 DBPR ES-11 December 2005 

### the estimated total number of bladder cases avoided a nd the resulting monetized benefits for the Stage 2 DBPR Preferred Regulatory Alternative. The cases avoided and monetized benefits are based on reductions in average TTHM and HAA5 concentrati ons that result from making treatment technology changes.

There are several categories of unquantified poten tial health and non-health benefits resulting from rule implementation that could contribute to the overall value of the benefits of the Stage 2 DBPR. Two important categories are colon and rectal cancer s, and reproductive and developmental effects. In addition to bladder cancer, human epidemiology studi es on chlorinated surface water have also reported associations with colon and rectal cancers. In the Stage 1 DBPR, EPA concluded that early studies suggested a small possible increase in rectal and colon cancers from exposure to chlorinated surface waters. Since the Stage 1 DBPR, the database of st udies on colon and rectal cancers continues to support a possible association, but evidence remains mixe d. For these reasons, EPA performed a sensitivity analysis to determine potential reductions in rect al and colon cancer cases as a result of the Stage 2 DBPR.

Scientific knowledge about the association of re productive and developmental health effects with DBP exposure is not known well enough to quantify these risks or the benefits of reduced DBP exposure in the primary benefits analysis. Nevertheless, although the results from different studies are mixed and no causal link has been established between adve rse reproductive or developmental effects and chlorinated drinking water, a weight of evidence eval uation of the health effects data suggests a potential association. EPA believes additional benefits from the Stage 2 DBPR could come from reducing potential reproductive and developmental effects risks a nd that it is therefore important to provide some quantitative indication of the possible magnitude of these benefits. To do this, EPA completed an illustrative calculation of potential benefits for one specific reproductive effects endpoint (fetal loss). Unquantified benefits are discussed in detail in Chapter 6 and are summarized in Exhibit 9.2.

EPA’s national cost estimate includes cost s incurred by CWSs and NTNCWSs for rule implementation, the IDSE, preparing Stage 2 monitoring plans, conducting additional routine monitoring, operational evaluations, and treatment technology changes (which account for the majority of the national costs) as well as estimated costs to States/Primacy Agencies. Exhibit ES.5 summarizes the total national annualized cost estimate for the Stage 2 DBPR Preferre d Regulatory Alternative. Note that the exhibit presents two estimates for national costs and moneti zed benefits, depending upon the discount rate used for present value calculations and annualizing one-time costs. 4



### Sections ES.5.1 through ES.5.3 summarize the methods used to estimate the number of plants making treatment technology changes to comply with the rule, and the benefits and costs resulting from these treatment technology changes. Chapters 3, 5, 6, and 7 and the appendices provide a more complete discussion of all data and calculations used to derive the results in Exhibits ES.5.Final Economic Analysis for the Stage 2 DBPR ES-12 December 2005Surface Water Disinfecting Ground Water< 10,000>10,000< 10,000>10,0009.0 260.5 3.5 5.8 278.9(102.9 - 540.9)Annualized Monetized Benefits of Bladder Cancer Cases AvoidedWTP for Lymphoma as the basis for non-fatal cases, $49.3 $1,430.3 $19.3 $31.9 $1,530.8smoking/lung cancer cessation lag model (90% confidence bounds) ($7.5 - $113.8) ($217.6 - $3304.0) ($2.9 - $44.6) ($4.9 - $73.7) ($232.9 - $3536.1)WTP for Chronic Bronchitis as the basis for non-fatal cases, $24.5 $712.7 $9.6 $15.9 $762.8smoking/lung cancer cessation lag model (90% confidence bounds)($5.3 - $54.5) ($154.3 - $1581.2) ($2.1 - $21.4) ($3.4 - $35.3) ($165.2 - $1692.3)Annualized Total Costs (90% confidence bounds)$12.2 $35.7 $18.1 $11.1 $1.7 $78.8($7.2 - $17.3) ($21.0 - $51.1) ($16.1 -$20.2) ($10.2 - $11.9) ($56.2 - $102.2)Annualized Monetized Benefits of Bladder Cancer Cases AvoidedWTP for Lymphoma as the basis for non-fatal cases, $39.6 $1,165.5 $15.5 $25.9 $1,246.5smoking/lung cancer cessation lag model (90% confidence bounds)($6.0 - $91.4) ($177.4 - $2690.6) ($2.4 - $35.8) ($3.9 - $59.9) ($189.8 - $2877.7)WTP for Chronic Bronchitis as the basis for non-fatal cases, $19.7 $580.3 $7.7 $12.9 $620.7smoking/lung cancer cessation lag model (90% confidence bounds)($4.3 - $43.7) ($85.5 - $873.2) ($1.7 - $17.1) ($2.8 - $28.6) ($134.6 - $1375.8)Annualized Total Costs (90% confidence bounds)$11.5 $36.1 $17.0 $10.5 $1.7 $76.8($7.0 - $16.2) ($21.3 - $51.1) ($15.0 -$19.0) ($9.6 - $11.3) ($54.6 - $99.4)Notes:Sources:TotalState/PrimacyAgenciesEstimated Number of Bladder Cancer Cases Avoided per YearIncludes both fatal and non-fatal cases. Expected value (90% confidence bounds) Basedon Villanvueva et al. 2003 for baseline risk estimates, smoking/lung cancer cessation lagmodel, and TTHM as an indicator of all DBPs.Type of Cost or BenefitTotals for bladder cancer cases avoided and monetized benefits from Exhibit 6.28. Detail for source and size provided in Appendices E and F.Annualized total costs and state/primacy agency costs derived from Exhibits 7.5a and 7.5b.Benefits andCosts Based onAnnualizationDiscount Rate of3 %Benefits for the Stage 2 DBPR are estimated using three approaches for estimating baseline risk, three different cessation lagmodels, and either TTHMs or HAA5s as an indicator. Nominalbenefits presented here are estimated using Villanueva et al. (2003) for baseline risk, TTHMs as an indicator, and the Smoking/Lung Cancer cessation lag model. Using TTHM or HAA5 as anindicator for all DBPs produces similar results. Because Villaneuva et al. (2003) and the Smoking/Lung Cancer cessation lag model result in benefits estimates that are in between the otheralternatives, results are presented as a representative comparison to costs. Chapter 6 presents results for the full range ofalternative approaches to estimating baseline risk and cessation lag.The 90 percent confidence bounds around cost address uncertainty in the compliance forecast methodology, potential impacts of the IDSE, and unit costs. 90 percent confidence bounds aroundcases avoided accounts for uncertainty in the compliance forecast, PAR, cessation lag model form, and predicted DBP reduction from the compliance forecast. 90 percent confidence boundsaround monetized benefits also reflect uncertainty in VSL and WTP inputs. Causality has not been established, so lower bound of potential risks may be as low as zero.Detail may not add due to independent rounding. Monetized benefits and costs are discounted and annualized in 2003 dollars.Costs are for CWSs and NTNCWSs and include treatment and nontreatment costs.Benefits andCosts Based onAnnualizationDiscount Rate of7 %Exhibit ES.5 Summary of Estimated National Benefits and Costs of the Stage 2 DBPRPreferred Regulatory Alternative ($ Million / Year)

### Final Economic Analysis for the Stage 2 DBPR ES-13 December 2005 

### ES.5.1 Derivation of the Stage 2 DBPR Complian ce Forecast and Consequent Reductions in DBPs

Changes in concentrations of DBPs are the dir ect result of changes in treatment technologies. Therefore, it is important that EPA uses consis tent methodologies for forecasting treatment technology changes and predicting reductions in DBPs (specifically, the reductions in TTHM and HAA5 concentrations that are used in the benefits anal ysis). This section summarizes the tools used and key assumptions for the Stage 2 DBPR compliance for ecasts and the consequent reductions in TTHM and HAA5 concentrations.

Since the rule was proposed, EPA has modifi ed the compliance forecast methodology in an attempt to further quantify uncertainties in the analysis. Specifically, EPA has developed a second method to predict the number of surface water plants making treatment technology changes and consequent reductions in TTHM and HAA5 concen tration, which supplements the Surface Water Analytical Tool (SWAT) predictions. EPA has also quantified uncertainty in the potential impacts of the Initial Distribution System Evaluation (IDSE). Uncertainties are characterized using Monte Carlo simulation in the cost and benefits models.

Predictive Tools Used to D evelop the Compliance Forecast

EPA uses different methods for different system sizes and source water types to develop the compliance forecasts, as shown in Exhibit ES.6. Because extensive data were available from the Information Collection Rule (ICR), analysis tools and methods drawing specifically on those data were used to develop compliance forecast s for large surface and ground water systems. For large surface water systems, EPA used two different methodologies, both drawing from ICR data: the Surface Water Analytical Tool (SWAT) and the ICR Matrix Method. The ICR Matrix Method uses TTHM and HAA5 distribution system data from the ICR to predict how many plants will need to make treatment technology changes for a specific regulatory alternative. SW AT uses a series of decision rules and algorithms to predict the number of plants making treatment technol ogy changes and the type of treatment they will install for a specific regulatory alternative, b ased on source water quality and existing treatment as reported in the ICR database. The ICR Matrix Me thod and SWAT produce different results; thus, results from both are incorporated into the cost model using a Monte Carlo simulation model to account for uncertainties in both methods.

The forecast for large ground water systems w as generated using the ICR Ground Water Delphi process, which convened a group of experts to eval uate plant configurati ons and predict technology selection for ground water plants that did not meet rule requirements. Compliance forecasts for large surface and ground water systems were used to generate forecasts for medium and small systems, making adjustments to account for different operational and wa ter quality characteristics that exist in the latter.

### Final Economic Analysis for the Stage 2 DBPR ES-14 December 2005 

## Exhibit ES.6 Tools Used to Develop the Stage 2 DBPR Compliance Forecasts

System Size (Population Served) Source Water Category

Surface Water Disinfecting Ground Water

Large (> 100,000 people) The Surface Water Analytical

Tool (SWAT) ICR Matrix Method ICR Ground Water Delphi Group

Medium (10,000 to 99,999 people) Extrapolation from SWAT Extrapolation from ICR Matrix Method Extrapolation from large ground water system results

Small (<10,000 people) Extrapolation from SWAT, adjusted to deal with small system-specific issues Extrapolation from ICR Matrix Method Extrapolation from large ground water system results,

adjusted to deal with small

system-specific issues



### Tools Used to Predict Changes in DBP Levels

For the benefits analysis, EPA needs informa tion on the changes in both average and peak TTHM and HAA5 levels that result from implementation of the Stage 2 DBPR. Estimates of bladder cancer cases avoided and the colon and rectal cancer sensitivity analysis are based on reductions in average levels, while the illustrative analysis of pot ential developmental and re productive health benefits is based on reductions in occurrences of peak concentrations.

To predict changes in average TTHM and HAA5 le vels for surface water systems, EPA uses two methods: the ICR Matrix Method and SWAT. As noted in the previous section, the ICR Matrix Method evaluates distribution system data to identify plants that would need to make treatment technology changes to meet a specific regulatory alternative. To predict average DBP concentrations occurring after treatment technology changes, EPA used TTHM a nd HAA5 occurrence data for those surface water plants already using chloramines and/or advanced technologies and in compliance with Stage 2 at the time of the ICR. The predicted average TTHM a nd HAA5 levels for all surface water plants is a weighted average for plants that do and do not change treatment technology.

SWAT is a model that uses a series of decision rules and algorithms to predict (1) which surface water plants need to change treatment technology to meet a specific regulatory alternative, (2) which treatment technology those plants will select based on a least cost decision tree, and (3) resulting changes in the national average TTHM and HAA5 levels in dist ribution systems. As with the compliance forecast, SWAT and the ICR Matrix Method produce different r esults for DBP levels; thus, both are incorporated into a Monte Carlo simulation model to further quan tify uncertainty in the national benefits estimate.

ICR ground water plant data were not robust e nough to develop a ground water model similar to SWAT; therefore, the ICR Matrix Method is the only approach used to predict reductions in average TTHM and HAA5 levels for these systems.5 

### Although the SWAT model was calibrated to national average TTHM and HAA5 concentrations in distribution systems and validated against industry treatme nt technology predictions, it was not calibrated to plantlevel DBP predictions and, thus, could not be used to a ssess changes in occurrence of peak levels. See Appendix A for more information on SWAT.

6 

### There is uncertainty in using the ICR data to re present pre-Stage 1 conditions because some plants may have begun making changes prior to the ICR in anticipa tion of the Stage 1 DBPR (McGuire et al., 2002). See Section 3.8 for a full discussion of uncertainties in ICR data.
Final Economic Analysis for the Stage 2 DBPR ES-15 December 2005 

### To predict changes in the o ccurrence of peak TTHM and HAA5 concentrations, EPA used only the ICR Matrix Method 5 

### . Similar to the way in which it is u sed to evaluate changes in average TTHM and HAA5 concentrations, the ICR Matrix Method eval uates distribution system data to identify plants that would need to make treatment technology cha nges to meet a specific regulatory alternative. To predict occurrence of peaks after treatment technology changes, EPA analyzed TTHM and HAA5 occurrence data for those surface water plants alread y using chloramines and/or advanced technologies at the time of the ICR. The predicted occurrence of peaks for all plants is a weighted average for plants that do and do not make treatment technology changes.

Accounting for the Stage 1 DBPR

For cost and benefit analyses, the compliance forecast and consequent reduction in DBPs needs to represent treatment technology changes from the pre- Stage 2 baseline (i.e., after implementation of the Stage 1 DBPR). The best data available to char acterize large plants are from the ICR, which were collected before the Stage 1 DBPR compliance deadlines and likely represent pre-Stage 1 conditions 6



### . The compliance forecast, therefore, needs to account for treatment technology changes as a result of the Stage 1 DBPR before predicting changes that are need ed for the Stage 2 DBPR. Similarly, the post-Stage 2 TTHM and HAA5 predictions need to take into acc ount changes as a result of the Stage 1 DBPR before predicting reductions that will occur as a result of the Stage 2 DBPR.

EPA uses a “delta” compliance forecast method that was developed by the Microbial / Disinfection Byproducts (M-DBP) Technical Working Group (TWG) to characterize the incremental changes due to Stage 2 relative to Stage 1. The met hod has four steps. First, EPA characterizes treatment technologies and TTHM and HAA5 occurrence for the Pre-Stage 1 baseline. Second, EPA predicts treatment technology changes and subsequent re duction in TTHM and HAA5 levels from Pre-Stage 1 baseline to post-Stage 1 DBPR conditions. Thir d, treatment technology changes and subsequent reductions in TTHM and HAA5 levels are predicte d from pre-Stage 1 baseline to post-Stage 2 DBPR conditions. Lastly, results from Step 2 are subtracted from Step 3 to calculate the incremental treatment technology change and TTHM/HAA5 reduction fro m post-Stage 1 DBPR to post-Stage 2 DBPR conditions.

This delta method was selected by the M-DBP TWG over a more direct, two step approach (i.e., predict pre-Stage 2 conditions and then use the pre- Stage 2 conditions to predict impacts for Stage 2) because modeling tools are not able to predict the treatment technology selection or TTHM, HAA5, bromate, and chlorite levels at the plant level. Th e delta approach compensates, at the national level, for potential errors in treatment technology selec tion and resulting TTHM and HAA5 concentrations predicted for Stage 2. The TWG believed that using the delta approach reduces the impact of uncertainty in SWAT predictive equations for TTHM and HAA5. The delta approach is used with both the ICR Matrix Method and SWAT analyses.7 

### EPA believes that the 20 percent safety margin accounts for potential impacts of the IDSE for small systems because their distribution system s are not as complex when compared to large systems. EPA also believes that the safety margin accounts for the IDSE for ground wate r systems because the year-to-year variability in source water quality (and thus, TTHM and HAA5 formation) is low. Chloramine systems generally observe lower spatial and temporal variability in TTHM and HAA5 distribution system levels (USEPA 2005k); thus, EPA believes the 20 percent safety margin accounts for potential impacts of the IDSE for these systems.
Final Economic Analysis for the Stage 2 DBPR ES-16 December 2005 

### Accounting for the IDSE

Because the purpose of the IDSE is to identify St age 2 compliance monitoring locations with high DBP levels, it is possible that systems may measure higher DBP levels at Stage 2 compliance monitoring sites than were measured under the ICR. This sugg ests that the number of plants predicted to make treatment technology changes, the level of treatment they select, and the resulting reductions in TTHM and HAA5 levels, all based on ICR data, could be underestimated.

The M-DBP TWG recommended that the Stage 1 and Stage 2 compliance forecast methodology incorporate an operational safety margin of 20 percen t to represent the operational level (i.e., 80 percent of the MCL) at which systems typically take some action to ensure consistent compliance with a new drinking water standard and the level at which syst ems target new treatment technologies to meet the standard. EPA believes that this safety margin already accounts for the impacts of the IDSE for some systems, including small systems, ground water systems, and those using chloramines 7 

### . EPA believes, however, that the 20 percent safety margin is not su fficient to account for the potential impacts of the IDSE on large and medium surface water systems because spatial variability of DBP levels and distribution system complexity are greatest in th ese systems. Since the proposal, EPA developed a methodology that analyzed ICR data from surface water plants to assess the extent of spatial variability of TTHM and HAA5 levels and used this as a basis for quantifying the impacts of the IDSE for large and medium surface water systems.

Results

Exhibit ES.7a shows the mean number and per cent of plants expected to make advanced treatment technologies changes to meet the requirements of the Stage 2 DBPR. Advanced technologies include alternative disinfectants such as ozone, UV, and chlorine dioxide, and DBP precursor removal technologies such as granular activated carbon adsorp tion and nanofiltration. The 90 percent confidence intervals around the mean estimate for surface water systems account for alternative compliance forecast methodologies (SWAT and the ICR Matrix Method) and uncertainty in the potential impacts of the IDSE. Because one method (instead of two) was used to predict ground water plants making treatment technology changes, Exhibit ES.7a only presents a mean value for these plants.

Exhibit ES.7b shows the reduction in the na tional average TTHM and HAA5 concentrations occurring in drinking water distribution systems as a result of treatment technology changes to meet Stage 2 DBPR requirements. The 90 percent confidence intervals around the mean estimate for surface water systems account for alternative methodologies (SWAT and the ICR Matrix Method) and the potential impacts of the IDSE.

### Final Economic Analysis for the Stage 2 DBPR ES-17 December 2005 Mean 5th %ile 95th %ile Mean 5th %ile 95th %ile
A B C D E=B/A F=C/A G = D/A

<10,000 3,996 352 196 506 8.8% 4.9% 12.7%

> 10,000 2,555 373 174 582 14.6% 6.8% 22.8% National Totals 6,552 724 371 1,088 11.1% 5.7% 16.6%

<10,000 40,376 1,170 2.9%

> 10,000 7,044 145 2.1% National Totals 47,419 1,314 2.8%

<10,000 760 68 38 98 9.0% 5.0% 12.9%

> 10,000 6 1 0 1 14.6% 6.8% 22.8% National Totals 766 69 39 100 9.0% 5.0% 13.0%

<10,000 5,480 153 2.8%

> 10,000 4 0 2.1% National Totals 5,483 154 2.8% 

### Grand Total All Plants 60,220 2,261 1,877 2,655 3.8% 3.1% 4.4%

Sources: (A) Exhibit 3.2, column AB. Represents 4th quarter 2003 SDWIS data. System baseline converted to plant baseline through four steps: 1) link purchased systems to their respective sellers, 2) estimate percent of ground water systems that disinfect, 3) categorize systems by primary source, and 4) multiply the system inventory by estimate of mean plants per system to produce plant inventory.
(B) - (D) Exhibit 7.3. The 90 percent confidence intervals for surface water systems represent alternative compliance forecast methodologies (SWAT and the ICR Matrix Method) and uncertainty in the potential impacts of the IDSE.

System Size (Population Served) Stage 2 DBPR Plant Baseline

Note: Detail may not add to totals due to independent rounding. Treatment changes include adding advanced technologies and/or chloramines. Primarily Ground Water NTNCWSs Primarily Surface Water CWSs

Primarily Ground Water CWSs

Primarily Suface Water NTNCWSs Number of Plants Making Treatment Technology Changes Percentage of Plants Making Treatment Technology Changes 

## Exhibit ES.7a Plants Making Treatme nt Technology Changes, Preferred Regulatory Alternative

### Final Economic Analysis for the Stage 2 DBPR ES-18 December 2005 Mean 5th 95th Mean 5th 95th A B Large (> 10,000) 160,935,736 35.5 32.2 33.7 30.7 9.2% 5.1% 13.5% Small (< 10,000) 8,422,403 35.5 32.9 33.8 32.0 7.2% 4.7% 9.7% Large (> 10,000) 65,152,168 13.2 13.0 1.4% Small (< 10,000) 28,514,211 14.7 14.4 2.0% 263,024,518 27.69 25.53 26.44 24.60 7.8% 4.5% 11.2%

Mean 5th 95th Mean 5th 95th A E Large (> 10,000) 160,935,736 25.0 22.5 23.7 21.1 9.9% 5.2% 15.5% Small (< 10,000) 8,422,403 25.0 23.1 23.8 22.4 7.6% 4.7% 10.5% Large (> 10,000) 65,152,168 7.0 6.6 4.5% Small (< 10,000) 28,514,211 7.8 7.4 6.3% 263,024,518 18.67 16.96 17.71 16.12 9.2% 5.2% 13.7% Notes: Detail may not add due to independent rounding. All TTHM and HAA5 concentrations represent the mean of all plant- mean concentrations. Total for "All Systems" is calculated us ing a Monte Carlo analysis.

* TTHM and HAA5 concentrations for all systems are the population-weighted values Sources: (A) SDWIS 2003 3rd quarter freeze, community water system population (Exhibit 3.3, CWSs only). All Systems* GW

System Size ( Po p ulation Served ) Percent Reduction Post-Stage 2 Level (μg/L) Pre-Stage 2 Level (μg/L) F G = (E - F) / E Source Water T yp e HAA5 SW Source Water T yp e System Size ( Po p ulation Served ) Pre-Stage 2 Level (μg/L) Population Post-Stage 2 Level (μg/L)

C TTHM Percent Reduction

D = (B - C) / B

Population

(B) and (E) Exhibit 5.22

(C) and (F) Outputs from the benefits Monte Carlo simulation model. Confidence bounds for large and medium SW systems account for uncertainties in compliance forecast methodologies and potential impacts of the IDSE. Confidence bounds for small SW systems account for uncertainties in compliance forecast methodologies. SW

GW All Systems*

Results for large SW systems for the preferred alternative represent the combined results for the 20% and 25% safety margins. 

## Exhibit ES.7b Estimated Reduction in Average TTHM and HAA5 from Pre-Stage 2 to Post-Stage 2, Preferred Regulatory Alternative

ES.5.2 Derivation of Benefits

Three categories of potential benefits are addressed in this EA: those associated with reductions in the incidence of bladder cancer, those associated with decreasing cases of colon and rectal cancers, and those associated with reductions in the inciden ce of adverse reproductive and developmental health effects. The primary benefits analysis in this EA is based on reductions in bladder cancer cases. Potential benefits associated with reduced incidence of co lon and rectal cancers are quantified in a sensitivity analysis, while potential benefits associated with decreasing adverse developmental and reproductive health effects (specifically, fetal losses) are presented as an illustrative calculation.

EPA used similar approaches to estimate the number of annual bladder cancer cases avoided (the primary benefits analysis), the number of annua l colon and rectal cancer avoided (the sensitivity analysis), and the number of annual fetal losses avoide d (the illustrative calculation). The major steps in deriving and characterizing cases avoided are:

• Estimate the current and future annual cases of illness from all causes

• Estimate how many cases can be attributed to DBP occurrence and exposure

• Estimate the reduction in future attributab le cases corresponding to anticipated reductions in DBP occurrence and exposure due to the Stage 2 DBPR

All benefit calculations were performed using the Stage 2 DBPR Benefits Model (USEPA 2005h).8 

### The American Cancer Society estimated in 2004 that 60,240 new cases of bladder cancer would occur in the U.S. population that year (ACS website, 2004).
Final Economic Analysis for the Stage 2 DBPR ES-19 December 2005 

### For bladder cancer, EPA computed the monetized benefits of the Stage 2 DBPR by multiplying the estimated number of bladder cases avoided by the estimated monetary value associated with avoiding both fatal and non-fatal cases of bladder cancer. The valu e of a statistical life (VSL) was used for fatal bladder cancers, while two alternate estimates of willingness to pay (WTP) to avoid non-fatal bladder cancer are used (one based on avoiding a case of curable lymphoma and the other based on avoiding a case of chronic bronchitis). EPA also computed the benefits for the reduction in colon and rectal cancer by using the same VSL and WTP estimates. EPA recognizes that there could be additional value associated with the number of potential avoided fe tal losses estimated in the illustrative calculation. However, the Agency is unable at this time either to develop a specific estimate of this value or to use a benefit transfer method to estimate the value from studies that address other endpoints because of associated uncertainty (see Section 6.8 for a full discussion of this issue).

Bladder Cancer

To calculate potential benefits from reduced incidence of bladder cancer cases, EPA began by estimating the number of new bladder cancer cases occurring per year from all causes. The National Cancer Institute’s Surveillance, Epidemiology, a nd End Results (SEER, 2004) program provides data on cancer rates (new cases per 100,000 population per year) as a function of age. EPA used this information in conjunction with population-by-a ge data from the 2000 U.S. Census to estimate the number of new cases of bladder cancer. Results show that the number of new bladder cancer cases per year starts to increase at about age 35 and peaks at 1,500 to 2,000 cases from about age 66 to 85. Although the annual rate of bladder cancer does not decline much afte r age 85, the incidence of new bladder cancers does, which represents the overall decline in the number of i ndividuals alive after that age. The resulting total number of new bladder cancer cases per year, 56,506, is slightly lower than that currently estimated by the American Cancer Society (ACS). 8 

### This likely represents EPA’s use of the census population data from 2000.

To estimate the baseline number of cases attributab le to DBP exposure, EPA used three different approaches:

• Using the range of Population Attributable Ri sk (PAR) values derived from consideration of 5 individual epidemiology studies used for the Stage 1 EA and the Stage 2 proposal EA (yields a pre-Stage 1 range of best estimates for PAR of 2% to 17%).

• Using the Odds Ratio (OR) of 1.2 from the Villa nueva et al. (2003) meta-analysis that reflects both sexes, ever exposed population from the st udies considered (yields a pre-Stage 1 best estimate for PAR of ~16%)

• Using the Villanueva et al. (2004) pooled da ta analysis to develop a dose-response relationship for OR as a function of Averag e TTHM. The dose-response relationship wasmodeled as linear with an intercept of OR = 1.0 at TTHM exposure level = 0 (yields a preStage 1 best estimate for PAR of ~17%)

Taken together, the three approaches provide a r easonable estimate of the range of potential risk. For the sake of simplicity, EPA carried only one these approaches, that based on Villanueva et al. (2003), through the entire benefits model. EPA notes th at the existing epidemiological evidence has not conclusively established causality between DBP exposur e and any health risk endpoints, so the lower bound of potential risks may be as low as zero.

### Final Economic Analysis for the Stage 2 DBPR ES-20 December 2005 

### To quantify the reduction in cases, EPA assumes that there is a linear relationship between average DBP concentration and bladder cancer risk. Thus, percent reductions in national average DBPs are used to determine the percent reductions in bl adder cancer cases attributable to DBPs. Predicted reductions in national average TTHM and HAA5 leve ls resulting from predicted treatment technology changes to comply with Stage 2 were used as i ndicators of overall chlorination DBP reductions. The baseline cases attributable to DBPs multiplied by the percent reductions in TTHM or HAA5 concentrations result in the estimated annual bla dder cancer cases “ultimately avoidable” for the Stage 1 and Stage 2 rules.

Over the long run, the annual cases ultimately a voidable (derived as described above) will be attained. They will not be achieved instantaneously , however. Research shows that a lag period (referred to as “cessation lag”) exists between the point in tim e when reduction in exposure to a carcinogen occurs and the point in time when the full risk reduction benef it of that exposure reduction is realized by affected individuals. Because there is no epidemiological or other empirical data available that specifically address the rate of achieving bladder cancer benef its resulting from DBP reductions, EPA uses data from three epidemiological studies that address the rate of risk reduction following exposure reduction to other carcinogens (cigarette smoke and lung cancer, ciga rette smoke and bladder cancer, and arsenic and bladder cancer) to generate three possible cessati on lag functions for bladder cancer and DBPs.

The cessation lag functions are used to project the number of bladder cancer cases avoided each year after implementation as a result of the Stage 2 DBPR over a 100-year period. A 100-year period was selected as the timeframe after which effectively all of the exposed population is composed of individuals exposed only to post-Stage 2 levels for their entire lif etime. At that time (and from that point forward) the annual bladder cancer cases ultimately avoidable are achieved for the exposed population. The projected number of cases avoided each year is then further adjusted forward in time to reflect when systems are expected to install new treatment to re duce DBPs based on the rule implementation schedule. Although a 100-year cessation lag period is modele d, annual avoided cases of bladder cancer are calculated primarily for the first 25 years after rule promulgation. A 25-year time period was used to coincide with the estimated life span of capital e quipment and a time lag of five to ten years for technology installation after rule promulgation.

The final step in the benefit calculation is to monetize the average annual cases avoided. This is done by applying economic values for avoided illnesses a nd deaths. EPA has estimated that 74 percent of bladder cancer cases are non-fatal (USEPA, 1999a). Th e value of avoiding non-fatal bladder cancer cases is based on people’s WTP for incremental reductions in the risk they face of contracting cancer. The metric of WTP to avoid an increased risk includes the desire to avoid treatment costs, pain and discomfort, productivity losses, and any other adverse consequences related to a non-fatal case of bladder cancer. Because specific estimates of WTP for avoidi ng non-fatal bladder cancer are not available, EPA estimated values from two other non-fatal illnesses: cura ble lymphoma and chronic bronchitis. Both are considered valid estimates of WTP for non-fatal cancer.

For fatal bladder cancer cases, VSL is used to cap ture the value of benefits. The VSL represents an estimate of the monetary value of reducing risks of premature death from cancer. Therefore, it is not an estimate of the value of saving a particular indivi dual’s life. Rather, it represents the sum of the values placed on small individual risk reductions across an exposed population. Other economic factors are taken into consideration when calcu lating benefits over time, such as income growth and social discount
rates.

### Final Economic Analysis for the Stage 2 DBPR ES-21 December 2005 

### There are several areas of uncertainty with resp ect to quantified benefits for bladder cancer. Many are described qualitatively in the analysis, wh ile others are incorporated explicitly as follows:

• There is uncertainty in the percent reduc tion in TTHM and HAA5 concentrations resulting from predicted treatment technology changes (i .e., the compliance forecasts). Uncertainty in SWAT and potential impacts of the IDSE are quantified in the primary analysis.

• Three approaches were used to estimate the baseline number of bladder cancer cases attributable to DBP exposure. For the sake of simplicity, one approach using data from Villanueva et al. (2003) was carried through the full benefits model.

• The estimated PAR values from the Villanueva et al. (2003) meta-analysis include confidence bounds resulting from statistical uncertainty in the odds ratio underlying the PAR calculation. The confidence bounds from Villanueva et al. (2003) capture a significant portion of the confidence intervals of the other two approaches.

• Three independent cessation lag models derived from three different epidemiological studies are used in the model. Also, two functional forms are used for each of these data sets and uncertainty in the parameters of those functions is included in the analysis.

• EPA uses two alternatives for valuing non-fatal bladder cancer.

Colon and Rectal Cancers

Human epidemiology studies on chlorinated surface water have reported associations with colon and rectal cancers. Colon and rectal cancers comb ined are the third most common type of new cancer cases and deaths in both men and women in the U.S., ex cluding skin cancers. Therefore, any benefit from reducing the incidence of colon and rectal cancers could be significant; hence EPA includes a quantitative sensitivity analysis for benefits from avoiding col on and rectal cancers due to Stage 2 DBP reductions.

EPA estimated the reduction in colon and rectal cancer in a similar manner to bladder cancer cases. Background incidence data were available fro m the SEER cancer registry and two quality studies were chosen to estimate a PAR value for DBPs. Us ing the percent reductions in DBPs and the smoking and lung cancer cessation lag model, the number of colon and rectal cancer cases avoided annually were estimated and monetized with the same VSL and WTP estimates as for bladder cancer.

Developmental And Reproductive Health Effects

As noted previously, EPA believes additional bene fits from this rule could come from reductions in developmental and reproductive health effects, although the relationship of these effects to DBP exposure is not known well enough to quantify risks or be nefits in the primary analysis. EPA was able to do an illustrative calculation of the potential benefits of reducing the risk of fetal loss, the non-cancer effect for which the most epidemiological data exis t in relation to DBP exposure. Because approximately one million of the six million pregnancies each year in the United States end in a miscarriage or stillbirth (Ventura et al. 2000), avoiding even a small risk a ttributable to DBP exposure by reducing DBP levels may result in a significant number of avoided fetal losses.

EPA estimated the reduction in potential fetal lo sses in a similar manner to bladder cancer cases. A range of possible PAR values for relating annua l fetal losses to DBP exposure was obtained from available epidemiological studies. Reductions in th e number of peak DBP events due to the Stage 1 DBPR and the Stage 2 DBPR were estimated. Reducti ons in exposure to peak DBPs were assumed to be proportional to reductions in peak DBP events. Like th e analysis of bladder cancer, there is uncertainty in

### Final Economic Analysis for the Stage 2 DBPR ES-22 December 2005 

### fetal loss PAR values, reflected in the range of valu es used in the analysis. There are other important uncertainties in this illustrative calculation, in cluding the assumed proportional relationship between reduction in fetal losses and reduction in exposure to peak levels due to the Stage 2 DBPR.

ES.5.3 Derivation of Costs

To estimate the total national costs of the Stag e 2 DBPR, EPA calculated the incremental costs to be incurred by PWSs and States/Primacy Agencies fro m the Stage 1 DBPR to the Stage 2 DBPR. Cost analyses for PWSs include identifying treatment pr ocess improvements that systems may make, as well as estimating the costs to implement the rule, conduct IDSEs, prepare monitoring plans, perform additional routine monitoring, and conduct operational evaluati ons (referred to as “non-treatment” activities in this document). The cost analysis for States/Primacy Agencies includes estimates of the labor burdens that they would face, such as training employees in the requirements of the Stage 2 DBPR, responding to PWS reports, and record keeping. Cost calculations are performed using the Stage 2 DBPR Cost Model (USEPA 2005i). The methodology for estimating treatment and non-treatment related costs for systems is discussed in the next several paragraphs, follo wed by a discussion of uncertainties. (A complete discussion of the cost analysis is provided in Chapter 7.)

All treatment costs are based on mean unit cost estimates for advanced technologies and chloramines. Technology unit cost estimates are in th e form of “dollars per plant” for initial capital and ongoing operation and maintenance (O&M) activities. Derivations of unit costs for a wide range of system sizes are described in detail in the document, Technologies and Costs Document for the Final Long Term 2 Enhanced Surface Water Treatment Rule and Final Stage 2 Disinfectants and Disinfection Byproducts Rule (USEPA 2005n). EPA combines the compliance forecast results (i.e., predicted number of plants changing technology and which technologies t hose plants select, derived as described in Section ES.5.1) with unit costs to produce total national treatment cost estimates.

Non-treatment costs for implementation, the IDSE, monitoring plans, additional routine monitoring, and operational evaluations are based on estimates of labor hours for performing these activities and on additional laboratory costs. For all non-treatment cost calculations, EPA used the Stage 2 DBPR system baseline shown in Exhibit ES.4a pl us the estimate of additional ground water systems expected to install disinfection for the GWR as s hown in ES.4b (for routine monitoring and monitoring plan costs only).

EPA recognizes that systems vary with respect to many of the attributes that are used as input parameters to the Stage 2 DBPR cost model (e.g ., plants per system, population served, flow per population, labor rates). In most cases, there is insufficient information to characterize fully the variability on a national scale. EPA believes that m ean values for the various input parameters are adequate to generate EPA’s best es timate of national costs for the rule.

EPA has quantified several large areas of uncerta inty in costs by quantifying uncertainty in compliance forecasts, as noted in section ES.5.1. Ther e is uncertainty in the national average unit capital and O&M costs for the various technologies expected to be implemented in response to the Stage 2 DBPR. This uncertainty has been incorporated into the cost model (using Monte Carlo simulation procedures). The national costs of the Stage 2 DBPR summarized in Exhibit ES.5 show both the expected values and the 90 percent confidence bounds on the national cost estimates obtained from the cost model.

### Final Economic Analysis for the Stage 2 DBPR ES-23 December 2005 Total Number of Households Served Mean Annual Household Cost Increase Median Annual Household Cost Increase 90th Percentile Annual Household Cost Increase 95th Percentile Annual Household Cost Increase Percentage of Annual Household Cost Increase < $12 Percentage of Annual Household Cost Increase < $120 All Systems 101,553,868 0.62 $ 0.03 $ 0.36 $ 0.98 $ 99% 100% All Small Systems 14,261,241 2.20 $ 0.10 $ 0.79 $ 2.57 $ 97% 100% SW < 10,000 3,251,893 4.58 $ 0.79 $ 2.69 $ 7.24 $ 95% 99% SW > 10,000 62,137,350 0.46 $ 0.02 $ 0.35 $ 1.81 $ 99% 100% GW < 10,000 11,009,348 1.49 $ 0.02 $ 0.39 $ 0.99 $ 98% 100% GW > 10,000 25,155,277 0.13 $ 0.00 $ 0.03 $ 0.08 $ 100% 100%

Total Number of Households Served Mean Annual Household Cost Increase Median Annual Household Cost Increase 90th Percentile Annual Household Cost Increase 95th Percentile Annual Household Cost Increase Percentage of Household Cost Increase < $12 Percentage of Household Cost Increase < $120 All Systems 10,161,304 5.53 $ 0.80 $ 10.04 $ 22.40 $ 92% 99% All Small Systems 591,623 46.48 $ 18.47 $ 168.85 $ 197.62 $ 38% 89% SW < 10,000 285,911 43.05 $ 13.79 $ 173.53 $ 177.93 $ 47% 85% SW > 10,000 9,060,119 2.83 $ 0.80 $ 6.98 $ 11.31 $ 96% 100% GW < 10,000 305,712 49.69 $ 16.65 $ 109.86 $ 197.62 $ 31% 92% GW > 10,000 509,562 5.97 $ 1.37 $ 26.82 $ 33.84 $ 79% 100%

Source: Exhibit 7.15 Notes: Detail may not add to total due to independent rounding. Number of households served by systems adding treatment will b e higher than households served by plants adding treatment because an ent ire system will incur costs even if only some of the plants for that system add treatment (this would result in Households Served by Plants Adding Treatment Households Served by All Plants 

## ES.6 Estimated Impacts on Household Costs

EPA assumes that systems will, to the extent po ssible, pass cost increases on to their customers through increases in water rates. Exhibit ES.8 presen ts estimated annual household cost increases for the Stage 2 DBPR Preferred Regulatory Alternative. The top half of the exhibit shows summary statistics for all households served by systems subject to the rule, including those that will not make treatment technology changes but will incur other minimal cost s, such as for rule implementation or additional routine monitoring. The bottom half shows statisti cs just for those households served by systems actually making treatment technology changes to comply with the rule (see Exhibit ES.7a for estimates of the percent of plants making treatment technology changes). Because treatment technology changes represent the majority of rule costs, this provides insi ght into how the rule will affect that segment of the population most impacted by the rule.



## Exhibit ES.8 Summary of Annual Household Cost Increases

As shown in Exhibit ES.8, the mean, median, and 90 th 

### percentile annual household cost increases for all systems are $0.62, $0.03, and $0.36 per year, respectively. The mean, median, and 90 th 

### percentile household cost increases for those served by plants making treatment technology changes are $5.53, $0.80, and $10.04, respectively. Households in small systems served by plants making treatment technology changes will experience the highest household cost increases because they must spread technology costs over a smaller customer base.9 

### Section 1415(e)(1) of SDWA allows States to grant va riances to small water systems in lieu of complying with an MCL if EPA determines that no nationally affordable compliance technologies exist for that system size/water quality combination. These variances also may be granted only where EPA has identified a variance technology under Section 1412(b)(15) for the contaminant, sy stem size, and source water quality in question. EPA is conducting a rigorous review of the methodology for the small system affordability analysis.

10 

### Only 15 small systems, all of which serve fewer than 500 people, are expected to install treatment technologies (i.e., integrated membranes with chlorami nes and granulated activated carbon (GAC) with advanced disinfectants) that are above the affordability threshold. EPA believes, however, that the number of plants in small systems predicted to add advanced technologies (incl uding GAC) is overstated for two reasons: 1) Stage 2 DBPR requirements for small systems are similar to Stage 1 DBPR requirements and may not trigger compliance violations, as explained in the minimal impacts sensitivity analysis in Chapter 7, and 2) distribution system modifications are not considered in the compliance forecast. A mo re detailed discussion is provided in Chapter 8.
Final Economic Analysis for the Stage 2 DBPR ES-24 December 2005 

### EPA analyzed the affordability of the Stage 2 DBPR to determine if variance technologies are needed for small systems. 9 

### The analysis was performed by comparing total expected household cost (i.e., the current annual household cost of water plus the household cost increase) to an "affordability threshold" equal to 2.5 percent of median household in come. The results of EPA's analysis show that, for each of the three categories of small systems, ther e are affordable compliance technologies available to small systems and that variance technologies are not required. 10



## ES.7 Comparison of Costs and Benefits for Four Regulatory Alternatives

Section ES.2 described the four regulatory altern atives considered in this economic analysis. Benefits and costs for these four alternatives are summarized in Exhibit ES.9 and ES.10, respectively. EPA recognizes that the quantified benefits based on reduced cases of bladder cancer could be zero for all alternatives since causality has not yet been estab lished between exposure to chlorinated water and bladder cancer.

The regulatory alternative EPA chose, which sp ecifically targets the highest DBP levels and potential risks in the distribution systems, is also the least-cost alternative by a substantial margin (Exhibit ES.10). Estimated costs for Alternative 1 are a pproximately three times those for the Preferred Alternative because of the more stringent bromate st andard. Quantified benefits based on bladder cancer cases avoided are nearly the same for Alternative 1 a nd the Preferred Alternative (Exhibit ES.9) because the benefits of avoiding potential cancer cases by lowe ring the bromate standard in Alternative 1 are unquantified. EPA did not favor this alternative becau se of a concern that lowering the bromate level to 5 μg/L could have adverse effects on microbial protection (see Chapter 4 for a full discussion). The range of quantified benefits increases significantly with A lternatives 2 and 3. However, these alternatives do not include the risk targeting strategy of the Preferre d Alternative. EPA has estimated that a large portion of the surface water systems covered by the rule would have to switch from their current treatment practice to more expensive advanced technologies to co mply with these alternativ es. The associated costs presented in ES.10 show mean estimated values be tween $421 and $634 million per year at a 3 percent discount rate. The M-DBP Advisory Committee did not favor Alternatives 2 and 3 because it believed that the health effects data are not certain enough to warrant such a drastic shift in the nation’s drinking water treatment practices.

### Final Economic Analysis for the Stage 2 DBPR ES-25 December 2005 Preferred A1 A2 A3
279 250 939 1296 (103 - 541) (127 - 397) (483 - 1,466) (675 - 1,988)

$1,531 $1,377 $5,167 $7,130
($233 - 3,536) ($209 - 3,180) ($786 - 11,936) ($1,085 - 16,468 ) $1,246 $1,126 $4,227 $5,832 ($190 - 2,878) ($172 - 2,600) ($644 - 9,758) ($888 - 13,464) $763 $686 $2,575 $3,552 ($165 - 1,692) ($149 - 1,522) ($558 - 5,712) ($769 - 7,880) $621 $561 $2,105 $2,904 ($135 - 1,376) ($122 - 1,243) ($457 - 4,665) ($630 - 6,436)

Sources: Exhibit 6.28 The 90 percent confidence bounds for cases avoided reflect uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cessation lag estimates. The 90 percent co nfidence bounds for benefits reflect uncertainty in monetization inputs relative to mean cases. EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. 

### Average Annual Number of Cases Avoided

Notes: Average annual avoided cases is based upon the 25-year period of analysis, so it is lower than the maximum cases to be avoided following the cessation lag period. Values are discoun ted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Assumes 26 percent of cases are fatal , 74 percent are non-fatal (USEPA 1999a). The cessation lag is explained in detail in Appendix E. 

### Annualized Mean Benefits of Cases Avoided (90% Confidence Bounds) 3%, Lymphoma

7%, Lymphoma

3%, Bronchitis

7%, Bronchitis Regulatory Alternative Discount Rate,WTP for NonFatal Cases

Lower (5th %tile) Upper (95th %tile) Lower (5th %tile) Upper (95th %tile)

Preferred $78.8 $56.2 $102.2 $76.8 $54.6 $99.4

A1 $254.1 $166.4 $346.2 $241.8 $158.1 $330.0

A2 $421.7 $367.8 $477.8 $406.4 $353.9 $461.3

A3 $634.2 $536.4 $736.2 $613.1 $517.7 $712.8

Sources: Appendix J.

For Alternative 1, see Exhibit J.3i for 3% and J.3m for 7%. For Alternative 2, see Exhibit J.4i for 3% and J.4m for 7%. For Alternative 3, see Exhibit J.5i for 3% and J.5m for 7%. For the Preferred Alternative, see Exhibit J.2as for 3% and J.2aw for 7%. Regulatory Alternative Discounted at 7%, 25 Years Discounted at 3%, 25 Years

90 Percent Confidence Bound Annualized Total Regulation Costs

Mean Value Mean Value 90 Percent Confidence Bound

Note: The 90 percent confidence bounds reflect uncertainty in compliance forecasts and unit treatment costs. 

## Exhibit ES.9 Comparison of Benefits fo r All Regulatory Alternatives ($Millions)

Exhibit ES.10 Comparison of Costs for All Regulatory Alternatives ($Millions)11 

### Alternative 1 appears to have fewer benefits than th e Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a re quirement that is included only in Alternative 1.
Final Economic Analysis for the Stage 2 DBPR ES-26 December 2005 

### 3%, 25 Years 7%, 25 Years Preferred 1,452 $ 1,170 $ 

## A1 1 

### 1,122 $ 885 $ A2 4,746 $ 3,821 $ A3 6,495 $ 5,219 $ Preferred $ 684 $ 544


## A1 1 

### 432 $ 319 $ A2 2,153 $ 1,698 $ A3 2,918 $ 2,291 $

Source: Exhibit 9.12 

## Annualized Value

Rule Alternative WTP for Non-Fatal

Bladder Cancer Cases

Lymphoma

Bronchitis

Notes: All values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model.Assumes 26 percent of cases are fatal, 74 percent are nonfatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer.

Footnote 1: Alternative 1 appears to have fewer benefits than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a requirement that is included only in Alternative 1. 

### A comparison of alternatives can also be made based upon net benefits: the difference between the annualized costs and the annualized monetized be nefits. Exhibit ES.11 shows that the Preferred Alternative has higher net benefits than Alternative 1 11 

### , but lower net benefits than Alternatives 2 and 3 using either estimate of WTP for non-fatal bladde r cancer. These net benefits do not include the unquantified benefits.



## Exhibit ES.11 Comparison of Annualized Mean Net Benefits for All Regulatory Alternatives ($Millions)12 

### The WTP values, shown here in 2003$, could be used to develop an annualized WTP value for the most accurate comparison to the annualized cost per case avoided va lues presented in Exhibit ES.12. First, they would be increased over the period of analysis to reflect the elasticity of WTP in response to increases in real income. Second, the WTP value would be weighted differentially over the pe riod of analysis to reflect in the annualized value the difference in the number of cases avoided, which varies on an annual basis. Third, because the cases avoided in the CEA ratio include both fatal and non-fatal cases, the VSL woul d be incorporated into a weighted average (to reflect that 26% of cases are fatal) with the WTP value after it, too, was increased over time to reflect the above 2 considerations. However, each of these factors would in crease the threshold used in this analysis; therefore annualized WTP values are not calculated because the Preferre d Alternative, and most of the other alternatives, have costs per case avoided that are already below the lowest of the thresholds ($0.80 million in 2003$).
Final Economic Analysis for the Stage 2 DBPR ES-27 December 2005 

### A further consideration is the relative cost e ffectiveness of each regulatory alternative of the Stage 2 DBPR. Cost effectiveness is generally used for determining which alternatives meet a certain criterion-a threshold for either maximum costs or minimum cases avoided. The rule is cost effective if it achieves the target level of cases avoided below an accep table cost; or if it yields an acceptable level of benefits below the maximum allowable cost. The most cost effective regulatory alternative would achieve the greatest benefits for a given expenditure, or would impose the least cost for achieving a given level of benefit. Cost effectiveness analysis (CEA ) usually produces a ratio consisting of a cost measure and an effectiveness measure, i.e., dollars per bla dder cancer case avoided. The problem with CEA is that, as with analysis of benefit cost ratios, it does not take into account the scale of benefits or costs. Therefore, when considering alternatives such as those in the Stage 2 DBPR, CEA does not provide enough information for choosing a regulatory alternativ e, and is only part of a complete analysis as presented in this EA. In general, EPA recommends that decisions as to whether a specific control strategy is justified be based on a complete comparison of benefits and costs.

However, the CEA provided in Exhi bit ES.12 does provide useful information. This exhibit shows the cost per bladder cancer case avoided for each alternative in order of increasing regulatory cost, using discount rates of 3 and 7 percent. As a thres hold for cost effectiveness, consider the WTP measures for avoidance of non-fatal lymphoma (mean of $4.49 million per case avoided in 2003$) or chronic bronchitis (mean of $0.80 million per case avoided in 2003$), which serve in this EA as surrogates for a measure of the amount society is willing to pay to avoid a non-fatal case of bladder cancer. 12 

### If an alternative is cost effective, it will have an average cost per case avoided that is equal to or less than the value of the WTP estimate. The Preferred Alternative meets this criterion: at discount rates of 3 and 7 percent, its unit costs of $0.33 and $0.41million, respectively, are less than the WTP values of $4.49 and $0.80 million, respectively. Furthermore, the Preferred Alternative is more cost effective than the other alternatives: it has the lowest cost per case avoided. Alternatives 2 and 3 are also below the minimum threshold of $0.80 million per case avoided. As described previously in this section, Alternative 1 has a relatively high cost per case avoided because it includ es the cost for more stringent bromate reductions but does not include the benefits of the reduction.

### Final Economic Analysis for the Stage 2 DBPR ES-28 December 2005 Preferred 0.33 $ 0.41 $ Alternative 1 1.18 $ 1.42 $ Alternative 2 0.52 $ 0.63 $ Alternative 3 0.57 $ 0.69 $

Source: Exhibit 9.14 Notes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for ba seline risk, and smoking/lung cancer cessation lag model. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (U SEPA 1999a). EPA recognizes that benefits may be as lo w as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer.

1) The cost effectiveness ratios are a conservative estimate in that the regulatory costs in the numerator are not adjusted by subtracting the medical costs associated with cases avoided to produce a net cost numerator. Adjustment of the numerator in this CEA would not alter the relative cost effe ctiveness of the alternat ives or change their rankings, because it involves the subtra ction of a constant. In the case where thresholds of maximum public expenditure or minimum cases to be avoided are prescribed, defining the numerator more precisely by making such adjustments would be appropriate. Rule Alternative Cost Per Case Avoided

3% 7% 

## Exhibit ES.12 Cost Per Discounted C ase Avoided, by Discount Rate and Regulatory Alternative ($Millions)

An alternative type of CEA considers the co st per life year saved from avoided premature mortality. In Appendix N to the Stage 2 DB PR Economic Analysis, a Quality Adjusted Life Years (QALYs) analysis is developed. Specifically, E xhibit ES.13 presents an approach to QALYs termed Morbidity Inclusive Life Years (MILYs), which in corporates life years saved from avoided morbidity (a time equivalent for lost quality of life during illness) with life years saved from avoided mortality. Exhibit ES.13 shows that the Preferred Alternative, whic h is the least stringent but has a targeted strategy (IDSE) of reducing DBP exposure, has the lowest cost per MILY, i.e., it is more cost effective than the other alternatives.

The cost per MILY ratios can also be compared to prima facie cost per MILY thresholds, with the understanding that the thresholds are arbitrary values, often derived by reference to the cost per QALY (or MILY) for interventions that public health specia lists agree are justified. The Harvard Cost Utility Analysis database presents a median cost-utility ratio of $31,000 per QALY (or MILY) (2002$) for respiratory and cardiovascular interventions, while Tengs et al. (1995) report a median cost per life-year saved for life-saving interventions of $48,000 (1993$). The health economics literature often uses either $50,000 or $100,000 per QALY (or MILY) as a threshold with ratios less than these values considered prima facie cost effective.

The Preferred Alternative is cost effective comp ared to the highest of these thresholds ($100,000) at a discount rate of 3 percent; it is not cost eff ective when considered at a 7 percent discount rate ($118,000 vs. $100,000 per MILY), although it is more cost effective than the other alternatives.

### Final Economic Analysis for the Stage 2 DBPR ES-29 December 2005 Net Cost

(ECOI) Net Cost (TCOI) MILYs Cost per MILY (ECOI) Cost per MILY (TCOI) (Years)
ABC D = A * 10 6 /C E = B * 10 6 /C

Preferred 43 $ 45 $ 725 $ 59,946 $ 62,001 $ Alternative 1 1 222 $ 224 $ 652 $ 340,584 $ 342,641 $ Alternative 2 302 $ 307 $ 2,449 $ 123,259 $ 125,315 $ Alternative 3 469 $ 476 $ 3,379 $ 138,754 $ 140,810 $

Preferred 60 $ 61 $ 510 $ 118,394 $ 119,540 $ Alternative 1 1 227 $ 227 $ 462 $ 491,295 $ 492,449 $ Alternative 2 350 $ 352 $ 1,733 $ 202,150 $ 203,303 $ Alternative 3 536 $ 538 $ 2,390 $ 224,024 $ 225,178 $

Source: Exhibit N.17 Footnote 1: Alternative 1 appears to have fewer benefits (MILYs) than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a requirement that is included only in Alternative 1. (Million $) ($) Rule Alternative

Abbreviations: MILYs = Morbidity Inclusive Life Years; ECOI = Enhanced Cost of Illness; TCOI = Traditional Cost of Illness

Notes: All values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and s moking/lung cancer cessation lag model. Some numbers may not add correctly due to rounding. EPA recognizes that benefits may be as low as zero since causality has not yet been establ ished between exposure to chlorinated water and bladder cancer. 7 Percent 3 Percent 

## Exhibit ES.13 Cost Effectiveness Anal ysis Using MILYs Saved from Cases of Bladder Cancer Avoided, by Rule Alte rnative, 3 and 7 Percent Discount Rates

ES.8 Conclusions

EPA is finalizing the Stage 2 DBPR to reduce th e potential risks that byproducts of chlorination pose to consumers of drinking water. Disinfection itself is important for protecting the public against waterborne microbes, and is practiced by over 48,000 PWSs in the United States. The chemicals commonly used, however, can react with substances in the source water to create potentially harmful DBPs. These DBPs include TTHM and HAA5, which ar e potentially associated with increased incidence of bladder and possibly other cancers. DBPs may al so be associated with potential adverse reproductive and developmental effects such as early-term miscarriage, stillbirth, low birth weight, and some birth defects. There is uncertainty in the scientific litera ture regarding the extent to which DBPs contribute to the incidence of these adverse effects in th e exposed population and EPA notes that existing epidemiological evidence has not conclusively established causality between DBP exposure and any health risk endpoints. Nevertheless, EPA believes th at the weight of evidence warrants concern for these potential hazards and justifies additional regulatory action beyond the Stage 1 DBPR.

### Final Economic Analysis for the Stage 2 DBPR 1-1 December 2005 

# 1. Introduction

This document presents an analysis of the costs and benefits of the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR). The analysis is performed in compliance with Executive Order 12866, Regulatory Planning and Review (USEPA 1993), which requires that the Environmental Protection Agency (EPA) estimate the economic imp act of rules costing over $100 million annually in an Economic Analysis (EA) and to submit the anal ysis in conjunction with publishing the rule.

This chapter provides a summary of the Stage 2 DBPR in Section 1.1. Section 1.2 outlines the organization of this EA and S ection 1.3 provides information re garding supporting calculations and citations in this EA.



## 1.1 Summary of the Stage 2 DBPR

The requirements of the Stage 2 DBPR apply to all community water systems (CWSs) and nontransient noncommunity water systems (NTNCWSs) th at add a disinfectant other than ultraviolet light (UV) or that deliver water that has been treated with a disinfectant other than UV. New since the Stage 1 DBPR, EPA has included requirements specifically for consecutive systems to ensure uniform regulation of consecutive systems in all States. The Stage 2 DBPR defines consecutive systems as public water systems that receive finished water from anothe r public water system (a wholesale system).

The Stage 2 DBPR builds on the 1979 Total Trihalomethanes Rule and the 1998 Stage 1 DBPR by requiring reduced levels of DBPs in distributi on systems. Each rule activity for the Preferred Regulatory Alternative and the associated rule sche dule are described below. Note that consecutive systems of any size must comply with the require ments of the Stage 2 DBPR at the same time as the largest system in the combined distribution system.

The numerical maximum contaminant levels (MCL s) for the Stage 2 DBPR are the same as for the Stage 1 DBPR MCLs: 80 micrograms per liter ( 

## : g/L) for total trihalomethanes (TTHM), and 60 

## : g/L for haloacetic acids (five) (HAA5). The Stage 2 DBPR is designed to reduce high TTHM and HAA5 levels in the distribution system by changing comp liance monitoring and calculation requirements. The compliance determination for the Stage 2 DBPR is based on a locational running annual average (LRAA) instead of the system-wide running annual average (RAA) used under the Stage 1 DBPR. LRAAs are essentially RAAs calculated separately for each sample location in the distribution system. With the Stage 2 LRAA requirement, the TTHM and HAA5 MCLs must be met at each monitoring location, while the Stage 1 RAA requires a system to average results over all monitoring locations. Exhibit 1.1 provides a comparison of Stage 1 and Stage 2 DBPR compliance calculations.

For many systems, compliance monitoring will be preceded by an initial distribution system evaluation (IDSE) to identify sample locations fo r Stage 2 compliance monitoring that represent distribution system sites with high TTHM and HAA5 levels. Systems may perform an IDSE either by completing a system specific study (SSS) or conducting standard monitoring , unless a system meets the criteria for a 40/30 certification or a very small systems waiver. To meet the criteria for a 40/30 certification, systems must have low Stage 1 monitoring results (every individual compliance sample is less than or equal to 40 

## : g/L and 30 

## : g/L for TTHM and HAA5, respectively) and no TTHM or HAA5 monitoring violations during a 2-year eligibility period. Systems can qualify for a very small systems waiver if they serve fewer than 500 people and have TTHM and HAA5 data. In addition, NTNCWSs serving fewer than 10,000 people are not required to conduct an IDSE.

### Final Economic Analysis for the Stage 2 DBPR 1-2 December 2005 

### The Stage 2 DBPR changes the way in wh ich compliance monitoring requirements are determined. Stage 1 compliance monitoring for TTHM and HAA5 is based on a system’s population served, source water type, system type, and number of pl ants treating water in that system. This “plantbased” approach is grounded in the assumption that larger systems have more treatment plants and thus greater system complexity. While this is ge nerally true, the plant-based approach created disproportionately burdensome monitoring requirements for some systems where the number of plants did not represent system size, such as larger systems with very large plants or smaller systems with many disinfecting wells. Moreover, a plant-based appr oach can complicate monitoring of purchased water systems, particularly complex ones with multiple connections. For these reasons, EPA has developed a “population-based” monitoring approach for the Stag e 2 DBPR, whereby the monitoring requirements are based on population served and source water type (not plants per system). Exhibit 1.2 shows the new, population-based standard monitoring requirements for the IDSE. Exhibit 1.3 presents the new, population-based Stage 2 DBPR compliance monitoring re quirements. EPA believes that the new Stage 2 population-based approach makes monitoring requirement s more protective of public health, simpler, and more equitable for systems of the same size and type.

Systems must develop a Stage 2 DBPR monitoring plan that includes monitoring locations, monitoring dates, and compliance calculation procedures. States have the option to implement a procedure for addressing modifications to wholesale system and consecutive system monitoring on a case-by-case basis.

The Stage 2 DBPR is being promulgated simulta neously with the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) to address comple x risk trade-offs between DBPs and microbial pathogens. The schedule for the Stage 2 DBPR is summa rized in Exhibit 1.4. Note that the compliance deadlines are based on population served. For c onsecutive and wholesale systems, the compliance schedule is based on the population served by the larg est system in a combined distribution system.

Because Stage 2 DBPR MCL compliance is based on individual DBP measurements at a location averaged over a four-quarter period, a system c ould find higher TTHM or HAA5 levels than the MCL values, while at the same time maintaining compliance with the Stage 2 DBPR. This is because the high concentration could be averaged with lower concentrati ons at a given location. For this reason, the Stage 2 DBPR includes a provision for “operational evaluations” as follows:

• A system has exceeded an operational evaluati on level at any monitoring location when the sum of the two previous quarters’ compliance monitoring results plus twice the current quarters result, divided by 4, exceeds 80 

## : g/L for TTHM or 60 

## : g/L for HAA5.

If an operational evaluation level is exceeded, the sy stem must conduct an “operational evaluation” and submit a written report of the evaluation to the State/ Primacy Agency no later than 90 days after being notified of the analytical resu lts that caused the excursion.

### Final Economic Analysis for the Stage 2 DBPR 1-3 December 2005 

## Stage 2B DBPR (Note that some sampling locations may change as a result of the IDSE.) Stage 1 DBPR Distribution System

Sampling Location



### First Quarter

Average of All Samples Second Quarter Third Quarter Fourth Quarter

Average of All Samples Average of All Samples Average of All Samples

Running Annual Average (RAA) of Quarterly Averages MUST BE AT OR BELOW COMPLIANCE LEVELS

First Quarter Second Quarter Third Quarter Fourth Quarter

First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS

First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS 

## Stage 2B DBPR (Note that some sampling locations may change as a result of the IDSE.) Stage 1 DBPR Distribution System

Sampling Location



### First Quarter

Average of All Samples Second Quarter Third Quarter Fourth Quarter

Average of All Samples Average of All Samples Average of All Samples

Running Annual Average (RAA) of Quarterly Averages MUST BE AT OR BELOW COMPLIANCE LEVELS

First Quarter Second Quarter Third Quarter Fourth Quarter

First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS

First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS First Quarter Second Quarter Third Quarter Fourth Quarter Locational Running Annual Average MUST BE AT OR BELOW COMPLIANCE LEVELS 

## Exhibit 1.1 Comparison of Stage 1 a nd Stage 2 DBPR Compliance Calculations

### Final Economic Analysis for the Stage 2 DBPR 1-4 December 2005 

## Exhibit 1.2 IDSE Standard Monitoring Requirements

System Size (Population Served) Number of Distribution System Sites 1 

### (by location type) per System Total Number of Sites per System Number of Monitoring Periods and Frequency for the 1-year IDSE period Near Entry Point 2 

### Average Residence Time High TTHM High HAA5 Systems Using Surface Water in Whole or in Part 3



### < 500 consecutive systems 1 - 1- 2 1 (during peak historical month) 4



### < 500 non-consecutive systems - - 11 2 1 (during peak historical month) 4


### 500 - 3,300 consecutive systems 1 - 1- 2 4 (every 90 days)

500 - 3,300 nonconsecutive systems - - 11 2 4 (every 90 days)

3,301 - 9,999 - 1 2 1 4 4 (every 90 days)
10,000 - 49,999 1 2 3 2 8 6 (every 60 days)
50,000 - 249,999 3 4 5 4 16 6 (every 60 days)
250,000 - 999,999 4 6 8 6 24 6 (every 60 days)
1 million - 4,999,999 6 8 10 8 32 6 (every 60 days)

> 5,000,000 8 10 12 10 40 6 (every 60 days) Systems Using Only Ground Water

< 500 consecutive systems 1 - 1- 2 1 (during peak historical month) 4



### < 500 non-consecutive systems - - 11 2 1 (during peak historical month) 4


### 500 - 9,999 - - 1 1 2 4 (every 90 days)
10,000 - 99,999 1 1 2 2 6 4 (every 90 days)
100,000 - 499,999 1 1 3 3 8 4 (every 90 days)

> 500,000 2 2 4 4 12 4 (every 90 days)

1 

### Samples must be taken at locations other than existing Stage 1 monitoring locations. Dual sample sets (i.e., a

TTHM and an HAA5 sample) must be taken at each monito ring location during each monitoring period. Sampling location must be distributed thr oughout the distribution system.

2 

### If the number of entry points to the di stribution system is fewer than the s pecified number of sampling locations,

additional samples must be taken equally at high TTHM and HAA5 locations. If there is an odd extra location number, a sample at a high TTHM location must be taken. If the number of entry points to the distribution system is more than the specified number of sample locations, sa mples must be taken at entry points to the distribution system having the highest water flows.

3 

### For the purposes of this EA, “surface water” system s are equivalent to “subpart H” systems and include systems

that use ground water under the direct influence of surface water (GWUDI).

4 

### The peak historical month is the mont h with the highest TTHM or HAA5 levels or the warmest water temperature.

### Final Economic Analysis for the Stage 2 DBPR 1-5 December 2005 

## Exhibit 1.3 Stage 2 Populati on-Based Monitoring Requirements

System Size (Population Served ) Distribution System Sample Locations 1



### Total Sample Locations per System 5 

### Monitoring Frequency 3 

### Highest TTHM Locations Highest HAA5 Locations Existing Stage 1 Compliance

Locations 2


### Systems Using Surface Water in Whole or in Part 4



### < 500 1 1 2 per year
500-3,300 1 1 2 per quarter
3,301-9,999 1 1 2 per quarter
10,000-49,999 2 1 1 4 per quarter

50,000-249,999 3 3 2 8 per quarter
250,000-999,999 5 4 3 12 per quarter
1 Mil-4,999,999 6 6 4 16 per quarter

> 5,000,000 8 7 5 20 per quarter Systems Using Only Ground Water
< 500 1 1 2 per year 500 - 9,999 1 1 2 per year 10,000 - 99,999 2 1 1 4 per quarter 100,000 - 499,999 3 2 1 6 per quarter

> 500,000 3 3 2 8 per quarter

1 

### Locations must be based on the system ’s recommendations for Stage 2 DBPR compliance monitoring locations in

its report to the State/Primacy Agency, unless the State/Primac y Agency requires different or additional locations. Locations should be distributed throughout the distribution syst em to the extent possible.

2 

### Alternate between highest HAA5 LRAA and highest TTH M LRAA locations among the existing Stage 1 average

resident time compliance locations. If the number of existing Stage 1 compliance locations is fewer than the specified number for Stage 2, alte rnate between highest HAA5 LRAA locations and highest TTHM LRAA locations from the IDSE.

3 

### All systems must monitor during the month of highest DBP concentrations. Systems on quarterly monitoring must

take dual sample sets approximately every 90 days.

4 

### For the purposes of this EA, “surface water” system s are equivalent to “subpart H” systems and include systems

that use GWUDI.

5 

### Systems on quarterly monitoring must take dual sample sets every 90 days at each monitoring location, except for

subpart H systems serving 500-3,300 people. Systems on annual monitoring and subpart H systems serving 500- 3,300 people are required to take individual TTHM and HAA5 samples (instead of a dual sample set) at the locations with the highest TTHM and HAA5 concentrations, re spectively. Only one location with a dual sample set per monitoring period is needed if the highest TTHM and HAA5 concentrations occur at a same location and month (if monitored annually).Final Economic Analysis for the Stage 2 DBPR 1-6 December 2005Schedule 1Systems serving> 100,0001IDSE Plan Due IDSE Report Due Begin ComplianceOctober 1, 2006 January 1, 2009 April 1, 2012Schedule 2Systems serving50,000 to 99,9991IDSE Plan Due IDSE Report Due Begin ComplianceApril 1, 2007 July 1, 2009 October 1, 2012Schedule 3Systems serving10,000 to 49,9991IDSE Plan Due IDSE Report Due Begin ComplianceOctober 1, 2007 January 1, 2010 October 1, 2013Schedule 4Systems serving< 10,0001IDSE Plan Due IDSE Report DueApril 1, 2008 July 1, 2010 Begin ComplianceOctober 1, 20132010 2011 20122006 2007 2008 2009Cryptomon.3Treatment InstallationPossible Extension22014 20152013Treatment InstallationPossible Extension2Treatment InstallationPossible Extension22006 2007 2008 2009 2014 20152010 2011 2012 2013Treatment InstallationPossible Extension2LT2CryptomonitoringIDSE mon.LT2CryptomonitoringLT2CryptomonitoringIDSE mon.IDSE mon.IDSE mon.E. Colimon.Exhibit 1.4 Stage 2 DBPR Implementation Schedule1Includes all systems that are part of a combined distribution system that have a largest system with this population.2A State may grant up to an additional 2 years for systems to complyif the State determines thatadditional time is necessary for capital improvements.3Subpart H systems that must conductCryptosporidiummonitoring have an additional12 months to comply with the Stage 2 DBPR MCLs.

### Final Economic Analysis for the Stage 2 DBPR 1-7 December 2005 

## 1.2 Document Organization

The rest of this EA is organized into the following chapters:

• Chapter 2 identifies public health concerns addressed by the rule and provides a 20-year regulatory history that includes a description of relevant National Primary Drinking Water Regulations (NPDWRs). It also explains the statutory authority for promulgating the Stage 2 DBPR and the economic rationale fo r choosing a regulatory approach.

• Chapter 3 characterizes conditions that exist (including system inventory, treatment, and water quality data) before systems make changes to meet the Stage 2 DBPR requirements.

• Chapter 4 reviews alternative regulatory approaches that EPA considered during the development of the rule and presents the ra tionale for selecting the Preferred Regulatory Alternative.

• Chapter 5 summarizes the methodology used to develop the compliance forecasts and predictions of reductions in DBPs. It also c ontains compliance forecast results for the Stage 1 DBPR and the Stage 2 DBPR Preferred Regulatory Alternative.

• Chapter 6 reviews available epidemiological a nd toxicological data related to DBPs. The public health and economic benef its of this rule, as well as several sensitivity analyses, are provided in this chapter.

• Chapter 7 presents an estimate of the costs of implementing the rule to industry, households, and States/Primacy Agencies. It also compares the costs of the four regulatory alternatives.

• Chapter 8 discusses analyses performed to eval uate the effects of the rule on different segments of the population and considers va rious executive orders and requirements, including the Regulatory Flexibility Act (R FA) and Unfunded Mandates Reform Act (UMRA).

• Chapter 9 compares the rule’s benefits and costs to evaluate whether projected benefits exceed costs. The results for the Prefe rred Regulatory Alternative are discussed and compared to the regulatory alternatives considered.

### Final Economic Analysis for the Stage 2 DBPR 1-8 December 2005 

## 1.3 Calculations and Citations

This EA presents results from detailed and comple x analyses. To help the reader track the various calculations and analyses, the following are provided:

• A detailed reference section (see Chapter 10).

• A row on most tabular exhibits throughout the document that gives the formulas used to compute the contents of each column.

• Sources for elements of exhibits throughout the document that are not calculated in the exhibits themselves.

• Supporting electronic files (Stage 2 DBPR cost model; Stage 2 DBPR benefits model; Stage 2 DBPR Surface Water Analytical Tool (SWAT) supporting files, and the ICR Matrix Method supporting files).1 

### The key outcomes of that regulatory negotiation effo rt were recommendations to proceed with rules addressing DBPs and microbial pathogens in two stages and to collect relevant information from public water supplies for use in the development of these rules and the an alysis of their impacts. This two-stage approach was subsequently incorporated into the 1996 Safe Drinking Wa ter Act (SDWA) Amendments. The first stage of the M-DBP rulemaking process culminated with the joint prom ulgation of the Stage 1 DBPR and the Interim Enhanced Surface Water Treatment Rule (IESWTR) by EPA in December 1998.

Final Economic Analysis for the Stage 2 DBPR 2-1 December 2005 

# 2. Need for the Rule

2.1 Introduction

This chapter first identifies the issue to be addressed by the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR) (section 2.1.1) and then summarizes in section 2.2 the public health concerns addressed by the rule. Section 2.3 provides the regulatory history leading up to the Stage 2 DBPR, and section 2.4 addresses the economic rati onale for choosing this regulatory approach.

2.1.1 Description of the Issue

Over 48,000 public water systems (PWSs), serving more than 260 million people in the United States, chemically disinfect their water to kill or inactivate microbial contaminants (USEPA 2001c). This is an essential public health measure. Chemical disinf ection, however, may pose health risks of its own. Disinfection byproducts (DBPs) result from reactions between chemical disinfectants and naturally occurring compounds in source waters. Research has shown that chlorinated waters and DBPs may be associated with increased risk of bladder and othe r cancers. While there are uncertainties in the quantitative relationship between the incidence of th ese cancers and the occurrence of DBPs in drinking water, EPA believes that additional reductions in these DBP levels in drinking water will reduce the incidence of bladder cancer and, possibly, other cancers.

In addition, results from toxicology and, particularly, epidemiology studies published in the last several years suggest a potential increased risk for pregnant women and their fetuses who are exposed to DBPs in drinking water. The studies have shown that early-term miscarriage, stillbirth, low birth weight, and some birth defects may be associated with drinking water containing DBPs. (These studies are discussed in detail in Chapter 6.) Uncertainties pertain to the extent to which reproductive and developmental effects may be associated with DBP exposure, which DBPs may be of greatest concern, what levels of DBPs may pose a risk, and at what period of development fetuses may be at the greatest risk. Although the levels of DBPs potentially associated with specific adverse reproductive and developmental effects are not known and no causal li nk has been established, EPA believes the evidence supports concern for these potential h azards and warrants regulatory action.

In a separate but concurrent action, EPA is promulgating the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) to improve c ontrol of microbial contaminants, particularly Cryptosporidium , in surface water and to ensure that microbial protection is not compromised by efforts to reduce exposure to DBPs. Together, the Stage 2 DBPR and LT2ESWTR represent the final stage of a two-stage strategy that was developed in a regulatory negotiation effort in 1992 and 1993. 1 

### They reflect recommendations presented by the Stage 2 Microbial and Disinfection Byproducts (M-DBP) Federal Advisory Committee Agreement in Principle, signed in September 2000 (USEPA 2000n).

### Final Economic Analysis for the Stage 2 DBPR 2-2 December 2005 

## 2.2 Public Health Concerns to Be Addressed

EPA’s primary mission is to protect human health and the environment. In carrying out this mission, EPA must often make regulatory decisions b ased on incomplete or uncertain information. The Agency believes it is appropriate and prudent to ta ke action to protect public health when evidence indicates that exposure to a contaminant could present significant risks to the public, rather than take no action until risks are unequivocally proven. Additi onally, the 1996 Amendments to the Safe Drinking Water Act (SDWA) require EPA to address DBP and microbial risks by certain statutory deadlines.

An important consideration in assessing public hea lth risks is the number of people who may be exposed to a particular contaminant. More than 260 million people in the United States potentially are exposed to DBPs via drinking water because they ar e served by public water systems (PWSs) that add chemical disinfectants (see Exhibit 3.3 for th e Stage 2 population baseline). While effective in controlling many harmful microorganisms, chemical disinfectants also form DBPs, some of which may pose health risks. Because of the large number of people potentially exposed to DBPs, EPA is concerned about any health risks that may be associated with DBPs. Information on these risks can come from two types of studies–epidemiological and toxicological.

Epidemiological studies have investigated the relationship between exposure to chlorinated drinking water and cancer. These studies suggest an association between bladder, rectal, and colon cancers and exposure to chlorinated drinking water. Numerous toxicology studies have shown several DBPs (such as bromodichloromethane, bromoform, di chloroacetic acid, and bromate) to be carcinogenic in laboratory animals (see Chapter 6 for details).

Other epidemiological studies indicate a poten tial link between DBP exposure and adverse reproductive and developmental health effects, par ticularly early-term miscarriage (see Chapter 6 for discussion). In addition, toxicological studies have shown that several DBPs cause adverse reproductive and developmental health effects in laboratory anim als. EPA believes that these studies together provide evidence that DBPs may present potential public hea lth risks to pregnant women and their fetuses.

Research, therefore, supports EPA’s conclusion th at chlorinated drinking water could potentially be a source of health risk to the general public. Ther e is uncertainty in the scientific literature regarding the extent to which DBPs contribute to the inciden ce of these adverse effects in the exposed population. Nevertheless, EPA believes that the weight of ev idence warrants concern for these potential hazards and justifies additional regulatory action beyond the Stage 1 DBPR.



## 2.3 Regulatory History

2.3.1 Statutory Authority for Promulgating the Rule

The primary responsibility for regulating the qua lity of drinking water lies with EPA. The SDWA establishes this responsibility and defines the mechanisms at the Agency’s disposal to protect public health. EPA sets standards by identifying which contaminants should be regulated and by establishing the maximum levels of the c ontaminants allowed in drinking water.

Section 1412(b)(1) of the 1996 SDWA reaut horization mandated new drinking water requirements. EPA’s general authority to set Ma ximum Contaminant Level Goals (MCLGs) and develop

### Final Economic Analysis for the Stage 2 DBPR 2-3 December 2005 

### the National Primary Drinking Water Regulations (NPDWR s) was modified to apply to contaminants that “may have an adverse effect on the health of pers ons,” are “known to occur or there is a substantial likelihood that the contaminant will occur in public wa ter systems with a frequency and at levels of public health concern,” and for which, “in the sole j udgment of the Administrator, regulation of such contaminant presents a meaningful opportunity for h ealth risk reductions for persons served by public water systems” (SDWA 1412(b)(1)(A)).

To regulate a contaminant, EPA sets an MCLG at a level at which no known or anticipated adverse health effects occur. MCLGs are established solely on the basis of protecting public health and are not enforceable. EPA simultaneously sets an enforceable Maximum Contaminant Level (MCL) as close as technologically feasible to the MCLG, while taking costs into consideration. If it is not feasible to measure the contaminant at levels presumed to have impacts on health, a treatment technique can be specified in place of an MCL. For water systems, compliance with a drinking water regulation means either not exceeding the MCL or meeting treatment technology requirements.

Additionally, EPA identifies maximum concentrations of residual disinfectants that can occur in water without harming human health and sets maximu m residual disinfectant level goals (MRDLGs) and maximum residual disinfectant levels (MRDLs). PWSs maintain residual levels of disinfectants in the distribution system, following treatment, to ensure consumer protection from microbial contaminants. Like MCLGs, MRDLGs are not enforceable, while MRDLs are.

In addition to the general authorities cited a bove, SDWA 1412(b)(2)(C) requires specifically that EPA promulgate the Stage 2 DBPR.

The Administrator shall promulgate an Inte rim Enhanced Surface Water Treatment Rule, a Final Enhanced Surface Water Treatment Rule, a Stage 1 Disinfectants and Disinfection Byproducts Rule, and a Stage 2 Disinfectan ts and Disinfection Byproducts Rule in accordance with the schedule published in Volume 29, Federal Register, Page 6361 (February 10, 1994), in Table III.13 of the proposed Information Collection Rule. (SDWA 1412(b)(2)(C))

The following sections summarize the development of relevant NPDWRs over the past 20 years.

2.3.2 1979 Total Trihalomethane Rule

Under the Total Trihalomethane Rule (44 Fe deral Register (FR) 68624, November 29, 1979), EPA set an MCL for TTHM (the sum of the concentr ations of chloroform, bromoform, bromodichloromethane, and dibromochloromethane) of 0.10 millig rams per liter (mg/L) as a running annual average (RAA) of quarterly measurements. This standard applied to CWSs using surface or ground water that served at least 10,000 people and that added a disinfect ant to the drinking water during any part of the treatment process. This 1979 rule was superseded by the 1998 Stage 1 DBPR (section 2.3.7) with which all CWSs and NTNCWSs must have complied by January 2004.

### Final Economic Analysis for the Stage 2 DBPR 2-4 December 2005 

### 2.3.3 1989 Total Coliform Rule

The Total Coliform Rule (TCR) (54 FR 27544, J une 29, 1989) applies to all PWSs. Because monitoring PWSs for every possible pathogenic organism is not feasible, coliform organisms are used as indicators of possible contamination. Coliforms are easily detected in water and are used to indicate a system’s vulnerability to pathogens. In the TCR, EPA set an MCLG of zero for total coliforms. EPA also set a monthly MCL for total coliforms and requi red testing of total-coliform-positive cultures for the presence of E. coli or fecal coliforms. E.coli and fecal coliforms indicate more immediate health risks from sewage or fecal contamination and are used as th e indicator of an acute MCL violation. Coliform monitoring frequency is determined by population served, the type of system (community or noncommunity) and the type of source water (surf ace water or ground water). In addition, the TCR required sanitary surveys every 5 years (or 10 years for noncommunity systems using disinfected ground water) for systems that collect fewer than 5 routin e total coliform samples per month (typically systems serving fewer than 4,100 people).

2.3.4 1989 Surface Water Treatment Rule

Under the Surface Water Treatment Rule (S WTR) (54 FR 27486, June 29, 1989), EPA set MCLGs of zero for Giardia lamblia , viruses, and Legionella and established requirements for all PWSs using surface water or GWUDI as a source. The SWTR includes treatment technique requirements for filtered and unfiltered systems that are intended to pr otect against the adverse health effects associated with Giardia lamblia , viruses, and Legionella , as well as many other pathogenic organisms. These requirements include:

• Maintenance of a disinfectant residual in wate r entering and within the distribution system.

• Removal or inactivation of at least 99.9 percent (3 logs) of Giardia and 99.99 percent (4 logs) of viruses.

• For filtered systems, meeting a turbidity perfo rmance standard for the combined filter effluent of 5 nephelometric turbidity units (NTUs) as a maximum and 0.5 NTU in 95 percent of monthly measurements, based on 4-hour monitoring for treatment plants using conventional treatment or direct filtration (with separate standards for other filtration technologies). These requirements were enhanced by the 1998 Interim Enhanced Surface Water Treatment Rule (IESWTR) and the 2002 Long Term 1 Enhanced Surface Water Treatment Rule (LT1ESWTR).

• Watershed control programs and other requirements for unfiltered systems.

2.3.5 1996 Information Collection Rule

The Information Collection Rule (ICR) (61 FR 24354, May 14, 1996) applied to PWSs serving more than 100,000 people. A more limited set of ICR requirements covered ground water systems serving 50,000 to 100,000 people.

The ICR authorized EPA to collect occurrence and treatment information from water treatment plants to help evaluate the possi ble need for changes to microbial requirements and microbial treatment practices and to help evaluate the need for future re gulation of disinfectants and DBPs. The ICR provided

### Final Economic Analysis for the Stage 2 DBPR 2-5 December 2005 

### EPA with information on the national occurrence of (1 ) chemical byproducts that form when disinfectants used for microbial control react with naturally o ccurring compounds and ions present in source water; and (2) disease-causing microorganisms including Cryptosporidium

, Giardia , viruses, and coliform bacteria. The ICR also mandated the collection of data on how wa ter systems currently treat for contaminants. The ICR monthly sampling data provided 18 months of in formation on the quality of the influent and treated water, including pH, alkalinity, turbidity, temperat ure, calcium, total hardness, total organic carbon, ultraviolet 254 

### (UV) absorbency, bromide, ammonia, and disinfectant residual. These data provide some indication of the “treatability” of the water, the o ccurrence of contaminants, and the potential for DBP formation. The data collected under the ICR have been analyzed to help develop the LT2ESWTR and Stage 2 DBPR.

2.3.6 1998 Interim Enhanced Surface Water Treatment Rule

The IESWTR (63 FR 69478, December 16, 1998) enhances the 1989 SWTR. It applies to PWSs serving at least 10,000 people and using surface water or GWUDI as a source. These systems began compliance with the IESWTR in January 2002. The pur pose of the IESWTR is to improve control of the protozoan Cryptosporidium and to address tradeoffs between the ri sks of microbial pathogens and those of DBPs. The requirements and guidelines include:

• An MCLG of zero for Cryptosporidium

. • Removal of 99 percent (2 logs) of Cryptosporidium for systems that use filters.

• For filtered systems, a turbidity performance st andard for the combined filter effluent of 1 NTU as a maximum and 0.3 NTU as a minimum in 95 percent of monthly measurements, based on 4-hour monitoring for treatment plan ts using conventional treatment or direct filtration.

• Continuous monitoring of individual filter effluent in conventional and direct filtration plants and recording turbidity readings ever y 15 minutes when these filters are on-line.

• A disinfection benchmark to assess the level of microbial protection provided before facilities change their disinfection practices to meet the requirements of the Stage 1 DBPR.

• Inclusion of Cryptosporidium in the definition of GWUDI and in the watershed control requirements for unfiltered PWSs.

• Covers for all new finished water storage facilities.

• A primacy provision that requires States to conduct sanitary surveys for all surface water systems, including those serving fewer than 10,000 people.

The IESWTR was promulgated concurrently with the Stage 1 DBPR, described in the next setion, so that systems could coordinate their response to the risks posed by DBPs and microbial pathogens.

### Final Economic Analysis for the Stage 2 DBPR 2-6 December 2005 

### 2.3.7 1998 Stage 1 Disinfectants and Disinfection Byproducts Rule

The Stage 1 DBPR (63 FR 69390, December 16, 1998) applies to all CWSs and NTNCWSs that add a chemical disinfectant to their water. Certai n requirements designed to provide protection against acute health effects from chlorine dioxide also apply to transient noncommunity water systems (TNCWSs). Compliance for surface water and GWUDI systems serving at least 10,000 people began in January 2002. Surface water and GWUDI system s serving fewer than 10,000 people and all ground water systems were required to comply by January 2004.

The Stage 1 DBPR sets MRDLGs for ch lorine (4 mg/L as chlorine (Cl 2 

### )), chloramines (4.0 mg/L as Cl 2 

### ), and chlorine dioxide (0.8 mg/L as ClO 2 

### ); and MCLGs for bromodichloromethane (0 mg/L), bromoform (0 mg/L), dibromochloromethane (0.06 mg /L), dichloroacetic acid (0 mg/L), trichloroacetic acid (0.3 mg/L), bromate (0 mg/L), and chlorite (0.8 mg/L). The rule sets MRDLs for chlorine (4.0 mg/L as Cl 2 

### ), chloramines (4.0 mg/L as Cl 2 

### ), and chlorine dioxide (0.8 mg/L as ClO 2 

### ); and MCLs for TTHM (0.080 mg/L), HAA5 (0.060 mg/L), bromate (0.010 mg/L), and chlorite (1.0 mg/L). The MRDLs and MCLs, except those for chlorite and chlorine dioxi de, are calculated as RAAs. For conventional surface water and GWUDI systems, a treatment technique—enha nced coagulation/softening—is specified for the removal of DBP precursors.

As noted in section 2.3.6, the Stage 1 DBPR w as promulgated concurrently with the IESWTR to coordinate the control of DBPs and microbial contaminants.

2.3.8 2000 Proposed Ground Water Rule

The proposed Ground Water Rule (65 FR 30194, May 10, 2000) addresses fecal contamination in ground water systems. It also builds on the TCR through provisions based on further evaluation of E. coli monitoring results measured under the TCR. Key components of the approach for protection of ground water included in the proposed rule are:

• Sanitary surveys for all ground water systems.

• Hydrogeologic sensitivity assessments to identif y ground water wells that are susceptible to fecal contamination.

• Triggered source water monitoring (based on TC monitoring) for an indicator of fecal contamination for all systems that do not achieve 4-log treatment, and in addition, routine source water monitoring for an indicator of fecal contamination for systems that have been determined to draw from sensitive ground water sources.

• Correction of significant deficiencies and fecal contamination by eliminating the source of contamination, correcting the deficiency, pr oviding an alternative source of water, or providing inactivation and/or removal of 99.99 percent (4 logs) of viruses.

• Compliance monitoring to ensure that disinf ection treatment is reliably operated when it is used.

### Final Economic Analysis for the Stage 2 DBPR 2-7 December 2005 

### 2.3.9 2001 Arsenic Rule

The Arsenic Rule (66 FR 6976, January 22, 2001) increases the level of public health protection against exposure to arsenic in drinking water. Th e rule revises the MCL for arsenic in drinking water from 0.05 mg/L to 0.010 mg/L and sets an MCLG of 0 mg/L for all CWSs and NTNCWSs. Clarification on how compliance is demonstrated for many inorga nic and organic contaminants in drinking water is also given. All existing CWSs and NTNCWSs must comply with the Arsenic Rule by January 23, 2006.

2.3.10 2001 Filter Backwash Recycling Rule

The Filter Backwash Recycling Rule (FBRR) (66 FR 31086, June 8, 2001) regulates systems that return filter backwash to the treatment process. The rule applies to surface water and GWUDI systems that use direct or conventional filtration and recy cle spent filter backwash water, sludge thickener supernatant, or liquids from dewatering processes. The rule requires that these recycled liquids be returned to a location such that all steps of a system’s conventional or direct filtration are employed. The rule also requires systems to notify the State that th ey practice recycling. Finally, systems must collect and maintain information for review by the State.

2.3.11 2002 Long Term 1 Enhanced Surface Water Treatment Rule

The LT1ESWTR (67 FR 1812, January 14, 2002) enhances the 1989 SWTR requirements for small systems. LT1ESWTR enhances control of Cryptosporidium and other disease-causing microbes for surface water and GWUDI systems that serve fewer than 10,000 people. Key provisions in the LT1ESWTR are very similar to those for the IESWTR, but provide additional flexibility for small systems.

2.3.12 2005 Long Term 2 Enhanced Surface Water Treatment Rule

Made final in concert with the Stage 2 DBPR, the LT2ESWTR strengthens control of Cryptosporidium , and applies to all PWSs that use surface wa ter or GWUDI as a source. It incorporates system-specific treatment requirements based on a “Mic robial Framework” approach that targets high-risk systems. This approach involves assigning systems to different categories (or “bins”) based on the levels of Cryptosporidium found in the source water. Additional treatment requirements, if any, are linked to the level of Cryptosporidium . A system will choose technologies and management practices from a “toolbox” of options appropriate to its bin.

Medium and large systems (those serving at least 10,000 people) that filter are required to conduct Cryptosporidium source water monitoring for 24 months to determine their bin classification. Small systems (those serving fewer than 10,000 people) that filter will monitor E. coli bacteria in their source water biweekly for 12 months. Based on their E. coli results, they may be required to monitor Cryptosporidium as well.

In addition to requirements for filtered systems, the LT2ESWTR requires unfiltered systems to continue to meet the filtration avoidance criteria under the 1989 SWTR and provide inactivation at 4 logs (99.99 percent) for virus, 3 logs (99.9 percent) for Giardia , and 2 to 3 logs (99 to 99.9 percent) for Cryptosporidium (depending on results of Cryptosporidium monitoring of source water). Building on the

### Final Economic Analysis for the Stage 2 DBPR 2-8 December 2005 

### SWTR requirements, inactivation requirements for unfiltered systems subject to the LT2ESWTR must be met using a minimum of two disinfectants.

Also, the LT2ESWTR requires systems with uncovered finished water reservoirs to cover the reservoirs or treat reservoir discharge to the distributi on system to achieve 4-log virus inactivation, 3-log Giardia inactivation, and 2-log Cryptosporidium inactivation.



## 2.4 Economic Rationale

This section addresses the economic rationale fo r choosing a regulatory approach. Such a rationale is required by Executive Order Number 12866, Regulatory Planning and Review (USEPA 1993), which states:

[E]ach agency shall identify the problem th at it intends to address (including, where applicable, the failures of the private mark ets or public institutions that warrant new agency action) as well as assess the significance of that problem. (Section 1, b(1))

In addition, Office of Management and Budge t (OMB) guidance dated January 11, 1996, states that “in order to establish the need for the propo sed action, the analysis should discuss whether the problem constitutes a significant market failure” (USEPA 1996b).

In a perfectly competitive market, prices and quan tities are determined solely by the aggregated decisions of buyers and sellers. Such a market occurs when many producers of a product are selling to many buyers, and both producers and consumers have perfect information on the characteristics and prices of each firm’s products. Barriers to entry in the industry cannot exist, and individual buyers and sellers must be “price takers”; i.e., their individual d ecisions cannot affect the price. Several properties of the public water supply do not satisfy the conditions fo r a perfectly competitive market and, thus, lead to market failures that require regulation.

First, many water systems are natural monopolies. A natural monopoly exists when it is impossible for more than one firm in each area to r ecover the costs of production and survive. There are high fixed costs associated with reservoirs and wells, transmission and distribution systems, treatment plants, and other facilities. For other potential supplie rs to enter the market, they would have to provide the same extensive infrastructure to realize simila r economies of scale and be competitive. A splitting of the market with increased fixed costs (for example, two supplier networks in a single market) usually makes this situation unprofitable for one or both supp liers. The result is a market suitable for a single supplier and one that is hostile to a lternative suppliers. In such natu ral monopolies, suppliers have fewer incentives for providing high-quality services or mainta ining competitive prices. In these situations, governments often intervene to help protect the public interest.

Because PWSs are legal, as well as natural, monopolies, they often are subject to price controls, if not outright public ownership. While customer s may demand improvements in water quality, the regulatory regime may not transmit that demand to th e water supplier or allow the supplier to raise its price to recover the cost of the improvements. If consumers do not believe that their drinking water is safe enough, they cannot simply switch to another wa ter utility. Other options for obtaining safe drinking water (e.g., buying bottled water or installing point-of-u se filtration) most often cost consumers more than the purchase from public water supplies. Therefor e, the water supplier may have little incentive to improve water quality.

### Final Economic Analysis for the Stage 2 DBPR 2-9 December 2005 

### Second, the public may not understand the health and safety issues associated with drinking water quality. Understanding the health risks potentia lly posed by trace quantities of drinking water contaminants involves analysis and synthesis of complex toxicological and health sciences data. Therefore, the public may not be aware of the risks it faces. EPA has implemented a Consumer Confidence Report Rule (CCR) (63 FR 44512 August 1998) that makes water quality information more easily available to consumers. This rule requir es CWSs to publish an annual report on local drinking water quality. Consumers, however, still have to analyze this information for its health risk implications. Even if informed consumers are able to engage wa ter systems in a dialogue about health issues, the transaction costs of such interaction (measured in personal time and monetary outlays) present another significant impediment to consumer expression of risk reduction preferences.

SDWA regulations are intended to provide a leve l of protection from exposure to drinking water contaminants. The regulations set minimum performance requirements to protect consumers from exposure to contaminants. They are not intended to restructure market mechanisms or to establish competition in supply; rather, they establish the level of service to be provided that best represents public preference for safety. The federal regulations redu ce the high information and transaction costs by acting on behalf of consumers in balanci ng risk reduction and the social costs of achieving this risk reduction.

### Final Economic Analysis for the Stage 2 DBPR 3-1 December 2005 

# 3. Baseline Conditions

3.1 Introduction

To quantify the effects of the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR), it is necessary to have a baseline against which to compar e the set of regulatory alternatives. The baseline is a characterization of the industry and its opera tions under the conditions expected to exist before systems make changes to meet requirements of th e Stage 2 DBPR. The baseline allows a consistent comparison of public health impacts (developed in Chapter 6) and the economic and financial impacts (developed in Chapters 7 and 8) of each regulatory alternative.

Development of the baseline consists of the following processes:

• Compiling an industry profile

• Characterizing the relevant properties of the raw water treated by the industry

• Characterizing the types and frequency of a dvanced treatment technologies being used at water treatment plants

• Characterizing disinfection byproduct (DBP) occurrence in finished water and in the distribution system

The appropriate baseline for assessing the impacts of Stage 2 would be conditions following implementation of Stage 1. However, the comp liance deadline for the Stage 1 DBPR occurred only recently (January 2004) for small surface water systems and all ground water systems and in January 2002 for large and medium surface water systems. T hus, the observed water qua lity data and occurrence of treatment technologies presented in this chapter re present pre-Stage 1 conditions. Predictions of preStage 2 baseline (as well as post-Stage 2) conditions are presented in Chapters 5, 6, and 7.

Some characteristics of the pre-Stage 1 baseline are modeled to allow for consistent comparison to post-Stage 1 and post-Stage 2 conditions. Th is chapter presents DBP occurrence and treatment technologies in place for large surface water plants as predicted using a tool developed by the Environmental Protection Agency (EPA), the Surface Water Analytical Tool (SWAT).

Sections 3.2 and 3.3 describe the data sources a nd tools used to characterize the pre-Stage 1 baseline. Section 3.4 characterizes the water industr y, including the baseline estimates of treatment plants and population subject to the Stage 2 DBPR. Influe nt water quality is summarized in section 3.5, and section 3.6 describes the types of treatment technologi es used by systems prior to the Stage 1 DBPR. Treated water quality, as it relates to the pre-Stage 1 b aseline, is presented in section 3.7. Lastly, section 3.8 itemizes and estimates the effects of uncertainties in the baseline analysis.

This chapter presents an analysis at a leve l of detail and precision appropriate to support subsequent analyses and regulatory decisions for the Stage 2 DBPR. Therefore, it does not give an exhaustive review of the water supply industr y, source waters, or industry practices.1 

### Throughout this document, the acronym “SDWIS/FED” is shortene d to “SDWIS.”Refer to EPA’s website for more information on SDWIS (http://www.epa. gov/safewater/sdwisfed/sdwis.htm)



### Final Economic Analysis for the Stage 2 DBPR 3-2 December 2005 

## 3.2 Data Sources

Several data sources were used to characterize the baseline and to predict treatment technology changes and water quality for different regulatory a lternatives. The Safe Drinking Water Information System-Federal Version (SDWIS/FED 1 

### ) data (4 th 

### Quarter Freeze Year 2003 data) is used to create system and population baselines (USEPA 2003t). SDWIS is EPA ’s national regulatory compliance database for the drinking water program. It includes information on the nation’s 170,000 public water systems (PWSs) and on violations of drinki ng water regulations. A second key sour ce of data used to develop the industry profile is the Third Edition of the Water Industry Baseline Handbook (Baseline Handbook) (USEPA 2001c) published in May 2001, which compil es data derived from the 1995 Community Water System Survey (CWSS) and SDWIS. The 1995 CWSS was a mail survey that covered ground and surface water systems of all sizes (based on population served). The survey was based on a two-phased, stratified sample design. Phase 1 was a telephone screening survey that provided a sampling frame for the main data collection in Phase 2. The survey sample in Phase 2 was stratified according to water system size (residential population served), ownership ( public, private, or ancillary), and primary water source (ground or surface). A total of 3,681 systems covering a range of source water types and system sizes were randomly selected to receive the main survey questionnaire. Of these, 1,980 systems responded. See the EPA Report, “Community Wate r System Survey, Volume 2” (USEPA 1997c), for more information on the 1995 CWSS sample design and data evaluation.

EPA also used the December 2000 document, “Geometries and Characteristics of Water Systems Report” (Model Systems Report) (USEPA 2000c). In this document, EPA analyzed 1995 CWSS data to develop equations relating flow and population, among other things.

The data source providing the most comprehe nsive information on influent water quality, treatment processes, and finished water quality came from the 1996 Information Collection Rule (ICR), which applied to all PWSs serving at least 100,000 pe ople, with a more limited set of ICR requirements pertaining to ground water systems serving 50,000 to 100,000. The purpose of the ICR was to collect DBP and microbial occurrence and treatment informati on to help evaluate the need for microbial and DBP rules. The ICR gathered plant-level data from approximately 300 water systems over 18 months (July 1997–December 1998). These data characterize the source waters and the water quality at each step in the treatment process and at points in the distribution system. The water quality data include information about the DBPs formed when chemi cal disinfectants react with naturally occurring compounds present in source water. In addition, the ICR collected treatment and process train data that were used in the predictive anal yses described in this chapter.

The American Water Works Association (AWWA ) submitted several comments in response to the proposed Stage 1 DBPR that underscore the n ecessity of the ICR in developing the Stage 2 DBPR. AWWA stated, “Promulgation of the Stage 2 D/DBPR and LT2ESWTR [Long Term 2 Enhanced Surface Water Treatment Rule] is contingent upon completion of necessary health effects research and analysis of the ICR data” (USEPA 1998b) and “AWWA believes that the data from both the ICR and complimentary research will ensure that a scientific database will be created to make important and cost effective decisions on the direction of both the final ESW TR and Stage 2 of the D/DBPR” (USEPA 1997d). In addition, AWWA concurred with the appropriateness of the phased approach to allow for analysis of ICR data in its comments on the 1994 rule versions. Comments on the 1994 rule from AWWA explain that data collected through the ICR would be used to de termine the occurrence of DBPs and DBP precursors as well as treatment capabilities associated with DBP control in developing the Stage 2 DBPR (USEPA 1994b).2 

### Although the language in EPA rules generally does not include systems serving exactly 10,000 people in the “small” category, this document places them in the small category to be consistent with the system and population data categories from the Baseline Handbook.

Final Economic Analysis for the Stage 2 DBPR 3-3 December 2005 

### For medium systems (serving 10,000 to 99,999 people) and small systems (serving fewer than 10,000 people), several additional data sources were u sed to characterize the source water and finished water quality: 2



### • ICR Supplemental Surveys

• The National Rural Water Association (NRWA) Survey

• The Ground Water Supply Survey

• Small surface and ground water plant data collected by various States (several States provided DBP data to EPA)

• The Water Utility Database (WATER:\STATS, AWWA 2000)

Data from these sources were also used to help predict treatment technologies changes to comply with regulatory alternatives. These data are presented in detail in the Occurrence Assessment for the Final Stage 2 Disinfectants and Disinfection Byproducts Rule (USEPA 2005k) (Occurrence Document). The Occurrence Document and Appendix L also discu ss the data quality of each of the sources used in this Economic Analysis (EA).



## 3.3 Surface Water Analytical Tool

Although observed DBP data are available for pr e-Stage 1 conditions, finished water quality is modeled to give a consistent basis to compare w ith the pre-Stage 2 and post-Stage 2 predictions. The SWAT is the main tool developed by EPA to model DBP occurrence for different regulatory alternatives. SWAT uses a series of algorithms and decision rules to predict the type of treatment a plant will use and the resulting DBP occurrence, given a specific regul atory alternative and source water quality based on ICR data. Additional description of SWAT is provided in Appendix A and the SWAT Operations Manual (USEPA 2000a).

SWAT was designed to provide answers to two broad questions:

• What treatment technologies will large surface water treatment plants implement (given a pre-determined, least-cost decision tree) to comp ly with a defined set of disinfection and DBP compliance criteria?

• What is the predicted finished and delivered water quality (particularly DBP levels) produced by large surface water treatment plants after implementation of a range of treatment technologies for a given set of disinfection criteria?

SWAT has four major components (Exhibit 3.1a), including:

• ICR Auxiliary Database 8 (AUX8) - It is a Microsoft AccessTM database that contains both inputs and outputs of the SWAT program. Inputs include ICR influent water quality and3 

### Empirical equations are used to estimate DBP fo rmation based on residence time (see Appendix A for discussion of these equations).

Final Economic Analysis for the Stage 2 DBPR 3-4 December 2005 

## Water Treatment Plant Model

Decision Tree Program User Interface

ICR Auxiliary Database 8 Inputs

(e.g., MCL criteria)

Treatment Plants and Water Quality (ICR Data)

Modified Plants and Selected

Outputs (e.g., DBP Levels) Water Treatment Plant Model

Decision Tree Program User Interface

ICR Auxiliary Database 8 Inputs

(e.g., MCL criteria)

Treatment Plants and Water Quality (ICR Data)

Modified Plants and Selected

Outputs (e.g., DBP Levels) plant process train data. Outputs consist of treatment technologies predicted for compliance, treated water quality, and modified process train data.

• Decision Tree Program—This part of SWAT determines how a treatment plant is modified to comply with defined regulatory alternatives. First, the program determines if an individual plant can be modified using the least expensive (and typically least effective) treatment technology to comply with the regulatory alternative. If not, the program moves to the next lowest-cost treatment technology. This process continues until the plant achieves compliance. The program receives inputs fro m the database (AUX8), and uses the Water Treatment Plant Model (described in the next bullet) to estimate treated water quality before and after predicted treatment technology chang es, and sends results back to the database.

• Water Treatment Plant Model—This model is the main predictive component of SWAT. It generates predictions of treated water quality (e.g., DBP levels) for the water treatment process trains defined by the Decision Tree Program. Predictive modules of the model were calibrated using the central tendency of the ICR data.

• User Interface—A WindowsTM interface enables the user to specify the disinfection and DBP criteria, as well as numerous other assumptions for a SWAT run.



## Exhibit 3.1a SWAT Components

The Water Treatment Plant Model and the Decision Tree Program work together to predict DBP occurrence levels and treatment plant modifications . The Water Treatment Plant Model computes DBP concentrations that represent the treatment process trai n for a plant, influent water quality characteristics, and specific treatment constraints. TTHM and HAA5 concentrations are predicted for two distribution system conditions–maximum water residence time and average water residence time. 3 

### If the DBP

### Final Economic Analysis for the Stage 2 DBPR 3-5 December 2005 

### concentrations do not meet regulatory constraints at the average residence time (for Stage 1) or at maximum residence time (for Stage 2), the Decision Tr ee Program modifies the process train to meet the specified water quality objectives using a least-cos t decision sequence. For the pre-Stage 2 DBPR baseline runs (conditions following implementation of the Stage 1 DBPR) and Stage 2 DBPR regulatory alternative runs, these criteria are based on specified maximum contaminant levels (MCLs) for DBPs. If a plant’s predicted DBP occurrence exceeds an MCL, the Decision Tree Program chooses a more effective treatment technology (i.e., the next highe r-cost option) in the decision tree, and the Water Treatment Plant Model generates a new DBP predicti on. This selection process continues until a selected treatment technology results in the plant meeting the regulatory alternative. SWAT was also run for a pre-Stage 1 baseline, where the model only predicte d the DBP levels based on the inputted process train and water quality data. This run was to serve as a co mparison to the ICR data to evaluate and calibrate predictions (see Appendix A).

SWAT was run using actual data from the ICR on influent water quality, treatment trains, and related characteristics of 273 ICR surface water plants . All SWAT results are based on a 12-month period using input data from months 7–18 (January 1998–D ecember 1998). The number of months with valid data input varied among plants; therefore, the output of SWAT does not contain 12 months of data for every plant, as is discussed in greater detail in Appendix A.

Exhibit 3.1b summarizes the inputs and outputs used in the SWAT modeling process. Appendix A describes the treatment technology selection process of the decision tree, the assumptions contained within SWAT, and the analysis of uncertainty . For further programming details, refer to The Surface Water Analytical Tool (SWAT) Version 1.1–Program Design and Assumptions (USEPA 2000a).



## 3.4 Industry Profile

This section provides the water industry characteriza tion used to derive costs and benefits for the Stage 2 DBPR. It is organized as follows:

• Section 3.4.1 is a background section with termi nology and definitions used to characterize the water industry baseline. It also identifies distinctions that are important for regulatory analysis.

• Section 3.4.2 presents the baseline numbers of systems, plants, and population subject to the Stage 2 DBPR.

• Section 3.4.3 presents mean plant design and average daily flows.

• Section 3.4.4 estimates the total number of households subject to the Stage 2 DBPR.

### Final Economic Analysis for the Stage 2 DBPR 3-6 December 2005 

## Exhibit 3.1b SWAT Inputs and Outputs

Input data
Source water quality • pH • Temperature (average and annual minimum) • Total organic carbon (TOC) • Ultraviolet 254 

### (UV) absorbance • Bromide • Alkalinity • Hardness (total and calcium) • Ammonia • Turbidity
Treatment plant characteristics • Flow (average and peak hourly) • Presence of, sequence of, and parameters (e.g., volumes and retention times) for unit processes, including rapid mix, flocculation, settling basin, filtration, contact tank, reservoir, granulated activated carbon, membranes, and ozone chambers • Dosages and chemical feeds (e.g., alum, ammonium sulfate, ammonia, CO 2 

### , NaOH, Cl 2 

### (gas), ClO 2 

### , ferric chloride, lime, ozone, potassium permanganate, soda ash, SO 2 

### , and H 2 

### SO 4 

### ) • Average and maximum distribution system residence times
Compliance measures (DBPs)

Finished water concentration • For each plant, and for each month for which data are available for that plant, the DBP concentration at the entry point to the distribution system is calculated, representing a residence time of 0. These monthly values can then be used in compliance calculations.
Distribution system average concentration • For each plant, and for each month for which data are available for that plant, the DBP concentration in the distribution system is calculated based on the average distribution system residence time reported by the system. These monthly values can then be used in different compliance calculations.
Distribution system maximum concentration • For each plant, and for each month for which data are available for that plant, the DBP concentration in the distribution system is calculated based on the maximum distribution system residence time reported by the system. These monthly values can then be used in different compliance calculations. Source: Appendix A



### .4 

### EPA also refers to the grouping of surface water and GWUDI systems as “subpart H” systems in the Stage 2 DBPR rule language. Surface water and GWUDI system s are grouped together because they fall under the same requirements in the Safe Drinking Water Act (SDWA) regulations.

Final Economic Analysis for the Stage 2 DBPR 3-7 December 2005 

### 3.4.1 Public Water System Categorization

Categorization of water systems is important because system size, ownership, and retail/wholesale relationships dictate the way in whic h costs and benefits are estimated. This section explains the water system categories as defined by EPA’s National Primary Drinking Water Regulations (NPDWRs) and describes further subdivisions accordi ng to water source, size (population served), and ownership for regulatory analysis purposes.

PWS Type

NPDWRs apply to all PWSs. A PWS is a system that provides water for human consumption through pipes or other constructed conveyances and th at has at least 15 service connections or regularly serves an average of at least 25 individuals per day for at least 60 days per year. PWSs are categorized as follows:

• A Community Water System ( CWS) is a PWS that has at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents.

• A Noncommunity Water System (NCWS ) is a PWS that is not a CWS. NCWSs are subdivided into two categories:

• A Nontransient Noncommunity Water System (NTNCWS) is a NCWS that regularly serves at least 25 of the same people more than 6 months per year.

• A Transient Noncommunity Water System (TNCWS) is a NCWS that does not regularly serve at least 25 of the same people more than 6 months per year.

Source Water Type

For the purposes of regulatory analysis, systems ar e typically categorized according to the source of their water. Types of sources include surface water (reservoirs, lakes, or flowing streams), ground water under the direct influence of surface wate r (GWUDI), ground water (aquifers not under the influence of surface water), and treated water that is purchased from other systems. For the purposes of this document, “surface water” includes GWUDI sources. 4



### In SDWIS and the Baseline Handbook (USEPA 2001c), systems are assigned a source type using the following hierarchy, in descending order: Su rface Water, Purchased Surface Water, Ground Water, and Purchased Ground Water. The presence of the first source in this list determines the source assignment for that system. As a result, all “mix ed systems” (systems with both a ground and surface water source) are placed in the surface water system category. Based on an analysis in the Model Systems Report (USEPA 2000c), it is estimated that 21 percent of surface water systems obtain some of their water from ground water sources. Approximately one-third of these, or 8 percent of all surface water systems in SDWIS and the Baseline Handbook, receive the majority of their flow from ground
water.

Other data sets classify systems differently. Fo r example, the ICR Auxiliary Database 1 (AUX1) classifies systems that receive more than 80 percen t of their source water from surface water as surface

### Final Economic Analysis for the Stage 2 DBPR 3-8 December 2005 

### water systems. Systems that rely on ground water for more than 80 percent of their supply are considered ground water systems. Systems that receive more than 80 percent of their supply from another system are considered purchased water systems. All other systems are considered mixed. The 1995 CWSS data are classified by primary source (the source that provid es more than 50 percent of average flow to the distribution system). In cases where there are three different sources (e.g., surface, ground, and purchased), systems in the 1995 CWSS are classified by the largest source.

This EA begins with numbers from the SDWIS database. These numbers are then reclassified according to primary source water type based on the CWSS data.

Ownership

Systems are categorized in SDWIS and in the Baseline Handbook according to three ownership types: “private,” “public,” and “other.” Private syst ems are owned by private corporations or individuals. Public systems are owned by public entities such as m unicipalities, counties, or special districts and have access to capital and means of financing that are not av ailable to private systems. The “other” category contains systems where ownership is not reported in SDWIS. These distinctions become important in calculating household costs (see Chapter 7) and in assessing Unfunded Mandates Reform Act (UMRA) requirements (see Chapter 8).

Purchased Water and Wholesale System Types

Systems are typically categorized according to whether they treat water themselves or purchase treated water from other systems. The Stage 2 DBPR defines a consecutive system as a PWS that buys or otherwise receives some or all of its finished water from one or more wholesale systems. A wholesale system is defined as a PWS that treats and then sells or otherwise delivers finished water to another PWS. Treatment modifications are generally not made by consecutive water systems, but are instead made by the associated wholesale systems. Costs of these treatment modifications are typically passed on to the consecutive systems in the form of water rate increases.

Population Served

The number of people served by systems (indicating system size) is a key parameter used to calculate benefits and costs of drinking water re gulations. This EA defines two types of system populations: the retail customers of a system who buy water directly from the system, and the wholesale customers of a system who are served by a second system that purchases treated water from the first. Systems are categorized in SDWIS and the Baseline Handbook by retail population served. Systems in the 1995 CWSS database are classified by total population (wholesale and retail).

Systems are categorized by population in order to group them for analyses. Mean estimates are used for each of the nine population size categories in this EA. Although some variability is lost by characterizing systems by nine population size categor ies, EPA believes the level of analysis provides adequate information to characterize national costs and benefits. This approach is consistent with other regulations developed by EPA.

### Final Economic Analysis for the Stage 2 DBPR 3-9 December 2005 

### 3.4.2 Systems, Plants, and Population Subject to the Stage 2 DBPR

To estimate costs and benefits attributable to the Stage 2 DBPR, EPA has developed the following industry baselines:

• The System Baseline (Appendix H) is used to estimate non-treatment costs incurred by systems for rule implementation, IDSEs, Stage 2 monitoring plans, additional routine monitoring, and operational evaluations. Systems are categorized based on the population breakouts used for Stage 2 DBPR monitoring requirements which are different than the standard nine size categories used in the rest of this EA. The derivation of the system baseline is in Appendix H, section H.1.

• The Plant Baseline (Exhibit 3.2) is used to estimate treatment costs (based on predictions of plants changing to various advanced treatment technologies).

• The Population Baseline (Exhibit 3.3) is used to estimate cancer cases avoided as a result of the Stage 2 DBPR and subsequent monetized benefits.

The purpose of this section is to define these baselines and describe how they were derived.

3.4.2.1 Plant Baseline

Exhibit 3.2, presented at the end of this subsec tion, shows the derivation of the baseline number of treatment plants subject to the Stage 2 DBPR (i .e., the plant baseline). The derivation is described below in four steps. Step 1 involves modifying th e surface water system inventory to better represent the size and number of plants that exist by “linking” purchasing surface water systems to their respective sellers. Only surface water systems were modified in this step (the number of purchasing ground water systems is such a small proportion of all ground water systems that linking them to sellers was not expected to change the characterization of the gr ound water plant baseline). Step 2 removes systems which do not disinfect from the baseline. In step 3, the system inventory was reclassified from the SDWIS source water categorization to the primary sour ce water type (i.e., the source type that provides more than 50 percent of the water to a system). The final step, step 4, involves converting the system inventory to a treatment plant inventory based on estimates of average treatment plants per system.

Step 1: Modify the Surface Water Syst em Inventory by Linking Buyers and Sellers

Because population served is used directly to estimate the volume of water treated, the type of system population reported is key to defining an accura te treatment plant baseline. As noted in section 3.4.1, system populations in SDWIS represent retail populations only. In other words, system populations reported in SDWIS do not include the populations of those consecutive systems to whom they sell water (purchased water systems are considered separate, stand-alone systems). More than half of the surface water systems are consecutive, stand-alone systems. Purchased-water systems comprise a much lower proportion of ground water systems (approximately five percent).

The advantage of classifying systems by retail population as done in SDWIS is that it appropriately accounts for both the total number of i ndividual PWSs in the United States and the total population served by all of those systems. However, a disadvantage (especially for surface water CWSs) when estimating national costs of regulations is that it does not directly account for the fact that the water delivered by the consecutive syst ems to their retail customers is actually treated by other systems . It is important to recognize that the total flow of surface wa ter is actually treated by fewer than half of the

### Final Economic Analysis for the Stage 2 DBPR 3-10 December 2005 

### surface water systems accounted for in SDWIS. Because of economies of scale, the cost of treatment (in cents per gallon) is less for systems treating larger flow s than it is for systems treating smaller flows. For example, it is typically more expensive to build and operate two treatment plants serving 5,000 people than one treatment plant serving 10,000 people. Failing to account for the fact that surface water is actually treated in larger quantities at a smaller num ber of systems than SDWIS suggests could result in an upward bias in national cost estimates of rules that affect a substantial portion of surface water systems.

To rectify this bias, an analysis was perform ed to “link” consecutive surface water systems to their respective wholesale system using data fro m SDWIS (each purchased system lists the PWS identification number(s) for systems that sell water to it) . If a consecutive system could be linked to a wholesaler, that system was removed from the sy stem count and its population was added to the population of the wholesale system.

The methodology used to link the SDWIS 2003 system inventory is described in detail below:

• If a system has multiple sources, (e.g., it has a primary source of surface water in addition to a purchased surface water source), it was assumed to be adequately represented as a non-purchased surface water system, and was not linked to its seller (i.e., only 100- percent-purchased-surface-water systems were linked).

• For systems that purchase water, all sellers were identified using SDWIS data (SDWIS has a table that lists the PWS identification number (PWSID) for each seller).

• If a purchased surface water system (System P) purchases all of its water from one nonpurchased surface water system (System S), its population was added to that of System S, and it was removed from the inventory of purchased systems.

• If the purchased surface water system buys water from multiple non-purchased systems, it was assigned to the most directly related non- purchased seller with the largest population. For example, a purchased system (Sys tem C) purchases from a non-purchased system (System B1) and a purchased system (Syste m B2), which in turn purchases from a nonpurchased system (System A). In this case, System C was linked to System B1; in other words, the population of System C was added to that of System B1. It was not linked to either System B2 or System A, even if those systems were larger.

• Some purchased systems have what is refe rred to as “cascading provider relationships.” For instance, a purchased system, System C, may purchase water from another system, System B. System B does not treat its own water but, instead purchases water from a non-purchased system, System A. For this analysis, the populations of both Systems B and C were added to the population of Syst em A, and Systems B and C were removed from the inventory of unlinked systems.

• When the purchased system and its seller are not of the same type (e.g., a CWS purchasing from a NTNCWS), they were not linked and are counted as separate, unlinked purchased systems. Systems purchasing from systems of different ownership type (e.g., a public water system purchasing from a private water system), however, were linked.

• If the ID number of the seller did not correspond to an active water system, the purchased system was counted as a separate, unlinked, purchased system.

### Final Economic Analysis for the Stage 2 DBPR 3-11 December 2005 

### • In a few cases, the seller could not be found, i.e., a purchased system (e.g., System C) cannot be linked to a non-purchased system. These purchased systems were counted as separate, unlinked, purchased systems.

Results of the linking exercise for surface water CWSs and NTNCWSs are shown in Exhibit 3.2, columns F through J.

As shown in Exhibit 3.2, the sum of the totals of columns F and G, there are approximately 2,700 purchasing systems remaining unlinked in the inventor y. These include surface water systems that could not be linked (e.g., many surface water NTNCWSs pur chase water from CWSs and were not be linked), purchased surface water systems with a non-purchased ground water source, and the unlinked purchased ground water systems. For the purposes of estimating treatment costs in this EA, EPA includes the remaining unlinked purchased water systems in the syst em inventory and evaluates them as if they are treating water themselves. As described previously , evaluating purchasing systems as if they are standalone, treating systems could result in an upward bias in national cost estimates. This bias is greatly reduced, however, by the linking effort described in this step.

While EPA believes that linking consecutive syst ems with their wholesalers will improve the accuracy of cost estimates, it is possible that purchased systems may be out of compliance even when the wholesaler is in compliance, thereby obligates them to incur treatment costs that are not being captured by this approach. EPA believes that the number of th ese systems, however, is small and will not have a measurable effect on the costs or benefits of the Stage 2 DBPR.

Step 2: Remove Systems which do not Disinfect

The Stage 2 DBPR applies only to systems which di sinfect their water. Therefore systems which do not disinfect were removed from the baseline. Th e inventory is reduced by the percent disinfecting (shown in Exhibit 3.2, column K) to produce the results shown in columns L through P.

The estimate of percent disinfecting in column P comes from several sources. The percent of ground water CWSs providing disinfection is deri ved from 1995 CWSS results, as summarized in Table B1.3.3 of the Baseline Handbook. The percent of ground water NTNCWSs that disinfect was derived from Ground Water Disinfection and Protective Practices in the United States (USEPA 1996a). These data sources do not include systems that may add disinfection to correct a significant deficiency under the Ground Water Rule (GWR). Because the GWR is expected to be promulgated within 8 months after the Stage 2 DBPR is promulgated, EPA expects new sy stems adding disinfection to meet GWR requirements to simultaneously achieve compliance with Stage 2 MCLs. Therefore, these systems are not included in the treatment baseline.

Step 3: Re-classify Systems by Primary Source Water Type

The characterization of distribution system DBP levels and predictions of treatment technology changes are very different for ground and surface wa ter plants. This is mainly because ground water sources generally have lower DBP precursor (e.g., TO C) concentrations than surface water; thus, DBP levels and predicted changes to meet Stage 2 DBPR requirements are generally less for plants treating ground water than for plants treating surface water.

As noted in section 3.4.1, all mixed systems (even those that are primarily ground water) are grouped with the 100 percent surface water systems in SDWIS. If EPA applied the compliance forecasts for surface water plants to systems that are primarily served by ground water sources, costs could be overstated. Therefore, systems were reclassified by primary source. This is consistent with

### Final Economic Analysis for the Stage 2 DBPR 3-12 December 2005 

### recommendations in the Arsenic NDWAC Final Re port (National Drinking Water Advisory Council 2001).

SDWIS does not contain information on whether or not a system is mixed or the relative proportions of surface and ground water flow used, indicating only whether it is served by all ground water or by at least some proportion of surface water. Therefore, to reclassify by primary source, EPA used flow data from the 1995 CWSS to estimate the proportion of surface water and mixed CWSs that received more than 50 percent of their flow from a ground water source (percentages are shown in Exhibit 3.2, column Q). These systems, originally classifi ed as surface water CWSs in SDWIS, were re-assigned to the ground water CWS category. Note that this adjustment was not made for NTNCWSs because these systems are most often a single building or in a small area, and are less likely to be served by more than one source type.

Step 4: Convert System Inventory to Plant Inventory

The 1995 CWSS data (question 18 from the CWSS questionnaire) were used to estimate the number of treatment plants per system for both surface and ground water CWSs for all system sizes. The analysis produced a distribution of plants per system within each system size category. For analyses in this EA, EPA uses the mean plant per system estimate (presented in column W of Exhibit 3.2). For NTNCWSs, EPA assumed a 1:1 plant per system ratio for all sizes and source water types because these systems are most often a single building or located in a small area.

### Final Economic Analysis for the Stage 2 DBPR 3-13 December 2005 Step 1: Use modified inventory for surface water systems

Public Private Public Private Public Private Public Private

A B C D E=A+B+C+D F G H I J = F+G+H+I

<100 413 293 183 196 1,085 23 8 149 193 373 100-499 855 630 433 294 2,212 41 24 416 290 771 500-999 655 385 333 97 1,470 21 11 309 98 439 1,000-3,299 1,189 302 940 157 2,588 53 23 862 153 1,091 3,300-9,999 862 139 955 86 2,042 48 15 947 90 1,100 10,000-49,999 704 91 878 100 1,773 32 2 967 103 1,104 50,000-99,999 111 18 174 31 334 7 0 222 36 265 100,000-999,999 59 9 188 25 281 7 0 238 28 273 1,000,000+ 0 0 15 3 18 0 0 20 3 23
Total 4,848 1,867 4,099 989 11,803 232 83 4,130 994 5,439

<100 156 106 1,243 10,395 11,900 156 106 1,243 10,395 11,900 100-499 613 252 4,294 9,569 14,728 613 252 4,294 9,569 14,728 500-999 326 103 2,794 1,613 4,836 326 103 2,794 1,613 4,836 1,000-3,299 332 83 4,197 1,257 5,869 332 83 4,197 1,257 5,869 3,300-9,999 113 19 2,117 412 2,661 113 19 2,117 412 2,661 10,000-49,999 34 6 1,040 200 1,280 34 6 1,040 200 1,280 50,000-99,999 1 0 113 28 142 1 0 113 28 142 100,000-999,999 1 0 52 12 65 1 0 52 12 65 1,000,000+ 0 0 3 0 3 0 0 3 0 3
Total 1,576 569 15,853 23,486 41,484 1,576 569 15,853 23,486 41,484

<100 15 36 90 90 231 15 31 90 90 226
100-499 31 44 124 118 317 31 42 123 116 312
500-999 10 16 38 42 106 10 14 39 43 106 1,000-3,299 17 12 26 37 92 16 12 25 38 91 3,300-9,999 8 3 5 9 25 8 3 6 8 25 10,000-49,999 2 2 0 1 5 2 2 0 1 5 50,000-99,999 0 0 0 0 0 0 0 0 0 0 100,000-999,999 1 0 0 0 1 1 0 0 0 1 1,000,000+ 0 0 0 0 0 0 0 0 0 0
Total 84 113 283 297 777 83 104 283 296 766

<100 11 10 1,703 6,872 8,596 11 10 1,703 6,872 8,596 100-499 17 17 3,166 4,141 7,341 17 17 3,166 4,141 7,341 500-999 7 3 1,224 798 2,032 7 3 1,224 798 2,032 1,000-3,299 8 1 432 411 852 8 1 432 411 852 3,300-9,999 5 1 28 40 74 5 1 28 40 74 10,000-49,999 3 0 5 3 11 3 0 5 3 11 50,000-99,999 0 0 1 0 1 0 0 1 0 1 100,000-999,999 0 0 0 1 1 0 0 0 1 1 1,000,000+ 0 0 0 0 0 0 0 0 0 0
Total 51 32 6,559 12,266 18,908 51 32 6,559 12,266 18,908 Grand Total, All Systems 6,559 2,581 26,794 37,038 72,972 1,942 788 26,825 37,042 66,597

Sources: (A) - (D) SDWIS 4th Quarter 2003 Frozen Database, systems with an other ownership designation were considered public (F) - (I) Analysis of data in the SDWIS 4th Quarter 2003 Frozen Database Surface Water and All Mixed CWSs

Surface Water and All Mixed NTNCWSs Total Number of

Linked Systems

Ground Water-Only CWSs Remaining Unlinked Systems No. of Linked Systems

System Size (population served) No. of Purchased Systems from SDWIS No. of Non-Purchased Systems from SDWIS Total No. of Systems from SDWIS

Ground Water-Only NTNCWSs 

## Exhibit 3.2 Derivation of th e Stage 2 DBPR Plant Baseline

### Final Economic Analysis for the Stage 2 DBPR 3-14 December 2005 Public Private Public Private K L=K*F M=K*G N=K*H O=K*I P=L+M+N+O

<100 100% 23 8 149 193 373
100-499 100% 41 24 416 290 771
500-999 100% 21 11 309 98 439 1,000-3,299 100% 53 23 862 153 1,091 3,300-9,999 100% 48 15 947 90 1,100 10,000-49,999 100% 32 2 967 103 1,104 50,000-99,999 100% 7 0 222 36 265 100,000-999,999 100% 7 0 238 28 273 1,000,000+ 100% 0 0 20 3 23
Total - 232 83 4,130 994 5,439

<100 53% 82 56 656 5,489 6,283
100-499 78% 478 196 3,345 7,454 11,473
500-999 84% 274 87 2,347 1,355 4,062 1,000-3,299 80% 265 66 3,345 1,002 4,678 3,300-9,999 87% 98 16 1,838 358 2,310 10,000-49,999 97% 33 6 1,004 193 1,235 50,000-99,999 86% 1 0 98 24 123 100,000-999,999 96% 1 0 50 12 63 1,000,000+ 100% 00 3 0 3
Total - 1,231 427 12,685 15,886 30,229

<100 100% 15 31 90 90 226
100-499 100% 31 42 123 116 312
500-999 100% 10 14 39 43 106 1,000-3,299 100% 16 12 25 38 91 3,300-9,999 100% 8 3 6 8 25 10,000-49,999 100% 22 0 1 5 50,000-99,999 100% 00 0 0 0 100,000-999,999 100% 10 0 0 1 1,000,000+ 100% 00 0 0 0
Total - 83 104 283 296 766

<100 29% 3 3 494 1,993 2,493
100-499 29% 5 5 918 1,201 2,129
500-999 29% 2 1 355 231 589 1,000-3,299 29% 2 0 125 119 247 3,300-9,999 29% 1 0 8 12 21 10,000-49,999 29% 10 1 1 3 50,000-99,999 29% 00 0 0 0 100,000-999,999 29% 00 0 0 0 1,000,000+ 29% 00 0 0 0
Total - 15 9 1,902 3,557 5,483 Grand Total, All Systems - 1,561 624 19,000 20,733 41,918

Sources: Sources: No. of Linked Disinfecting Systems Remaining Disinfecting

Unlinked Systems

Percent Disinfecting

(K) Percentage of ground water CWSs that disinfect is estimated using percentage of treatment in place from the Third Edition of the Baseline Handbook ( Table B1.3.3 ) , ori g inall y derived from the 1995 CWSS. LINKED Disinfecting Surface Water and All Mixed CWSs

Disinfecting Ground Water-Only CWSs Total No. of Linked Disinfecting

Systems Step 2: Calculate Disinfecting Systems

System Size (population served)

LINKED Disinfecting Surface Water and All Mixed NTNCWSs

Disinfecting Ground Water-Only NTNCWSs 

## Exhibit 3.2 Derivation of the St age 2 DBPR Plant Baseline (Continued)

### Final Economic Analysis for the Stage 2 DBPR 3-15 December 2005 Public Private Public Private Q R S T U V=R+S+T+U

<100 3.7% 22 8 143 186 359
100-499 9.6% 37 22 376 262 697
500-999 0.0% 21 11 309 98 439 1,000-3,299 5.9% 50 22 811 144 1,027 3,300-9,999 12.0% 42 13 833 79 968 10,000-49,999 10.0% 29 2 870 93 994 50,000-99,999 8.9% 6 0 202 33 241 100,000-999,999 14.0% 6 0 205 24 235 1,000,000+ 0.0% 0 0 20 3 23
Total 8.4% 214 77 3,770 922 4,983 Primarily Ground Water CWSs
<100 - 83 56 662 5,496 6,297 100-499 - 481 199 3,385 7,482 11,547 500-999 - 274 87 2,347 1,355 4,062 1,000-3,299 - 268 68 3,396 1,011 4,742 3,300-9,999 - 104 18 1,951 368 2,442 10,000-49,999 - 36 6 1,100 203 1,346 50,000-99,999 - 1 0 117 27 146 100,000-999,999 - 2 0 83 15 101 1,000,000+ - 0 0 3 0 3
Total - 1,250 433 13,045 15,958 30,686

<100 0.0% 15 31 90 90 226
100-499 0.0% 31 42 123 116 312
500-999 0.0% 10 14 39 43 106 1,000-3,299 0.0% 16 12 25 38 91 3,300-9,999 0.0% 8 3 6 8 25 10,000-49,999 0.0% 2 2 0 1 5 50,000-99,999 0.0% 0 0 0 0 0 100,000-999,999 0.0% 1 0 0 0 1 1,000,000+ 0.0% 0 0 0 0 0
Total 0.0% 83 104 283 296 766

<100 - 3 3 494 1,993 2,493 100-499 - 5 5 918 1,201 2,129
500-999 - 2 1 355 231 589 1,000-3,299 - 2 0 125 119 247 3,300-9,999 - 1 0 8 12 21 10,000-49,999 - 1 0 1 1 3 50,000-99,999 - 0 0 0 0 0 100,000-999,999 - 0 0 0 0 0 1,000,000+ - 0 0 0 0 0
Total - 15 9 1,902 3,557 5,483 Grand Total, All Sys.

- 1,561 624 19,000 20,733 41,918

Sources: LINKED Primarily Surface Water CWSs Step 3: Re-allocate such that systems are categorized by primary source water type

(Q) Percentage of SW systems that are primarily GW from "Geometries and Characteristics of Public Water Supplies" (USEPA 2000c), Exhibit 2.9. ( R ) For surface water, R=L* ( 1-Q ) ; for g round water, R=L+ (( Q for SW ) * ( L for SW ))

. % SW that

are Primarily

GW

Primarily Ground Water NTNCWSs No. of Disinfecting, Purchased Systems by Primary Source

System Size (population served)

LINKED Primarily Surface Water NTNCWSs Total No. of Disinfecting, Linked Systems No. of Disinfecting,

Non-Purchased Systems by Source 

## Exhibit 3.2 Derivation of the St age 2 DBPR Plant Baseline (Continued)

### Final Economic Analysis for the Stage 2 DBPR 3-16 December 2005 Public Private Public Private W X=W*R Y=W*S Z=W*T AA=W*U AB = X+Y+Z+AA

<100 1.0 22 8 143 186 359
100-499 1.1 41 24 414 288 767
500-999 1.1 23 12 340 108 483 1,000-3,299 1.1 55 24 892 158 1,129 3,300-9,999 1.3 55 17 1,083 103 1,258 10,000-49,999 1.3 37 2 1,131 121 1,292 50,000-99,999 2.4 15 0 485 79 579 100,000-999,999 2.6 16 0 532 63 610 1,000,000+ 3.2 0 0 64 10 74
Total

- 264 87 5,086 1,115 6,552 Disinfecting Primarily Ground Water CWSs
<100 1.0 85 57 675 5,606 6,423 100-499 1.3 636 262 4,468 9,876 15,242 500-999 1.5 411 130 3,520 2,032 6,093 1,000-3,299 1.6 428 108 5,433 1,617 7,587 3,300-9,999 2.1 214 38 4,019 759 5,030 10,000-49,999 4.0 144 24 4,401 813 5,382 50,000-99,999 4.9 7 0 575 134 716 100,000-999,999 9.1 18 0 759 141 918 1,000,000+ 9.1 0 0 27 0 27
Total

- 1,942 619 23,879 20,979 47,419

<100 1.0 15 31 90 90 226
100-499 1.0 31 42 123 116 312
500-999 1.0 10 14 39 43 106 1,000-3,299 1.0 16 12 25 38 91 3,300-9,999 1.0 8 3 6 8 25 10,000-49,999 1.0 2 2 0 1 5 50,000-99,999 1.0 0 0 0 0 0 100,000-999,999 1.0 1 0 0 0 1 1,000,000+ 1.0 0 0 0 0 0
Total

- 83 104 283 296 766

<100 1.0 3 3 494 1,993 2,493 100-499 1.0 5 5 918 1,201 2,129
500-999 1.0 2 1 355 231 589 1,000-3,299 1.0 2 0 125 119 247 3,300-9,999 1.0 1 0 8 12 21 10,000-49,999 1.0 1 0 1 1 3 50,000-99,999 1.0 0 0 0 0 0 100,000-999,999 1.0 0 0 0 0 0 1,000,000+ 1.0 0 0 0 0 0
Total

- 15 9 1,902 3,557 5,483 Grand Total, All Sys.

- 2,304 819 31,150 25,947 60,220

Sources: LINKED Primarily Surface Water CWSs

(W) Derived from Question 18 of the 1995 CWSS, calculations based on classification of systems by primary source. Methodology to be included in subsequent drafts of the Geometries Document (USEPA 2000c). LINKED Disinfecting Primarily Ground Water NTNCWSs Disinfecting, Purchased Plants Disinfecting, NonPurchased Plants Step 4: Convert system in ventory to plant inventory

Total No.of Disinfecting Plants Plants per System System Size (population served) Baseline Number of Plants Subject to the Stage 2 DBPR

LINKED Primarily Surface Water NTNCWSs 

## Exhibit 3.2 Derivation of the St age 2 DBPR Plant Baseline (Continued)

### Final Economic Analysis for the Stage 2 DBPR 3-17 December 2005 

### 3.4.2.2 Population Baseline

The population baseline is used in the Stage 2 DBPR benefits analysis to help derive the cases of bladder cancer avoided as a result of treatment technology changes resulting from the Stage 2 DBPR (see chapter 6). Because the benefits of the rule ar e a function of treatment technology changes and subsequent DBP reductions, the population baseline must be consistent with the plant baseline. Thus, the derivation of the Stage 2 DBPR population baseline is si milar to that of the Stage 2 DBPR plant baseline (with the exception of Step 4– convert system inventory to plant inventory; this is not needed given that the population served by all plants in a size category and all systems in a size category are the same).

Note that because NTNCWs are most often bus inesses such as restaurants, schools, campgrounds, etc., their population generally duplicates the popula tion served by CWSs. Total population served by disinfecting systems as derived in this section is just the total of the CWS population served, or 264,513,763 (169,358,139 + 95,155,624) from Exhibit 3.3, column Q.

Step 1: Modify the Surface Water Syst em Inventory by Linking Buyers and Sellers

As with the plant baseline, EPA has modified th e system-level population data in SDWIS to add, for surface water systems, populations served by purchasing systems to the population served by their wholesale seller. The overall effect of this step shifts population into higher system size categories; however, it does not alter the total population served by all surface water systems as reported in SDWIS. Section 3.4.2.1 provides the rationale and detaile d methodology used to modify the surface water inventory.

Step 2: Remove Population of Systems that do not Disinfect

The Stage 2 DBPR applies only to systems that disi nfect their water. Therefore, systems that do not disinfect were removed from the baseline. The inventory is reduced by the percent disinfecting (shown in Exhibit 3.3, column F) to produce the results shown in columns G through K.

The estimate of percent disinfecting in column P is derived in section 3.4.2.1

Step 3: Re-classify Population by Primary Source Water Type

As with the plant baseline, EPA modified population data from SDWIS to represent populations served either by primarily ground or primarily surface water systems. Given that SDWIS does not contain information on whether or not a system is mixed or the relative proportions of surface and ground water flow used, EPA used flow data from the 1995 CWSS to estimate the proportion of surface water and mixed CWSs that received more than 50 pe rcent of their flow from a ground water source (percentages are shown in Exhibit 3.3, column L). This population, originally classified as served by surface water CWSs in SDWIS, was re-assigned to th e ground water CWS category. This adjustment was not made for NTNCWSs because these systems are most of ten a single building or in a small area, and are less likely to be served by more than one source type.

### Final Economic Analysis for the Stage 2 DBPR 3-18 December 2005 Public Private Public Private

A B C D E=A+B+C+D

<100 1,124 399 8,260 10,743 20,526 100-499 10,817 5,925 114,084 68,769 199,595 500-999 15,425 7,381 229,498 65,606 317,910 1,000-3,299 107,441 47,998 1,735,746 284,439 2,175,624 3,300-9,999 287,030 76,258 5,746,521 545,907 6,655,716 10,000-49,999 693,894 38,135 22,634,702 2,537,058 25,903,789 50,000-99,999 476,236 0 15,181,764 2,591,527 18,249,527 100,000-999,999 2,432,694 0 63,779,714 9,798,126 76,010,534 1,000,000+ 0 0 48,565,698 7,062,250 55,627,948
Total 4,024,661 176,096 157,995,987 22,964,425 185,161,169

<100 9,640 6,456 73,773 604,212 694,081 100-499 165,625 64,176 1,180,056 2,054,329 3,464,186 500-999 239,129 70,939 2,022,042 1,111,269 3,443,379 1,000-3,299 594,474 146,219 7,850,024 2,201,328 10,792,045 3,300-9,999 586,870 97,393 11,954,771 2,347,681 14,986,715 10,000-49,999 644,110 82,397 21,421,779 4,180,506 26,328,792 50,000-99,999 87,933 0 7,191,107 1,955,231 9,234,271 100,000-999,999 140,000 0 11,222,309 2,108,763 13,471,072 1,000,000+ 0 0 3,933,533 0 3,933,533
Total 2,467,781 467,580 66,849,394 16,563,319 86,348,074

<100 752 1,578 4,285 4,486 11,101 100-499 7,481 9,531 27,491 27,624 72,127 500-999 6,659 8,568 25,534 29,560 70,321 1,000-3,299 29,057 18,649 45,178 60,403 153,287 3,300-9,999 49,231 13,003 24,537 38,642 125,413 10,000-49,999 72,000 43,055 0 13,000 128,055 50,000-99,999 0 0 0 0 0 100,000-999,999 169,846 0 0 0 169,846 1,000,000+ 0 0 0 0 0
Total 335,026 94,384 127,025 173,715 730,150

<100 545 487 87,724 344,860 433,616 100-499 3,984 3,080 806,717 845,693 1,659,474 500-999 4,510 1,950 824,809 535,712 1,366,981 1,000-3,299 13,823 1,350 657,954 649,238 1,322,365 3,300-9,999 23,800 4,800 145,634 207,114 381,348 10,000-49,999 55,200 0 137,008 36,200 228,408 50,000-99,999 0 0 66,000 0 66,000 100,000-999,999 0 0 0 110,000 110,000 1,000,000+ 0 0 0 0 0
Total 101,862 11,667 2,725,846 2,728,817 5,568,192 Grand Total, All Systems 6,929,330 749,727 227,698,252 42,430,276 277,807,585 System Size (population served) Population Served by Unlinked Systems Population Served by Linked Systems Total Population

Served Step 1: Use linked inventory for SW systems

(A-D) for Surface Water CWSs & NTNCWSs: "Linked" system inventory derived from SDWIS 4th Quarter Year 2003 Freeze data. See section 3.4.2.2 for a description of linking methodology. Ground Water-Only NTNCWSs LINKED Surface Water and All Mixed CWSs

Sources: LINKED Surface Water and All Mixed NTNCWSs Ground Water-Only CWSs 

## Exhibit 3.3 Derivation of the Stage 2 DBPR Population Baseline

### Final Economic Analysis for the Stage 2 DBPR 3-19 December 2005 Public Private Public Private F G=A*F H=B*F I =C*F J=D*F K=G+H+I+J

<100 100% 1,124 399 8,260 10,743 20,526 100-499 100% 10,817 5,925 114,084 68,769 199,595 500-999 100% 15,425 7,381 229,498 65,606 317,910 1,000-3,299 100% 107,441 47,998 1,735,746 284,439 2,175,624 3,300-9,999 100% 287,030 76,258 5,746,521 545,907 6,655,716 10,000-49,999 100% 693,894 38,135 22,634,702 2,537,058 25,903,789 50,000-99,999 100% 476,236 0 15,181,764 2,591,527 18,249,527 100,000-999,999 100% 2,432,694 0 63,779,714 9,798,126 76,010,534 1,000,000+ 100% 0 0 48,565,698 7,062,250 55,627,948
Total - 4,024,661 176,096 157,995,987 22,964,425 185,161,169

<100 53% 5,090 3,409 38,952 319,024 366,475 100-499 78% 129,022 49,993 919,264 1,600,322 2,698,601 500-999 84% 200,868 59,589 1,698,515 933,466 2,892,438 1,000-3,299 80% 473,796 116,537 6,256,469 1,754,458 8,601,260 3,300-9,999 87% 509,403 84,537 10,376,741 2,037,787 13,008,469 10,000-49,999 97% 621,566 79,513 20,672,017 4,034,188 25,407,284 50,000-99,999 86% 75,886 0 6,205,925 1,687,364 7,969,176 100,000-999,999 96% 134,960 0 10,818,306 2,032,848 12,986,113 1,000,000+ 100% 0 0 3,933,533 0 3,933,533
Total - 2,150,591 393,577 60,919,722 14,399,458 77,863,349

<100 100% 752 1,578 4,285 4,486 11,101 100-499 100% 7,481 9,531 27,491 27,624 72,127 500-999 100% 6,659 8,568 25,534 29,560 70,321 1,000-3,299 100% 29,057 18,649 45,178 60,403 153,287 3,300-9,999 100% 49,231 13,003 24,537 38,642 125,413 10,000-49,999 100% 72,000 43,055 0 13,000 128,055 50,000-99,999 100% 0 0 0 0 0 100,000-999,999 100% 169,846 0 0 0 169,846 1,000,000+ 100% 0 0 0 0 0
Total - 335,026 94,384 127,025 173,715 730,150

<100 29% 158 141 25,440 100,009 125,749 100-499 29% 1,155 893 233,948 245,251 481,247 500-999 29% 1,308 566 239,195 155,356 396,424 1,000-3,299 29% 4,009 392 190,807 188,279 383,486 3,300-9,999 29% 6,902 1,392 42,234 60,063 110,591 10,000-49,999 29% 16,008 0 39,732 10,498 66,238 50,000-99,999 29% 0 0 19,140 0 19,140 100,000-999,999 29% 0 0 0 31,900 31,900 1,000,000+ 29% 0 0 0 0 0
Total - 29,540 3,383 790,495 791,357 1,614,776 Grand Total, All Systems - 6,539,818 667,441 219,833,230 38,328,955 265,369,444

(F) Percentage of ground water CWSs that disinfect is estimated using percentage of treatment in place from the Third Edition of the Baseline Handbook (Table B1.3.3), originally derived from the 1995 CWSS. Disinfecting Ground Water-Only NTNCWSs

Sources: Step 2: Remove population which doesn't disinfect

System Size (population served) Population Served by Remaining Unlinked, Disinfecting Systems Population Served by Linked, Disinfecting Systems Total Population Served by Disinfecting Systems Percent Disinfecting

LINKED Surface Water and All Mixed CWSs

Disinfecting Ground Water-Only CWSs

LINKED Surface Water and All Mixed NTNCWSs 

## Exhibit 3.3 Derivation of the Stag e 2 DBPR Population Baseline (Continued)

### Final Economic Analysis for the Stage 2 DBPR 3-20 December 2005 Public Private Public Private L M N O P Q=M+N+O+P

<100 3.7% 1,082 384 7,954 10,346 19,767 100-499 9.6% 9,779 5,356 103,132 62,167 180,434 500-999 0.0% 15,425 7,381 229,498 65,606 317,910 1,000-3,299 5.9% 101,102 45,166 1,633,337 267,657 2,047,262 3,300-9,999 12.0% 252,586 67,107 5,056,938 480,398 5,857,030 10,000-49,999 10.0% 624,505 34,322 20,371,232 2,283,352 23,313,410 50,000-99,999 8.9% 433,851 0 13,830,587 2,360,881 16,625,319 100,000-999,999 14.0% 2,092,117 0 54,850,554 8,426,388 65,369,059 1,000,000+ 0.0% 0 0 48,565,698 7,062,250 55,627,948
Total - 3,530,447 159,716 144,648,931 21,019,046 169,358,139

<100 - 5,132 3,424 39,258 319,421 367,234 100-499 - 130,060 50,562 930,216 1,606,924 2,717,762 500-999 - 200,868 59,589 1,698,515 933,466 2,892,438 1,000-3,299 - 480,135 119,368 6,358,878 1,771,240 8,729,622 3,300-9,999 - 543,847 93,688 11,066,324 2,103,296 13,807,155 10,000-49,999 - 690,956 83,327 22,935,487 4,287,894 27,997,663 50,000-99,999 - 118,271 0 7,557,102 1,918,010 9,593,384 100,000-999,999 - 475,537 0 19,747,466 3,404,585 23,627,588 1,000,000+ - 0 0 3,933,533 0 3,933,533
Total - 2,644,806 409,957 74,266,779 16,344,837 93,666,379

<100 0.0% 752 1,578 4,285 4,486 11,101 100-499 0.0% 7,481 9,531 27,491 27,624 72,127 500-999 0.0% 6,659 8,568 25,534 29,560 70,321 1,000-3,299 0.0% 29,057 18,649 45,178 60,403 153,287 3,300-9,999 0.0% 49,231 13,003 24,537 38,642 125,413 10,000-49,999 0.0% 72,000 43,055 0 13,000 128,055 50,000-99,999 0.0% 0 0 0 0 0 100,000-999,999 0.0% 169,846 0 0 0 169,846 1,000,000+ 0.0% 0 0 0 0 0
Total - 335,026 94,384 127,025 173,715 730,150

<100 - 158 141 25,440 100,009 125,749 100-499 - 1,155 893 233,948 245,251 481,247 500-999 - 1,308 566 239,195 155,356 396,424 1,000-3,299 - 4,009 392 190,807 188,279 383,486 3,300-9,999 - 6,902 1,392 42,234 60,063 110,591 10,000-49,999 - 16,008 0 39,732 10,498 66,238 50,000-99,999 - 0 0 19,140 0 19,140 100,000-999,999 - 0 0 0 31,900 31,900 1,000,000+ - 0 0 0 0 0
Total - 29,540 3,383 790,495 791,357 1,614,776 Grand Total, All Systems

- 6,539,818 667,441 219,833,230 38,328,955 265,369,444 Step 3: Re-allocate such that systems are categorized by primary source water type

LINKED Primarily Surface Water NTNCWSs

Disinfecting Primarily Ground Water NTNCWSs Total Population Served by Disinfecting

Systems Baseline Number of People Subject to the Stage 2 DBPR

% SW that are Primarily

GW Population Served by Remaining Unlinked, Disinfecting Systems Population Served by Linked, Disinfecting Systems System Size (population served)

(P) For surface water, P=J*(1-L); For ground water, P=J+((J for SW)*(L for SW)). (M) For surface water, M=G*(1-L); For ground water, M=G+((G for SW)*(L for SW)). (O) For surface water, O=I*(1-L); For ground water, O=I+((I for SW)*(L for SW)). Sources: (L) Percentage of SW systems that are primarily GW from "Geometries and Characteristics of Public Water Supplies" (USEPA 2000c), Exhibit 2.9. LINKED Primarily Surface Water CWSs

Disinfecting Primarily Ground Water CWSs

Note: Detail may not add due to independent rounding. 

## Exhibit 3.3 Derivation of the Stag e 2 DBPR Population Baseline (Continued)5 

### Equations are from the 3 nd 

### Edition of the Baseline Handbook as derived in December 2000 Model Systems Report (USEPA 2000c).

Final Economic Analysis for the Stage 2 DBPR 3-21 December 2005 

### 3.4.3 Water Treatment Plant Design and Average Daily Flows

Treatment technology costs depend on the volume of water treated per day. The cost analysis described in Chapter 7 uses two types of treatment plant flow: (1) design flow, which is the maximum capacity at which the plant was intended to operate, expressed in millions of gallons per day (MGD), and (2) average daily flow, which is the flow produced by a treatment plant in one day, an average derived from 365 days of flow measurements, expressed in M GD. Design flows are used to estimate the capital costs of the treatment technology that will be installed to meet the requirements of the Stage 2 DBPR. Average daily flows are used to estimate the annua l cost of ongoing operations and maintenance (O&M).

To estimate flows for different sized systems, EPA developed the following regression equations:

Surface Water: Design Flow (MGD) = 0.36971 X 0.97757 

### / 1,000 Average Daily Flow (MGD) = 0.10540 X 1.02058 

### / 1,000

Ground Water: Design Flow (MGD) = 0.39639 X 0.97708 

### / 1,000 Average Daily Flow (MGD) = 0.06428 X 1.07652 

### / 1,000

Where X = mean population served per system. 5



### These equations are based on 1995 CWSS data. Their derivation is presented in detail in the Model Systems Report (USEPA 2000c) and summarized in the Baseline Handbook (USEPA 2001c). The equations are used in this EA to estimate mean flows per plant for each size category, using the mean population served per plant. (The mean population ser ved per plant can be calculated by dividing the total population for a given size category presented in Exhibit 3.3, column Q, by the baseline number of plants in that size category as presented in Exhibit 3.2, column V.). Exhibit 3.4 shows the population per system, the number of plants per system, and the design and average flows per plant.

This EA uses a single regression equation to estimat e flows for either public or privately owned systems. There is, however, a slight difference in the flow characteristics for these two ownership types, as discussed in the Model Systems Report (USEPA 2000c). The use of different flow equations for public and private systems would not affect tota l national costs, although per-household costs may be slightly affected. EPA has evaluated the equations and believes that the differences are small and would have a negligible effect on household costs.

### Final Economic Analysis for the Stage 2 DBPR 3-22 December 2005 System Size (Population

Served) Average No. of Population Served per System Average No. of Plants/System Design Flows (MGD) Per Plant A verage Daily Flow (MGD) Per Plant

X Y Z = 0.36971 X 0.97757 /1000Y AA = 0.10540 X 1.02058 /1000Y <100 55.0 1.0 0.019 0.006 100-499 258.9 1.1 0.077 0.028 500-999 724.2 1.1 0.210 0.079 1,000-3,299 1,994.2 1.1 0.565 0.223 3,300-9,999 6,050.7 1.3 1.415 0.587 10,000-49,999 23,463.6 1.3 5.325 2.340 50,000-99,999 68,866.1 2.4 8.263 3.804 100,000-999,999 278,426.9 2.6 29.886 14.609 1,000,000+ 2,418,606.4 3.2 200.952 107.803

X Y Z = 0.39639 X 0.97708 /1000Y AA = 0.06428 X 1.07652 /1000Y
<100 58.3 1.0 0.021 0.005 100-499 235.4 1.3 0.062 0.017 500-999 712.0 1.5 0.162 0.050 1,000-3,299 1,840.9 1.6 0.384 0.131 3,300-9,999 5,654.6 2.1 0.893 0.342 10,000-49,999 20,806.8 4.0 1.642 0.716 50,000-99,999 65,649.2 4.9 4.119 2.012 100,000-999,999 234,214.8 9.1 7.685 4.261 1,000,000+ 1,311,177.7 9.1 41.355 27.216

X Y Z = 0.36971 X 0.97757 /1000Y AA = 0.10540 X 1.02058 /1000Y <100 49.1 1.0 0.017 0.006 100-499 231.2 1.0 0.076 0.027 500-999 663.4 1.0 0.212 0.080 1,000-3,299 1,684.5 1.0 0.527 0.207 3,300-9,999 5,016.5 1.0 1.532 0.630 10,000-49,999 25,611.0 1.0 7.541 3.327 50,000-99,999 - 1.0 - - 100,000-999,999 169,846.0 1.0 47.930 22.937 1,000,000+ - 1.0 - -

X Y Z = 0.39639 X 0.97708 /1000Y AA = 0.06428 X 1.07652 /1000Y
<100 50.4 1.0 0.018 0.004 100-499 226.1 1.0 0.079 0.022 500-999 672.7 1.0 0.230 0.071 1,000-3,299 1,552.1 1.0 0.520 0.175 3,300-9,999 5,153.4 1.0 1.679 0.637 10,000-49,999 20,764.4 1.0 6.554 2.856 50,000-99,999 66,000.0 1.0 20.286 9.918 100,000-999,999 110,000.0 1.0 33.417 17.188 1,000,000+ - 1.0 - -

Source: Equations relating mean population to flow are from the Baseline Handbook (USEPA 2001c).
X is the total population for the size category (Exhibit 3.3, column Q) divided by the total number of systems for the size cate gory (Exhibit 3.2, column V). Note: Formulas may not produce exact results due to independent rounding (average people per plant includes fractions). Disinfecting Primarily Ground Water NTNCWSs LINKED, Primarily Surface Water NTNCWSs LINKED, Primarily Surface Water CWSs

Disinfecting Primarily Ground Water CWSs 

## Exhibit 3.4 Design Flows and Average Daily Flows per Plant (MGD)

### Final Economic Analysis for the Stage 2 DBPR 3-23 December 2005 Number of Households Served Linked, Primarily Surface Water Primarily Disinfecting Ground Water
<100 7,632 141,789
100-499 69,666 1,049,329
500-999 122,745 1,116,772 1,000-3,299 790,449 3,370,510 3,300-9,999 2,261,402 5,330,948 10,000-49,999 9,001,317 10,809,909 50,000-99,999 6,419,042 3,704,009 100,000-999,999 25,239,019 9,122,621 1,000,000+ 21,477,972 1,518,739
Total 65,389,243 36,164,625 National Total Note: Detail may not add due to independent rounding. System Size (Population Served)

101,553,868

Source: Calculated by dividing the total p opulation served (Exhibit 3.3, column Q) by 2.59, the average number of people per household (U.S. Census Bureau 2001). 

### Comparable analyses relating average daily and design flow to population was not performed for the NTNCWSs. Other drinking water rules have evaluated flows for NTNCWSs according to service categories (e.g., schools, restaurants, hotels, industry) instead of size. EPA considered using this method for evaluating NTNCWSs for the Stage 2 DBPR, but decided against it for the following reasons:

• Service category flows are based on mean population served for all systems in that category, regardless of source water type. EPA expects that surface water and GWUDI sources would be more prevalent in larger NTNCWSs, but has no basis for developing revised population estimates for each service category by source.

• The prediction of treatment technology selec tion in Chapter 7 is a function of population served and does not directly apply to service categories that may include a wide range of water system sizes and flows (e.g., schools can be very small local buildings or metropolitan high schools).

EPA, therefore, applied the CWS regression equati ons to NTNCWSs, recognizing that this may over-estimate flows and, therefore, costs. This ove r-estimation is addressed as part of the uncertainties summarized in section 3.8. Note that because the ratio of plants per system was assumed to be 1:1 for all NTNCWSs, plant flows equal system flows. Mean plant flows for CWSs and NTNCWSs may differ from each other because of the difference in mean population per plant within each size category.

3.4.4 Number of Households Served

The number of households served by CWSs expect ed to be subject to the Stage 2 DBPR is estimated by dividing the population for each system size category by the average number of people per household (2.59) (U.S. Census Bureau 2001). As s hown in Exhibit 3.5, PWSs serve about 102 million households.



## Exhibit 3.5 Number of Households Subject to the Stage 2 DBPR

Note: Detail may not add due to independent rounding. Source: Calculated by dividing the total population serv ed (Exhibit 3.3, column Q) by 2.59, the average number of people per household (U.S. Census Bureau 2001).

### Final Economic Analysis for the Stage 2 DBPR 3-24 December 2005 

## 3.5 Influent Water Quality Characterization

3.5.1 Summary of Available Influent Water Quality Data

Predictions of compliance forecasts assume that a system will choose a treatment technology for the Stage 2 DBPR that best addresses its water quality improvement needs. The quality of the source water plays a key role in evaluating treatment altern atives to meet regulatory requirements. This section provides an overview of influent water quality on a national level.

Exhibit 3.6 summarizes the influent water quality da ta that were collected under the ICR for large surface and ground water systems. These data represent th e parameters that most affect DBP formation in disinfected waters. The median, 90 th 

### percentile, and range provide some insight into the variability in plant means among all large surface and ground water plants.

Monthly plant data collected during the last 12 months of the ICR collection period (January 1998–December 1998) were averaged to estimate a “plant -mean” value for each plant. Only the last 12 months were evaluated because they appear to be of higher quality than data collected during the first 6 months of the survey. In addition, using all 18 mont hs of data could skew results (data from the last 2 quarters of the year would be counted twice).

ICR summary data in Exhibit 3.6 are grouped according to “Ground” or “Surface” water plants types. This designation is based on the source wate r type reported by the plant for each month from July 1997 to December 1998. The types of sources record ed were surface water, ground water, mixed, or purchased. Most plants reported on one source type for all months, but some plants reported surface water for some months and mixed for others. These plants were considered surface water plants. One ground water plant reported ground water for some months and mixed for others—this plant was considered a ground water. Analyses of all plants includes “blended,” “mixed,” and “purchased” planttypes from the ICR database. These plant types make up a small portion (less than 10 percent) of the total—most ICR plants are categorized as either surface or ground water plants.

Data for these influent water quality parameters were part of the input data for SWAT. They were also used by the Delphi Group, a group of industry experts gathered to advise EPA on technical issues surrounding the rule, and the small-system experts (both for surface water and ground water) to assess treatment alternatives. For a complete char acterization and discussion of these parameters, see Chapter 3 of the Occurrence Document (USEPA 2005k).

### Final Economic Analysis for the Stage 2 DBPR 3-25 December 2005 

## Exhibit 3.6 ICR Large System Influent Water Quality Parameters— Summary of Pre-Stage 1 Plant-Mean Data

Parameter Source Type Number of Plants Mean of Plant Means Median of Plant Means 90 th 

### Percentile of Plant Means Range of Plant Means

Alkalinity (mg/L as CaCO 3 

### ) Surface 336 81 79 165 2.75 – 273

Ground 121 159 156 264 1.00 – 415

Bromide (mg/L) Surface 320 0.055 0. 027 0.115 0 – 1.325

Ground 118 0.103 0.066 0.190 0 – 1.325

pH Surface 336 7.6 7.7 8.2 6.0 – 8.5

Ground 119 7.3 7.4 8.0 4.1 – 8.8

Temperature (°C) Surface 334 16.0 16.1 20.7 3.7 – 27.7

Ground 121 19.9 20.1 26.3 9.5 – 30.5

Total Hardness (mg/L as CaCO 3 

### ) Surface 326 117 109 251 3.1 – 501

Ground 116 194 181 352 3.6 – 778
Total Organic Carbon (mg/L as C) Surface 307 3.14 2. 71 5.29 0 – 21.4

Ground 103 1.46 0.19 3.36 0 – 16.1

Turbidity (Nephelometric Turbidity Units) Surface 328 18.2 6.7 34.0 0.06 – 529

Ground 116 1.3 0.2 2.6 0.03 – 38.7

UV 254 

### Absorbance (cm -1 

### ) Surface 306 0.098 0. 079 0.176 0 – 0.880

Ground 104 0.062 0.009 0.266 0 – 0.606 Note: The maximum surface water bromide mean value, 3.13 m illigrams per liter (mg/L), is not shown. This value was calculated based on a one-month reported bromide concentra tion of 28 mg/L, which EPA assumes to be a reporting error. (Laboratories often report bromi de values in μg/L, rather than mg/L; this value may not have been converted to mg/L.) All the other values for that plant in t he last 12 months of the ICR were below 0.1 mg/L. Source: ICR AUX1 database (USEPA 2000h). Represents dist ribution of plant-mean data as calculated using ICR monthly data from the last 12 months of the ICR (January 1998 - December 1998). Only plants with reported data for at least 9 of the 12 months are included in this summary table. Does not include blended, mixed, or purchased plants.

### Final Economic Analysis for the Stage 2 DBPR 3-26 December 2005 

### A key influent water quality parameter related to Stage 2 DBPR compliance is TOC. TOC is a measure of organic content in the water and is gene rally a good indicator of the concentrations of total trihalomethanes (TTHM) and haloacetic acid (HAA5) precursors. The distribution of plant-mean TOC concentrations for plants with surface water sources covers a large range (from 0 to 21.4 mg/L (Exhibit 3.6)); however, 90 percent of the plants had mean TOC concentrations below 5.3 mg/L. Exhibit 3.7 shows the distribution of plant-mean TOC concentr ations for surface water and ground water plants for the subset of plants shown in Exhibit 3.6. For ground water plants, 70 percent had mean TOC concentrations below 1 mg/L; the highest values were close to those for surface water plants. A large percentage of ICR ground water plants (approximately 25 percent) are located in Florida, where high levels of TOC occur in ground water.

Bromide in source water can affect the amount and type of DBPs formed, shifting the distribution of DBPs more to the brominated species. Also, br omide can react with ozone and chlorine dioxide to form bromate, another byproduct of concern. As show n in Exhibit 3.6, most of the plant-mean bromide levels are relatively low (the 90 th 

### percentiles were 0.122 and 0.190 mg/L for surface water and ground water sources, respectively). Exhibit 3.8 shows the di stribution of mean bromide concentrations for large surface water and ground water plants.

There is no extensive data set similar to the I CR that provides comparable influent water quality data for medium and small systems. Therefore, as noted in section 3.2, several alternative data sources were used to characterize these systems and compare their water quality to the large systems. These data sources include the ICR supplemental survey (ICR SS), NRWA data, AWWA WATER:\Stats data, and data from individual states. The ICRSS is a survey m eant to compliment the ICR data set. It is a survey of raw source water quality and DBP concentrations from 40 random plants each from the small, medium, and large size categories. The NRWA surveyed 117 random small plants nationwide and determined treatment process, source water quality, and DBP concentrations. The WATER:\STATS database was compiled by AWWA and contains source water quality , treatment processes, and DBP concentrations for 872 member plants of mostly medium and large size categories. The State data include DBP monitoring data from 10 States representing 562 small surface water systems and 2,336 small ground water systems. The Occurrence Document (USEPA 2005k) provides an overview of each alternative data set and compares source water quality for medium and small systems. Exhibit 3.9 provides a summary of influent water quality data from these sources for me dium and small surface and ground water systems. Appendices A and B provide additional detail regarding influent water qua lity data that are relevant to compliance forecast analyses for surface and ground water systems, respectively.

3.5.2 Regional Differences in Water Quality

EPA evaluated ICR data for surface and ground water systems to determine if there were differences in influent water quality among regions. Exhibits 3.10 and 3.11 show the range of average TOC concentrations by State for surface and ground wate r systems, respectively, using ICR data. Exhibit 3.12 shows average TOC concentrations by Stat e for ground water systems using Ground Water Supply Survey (GWSS) data. Surface water systems did not exhibit any notable regional trends; however, ICR data and GWSS data show that Florida has very high TOC concentrations compared to other States. Florida also has the largest proportion of large ground water systems of all the States. The ICR Ground Water Delphi Group estimated that, of the large and medium ground water plants that will need to make changes to comply with the Stage 2 DBPR (which includes a requirement for the IDSE), more than 80 percent are in Florida (see Appendix B, Exhibit B.4 for compliance forecast data on ground water systems).

### Final Economic Analysis for the Stage 2 DBPR 3-27 December 2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 2 4 6 8 10 12 14 16 18 20 22 24 Plant-Mean TOC (mg/L as C)Percentile SW ICR TOC Plant-Means (N=307) GW ICR TOC Plant-Means (N=103) 

## Exhibit 3.7 Cumulative Distribution of TOC in Influent Water of Large System ICR Plant-Mean Data

Source: ICR AUX1 database (USEPA 2000h).

### Final Economic Analysis for the Stage 2 DBPR 3-28 December 2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 0.2 0.4 0.6 0.8 1 1.2 1.4 Plant-Mean Bromide (mg/L)Percentile SW ICR Bromide Plant-Means (N=320) GW ICR Bromide Plant-Means (N=118) 

## Exhibit 3.8 Cumulative Dist ribution of Bromide in Influent Water of Large System ICR Plant-Mean Data

Source: Each data point in the distri bution represents the mean va lue of monthly data collect ed at a single plant over a 12-month period (January 1998–December 1998). Only plants with reported data for at leas t 9 of the 12 months are included in this summary table (USEPA 2000h).

### Final Economic Analysis for the Stage 2 DBPR 3-29 December 2005 

## Exhibit 3.9 Medium and Small System Influent Water Quality Parameters– Summary of Pre-Stage 1 Plant-Mean Data

Data Source/Size Category N Mean ofPlantMeans Median ofPlantMeans 90 th 

### Percentile of Plant-Means Range of Plant-Means Source Water Alkalinity (mg/L as CaCO 3 

### )
NRWA Small Surface Water (SW) Plants 95 81 74 146 0 - 281
ICR Supplemental Survey (ICR SS) Medium SW Plants 40 82 74 159 4.8 - 240

ICR SS Small SW Plants 38 66 55 123 4.4 - 249 Source Water Bromide (mg/L)
NRWA Small SW Plants 95 0.063 0.021 0.107 0-1.72
ICR SS Medium SW Plants 40 0.050 0.016 0.092 0 - 0.53
ICR SS Small SW Plants 38 0.02 0 0.044 0 - 0.27 Source Water pH
NRWA Small SW Plants 78 7.3 7.5 8.1 3.8 - 8.8
ICR SS Medium SW Plants 40 7.6 7.6 8.2 5.9 - 8.4
ICR SS Small SW Plants 36 7.3 7.4 8.0 5.8 - 8.3 Source Water TOC (mg/L as C)
NRWA Small SW Plants 96 3.0 2.6 5.4 0.3 - 9.0
ICR SS Medium SW Plants 40 3.6 3.7 5.5 0.2 - 7.9
ICR SS Small SW Plants 38 2.4 2.1 4.5 0.1 - 7.1
WATER:\STATS Medium SW Plants 102 5.6 3.2 6.4 0 - 200

WATER:\STATS Medium GW Plants 51 2.3 0.79 7.0 0 - 25
Source Water Turbidity (NTU)
NRWA Small SW Plants 76 7.8 4.1 18 0.1 - 65
ICR SS Medium SW Plants 40 13 5.9 33 1 - 103
ICR SS Small SW Plants 36 6.2 3.5 13 0.3 - 43 Source Water UV-254 (cm -1 

### )
NRWA Small SW Plants 96 0.082 0.074 0.127 0.01 - 0.23
ICR SS Medium SW Plants 40 0.093 0.083 0.171 0.03 - 0.21
ICR SS Small SW Plants 38 0.074 0.051 0.113 0.02 - 0.44 Note: ICR SS data are the plant-means for plants that took at least three-four ths of the total possible samples for each parameter. Only plants that had both a Winter and Summer sample are included in the NRWA data for this analysis.

### Final Economic Analysis for the Stage 2 DBPR 3-30 December 2005 

### TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L C

C C C C

C C

C C C C

C C C

C C
C A

A

A A B B

B B

B

C

D D D

D D D D D D D



### TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L C

C C C C

C C

C C C C

C C C

C C
C A

A

A A B B

B B

B

C

D D D

D D D D D D D



### TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L A

A A A A

A A

A

A

A A A

A

A A

A A
A A A

A A

B B

B B

A C

C

C D

D 

### TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L A

A A A A

A A

A

A

A A A

A

A A

A A
A A A

A A

B B

B B

A C

C

C D

D 

## Exhibit 3.10 Influent Water TOC Dist ribution for ICR Surface Water Systems

Source: ICR AUX1 Database (USEPA 2000h); m ean of all plant-means for each State.



## Exhibit 3.11 Influent Water TOC Di stribution for ICR Ground Water Systems

Source: ICR AUX1 Database (USEPA 2000h); m ean of all plant-means for each State.

### Final Economic Analysis for the Stage 2 DBPR 3-31 December 2005 

### TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L A A A A

A

A A A

A

A

A A A A A A A A A A A

A
A A A A

A B

B B

B

B B B

B B

B

B B B

B

B B

B B

A C C

C C C

D

D D 

### TOC >= 2 to 3 mg/L TOC >= 3 to 4 mg/L No Data TOC < 1 to mg/L TOC >= 1 to 2 mg/L

TOC >= 4 mg/L A A A A

A

A A A

A

A

A A A A A A A A A A A

A
A A A A

A B

B B

B

B B B

B B

B

B B B

B

B B

B B

A C C

C C C

D

D D 

## Exhibit 3.12 Influent Water TOC Dist ribution for Ground Water Systems Derived from the Ground Water Supply Survey

Source: Ground Water Supply Survey (USEPA 1983)



## 3.6 Treatment Characterization for the Pre-Stage 1 Baseline

This section summarizes treatment conditions for the pre-Stage 1 baseline estimate of treatment technologies-in-place for the baseline. Chapter 5 provides further detail on treatment technologies and compliance forecast methodology used in this EA.

Although cost analyses in Chapter 7 are performed for each of the nine system size categories separately, treatment characterizations are predicte d according to the following aggregated categories by population served:

• Small systems

• Serving fewer than 100 people

• Serving 100 to 999 people

• Serving 1,000 to 9,999 people

• Medium systems—serving 10,000 to 99,999 people

• Large systems—serving 100,000 or more people

Small systems were stratified by the three population categories shown above to represent differences in the number of systems needing to change treatment technologies and the technology6 

### As described in Appendix A, the treatment technologies used to characterize th e pre-Stage 1and pre-Stage 2 baselines are different than those presented in the Stag e 1 RIA. New tools to charact erize treatment technologiesin-place have been made available since the Stage 1 DBPR was promulgated, notably the SWAT Model.

Final Economic Analysis for the Stage 2 DBPR 3-32 December 2005 

### options available to each category. The treatment ch aracterizations presented here and in Chapter 7 show the nine population size categories used for costing, but present one compliance forecast (as a percentage of the total baseline number of plants) for each of the population size categories listed above.

Exhibits 3.13 and 3.14 summarize the pre-Stage 1 DBPR baseline treatment technologies in place for surface and ground water treatment plants, respectively 6 

### . For plants in large and medium ground water systems, ICR treatment data were used to derive the estimated percent of plants using each treatment technology as no other model or data source exists to characterize treatment technologies. For plants in large and medium surface water systems, SWAT-predicted results from the “initial plant run” (USEPA 2001b) are used to characterize the percent of plants using each treatment technology in Exhibit 3.13. SWAT-predicted results were used instead of available ICR-observed data to allow for consistent comparison of pre-Stage 1 data to modeled pre-St age 2 and post-Stage 2 data. (If observed data wereused for pre-Stage 1 treatment technology-in-place estimates, differences between pre-Stage 1 and preStage 2 results would represent potential inconsistenc ies in observed vs. predicted data, not just the expected treatment technology change from pre-Stage 1 to pre-Stage 2. The SWAT Model uses a subset of the ICR plants, so while percentages are similar, they are not exact.) In addition, the SWAT initial plant run is used to calculate DBP reductions from pre-Stage 1 to post-Stage 1 for use in the benefits models.

For all small systems, the only significant use of advanced treatment technologies was reported in the NRWA database for small surface water systems (appr oximately 3.6 percent are estimated to be using microfiltration/ultrafiltration (MF/UF), as shown in Exhibit 3.14). The percent using each treatment technology is based on evaluation of CWS data; EPA assumed that NTNCWSs use similar treatment technologies for the size categories shown.Final Economic Analysis for the Stage 2 DBPR Proposal 3-33 December 2005Chlorine Dioxide UVOzone MF/UF GAC10CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLMA BCDEFG H I J K L<10096.4% 346 0.0% 03.6% 13 0.0% 0100-49996.4% 739 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 28 0.0% 0500-99996.4%4660.0%00.0%0 0.0% 00.0%00.0%03.6%170.0%01,000-3,29996.4% 1,089 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 41 0.0% 03,300-9,99996.4%1,2130.0%00.0%0 0.0% 00.0%00.0%03.6%450.0%010,000-49,99953.4% 689 31.6% 408 5.1% 65 3.0% 39 3.2% 42 1.9% 25 0.2% 3 0.1% 2 0.9% 12 0.5% 750,000-99,99953.4%30931.6%1835.1%293.0%173.2%191.9%110.2%10.1%10.9%50.5%3100,000-999,99953.4% 326 31.6% 193 5.1% 31 3.0% 18 3.2% 20 1.9% 12 0.2% 1 0.1% 1 0.9% 6 0.5% 3=1,000,00053.4%3931.6%235.1%43.0%23.2%21.9%10.2%00.1%00.9%10.5%0Total Plants, %79.6% 5,216 12.3% 808 2.0% 129 1.2% 77 1.3% 82 0.7% 49 2.3% 150 0.1% 3 0.4% 24 0.2% 14GAC10 + ADGAC20GAC20 + ADMembranes TOTALCL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLMMN OPQRS TA+C+E+G+I+K+M+O+Q+S B+D+F+H+J+L+N+P+R+T<1000.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0100-4990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0500-9990.0%00.0%00.0%00.0%00.0%00.0%01,000-3,2990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 03,300-9,9990.0%00.0%00.0%00.0%00.0%00.0%010,000-49,9990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 050,000-99,9990.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0100,000-999,9990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0=1,000,0000.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0Total Plants, %0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0Note: Detail may not add to totals due to independent rounding.1"No Adv" includes conventional, non-conventional, and softening plants.Percentage using chloramine is taken from the Occurrence Document (USEPA 2005k) and ICR AUX1 data (USEPA 2000h).0.0%05,601 14.5% 95148337.2%1,1291,2588110.0% 0767 0.0% 062.8%62.8%85.5%359100.0%100.0%100.0%62.8%System Size(PopulationServed)y(PopulationServed)100.0%100.0%No AdvancedTreatmentTechnologies1CL2No AdvancedTreatmentTechnologies1CLM21637.2%Source: Surface water systems serving <10,000 people: National Rural Water Survey (USEPA 2001a). Surface water systems serving10,000 or more people: SWAT initial plant run (USEPA 2001b).227273834636437.2%62.8%0.0% 004810.0%37.2%Exhibit 3.13a Pre-Stage 1 DBPR Treatment Technologies-in-Place for CWS Surface Water PlantsFinal Economic Analysis for the Stage 2 DBPR Proposal 3-34 December 2005Chlorine Dioxide UVOzone MF/UF GAC10CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLMA BC DEFGH I J K L<10096.4% 218 0.0% 03.6% 8 0.0% 0100-49996.4% 301 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 11 0.0% 0500-99996.4%1020.0%00.0%0 0.0% 00.0%00.0%03.6%40.0%01,000-3,29996.4% 89 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 3 0.0% 03,300-9,99996.4%240.0%00.0%0 0.0% 00.0%00.0%03.6%10.0%010,000-49,99953.4% 3 31.6% 2 5.1% 0 3.0% 0 3.2% 0 1.9% 0 0.2% 0 0.1% 0 0.9% 0 0.5% 050,000-99,99953.4%031.6%05.1%03.0%03.2%01.9%00.2%00.1%00.9%00.5%0100,000-999,99953.4% 1 31.6% 0 5.1% 0 3.0% 0 3.2% 0 1.9% 0 0.2% 0 0.1% 0 0.9% 0 0.5% 0=1,000,00053.4%031.6%05.1%03.0%03.2%01.9%00.2%00.1%00.9%00.5%0Total Plants, %96.1% 737 0.2% 2 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 27 0.0% 0 0.0% 0 0.0% 0GAC10 + ADGAC20GAC20 + ADMembranes TOTALCL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLMMN OPQRSTA+C+E+G+I+K+M+O+Q+S B+D+F+H+J+L+N+P+R+T<1000.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0100-4990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0500-9990.0%00.0%00.0%00.0%00.0%00.0%01,000-3,2990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 03,300-9,9990.0%00.0%00.0%00.0%00.0%00.0%010,000-49,9990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 050,000-99,9990.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0100,000-999,9990.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0=1,000,0000.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0Total Plants, %0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0Note: Detail may not add to totals due to independent rounding.1"No Adv" includes conventional, non-conventional, and softening plants.The NTNCWS technology distribution is assumed to be the sameas the CWS technology distribution presented in Exhibit 3.13a.Percentage using chloramine is taken from the OccurrenceDocument (USEPA 2005k) and ICR AUX1 data (USEPA 2000h).00002037.2%37.2%92250.0%0.0%37.2%37.2%99.7%30System Size(PopulationServed)y(PopulationServed)100.0%100.0%100.0%100.0%100.0%10662.8%62.8%62.8%62.8%100312 0.0% 0Source: Surface water systems serving <10,000 people: NationalRural Water Survey (USEPA 2001a). Surface water systems serving10,000 or more people: SWAT initial plant run (USEPA 2001b).No AdvancedTreatmentTechnologies1CL2No AdvancedTreatmentTechnologies1CLM0.0%0765 0.3% 2226 0.0%Exhibit 3.13b Pre-Stage 1 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water PlantsFinal Economic Analysis for the Stage 2 DBPR Proposal 3-35 December 2005System Size(Population Served) No Adv1CL2 No Adv1CLM UV CL2 UV CLM Ozone CL2 Ozone CLM GAC20 CL2 GAC20 CLM Membranes CL2MembranesCLM TOTAL USING CL2 TOTAL USING CLMA B C D E F G H I J K = B + D + F + H + J L = B+D+F+H+J<100 100.0% 6,423 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 6,423 0.0% 0100-499 100.0% 15,242 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 15,242 0.0% 0500-999100.0%6,0930.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0100.0%6,0930.0%01,000-3,299 100.0% 7,587 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 7,587 0.0% 03,300-9,999100.0%5,0300.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0100.0%5,0300.0%010,000-49,999 92.3% 4,968 5.4% 290 0.8% 41 0.0% 0 0.0% 0 0.0% 0 1.5% 83 0.0% 0 94.6% 5,093 5.4% 29050,000-99,99992.3%6615.4%390.8%60.0%00.0%00.0%01.5%110.0%094.6%6775.4%39100,000-999,999 92.3% 847 5.4% 49 0.8% 7 0.0% 0 0.0% 0 0.0% 0 1.5% 14 0.0% 0 94.6% 869 5.4% 49?1,000,00092.3%255.4%10.8%00.0%00.0%00.0%01.5%00.0%094.6%265.4%1Total Plants 98.9% 46,878 0.8% 379 0.0% 0 0.0% 0 0.1% 54 0.0% 0 0.0% 0 0.0% 0 0.2% 108 0.0% 0 99.2% 47,040 0.8% 379Note: Detail may not add to totals due to independent rounding1"No Adv" includes conventional, non-conventional, and softening plants.Source: Ground water systems serving <10,000 people - limited data available. Assumed only cholrine usage and no advanced technologies; Ground water systems serving 10,000 or more people - based on ICR data for 130 large GWplants.System Size(Population Served) No Adv1CL2 No Adv1CLM UV CL2 UV CLM Ozone CL2 Ozone CLM GAC20 CL2 GAC20 CLM Membranes CL2MembranesCLM TOTAL USING CL2 TOTAL USING CLMA B C D E F G H I J K = B + D + F + H + J L = B+D+F+H+J<100 100.0% 2,493 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 2,493 0.0% 0100-499 100.0% 2,129 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 2,129 0.0% 0500-999100.0%5890.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0100.0%5890.0%01,000-3,299 100.0% 247 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 247 0.0% 03,300-9,999100.0%210.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%00.0%0100.0%210.0%010,000-49,999 92.3% 3 5.4% 0 0.8% 0 0.0% 0 0.0% 0 0.0% 0 1.5% 0 0.0% 0 94.6% 3 5.4% 050,000-99,99992.3%05.4%00.8%00.0%00.0%00.0%01.5%00.0%094.6%05.4%0100,000-999,999 92.3% 0 5.4% 0 0.8% 0 0.0% 0 0.0% 0 0.0% 0 1.5% 0 0.0% 0 94.6% 0 5.4% 0=1,000,00092.3%05.4%00.8%00.0%00.0%00.0%01.5%00.0%094.6%05.4%0Total Plants 100.0% 5,483 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100.0% 5,483 0.0% 0Note: Detail may not add to totals due to independent roundingThe NTNCWS technology distribution is assumed to be the sameas the CWS technology distribution presented in Exhibit 3.14a.1"No Adv" includes conventional, non-conventional, and softening plants.Source: Ground water systems serving <10,000 people - limited data available. Assumed only cholrine usage and no advanced technologies; Ground water systems serving 10,000 or more people - based on ICR data for 130 large GWplants.Exhibit 3.14a Pre-Stage 1 DBPR Treatment Technologies-in-Place for CWS Ground Water PlantsExhibit 3.14b Pre-Stage 1 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants

### Final Economic Analysis for the Stage 2 DBPR 3-36 December 2005 

## 3.7 DBP Occurrence for the Pre-Stage 1 Baseline

For pre-Stage 1 DBPR conditions, observed DBP data are available from the ICR for large systems; and from the NRWA survey, WATER:\STATS, and other data sources for medium and small systems (see section 3.2 for a summary of data sources). The pre-Stage 1 DBPR baseline DBP occurrence is also predicted using SWAT. In Chap ter 5, SWAT-modeled DBP occurrence is used instead of observed (ICR) values so that any changes show n in the analysis would be a result of treatment technology changes and operating c onditions and not differences betw een observed and modeled data.

Section 3.7.1 provides background information describing the ICR and SWAT DBP data used in this EA. Section 3.7.2 summarizes pre-Stage 1 DBP occurrence data for large surface water systems. Pre-Stage 1 DBPR occurrence levels for large ground water systems are provided in section 3.7.3. DBP occurrence for medium systems is discussed in secti on 3.7.4, followed by presentation of small system data in section 3.7.5. Section 3.8 describes the uncertainties in observed and predicted DBP data.

3.7.1 Description of ICR and SWAT DBP Data

3.7.1.1 ICR DBP Data

The analysis of ICR DBP data in this EA is consistent with the methodology used in the Occurrence Document (USEPA 2005k), Chapter 5, and A ppendix A. A brief description of the data and assumptions used in the analyses are provided below.

Sampling Period

Consistent with the influent water quality date summarized in Section 3.5, DBP data represent that last 12 months (four quarters) of the ICR collection period (January - December, 1998). Data collected appear to be of higher quality than data co llected during the first 6 months of the survey. In addition, 6 quarters were not used because 2 quarter s would be counted twice and could skew results

Plant Source Water Type

DBP data for ground and surface water plants are analyzed in this section. Consistent with influent water quality data described in Section 3.5, plant-source water type designation is based on the source water type reported by the plant for each month from July 1997 to December 1998. The types of sources recorded were surface water, ground water, mixed, or purchased. Most plants reported on one source type for all months, but some plants reported surface water for some months and mixed for others. These plants were considered surface water plants. One plant reported ground water for some months and mixed for others—this plant was considered a ground water plant.

Distribution System Sampling Locations

Quarterly TTHM and HAA5 data were collected at the following distribution system sampling locations (note that these locations are each associated with one ICR plant):

• Average 1 (AVG 1) and Average 2 (AVG 2 )—two sample locations in the distribution system, each representing an approximate average reside nce time, as designated by the water system.

• Distribution System Maximum (DS Maximum) —the sample location in the distribution system that has the longest residence time, as designated by the water system.

### Final Economic Analysis for the Stage 2 DBPR 3-37 December 2005 

### • Distribution System Equivalent Location (DSE) —a sample location in the distribution system that has a known residence time, where no additi onal disinfectant has been added between the plant and sample location, and where there has been no blending with water from other plants.

Initial Plant Screening

All ICR plants (there are approximately 500 plants in the ICR database) were screened to ensure that at least 3 of 4 quarters have TTHM and HAA5 data for at least 3 of 4 distribution system locations. Note that the total number of plants that meet the minimum screening criteria (311 plants, of which 213 are surface water plants, 83 are ground water plants, and 15 are either blended, mixed, or purchased water plants) represents more than 60 percent of all large pl ants that participated in the ICR data collection effort.

The initial plant screening was done to minimize biases in RAA and LRAA calculations (e.g., LRAAs could be skewed if data from multiple quarters is missing). While the screening process is intended to reduce biases in data analysis, EPA recognizes that biases in the RAA and LRAA calculations may still exist. First, missing data poi nts from locations may skew the quarterly average. For example, a plant with less than 3 of 4 quarters of data for the maximum residence time location (but having at least 3 of 4 quarters of data for all other locations, allowing it to be included in the analysis) would probably have an RAA that is skewed low. Second, missing quarterly data could skew the yearly average. For example, because higher DBP levels are typically seen during the warmest months, missing data in the warmest quarter may lower the annual average at that location. The screening criteria described above were selected to strike a ba lance between minimizing biases in RAA and LRAA calculations and maximizing the number of plants evaluated.

3.7.1.2 SWAT DBP Data

SWAT produces monthly estimates of DBP occu rrence for surface water plants at two distribution system locations:

• Distribution System Average (DS Average) —theoretical location with average residence time (calculated by averaging the residence times reported by the water system for the four locations listed above).

• Distribution System Maximum (DS Maximum) —theoretical location with the maximum residence time (highest residence time reported for the four locations above).

Data from these locations are compared and summarized in subsequent sections.

3.7.2 Pre-Stage 1 DBP Occurrence for Large Surface Water Plants

Exhibit 3.15 summarizes the TTHM, HAA5, bromate, and chlorite occurrence for pre-Stage 1 baseline conditions. Pre-Stage 1 occurrence is s hown for both observed ICR data and SWAT-predicted data. Exhibits 3.16 through 3.19 show the cumulative di stributions of the same plant-mean and individual observations (monthly DBP concentrations) for SWAT data. SWAT plant-mean data represent plantmean concentrations at the DS Average (average resi dence time) location. ICR plant-mean data represent the average of four distribution system locations (AVG1, AVG2, DSE, and DS Maximum). Bromate and

### Final Economic Analysis for the Stage 2 DBPR 3-38 December 2005 

### chlorite data represent finished-water concentra tions from both ICR and SWAT data sets, although ICR chlorite data show the maximum finished-water concen tration at each plant, rather than the plant-mean. Statistical calculations of individual observations ar e for SWAT monthly data and ICR quarterly data.

Exhibit 3.15 reveals differences between SWAT-p redicted and ICR observed data. Although the predicted SWAT data is calibrate d to the national averages reported in the ICR, differences still exist because of differences in calculated versus actual r esidence times, uncertainty in the SWAT predictive equations, uncertainty in variability in the sampling data, and the fact that SWAT essentially follows a single slug of water through the distribution system, while the ICR measures instantaneous values of different parcels of water. See section 3.8 for a discussion of uncertainty in each data set.

3.7.3 Pre-Stage 1 DBP Occurrence in Large Ground Water Plants

ICR data were the only source of pre-Stage 1 DBP data for large ground water systems. There are limited or no data on bromate and chlorite, sin ce these DBPs were monitored only by plants using ozone or chlorine dioxide, and only one ground water plant in the ICR used these disinfectants (USEPA 2005k). TTHM and HAA5 data for these plants are summa rized in Exhibit 3.20. DBP levels in ground water are significantly less than in large surface wa ter plants (see Exhibit 3.15); mean TTHM levels in ground water plants are less than half those in la rge surface water plants, compared to observed or modeled surface water data. Ground water data are more skewed than surface water data; there is a much bigger difference between the median and the mean values for ground water.

### Final Economic Analysis for the Stage 2 DBPR 3-39 December 2005 

## Exhibit 3.15 Summary of Pre-Stage 1 DBP Occurrence for Large Surface Water Plants, DS Average Data

Parameter Plant-Mean Data Individual Observations

N Mean Median 90 th 

### %ile Range N Mean Median 90 th 

### %ile Range
TTHM (μg/L) Pre-Stage 1 (ICR) 213 42 40 70 0-117 3,083 42 37 78 0-177

Pre-Stage 1 (SWAT) 273 49 42 9 3-207 2,784 49 39 100 0-356

HAA5 (μg/L) Pre-Stage 1 (ICR) 213 29 24 52 0-116 3,083 29 23 55 0-188

Pre-Stage 1 (SWAT) 273 36 30 71 1-146 2,784 36 27 77 0-294

Bromate (μg/L) (Ozone plants only) Pre-Stage 1 (ICR) 14 2.6 2.2 5.4 0.02-7.2 157 2.6 1.9 6.7 0-14.6

Pre-Stage 1 (SWAT) 15 6.3 1.8 24.3 0.2-28 156 6.1 1.9 18 0.1-62

Chlorite (μg/L) (Chlorine dioxide plants only) Pre-Stage 1 (ICR) 18 429 465 701 2.2-1,105 192 435 435 830 0-1,719

Pre-Stage 1 (SWAT) 22 819 812 1394 140-1,680 177 636 700 1330 42-1,680

Note: For TTHM and HAA5 data, SWAT data are from the DS Average location and ICR dat a are the average of four distribution system locations for the last 12 months of the ICR collection period (January 1998-December 1998). For bromate and chlorite data, finished water data from both SWAT and ICR ar e used. All SWAT data are based on monthly predicted observations, ICR TTHM and HAA5 dat a are based on quarterly observations, and ICR chlorite and bromate data are based on m onthly observations. For ICR data, only i ndividual observations used to calculate plant means are shown. For ICR data, only plants that have data fo r 3 of the last 4 quarters were included, and, for ICR TTHM and HAA5 data, only plants with at least 3 of t he 4 required distribution system samples each quarter were included.
Sources: SWAT Initial Plant Run and Run 300 (USEPA 2001b); ICR AUX1 Database (USEPA 2000h), screened data.

### Final Economic Analysis for the Stage 2 DBPR 3-40 December 2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 50 100 150 200 Plant Mean HAA5 (ug/L)Cumulative Percentile

Plant Mean Monthly 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 50 100 150 200 250 300 350 400 Plant Mean TTHM (ug/L)Cumulative Percentile

Plant Mean Monthly 

## Exhibit 3.16 Cumulative Distributions of TTHM Data Predicted by SWAT, PreStage 1 (DS Average)

Note: DS Average data from SWAT Source: SWAT Initial Plant Run (USEPA 2001b).



## Exhibit 3.17 Cumulative Distributions of HAA5 Data Predicted by SWAT, PreStage 1 (DS Average)

Note: DS Average data from SWAT Source: SWAT Initial Plant Run (USEPA 2001b)

### Final Economic Analysis for the Stage 2 DBPR 3-41 December 2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 5 10 15 20 25 30 35 40 Bromate (ug/L)Cumulative Percent

Monthly
Plant Mean

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 200 400 600 800 1000 1200 1400 1600 1800 Chlorite (ug/L)Cumulative Percent
Monthly
Plant Mean 

## Exhibit 3.18 Cumulative Distributions of Bromate Data Predicted by SWAT, PreStage 1 (Finished Water)

Note: Finished water data from SWAT for ozone plants only. Source: SWAT Initial Plant Run (USEPA 2001b).



## Exhibit 3.19 Cumulative Distributions of Chlorite Data Predicted by SWAT (Finished Water)

Note: Finished water data from SWAT. Includes chlorine dioxide plants only. Source: SWAT Initial Plant Run (USEPA 2001b)

.

### Final Economic Analysis for the Stage 2 DBPR 3-42 December 2005 

## Exhibit 3.20 Summary of Pre-Stage 1 DBP Occurrence for Large Ground Water Plants, ICR Data

Parameter Plant-Mean Data Individual Observations

N Mean Median 90 th 

### %ile Range N Mean Median 90 th 

### %ile Range
TTHM (μg/L)
Pre-Stage 1 82 15.4 6.8 37 0-123 1,196 15.6 5.8 45 0-300

HAA5 (μg/L)
Pre-Stage 1 82 8.5 2.2 22 0-71 1,196 8.8 1.5 26 0-124

Source: AUX1 database (USEPA 2000h),screened data.



### 3.7.4 Pre-Stage 1 DBP Occurrence for Medium Surface and Ground Water Plants

DBP occurrence data for medium ground water and surface water plants are limited. Plant-mean data on TTHM are available from WATER:\STATS, a da tabase compiled by the American Water Works Association (AWWA 2000). Graphs of WATER:\STATS da ta in Appendix A show that DBP levels and water quality parameter levels are similar in medium and large surface water plants. Graphs for ground water in the Occurrence Document (USEPA 2005k) also show similarities between medium and large ground water plants. Therefore, EPA assumed that DBP occurrence for medium surface water and ground water plants is roughly equivalent to DBP occurrence for large surface water and ground water plants, respectively.

3.7.5 Pre-Stage 1 DBP Occurrences for Small Surface and Ground Water Plants

The small-system experts used NRWA survey data and TTHM data s ubmitted to EPA from eight States to assess pre-Stage 1 DBP occurrence levels fo r small surface and ground water plants. Exhibit 3.21 summarizes the TTHM and HAA5 data from these two data sets.

Although Exhibit 3.21 shows that TTHM levels from the State data set are higher than the levels from the NRWA data set, NRWA data are considered more reliable and representative of national preStage 1 DBP occurrence than the Stat e surface water data. (For further characterization of small surface and ground water plant data sets, refer to Chap ter 3 of the Occurrence Document (USEPA 2005k)). Therefore, NRWA observed data were used to describe occurrence for sm all surface water plants.

### Final Economic Analysis for the Stage 2 DBPR 3-43 December 2005 

## Exhibit 3.21 Summary of Pre-Stage 1 DBP Occurrence Data for Small Systems, DS Average Data

Parameter

(source) Plant-Mean Data Individual Observations

N Mean Median 90 th 

### %ile Range N Mean Median 90 th 

### %ile Range
TTHM (μg/L)
Pre-Stage 1 (NRWA survey, SW systems) 96 83 62 179 0-328 384 85 64 169 0-451

Pre-Stage 1 (State data, SW systems) 562 99 65 215 0-687 N/A N/A N/A N/A N/A

Pre-Stage 1 (State data, GW systems) 2,336 17 3 46 0-655 N/A N/A N/A N/A N/A

HAA5 (μg/L)
Pre-Stage 1 (NRWA survey, SW systems) 96 45 34 84 0-262 384 44 34 90 0-475

Source: Pre-Stage 1 data: NRWA data (USEPA 2001a) are weighted averages of data at locations having average and maximum residence times in the distribution system. Aver age residence time data are weighted three times more than maximum residence time data to make data equivalent to DS Averages calculated for ICR TTHM and HAA5. NRWA plant-mean data include only those plants that had data for both sampling peri ods and for both distribution system locations. Only those individual observations that were used to calculate plant-mean data are shown here. State data (USEPA 2005k) are a mixed data set from eight States for surf ace water and seven States for ground water; N/A indicates no individual observations were available for this data set.



## 3.8 Uncertainties in Development of the Pre-Stage 1 Baseline

There is uncertainty in this baseline analys is due to measurement error and incomplete information that could result in either an over-estim ate or under-estimate of the benefits and/or costs as presented in Chapters 6 and 7. These uncertainties we re not modeled as the impacts of these uncertainties is unknown and EPA believes these uncertainties have less of an effect than those which are modeled in Chapters 6 and 7.

Exhibit 3.22 presents key uncertainties and an estimate of the effects that each may have on subsequent analyses. Note the effects on benefits and costs is unknown for most of the uncertainties listed in Exhibit 3.22. A detailed discussion of each uncertainty follows the exhibit.

### Final Economic Analysis for the Stage 2 DBPR 3-44 December 2005 

## Exhibit 3.22 Summary of Uncer tainties Affecting Stage 2 DBPR Baseline Estimates

Uncertainty Section Where Estimates

are Presented Effect on Benefit Estimate Effect on Cost Estimates

Underestimate Overestimate Unknown Impact Underestimate Overestimate Unknown Impact
Uncertainty in baseline data inputs used to generate the industry baseline (SDWIS and 1995 CWSS data) 3.4 X X

CWS flow equations for NTNCWSs 3.4.3 No impact on benefits X

Uncertainty in use of chloramines and advanced treatment technologies 3.6 X X

Uncertainty in observed data and predictive tools used to characterize DBP occurrence for the pre-Stage 1 baseline 3.7 X X



### Uncertainty in the Industry Baseline

EPA recognizes that there is uncertainty related to the various data sources used to define the system inventory for the Stage 2 DBPR. The uncertainty in the system inventory data inputs is not quantified in this EA; however, a qualitative discussion of the identified uncertainties is provided below.

As noted above, SDWIS and the 1995 CWSS are the primary sources of system inventory data. SDWIS is EPA’s primary drinking water database, containing data for over 170,000 PWSs. SDWIS stores State-reported information on each water syst em, including name, ID number, number of people served, type of system (year-round or seasonal), a nd source of water (ground water or surface water), along with monitoring and violation information. In 1998, EPA began a major effort to assess the quality of its drinking water data in SD WIS. The results, published in Data Reliability Analysis of the EPA Safe Drinking Water Information System/Federal Version , found that the quality of the required inventory data was high (USEPA 2000e). Thus, EPA believes that un certainty in the system inventory data from SDWIS with respect to numbers of systems, source information, and size classification is low.

The 1995 CWSS was developed to gather data on wa ter systems in the United States. A total of 3,681 systems covering a range of source water types and system sizes were statistically selected to receive the main survey questionnaire. Of these, 1,980 systems responded. These responses were weighted to maintain statistical representation of the total universe of CWSs. The EPA report Community Water System Survey (USEPA 1997c) provides information on the 1995 CWSS survey design and data evaluation.

### Final Economic Analysis for the Stage 2 DBPR 3-45 December 2005 

### The 1995 CWSS was the primary data source used to develop the following industry baseline characteristics:

• Percent of ground water systems that disinfect

• Percent of SDWIS surface water systems that use primarily ground water

• Treatment plants per system

• Average and design flow based on popula tion served (presented in section 3.4.3)

Because the CWSS is a survey of CWSs, estimat es based on the data will contain uncertainty because of sampling errors. To help define these uncertainties, the CWSS report provides the confidence intervals on certain parameters. The report does not , however, contain data for percent disinfecting, percent of SDWIS surface water systems providing ground water, and treatment plants per system that are used in this EA. The confiden ce intervals for similar parameters can provide some information on uncertainty. For example, an analysis of the percent of ground water systems with no treatment (which uses similar data to the analysis of percent disinf ecting), yielded 95 percent confidence intervals of less than + 10 percent.

For average and design flow regression equations, one measure of uncertainty is the R-value for the regressions. The regressions both for average daily flow and for design flow had very high R-values (0.97 and 0.90, respectively), indicating a low level of uncertainty.

Uncertainties in Flow Equations for NTNCWs

As noted in Section 3.4.3, the relationship be tween population served and average daily flow and design flow was not modeled for NTNCWSs. As a surrogate, the population-flow equations derived for CWSs based on data collected during the 1995 CWSS were used. EPA recognizes that the CWS flow equations likely overestimate average daily and design flows for NTNCWSs since NTNCWSs generally only operate for part of the day and may not have high volume water uses such as showering or washing clothes. This overestimation could lead to an over estimation in treatment technology costs and therefore, national costs of the rule. The number of NTNC WSs relative to CWSs, however, is small, so EPA anticipates that the impact of this overestimate will be minor.

Uncertainties in pre-Stage 1 Use of Advanced Treatment Technology

The estimated use of chloramines and adva nced treatment technologies prior to the implementation of the Stage 1 DBPR is based on di fferent data sources depending on system size. For large surface and ground water systems, use of treatment technologies is based on ICR data. EPA expects that the treatment technology characterization for la rge systems has a relatively high degree of certainty because the ICR database represents a census of a ll plants serving more than 100,000 people. For medium and small systems, use of chloramines or advanced treatment technologies is based on data gathered during the 1995 CWSS. As described earlier in this section, the CWSS is a statistically designed survey and is expected to contain sample error. Thus, the estimated pre-Stage 1 treatment technologies in place for medium and small systems are less certain th an the estimated pre-Stage 1 treatment technologies in place for large systems.

Uncertainties in ICR DBP Data

The are several sources of uncertainty in the DB P data collected under the ICR. The American Water Works Association Research Foundation (AWWARF) has compiled a thorough description of the

### Final Economic Analysis for the Stage 2 DBPR 3-46 December 2005 

### ICR data collection challenges and ultimat e quality of the data in a publication, Information Collection Rule Data Analysis (the AWWARF ICR Report) (McGuire et al. 2002). Data quality controls were developed by a group of industry expe rts and strictly enforced; thus, EPA believes that the data quality in the ICR database is very high.

One key area of uncertainty that is addressed in the AWWARF ICR Report relates to the representativeness of all data collected during the ICR. The authors try to answer the question, how does the water quality during the year of ICR data coll ection (1998) represent past years and adequately be used to predict future DBP occurrence? Weat her and rainfall during the ICR sample period were compared to historical data to make this assessment.

On a nationwide basis, 1998 was hotter and wetter th an normal. Approximately 75 percent of the country experienced warmer than usual temperatur es during 1998. Overall it was also a very wet year with an average rainfall of 32.6 inches compared to the average of 27.2 inches. Twenty-two percent of the country experienced wetter-than-normal conditi ons. Increased rainfall could bias the results, increasing levels of constituents that derive mainly from runoff such as TOC. Other constituent could be lower than normal such as bromide, which tends to rise during droughts. It should also be noted that these trends are for the national data and that on a regional basis the trends may be different. For example, even though 1998 was a very wet year on a national basis several mid-Atlantic states experienced severe droughts during the summer of 1998. Chapter 3 of the AWWARF ICR Report (McGuire et al. 2002) provides additional details for this assessment.

It is unknown how year-to-year variability in source water quality will affect estimated DBP occurrence. The year of data collection (1998) c ould represent a worst-case, best-case, or typical year depending on water-quality trends for a given plant. It is likely that some plants may experience higher DBP occurrence in future years than wh at is represented in the ICR database.

Because of the nature of distribution system monitoring, the representativeness of a single grab sample is uncertain. Pereira et al. (2004) showed th at DBP levels can fluctuate even on a daily basis. Over a 1 week sampling period where samples were ta ken every 6 hours that the coefficient of variability ranged from 6 to 20 percent depending on the DBP measured and the sampling location. One grab sample collected at a discreet point in time for th e ICR does not represent this potential variability. In addition to hourly variations, the ICR data were not re quired to be collected at evenly spaced intervals. Thus, there is uncertainty in assuming that a single da ta point represents typical occurrence over the entire quarter.

Based on comparisons of ICR and historical DBP databases, researchers suspect that plants changed their treatment technology in anticipation of the Stage 1 DBPR prior to the ICR data collection period (McGuire et al. 2002). The ICR then is not likely the true pre-Stage 1 baseline. EPA believes, however, that because costs and benefits for th e Stage 2 DBPR are based on treatment technology changes from a predicted pre-Stage 2 baseline, the impact of this uncertainty is small.

Uncertainties in SWAT Predictions

Part II of Appendix A is dedicated to the discussion of uncertainties in the SWAT model. Major areas of uncertainty in the SWAT predictions for the pre-Stage 1 baseline are (1) the uncertainty in ICR observed data, upon which the SWAT model is based, (2) uncertainty in predictive equations for DBP formation, (3) uncertainty in the SWAT complia nce determination, and (4) uncertainty in SWAT treatment technology selection. See Appendix A for a detailed discussion of uncertainties and information on how the SWAT model was validated.

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-1 

# 4. Consideration of Regulatory Alternatives

4.1 Introduction

To address the public health concerns presented in Chapter 2 and discussed in more detail in Chapter 6, the Environmental Protection Agency (EPA) convened the Microbial-Disinfectants / Disinfection Byproducts (M-DB\P) Advisory Committee under the Federal Advisory Committees Act (FACA) to explore a number of regulatory alternatives for the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR). The M-DBP Advisory Committee was composed of representatives from the following groups:

All Indian Pueblo Council, Pueblo Office of Environmental Protection American Water Works Association Association of Metropolitan Water Agencies Association of State Drinking Water Administrators Chlorine Chemistry Council Clean Water Action Conservation Law Foundation Environmental Council of the States International Ozone Association National Association of County and City Health Officials National Association of People with AIDS National Association of Regulatory Utility Commissioners National Association of State Utility Consumer Advocates National Association of Water Companies National Environmental Health Association National League of Cities National Resources Defense Council National Rural Water Association Physicians for Social Responsibility Unfiltered Systems U.S. Environmental Protection Agency Water and Wastewater Equipment Manufacturers Association

M-DBP Advisory Committee deliberations, which began the Spring of 1999 and culminated in December 2000 with an Agreement in Principle (USEPA 2000m), are documented on EPA’s website in the form of meeting summaries (USEPA 2000n).

This chapter describes the process for developing regulatory alternatives, then summarizes the four Stage 2 DBPR regulatory alternatives considered in this Economic Analysis (EA). Among the four alternatives is the Preferred Alternative, which represents the recommendation of the M-DBP Advisory Committee.

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-2 

## 4.2 Process for Development of Regulatory Alternatives

The process that led to the development of the Stage 2 DBPR began with the initiation of a negotiated rulemaking by EPA in 1992 to address public health concerns related to disinfectants, disinfection byproducts (DBPs), and microbial pathogens. The Regulatory Negotiation Committee met from November 1992 through June 1993. The Committee included representatives of State and local public health and regulatory agencies, public water systems, elected officials, consumer groups, and environmental groups. As a result of its deliberations, the Committee recommended the development of three sets of rules:

• A two-stage approach for regulations to control risks from DBPs.

• A two-stage approach for regulations to control risks from microbial contaminants (the Interim Enhanced Surface Water Treatment Rule (IESWTR) and the Long Term Enhanced Surface Water Treatment Rule).

• An information collection rule to support the above.

The Information Collection Rule (ICR) was promulgated in May 1996. The DBP and microbial regulatory process recommendations of the Committee were subsequently incorporated into the 1996 Safe Drinking Water Act (SDWA) Amendments as statutory requirements. The Stage 1 DBPR and the IESWTR were both promulgated by EPA in December 1998.

Results from the 1996 ICR, which were gathered between July 1997 and December 1998, provided the M-DBP Advisory Committee and EPA with a wealth of information on large water systems, their treatment processes, and the quality of their source and finished waters. This information was used to develop and run the Surface Water Analytical Tool (SWAT) (a model that uses a series of algorithms and decision rules to predict treatment technology changes and DBP occurrence for regulatory alternatives; see Appendix A). The output from SWAT formed much of the basis for estimates of national cost and exposure to DBPs for the regulatory alternatives under consideration. Additional data were obtained from a survey conducted by the National Rural Water Association (NRWA) of 120 smaller systems—serving fewer than 10,000 people—as well as from a variety of State data sources.

The M-DBP Advisory Committee considered several key questions during the negotiation process, including:

• What health effects will the Stage 2 DBPR address?

• Should disinfectants and DBPs not regulated under the Stage 1 DBPR now be regulated?

• Should standards for disinfectants and DBPs set under the Stage 1 DBPR be amended?

• Should monitoring requirements under the Stage 1 DBPR be amended?

• Should compliance standards be calculated differently than those in the Stage 1 DBPR?

• What are the risk tradeoffs that need to be considered?

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-3 

### EPA used SWAT to develop rough estimates of costs and exposure reductions for over a hundred possible rule alternatives. Of these, the M-DBP Advisory Committee focused its attention on those alternatives that would reduce peaks in DBPs that may occur throughout the distribution system.



## 4.3 Regulatory Alternatives Considered

Four Stage 2 DBPR regulatory options are considered in this EA. They include what is referred to as the Preferred Alternative, representing the recommendation of the M-DBP Advisory Committee, and three other alternatives studied by the Committee, but not selected for reasons noted below. Though not selected as a preferred option, the Committee considered each of those other three options as alternatives worth careful consideration; EPA carried them through the benefits and cost calculations for comparison with the Preferred Alternative. EPA chose the least-cost alternative that targets the highest risks as the Preferred Alternative.

The goal of the M-DBP Advisory Committee was to increase the stringency of the total trihalomethanes (TTHM) and haloacetic acids (HAA5) compliance standards by reducing peak concentrations of DBPs in distribution systems. The Advisory Committee debated three different compliance determination approaches. The first, a running annual average (RAA), bases compliance on the average of all samples taken over a 12-month period, and allows certain monitoring locations to have DBP levels higher than the maximum contaminant level (MCL) as long as the average does not exceed the MCL. The second approach, a locational running annual average (LRAA), bases compliance on the average of all samples taken at each specific monitoring location over a 12-month period, and requires the average of all samples at individual monitoring locations to be no higher than the MCL. The third, a single highest (SH) value, bases compliance on each individual sample meeting the MCL, and requires all monitoring locations never to have DBP levels higher than the MCL. Compared to RAA compliance options, the options involving LRAA and SH compliance measures focus on reducing peak exposures—and the potential inequalities in exposure resulting from them—to customers served in some parts of distribution systems. The LRAA has the added benefit of reducing average DBP exposures.

The following discussion provides details of the four main regulatory alternatives considered.

Preferred Alternative

The Stage 1 DBPR set MCLs for total TTHM at 80 μg/L and HAA5 at 60 μg/L, each measured as a RAA based on quarterly averages of all samples taken. The Stage 2 Preferred Alternative retains these MCL values, but modifies how compliance is determined for TTHM and HAA5 under the Stage 1 DBPR. Its components include:

• MCL of 80 micrograms per liter (μg/L) TTHM measured as an LRAA

• MCL of 60 μg/L HAA5 measured as an LRAA

• MCL of 10 μg/L bromate measured as an RAA, based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR)

• Compliance monitoring preceded by the Initial Distribution System Evaluation (IDSE)

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-4 

### Under the Preferred Alternative for the Stage 2 DBPR, systems are required to identify the compliance monitoring sites that best represent high TTHM and HAA5 levels through the IDSE and to monitor at these locations. The purpose of this alternative is to control the levels of TTHM and HAA5 at locations in the distribution system with the highest levels of these DBPs as determined from the IDSE in order to meet the MCLs throughout the entire distribution system.

Alternative 1

This alternative has the same TTHM and HAA5 requirements as the Preferred Alternative, but has a lower MCL for bromate . Its components include:

• MCL of 80 μg/L TTHM measured as an LRAA

• MCL of 60 μg/L HAA5 measured as an LRAA

• MCL of 5 μg/L bromate measured as an RAA, based on monthly samples taken at the finished water point

Members of the M-DBP Advisory Committee did not favor this alternative because they were concerned that lowering the bromate level to 5 μg/L could have adverse effects on microbial protection. In addition, the more stringent bromate standard led to estimated costs that are approximately three times those for the Preferred Alternative. Alternative 1 would probably cause some systems to stop using ozone or not consider ozone for microbial protection—developments that the M-DBP Advisory Committee and EPA did not want to encourage because ozone is more effective than chlorine against Cryptosporidium (Clark et al. 1994).

Alternative 2

This alternative measures TTHM and HAA5 concentrations as a SH value for the MCL and maintains the MCL for bromate. Its components include:

• MCL of 80 μg/L TTHM measured as the SH value for any sample taken

• MCL of 60 μg/L HAA5 measured as the SH value for any sample taken

• MCL of 10 μg/L bromate measured as an RAA (no change from the Stage 1 DBPR)

This alternative is more stringent than the Preferred Alternative and Alternative 1. Under Alternative 2, no TTHM or HAA5 sample can exceed the MCL. EPA has estimated that a large portion of the surface water systems covered by the rule would have to switch from their current treatment practice to more expensive advanced treatment technologies to comply with this alternative. The M-DBP Advisory Committee did not favor this alternative because it believed that the health effects data are not certain enough to warrant such a drastic shift in the Nation’s drinking water treatment practices. In addition, Alternative 2 does not include the risk targeting strategy of the Preferred Alternative.

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-5 

### Alternative 3

This alternative reduces MCLs for TTHM and HAA5 and maintains the MCL for bromate, but does not modify how compliance is measured under the Stage 1 DBPR. Its components include:

• MCL of 40 μg/L TTHM measured as an RAA

• MCL of 30 μg/L HAA5 measured as an RAA

• MCL of 10 μg/L bromate measured as an RAA (no change from Stage 1 DBPR MCL)

This alternative reduces the average level of TTHM and HAA5 in the distribution system, but does not necessarily reduce peaks in the distribution system as an LRAA compliance strategy is expected to do. As with Alternative 2, a large portion of the surface water systems covered by the rule would have to switch from their current treatment practices to expensive advanced treatment technologies to comply with this alternative. Similarly, the M-DBP Advisory Committee did not favor this alternative because it believed that the health effects data are not certain enough to warrant such a drastic shift in the Nation’s drinking water treatment practices and because it does not include the risk targeting strategy of the Preferred Alternative.

Exhibit 4.1 shows how compliance would be determined for the Stage 1 DBPR and for each of the Stage 2 regulatory alternatives described above when applied to a hypothetical large surface water system. This hypothetical system has one treatment plant and measures TTHM in the distribution system in four locations per quarter (the calculations shown would be the same for HAA5). Note that the measured concentrations of TTHM and HAA5 are the same in all cases. In this example, the system is in compliance with the Stage 1 DBPR, but would be in violation of all four Stage 2 DBPR regulatory options.

In addition to the four regulatory options addressed above, the M-DBP Advisory Committee considered other changes in the approach to regulating DBPs to be worth noting. Both the current Stage 1 DBPR and the Stage 2 DBPR alternatives considered in this analysis use TTHM and HAA5 as the specific chlorination DBPs measured for compliance. The M-DBP Advisory Committee determined that TTHM and HAA5 are reliable indicators for all halogenated DBPs that exist in chlorinated drinking water, including known DBPs that are unmeasurable and others that have yet to be identified. The Committee considered having EPA modify the group of indicators to include a total of six haloacetic acids (HAA6), a total of nine haloacetic acids (HAA9), or other chlorination DBPs. However, the M-DBP advisory committee did not recommend that EPA expand the DBP indicators to include HAA6 or HAA9. Fewer plants measured for HAA9 under the ICR because of analytical method problems for detecting HAA9 (USEPA 1999c).

The Stage 1 DBPR also set a bromate MCL at 10 μg/L measured as an RAA, based on monthly measurements for ozone systems and a chlorite MCL at 1.0 mg/L based on measurements required for chlorine dioxide systems. The M-DBP Advisory Committee debated whether the bromate MCL should be lowered (Regulatory Alternative 1). The Stage 1 DBPR set the MCL for bromate at 10 μg/L partly because that was the limit of EPA’s analytical capability at that time. New methods now exist to measure lower concentrations of bromate, which would allow a lower limit to be set. However, the Committee was concerned that a lower bromate MCL might discourage systems from switching to (or continuing to use) ozone to increase microbial protection. Unlike chlorine, ozone is effective in the

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-6 

### disinfection of Cryptosporidium —a focus of the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR). Therefore, the M-DBP Advisory Committee recommended that EPA not change the bromate MCL. The M-DBP Advisory Committee did not discuss the chlorite standard, and EPA does not believe it needs to be revised.

Last, it should be noted that reductions in exposure to DBPs could also be achieved through treatment techniques in lieu of or in addition to setting MCLs. For example, reducing organic precursor compounds, measured as Total Organic Carbon (TOC), by such means as enhanced coagulation has been shown to lower DBP formation. The M-DBP Advisory Committee considered regulatory alternatives for reducing precursors and determined that the Stage 1 DBPR reduced TOC to a sufficient degree. Further, removing ever-smaller quantities of these compounds will be more difficult, less efficient, and increasingly costly. While analysis of ICR data shows that some systems could improve performance in this way (and SWAT incorporated that into its decision tree), a regulatory requirement for reducing TOC levels was deemed unnecessary.

### Final Economic Analysis for the Stage 2 DBPR December 2005 4-7 

### Basis of Compliance

Violation of MCL

Stage 1 DBPR TTHM MCL = 80 μg/L measured as an RAA No exceedance of MCL

Loc. 1 Loc. 2 Loc. 3 Loc. 4 Qtrly Avg. Q1 100 40 50 50 60 Q2 75 50 40 100 66 Q3 55 45 55 110 66 Q4 60 55 40 75 58 RAA 63

Preferred Stage 2 DBPR Alternative and Alternative 1* TTHM MCL = 80 μg/L measured as an LRAA LRAA at Location 4 exceeds MCL

Loc. 1 1 

### Loc. 2 1 

### Loc. 3 1 

### Loc. 4 1


### Q1 100 40 50 50 Q2 75 50 40 100 Q3 55 45 55 110 Q4 60 55 40 75 LRAA 73 48 46 84 *The Preferred Alternative and Alternative 1 have the same TTHM MCL; they differ only in regard to the bromate MCL. Footnote 1: Based on the IDSE, new locations targeted for high DBPs.



### Alternative 2 TTHM MCL = 80 μg/L measured as a single highest value Three samples at Locations 1 and 4 exceed MCL

Loc. 1 Loc. 2 Loc. 3 Loc. 4 Q1 100 40 50 50 Q2 75 50 40 100 Q3 55 45 55 110 Q4 60 55 40 75

Alternative 3 TTHM MCL = 40 μg/L measured as an RAA RAA exceeds MCL

Loc. 1 Loc. 2 Loc. 3 Loc. 4 Qtrly Avg. Q1 100 40 50 50 60 Q2 75 50 40 100 66 Q3 55 45 55 110 66 Q4 60 55 40 75 58
RAA 63 

## Exhibit 4.1 Comparison of Hypothetical Compliance Calculations for Stage 1 and Stage 2 Regulatory Alternatives

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-1 

# 5. Compliance Forecast and Conseque nt Reduction in Chlorination DBPs

5.1 Introduction

The compliance forecast represents the changes systems are predicted to make in treatment technologies to comply with a new drinking water regulation. Treatment technology changes to meet the Stage 2 Disinfectants and Disinfection Byproducts Ru le (DBPR) result in costs incurred by the water systems as well as reductions in concentrations of disinfection byproducts (DBPs) that determine the benefits achieved by the rule.

Section 5.2 provides an overview of the methodol ogies used to develop the compliance forecasts and consequent reduction in the leve ls of DBPs. Section 5.3 provid es details on the derivation of the compliance forecast, and Section 5.4 presents the for ecast results for the Stage 1 DBPR and the Stage 2 DBPR Preferred Alternative (forecasts for the other regulatory alternatives are presented in Appendix C). Predicted reductions in the levels of two key classes of DBPs, total trihalomethane (TTHM) and five haloacetic acids (HAA5), are discussed in Sections 5. 5 and 5.6. Uncertainties in the compliance forecast and DBP reduction estimates are summarized in Section 5.7.

In support of this chapter:

• Appendices A and B explain the derivation of the compliance forecasts (i.e., the number of plants making treatment technology changes and which treatment technologies they select) for surface water and disinfecting ground water systems, respectively.

• Appendix C provides supplemental compliance fo recasts for the Stage 1 DBPR and Stage 2 DBPR regulatory alternatives.

Note that “compliance forecast” and “technology selection forecast” are used interchangeably in this Economic Analysis (EA) to denote the overall pe rcentage of plants predicted to change treatment technology, along with the specific technologies those plan ts are predicted to select to achieve compliance with the Stage 1 or Stage 2 DBPRs.



## 5.2 Overview of Methodologies used in the Primary Analysis

Changes in concentrations of DBPs are the dir ect result of changes in treatment technologies. Therefore, it is important that EPA use consis tent methodologies for forecasting treatment technology changes and predicting reductions in DBPs (specifically, the reductions in TTHM and HAA5 concentrations that are used in the benefits anal ysis). This section summarizes the tools used and key assumptions for the Stage 2 DBPR compliance for ecasts and the consequent reductions in TTHM and HAA5 concentrations.

Since the rule was proposed, EPA has modifi ed the compliance forecast methodology in an attempt to quantify uncertainties in the analysis . Specifically, EPA has developed a second method to predict the number of surface water plants maki ng treatment technology changes and consequent reductions in TTHM and HAA5 concentration, whic h supplements the Surface Water Analytical Tool (SWAT) predictions. EPA has also quantified un certainty in the potential impacts of the Initial

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-2 

### Distribution System Evaluation (IDSE). Uncertainties are characterized using Monte Carlo simulation in the cost and benefits models.

Predictive Tools Used to Develop the Compliance Forecast

EPA uses different methods for different system sizes and source water types to develop the compliance forecasts, as shown in Exhibit 5.1. Because extensive data were available from the Information Collection Request (ICR), detailed analysis tools were used to develop compliance forecasts for large surface and ground water systems. For la rge surface water systems, EPA used two different methodologies, both drawing from ICR data: the Surface Water Analytical Tool (SWAT) and the ICR Matrix Method. The ICR Matrix Method uses TTHM and HAA5 distribution system data from the ICR to predict how many plants will need to change t echnology for a specific regulatory alternative. SWAT uses a series of decision rules and algorithms to pred ict (1) which surface water plants need to change treatment technology to meet a specific regulatory alternative, (2) which treatment technology those plants will select based on a least cost decision tree. The ICR Matrix Method and SWAT produce different results; thus, both are incorporated into a Monte Carlo simulation model to account for uncertainties in both methods. The forecast for larg e ground water systems was generated using the ICR Ground Water Delphi process, which convened a group of experts to evaluate plant configurations and predict treatment technology selections for ground wate r plants that did not meet rule requirements. Compliance forecasts for large surface and ground water systems were used to generate forecasts for medium and small systems, making adjustments to account for operational and water quality characteristics in medium and small system s that differ from those in large systems.



## Exhibit 5.1 Tools Used to Develop the Stage 2 DBPR Compliance Forecasts

System Size (Population Served) Source Water Category

Surface Water Disinfecting Ground Water

Large (> 100,000 people) The Surface Water Analytical

Tool (SWAT) (Appendix A) ICR Matrix Method (Section 5.5) ICR Ground Water Delphi Group (Appendix B)

Medium (10,000 to 99,999 people) Extrapolation from SWAT (Appendix A) Extrapolation from ICR Matrix Method (Section 5.5) Extrapolation from large ground water system results (Appendix B)

Small (<10,000 people) Extrapolation from SWAT, adjusted to deal with small

system-specific issues (Appendix A) Extrapolation from ICR Matrix Method (Section 5.5) Extrapolation from large ground water system results,

adjusted to deal with small

system-specific issues (Appendix B)1 

### Although the SWAT model was calibrated to national average TTHM and HAA5 concentrations in distribution systems and validated against industry treatme nt technology predictions, it was not calibrated to plantlevel DBP predictions and, thus, could not be used to a ssess changes in occurrence of peak levels. See Appendix A for more information on SWAT.

Final Economic Analysis for the Stage 2 DBPR December 2005 5-3 

### Tools Used to Predict Changes in DBP Levels

For the benefits analysis, EPA needs informa tion on the changes in both average and peak TTHM and HAA5 levels that result from implementation of the Stage 2 DBPR. Estimates of bladder cancer cases avoided are based on reductions in average levels, while the illustrative analysis of potential developmental and reproductive h ealth benefits is based on reductions in occurrences of peak concentrations.

To predict changes in average TTHM and HAA5 le vels for surface water systems, EPA uses two methods: the ICR Matrix Method and SWAT. As noted in the previous section, the ICR Matrix Method evaluates distribution system data to identify plants that would need to make treatment technology changes to meet a specific regulatory alternative. To predict average DBP concentrations occurring after treatment technology changes, EPA used TTHM a nd HAA5 occurrence data for those surface water plants already using chloramines and/or advanced technologies at the time of the ICR. The predicted average TTHM and HAA5 levels for all surface water plan ts is a weighted average for plants that do and do not change treatment technology.

SWAT is a model that uses a series of decision rules and algorithms to predict (1) which surface water plants need to change treatment technology to meet a specific regulatory alternative, (2) which treatment technology those plants will select based on a least cost decision tree, and (3) resulting changes in the national average TTHM and HAA5 levels in dist ribution systems. As with the compliance forecast, SWAT and the ICR Matrix Method produce different r esults; thus, both are incorporated into a Monte Carlo simulation model to account for uncertainties in both methods.

ICR ground water plant data were not robust e nough to develop a ground water model similar to SWAT; therefore, the ICR Matrix Method is the only approach used to predict reductions in average TTHM and HAA5 levels for these systems.

To predict changes in the o ccurrence of peak TTHM and HAA5 concentrations, EPA used only the ICR Matrix Method 1 

### . Similar to the way in which it is u sed to evaluate changes in average TTHM and HAA5 concentrations, the ICR Matrix Method eval uates distribution system data to identify plants that would need to make treatment technology cha nges to meet a specific regulatory alternative. To predict occurrence of peaks after treatment technology changes, EPA analyzed TTHM and HAA5 occurrence data for those surface water plants alread y using chloramines and/or advanced technologies at the time of the ICR. The predicted occurrence of peaks for all plants is a weighted average for plants that do and do not make treatment technology changes.

Accounting for the Stage 1 DBPR

For cost and benefit analyses, the compliance forecast and consequent reduction in DBPs needs to represent treatment technology changes from the pre- Stage 2 baseline (i.e., after implementation of the Stage 1 DBPR). The best data available to char acterize large plants are from the ICR, which were2 

### There is uncertainty in using the ICR data to re present pre-Stage 1 conditions because some plants may have begun making changes prior to the ICR in anticipa tion of the Stage 1 DBPR (McGuire et al., 2002). See Section 3.8 for a full discussion of uncertainties in ICR data.

Final Economic Analysis for the Stage 2 DBPR December 2005 5-4 

### collected before the Stage 1 DBPR compliance deadlines and likely represent pre-Stage 1 conditions 2



### . The compliance forecast, therefore, needs to account for treatment technology changes as a result of the Stage 1 DBPR before predicting changes that are need ed for the Stage 2 DBPR. Similarly, the post-Stage 2 TTHM and HAA5 predictions need to take into acc ount changes as a result of the Stage 1 DBPR before predicting reductions that will occur as a result of the Stage 2 DBPR.

EPA uses a “delta” compliance forecast method that was developed by the Microbial / Disinfection Byproducts (M-DBP) Technical Working Group (TWG). The method has four steps. First, EPA characterizes treatment technologies and TTHM a nd HAA5 occurrence for the Pre-Stage 1 baseline. Second, EPA predicts treatment technology ch anges and subsequent reduction in TTHM and HAA5 levels from Pre-Stage 1 baseline to post-Stage 1 DBPR conditions. Third, treatment technology changes and subsequent reductions in TTH M and HAA5 levels are predicted from pre-Stage 1 baseline to postStage 2 DBPR conditions. Lastly, results from Step 2 are subtracted from Step 3 to calculate the incremental treatment technology change and TTH M/HAA5 reduction from post-Stage 1 DBPR to postStage 2 DBPR conditions.

This delta method was selected by the M-DBP TWG over a more direct, two step approach (i.e., predict pre-Stage 2 conditions and then use the pre- Stage 2 conditions to predict impacts for Stage 2) because modeling tools are not able to predict the treatment technology selection or TTHM, HAA5, bromate, and chlorite levels at the plant level. Th e delta approach allows for potential errors in treatment technology selection and TTHM and HAA5 levels to be cancelled out for national level estimates. The TWG believed that using the delta approach reduces the impact of uncertainty in SWAT predictive equations for TTHM and HAA5. The delta approach is used with both the ICR Matrix Method and SWAT analyses.

Accounting for the IDSE

Because the purpose of the IDSE is to identify St age 2 compliance monitoring locations with high DBP levels, it is possible that systems may measure higher DBP levels at Stage 2 compliance monitoring sites than were measured under the ICR. This sugg ests that the number of plants predicted to make treatment technology changes, the level of treatment they select, and the resulting reductions in TTHM and HAA5 levels, all based on ICR data, could be underestimated.3 

### EPA believes that the 20 percent safety margin accounts for potential impacts of the IDSE for small systems because their distributions are not as complex when compared to large systems. EPA also believes that the safety margin accounts for the IDSE for ground water system s because the year-to-year variability in source water quality (and thus, TTHM and HAA5 formation) is low. Ch loramine systems generally observe lower spatial and temporal variability in TTHM and HAA5 distribution sy stem levels (USEPA 2005k); thus, EPA believes the 20 percent safety margin accounts for potential impacts of the IDSE for these systems.

Final Economic Analysis for the Stage 2 DBPR December 2005 5-5 

### The M-DBP TWG recommended that the Stage 1 and Stage 2 compliance forecast methodology incorporate an operational safety margin of 20 percen t to represent the operational level (i.e., 80 percent of the MCL) at which systems typically take some action to ensure consistent compliance with a new drinking water standard and the level at which syst ems target new treatment technologies to meet the standard. EPA believes that this safety margin already accounts for the impacts of the IDSE for some systems, including small systems, ground water systems, and those using chloramines 3 

### . EPA believes, however, that the 20 percent safety margin is not su fficient to account for the potential impacts of the IDSE on large and medium surface water systems because spatial variability of DBP levels and distribution system complexity are greatest in th ese systems. Since the proposal, EPA developed a methodology that analyzed ICR data from surface water plants to assess the extent of spatial variability of TTHM and HAA5 levels and used this as a basis for quantifying the impacts of the IDSE for large and medium surface water systems.



## 5.3 Compliance Forecast Methodology

This section summarizes the tools used for the compliance forecast and provides details on how EPA accounted for the Stage 1 DBPR and potential impacts of the IDSE on the compliance forecast. Section 5.4 presents the results of the compliance forecast.

5.3.1 Tools for Surface and Ground Water Systems

EPA uses several tools to predict changes in tr eatment technology that will result from the Stage 2 DBPR. For surface water systems, EPA used results from both SWAT and the ICR Matrix Method. For ground water systems, predictions were made us ing the ICR Ground Water Delphi Process. Exhibit 5.1 summarizes the tools used to develop the co mpliance forecasts. Detailed information on these methodologies can be found in the referenced sections and appendices.

Surface Water Systems

The two tools used to predict changes in tr eatment technology and the resulting reductions in DBP levels for surface water systems are SWAT and the ICR Matrix Method. SWAT is a modeling tool developed by EPA during the M- DBP Federal Advisory Committee Act (FACA) process to evaluate regulatory alternatives. SWAT uses source water and treatment data from the ICR along with a series of empirical equations developed by researchers to m odel TTHM and HAA5 levels in distribution systems. For each plant, SWAT predicts TTHM, HAA5, chlorite , and bromate levels and compares them to MCLs for a given regulatory alternative. If the plant does not meet the MCLs, SWAT modifies the plant’s treatment until it can achieve compliance. SWAT u ses a decision tree, arranged from lowest-cost to the highest-cost treatment technology, to determine which ne w technology is selected for the plant. A total of4 

### As will be explained in Sections 5.5 and 5.6, revi sed TTHM and HAA5 levels for these plants are based on an analysis of ICR data for plants that are Stage 2 compliant and already used chloramines and/or advanced technologies prior to the ICR.

Final Economic Analysis for the Stage 2 DBPR December 2005 5-6 

### 273 of the 350 ICR surface water plants had sufficient data to allow for modeling in SWAT. (For characterization of the 273 modeled plants, see Appendix A.)

The ICR Matrix Method evaluates TTHM and HAA5 distribution system data from the ICR to identify plants that would need to make treatment technology changes to meet specific regulatory alternatives. ICR plants are first screened to en sure that there are enough TTHM and HAA5 distribution system data so as not to skew the analysis. (See Chapter 3 for a discussion of the screening process, including a discussion of data representativeness.) The method then places the screen ed plants into “bins” based on their running annual average (RAA) and lo cational running annual average (LRAA) TTHM and HAA5 concentrations. Plants in bins that are non-co mpliant with Stage 1 are moved into compliant bins 4



### . The remaining plants that are non-compliant with Stage 2 regulatory alternatives are moved into compliant bins. The ICR Matrix Method is limited in that it does not predict the specific technologies plants will select; this information is only available from SWAT.

Results from both SWAT and the ICR Matrix Met hod are used in the primary analysis for all regulatory alternatives. Section 5.3.6 compares the r esults and explains how they are incorporated into the cost and benefits model.

SWAT and ICR Matrix Method compliance forecast results for large surface water systems were applied directly to medium surface water systems, as the two size categories share similar source types and operational capabilities. Adjustments were made to the forecasts for small surface water systems to account for differences in water qua lity and operational constraints.

Ground Water Systems

The compliance forecasts for large ground water systems were generated using the ICR Ground Water Delphi Process, which convened a group of expe rts to determine the treatment technology changes that would be needed by systems that were not in compliance with the Stage 1 and Stage 2 DBPR. The results were stratified based on plant location (Florida or Non-Florida) and extra polated to national levels. Because large and medium ground water systems are similar with respect to treatment configurations and the well fields from which they draw, the complian ce forecasts for large ground water systems were also used for medium ground water systems (the ratio of Florida to Non-Florida ground water systems was considered similar enough to allow for this extra polation; see Appendix B for more details). The compliance forecast for small ground water plants was based on results of the ICR Ground Water Delphi process, but was adjusted to account for differences in total organic carbon (TOC), softening use, and the ratio of Florida to Non-Florida ground water systems.

5.3.2 Accounting for the Stage 1 DBPR

For cost and benefit analyses, the complian ce forecast needs to represent treatment technology changes from the pre-Stage 2 baseline (i.e., after impl ementation of the Stage 1 DBPR). The best data available to characterize large plan ts are from the ICR, which were collected before the Stage 1 DBPR5 

### There is uncertainty in using the ICR data to re present pre-Stage 1 conditions because some plants may have begun making changes prior to the ICR in anticipati on of the Stage 1 DBPR (McGuire 2003). See Section 3.8 for a full discussion of uncertainties in ICR data.

Final Economic Analysis for the Stage 2 DBPR December 2005 5-7 

### compliance deadlines and likely represent pre-Stage 1 conditions 5 

### . The compliance forecast, therefore, needs to account for treatment technology changes as a result of the Stage 1 DBPR before predicting changes that are needed for the Stage 2 DBPR.

The Stage 1 Regulatory Impact Analysis (RIA) (USEPA 1998a) includes a prediction of chloramine and advanced technology use that will result from the Stage 1 DBPR. EPA did not use this prediction as the post-Stage 1 baseline in this EA b ecause new data and tools became available since the Stage 1 RIA was developed (namely ICR and SWAT) th at provide better characterization of plants and allow for better prediction of treatment technology changes.

For surface water systems, a straightforward, 2- step approach for generating the Stage 2 DBPR compliance forecast was originally considered dur ing the M-DBP FACA deliberations. Under this approach, SWAT would be used to predict treatme nt technology use and TTHM or HAA5 levels for each plant for post-Stage 1 conditions (i.e., after plants ma ke changes to comply with Stage 1). Then SWAT would evaluate post-Stage 1 conditions for each plant to predict treatment technology changes needed to comply with the Stage 2 DBPR. In other word s, SWAT would assess each plant for compliance with Stage 1, make a treatment technology change if needed to comply with Stage 1, then evaluate post-Stage 1 TTHM and HAA5 levels to determine if further changes are needed for Stage 2.

The M-DBP TWG identified a problem with this approach. SWAT predictive equations for TTHM and HAA5 were calibrated by comparing the I CR-observed values to the SWAT-predicted values for the 273 plants used by the SWAT model. While the national average TTHM and HAA5 levels predicted by SWAT and observed by the ICR had good agreement, differences between SWAT and ICR data were large for some plants. Thus, although national predictions were considered dependable, SWAT-predicted TTHM and HAA5 data for indivi dual plants were uncertain. Because of this uncertainty, the M-DBP TWG decided that it was inappr opriate to evaluate SWAT-predicted post-Stage 1 TTHM and HAA5 occurrence for each plant separatel y to assess compliance with the Stage 2 DBPR.

To minimize the impacts of uncertainty in SW AT plant-level predictions on the Stage 2 DBPR compliance forecast, the M-DBP FACA developed a f our-step, or “delta,” approach for SWAT:

• Step 1: Model TTHM and HAA5 occurrence fo r the pre-Stage 1 baseline conditions (for SWAT, predict using the model; for the ICR Matrix Method, use observed data).

• Step 2: Predict treatment technology changes from the pre-Stage 1 DBPR baseline to postStage 1 DBPR conditions.

• Step 3: Predict treatment technology changes from the pre-Stage 1 DBPR baseline to postStage 2 DBPR conditions.

• Step 4: Take the difference, or delta, between the results from Steps 2 and 3 to calculate the incremental treatment technology selection forecast and TTHM and HAA5 reduction for Stage 2 (from post-Stage 1 conditions).

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-8 

### Because the same pre-Stage 1 base line was used to evaluate co mpliance with both Stage 1 and Stage 2, potential errors in selection of treatment technology and changes in TTHM and HAA5 levels are cancelled out for national level estimates in Step 4. The TWG believed that using the delta approach reduces the impact of uncertainty in SWAT’s predictive equations for TTHM and HAA5. The delta approach was used for both SWAT and the ICR Matrix Method.

A similar delta approach was developed and used for ground water plants. Because ICR data for ground water plants were not robus t enough to allow for modeling in SWAT, ICR observed data were used as the pre-Stage 1 baseline. Using the ICR Gr ound Water Delphi results, EPA calculated the percent of plants exceeding Stage 1 and Stage 2 MCLs from the Pre-Stage 1 baseline and took the difference as the percent of plants needing to change treatment technologies from post-Stage 1 to Stage 2.

An uncertainty in the delta approach is th e implicit assumption that plants making treatment technology changes to comply with the Stage 1 DB PR also meet the Stage 2 DBPR MCLs (Stage 1 forecasts are subtracted from Stage 2; only the delta is c onsidered for costs and benefits). To illustrate the mechanisms of the delta approach and this uncerta inty, consider the plots of the maximum locational running annual average (LRAA) and running annual average (RAA) for each plant in Exhibit 5.2. Quadrant I contains those plants in compliance w ith both Stage 1 and Stage 2 considering a 20 percent safety margin. Quadrant II contains plants that are in compliance with Stage 1, but not Stage 2. Plants in Quadrant III are those that exceed the MCLs for both Stage 1 and Stage 2. With the delta approach, plants in Quadrant III that make a treatment technology change to meet the Stage 1 DBPR move to Quadrant I (i.e., they are Stage 2-compliant). Although EPA recognizes this uncertainty, the Agency believes this the delta approach is reasonable for the following reasons:

• The Stage 2 DBPR is a required rule in th e Safe Drinking Water Act (SDWA) Amendments of 1996. Details of the Stage 2 DBPR were pub lished in the Agreement in Principle, which includes the Stage 2 MCLs, in December 2000, wh ich is well before the Stage 1 compliance deadlines. It is less costly and, therefore, in a water system’s best interest to develop a comprehensive treatment strategy to achieve simultaneous compliance with both Stage 1 and Stage 2.

• A large portion of systems use chloramines to achieve compliance with the Stage 1 DBPR. Chloramines generally result in lower spa tial and temporal variability of TTHM and HAA5 concentrations in distribution systems compared to chlorine, as discussed in Section 5.3.6 and shown in Exhibit 5.8a. Therefore, systems that have switched to chloramines to comply with Stage 1 DBPR will likely have LRAA values already below 80 μg/L for TTHM and 60 μg/L for HAA5 and will not need to make a second treatment technology change to comply with the Stage 2 DBPR.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-9 0 20 40 60 80 100 120 140

0 20 40 60 80 100 120 140 160 TTHM RAA, ug/LMaximum TTHM LRAA, ug/L



### II

IV III

I 64

64

0 20 40 60 80 100 120 140

0 20 40 60 80 100 120 HAA5 RAA, ug/LMaximum HAA5 LRAA, ug/L



### II

IV III

I

48 48

Note: Each point on the graph represents one plant
MCL (with 20% Safety Factor) RAA = LRAA representation Plants under the Stage 1 DBPR, but above the Stage 2 DBPR compliance targets
Source: analysis of ICR screened ground and surface water plants (N = 311) Note: Each point on the graph represents one plant
MCL (with 20% Safety Factor) RAA = LRAA representation Plants under the Stage 1 DBPR, but above the Stage 2 DBPR compliance targets
Source: analysis of ICR screened ground and surface water plants (N = 311) 

## Exhibit 5.2 Compliance Evaluation of Screened ICR Surface and Ground Water Plants

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-10 

### One disadvantage of the delta approach is that compliance forecasts for the Stage 2 DBPR do not take into account the specific advanced treatment t echnologies predicted for Stage 1. In some cases, a more advanced treatment technology can be predicted for a plant to meet Stage 1 requirements as compared to Stage 2. There are two reasons this can happen:

• A plant is allowed to use chloramines in the Stage 2 model run, but not for the Stage 1 run. For all SWAT model runs, 77 percent of plants were allowed to convert from free chlorine to chloramines to comply with DBP rules. Th e 77 percent cap on chloramine conversion was developed during the M-DBP FACA deliberati ons to represent site-specific circumstances and other local factors that would preclude chlo ramine usage for reasons other than technical suitability. The percentage is applied to each plant randomly by a Monte Carlo simulation model. Because the number is assigned randomly, the situation can (and does) occur where a plant is not allowed to use chloramines for St age 1 compliance, but is allowed to use it for Stage 2 compliance.

• EPA considered ultraviolet (UV) an availa ble technology for meeting Stage 2 requirements, but not Stage 1 requirements, when developing the compliance forecasts. UV is an emerging technology that has just recently been show n to be an effective disinfectant for many microorganisms of concern in drinking water. Since the model starts with the same pre-Stage 1 baseline, some plants that are predicted to use more expensive technologies, such as ozone, microfiltration/ultrafiltration (MF/UF), or granular activated carbon (GAC), to comply with Stage 1 can achieve compliance with UV on ce it is included in the Stage 2 runs.

The first factor (randomness in chloramine use) has a relatively small impact on the compliance forecast. The second factor, assumptions for UV ava ilability, however, causes the delta from Stage 1 to Stage 2 to be negative for some advanced technologi es. EPA developed an approach to adjust the technology selection forecast to correct for the negativ es (called the “adjustment for negatives” step). In summary, plants selecting UV for the Stage 2 DBPR we re reallocated to advanced technologies (ozone, MF/UF, and GAC-10-Minute Contact Time (GAC10)) wh en overall predictions were lower for Stage 2 than for Stage 1. Details on the adjustment-fo r-negatives step, including flow charts and sample calculations, are provided in Appendices A and B.

5.3.3 Operational Safety Margins

The M-DBP TWG recommended that a 20 percent operational safety margin be used for DBP MCLs (TTHM, HAA5, bromate, and chlorite) when evaluating Stage 1 and all Stage 2 regulatory alternatives. This safety margin is intended to repr esent the level (i.e., 80 percent of the MCL) at which systems typically take some action to ensure cons istent compliance with a new drinking water standard and the level at which systems target new treatment technologies to meet the standard. In addition to representing industry practices, the safety margin also is intended to account for year-to-year fluctuations in DBP levels. (ICR data are limited to 1 year a nd might not represent the highest DBP concentrations that occur in a system.) Individual systems may use higher or lower safety margins based on systemspecific conditions. Use of a safety margin is consis tent with prior DBP regulatory development efforts.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-11 

### 5.3.4 Accounting for the IDSE

For most systems, compliance monitoring for the Stage 2 DBPR is preceded by an IDSE. The purpose of the IDSE is to identify Stage 2 DBPR compliance monitoring sites that represent high TTHM and HAA5 concentrations in the distribution syst em. Not all systems must perform an IDSE. Nontransient noncommunity water system (N TNCWSs) serving fewer than 10,000 people are not required to conduct an IDSE. Other systems may not need to perform the IDSE if they demonstrate low historic DBP distribution system concentrations or if they serve fewer than 500 people. Most systems that conduct an IDSE are expected to monitor for one year.

There are several reasons why Stage 2 complian ce monitoring sites identified by the IDSE may have higher TTHM and HAA5 levels than data collected under the ICR. First, monitoring under the IDSE includes more sites, and samples are taken more frequently than the ICR. In addition, sites are selected to represent both high TTHM and high HAA5 leve ls. Whether an individual system finds sites with higher TTHM and HAA5 levels than those repor ted in the ICR depends on a number of factors, including but not limited to:

• Spatial variability in TTHM and HAA5 levels. The more variability in a system, the more likely the system will find higher LRAAs during the IDSE. Spatial variability is influenced by residual disinfectant type (free chlorine versus chloramines).

• Temporal variability in TTHM and HAA5 levels . Seasonal variability related to increased temperature and changes in source water quality may be better characterized during the IDSE because they take more frequent samples (for large systems, 6 per year for the IDSE compared to 4 per year required for the Stage 1 DBPR). Temporal variability is a much larger factor for surface water systems than for ground water systems.

• Number of plants per system. Systems with a higher than average number of plants are already sampling at many locations under the Stage 1 DBPR and thus have most likely already captured much of the spatial variability of DBP levels in their distribution systems. Conversely, systems with a lower than aver age number of plants have collected fewer samples under Stage 1 and are thus more likely to find higher LRAAs when monitoring for the IDSE.

• System configuration. Systems having more complicated distribution systems (e.g., welllooped systems with several large users, system s with multiple storage facilities, and systems with pumping stations) are more likely to find higher LRAAs during their IDSEs. Complexity of the system generally decreases with system size.

• Technical resources used to select ICR and/or St age 1 sites. Systems with extensive residual data, extensive DBP data, hydraulic models, and those that have performed tracer studies should already have well-defined maximum residence time sites. These systems are less likely to see significantly higher LRAAs as a result of their IDSEs.

The IDSE can potentially affect the compliance for ecast in two ways. First, systems that appear to be in compliance with the Stage 2 DBPR based on an evaluation of their ICR data might find increased DBP concentrations at their new Stage 2 DBPR monitoring locations (post-IDSE), high enough to cause them to make treatment technology changes. Second, systems that expect to be out of compliance with6 

### This method was selected over other alternatives sin ce the highest location in the ICR data was not always at the maximum residence time location. In addition, this approach more conservatively predicts spatial variability, as it represents the highest two locations, and thus th e smallest difference between two ICR-observed locations.

Final Economic Analysis for the Stage 2 DBPR December 2005 5-12 

### the Stage 2 DBPR based on an evaluation of their I CR data may need to use more advanced technology changes to meet rule requirements at new sites identified during the IDSE.

One limitation of EPA’s compliance forecast methodology is the use of ICR data as model inputs; ICR data are the best available data, but they may not represent Stage 2 DBPR compliance monitoring results due to systems conducting the IDSE. This lim itation is, in part, accounted for by the use of a 20 percent safety margin. EPA believes that the 20 pe rcent safety margin accounts for potential impacts of the IDSE for small surface water systems because their distribution systems are not usually as complex as large systems. For ground water systems, EPA belie ves that a 20 percent safety margin already accounts for the IDSE because the year-to-year variability in source water quality (and TTHM and HAA5 levels) is low. Similarly, chloramine systems generally ob serve lower spatial and temporal variability in TTHM and HAA5 distribution system levels (USEPA 2005k); thus, EPA believes the 20 percent safety margin accounts for potential impacts of the IDSE for these systems.

EPA believes, however, that the 20 percent safet y margin is not sufficient to account for the potential impacts of the IDSE on large and medium surface water systems because spatial variability of DBP levels and distribution system complexity ar e greatest in these systems. Since the proposal, EPA developed a methodology to quantify the potential impacts of the IDSE on these systems.

5.3.4.1 Analysis of Spatial Variability in Large and Medium Surface Water Systems

ICR screened data, consisting of data from 213 su rface water plants, were used to assess spatial variability of DBPs in distribution systems of large and medium surface water systems. See section 3.7.1 for a description of the ICR data set and the screening method. (Only those plants with 3 of 4 quarters of data that have TTHM and HAA5 data for at least 3 of 4 distribution system locations are considered in the analysis.)

EPA began with the simplifying assumption that th e spatial variability in the ICR data represents the variability that systems can find through IDSE monitoring. That is, all plants can find post-IDSE maximum LRAA values according to the following formula 6 

### :
post-IDSE LRAA
max 

### = ICR LRAA
max 

### + (ICR LRAA
max 

### - ICR LRAA 2ndHi 

### ) (Equation 5.1)

Where:

post-IDSE LRAA
max 

### = the maximum LRAA value found after the system has conduced the IDSE ICR LRAA
max 

### = the maximum LRAA value as reported for the last four quarters of the ICR ICR LRAA 2ndHi 

### = the second highest LRAA as reported for the last four quarters of the ICR

Exhibit 5.3 predicts the potential increase in the percent of plants making treatment technology changes from Stage 1 to Stage 2 by assuming that th e Stage 2-compliant ICR surface water plants have post-IDSE LRAAs according to Equation 5.1. Exhibit 5.4a characterizes the difference between the ICR LRAA
max 

### and ICR LRAA 2ndHi 

### for those surface water plants that are predicted to make treatment

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-13 

### Number Percent of All Plants Number Percent of All Plants A B C = B / A D E = D / A CL2 133 29 21.8% 11 8.3% CLM 80 7 8.8% 4 5.0% All 21 3 36 16.9 % 15 7.0 % Notes: CL2 = free chlorine, CLM = chloramines LRAA MAX 

### = the maximum LRAA value for each plant.
LRAA 2ndHI 

### = the second highest LRAA value for each plant. Sources: 

### Disinfectant Type Number of Screened ICR Surface Water Plants

A) See Section 3.7.1 for a detailed description of the ICR data set and screening method. B) & D) Stage 1 and Stage 2 compliance is based on an assessment of ICR TTHM and HAA5 occurrence data applying a 20 percent safety margin. 

### S2-Compliant Plants that will be S2 Non-Compliant after the

IDSE based on LRAA MAX 

### + (LRAA MAX 

### - LRAA 2ndHI 

### ) S1-Compliant Plants that are

already S2 Non-Compliant

TTHM HAA5 CL2 CLM All CL2 CLM All Number of Screened Sta g e 2 non-com p liant Plants 1 40 11 51 40 11 51 Average of LRAA MAX 64.50 57.63 63.02 37.57 38.97 37.87 Average of LRAA 2ndHI 49.79 52.31 50.33 32.93 35.58 33.50 Delta of Average of LRAA MAX and Average of LRAA 2ndHI 14.71 5.31 12.68 4.64 3.39 4.37 Notes

LRAA MAX = the maximum LRAA value for each plant.
LRAA 2ndHI = the second highest LRAA value for each plant. 1 Represents all screened ICR SW plants that are in compliance with Stage 1 but have Post-IDSE LRAAmax values > 64 ug/L for TTHM or 48 ug/L for HAA5. See Section 3.7.1 for a detailed description of the ICR data set and screening method. CL2 = free chlorine, CLM = chloramines 

### technology changes for Stage 1 and Stage 2, including those plants that are predicted to make treatment technology changes if Equation 5.1 was applied. Note that the average di fference between the ICR LRAA
max 

### and ICR LRAA 2ndHi 

### is 12.7 : 

### g/L for TTHM and 4.4 : 

### g/L for HAA5. The cumulative distribution for ICR LRAA
max 

### - LRAA 2ndHi 

### for TTHM and HAA5 are shown in Exhibits 5.4b and 5.4c, respectively.



## Exhibit 5.3 Predicted Increase in Per cent Making Treatment Technology Changes based on Spatial Variability Analysis

Exhibit 5.4a Analysis of Variabilit y for Stage 2 Non-Compliant Plants

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-14 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60
LRAA MAX - LRAA 2ndHI for TTHM (μg/L)Cumulative Percentage (N=51) 

## Exhibit 5.4b Cumulative Distribution of ICR LRAA MAX 

## - ICR LRAA 2ndHI 

## for Stage 2 Non-Compliant Plants (TTHM data)

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-15 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
LRAA MAX - LRAA 2ndHI for HAA5 (μg/L)Cumulative Percentage (N=51) 

## Exhibit 5.4c Cumulative Distribution of ICR LRAA MAX 

## - ICR LRAA 2ndHI 

## for Stage 2 Non-Compliant Plants (HAA5 Data)

EPA believes this analysis of spatial variability over-predicts the potential impacts of the IDSE for three reasons. First, the calculation is genera lly based on the difference between a calculated LRAA from a maximum residence time and an average resi dence site. Because sampling was required at only one maximum residence time site for the ICR, EPA is not able to compare two or more LRAA results from maximum residence time sites. Second, this calculation assumes all plants find sites with higher DBP levels during the IDSE and does not take into account other factors, such as the use of advanced tools to locate maximum residence time sites prior th e ICR. Systems that used advanced tools are not expected to find drastic differences between their ICR LRAA
max 

### and their post-IDSE LRAA
max 

### . Third, systems using chloramines for residual disinfection ge nerally find less spatial and temporal variability in distribution system DBP data as compared to systems using chlorine. This statement is supported by results in Exhibit 5.4a, which presents the difference in between the LRAA
max 

### and the LRAA 2ndHi 

### for screened ICR surface water plants that are in complian ce with Stage 1 but out of compliance with Stage 2, based on the post-IDSE LRAA
max 

### calculation (see equation 5.1). The average between the TTHM LRAA
max 

### and TTHM LRAA 2ndHi 

### for chlorine plants is 14.7 : 

### g/L, whereas the average difference for chloramine plants is 5.3 : 

### g/L. Even though EPA believes that the analysis of spatial variability in this section over-predicts the potential impacts of the IDSE for reasons described above, the Agency believes that it has value in informing an upper-end estimate.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-16 

### 5.3.4.2 Modifying the Operational Safety Margin

The next step in the process of quantifying the potential impacts of the IDSE is applying the results in the previous section to the large and medium surface water compliance forecast methodology. Changes to the compliance for ecast must take into account:

• The potential changes in the number of systems making treatment technology changes

• The potential treatment technology changes selected by all systems

• The resulting reduction in TTHM and HAA5 concentrations

EPA determined that the most effective way to revise the compliance forecast was to modify the operational safety margin. A larger safety margin would affect the compliance forecasts of both SWAT and the ICR Matrix Method (i.e., cause more plants to make treatment technology changes). A larger safety margin would also impact the distribution of treatment technologies predicted by SWAT. (SWAT re-evaluates compliance with the Stage 2 DBPR by considering the safety margin after evaluating each treatment type in the decision tree.) Lastly, an incr eased safety margin could result in plants predicted to install higher-cost treatment technologies to meet co mpliance with a lower numeric MCL value. The decrease in TTHM and HAA5 levels resulting from th e additional plants making treatment technology changes, and more advanced treatment technologies being selected would be automatically calculated by SWAT.

To identify the most appropriate safety margin, EPA compared the results in Exhibits 5.3 and 5.4 to the compliance assessment in Exhibit 5.5. The vari ability analysis in Exhibit 5.3 predicts that an additional 7 percent of all plants could make treatme nt technology changes as a result of the IDSE. This prediction falls in between the compliance analys is of ICR for a 25 and a 30 percent safety margin (compare to column E in Exhibit 5.5). Because EPA believes the analysis of spatial variability produces an overestimate of potential impacts as discussed previ ously, a safety margin of 25 percent was chosen to model the impacts of the IDSE. Although the true magnitude of the influence of the IDSE is unknown, EPA believes that the compliance forecast based on an alysis of spatial variability provides a plausible prediction.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-17 TTHM/HAA5 LRAA Values Number of Plants Percent of All Plants Delta from Stage 1 to

Stage 2 I ncrementa l P ercent More Plants Compared to 20 Percent SM for Stage 2 R at i o o f Additional Plants to the 20 Percent SM for Stage 2 A B C = B / 213 D = C - 19.2% E = D - 16.9% F = D/16.9% All Screened Plants 213 Stage 1 non-compliant at 20% SM 41 19.2% Stage 2 non-compliant at 20% SM 64 / 48 77 36.2% 16.9% Stage 2 non-compliant at 25% SM 60 / 45 88 41.3% 22.1% 5.2% 1.3 Stage 2 non-compliant at 30% SM 56 / 42 95 44.6% 25.4% 8.5% 1.5 Sources: A. MCL * (1 - safety margin) Plant Subset

B. Assessment of ICR screened DBP dataset for surface water systems. See section 3.7.1 for a description of the dataset and screening process 

## Exhibit 5.5 Compliance Analysis of ICR Sc reened Plants at Different Operational Safety Margins

5.3.4.3 Incorporating Potential Impacts of the IDSE into the Compliance Forecast

EPA believes that the 20 percent operational saf ety margin already accounts for the impacts of the IDSE for some systems, including small systems, ground water systems, and those using chloramines. As discussed earlier, EPA believes that the 20 percen t safety margin accounts for potential impacts of the IDSE for small systems because their distribution system s are less complex than those of large systems. EPA also believes that the safety margin account s for the IDSE for ground water systems because the year-to-year variability in source water quality (and thus, TTHM and HAA5 formation) is low. Chloramine systems generally observe lower sp atial and temporal variability in TTHM and HAA5 distribution system levels (USEPA 2005k); thus, EPA believes the 20 percent safety margin accounts for potential impacts of the IDSE for these systems.

EPA believes that the compliance forecast for some large and medium surface water systems using a 20 percent safety margin is already conser vative because the treatment technology decision tree (see Appendix A) does not include distribution system operational improvements that systems are more likely to use for compliance with St age 2 as compared to Stage 1. However, for the reasons given in Section 5.3.4, EPA believes that a compliance fo recast based on a safety margin of 25 percent also provides a plausible prediction for some large and medium surface water systems. Because both operational safety margins are considered plausibl e, EPA’s compliance forecast for large and medium surface water systems assigns equal probability to the 20 and 25 percent safety margins. Compliance forecast results in this chapter reflect this assumption.

Although the magnitude of the impacts of the IDSE is uncertain, it is important to note that the IDSE will affect costs and benefits in the same dir ection. If higher DBPs are identified through the IDSE, more systems make treatment technology changes and more advanced treatment technologies are selected, increasing both costs and benefits. A sensitivity analysis in Section 5.7 shows that the magnitude of the impact of the IDSE on the benefits an alysis is expected to be higher than the magnitude of the impact on the cost analysis.7 

### One possible explanation for this difference is that the maximum residence time reported in the ICR is not consistent with the maximum TTHM and HAA5 value reporte d for the system. As shown in Section A.6.4, the maximum TTHM and HAA5 LRAAs often occur at locations other than the one designated as the maximum residence time location (MAX). Uncertainty, and especia lly underestimates, of the maximum residence time may result in a lower percent of plants predicted by SWAT to make treatment technology changes for Stage 2, but not Stage 1. The average residence time in SWAT is deemed to be more certain because it is based on the mean of the four distribution system residence times reported in the ICR (for the distribution system equivalent sample point (DSE), average sample point number 1 (AVE1), averag e sample point number 2 (AVE2), and MAX locations).

Final Economic Analysis for the Stage 2 DBPR December 2005 5-18 

### 5.3.5 Methodology for Incorporating SWAT and ICR Matrix Method Results into the Compliance Forecast

As described in Section 5.3.1, EPA uses two tool s to predict changes in treatment technology and resulting reductions in DBP levels: SWAT and the I CR Matrix Method. Exhibit 5.6 compares the SWAT and ICR Matrix Method predictions of plants cha nging treatment technology for Stage 1 and Stage 2. (Comparisons of the predicted re ductions in TTHM and HAA5 concentr ations are presented in Sections 5.6 and 5.7.) Exhibit 5.6 shows that although the to tal predicted percentages of plants making treatment technology changes for Stage 2 are similar for both methods, the percent of plants making treatment technology changes to comply with Stage 1 differ between the ICR Matrix Method and SWAT by more than 10 percentage points. Such differences are e xpected given the inherent differences in the two methods and uncertainties associated with each 7



### . Because both SWAT and the ICR Matrix Method have associated uncertainty, results from both are used to generate the compliance forecast for su rface water systems. The ICR Matrix Method does not predict the specific treatment technologies that plants will install. Thus, results from the ICR matrix method are incorporated by comparing the predicted percent of plants making treatment technology changes with SWAT results to create a ICR Matrix Method-to-SWAT multiplier, shown in Exhibit 5.6, column G. EPA generated a uniform distribution with 1.0 as the 5 th 

### percentile value and the ICR Matrix Method-to-SWAT multiplier for plants making treatment technology changes as the 95 th 

### percentile value. Two separate distributions were used, one for the 20 percent safety margin and one for the 25 percent safety margin, as shown in Exhibit 5.6. Exhibit 5.7 provides a graphical depiction of the two uniform distributions for the Preferred Alternative.Final Economic Analysis for the Stage 2 DBPRAugust 20055-19ICR Matrix Method SWAT% Changingfrom pre-S1to pre-S2% Changingfom pre-S1to post-S2% Changingfrom pre-S2to post-S2% Changingfrom pre-S1to pre-S2% Changingfom pre-S1to post-S2% Changingfrom pre-S2to post-S2A B C = B - A D E F = E - DG = C / FPreferred Reg. Alternative (80/60 LRAA, IDSE), 20% SM 19.2% 36.2% 16.9% 32.5% 39.1% 6.6% 2.57Preferred Reg. Alternative (80/60 LRAA, IDSE), 25% SM 19.2% 41.3% 22.1% 32.5% 45.4% 12.9% 1.71Reg. Alternative 1 (Bromate = 5),20% SM 19.2% 36.2% 16.9% 32.5% 39.4% 6.8% 2.47Reg. Alternative 2 (80/60 Single High), 20% SM 19.2% 58.2% 39.0% 32.5% 63.1% 30.6% 1.27Reg. Alternative 3 (40/30 RAA), 20%SM 19.2% 72.8% 53.5% 32.5% 72.1% 39.6% 1.35ICR MatrixMethod-toSWATMultiplierRegulatory AlternativeExhibit 5.6 Predicted Percent of Plants Making Treatment Technology Changes to Meet Stage 1 and Stage 2Regulatory Alternatives for the ICR Matrix Method and SWATNotes: 1) The operational safety marginfor Stage 1 is 20 percent for all analyses.Sources: SWAT run summaries (USEPA 2001b),ICR Matrix Method results (USEPA 2005a).(A) Percent of plants changing from bin B2 from Exhibit 5.16a. ICR MatrixMethod results for Reg. Alternative 1 are the same as for the PreferredAlternative, assuming a 20 percent safety margin.(B) The sum of percent of plants changing inbins B2 and A2 from Exhibits 5.16b through 5.16d. ICR Matrix Method results for Reg. Alternative 1 arethe same as for the Preferred Alternative, 20 percent safety margin.(D) and (E) SWAT run summaries (USEPA 2001b).

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-20 (2.57) (1.71) 1 2 3Probability

Multiplier 1 2 3Probability

Multiplier 

### Uniform Distribution for a 20% Safety Margin Uniform Distribution for a 25% Safety Margin

(2.57) (1.71) 1 2 3Probability

Multiplier 1 2 3Probability

Multiplier 

### Uniform Distribution for a 20% Safety Margin Uniform Distribution for a 25% Safety Margin 

## Exhibit 5.7 Uniform Distributions for In corporating Results from SWAT and the

ICR Matrix Method into the Compliance Forecast for Surface Water Systems

Note: The uniform distributions are used when generating t he Stage 2 DBPR compliance forecasts for surface water systems for the Preferred Alternative. The same method is applied to the ot her regulatory alternatives using the multipliers in Exhibit 5.6.

Source: Exhibit 5.6.



### 5.3.6 Compliance Forecast Simulation Model

To include results from two surface water co mpliance forecast tools and to account for uncertainty in the potential impacts of the IDSE in the compliance forecast, EPA developed a Monte Carlo simulation model. The model follows three basic steps:

• Step 1: For large and medium surface water systems, the model randomly selects the SWATpredicted treatment technology selection delta for either the 20 or 25 percent safety margin runs. Each safety margin has an equal (50 percent) chance of being selected. For small surface water systems and all ground water systems, the treatment technology selection delta for the 20 percent safety margin is always selected.

• Step 2: For large, medium, and small surface water systems, the model randomly selects the ICR-to-SWAT multiplier from the appropriate uniform distribution from Exhibit 5.7 for the safety margin selected in Step 1.

• Step 3: For large, medium, and small surface water systems, the model multiplies the result from Step 2 by the treatment technology selection delta results identified in Step 1 to calculate the percent and number of plants making treatment technology changes from Stage 1 to Stage 2. For ground water systems, the treatment technology selection delta for a 20 percent safety margin is always used to calcula te the percent of plants changing from Stage 1 to Stage 2.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-21 

### The process is repeated 10,000 times to produce a distribution of plants making treatment technology changes from Stage 1 to Stage 2 (results from Step 3). This distribution is carried through the cost model, as described in Chapter 7. Note that for large and medium surface water systems, only the treatment technology selection delta for the 20 percent safety margin is used in Step 1 for Regulatory Alternatives 1, 2, and 3 as the IDSE is not a component of these alternatives.



## 5.4 Compliance Forecast Results

Three types of compliance forecasts are presented in this EA:

1) Treatment Technologies-in-Place forecasts show the number and percent of plants that are using a given treatment technology either before or after rule implementation. For pre-Stage 2 and post-Stage 2 treatment technologies-in-place, the calculated number and percent of plants represent plants predicted to make treat ment technology changes to comply with the rule, added to the number and percent of plants already using the treatment technology before rule promulgation.

2) Treatment Technology Selection forecasts show the number and percent of plants that are predicted to add a given treatment technology to comply with the Stage 1 DBPR or Stage 2 DBPR regulatory alternatives. These results include only the number of plants that exceed the rule’s MCLs and that therefore must make treatment technology changes to comply with the rule. Treatment technology selection results are always based on pre-Stage 1 conditions.

3) Treatment Technology Selection Delta forecasts show the incremental number and percent of plants that must add a given treatment technology following the Stage 1 DBPR to meet Stage 2 DBPR MCLs. Treatment technology selection deltas are calculated by subtracting the Stage 1 DBPR treatment technology selection from the Stage 2 DBPR treatment technology selection. These treatment technology selection delta are used for costing.

Note that all forecast results in subsequent sections have the same formatting, with system size down the left hand column and treatment technology across the top. Exhibit 5.8 provides the exhibit numbers for the compliance forecasts for the Stage 2 DBPR Preferred Alternative. Exhibit 5.9 summarizes compliance forecast resu lts for community water systems (CWSs) and NTNCWSs for all regulatory alternatives. Surface water system results in Exhibit 5.9 represent the mean values from the Monte Carlo simulation model. These results with 90 percent confidence intervals and further population breakouts are shown in Exhibit 7.3.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-22 

## Exhibit 5.8 Compliance Forecast Exhibits for the Stage 2 DBPR Preferred Alternative

Compliance Forecast Type Exhibit Containing Results for the Preferred Alternative 1


### Pre-Stage 1 Treatment Technologies-in-Place Exhibits 3.13 and 3.14
Stage 1 DBPR Treatment Technology Selection Exhibits C.1 and C.2
Pre-Stage 2 Treatment Technologies-in-Place Exhibit 5.10 and 5.13
Stage 2 DBPR Treatment Technology Selection Delta Exhibits 5.11 and 5.14 2


### Post-Stage 2 Treatment Technologies-in-Place Exhibits 5.12 and 5.15
Notes: 1. The first exhibit contains results for surface water plants, the second for ground water plants. 2. Treatment technology selection delta tables are used for costing.



### Surface Water Systems

Section 3.6.2 describes how predictions of pre-Stage 1 treatment technologies-in-place were developed. The pre-Stage 1 baseline for surface water systems is shown in Exhibit 3.13.

Exhibit 5.10 shows the predicted treatment technologies-in-place for surface water plants following the Stage 1 DBPR (i.e., the pre-Stage 2 DB PR baseline). The treatment technologies-in-place for small surface and ground water systems can be de rived by adding the treatment technology selection for the Stage 1 DBPR (Exhibit C.1a for CWS, C.1b for NTNCWS) to the treatment technologies-in-place for the pre-Stage 1 baseline (Exhibit 3.13). This is not true, however, for plants in large and medium systems. EPA assumes that pre-Stage 2, only medium and large systems employ advanced treatment technologies. Therefore, the SWAT program produces a different type of result, called “ending technologies,” that accounts for these plants that alread y have an advanced treatment technology but must select another to meet the rule alternative.

Exhibits 5.11a through 5.11d present the treatment technology selection deltas, as a percentage (5.11a for CWS, 5.11c for NTNCWS) and as the number of plants (5.11b for CWS, 5.11d for NTNCWS), for the Stage 2 DBPR Preferred Alternative for surface water systems. These exhibits are used to predict costs for surface water systems. As described in Section 5.3, technology selection deltas for surface water systems incorporate two compliance forecast methods : SWAT and the ICR Matrix Method. For the Preferred Alternative, the Stage 2 treatment tec hnology selection deltas for large and medium surface water systems represent equal probability of a 20 a nd 25 percent safety margin to model the potential impact of the IDSE.

Exhibits 5.12a through 5.12d present the final post-Stage 2 treatment technologies-in-place for surface water systems under the Stage 2 DBPR Preferred Alternative.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-23 

### Ground Water Systems

Exhibit 3.14 summarizes the pre-Stage 1 DBPR baseline treatment technologies-in-place for ground water treatment plants. The derivation of this b aseline is discussed in Section 3.6.2. For plants in large and medium ground water systems, ICR treatment da ta were used to derive the predicted percent of plants using each treatment technology. The pe rcentage of small ground water plants using each treatment technology is based on evaluation of CWS data; EPA assumed that NTNCWSs use similar treatment technologies for the size categories shown.

Exhibit 5.13 shows the predicted treatment technologies-in-place for ground water plants following the Stage 1 DBPR (pre-Stage 2 DBPR base line). The treatment technologies-in-place for the pre-Stage 2 baseline can be derived by adding the treatment technology selection for the Stage 1 DBPR (Exhibit C.2a for CWS, C.2b for NTNCWS) to the treatment technologies-in-place for the pre-Stage 1 baseline (Exhibit 3.14a for CWS, 3.14b for NTNCWS).

Exhibits 5.14a through 5.14d present the treatme nt technology selection deltas for ground water systems. EPA used these deltas to predict the costs to ground water systems of complying with the Stage 2 DBPR. Exhibits 5.15a through 5.15d present EPA’s prediction of treatment technologies that will be employed after systems comply with the Stage 2 DBPR. For all plants, post-Stage 2 treatment technologies-in-place can be derived by adding the pr edicted pre-Stage 2 treatment technologies-in-place (Exhibit 5.13) to the treatment technology selections in Exhibit 5.14.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-24



### Number Percent A B C = B / A Preferred Regulatory Alternative SW > 10K 2,561 373 14.6% SW < 10K 4,757 420 8.8% GW > 10K 7,048 145 2.1% GW < 10K 45,855 1,323 2.9% All Plants 60,221 2,261 3.8% Alternative 1 (BR 5) SW > 10K 2,561 305 11.9% SW < 10K 4,757 420 8.8% GW > 10K 7,048 145 2.1% GW < 10K 45,855 1,327 2.9% All Plants 60,221 2,197 3.6% Alternative 2 (80/60 SH) SW > 10K 2,561 892 34.8% SW < 10K 4,757 1,052 22.1% GW > 10K 7,048 488 6.9% GW < 10K 45,855 2,377 5.2% All Plants 60,221 4,810 8.0% Alternative 3 (40/30 RAA) SW > 10K 2,561 1,193 46.6% SW < 10K 4,757 1,306 27.4% GW > 10K 7,048 334 4.7% GW < 10K 45,855 1,613 3.5% All Plants 60,221 4,446 7.4%

Notes:

Sources:

(B) Exhibits 5.11 and 5.14 for the Preferred Regulatory Alternative, Exhibits C.3 and C.4 for Alternative 1, Exhibits C.7 and C.8 for Alternative 2, and Exhibits C.11 and C.12 for Alternative 3 Mean Estimate of Plants Changing Treatment Technology from Stage 1 to Stage 2 (including CLM) System Size

and Type

Uncertainty in the impacts of the IDSE for SW systems serving >10,000 people are reflected in estimates for the Preferred Regulatory Alternative only. Estimates for all SW systems are the averages of the results of two methods: SWAT and the ICR Matrix Method. Baseline Number of CWS and NTNCWS Plants

(A) Baseline Number of CWS and NTNCWS plants from Chapter 3, Exhibit 3.2. 

## Exhibit 5.9 Plants in CWSs a nd NTNCWSs Making Treatment Technology Changes From Stage 1 For Stag e 2 DBPR Regulatory AlternativesExhibit 5.10a Pre-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent and Number of Plants by Residual Disinfection Type)

No Advanced Treatment Technologies 1 Chlorine Dioxide UV Ozone MF/UF GAC10
CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM
A B C D E F G H I J K L
% # % # % # % # % # % # % # % # % # % # % # % #
<100 41.8% 150 29.7% 107 14.5% 52 7.1% 26
100-499 35.6% 273 35.4% 272 1.0% 7 0.9% 7 5.1% 39 4.6% 35 8.9% 68 4.8% 37
500-999 35.6% 172 35.4% 171 1.0% 5 0.9% 4 5.1% 24 4.6% 22 8.9% 43 4.8% 23 1,000-3,300 33.4% 378 41.3% 467 1.9% 22 2.1% 24 4.0% 45 4.5% 51 6.2% 70 2.9% 32 3,301-9,999 33.4% 421 41.3% 520 1.9% 24 2.1% 27 4.0% 50 4.5% 56 6.2% 78 2.9% 36 10,000-49,999 35.0% 452 39.0% 504 3.3% 42 3.7% 47 6.1% 78 6.8% 87 0.9% 11 1.0% 12 1.0% 13 1.2% 15 50,000-99,999 35.0% 203 39.0% 226 3.3% 19 3.7% 21 6.1% 35 6.8% 39 0.9% 5 1.0% 6 1.0% 6 1.2% 7 100,000-999,999 35.0% 213 39.0% 238 3.3% 20 3.7% 22 6.1% 37 6.8% 41 0.9% 5 1.0% 6 1.0% 6 1.2% 7 >=1,000,000 35.0% 26 39.0% 29 3.3% 2 3.7% 3 6.1% 4 6.8% 5 0.9% 1 1.0% 1 1.0% 1 1.2% 1 Total Plants, % 34.9% 2,287 38.7% 2,534 2.2% 141 2.4% 155 4.8% 314 5.1% 337 5.1% 333 2.7% 179 0.4% 27 0.5% 30 GAC10 + AD GAC20 GAC20 + AD Membranes TOTAL
CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM
M N O P Q R S T U = A+C+E+G+I+K+M+O+Q+S V = B+D+F+H+J+L+N+P+R+T
% # % # % # % # % # % # % # % # % # % #
<100 2.0% 7 1.3% 5 0.0% 0 0.0% 0 2.1% 8 1.4% 5
100-499 1.1% 8 1.0% 7 0.5% 4 0.4% 3 0.5% 3 0.4% 3
500-999 1.1% 5 1.0% 5 0.5% 2 0.4% 2 0.5% 2 0.4% 2 1,000-3,300 1.0% 12 1.2% 13 0.5% 6 0.6% 7 0.2% 2 0.2% 2 3,301-9,999 1.0% 13 1.2% 15 0.5% 7 0.6% 7 0.2% 2 0.2% 2 10,000-49,999 0.5% 7 0.6% 7 0.2% 2 0.2% 2 0.0% 0 0.0% 0 0.3% 4 0.4% 5 50,000-99,999 0.5% 3 0.6% 3 0.2% 1 0.2% 1 0.0% 0 0.0% 0 0.3% 2 0.4% 2 100,000-999,999 0.5% 3 0.6% 4 0.2% 1 0.2% 1 0.0% 0 0.0% 0 0.3% 2 0.4% 2 >=1,000,000 0.5% 0 0.6% 0 0.2% 0 0.2% 0 0.0% 0 0.0% 0 0.3% 0 0.4% 0 Total Plants, % 0.2% 13 0.2% 15 0.8% 49 0.8% 49 0.3% 18 0.3% 19 0.4% 26 0.4% 25 Note: Detail may not add to totals due to independent rounding

1 "No Adv" includes conventional, non-conventional, and softening plants.

Exhibit 5.10b Pre-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent and Number of Plants by Residual Disinfection Type)

No Advanced Treatment Technologies 1 Chlorine Dioxide UV Ozone MF/UF GAC10
CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM
A B C D E F G H I J K L
% # % # % # % # % # % # % # % # % # % # % # % #
<100 41.8% 95 29.7% 67 14.5% 33 7.1% 16
100-499 35.6% 111 35.4% 111 1.0% 3 0.9% 3 5.1% 16 4.6% 14 8.9% 28 4.8% 15
500-999 35.6% 38 35.4% 38 1.0% 1 0.9% 1 5.1% 5 4.6% 5 8.9% 9 4.8% 5 1,000-3,300 33.4% 31 41.3% 38 1.9% 2 2.1% 2 4.0% 4 4.5% 4 6.2% 6 2.9% 3 3,301-9,999 33.4% 8 41.3% 10 1.9% 0 2.1% 1 4.0% 1 4.5% 1 6.2% 2 2.9% 1 10,000-49,999 35.0% 2 39.0% 2 3.3% 0 3.7% 0 6.1% 0 6.8% 0 0.9% 0 1.0% 0 1.0% 0 1.2% 0 50,000-99,999 100.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100,000-999,999 35.0% 0 39.0% 0 3.3% 0 3.7% 0 6.1% 0 6.8% 0 0.9% 0 1.0% 0 1.0% 0 1.2% 0 >=1,000,000 100.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 Total Plants, % 37.1% 285 34.7% 266 0.8% 6 0.8% 6 3.4% 26 3.2% 25 10.1% 77 5.2% 40 0.0% 0 0.0% 0 GAC10 + AD GAC20 GAC20 + AD Membranes TOTAL
CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM
M N O P Q R S T U = A+C+E+G+I+K+M+O+Q+S V = B+D+F+H+J+L+N+P+R+T
% # % # % # % # % # % # % # % # % # % #
<100 2.0% 4 1.3% 3 0.0% 0 0.0% 0 2.1% 5 1.4% 3
100-499 1.1% 3 1.0% 3 0.5% 2 0.4% 1 0.5% 1 0.4% 1
500-999 1.1% 1 1.0% 1 0.5% 1 0.4% 0 0.5% 0 0.4% 0 1,000-3,300 1.0% 1 1.2% 1 0.5% 0 0.6% 1 0.2% 0 0.2% 0 3,301-9,999 1.0% 0 1.2% 0 0.5% 0 0.6% 0 0.2% 0 0.2% 0 10,000-49,999 0.5% 0 0.6% 0 0.2% 0 0.2% 0 0.0% 0 0.0% 0 0.3% 0 0.4% 0 50,000-99,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 100,000-999,999 0.5% 0 0.6% 0 0.2% 0 0.2% 0 0.0% 0 0.0% 0 0.3% 0 0.4% 0 >=1,000,000 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 Total Plants, % 0.0% 0 0.0% 0 1.3% 10 1.1% 8 0.3% 3 0.3% 3 0.9% 7 0.7% 5 Note: Detail may not add to totals due to independent rounding

1 "No Adv" includes conventional, non-conventional, and softening plants. System Size (Population Served)

System Size (Population Served)

60.4% 217 39.6% 142
364 47.5% 403
229
596
664
681
306
322
39 3,342 51.0% 52.7% 52.7% 52.7% 52.5%

52.7% 52.7% 52.7% 47.5%

49.0% 47.3% 47.3% 534

595
610
274 52.5%

Source: Surface water systems serving <10,000 people: Add Treatment Technologies-in-Place for the Pre-Stage 1 DBPR Baseline (Exhibit 3.13a) to Stage 1 Treatment Technology Selection (Exhibit C.1a). Surface water systems serving 10,000 or more people: Use ending Treatment Technology predictions from SWAT (FACA Screen Series3 v3.0 Database) (USEPA, 2001b). 47.3% 47.3% 47.3% 47.3%

288

35 3,209 254

60.4% 137 39.6%

47.5% 52.5%
52.5% System Size (Population Served)

System Size (Population Served)

46.0% 0.0% 52.7% 0.0% 52.7% 52.7% 52.7%

0 2

0
414 0 100.0% 47.3% 47.3% 47.3% 43

12 89
148
50
49 47.5% 164
56

0
353

Source: Surface water systems serving <10,000 people: Add Treatment Technologies-in-Place for the Pre-Stage 1 DBPR Baseline (Exhibit 3.13b) to Stage 1 Treatment Technology Selection (Exhibit C.1b). Surface water systems serving 10,000 or more people: Use ending Treatment Technology predictions from SWAT (FACA Screen Series3 v3.0 Database) (USEPA, 2001b). 13
3
0
1

54.0% 100.0% 47.3%

Final Economic Analysis for the Stage 2 DBPR December 2005 5-25Exhibit 5.11a Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type) Preferred Alternative Chlorine Dioxide UV Ozone MF/UF GAC10 CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K <100 1.9% 1.0% 2.7% 4.1% 2.3% 5.9% 3.0% 1.7% 4.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100-499 4.1% 2.3% 5.9% 0.1% 0.1% 0.2% 0.4% 0.2% 0.5% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 500-999 4.1% 2.3% 5.9% 0.1% 0.1% 0.2% 0.4% 0.2% 0.5% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1,000-3,300 4.2% 2.4% 6.1% 0.2% 0.1% 0.2% 0.9% 0.5% 1.3% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3,301-9,999 4.2% 2.4% 6.1% 0.2% 0.1% 0.2% 0.9% 0.5% 1.3% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10,000-49,999 8.6% 4.8% 12.0% 0.0% 0.0% 0.0% 0.3% 0.4% 0.1% 2.8% 0.6% 5.7% 0.8% 0.1% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50,000-99,999 8.6% 4.8% 12.0% 0.0% 0.0% 0.0% 0.3% 0.4% 0.1% 2.8% 0.6% 5.7% 0.8% 0.1% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100,000-999,999 8.6% 4.8% 12.0% 0.0% 0.0% 0.0% 0.3% 0.4% 0.1% 2.8% 0.6% 5.7% 0.8% 0.1% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% >=1,000,000 8.6% 4.8% 12.0% 0.0% 0.0% 0.0% 0.3% 0.4% 0.1% 2.8% 0.6% 5.7% 0.8% 0.1% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 5.8% 3.2% 8.2% 0.1% 0.0% 0.1% 0.5% 0.4% 0.6% 1.9% 0.7% 3.4% 1.2% 0.5% 1.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% GAC10 + Advanced Disinfectants GAC20 GAC20 + Advanced Disinfectants Membranes CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

L M N O P Q R S T=A+C+E+G+I+K+M+O+ Q+S L = SUM(A:S)
<100 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.7% 0.4% 1.0% 0.5% 0.3% 0.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5.4% 3.0% 7.8% 10.2% 5.7% 14.6% 100-499 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% 0.3% 0.8% 0.7% 0.4% 1.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 6.5% 3.6% 9.3% 8.4% 4.7% 12.1% 500-999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% 0.3% 0.8% 0.7% 0.4% 1.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 6.5% 3.6% 9.3% 8.4% 4.7% 12.1% 1,000-3,300 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% 0.3% 0.7% 0.8% 0.5% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.2% 4.0% 10.4% 8.8% 4.9% 12.7% 3,301-9,999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% 0.3% 0.7% 0.8% 0.5% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.2% 4.0% 10.4% 8.8% 4.9% 12.7% 10,000-49,999 1.2% 0.6% 1.7% 0.5% 0.2% 0.7% 0.2% 0.0% 0.7% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10.3% 5.6% 14.6% 14.6% 6.8% 22.8% 50,000-99,999 1.2% 0.6% 1.7% 0.5% 0.2% 0.7% 0.2% 0.0% 0.7% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10.3% 5.6% 14.6% 14.6% 6.8% 22.8% 100,000-999,999 1.2% 0.6% 1.7% 0.5% 0.2% 0.7% 0.2% 0.0% 0.7% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10.3% 5.6% 14.6% 14.6% 6.8% 22.8% >=1,000,000 1.2% 0.6% 1.7% 0.5% 0.2% 0.7% 0.2% 0.0% 0.7% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10.3% 5.6% 14.6% 14.6% 6.8% 22.8% Total % 0.5% 0.2% 0.7% 0.2% 0.1% 0.3% 0.1% 0.0% 0.3% 0.0% 0.0% 0.1% 0.3% 0.2% 0.5% 0.5% 0.3% 0.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 8.2% 4.5% 11.7% 11.1% 5.7% 16.6% 11.1% 5.7% 16.6% Note: Detail may not add to totals due to independent rounding Source: Treatment Technology Selection for the Preferred Alternative minus the Stage 1 Treatment Technology Selection from Appendix C, Exhibit C.1a.

Exhibit 5.11b Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type) Preferred Alternative Chlorine Dioxide UV Ozone MF/UF GAC10 CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K <100 7 4 10 15 8 21 11 6 16 0 0 0 0 0 0 100-499 31 17 45 1 1 1 3 2 4 9 5 13 10 5 14 0 0 0 0 0 0 0 0 0 0 0 0 500-999 20 11 28 1 0 1 2 1 3 6 3 8 6 3 9 0 0 0 0 0 0 0 0 0 0 0 0 1,000-3,300 48 27 69 2 1 3 10 6 15 11 6 15 14 8 20 0 0 0 0 0 0 0 0 0 0 0 0 3,301-9,999 53 30 77 2 1 3 11 6 16 12 7 17 16 9 23 0 0 0 0 0 0 0 0 0 0 0 0 10,000-49,999 112 62 155 0 0 0 4 6 1 37 8 74 10 2 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50,000-99,999 50 28 69 0 0 0 2 3 1 17 4 33 5 1 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100,000-999,999 53 29 73 0 0 0 2 3 1 17 4 35 5 1 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >=1,000,000 6 4 9 0 0 0 0 0 0 2 0 4 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Plants 379 211 534 6 3 8 34 26 40 125 46 221 77 35 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GAC10 + Advanced Disinfectants GAC20 GAC20 + Advanced Disinfectants Membranes CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

L M N O P Q R S T=A+C+E+G+I+K+M+O+ Q+S L = SUM(A:S)
<100 0 0 0 0 0 0 2 1 3 2 1 3 0 0 0 0 0 0 19 11 28 36 20 53 100-499 0 0 0 0 0 0 4 2 6 5 3 8 0 0 0 0 0 1 50 28 72 64 36 92 500-999 0 0 0 0 0 0 3 1 4 3 2 5 0 0 0 0 0 0 31 17 45 40 23 58 1,000-3,300 0 0 0 0 0 0 5 3 8 9 5 13 0 0 0 0 0 0 81 45 117 100 56 143 3,301-9,999 0 0 0 0 0 0 6 3 9 10 6 15 0 0 0 0 0 0 91 51 131 111 62 160 10,000-49,999 15 7 23 6 3 9 3 0 9 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 133 72 189 188 88 294 50,000-99,999 7 3 10 3 1 4 1 0 4 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 60 33 85 84 40 132 100,000-999,999 7 4 11 3 1 4 1 0 4 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 63 34 89 89 42 139 >=1,000,000 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 4 11 11 5 17 Total Plants 30 15 45 12 5 18 6 0 18 3 1 6 21 12 30 30 17 43 0 0 0 1 0 1 536 296 766 724 371 1,088 724 371 1,088 Note: Detail may not add to totals due to independent rounding Source: Above table with Treatment Technologies switching from an advanced Treatment Technology with Cl2 to the same advanced Treatment Technology with CLM being moved into the CLM only column 352 196 506

373 174 582

22.8%

System Size (Population Served) Converting to CLM Only

System Size (Population Served) Total Converting to CLM Plants Making Treatment Technology Changes 14.6% 6.8% Plants Making Treatment Technology Changes

8.8% 4.9% 12.7% Converting to CLM Only System Size (Population Served)

System Size (Population Served) Total Converting to CLM

Final Economic Analysis for the Stage 2 DBPR December 2005 5-26Exhibit 5.11c Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type) Preferred Alternative Chlorine Dioxide UV Ozone MF/UF GAC10 CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K <100 1.9% 1.0% 2.7% 4.1% 2.3% 5.9% 3.0% 1.7% 4.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100-499 4.1% 2.3% 5.9% 0.1% 0.1% 0.2% 0.4% 0.2% 0.5% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 500-999 4.1% 2.3% 5.9% 0.1% 0.1% 0.2% 0.4% 0.2% 0.5% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1,000-3,300 4.2% 2.4% 6.1% 0.2% 0.1% 0.2% 0.9% 0.5% 1.3% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3,301-9,999 4.2% 2.4% 6.1% 0.2% 0.1% 0.2% 0.9% 0.5% 1.3% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10,000-49,999 8.6% 4.8% 12.0% 0.0% 0.0% 0.0% 0.3% 0.4% 0.1% 2.8% 0.6% 5.7% 0.8% 0.1% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50,000-99,999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100,000-999,999 8.6% 4.8% 12.0% 0.0% 0.0% 0.0% 0.3% 0.4% 0.1% 2.8% 0.6% 5.7% 0.8% 0.1% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% >=1,000,000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 3.5% 1.9% 5.0% 0.1% 0.1% 0.1% 0.3% 0.2% 0.5% 2.0% 1.1% 2.9% 1.8% 1.0% 2.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% GAC10 + Advanced Disinfectants GAC20 GAC20 + Advanced Disinfectants Membranes CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

L M N O P Q R S T=A+C+E+G+I+K+M+O+ Q+S L = SUM(A:S)
<100 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.7% 0.4% 1.0% 0.5% 0.3% 0.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5.4% 3.0% 7.8% 10.2% 5.7% 14.6% 100-499 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% 0.3% 0.8% 0.7% 0.4% 1.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 6.5% 3.6% 9.3% 8.4% 4.7% 12.1% 500-999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% 0.3% 0.8% 0.7% 0.4% 1.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 6.5% 3.6% 9.3% 8.4% 4.7% 12.1% 1,000-3,300 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% 0.3% 0.7% 0.8% 0.5% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.2% 4.0% 10.4% 8.8% 4.9% 12.7% 3,301-9,999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% 0.3% 0.7% 0.8% 0.5% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 7.2% 4.0% 10.4% 8.8% 4.9% 12.7% 10,000-49,999 1.2% 0.6% 1.7% 0.5% 0.2% 0.7% 0.2% 0.0% 0.7% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10.3% 5.6% 14.6% 14.6% 6.8% 22.8% 50,000-99,999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100,000-999,999 1.2% 0.6% 1.7% 0.5% 0.2% 0.7% 0.2% 0.0% 0.7% 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10.3% 5.6% 14.6% 14.6% 6.8% 22.8% >=1,000,000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% 0.3% 0.8% 0.7% 0.4% 0.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 6.3% 3.5% 9.1% 9.0% 5.0% 13.0% 9.0% 5.0% 13.0% Note: Detail may not add to totals due to independent rounding Source: Treatment Technology Selection for the Preferred Alternative minus the Stage 1 Treatment Technology Selection from Appendix C, Exhibit C.1b.

Exhibit 5.11d Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type) Preferred Alternative Chlorine Dioxide UV Ozone MF/UF GAC10 CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K <100 4 2 6 9 5 13 7 4 10 0 0 0 0 0 0 100-499 13 7 18 0 0 1 1 1 2 4 2 5 4 2 6 0 0 0 0 0 0 0 0 0 0 0 0 500-999 4 2 6 0 0 0 0 0 1 1 1 2 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 1,000-3,300 4 2 6 0 0 0 1 0 1 1 0 1 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 3,301-9,999 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10,000-49,999 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50,000-99,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100,000-999,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >=1,000,000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Plants 27 15 39 1 0 1 3 1 4 16 9 23 14 8 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GAC10 + Advanced Disinfectants GAC20 GAC20 + Advanced Disinfectants Membranes CL2 CLM CL2 CLM CL2 CLM CL2 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

L M N O P Q R S T=A+C+E+G+I+K+M+O+ Q+S L = SUM(A:S)
<100 0 0 0 0 0 0 2 1 2 1 1 2 0 0 0 0 0 0 12 7 18 23 13 33 100-499 0 0 0 0 0 0 2 1 2 2 1 3 0 0 0 0 0 0 20 11 29 26 15 38 500-999 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 7 4 10 9 5 13 1,000-3,300 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 7 4 10 8 5 12 3,301-9,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 3 2 1 3 10,000-49,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 50,000-99,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100,000-999,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >=1,000,000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Plants 0 0 0 0 0 0 0 0 0 0 0 0 4 2 6 5 3 7 0 0 0 0 0 0 48 27 70 69 39 100 69 39 100 Note: Detail may not add to totals due to independent rounding Source: Above table with Treatment Technologies switching from an advanced Treatment Technology with Cl2 to the same advanced Treatment Technology with CLM being moved into the CLM only column 68 38 98

1 0 1

22.8%

System Size (Population Served) Converting to CLM Only

System Size (Population Served) Total Converting to CLM Plants Making Treatment Technology Changes 14.6% 6.8% Converting to CLM Only System Size (Population Served)

System Size (Population Served) Total Converting to CLM Plants Making Treatment Technology Changes

9.0% 5.0% 12.9%

Final Economic Analysis for the Stage 2 DBPR December 2005 5-27Exhibit 5.12a Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type) Preferred Alternative

No Advanced Treatment Technologies CL2 1 No Advanced Treatment Technologies CLM 1 Chlorine Dioxide CL2 Chlorine Dioxide

CLM UV CL2 UV CLM Ozone CL2 Ozone CLM MF/UF CL2 MF/UF CLM GAC 10 CL2 GAC 10 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K L <100 31.7% 27.2% 36.2% 31.6% 30.8% 32.4% 4.1% 2.3% 5.9% 3.0% 1.7% 4.4% 14.5% 14.5% 14.5% 7.1% 7.1% 7.1% 100-499 27.2% 23.5% 30.9% 39.5% 37.7% 41.3% 1.1% 1.0% 1.1% 1.2% 1.1% 1.4% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 5.1% 5.1% 5.1% 4.6% 4.6% 4.6% 8.9% 8.9% 8.9% 4.8% 4.8% 4.8% 500-999 27.2% 23.5% 30.9% 39.5% 37.7% 41.3% 1.1% 1.0% 1.1% 1.2% 1.1% 1.4% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 5.1% 5.1% 5.1% 4.6% 4.6% 4.6% 8.9% 8.9% 8.9% 4.8% 4.8% 4.8% 1,000-3,300 24.6% 20.7% 28.5% 45.6% 43.7% 47.4% 2.1% 2.0% 2.2% 3.0% 2.6% 3.4% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 4.0% 4.0% 4.0% 4.5% 4.5% 4.5% 6.2% 6.2% 6.2% 2.9% 2.9% 2.9% 3,301-9,999 24.6% 20.7% 28.5% 45.6% 43.7% 47.4% 2.1% 2.0% 2.2% 3.0% 2.6% 3.4% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 4.0% 4.0% 4.0% 4.5% 4.5% 4.5% 6.2% 6.2% 6.2% 2.9% 2.9% 2.9% 10,000-49,999 29.3% 25.5% 33.2% 41.2% 41.0% 41.4% 2.7% 2.3% 3.0% 3.7% 3.5% 4.0% 1.1% 0.3% 1.9% 1.6% 0.4% 2.9% 5.3% 5.1% 5.5% 7.5% 7.3% 7.7% 0.8% 0.7% 0.8% 1.1% 1.0% 1.1% 0.9% 0.9% 0.9% 1.3% 1.2% 1.3% 50,000-99,999 29.3% 25.5% 33.2% 41.2% 41.0% 41.4% 2.7% 2.3% 3.0% 3.7% 3.5% 4.0% 1.1% 0.3% 1.9% 1.6% 0.4% 2.9% 5.3% 5.1% 5.5% 7.5% 7.3% 7.7% 0.8% 0.7% 0.8% 1.1% 1.0% 1.1% 0.9% 0.9% 0.9% 1.3% 1.2% 1.3% 100,000-999,999 29.3% 25.5% 33.2% 41.2% 41.0% 41.4% 2.7% 2.3% 3.0% 3.7% 3.5% 4.0% 1.1% 0.3% 1.9% 1.6% 0.4% 2.9% 5.3% 5.1% 5.5% 7.5% 7.3% 7.7% 0.8% 0.7% 0.8% 1.1% 1.0% 1.1% 0.9% 0.9% 0.9% 1.3% 1.2% 1.3% >=1,000,000 29.3% 25.5% 33.2% 41.2% 41.0% 41.4% 2.7% 2.3% 3.0% 3.7% 3.5% 4.0% 1.1% 0.3% 1.9% 1.6% 0.4% 2.9% 5.3% 5.1% 5.5% 7.5% 7.3% 7.7% 0.8% 0.7% 0.8% 1.1% 1.0% 1.1% 0.9% 0.9% 0.9% 1.3% 1.2% 1.3% Total % 27.3% 23.5% 31.2% 41.9% 40.8% 43.1% 2.0% 1.8% 2.2% 2.8% 2.5% 3.1% 1.2% 0.6% 1.9% 1.5% 0.6% 2.4% 4.5% 4.4% 4.6% 5.4% 5.3% 5.5% 5.0% 5.0% 5.1% 2.8% 2.8% 2.8% 0.4% 0.3% 0.4% 0.5% 0.5% 0.5% GAC10 + AD CL2 GAC10 + AD CLM GAC20 CL2 GAC20 CLM GAC20 + AD CL2 GAC20 + AD CLM Membranes CL2 Membranes CLM TOTAL CL2 TOTAL CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

M N O P Q R S T U = A+C+E+G+I+K+M+O+Q+S V = B+D+F+H+J+L+N+P+R+T
<100 2.0% 2.0% 2.0% 1.3% 1.3% 1.3% 0.7% 0.4% 1.0% 0.5% 0.3% 0.7% 2.1% 2.1% 2.1% 1.4% 1.4% 1.4% 100-499 1.1% 1.1% 1.1% 1.0% 1.0% 1.0% 1.0% 0.8% 1.3% 1.1% 0.8% 1.4% 0.5% 0.5% 0.5% 0.5% 0.4% 0.5% 500-999 1.1% 1.1% 1.1% 1.0% 1.0% 1.0% 1.0% 0.8% 1.3% 1.1% 0.8% 1.4% 0.5% 0.5% 0.5% 0.5% 0.4% 0.5% 1,000-3,300 1.0% 1.0% 1.0% 1.2% 1.2% 1.2% 1.0% 0.8% 1.2% 1.4% 1.0% 1.8% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 3,301-9,999 1.0% 1.0% 1.0% 1.2% 1.2% 1.2% 1.0% 0.8% 1.2% 1.4% 1.0% 1.8% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 10,000-49,999 0.9% 0.8% 1.0% 1.3% 1.0% 1.5% 0.2% 0.1% 0.3% 0.3% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 50,000-99,999 0.9% 0.8% 1.0% 1.3% 1.0% 1.5% 0.2% 0.1% 0.3% 0.3% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 100,000-999,999 0.9% 0.8% 1.0% 1.3% 1.0% 1.5% 0.2% 0.1% 0.3% 0.3% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% >=1,000,000 0.9% 0.8% 1.0% 1.3% 1.0% 1.5% 0.2% 0.1% 0.3% 0.3% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% Total % 0.4% 0.3% 0.4% 0.5% 0.4% 0.6% 0.8% 0.7% 0.8% 0.8% 0.8% 0.8% 0.6% 0.5% 0.7% 0.8% 0.6% 1.0% 0.4% 0.4% 0.4% 0.4% 0.4% 0.4% Note: Detail may not add to totals due to independent rounding
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants.

Exhibit 5.12b Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type) Preferred Alternative

No Advanced Treatment Technologies CL2 1 No Advanced Treatment Technologies CLM 1 Chlorine Dioxide CL2 Chlorine Dioxide

CLM UV CL2 UV CLM Ozone CL2 Ozone CLM MF/UF CL2 MF/UF CLM GAC 10 CL2 GAC 10 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K L <100 114 98 130 114 111 117 15 8 21 11 6 16 52 52 52 26 26 26 100-499 209 180 237 303 289 317 8 8 9 9 8 11 9 5 13 10 5 14 39 39 39 35 35 35 68 68 68 37 37 37 500-999 131 114 149 191 182 199 5 5 5 6 5 7 6 3 8 6 3 9 24 24 24 22 22 22 43 43 43 23 23 23 1,000-3,300 278 234 322 515 494 536 23 23 24 34 30 39 11 6 15 14 8 20 45 45 45 51 51 51 70 70 70 32 32 32 3,301-9,999 310 261 359 574 550 597 26 25 27 38 33 43 12 7 17 16 9 23 50 50 50 56 56 56 78 78 78 36 36 36 10,000-49,999 379 329 429 532 529 534 35 30 39 48 45 51 14 4 25 21 5 37 69 66 72 97 94 99 10 9 10 14 13 14 12 11 12 17 16 17 50,000-99,999 170 147 193 239 237 240 15 14 17 22 20 23 6 2 11 9 2 17 31 30 32 43 42 45 4 4 5 6 6 6 5 5 6 7 7 8 100,000-999,999 179 155 203 251 250 253 16 14 18 23 21 24 7 2 12 10 3 17 33 31 34 46 44 47 5 4 5 7 6 7 6 5 6 8 8 8 >=1,000,000 22 19 24 30 30 30 2 2 2 3 3 3 1 0 1 1 0 2 4 4 4 6 5 6 1 1 1 1 1 1 1 1 1 1 1 1 Total Plants 1,792 1,538 2,046 2,747 2,672 2,822 131 120 142 183 166 200 81 38 124 99 42 155 295 290 301 356 350 361 330 329 331 181 181 182 23 22 24 33 32 34 GAC10 + AD CL2 GAC10 + AD CLM GAC20 CL2 GAC20 CLM GAC20 + AD CL2 GAC20 + AD CLM Membranes CL2 Membranes CLM TOTAL CL2 TOTAL CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

M N O P Q R S T U = A+C+E+G+I+K+M+O+Q+S V = B+D+F+H+J+L+N+P+R+T
<100 7 7 7 5 5 5 2 1 3 2 1 3 8 8 8 5 5 5
100-499 8 8 8 7 7 7 8 6 10 9 6 11 3 3 3 4 3 4
500-999 5 5 5 5 5 5 5 4 6 5 4 7 2 2 2 2 2 2 1,000-3,300 12 12 12 13 13 13 11 9 14 16 12 20 2 2 2 2 2 2 3,301-9,999 13 13 13 15 15 15 13 10 15 18 13 22 2 2 2 2 2 2 10,000-49,999 12 10 13 17 13 20 3 2 4 4 3 5 0 0 0 0 0 0 4 4 4 6 5 6 50,000-99,999 5 5 6 7 6 9 1 1 2 2 1 2 0 0 0 0 0 0 2 2 2 2 2 3 100,000-999,999 6 5 6 8 6 9 1 1 2 2 1 3 0 0 0 0 0 0 2 2 2 3 3 3 >=1,000,000 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Total Plants 23 20 26 33 27 39 51 49 53 52 50 55 39 30 49 49 36 63 25 25 26 26 26 27 Note: Detail may not add to totals due to independent rounding
1 No advanced Treatment Technologies includes conventional, non-conventional, and softening plants. System Size (Population Served)

42.6% 48.5% 41.5% 41.5% 35.5%

41.6% 40.0% 35.5%

36.1%

System Size (Population Served) System Size (Population Served) System Size (Population Served)

40.0% 41.6% 41.6% 41.6% 55.0% 46.0% 46.0%

36.1%

36.1% 36.1% 37.6% 61.6%

50.6% 50.6% 44.7% 44.7% 47.1% 47.1% 47.1% 47.1% 47.7% 45.0%

54.0% 54.0% 60.0% 60.0% 58.4% 58.4% 58.4% 58.4% 57.4% 42.6%

51.1% 51.1% 56.8% 56.8% 56.1% 56.1% 56.1% 56.1% 54.7% 47.3%

56.8% 56.8% 63.1% 63.1% 60.7% 60.7% 60.7% 60.7% 60.1%

198
353 174

318 221

388 162

414
222
452
504
537
241
254
31 2,792 201

400
446
466
209
220
27 2,461 244

504
562
608
273
287
35 3,122 261

677
754
755
339
357
43 3,760 153

392
247
641
714
725
325
343 794

784
352
371

Source: Surface water systems serving <10,000 people: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.10a) to the Treatment Technology Selection Delta for the Preferred Alternative. Surface water systems serving 10,000 or more people: Use ending Treatment Technology predictions from SWAT (FACA Screen Series3 v3.0 Database) for the Preferred Alternative. Source: Surface water systems serving <10,000 people: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.10a) to the Treatment Technology Selection Delta for the Preferred Alternative. Surface water systems serving 10,000 or more people: Use ending Treatment Technology predictions from SWAT (FACA Screen Series3 v3.0 Database) for the Preferred Alternative.

45 3,938 41 3,582 170

436
274
713

Final Economic Analysis for the Stage 2 DBPR December 2005 5-28Exhibit 5.12c Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type) Preferred Alternative

No Advanced Treatment Technologies CL2 1 No Advanced Treatment Technologies CLM 1 Chlorine Dioxide CL2 Chlorine Dioxide

CLM UV CL2 UV CLM Ozone CL2 Ozone CLM MF/UF CL2 MF/UF CLM GAC 10 CL2 GAC 10 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K L <100 31.7% 27.2% 36.2% 31.6% 30.8% 32.4% 4.1% 2.3% 5.9% 3.0% 1.7% 4.4% 14.5% 14.5% 14.5% 7.1% 7.1% 7.1% 100-499 27.2% 23.5% 30.9% 39.5% 37.7% 41.3% 1.1% 1.0% 1.1% 1.2% 1.1% 1.4% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 5.1% 5.1% 5.1% 4.6% 4.6% 4.6% 8.9% 8.9% 8.9% 4.8% 4.8% 4.8% 500-999 27.2% 23.5% 30.9% 39.5% 37.7% 41.3% 1.1% 1.0% 1.1% 1.2% 1.1% 1.4% 1.2% 0.7% 1.8% 1.3% 0.7% 1.8% 5.1% 5.1% 5.1% 4.6% 4.6% 4.6% 8.9% 8.9% 8.9% 4.8% 4.8% 4.8% 1,000-3,300 24.6% 20.7% 28.5% 45.6% 43.7% 47.4% 2.1% 2.0% 2.2% 3.0% 2.6% 3.4% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 4.0% 4.0% 4.0% 4.5% 4.5% 4.5% 6.2% 6.2% 6.2% 2.9% 2.9% 2.9% 3,301-9,999 24.6% 20.7% 28.5% 45.6% 43.7% 47.4% 2.1% 2.0% 2.2% 3.0% 2.6% 3.4% 1.0% 0.5% 1.4% 1.3% 0.7% 1.8% 4.0% 4.0% 4.0% 4.5% 4.5% 4.5% 6.2% 6.2% 6.2% 2.9% 2.9% 2.9% 10,000-49,999 29.3% 25.5% 33.2% 41.2% 41.0% 41.4% 2.7% 2.3% 3.0% 3.7% 3.5% 4.0% 1.1% 0.3% 1.9% 1.6% 0.4% 2.9% 5.3% 5.1% 5.5% 7.5% 7.3% 7.7% 0.8% 0.7% 0.8% 1.1% 1.0% 1.1% 0.9% 0.9% 0.9% 1.3% 1.2% 1.3% 50,000-99,999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100,000-999,999 29.3% 25.5% 33.2% 41.2% 41.0% 41.4% 2.7% 2.3% 3.0% 3.7% 3.5% 4.0% 1.1% 0.3% 1.9% 1.6% 0.4% 2.9% 5.3% 5.1% 5.5% 7.5% 7.3% 7.7% 0.8% 0.7% 0.8% 1.1% 1.0% 1.1% 0.9% 0.9% 0.9% 1.3% 1.2% 1.3% >=1,000,000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 28.2% 24.2% 32.1% 38.1% 36.6% 39.6% 0.9% 0.9% 1.0% 1.2% 1.0% 1.3% 2.0% 1.1% 2.9% 1.8% 1.0% 2.6% 3.4% 3.4% 3.4% 3.2% 3.2% 3.2% 10.1% 10.1% 10.1% 5.2% 5.2% 5.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% GAC10 + AD CL2 GAC10 + AD CLM GAC20 CL2 GAC20 CLM GAC20 + AD CL2 GAC20 + AD CLM Membranes CL2 Membranes CLM TOTAL CL2 TOTAL CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

M N O P Q R S T U = A+C+E+G+I+K+M+O+Q+S V = B+D+F+H+J+L+N+P+R+T
<100 2.0% 2.0% 2.0% 1.3% 1.3% 1.3% 0.7% 0.4% 1.0% 0.5% 0.3% 0.7% 2.1% 2.1% 2.1% 1.4% 1.4% 1.4% 100-499 1.1% 1.1% 1.1% 1.0% 1.0% 1.0% 1.0% 0.8% 1.3% 1.1% 0.8% 1.4% 0.5% 0.5% 0.5% 0.5% 0.4% 0.5% 500-999 1.1% 1.1% 1.1% 1.0% 1.0% 1.0% 1.0% 0.8% 1.3% 1.1% 0.8% 1.4% 0.5% 0.5% 0.5% 0.5% 0.4% 0.5% 1,000-3,300 1.0% 1.0% 1.0% 1.2% 1.2% 1.2% 1.0% 0.8% 1.2% 1.4% 1.0% 1.8% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 3,301-9,999 1.0% 1.0% 1.0% 1.2% 1.2% 1.2% 1.0% 0.8% 1.2% 1.4% 1.0% 1.8% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 10,000-49,999 0.9% 0.8% 1.0% 1.3% 1.0% 1.5% 0.2% 0.1% 0.3% 0.3% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 50,000-99,999 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100,000-999,999 0.9% 0.8% 1.0% 1.3% 1.0% 1.5% 0.2% 0.1% 0.3% 0.3% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% >=1,000,000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.3% 1.3% 1.3% 1.1% 1.1% 1.1% 0.9% 0.7% 1.2% 1.0% 0.7% 1.3% 0.9% 0.9% 0.9% 0.7% 0.7% 0.7% Note: Detail may not add to totals due to independent rounding
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants.

Exhibit 5.12d Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type) Preferred Alternative

No Advanced Treatment Technologies CL2 1 No Advanced Treatment Technologies CLM 1 Chlorine Dioxide CL2 Chlorine Dioxide

CLM UV CL2 UV CLM Ozone CL2 Ozone CLM MF/UF CL2 MF/UF CLM GAC 10 CL2 GAC 10 CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

A B C D E F G H I J K L <100 72 62 82 71 70 73 9 5 13 7 4 10 33 33 33 16 16 16 100-499 85 73 96 123 118 129 3 3 4 4 3 4 4 2 5 4 2 6 16 16 16 14 14 14 28 28 28 15 15 15 500-999 29 25 33 42 40 44 1 1 1 1 1 1 1 1 2 1 1 2 5 5 5 5 5 5 9 9 9 5 5 5 1,000-3,300 23 19 26 42 40 44 2 2 2 3 2 3 1 0 1 1 1 2 4 4 4 4 4 4 6 6 6 3 3 3 3,301-9,999 6 5 7 11 11 12 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 1 1 1 10,000-49,999 1 1 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50,000-99,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100,000-999,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >=1,000,000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Plants 216 186 246 292 281 304 7 7 7 9 8 10 16 9 22 14 8 20 26 26 26 25 25 25 77 77 77 40 40 40 0 0 0 0 0 0 GAC10 + AD CL2 GAC10 + AD CLM GAC20 CL2 GAC20 CLM GAC20 + AD CL2 GAC20 + AD CLM Membranes CL2 Membranes CLM TOTAL CL2 TOTAL CLM Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th

M N O P Q R S T U = A+C+E+G+I+K+M+O+Q+S V = B+D+F+H+J+L+N+P+R+T
<100 4 4 4 3 3 3 2 1 2 1 1 2 5 5 5 3 3 3 100-499 3 3 3 3 3 3 3 2 4 4 3 5 1 1 1 1 1 2 500-999 1 1 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 1 1,000-3,300 1 1 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 0 3,301-9,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10,000-49,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50,000-99,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100,000-999,999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >=1,000,000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Plants 0 0 0 0 0 0 10 10 10 8 8 8 7 5 9 8 5 10 7 7 7 5 5 5 Note: Detail may not add to totals due to independent rounding
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. System Size (Population Served)

47.7% 48.5% 41.5% 41.5% 35.5%

0.0% 40.0% 35.5%

36.1%

System Size (Population Served) System Size (Population Served) System Size (Population Served)

40.0% 41.6% 0.0% 41.6% 55.0% 46.0% 46.0%

0.0%

36.1% 0.0% 42.6% 61.6%

50.6% 50.6% 44.7% 44.7% 47.1% 0.0% 47.1% 0.0% 52.9% 45.0%

54.0% 54.0% 60.0% 60.0% 58.4% 0.0% 58.4% 0.0% 52.3% 42.6%

51.1% 51.1% 56.8% 56.8% 56.1% 0.0% 56.1% 0.0% 49.5% 47.3%

56.8% 56.8% 63.1% 63.1% 60.7% 0.0% 60.7% 0.0% 55.0%

124 144 110

130 139

158 102

168 49 37 10 2 0 0 0 366 44

33 9 2 0 0 0 327 54

41 11 2 0 0 0 406 57

55 15 3 0 1 0 401 96

159 54 52 14 3 0 1 16

3 0 1

Source: Surface water systems serving <10,000 people: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.10b) to the Treatment Technology Selection Delta for the Preferred Alternative. Surface water systems serving 10,000 or more people: Use ending Treatment Technology predictions from SWAT (FACA Screen Series3 v3.0 Database) for the Preferred Alternative. Source: Surface water systems serving <10,000 people: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.10b) to the Treatment Technology Selection Delta for the Preferred Alternative. Surface water systems serving 10,000 or more people: Use ending Treatment Technology predictions from SWAT (FACA Screen Series3 v3.0 Database) for the Preferred Alternative.

0 422 0 380 107

177 60 58

Final Economic Analysis for the Stage 2 DBPR December 2005 5-29

### Exhibit 5.13a Pre-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent and Number of Plants, by Residual Disinfectant Type)

No Advanced Treatment Technologies CL2 1 No Advanced Treatment Technologies CLM 1 UV CL2 UV CLM Ozone CL2 Ozone CLM GAC20 CL2 GAC20 CLM Membranes CL2 Membranes

CLM TOTAL USING

CL2 TOTAL USING

CLM
A B C D E F G H I J K = A+C+E+G+I L = B+D+F+H+J
# # % # % # % # % # % # % # % # % # % # % # % #
<100 95.9% 6,161 2.4% 155 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.9% 56 0.3% 22 0.5% 29 96.3% 6,183 3.7% 240
100-499 95.3% 14,522 2.8% 426 0.0% 0 0.0% 0 0.2% 25 0.5% 74 0.0% 0 0.6% 96 0.1% 20 0.5% 79 95.6% 14,567 4.4% 676
500-999 95.3% 5,806 2.8% 170 0.0% 0 0.0% 0 0.2% 10 0.5% 29 0.0% 0 0.6% 39 0.1% 8 0.5% 32 95.6% 5,823 4.4% 270 1,000-3,300 95.7% 7,264 2.5% 192 0.0% 0 0.0% 0 0.3% 22 0.9% 66 0.0% 0 0.1% 4 0.1% 4 0.5% 36 96.1% 7,290 3.9% 297 3,301-9,999 95.7% 4,816 2.5% 127 0.0% 0 0.0% 0 0.3% 15 0.9% 44 0.0% 0 0.1% 3 0.1% 3 0.5% 24 96.1% 4,833 3.9% 197 10,000-49,999 89.2% 4,801 7.2% 389 0.8% 46 0.8% 42 0.0% 0 0.0% 2 1.7% 90 0.3% 14 91.7% 4,936 8.3% 446 50,000-99,999 89.2% 639 7.2% 52 0.8% 6 0.8% 6 0.0% 0 0.0% 0 1.7% 12 0.3% 2 91.7% 657 8.3% 59 100,000-999,999 89.5% 821 7.1% 65 0.8% 8 0.7% 6 0.0% 0 0.0% 0 1.7% 15 0.2% 2 92.0% 844 8.0% 74 >=1,000,000 89.5% 24 7.1% 2 0.8% 0 0.7% 0 0.0% 0 0.0% 0 1.7% 0 0.2% 0 92.0% 25 8.0% 2
Total Plants 94.6% 44,854 3.3% 1,578 0.0% 0 0.0% 0 0.3% 130 0.6% 267 0.0% 0 0.4% 200 0.4% 173 0.5% 217 95.2% 45,157 4.8% 2,262 Note: Detail may not add to totals due to independent rounding

1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. Source: Add Treatment Technologies-in-Place for the Pre-Stage 1 DBPR Baseline (Exhibit 3.14a) to Stage 1 Treatment Technology Selection (Exhibit C.2a).



### Exhibit 5.13b Pre-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent and Number of Plants, by Residual Disinfectant Type)

No Advanced Treatment Technologies CL2 1 No Advanced Treatment Technologies CLM 1 UV CL2 UV CLM Ozone CL2 Ozone CLM GAC20 CL2 GAC20 CLM Membranes CL2 Membranes

CLM TOTAL USING

CL2 TOTAL USING

CLM
A B C D E F G H I J K = A+C+E+G+I L = B+D+F+H+J
# # % # % # % # % # % # % # % # % # % # % # % #
<100 95.9% 2,391 2.4% 60 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.9% 22 0.3% 8 0.5% 11 96.3% 2,400 3.7% 93
100-499 95.3% 2,028 2.8% 60 0.0% 0 0.0% 0 0.2% 3 0.5% 10 0.0% 0 0.6% 13 0.1% 3 0.5% 11 95.6% 2,035 4.4% 94
500-999 95.3% 561 2.8% 16 0.0% 0 0.0% 0 0.2% 1 0.5% 3 0.0% 0 0.6% 4 0.1% 1 0.5% 3 95.6% 563 4.4% 26 1,000-3,300 95.7% 237 2.5% 6 0.0% 0 0.0% 0 0.3% 1 0.9% 2 0.0% 0 0.1% 0 0.1% 0 0.5% 1 96.1% 237 3.9% 10 3,301-9,999 95.7% 21 2.5% 1 0.0% 0 0.0% 0 0.3% 0 0.9% 0 0.0% 0 0.1% 0 0.1% 0 0.5% 0 96.1% 21 3.9% 1 10,000-49,999 89.2% 3 7.2% 0 0.8% 0 0.8% 0 0.0% 0 0.0% 0 1.7% 0 0.3% 0 91.7% 3 8.3% 0 50,000-99,999 89.2% 0 7.2% 0 0.8% 0 0.8% 0 0.0% 0 0.0% 0 1.7% 0 0.3% 0 91.7% 0 8.3% 0 100,000-999,999 89.5% 0 7.1% 0 0.8% 0 0.7% 0 0.0% 0 0.0% 0 1.7% 0 0.2% 0 92.0% 0 8.0% 0 >=1,000,000 89.5% 0 7.1% 0 0.8% 0 0.7% 0 0.0% 0 0.0% 0 1.7% 0 0.2% 0 92.0% 0 8.0% 0
Total Plants 95.6% 5,241 2.6% 143 0.0% 0 0.0% 0 0.1% 5 0.3% 15 0.0% 0 0.7% 39 0.2% 12 0.5% 27 95.9% 5,259 4.1% 225 Note: Detail may not add to totals due to independent rounding

1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. Source: Add Treatment Technologies-in-Place for the Pre-Stage 1 DBPR Baseline (Exhibit 3.14b) to Stage 1 Treatment Technology Selection (Exhibit C.2b). System Size (Population Served)

System Size (Population Served) 

### Preferred Alternative

Preferred Alternative

Final Economic Analysis for the Stage 2 DBPR December 2005 5-30Exhibit 5.14a Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) CLM Only UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Converting

to CLM Plants Making

Treatment Technology Changes
A B C D E F G H I J = A+C+E+G+I K = SUM(A:I) <100 1.0% 0.0% 1.1% 0.0% 0.0% 0.4% 0.0% 0.0% 0.0% 2.1% 2.4% 100-499 1.4% 0.0% 1.6% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 3.0% 3.2% 500-999 1.4% 0.0% 1.6% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 3.0% 3.2% 1,000-3,300 1.1% 0.0% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.7% 2.7% 3,301-9,999 1.1% 0.0% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.7% 2.7% 10,000-49,999 1.4% 0.1% 0.2% 0.0% 0.2% 0.0% 0.2% 2.0% 2.1% 50,000-99,999 1.4% 0.1% 0.2% 0.0% 0.2% 0.0% 0.2% 2.0% 2.1% 100,000-999,999 1.3% 0.1% 0.2% 0.0% 0.1% 0.0% 0.2% 1.9% 2.0% >=1,000,000 1.4% 0.1% 0.2% 0.0% 0.1% 0.0% 0.2% 2.0% 2.1% Total % 1.3% 0.0% 1.3% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 2.6% 2.8% 2.8% Note: Detail may not add to totals due to independent rounding. Source: Treatment Technology Selection for the Preferred Alternative minus the Stage 1 Treatment Technology Selection from Appendix C, Exhibit C.2a.

Exhibit 5.14b Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) CLM Only UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Converting

to CLM Plants Making

Treatment Technology Changes
A B C D E F G H I J = A+C+E+G+I K = SUM(A:I) <100 62 0 70 0 0 23 0 0 0 132 155 100-499 213 0 242 0 0 27 0 0 0 456 483 500-999 85 0 97 0 0 11 0 0 0 182 193 1,000-3,300 82 0 118 0 0 0 4 0 0 204 204 3,301-9,999 54 0 78 0 0 0 2 0 0 135 135 10,000-49,999 75 3 12 0 8 2 11 107 111 50,000-99,999 10 0 2 0 1 0 2 14 15 100,000-999,999 12 0 2 0 1 0 2 17 18 >=1,000,000 0 0 0 0 0 0 0 1 1 Total Plants 595 0 606 4 15 61 17 2 15 1,247 1,314 1,314 Note: Detail may not add to totals due to independent rounding. Source: Above table with Treatment Technologies switching from an advanced Treatment Technology with Cl2 to the same advanced Treatment Technology with CLM being moved into the CLM only column. 145 1,170 2.9%

2.1%



### Final Economic Analysis for the Stage 2 DBPR December 2005 5-31Exhibit 5.14c Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) CLM Only UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Converting

to CLM Plants Making

Treatment Technology Changes
A B C D E F G H I J = A+C+E+G+I K = SUM(A:I) <100 1.0% 0.0% 1.1% 0.0% 0.0% 0.4% 0.0% 0.0% 0.0% 2.1% 2.4% 100-499 1.4% 0.0% 1.6% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 3.0% 3.2% 500-999 1.4% 0.0% 1.6% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 3.0% 3.2% 1,000-3,300 1.1% 0.0% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.7% 2.7% 3,301-9,999 1.1% 0.0% 1.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2.7% 2.7% 10,000-49,999 1.4% 0.1% 0.2% 0.0% 0.2% 0.0% 0.2% 2.0% 2.1% 50,000-99,999 1.4% 0.1% 0.2% 0.0% 0.2% 0.0% 0.2% 2.0% 2.1% 100,000-999,999 1.3% 0.1% 0.2% 0.0% 0.1% 0.0% 0.2% 1.9% 2.0% >=1,000,000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 1.2% 0.0% 1.4% 0.0% 0.0% 0.3% 0.0% 0.0% 0.0% 2.5% 2.8% 2.8% Note: Detail may not add to totals due to independent rounding. Source: Treatment Technology Selection for the Preferred Alternative minus the Stage 1 Treatment Technology Selection from Appendix C, Exhibit C.2b.

Exhibit 5.14d Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) CLM Only UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Converting

to CLM Plants Making

Treatment Technology Changes
A B C D E F G H I J = A+C+E+G+I K = SUM(A:I) <100 24 0 27 0 0 9 0 0 0 51 60 100-499 30 0 34 0 0 4 0 0 0 64 67 500-999 8 0 9 0 0 1 0 0 0 18 19 1,000-3,300 3 0 4 0 0 0 0 0 0 7 7 3,301-9,999 0 0 0 0 0 0 0 0 0 1 1 10,000-49,999 0 0 0 0 0 0 0 0 0 50,000-99,999 0 0 0 0 0 0 0 0 0 100,000-999,999 0 0 0 0 0 0 0 0 0 >=1,000,000 0 0 0 0 0 0 0 0 0 Total Plants 65 0 75 0 0 14 0 0 0 140 154 154 Note: Detail may not add to totals due to independent rounding. Source: Above table with Treatment Technologies switching from an advanced Treatment Technology with Cl2 to the same advanced Treatment Technology with CLM being moved into the CLM only column. 2.8%

2.1%

153

0



### Final Economic Analysis for the Stage 2 DBPR December 2005 5-32Exhibit 5.15a Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) No Advanced Treatment Technologies CL2 1 No Advanced

Treatment Technologies CLM 1 UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Using CL2 Total Using CLM
A B C D E F G H I J K = A+C+E+G+I L = B+D+F+H+J <100 93.5% 3.4% 0.0% 1.1% 0.0% 0.0% 0.4% 0.9% 0.3% 0.5% 94.2% 5.8% 100-499 92.1% 4.2% 0.0% 1.6% 0.2% 0.5% 0.2% 0.6% 0.1% 0.5% 92.6% 7.4% 500-999 92.1% 4.2% 0.0% 1.6% 0.2% 0.5% 0.2% 0.6% 0.1% 0.5% 92.6% 7.4% 1,000-3,300 93.1% 3.6% 0.0% 1.6% 0.3% 0.9% 0.0% 0.1% 0.1% 0.5% 93.4% 6.6% 3,301-9,999 93.1% 3.6% 0.0% 1.6% 0.3% 0.9% 0.0% 0.1% 0.1% 0.5% 93.4% 6.6% 10,000-49,999 87.1% 8.6% 0.9% 1.0% 0.0% 0.2% 1.7% 0.5% 89.7% 10.3% 50,000-99,999 87.1% 8.6% 0.9% 1.0% 0.0% 0.2% 1.7% 0.5% 89.7% 10.3% 100,000-999,999 87.5% 8.4% 0.9% 0.9% 0.0% 0.2% 1.7% 0.4% 90.1% 9.9% >=1,000,000 87.4% 8.5% 0.9% 0.9% 0.0% 0.2% 1.7% 0.4% 90.0% 10.0% Total % 91.8% 4.6% 0.0% 1.3% 0.3% 0.6% 0.1% 0.5% 0.4% 0.5% 92.6% 7.4% Note: Detail may not add to totals due to independent rounding.
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. Source: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.13a) to the Treatment Technology Selection Delta for the Preferred Alternative.

Exhibit 5.15b Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) No Advanced Treatment Technologies CL2 1 No Advanced

Treatment Technologies CLM 1 UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Using CL2 Total Using CLM
A B C D E F G H I J K = A+C+E+G+I L = B+D+F+H+J
<100 6,006 217 0 70 0 0 23 56 22 29 6,051 372 100-499 14,040 640 0 242 25 74 27 96 20 79 14,111 1,131 500-999 5,613 256 0 97 10 29 11 39 8 32 5,641 452 1,000-3,300 7,060 274 0 118 22 66 0 8 4 36 7,086 501 3,301-9,999 4,681 181 0 78 15 44 0 5 3 24 4,698 332 10,000-49,999 4,690 464 48 53 0 10 91 25 4,829 553 50,000-99,999 624 62 6 7 0 1 12 3 642 74 100,000-999,999 803 77 8 8 0 2 15 4 827 91 >=1,000,000 24 2 0 0 0 0 0 0 25 3 Total Plants 43,539 2,173 0 606 134 282 61 217 175 232 43,910 3,510 Note: Detail may not add to totals due to independent rounding.
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. Source: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.13a) to the Treatment Technology Selection Delta for the Preferred Alternative.



### Final Economic Analysis for the Stage 2 DBPR December 2005 5-33Exhibit 5.15c Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) No Advanced Treatment Technologies CL2 1 No Advanced

Treatment Technologies CLM 1 UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Using CL2 Total Using CLM
A B C D E F G H I J K = A+C+E+G+I L = B+D+F+H+J <100 93.5% 3.4% 0.0% 1.1% 0.0% 0.0% 0.4% 0.9% 0.3% 0.5% 94.2% 5.8% 100-499 92.1% 4.2% 0.0% 1.6% 0.2% 0.5% 0.2% 0.6% 0.1% 0.5% 92.6% 7.4% 500-999 92.1% 4.2% 0.0% 1.6% 0.2% 0.5% 0.2% 0.6% 0.1% 0.5% 92.6% 7.4% 1,000-3,300 93.1% 3.6% 0.0% 1.6% 0.3% 0.9% 0.0% 0.1% 0.1% 0.5% 93.4% 6.6% 3,301-9,999 93.1% 3.6% 0.0% 1.6% 0.3% 0.9% 0.0% 0.1% 0.1% 0.5% 93.4% 6.6% 10,000-49,999 87.1% 8.6% 0.9% 1.0% 0.0% 0.2% 1.7% 0.5% 89.7% 10.3% 50,000-99,999 87.1% 8.6% 0.9% 1.0% 0.0% 0.2% 1.7% 0.5% 89.7% 10.3% 100,000-999,999 87.5% 8.4% 0.9% 0.9% 0.0% 0.2% 1.7% 0.4% 90.1% 9.9% >=1,000,000 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Total % 92.8% 3.8% 0.0% 1.4% 0.1% 0.3% 0.3% 0.7% 0.2% 0.5% 93.4% 6.6% Note: Detail may not add to totals due to independent rounding.
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. Source: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.13b) to the Treatment Technology Selection Delta for the Preferred Alternative.

Exhibit 5.15d Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type) Preferred Alternative

System Size (Population Served) No Advanced Treatment Technologies CL2 1 No Advanced

Treatment Technologies CLM 1 UV CL2 UV CLM Ozone CL2 Ozone

CLM GAC20

CL2 GAC20

CLM Membranes

CL2 Membranes

CLM Total Using CL2 Total Using CLM
A B C D E F G H I J K = A+C+E+G+I L = B+D+F+H+J
<100 2,331 84 0 27 0 0 9 22 8 11 2,348 144 100-499 1,961 89 0 34 3 10 4 13 3 11 1,971 158 500-999 543 25 0 9 1 3 1 4 1 3 546 44 1,000-3,300 230 9 0 4 1 2 0 0 0 1 231 16 3,301-9,999 20 1 0 0 0 0 0 0 0 0 20 1 10,000-49,999 3 0 0 0 0 0 0 0 3 0 50,000-99,999 0 0 0 0 0 0 0 0 0 0 100,000-999,999 0 0 0 0 0 0 0 0 0 0 >=1,000,000 0 0 0 0 0 0 0 0 0 0 Total Plants 5,088 208 0 75 5 15 14 39 12 27 5,119 364 Note: Detail may not add to totals due to independent rounding.
1 No Advanced Treatment Technologies includes conventional, non-conventional, and softening plants. Source: Add Treatment Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 5.13b) to the Treatment Technology Selection Delta for the Preferred Alternative.



### Final Economic Analysis for the Stage 2 DBPR December 2005 5-34

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-35 

## 5.5 Reduction in National Average TTHM and HAA5 Levels

This section presents the predicted reductions in average TTHM and HAA5 levels in distribution systems as a result of the Stage 2 DBPR. The reducti ons in average levels are used in Chapter 6 to approximate the reduction in bladder cancer cases as a result of the Stage 2 DBPR. This section presents an overview of the methodology first, followed by detailed derivations for large and medium surface water systems, small surface water systems, large and medium ground water systems, and small ground water systems. Results are summarized in Section 5.5.6.

5.5.1 Overview of Methodology

The methodology for estimating the percent reduction in average TTHM and HAA5 values resulting from the Stage 2 DBPR is as follows:

1) Predict TTHM and HAA5 levels for the pre- Stage 1 baseline (presented in Section 3.7.1)

2) Predict the percent reduction resulting from the Stage 1 DBPR and the resulting TTHM and HAA5 levels for the pre-Stage 2 baseline

3) Predict the percent reduction resulting from the Stage 2 DBPR

Reductions are predicted separately for ground and surface water systems and for large and small systems (4 predictions). A population-weighted per cent reduction is then calculated for all systems.

5.5.2 Reductions for Large and Medium Surface Water Systems The methodology for surface water systems is based on SWAT model output and the ICR Matrix Method. Results from both methods are used to pred ict percent reduction in average DBPs. The first section summarizes the methodology using SWAT. Sec tion 5.5.2.2 shows how the ICR Matrix Method is used to predict percent reductions. Section 5.5. 2.3 compares results for the individual methods and explains how they are combined using a Monte Carlo simulation model for the primary analysis.

5.5.2.1 SWAT Methodology

For each model run, SWAT predicted monthly TTHM and HAA5 levels for each of 273 plants evaluated (273 plants out of the possible 350 ICR plan ts were used in the SWAT model). See Appendix A for more information on SWAT, including a discussion of plant representativeness. Monthly data were averaged for each plant to produce plant-mean data. All plant-means were averaged together to produce a “mean of plant-means” value. The means of plant- means for Stage 1 and Stage 2 SWAT model runs were compared to the SWAT initial plant run (simulating pre-Stage 1 conditions) to compute percent reduction.

SWAT-predicted TTHM and HAA5 results were used instead of available ICR-observed data for the pre-Stage 1 DBPR baseline to allow for consiste nt comparison of pre-Stage 1 data to modeled preStage 2 and post-Stage 2 TTHM and HAA5 results. If observed data were used for pre-Stage 1 predictions, differences between pre-Stage 1 and pr e-Stage 2 or post-Stage 2 results would reflect

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-36 

### potential inconsistencies between ob served and predicted data sets, not just the expected treatment technology change from pre-Stage 1 to pre-Stage 2 or post-Stage 2.

5.5.2.2 The ICR Matrix Method

Exhibits 5.16a through 5.16d show the derivation of percent reduction in national average TTHM and HAA5 levels using the ICR Matrix Method. Th e ICR Matrix Method for the Preferred Regulatory Alternative with safety margins of 20 and 25 percen t is presented in Exhibits 5.16a and 5.16b (the 25 percent safety margin is used to account for the poten tial impacts of the IDSE). Exhibits 5.16c and 5.16d show the ICR Matrix Method for Regulatory Altern atives 2 and 3. Regulatory Alternative 1 was not analyzed separately, as it contains the same MC Ls for TTHM and HAA5 and therefore has the same matrix as the Preferred Regulatory Alternative.

To illustrate how the ICR matrix method works, c onsider the data presented in Exhibit 5.16a for the Preferred Alternative for a 20 percent safety margin. The left side of the exhibit contains three tables or matrices that are divided into different “bins.” The bins are cells defined by ranges of RAA values for TTHM and HAA5 across the top, and maximum LRAA values for TTHM and HAA5 down the left-hand side. The method works by moving plants from non-compliant bins (Bins B2 and A2) into the compliant bin (Bin A1) in the second and third tables, representing their actions to comply with Stage 1 and Stage 2, respectively.

The number and percent of plants in each bin unde r pre-Stage 1 conditions is shown in the tables on the right-hand side of Exhibit 5.16a. Plants are assigned to a bin based on their RAA and LRAA observations as calculated from the ICR data. Note that a plant is considered in one of the non-compliant bins if it exceeds either the TTHM or HAA5 MCL. EPA assumes that plants making treatment technology changes to comply with Stage 1 will also meet Stage 2 MCLs (e.g., be moved into the Stage 1- AND Stage 2-compliant Bin A1). This is cons istent with the SWAT methodology, which considers only the delta or additional plants that need to ma ke changes to comply with the Stage 2 DBPR. EPA recognizes there is uncertainty in this assumption (see the discussion in the compliance forecast section, Section 5.3.2) but believes it is a reasonable approximation.

Unlike SWAT, the ICR Matrix Method does not use a model to predict which treatment technologies will be used by plants for compliance, or to calculate the resulting changes in average TTHM and HAA5 concentrations. EPA developed an alternative methodology to predict these effects. Based on comparisons of ICR and historical DBP data bases, researchers suspect th at plants changed their treatment technology in anticipation of the Stage 1 DBPR prior to the ICR data collection period (McGuire et al. 2002). It follows, then, that at least some portion of the plants reporting advanced treatment technology and/or chloramine used in the ICR had installed these treatment technologies to reduce DBPs. Therefore, the TTHM and HAA5 levels of the ICR plants using advanced treatment technologies and/or chloramines can provide an indi cation of the final TTHM and HAA5 levels for plants that add these treatment technologies to comply with the Stage 2 DBPR.

The results of the analysis of TTHM and HAA5 le vels for Stage 2-compliant plants that use advanced treatment technologies and/or chloramines dur ing the ICR are summarized in Exhibit 5.17. The average of the plant-average TTHM and HAA5 con centrations for all four analyses are assumed to represent the average TTHM and HAA5 concentrations for plants that will make treatment technology changes to meet the Stage 1 and Stage 2 rules. The resulting change in the national average TTHM and HAA5 concentrations is calculated as the weighted av erage for the Stage 1/Stage 2 compliant plants and

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-37 TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 136 64% 31.64 20.67

<64/48 A1 A2 36 17% 51.64 33.12

>= 64/48(S2 noncompliant) A2 B2 B2 41 19% 69.34 53.36

All Plants 213 100% 42.28 29.07

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 136 64% 31.64 20.67

<64/48 A1 A2 36 17% 51.64 33.12

>= 64/48(S2 noncompliant) A2 A1 41 19% 31.48 19.14

All Plants 213 100% 34.99 22.48

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 136 64% 31.64 20.67

<64/48 A1 A1 36 17% 31.48 19.14

>= 64/48(S2 noncompliant) A1 41 19% 31.48 19.14

All Plants 213 100% 31.58 20.11 Percent of

Plants

Percent of

Plants

Percent of

Plants RAA Bin Assignment Number of Plants

Max LRAA Average of Plant Averages (ug/L) Number of Plants Bin Assignment RAA

Pre-Stage 2 Max LRAA

Average of Plant Averages (ug/L)

Bin Assignment Number of Plants Average of Plant Averages (ug/L) Post-Stage 2 Max LRAA

RAA 

### the non-compliant changers. EPA recognizes that there is uncertainty in using the subset of ICR plants using advanced technology and/or chloramines to m odel future changes in DBP occurrence but believes it provides a plausible result.



## Exhibit 5.16a ICR Matrix Method for th e Stage 2 DBPR Preferred Alternative (80/60 LRAA, IDSE)- 20 Percent Safety Margin

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 213 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines a plant’s bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are compli ant with Stage 1 and Stage 2 and that use either an advanced treatment technology, chloramines, or both (64 plants) from Exhibit 5.17.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-38 TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 125 59% 30.14 19.39

<60/45 A1 A2 47 22% 50.95 33.60

>= 60/45(S2 noncompliant) A2 B2 B2 41 19% 69.34 53.36

All Plants 213 100% 42.28 29.07

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 125 59% 30.14 19.39

<60/45 A1+B2 A2 47 22% 50.95 33.60

>= 60/45(S2 noncompliant) A2 B2 41 19% 29.33 17.56

All Plants 213 100% 34.58 22.17

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 125 59% 30.14 19.39

<60/45 A1+B2+A2 A2 47 22% 29.33 17.56

>= 60/45(S2 noncompliant) B2 41 19% 29.33 17.56

All Plants 213 100% 29.80 18.63 Percent of

Plants

Percent of

Plants

Percent of

Plants RAA Bin Number of Plants

Max LRAA 

### Pre-Stage 1 Average of Plant Averages (ug/L) Number of Plants Bin RAA



### Pre-Stage 2 Max LRAA

Average of Plant Averages (ug/L)

Bin Number of Plants Average of Plant Averages (ug/L) 

### Post-Stage 2 Max LRAA

RAA 

## Exhibit 5.16b ICR Matrix Method for a Stage 2 DBPR Preferred Alternative (80/60 LRAA, IDSE)- 25 Percent Safety Margin

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 213 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines a plant’s bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are compli ant with Stage 1 and Stage 2 and that use either an advanced treatment technology, chloramines, or both (64 plants) from Exhibit 5.17.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-39 TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 89 42% 25.18 16.52

<64/48 A1 A2 83 39% 47.25 30.51

>= 64/48(S2 noncompliant) A2 B2 B2 41 19% 69.34 53.36

All Plants 213 100% 42.28 29.07

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 89 42% 25.18 16.52

<64/48 A1+B2 A2 83 39% 47.25 30.51

>= 64/48(S2 noncompliant) A2 B2 41 19% 24.99 15.12

All Plants 213 100% 33.74 21.70

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 89 42% 25.18 16.52

<64/48 A1+B2+A2 A2 83 39% 24.99 15.12

>= 64/48(S2 noncompliant) B2 41 19% 24.99 15.12

All Plants 213 100% 25.07 15.71 Percent of

Plants

Percent of

Plants

Percent of

Plants RAA Bin Number of Plants

Single High 

### Pre-Stage 1 Average of Plant Averages (ug/L) Number of Plants Bin RAA



### Pre-Stage 2 Single High

Average of Plant Averages (ug/L)

Bin Number of Plants Average of Plant Averages (ug/L) 

### Post-Stage 2 Single High

RAA 

## Exhibit 5.16c ICR Matrix Method for Regulatory Alternative 2 (80/60 SH)

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 213 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines a plant’s bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are compli ant with Stage 1 and Stage 2 and that use either an advanced treatment technology, chloramines, or both (64 plants) from Exhibit 5.17.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-40 TTHM HAA5
<64/48 >=64/48 (S1 non-compliant) A1 58 27% 19.71 12.91

<32/24 A1 A2 114 54% 44.03 28.55

>= 32/24(S2 noncompliant) A2 B2 B2 41 19% 69.34 53.36

All Plants 213 100% 42.28 29.07

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 58 27% 19.71 12.91

<32/24 A1+B2 A2 114 54% 44.03 28.55

>= 32/24(S2 noncompliant) A2 B2 41 19% 19.73 13.04

All Plants 213 100% 32.73 21.30

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 58 27% 19.71 12.91

<32/24 A1+B2+A2 A2 114 54% 19.73 13.04

>= 32/24(S2 noncompliant) B2 41 19% 19.73 13.04

All Plants 213 100% 19.72 13.00 Average of Plant Averages (ug/L)

Bin Number of Plants Average of Plant Averages (ug/L) 

### Post-Stage 2 RAA

RAA

RAA 

### Pre-Stage 1 Average of Plant Averages (ug/L) Number of Plants Bin RAA



### Pre-Stage 2 RAA Percent of Plants

Percent of

Plants

Percent of

Plants RAA Bin Number of Plants 

## Exhibit 5.16d ICR Matrix Method fo r Regulatory Alternative 3 (40/30 RAA)

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 213 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines a plant’s bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are compli ant with Stage 1 and Stage 2 and that use either an advanced treatment technology, chloramines, or both (64 plants) from Exhibit 5.17.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-41 

### Preferred Regulatory Alternative (20 Percent Safety Margin) Number of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 47 34.50 20.24 ADV tech only 5 32.20 23.19 CLM & Adv. tech 12 19.33 13.14 Total 64 31.48 19.14

Preferred Regulatory Alternative (25 Percent Safety Margin) Number of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 43 32.44 18.97 ADV tech only 4 31.07 19.06 CLM & Adv. tech 12 19.33 13.14 Total 59 29.68 17.79

Regulatory Alternative 2 Number of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 42 32.02 18.68 ADV tech only 4 31.07 19.06 CLM & Adv. tech 12 19.33 13.14 Total 58 29.33 17.56

Regulatory Alternative 3 Number of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 28 27.37 16.10 ADV tech only 4 31.07 19.06 CLM & Adv. tech 11 16.74 11.20 Total 43 24.99 15.12

Subset of Stage 2 Compliant Plants

Subset of Stage 2 Compliant Plants

Subset of Stage 2 Compliant Plants

Subset of Stage 2 Compliant Plants 

## Exhibit 5.17 TTHM and HAA5 Levels for Stage 2-Compliant Plants Using Chloramines and/or an Advanced Treatment Technology

Notes: All TTHM and HAA5 values represent the m ean of plant-means CLM = chloramine

Source: ICR Aux 1 database (USEPA 2000h), analysis of ICR screened data (213 surface water plants)

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-42 

### 5.5.2.3 Combining SWAT and ICR Matrix Method Results

Exhibit 5.18 presents the results of SWAT and the ICR Matrix Method for post-Stage 2 national average TTHM and HAA5 levels. Note that predicti ons for a 20 and 25 percent safety margin are shown separately for the Preferred Alternative. Both pred ictions are used in a Monte Carlo simulation model to account for uncertainty in the impacts of the IDSE. (See Section 5.3.4 for a detailed description of the methodology used to quantify uncertainty in the potential impacts of the IDSE on large and medium surface water systems.) As shown in Exhibit 5.18, the ICR Matrix Method predicts a greater reduction in TTHM and HAA5 levels compared to SWAT for the Preferred Alternative. Such differences are expected given the inherent differences in the tw o methods and uncertainties associated with each one. (Possible reasons for the differences between the two methods are noted in Section 5.3.6.) Because both SWAT and the ICR Matrix Method have associated uncer tainty, results from both are used to generate the estimated percent reduction in TTHM and HAA5 con centrations for large and medium surface water systems.

Similar to the compliance forecast model, EPA developed two uniform distributions based on calculated the ICR Matrix Method-to-SWAT multip liers. The distributions use 1.0 as the 5 th 

### percentile value and the ICR Matrix Method-to-SWAT multiplier as the 95 th 

### percentile value. There are two distributions, one for the 20 percent safety margin and one for the 25 percent safety margin. Exhibit 5.19 provides a graphical depiction of the two uniform distributions for the Preferred Alternative.

To produce final estimates of percent reduction in TTHM and HAA5 for large surface water systems, EPA developed a Monte Carlo simulation model, similar to the compliance forecast model described in Section 5.3.6, with three basic steps. Note that results for TTHM and HAA5 are generated independently.

Step 1: The model randomly selects the predicted TTHM or HAA5 reduction from SWAT, as shown in column F of Exhibit 5.18 for either th e 20 or 25 percent safety margin runs. Results for each of the two safety margins have an equal (50 percent) chance of being selected.

Step 2: The model randomly selects the ICR-to-SWAT multiplier from the appropriate uniform distribution from Exhibit 5.19 for the safety margin selected in Step 1.

Step 3: The model applies the multipliers from Step 2 to the TTHM or HAA5 reductions identified in Step 1 to calculate the percent reduc tion from Stage 1 to Stage 2 for that iteration.

The process is repeated 10,000 times to produce a distribution of TTHM or HAA5 reductions from Stage 1 to Stage 2 for large surface water system s. This distribution is carried through the benefits model, as described in Chapter 6. Note that only TTHM or HAA5 reduction for the 20 percent safety margin is used for Regulatory Alternatives 1, 2, and 3 as the IDSE is not a component of these alternatives. Final estimates of predicted reduc tion in average TTHM and HAA5 concentrations are presented in Section 5.5.5 for all system types and sizes.Economic Analysis for the Stage 2 DBPRDecember 20055-43ICR Matrix Method SWATMean of PlantMeans preS2 (μg/L)Mean of PlantMeans post-S2(μg/L)% Reductionfrom pre-S2to post-S2Mean of PlantMeans preS2 (μg/L)Mean of PlantMeans postS2 (μg/L)% Reductionfrom pre-S2to post-S2A B C = (B - A)/A D E F = (E - D)/DG = C / FTTHMPreferred Reg. Alternative (80/60 LRAA, IDSE), 20% SM 35.0 31.6 9.7% 35.5 33.8 4.7% 2.06Preferred Reg. Alternative (80/60 LRAA, IDSE), 25% SM 34.6 29.8 13.8% 35.5 32.5 8.4% 1.65Reg. Alternative 1 (Bromate = 5), 20% SM 35.0 31.6 9.7% 35.5 33.0 6.9% 1.42Reg. Alternative 2 (80/60 Single High), 20% SM 33.7 25.1 25.7% 35.5 23.7 33.2% 0.77Reg. Alternative 3 (40/30 RAA), 20% SM 32.7 19.7 39.7% 35.5 21.0 40.8% 0.97HAA5Preferred Reg. Alternative (80/60 LRAA, IDSE), 20% SM 22.5 20.1 10.5% 25.0 23.8 4.7% 2.23Preferred Reg. Alternative (80/60 LRAA, IDSE), 25% SM 22.2 18.6 16.0% 25.0 22.9 8.3% 1.92Reg. Alternative 1 (BR = 5), 20% SM 22.5 20.1 10.5% 25.0 23.6 5.6% 1.87Reg. Alternative 2 (80/60 SH), 20% SM 21.7 15.7 27.6% 25.0 16.5 33.8% 0.82Reg. Alternative 3 (40/30 RAA), 20% SM 21.3 13.0 39.0% 25.0 13.9 44.3% 0.88ICR MatrixMethod-toSWATMultiplierRegulatory AlternativeExhibit 5.18 Inputs to Monte Carlo Simulation Model: Estimated DBP Reduction from SWAT and ICR MatrixMethodSources: SWAT run summaries (USEPA 2001b), ICR Matrix Method Results (USEPA 2005a)(A) Average of plant-average values for allplants, pre-Stage 2 conditions from Exhibit5.16a. ICR Matrix Method results for Reg. Alternative 1 are thesame as for the Preferred Alternative, Unadjusted.(B) Average of plant-average values for all plants, post-stage 2 conditions from Exhibits 5.16b through 5.16d. ICR Matrix Method results for Reg.Alternative 1 are the same as for the Preferred Alternative, Unadjusted.(D) and (E) SWAT run summaries (USEPA 2001b).

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-44 (2.06) (1.65) 

### 1 2 3Probability



### Multiplier 1 2 3Probability



### Multiplier 

## Uniform Distribution for a 20% Safety Margin Uniform Distribution for a 25% Safety Margin

(2.06) (1.65) 

### 1 2 3Probability



### Multiplier 1 2 3Probability



### Multiplier 

## Uniform Distribution for a 20% Safety Margin Uniform Distribution for a 25% Safety Margin 

## Exhibit 5.19 Inputs to the Monte Carlo Simulation Model: Uniform Distributions Based on ICR Matrix Method-to-SWAT Multiplier

Note: The uniform distributions are used when generating t he predicted DBP reductions for surface water systems for the Preferred Alternative. The same method is applied to the other regulatory alternatives using the multipliers in Exhibit 5.18.

Source: Exhibit 5.18.



### 5.5.3 Reductions for Small Surface Water Systems

National Rural Water Association (NRWA) survey data were the basis for estimating baseline TTHM and HAA5 levels in small surface water system s (USEPA 2001a). Pre-Stage 2 and Post-Stage 2 TTHM and HAA5 levels for small surface water systems were assumed to be similar to large system levels. Thus, both methods used for large and medium surface water systems (SWAT and ICR Matrix Method) were used to calculate percent reducti on in TTHM and HAA5 levels for small surface water systems. Note that EPA believes that the 20 per cent safety margin already accounts for potential impacts of the IDSE for small surface water systems becau se their distribution systems as not as complex compared to large ground water systems. Therefore, an alternative percent reduction for a 25 percent safety margin was not used for small surface water sy stems, as it was for large and medium surface water systems.

Final estimated percent reductions in averag e TTHM and HAA5 concentrations are presented in Section 5.5.5 for all system types and sizes.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-45 

### 5.5.4 Reductions for Large and Medium Ground Water Systems
As described in Section 3.7, ICR data were u sed to characterize TTHM and HAA5 levels for the ground water system pre-Stage 1 baseline. EPA u sed the ICR Matrix Method to predict changes in average TTHM and HAA5 levels for large ground water systems following the Stage 1 and Stage 2 rules. A detailed description of the method can be found in Section 5.5.2.2

The analysis of Stage 2-compliant, screened ground water plants using chloramines and/or an advanced treatment technology at the time of the I CR data collection is shown in Exhibit 5.20. TTHM and HAA5 levels for these plants are used to es timate the TTHM and HAA5 levels for those plants changing treatment technology to meet Stage 1 and Stage 2 rules. The number of ICR GW plants that use chloramines and/or advanced disinfectants and comply with the Stage 2 DBPR is low: 12 plants for the Preferred Alternative (considering a 20 percent safety margin on compliance). This is roughly 15 percent of the total number of screened ground water plants. EPA compared TOC levels for the Stage 2-compliant ground water plants that use chloramines and/or an a dvanced treatment technology to levels for the Stage 2 non-compliant plants and found them to be similar.

Exhibit 5.21a shows the ICR Matrix Method fo r the Stage 2 DBPR Preferred Alternative. Exhibits 5.21b and 5.21c show the me thod for Alternatives 2 and 3. Note that the ICR Matrix Method for ground water plants is not performed for the 25 pe rcent safety margin. EPA believes that for ground water systems, the 20 percent safety margin analysis already accounts for the impacts of the IDSE since these systems do not typically observe high year-to-year or seasonal variability in water quality (see Section 5.3.4. for additional information). S ection 5.5.5 summarizes percent reduction in TTHM and HAA5 for all system sizes and source water types.

5.5.5 Reductions for Small Ground Water Systems

Data from seven states were u sed to characterize TTHM and HAA5 levels for small ground water systems (USEPA 2000d). To derive the percent reduc tion in average TTHM and HAA5 levels for small ground water systems, EPA compared the predicted percent of plants making treatment technology changes for small ground water systems to large ground water systems. The percent reduction in DBP concentrations as predicted by the ICR Matr ix Method for large ground water systems (GW L 

### ) is multiplied by the ratio of small ground water plants changing treatment technology (GCT
s 

### ) to large ground water plants changing treatment technology (GCT L 

### ), or

GW S 

### = GW L 

### * (GCT S 

### /GCT L 

### )

where,

GW L 

### and GW S 

### = predicted percent DBP reduction for large and small ground water systems, respectively.

GCT L 

### and GCT S 

### = the percent of plants changing treatment technology for large and small ground water plants (weighted average across large or small system size categories), respectively.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-46 

## No. of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 10 29.0 19.4 A dv. tech with CLM 2 19.9 16.5 A dv. tech w/o CLM 0 0.0 0.0 Total 12 27.5 18.9

No. of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 6 21.7 14.2 A dv. tech with CLM 2 19.9 16.5 A dv. tech w/o CLM 0 0.0 0.0 Total 8 21.2 14.8

No. of Plants Mean TTHM ( μ g/L) Mean HAA5 ( μ g/L) CLM only 6 18.5 11.3 A dv. tech with CLM 2 19.9 16.5 A dv. tech w/o CLM 0 0.0 0.0 Total 8 18.8 12.6 Subset of Stage 2 Compliant Plants Regulatory Alternative 3 Subset of Stage 2 Compliant Plants Preferred Regulatory Alternative

Subset of Stage 2 Compliant Plants Regulatory Alternative 2 

## Exhibit 5.20 TTHM and HAA5 Levels for Stage 2-Compliant Ground Water Plants Using Chloramines and/or an Advanced Treatment Technology

Notes: All TTHM and HAA5 values represent the m ean of plant-means. ICR Matrix Method results for Reg. Al ternative 1 are the same as for t he Preferred Alternative, assuming a 20 percent safety margin. While the number of plants is identical for Regulatory Alternatives 2 and 3, they represent a different subset of plants. Source: ICR Aux 1 database (USEPA 2000h), analysis of ICR screened data (82 ground water plants)

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-47 TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 75 91% 11.62 5.50

<64/48 A1 A2 2 2% 35.29 31.71

>= 64/48(S2 noncompliant) A2 B2 B2 5 6% 63.54 43.37

All Plants 82 100% 15.36 8.45

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 75 91% 11.62 5.50

<64/48 A1+B2 A2 2 2% 35.29 31.71

>= 64/48(S2 noncompliant) A2 B2 5 6% 27.50 18.95

All Plants 82 100% 13.16 6.96

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 75 91% 11.62 5.50

<64/48 A1+B2+A2 A2 2 2% 27.50 18.95

>= 64/48(S2 noncompliant) B2 5 6% 27.50 18.95

All Plants 82 100% 12.97 6.64 Average of Plant Averages (ug/L)

Bin Number of Plants Average of Plant Averages (ug/L) 

### Post-Stage 2 Max LRAA

RAA

Max LRAA 

### Pre-Stage 1 Average of Plant Averages (ug/L) Number of Plants Bin RAA



### Pre-Stage 2 Max LRAA Percent of Plants

Percent of

Plants

Percent of

Plants RAA Bin Number of Plants 

## Exhibit 5.21a ICR Matrix Method for Ground Water Plants for the Stage 2 DBPR Preferred Alternative

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 82 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines the bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are comp liant with Stage 1 and Stage 2 and us e an advanced treatment technology and/or chloramines (12 plants), from Exhibit 5.20.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-48 TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 67 82% 8.76 3.89

<64/48 A1 A2 10 12% 35.47 21.51

>= 64/48(S2 noncompliant) A2 B2 B2 5 6% 63.54 43.37

All Plants 82 100% 15.36 8.45

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 67 82% 8.76 3.89

<64/48 A1+B2 A2 10 12% 35.47 21.51

>= 64/48(S2 noncompliant) A2 B2 5 6% 21.23 14.80

All Plants 82 100% 12.78 6.70

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 67 82% 8.76 3.89

<64/48 A1+B2+A2 A2 10 12% 21.23 14.80

>= 64/48(S2 noncompliant) B2 5 6% 21.23 14.80

All Plants 82 100% 11.04 5.88 Percent of

Plants

Percent of

Plants

Percent of

Plants RAA Bin Number of Plants

Single High 

### Pre-Stage 1 Average of Plant Averages (ug/L) Number of Plants Bin RAA



### Pre-Stage 2 Single High

Average of Plant Averages (ug/L)

Bin Number of Plants Average of Plant Averages (ug/L) 

### Post-Stage 2 Single High

RAA 

## Exhibit 5.21b ICR Matrix Method for Ground Water Plants for Regulatory Alternative 2

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 82 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines the bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are comp liant with Stage 1 and Stage 2 and us e an advanced treatment technology and/or chloramines (8 plants), from Exhibit 5.20.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-49 TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 67 82% 7.90 3.51

<32/24 A1 A2 10 12% 41.27 24.03

>= 32/24(S2 noncompliant) A2 B2 B2 5 6% 63.54 43.37

All Plants 82 100% 15.36 8.45

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 67 82% 7.90 3.51

<32/24 A1+B2 A2 10 12% 41.27 24.03

>= 32/24(S2 noncompliant) A2 B2 5 6% 18.84 12.57

All Plants 82 100% 12.63 6.57

TTHM HAA5

<64/48 >=64/48 (S1 non-compliant) A1 67 82% 7.90 3.51

<32/24 A1+B2+A2 A2 10 12% 18.84 12.57

>= 32/24(S2 noncompliant) B2 5 6% 18.84 12.57

All Plants 82 100% 9.90 5.17 Average of Plant Averages (ug/L)

Bin Number of Plants Average of Plant Averages (ug/L) 

### Post-Stage 2 RAA

RAA

RAA 

### Pre-Stage 1 Average of Plant Averages (ug/L) Number of Plants Bin RAA



### Pre-Stage 2 RAA Percent of Plants

Percent of

Plants

Percent of

Plants RAA Bin Number of Plants 

## Exhibit 5.21c ICR Matrix Method for Ground Water Plants for Regulatory Alternative 3

Notes: 1) In the first table on the left, A1 through B2 are t he number of ICR plants that m eet the criteria for each bin under pre-Stage 1 conditions. Their calculated aver age TTHM and HAA5 values based on the averages of all plant-averages are shown in the first table on the right. A total of 82 ICR plants were evaluated. 2) Each cell (bin) represents a range of the TTHM and HAA5 RAA concentrations and maximum LRAA concentrations in 

### : g/L (i.e., RAA <64/48 means the plant needs to have its TTHM RAA level below 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed in to the bin). The maximum TTHM or HAA5 result determines the bin placement. 3) Crossed-out bins represent plant s that have moved from out of comp liance bins to in compliance bins. 4) The gray bins on the right-hand side represent bins that have mov ed into compliance with pre-Stage 2 and post-Stage 2. The TTHM and HAA5 concentrations for these plants are the aver ages of the values for those ICR plants that are comp liant with Stage 1 and Stage 2 and us e an advanced treatment technology and/or chloramines (8 plants), from Exhibit 5.20.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-50 

### 5.5.6 Results for All Systems

Exhibit 5.22 presents predicted pre-Stage 1 and pre-Stage 2 TTHM and HAA5 mean distribution system concentrations for surface and ground water plants . This analysis shows that the largest percent reduction in DBP concentrations is for surface wate r plants from pre-Stage 1 to pre-Stage 2 conditions (reductions range from approximately 27 to 57 percent for all plants). The reduction in TTHM and HAA5 levels for ground water plants is less, ranging from 11 to 18 percent. The percent reduction for small surface water plants is greater than the pe rcent reduction for large surface water plants because plants in small systems did not have to meet the TTHM rule MCL of 100 : 

### g/L prior to the Stage 1 DBPR.

Exhibit 5.23 shows the predicted percent reduc tion in TTHM and HAA5 concentrations from preStage 2 to post-Stage 2 DBPR conditions for the Pr eferred Alternative. Exhibit 5.24a, 5.24b, and 5.24c show similar information for regulatory alternatives 1, 2, and 3, respectively. Note that the combined results for “All Systems” are generated using a Monte Carlo analysis. Also note that the percent reduction for Regulatory Alternative 1 is slightly less than the percent reduction for the Preferred alternative because none of the Regulatory Alternativ es except the preferred include the IDSE component.

It is important to note that the calculation of the percent reduction in all plant-mean DBP levels includes plants that make minor process chang es as well as those that do not make any treatment technology changes to meet the Stage 2 DBPR. The predicted percent reduction for the subset of plants that add advanced treatment technologies or chloramines, therefore, is much higher. Among those plants reducing DBP levels from Stage 1 to Stage 2, it is pred icted that the average reductions in DBP levels will be approximately 30 percent, and may range from less than 5 up to 60 percent.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-51 

### Pre-Stage 1 ( μ g/L) Pre-Stage 2 ( μ g/L) Percent Reduction A B C D = (B-C)/B Large (= 10,000) 160,935,736 48.7 35.5 27.2% Small (< 10,000) 8,422,403 82.8 35.5 57.2% Large (= 10,000) 65,152,168 15.4 13.2 14.3% Small (< 10,000) 28,514,211 16.5 14.7 11.1% 263,024,518 38.05 27.69 27.2%

Pre-Stage 1 ( μ g/L) Pre-Stage 2 ( μ g/L) Percent Reduction A E F G = (E-F)/E Large (= 10,000) 160,935,736 35.5 25.0 29.5% Small (< 10,000) 8,422,403 45.3 25.0 44.8% Large (= 10,000) 65,152,168 8.4 7.0 17.6% Small (< 10,000) 28,514,211 9.1 7.8 13.7% 263,024,518 26.24 18.67 28.8% Notes: All TTHM and HAA5 concentrations represent the mean of all plant-mean concentrations. * TTHM and HAA5 concentrations for all systems are the population-weighted values. Sources: (A) SDWIS 2003 3rd quarter freeze, community water system population (Exhibit 3.3, CWSs only).

(C) and (F) For SW, Pre-Stage 2 runs from SWAT (USEPA 2001e). For large GW, pre-Stage 2 based on the ICR matrix method. For small GW, pre-Stage 2 based on percent reduction in large GW and comparison of percent changing technology (see methodology in Section 5.5).



### Source Water T yp e System Size ( Po p ulation Served ) Population

SW

GW All Systems*

(E) Large SW: Exhibit 3.15, SWAT plant-mean data. Small SW: Exhibit 3.21, NRWA plant-mean data. Large GW: Exhibit 3.20, ICR plant-mean data. Small GW: large GW HAA5 * (small GW TTHM / large GW TTHM). (B) Large SW: Exhibit 3.15, SWAT plant-mean data. Small SW: Exhibit 3.21, NRWA plant-mean data. Large GW: Exhibit 3.20, ICR plant-mean data. Small GW(B): Exhibit 3.21, State plant-mean data.Small GW. 

### All Systems* Population

HAA5 GW TTHM

SW Source Water T yp e System Size ( Po p ulation Served ) 

## Exhibit 5.22 Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 1 to Pre-Stage 2Final Economic Analysis for the Stage 2 DBPRDecember 20055-52Mean 5th 95th Mean 5th 95thABLarge (>10,000)160,935,736 35.5 32.2 33.7 30.7 9.2% 5.1% 13.5%Small (< 10,000) 8,422,403 35.5 32.9 33.8 32.0 7.2% 4.7% 9.7%Large (>10,000)65,152,168 13.2 13.0 1.4%Small (< 10,000) 28,514,211 14.7 14.4 2.0%263,024,518 27.69 25.53 26.44 24.60 7.8% 4.5% 11.2%Mean 5th 95th Mean 5th 95thAELarge (>10,000)160,935,736 25.0 22.5 23.7 21.1 9.9% 5.2% 15.5%Small (< 10,000) 8,422,403 25.0 23.1 23.8 22.4 7.6% 4.7% 10.5%Large (>10,000)65,152,168 7.0 6.6 4.5%Small (< 10,000) 28,514,211 7.8 7.4 6.3%263,024,518 18.67 16.96 17.71 16.12 9.2% 5.2% 13.7%Notes: Detail may not add due to independent rounding.All TTHM and HAA5 concentrations represent the mean of all plant-mean concentrations. Total for "All Systems" is calculated using a Monte Carlo analysis.* TTHM and HAA5 concentrations for all systems are the population-weighted valuesSources:(A) SDWIS 2003 3rd quarter freeze, community water system population (Exhibit 3.3, CWSs only).Population(B) and (E) Exhibit 5.22(C) and (F) Outputs from the benefits Monte Carlo simulation model. Confidence bounds for large and medium SW systems accountfor uncertainties in complianceforecast methodologies and potential impacts of the IDSE. Confidence bounds for small SW systems account for uncertainties incompliance forecast methodologies.SWGWAll Systems*Results for large SW systems for the preferred alternative represent the combined results for the 20% and 25% safety margins.Post-Stage 2 Level (μg/L)CTTHMPercent ReductionD = (B - C) / BSWSource WaterTypeSystem Size(Population Served)Pre-Stage 2Level (μg/L)PopulationAll Systems*GWSystem Size(Population Served)Percent ReductionPost-Stage 2 Level (μg/L)Pre-Stage 2Level (μg/L)F G = (E - F) / ESource WaterTypeHAA5Exhibit 5.23 Reduction in Average TTHM and HAA5 Concentrations fromPre-Stage 2 to Post-Stage 2, Preferred AlternativeFinal Economic Analysis for the Stage 2 DBPRDecember 20055-53Mean 5th 95th Mean 5th 95thABLarge (= 10,000) 160,935,736 35.5 32.5 33.0 32.0 8.3% 6.9% 9.7%Small (< 10,000) 8,422,403 35.5 32.5 33.0 32.0 8.3% 6.9% 9.7%Large (= 10,000) 65,152,168 13.2 13.0 1.4%Small (< 10,000) 28,514,211 14.7 14.4 2.0%263,024,518 27.69 25.71 26.05 25.38 7.1% 5.9% 8.4%Mean 5th 95th Mean 5th 95thAELarge (= 10,000) 160,935,736 25.0 23.0 23.6 22.4 8.1% 5.6% 10.5%Small (< 10,000) 8,422,403 25.0 23.0 23.6 22.4 8.1% 5.6% 10.5%Large (= 10,000) 65,152,168 7.0 6.6 4.5%Small (< 10,000) 28,514,211 7.8 7.4 6.3%263,024,518 18.67 17.23 17.62 16.85 7.7% 5.7% 9.8%Notes: Detail may not add due to independent rounding.All TTHM and HAA5 concentrations represent the mean of all plant-mean concentrations. Total for "All Systems" is calculated using a Monte Carlo analysis.* TTHM and HAA5 concentrations for all systems are the population-weighted values.Sources:(A) SDWIS 2003 3rd quarter freeze, community water system population (Exhibit 3.3, CWSs only).All Systems*GWSystem Size(Population Served)Percent ReductionPost-Stage 2 Level (μg/L)Pre-Stage 2Level (μg/L)F G = (E - F) / ESource WaterTypeHAA5SWSource WaterTypeSystem Size(Population Served)Pre-Stage 2Level (μg/L)PopulationPost-Stage 2 Level (μg/L)CTTHMPercent ReductionD = (B - C) / BPopulation(B) and (E) Exhibit 5.22(C) and (F) Outputs from the benefits Monte Carlo simulation model. Confidence bounds for SW systems account for uncertaintiesin compliance forecastmethodologies.SWGWAll Systems*Exhibit 5.24a Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2,Regulatory Alternative 1Final Economic Analysis for the Stage 2 DBPRDecember 20055-54Mean 5th 95th Mean 5th 95thABLarge (= 10,000) 160,935,736 35.5 25.0 26.4 23.7 29.4% 25.7% 33.2%Small (< 10,000) 8,422,403 35.5 25.0 26.4 23.7 29.4% 25.7% 33.2%Large (= 10,000) 65,152,168 13.2 11.0 16.1%Small (< 10,000) 28,514,211 14.7 12.9 12.0%263,024,518 27.69 20.24 21.16 19.33 26.9% 23.6% 30.2%Mean 5th 95th Mean 5th 95thAELarge (= 10,000) 160,935,736 25.0 17.3 18.1 16.5 30.7% 27.6% 33.8%Small (< 10,000) 8,422,403 25.0 17.3 18.1 16.5 30.7% 27.6% 33.8%Large (= 10,000) 65,152,168 7.0 5.9 15.4%Small (< 10,000) 28,514,211 7.8 6.9 11.5%263,024,518 18.67 13.36 13.92 12.80 28.5% 25.5% 31.4%Notes: Detail may not add due to independent rounding.All TTHM and HAA5 concentrations represent the mean of all plant-mean concentrations. Total for "All Systems" is calculated using a Monte Carlo analysis.* TTHM and HAA5 concentrations for all systems are the population-weighted values.Sources:(A) SDWIS 2003 3rd quarter freeze, community water system population (Exhibit 3.3, CWSs only).Population(B) and (E) Exhibit 5.22(C) and (F) Outputs from the benefits Monte Carlo simulation model. Confidence bounds for SW systems account for uncertaintiesin compliance forecastmethodologies.SWGWAll Systems*Post-Stage 2 Level (μg/L)CTTHMPercent ReductionD = (B - C) / BSWSource WaterTypeSystem Size(Population Served)Pre-Stage 2Level (μg/L)PopulationAll Systems*GWSystem Size(Population Served)Percent ReductionPost-Stage 2 Level (μg/L)Pre-Stage 2Level (μg/L)F G = (E - F) / ESource WaterTypeHAA5Exhibit 5.24b Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2,Regulatory Alternative 2Final Economic Analysis for the Stage 2 DBPRDecember 20055-55Mean 5th 95th Mean 5th 95thABLarge (= 10,000) 160,935,736 35.5 21.2 21.4 21.0 40.2% 39.7% 40.8%Small (< 10,000) 8,422,403 35.5 21.2 21.4 21.0 40.2% 39.7% 40.8%Large (= 10,000) 65,152,168 13.2 9.9 24.8%Small (< 10,000) 28,514,211 14.7 12.0 18.4%263,024,518 27.69 17.41 17.72 17.10 37.1% 36.0% 38.2%Mean 5th 95th Mean 5th 95thAELarge (= 10,000) 160,935,736 25.0 14.6 15.3 13.9 41.6% 39.0% 44.3%Small (< 10,000) 8,422,403 25.0 14.6 15.3 13.9 41.6% 39.0% 44.3%Large (= 10,000) 65,152,168 7.0 5.2 25.7%Small (< 10,000) 28,514,211 7.8 6.4 19.0%263,024,518 18.67 11.37 11.90 10.83 39.1% 36.2% 42.0%Notes: Detail may not add due to independent rounding.All TTHM and HAA5 concentrations represent the mean of all plant-mean concentrations. Total for "All Systems" is calculated using a Monte Carlo analysis.* TTHM and HAA5 concentrations for all systems are the population-weighted values.Sources:(A) SDWIS 2003 3rd quarter freeze, community water system population (Exhibit 3.3, CWSs only)All Systems*GWSWSource WaterTypeSystem Size(Population Served)Pre-Stage 2Level (μg/L)PopulationPost-Stage 2 Level (μg/L)CTTHMPercent ReductionD = (B - C) / B(C) and (F) Outputs from the benefits Monte Carlo simulation model. Confidence bounds for SW systems account for uncertaintiesin compliance forecastmethodologies .SWGWAll Systems*Source WaterTypeHAA5Population(B) and (E) Exhibit 5.22System Size(Population Served)Percent ReductionPost-Stage 2 Level (μg/L)Pre-Stage 2Level (μg/L)F G = (E - F) / EExhibit 5.24c Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to Post-Stage 2,Regulatory Alternative 3

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-56 

## 5.6 Reduction in Frequency of Pe ak TTHM and HAA5 Concentrations

Treatment technology changes to meet the Stag e 1 and Stage 2 DBPRs can reduce the frequency of peak TTHM and HAA5 values and reduce all levels of TTHM and HAA5 concentrations. Both effects have potential health benefits, which are discussed in detail in Chapter 6. This section summarizes the methodology for estimating reduced frequency of percent locations with peak TTHM and HAA5 concentrations as a result of the Stage 1 and Stage 2 Preferred Alternative. Appendix G contains an analysis of percent reductions of peaks considered as individual measurements.

5.6.1 Methodology and Assumptions

For the purposes of this section, a “peak” TTHM or HAA5 is defined as any individual measurement greater than a specified threshold concen tration. The level does not have to be sustained over any period of time to be considered a peak m easurement, and it can be a measurement taken at any time in the year. As discussed in Chapter 6, the h ealth data do not conclusively identify a peak TTHM or HAA5 level of concern. Therefore, the analyses in this section use the following alternative threshold concentrations (or “study levels”) for the purposes of defining peaks: 60, 75, 80, and 100 : 

### g/L for TTHM and 45, 60, and 75 : 

### g/L for HAA5. The analyses in this section predict the number of locations with at least one TTHM and HAA5 observation greater than each study level for pre-Stage 1, simulated pre-Stage 2 and simulated post-Stage 2 conditions. This inform ation is used in the exposure assessment in Chapter 6.

EPA evaluated TTHM and HAA5 data from both ground and surface water plants for the four ICR distribution system sampling locations (DSE, AVG1, AVG2, and DS Maximum) to predict how the frequency of peak TTHM and HAA5 concentrations cha nges as a result of the Stage 1 and Stage 2 rules. For surface water plants, ICR-observed data is u sed instead of SWAT-predicted data because, as explained in Appendix A, SWAT-predicted TTH M and HAA5 concentrations are valid only when considering national averages, not at the plant level.

The method used to predict reduction in locations is the ICR Matrix Method. Section 5.5.2.2 provides a detailed description of how the matrix method predicts reductions in average DBP concentrations. The method works in the same way to predict reduction in locations with peaks. Plants are assigned to a bin (bins are defined in the tables in the left-hand side of the exhibit) based on their RAA and LRAA observations, as calculated from the ICR data. The method works by moving plants from non-compliant bins (Bins B2 and A2) into the co mpliant bin (Bin A1) in the second and third tables, representing their actions to comply with Stage 1 and Stage 2, respectively.Final Economic Analysis for the Stage 2 DBPRDecember 20055-57Pre-Stage 1BinNumber ofLocationsLocations withTTHM >60 ug/LLocations withTTHM >75 ug/LLocations withTTHM >80 ug/LLocations withTTHM >100 ug/LLocations withHAA5 >45 ug/LLocations withHAA5 >60 ug/LLocations withHAA5 >75 ug/LA1 880 122 31 19 4 80 13 3<64/48>=64/48 (S1 non-compliant)A2 155 96 65 54 17 82 32 9<64/48 A1 B2 195 173 151 142 83 136 112 72>= 64/48(S2 noncompliant)A2 B2 All Plants 1,230 391 247 215 104 298 157 84Pre-Stage 2BinNumber ofLocationsLocations withTTHM >60 ug/LLocations withTTHM >75 ug/LLocations withTTHM >80 ug/LLocations withTTHM >100 ug/LLocations withHAA5 >45 ug/LLocations withHAA5 >60 ug/LLocations withHAA5 >75 ug/LA1 880 122 31 19 4 80 13 3<64/48>=64/48 (S1 non-compliant)A2 155 96 65 54 17 82 32 9<64/48 A1+B2 B2 195 40 5 1 0 21 3 0>= 64/48(S2 noncompliant)A2 All Plants 1,230 258 101 74 21 183 48 12Post-Stage 2BinNumber ofLocationsLocations withTTHM >60 ug/LLocations withTTHM >75 ug/LLocations withTTHM >80 ug/LLocations withTTHM >100 ug/LLocations withHAA5 >45 ug/LLocations withHAA5 >60 ug/LLocations withHAA5 >75 ug/LA1 880 122 31 19 4 80 13 3<64/48>=64/48 (S1 non-compliant)A2 155 32 4 1 0 17 2 0<64/48 A1+B2+A2 B2 195 40 5 1 0 21 3 0>= 64/48(S2 noncompliant)All Plants 1,230 193 40 21 4 117 18 3MaxLRAARAAMaxLRAARAAMaxLRAARAAExhibit 5.25a ICR Matrix Method for Peak Locations forthe Stage 2 DBPR, 20 Percent Safety Margin, LargeSurface and Ground Water PlantsNotes: 1) In the Pre-Stage 1 tables, A1 through B2 are the number of locations for ICRplants that meet thecriteria for each bin under pre-Stage 1 conditions. Atotal of 1,230 locations for 311 screened ICR surface and ground water plants were evaluated.2) Each cell (bin) represents a range of the TTHM and HAA5RAA concentrations and Maximum LRAA concentrations in:g/L (i.e., RAA <64/48 meansthe plant needs to have its TTHM RAA levelbelow 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed into the bin). Themaximum TTHM orHAA5 result determines the bin placement. Note that binsare based on a 20 percent safetymargin on the Stage 1 and Stage 2 MCLs.3) Crossed-out bins represent plants that have moved from out of compliance bins to in compliance bins.4) The gray bins on the right-hand side represent bins that have moved into compliance with pre-Stage 2 and post-Stage 2. Thepercent locations withTTHM and HAA5 concentrations above each studylevel is the percent of locations abovethe study level forthose ICR plants thatare compliant withStage 1 and Stage 2 and use an advanced treatment technology and/or chloramines (as shown in Exhibit 5.26).Final Economic Analysis for the Stage 2 DBPRDecember 20055-58Pre-Stage 1BinNumber ofLocationsLocations withTTHM >60 ug/LLocations withTTHM >75 ug/LLocations withTTHM >80 ug/LLocations withTTHM >100 ug/LLocations withHAA5 >45 ug/LLocations withHAA5 >60 ug/LLocations withHAA5 >75 ug/LA1 832 90 18 11 2 52 4 1<64/48>=64/48 (S1 non-compliant)A2 203 128 78 62 19 110 41 11<60/45 A1 B2 195 173 151 142 83 136 112 72>= 60/45 (S2noncompliant)A2 B2 All Plants 1,230 391 247 215 104 298 157 84Pre-Stage 2BinNumber ofLocationsLocations withTTHM >60 ug/LLocations withTTHM >75 ug/LLocations withTTHM >80 ug/LLocations withTTHM >100 ug/LLocations withHAA5 >45 ug/LLocations withHAA5 >60 ug/LLocations withHAA5 >75 ug/LA1 832 90 18 11 2 52 4 1<64/48>=64/48 (S1 non-compliant)A2 203 128 78 62 19 110 41 11<60/45 A1+B2 B2 195 40 5 1 0 21 3 0>= 60/45 (S2noncompliant)A2 All Plants 1,230 258 101 74 21 183 48 12Post-Stage 2BinNumber ofLocationsLocations withTTHM >60 ug/LLocations withTTHM >75 ug/LLocations withTTHM >80 ug/LLocations withTTHM >100 ug/LLocations withHAA5 >45 ug/LLocations withHAA5 >60 ug/LLocations withHAA5 >75 ug/LA1 832 90 18 11 2 52 4 1<64/48>=64/48 (S1 non-compliant)A2 203 41 5 1 0 22 3 0<60/45 A1+B2+A2 B2 195 40 5 1 0 21 3 0>= 60/45 (S2noncompliant)All Plants 1,230 171 28 14 2 94 9 1MaxLRAARAAMaxLRAARAAMaxLRAARAAExhibit 5.25b ICR Matrix Method for Peak Locations for the Stage 2 DBPR, 25 Percent Safety Margin, LargeSurface and Ground Water PlantsNotes: 1) In the Pre-Stage 1 tables, A1 through B2 are the number of locations for ICRplants that meet thecriteria for each bin under pre-Stage 1 conditions. Atotal of 1,230 locations for 311 screened ICR surface and ground water plants were evaluated.2) Each cell (bin) represents a range of the TTHM and HAA5RAA concentrations and Maximum LRAA concentrations in:g/L (i.e., RAA <64/48 meansthe plant needs to have its TTHM RAA levelbelow 64 μg/L and its HAA5 RAA level below 48 μg/L to be placed into the bin). Themaximum TTHM orHAA5 result determines the bin placement. Note that binsare based on a 20 percent safety margin on the Stage 1 and on a 25 percent safety margin onthe Stage 2 MCLs.3) Crossed-out bins represent plants that have moved from out of compliance bins to in compliance bins.4) The gray bins on the right-hand side represent bins that have moved into compliance with pre-Stage 2 and post-Stage 2. Thepercent locations withTTHM and HAA5 concentrations above each studylevel is the percent of locations abovethe study level forthose ICR plants thatare compliant withStage 1 and Stage 2 and use an advanced treatment technology and/or chloramines (as shown in Exhibit 5.26).

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-59 60 μg/L 75 μg/L 80 μg/L 100 μg/L 45 μg/L 60 μg/L 75 μg/L A CDE F I J K Stage 2 Compliance Based on a 20 Percent Safety Margin CLM only 235 24.7% 3.0% 0.9% 0.0% 11.9% 1.7% 0.0% Adv. tech with CLM 55 7.3% 1.8% 0.0% 0.0% 1.8% 0.0% 0.0% Adv. tech w/o CLM 20 5.0% 0.0% 0.0% 0.0% 20.0% 0.0% 0.0% All plants 310 20.3% 2.6% 0.6% 0.0% 10.6% 1.3% 0.0% Stage 2 Compliance Based on a 25 Percent Safety Margin CLM only 219 19.2% 1.8% 0.9% 0.0% 9.1% 0.9% 0.0% Adv. tech with CLM 55 7.3% 1.8% 0.0% 0.0% 1.8% 0.0% 0.0% Adv. tech w/o CLM 16 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% All plants 290 15.9% 1.7% 0.7% 0.0% 7.2% 0.7% 0.0%
Notes:

The 25 percent safety margin results include ground water systems.
Source: ICR database (USEPA 2000h), analysis of 311 screened ICR surface and ground water plants. Advanced technologies include chlorine dioxide,ozone, GAC, and membranes. Advanced technologies DO NOT consider enhanced coagulation or enhanced softening. Percent of Locations with TTHM Peaks Above Technology

Cate g or y Percent of Locations with HAA5 Peaks Above Number of Locations 

### Characterization of peak TTHM and HAA5 levels fo r plants in each bin is shown in the tables on the right-hand side of Exhibit 5.25a for the 20 percent safety margin and 5.25b for the 25 percent safety margin. The first column shows the total number of lo cations in the bin. Subsequent columns show the percent of the locations that have at least one TTHM sampling result above study levels of 60, 75, 80, and 100 μg/L followed by the percent of locations with at least one HAA5 result above 45, 60, and 75 μg/L. Shaded rows represent those sampling locations associated with non-compliant plants that are expected to make treatment technology changes to meet Stage 1, then Stage 2 compliance.

Similar to the method explained in Section 5.5.1.2, EPA used information on the occurrence of peaks for ICR plants using advanced treatment tec hnologies and/or chloramines at the time of the ICR to estimate the occurrence of peaks for plants predicted to change treatment technology to comply with the Stage 2 DBPR. The results of the analysis of TTHM and HAA5 peaks for this subset of plants is summarized in Exhibit 5.26. The frequency of peak TTHM and HAA5 concentrations in Exhibit 5.26 are assumed to represent the frequency of peak TTHM and HAA5 concentrations for plants that will make treatment technology changes to meet the Stage 1 and Stage 2 rules (identified as shaded rows for the preStage 2 and post-Stage 2 tables in Exhibits 5.25a and 5.25b).



## Exhibit 5.26 Frequency of Occurrence of Peak Locations for ICR Surface and Ground Water Plants Using Chloramines and/or Advanced Treatment Technologies

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-60 No. of Locations Evaluated No. of Locations with Peaks Percent of Locations with Peaks No. of Locations Evaluated No. of Locations with Peaks Percent of Locations with Peaks No. of Locations Evaluated No. of Locations with Peaks Percent of Locations with Peaks A B C = B/A D E F = E/D G H I = H/G 60 μg/L 1,230 391 31.8% 1,230 258 20.9% 1,230 193 15.7% 75 μg/L 1,230 247 20.1% 1,230 101 8.2% 1,230 40 3.3% 80 μg/L 1,230 215 17.5% 1,230 74 6.0% 1,230 21 1.7% 100 μg/L 1,230 104 8.5% 1,230 21 1.7% 1,230 4 0.3% Sources:

(B), (E), and (H) are number of locations with at least one TTHM observation over the TTHM study level as derived in Exhibits 5.25. TTHM Study Level Evaluated Pre-Stage 1 Conditions Pre-Stage 2 Conditions Post-Stage 2 Conditions

(A), (D), and (G) are the number of distribution system locations evaluated for 311 screened ICR surface and g round water p lants. 

### 5.6.2 Results

Exhibit 5.27 summarize the results for each of four TTHM study levels assuming a 20 percent safety margin on compliance. Using the ICR Matrix A pproach, the predicted percent of locations with at least one peak observation declines from 20.1 percent for pre-Stage 1 to 8.2 percent for pre-Stage 2 to 3.3 percent for post-Stage 2 DBPR conditions at a TTHM study level of 75 : 

### g/L. EPA believes that results based on a 20 percent safety margin are conservativ ely low because they do not consider the potential impacts of the IDSE on large surface water plants (i.e., more plants may need to make treatment changes as a result of the IDSE, causing additional reduction in occurrence of peaks). Results based on a 25 percent safety margin, however, are conservativel y high because the analysis includes ground water systems.



## Exhibit 5.27 Predicted Percent of Dist ribution System Sampling Locations with Peaks for Pre-Stage 1, Pre-Stage 2, and Post-Stage 2 Conditions, 20 Percent safety Margin

5.7 Uncertainties in the Compliance Forecast and Subsequent DBP Reduction

There are numerous sources of uncertainty in th e compliance forecast, as discussed previously in this chapter and in detail in Appendices A and B. Exhibit 5.28 summarizes the key uncertainties in the compliance forecast, with the exception of uncertainties in baseline data inputs (e.g., the ICR data), which are discussed in detail in Section 3.8.

EPA believes that two of these uncertainties could have a potentially large impact on Stage 2 DBPR cost and benefit analysis:

• Uncertainty in the impacts of the Initial Distribution System Evaluation (IDSE) on the Compliance Forecast for large and medium surface water sytems.

• Uncertainty in compliance forecast tools.

EPA has adjusted the compliance forecast methodol ogy to quantify and incorporate both of these uncertainties into the compliance for ecast results and the cost and benefits models. Section 5.3 discusses the methods used to quantify these uncertainties. To help inform the reader of the potential magnitude of

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-61 

### these uncertainties, EPA has conducted several sensitiv ity analyses. Exhibit 5.29 shows the predicted percent of all plants changing technology and resu lting decrease in average TTHM concentration for the primary analysis and four sensitivity analyses. No te that results reflect combined surface water and ground water system analyses, with the ground water system inputs staying the same for each estimate. Analyses 1 and 2 (beneath the primary analysis) show results assuming a 20 percent safety margin and 25 percent safety margin for large and medium surface wate r systems, respectively. The impact of the safety margin is more pronounced for DBP reduction than fo r percent changing technology. This implies that the uncertainty in the IDSE will have a larger impact on the national benefits estimates compared to the national cost estimates.

Analyses 3 and 4 show results using the SWAT compliance forecast methodology for all surface water systems, and the ICR Matrix Method compliance forecast methodology for all surface water systems, respectively. The compliance forecast t ool has a greater impact on the compliance forecast results compared to the operational safety margin. Th e percent of all plants changing technology for the ICR Matrix Method is approximately 15 percent highe r than the primary analysis. The percent DBP reduction for the ICR Matrix Method is about 30 per cent higher than the primary analysis. EPA carried this analysis through the entire benefits and cost models and found that overall, the benefits estimates using the SWAT model are about 30 percent lower than the midpoint estimates, while those using the ICR Matrix Method are about 30 percent higher. For na tional costs, estimates using the SWAT model are about 25 percent lower then the midpoint estimate, wh ile those using the ICR Matrix Method are about 25 percent higher. (Additional surface water systems changing technology have a disproportionately high impact on total costs because surface water systems ar e typically much larger than ground water systems and therefore, have higher unit costs.)

The remaining uncertainties listed in Exhibit 5. 28 fall into two categories—uncertainty in the DBP data and uncertainty in the models. These two categories are discussed in Sections 5.7.1 and 5.7.2, respectively.

It is important to note that any biases in th e compliance forecasts affect cost and benefits similarly. If more plants make treatment technol ogy changes than predicted, costs for the treatment technology changes would be higher and there would also be a higher overall reduction in DBP levels. Conversely, if fewer plants make treatment technol ogy changes than predicted, treatment costs and benefits from DBP reduction would be less.

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-62 

## Exhibit 5.28 Summary of Uncertainties in the Compliance Forecast

Source Water Type Uncertainty Section With Additional Discussion of Uncertainty Effect on Benefit Predictions Effect on Cost Predictions

Underestimate Overestimate Unknown Impact Underestimate Overestimate Unknown Impact


### Surface Uncertainty in tools used to derive compliance forecast and DBP reductions for large surface water systems A.6 Quantified in the primary analysis (addresses potential underestimate or overestimate) Quantified in the primary analysis (addresses potential underestimate or overestimate)

Ground Uncertainty in Ground Water Delphi results B.2.2 X X

All Uncertainty in extrapolating compliance forecasts from large to small systems A.9.1 & B.3.1 X X

All Uncertainty in using the delta approach (effectively assuming that plants changing for Stage 1 meet Stage 2 MCLs) 5.3.2 X X

All Operational safety margin of 20 percent for Stage 1 and Stage 2 5.3.3 X X

Surface Impacts of the IDSE on the compliance forecast for the preferred regulatory alternative 5.3.4 Quantified in the primary analysis (addresses potential underestimate) Quantified in the primary analysis (addresses potential underestimate)

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-63 Total Percent Changing Technology from Stage 1 to Stage 2 (all plants) Percent Reduction in Average TTHM from Stage

1 to Stage 2 (all plants)
A B
Equally weighted between 20% and 25% Safety Margin for large SW systems, SWAT and ICR MM for all SW Systems (Primary Analysis) 3.8% 7.8%

1) 20% Safety Margin for Large SW systems 3.6% 6.3%
2) 25% Safety Margin for Large SW systems 3.9% 9.3%
3) ICR Matrix Method for all SW systems 4.3% 9.9%
4) SWAT Method for all SW systems 3.2% 5.7%

Notes:

Sources: (A) Stage 2 DBPR Cost Model (USEPA 2005i) (B) Stage 2 DBPR Benefits Model (USEPA 2005h) 

### Sensitivity Analysis for Surface Water Systems

Costs and benefits shown are mean estimates for all systems. Percent results are indicated for all systems. Sensitivity analysis criteria pertain only to the surface water system portion of the analysis. The ground water system portion of the analysis is the same for each sensitivity analysis. For each sensitivity analysis, only one factor was changed (e.g., for the 20% safety margin analysis, both SWAT and the ICR Matrix Method are considered). 

## Exhibit 5.29 Potential Impact of Uncertainties on the Compliance Forecast and DBP Reduction Analysis

5.7.1 Uncertainty in DBP data

One factor that influences the compliance for ecast results is the representativeness of the ICR data. For example, ICR observed data are used as the basis for the pre-Stage 1 baseline, although EPA recognizes that some plants had already made chang es to comply with Stage 1 by the time the ICR was conducted. Additionally, there are limitations associated with the subsets of the ICR data that were used for the analysis with both SWAT and the ICR Matrix Method. Limitations of the ICR data are discussed in Section 3.8. Data limitations related to SWAT are discussed in greater detail in Appendix A, and limitations related to the ICR Matrix Method are discussed in this section.

5.7.1.1 Representativeness of the ICR data

There are uncertainties regarding the ICR TTHM and HAA5 data in general, as explained in detail in Section 3.8. EPA examined the climate conditions during the ICR sampling period to determine the representativeness of DBP levels over time. On a nationwide basis, 1998 was hotter and wetter than normal. Increased rainfall may have biased the results by increasing levels of contaminants from runoff,

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-64 

### such as TOC. Other constituents may have been lowe r than normal, such as bromide, which tends to rise during droughts. Higher temperatures in 1998 could have caused the DBP levels in the ICR data to be unusually high as compared to an average year.

In addition, the representativeness of the DBP sampl e results are uncertain due to the nature of distribution system monitoring. Research has show n that TTHM and HAA5 levels can vary as much as 20 percent over the course of a day at locations in the distribution system (Pereira et al. 2004). One grab sample collected at a discrete point in time for th e ICR does not represent this potential variability. In addition to hourly variations, the ICR data were not re quired to be collected at evenly spaced intervals. Thus, there is uncertainty in assuming that a single da ta point represents the typical level over the entire quarter.

5.7.1.2 Uncertainty in the subset of ICR data used for the ICR Matrix Method

The data set used for the ICR Matrix Method c ontains 213 of the 353 surface water plants in the ICR database, or roughly 60 percent. To evalua te the representativeness of the ICR Matrix Method subset, EPA evaluated source water TOC and distributi on system DBP data. Plants in the screened data set have a mean influent TOC level of 3.21, and the plants excluded from this data set have mean influent TOC level of 3.19, indicating relativ ely little bias from source water quality.

TTHM and HAA5 data for plants that passed the da ta quality screen were compared to data for the plants that were excluded from the analyses. For the excluded plants, only those that had matching TTHM and HAA5 samples were considered, leaving 75 of the 140 SW plants that were excluded. The TTHM levels were higher in the excluded plants, with a plant mean of 50.5, as opposed to 42.3 for the screened plants. The excluded plants may have highe r overall DBP levels, and th eir inclusion would raise the percentage of plants out of compliance with Stage 1 and Stage 2. However, these plants were excluded due to missing data, so it is possible that the complete data set would have DBP levels more similar to the screened plants.

5.7.2 Uncertainty in the Delta Approach

This section briefly describes some of the la rger uncertainties in the compliance forecast. Uncertainties associated with SWAT are di scussed in greater detail in Appendix A.

Both SWAT and the ICR Matrix Method are limited in allowing systems to make multiple treatment technology changes. In both models, when systems make treatment technology changes to come into compliance with Stage 1, they will simu ltaneously come into compliance with Stage 2 MCLs. It is possible that some plants may need to make a second treatment technology change after achieving compliance with Stage 1 to achieve compliance with Stage 2, although EPA believes this is unlikely for most systems. The Agency believes this uncertainty is small and the delta approach is reasonable for reasons presented in Section 5.3.2.

There is also uncertainty associated with the 20 percent safety margin. Individual systems may use higher or lower safety margins based on system-specific conditions. The M-DBP TWG recommended that a 20 percent operational safety margin be used for DBP MCLs (TTHM, HAA5, bromate, and chlorite) when evaluating all regulatory a lternatives. This safety margin is consistent with practices in prior DBP regulatory development efforts. It is intended to represent the level at which

### Final Economic Analysis for the Stage 2 DBPR December 2005 5-65 

### systems typically take some action to ensure cons istent compliance with a new drinking water standard and the level at which systems target new treatment to meet the standard. In addition to representing industry practices, the safety margin also is intended to account for year-to-year fluctuations in DBP data. (ICR data are limited to 1 year and must not represen t the highest DBP concentrations that occur in a system.)

### Final Economic Analysis for the Stage 2 DBPR 6-1 December 2005 

# 6. Benefits Analysis

6.1 Introduction

The mission of the Environmental Protection Agency (EPA) is to protect human health and to safeguard the natural environment (USEPA 2000m). The Safe Drinking Water Act (SDWA) provides that the Administrator shall...publish a maximum contam inant level goal and promulgate a national primary drinking water regulation for a contaminant...if the Administrator determines that - (i) the contaminant may have an adverse effect on the health of persons; (ii) the contaminant is known to occur or there is a substantial lik elihood that the contaminant will occur in public water systems with a frequency and at levels of public health concern; and (iii) in the sole judgment of the Administrator, regulation of such contaminant presents a meaningful opportunity for health risk reduction for pers ons served by public water systems. (42 USC §300g-1(b)(1)(A))

When carrying out its statutory mandate, EPA must often make regulatory decisions using incomplete or uncertain information. EPA believes it is appropriate and prudent to act to protect public health when there are indications that exposure to a contaminant could present significant risks to the public, rather than take no action until risks ar e unequivocally proven. Evidence from both human epidemiology and animal toxicology studies indicate that the consumption of drinking water containing disinfection byproducts (DBPs) may result in adverse health effects. The two main categories of such potential effects that have been associated with DBPs are reproductive and developmental effects and cancer (particularly bladder cancer). EPA has concluded that DBPs occur at levels that are a public health concern in some public water systems (PWSs) that a pply a chemical disinfectant, and that the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR) presents a meaningful opportunity for a reduction in the risk of adverse health effects.

Under Executive Order 12866, EPA must conduct an Economic Analysis (EA) for rules costing over $100 million annually. The benefits analyses presen ted in this chapter follow the requirements of the executive order and related Office of Manage ment and Budget (OMB) and EPA guidance, and provide a reasonable basis for estimating potential h ealth benefits using the best available science.

EPA has quantified the benefits associated with e xpected reductions in the incidence of bladder cancer. EPA also includes a sensitivity analysis fo r potential benefits from avoiding colon and rectal cancers. Scientific knowledge about the association of reproductive and developmental health effects with DBP exposure is not adequate to quantify these ri sks or the benefits of reduced DBP exposure in the primary analysis. Nevertheless, although the results from different studies do not support a conclusion at this time as to whether exposure to chlorinated dr inking water or disinfection byproducts cause adverse developmental or reproductive health effects, they do support a potential health concern. EPA believes additional benefits from the Stage 2 DBPR c ould come from reducing potential reproductive and developmental risks and that it is important to provi de some quantitative indication of the potential risk. To do this, EPA completed an illustrative calculation of potential benefits for one specific reproductive effects endpoint (fetal loss).

Section 6.1.1 provides an overview of the methodology and key assumptions used to estimate the benefits that may be attributed to the Stage 2 DBPR (including the illustrative calculations for a developmental and reproductive health endpoint). Section 6.1.2 summarizes results.

Section 6.2 presents the problem identification and the assessment of potential hazard. Carcinogenic and non-carcinogenic (e.g., reproductive and developmental) risks are presented with

### Final Economic Analysis for the Stage 2 DBPR 6-2 December 2005 

### toxicological and epidemiological evidence. Secti on 6.3 follows with an assessment of exposure. The rule’s benefits, including cancer cases avoided and the associated value of those benefits, are addressed in Sections 6.4 and 6.5. Section 6.6 summarizes uncerta inties of national benefits estimates. Section 6.7 contains a sensitivity analysis for benefits from avoidi ng colon and rectal cancers. Potential benefits from reductions in one reproductive a nd developmental endpoint—fetal loss—are evaluated through an illustrative calculation in Section 6.8.

The following provide additional deta ils in support of this chapter:

• Chapter 5 estimates reduction in TTHM and HAA5 occurrence as a result of DBP risks in support of the exposure assessment in Section 6.3.

• Appendix D shows the schedule for all rule activiti es (this information is used as input for the quantified benefits calculation).

• Appendix E provides general background informa tion on population attributable risk (PAR) and a detailed description of the derivation of PAR values used to quantify benefits associated with reduction in cancer cases. The development and modeling of the cessation lag equations are described. Lastly, Appendi x E shows detailed calculations for estimating the number of bladder cancer cases avoided as a result of the Stage 2 DBPR.

• Appendix E2 provides calculations for the colon and rectal cancer sensitivity analysis.

• Appendix F describes the valuation of Stage 2 DBPR benefits and presents results for all regulatory alternatives and sensitivity analyses.

• Appendix G provides detailed calculations for the illustrative calculation of potential reproductive and developmental health impacts.

6.1.1 Overview of Methodology for Quantifying Stage 2 DBPR Benefits

Three categories of potential benefits are addressed in this EA: those associated with reductions in the incidence of bladder cancer, those associated w ith reductions in the incidence of colon and rectal cancers, and those associated with reductions in the incidence of adverse reproductive and developmental health effects. The primary benefits analysis in this EA is based on reductions in bladder cancer cases. Potential benefits associated with reduced inciden ce of colon and rectal cancers are quantified in a sensitivity analysis, while potential benefits associ ated with decreasing adverse developmental and reproductive health effects (specifically, fetal losses) are presented as an illustrative calculation.

EPA used similar approaches to estimate the number of bladder cancer cases avoided (the primary benefits analysis), the number of colon and rectal cancer avoided (the sensitivity analysis), and the number of avoided incidence of fetal loss (the illustrative calculation). The major steps in deriving and characterizing cases avoided are:

• Estimate the current and future annual cases of illness from all causes

• Estimate how many cases can be attributed to current DBP occurrence and exposure

• Estimate the reduction in future cases corresponding to anticipated reductions in DBP occurrence and exposure due to the Stage 2 DBPR1 

### The American Cancer Society estimated in 2004 that 60,240 new cases of bladder cancer would occur in the U.S. population that year (ACS website, 2004). Final Economic Analysis for the Stage 2 DBPR 6-3 December 2005 

### All benefit calculations were performed using the Stage 2 DBPR Benefits Model (USEPA 2005h).

For bladder cancer, EPA computed the monetized benefits of the Stage 2 DBPR by multiplying the estimated number of bladder cases avoided by the estimated monetary value associated with avoiding both fatal and non-fatal cases of bladder cancer. The valu e of a statistical life (VSL) was used for fatal bladder cancers, while two alternate estimates of w illingness-to-pay to avoid non-fatal bladder cancer are used (one based on avoiding a case of curable lymphoma and the other based on avoiding a case of chronic bronchitis). EPA also computed the benefits for the reduction in colon and rectal cancer by using the same VSL and willingness to pay (WTP) estimates. EPA recognizes that there could be a additional value associated with the number of avoided feta l losses estimated in the illustrative calculation. However, the Agency is unable at this time either to develop a specific estimate of this value or to use a benefit transfer method to estimate the value from studies that address other endpoints (see Section 6.8 for a full discussion of this issue).

Bladder Cancer

To calculate potential benefits from reduced incidence of bladder cancer cases, EPA began by estimating the number of new bladder cancer cases occurring per year from all causes. The National Cancer Institute’s Surveillance, Epidemiology, a nd End Results (SEER, 2004) program provides data on cancer rates (new cases per 100,000 population per year) as a function of age. EPA used this information in conjunction with population-by-a ge data from the 2000 U.S. Census to estimate the number of new cases of bladder cancer. Results show that the number of new bladder cancer cases per year starts to increase at about age 35 and peaks at 1,500 to 2,000 cases from about age 66 to 85. Although the annual rate of bladder cancer does not decline much afte r age 85, the number of new bladder cancer cases does, which represents the overall decline in the number of i ndividuals alive after that age. The resulting total number of new bladder cancer cases per year, 56,506, is slightly lower than that currently estimated by the American Cancer Society (ACS). 1 

### This likely represents EPA’s use of the census population data from 2000.

To estimate the baseline number of cases attributab le to DBP exposure, EPA used three different approaches:

• Using the range of Population Attributable Ri sk (PAR) values derived from consideration of 5 individual epidemiology studies used for the Stage 1 EA and the Stage 2 proposal EA (yields a pre-Stage 1 range of best estimates for PAR of 2% to 17%).

• Using the Odds Ratio (OR) of 1.2 from the Villa nueva et al. (2003) meta-analysis that reflects both sexes, ever exposed population from the st udies considered (yields a pre-Stage 1 best estimate for PAR of ~16%)

• Using the Villanueva et al. (2004) pooled da ta analysis to develop a dose-response relationship for OR as a function of Averag e TTHM. The dose-response relationship wasmodeled as linear with an intercept of OR = 1.0 at TTHM exposure level = 0 (yields a preStage 1 best estimate for PAR of ~17%)

Taken together, the three approaches provide a r easonable estimate of the range of potential risks. For the sake of simplicity, EPA carried only one of these approaches, that based on Villanueva et al. (2003), through the entire benefits model.

### Final Economic Analysis for the Stage 2 DBPR 6-4 December 2005 

### To quantify the reduction in cases, EPA assumes that there is a linear relationship between average DBP concentration and relative risk of bl adder cancer. Thus, percent reductions in national average DBPs are used to determine the percent re ductions in bladder cancer cases attributable to DBPs. Predicted reductions in national average TTHM a nd HAA5 levels resulting from predicted treatment technology changes to comply with Stage 2 were used as indicators of overall chlorination DBP reductions. The baseline cases attributable to DBPs multiplied by the percent reductions in TTHM or HAA5 concentrations result in the estimated annua l bladder cancer cases “ultimately avoidable” for the Stage 1 and Stage 2 rules.

Over the long run, the cases ultimately avoidable (derived as described above) will be attained. They will not be achieved instantaneously, however. Research shows that a lag period (referred to as “cessation lag”) exists between the point in time when reduction in exposure to a carcinogen occurs and the point in time when the full risk reduction benefit of that exposure reduction is realized by affected individuals. Because there is no epidemiological or other empirical data available that specifically address the rate of achieving bladder cancer benef its resulting from DBP reductions, EPA uses data from three epidemiological studies that address the rate of risk reduction following exposure reduction to other carcinogens (namely cigarette smoke and arsenic) to generate three possible cessation lag functions for bladder cancer and DBPs.

The cessation lag functions are used to project the number of bladder cancer cases avoided each year after implementation as a result of the Stage 2 DBPR over a 100-year period. A 100-year period was selected as the timeframe after which effectively all of the exposed population is composed of individuals exposed only to post-Stage 2 levels for their entire lif etime. At that time (and from that point forward) the annual bladder cancer cases ultimately avoidable is achieved for the exposed population. The projected number of cases avoided each year is also ad justed to reflect when systems are expected to install new treatment to reduce DBPs based on the rule implementation schedule. Although a 100-year cessation lag period is modeled, a nnual avoided cases of bladder cancer are calculated primarily for the first 25 years after rule promulgation. A 25-year time period was used to coincide with the estimated life span of capital equipment and a time lag of five to ten years for technology installation after rule promulgation.

The final step in the benefit calculation is to monetize the average annual cases avoided. This is done by applying economic values for avoided illnesses a nd deaths. EPA has estimated that 74 percent of bladder cancer cases are non-fatal (USEPA, 1999a). Th e value of avoiding non-fatal bladder cancer cases is based on people’s WTP for incremental reductions in the risk they face of contracting cancer. The metric of WTP to avoid an increased risk includes the desire to avoid treatment costs, pain and discomfort, productivity losses, and any other adverse consequences related to a non-fatal case of bladder cancer. Because specific estimates of WTP for avoidi ng non-fatal bladder cancer are not available, EPA estimated values from two other non-fatal illnesses: cura ble lymphoma and chronic bronchitis. Both are considered valid estimates of WTP for non-fatal cancer.

For fatal bladder cancer cases, VSL is used to cap ture the value of benefits. The VSL represents an estimate of the monetary value of reducing risks of premature death from cancer. Therefore, it is not an estimate of the value of saving a particular indivi dual’s life. Rather, it represents the sum of the values placed on small individual risk reductions across an exposed population. Other economic factors are taken into consideration when calcu lating benefits over time, such as income growth and social discount
rates.

### Final Economic Analysis for the Stage 2 DBPR 6-5 December 2005 

### There are several areas of uncertainty with resp ect to quantified benefits for bladder cancer. Many are described qualitatively in the analysis, wh ile other are incorporated explicitly as follows:

• There is uncertainty in the percent reduc tion in TTHM and HAA5 concentrations resulting from predicted treatment technology changes (i .e., the compliance forecasts). Uncertainty in SWAT and potential impacts of the IDSE are quantified in the primary analysis.

• Three approaches were used to estimate the baseline number of bladder cancer cases attributable to DBP exposure. For the sake of simplicity, one approach using data from Villanueva et al. (2003) was carried through the full benefits model.

• The estimated PAR values from the Villanueva et al. (2003) meta-analysis include confidence bounds resulting from statistical uncertainty in the odds ratio underlying the PAR calculation. The confidence bounds from Villanueva et al. (2003) capture a significant portion of the confidence intervals of the other two approaches.

• Three independent cessation lag models derived from three different epidemiological studies are used in the model. Also, two functional forms are used for each of these data sets and uncertainty in the parameters of those functions is included in the analysis.

• EPA uses two alternatives for valuing non-fatal bladder cancer.

Colon and Rectal Cancers

Human epidemiology studies on chlorinated surface water have reported associations with colon and rectal cancers. Colon and rectal cancers comb ined are the third most common type of new cancer cases and deaths in both men and women in the U.S., ex cluding skin cancers. Therefore, any benefit from reducing the potential incidence of colon and rectal cancers could be significant. EPA is including a quantitative sensitivity analysis for benefits accrue d from the Stage 2 DBPR from avoiding colon and rectal cancers.

EPA estimated the reduction in colon and rectal cancer in a similar manner to bladder cancer cases. Background incidence data were available fro m the SEER cancer registry and two quality studies were chosen to estimate a PAR value. Using the percent reductions in DBPs and the smoking and lung cancer cessation lag model, the number of colon a nd rectal cancer cases avoided annually was estimated and monetized with the same VSL and WTP estimates as for bladder cancer.

Developmental And Reproductive Health Effects

As noted previously, EPA believes that additi onal benefits of this rule could come from reductions in potential developmenta l and reproductive health effects, although the relationship of these effects to DBP exposure is not known well enough to quantify risks or benefits in the main analysis. EPA prepared an illustrative calculation of the benefits of reducing the potential risk of fetal loss, the noncancer effect for which the most epidemiological data exist in relation to DBP exposure. Because approximately one million of the six million pregnancies that occur each year in the United States end in a miscarriage or stillbirth (Ventura et al. 2000), avoidi ng even a small risk attributable to DBP exposure by reducing DBP levels may potentially result in a significant number of avoided fetal losses.

EPA estimated the potential reduction in fetal lo sses in a similar manner to bladder cancer cases. A range of possible PAR values for relating annua l fetal losses to DBP exposure was obtained from available epidemiological studies. Reductions in th e number of peak DBP events due to the Stage 1 DBPR and the Stage 2 DBPR were estimated. Reducti ons in exposure to peak DBPs were assumed to be

### Final Economic Analysis for the Stage 2 DBPR 6-6 December 2005 Mean 5th 95th Mean 5th 95th 3 %, Lymphoma $ 1,531 $ 233 $ 3,536 7 % Lymphoma $ 1,246 $ 190 $ 2,878 3 % Bronchitis $ 763 $ 165 $ 1,692 7 % Bronchitis $ 621 $ 135 $ 1,376 3 %, Lymphoma $ 1,032 $ 157 $ 2,384 7 % Lymphoma $ 845 $ 129 $ 1,950 3 % Bronchitis $ 514 $ 111 $ 1,141 7 % Bronchitis $ 420 $ 91 $ 932 3 %, Lymphoma $ 1,852 $ 282 $ 4,276 7 % Lymphoma $ 1,545 $ 235 $ 3,566 3 % Bronchitis $ 922 $ 200 $ 2,045 7 % Bronchitis $ 769 $ 167 $ 1,704 Cessation Lag Model used to estimate Annual

Bladder Cancer Cases Avoided Annual Average Bladder Cancer Cases Avoided for the first 25 years Discount Rate, WTP for Non-Fatal Bladder

Cancer Cases A nnualized Benefits of Bladder Cancer Cases Avoided ($Millions)

Smoking/Lung Cancer Model 279 103 541

188 61 399

Arsenic/Bladder Cancer Model 333 138 610 Smoking/Bladder

Cancer Model 

### proportional to reductions in peak DBP events. Like th e analysis of bladder cancer, there is uncertainty in fetal loss PAR values, which is reflected in the range of values used in the analysis. There are other important uncertainties in this illustrative calcula tion, including the assumed proportional relationship between reduction in fetal losses and reduction in exposure to peak levels due to the Stage 2 DBPR.

6.1.2 Summary of National Benefits of the Stage 2 DBPR

Exhibit 6.1 summarizes the estimated number of bladder cancer cases avoided as a result of the Stage 2 DBPR and the monetized value of those cases. The benefits in Exhibit 6.1 are for the Preferred Regulatory Alternative (which includes a requirement for the IDSE) for the Stage 2 DBPR. Benefit estimates for the other regulatory alternatives were derived using the same methods as for the Preferred Regulatory Alternative and are presented in Section 6.5.4.

The confidence bounds shown for the results in E xhibit 6.1 incorporate uncertainty in the PAR, uncertainty in the compliance forecast and resulting reduction in DBP concentrations, and uncertainty in cessation lag. The confidence bounds of the monetized benefits also incorporate uncertainty in the valuation parameters. An estimated 26 percent of bladder cancer cases avoided are fatal, and 74 percent are non-fatal. The monetized benefits therefore re present the estimate of avoiding both fatal and non-fatal cancers in those proportions.

In addition to bladder cancer cases avoided, EPA provides an illustrative calculation of the potential number of fetal losses that might be avoide d per year, ranging from 0 to 3,700. The value of other health benefits, including the potential reduction in other types of cancer such as colon or rectal, could be significant, and is calculated in a sensitivity analysis. Also, the value of non-health benefits, such as improved taste and odor of water, are exp ected to be positive. These are discussed further in Section 6.4



## .

Exhibit 6.1 Summary of Quantified Benefits for the Stage 2 DBPR

Note: Based on TTHM as an indicator, benefits were calcul ated using the Villanueva et al. (2003) PAR. Assumes 24 percent of cases are fatal, 76 percent are non-fatal (USEPA 1999a). The 90 percent confidence interval for cases incorporates uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cessation lag. The 90 percent confidence bounds for monetized benefit s reflect uncertainty in monetization inputs relative to mean cases. Values are discounted and annualized in 2003$. EPA recognizes t hat benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. Sources: Summarized from detailed figures presented in Appendix E (Exhibits E.38d, E.38h and E.38l) and F (Exhibits F.2v and F.2w, F.3v and F.3w).

### Final Economic Analysis for the Stage 2 DBPR 6-7 December 2005 

## 6.2 Problem Identification and Assessment of Potential Hazard

This section provides detailed information from the toxicological and epidemiological literature for the key adverse health effects that have been asso ciated with exposure to DBPs. In addition to the studies and reviews presented here, EPA has a ddressed reproductive and developmental effects, carcinogenicity, and other adverse health effects at length in several Health Criteria Documents. Specifically, EPA has developed Drinking Water Criteria Documents for the following DBPs: brominated trihalomethanes (USEPA 2005b), brominated haloacetic acids (USEPA 2005c), trichloroacetic acid (2005 d), and monochloroacetic acid (USEPA 2005e). EPA has also completed toxicological reviews of dichloroacetic acid (IRIS 2003), bromate (IRIS 2001a), chloroform (IRIS 2001b), chlorine dioxide and chlorite (IRIS 2000), and an addendum for dichlo roacetic acid (USEPA 2005j). A similar document exists for 3-Chloro-4-(dichloromethyl)-5 -hydroxy-2(5H)-furanone (MX) (USEPA 2000f).

EPA’s weight of evidence evaluation of the best available science on carcinogenicity and reproductive and developmental effect s, in conjunction with the widespread exposure to DBPs, supports the incremental regulatory changes in today’s rule that target lowering DBPs and providing equitable public health protection.

6.2.1 Cancer

Several DBPs have been identified by EPA as probable or possible human carcinogens. EPA believes that the cancer epidemiology and toxicol ogy literature provides important information that contributes to the weight of evidence for potential health risks from exposure to chlorinated drinking water. At this time, the cancer epidemiology studies support a potential association between exposure to chlorinated drinking water and cancer, but evidence is insufficient to establish a causal relationship. The epidemiological evidence for an asso ciation between DBP exposure and colon and rectal cancers is not as consistent as it is for bladder cancer, although similarity of effects reported in animal toxicity and human epidemiology studies strengthens the evidence for an association with colon and rectal cancers. EPA believes that the overall cancer epidemiology and toxicology data support the decision to pursue additional DBP control measures as reflected in th e Stage 2 DBPR. The following sections provide an overview of the epidemiological and toxicological evidence for the carcinogenicity of key DBPs.

6.2.1.1 Epidemiological Evidence of DBP Carcinogenicity

A number of epidemiological studies have been conducted to investigate the relationship between exposure to chlorinated drinking water and various cancers. These studies contribute to the overall evidence on potential human health hazards fro m exposure to chlorinated drinking water.

Epidemiology studies provide useful health effects information because they reflect human exposure to a drinking water DBP mixture through multiple routes of intake such as ingestion, inhalation and dermal absorption. The greatest difficulty with conducting cancer epidemiology studies is the length of time between exposure and effect. Higher quality studies have adequately controlled for confounding and have limited the potential for exposure misclassifica tion, for example, using DBP levels in drinking water as the exposure metric as opposed to type of source water. Study design considerations for interpreting cancer epidemiology data include su fficient follow-up time to detect disease occurrence, adequate sample size, valid ascertainment of cause of the cancer, and reduction of potential selection bias in case-control and cohort studies (by having comp arable cases and controls and by limiting loss to follow-up). Epidemiology studies provide extremely useful information on human exposure to chlorinated water, which complement single chemical, high dose animal data.

### Final Economic Analysis for the Stage 2 DBPR 6-8 December 2005 

### Bladder Cancer - Causes and Risk Factors

The National Cancer Institute (NCI) lists the fo llowing primary risk factors for bladder cancer: age, tobacco, occupation, certain infections, certain drug treatments, race, being male, family history and personal history (NCI 2002). The American Cancer Society (ACS) estimates that there will be about 60,240 new cases of bladder cancer diagnosed in the United States (about 44,460 men and 15,600 women) in 2004 (ACS 2004). The cancer is more common in men than in women, although women who smoke have twice the risk of men who smoke (Mayo Clinic 2004). Whites are about two times more likely to develop bladder cancer than African Americans or Hispanics (ACS 2004).

The literature on bladder cancer and its causes and risk factors describes several well-known etiologic agents. Two useful review articles on bl adder cancer in humans are Cohen et al. (2000) and Fukushima and Wanibuchi (2000). The follo wing is derived largely from these reviews.

Bladder cancer involves a heterogenous group of tumo rs, but is often categorized into one of two main types: squamous cell carcinomas (which are mainly seen as secondary to schistosomiasis infections) and transitional (urothelial) cell carcinomas.

Schistosomiasis is caused by the parasite Schistosoma haematobium , which is most prevalent in Egypt and other Nile River countries, but is found in other parts of Africa, the Middle East and India. Generally, bladder cancers in areas that have ende mic schistosomiasis involve squamous cell carcinomas rather than transitional cell carcinomas, and also te nd to occur in somewhat younger individuals (40s or 50s) than those typically experiencing transitional cell carcinomas. Cohen et al. (2000) indicated that the incidence and mortality rates for bladder tumors of this etiology are not well characterized. Bladder cancer related to Schistosoma infections does not appear to be directly relevant to that associated with DBPs or other chemical agents, and is therefore not considered further here.

Bladder cancers not associated with Schistosoma infections occur throughout the developed world, and these are almost all of the transitional cell carcinoma type, and include low-grade, recurrent papillary tumors and high-grade invasive malignanc ies. In countries without schistosomiasis, over 95 percent of bladder cancers involve transitional ce ll tumors. There is a relatively long history of association of these bladder cancers with specific envi ronmental and occupational factors. For example, an association of bladder cancer with workers in the aniline dyes industry dates back to a 1895 German study by Rehn, with similar observations between exposure to certain aromatic amines and related compounds in the dye industry throughout the twentieth century. A number of specific aromatic amine compounds considered to be bla dder carcinogens have been identified through epidemiological studies, occupational studies, and animal toxicity studies. Well-recognized among these are:

• 2-Naphthylamine • 4-Aminobiphenyl • Benzidine (and some benzidine-related "azo" dyes) • 4,4-Methylenebis(2-chloraoaniline) (MBOCA) • o-Toluidine • 4-Chloro-o-toluidine • Methylenedianiline (MDA)

Other specific chemical compounds that have b een associated with bladder cancer include the analgesic phenacetin and some chemotherapeutic agents, such as cyclophosphamide and chlornaphazine (N,N-bis(2-chloroethyl)-2-naphthylamine).

Although there has been some indication of a re lationship between certain artificial sweeteners (saccharin and cyclamates) with bladder cancer, these are now generally considered very weak

### Final Economic Analysis for the Stage 2 DBPR 6-9 December 2005 

### associations at best (Mayo Clinic 2004). Also, ther e do not appear to be any dietary factors associated with bladder cancer beyond the associations made betw een cancer in general and diets high in fats, red meats and fried foods. In a systematic literature re view, Zeegers et al. (2004) concluded that coffee and tea consumption are probably not associated with bladder cancer. While this study found some convincing evidence of an association of alcohol cons umption and bladder cancer, this risk was not found to be statistically significant.

The most significant environmental factor that is associated with bladder cancer is tobacco smoke (ACS 2004). The causative agent(s) in tobacco smoke is not known, but it should be noted that cigarette smoke contains aromatic amine compounds, including so me of those that have been specifically linked with bladder cancer in other studies as noted above. Ingestion of arsenic, notably as a drinking water contaminant, has been associated with bladder cancer. Consumption of chlorinated drinking water has also been associated with bladder cancer.

It has been reported that smoking is the attributab le factor for about 50 percent of bladder cancers in men and 30 percent in women (NCI 2004). The NC I also indicates that up to 25 percent of bladder cancers may be attributable to occupational factor s, again notably those involving exposure to certain aromatic amine compounds (Mayo Clinic 2004). Other studies have estimated that occupational exposure may be the attributable factor in 20 percent of cases (Silverman et al. 1989a, Silverman et al. 1989b, and Silverman et al. 1990). Less than half a percent of bla dder cancers can be attributed to a rare bladder birth defect, and hereditary factors may account for 1 percent (ACS 2004).

In the benefits analysis supporting the January 2001 arsenic rule, EPA did not derive an estimate of the bladder cancer attributable factor for arsenic in drinking water, and no other estimate was found in the literature. However, based on estimates of annua l bladder cancer cases avoided by the various arsenic regulations considered, including the most stringent of them at 3 

## : g/L (approximately 30 to 80 cases per year), it would appear that the total cases attributable to arsenic in drinking water is a small fraction of the approximately 60,000 new cases reported each year.

For both the Stage 1 Final Rule (November 1998) and the proposed Stage 2 Rule (August 2003), EPA provided estimates that chlorinated drinking wate r consumed prior to these rules maybe responsible for between 2 and 17 percent of bladder cancers.

It is not appropriate simply to add the estimat es of percent contribution to total bladder cancer estimated separately for each of the individual cau ses described above, as there may be overlap among them. The Venn diagram shown in Exhibit 6.2 is in tended to provide a schematic depiction of possible overlaps among the major attributable causes of bl adder cancer noted above: smoking, occupational exposures, and drinking water.

### Final Economic Analysis for the Stage 2 DBPR 6-10 December 2005 

## C

D A B

Drinking Water Occupational

Smoking Only Occupational

Only Drinking

Water Only Smoking 

## E

G F H Exhibit 6.2 Venn Diagram of Bladde r Cancer in the U.S. Population

The total bladder cancers each year is represented by the space in the rectangle, and is the sum of A+B+C+D+E+F+G+H = 100 percent. Smoking, noted previously as being associated with approximately 50 percent of bladder cancers, is represented by the circle with areas A+B+C+D. Occupational sources, estimated as the principal factor for 25 percent of bladder cancer cases, is the circle composed of areas B+C+E+F. Drinking water is represented by the circle with areas C+D+F+G.

The overlap areas among these circles (that is, areas B, C, D and F) represent circumstances where contributions from multiple sources may not be fu lly accounted for in the epidemiological data. To the extent this is the case, the sum of the two major individual sources noted above – smoking at 50 percent, occupational at 25 percent – would be l ess than the 75 percent implied by adding those two values.

Bladder Cancer - Studies Supporting EPA’s PAR Analysis

More evidence is available to support a po ssible association between bladder cancer and chlorinated water or DBP exposure than for othe r cancers. The Stage 1 DBPR Regulatory Impact Assessment (USEPA 1998a) presents EPA’s review of th e large body of epidemiology literature for bladder cancer and its association with DBPs in dri nking water. From this review, EPA concluded that although causality has not been established, the data support a potential association, which is a concern. Particular gaps in EPA’s understa nding include the reason for incons istent results across subpopulations in the different studies, especially for males versus females and smoke rs versus nonsmokers.

### Final Economic Analysis for the Stage 2 DBPR 6-11 December 2005 

### For both the Stage 1 DBPR EA and the Stage 2 proposal EA, EPA used five epidemiological studies conducted in the 1980s and 1990s to calcula te a range of PAR values for bladder cancer associated with exposure to chlorinated drinking wate r. The five epidemiological studies used by EPA are as follows (note that Cantor et al. 1985 and Cantor et al. 1987 use the same epidemiological data):

• Cantor et al. (1985; 1987) • McGeehin et al. (1993) • King and Marrett (1996) • Freedman et al. (1997) • Cantor et al. (1998)

Exhibit 6.3 provides relevant summary informa tion for each of these studies and the PAR values calculated from them by EPA. Appendix E provides additional information on the derivation and use of PAR values in general, as well as additional deta ils on the PAR values derived from these studies by EPA.

All of these studies include adjustments in th eir analyses to account for possible confounding by other factors that may contribute to bladder cancer, notably sex, age, and smoking. Cantor et al. and McGeehin et al. also included adjustments for occupational exposure.

As shown in Exhibit 6.3, the estimated PAR pe rcentages from the studies range from 2 percent to 17 percent. Those values are “best estimates” derive d from the study data as described in Appendix E. EPA has also estimated 95 percent confidence inte rvals for those PAR values using a Monte Carlo simulation procedure, which is also described in Appendix E. In most cases, the lower confidence bound has been truncated at 0 percent based on biological pl ausibility considerations. That is, notwithstanding statistical indications of PAR values < 0 percent imp lied by odds ratios < 1.0, there is no toxicological or epidemiological data to support a conclusion that increased DBP exposure would reduce bladder cancer.

### Final Economic Analysis for the Stage 2 DBPR 6-12 December 2005 

## Exhibit 6.3 Summary of Epidemiology Studies for Bladder Cancer Associated with Chlorinated Drinking Water and EPA Calculated PAR Values

Study Description Summary of Results Comments PAR (95% CI) 1



### Cantor et al. (1985) Case-control study of association between bladder cancer and consumption of chlorinated surface water - Odds ratio for all whites with over 59 years of exposure is 1.1 (95% Confidence Interval: 0.8- 1.5) - Odds ratio for nonsmokers is 2.3 (95% Confidence Interval: 1.3-4.2) - Odds ratio for current smokers is 0.6 (95% Confidence Interval: 0.3-1.2) Majority of water systems contained less than 20 μ g/L THMs. 2% (0% - 15%)

Cantor et al. (1987) 2 

### Case-control study of association between bladder cancer and consumption of chlorinated surface water - Odds ratio for both sexes with over 59 years of exposure to tap water is 1.4 (95% Confidence Interval: 0.9-2.3) - Odds ratio for nonsmokers with over 59 years of exposure to tap water is 3.1 (95% Confidence Interval: 1.3-7.3) Results were statistically significant for non-smokers only 15% (0% - 31%)

McGeehin et al. (1993) Case-control study of association between bladder cancer and consumption of chlorinated surface water - Odds ratio for bladder cancer with over 30 years of exposure is 1.8 (95% Confidence Interval: 1.1-2.9) - Odds ratio for cases consuming over 5 glasses of tap water per day is 2.0 (95% Confidence Interval: 1.1-2.8) Level of total THMs, residual chlorine, or nitrates not associated with bladder cancer risk controlling for years of exposure. 17% (0% - 33%)

King and Marrett (1996) Case-control study of association between bladder cancer and consumption of chlorinated surface water - Odds ratio for bladder cancer for 35 years of exposure compared to 10 years is 1.42 (95% Confidence Interval: 1.10-1.81) - Bladder cancer risk increased with years of exposure - Risk increases by 11 percent with each 1,000 μg/L THM-year 3 

### Statistically significant only for lengthy exposures. Results provide no support for an interaction between volume of water consumed and years of exposure to THMs level > 49 μg/L. 17% (1% - 28%)

Freedman et al. (1997) Nested case-control study of association between bladder cancer and consumption of chlorinated drinking water - Odds ratio for bladder cancer using 1975 measure of exposure is 1.2 (95% Confidence Interval: 0.9-1.6) - Slight gradient of increasing risk with increasing duration noted only among smokers Further stratification by gender showed elevated odds ratios to be restricted to male smokers. 3% (0% - 22%)

Cantor et al. (1998) Case-control study of association between bladder cancer and consumption of chlorinated surface water - Little overall association between bladder cancer risk and exposure to chlorination byproducts - Bladder cancer risk increased with exposure duration Opposite trends were found in males and females. Total lifetime and average lifetime TTHM levels show all risk increases are apparently restricted to male smokers. 3% (0% - 8%)

1 

### Confidence intervals truncated at zero to reflect biological pl ausibility. The actual lower c onfidence level is often negative.

2 

### The Cantor et al. 1987 study is based upon the same data set as the Cantor et al. 1985 study. OR and PAR values for Cantor 1987 reflect modifications to the inclusion crit eria and adjustments for confounders relative to the analysis performed in the 1985 study.

3 

### THM-years are the product of the continuous estimate of a given THM level (in μg/ L) and years at that level, analogous to pack-years of cigarette smoking.

### Final Economic Analysis for the Stage 2 DBPR 6-13 December 2005 

### Just prior to completion of the Stage 2 DBPR proposal, Villanueva et al. (2003) published a meta-analysis of epidemiological studies addressing bl adder cancer related to exposure to chlorinated drinking water. The Villanueva et al. (2003) meta-a nalysis included most of the studies that EPA had used for Stage 1 and the Stage 2 proposal (see Exhi bit 6.3). The specific studies considered by these authors are as follows:

• Cantor et al. (1998) • Koivusalo et al. (1998) • King and Marrett (1996) • McGeehin et al. (1993) • Vena et al. (1993) • Cantor et al. (1987) • Wilkins and Comstock (1981) • Doyle et al. (1997)

The first six of these are case-control studies, the latter two are cohort studies.

Villanueva et al. (2003) developed several sets of ORs reflecting different exposure conditions (mid-term, long-term, and ever-exposed), separatel y for men, women as well as for both sexes combined. In addition to estimating overall OR values for those several populations and exposure conditions, Villanueva et al. (2003) presented da ta showing an increase in OR with increased duration of exposure. The authors present a dose-response analysis quantifying that relationship.

For the purposes of supporting the final Stage 2 an alysis, EPA has chosen to use the estimated OR from Villanueva et al. (2003) for the "ever-expo sed, both sexes" category. Exhibit 6.4 provides a summary of the 6 studies that were used by Villanueva et al. (2003) for this exposure group; Exhibit 6.5 summarized the weights, the OR values for those indi vidual studies and the combined OR obtained by the authors.

### Final Economic Analysis for the Stage 2 DBPR 6-14 December 2005 

## Exhibit 6.4 Summary of Epidemiology Studies from Villanueva et al. (2003) for Bladder Cancer Associated with Chlorinated Drinking Water used in Developments of the PAR Analysis

Study Description Summary of Results Comments


### Cantor et al. (1998) Case-control study of association between bladder cancer and consumption of chlorinated surface water. - Little overall association between bladder cancer risk and exposure to chlorination byproducts - Bladder cancer risk increased with exposure duration Opposite trends were found in males and females. Total lifetime and average lifetime TTHM levels show all risk increases are apparently restricted to male smokers.

Koivusalo et al. (1998) Case-control study of association between estimated historical exposure to drinking water mutagenicity and kidney and bladder

cancers. - Non-significant excess risk of bladder cancer associated with mutagenic drinking water for men and

women - Statistically significant OR (2.59, 95% CI 1.13-5.94) for a 3,000 net revertants/L increase in average exposure for nonsmoking men with 

## # 30 years estimable exposure history - Authors claim their study (at publication) was first to report an exposure-response relationship between the quantitative level of drinking water chlorination by-products and kidney cancer. - Authors acknowledge that higher OR for those with 

## # 30 years in highest exposure category (in this study, 3,000 net revertants/L) could indicate that exposure period used was too short to appropriately study the relationship w/bladder cancer, and risk may be underestimated in this study. King and Marrett (1996) Case-control study of association between bladder cancer and consumption of chlorinated surface water. - Odds ratio for bladder cancer for 35 years of exposure compared to 10 years is 1.42 (95% Confidence Interval: 1.10-1.81) - Bladder cancer risk increased with years of exposure - Risk increases by 11 percent with each 1,000 μg/L THM-year Statistically significant only for lengthy exposures. Results provide no support for an interaction between volume of water consumed and years of exposure to THMs levels > 49 μg/L.

McGeehin et al. (1993) Case-control study of association between bladder cancer and consumption of chlorinated surface water. - Odds ratio for bladder cancer with over 30 years of exposure is 1.8 (95% Confidence Interval: 1.1-2.9) - Odds ratio for cases consuming over 5 glasses of tap water per day is 2.0 (95% Confidence Interval: 1.1-2.8) Level of total THMs, residual chlorine, or nitrates are not associated with bladder cancer risk, controlling for years of exposure.

Cantor et al. (1987) 1 

### Case-control study of association between bladder cancer and consumption of chlorinated surface water. - Odds ratio for both sexes with over 59 years of exposure to tap water is 1.4 (95% Confidence Interval: 0.9-2.3) - Odds ratio for nonsmokers with over 59 years of exposure to tap water is 3.1 (95% Confidence Interval: 1.3-7.3) Long-term bladder cancer risks are more prominent in nonsmokers.

Wilkins and Comstock (1981) 2 

### Cohort study of association between bladder cancer and chlorinated surface water. - Relative Risk of 1.8 for men (95% Confidence Interval: 0.8-4.75) - Relative Risk of 1.6 for women (95% Confidence Interval: 0.54-6.32) Results not statistically significant.

1 The Cantor et al. 1987 study is based upon the same data set as the Cantor et al. 1985 study. OR and PAR values for Cantor 1987 represent modifications to the inclus ion criteria and adjustments for confounders re lative to the analysis performed in th e 1985 study.

2 Freedman et al. (1997) is a subset of the Wilkins and Coms tock 1981 study and was used in Stage 1 and the Stage 2 proposal for calculation of bladder c ancer PAR range values.

### Final Economic Analysis for the Stage 2 DBPR 6-15 December 2005 

## Exhibit 6.5 Estimated OR for Ever-Exposed, Both Sexes Category from Villanueva et al. (2003) Meta-Analysis

Study Weight OR 95% CI
Cantor et al. (1998) 34.9 1.1 0.9 - 1.3
Koivusalo et al. (1998) 6.6 1.4 0.9 - 2.1
King and Marrett (1996) 19.2 1.4 1.1 - 1.8
McGeeehin et al. (1993) 8.5 1.3 0.9 - 1.9
Cantor et al. (1987) 28.7 1.2 1.0 - 1.5
Wilkins & Comstock (1981) 2.2 1.7 0.8 - 3.6

Combined 1.2 1.1 - 1.4



### Based on the combined OR value of 1.2 and the corresponding 95 percent CI values of 1.1 and 1.4, respectively, EPA calculated a PAR from the V illanueva et al. (2003) study of 15.8 percent (95 percent CI = 8.5 percent - 27.2 percent). S ee Appendix E for details of the PAR calculation.

Subsequent to the Stage 2 DBPR proposal, Villa nueva et al. (2004) published a pooled-analysis of studies addressing the potential association between DBPs and bladder cancer. This pooled analysis included the six studies summarized in Exhibit 6.6.



## Exhibit 6.6 Summary of Epidemiology Studies from Villanueva et al. (2004) for Bladder Cancer Associated with Chlorinated Drinking Water used in Developments of the PAR Analysis

Study Description Summary of Results Comments
Lynch et al. (1989) Case control study of association between years of exposure to chlorinated drinking water and bladder

cancer. - Adjusted OR for both sexes = 1.52 (95% CI 1.10- 2.10) for average exposure more than 1 

## : g/L THM compared with 

## # 1 

## : g/L THM Statistically significant for both sexes combined, and also increased for men and women separately.

Cordier et al. (1993) Hospital-based case control study of occupational risks of bladder cancer. TTHM data previously unpublished. - Adjusted OR for both sexes = 1.02 (95% CI 0.66- 1.57) for average exposure more than 1 

## : g/L THM compared with 

## # 1 

## : g/L THM TTHM data was previously unpublished.

Cantor et al. (1998) Case-control study of association between bladder cancer and consumption of chlorinated surface water. - Little overall association between bladder cancer risk and exposure to chlorination byproducts - Bladder cancer risk increased with exposure duration Opposite trends were found in males and females. Total lifetime and average lifetime TTHM levels show all risk increases are apparently restricted to male smokers.

### Study Description Summary of Results Comments

Final Economic Analysis for the Stage 2 DBPR 6-16 December 2005

Koivusalo et al. (1998) Case-control study of association between estimated historical exposure to drinking water mutagenicity and kidney and bladder cancers. - Non-significant excess risk of bladder cancer associated with mutagenic drinking water for men and women - Statistically significant OR (2.59, 95% CI 1.13-5.94) for a 3,000 net revertants/L increase in average exposure for nonsmoking men with 

## # 30 years estimable exposure history - Authors claim their study (at publication) was first to report an exposure-response relationship between the quantitative level of drinking water chlorination by-products and kidney

cancer. - Authors acknowledge that higher OR for those with 

## # 30 years in highest exposure category (in this study, 3,000 net revertants/L) could indicate that exposure period used was too short to appropriately study the relationship w/bladder cancer, and risk may be underestimated in this study. King and Marrett (1996) Case-control study of association between bladder cancer and consumption of chlorinated surface water. - Odds ratio for bladder cancer for 35 years of exposure compared to 10 years is 1.42 (95% Confidence Interval: 1.10- 1.81) - Bladder cancer risk increased with years of exposure - Risk increases by 11 percent with each 1,000 μg/L THM-year Statistically significant only for lengthy exposures. Results provide no support for an interaction between volume of water consumed and years of exposure to THMs levels > 49 μg/L.

Porru (2003) Hospital-based case control study of association between years of exposure to chlorinated drinking water and bladder cancer. TTHM data previously unpublished. - Adjusted OR for men = 4.74 (95% CI 0.76-29.6) for average exposure more than 1 

## : g/L THM compared with 

## # 1 

## : g/L THM TTHM data was previously unpublished.



### This pooled analysis (Villanueva et al. 2004) focused on TTHM exposure specifically and presented OR estimates (adjusted for age, smoking, o ccupation, coffee consumption and education) for men and women separately, as well as for both sexes combined for any exposure to TTHMs and as a function of average TTHM exposure and cumulative TTH M exposure. The authors also evaluated the relationship between OR for bladder cancer a nd duration of exposure to chlorinated water.

For support of the Stage 2 DBPR, EPA is usi ng information based on the relationship between OR and average TTHM exposure from the Villanueva et al. (2004) study for both sexes combined. These estimates as presented in Villanueva et al. (2004) are shown in Exhibit 6.7.

### Final Economic Analysis for the Stage 2 DBPR 6-17 December 2005 

## Exhibit 6.7 Summary of Estimated OR Values Associated with Average TTHM Exposures for Both Sexes from Villanueva et al. (2004)

Average TTHM (μg/L) OR 95% CI

0 1.0 NA

> 0 1.2 1.0 - 1.4

0 - 1 1.0 NA

> 1 1.2 1.1 - 1.3

0 - 1 1.0 NA

> 1 - 5 1.1 0.9 - 1.3

> 5 - 25 1.2 1.0 - 1.4

> 25 - 50 1.2 1.0 - 1.4

> 50 1.3 1.1 - 1.5



### EPA also obtained additional deta iled data on the relationship be tween average TTHM levels and OR from the authors of the Villanueva et al. (2004) study (Kogevinas and Villanueva 2005). These additional data are presented in Exhibit 6.8. Using a linear relationship fit to the data in Exhibit 6.8 provided by Kogevinas and Villanue va (2005), and an estimate of a national average Pre-Stage 1 TTHM concentration of 38.05 μg/L, EPA has estimated a pr e-Stage 1 PAR value of 17.1 percent (95 percent CI = 2.5 percent - 33.1 percent). See Appendix E for details of the PAR calculation.

Exhibit 6.9 provides a summary of the estimated annual bladder cancer cases attributable to DBPs reflecting pre-Stage 1 occurrence and exposure leve ls for the three approaches to estimating risk described above:

• Range of PAR values from five individual studies • Villanueva et al. (2003), and • Villanueva et al. (2004).

Taken together, the three approaches provide a reas onable estimate of the range of potential risks. These estimates, summarized in Exhibit 6.9, assume an annual total of 56,506 new cases of bladder cancer from all causes. These values are obtained by multiplying the 56,506 total cases by the appropriate Pre-Stage 1 PAR values presented.

### Final Economic Analysis for the Stage 2 DBPR 6-18 December 2005 

## Exhibit 6.8 Detailed Data on OR as a Function of Average TTHM Exposure Level Provided by Kogevinas and Villanueva (2005)

Average TTHM (μg/L) Odds Ratio Lower 95% CI Upper 95% CI

0 1.0 -- --

10 1.1 1.0 1.3

20 1.2 1.0 1.4

30 1.2 1.0 1.4

40 1.2 1.0 1.4

50 1.2 1.0 1.4

60 1.3 1.1 1.5

70 1.3 1.1 1.6

80 1.4 1.1 1.7

90 1.5 1.1 1.9

100 1.6 1.1 2.2

110 1.7 1.1 2.6

120 1.8 1.0 3.1

130 1.9 1.0 3.7



## Exhibit 6.9 Estimates of Pre-St age 1 Annual Bladder Cancer Cases Attributable to DBPs

Study Lower 95% CI Best Estimate of Annual Cases Attributable to DBPs Upper 95% CI
Five Studies Used in Stage 1 and Stage 2 Proposal 1 

### 0 1,130 - 9,606 18,647

Villanueva et al. (2003) 4,830 8,899 15,376
Villanueva et al. (2004) 1,412 9,670 18,716

1 

### For the estimates for the five studies used in Stage 1 and the Stage 2 proposal, the range shown for the "Best Estimate" reflects the 2 percent and 17 percent PAR values ; the lower 95 percent CI reflects 0 percent PAR and the upper 95 percent CI reflects 33 percent PAR (the highest of the upper 95 perc ent CI estimates for the individual studies).



### The details of the calculations of the Pre-Stage 1 attributable cases shown in Exhibit 6.9 are provided in Appendix E.

### Final Economic Analysis for the Stage 2 DBPR 6-19 December 2005 

### Other New Cancer Studies

In the Stage 1 DBPR, EPA concluded that the epidemiological evidence suggested a potential increased risk for bladder cancer due to DBP exposur e. Some key studies EPA considered for Stage 1 include Cantor et al. (1998), Doyle et al. ( 1997), Freedman et al. (1997), King and Marrett (1996), McGeehin et al. (1993), Cantor et al. (1987), and Cant or et al. (1985). Several studies published since the Stage 1 DBPR continue to support an association be tween increased risk of bladder cancer and exposure to chlorinated surface water (Chevrier et al. 2004, Koivusalo et al. 1998, Yang et al. 1998). One study found no effects on a biomarker of genotoxicity in urinary bladder cells from TTHM exposure (Ranmuthugala et al. 2003). Epidemiological revi ews and meta-analyses generally support the possibility of an association between chlorinated water or THMs and bladder cancer (Villanueva et al. 2004, Villanueva et al. 2003, Villanueva et al. 2001, Mills et al. 1998). The World Health Organization (WHO 2000) found data inconclusive or insufficient to de termine causality between chlorinated water and any health endpoint, although they concluded that the ev idence is better for bladder cancer than for other

cancers.
In the Stage 1 DBPR, EPA concluded that ear ly studies suggested a small possible increase in rectal and colon cancers from exposure to chlorinate d surface waters. The database of studies on colon and rectal cancers continues to support a possible asso ciation, but evidence remains mixed. For colon cancer, one newer study supports the evidence of an association (King et al. 2000a) while others showed inconsistent findings (Hildesheim et al. 1998, Yang et al. 1998). Rectal cancer studies are also mixed. Hildesheim et al. (1998) and Yang et al. (1998) support an association with rectal cancer while King et al. (2000a) did not. A review of colon and rectal cancer concluded evidence was inconclusive but that there was a stronger association for rectal cancer and chlo rination DBPs than for colon cancer (Mills et al. 1998). The WHO (2000) review reported that studies s howed weak to moderate associations with colon and rectal cancers and chlorinated surface water or TH Ms but that evidence is inadequate to evaluate these associations.

Recent studies on kidney, brain, and lung cancers and DBP exposure support a possible association (kidney: Yang et al. 1998, Koivusalo et al. 1998; brain: Cantor et al. 1999; lung: Yang et al. 1998). However, so few studies have examined th ese endpoints that definitive conclusions cannot be made. Studies on leukemia found little or no associa tion with DBPs (Infante-Rivard et al. 2002, Infante-Rivard et al. 2001). A recent study did not find an association between pancreatic cancer and DBPs (Do et al. 2005). A study researching multiple cancer endpoints found an association between THM exposure and all cancers when grouped together (V inceti et al. 2004). In the development of the Stage 2 DBPR, EPA has evaluated several key bla dder cancer studies. Summary information on these studies can be found in Exhibit 6.10.Final Economic Analysis for the Stage 2 DBPR 6-20 December 2005Exhibit 6.10 Summary of Bladder Cancer Epidemiology Studies and Review/Meta-analysis StudiesReviewed for Stage 2 DBPRAuthor(s) Study Type Exposure(s)StudiedOutcome(s)MeasuredFindingsDoet al.2005 Case-controlstudy inCanada, 1994-1997.Estimated chlorinatedDBPs, chloroform,BDCM concentrations.Pancreaticcancer.No association was found between pancreatic cancer andexposure to chlorinated DBPs, chloroform, or BDCM.Chevrieret al.2004Case-controlstudy inFrance, 1985-1987.Compared THM levels,duration of exposure,and 3 types of watertreatment (ozonation,chlorination,ozonation/chlorination).Bladdercancer.A statistically significant decreased risk of bladder cancer wasfound as duration of exposure to ozonated water increased. Thiswas evident with and without adjustment for other exposuremeasures. A small association was detected for increased bladdercancer risk and duration of exposure to chlorinated surface waterand with the estimated THM content of the water, achievingstatistical significance only whenadjusted for duration of ozonatedwater exposures. Effect modification by gender was noted in theadjusted analyses.Vincetiet al.2004Retrospectivecohort study inItaly, 1987-1999.Standardized mortalityratios from all causesvs. cancer forconsumers drinkingwater with high THMs.15 cancersincludingcolon,rectum, andbladder.Mortality ratio from all cancers showed a statistically significantsmall increase for males consuming drinking water with highTHMs. For females, an increased mortality ratio for all cancerswas seen but was not statistically significant. Stomach cancer inmen was the only individual cancer in which a statisticallysignificant excess in mortality was detected for consumption ofdrinking water with high THMs.Ranmuthugalaet al.2003Cohort study in3 Australiancommunities,1997.Estimated dose ofTTHM, chloroform,and bromoform fromroutinely-collectedTHM measurementsand fluid intake diary.Frequency ofmicronuclei inurinarybladderepithelialcells.Relative risk estimates for DNA damage to bladder cells for THMdose metrics were near 1.0. The study provides no evidence thatTHMs are associated with DNA damage to bladder epithelial cells,and dose-response patterns were not detected.Infante-Rivardet al.2002Populationbased casecontrol study inQuebec, 1980-1993.Estimated prenataland postnatalexposure to THMs andpolymorphisms in twogenes.Acutelymphoblasticleukemia.Data are suggestive, but imprecise, linking DNA variants with riskof acute lymphoblastic leukemia associated with drinking waterDBPs. The number of genotyped subjects forGSTT1andCYP2E1 genes was too small to be conclusive.Author(s) Study Type Exposure(s)StudiedOutcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-21 December 2005Infante-Rivardet al.2001Populationbased casecontrol study inQuebec, 1980-1993.Compared waterchlorination (never,sometimes, always)and exposure toTTHMs, metals, andnitrates.Acutelymphoblasticleukemia.No increased risk for lymphoblastic leukemia was observed forprenatal exposure at average levels of TTHMs, metals or nitrates.However, a non-statistically significant, small increased risk wasseen for postnatal cumulative exposure to TTHMs and chloroform(both at above the 95thexposure percentile of the distribution forcases and controls), for zinc, cadmium, and arsenic, but not othermetals or nitrates.Kinget al.2000aPopulationbased casecontrol study insouthernOntario, 1992-1994.Compared source ofdrinking water andchlorination status.Estimated TTHMlevels, duration ofexposure, and tapwater consumption.Colon andrectal cancer.Colon cancer risk was statistically associated with cumulative longterm exposure to THMs, chlorinated surface water, and tap waterconsumption metrics among males only. Exposure-responserelationships were evident for exposure measures combiningduration and THM levels. Associations between the exposuremeasures and rectal cancer were not observed for either gender.Cantoret al.1999Populationbased casecontrol study inIowa, 1984-1987.Compared level andduration of THMexposure (cumulativeand average), sourceof water, chlorination,and waterconsumption.Brain cancer. Among males, a statistically significant increased risk of braincancer was detected for duration of chlorinated versus nonchlorinated source water, especially among high-level consumersof tap water. An increased risk of brain cancer for high waterintake level was found in men. No associations were found forwomen for any of the exposure metrics examined.Cantoret al.1998Populationbased casecontrol study inIowa, 1986-1989.Compared level andduration of THMexposure (cumulativeand average), sourceof water, chlorination,and waterconsumption.Bladdercancer.A statistically significant positive association between risk ofbladder cancer and exposure to chlorinated groundwater orsurface water reported for men and for smokers, but noassociation found for male/female non-smokers, or for womenoverall. Limited evidence was found for an association betweentapwater consumption and bladder cancer risk. Suggestiveevidence existed for exposure-response effects of chlorinatedwater and lifetime THM measures on bladder cancer risk.Hildesheimetal.1998Populationbased casecontrol study inIowa, 1986-1989.Compared level andduration of THMexposure (cumulativeand average), sourceof water, chlorination,and waterconsumption.Colon andrectal cancer.Increased risks of rectal cancer was associated with duration ofexposure to chlorinated surface water and any chlorinated water,with evidence of an exposure-response relationship. Risk of rectalcancer is statistically significant increased with >60 years lifetimeexposure to THMs in drinking water, and risk increased forindividuals with low dietary fiber intake. Risks were similar for menand women and no effects were observed for tapwater measures.No associations were detected for water exposure measures andrisk of colon cancer.Author(s) Study Type Exposure(s)StudiedOutcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-22 December 2005Koivusaloetal.1998Populationbased casecontrol study inFinland, 1991-1992.Estimated residentialduration of exposureand level of drinkingwater mutagenicity.Bladder andkidneycancer.Drinking water mutagenicity was associated with a small,statistically significant, exposure-related excess risk for kidney andbladder cancers among men; weaker associations were detectedfor mutagenic water and bladder or kidney cancer among women.The effect of mutagenicity on bladder cancer was modified bysmoking status, with an increased risk among non-smokers.Yanget al.1998Cross-sectionalstudy inTaiwan, 1982-1991.Examined residence inchlorinated (mainlysurface water sources)relative to nonchlorinated (mainlyprivate well) water.Cancer ofrectum, lung,bladder,kidney, colon,and 11others.Residence in chlorinating municipalities (vs. non-chlorinating) wasstatistically significantly associated with the following types ofcancer in both males and females: rectal, lung, bladder, and kidneycancer. Liver cancer and all cancers were also statisticallysignificantly elevated in chlorinated towns for males only. Mortalityrates for cancers of the esophagus, stomach, colon, pancreas,prostate, brain, breast, cervix uteri and uterus, and ovary werecomparable for chlorinated and non-chlorinated residence.Doyleet al.1997Prospectivecohort study inIowa, 1987-1993.Examined chloroformlevels and source ofdrinking water.Colon,rectum,bladder, and8 othercancers inwomen.Statistically significant increased risk of colon cancer, breastcancer and all cancers combined was observed for womenexposed to chloroform in drinking water, with evidence ofexposure-response effects. No associations were detectedbetween chloroform and bladder, rectum, kidney, upper digestiveorgans, lung, ovary, endometrium, or breast cancers, or formelanomas or non-Hodgkin’s lymphoma. Surface water exposure(compared to ground water users) was also a significant predictorof colon and breast cancer risk.Freedmanetal.1997Populationbased casecontrol study inMaryland,1975-1992.Estimated duration ofexposure tochlorinated water.Compared exposure tochlorinated municipalwater (yes/no).Bladdercancer.There was a weak association between bladder cancer risk andduration of exposure to municipal water for male cigarettesmokers, as well as an exposure-response relationship. Noassociation was seen for those with no history of smoking,suggesting that smoking may modify a possible effect ofchlorinated surface water on the risk of bladder cancer.King andMarrett 1996Case-controlstudy inOntario,Canada, 1992-1994.Compared source ofdrinking water andchlorination status.Estimated TTHMlevels, duration ofexposure, and tapwater consumption.Bladdercancer.Statistically significant associations were detected for bladdercancer and chlorinated surface water, duration or concentration ofTHM levels and tap water consumption metrics. Populationattributable risks were estimated at 14 to 16 percent. An exposureresponse relationship was observed for estimated duration of highTHM exposures and risk of bladder cancer.Author(s) Study Type Exposure(s)StudiedOutcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-23 December 2005Cordieret al.1993Hospital-basedcase-controlstudy inFrance, 1984-1987.Estimated duration ofexposure to TTHMs.Bladdercancer.No associations were not detected for bladder cancer andexposure to TTHMs.McGeehinetal.1993Populationbased casecontrol study inColorado,1990-1991.Compared source ofdrinking water, watertreatment, and tapwater versus bottledwater. Estimatedduration of exposure toTTHMs and levels ofTTHMs, nitrates, andresidual chlorine.Bladdercancer.Statistically significant associations were detected for bladdercancer and duration of exposure to chlorinated surface water. Therisk was similar for males and females and among nonsmokersand smokers. The attributable risk was estimated at 14.9 percent.High tap water intake was associated with risk of bladder cancer ina exposure-response fashion. No associations were detectedbetween bladder cancer and levels of TTHMs, nitrates, andresidual chlorine.Venaet al.1993Case-controlstudy inwestern NY,1979-1985.Comparedconsumption of fluids,including chlorinatedtap water.Bladdercancer.Bladder cancer and consumption of total fluids and tap water aloneshowed a significant finding for both age categories (under 65years and over 65 years). A dose-response relationship wasobserved for consumption of tap water and total fluid intake. Risksassociated with tap water consumption were higher fornonsmokers.Lynchet al.1989Case-controlstudy in Iowa,1894-1979.Compared source ofdrinking water.Estimated chlorinatedwater consumptionand duration ofexposure.Bladdercancer.Bladder cancer was statistically associated with duration ofexposure to chlorinated drinking water sources for both sexes.Cantoret al.1987 (andCantoret al.1985)Populationbased casecontrol study in10 areas of theU.S., 1977-1978.Compared source ofdrinking water.Estimated totalbeverage and tapwater consumptionand duration ofexposure.Bladdercancer.Bladder cancer was statistically associated with duration ofexposure to chlorinated surface water for women and nonsmokersof both sexes. The largest risks were seen when both exposureduration and level of tap water ingestion were combined. Noassociation was seen for total beverage consumption.Wilkins andComstock1981Cohort study inWashingtonCounty, MD,1963-1975Compared chlorinatedsurface water.Bladdercancer.Incidence rates for bladder cancer among men were nearly twofoldhigher in the chlorinated surface water group than in the referentgroup (results not statistically significant).Author(s) Study Type Exposure(s)StudiedOutcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-24 December 2005Reviews/Meta-analysesVillanuevaetal.2004Review andmeta-analysisof 6 casecontrol studies.Individual-basedexposure estimates toTHMs and waterconsumption over a40-year period.Bladdercancer.The meta-analysis suggests that risk of bladder cancer in menincreases with long-term exposure to TTHMs. An exposureresponse pattern was observed among men exposed to TTHMs,with statistically significant risk seen at exposures higher than 50:g/L. No association between TTHMs and bladder cancer wasseen for women.Villanuevaetal.2003 (andGoebellet al.2004)Review andmeta-analysisof 6 casecontrol studiesand 2 cohortstudies.Compared source ofwater and estimatedduration of exposure tochlorinated drinkingwater.Bladdercancer.The meta-analysis findings showed a moderate excess risk ofbladder cancer attributable to long-term consumption of chlorinateddrinking water for both genders, particularly in men. Statisticallysignificance seen with men and combined both sexes. The riskwas higher when exposure exceeded 40 years.Villanuevaetal.2001Qualitativereview of 31cancer studies.Compared exposure toTTHM levels,mutagenic drinkingwater, waterconsumption, sourcewater, types ofdisinfection(chlorination andchloramination), andresidence times.Cancer ofbladder,colon,rectum, and 5othercancers.Review found that although results for cancer studies varied andwere not always statistically significant, evidence for bladdercancer is strongest, and all 10 of the bladder cancer studiesshowed increased cancer risks with ingestion of chlorinated water.The authors felt associations with chlorinated water and cancer ofthe colon, rectum, pancreas, esophagus, brain, and other cancerswere inconsistent.WHO 2000 Qualitativereviews ofvarious studiesin Finland,U.S., andCanada.Various exposures toTHMs.Variouscancers.Studies reviewed reported weak to moderate increasedrelative risks of bladder, colon, rectal, pancreatic, breast, brain orlung cancer associated with long-term exposure to chlorinateddrinking water. The authors felt evidence is inconclusive for anassociation between colon cancer and long-term exposure toTHMs, that evidence is insufficient to evaluate a causalrelationship between THMs and rectal, bladder, and other cancers.They found no association between THMs and increased risk ofcardiovascular disease.Author(s) Study Type Exposure(s)StudiedOutcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-25 December 2005Millset al.1998Qualitativereview of 22studies.Examined TTHMlevels and waterconsumption.Compared source ofwater and 2 types ofwater treatment(chlorination andchloramination).Cancer ofcolon,rectum, andbladder.Review suggests possible increases in risks of bladder cancer withexposure to chlorinated drinking water. The authors felt evidencefor increased risk of colon and rectal cancers is inconclusive,though evidence is stronger for rectal cancer.

### Final Economic Analysis for the Stage 2 DBPR 6-26 December 2005 

### 6.2.1.2 Toxicological Evidence of DBP Carcinogenicity

Toxicological studies provide important informa tion on the potential carcinogenicity of DBPs in humans. EPA’s Integrated Risk Information System (IRIS), which is accessible at http://www.epa.gov/iris, provides detailed descripti ons of cancer risk assessments that EPA has performed for seven DBPs. Included on IRIS ar e weight-of-evidence characterizations of the carcinogenic potential of those seven DBPs and lifetim e unit cancer risk factors for five of the seven, based primarily on animal toxicological data. As with all risk evaluations based on animal toxicological studies, several extrapolations were required to est ablish lifetime unit cancer risks for humans (e.g., from high to low doses, from nonhuman species to humans, and for DBPs, from gavage to ingestion of water). Exhibit 6.11 provides a summary of the cancer risk assessments for those seven DBPs as presented on the IRIS database.

Analyses done for the Stage 2 DBPR follow th e 1999 EPA Proposed Guidelines for Carcinogenic Risk Assessment (USEPA 1999b). In March 2005, EPA upda ted and finalized the Cancer Guidelines and a Supplementary Children’s Guidan ce, which include new considerations on mode of action for cancer risk determination and additional potential risk s due to early childhood exposure (USEPA 2005f, USEPA 2005g). Conducting the cancer evaluation using the 2005 Cancer Guidelines would not result in any change from the existing analysis. With the ex ception of chloroform, no mode of action has been established for other specific regulated DBPs. Although some of the DBPs have given mixed mutagenicity and genotoxicity results, having a positive mutagenicity study does not necessarily mean that a chemical has a mutagenic mode of action. The extra factor of safety for children’s health protection does not apply, because the new Supplementary Ch ildren’s Guidance requires application of the children’s factor only when a mutagenic mode of action has been identified.

The lifetime unit risk factors shown in Exhibit 6.11 for bromoform, bromodichloromethane, and dibromochloromethane were included in the cancer risk assessment and benefit analyses performed by EPA in support of the Stage 1 DBPR promulgated in 1998. Since the Stage 1 DBPR, EPA has updated the quantitative risk assessments for these three DBPs in order to represent the methodology proposed in the 1996/1999 draft cancer guidelines (USEPA 1996c and 1999b), resulting in revisions to the unit risk factors. Also, a new study of dichloroacetic acid (DCAA) tumorigenicity in mice by DeAngelo et al. (1999) examined doses lower than those used in previously published studies and has been judged by EPA to be suitable for quantification of risk, also using the newer methodology.

Except for DCAA, these updated cancer risk assessments do not yet appear on the IRIS database. A toxicological review for DCAA exists on IRIS. EPA is completing a new brominated THM Criteria Document for bromoform, bromodichloromethane, a nd dibromochloromethane that supports the Stage 2 Rule, and this document provides details on the animal toxicological data used to derive the new cancer unit risk factors for these DBPs.

The updated cancer risk factors for these four DBPs are presented in Exhibit 6.12. They have been used to estimate the pre-St age 2 baseline cancer cases, the pre- Stage 1 concentrations of these compounds, the changes in those concentrations follo wing Stage 1, and the estimated number of people exposed. As described in the Crite ria Documents, cancer risk values were developed by fitting the key animal toxicological data to linearized multista ge models using the Maximum Likelihood Estimation (MLE) method. MLE method is a standard statistical procedure used to estimate model parameter values (in this case, parameters for the linearized multistage model) that have the highest likelihood from among all possible parameters of generating the observed da ta. The risk factors shown in Exhibit 6.12 are then computed from the linearized multistage dose-res ponse model based on the MLE parameters obtained using the study data.

### Final Economic Analysis for the Stage 2 DBPR 6-27 December 2005 

### The first risk factor is based on the estimated dose that the model predicts will result in a carcinogenic response in 10 percent of the subjects (referred to as the Effective Dose for 10 percent response, or ED 10 

### ). (Note: This unit risk factor is also sometimes referred to as the MLE estimate since it represents the dose taken directly from the curve fit by the MLE method.)

The second risk factor, which represents a more conservative estimate of the risk (and which corresponds more directly to the lifetime unit risk s shown in Exhibit 6.11 from the IRIS database), is based on the lower 95 percent confidence bound on the dose that the model predicts will result in a carcinogenic response in 10 percent of those exposed to the chemical, relative to control (referred to as the Lower Bound on the Effective Dose for 10 percent response, or LED 10 

### ).



## Exhibit 6.11 Summary of EPA’s Cancer Risk Assessments as currently presented on IRIS for Specific DBPs

Chemical EPA’s Human Carcinogen Assessment Lifetime Unit Cancer Risk Factor Date and Source

Bromoform Probable 1 

### 2.3 X 10 -7 

### ( 

## : g/L) -1 

### 1993 (IRIS)

Bromodichloromethane Probable 1 

### 1.8 X 10 -6 

### ( 

## : g/L) -1 

### 1993 (IRIS)

Chloroform Probable 1 

### Likely human carcinogen under high-exposure conditions that lead to cytotoxicity and regenerative hyperplasia in susceptible tissues. Not likely without cytotoxicity and cell regeneration. 2 

### Not Available 2001 (IRIS)

Dibromochloromethane Possible 1 

### 2.4 X 10 -6 

### ( 

## : g/L) -1 

### 1992 (IRIS)
Dichloroacetic Acid Likely 2 

### 1.4 X 10 -6 

### ( 

## : g/L) -1 

### 2003 (IRIS)

Trichloroacetic Acid Possible 1 

### Not Available 1996 (IRIS)

Bromate Probable 1 

### Likely to be carcinogenic via oral route of exposure 2 

### 2 X 10 -5 

### ( 

## : g/L) -1 

### 2001 (IRIS)

1 

### EPA’s Human Carcinogen Assessment reported, as cla ssified under EPA 1986 Cancer Risk Assessment Guidelines (USEPA 1986).
2 

### EPA’s Human Carcinogen Assessment reported, as classified under EPA 1996 and 1999 Proposed Cancer Risk Assessment Guidelines (USEPA 1996c and 1999b).



### In both cases, EPA derives unit risk values assuming low-dose linearity and no threshold to estimate risk. As shown in Exhibit 6.12, the baseline number of annual Pre-Stage 2 cancer cases calculated from the risk factors for these four DBPs are 39 cases for the ED 10 

### risk factors and 91 cases for the LED 10 

### risk factors. Assuming that DBP risk reduc tions for Stage 2 for the entire population average 7.76 percent, corresponding to the reduction in average TTHM levels (see Exhibit 6.19), Stage 2 cancer cases avoided based on the toxicological data range from 1.7 to 4.0 cases per year.

### Final Economic Analysis for the Stage 2 DBPR 6-28 December 2005 

### Several limitations must be considered in conj unction with the interpretation and use of these cancer risk estimates. There are only seven DBPs (those shown in Exhibit 6.11) for which EPA has determined that adequate toxicology studies are av ailable to support an assessment of their potential for carcinogenicity in humans. As discussed elsewhere in this document, a large number of DBPs are present in drinking water that has been disinfected, including many substances that have not yet been specifically identified. It must also be recognized that these highly controlled toxicology studies involve exposure to each respective DBP separately, while actual exposure to humans is to a mixture that includes many other DBPs in a wide array of relative proportions. Las tly, these toxicology studies limit exposure to the oral route only, whereas humans are generally exposed to DBPs in drinking water not only by the oral route but by dermal exposure and inhalation as well.Final Economic Analysis for the Stage 2 DBPR 6-29 December 2005Lifetime unitrisk (cases/person)/(mg/kg-day)Lifetime unit riskconc. (cases/person)/ (ug/L)Annual unit riskconc. (cases/person)/ (ug/L)BaselineCasesLifetime unitrisk (cases/person)/(mg/kg-day)Lifetime unit riskconc. (cases/person)/ (ug/L)Annual unit riskconc. (cases/person)/ (ug/L)BaselineCasesABCD=C*(1/1000)*(2L/day)*(1/70 kD) E=D/70 years F=A*B*E GH=G*(1/1000)*(2L/day)*(1/70 kD) I=H/70 years J=A*B*IBDCMSW 8.20 169,358,139 2.2E-02 6.29E-07 8.98E-09 12.5 3.4E-02 9.71E-07 1.39E-08 19.3GW 3.05 93,666,379 2.2E-02 6.29E-07 8.98E-09 2.6 3.4E-02 9.71E-07 1.39E-08 4.0Total 263,024,51815.0 23.2BromoformSW 2.69 169,358,139 3.4E-03 9.71E-08 1.39E-09 0.6 4.5E-03 1.29E-07 1.84E-09 0.8GW 2.22 93,666,379 3.4E-03 9.71E-08 1.39E-09 0.3 4.5E-03 1.29E-07 1.84E-09 0.4Total 263,024,5180.9 1.2DBCMSW 5.50 169,358,139 1.7E-02 4.86E-07 6.94E-09 6.5 4.0E-02 1.14E-06 1.63E-08 15.2GW 3.13 93,666,379 1.7E-02 4.86E-07 6.94E-09 2.0 4.0E-02 1.14E-06 1.63E-08 4.8Total 263,024,5188.5 20.0DCAASW 11.98 169,358,139 1.5E-02 4.29E-07 6.12E-09 12.4 4.8E-02 1.36E-06 1.94E-08 39.4GW 4.28 93,666,379 1.5E-02 4.29E-07 6.12E-09 2.5 4.8E-02 1.36E-06 1.94E-08 7.8Total 263,024,51814.9 47.2Grand Total39.3 91.7Derivation of cases using ED10Derivation of cases using LED10Source WaterType PopulationPre-Stage 2Conc (ug/L),Mean of PlantMeans,DS AverageExhibit 6.12 Quantification of Cancer Risk forBDCM, Bromoform, DBCM, and DCAA, Pre-Stage 2 BaselineNote: Unit risk factors are different fromExhibit 6.5 - see text for discussion.Sources: A) SW: SWAT DBP Summary Statistics,Run 300 (Pre-Stage 2); GW: Stage 2 Benefits ModelB) Stage 2 Population Baseline: Exhibit 3.3C) The unit risk factor is based on the ED10(effective dose for 10 percent response) basedon the Maximum Likelihood Estimation (MLE) method(provided by Nancy Chiu of EPA's Health and Ecological Criteria Division inemail (5/22/03)).D) This calculation assumes a 70 kg person with the average drinking water consumption rate of 2L/day. kD = kilogram-Day.G) The unit risk factor is based on the LED10(lower 95 percent confidencebound on effective dose for 10 percent response) based on the MLEmethod (provided by Nancy Chiu in email (5/22/03)).

### Final Economic Analysis for the Stage 2 DBPR 6-30 December 2005 

### More research on DBPs is underway at EPA a nd other research institutions. Summaries of ongoing studies may be found on EPA’s DRINK website (http://www.epa.gov/safewater/drink/intro.html). Two-year bioassays by the National Toxicology Program (NTP) released in abstract form have recently been completed on BDCM and chlorate. The draft abstract on BDCM reported no evidence of carcinogenicity when BDCM was administered via dri nking water (NTP 2005a). The results of this draft report do not affect the Stage 2 benefits analysis, because the quantified benefits are based on data from epidemiological studies as presented in Section 6.4 and Appendix E. Another recent study, a modified two-year bioassay on BDCM in the drinking water, reported little evidence of carcinogenicity (George et al. 2002). In a previous NTP study, tumors were observed, including an increased incidence of kidney, liver, and colon tumors, when BDCM was administer ed at higher doses by gavage in corn oil (NTP 1987). EPA will examine new information on BDCM as it becomes available. In the chlorate draft abstract, NTP found some evidence that it may be a carcinogen (NTP 2004). Chlorate is a byproduct of hypochlorite and chlorine dioxide systems. A l ong-term, two-year bioassay NTP study on DBA is also complete but has not yet undergone peer review (NTP 2005b).

Another significant advancement beyond the Stage 1 DBPR was the evaluation of the chloroform tumorigenicity data on the basis of its nonlin ear mode of action following the draft 1999 proposed Guidelines for Carcinogen Risk Assessment (USEPA 1999b). The new chloroform assessment became available on IRIS in October 2001.

An International Life Sciences Institute (ILS I) Expert Panel recommended that DBP risk cannot be assessed by single-chemical testing approaches alone (ILSI and RSI 1998). The report suggested the use of modern approaches (e.g., studies relating chemical structure to toxicity, application of molecular biology techniques, studies of mechanism of action), th e use of a 3-tiered testing approach (i.e., in-vitro tests; short-term screening tests or 90-day animal studies; long-term chronic bioassays). It also recommended a focus on three scenarios: (1) defined (s imple) mixtures of less than 10 DBPs; (2) whole mixtures produced by simulating disinfection scenarios; and (3) real drinking water samples or their
extracts.

Other byproducts with carcinogenic potential

Along with the reduction in DBPs from chlorination such as TTHM and HAA5 as a result of the Stage 2 DBPR, there may be increases in other DBPs as systems switch from chlorine to alternative disinfectants. For all disinfectants, many DBPs ar e not regulated and many others have not yet been identified. EPA will continue to review new studies on DBPs and their occurrence levels to determine if they pose possible health risks. EPA continues to support regulation of TTHM and HAA5 as indicators for chlorination DBP occurrence and believes that operational and treatment technology changes made because of the Stage 2 DBPR will r esult in an overall decrease in risk.

Emerging DBPs

Iodo-DBPs and nitrogenous DBPs including halonitromethanes are DBPs that have recently been reported (Richardson et al. 2002, Richardson 2003). One recent occurrence study sampled quarterly at twelve plants using different disinfectants across the U.S. for several iodo-THMs and halonitromethane species (Weinberg et al. 2002). The concentrations of iodo-THMs and halonitromethane in the majority of samples in this study were less than the an alytical minimum reporting levels; plant-average concentrations of iodo-THM and ha lonitromethane species were typically less than 0.002 mg/L, which is an order of magnitude lower than the corresponding average concentrations of TTHM and HAA5 at those same plants. Chloropicrin, a halonitromethane sp ecies, was also measured in the ICR with a median concentration of 0.0002 mg/L across all surface wate r samples. No occurrence data exist for the iodoacids due to the lack of a quantitative method and standards. Further work on chemical formation of iodo-DBPs and halonitromethanes is needed.

### Final Economic Analysis for the Stage 2 DBPR 6-31 December 2005 

### Iodoacetic acid was found to be cytotoxic a nd genotoxic in Salmonella and mammalian cells (Plewa et al. 2004a) as were some of the halonitromethanes (Kundu et al. 2004; Plewa et al. 2004b). Although potent in these in vitro screening studies, furt her research is needed to determine if these DBPs are active in living systems. No conclusions on human health risk can be drawn from such preliminary studies.

N-nitrosamines

Another group of nitrogenous DBPs are the N-nitrosamines. A number of N-nitrosamines exist, and N-nitrosodimethylamine (NDMA), a probable human carcinogen (IRIS 1993), has been identified as a potential health risk in drinking water. NDMA is a contaminant from industrial sources and a potential disinfection byproduct from reactions of chlorine or chloramine with nitrogen containing organic matter and from some polymers used as coagulant aids. Studies have produced new information on the mechanism of formation of NDMA, but there is not enough information at this time to draw conclusions regarding a potential increase in NDMA occurrence as systems change treatment. Although there are studies that examined the occurre nce of NDMA in some water systems, there are no systematic evaluations of the occurrence of NDMA and other nitrosamines in US waters. Recent studies have provided new occurrence information that shows NDM A forms in both chlorinated and chloraminated systems. Barrett et al. (2003) reported median concen trations of less than 2 ng/L for the seven chlorine systems studied and less than 3 ng/L for 13 chloramine systems. Another study demonstrated that factors other than disinfectant type may play an important role in the formation of NDMA (Schreiber and Mitch 2005). More research is underway to determine th e extent of NDMA occurrence in drinking water systems. EPA has proposed monitoring for NDMA under Unregulated Contaminant Monitoring Rule 2 (USEPA 2005q).

Risk assessments have estimated that the 10 -6 

### lifetime cancer risk level is 7 ng/L based on induction of tumors at multiple sites. NDMA is also present in food, tobacco smoke, and industrial emissions, and additional research is underway to de termine the relative exposure of NDMA in drinking water to these other sources.

Other DBPs

Some systems, depending on bromide and organi c precursor levels in the source water and treatment technology selection, may experience a shift to higher ratios, or concentrations, of brominated DBPs while the overall TTHM or HAA5 concentra tion may decrease. In some instances where alternative disinfectants are used, levels of chlo rite and bromate may increase as a result of systems switching to chlorine dioxide or ozone, respectively. However, EPA anticipates that changes in chlorite and bromate concentration as a result of the Stage 2 DBPR will be minimal (see Section 6.4). For most systems, overall levels of DBPs, as well as brominated DBP species, should decrease as a result of this rule. EPA continues to believe that precursor remova l is a highly effective strategy to reduce levels of DBPs.

Other toxicological effects

The Agency has modified the refe rence dose (RfD) values of the ch lorinated acetic acids since the Stage 1 DBPR. Under the Stage 1 DBPR there was no established RfD for monochloroacetic acid (MCAA). Data from a drinking water exposure study of MCAA in rats by DeAngelo et al. (1997) were used to establish an RfD of 0.01 mg/kg-day based on observed increases in spleen weights. Data from DeAngelo et al. (1997) were also used to calculate a new RfD of 0.03 mg/kg-day for trichloroacetic acid based on observed effects on body weight and liver effects.2 

### Site concordance has been found for colon cancer and for reproductive and toxicological endpoints. Final Economic Analysis for the Stage 2 DBPR 6-32 December 2005 

### WHO review of toxicology literature (2000)

The IPCS report on Disinfectants and Disinfection Byproducts (WHO 2000) emphasizes that the bulk of the toxicology data focus primarily on carcinogenesis. The Task Group found BDCM to be of particular interest because it produces tumors in both rats and mice at several sites. Although the HAAs appear to be without significant genotoxic activity, the brominated HAAs appear to induce oxidative damage to deoxyribose nucleic acid (DNA), leading to tumor formation.

6.2.1.3 Issues with Human and An imal Cancer Data Concordance

According to the Guidelines for Carcinogen Risk Assessment (USEPA 2005f), tumor site concordance between human and test animal is not necessary to determine carcinogenic potential; mechanistic considerations should only be applied when there is sufficient data to support a mode of action. The guidelines state that “Target organ conc ordance is not a prerequisite for evaluating the implications of animal study results for humans. ” Although concordance of effects between the test species and humans is highly desirable and lends creden ce and support in the analysis of mode of action, it is not necessary to cite animal and human tumor s ite concordance in order to justify a quantitative cancer risk assessment. In addition, there is insufficient data to support potential mode(s) of action for DBPs, with the exception of chloroform. Therefore, consideration of site concordance across species for cancer is not appropriate at this time. 2



### It is important to consider some key simila rities and differences across species when conducting such a cross-species concordance evaluation for bla dder cancer. The mechanisms controlling cell growth and differentiation are similar across species, but there are marked differences in the way these mechanisms are managed in various tissues within a give n species. In addition, it is important to consider the differences in exposure routes for the animal st udies (ingestion and gavage) and the epidemiological studies, which may include inhalation and dermal exposure.

Disinfection byproducts may be associated with bladder cancer in humans, but may appear as kidney or liver cancers in laboratory animal toxicology studies in rodents; laboratory animal toxicology studies of individual DBPs have reported cancer of the liver and kidney. Although this appears to indicate a lack of concordance in target organs, c oncordance on the general tissue type may be present in different organs. Kidney cancer in the renal pelv is, which has been reported for a few DBPs, may be linked with bladder cancer, as the renal pelvis is lin ed with the same transitional cell epithelium as found in the bladder (Cohen et al. 1988). Further, the pat hogenesis of transitional cell carcinomas in the urinary bladder appears to be similar throughout the renal pe lvis, ureter, and urinary bladder (Cohen et al. 1988). For example, the DBP MX has been reported to cause individual transitional cell hypertrophy with karyomegaly in the urinary bladder of rats (Komulai nen et al. 1997). A review of occupational cancer of the urinary tract reported that approximately 15 per cent of kidney neoplasms are in the renal pelvis and appear to be caused by the same carcinogens as bla dder neoplasms (Schulte et al. 1987). Finally, over 90 percent of human bladder cancers involve the transiti onal cell epithelium or urothelium (Silverman et al. 1996), suggesting that a component of the kidney and bl adder can be considered part of the same target tissue, known as the urinary tract.

DBPs may not be unique with respect to an appa rent lack of tissue-specific concordance across species. A majority of the non-DBP agents that clearly act through genotoxic mechanisms, including benzidine and benzidine-derived azo dyes (Cohe n and Johansson 1992), are known to cause urinary bladder carcinoma in humans and to cause cancer in rodents at various sites that do not always include the urinary bladder (Rice et al. 1999). Other examples of this include inorganic arsenicals, which are human

### Final Economic Analysis for the Stage 2 DBPR 6-33 December 2005 

### bladder carcinogens but are negative in anim als studies (NRC 1990, USEPA 1994a). Conversely, nitrosamines (i.e., N-methyl-N-nitrosourea) are bladder carcinogens in laboratory animals, but not in humans (Cohen and Johansson 1992). Similar to disinf ection byproducts, caffeine is a risk factor for bladder cancer in humans, but there is no evidence of increased risk reported in laboratory animals (Cohen and Johansson 1992). Although 40 percent of the NTP chemicals that cause bladder tumors are not mutagenic, EPA concludes that a majority of the agents that clearly act through genotoxic mechanisms are known to cause urinary bladder carcinoma in humans and rodents at various sites that do not always include the urinary bladder.

Another potential influence on the difference in observations in animals and humans is the difference in exposure route in animal studies versus human studies. The animal studies all use ingestion or gavage as the route of administration, whereas human drinking water exposure includes inhalation and dermal exposure. For example, tumor responses from chloroform exposure in the liver and kidney of rats varied by route of exposure, sex and strain (ILSI 1997).

EPA has completed a comprehensive review of the cancer data on disinfection byproducts; and while there is evidence from human cancer epidemiology studies that lifetime consumption of the DBP mixture within chlorinated surface water poses a bl adder cancer risk, the specific causative constituents have not been identified. Since there is no definitiv e support provided by studies to date for or against the generalization of adverse effects across different orga ns or between or within species, EPA concludes that target organ concordance for cancer is not a prerequi site for evaluating the implications of animal study results for humans at this time. EPA will reevaluate this issue as new data become available to support a mode-of-action.

6.2.1.4 Conclusions

EPA concludes that the epidemiological and toxicological studies support a weight-of-evidence conclusion that there may be an association between DBPs and cancer. The evidence is insufficient to establish a causal relationship. The following are the key factors used to support EPA’s weight-ofevidence conclusion:

• There is some evidence from animal studies for the carcinogenicity of individual DBPs included in this rule. Exhibit 6.11 summarizes the Agency findings on the carcinogenicity of seven DBPs. They have all been characterized on IRIS as either “possible” or “probable” carcinogens under EPA’s 1986 guidelines, and in some cases also as “likely” carcinogens under EPA’s 1996/1999 guidelines. One of these (chloroform) has been evaluated based on its mode of action, with the finding that it is likely to be carcinogenic only under high-exposur e conditions that lead to cytotoxicity and regenerative hyperplasia.

• Epidemiological data from individual i nvestigations and from a meta-analysis (Villanueva et al. 2003) links exposure to chlo rinated water with an increased risk for bladder cancer in some population subgroups.

• An analysis of pooled data by Villanueva et al. (2004) links exposure to TTHMs with an increased risk of bladder cancer.

• The epidemiological data cannot link sp ecific DBPs with cancer risk because of difficulties in characterizing the exposure. Exposure in some epidemiology studies was monitored purely in terms of chlorinated wate r, which contains a mixture of DBPs, some of which have not yet been identified, as well as a variety of other drinking water contaminants. In other studies the DBP exposure was monitored in terms of

### Final Economic Analysis for the Stage 2 DBPR 6-34 December 2005 

### trihalomethane concentrations (a variable mi xture of four individual DBPs). Thus, the Agency must rely on both the bioassays for individual chemicals as well as the epidemiology data in making a weight-of-evidence determination.

• Associations of chlorinated water to cancer of the colon, rectum, and kidney were found in some cases but the data are less robust than the data for bladder cancer.

EPA has a research program that continues to examine the relationship between exposure to DBPs and carcinogenicity. Additional data needs include information on modes of action, the reasons for inconsistencies in findings between men and wome n, inconsistencies across studies in the role of smoking, and carcinogenicity testing for selected brominated and chlorinated DBPs administered in drinking water. New studies are under way or planne d that would help provide this type of data.

6.2.2 Reproductive and Developmental Health Effects

Both human epidemiology studies and animal toxicology studies have examined associations between chlorinated drinking water or DBPs and re productive and developmental health effects. Based on the weight-of-evidence evaluation of the repr oductive and developmental epidemiology data EPA concludes that a causal link between adverse reproduc tive or developmental health effects and exposure to chlorinated drinking water or DBPs has not been established, but that there is a potential association. Despite inconsistent findings across studies, some r ecent studies continue to suggest associations between DBP exposure and various adverse re productive and developmental effect s. In addition, data from a number of toxicology studies, although the majo rity of them were conducted using high doses, demonstrate biological plausibility for some of the effects observed in epidemiology studies. EPA concludes that no dose-response relationship or cau sal link has been established between exposure to chlorinated drinking water or disinfection byproducts and adverse reproductive or developmental health effects. EPA’s evaluation of the best available studies, particularly epidemiology studies, is that they do not support a conclusion at this time as to whether e xposure to chlorinated drinking water or disinfection byproducts cause adverse reproductive a nd developmental health effects, but do provide an indication of a potential health hazard concern that warrants incr emental regulatory action beyond the Stage 1 DBPR.

The Centers for Disease Control (CDC) reports that, for the 10 year period between 1986 and 1996, spontaneous fetal losses were estimated to be between 0.8 million and 1.0 million per year. For births in the United States in the year 2002 (as reported by the CDC, 2005), 1.4 percent of births are considered very low birth weight (defined by CDC as below 1,500 g) and 7.8 percent are considered low birth weight (defined by the CDC as below 2,500 g). Birth defects are reported to occur in approximately 1 in 33 live births per year in the United States (CDC 2005). Although research has identified some risk factors for these adverse birth outcomes, including nutritional factors (e.g., lack of folic acid supplementation) and fetal exposure to tobacco smoke and alcohol, the causes of most such outcomes are unknown.

A variety of research is underway to examine the potential role that maternal exposure to specific contaminants might play in these adverse outcomes. The following sections provide a review of the literature addressing the potential relationship between DBPs and adverse reproductive and developmental outcomes.

### Final Economic Analysis for the Stage 2 DBPR 6-35 December 2005 

### 6.2.2.1 Epidemiological Evidence of Adverse Repr oductive and Developmental Health Effects

As discussed previously, epidemiology studies ha ve the strength of relating human exposure to DBP mixtures through multiple intake routes. A lthough the critical exposure window for reproductive and developmental effects is much smaller than that for cancer (generally weeks versus years), exposure assessment is also a main limitation of reproductive a nd developmental epidemiology studies. Exposure assessment uncertainties and possible exposure classification errors arise from limited data on DBP concentrations and maternal water usage and s ource over the course of the pregnancy. However, classification errors typically push the true risk es timate towards the null value (Vineis 2004). According to Bove et al. (2002), “Difficulties in assessing exposure may result in exposure misclassification biases that would most likely produce substantial underes timates of risk as well as distorted or attenuated exposure–response trends.” Studies of rare outcomes (e.g., individual birth defects) often have limited statistical power because of the small number of cases being examined. This limits the ability to detect statistically significant associations for small to mode rate relative risk estimates. Small sample sizes also result in imprecision around risk estimates reflected by wide confidence intervals. In addition to the limitations of individual studies, evaluating repr oductive and developmental epidemiology studies collectively is difficult because of the methodological differences between studies and the wide variety of endpoints examined. These factors may contribute to in consistencies in the scientific body of literature as noted below.

More recent studies tend to be of higher quality because of improved exposure assessments and other methodological advancements. For example, st udies that use THM levels to estimate exposure tend to be higher quality than studies that define exposur e by source or treatment. These factors were taken into account by EPA when comparing and making conclusions on the reproduc tive and developmental epidemiology literature. What follows is a summary of available epidemiology literature on reproductive and developmental endpoints such as s pontaneous abortion, stillbirth, neur al tube and other birth defects, low birth weight, and intrauterine growth retardati on. Information is grouped, where appropriate, into three categories on fetal growth, viability, and malformations, and reviews are described separately afterward.

Epidemiology reports and reviews

Fetal growth

Many studies looked for an association between fe tal growth (mainly small for gestational age, low birth weight, and pre-term delivery) and chlorina ted water or DBPs. The results from the collection of studies as a whole are inconsistent. A number of studies support the possibility that exposure to chlorinated water or DBPs are associated with adve rse fetal growth effects (Infante-Rivard 2004, Wright et al. 2004, Wright et al. 2003, Källén and Robert 2000, Gallagher et al. 1998, Kanitz et al. 1996, Bove et al. 1995, Kramer et al. 1992). Other studies showed mixed results (Porter et al. 2005, Savitz et al. 2005, Yang 2004) or did not provide evidence of an association (Toledano et al. 2005, Jaakkola et al. 2001, Dodds et al. 1999, Savitz et al. 1995) between DBP exposure and fetal growth. EPA notes that recent, higher quality studies provide some evidence of an in creased risk of small for gestational age and low birth weight.

Fetal viability

While the database of epidemiology studies fo r fetal loss endpoints (spontaneous abortion or stillbirth) remains inconsistent as a whole, there is suggestive evidence of an association between fetal loss and chlorinated water or DBP exposure. Va rious studies support the possibility that exposure to chlorinated water or DBPs is associated with d ecreased fetal viability (Toledano 2005, Dodds et al. 2004, King et al. 2000b, Dodds et al. 1999, Waller et al. 1998, Aschengrau et al. 1993, Aschengrau et al. 1989). Other studies did not support an association (Bove et al . 1995) or reported inconclusive results (Savitz et

### Final Economic Analysis for the Stage 2 DBPR 6-36 December 2005 

### al. 2005, Swan et al. 1998, Savitz 1995) between feta l viability and exposure to THMs or tapwater. A recent study by King et al. (2005) found little evidence of an association between stillbirths and haloacetic acids after controlling for trihalomethane expos ures, though non-statistically significant increases in stillbirths were seen across various exposure levels.

Fetal malformations

A number of epidemiology studies have examined the relationship between fetal malformations (such as neural tube, oral cleft, cardiac, or urinary defects, and chromosomal abnormalities) and chlorinated water or DBPs. It is difficult to assess fe tal malformations in aggregate due to inconsistent findings and disparate endpoints being examined in the available studies. Some studies support the possibility that exposure to chlorinated water or DB Ps is associated with various fetal malformations (Cedergren et al. 2002, Hwang et al. 2002, Dodds and King 2001, Klotz and Pyrch 1999, Bove et al. 1995, Aschengrau et al. 1993). Other studies found little evidence (Shaw et al. 2003, Källén and Robert 2000, Dodds et al. 1999, Shaw et al. 1991) or in conclusive results (Magnus et al. 1999) between chlorinated water or DBP exposure and fetal malformati ons. Birth defects most consistently identified as being associated with DBPs include neural t ube defects and urinary tract malformations.

Other endpoints have also been examined in r ecent epidemiology studies. One study suggests an association between DBPs and decreased menstrua l cycle length (Windham et al. 2003), which, if corroborated, could be linked to the biological b asis of other reproductive endpoints observed. No association between THM exposure and semen quality was found (Fenster et al. 2003). More work is needed in both areas to support these results.

Reviews

An early review supported an association between measures of fetal viability and tap water (Swan et al. 1992). Three other reviews found data inade quate to support an association between reproductive and developmental health effect s and THM exposure (Reif et al. 1996, Craun 1998, WHO 2000). Mills et al. (1998) examined data on and found support fo r an association between fetal viability and malformations and THMs. Another review presente d to the Stage 2 MDBP FACA found some evidence for an association with fetal viability and some feta l malformations and exposure to DBPs but reported that the evidence was inconsistent for these endpoints as well as for fetal growth (Reif et al. 2000). Reif et al. (2000) concluded that the weight of eviden ce from epidemiology studies suggests that “DBPs are likely to be reproductive toxicants in humans under appropriate exposure conditions,” but from a risk assessment perspective, data are primarily at the hazar d identification stage. Nieuwenhuijsen et al. (2000) found some evidence for an association between feta l growth and THM exposur e and concluded evidence for associations with other fetal endpoints is w eak but gaining weight. A qualitative review by Villanueva et al. (2001) found evidence generally supports a possible association between reproductive effects and drinking chlorinated water. Graves et al. (2001) supports a possible association for fetal growth but not fetal viability or malformations. More recently, Bove et al. (2002) examined and supported an association between small for gestational age, neural tube defects and spontaneous abortion endpoints and DBPs. Following a meta-analysis on five malformation studies, Hwang and Jaakkola (2003) concluded that there was evidence which suppor ted associations between DBPs and risk of birth defects, especially neural tube defects and urinary tract defects. More detail on some of these critical reviews is presented later in this section. E xhibit 6.13 provides summary information for key epidemiological reports and reviews to the Stage 2 DBPR.Final Economic Analysis for the Stage 2 DBPR 6-37 December 2005Exhibit 6.13 Summary of Reproductive/Developmental Epidemiology StudiesAuthor(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsPorteret al.2005Crosssectional studyin Maryland,1998-2002.Estimated trimesterspecific andpregnancy-averageexposures to THMsand HAAs, includingindividual DBPs.Intrauterinegrowthretardation.No consistent association or dose-response relationship wasfound between exposure to either TTHM or HAA5 andintrauterine growth retardation. Results suggest an increasedrisk of intrauterine growth retardation associated with TTHMand HAA5 exposure in the third trimester, although only HAA5results were statistically significant.Savitzet al.2005Populationbasedprospectivecohort study inthreecommunitiesaround theU.S., 2000-2004.Estimated TTHM,HAA9, and TOXexposures duringpregnancy. Individualbrominated THMs andHAA species wereexamined. Indicesexamined includedconcentration,ingested amount,exposure fromshowering andbathing, and anintegration of allexposures combined.Early and latepregnancy loss,preterm birth,small forgestational age,and term birthweight.No association with pregnancy loss was seen when high TTHMexposures were compared to low exposures. When examiningindividual THMs, a statistically significant association was foundbetween bromodichloromethane (BDCM) and pregnancy loss.Although non-statistically significant, an increased risk similar inmagnitude was seen between dibromochloromethane (DBCM)and pregnancy loss. Some increased risks were seen forlosses at greater than 12 weeks’ gestation for TTHM, BDCM,and TOX (total organic halide), but most results generally didnot provide support for an association. Preterm birth showed asmall inverse relationship with DBP exposure (i.e. higherexposures were less likely to have a preterm birth), but thisassociation was weak. TTHM exposure of 80 μg/L wassignificantly associated with twice the risk for small forgestational age during the third trimester.Toledanoet al.2005Large crosssectional studyin England,1992-1998.Linked mother’sresidence at time ofdelivery to modeledestimates of TTHMlevels in water zones.Stillbirth, lowbirth weight.A significant association between TTHM and risk of stillbirth,low birth weight, and very low birth weight was observed in oneof the three regions. When all three regions were combined,small, but non-significant, excess risks were found between allthree outcomes and TTHM and chloroform. No associationswere observed between reproductive risks and BDCM or totalbrominated THMs.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-38 December 2005Doddset al.2004 (and Kinget al.2005)Populationbased casecontrol studyin Nova Scotiaand EasternOntario, 1999-2001.Estimated THM andHAA exposure atresidence duringpregnancy. Linkedwater consumptionand showering/bathingto THM exposure.Stillbirth. A statistically significant association was observed betweenstillbirths and exposure to total THM, BDCM, and chloroform.Associations were also detected for metrics which incorporatedwater consumption, showering and bathing habits. Elevatedrelative risks were observed for intermediate exposures for totalHAA and DCAA measures; TCAA and brominated HAAexposures showed no association. No statistically significantassociations or dose-response relationships between any HAAsand stillbirth were detected after controlling for THM exposure.Infante-Rivard2004Case-controlstudy ofnewborns inMontreal,1998-2000.Estimated THM levelsand waterconsumption duringpregnancy. Exposurefrom showering andpresence of twogeneticpolymorphisms.Intrauterinegrowthretardation.No associations were found between exposure to THMs andintrauterine growth retardation. However, a significant effectwas observed between THM exposure and intrauterine growthretardation for newborns with theCYP2E1gene variant.Findings suggest that exposure to THMs at the highest levelscan affect fetal growth but only in genetically susceptiblenewborns.Wrightet al.2004Large crosssectionalstudy:Massachusetts, 1995-1998.Estimated maternalthird-trimesterexposures to TTHMs,chloroform, BDCM,total HAAs, DCA, TCA,MX and mutagenicityin drinking water.Birth weight,small forgestational age,pretermdelivery,gestational age.Statistically significant reductions in mean birth weight wereobserved for BDCM, chloroform, and mutagenic activity. Anexposure-response relationship was found between THMexposure and reductions in mean birth weight and risk of smallfor gestational age. There was no association between pretermdelivery and elevated levels of HAAs, MX, or mutagenicity. Areduced risk of preterm delivery was observed with high THMexposures. Gestational age was associated with exposure toTHMs and mutagenicity.Yang 2004 (andYanget al.2000)Large crosssectionalstudies inTaiwan, 1994-1996.Compared maternalconsumption ofchlorinated drinkingwater (yes/no).Low birthweight, pretermdelivery.Residence in area supplied with chlorinated drinking watershowed a statistically significant association with pretermdelivery. No association was seen between chlorinateddrinking water and low birth weight.Fensteret al.2003Smallprospectivestudy inCalifornia,1990-1991.Examined TTHMlevels within the 90days preceding semencollection.Sperm motility,spermmorphology.No association between TTHM level and sperm mobility ormorphology. BDCM was inversely associated with linearity ofsperm motion. There was some suggestion that waterconsumption and other ingestion metrics may be associatedwith different indicators of semen quality.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-39 December 2005Shawet al.2003 2 case-controlmaternalinterviewstudies: CA,1987-1991.Estimated THM levelsfor mothers'residences from beforeconception throughearly pregnancy.Neural tubedefects, oralclefts, selectedheart defects.No associations or exposure-response relation were observedbetween malformations and TTHMs in either study.Windhamet al.2003Prospectivestudy: CA,1990-1991.Estimated exposure toTHMs throughshowering andingestion over averageof 5.6 menstrual cyclesper woman.Menstrualcycle, follicularphase length(in days).Findings suggest that THM exposure may affect ovarianfunction. All brominated THM compounds were associated withsignificantly shorter menstrual cycles with the strongest findingfor chlorodibromomethane. There was little associationbetween TTHM exposure and luteal phase length, menseslength, or cycle variability.Wrightet al.2003Crosssectionalstudy:Massachusetts, 1990.Estimated TTHMexposure in womenduring pregnancy(average forpregnancy and duringeach trimester).Birth weight,small forgestational age,pretermdelivery,gestational age.Statistically significant associations between 2ndtrimester andpregnancy average TTHM exposure and small for gestationalage and fetal birth weight were detected. Small, statisticallysignificant increases in gestational duration/age were observedat increased TTHM levels, but there was little evidence of anassociation between TTHM and preterm delivery or low birthweight.Cedergrenet al.2002Retrospectivecase-controlstudy:Sweden,1982-1997.Examined maternalpericonceptional DBPlevels and used GIS toassign water supplies.Cardiacdefects.Exposure to chlorine dioxide in drinking water showed statisticalsignificance for cardiac defects. THM concentrations of 10:g/Land higher were significantly associated with cardiac defects.No excess risk for cardiac defect and nitrate were seen.Hwanget al.2002Large crosssectional studyin Norway,1993-1998.Compared exposure tochlorination (yes/no)and water color levelsfor mother's residenceduring pregnancy.Birth defects(neural tubedefects,cardiac,respiratorysystem, oralcleft, urinarytract).Risk of any birth defect, cardiac, respiratory system, and urinarytract defects were significantly associated with waterchlorination. Exposure to chlorinated drinking water wasstatistically significantly associated with risk of ventricular septaldefects, and an exposure-response pattern was seen. No otherspecific defects were associated with the exposures that wereexamined.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-40 December 2005Dodds and King2001Populationbasedretrospectivecohort in NovaScotia, 1988-1995.Estimated THM,chloroform, andbromodichloromethane(BDCM) exposure.Neural tubedefects,cardiovasculardefects, cleftdefects,chromosomalabnormalities.Exposure to BDCM was associated with increased risk ofneural tube defects, cardiovascular anomalies. Chloroform wasnot associated with neural tube defects, but was associatedwith chromosomal abnormalities. No association between THMand cleft defects were detected.Jaakkolaet al.2001Large crosssectional studyin Norway,1993-1995.Compared chlorination(yes/no) and watercolor (high/low) formother duringpregnancy.Low birthweight, smallfor gestationalage, pretermdelivery.No evidence found for association between prenatal exposureto chlorinated drinking water and low birth weight or small forgestational age. A reduced risk of preterm delivery was notedfor exposure to chlorinated water with high color content.Källén andRobert 2000Large crosssectionalcohort study inSweden,1985-1994.Linked prenatalexposure to drinkingwater disinfected withvarious methods (nochlorine, chlorinedioxide only, sodiumhypochlorite only).Gestationalduration, birthweight,intrauterinegrowth,mortality,congenitalmalformations,and other birthoutcomes.A statistically significant difference was found for shortgestational duration and low birth weight among infants whosemother resided in areas using sodium hypochlorite, but not forchlorine dioxide. Sodium hypochlorite was also associated withother indices of fetal development but not with congenitaldefects. No other effects were observed for intrauterine growth,childhood cancer, infant mortality, low Apgar score, neonataljaundice, or neonatal hypothyroidism in relation to eitherdisinfection method.Doddset al.1999 (and Kinget al.2000b)Populationbasedretrospectivecohort study inNova Scotia,1988-1995.Estimated TTHM levelfor women duringpregnancy.Low birthweight, pretermbirth, small forgestational age,stillbirth,chromosomalabnormalities,neural tubedefects, cleftdefects, majorcardiac defects.A statistically significant increased risk for stillbirths and hightotal THMs and specific THMs during pregnancy was detected,with higher risks observed among asphyxia-related stillbirths.Bromodichloromethane had the strongest association andexhibited an exposure-response pattern. There was limitedevidence of an association between THM level and otherreproductive outcomes. No congenital anomalies wereassociated with THM exposure, except for a non-statisticallysignificant association with chromosomal abnormalities.Klotz and Pyrch1999 (and Klotzand Pyrch 1998)Populationbased casecontrol studyin New Jersey,1993-1994.Estimated exposure ofpregnant mothers toTTHMs and HAAs,and compared sourceof water.Neural tubedefects.A significant association was seen between exposure to THMsand neural tube defects. No associations were observed forneural tube defects and haloacetic acids or haloacetonitriles.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-41 December 2005Magnuset al.1999Large crosssectional studyin Norway,1993-1995.Compared chlorination(yes/no) and watercolor (high/low) atmothers' residences attime of birth.Birth defects(neural tubedefects, majorcardiac,respiratory,urinary, oralcleft).Statistically significant associations were seen between urinarytract defects and chlorination and high water color (high contentof organic compounds). No associations were detected forother outcomes or all birth defects combined. A non-statisticallysignificant, overall excess risk of birth defects was seen withinmunicipalities with chlorination and high water color comparedto municipalities with no chlorination and low color.Gallagheret al.1998Retrospectivecohort study ofnewborns inColorado,1990-1993.Estimated THM levelsin drinking waterduring third trimesterof pregnancy.Low birthweight, termlow birthweight,and pretermdelivery.Weak, non-statistically significant association with low birthweight and TTHM exposure during the third trimester. Largestatistically significant increase for term low birthweight athighest THM exposure levels. No association between pretermdelivery and THM exposure.Swanet al.1998 Prospectivestudy inalifornia, 1990-1991.Comparedconsumption of coldtap water to bottledwater during earlypregnancy.Spontaneousabortion.Pregnant women who drank cold tap water compared to thosewho consumed no cold tap water showed a significant findingfor spontaneous abortion at one of three sites.Walleret al.1998 (andWalleret al.2001)Prospectivecohort inCalifornia,1989-1991.Estimated TTHMlevels during firsttrimester of pregnancyvia ingestion andshowering.Spontaneousabortion.Statistically significant increased risk between high intake ofTTHMs and spontaneous abortion compared to low intake.BDCM statistically associated with increased spontaneousabortion; other THMs not. Reanalysis of exposure yielded lessexposure misclassification and relative risks similar inmagnitude to earlier study. An exposure-response relationshipwas seen between spontaneous abortion and ingestionexposure to TTHMs.Kanitzet al.1996Crosssectional studyin Italy, 1988-1989.Compared 3 types ofwater treatment(chlorine dioxide,sodium hypochlorite,and chlorinedioxide/sodiumhypochlorite).Low birthweight, bodylength, cranialcircumference,pretermdelivery, andother effects.Smaller body length and small cranial circumference showedstatistical significant association with maternal exposure tochlorinated drinking water. Neonatal jaundice linked statisticallyto prenatal exposure to drinking water treated with chlorinedioxide. Length of pregnancy, type of delivery, and birthweightshowed no association.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-42 December 2005Boveet al.1995(and Boveet al.1992a & 1992b)Large cohortcrosssectional studyin New Jersey,1985-1988.Examined maternalexposure to TTHM andvarious othercontaminants.Low birthweight, fetaldeaths, smallfor gestationalage, birthdefects (neuraltube defects,oral cleft,central nervoussystem, majorcardiac).Weak, statistically significant increased risk found for higherTTHM levels with small for gestational age, neural tube defects,central nervous system defects, oral cleft defects, and majorcardiac defects. Some association with higher TTHM exposureand low birth weight. No effect seen for preterm birth, very lowbirth weight, or fetal deaths.Savitzet al.1995Populationbased casecontrol study:NorthCarolina,1988-1991.Examined TTHMconcentration atresidences and waterconsumption (duringfirst and thirdtrimesters).Spontaneousabortion,pretermdelivery, lowbirth weight.There was a statistically significant increased miscarriage riskwith high THM concentration, but THM intake (based onconcentration times consumption level) was not related topregnancy outcome. No associations were seen for pretermdelivery or low birth weight. Water source was not related topregnancy outcome either, with the exception of a nonsignificant, increased risk of spontaneous abortion for bottledwater users. There was a non-statistically significant pattern ofreduced risk with increased consumption of water for all threeoutcomes.Aschengrauetal.1993Case-controlstudy inMassachusetts, 1977-1980.Source of water and 2types of watertreatment (chlorination,chloramination).Neonatal death,stillbirth,congenitalanomalies.There was a non-significant, increased association betweenfrequency of stillbirths and maternal exposure to chlorinatedversus chloraminated surface water. An increased risk ofurinary track and respiratory track defects and chlorinated waterwas detected. Neonatal death and other major malformationsshowed no association. No increased risk seen for any adversepregnancy outcomes for surface water versus ground andmixed water use.Krameret al.1992Populationbased casecontrol studyin Iowa, 1989-1990.Examined chloroform,DCBM, DBCM, andbromoform levels andcompared type ofwater source (surface,shallow well, deepwell).Low birthweight,prematurity,intrauterinegrowthretardation.Statistically significant increased risk for intrauterine growthretardation effects from chloroform exposure were observed.Non-significant increased risks were observed for low birthweight and chloroform and for intrauterine growth retardationand DCBM. No intrauterine growth retardation or low birthweight effects were seen for the other THMs, and no effects onprematurity were observed for any of the THMs.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-43 December 2005Shawet al.1991(and Shawet al.1990)Small casecontrol study:Santa ClaraCounty, CA,1981-1983.Estimated chlorinatedtap waterconsumption, meanmaternal TTHM level,showering/bathingexposure at residenceduring first trimester.Congenitalcardiacanomalies.Following reanalysis, no association between cardiacanomalies and TTHM level were observed.Aschengrauetal.1989Case-controlstudy inMassachusetts, 1976-1978.Source of water andexposure to metalsand othercontaminants.Spontaneousabortion.A statistically significantly association was detected betweensurface water source and frequency of spontaneous abortion.Reviews/Meta-analysesHwang andJakkola 2003Review andmeta-analysisof 5 studies.Compared DBP levels,source of water,chlorine residual, color(high/low), and 2 typesof disinfection:chlorination andchloramination.Birth defects(respiratorysystem, urinarysystem, neuraltube defects,cardiac, oralcleft).The meta-analysis supports an association between exposureto chlorination by-products and the risk of any birth defect,particularly the risk of neural tube defects and urinary systemdefects.Boveet al.2002 Qualitativereview of 14studies.Examined THM levels.Compared drinkingwater source and typeof water treatment.Birth defects,small forgestational age,low birthweight, pretermdelivery,spontaneousabortion, fetaldeath.Review found the studies of THMs and adverse birth outcomesprovide moderate evidence for associations with small forgestational age, neural tube defects, and spontaneousabortions. Authors felt risks may have been underestimatedand exposure-response relationships distorted due to exposuremisclassification.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-44 December 2005Graveset al.2001Review oftoxicologicalandepidemiological studiesusing a weightof evidenceapproach.Examined waterconsumption, durationof exposure, THMlevels, HAA levels, andother contaminants.Compared source ofwater, water treatment,water color (high/low),etc.Low birthweight, pretermdelivery, smallfor gestationalage,intrauterinegrowthretardation,specific birthdefects,neonatal death,decreasedfertility, fetalresorption, andother effects.Weight of evidence suggested positive association with DBPexposure for growth retardation such as small for gestationalage or intrauterine growth retardation and urinary tract defects.Review found no support for an association between DBPexposure and low birth weight, preterm delivery, some specificbirth defects, and neonatal death. The review resulted ininconsistent findings for all birth defects, all central nervoussystem defects, neural tube defects, spontaneous abortion, andstillbirth.Villanuevaet al.2001Qualitativereview of 14reproductiveanddevelopmentalhealth effectstudies.Compared exposure toTTHM levels,mutagenic drinkingwater, waterconsumption, sourcewater, types ofdisinfection(chlorination andchloramination), andresidence times.Spontaneousabortion, lowbirth weight,small forgestational age,neural tubedefects, otherreproductiveanddevelopmentaloutcomes.Review found positive associations between increasedspontaneous abortion, low birth weight, small for gestationalage, and neural tube defects and drinking chlorinated water inmost studies although not always with statistical significance.Nieuwenhuijsenet al.2000Qualitativereview ofnumeroustoxicologicalandepidemiological studies.Examined levels ofvarious DBPs, waterconsumption, andduration of exposure.Compared water color,water treatment,source of water, etc.Low birthweight, pretermdelivery,spontaneousabortions,stillbirth, birthdefects, etc.The review supports some evidence of association betweenTHMs and low birth weight, but inconclusive. Review found noevidence of association between THMs and preterm delivery,and that associations for other outcomes (spontaneousabortions, stillbirth, and birth defects) were weak but gainingweight.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsFinal Economic Analysis for the Stage 2 DBPR 6-45 December 2005Reifet al.2000 Qualitativereviews ofnumerousepidemiological studies.Compared source ofwater supply andmethods ofdisinfection.Estimated TTHMlevels.Birth weight,low birthweight,intrauterinegrowthretardation,small forgestational age,preterm deliver,somaticparameters,neonataljaundice,spontaneousabortion,stillbirth,developmentalanomalies.Weight of evidence suggested DBPs are reproductive toxicantsin humans under appropriate exposure conditions. The reviewreports findings between TTHMs and effects on fetal growth,fetal viability, and congenital anomalies as inconsistent.Reviewers felt data are at the stage of hazard identification anddid not suggest a dose-response pattern of increasing risk withincreasing TTHM concentration.WHO 2000 Qualitativereviews ofvariousstudies inFinland, U.S.,and Canada.Various exposures toTHMs.Variousreproductiveanddevelopmentaleffects.Review found some support for an association betweenincreased risks of neural tube defects and miscarriage andTHM exposure. Other associations have been observed, butthe authors believed insufficient data exists to assess any ofthese associations.Craun, ed. 1998 Qualitativereview of 10studies, focuson Californiacohort study.Examined THM levelsand waterconsumption, andcompared source ofwater and watertreatment (chlorine,chloramines, chlorinedioxide).Stillbirth,neonatal death,spontaneousabortion, lowbirth weight,pretermdelivery,intrauterinegrowthretardation,neonataljaundice, birthdefects.Associations between DBPs and various reproductive effectswere seen in some epidemiological studies, but the authors feltthese results do not provide convincing evidence for a causalrelationship between DBPs and reproductive effects.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsEconomic Analysis for the Stage 2 DBPR 6-46 December 2005Millset al.1998 Qualitativereview of 22studies.Examined TTHMlevels and waterconsumption.Compared source ofwater and 2 types ofwater treatment(chlorination andchloramination).Variousreproductiveanddevelopmentaleffects.Review found studies suggest possible increases in adversereproductive and developmental effects, such as increasedspontaneous abortion rates, small for gestational age, and fetalanomalies, but that insufficient evidence exists to establish acausal relationship.Reifet al.1996 Review of 3case-controlstudies and 1crosssectionalstudy.Examined THM levelsat residences, doseconsumption,chloroform. Comparedsource of waters and 2types of watertreatment (chlorinationand chloramination).Birth defects(centralnervoussystem, neuraltube defects,cardiac, oralcleft,respiratory,urinary tract),spontaneousabortion, lowbirth weight,growthretardation,pretermdelivery,intrauterinegrowthretardation,stillbirth,neonatal death.Studies reviewed suggest that exposure to DBPs may increaseintrauterine growth retardation, neural tube defects, major heartdefects, and oral cleft defects. Review found epidemiologicevidence supporting associations between exposure to DBPsand adverse pregnancy outcomes to be sparse and to providean inadequate basis to identify DBPs as a reproductive ordevelopmental hazard.Author(s) Study Type Exposure(s) Studied Outcome(s)MeasuredFindingsEconomic Analysis for the Stage 2 DBPR 6-47 December 2005Swanet al.1992Qualitativereview of 5studies inSanta ClaraCounty, CA(Deaneet al.1992,Wrenschet al.1992, HertzPicciottoet al.1992,Windhametal.1992,Fensteret al.1992).Compared maternalconsumption ofresidence tap water tobottled water.Spontaneousabortion.Four of the studies reviewed suggest that women drinkingbottled water during the first trimester of pregnancy may havereduced risk of spontaneous abortion relative to drinking tapwater. No association seen in the fifth study. Reviewconcluded that if findings are causal and not due to chance orbias, data suggest a 10-50% increase in spontaneous abortionrisk for pregnant women drinking tap water over bottled water.

### Final Economic Analysis for the Stage 2 DBPR 6-48 December 2005 

### Critical review of epidemiology literature by Reif et al. (2000)

Reif et al. (2000) conducted a critical review of the epidemiology literature pertaining to potential reproductive and developmental effects of exposure to DBPs in drinking water. Reif presented much of this data during the FACA process and the critical revi ew of the literature was important in this process. The review included 16 peer-reviewed scientific ma nuscripts and published re ports of which 10 were previously discussed in the Stage 1 DBPR. The authors evaluated associations between DBPs and outcomes grouped as effects on (1) fetal growth (birth weight [as a continuous variable]; low birth weight [defined as <2,500 grams]; term low birth weight [defined as <2,500 grams]; very low birth weight [defined as <1,500 grams]; preterm delivery [defined as <37 weeks of gestation] and intrauterine growth retardation [or decreased rate of growth of the fe tus]); (2) fetal viability (spontaneous abortion and stillbirth); and, (3) risk of fetal malformations (all malformations, oral cleft defects, major cardiac defects, neural tube defects, and chromosomal abnormalities).

Reif et al. (2000) found mixed evidence in the epidemiological literature they reviewed for associations between DBPs and effects on fetal gr owth. Studies using TTHM concentrations reached differing conclusions. Some studies found weak but sta tistically significant associations (Gallagher et al. 1998, Bove et al. 1992b, Bove et al. 1995), but two found none (Dodds et al. 1999, Savitz et al. 1995). Studies with qualitative exposure assessment designs are sim ilarly variable in their findings (Kanitz et al. 1996, Källén and Robert 2000, Yang et al. 2000).

For effects on fetal viability, the authors reported that some evidence exis ts for an increased risk of spontaneous abortion and stillbirth. Increased ra tes of spontaneous abortion associated with TTHM levels of 75 μg/L or more were reported by Waller et al. (1998). A schengrau et al. (1989) reported a doubling of risk of spontaneous abortion for expos ure to surface water compared to ground and mixed water. Although Savitz et al. (1995) found an associ ation between high levels of THMs and spontaneous abortion, no relationship with do se or water source was discovered. As discussed previously, an increased risk of stillbirth was found to be associat ed with THM and BDCM exposure (Dodds et al. 1999, King et al. 2000b). Aschengrau et al. (1993) found an association between stillbirth and the use of chlorinated versus chloraminated water systems. A weak association was found for the use of surface water systems and risk of stillbirth, but these au thors found little evidence for an association between TTHM and risk of stillbirth (Bove et al. 1992a, Bove et al. 1995).

For congenital abnormalities related to DBP expos ure, Reif et al. (2000) reported that the relatively few studies available in the technical literatu re provide an inconsistent pattern both in terms of associating exposure with the occurrence of anomalies in general, and with respect to identifying specific anomalies that result from exposure. The authors c onceded that an assessment of congenital anomalies is difficult due to the small number of cases available for evaluation and possible selection bias due to elective terminations of pregnancy. In addition, the authors stated that (1) categorizing defects may yield etiologically dissimilar aggregations and may dilute the estimated risk; (2) at higher DBP concentrations, multiple or lethal defects may be induced and th e outcomes may be expressed as spontaneous abortion or stillbirth, or cause unrecognized fetal loss; and (3) cases with recognized, single anomalies may represent only a portion of the full range of the potential effects.

Reif et al. provide several possible explanations for the discrepancies and inconsistencies between the epidemiologic studies: (1) substantial differe nces existed between methods of exposure assessment and, in some cases, definition of the outcome; (2 ) referent groups varied across studies; (3) the composition of DBP mixtures may have varied across locales and studies; and (4) other classes of DBPs may be the causal agents and THMs may or may not be an appropriate exposure indicator for those DBPs. Exposure misclassification in the studies may either hide a true effect or, in rare circumstances, create an artificial effect.

### Final Economic Analysis for the Stage 2 DBPR 6-49 December 2005 

### Reif et al. also reviewed the epidemiology literature for dose-response relationships. The researchers did not find a continuous pattern of in creasing risk with increasing concentration of TTHM, but they did observe a general trend of small incr eases in risk for concentrations of TTHM greater than 100 

## : g/L.

Based on information provided in the literature, Reif et al. estimated PAR for each outcome in each study. Appendix E provides a detailed di scussion of the derivation of PAR values from epidemiological studies and their use in risk and benefits assessments.

Reif et al. explored the difference in potential health risk across TTHM thresholds of 80 and 60 μg/L. ORs with 95 percent confidence intervals fro m various studies are compared in Exhibit 6.14 and PAR values with 95 percent confidence intervals (tr uncated at zero to represent biological relevance) from various studies are compared in Exhibit 6.15. The distribution of exposure levels differed among the studies, but when normalized in this way the st udies appear to provide some support for establishing a threshold level for TTHM. The point estimates of PAR in Exhibit 6.15 are generally higher when 60


## : g/L is used as the cut-point rather than 80 

## : g/L. This seems to indicate that an important reduction in disease occurrence may be obtained by elimina ting not only TTHM expos ure levels above 80 

## : g/L, but also levels between 60 

## : g/L and 80 

## : g/L. The authors note that this conclusion is tentative because many of the 95 percent confidence intervals on the ORs were very wide and extended to values of one and lower. Moreover, they suggest caution when interpreting the PAR values and note that “[s]ince a number of assumptions regarding attributable fraction do not appear to hold, population attributable risks are unlikely to be useful with the current data set.”

The findings for low birth weight are varied a nd do not strongly support a threshold of 80 or 60 μg/L. Reif et al. noted that the higher outcomes in the Gallagher et al. (1998) study may result from decreased non-differential misclassification (a type of bias) by taking spatial variability into account. Also, the DBP mixture may have been different from the mixtures used in other studies. There does not appear to be an increased association between TTHM and intrauterine growth retardation or preterm birth above the thresholds in question, based on the findi ngs presented in Exhibit 6.14. The Waller et al. (1998) study presents higher ORs for spontaneous abor tions than Savitz et al. (1995). The ORs for spontaneous abortion varied from re gion to region, possibly due to a difference in concentrations of BDCM and other byproducts (Waller et al. 1998). The ORs for neural tube defects were generally higher than for other defects above both thresholds. Th ere was no strong evidence of increased risk, however, for oral cleft defects or major cardiac defects (the ORs for both defects, based on Bove et al. (1995), were high). Overall, the ORs across the 60 and 80 μg/L th resholds were similar, but tended to be slightly higher for 80 μg/L.

### Final Economic Analysis for the Stage 2 DBPR 6-50 December 2005 

## Exhibit 6.14 Odds Ratios (and 95 Percent Confidence Intervals 1 

## ) Calculated by Reif et al. (2000) for Reproductive and Developmental Health Endpoints at TTHM Levels of > 80 μg/L versus < 80 μg/L and > 60 μg/L versus < 60 μg/L

Health Endpoint Dodds et al. (1999) Bove et al. (1995) Klotz and Pyrch (1998) Savitz et al. (1995) Waller et al. (1998) Gallagher et al. (1998)

> 80 μg/L versus < 80 μg/L TTHM
Low Birth Weight 1.09 (0.99,1.19) 1.20 (1.02,1.41) N/A 1.01 (0.69,1.50) N/A N/A

Intrauterine Growth Retardation 1.05 (0.98,1.12) 1.12 (1.03,1.22) N/A N/A N/A N/A

Preterm Birth 1.01 (0.92,1.10) 1.09 (0.99,1.19) N/A 0.74 (0.51,1.07) N/A N/A

Spontaneous Abortion N/A N/A N/A 1.06 (0.63,1.78) 1.29 (0.98,1.69) N/A

Stillbirths 1.59 (1.21,2.10) 0.65 (0.45,0.95) N/A N/A N/A N/A

Neural Tube Defects 1.37 (0.88,2.15) 2.12 (1.00,4.49) 1.35 (0.65,2.79) N/A N/A N/A

Oral Cleft Defects 1.01 (0.63,1.63) 1.95 (0.87,2.90) N/A N/A N/A N/A

Major Cardiac Defects 0.87 (0.70,1.08) 1.59 (0.87,2.90) N/A N/A N/A N/A



### > 60 μg/L versus < 60 μg/L TTHM
Low Birth Weight 1.06 (0.98,1.16) 1.07 (0.96,1.18) N/A 1.35 (0.90,2.01) N/A 2.24 (1.03,4.88)

Intrauterine Growth Retardation 1.05 (0.99,1.12) 1.04 (0.99,1.09) N/A N/A N/A N/A

Preterm Birth 0.98 (0.91,1.06) 0.96 (0.91,1.02) N/A 1.01 (0.71,1.44) N/A N/A

Spontaneous Abortion N/A N/A N/A 0.97 (0.56,1.67) 1.22 (0.98,1.53) N/A

Stillbirths 1.56 (1.18,2.06) 0.80 (0.65,0.97) N/A N/A N/A N/A

Neural Tube Defects 1.01 (0.66,1.56) 1.34 (0.76,2.38) 1.79 (1.08,2.95) N/A N/A N/A

Oral Cleft Defects 0.91 (0.59,1.40) 1.25 (0.78,2.02) N/A N/A N/A N/A

Major Cardiac Defects 0.94 (0.78,1.14) 0.93 (0.59,1.45) N/A N/A N/A N/A



### Notes: 1 

### Lower confidence limit is truncated at zero N/A indicates that data for that health endpoint was not presented in the study.

Source: Adapted from Reif et al. (2000).

### Final Economic Analysis for the Stage 2 DBPR 6-51 December 2005 

## Exhibit 6.15 PAR Values (and 95 Percent Confidence Intervals 1 

## ) Calculated by Reif et al. (2000) for Reproductive and Developmental Health Endpoints at TTHM Levels of > 80 μg/L versus < 80 μg/L and > 60 μg/L versus < 60 μg/L (Values are Percentages)

Health Endpoint Dodds et al. (1999) Bove et al. (1995) Klotz and Pyrch (1998) Savitz et al. (1995) Waller et al. (1998) Gallagher et al. (1998) > 80 μg/L versus < 80 μg/L TTHM
Low Birth Weight 2.4% (0,4.9 ) 1.5% (0.1,2.9) N/A 0.5% (0,13.1) N/A N/A

Intrauterine Growth Retardation 1.3% (0,3.1) 0.9% (0.2,1.6) N/A N/A N/A N/A

Preterm Birth 0.2% (0,2.2) 0.7% (0,1.5) N/A N/A N/A N/A Spontaneous Abortion N/A N/A N/A 1.9% (0,18.2) 4.5% (0,9.5) N/A

Stillbirths 14.1% (4.6,22.7) N/A N/A N/A N/A N/A

Neural Tube Defects 9.8% (0,23.3) 7.6% (0,17.0) 3.0%(0,10.2) N/A N/A N/A

Oral Cleft Defects 0.4% (0,13.1) 6.4% (0,13.9) N/A N/A N/A N/A Major Cardiac Defects N/A 4.1% (0,10.2) N/A N/A N/A N/A



### > 60 μg/L versus < 60 μg/L TTHM
Low Birth Weight 3.2% (0,7.3) 1.8% (0,4.6) N/A 18.8% (0,39.0) N/A 6.4% (0,14.1)

Intrauterine Growth Retardation 2.7% (0,5.8) 1.0% (0,2.4) N/A N/A N/A N/A

Preterm Birth N/A N/A N/A 0.7% (0,20.4) N/A N/A Spontaneous Abortion N/A N/A N/A N/A 6.5% (0,13.6) N/A

Stillbirths 22.5% (7.7,34.9) N/A N/A N/A N/A N/A

Neural Tube Defects 0.5% (0,20.0) 7.8% (0,22.5) 14.1% (1,25.5) N/A N/A N/A

Oral Cleft Defects N/A 5.9% (0,18.0) N/A N/A N/A N/A Major Cardiac Defects N/A N/A N/A N/A N/A N/A



### Notes: 1 

### Lower confidence limit is truncated at zero N/A indicates that data for that health endpoint were not presented in the st udy. Note that Reif et al. (2000) did not present PAR values for effects where the OR as shown in Exhibit 6.14 was < 1 (PAR considered by the authors in those cases as undefined). Source: Adapted from Reif et al. (2000).



### Critical review of epidemiology literature by Bove et al. (2002)

Bove et al. (2002) conducted a qualitative re view of 14 reproductive and developmental epidemiology studies on exposure to chlorination byproducts in drinking water (many studies are the same as those reviewed by Reif et al. [2000]). Endpoints reviewed include small size for gestational age, birth defects (e.g., neural tube defects, cleft def ects, and cardiac effects), and spontaneous abortions. Studies that evaluated the end point of “small for g estational age” were limited due to lack of adequate

### Final Economic Analysis for the Stage 2 DBPR 6-52 December 2005 

### exposure information and low study participation ra te. Studies conducted in Denver (Gallagher et al. 1998), northern New Jersey (Bove et al. 1995), central North Carolina (Savitz et al. 1995), and Nova Scotia (Dodds et al. 1999) based their exposur e estimates on tap water samples of THMs taken concurrently with the pregnancy period. However, only one study (Gallagher et al. 1998) involved modeling the distribution system characteristics and matching the residence with the appropriate sample location; this likely minimized exposure misclassifica tion, and further strengthe ned the relationship found in the Denver study between TTHM and small for g estational age (OR = 5.9; no confidence intervals reported).

Bove et al. (2002) believed that there was some c onsistency in the findings for neural tube defects and oral cleft defects, but not for cardiac defects. In the two studies that evaluated neural tube defects and individual THMs, one obtained similar associations fo r neural tube defects for chloroform and BDCM (Klotz and Pyrch 1998), while the other study found a much stronger association with BDCM (Dodds and King 2001). Of the two studies that evaluated oral cleft defects and levels of THMs, one found an association with TTHM (Bove et al. 1995) and the ot her found an association with chloroform, but not TTHM (Dodds and King 2001).

The California prospective cohort study (W aller et al. 1998) found a correlation between spontaneous abortion and specific THMs, especially BDCM. An association was also reported for spontaneous abortion when TTHM leve ls were evaluated, but this re lationship disappeared when water consumption habits were taken into account. Bove et al. (1995) noted th at this study’s low participation rate was a notable weakness. In addition, because th e maternal interviews were conducted after the loss had occurred, the potential for recall bias in water consumption habits during pregnancy was introduced. A Massachusetts study (Aschengrau et al. 1993) found no excess spontaneous abortion correlating with treatment type (i.e., chlorination vs. chloramination) , but significant effects were found for water source (surface water, ground water).

Bove et al. (2002) evaluated three studies on the incidence of fetal deaths and THM levels that had very different results. The Nova Scotia study (Dodds and King 2001) found a strong association, especially with BDCM levels. In contrast, the northern New Jersey study (Bove et al. 1995) could not evaluate the individual THM levels or information on the cause of death. Therefore, its finding of no excess could be the result of misclassification biases due to the failure to evaluate individual THMs and specific causes of death. The Massachusetts study (Aschengrau et al. 1993) found an association between stillbirths and chlorinated surface water when compared with chloraminated surface water.

Critical review of epidemiology literature by Nieuwenhuijsen et al. (2000)

Nieuwenhuijsen et al. (2000) reviewed the toxi cological and epidemiological literature and evaluated the potential risk of chlorination DBPs on human reproductive health. The authors reviewed 10 epidemiological studies according to the exposure measure used: water source and water treatment, routinely collected measurements of THMs, and r outinely collected THM measurements and estimation of individual THM ingestion. The authors commented that assessment of exposure is one of the weakest aspects of the available epidemiological studies.

The authors concluded that the evidence fro m a small number of studies suggests a weak association for spontaneous abortions, stillbirths, and birth defects, and the weight of evidence for a stronger association is increasing as more quality studies are completed. Nieuwenhuijsen et al. concluded that, “although studies report small risks that are difficu lt to interpret, the large number of people exposed to chlorinated water supplies constitutes a public health concern.”

### Final Economic Analysis for the Stage 2 DBPR 6-53 December 2005 

### Critical review of epidemiology literature by Graves et al. (2001)

Graves et al. (2001) considered the toxicol ogical and epidemiological evidence for various reproductive and developmental effects based on outco me, using a weight-of-evidence procedure. The studies included in the review examined water c onsumption, duration of exposure, THM levels, HAA levels, and presence of other contaminants. Many compared source water type, water treatment method, water color (high/low), and other physical properties. Endpoints which the authors found that the weight of evidence showed no association included low birth weight (5 articles), very low birth weight (2 articles), preterm delivery (7 articles), cesarean deliver y (1 article), congenital anomalies by severity (1 article), spina bifida (1 article), cleft lip and palate (4 articles), cardiac anomalies (4 articles), gastrointestinal anomalies (1 article), genital anom alies (1 article), integument anomalies (1 article), musculoskeletal anomalies (1 article), chromoso mal abnormalities (1 article), and neonatal death (1 article). Endpoints which the authors found that the weight of evidence showed mixed, inconsistent or weak results included neonatal jaundice (1 article), all congenital anomalies/birth defects (3 articles), all CNS anomalies (2 articles), neural tube defects (4 articles), respiratory anomalies (2 articles), SAB/miscarriage (3 articles), and stillbirth/fetal death (4 articles). Those endpoints that were suggestive of an association included growth retardation including term low birth weight (3 articles), IUGR or SGA (3 articles), and small body length and cranial circum ference (1 article), and urinary tract defects (2 articles). The authors note that the exposure character ization in the epidemiological studies to may not be adequate to show an association of small magnitude . The authors also caution the use of quarterly or routine monitoring of THMs matched to mate rnal residence as a representation of exposure.

Critical review of the epidemiology literature by Hwang and Jaakkola (2003)

Hwang and Jaakkola (2003) reviewed epidemiological studies for birth defects and performed a meta-analysis of the studies which provided estimates of exposure on one or more birth defects. The review presented studies including the following endpoints: any birth defect (3 studies), neural tube defect (4 studies), major cardiac effect (3 studies), respiratory defect (2 studies), oral cleft defect (3 studies), and urinary system defect (2 studies). The meta-analysis supports an association between exposure to chlorination by-products and the risk of any birth defect , particularly the risk of neural tube defects and urinary system defects. Results for cardiac defects, r espiratory defects, and oral clefts were inconsistent.

EPA’s epidemiology research program

EPA’s epidemiology research program continu es to examine the potential relationship between exposure to DBPs and adverse deve lopmental and reproductive effects. The Agency is supporting several studies using improved study designs to provide be tter information for characterizing potential risks.

6.2.2.2 Toxicological Evidence of Adverse Repro ductive and Developmental Health Effects

EPA has evaluated published studies of the potentia l adverse effects of DBPs on the reproductive and developmental health of laboratory animals. Es pecially pertinent information comes from reviews of the toxicology literature by Dr. Rochelle Tyl (2000): “Review of Animal Studies for Reproductive and Developmental Toxicity Assessment of Drinking Wate r Contaminants: DBPs” and by the World Health Organization (2000): “Environmental Health Criteri a 216: Disinfectants and Disinfection Byproducts.”

Review of Tyl (2000)

Tyl evaluated the literature using the EPA developmental (USEPA 1991b) and reproductive (USEPA 1996d) toxicity risk assessment guidelines. Tyl presented this critical review during the FACA process, and the analysis was important in deliberations. Tyl focused her analysis on making determinations regarding hazard identification (that is, identifying the specific types of adverse effects

### Final Economic Analysis for the Stage 2 DBPR 6-54 December 2005 

### caused by these substances) and the adequacy of data from the available studies to support the development of dose-response assessments.

Exhibit 6.16, adapted and updated from Tyl (2000), lists the types of reproductive and developmental toxicology studies that have been pe rformed for various disinfectants and specific DBPs. In Exhibit 6.16, the study types are classified as either screening studies or as dose-response studies.

Tyl concluded, based upon a weight-of-evidence a pproach to the analysis of the available, relevant literature, that “some of the DBPs have th e intrinsic capacity to do harm, specifically to the developing conceptus and the male (and possibly the female) reproductive system.” Specific reproductive and developmental hazards that have been identifie d and associated with exposure to various DBPs are summarized in Exhibit 6.17.

Notwithstanding the evidence supporting the iden tification of developm ental and reproductive effects from DBP exposure, Tyl also concluded th at the weight of evidence does not support a doseresponse evaluation based on existing studies. (She notes as “one possible exception” the 1996 Chemical Manufacturers Association’s two-generation rat study on chlorite.)

Tyl also noted in her summary and conclusions th at in a review of animal literature for the purpose of risk assessment, “biological plausibility” is a major concern. Tyl pointed to several aspects of both the in vitro and in vivo studies that support the biological plausibility that DBPs can cause adverse reproductive and developmental effects. In partic ular, there was an observed temporal relationship between the exposures in toxicological studies and th e occurrence of the developmental (e.g., embryonic neural tube, embryonic heart) or reproductive process (e.g., spermatogenesis). The observed effects were reproducible in the same or similar study designs. Th e effects were consistent across study designs. The effects observed in animal toxicological studies we re comparable to those observed in some human epidemiological studies (e.g., embryonic heart and ne ural tube defects, full litter resorption/miscarriage, spontaneous abortion, or stillbirth).

### Final Economic Analysis for the Stage 2 DBPR 6-55 December 2005 

## Exhibit 6.16 Availability of Reproduc tive and Developmental Toxicology Studies for Specific DBPs

Disinfectant or DBP Screens - Hazard Identification Dose Response

WEC NTP 35 Day CKA CKA++ Male Repro Seg II MultiGEN DISINFECTANTS Chlorine X Chlorine Dioxide X X Chloramine X TRIHALOMETHANES Chloroform X X X X Bromoform X X X Bromodichloromethane X X X X X Dibromochloromethane X X HALOACETIC ACIDS Monochloroacetic acid X X Dichloroacetic acid X X X Trichloroacetic acid X X X Monobromoacetic acid X X X Dibromoacetic acid X X X X X X Tribromoacetic acid X X Bromochloroacetic acid X X X P Bromodichloroacetic acid X Dibromochloroacetic acid X X HALOACETONITRILES Chloroacetonitrile X Dichloroacetonitrile X X Trichloroacetonitrile X X Bromoacetonitrile X X Dibromoacetonitrile X X Tribromoacetonitrile Bromochloroacetonitrile X X ALDEHYDES Formaldehyde X X X X Acetaldehyde X X Propanal X X MISCELLANEOUS 1,1-Dichloropropanone X Hexachloropropanone X Dichloromethane X Dibromomethane X MX X X Bromate X Chlorite X X X Notes: X = Completed and published in t he literature; P = In planning stage; WE C = Whole embryo culture; NTP 35 Day = NTP 35-day reproductive/ developmental toxicity screen; CKA = Chernoff-Kavlock Assay; CKA (++) = Chernoff-Kavlock Assay (modified); Male Repro. = Short-term adult male reproductive toxicity screen; Seg II = Segment II developmental toxicity study; Mult i-GEN = Multigeneration reproductive toxicity study. Source: Adapted and updated from Tyl (2000).

### Final Economic Analysis for the Stage 2 DBPR 6-56 December 2005 

## Exhibit 6.17 Reproductive and Developmental Health Effects Associated with DBPs in Toxicological Studies

Type of Effect DBP
Developmental defects Trichloroacetic acid (TCAA), dichloroacetic acid (DCAA), and monochloroacetic acid (MCAA)
Whole litter resorption (miscarriage/spontaneous abortion) Chloroform, bromoform, bromodichloromethane (BDCM), dibromochloromethane (DBCM), DCAA, TCAA, dichloroacetonitrile (DCAN), and trichloroacetonitrile (TCAN)
Fetotoxicity (reduced fetal body weights, increased anomalies like chromosomal defects) Chloroform, BDCM, DBCM, DCAA, TCAA, DCAN, TCAN, dibromoacetonitrile (DBAN), bromochloroacetonitrile (BCAN), monochloroacetonitrile (MCAN) acetaldehyde, formaldehyde
Male reproductive defects DCAA, dibromoacetic acid (DBAA), BDCM, formaldehyde Source: Adapted from Tyl (2000).



### Critical Review of Toxicological Literature by Graves et al. (2001)

As described in the prior Section 6.2.2.1 on epidemiological evidence , Graves, et al. conducted a weight of evidence analysis on epidemiologic and toxicologic studies of the association between DBPs and reproductive or developmental effects. Study results for the epidemiological and toxicological weight of evidence analyses were combined and are explained in summary form in section 6.2.2.1.

The authors noted that the toxicological data support that normal exposure to DBPs through tap water would generally not cause “adverse effects,” how ever, for the effects listed as suggestive of positive associations or producing mixed, in consistent, or weak results, further epidemiological research is warranted. One problem highlighted by the authors is that the current literature lacks accurate or detailed exposure assessment data. Accurate measurement of consumption (and exposure through dermal contact and inhalation) is needed, along with information on the DBP components of the drinking (or bathing) water. Alternatively, the authors indicate that when biomarkers are developed, they will potentially provide information on individual exposure.

Review of WHO (2000)

The International Programme on Chemical Safe ty (IPCS) of the World Health Organization (WHO) published an evaluation of Disinfectants a nd DBPs in its Environmental Health Criteria monograph series (WHO 2000). In this review of the toxicology data on repr oductive and developmental effects from DBP exposure, the WHO concludes that although the data on these effects are not as robust as the cancer database, these effects are of potential hea lth concern. They also conclude that reproductive effects in females have been principally embryole thality and fetal resorptions associated with the haloacetonitriles (HANs) and the dihaloacetates, while DCAA and DBAA have both been associated with adverse effects on male reproduction, including t esticular toxicity and spermatoxic effects.

New Toxicology Data Since the Stage 1 DBPR

Since promulgating the Stage 1 DBPR, more r esearch on DBPs is underway at EPA and other research institutions. For more information, on-going studies may be found on EPA’s DRINK website (http://www.epa.gov/safewater/drink/intro.html). Summaries of new studies are provided below.

### Final Economic Analysis for the Stage 2 DBPR 6-57 December 2005 

### Chen et al. (2003) studied the in vitro effect of bromodichloromethane on chorionic gonadotropin (CG) secretion by human placental trophoblast cultur es. Exposure to bromodichloromethane caused a significant dose-dependent decrease in the secretion of immunoreactive and bioactive CG. The lowest concentration that produced a statistically significant response was 0.02 

## : M, the lowest concentration tested. Chen et al. (2004) also reported that addition of 0.02 to 2 mM of bromodichloromethane inhibited morphological differentiation of human mononucleated cytotrophoblast cells to multinucleated syncytiotrophoblast-like colonies. The significance of the findings reported by Chen et al. (2003, 2004) for human health is that placental trophoblasts are th e sole source of CG during normal human pregnancy and play a major role in the maintenance of the conceptus.

Christian et al. (2001) conducted a developmenta l toxicity study with pregnant New Zealand White rabbits exposed to BDCM in drinking water at concentrations of 0, 15, 150, 450, and 900 parts per million (ppm) in drinking water on gestation days 6-29. The No-Observed-Adverse-Effect-Level (NOAEL) and Lowest-Observed-Adverse-Effect-Level (LOAEL) identified for maternal toxicity in this study were 13.4 milligrams per kilogram per day (mg/kg-day) (150 ppm) and 35.6 mg/kg-day (450 ppm), respectively, based on decreased body weight gain . The developmental NOAEL was 55.3 mg/kg-day (900 ppm) based on the absence of statistically si gnificant, dose-related effects at any tested concentration. Christian et al. also conducted a developmental study of BDCM in a second species, Sprague-Dawley rats. Rats were e xposed to BDCM in the drinking water at concentrations of 0, 50, 150, 450, and 900 ppm on gestation days 6 to 21. The c oncentration-based maternal NOAEL and LOAEL for this study were 150 ppm and 450 ppm, respectively, based on statistically significant, persistent reductions in maternal body weight and body weight gains. Based on the mean consumed dosage of bromodichloromethane, these concentrations corres pond to doses of 18.4 mg/kg-day and 45.0 mg/kg-day, respectively. The concentration-based devel opmental NOAEL and LOAEL were 450 ppm and 900 ppm, respectively, based on a significantly decreased number of ossification sites per fetus for the forelimb phalanges (bones of the hand) and the hindlimb me tatarsals and phalanges. These concentrations correspond to mean consumed doses of 45.0 mg /kg-day and 82.0 mg/kg-day, respectively.

Christian et al. (2002a) summarized the results of a two-generation reproductive toxicity study on bromodichloromethane conducted in Sprague-Daw ley (SD) rats. Bromodichloromethane was continuously provided to test animals in the drinking water at concentrations of 0, 50, 150, or 450 ppm. Average daily doses estimated for the 50, 150 and 450 ppm concentrations were reportedly 4.1 to 12.6, 11.6 to 40.2, and 29.5 to 109 mg/kg-day, respectiv ely. The parental NOAEL and LOAEL were 50 and 150 ppm, respectively, based on statistically signifi cant reduced body weight and body weight gain; F1 and F2 generation pup body weights were reduced in the 150 and 450 ppm groups during the lactation period after the pups began to drink the water provide d to the dams. Body weight and body weight gain were also reduced in the 150 and 450 ppm F1 generati on males and females. A marginal effect on estrous cyclicity was observed in F1 females in the 450 ppm exposure group. Small ( 

## # 6 percent), but statistically significant, delays in F1 generation sexual matu ration occurred at 150 ppm (males) and 450 ppm (males and females) as determined by timing of vaginal pa tency or preputial separation. The study authors considered these effects to be a secondary response associated with reduced body weights cause appears to be dehydration brought about by taste aversion to the compound. The results of this study identify NOAEL and LOAEL values for reproductive effects of 50 ppm (4.1 to 12.6 mg/kg-day) and 150 ppm (11.6 to 40.2 mg/kg-day), respectively, based on delayed sexual maturation.

Bielmeier et al. (2001) conducted a series of experiments to investigate the mode of action in bromodichloromethane-induced full litter resorption ( FLR). The study included a strain comparison of F344 and SD rats. In the strain comparison experime nt, female SD rats (13 to 14/dose group) were dosed with 0, 75, or 100 mg/kg-day by aqueous gavage in 10 percent Emulphor ® 

### on gestation day (GD) 6 to 10. F344 rats (12 to 14/dose group) were dosed with 0 or 75 mg/kg-day administered in the same vehicle. The incidence of FLR in the bromodichloromethane-tr eated F344 rats was 62 percent, while the incidence of FLR in SD rats treated with 75 or 100 mg/kg- day of bromodichloromethane was 0 percent. Both strains of rats showed similar signs of maternal t oxicity, and the percent body weight loss after the first

### Final Economic Analysis for the Stage 2 DBPR 6-58 December 2005 

### day of dosing was comparable for SD rats and the F 344 rats that resorbed their litters. The rats were allowed to deliver and pups were examined on postnat al days 1 and 6. Surviving litters appeared normal and no effect on post-natal survival, litter size, or pup weight was observed. The series of experiments conducted by Bielmeier et al. identified a LOAEL of 75 mg/kg-day (the lowest dose tested) based on FLR in F344 rats. A NOAEL was not identified. Mechanistic studies reported by Bielmeier et al. indicate that BDCM-induced pregnancy loss is likely to be luteiniz ing hormone (LH)-mediated (Bielmeier et al. 2004). In this more recent study, Bielmeier et al. hypothesi zes that BDCM alters LH levels by disrupting the hypothalamic-pituitary-gonadal axis or by altering the responsiveness of the corpora lutea to LH. These possible mechanisms are potentially relevant to pr egnancy maintenance in humans. EPA believes the finding of BDCM-induced pregnancy loss in F344 rats is relevant to risk assessment, and may provide insight into the epidemiological finding of incr eased risk of spontaneous abortion associated with consumption of BDCM (Waller et al. 1998, 2001).

Christian et al. (2002b) performed a two-generati on drinking water study of DBAA in rats. Male and female Sprague-Dawley rats (30/sex/exposure group) were administered DBAA in drinking water at concentrations of 0, 50, 250, or 650 ppm continuous ly from initiation of exposure of the parental (P) generation male and female ra ts through weaning of the F 2 

### offspring. Based on testicular histomorphology indicative of abnormal spermatogenesis in P and F 1 

### males, the parental and reproductive/developmental toxicity LOA EL and NOAEL are 250 and 50 ppm, respectively.

Previous studies by EPA have reported adverse e ffects of DBAA, administered via oral gavage, on spermatogenesis that impacted male fertility (Linder et al. 1994, 1995, 1997) at doses comparable to those achieved in the Christian et al. (2002b) study. Based on these studies collectively, DBAA is spermatotoxic. Moreover, Veeramachaneni et al. ( 2000) reported in an abstract that sperm from male rabbits exposed to DBA in utero from gestation days 15 and throughout life reduced the fertility of artificially inseminated females as evidenced by redu ced conceptions. When published, this study may support the evidence that DBA is a male reproductive system toxicant.

In addition, research on DBAA by Klinefelter et al. (2001) has demonstrated statistically significant delays in both vaginal opening and preputial separation us ing the body weight on the day of acquisition (at postnatal day 45) as the co-variant. Th is was not found by Christian et al. (2002b) using the body weight at weaning as the statistical covarian t. However, the authors analyzed the data for preputial separation and vaginal ope ning with body weight on the day of weaning as a co-variant rather than body weight on the day of acquisition, i.e. the day that the prepuce separates or the day the vagina opens. It is likely that there was an increase in body weight from postnatal day 21 (weaning) until preputial separation (day 45) that was independent of the delay in sexual maturation. A more recent study by Klinefelter et al. (2004) found that exposure to either 4, 40, or 400 ppm of DBAA from GD 15 through post-natal day (PND) 21 did not result in any signi ficant reproductive alterations, but effects were seen with continuous exposure until adulthood. Males a nd females exposed to 400 ppm DBAA were reported to have delayed preputial separ ation and vaginal openings by 4 and 3 days, and also an increased responsiveness of both the testis ad ovary to hum an choriogonadotropin (hCG). At 4 ppm DBAA and higher, hCG-stimulated testosterone production by testicular parenchyma on PND 56 was increased. Also, continuous exposure to DBAA compromised the quality of proximal cauda epididymal sperm.

Kaydos et al. (2004) administered DBA and BCA, individually and in combination to investigate the effect on fertility and spermatogenesis in the rat. Since humans are exposed to a complex mixture of DBPs in disinfected drinking water, it is important to study the potential effects of mixtures of DBPs that may elicit similar reproductive effects. The authors of this study were able to find dose and effect additivity, and in some cases synergism, for haloacid-induced decrease in fertility.

Although the Christian et al. (2002b) study was conducted in accordance with EPA’s 1998 testing guidelines, EPA has incorporated newer, more s ophisticated measures into recent intramural and

### Final Economic Analysis for the Stage 2 DBPR 6-59 December 2005 

### extramural studies that have not yet been incorporat ed into the testing guidelines. Such measures include changes in specific proteins in the sperm membra ne proteome and fertility assessments via in utero insemination. EPA believes that additional research is needed, using these newer toxicological measures, to clarify the extent to which DBAA poses human reproductive or developmental risk. The database on male reproductive effects from exposure to DBAA is in complete and is not suitable for quantitative risk assessment at this time. It does identify reproductive effects as an area of concern.

In addition, EPA has prepared individual suppor ting documents that provide detailed summaries of the relevant new information, as well as an overa ll characterization of the human health risks from exposure to these DBPs (USEPA 2000f, 2005b-e, 2005j; IRIS 2000, 2001a-b, 2003). Overall, reproductive and developmental toxicology studies i ndicate a possible reproductive/developmental health hazard although they are preliminary in nature fo r the majority of DBPs, and the dose-response characteristics of most DBPs have not been quantif ied. Some of the reproductive effects of DCAA were quantified as part of the RfD development process, and impacts of DCAA on testicular structure are one of the critical effects in the study that is the basis of the RfD (IRIS 2003).

Biological plausibility for the effects observed in reproductive and developmental epidemiological studies has been demonstrated through various toxicological studies on some individual DBPs (e.g., Bielmeier et al. 2001, Bielmeier et al. 2004, Narotsky et al. 1992, Chen et al. 2003, Chen et al. 2004). Some of these studies were conducted at hi gh doses, but similarity of effects observed between toxicology studies and epidemiology studies strengthens the weight of evidence for a possible association between adverse reproductive and developmental hea lth effects and exposure to chlorinated surface
water.

6.2.2.3 Conclusions

EPA believes that toxicology and epidemiology data do not support a conclusion at this time as to whether exposure to chlorinated drinking water causes adverse reproductive or developmental effects, but do support a potential health concern. Alt hough scientific knowledge about the association of reproductive and developmental health effects with DBP exposure is not know n well enough to quantify these risks or the benefits of reduced DBP exposur e, EPA concludes that the data are sufficient to determine that a concern exists that warrants additi onal regulatory action. The following are the specific key factors used to support EPA’s weight-of-evidence conclusion:

• The results of several studies performed by different researchers with different methods at different research sites show similar trends.

• Some health effects observed in animal toxicological studies are comparable to those observed in some human epidemiological studies (e.g., embryonic heart and neural tube defects, full litter resorption/miscarriage, s pontaneous abortion, or stillbirth) showing similarity of effects between animal toxicity and human epidemiology studies.

• Difficulties in assessing exposure to DBPs, resulting in exposure misclassification, may underestimate reproductive and developmental risk s associated with DBPs. It is possible that some of the inconsistencies reported in epidemiological and toxicological study results are due to these misclassifications, and the true effects may be greater than demonstrated. A spurious effect would be produced only in rare cases, and is unlikely as described in Reif et al. (2000) and Bove et al. (2002).

EPA’s epidemiology and toxicology research programs continue to examine the relationship between exposure to DBPs and poten tial adverse reproductive and developm ental health effects. EPA is

### Final Economic Analysis for the Stage 2 DBPR 6-60 December 2005 

### System Size (Population Served) Population Served by Disinfecting Systems Percent of Total Population Served by Disinfecting

Systems <100 387,001 0.15% 101-500 2,898,196 1.10% 501-1,000 3,210,348 1.22% 1,001-3,300 10,776,884 4.10% 3,301-10,000 19,664,185 7.48% 10,001-50,000 51,311,073 19.51% 50,001-100,000 26,218,703 9.97% 100,001-1,000,000 88,996,647 33.84% 1,000,000 + 59,561,481 22.64% Total 263,024,518 100.00%

Source: Derived from the Stage 2 DBPR population baseline for surface and disinfecting ground water CWSs in Exhibit 3.3. 

### also supporting several studies using improved study designs to provide better information for characterizing potential risks.



## 6.3 Exposure Assessment

6.3.1 Population Exposed

Because DBPs are formed when disinfectants combine with organic compounds, the population at risk is identified as the population served by drinking water systems that disinfect. A very large portion of the United States population—approximately 94 percent—is potentially exposed to DBPs in disinfected drinking water. Exhibit 6.18 contains EPA’s estimates of the population potentially exposed. Nearly 260 million people in the United States are served by community water systems (CWSs) that apply a disinfectant to water to protect against microbial contaminants. In addition to those served by CWSs, just over 2 million individuals are served regul arly by nontransient noncommunity water systems (NTNCWSs). (See Exhibit 3.3 for population served by different system types.)

Two population subgroups of concern are women of child-bearing age and developing fetuses. Women of child-bearing age are generally considered to be those in the age range of 15 to 45. The estimated U.S. population for the year 2000 is 281 million, of which approximately 64 million (23 percent) are females between the ages of 15 and 45. Because approximately 94 percent of the population is served by PWSs that disinfect, it can be estimated that about 60 million women of child-bearing age are served by these water supplies. Currently, there are approximately 4 million live births each year. Again, using the factors above, it can roughly be estimated that more than 3.8 million infants are born each year to mothers served by a disinfecting water supply.



## Exhibit 6.18 Estimated Population Exposed to DBPs in Drinking Water

### Final Economic Analysis for the Stage 2 DBPR 6-61 December 2005 

### 6.3.2 Routes of Exposure

An important route of exposure to DBPs is from the direct ingestion of drinking water and from the consumption of food that has been cleaned, pro cessed or prepared with drinking water. EPA has examined drinking water consumption data from the 1994–1996 USDA Continuing Survey of Food Intakes by Individuals (USDA 1997) and determined that mean daily drinking water consumption across all ages, sexes, and regions in the United States rang es from 0.9 to 1.2 liters per day, with an upper 95 th 

### percentile range of 2.5 to 2.9 liters per day (USEPA 2000i).

People also can be exposed to some contaminants in drinking water by routes of exposure other than ingestion, particularly by inhalation and dermal contact from s howering, bathing, washing dishes, washing clothes, or swimming. An interna tional work group was convened in 2000 to assess the challenges involved in assessing exposure to DBPs in epidemiological studies. The workgroup concluded that accurate exposure characterization (from all routes) is extremely important for a valid risk assessment (Arbuckle et al. 2002). The remainder of this sec tion focuses on routes of exposure other than direct ingestion.

Some studies have found that exposure due to i nhalation and skin absorption during showering may actually be higher than ingesti on-related exposure (Kuo et al. 1998, Backer et al. 2000, Miles et al. 2002). Backer et al. (2000) found that when volunteer s either showered with tap water for 10 minutes, bathed in tap water for 10 minutes, or drank 1 liter of tap water over 10 minutes, the highest levels of THMs were found in the blood samples from people who took 10 minute showers, whereas the lowest levels were found in the blood samples from people w ho drank 1 L of water in 10 minutes. The results from this study indicate that household activities such as bathing and showering are important routes for human exposure to THMs.

Singer et al. (2003) examined the extent to which everyday uses of disinfected drinking water activities in the household effect levels of THMs in th e blood. It was concluded that the average ratio of chloroform in blood to chloroform in water is greatest for activities where there is a potential for significant inhalation or dermal exposure (i.e. s howering, bathing, and washing dishes by hand).

The route of exposure may depend on the volatility and chemical phase of the DBP in question (Weisel et al. 1999, Xu et al. 2002, Xu and Weisel 2003, 2004, 2005). THMs are more volatile than HAAs and exist in the air at background levels (Weisel et al. 1999). Xu and Weisel (2003) found that the estimated dose from inhalation of particulate-phase DBPs represented less than 1 percent of the ingestion dose, and the vapor-phase DBPs can contribute to ove r 10 percent of the ingestion dose during a shower. Weisel et al. (1999) found a significant correlation be tween breath concentrations of chloroform after showering and water concentration. They were unable to show a relationship between breath concentration and overall exposure due to variability in when breath samples were taken and the fast rate of THM metabolization. However, almost all breat h concentrations, except for chloroform, were below the detection limit, most likely due to low water con centrations. Weisel et al. also found a link between urinary trichloroacetic acid (TCAA) excretion rat es and TCAA exposure, calculated as the water’s TCAA concentration multiplied by the volume of water c onsumed by volunteers over a 48 hour period, adjusted for home filters and boiling. TCAA exposure gene rally fell below 10 ng over 48 hours, but ranged up to 50 ng. In addition, Batterman et al. (2000) qua ntified volatization rates for several TTHMs when preparing, storing and serving tap water and found that heating up tap water (as in hot beverages) reduces the amount of exposure upon ingestion.

Exposure appears to differ between males and fe males, with males absorbing more chloroform than females (Corley et al. 2000). Most significan tly, dermal absorption, especially in women, is significantly affected by water temperature, since at higher temperatures blood flow to the skin is higher, allowing the blood to take in more DBPs (Corle y et al. 2000, Gordon et al. 1998). Measuring the chloroform breath concentrations of test subjects immer sed in bath water at concentrations of ~90 μg/L at

### Final Economic Analysis for the Stage 2 DBPR 6-62 December 2005 

### 35°C for 30 minutes, Corley et al. calculated that men would absorb 42 μg and women would absorb 12 μg of chloroform. These exposures were based on models taking blood flow to the skin and skin permeability into consideration. At 40°C, this incr eased to 44 μg for men and 40 μg/L for women. (This includes chloroform exhaled, metabolized, and main tained in the body.) At 30°C, on the other hand, most subjects had chloroform breath levels below de tection limits. For example in comparison to dermal absorption, if 2 liters of water with the same chloro form concentration were ingested, the exposure from ingestion would range from 79 to 194 μg.

6.3.2.1 Special Exposure Issues for Pregnant Women

Because of the potential reproductive and devel opmental effects of DBPs, pregnant women represent a population subset of special concern with r espect to the intake of DBPs from drinking water. Because the kidneys work harder in pregnancy to expel waste material from the body, drinking an adequate volume of water is extremely important. Pregnant women can become dehydrated easily, which can lead to fetotoxicity. Thus, women who are pre gnant are encouraged to drink a minimum of eight 8- ounce glasses of water a day to ensure proper hydration (March of Dimes 1999).

Pregnant women cannot avoid tap water completely . As discussed in the previous section, DBP exposure can occur through inhalation and dermal contact from a variety of activities, including showering and bathing. Zender et al. (2001) found that pregnant wo men bathed more often and for a longer duration than non-pregnant women but shower ing patterns were similar. Bottled water is not necessarily safer than tap water and is more expensive. Bottled water, even if safer, may not be an option for economically disadvantaged pregnant women. Because pregnant women cannot avoid tap water, the expected reduction in DBP exposure that is estimated for the Stage 2 DBPR is especially important in providing public health protection to them and their developing children.

6.3.3 Exposure Reduction

It is well recognized that DBP concentrations can vary greatly throughout a distribution system and over time at the same location in a distribution system. The Stage 1 DBPR requires systems to meet the maximum contaminant level (MCL) standards a nd associated compliance monitoring requirements as running annual averages (RAAs) of 80 

## : g/L for TTHM and 60 

## : g/L for HAA5 as averaged across all monitoring locations. It is possible that some syst ems can achieve the average concentration targeted by the Stage 1 DBPR, and yet still have some locations in the distribution system where average DBP levels are far in excess of the system-wide target at some, or even at all, times. The peak exposures resulting from these high concentrations are of particular con cern in regard to potential adverse reproductive and developmental health effects. Exposure at locations having repeatedly high sample concentrations are of particular concern for pregnant women, who are enc ouraged to drink more water than the average person and who may be especially sensitive to the potential eff ects of DBPs, as explained in the previous section.

Under the Stage 2 DBPR preferred regulatory alte rnative (which includes a requirement for the IDSE), TTHM and HAA5 MCLs will remain at 80 μg/L and 60 μg/L, respectively, but compliance will be based on the locational running annual average (L RAA). Exhibits 1.3 and 4.1 illustrate how the LRAA and RAA are calculated. This revised comp liance calculation requirement will reduce average DBP levels in the entire distribution system as well as avoid having average DBP levels at any sampling location exceed 80 and 60 

## : g/L for TTHM and HAA5, respectively. Systems are required to meet the Stage 2 DBPR MCLs at revised sampling locations that will be identified through the IDSE to further ensure that peak occurrence ev ents are captured and controlled.

### Final Economic Analysis for the Stage 2 DBPR 6-63 December 2005 Mean 5th %ile Lower CB 95th %ile Upper CB Preferred Alternative 7.81% 4.53% 11.18% Alternative 1 7.15% 5.93% 8.36% Alternative 2 26.91% 23.61% 30.20% Alternative 3 37.12% 36.00% 38.25% Mean and 90% Confidence Bounds on

Percent TTHM Reduction from PreStage 2 to Post-Stage 2 

### The Stage 2 DBPR Preferred Regulatory Alternativ e is expected to yield health benefits by achieving the following effects in t hose systems subject to the rule:

1) Reducing exposures to all DBPs levels.

2) Reducing exposures to single peak occurrences or repeated peak o ccurrence at location that consistently exceed TTHM and HAA5 MCL levels.

The next sections discuss these two ways that the Stage 2 DBPR reduces exposures to DBPs.

6.3.3.1 Reducing Exposure to All Levels of DBPs

In Chapter 5, EPA estimates the reduction in the national average TTHM and HAA5 concentrations occurring in drinking water distribution systems as a result of new treatment to meet Stage 2 DBPR requirements. Results from this analys is for TTHM are summarized below in Exhibit 6.19.

The average reduction in plant-mean TTHM and HAA5 concentrations is assumed to represent the range of reductions for all chlorination DBPs. Using these two DBP classes as “indicators” for all chlorination DBPs may overestimate or underestimate the true concentration reduction (see Section 6.6 for a summary of uncertainties). However, because measurable halogen-substituted DBP concentrations, comprised primarily of TTHM and HAA5, are estimated to make up 30 to 60 percent of the measured total organic halide (TOX) concentration (Singe r 1999), TTHM and HAA5 reductions are assumed to be reasonable indicators of the overall chlorination DB P reductions. Separate evaluations for TTHM and HAA5 are carried throughout the analyses.

The average reduction in TTHM and HAA5 concentr ation is a key input in the estimation of benefits of the Stage 2 DBP Rule. The reduction in concentration leads to a reduction in exposure, which in turn reduces the incidence of disease. Section 6.4 de tails the estimation of the benefits of the Stage 2 DBP Rule.



## Exhibit 6.19 National Average TTHM 1 

## Reduction Estimates

Note: Estimates of mean and 90% confidenc e bounds (CB) incorporate uncertainty in compliance forecast methodologies.

1 

### Reductions in HAA5s are very similar. See Section 5.5 for more detail. Source: Stage 2 Benefits Model (USEPA 2005h)

### Final Economic Analysis for the Stage 2 DBPR 6-64 December 2005 

### 6.3.3.2 Reducing Exposure to Peak DBP Occurrences

EPA used distribution system data from the Info rmation Collection Rule (ICR) to estimate the reduction in occurrences of peak DBP concentrations that result from the Stage 2 DBPR. Section 5.6.1 provides a detailed explanation of the methodology u sed to generate these estimates and presents results in Exhibit 5.27. Section G.3 presents an analys is of the reduction in peak observations as well as reduction in three-quarter averages that are greater than 75 

## : g/L. Note that since the developmental and reproductive health data described in Section 6.2 do not conclusively identify the peak level of concern, Section 5.6 and G.3 provide an analysis for sever al possible peak TTHM and HAA5 concentrations, or study levels.

Exhibit 5.27 shows that, at a TTHM study level of 75 

## : g/L, the percent of distribution system sampling locations with at least one peak observati on declines from 20.1 percent for pre-Stage 1 to 8.2 percent for pre-Stage 2 to 3.3 percent for post-Stage 2 DBPR conditions. To translate estimated changes in peak DBP occurrence as a result of the Stage 2 DB PR to changes in peak DBP exposure , the following assumptions are used:

1) Each ICR sampling-location (DSE, AVG1, AVG2, and DS Maximum) represents an equal portion (25 percent) of the total population served by the plant.

2) Peak DBP occurrence for the 311 large ICR surface and ground water plants evaluated in Section 5.5 is representative of the peak DBP occurrence for all plants (large and small).

The first assumption may overestimate the population represented by the DS Maximum location (i.e., 25 percent may be too high) and thus, may ove restimate the population exposed to peaks. This potential overestimate, however, is minimized becau se ICR data showed that the peak TTHM level occurred somewhere other than the DS maximum location approximately 52 percent of the time (see Chapter 3 of the Stage 2 DBPR Occurrence Docu ment (USEPA 2005k)). The rationale for the second assumption is provided in the next three paragraphs.

ICR data pertains to all systems serving 100, 000 or more people. The 311 plants evaluated represent 62 percent (311/500) of all plants in the ICR. Systems serving 100,000 or more people serve approximately 149 million people, or 56 percent ( 149 million/264 million) of the total population served by disinfecting systems (Exhibit 6.18 provides a summary of the population served by each disinfecting system size category). Thus, the 311 plants encompasses approximately 35 percent (56 percent x 62 percent) of the total population served by disinfecting systems.

Because medium-sized systems serving 10,000 to 99,999 people are expected to have treatment technologies and source water quality very similar to large systems serving 100,000 or more people, EPA believes that ICR large-system data is adequate for characterizing peak DBP occurrence for medium systems. (See Appendices A and B for comparis ons of source water quality data and treatment technologies in place for medium and large systems.)

For small systems serving fewer than 10,000 people, using ICR data to characterize pre-Stage 1 peak occurrence may bias the results of this analysis for two reasons. First, small systems serving fewer than 10,000 people were not required to co mply with the 1979 TTHM standard of 100 

## : g/L and may have higher DBP levels than indicated by ICR data . Alternately, small systems may have lower DBP levels than indicated by ICR data since they are made up of a higher proportion (more than 75 percent) of ground-water-only systems compared to large systems. It is expected that these two biases offset each other to some extent in the analysis of pre-Stage 1 data. It is important to note that biases in characterization of DBP peaks nationally that are cau sed by differences in small-system occurrence are minimized because systems serving fewer than 10,000 people represent only 14.8 percent of the total population served.

### Final Economic Analysis for the Stage 2 DBPR 6-65 December 2005 

### TTHM and HAA5 concentrations are highly variable in distribution systems; it is probable that this analysis does not capture the true variability in exposure to peaks. Uncertainties with interpretation of ICR data for the purposes of this exposure assessment include:

• The extent to which small-system occurrence is represented;

• Year-to-year variability of DBP occurrence data that might be affected by changes in source water quality (e.g., drought years versus non-drought years);

• The extent to which each ICR sampling point represents an equal fraction of the population served; and

• The extent to which ICR sampling locations represent compliance monitoring locations when trying to estimate reductions in exposure resulting from compliance with Stage 1 and Stage 2 DBPRs.

The assumptions in this section are necessary, however , for predicting exposure changes given the limited data on DBP occurrence in small systems and in distribution systems in general. Using the two assumptions listed above, the reduction in plant-loca tions with peaks as a result of the Stage 2 DBPR (shown in Exhibit 5.27) can be taken to represent th e reduction in exposure to peaks nationally as a result of the Stage 2 DBPR. For example, for a TTHM study level of 80 

## : g/L, the percent of the population exposed to peak DBPs is predicted to decline from 6.0 to 1.7 percent (a 70 percent reduction) as a result of the Stage 2 DBPR.



## 6.4 Benefits of the Stage 2 DBPR: Reduced Incidence of Adverse Effects

6.4.1 Reduced Incidence of Bladder Cancer Cases

This section presents EPA’s estimates of the exp ected reduction in the incidence rate of new bladder cancer cases as a result of the Stage 2 DBPR. The methodology used to obtain these estimates is also discussed in this section. Additional details on the methodology are provided in Appendix E. Also, Section 6.2.1 presents information on the annual incide nce of new bladder cancer cases attributable to all sources and, in particular, to DBPs prior to Stage 1, that is used as a key input to the estimation of the reduction in annual cases due to the Stage 2 DBPR. The estimates of Pre-Stage 1 bladder cancer cases attributable to DBPs are presented in Section 6.2. 1 for PAR values derived from three data sources: five studies used for Stage 1 and Stage 2 Proposal, Villa nueva et al. (2003), and Villanueva et al. (2004). Similarly, this section provides separate estimates of the avoidable cases based on the attributable cases estimated from those three sources.

Several key assumptions underlie the calculation of bladder cancer cases avoided by the Stage 2 DBPR. The most important one is that a causal re lationship exists between exposure to chlorinated surface water and bladder cancer. However, EPA a nd the international bodies (e.g. WHO) that classify risk recognize that such causality has not yet been established.

Other important assumptions regarding the bladde r cancer risk from DBPs, and the risk reduction from lower DBP levels, are:

• That there is no threshold below which there is no risk, • That the risk is linearly related to DBP e xposure levels resulting from the range of DBP levels in drinking water, and

### Final Economic Analysis for the Stage 2 DBPR 6-66 December 2005 

### • That reduction in bladder cancer risks can be estimated from reduction in levels of TTHMs and HAA5s acting as indicators of chlorination DBPs in drinking water.

Taken together, these assumptions provide the b asis for calculating the reductions in expected annual cases of bladder cancer in the population exposed to DBPs in drinking water resulting from the Stage 2 DBPR. So, for example, if the annual inciden ce of bladder cancer attributable to DBPs remaining after Stage 1 is X cases, and the Stage 2 DBPR is de termined to reduce the overall average concentrations of TTHMs or HAA5s by 5 percent, then it would be estimated that, over time, there would be 0.05X fewer new bladder cancer cases occurring each year as a result of the Stage 2 rule.

The number of cases avoided (and the resulting m onetized benefits discussed in subsequent sections) was calculated using TTHM and HAA5 as indicat ors for exposure to all chlorination DBPs (see Section 6.3.3.2 for discussion of the use of TTHM and HAA5 as indicators). However, for analyses presented in the rest of this chapter, only the results of calculations using TTHM are presented, to simplify the discussion. Benefits calculated using TTH M as an indicator are similar to those calculated using HAA5. Detailed results for all analyses us ing both TTHM and HAA5 as indicators are presented in Appendices E and F.

Another key set of assumptions used to estimate the reduction of bladder cancer incidence relates to when the expected reductions in new cancer cases be gin to occur. Individual cancer risks at any point in time generally represent lifetime exposure levels a nd not just current or very recent exposure levels. Therefore, it would not be appropriate to assume that individuals exposed to some level of DBPs for a substantial portion of their lifetime would immediately attain a reduced risk of bladder cancer when the DBP levels in their water system are reduced as a result of compliance with the Stage 2 DBPR. A transition from pre-Stage 2 risks to the post-Stag e 2 risks—referred to here as the “cessation lag”—has therefore been included in the calculation of cancer cases avoided each year to account for this factor.

Three subsections describing the estimation of cancer cases avoided follow. In the first, the annual cancer cases avoided are calculated without ta king the cessation lag transition into account. This establishes the “annual cases ultimately avoidable” that will be achieved once the full effect of the reduced DBP exposure is realized. The second subsec tion takes into account the effect of the cessation lag transition period between current and post-regulatory risk levels. A final subsection further accounts for the timing of the avoided cancer cases by considering the implementation schedule of the rule (that is, not all affected systems will implement the rule simultaneously).

As noted previously, EPA has developed three approaches to estimating the number of bladder cancer cases attributable to DBPs (five studies; Villa nueva et al. (2003); and Villanueva et al. (2004)). This section presents the estimates of the bladder cancer cases ultimately avoidable using all three methods for estimating the baseline population attri butable risk (PAR). However, for the sake of simplicity, one method for estimating PAR based on V illanueva et al. (2003) is carried through the full benefits analysis to account for cessation lag, implem entation schedule, and monetization. A perspective on how using one of the other two approaches would impact the benefits is provided in Section 6.5.

6.4.1.1 Annual Cancer Cases Ultimately Avoidable

Once the Stage 2 DBPR has been fully implem ented, the incidence of bladder cancer cases annually is anticipated to decline to a new, lo wer value representing the lower average DBP exposure levels. That new value will be achieved over time as lifetime risks for individuals who currently consume water at the high, pre-Stage 2 DBP levels become mo re influenced by the lower, post-Stage 2 levels. Over the long-term, the lower incidence of new bl adder cancer cases will represent the difference in the risk of new generations of individuals who are exposed largely or solely to the post-Stage 2 levels for their lifetimes instead of the higher pre-Stage 2 levels.

### Final Economic Analysis for the Stage 2 DBPR 6-67 December 2005 

### This long-term, steady-state difference between the annual bladder cancer cases attributable to DBPs before and after the Stage 2 rule is referred to as the “annual cases ultimately avoidable.” Note that this term “ultimately avoidable” is different from th e “total attributable” cases. The total attributable refers to all the annual bladder cancer cases due to DBPs as presented in Section 6.2; the cases ultimately avoidable refers to that portion of the total attr ibutable cases that a specific reduction in average DBP concentrations would eliminate.

To calculate the post-Stage 2 ultimately-avoida ble cancer cases, it is necessary to begin with the pre-Stage 1 cancer incidence from all causes, determine the total cases attributable to DBPs, determine the cases ultimately avoidable by Stage 1, and then determ ine the cases ultimately avoidable by Stage 2. This is done as follows:

• Estimate of the total cases attributable to DB Ps (under pre-Stage 1 conditions) in drinking water by applying the age-based PAR valu es to each of the age-based cases per year attributable to all causes.

• Estimate the maximum number of those total attributable cases that are avoided by the Stage 1 rule based on the percent reduction in average DBP levels due to Stage 1.

• Subtract the maximum number avoidable by Stage 1 from the total pre-Stage 1 attributable cases to obtain the post-Stage 1 (pre-Stage 2) attributable cases.

• Estimate the maximum number of the remaining pre-Stage 2 attributable cases that are avoided by the Stage 2 rule based on the pe rcent reduction in average DBP levels due to Stage 2.

As discussed in Section 6.2.1, EPA has devel oped three approaches for estimating the total attributable pre-Stage 1 cases:

• Using the range of Population Attributable Ri sk (PAR) values derived from consideration of 5 individual epidemiology studies used for the Stage 1 EA and the Stage 2 proposal EA (yields a pre-Stage 1 range of best estimates for PAR of 2% to 17%).

• Using the OR of 1.2 from the Villanueva et al. (2003) meta-analysis that reflects both sexes, ever exposed population from the studies consider ed (yields a pre-Stage 1 best estimate for PAR of ~16%)

• Using the Villanueva et al. (2004) pooled da ta analysis to develop a dose-response relationship for OR as a function of Averag e TTHM. The dose-response relationship wasmodeled as linear with an intercept of OR = 1.0 at TTHM exposure level = 0 (yields a preStage 1 best estimate for PAR of ~17%)

These three approaches were then used to gauge the percentage of cases attributable to DBP exposure (i.e., PAR). Taken together, the three appr oaches provide a reasonable estimate of the range of potential risks. EPA has long recognized that while the several epidemiology studies described in this chapter indicate a potential association between expos ure to DBPs in drinking water and bladder cancer incidence, uncertainty remains with respect to quantifying the number of new bladder cases that occur each year that can be attributed to that exposure. EPA notes that existing epidemiological evidence has not conclusively established causality between DBP expos ure and any health risk endpoints, so the lower bound of potential bladder cancer cases may be as low as zero.

Two basic methodologies for using the epidemiology data are represented in the three approaches. The first is to consider multiple studies separately rather than combining the information into

### Final Economic Analysis for the Stage 2 DBPR 6-68 December 2005 

### a single estimate of the attributable risk. The second is to combine the information provided by multiple epidemiology studies using either a meta-analysis or a pooled data analysis. Each methodology has advantages and disadvantages.

One advantage to keeping estimates of indivi dual studies separate and presenting them as a full range of plausible results is that an explicit depicti on of the extent of uncertainty that exists in the quantitative risk estimate is retained. EPA chose to consider studies separately in the economic analyses for both the Stage 1 DBP rule and the proposal for th e Stage 2 DBP rule. EPA relied upon a range of risk estimates derived separately from 5 key studies that were published in the 1980's and 1990's. The individual estimates of the fraction of bladder cancer cases attributable to DBP exposure (or more specifically to chlorinated water exposure) obtained from each of these five studies covered a wide range: 2% to 17%. Further, as EPA noted, consideration of uncertainty for each of the individual estimates leads a wider range of values and, on the low end, includes the possibility of 0%.

One criterion to consider when deciding whether to combine multiple studies is the heterogeneity of the data. In developing the Stage 1 rule, EPA evaluated two meta-analyses available at that time (Poole et al. 1997, Morris et al. 1992) and concluded that the existing studies were too heterogeneous to be combined in any way.

Meta-analyses and pooled data analyses are two approaches that are used to combine the information provided by multiple epidemiology studies. In a meta-analysis, the measures of an effect size obtained in the individual studies (such as the OR) are weighted, typically by the inverse of the variance of the effect size, and the weighted values combined to obtain the overall estimate of that effect. In a pooled data analysis, the underlying data of the multip le studies are combined together, typically without weighting, and an estimate of the effect is made from the combined data as though it were obtained from a single study.

Meta-analysis is more commonly used for co mbining multiple epidemiology studies than is pooled data analysis. If heterogeneity is not prope rly controlled for across the studies used, pooled data analysis can be subject to outcomes that are greater, less, and often opposite that of the outcomes observed in the individual studies (Bravata and Ol kin 2001). Although the results of meta-analysis can also be affected by heterogeneity across the studi es used, it is not as subject to these same effects. Meta-analysis can also combine data by weighting certain studies more than others, while pooled data analysis cannot do this. However, whereas meta-analysi s is limited to consideration of the specific effect measures studied by the author's of the underlying studies, pooled data analysis can provide an opportunity to evaluate an effect that was not speci fically considered in some or all of the underlying studies.

EPA determined that the meta-analysis published by Villanueva et al. (2003) and the pooled data analysis published by Villanueva et al. (2004), bot h of which combine the results of multiple select studies, offer reasonable approaches to arriving at a si ngle, overall estimate of attributable risk while still retaining an appropriate characterization of the uncertainty in that risk estimate.

The Villanueva et al. (2003) meta-analysis, which considered four of five of the same studies that EPA has used historically for its PAR analyses in a ddition to two other lower weighted studies, obtained results that are consistent with the five study es timates. The meta-analysis found a relationship between duration of exposure to DBPs (or chlorinated wate r) and risk of bladder cancer, which EPA used to inform the relationship between exposure and risk. With this approach to estimating risk, EPA assumes that the exposure of the study populations is charact eristic of the national pre-Stage 1 exposure without knowing the exposure levels explicitly.

The Villanueva et al. (2004) pooled data analysis produced results that are consistent with the other approaches. The Villanueva et al. (2004) pa per provided a dose response relationship between OR

### Final Economic Analysis for the Stage 2 DBPR 6-69 December 2005 

### and TTHM concentrations that allowed EPA to es timate PAR values based specifically on the estimated average concentrations of TTHMs before and after implementation of the Stage 2 rule, a unique feature not possible with the other two approaches. A va riety of methods, including modeling, were used to estimate TTHM concentrations. In using the Villanue va et al. (2004) analysis to estimate risk, EPA assumes that these estimated exposures represent the exposure of the study populations and that the study population exposures are characteristic of the national pre-Stage 1 exposure. In addition, the Villanueva et al. (2004) paper used different studies, one of which is unpublished, than the other approaches. In using the analysis, EPA assumes that the relationship found between exposure and risk is valid for the US population although the study populations in the pooled analysis are from Italy, Canada, France, and Finland as well as the U.S.

Exhibit 6.20 summarizes the estimates of ultimat ely avoidable cases based on these three sets of PAR value estimates. The details for the calculations resulting in the estimates provided in Exhibit 6.20 are presented in Appendix E, Section E.4.2.1. It is important to note that in quantifying the reduction in cases, EPA assumed a linear relationship between averag e DBP concentration and bladder cancer risks. Because of this, EPA considers these estimates to be an upper bound on the annua l reduction in bladder cancer cases due to the rule.

There are two major sources of uncertainty reflect ed in the estimates presented in Exhibit 6.20 for each of the three PAR estimate sources.

First, each shows three “rows” indicating the uncer tainty in the percent DBP reductions that are predicted to occur between Stage 1 and Stage 2. These three estimates provided for each of the PAR estimate sources correspond to the Best Estimate of the Stage 2 reduction in DBPs, along with the lower and upper 95 percent CI bounds on those percent DBP reductions.

The second source of uncertainty reflected in each estimate corresponds to uncertainty in the OR values provided in each of the data sets and the PAR values derived from them. The low and high estimates shown in each row correspond to the lowe r and upper 95 percent confidence intervals on the OR/PAR estimates, with the best estimates reflected in the numbers circled between them. Note that for the five studies used for Stage 1 and the Stage 2 proposal, the “best estimates” include numbers based on both the 2 percent and 17 percent PAR values that underlie these calculations.

Focusing on the best estimate of percent DBP reduc tions, the range of estimates of Stage 2 annual bladder cancer cases ultimately avoidable derived from the “five studies” set of PARs extends from 0 cases to 1,060 cases per year, with best estimates of 64 (for the 2 percent PAR estimate) and 546 for the 17 percent PAR estimate. For the estimates derived from the Villanueva et al. (2003) PAR values, the annual cases ultimately avoidable for the best estimat e of percent DBP reductions ranges from 275 to 874, with a best estimate of 506. For the estimates deri ved from the Villanueva et al. (2004) PAR values, the annual cases ultimately avoidable for the best estimate of percent DBP reductions ranges from 80 to 1,064, with a best estimate of 550.

As noted previously, the estimates of ultimately avoidable cases from Stage 2 shown here set an upper bound on the annual reduction in bladder cancer cases due to the DBP reductions from this rule. As a result of cessation lag and the phasing in of reductions over time in accordance with the implementation schedule, these annual reductions are not expected to be realized for a substantial number of years after the rule is promulgated. The sections that follow account for both of these factors in estimating the annual benefits of the Stage 2 rule.

For the calculation of the annual benefits that include consideration of cessation lag, implementation schedule and monetization, EPA is using as its starting point the annual cases ultimately avoidable derived using the OR and PAR values obtai ned from the Villanueva et al. (2003) study. The estimates of total attributable cases and cases ultimately avoidable for Stage 2 based on Villanueva et al.

### Final Economic Analysis for the Stage 2 DBPR 6-70 December 2005 

### (2003) fall in the middle of all of the estimates a nd capture a substantial portion of the overall range reflecting the uncertainty in both the underlying OR and PAR values as well as the range of uncertainty in DBP reductions for Stage 2.

It is important to note that in running the full benefits simulation mode l using the Villanueva et al. (2003) inputs for PAR and incorporating all of th e uncertainties for DBP concentration changes, the mean estimate of annual cases ultimately avoidable produced by the full model is 581 (CB=234-1,083) as compared to the “best estimate” of 506 (CB=275-874) calculated above.Final Economic Analysis for the Stage 2 DBPR 6-71 December 2005Abbreviation: PAR = Population Attributable Risk (values shown are best estimates). CB = Confidence BoundNotes: Estimated annual cases ultimately avoidable are based on predicted DBP reduction from Stage 1 to Stage 2. Results shownassume that percent reduction in average TTHMconcentrations is an indicator of percent reduction in concentrations of all DBPs. Three contributions to the uncertainty in the estimate of the annual cases ultimately avoidable by Stage 2 aredisplayed in this exhibit: (1) uncertainty in the approach used to estimate PAR; (2) uncertainty in the underlying data used toderive the PAR estimates for each approach, represented by the95 percent confidence intervals displayed in each horizontal bar; and (3) uncertainty in the percent reduction in the nationalaverage DBP levels achieved by Stage 2 is represented by the lower90 percent CB, best estimate, and upper 90 percent CB values shown for each approach. For Approach 1, the hatched boxes represent the 2 percent to 17 percent range of best estimatesfrom the five separate studies considered.in the Odds Ratios underlying the PAR values. The estimates in boxes are the overallmean (best) estimates for each approach.Approach 2Villanueva et al.(2003)(PAR = 15.7%)Approach 3Villanueva et al.(2004)(PAR = 17.1%)Annual Cases Ultimately Avoidable1,0002,000615317371,060546641,51878292Approach 1“5 Studies”(PAR = 2%-17%)1592935072758745061,252393724476173191,064805501,523787115DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)0DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)Abbreviation: PAR = Population Attributable Risk (values shown are best estimates). CB = Confidence BoundNotes: Estimated annual cases ultimately avoidable are based on predicted DBP reduction from Stage 1 to Stage 2. Results shownassume that percent reduction in average TTHMconcentrations is an indicator of percent reduction in concentrations of all DBPs. Three contributions to the uncertainty in the estimate of the annual cases ultimately avoidable by Stage 2 aredisplayed in this exhibit: (1) uncertainty in the approach used to estimate PAR; (2) uncertainty in the underlying data used toderive the PAR estimates for each approach, represented by the95 percent confidence intervals displayed in each horizontal bar; and (3) uncertainty in the percent reduction in the nationalaverage DBP levels achieved by Stage 2 is represented by the lower90 percent CB, best estimate, and upper 90 percent CB values shown for each approach. For Approach 1, the hatched boxes represent the 2 percent to 17 percent range of best estimatesfrom the five separate studies considered.in the Odds Ratios underlying the PAR values. The estimates in boxes are the overallmean (best) estimates for each approach.Approach 2Villanueva et al.(2003)(PAR = 15.7%)Approach 3Villanueva et al.(2004)(PAR = 17.1%)Annual Cases Ultimately Avoidable1,0002,000615317371,060546641,51878292Approach 1“5 Studies”(PAR = 2%-17%)1592935072758745061,252393724476173191,064805501,523787115DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)0DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)Approach 2Villanueva et al.(2003)(PAR = 15.7%)Approach 3Villanueva et al.(2004)(PAR = 17.1%)Annual Cases Ultimately Avoidable1,0002,000615317371,060546641,51878292Approach 1“5 Studies”(PAR = 2%-17%)1592935072758745061,252393724476173191,064805501,523787115DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)0DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)DBP Red. = 4.5%(lower CB)DBP Red. = 7.8%(best estimate)DBP Red. = 11.2%(upper CB)Exhibit 6.20 Comparison of Range of Estimates of Stage 2 Cases Ultimately Avoidablefor Three PAR Approaches and DBP Reductions3 

### The SAB included the following statement in its re port on arsenic, to emphasize this difference: “An important point is that the time to benefits from reducing arsenic in drinking water may not equal the estimated time since first exposure to an adverse effect. A good exampl e is cigarette smoking: the latency between initiation of exposure and an increase in lung cancer risk is approximate ly 20 years. However, after cessation of exposure, risk for lung cancer begins to decline rather quickly. A be nefits analysis of smoking cessation programs based on the observed latency would greatly underestimate the actual benefits.” Final Economic Analysis for the Stage 2 DBPR 6-72 December 2005 

### 6.4.1.2 Annual Cancer Cases Avoided Accounting for Cessation Lag

Recently, the Arsenic Rule Benefits Review Pa nel (ARBRP) of EPA’s Science Advisory Board (SAB) addressed cessation lag in detail and provided gui dance to account for the transition period between higher and lower steady-state risks (USEPA 2001d). The ARBRP coined the term “cessation-lag” to emphasize the focus on the timing of the attenuation of ri sk after reduction in exposures. They did this to avoid confusion with the more traditional term of “latency,” which represents the time period from when initial exposure occurs to when an in crease in risk from a carcinogen occurs. 3



### Although the focus of the cessation lag discussion in the SAB review was on reducing levels of arsenic in drinking water, much of their considerati on of this issue had more general applications beyond the arsenic issue they were considering at th at time. In particular, the SAB noted that:

• The same model should be used to estimate the time pattern of exposure and response as is used to estimate the potency of the carcinogen.

• If possible, information about the mechanis m by which cancer occurs should be used in estimating the cessation lag (noting that late-sta ge mechanisms in cancer formation imply a shorter cessation lag than early-stage mechanisms). The cessation lag tends to be shorter (i.e. the curve steeper) in late-stage mechanis ms such as cancer because a small reduction in exposure leads to a large decrease in risk.

• If specific data are not available for characterizing the cessation lag, an upper bound for benefits can be provided based on the assumption of immediately attaining steady-state results.

• In the absence of specific cessation lag data, other models should be considered to examine the influence of the lag.

Following the release of the SAB’s report, EPA began to explore approaches for including the cessation lag in risk reduction models and for calcu lating benefits for the arsenic regulation. EPA recognized, however, that the concept of cessation lag ma y be applicable not only to arsenic, but also to other drinking water contaminants having a cancer endpoint.

In response to the SAB cessation lag recommendations, EPA has:

• Conducted a study that considered peer review comments and resulted in the 2003 final report Arsenic in Drinking Water: Cessation Lag Model (USEPA 2003r).

• Presented a poster of the above report at the Society of Toxicology Conference (Schulman et al. 2004).

• Initiated development of general criteria for incorporating cessation lag modeling in benefits analyses for other drinking water regulations. In the effort to develop a cessation lag model specific to DBPs, EPA reviewed the available epidemiological literature for information relating to the timing of exposure and response, but could not

### Final Economic Analysis for the Stage 2 DBPR 6-73 December 2005 

### identify any studies that were adequate, alone or in combination, to support a model specifically for DBPs in drinking water. Thus, in keeping with the SA B recommendation to consider other models in the absence of substance-specific cessation lag information, EPA explored the use of information on other carcinogens that could be used as an indicator to ch aracterize the influence of cessation lag in calculating benefits. EPA investigated three different models and determined that using all three as separate alternatives would better characterize the uncertainty than selecting one or combining the three together into a single cessation lag model.

The carcinogen for which the most extensive da tabase was available for characterizing cessation lag was for cigarette smoking. EPA examined sever al extensive epidemiological studies on the risks of adverse health effects, including lung cancer and bl adder cancer, for smokers and former smokers. In addition, EPA included data for arsenic in drinking water and bladder cancer. The three studies used, and the cancer end-point and risk factor they each consider, are:

• Hrubec and McLaughlin (1997a): Smoking and Lung Cancer

• Hartge et al. (1987): Smoking and Bladder Cancer

• Chen and Gibb (2003): Arsenic and Bladder Cancer

Each of these studies provides information on how the cancer risk for individuals having some high level of exposure to the risk factor for a substa ntial portion of their lives changes over time toward the risk observed for other individuals who have experienced some lower le vel of exposure. The first two involve a change from smoking to non-smoking (complete cessation) while the third involves a change from a high arsenic exposure level of 50 : 

### g/L in drinking water to a lower, but non-zero, level of 10 : 

### g/L. The Hrubec and McLaughlin (1997a) report is a comp rehensive study involving a 26-year follow-up of almost 300,000 U.S. male veterans. Hartge et al. (1987) used data from 8,764 subjects in the National Bladder Cancer Study and their cigarette smoking hist ories. In addition, EPA selected Chen and Gibb (2003) to develop a cessation lag model based on arsen ic exposure in drinking water and bladder cancer.

Aside from chloroform, no mode of action has been established either for specific DBPs or for chlorinated water in general relate d to bladder cancer. Thus, EPA assumes that the mode of action may be adequately represented by the mixed initiator and pr omoter aspects of the cigarette smoking model. As discussed in the SAB report and the EPA C essation Lag report (USEPA 2001d and USEPA 2003r), carcinogens that act solely or primarily as initiators would tend to show a longer cessation lag (lower rate of risk reduction following reductions in exposure) than carcinogens that act solely or primarily as promoters. The available information on tobacco smoke and lung cancer suggests that it involves both initiators and promoters, so the cessation lag derive d from smoking data is expected to represent the combined influence of these different mechanisms.

The smoking/bladder cancer data is appropriate to use because the target organ is the same. “Portal of entry” differences are particularly impor tant in differentiating between smoking/lung cancer and DBPs/bladder cancer. The route of exposure for smoki ng/bladder cancer is similar to DBP ingestion in that there is expected to be a first pass through th e liver followed by entry into the bloodstream prior to reaching the bladder.

In the case of dermal exposure, which is consid ered a major route, DBPs enter the bloodstream directly and can reach the bladder without that “f irst pass” through the liver. This could argue for a similarity with the smoking/lung cancer model because it involves a more direct “portal of entry”.

While there are some reasons to favor the smoking/bladder cancer data, the largest area of uncertainty is the appropriateness of using any sm oking/cancer data to derive a cessation lag model for DBPs. In light of the overarching uncertainties in the use of smoking data, developing separate cessation lag models based on smoking/lung cancer data and smoki ng/bladder cancer data is a reasonable approach. EPA has therefore judged that these two models, pl us the arsenic/bladder cancer model, are equally

### Final Economic Analysis for the Stage 2 DBPR 6-74 December 2005 

### plausible for characterizing cessation lag in order to es timate benefits attributable to DBP reduction. The equations representing the three cessation lag models as well as details regarding their derivation and implementation can be found in Appendix E.

Another important consideration is that two out of three cessation lag models (those based on smoking) involve complete cessation of exposure, whereas in the case of DBPs, the exposure is only reduced. In some water systems the reduction is only 10 percent, whereas in others it may be as high as 60 percent, with an average of approximately 30 percen t. This moderate reduction in exposure may prevent full DNA repair, which some scientists interpret as the basis for the short cessation lag associated with full cessation of smoking. However, the third cessation lag model considers only a partial reduction in exposure.

Exhibit 6.21a illustrates the effect of the cessati on lag model. The results from the cessation lag models show that the majority of the potential cases avoi ded occur within the first fifteen years after initial reduced exposure to DBPs. For example, fifteen y ears after the exposure reduction has occurred, the annual cases avoided will be 489 for the smoking / l ung cancer cessation lag model, 329 for the smoking / bladder cancer cessation lag model, and 534 cases for th e arsenic / bladder cancer cessation lag model. These represent approximately 84%, 57%, and 92%, respectively, of the estimated 581 annual cases ultimately avoidable by the Stage 2 DBPR based on the Villanueva et al. (2003) PAR estimates. Note that the estimate of 581 annual cases ultimately avoidable differs slightly from the 506 cases ultimately avoidable shown for the Villanueva et al. (2003) PA R estimates in the preceding section. The 581 number is generated by the full Monte Carlo simulation bene fits model taking uncertainty in the PAR and DBP reductions into account, whereas the 506 number is a direct calculation based on the individual "best" estimates of PAR and DBP reduction values without accounting for those uncertainties.

6.4.1.3 Adjustments in Annual Cancer Cases Avoi ded to Account for the Rule Implementation Schedule

In addition to the delay in reaching a steady-st ate level of risk reduction as a result of cessation lag, there is a delay in attaining maximum expos ure reduction across the entire affected population that results from the Stage 2 DBPR implementation schedule. For example, large surface water PWSs have 6 years from rule promulgation to meet the new Stage 2 MCLs, with an additional 2-year extension possible for capital improvements. For the benefit and cost analysis, EPA estimates that some percentage will make treatment technology changes in year 4, some will make treatment technology changes in year 5, and so on. Appendix D shows the assumptions regard ing the schedule for installation of treatment technologies to meet Stage 2 DBPR requirements. In general, EPA assumes that a fairly constant increment of systems will complete installation of new treatment technologies each year, with the last systems installing treatment by 2016.

The delay in exposure reduction resulting from the rule implementation schedule is incorporated into the benefits model by adjusting the cases avoided for the given year. For example, if 10 percent of systems install treatment equipment (and start realizing reductions in cancer cases) in year 1, only that portion of the cases will begin the cessation lag equilibrium process in that year. Exhibit 6.21b provides ths estimated cases avoided each of the first 25 years after the rule promulgation considering both cessation lag and implementation schedule.

EPA analyses of available data indicate that 26 pe rcent of bladder cancers are fatal and 74 percent are non-fatal (USEPA 1999a). Annual cases avoided we re apportioned to each category proportionately.

### Final Economic Analysis for the Stage 2 DBPR 6-75 December 2005 0 100 200 300 400 500 600

0 10 20 30 40 50 60 70 80 90 100 Years After DBP Exposure Reduction BeginsAnnual Baldder Cancer Cases Avoided

Arsenic / Bladder Cancerr Model Smoking / Lung Cancer Model Smoking / Bladder Cancer Model 

## Exhibit 6.21a Comparison of Alternati ve Cessation Lag Models: Estimates of Annual Cases Avoided by Year Following Exposure Reduction (TTHM as an indicator)

### Final Economic Analysis for the Stage 2 DBPR 6-76 December 2005 Total Percent 1 Total Percent 1 Total Percent 1
1 0 0% 0 0% 0 0% 2 0 0% 0 0% 0 0% 3 0 0% 0 0% 0 0% 4 0 0% 0 0% 0 0% 5 0 0% 0 0% 0 0% 6 24 4% 23 4% 45 8% 7 62 11% 54 9% 110 19% 8 111 19% 90 16% 187 32% 9 170 29% 132 23% 275 48% 10 220 38% 161 28% 334 58% 11 265 46% 184 32% 379 65% 12 305 53% 204 35% 412 71% 13 341 59% 221 38% 438 76% 14 371 64% 237 41% 458 79% 15 396 68% 251 43% 475 82% 16 416 72% 265 46% 488 84% 17 433 75% 278 48% 499 86% 18 448 77% 289 50% 509 88% 19 460 79% 301 52% 516 89% 20 471 81% 311 54% 523 90% 21 481 83% 321 55% 528 91% 22 489 84% 330 57% 533 92% 23 496 86% 339 59% 537 93% 24 503 87% 347 60% 541 93% 25 509 88% 355 61% 544 94% Year Smoking/Lung Cancer Cessation Lag Model Smoking/Bladder Cancer Cessation Lag Model Arsenic /Bladder Cancer Cessation Lag Model 

## Exhibit 6.21b Estimates of Annual C ases Avoided by Year For Three Cessation Lag Models, Considering Rule Implemen tation Schedule (TTHM as an Indicator)

Note: 1 

### Percent of annual cases ultimately avoidable achieved during each of the first 25 years. The benefits model estimates 581 (90% CB = 234 - 1,083) annual cases ulti mately avoidable using the Villanueva et al. (2003) PAR inputs and including uncertainty in these and DBP reductions. EPA recognizes that cases may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. Source: Exhibits E.38a, E.38e and E.38i.



### 6.4.2 Reduced Incidence of Reproductive and Developmental Effects

As discussed earlier, both epidemiological and t oxicological evidence suggest the potential for increased health risk for pregnant women and their fetuses exposed to DBPs in drinking water. Although the levels of DBPs associated with specific potentia l adverse reproductive and de velopmental effects is not known and no causal link has been established, EPA belie ves that lowering the overall levels of DBPs in distribution systems and in particular by reducing th e incidence of peak levels is important from a public health perspective.

EPA believes that the current scientific know ledge on reproductive and developmental health effects is not strong enough to quantify risk in the pr imary benefits analysis. However, an illustrative calculation considering the range of possible benefits for one specific effect in this category—avoided cases of fetal loss—is presented in Section 6.8. The discussion in Section 6.2 and the illustrative calculation in Section 6.8 suggest that the benefits from reduced DBP exposure in terms of both avoided incidence of potential reproductive and developmenta l effects and in terms of the potential monetized value of those avoided cases could be significant.

### Final Economic Analysis for the Stage 2 DBPR 6-77 December 2005 

### 6.4.3 Other Health-Related Benefits

The scientific literature indicates that exposure to DB Ps may be related to health effects other than reproductive, developmental, and bl adder cancer effects. Some studies have indicated an association between consumption of chlorinate d drinking water and colon and rectal cancer, while other studies have shown no association. Since 1998, several new studies have been published that contribute to the weight of evidence relating DBP exposure with colon and r ectal cancer. As TTHM and HAA5 levels are reduced under the Stage 2 DBPR, other potentially carci nogenic chlorination DBPs (both known and unknown) will be reduced as well. These collateral effects may further reduce the number of colon and rectal cancer cases. Both toxicology and epidemiology studies indi cate that other cancers may be associated with DBP exposure but currently there is not enough data to quantify or monetize these risks. However, EPA believes that the association between exposure to DBPs and colon and rectal cancers is possibly significant, so an analysis of benefits is pr esented as a sensitivity analysis (see Section 6.7).

6.4.4 Non-Health-Related Benefits

The Stage 2 DBPR may increase consumer confidence in the quality of drinking water. Drinking water consumers may be willing to pay a premium for regulatory action if it reduces their risk of becoming ill. Consumers’ WTP depends on several factors, incl uding their degree of risk aversion, their perceptions about drinking water quality, and the expected probab ility and severity of potential human health effects associated with DBPs.

Most people who switch to bottled water or use filtration devices do so because of taste and odor problems and health-related concerns. Chlorine dioxi de, ozone, and chloramines have historically been used to address taste and odor. To the extent that the Stage 2 DBPR changes perceptions of the health risks associated with drinking water and improves taste and odor, it may reduce actions such as buying bottled water or installing filtration devices. Any r esulting cost savings would be a regulatory benefit.

As PWSs move away from conventional treatment to more advanced treatment technologies, other non-health benefits are anticipated besides better-t asting and smelling water. Installation of certain advanced treatment technologies can remove many cont aminants in addition to those specifically targeted by the Stage 2 DBPR, including those that EPA may regulate in the future. For example, membrane technology (depending on pore size), can be used to lower DBP formation, but it can also remove many other contaminants (e.g. bacteria and protozoans) th at EPA is in the process of regulating. GAC lowers nutrient availability for bacterial growth, produces a biol ogically more stable finished water, and facilitates management of water quality in the distribution system. Since GAC also removes synthetic organic chemicals( SOCs), it provides additional protection from exposure to chemicals associated with accidental spills or environmental runoff. Removal of any c ontaminants that may face regulation could result in future cost savings to a water system.

6.4.5 Potential Increases in Health Risks

It is important to maintain a balance betw een the risks from DBPs and those from microbial pathogens in drinking water. The Microbial-Disin fectants/Disinfection Byproducts (M-DBP) Advisory Committee considered the impact of DBP control on microbial protection when they recommended the MCLs in the Stage 2 DBPR. For example, as describ ed in Chapter 4 of this EA, the M-DBP Advisory Committee debated whether the bromate MCL should be lowered. The Stage 1 DBPR set the MCL for bromate at 10 μg/L, partly because that was the limit of EPA’s measuring capability at that time. Methods now exist to measure lower concentrations of br omate, which would allow a lower limit to be set. However, the committee was concerned that a lo wer bromate MCL might discourage systems from switching to (or continuing to use) ozone to incr ease microbial protection. Unlike chlorine, ozone can

### Final Economic Analysis for the Stage 2 DBPR 6-78 December 2005 

### inactivate Cryptosporidium , a focus of the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR). Therefore, to encourage the use of ozone, M-DBP Advisory Committee recommended that EPA not change the bromate MCL.

Along with the reduction in chlorination DBPs such as TTHM and HAA5, there may be increases in other DBPs as systems change from chlorine to ot her disinfectants. Exhibits 6.22 and 6.23 compare the SWAT-predicted monthly average and plant-mean av erage concentrations for chlorite (a potential byproduct of chlorine dioxide disinf ection) and bromate (a potential by product of ozone disinfection) for pre-Stage 2 and post-Stage 2 conditions. These exhibits show that the predicted changes in bromate and chlorite concentration as a result of the Stage 2 DBPR are expected to be minimal. These changes are minimal in part because bromate and chlorite MCLs already exist.

Another potential increase in health risks is due to increases in N-nitrosodimethylamine (NDMA), which is formed during both the chlorination and the chloramination process (see Section 6.2.1.2). Chapter 5 shows that many systems that do not currently meet the Stage 2 requirements will do so by switching to chloramines.

In addition, the baseline of disinfecting systems will increase as ground water systems begin adding disinfection by complying with the Ground Water Rule. In these circumstances, it is assumed that when a system adds disinfection, it will ensure compliance with the Stage 2 DBPR. Essentially, these systems will move from a position of little to no risk (non-disinfecting) to a Post-Stage 2 risk. This increase in risk due to the Ground Water Rule will be small on a national level because of the small population served by most non-disinfecting systems. More detail on this analysis can be found in Appendix M.

### Final Economic Analysis for the Stage 2 DBPR 6-79 December 2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 100 200 300 400 500 600 700 800 900 Chlorite (ug/L)Cumulative Percentile

Post-Stage 2 Pre-Stage 2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 100 200 300 400 500 600 700 800 Chlorite (ug/L)Cumulative Percentile

Post-Stage 2 Pre-Stage 2 

## Exhibit 6.22a Predicted Chlorite Plant-M ean Concentration for Pre-Stage 2 and Post-Stage 2

Exhibit 6.22b Predicted Chlorite Monthl y Average Concentrations for Pre-Stage 2 and Post-Stage 2

Source: DS Average data from SWAT runs 300 and 303 (USEPA 2001b).

### Final Economic Analysis for the Stage 2 DBPR 6-80 December 2005 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

012345678 Bromate (ug/L)Cumulative Percentile

Post-Stage 2 Pre-Stage 2 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 5 10 15 20 25 Bromate (ug/L)Cumulative Percentile

Post-Stage 2 Pre-Stage 2 

## Exhibit 6.23a Predicted Bromate Plant-Mean Concentrations for Pre-Stage 2 and Post-Stage 2

Exhibit 6.23b Predicted Bromate Monthly Average Concentrations for Pre-Stage 2 and Post-Stage 2

Source: DS Average data from SWAT runs 300 and 303 (USEPA 2001b).

### Final Economic Analysis for the Stage 2 DBPR 6-81 December 2005 

## 6.5 Valuation of Health Benefits for the Stage 2 DBPR

Once the benefits of implementing the Stage 2 DBPR have been identified, a monetary value must be assigned to allow comparison with the costs of the regulation. The following sections draw on the valuation literature to attribute the most appropriate values to each type of benefit derived from the rule. Where the available information is not sufficient to quantify monetary benefits, a qualitative discussion of potential value is presented.

To augment the valuation data from the literatu re, EPA incorporated into the Stage 2 DBPR analyses recommendations from reviews of previous re gulations. In particular, recommendations made by EPA’s SAB with regard to the benefits analysis of the recently promulgated Arsenic Rule (66 FR 6976, January 22, 2001) were incorporated into the Stag e 2 DBPR analyses as appropriate. Even though these recommendations were made in the context of the arsenic regulation (USEPA 2001d), certain recommendations regarding methodology (e.g., incorpor ation of cessation lag, calculation and presentation of uncertainties, etc.) are applicable to other impact analyses, including the Stage 2 DBPR. This section is organized as follows:

Section 6.5.1 Presents a qualitative discussion of the value of reductions in potential adverse reproductive developmental health effects derived from the Stage 2 DBPR.

Section 6.5.2 Explains the methodology used for quantitative valuation of reductions in bladder cancer cases attributable to the Stage 2 DBPR.

Section 6.5.3 Summarizes the total potential benefits attributable to the Stage 2 DBPR from implementation of the preferred regulatory alternative (which includes the requirement for the IDSE).

Section 6.5.4 Compares estimated benefits attri butable to the preferred regulatory alternative (which includes a requirement for the IDSE) to those estimated for other alternatives.

6.5.1 Value of Reductions in Potential Adverse Re productive and Developmental Health Effects

Potential adverse reproductive and developmental effects may impose a large economic cost on the nation. Although many miscarriages do not have associated medical costs, some do, with costs ranging from $5,000 to $11,000 depending on the conditions of th e miscarriage and the length of stay in the hospital (HCUPnet 2000). The full economic benefit of avoiding a miscarriage would also include the monetary value of forgoing the associated pain, sufferi ng, and loss. Another potential benefit is that the life of the fetus is saved. Avoiding these costs re presents one potential economic benefit of the rule.

Low birth weight also imposes costs on society. A report from The Future of Children (Lewit et al. 1995) estimates that approximately 40,000 infants die each year as a result of low birth weight and that $5.4 billion is spent each year on the additional services that low-birth-weight children require for health care and, eventually, special education and child care. The Lewit et al. (1995) study also estimates that $5.5 to $6 billion are spent each year caring for low-bi rth-weight infants and children. It estimates that such children are almost 50 percent more likely th an normal-birth-weight children to require special education. Additionally, the costs of caring for low- birth-weight infants is increasing as their chances for survival increase.

### Final Economic Analysis for the Stage 2 DBPR 6-82 December 2005 

### The cost for treating and caring for those with birt h defects is high. For example, the lifetime cost for a case of spina bifida is estimated at $300,000. Es timates for the lifetime cost of a heart defect range from $100,000 to $400,000 (CDC 1995). These costs account only for the estimated medical, developmental, and special education services attribut ed to each case. They do not include the pain and suffering of the children with these conditions or the emotional strain on their parents and other family members.

If even a small proportion of fetal losses, low birth weights, premature births, congenital anomalies, and infertility problems are attributable to DBPs, the associated monetary value of benefits from the Stage 2 DBPR would still be great. Given the high cost of medical care, the many ways to value a pregnancy saved, the unknown pain and suffering as a result of potential adverse reproductive and developmental effects, the high percentage of the U.S. population exposed to DBPs, and the large number of pregnant women, the Stage 2 DBPR presents a pot ential for substantial savings to society. Although uncertainties in the estimation of potentially avoi ded adverse reproductive a nd developmental health effects preclude a definitive evaluation of associated benefits in the primary analysis, EPA has conducted an illustrative calculation to estimate a range of possi ble benefits associated with fetal losses (see Section 6.8).

6.5.2 Value of Reductions in Bladder Cancer Cases

EPA analyses of available data indicate that 26 pe rcent of bladder cancers are fatal and 74 percent are non-fatal (USEPA 1999a). Annual cases avoided we re apportioned to each category proportionately.

Valuation data for fatal and non-fatal bladder cancer cases are summarized below. This is followed by an explanation of how these data are adjust ed to current price levels and for income elasticity effects, allowing proper incorporation into the Stage 2 DBPR benefits model. The next section (6.5.3) describes how these values are combin ed with the bladder cancer case reductions to yield the total estimated benefits resulting from the Stage 2 DBPR.

Value of Avoiding a Fatal Case of Bladder Cancer

For fatal bladder cancer cases, the value of a statisti cal life (VSL) is used to calculate the value of benefits. The VSL represents an estimate of the mone tary value of reducing risks of premature death from cancer. The VSL does not represent the value of saving a particular individual’s life; it represents the sum of the values placed on small individual risk reductions across an exposed population. For example, if a regulation were to reduce the risk of premature death from cancer by 1/1,000,000 for 1 million exposed individuals, the regulation would “save” one statis tical life (1,000,000 × 1/1,000,000). If each of the 1,000,000 people were (on average) willing to pay $5 to achieve their risk reduction of 1/1,000,000 anticipated from the regulation, the VSL would be $5 million ($5 × 1,000,000).

An EPA study characterized the range of possible VSL values as a Weibull distribution with a mean of $4.8 million (1990 price level), based on 26 studies (USEPA 1997a). This is the value recommended for use in EPA’s Guidelines for Preparing Economic Analyses (USEPA 2000j) and endorsed by the SAB (USEPA 2001d). For purposes of the Stage 2 DBPR benefits analysis, the VSL Weibull distribution (with parameters of location = 0, scale = 5.32, shape = 1.51) was incorporated into the benefits model using a Monte Carlo simulation. This allows quantification of the uncertainty surrounding the benefit estimates.4 

### Previous EPA analyses (Stage 1 DBPR and Arseni c Rule) used the WTP value for avoiding a case of chronic bronchitis for benefits transfer calculations. The SA B review of the Arsenic benefits analysis identified the curable lymphoma WTP value as another metric that coul d be used in benefits valuation because “...the endpoint being valued more nearly corresponds to nonfatal bladder cancer...” (USEPA 2001d). The SAB suggested, however, that calculations using the WTP for chronic bronchitis also be presented. This analysis follows the SAB’s recommendation. Final Economic Analysis for the Stage 2 DBPR 6-83 December 2005 

### Value of Avoiding the Morbidity Increm ent of a Fatal Case of Bladder Cancer

The VSL represents the value of avoiding a premature death. This valuation, however, does not explicitly take into account the medical costs associat ed with the period of illness (morbidity increment) leading up to death. In its review of the Arsenic Rule, the SAB suggested that the appropriate measure to use in valuing the avoidance of the morbidity increm ent is the medical cost attributable to a cancer case (USEPA 2001d). Based on available data, EPA estimates the medical costs for a fatal bladder cancer case to be $93,927 at 1996 price levels (U SEPA 1999a). This cost (updated to 2003 price levels) is applied as a point estimate to each fatal case of bladder cancer in the benefits model.

Value of Avoiding a Non-fatal Case of Bladder Cancer

For a case of non-fatal bladder cancer, a willingness to pay (WTP) measure is used to estimate the value a person would place on reducing the risk of a case of non-fatal bladder cancer. It accounts for the desire to avoid treatment costs, pain and di scomfort, productivity losses, and any other adverse consequences related to contraction of a non-fata l case of bladder cancer. As is the case with VSL valuation, the cumulative WTP for this risk re duction across an exposed population can be used to represent the statistical value of avoiding the illness itsel f. WTP is a more comprehensive measure of the total value that a person would place on avoiding a can cer case than the much simpler cost of illness (COI)

measure.

A review of the available literature did not rev eal any studies that specifically measured the WTP to avoid the risk of non-fatal bladder cancer. Inst ead, two surrogate estimates are used: one based on avoiding a case of curable lymph cancer (lymphoma) and the other based on avoiding a case of chronic bronchitis 4 

### . Results using both WTP estimates are presented throughout the remainder of the analyses.

The WTP to avoid the risk of contracting curabl e lymphoma is derived from a survey by Magat et al. (1996) that evaluates the risk-risk trade-off be tween curable lymphoma and death using a reference lottery metric. A reference lottery is a methodol ogy that educates survey respondents of the health consequences of a particular disease (in this case cu rable lymphoma) and, based on this information, presents them with choices related to health outcom es. The choices in health outcomes made by the respondents can be further evaluated to derive quan titative measures of relative risk aversion. Based on the outcomes of the Magat et al. study, it was determin ed that the median risk-ri sk trade-off (relative risk aversion) for contracting a curable case of lymphoma was equivalent to 58.3 percent of the risk attributed to reducing the chances of sudden death (i.e., the av erage person would pay 58.3 percent of what they would pay to reduce the risk of sudden death to achie ve an equal risk reduction for contracting curable lymphoma). Based on the Magat et al. study results, EPA calculated a WTP distribution for non-fatal bladder cancer as a percentage of the VSL distribution, resulting in a mean WTP value of $2.8 million ($4.8 million × 58.3 percent) at 1990 price levels (see Appendix F, Section F.1 for additional information on the derivation of this WTP estimate).

The WTP values for avoiding the risk of chronic br onchitis are consistent with those defined for the Stage 1 DBPR. They are best represented by a lognormal distribution with a mean of $587,500, standard deviation of $264,826, and a maximum value of $1.5 million at 1998 price values (USEPA 1998a, Viscusi et al. 1991).

### Final Economic Analysis for the Stage 2 DBPR 6-84 December 2005 Mean 5th %tile 95th %tile
Morbidity Increment 1996 0.1 $ 1.30 0.1 $ N/A N/A

VSL 1990 4.8 $ 1.41 7.8 $ 1.2 $ 17.9 $
WTP - Non-Fatal Lymphoma 1990 2.8 $ 1.41 4.4 $ 0.7 $ 10.1 $

WTP - Chronic Bronchitis 1998 0.6 $ 1.13 0.8 $ 0.4 $ 1.4 $ Base Year Mean Value in Base

Year ( Millions ) CPI Update Factor V aluation Parameter Values at Year 2003 Price Level (Millions) 

### Although the WTP to avoid curable lymphoma or chr onic bronchitis is not a perfect substitute for the WTP to avoid a case of non-fatal bladder cancer, it is a reasonable value to use in a benefits transfer methodology. Non-fatal internal cancers, regardless of type, generally present patients with very similar treatment, health, and long-term quality of life impli cations, including surgery, radiation or chemotherapy treatments (with attendant side effects), and generally diminished vitality over the duration of the illness. In the absence of more specific studies, the WTP va lues for avoiding a case of curable lymphoma or a case of chronic bronchitis provide a reasonable, though not definitive, substitute for the value of avoiding nonfatal bladder cancer.

Updating Price Levels

All valuation parameters must be updated to the same price level so comparisons can be made in real terms. Values for VSL, WTP, and the morbid ity increment used in the model are updated based on adjustment factors derived from Bureau of Labor Sta tistics (BLS) consumer price index (CPI) data so that each represents a year 2003 price level. Exhibit 6.24 presents these updates.



## Exhibit 6.24 VSL, WTP, and Morbidity Increment Price Level Updates

Note: Morbidity increment value is presented as a point estimate. Source: Derived from Appendix F (Exhibits F.1a, F.1b, and F.1f).



### Adjustments for Real Income Growth and Elasticity

Although the price level (year 2003) is held consta nt throughout the benefits model, projections of benefits in future years are subject to income elas ticity adjustments. These re present changes in valuation in relation to changes in real income. For example, if, for every 1 percent increase in real income, a particular consumer’s WTP for a particular item in creases by 1 percent, this would be represented by an income elasticity of 1. For most items, income el asticity values are actually less than 1, indicating that valuation of most items does not increase as fast as real income levels.

Based on an evaluation of the income elasticity literature, Kleckner and Neuman (2000) identified published studies from which elasticity values could be derived for both fatal a nd non-fatal potential health effects. For fatal cancers, they identified a triangular distribution with a central estimate of 0.40 (low end: 0.08; high end:1.00) to represent the uncertainty of th at income elasticity value. For non-fatal cancers, a triangular distribution with a central estimate of 0.45 (low end: 0.25; high end: 0.60) best represents the value. These distributions are used as assumptions in the Monte Carlo simulation to further characterize uncertainty in benefits estimates.

To apply the income elasticity values in the model, they must be combined with projections of real income growth over the time frame for analysis. Population and real gross domestic product (GDP)5 

### Ideally, income elasticity adjustments would be calcu lated using real per capita personal income growth. However, real per capita GDP is used as a proxy for r eal per capita personal income growth due to lack of appropriate projections for real personal income growth. Historical data suggests that GDP and personal income grow at similar rates (i.e., Table B-31 of the 2002 Economic Report of the President shows that both real per capita GDP and disposable personal income grew at an av erage annual rate of 2.3 percent between 1959 and 2000).

6 

### See Appendix A of Kleckner and Neuman (2000) for additional information on the derivation and application of income elasticity adjustments.

7 

### The distribution of VSL values used in this EA is derived from a meta-analysis of 26 different VSL studies, all representing different years price levels. Th ese price levels were updated to a common 1990 price level as part of the analysis in “The Benefits and Costs of the Clean Air Act, 1970-1990” (USEPA 1997b), from which the distribution used in this EA is taken.

8 

### Because the morbidity increment represents a point es timate of direct medical costs, and income elasticity figures used in this analysis are based on WTP values, income elasticity adjustments were not applied to the projected morbidity increment values ( only the CPI update factor is applied).

9 

### A 25-year analysis time frame was chosen to repres ent the period before which most systems would need to reinvest in capital equipment replacement (a 20-year useful life is assumed for the analysis). Since the benefits, as derived in this analysis, are a result of installing treatment equipment, this time frame was also applied to benefit projections. Final Economic Analysis for the Stage 2 DBPR 6-85 December 2005 

### projections are combined to calculate per-capita real GDP values. 5 

### Percent changes in these values over time can then be combined with income elasticity figures to derive a single adjustment factor. 6 

### Given any two time periods, this factor can be calculated as follows:

Income elasticity adjustment factor = (eI 1 

### - eI 2 

### - I 2 

### - I 1 

### ) / (eI 2 

### - eI 1 

### - I 2 

### - I 1 

### )

where: e = income elasticity I 1 

### = real income (per-capita GDP) in the base year I 2 

### = real income (per-capita GDP) in the year of analysis

Income elasticity adjustment factors are calcula ted from the same base year as the values subject to adjustment. For example, income elasticity fact ors for fatal cancers are calculated from a 1990 base year because that is the base year used in th e study from which VSL estimates are derived. 7 

### The mean values of the income adjustment factors calculated for the Stag e 2 benefits model range from 1.160 to 1.488 for fatal cancer valuation and 1.063 to 1.400 for non-fatal valuation over the 25-year analysis time frame (Appendix F presents detailed spreadsheets of these calcula tions). The adjusted yearly values for the VSL and WTP (at a 2003 price level) are then calculated by multiplying the base value of each by the appropriate income elasticity adjustment factor. 8 

### Exhibit 6.25 presents the results of the income elasticity adjustments for the 25-year analysis time frame. 9



### In the Stage 2 benefits analysis the income-a djusted VSL estimates are applied to the year in which cases have been avoided. An alternative appro ach supported by some economists, and used in other EPA analyses, is for the income adjustments to be applied only up to the time that exposures are reduced rather than over the cessation lag. Because of the s horter time period over which income growth would be calculated the alternative would result in smaller inco me adjustment. To use the alternative EPA would need to link the year cancers are avoided to a speci fic year of exposure reduction. This cannot be done with the risk assessment and cessation lag applicati on in the Stage 2 analysis, where estimated cases avoided are based on a transition from one steady state to another. The VSL income adjustment approach used in this EA will tend to oversta te benefits somewhat relative to the alternative described above. EPA recognizes this potential bias, but notes that is small in comparison to other uncertainties in valuation, as well as uncertainties in the risk assessment and estimates of cases avoided.Final Economic Analysis for the Stage 2 DBPR 6-86 December 2005PointEstimate 5th %tile 95th %tile 5th %tile 95th %tile 5th %tile 95th %tile2005 0.12 7.76 6.73 1.19 17.91 4.43 3.85 0.68 10.13 0.80 0.74 0.36 1.452006 0.12 7.85 6.81 1.20 18.12 4.48 3.89 0.69 10.24 0.81 0.75 0.37 1.462007 0.12 7.95 6.88 1.21 18.33 4.53 3.93 0.70 10.36 0.82 0.75 0.37 1.482008 0.12 8.03 6.95 1.23 18.55 4.57 3.97 0.70 10.45 0.82 0.76 0.37 1.492009 0.12 8.12 7.03 1.24 18.76 4.61 4.00 0.71 10.57 0.83 0.77 0.38 1.512010 0.12 8.22 7.10 1.25 18.99 4.66 4.04 0.71 10.67 0.84 0.78 0.38 1.522011 0.12 8.31 7.18 1.26 19.24 4.70 4.08 0.72 10.76 0.85 0.78 0.38 1.542012 0.12 8.40 7.25 1.28 19.43 4.74 4.12 0.73 10.85 0.86 0.79 0.39 1.552013 0.12 8.49 7.33 1.29 19.64 4.79 4.16 0.74 10.95 0.86 0.80 0.39 1.562014 0.12 8.59 7.41 1.30 19.87 4.83 4.20 0.74 11.07 0.87 0.81 0.40 1.582015 0.12 8.68 7.48 1.31 20.13 4.88 4.24 0.75 11.16 0.88 0.81 0.40 1.602016 0.12 8.78 7.57 1.32 20.34 4.92 4.28 0.76 11.27 0.89 0.82 0.40 1.612017 0.12 8.88 7.64 1.34 20.62 4.97 4.32 0.77 11.38 0.90 0.83 0.41 1.632018 0.12 8.98 7.72 1.35 20.86 5.02 4.36 0.77 11.49 0.91 0.84 0.41 1.642019 0.12 9.08 7.81 1.36 21.15 5.06 4.40 0.78 11.61 0.91 0.85 0.41 1.662020 0.12 9.18 7.89 1.37 21.40 5.11 4.44 0.79 11.72 0.92 0.85 0.42 1.672021 0.12 9.28 7.97 1.38 21.67 5.16 4.48 0.79 11.82 0.93 0.86 0.42 1.692022 0.12 9.38 8.05 1.40 21.95 5.20 4.52 0.80 11.94 0.94 0.87 0.43 1.712023 0.12 9.49 8.15 1.41 22.20 5.25 4.56 0.81 12.06 0.95 0.88 0.43 1.722024 0.12 9.59 8.23 1.42 22.45 5.30 4.60 0.82 12.17 0.96 0.89 0.43 1.742025 0.12 9.70 8.32 1.43 22.71 5.34 4.64 0.82 12.28 0.97 0.89 0.44 1.762026 0.12 9.80 8.41 1.45 22.96 5.39 4.69 0.83 12.40 0.98 0.90 0.44 1.782027 0.12 9.91 8.49 1.46 23.28 5.44 4.73 0.84 12.52 0.99 0.91 0.45 1.792028 0.12 9.87 8.46 1.46 23.17 5.42 4.71 0.84 12.48 0.98 0.91 0.44 1.792029 0.12 9.95 8.52 1.47 23.40 5.46 4.74 0.84 12.56 0.99 0.91 0.45 1.80WTP - BronchitisMeanValueMedianValue90% Confidence IntervalWTP - Non-Fatal LymphomaMeanValueMedianValue90% Confidence IntervalMorbidityIncrementFatal Cancer Cases Non-Fatal Cancer CasesYear90% Confidence IntervalVSLMeanValueMedianValueExhibit 6.25 Value of Morbidity Increment, VSL,and WTP by Year, Adjusted for Income ElasticityNotes: All values in millions of year 2003 dollars. Detail may not add exactly due to independent rounding.Source: Exhibit F.1f10 

### See EPA’s Guidelines for Preparing Economic Analyses (USEPA 2000j) for a full discussion of the use of discount rates in the evaluation of policy decisions. Final Economic Analysis for the Stage 2 DBPR 6-87 December 2005 

### 6.5.3 Value of Benefits Resulting from the Stage 2 DBPR for the Preferred Alternative

To assess the total value of benefits resulting from the Stage 2 DBPR, both the qualitative and quantitative benefits must be consid ered. Although information is not sufficient to quantify the value of preventing potential adverse reproductive and developm ental health effects in the primary benefits analysis, the number of cases avoided and associated value could be significant (see Section 6.8). Likewise, the value of other health and non-health be nefits could be substantia l, for example reduction in risk of other cancers and reductions in other regulat ed contaminants. Thus, the primary, quantitative benefits analysis is a conservative estimate of the total benefits of this regulation.

To calculate the total value of benefits deri ved from reductions in bladder cancer cases as a result of the Stage 2 DBPR, a stream of monetary benef its is calculated by combining the annual cases avoided (Exhibit 6.21) with valuation inputs (Exhibit 6.25) using a Monte Carlo simulation. The Monte Carlo simulation allows the characterization of uncertainty around the modeling outputs based on the uncertainty in the various inputs. The benefits model uses distri butions of VSL, WTP, and income elasticity values to attribute monetary values (with uncertainty bounds) to the mean number of bladder cancer cases avoided. The values for cancer cases avoided for the three cessa tion lag models and for both WTP estimates (for curable lymphoma and chronic bronchitis) were calcula ted and carried through the Stage 2 DBPR benefits model. The results for fatal, non-fatal, and total be nefits are presented in Exhibits 6.26 (note that the alternative analysis assumptions, e.g. Arsenic/B ladder Cancer Cessation Lag model and WTP for Lymphoma, are shown in the table headings).

Calculating and Discounting the Stream of Benefits

To allow comparison of future streams of costs and benefits, it is common practice to adjust both streams to a present value (PV) using a discount rate . This process takes into account the time preference that society places on expenditures and benefits and a llows comparison of cost and benefit streams that vary over a given time period. 10 

### A present value for any future period can be calculated using the following equation:

PV = V(t) / (1 + R) t



### Where: t = The number of years from the reference period (year 0 of the benefits stream) R = Discount rate V(t) = The benefits occurring t years from the reference period

There is much discussion among economists of the pr oper discount rate to use for policy analysis. Therefore, for Stage 2 DBPR benefits analyses, PV calculations are made using two rates thought to best represent current policy evaluation methodologies, 3 and 7 percent. Historically, the use of 3 percent is based on rates of return on relatively ri sk-free investments, as described in the Guidelines for Preparing Economic Analyses (USEPA 2000j). The rate of 7 percent is recommended by OMB as an estimate of “before-tax rate of return to incremental privat e investment” (USEPA 1996b). To allow evaluation on an annual basis, the PV of benefits is annualized usi ng the same discount rates. Exhibit 6.28 presents the annualized present value of estimated benefits for the Stage 2 DBPR over the 25-year time frame for analysis.

### Final Economic Analysis for the Stage 2 DBPR 6-88 December 2005 5th %tile 95th %tile 5th %tile 95th %tile 5th %tile 95th %tile
2005 - $ - $ - $ - $ - $ - $ - $ - $ - $ 2006 - $ - $ - $ - $ - $ - $ - $ - $ - $ 2007 - $ - $ - $ - $ - $ - $ - $ - $ - $ 2008 - $ - $ - $ - $ - $ - $ - $ - $ - $ 2009 - $ - $ - $ - $ - $ - $ - $ - $ - $ 2010 135 $ 21 $ 310 $ 127 $ 20 $ 293 $ 252 $ 39 $ 580 $ 2011 349 $ 53 $ 803 $ 302 $ 46 $ 694 $ 618 $ 95 $ 1,422 $ 2012 631 $ 97 $ 1,450 $ 515 $ 79 $ 1,182 $ 1,068 $ 163 $ 2,453 $ 2013 978 $ 150 $ 2,246 $ 762 $ 117 $ 1,749 $ 1,583 $ 242 $ 3,637 $ 2014 1,277 $ 195 $ 2,935 $ 933 $ 143 $ 2,144 $ 1,942 $ 297 $ 4,464 $ 2015 1,557 $ 238 $ 3,581 $ 1,080 $ 165 $ 2,483 $ 2,224 $ 340 $ 5,116 $ 2016 1,810 $ 277 $ 4,164 $ 1,206 $ 184 $ 2,774 $ 2,442 $ 373 $ 5,616 $ 2017 2,040 $ 312 $ 4,697 $ 1,323 $ 202 $ 3,045 $ 2,621 $ 400 $ 6,032 $ 2018 2,242 $ 342 $ 5,166 $ 1,432 $ 218 $ 3,299 $ 2,771 $ 422 $ 6,383 $ 2019 2,416 $ 368 $ 5,574 $ 1,536 $ 234 $ 3,544 $ 2,899 $ 442 $ 6,690 $ 2020 2,566 $ 391 $ 5,924 $ 1,635 $ 249 $ 3,774 $ 3,011 $ 458 $ 6,951 $ 2021 2,698 $ 410 $ 6,230 $ 1,729 $ 263 $ 3,993 $ 3,110 $ 473 $ 7,181 $ 2022 2,817 $ 428 $ 6,515 $ 1,821 $ 277 $ 4,211 $ 3,199 $ 486 $ 7,398 $ 2023 2,924 $ 445 $ 6,765 $ 1,909 $ 290 $ 4,416 $ 3,279 $ 499 $ 7,586 $ 2024 3,023 $ 459 $ 6,995 $ 1,994 $ 303 $ 4,616 $ 3,353 $ 510 $ 7,761 $ 2025 3,113 $ 473 $ 7,205 $ 2,078 $ 315 $ 4,808 $ 3,422 $ 519 $ 7,920 $ 2026 3,198 $ 485 $ 7,407 $ 2,158 $ 327 $ 5,000 $ 3,487 $ 529 $ 8,077 $ 2027 3,277 $ 496 $ 7,603 $ 2,237 $ 339 $ 5,190 $ 3,548 $ 537 $ 8,231 $ 2028 3,309 $ 501 $ 7,672 $ 2,284 $ 346 $ 5,297 $ 3,560 $ 539 $ 8,253 $ 2029 3,371 $ 510 $ 7,823 $ 2,353 $ 356 $ 5,461 $ 3,606 $ 546 $ 8,369 $ Ann. Avg. 1,749 $ 266 $ 4,043 $ 1,177 $ 179 $ 2,719 $ 2,080 $ 316 $ 4,805 $ Arsenic/Bladder Cancer Cessation Lag Model

Mean Value 90% Confidence Interval Smoking/Bladder Cancer Cessation Lag Model

Mean Value 90% Confidence Interval 90% Confidence Interval Smoking/Lung Cancer Cessation Lag Model

Mean Value Yea r 

## Exhibit 6.26a Non-Discounted Stream of Benefits from the Stage 2 DBPR Preferred Regulatory Alternative, All Systems, WTP Curable Lymphoma, TTHM as Indicator

Notes: All values in millions of year 2003 dollars . Detail may not add to totals due to independent rounding. EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. The 90 percent confidence bounds for cases reflect uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cessation lag. Benefits were calculated using the Villanueva et al. (2003) PAR.

Source: Derived from Exhibit F.2u.

### Final Economic Analysis for the Stage 2 DBPR 6-89 December 2005 5th %tile 95th %tile 5th %tile 95th %tile 5th %tile 95th %tile 2005

- $ - $ - $ - $ - $ - $ - $ - $ - $ 2006

- $ - $ - $ - $ - $ - $ - $ - $ - $ 2007

- $ - $ - $ - $ - $ - $ - $ - $ - $ 2008

- $ - $ - $ - $ - $ - $ - $ - $ - $ 2009

- $ - $ - $ - $ - $ - $ - $ - $ - $ 2010 67 $ 15 $ 146 $ 63 $ 14 $ 138 $ 125 $ 27 $ 274 $ 2011 172 $ 38 $ 380 $ 149 $ 33 $ 329 $ 306 $ 67 $ 673 $ 2012 312 $ 69 $ 687 $ 255 $ 56 $ 560 $ 528 $ 116 $ 1,162 $ 2013 484 $ 106 $ 1,065 $ 377 $ 83 $ 829 $ 784 $ 172 $ 1,725 $ 2014 632 $ 139 $ 1,392 $ 462 $ 101 $ 1,017 $ 962 $ 211 $ 2,118 $ 2015 772 $ 169 $ 1,702 $ 535 $ 117 $ 1,180 $ 1,103 $ 241 $ 2,432 $ 2016 898 $ 196 $ 1,980 $ 598 $ 131 $ 1,319 $ 1,212 $ 265 $ 2,671 $ 2017 1,013 $ 221 $ 2,238 $ 657 $ 143 $ 1,451 $ 1,301 $ 284 $ 2,874 $ 2018 1,114 $ 243 $ 2,462 $ 712 $ 155 $ 1,572 $ 1,377 $ 300 $ 3,042 $ 2019 1,201 $ 261 $ 2,660 $ 764 $ 166 $ 1,691 $ 1,442 $ 313 $ 3,193 $ 2020 1,277 $ 277 $ 2,830 $ 814 $ 177 $ 1,803 $ 1,499 $ 325 $ 3,321 $ 2021 1,344 $ 291 $ 2,983 $ 861 $ 187 $ 1,911 $ 1,549 $ 336 $ 3,437 $ 2022 1,404 $ 304 $ 3,122 $ 908 $ 196 $ 2,018 $ 1,595 $ 345 $ 3,545 $ 2023 1,459 $ 315 $ 3,244 $ 952 $ 206 $ 2,118 $ 1,636 $ 353 $ 3,638 $ 2024 1,509 $ 325 $ 3,358 $ 996 $ 215 $ 2,216 $ 1,675 $ 361 $ 3,725 $ 2025 1,556 $ 335 $ 3,464 $ 1,038 $ 223 $ 2,311 $ 1,711 $ 368 $ 3,808 $ 2026 1,600 $ 344 $ 3,562 $ 1,080 $ 232 $ 2,404 $ 1,744 $ 375 $ 3,884 $ 2027 1,641 $ 352 $ 3,663 $ 1,120 $ 240 $ 2,501 $ 1,776 $ 381 $ 3,965 $ 2028 1,656 $ 355 $ 3,693 $ 1,143 $ 245 $ 2,550 $ 1,782 $ 382 $ 3,974 $ 2029 1,688 $ 362 $ 3,772 $ 1,179 $ 253 $ 2,633 $ 1,806 $ 387 $ 4,035 $ Ann. Avg. 872 $ 189 $ 1,936 $ 587 $ 127 $ 1,302 $ 1,036 $ 224 $ 2,300 $ Yea r Arsenic/Bladder Cancer Cessation Lag Model

Mean Value 90% Confidence Interval Smoking/Bladder Cancer Cessation Lag Model

Mean Value 90% Confidence Interval 90% Confidence Interval Smoking/Lung Cancer Cessation Lag Model

Mean Value 

## Exhibit 6.26b Non-Discounted Stream of Benefits from the Stage 2 DBPR Preferred Regulatory Alternative, All Sy stems, WTP Chronic Bronchitis, TTHM as Indicator

Notes: All values in millions of year 2003 dollars . Detail may not add to totals due to independent rounding. EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. The 90 percent confidence bounds for cases reflect uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cessation lag. Benefits were calculated using the Villanueva et al. (2003) PAR.

Source: Derived from Exhibit F.3u.

### Final Economic Analysis for the Stage 2 DBPR 6-90 December 2005 Best Estimate

Pre-Stage 1 PAR B est E st i mate Annual Cases

Ultimately Avoided 1 A verage Annualized Cases Avoided

for 25 years A nnua li ze d Expected Benefits
3% 2 (Millions, 2003$) A nnua li ze d Expected Benefits
7% 2 (Millions, 2003$) Approach (PAR Source) A BC D E Approach 1: "5 Studies" 2% or 17% (0%, 33%) 64 or 546 (0, 1060) 35 or 301 (0, 656) $194 or $1,652 ($0, $4,288) $158 or $1,346 ($0, $3,490)
Approach 2: Villaneuva 2003 15.7% (8.5%, 27.2%) 506 (275, 874) 279 (103, 541) $1,531 ($233, $3,536) $1,246 ($190, $2,878)
Approach 3: Villaneuva 2004 17.1% (2.5%, 33.1%) 550 (80, 1064) 303 (30, 658) $1,664 ($068, $4,304) $1,355 ($055, $3,503) 

## Exhibit 6.27 Benefits Summary for the Stage 2 DBPR, Preferred Regulatory Alternative (Millions, 2003$)

Notes: Detail may not add to totals due to independent rounding. Smoking-lung cessation lag model and chronic lymphoma WTP used to calculate benefits.

1 

### Based on TTHM as indicator. EPA recognizes that benef its may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. The estimate of annual cases ultimately avoided shown here are those calculated from the PAR values as described in 6.4.1.1. The full benefits simulation model, which incorporates uncerta inty in the impacts of the IDSE and uncertainty in SWAT predictive equations, and in the estimates of PAR, produces slightly higher mean estimates. Specifically, the model estimates 581 cases versus the 506 cases shown here for Approach 2. Cases avoided have not yet been discounted.

2 

### The 90 percent confidence bounds fo r benefits incorporates uncertainty in the VSL, WTP, and income elasticity adjustment relative to mean cases. Source: (A) Exhibit 6.20. (B) Exhibit E.19. (C, D) The cases avoided from column B multiplied by the annualized benefits per ca se, derived from Exhibit 6.28.

### Final Economic Analysis for the Stage 2 DBPR 6-91 December 2005 Mean 5th 95th Mean 5th 95th 3 %, Lymphoma $ 1,531 $ 233 $ 3,536 7 % Lymphoma $ 1,246 $ 190 $ 2,878 3 % Bronchitis $ 763 $ 165 $ 1,692 7 % Bronchitis $ 621 $ 135 $ 1,376 3 %, Lymphoma $ 1,032 $ 157 $ 2,384 7 % Lymphoma $ 845 $ 129 $ 1,950 3 % Bronchitis $ 514 $ 111 $ 1,141 7 % Bronchitis $ 420 $ 91 $ 932 3 %, Lymphoma $ 1,852 $ 282 $ 4,276 7 % Lymphoma $ 1,545 $ 235 $ 3,566 3 % Bronchitis $ 922 $ 200 $ 2,045 7 % Bronchitis $ 769 $ 167 $ 1,704 Adverse Reproductive and Developmental Health Effects Avoided Causality has not been established, and numbers and types of cases avoided, as well as the value of such cases, were not quanti fied in the primary benefits analysis. Given the numbers of women of child-bearing age exposed (58 million), the evidence indicates that the number of cases and the value of preventing those cases could be significant. See results of the illustrative calculation in Section 6.8. Number and Value of Estimated Bladder Cancer Cases Avoided 1 Causality has not been established; however, the weight of evidence supports PAR estimates of potential benefits.

399 Smoking/Bladder

Cancer Model

610 Annualized Benefits of Bladder Cancer Cases Avoided (Millions, 2003$) 3 Annual Average Bladder Cancer

Cases Avoided for the first 25
years 2

279 Discount Rate, WTP for Non-Fatal Bladder

Cancer Cases

541 Smoking/Lung Cancer Model Cessation Lag Model

used to estimate Annual Bladder Cancer Cases Avoided

103

333 138 Arsenic/Bladder Cancer Model 188 61

Other Health Benefits

Non-Health Benefits Qualitative assessment indicates that the va lue of non-health benefits could be positive. Qualitative assessment indicates that the value of other health benefits could be positive and significant. 

## Exhibit 6.28 Benefits Summary for the Stage 2 DBPR, Preferred Regulatory Alternative (Millions, 2003$)

Notes: Detail may not add to totals due to independent rounding.

1 

### Based on TTHM as indicator, Villanueva et al. (2003) for PAR. EPA recognizes that benefits may be as low as zero since causality has not yet been estab lished between exposure to chlorinated water and bladder

cancer.

2 

### The 90 percent confidence interval for cases incor porates uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cessation lag.

3 

### . The 90 percent confidence bounds for monetized benefits reflect uncertainty in monetization inputs relative to mean cases. Assumes 24 percent of cases are fatal, 76 percent are non-fatal (USEPA 1999a).

Source: Summarized from detailed figures presented in Appendix E (Exhibits E.38d, E. 38h, E.38l) and F (Exhibits F.2v and F.2w, F.3v and F.3w).



### 6.5.4 Comparison of the Value of Be nefits for Regulatory Alternatives

This section compares the benefits of d ecreasing DBP occurrence under the Stage 2 DBPR preferred alternative with the three evaluated alte rnatives. The alternativ es are summarized below (see Chapter 4 for a more detailed discussion):

Preferred Alternative: 80 μg/L TTHM and 60 μg/L HAA5 as an LRAA; bromate MCL of 10 μg/L as an RAA based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR for bromate); compliance monitoring proceeded by the IDSE.

Alternative 1: 80 μg/L TTHM and 60 μg/L HAA5 as an LRAA; bromate MCL of 5μg/L as an RAA based on monthly samples taken at the finished water point.

### Final Economic Analysis for the Stage 2 DBPR 6-92 December 2005 Mean 5th 95th Mean 5th 95th Mean 5th 95th Mean 5th 95th Average Annual Number of Cases Avoided 2 – 279 103 541 250 127 397 939 483 1,466 1,296 675 1,988

3%, Lymphoma $ 1,531 $ 233 $ 3,536 $ 1,377 $ 209 $ 3,180 $ 5,167 $ 786 $ 11,936 $ 7,130 $ 1,085 $ 16,468 7%, Lymphoma $ 1,246 $ 190 $ 2,878 $ 1,126 $ 172 $ 2,600 $ 4,227 $ 644 $ 9,758 $ 5,832 $ 888 $ 13,464 3%, Bronchitis $ 763 $ 165 $ 1,692 $ 686 $ 149 $ 1,522 $ 2,575 $ 558 $ 5,712 $ 3,552 $ 769 $ 7,880 7%, Bronchitis $ 621 $ 135 $ 1,376 $ 561 $ 122 $ 1,243 $ 2,105 $ 457 $ 4,665 $ 2,904 $ 630 $ 6,436 Discount Rate,WTP for NonFatal Cases

Annualized Benefits of Cases
Avoided 3 Preferred Alternative Alternative 1 Alternative 2 Alternative 3 

### Alternative 2: 80 μg/L TTHM and 60 μg/L HAA5 as the single maximum value for any sample taken during the year; bromate MCL of 10 μg/L as an RAA based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR for bromate).

Alternative 3: 40 μg/L TTHM and 30 μg/L HAA5 as an RAA of all distribution samples taken; bromate MCL of 10 μg/L as an RAA based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR for bromate).

Exhibit 6.29 compares the value of benefits for all Stage 2 DBPR regulatory alternatives, using TTHM as an indicator for reduction in all chlorination DBPs. Although the primary objective of the Stage 2 DBPR is to reduce the non-quantified risks of a dverse potential reproductive and developmental health effects, the exhibit also shows that the quantif ied benefits of reducing cancer cases are considerable regardless of which alternative is chosen. Chapter 7 examines the costs of these alternatives, and Chapter 9 compares their benefits and costs. An additional an alysis in Appendix N considers the cost effectiveness of the alternatives in terms of quality-adjusted life years saved.



## Exhibit 6.29 Number and Annualized Value of Estimated Bladder Cancer Cases Avoided for All Stage 2 DBPR Regulatory Al ternatives, Villanueva et al. (2003) for Baseline Risk (Millions, 2003$) 1



### Notes: 1 

### Based on TTHM as indicator, and the smoking and l ung cancer cessation lag model. EPA recognizes that benefits may be as low as zero since causality has not yet been established betw een exposure to chlorinated water and bladder cancer.

2 

### The 90 percent confidence interval for cases incor porates uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cessation lag.

3 

### The 90 percent confidence bounds for monetized benefits reflect uncertainty in monetization inputs relative to mean cases.
4 

### The Preferred Alternative avoids more cases of bladder cancer than the next least expensive alternative–Alternative 1–because it is the only alternative for wh ich the IDSE is considered, resulting in increased benefits. However, Alternat ive 1 is the only alternative requi ring a lower bromate standard, whose benefits are not considered in this anal ysis. Should potential cancer case s averted by lowering the bromate standard be quantified, Al ternative 1 would c apture more benefits. Assumes 24 percent of cases are fata l, 76 percent are non-fatal (USEPA 1999a).

Source: Summarized from detailed figures presented in Appendix E (Exhibits E. 38d, E.40d, E.41d, E.42d) and Appendix F (Exhibits F.2v and w, F.3v and w, F.6b, F.7b, F.8b, F.9b, F.10b, F.11b).

### Final Economic Analysis for the Stage 2 DBPR 6-93 December 2005 

## 6.6 Uncertainties

Many factors contribute to uncertainty in national benefits estimates. Uncertainty exists in model inputs such as the estimated PAR values and the cessation lag models. To assess uncertainty in the approach used to estimate the number of bladder cancer cases in the baseline that can be attributed to DBP occurrence and exposure, and the number of cases that can be avoided by implementation of the Stage 2 DBPR, three approaches were used to estimate Pre-Stage 1 PAR (see Appendix E for more detail). To quantify uncertainty in cessation lag, three independe nt cessation lag models derived from three different epidemiological studies are used. Also, two functi onal forms are used for each of these data sets and uncertainty in the parameters of those functions is included in the analysis (see Appendix E for more detail). For monetization of benefits, EPA uses tw o alternatives for valuing non-fatal bladder cancer. Other uncertainties, such as the linear relationship between DBP reductions a nd reductions in bladder cancer cases avoided, are discussed qualitatively. A su mmary of the key uncertainties and the effects of uncertainty in those assumptions on the benefits and cost analyses are presented in Exhibit 6.29.

EPA believes that uncertainty in the complian ce forecast has a potentially large influence on benefit (and cost) estimates in this EA. Thus, the Agency has attempted to quantify the uncertainty by giving equal weight to two different compliance for ecast approaches. One comp liance forecast approach is based on the SWAT predictions, and the other is based on the ICR matrix method. The ICR Matrix Method uses the same basic approach as SWAT, but uses TTHM and HAA5 data from the ICR directly to estimate the percent of plants changing technology to comply with the Stage 2 DBPR and the resulting DBP reduction (see Chapter 5 for more information on these approaches). To characterize the uncertainty of the compliance forecast results, EPA assumes a uni form distribution between SWAT and ICR Matrix Method results. That is, the national benefits es timates presented in this chapter represent the midpoint between benefits estimated using the SWAT model, and those estimated using the ICR Matrix Method. Benefits estimates using the SWAT model are about 30% lower than the midpoint estimates while those using the ICR Matrix Method are about 30% higher.

Two of the greatest uncertainties affecting the be nefits of the Stage 2 DBPR are related to nonquantified benefits estimates. Both of these factor s result in an underestimation of quantified Stage 2 DBPR benefits. To inform the reader of the poten tial magnitude of these benefits, Section 6.7 provides results from a sensitivity analysis for colon and r ectal cancers combined. An illustrative analysis of potential developmental and reproduc tive benefits of the Stage 2 DBPR is discussed in Section 6.8.

In addition to the uncertainties listed in Exhibit 6. 30, the potential costs or benefits of a possible interactive effect from the promulgation of more th an one rule in a short period of time are also not quantified. EPA has taken into account compliance w ith the Stage 1 DBPR and considered the potential impacts of the Ground Water Rule on non-treatment costs in Appendix H, and considered potential impacts of the Arsenic Rule, and the LT2ESWTR. EPA addresses potential increased risk due to ground water systems adding disinfection under the Ground Water Rule in Appendix M.

### Final Economic Analysis for the Stage 2 DBPR 6-94 December 2005 

## Exhibit 6.30 Uncertainties and Possibl e Effect on Estimate of Benefits

Uncertainty Section with Full Discussion of Uncertainty Effect on Benefit Estimate

Underestimate Overestimate Unknown Impact Uncertainty in DBP reductions for surface water systems Chapter 5 Quantified in the primary analysis (addresses potential underestimate or overestimate) Uncertainty in DBP reductions for ground water systems Chapter 5 X

Analysis of reduction in DBP occurrence does not include results of IDSE 5.3 Quantified in the primary analysis (addresses for potential underestimate)

Uncertainty in valuation inputs (WTP and VSL) 6.5.2 Quantified in the primary analysis (addresses for potential underestimate or overestimate) Uncertainty in the bladder cancer PAR value 1 

### 6.1.1 Appendix E Quantified in the primary analysis (addresses range of potential effects, but

true values could lie outside the range) Analysis of exposure reduction assumes TTHM and HAA5 to be proxies for all chlorination DBPs 6.3.3 X

DBPs have a linear no-threshold dose-response relationship for bladder cancer effects 6.2.1 X

Uncertainty in cessation lag function 6.4.2.2 Appendix E Quantified in the primary analysis (addresses potential underestimate or overestimate)
Benefits of reduced cancers other than bladder cancer are not included in the quantitative analysis 6.7 Quantified in a sensitivity analysis (addresses potential underestimate)

Potential reproductive and developmental health effects avoided are not quantified in the primary analysis 6.8 X

1 

### To assess uncertainty in PAR estimates, three approaches were used to estimate Pre-Stage 1 PAR, as shown in Appendix E of the EA. PAR value average estimate of 16 percent used to calculate the number of bladder cancer cases avoided is not absolute. EPA rec ognizes that the number of cases may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer.



## 6.7 Sensitivity Analysis for Other Cancers

Colon and rectal cancers combined are the third mo st common site (excluding skin) of new cancer cases and deaths in both men and women in the U.S. The American Cancer Society (ACS) estimated that 104,950 new colon and rectal cancer cases will be diagnosed in 2005, with 56,290 resulting in deaths (ACS 2005). Human epidemiology studies on chlorina ted surface water have reported associations with colon and rectal cancers.

### Final Economic Analysis for the Stage 2 DBPR 6-95 December 2005 

### In the development of the Stage 1 DBPR, EPA investigated estimating a PAR for colon and rectal cancers and concluded that the data was not conclusive enough to calculate a PAR. Prior to the Stage 1 DBPR, several population-based case control and pr ospective cohort studies had been published that evaluated the association between consumption of chlo rinated drinking water and colon or rectal cancer (Wilkins and Comstock 1981, Bean et al. 1982, Cragle et al. 1985, Young 1987, Doyle 1997, Koivusalo et al. 1997, Hildesheim et al. 1998).

Wilkins and Comstock (1981) examined annual mortality rates in Washington County, MD from 1963-1975 comparing municipal residents with deep well u sers. For rectal cancer, a relative risk (RR) of 1.42 was reported, but this was not statistically signi ficant. No difference was observed for colon cancer. Bean et al. (1982) examined cancer incidence da ta for the state of Iowa for the years 1969-1978 considering differences for surface and ground water supplies (and well depth for ground water) and for different population-size municipalities. They observed higher incidence of rectal cancer for both males and females in surface water versus ground water for all size municipalities. No difference was observedfor colon cancer. Cragle et al. (1985), a hospital-based case-control study, and Young et al. (1987), a casecontrol study, were excluded due to inadequate e xposure assessment. Doyle et al. (1997), a prospective cohort study, indicated that in comparison with wo men who used municipal ground water sources, women with municipal surface water sources were at an incr eased risk of colon cancer and all cancers combined. Doyle (1997) did not report an association between consumption of chlorinated surface water and cancer of the rectum and anus. Koivusalo et al. (1997) conducted an historical c ohort study of 621,431individuals in 56 towns in Finland and compared cancer inciden ce between locations based on a measure of raw water and chlorination treatment. Comparing locations re presenting exposure to chlorinated surface water and no exposure to chlorinated surface water, they observe d a statistically significant increased risk of rectal cancer for women (RR = 1.38), but no increased risk fo r rectal cancer for men nor for colon cancer for either sex. Based on these studies for colon and for rectal cancer, it was not possible to estimate a PAR range at the time of the Stage 1DBPR. Hildesheim et al. (1998) indicated an association between rectal cancer and exposure to chlorinated water, but did not observe a significant increase in risk of colon cancer associated with chlorinated surface water use.

Since the Stage 1 DBPR, additional human ep idemiology studies have been published that investigated the potential relationship between col on and rectal cancers and exposure to chlorinated surface water (King et al. 2000a and Yang et al. 1998). The database of studies on colon and rectal cancers continues to support a possible association, but evidence remains mixed. King et al. (2000a), a population-based case-control study, found evidence of an increased colon cancer risk for males with cumulative exposure to THMs and dur ation of exposure to chlorinated surface water. No associations were observed between exposure measures and rect al cancer. Yang et al. (1998) conducted a crosssectional mortality study in Taiwan which co mpared cancer mortality rates from chlorinating municipalities and non-chlorinating municipalities. The resulting Ratios of Age-adjusted Mortality Rates (SRR) were significantly higher for males and for fema les for rectal cancer but not for colon cancer. This study did not control for diet or smoking. Also, because this study is based on mortality, not incidence, it makes this study problematic for doing risk analysis for bladder cancer incidence.

Because the area of the colon and rectum is the third most common site (excluding skin) of new cancer cases and deaths in both men and women in the U.S., and human epidemiology studies on chlorinated surface water have reported potential associa tions with colon and rectal cancers, EPA chose to perform a sensitivity analysis on potential benefits from avoiding colon and rectal cancers. Of the available studies, EPA has identified Hildesheim et al. (1998) and King et al. (2000a) as providing adequate data to estimate PAR values for colon and rectal cancers. Additional details on these studies are presented below.

Hildesheim et al. (1998) conducted a population-b ased case-control study and found an association between duration of chlorinated surface water use a nd rectal cancer in Iowa residents in 1986-1989. It should be noted that this study is essentially the same research group and study setting as the Cantor et al.

### Final Economic Analysis for the Stage 2 DBPR 6-96 December 2005 

### (1998) study on bladder cancer that was used in the ma in benefits analysis. No important association was found for colon cancer and related sites for either duration of exposure or TTHM estimates. ORs were presented for several characterizations of exposur e such as whether the study group was exposed to chlorinated surface, ground water, or any chlorinate d water, duration of exposure, total lifetime THM level, and lifetime average THM concentration. This study was used to estimate a PAR for rectal cancer.

King et al. (2000a) conducted a population-based case-c ontrol study in southern Ontario, Canada. This study is essentially the same research group and study setting as the King and Marrett (1996) study that was used in the Villanueva et al. (2003) meta-a nalysis. ORs were presented for duration of exposure to chlorinated water and for exposure to various levels of TTHMs. An association was found among males for colon cancer risk with cumulative exposure to TTHMs, duration of exposure to chlorinated surface water, and duration of exposure to 

## $ 50 

## : g/L and to 75 

## : g/L of TTHM. These relationships were not observed in females. No association was found betw een rectal cancer risk by any of the measures of exposure to disinfection byproducts in this study. Th is study was used to estimate a PAR for colon cancer (for males only).

PAR for colon cancer

EPA estimated a PAR value for colon cancer for males only using data from the King et al. (2000) study. PAR was calculated using the method describ ed in Appendix E for calculating PAR for bladder cancer for the five epidemiological studies used in Approach 1 for bladder cancer. Using the data from Table 2 of the King et al. (2000) study reflecting year s of exposure to chlorinated water resulted in a PAR value estimate of 24.5%. Note again that this onl y applies to males since the authors did not detect associations for females.

PAR for rectal cancer

EPA estimated a PAR value for rectal cancer fo r both sexes only using data from the Hildesheim et al. (1998) study. As for colon cancer, PAR for rectal was calculated using the method described in Appendix E for calculating PAR for bladder cancer for the five epidemiological studies used in Approach 1 for bladder cancer. Using the data from Table 1 of the Hildesheim et al. (1998) study, reflecting years of exposure to any chlorinated water, resulted in a PAR value estimate of 11.8%.

Estimated Benefits for Reductions in Colon and Rectal Cancers

Using the above PAR values with data from SEER a nd the American Cancer Society for colon and rectal cancer incidence, EPA estimated the annuali zed value of the cases of colon cancer avoided from Stage 2 for males only and of cases of rectal cancer avoided for both sexes. The results are presented in Exhibit 6.31, which also shows the annualized valu e of the bladder cancer cases avoided from Stage 2. These results are based on TTHM reductions and incl ude the smoking / lung cancer cessation lag model. Additionally, a sensitivity analysis in Appendix N to this document incorporates into a cost effectiveness analysis the quality-adjusted life years that are poten tially saved for avoided colon and rectal cancer cases

EPA recognizes that actual risks and PAR values could be zero due to uncertainties in the scientific evidence. Detailed benefits information can be found in Appendix E2.

### Final Economic Analysis for the Stage 2 DBPR 6-97 December 2005 

## Exhibit 6.31 Annualized Value 1 

## of Estimated Bladder Cancer Cases Avoided for the Primary Analysis, and Estimated Colon and Rectal Cancer Cases Avoided for the Sensitivity Analysis (Millions, 2003$) 2



### Notes: 1 

### The 90 percent confidence bounds show n in the exhibit incorporate uncertainty in the VSL, WTP, and income elasticity adjustment.

2 

### Based on TTHM as indicator for the Preferred Regulator y Alternative. EPA recogni zes that the benefits may be as low as zero since causality has not yet been established between exposur e to chlorinated water and bladder, colon, or rectal cancer.
Source: Quantitative values are summa rized from detailed figures presented in Appendix F (Exhibits F.2ab, F.2ac, F.3 ab, F.3ac, F.12c and d, F.13c and d, F.14c and d, F.15c and d).



### Caveats to analysis

The uncertainties that pertain to the PAR analysis for bladder cancer (see Section 6.4 - 6.6) also pertain to the PAR analysis for colon and rectal can cer. However, additional uncertainties pertain to the risk estimates for colon and rectal cancer. Exposure levels and associated risk from limited epidemiological studies may not be representative of the general population; a nd inconsistency of the relative risk for cancer type and gender (the King et al study shows an association for males only while the Hildesheim et al study reported an association for r ectal but not for colon cancer) are significant and difficult to explain. Therefore, the estimates from th is sensitivity analysis should only be interpreted as indication of possible benefits from this rule while also recognizing that the benefits may be as low as

zero.



## 6.8 Potential Fetal Losses Avoided

EPA believes that additional benefits from this rule could come from reduction in developmental and reproductive health effects. EPA does not believe the available evidence pr ovides an adequate basis for quantifying potential reproductive or developmental ri sks in the main benefits analysis. Furthermore, no causal link between DBPs and these risks has yet b een established. (See Section 6.2.2 for a complete discussion of the reproductive and developmental health effects of DBPs.) Nevertheless, given the widespread exposure to DBPs, the importance soci ety places on reproductive and developmental health, and the nearly 1 million fetal losses each year in the U.S., the Agency believes that it is appropriate to provide some quantitative indication of the potential ri sk in these categories. To do this, PAR calculations from several studies on the relationship between chlo rinated water exposure and fetal loss have been adapted and applied to national sta tistics on the annual incidence of fetal loss (shown in Section 6.8.1). Section 6.8.2 discusses valuation of these potential fetal losses avoided. Smoking/Lung Cancer Cessation Lag Model Bladder Cancer - Primary Analysis Colon Cancer Sensitivity A nalysis Rectal Cancer Sensitivity Analysis Mean 5th 95th Mean 5th 95th Mean 5th 95th 3 %, Lymphoma $1,531 $233 $3,536 $2,395 $364 $5,530 $819 $125 $1,890 7 % Lymphoma $1,246 $190 $2,878 $1,991 $303 $4,594 $680 $104 $1,570 3 % Bronchitis $763 $165 $1,692 $1,193 $259 $2,645 $408 $88 $904 7 % Bronchitis $621 $135 $1,376 $991 $215 $2,195 $339 $74 $750 Discount Rate, WTP for Non-Fatal Cases11 

### Use of unadjusted OR or RR estimates has the effect of including possible biases from known confounders; however, EPA believes the unadjusted estimat es are adequate for purposes of the illustrative calculations presented here. It was not possible to calculate ORs adju sted for the same confounders from the four studies



## . 12 

### The calculated lower 95 percent confidence intervals on PA R for all four studies were less than zero. This means the possibility of no effect cannot be ruled out w ith 95 percent confidence on the basis of any single study. The upper 95 percent confidence intervals for the PAR estimates were 3, 4, 4, and 6 percent. Final Economic Analysis for the Stage 2 DBPR 6-98 December 2005 

### 6.8.1 Reproductive Effects Illustrative Calculation

EPA has calculated the unadjusted ORs or RRs associated with each of four population-based epidemiological studies of fetal loss: Waller et al. 2001, King et al. 2000b, Savitz et al. 1995, and Savitz et al. 2005. All are high-quality studies that have sufficient sample sizes and high study response rates, adjustments for known confounders 11 

### , have exposure assessment information from water treatment data, residential histories, and THM measurements. These are summarized in Exhibit 6.32. Because the populations in these four studies appear to have TTHM exposures significantly greater than those of the general U.S. population, EPA has chosen to scale the r esults using ICR data to derive PAR values that are more relevant to the ge neral population (Appendix G).

The four studies (using unadjusted data to allo w for comparability, and scaled to the TTHM levels reported in the ICR data base) yield median PARs of 0, 0.4, 1.7, and 1.9 percent 12 

### . Using the annual incidence of fetal loss reported by CDC, the median PARs for these four studies suggest that the incidence of fetal loss attributable to exposure to chlorina ted drinking water could ra nge from 0 to 18,700 annually. EPA assumed that this potential risk range pertained to pre-Stage 1 exposure conditions. Thus, to evaluate potential reduction in fetal loss for the Stage 2 DBPR , EPA had to first estimate the potential reductions for the Stage 1 DBPR and then the subsequent percen t reductions for Stage 2 DBPR. For this analysis, EPA assumed that reduction in risk is proportional to the percent reductions in the number of observations having quarterly TTHM concentration measurements a bove the study population cut offs. This analysis would imply that the Stage 1 DBPR would reduce potential fetal loss by 73 percent and that the Stage 2 DBPR would further reduce remaining potential fetal lo ss by 75 percent. This analysis would further imply that a range of 0 to 3,700 fetal losses could be avoided per year as a result of the Stage 2 DBPR. Using the three-quarter average, the number of fetal losses attributable to DBPs is reduced by 90 percent for Stage 1, with the remaining fetal losses eliminated by Stage 2. Refer to Appendix G for derivation of PARs and detailed calculation of potential fetal losses avoided.

Caution is required in interpreting the numbers derived above because there may be significant differences in exposure patterns of the study populati ons and the national populati on (e.g., different types of DBP mixtures having similar TTHM levels). Th e estimates presented here are not part of EPA’s primary benefits analysis, and the ranges are not meant to suggest upper and lower bounds. Rather, they are intended to illustrate quantitatively the potential ri sk implications of some of the published results. EPA reiterates that causality has not yet been estab lished and that each of the studies has a lower 95 percent confidence bound for the estimated attributable risk below zero.

### Final Economic Analysis for the Stage 2 DBPR 6-99 December 2005 

## Exhibit 6.32 Summary of the Fetal Loss Human Epidemiology Studies

Study Population Exposure Assessment Outcome Results 1 

### Potential Confounders

Evaluated Waller et al. 2001 Prospective cohort of 4,209 pregnant women in prepaid health plan in CA 1989- 91 Used utility total trihalomethane (TTHM) data to estimate exposure via ingestion and showering during first trimester of pregnancy. Spontaneous abortion ( 

## # 20 weeks of gestation) Recalculated 80


## : g/L compared to <80 

## : g/L forunweighted utilitywide average RR=1.25 (0.99,1.6)

Study based prevalence of exposure = 15% Gestational age at interview, maternal age, cigarette smoking, history of pregnancy loss, maternal race, employment during pregnancy

King et al. 2000b Populationbased retrospective cohort of 47,275 births in Nova Scotia, Canada 1988-1995 Linked mother’s residence at time of delivery to the levels of specific TTHMs monitored in the distribution system of the utility and averaged predicted values of TTHM levels for all the months covering the pregnancy. Stillbirth Recalculated 75



## : g/L compared to <75 

## : g/L RR=1.28 (95% CI 0.98, 1.7)

Study based prevalence of exposure = 32% Smoking, maternal age

Savitz et al. 1995 Populationbased casecontrol study of 126 cases and 122 controls in NC 1988-91 Linked existing distribution system TTHM concentration data to maternal residence and water consumption data. The fourth week of pregnancy used to assign the reported quarterly average TTHM. Spontaneous abortion Recalculated 81


## : g/L compared to <81 

## : g/L OR=1.06 (0.6,1.8)

Study based prevalence of exposure = 35% Maternal age, race, education, marital status, poverty level, smoking, alcohol use, nausea, employment

Savitz et al. 2005 2 

### Prospective cohort of 2,413 pregnant women from 3 water systems in the U.S., 2000-2004 Weekly or biweekly distribution system DBP concentration data were collected and linked with maternal residence and water consumption data (during first and second trimesters). Periconceptual, early and late gestational exposure windows were examined. Spontaneous abortion, including early (<12 wks) and late (> 12 wks) fetal losses Utilized reported statistic of 75 

## : g/L compared to <75


## : g/L OR=0.96 (0.68-1.35) (Table 6.1, pg. 75)

Study based prevalence of exposure = 18% Maternal age, tobacco use, race, ethnicity, education, marital status, income, alcohol use, caffeine consumption, body mass index, age at menarche, employment, diabetes, pregnancy history, prior fetal loss, induced abortion history, vitamin use

1 

### EPA recalculated OR and RR values using crude Odds Ratios for the fetal loss sensitivity analysis



## . 2 

### This study was added to the analysis since the proposal.

### Final Economic Analysis for the Stage 2 DBPR 6-100 December 2005 

## 6.8.2 Value of Potential Reductions in Fetal Losses Avoided

EPA has not monetized the value of potentially avoided fetal loss but recognizes the significant value of improvement in developm ental and reproductive health in ge neral. See Section 6.5.1 for a discussion of the value of reducing repr oductive and developmental health risks.

The agency is considering further work specifi c to fetal loss valuation. One possible area of further research is the value that prospective parent s have for reducing risks during pregnancy. In this regard, the substantial lifestyle changes that prosp ective parents often undertake during pregnancy suggests that reducing these kinds of risks are of value. A second possible area would be benefit transfer methodologies that address how existing studies can inform the estimation of the benefits of reduced fetal loss.

When valuation studies specific to the health e ndpoints of a regulation are lacking, the Agency typically draws upon existing studies of similar health e ndpoints to estimate benefits. The “transfer” of the results of these studies to value similar health endpoints must be done carefully and methodically, controlling for differences in the health endpoints and in the relevant populations. Some researchers have attempted to transfer values using sophisticated analytical techniques such as preference calibration methods (e.g., Smith et al. 2002). Regardless of the approach used, “benefit transfer” requires systematic comparison of the similarities and differences in the health effects in the studies and those resulting from the regulation. Application of benefit transfer leads to a detailed qualitative examination of the implications of using those studies and potentially to empirical adjustments to the results of the existing studies.

Until more information on these subjects is available, EPA cannot estimate a monetized value for avoiding fetal loss. However, research on valuation and benefit transfer continues to progress. The Agency anticipates that new research will support furt her efforts to value reproductive and developmental endpoints.

### Final Economic Analysis for the Stage 2 DBPR 7-1 December 2005 

# 7. Cost Analysis

7.1 Introduction

This chapter estimates the national costs of the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR). National costs include treatment tec hnology changes to comply with the rule as well as non-treatment costs, such as Initial Distribution System Evaluations (IDSEs), additional routine monitoring, and operational evaluations.

The data presented in this chapter are derived from analyses of Information Collection Rule (ICR) data, ICR Supplemental Survey data, National Rural Water Survey data, Surface Water Analytical Tool (SWAT) model results, the American Water Work s Association Water Utility Database (Water:\STATS) (AWWA 2000), State/Primacy Agency data, and the results of two expert opinion processes for small systems. For a complete explanation of these data sources, see Chapter 3 and Appendices A and B.

Section 7.1.1 of this chapter summarizes the methodology and data inputs used to estimate national costs. Sections 7.2 through 7.4 discuss the i nputs that are used in the cost model. These are: labor rates and laboratory fees (7.2), non-treatment costs (7.3), technology unit costs (7.4), and compliance forecasts (derived in Ch apter 5). Section 7.5 discusses the cost model itself, including how it accounts for uncertainty in some inputs, the calcula tion of the costs, and projection and discounting of those costs. The results of the cost model ar e discussed in Section 7.6. Section 7.7 discusses unquantified costs and Section 7.8 gives a summary of the uncertainties in the cost calculations. Section 7.9 presents a comparison of the costs of the final ru le option compared to other regulatory alternatives that were analyzed.

In support of this chapter:

• Appendix D contains the rule implementation schedule for different system types and rule activities (used in projecting and annualizing costs over a 25-year period).

• Appendix H offers a more complete explanati on of the laboratory costs and labor hours for implementation, IDSE, monitoring plans, additional routine monitoring, as well as the assumptions and calculations for the operational evaluation costs.

• Appendix I presents additional detail for technology unit costs.

• Appendix J presents cost projections, present va lue estimates, and annualization of costs for all Stage 2 DBPR regulatory alternatives and sensitivity analyses.

• Appendix K contains documentation for the Stag e 2 DBPR cost model, including a detailed file list and flow charts.

7.1.1 Overview of Methodology for Quantifying Stage 2 DBPR Costs

To estimate the national costs of the Stage 2 DBPR, the Environmental Protection Agency (EPA) calculated the incremental costs that public water sy stems (PWSs) and their States/Primacy Agencies are expected to incur from the Stage 1 DBPR to the Stage 2 DBPR. Cost analyses for PWSs include an identification of treatment process improvements that systems may make, as well as estimates of the costs1 

### For the purposes of this Economic Analysis (EA), rule implementation activities are assumed to include activities such as reading the rule and training personne l. The activities are summarized in Section 7.3.1 and discussed in more detail in Appendix H.

2 

### For more information on discounting costs see EPA’s Guidelines for Preparing Economic Analyses (USEPA 2000j).

Final Economic Analysis for the Stage 2 DBPR 7-2 December 2005 

# Inputs - Industry Baseline - Compliance Forecasts - Labor Rates and Lab Fees - Technology Unit Costs - Non-treatment costs - Stage 2 rule Implementation Schedule 

# Outputs
- Nominal Costs (one time

and annual) - Annualized Costs,

Present Value in 2003 $ - Household Costs 

# SAS Cost Model Inputs - Industry Baseline - Compliance Forecasts - Labor Rates and Lab Fees - Technology Unit Costs - Non-treatment costs - Stage 2 rule Implementation Schedule 

# Outputs
- Nominal Costs (one time

and annual) - Annualized Costs,

Present Value in 2003 $ - Household Costs 

# SAS Cost Model to implement the rule 1 

### , conduct IDSEs, prepare monitoring plans, perform additional routine monitoring, and perform operational evaluations. System costs we re estimated for different system types (community water systems (CWS) or nontransient noncommunity water systems (NTNCWS)), source water types (ground or surface), and size categories (nine categori es based on population served, consistent with the Drinking Water Baseline Handbook). Estimates of costs to State/Primacy Agencies represent estimated labor burdens that States/Primacy Agencies would face, such as training employees on the requirements of the Stage 2 DBPR, responding to PWS reports, and recordkeeping.

Costs were calculated using a cost model pr ogrammed in SAS version 9.1. Exhibit 7.1 summarizes the model inputs and outputs. The model combines baseline data di scussed in Chapter 3 with inputs for labor rates, laboratory fees, non-treatment costs, technology unit costs, and compliance forecasts from Chapter 5 to calculate nominal costs fo r the rule. All costs are calculated in 2003 dollars. Costs are then distributed in the years in which th ey are expected to be incurred and are discounted to give a net present value of costs using both a 3 and 7 percent social discount rate. The present value costs are then annualized using the same social discount rate 2



### . Household costs are also generated by the co st model using assumptions regarding household water usage and total households served per plant.



## Exhibit 7.1 Stage 2 DBPR Co st Model Inputs and Outputs

### Final Economic Analysis for the Stage 2 DBPR 7-3 December 2005 

### 7.1.2 Cost Summary

Number of systems performing non-treatment activities

Exhibit 7.2a shows the baseline number of systems subject to the Stage 2 DBPR and the estimated number of those systems performing various rule activities (implementation, IDSE monitoring, monitoring plans, additional routine monitoring, a nd operational evaluations). Appendix H provides the derivation of these values.

As shown in columns B and C, EPA estimates that all disinfecting CWSs and NTNCWSs will have to perform at least minimal implementation activities (reading and understanding the rule, training, etc.). As shown in column F, all large systems a nd most small systems are required to prepare a Stage 2 compliance monitoring plan. The number of systems performing IDSE monitoring (shown in column D) is only a fraction of all systems because some will c hoose to perform studies, meet the criteria for a 40/30 certification, or receive waivers from IDSE requirements.

EPA has established a population-based monitori ng approach for the Stage 2 DBPR, where monitoring requirements are no longer based on number of plants per system as under the Stage 1 DBPR. Depending on the number of plants in a given system, the number of Stage 2 compliance samples required per year may stay the same, decrease, or increase from Stage 1 requirements. See Exhibit H.8 for information on changes in monitoring burden.

EPA expects that some number of Stage 2-compliant systems will find TTHM and HAA5 levels high enough to trigger the requirement for an opera tional evaluation. Column H shows the estimated number of systems that may require operational evaluations.

In addition to those ground water systems that currently disinfect, EPA predicts that some systems will install disinfection to comply with th e anticipated Ground Water Rule (GWR). Exhibit 7.2b shows the number of systems predicted to install chemical disinfection as a result of the Ground Water Rule, based on the Regulatory Impact Analysis (RIA) prepared for the May 2000 GWR proposal and current system information available. Because the GW R is expected to be promulgated within 8 months after the Stage 2 DBPR is promulgated, EPA expects new systems adding disinfection to meet GWR requirements to simultaneously achieve compliance w ith Stage 2 MCLs. Therefore, these systems are not included in the treatment baseline. The IDSE will likely not apply to these systems because they are expected to install disinfection after the IDSE require ment is complete. Systems installing disinfection for the GWR will, however, be required to prepare m onitoring plans and monitor DBPs for the first time under Stage 2. Exhibit 7.2b shows that all newly disinfecting ground water systems will prepare monitoring plans and will be required to conduct monito ring for the first time. It should be noted that actual monitoring-related costs for newly disinfecti ng ground water systems may be different from those estimated here depending upon the details of the final GWR.

### Final Economic Analysis for the Stage 2 DBPR 7-4 December 2005 A B C=B/A*100 D E=D/A*100 F G=F/A*100 H I=H/A*100

<100 1,085 1,085 100% 678 62% 678 62% 4 0% 100-499 2,212 2,212 100% 1,382 62% 1,382 62% 8 0% 500-999 1,470 1,470 100% 1,385 94% 1,470 100% 10 1% 1,000-3,299 2,588 2,588 100% 2,438 94% 2,588 100% 18 1% 3,300-9,999 2,042 2,042 100% 1,888 92% 2,042 100% 57 3% 10,000-49,999 1,773 1,773 100% 1,524 86% 1,773 100% 189 11% 50,000-99,999 334 334 100% 273 82% 334 100% 68 20% 100,000-999,999 281 281 100% 226 81% 281 100% 64 23% 1,000,000+ 18 18 100% 15 83% 18 100% 6 33% National Totals 11,803 11,803 100% 9,809 83% 10,566 90% 424 4%

<100 7,935 7,935 100% 336 4% 336 4% 0 0% 100-499 9,821 9,821 100% 416 4% 416 4% 0 0% 500-999 3,998 3,998 100% 708 18% 3,998 100% 0 0% 1,000-3,299 4,852 4,852 100% 859 18% 4,852 100% 0 0% 3,300-9,999 2,200 2,200 100% 389 18% 2,200 100% 0 0% 10,000-49,999 1,222 1,222 100% 216 18% 1,222 100% 0 0% 50,000-99,999 136 136 100% 24 18% 136 100% 0 0% 100,000-999,999 63 63 100% 18 29% 63 100% 0 0% 1,000,000+ 3 3 100% 0 15% 3 100% 0 0% National Totals 30,229 30,229 100% 2,966 10% 13,225 44% 0 0%

<100 231 231 100% 0 0% 0 0% 0 0% 100-499 317 317 100% 0 0% 0 0% 0 0% 500-999 106 106 100% 0 0% 0 0% 0 0% 1,000-3,299 93 93 100% 0 0% 0 0% 0 0% 3,300-9,999 24 24 100% 0 0% 0 0% 0 0% 10,000-49,999 5 5 100% 4 80% 5 100% 0 0% 50,000-99,999 0 0 - 0 - 0 - 0 - 100,000-999,999 1 1 100% 1 100% 1 100% 0 0% 1,000,000+ 0 0 - 0 - 0 - 0 -
National Totals 777 777 100% 5 1% 6 1% 0 0%

<100 2,493 2,493 100% 0 0% 0 0% 0 0% 100-499 2,129 2,129 100% 0 0% 0 0% 0 0% 500-999 589 589 100% 0 0% 0 0% 0 0% 1,000-3,299 247 247 100% 0 0% 0 0% 0 0% 3,300-9,999 21 21 100% 0 0% 0 0% 0 0% 10,000-49,999 3 3 100% 1 18% 3 100% 0 0% 50,000-99,999 0 0 100% 0 18% 0 100% 0 0% 100,000-999,999 0 0 100% 0 100% 0 100% 0 0% 1,000,000+ 0 0 - 0 - 0 - 0 - National Totals 5,483 5,483 100% 1 0% 4 0% 0 0% GRAND TOTAL 48,293 48,293 100% 12,780 26% 23,800 49% 424 1%

Sources: Ground Water Only CWSs

Surface Water and Mixed NTNCWSs

Ground Water Only NTNCWSs Stage 2 Monitoring Plans IDSE Monitoring System Size (Population Served) Stage 2 DBPR System Baseline Number and Percent of Systems Performing Various Rule Activities

Implementation Operational Evaluations

Non-treatment-related rule activities, in ad dition to those shown in the table, also include routine compliance monitoring. So me systems are expected to take more samples and some less from Stage 1 to Stage 2 depending on the number of plants in their systems. Ov erall the Stage 2 DBPR results in an increase in the total number of complian ce samples taken from the St age 1 DBPR. See Exhibit H.8 a, column H, for the c hange in total samples for th e different size categories.

(A), (B), (D), (F), and (H): Appendix H, Exhibit H.15a. Notes: Surface Water and Mixed CWSs

Detail may not add to totals due to independent rounding. 

## Exhibit 7.2a Baseline Systems Subject to Non-Treatment-Related Rule Activities

### Final Economic Analysis for the Stage 2 DBPR 7-5 December 2005 A B C=B/A*100

<100 0 0 - 100-499 0 0 - 500-999 0 0 - 1,000-3,299 0 0 - 3,300-9,999 0 0 - 10,000-49,999 0 0 - 50,000-99,999 0 0 - 100,000-999,999 0 0 - 1,000,000+ 0 0 -
National Totals 0 -

-

<100 354 354 100% 100-499 439 439 100% 500-999 86 86 100% 1,000-3,299 104 104 100% 3,300-9,999 47 47 100% 10,000-49,999 10 10 100% 50,000-99,999 1 1 100% 100,000-999,999 2 2 100% 1,000,000+ 0 0 100% National Totals 1,042 1,042 100%

<100 0 0 - 100-499 0 0 - 500-999 0 0 - 1,000-3,299 0 0 - 3,300-9,999 0 0 - 10,000-49,999 0 0 - 50,000-99,999 0 0 - 100,000-999,999 0 0 - 1,000,000+ 0 0 -
National Totals 0 -

-

<100 669 669 100% 100-499 572 572 100% 500-999 184 184 100% 1,000-3,299 77 77 100% 3,300-9,999 7 7 100% 10,000-49,999 1 1 100% 50,000-99,999 0 0 100% 100,000-999,999 0 0 100% 1,000,000+ 0 0 - National Totals 1,510 1,510 100% GRAND TOTAL 2,552 2,552 100%

Sources: Notes: System Size (Population Served) Baseline No. of Systems Adding Disinfection for the GWR

(A), (B): Appendix H, Exhibit H.12b. Detail may not add to totals due to independent rounding. Stage 2 Monitoring Plans

Non-Treatment-Related Rule Activities, in addition to those shown in the table, include routine compliance monitoring for all systems. Surface Water and Mixed CWSs

Ground Water Only CWSs

Surface Water and Mixed NTNCWSs

Ground Water Only NTNCWSs 

## Exhibit 7.2b Non-Treatment-Related Rule Activities for Systems Installing Disinfection to Comply with the Ground Water Rule

### Final Economic Analysis for the Stage 2 DBPR 7-6 December 2005 

### Number of plants making treatment technology changes

Exhibit 7.3 shows the baseline number of plants a nd the estimated percentage of those plants that are predicted to make treatment technology changes. The mean estimated percentage of plants making treatment technology changes is approximately 4 per cent for all systems, with a much higher percentage for surface water plants. The baseline number of ground wa ter plants is larger than that of surface water plants, however, there is a larger absolute number of ground water plants that are predicted to make treatment technology changes.

The 90-percent confidence interval around the m ean for the surface water plant estimate accounts for alternative compliance forecast methodologies (S WAT and the ICR Matrix Method) and uncertainty in the potential impacts of the IDSE. Derivation of the compliance forecast is discussed in detail in Chapter 5.

One-time costs

One-time costs for systems include initial capita l, implementation, IDSE, and monitoring plan costs. State/Primacy Agency costs include those associated with implementation, IDSEs, and monitoring plans. Exhibit 7.4 summarizes estimated total initia l capital and other one-time costs of the Stage 2 DBPR for systems and States/Primacy Agencies.

Annualized Costs

Exhibit 7.5a and b summarize the average annuali zed costs for the Stage 2 DBPR at 3 and 7 percent discount rates, respectively. System costs range from approximately $56 to $102 million annually at a 3 percent discount rate, with a mean es timate of approximately $79 million per year. At a 7 percent discount rate, system costs range from a pproximately $55 to $99 million annually, with a mean estimate of approximately $77 million per year. Stat e costs are estimated to be between $1.70 and $1.71 million per year, depending on the discount rate.

### Final Economic Analysis for the Stage 2 DBPR 7-7 December 2005 Number of Plants Making Treatment Technology Changes Percentage of Plants Making Treatment Technology Changes
Mean 5th Percentile 95th Percentile Mean 5th Percentile 95th Percentile

A B C D E=B/A F=C/A G = D/A

<100 359 36 20 53 10.2% 5.7% 14.6% 100-499 767 64 36 92 8.4% 4.7% 12.1% 500-999 483 40 23 58 8.4% 4.7% 12.1% 1,000-3,299 1,129 100 56 143 8.8% 4.9% 12.7% 3,300-9,999 1,258 111 62 160 8.8% 4.9% 12.7% 10,000-49,999 1,292 188 88 294 14.6% 6.8% 22.8% 50,000-99,999 579 84 40 132 14.6% 6.8% 22.8% 100,000-999,999 610 89 42 139 14.6% 6.8% 22.8% 1,000,000+ 74 11 5 17 14.6% 6.8% 22.8% National Totals 6,552 724 371 1,088 11.1% 5.7% 16.6%

<100 6,423 155 2.4% 100-499 15,242 483 3.2% 500-999 6,093 193 3.2% 1,000-3,299 7,587 204 2.7% 3,300-9,999 5,030 135 2.7% 10,000-49,999 5,382 111 2.1% 50,000-99,999 716 15 2.1% 100,000-999,999 918 18 2.0% 1,000,000+ 27 1 2.1% National Totals 47,419 1,314 2.8%

<100 226 23 13 33 10.2% 5.7% 14.6% 100-499 312 26 15 38 8.4% 4.7% 12.1% 500-999 106 9 5 13 8.4% 4.7% 12.1% 1,000-3,299 91 8 5 12 8.9% 5.0% 12.8% 3,300-9,999 25 2 1 3 8.8% 4.9% 12.7% 10,000-49,999 5 1 0 1 14.6% 6.8% 22.8% 50,000-99,999 0 0 0 0 - - - 100,000-999,999 1 0 0 0 14.6% 6.8% 22.8% 1,000,000+ 0 0 0 0 - - - National Totals 766 69 39 100 9.0% 5.0% 13.0%

<100 2,493 60 2.4% 100-499 2,129 67 3.2% 500-999 589 19 3.2% 1,000-3,299 247 7 2.7% 3,300-9,999 21 1 2.7% 10,000-49,999 3 0 2.1% 50,000-99,999 0 0 2.1% 100,000-999,999 0 0 2.0% 1,000,000+ 0 0 - National Totals 5,483 154 2.8% Grand Total All Plants 60,220 2,261 1,877 2,655 3.8% 3.1% 4.4% Note: Detail may not add to totals due to independent rounding. Estimated from the pre-Stage 2 Baseline.
Sources: (A) Exhibit 3.2, column AB. (B)-(D) Compliance Forecast Inputs in Chapter 5, and the Stage 2 DBPR Cost Model. The 90 percent confidence intervals for surface water systems represent alternative compliance forecast methodologies (SWAT and the ICR Matrix Method) and uncertainty in the potential impacts of the IDSE. System Size (Population Served) Stage 2 DBPR Plant Baseline

Primarily Ground Water NTNCWSs Primarily Surface Water CWSs

Primarily Ground Water CWSs

Primarily Suface Water NTNCWSs 

## Exhibit 7.3 Number and Percent of Plants Making Treatment Technology Changes for the Stage 2 DBPRFinal Economic Analysis for the Stage 2 DBPR 7-8 December 2005Surface Water Systems Disinfecting Ground Water SystemsServing < 10,000Serving >10,000Serving < 10,000Serving >10,000TotalTotal Initial Capital Costs for the Rule100.9$ 451.57$ 179.8$ 107.3$ 839.5$(90% Confidence Bounds)(55.51 - 149.42) (246.01 - 630.04) (148.85 - 210.88) (96.31 - 118.23) (546.68 - 1,108.57)CWS Total Initial Capital94.83$ 450.6$ 167.1$ 107.1$ 819.6$(90% Confidence Bounds)(52.22 - 140.40) (245.48 - 628.71) (138.11 - 196.13) (96.18 - 118.06) (531.99 - 1,083.30)NTNCWS Total Initial Capital6.03$ 1.0$ 12.8$ 0.1$ 19.9$(90% Confidence Bounds)(3.29 - 9.02) (0.53 - 1.33) (10.74 - 14.75) (0.13 - 0.16) (14.69 - 25.27)CWS One-Time Costs21.8$ 33.5$ 13.5$ 3.3$ 72.0$Implementation2.4$ 1.7$ 6.0$ 0.9$ 11.0$IDSE18.4$ 31.1$ 5.8$ 2.0$ 57.4$Monitoring Plans1.0$ 0.7$ 1.6$ 0.4$ 3.6$NTNCWS One-Time Costs0.2$ 0.1$ 1.3$ 0.0$ 1.6$Implementation0.2$ 0.0$ 1.1$ 0.0$ 1.3$IDSE-$ 0.1$ -$ 0.0$ 0.1$Monitoring Plans0.0$ 0.0$ 0.2$ 0.0$ 0.2$State/Primacy Agency One-Time Costs10.9$Implementation7.8$IDSE2.2$Monitoring Plans0.9$Note: Detail may not add due to independent rounding. 90-percent confidence bounds reflect uncertainty in unit treatment costs.Sources: Initial Capital Costs from Exhibit 7.13. Implementation, IDSE, and MonitoringPlan costs from Exhibit 7.7.Stage/Primacy Agency One-Time Costs from Exhibit H.21Exhibit 7.4 Initial Capital and One-Time Costs for the Stage 2 DBPR ($Millions)Final Economic Analysis for the Stage 2 DBPR 7-9 December 2005Total System CostsLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)Implementation IDSEMonitoringPlansMoni-toringOperationalEvalationsLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)< 10,000 $4.21 $2.32 $6.23 $6.10 $3.41 $8.83 $0.12 $0.93 $0.05 -$0.07 $0.02 $11.34 $6.76 $16.10>10,000$20.60 $11.22 $28.75 $14.33 $9.03 $21.55 $0.09 $1.59 $0.03 -$1.14 $0.11 $35.61 $20.93 $50.97< 10,000 $0.27 $0.15 $0.40 $0.57 $0.32 $0.82 $0.01 $0.00 $0.00 $0.02 $0.00 $0.86 $0.49 $1.25>10,000$0.04 $0.02 $0.06 $0.03 $0.02 $0.04 $0.00 $0.00 $0.00 $0.00 $0.00 $0.08 $0.05 $0.11< 10,000 $7.41 $6.13 $8.70 $7.20 $6.60 $7.79 $0.30 $0.29 $0.08 $1.05 $0.00 $16.33 $14.45 $18.21>10,000$4.87 $4.37 $5.36 $6.00 $5.64 $6.37 $0.05 $0.10 $0.02 $0.00 $0.00 $11.04 $10.18 $11.90< 10,000 $0.57 $0.48 $0.65 $0.75 $0.69 $0.81 $0.06 $0.00 $0.01 $0.42 $0.00 $1.80 $1.65 $1.95>10,000$0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.00 $0.00 $0.00 $0.01 $0.00 $0.03 $0.02 $0.03TOTAL $37.97 $24.69 $50.17 $34.98 $25.72 $46.22 $0.62 $2.91 $0.19 $0.28 $0.12 $77.08 $54.53 $100.51 $1.71 $78.80 $56.24 $102.22Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecast and unit treatment costs.Estimates are discounted to 2003 and given in 2003 dollars.Sources Capital Costs: SW CWS, Exhibit J.2bb; SW NTNCWS, Exhibit J.2bf; GW CWS, Exhibit J.2bj; GW NTNCWS, Exhibit J.2bn.O&M Costs: SW CWS, Exhibit J.2bc; SW NTNCWS, Exhibit J.2bg; GW CWS, Exhibit J.2bk; GW NTNCWS, Exhibit J.2bo.Non-Treatment Costs: SW CWS, Exhibit J.2bd; SW NTNCWS, Exhibit J.2bh; GW CWS, Exhibit J.2bl; GW NTNCWS, Exhibit J.2bp.State Costs; Appendix J, Exhibit J.2asGround Water NTNCWSsSystem Size(PopulationServed)Non-Treatment Costs(Point Estimate)MeanValueMeanValue90 PercentConfidence Bound90 PercentConfidence BoundSystem Costs90 PercentConfidence BoundStateCostsTotal Costs of the RuleSurface Water CWSsSurface Water NTNCWSsMeanValue90 PercentConfidence BoundCapital Costs O&M CostsMeanValueGround Water CWSsExhibit 7.5a Total Annualized Costs for Stage 2 DBPR Rule Activities ($Millions/Year, 3 Percent Discount Rate)Final Economic Analysis for the Stage 2 DBPR 7-10 December 2005Total System CostsLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)Implementation IDSEMonitoringPlansMoni-toringOperationalEvaluationsLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)< 10,000 $4.53 $2.50 $6.71 $4.86 $2.72 $7.04 $0.15 $1.16 $0.06 -$0.06 $0.01 $10.72 $6.54 $15.08>10,000$23.00 $12.53 $32.10 $11.66 $7.35 $17.54 $0.11 $2.06 $0.04 -$0.90 $0.08 $36.06 $21.27 $51.03< 10,000 $0.29 $0.16 $0.43 $0.45 $0.25 $0.66 $0.01 $0.00 $0.00 $0.01 $0.00 $0.76 $0.43 $1.11>10,000$0.05 $0.03 $0.07 $0.02 $0.01 $0.03 $0.00 $0.00 $0.00 $0.00 $0.00 $0.08 $0.05 $0.11< 10,000 $7.98 $6.60 $9.37 $5.74 $5.26 $6.21 $0.38 $0.36 $0.09 $0.84 $0.00 $15.38 $13.53 $17.24>10,000$5.39 $4.84 $5.94 $4.87 $4.57 $5.16 $0.06 $0.13 $0.02 $0.00 $0.00 $10.46 $9.62 $11.31< 10,000 $0.61 $0.51 $0.70 $0.60 $0.55 $0.65 $0.07 $0.00 $0.01 $0.33 $0.00 $1.62 $1.48 $1.77>10,000$0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.00 $0.00 $0.00 $0.01 $0.00 $0.02 $0.02 $0.02TOTAL $41.86 $27.16 $55.33 $28.21 $20.73 $37.29 $0.78 $3.71 $0.23 $0.23 $0.10 $75.11 $52.94 $97.67 $1.70 $76.81 $54.64 $99.36Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecast and unit treatment costs.Estimates are discounted to 2003 and given in 2003 dollars.Sources Capital Costs: SW CWS, Exhibit J.2br; SW NTNCWS, Exhibit J.2bv; GW CWS, Exhibit J.2bz; GW NTNCWS, Exhibit J.2cd.O&M Costs: SW CWS, Exhibit J.2bs; SW NTNCWS, Exhibit J.2bw; GW CWS, Exhibit J.2ca; GW NTNCWS, Exhibit J.2ce.Non-Treatment Costs: SW CWS, Exhibit J.2bt; SW NTNCWS, Exhibit J.2bx; GW CWS, Exhibit J.2cb; GW NTNCWS, Exhibit J.2cf.State Costs: Appendix J, Exhibit J.2awTotal Costs of the RuleSurface Water CWSsSurface Water NTNCWSsMeanValue90 PercentConfidence BoundCapital Costs O&M CostsMeanValueGround Water CWSs90 PercentConfidence BoundSystem Costs90 PercentConfidence BoundStateCostsGround Water NTNCWSsSystem Size(PopulationServed)Non-Treatment Costs(Point Estimate)MeanValueMeanValue90 PercentConfidence BoundExhibit 7.5b Total Annualized Costs for Stage 2 DBPR Rule Activities ($Millions/Year, 7 Percent Discount Rate)

### Final Economic Analysis for the Stage 2 DBPR 7-11 December 2005 Technical Managerial Combined
A BC <100 $21.44 $44.36 $21.44 100-499 $23.09 $47.78 $23.09 500-999 $24.74 $51.20 $24.74 1,000-3,299 $24.74 $51.20 $24.74 3,300-9,999 $25.34 $51.20 $30.51 10,000-49,999 $26.05 $51.20 $31.08 50,000-99,999 $26.05 $51.20 $31.08 100,000-999,999 $31.26 $51.20 $35.25 1,000,000+ $31.26 $51.20 $35.25

Source: Labor Costs for National Drinking Wa ter Rules, Exhibits 20 and 21 (U SEPA, 2003s) Labor Rate (2003$/hr) System Size (Population Served)

Notes: EPA estimates t hat systems with populations greater than 3,300 use a combination of operators (technical) and engineers (managerial), with an 80/20 ratio between the two, respectively. 

## 7.2 Labor Rates and Laboratory Fees

Labor Rates

Labor costs to PWSs are estimated using hourly labor rates for technical and managerial labor categories. Labor rates representative of national averages, based on Bureau of Labor Statistics (BLS) figures as reported in Labor Costs for National Drinking Water Rules (USEPA 2003e), are used in all analyses. The technical and managerial wage rates vary with system size and include fringe benefits. Labor rates do not differ between surface and ground water systems.

Exhibit 7.6a shows the technical and managerial rates for each of the nine standard system size categories used in this EA. Exhibit 7.6b shows the technical and managerial rates according to the system size categories used to specify Stage 2 monitoring re quirements. All rates have been adjusted to 2003 dollars using the Employment Cost Index (ECI) (BLS 2003).

To account for the composition of staff at PWSs of varying sizes, EPA uses only the technical rate for systems serving fewer than 3,300 people. For systems serving 3,300 or more people, EPA uses a ratio of 80 percent technical labor to 20 percent manageri al labor to arrive at a weighted labor rate. The final labor rates used for the cost analysis are shown in the combined column (column C) in Exhibits 7.6a and b.



## Exhibit 7.6a System Wage Rates by Standard Size Categories

### Final Economic Analysis for the Stage 2 DBPR 7-12 December 2005 Technical Managerial Combined
A BC

<500 $22.55 $46.65 $22.55 500-3,299 $24.74 $51.20 $24.74 3,300-9,999 $25.34 $51.20 $30.51 10,000-49,999 $26.05 $51.20 $31.08 50,000-249,999 $28.00 $51.20 $32.64 250,000-999,999 $31.26 $51.20 $35.25 1,000,000-4,999,999 $31.26 $51.20 $35.25 5M+ $31.26 $51.20 $35.25

<500 $22.35 $46.25 $22.35 500-9,999 $24.86 $51.20 $24.86 10,000-99,999 $26.05 $51.20 $31.08 100,000-499,999 $31.26 $51.20 $35.25 500,000+ $31.26 $51.20 $35.25

<500 $22.39 $38.84 $22.39 500-3,299 $24.74 $51.20 $24.74 3,300-9,999 $25.34 $51.20 $30.51 10,000-49,999 $26.05 $51.20 $31.08 50,000-249,999 $31.26 $51.20 $35.25 250,000-999,999 N/A N/A N/A 1,000,000-4,999,999 N/A N/A N/A 5M+ N/A N/A N/A

<500 $22.20 $45.94 $22.20 500-9,999 $24.76 $51.20 $24.76 10,000-99,999 $26.05 $51.20 $31.08 100,000-499,999 $31.26 $51.20 $35.25 500,000+ N/A N/A N/A



### Source: Labor Costs for National Drinking Water Rules, Exhibits 20 and 21 (USEPA, 2003s) Notes: EPA estimates that systems with populations greater than 3,300 use a combination of operators (technical) and engineers (managerial), with an 80/20 ratio between the two, respectively. Surface Water and Mixed NTNCWSs

Disinfecting Ground Water Only NTNCWSs Surface Water and Mixed CWSs

Disinfecting Ground Water Only CWSs System Size (Population Served) 

### Labor Rate (2003$/hr) 

## Exhibit 7.6b System Wage Rates by Monitoring Size Categories

EPA recognizes that there may be significant variation in labor rates across all PWSs. However, data are not currently available that would allow assignment of labor rates to specific PWSs based on characteristics such as size, classification, or geogra phical region. In the absence of such data and because analyses in this EA are perform ed on a national level, the data from Labor Costs for National Drinking Water Rules are used for all systems.

Labor costs attributable to States for administ rative tasks are estimated based on an average annual full-time-equivalent (FTE) labor cost, incl uding overhead and fringe be nefits, of $65,255 (2001$). This rate was established based on data from th e 2001 State Drinking Water Needs Analysis (ASDWA 2001). For use in the Stage 2 EA analyses, the $65,255 annual rate was updated to 2003 dollars ($70,132) using the ECI (BLS 2003) and converted to an hourly basis (1 FTE = 2,080 hours) to establish a State rate of $33.60 per hour.

### Final Economic Analysis for the Stage 2 DBPR 7-13 December 2005 

### Laboratory Fees

A laboratory fee, expressed as a cost per sample, is associated with TTHM and HAA5 monitoring costs for IDSE and additional routine monitoring. Based on laboratory costs reported in the 1996 ICR, EPA estimated the laboratory fee at $200 per sample for all size categories. This estimate does not include shipping costs. For systems serving 10,000 people or more, a shipping cost of $10 was added to account for the fact that larger systems often have in-house laboratory facilities and can take advantage of bulk discounts. For systems serving fewer than 10,000 people, a shipping cost of $40 is added because very few small systems have in-house laboratory facilities and they have less opportunity to take advantage of bulk discounts. Laboratory fees are not e xpected to differ substantially between disinfecting ground water systems and surface water systems.



## 7.3 Non-Treatment Costs for Systems and States/Primacy Agencies

This section presents the estimated national co sts for systems and States/Primacy Agencies to perform Stage 2 DBPR activities that are not related to treatment. These activities have been described in Chapter 1 and are described in detail in Appendix H. The following subsections provide a brief summary of each activity and key assumptions used to estimate costs for each:

7.3.1 Rule Implementation 7.3.2 Initial Distribution System Evaluations 7.3.3 Monitoring Plans 7.3.4 Additional Routine Monitoring 7.3.5 Operational Evaluations 7.3.6. Results (One-Time and Yearly Costs)

Appendix H provides the methodology and calculati ons for all non-treatment-related costs. Note that cost calculations in Appendix H are performed using system inventory data broken out by system size categories that are different from the standard nine system size categories used elsewhere in this EA. EPA believes these alternate categories to be more appropriate for establishing the number of samples required per system. Section 7.3.6 summarizes the one-time and yearly costs for all non-treatment-related Stage 2 DBPR activities.

Monitoring requirements for the Stage 1 DBPR we re dependent on the system type (NTNCWS or CWS), the source water type (surface or ground) a nd the number of plants per system. EPA has identified several potential issues when requirement s are based on the number of plants per system. These include:

• The number of required sample sites may be either excessive or insufficient to represent TTHM and HAA5 occurrence throughout the system, pa rticularly in situations where a small system uses multiple plants, or where a very large system uses a small number of plants.

• Plant-based sampling requirements for mixed systems (i.e., those receiving disinfected surface water and ground water in their distribution system) may be excessive, depending upon the system’s characteristics.

• Plant-based monitoring requirements pose unique implementation issues for systems that use temporary supplies during the year.

### Final Economic Analysis for the Stage 2 DBPR 7-14 December 2005 

### An alternative population-based approach was in cluded in the proposed Stage 2 DBPR, and many positive comments were received. For this reason and those listed above, a population-based monitoring approach was adopted for the final rule whereb y IDSE and Stage 2 monitoring requirements are based only on system type, source water type, and retail population served.

7.3.1 Rule Implementation

Public Water Systems

All systems subject to the Stage 2 DBPR will incu r one-time costs for staff to read the rule and become familiar with its provisions and to be traine d on its requirements. The technical and managerial labor rates presented in Section 7.2 were used along with estimates of labor hours to generate implementation costs for all systems. The mix of la bor rates used to estimate implementation costs varies by activity and system size as summarized in Appendix H.

States/Primacy Agencies

State/Primacy Agency implementation activities include:

• Public notification • Regulation adoption and program development • Training State/Primacy Agency staff • Training PWS staff • Technical assistance • Updating the data management system

The number of FTEs required per activity was es timated by EPA based on previous experience with other rules. State/Primacy Agency activiti es include public notification (0.1 FTEs), regulation adoption and program implementation (0.50 FTEs), training State/Primacy Agency staff (0.25 FTEs), training PWS staff and technical assistance (1.00 FTE), and updating the management system (0.10 FTEs). The labor rates used to estimate State/Primacy Agency costs are presented in Section 7.2. The number of States and territories included the 50 States (or EPA regions where States do not have primacy), 6 territories, and 1 tribal government.

7.3.2 Initial Distribution System Evaluations

Public Water Systems

The purpose of the IDSE is to identify compliance monitoring sites that are representative of the highest TTHM and HAA5 levels in the distribution system. IDSEs can be performed by either (1) conducting standard monitoring or (2) completing an System Specific Study (SSS) that may include existing monitoring data or hydraulic modeling r esults. NTNCWSs serving fewer than 10,000 people are not subject to the IDSE requirements of the Stage 2 DBPR. A CWS or NTNCWS does not have to perform the IDSE if: (1) all Stage 1 DBPR co mpliance samples are less than or equal to 40 

# : g/L for TTHM and 30 

## : g/L for HAA5, or (2) the system serves fewe r than 500 people and qualifies for the very small system waiver.

Systems performing an IDSE will incur costs for ev aluating their distribution systems to identify sampling sites, preparing an IDSE monitoring plan, sampling, and reporting results. Systems electing to

### Final Economic Analysis for the Stage 2 DBPR 7-15 December 2005 

### complete an SSS may not incur sampling costs; however , they will still incur labor costs for preparing an IDSE study plan and preparing the report. Also, some systems that do not perform the IDSE may have more sampling sites required under the Stage 2 DBPR than under the Stage 1 DBPR and, thus, will incur a small labor cost for selecting new Stage 2 sites. A detailed description of the process that EPA used to estimate the total national IDSE costs, as well as deta iled calculation tables, are presented in Appendix H.

States/Primacy Agencies

States/Primacy Agencies also will incur costs as a result of the IDSEs. The activities they will conduct include analyzing PWS IDSE reports, determin ing which systems cannot receive a very small system waiver, consulting with PWSs, and IDSE r ecordkeeping. The estimates of burden and costs depend on the IDSE option (standard monitoring plan or SSS) and system size. Total costs for States/Primacy Agencies for the IDSE are estimated to be $2.2 million.

7.3.3 Monitoring Plans

Public Water Systems

Most systems are required to prepare a monitori ng plan for routine Stage 2 DBPR monitoring. Many base the new monitoring plan on the IDSE results.

As described in Section 7.1.2, some ground water systems may choose to add disinfection to meet the requirements of the GWR after it becomes final. Because the GWR is expected to be promulgated within 8 months after the Stage 2 DBPR is promul gated, EPA expects new systems adding disinfection to meet GWR requirements to simultaneously achieve compliance with Stage 2 MCLs. Therefore, as discussed in Chapter 3 of this EA, these systems are not included in the treatment baseline. The IDSE will likely not apply to these systems because they are expected to add disinfection after the IDSE requirement is complete. These systems will, how ever, need to prepare Stage 2 monitoring plans.

Estimates of labor hours for systems to complete the monitoring plans are in Appendix H.

States/Primacy Agencies

States/Primacy Agencies will also incur costs to review and approve monitoring plans submitted by systems. Estimates of burden and cost for monitoring plans are found in Appendix H. The estimated costs for State/Primacy Agency review and approval of monitoring plans is $926,016.

### Final Economic Analysis for the Stage 2 DBPR 7-16 December 2005 

### 7.3.4 Additional Routine Monitoring

Public Water Systems

EPA has established a population-based monitori ng approach for the Stage 2 DBPR, where monitoring requirements are no longer based on number of plants per system as under the Stage 1 DBPR. As a result, systems may have the same, fewer, or more monitoring sites for the Stage 2 DBPR compared to Stage 1, depending on number of plants and how the State/Primacy Agency determined their Stage 1 monitoring requirements. Some systems will have the same monitoring requirements under the Stage 2 DBPR as they did under the Stage 1 DBPR, and will, th erefore, incur no additional costs for this activity. In other cases, incremental costs will be negative (t hat is, costs will be reduced) under the Stage 2 DBPR for systems that have fewer sites than the Stage 1 DBPR (see Exhibit H.8 in Appendix H). Systems with more monitoring sites under the Stage 2 DBPR than unde r the Stage 1 DBPR will incur costs. Changes in numbers of samples from Stage 1 to Stage 2 and total costs for additional routine monitoring are provided in Appendix H.

As described in Section 7.1.2, some ground water systems may choose to add disinfection to meet the requirements of the GWR after it becomes final. These are systems that do not disinfect currently that will elect to install disinfection and monitor for DBPs for the first time to comply with the GWR requirements. Because the GWR is expected to be promulgated within 8 months after the Stage 2 DBPR is promulgated, EPA expects new systems adding disinfection to meet GWR requirements to simultaneously achieve compliance with Stage 2 MCLs. Therefore, these systems are not included in the treatment baseline. The IDSE will likely not apply to these systems because they are expected to add disinfection after the IDSE requirement is complete. Systems adding disinfection for the GWR, however, will need to monitor DBPs for the first time under Stag e 2. The derivation of routine monitoring costs for systems that add disinfection to achieve complian ce with the GWR is provided in Appendix H. The actual monitoring costs for newly disinfecting ground water systems may be different from those estimated here depending upon the details of the final GWR.

Costs for additional routine monitoring include laboratory analysis and labor for taking the sample.

States/Primacy Agencies

States/Primacy Agencies will incur costs related to review and evaluation of monitoring data submitted by systems. EPA estimates states will requi re 0.4 FTEs to track compliance data, update data management systems, and file records. These costs are estimated to total $1.6 million per year.

### Final Economic Analysis for the Stage 2 DBPR 7-17 December 2005 

### 7.3.5 Operational Evaluations

Public Water Systems

To address excess DBP levels that may occasionally occur (but do not cause rule violations), the Stage 2 DBPR contains a provision for operational evaluations. An operational evaluation level is exceeded when a sample result when multiplied by two, added to the sum of the previous two quarters’ samples, and divided by four would result in a concentration greater than 80 

## : g/L for TTHM or 60 

## : g/L for HAA5. The equation for calculating the concentration for an operational evaluation (C OL 

### ) is:

C OL 

### = (Q 1 

### + Q 2 

### + 2*Q 3 

### )/4

where Q 1 

### is the concentration of the of the DBP two quarters ago, Q 2 

### is the concentration of the DBP in the previous quarter, and Q 3 

### is the concentration of the DBP in the current quarter. If C OL 

### is greater than the maximum contaminant level (MCL) for the DBP measured, then the operational evaluation level is exceeded. For example, if a system had first-quarter and second-quarter results of 75 μ g/L for TTHM and had a third-quarter result of 90 μ g/L, then the calculation would yield:

(75 μ g/L +75 μ g/L + 2*(90 μ g/L))/4 = 82.5 μ g/L,

meaning the operational evaluation level is exceeded.

If an operational evaluation level is exceeded, sy stems must conduct an “operational evaluation” to investigate and document the cause. Appendix H provides estimates of the number of systems expected to exceed an operational eval uation level and the associated costs.

States/Primacy Agencies

States will incur some costs to review operationa l evaluations submitted by systems. Appendix H estimates the time and costs that will be spent by Stat es to review these operational evaluations. Costs for States/Primacy Agencies to review the operational evaluations are estimated to be $114,173 per year.

7.3.6 Results (One-Time and Yearly Costs)

Exhibit 7.7 summarizes the one-time system costs for implementation, IDSEs, and development of Stage 2 monitoring plans, along with the yearly costs for additional routine monitoring and operational evaluations. Note that IDSE costs make up the majo rity of the one-time costs. Total one-time system costs for implementation, IDSEs, and Stage 2 monitoring plans are approximately $73.6 million (including $12.3 million for implementation activities, $57.4 million for IDSE activities, and $3.8 million for monitoring plans). Total annual system cost s for additional routine monitoring and operational evaluations are approximately $0.6 million (including $0.4 million for additional routine monitoring, $0.2 million for operational evaluations). As shown in E xhibit 7.7, additional routine monitoring costs are positive for some systems and negative for others (b ecause of the redefined monitoring requirements from a plant-based to a population-based approach). On e-time and annual costs for States/Primacy Agencies are summarized in Appendix H, Exhibit H.21.

### Final Economic Analysis for the Stage 2 DBPR 7-18 December 2005 One Time-Costs ($) Annual Costs ($)

Implementation IDSE Stage 2 Monitoring

Plans Total Additional Routine Monitoring Operational Evaluations Total

A B C D=A+B+C E F G=E+F

<100 244,635 $ 447,582 $ 76,408 $ 768,625 $ (52,103) $ 534 $ (51,568) $ 100-499 498,740 $ 912,489 $ 155,774 $ 1,567,003 $ (106,222) $ 1,089 $ (105,133) $ 500-999 363,678 $ 3,140,721 $ 181,839 $ 3,686,237 $ (331,698) $ 3,011 $ (328,686) $ 1,000-3,299 640,272 $ 5,529,388 $ 320,136 $ 6,489,795 $ (583,969) $ 5,301 $ (578,668) $ 3,300-9,999 623,055 $ 8,379,826 $ 258,721 $ 9,261,602 $ 953,611 $ 20,870 $ 974,481 $ 10,000-49,999 1,212,306 $ 17,851,398 $ 461,867 $ 19,525,571 $ (2,477,619) $ 98,959 $ (2,378,660) $ 50,000-99,999 239,846 $ 6,426,075 $ 93,524 $ 6,759,446 $ 216,219 $ 39,199 $ 255,418 $ 100,000-999,999 212,143 $ 6,113,574 $ 93,984 $ 6,419,700 $ 276,429 $ 38,699 $ 315,128 $ 1,000,000+ 15,227 $ 731,365 $ 11,566 $ 758,158 $ 36,517 $ 5,076 $ 41,593 $ National Totals 4,049,902 $ 49,532,418 $ 1,653,819 $ 55,236,138 $ (2,068,834) $ 212,739 $ (1,856,095) $

<100 1,596,365 $ 221,266 $ 77,140 $ 1,894,770 $ 96,758 $ - $ 96,758 $ 100-499 1,975,736 $ 273,849 $ 95,472 $ 2,345,057 $ 119,753 $ - $ 119,753 $ 500-999 894,469 $ 1,931,945 $ 507,572 $ 3,333,986 $ 553,626 $ - $ 553,626 $ 1,000-3,299 1,085,531 $ 2,344,617 $ 615,991 $ 4,046,139 $ 671,883 $ - $ 671,883 $ 3,300-9,999 492,179 $ 1,063,047 $ 279,290 $ 1,834,515 $ 304,631 $ - $ 304,631 $ 10,000-49,999 797,681 $ 1,642,671 $ 320,895 $ 2,761,247 $ 117,333 $ - $ 117,333 $ 50,000-99,999 88,492 $ 182,233 $ 35,599 $ 306,325 $ 13,017 $ - $ 13,017 $ 100,000-999,999 46,421 $ 166,938 $ 30,689 $ 244,048 $ (92,140) $ - $ (92,140) $ 1,000,000+ 2,180 $ 5,964 $ 1,868 $ 10,013 $ (25,261) $ - $ (25,261) $ National Totals 6,979,054 $ 7,832,529 $ 1,964,515 $ 16,776,099 $ 1,759,600 $ - $ 1,759,600 $

<100 46,558 $ - $ - $ 46,558 $ - $ - $ - $ 100-499 63,891 $ - $ - $ 63,891 $ - $ - $ - $ 500-999 23,602 $ - $ 5,245 $ 28,847 $ - $ - $ - $ 1,000-3,299 20,707 $ - $ 4,602 $ 25,309 $ - $ - $ - $ 3,300-9,999 6,591 $ - $ 1,216 $ 7,807 $ 25,473 $ - $ 25,473 $ 10,000-49,999 3,263 $ 46,876 $ 1,303 $ 51,442 $ - $ - $ - $ 50,000-99,999

- $ - $ - $ - $ - $ - $ - $ 100,000-999,999 740 $ 23,725 $ 313 $ 24,778 $ 3,860 $ - $ 3,860 $ 1,000,000+

- $ - $ - $ - $ - $ - $ - $ National Totals 165,353 $ 70,601 $ 12,678 $ 248,632 $ 29,333 $ - $ 29,333 $

<100 498,070 $ - $ 74,304 $ 572,375 $ 175,519 $ - $ 175,519 $ 100-499 425,353 $ - $ 63,456 $ 488,809 $ 149,893 $ - $ 149,893 $ 500-999 131,289 $ - $ 51,924 $ 183,212 $ 253,333 $ - $ 253,333 $ 1,000-3,299 55,048 $ - $ 21,771 $ 76,819 $ 106,220 $ - $ 106,220 $ 3,300-9,999 4,781 $ - $ 1,891 $ 6,672 $ 9,226 $ - $ 9,226 $ 10,000-49,999 2,082 $ 855 $ 1,143 $ 4,080 $ 13,563 $ - $ 13,563 $ 50,000-99,999 189 $ 78 $ 104 $ 371 $ 1,233 $ - $ 1,233 $ 100,000-999,999 215 $ - $ 192 $ 406 $ 2,085 $ - $ 2,085 $ 1,000,000+

- $ - $ - $ - $ - $ - $ - $ National Totals 1,117,027 $ 932 $ 214,785 $ 1,332,744 $ 711,072 $ - $ 711,072 $ Grand Total All Systems 12,311,336 $ 57,436,480 $ 3,845,797 $ 73,593,613 $ 431,171 $ 212,739 $ 643,910 $
Notes: Detail may not add due to independent rounding. Source: Appendix H, Exhibit H.16. Costs for Stage 2 monitoring plans and additional routine monitoring include those costs fo r systems that are projected to add disinfection to comply with the Ground Water Rule. 100% Ground Water NTNCWSs System Size (Population Served)

Surface Water and Mixed CWSs

100% Ground Water CWSs

Surface Water and Mixed NTNCWSs 

## Exhibit 7.7 Summary of System Costs for Non-Treatment Related Stage 2 DBPR Rule Activities (One-Time and Yearly)

7.4 Technology Unit Costs

Available treatment technologies for reducing DBPs were identified during the Stage 2 Microbial-Disinfectants/Disinfection Byproducts (M-DBP) Federal Advisory Committees Act (FACA) deliberations (USEPA 2000n). They include alterna tive disinfectants such as ozone, ultraviolet light

### Final Economic Analysis for the Stage 2 DBPR 7-19 December 2005 

### (UV), and chlorine dioxide, as well as DBP precursor removal treatment technologies such as microfiltration or ultrafiltration. Converting to chlo ramines for residual disinfection was also identified as a relatively inexpensive treatment technology that can limit DBP formation in many distribution systems.

Unit cost estimates for these treatment technologies are in units of dollars per plant for initial capital and yearly O&M activities. Household unit cost s are in units of dollars per household per year. Derivation of unit costs for a wide range of plant sizes, represented by different design and average daily flow rates, are provided in the document, Technologies and Costs for Control of Microbial Contaminants and Disinfection Byproducts (USEPA 2005n). EPA uses mean design flow and average daily flow for each of the nine system size categories (shown in E xhibit 3.4) to estimate unit costs for each treatment technology for each system type, source water type, and size category.

Section 7.4.1 describes the treatment technologies and operating conditions that are used to predict national treatment costs of the Stage 2 DBPR. Section 7.4.2 discusses alternatives to treatment identified by EPA and others during the FACA pr ocess and explains why these processes are not included in the cost analysis for the Stage 2 DBPR. Sec tion 7.4.3 discusses uncertainty in the unit costs and explains how the uncertainties were explicitly accounted for in the Stage 2 cost model.

7.4.1 Treatment Technologies Used to Estimate Costs

This section discusses the treatment technologies ava ilable for surface water plants first, followed by the treatment technologies used for ground water plants. A discussion of uncertainties in unit costs follows. A summary of unit treatment costs is presented at the end of the section. Appendix I supports the data in Section 7.4.1, showing the detailed deri vation of unit costs over the entire range of expected plant flows.

Treatment Technologies for Surface Water Plants

Exhibit 7.8a lists the treatment technologies th at are available to surface water plants for complying with Stage 1 DBPR and Stage 2 DBPR re gulatory alternatives. The SWAT decision tree, as noted in Appendix A, includes both installation of treatment technologies and changes to operational practices. Although changes in operational practices may result in small increases in chemical costs or minor capital improvements, EPA assumes their costs ar e negligible as compared to the costs of the advanced treatment technologies (e.g., UV, ozone, granular activated carbon, microfiltration/ultrafiltration). Therefore, the predicted costs for th e Stage 2 DBPR do not include costs for operational changes (Section 7.8 summarizes uncertainties in nati onal cost estimates). The treatment technologies presented in the SWAT Decision Tree (Appendix A) that are not costed in this EA because they are assumed to incur negligible costs are:

• Adjusting disinfection dose

• Moving the point of chlorination

• Enhanced coagulation/enhanced softening (required for the Stage 1 DBPR)

• Turbo coagulation

Although EPA assumes that large surface water plants serving 100,000 people or more can select any of the treatment technologies presented in Exhibit 7.8a, small plants may not be able to use a particular treatment technology because of operational constraints or other reasons. Limitations on the

### Final Economic Analysis for the Stage 2 DBPR 7-20 December 2005 

### use of treatment technologies by small systems, su mmarized in the second column in Exhibit 7.8a, were identified during the small surface water expert review process (see Appendix A for details).

The last column in Exhibit 7.8a identifies the design criteria and operating conditions for each treatment technology in this EA for which costs are calculated. To capture the range of costs, the Technology and Cost (T&C) document evaluated treat ment technologies over a range of possible influent water qualities and operating conditions (USEPA 2005n). For the purposes of estimating the costs of the Stage 2 DBPR, the Technical Workgroup (TWG) select ed water quality and operating parameters that would capture the typical circumstances under which plants may use the treatment technology. EPA does not propose that all systems would operate under these conditions, but they suffice to generate capital and O&M costs typical of the range of system types and sizes. While these assumptions simplify the true variety of operating conditions, EPA believes they cap ture reasonable estimates of national costs. The uncertainties associated with selecting these operati onal parameters and conditions are summarized in Section 7.8.

Appendix A presents additional information on how each treatment technology was modeled in SWAT, including log removal and disinfection credits for Giardia and viruses. Note that, for UV, the disinfection credit for viruses was 2.0 logs for all runs . The primary disinfectant (usually chlorine) dose was then modified to meet the remaining disinfection requirements.

Some advanced treatment technologies were consid ered in combination for surface water systems to meet the Stage 2 DBPR (as modeled in SWAT). When treatment technologies are a combination of two or more unit processes (e.g., Granular Activat ed Carbon—20-Minute Contact Time (GAC20) with Advanced Disinfectants such as Chlorine Dioxide or Ozone), the technology unit costs are assumed to be the sum of the costs for each unit process. For example, the cost for implementing GAC20 with an Advanced Disinfectant (e.g., Ozone) is equal to the sum of GAC20 and Ozone unit costs. This may overestimate unit costs since some economies of scale are expected when two or more treatment technologies are installed at the same time.

### Final Economic Analysis for the Stage 2 DBPR 7-21 December 2005 

## Exhibit 7.8a Treatment Technologies for Surface Water Plants

Technology Constraints Design Crit eria and Operating Conditions

Switching to chloramines (CLM) as a residual disinfectant Can be used alone or in conjunction with all other treatment technologies in this exhibit Ammonia dose = 0.55 mg/L (output from SWAT)

Chlorine dioxide (CLO2) Assumed not practical for systems fewer than 100 people EPA assumed that plants will not build a new contact basin for chlorine dioxide ClO 2 

### dose = 1.25 mg/L

Ultraviolet light disinfection (UV) 1 

### None Median water quality parameters— UV 254 

### = 0.051 cm -1 

### , turbidity = 0.1 NTU, alkalinity = 60 mg/L as CaCO 3 

### , hardness = 100 mg/L as CaCO 3 

### Dose = 40 mJ/cm 2



### Ozone Assumed not practical for systems serving fewer than 100 people Design dose = 3.2 mg/L, contact time = 12 minutes 2



### Microfiltration / Ultrafiltration (MF/UF) None Median water quality parameters— Temperature=10°C, disposal to sewer
Granular activatedcarbon filtration, emptybed contact time of 10 minutes (GAC10) Assumed not practical for small systems serving fewer than 10,000 people Reactivation frequency = 360 days 3 

### On-site regeneration

GAC10 + Advanced Disinfectants Assumed not practical for small systems serving fewer than 10,000 people Chlorine dioxide as the advanced disinfectant Reactivation frequency = 360 days 3 

### On-site regeneration
Granular activatedcarbon filtration, emptybed contact time of 20 minutes (GAC20) None Reactivation frequency = 90 days 3 

### Onsite regeneration used for systems serving > 10,000 Media replacement used for systems serving < 10,000

GAC20 + Advanced Disinfectants None Systems serving > 10,000 (GAC20 + chlorine dioxide) Systems serving 100 - 9,999 (GAC20 + ozone) Systems serving < 100 (GAC20 + UV) On-site media regeneration used for systems serving > 10,000 Media replacement used for systems serving < 10,000

Membranes (MF/UF + nanofiltration [NF]) None Median water quality parameters— MF/UF: 10°C, disposal to sewer NF: 10°C, ocean discharge
Notes: 1 Available for Stage 2 DBPR regulatory alternatives only; not considered available for the Stage 1 DBPR. UV was assumed to be used as a supplement to ch lorine to achieve some of the required Giardia and virus inactivation, thereby reducing chlorine dosages. 2 Dose does not consider Cryptosporidium inactivation, and, therefore, may not represent what systems would do to meet Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) requirements. However, the higher dose is accounted for in the LT2ESWTR EA. 3 Median reactivation frequency generated by SWAT. Source: T&C document (USEPA 2005n), FACA deliberati ons for Stage 2 treatment technologies (USEPA 2000n). SWAT Decision Tree (Appendix A), and Small Surface Water Delphi Groups (Appendix A).3 

### Note, EPA updated the 40 mJ/cm 2 

### UV unit costs based on data obtained for recent installations of this technology. Similar data for 200 mJ/cm 2 

### UV systems were not available within the time frame required to include in this analysis

Final Economic Analysis for the Stage 2 DBPR 7-22 December 2005 

### Treatment Technologies for Ground Water Plants

Exhibit 7.8b lists the treatment technologies used to estimate costs of the Stage 1 DBPR and Stage 2 DBPR regulatory alternatives for ground wa ter plants. Fewer treatment technologies are applicable to disinfecting ground water plants than to surface water plants. As noted in Appendix B, the ICR Ground Water Delphi process concluded that disinfecting ground water systems would choose primarily from four treatment technologies—c onversion to chloramines, ozone, GAC20, and nanofiltration. Limitations on treatment technology use by small systems, as identified during the small ground water system expert review process, are provided in the second column.

Because UV was still very much an emerging treatment technology when the Ground Water Delphi process was conducted (Spring, 2000), UV was not considered as a treatment option for large ground water plants for either the Stage 1 or Stage 2 DBPRs. UV was, however, considered an available treatment technology for small ground water systems to meet Stage 2 DBPR requirements. The small-system expert reviewers assumed UV would be used instead of chlorine to achieve 4.0 logs of virus inactivation in all circumstances. Current UV validation techniques as outlined in the UV Disinfection Guidance Manual (USEPA 2003g) are not currently feasible for validation of 4-log virus inactivation. Therefore it will be necessary to use two UV reactors in series, each achieving 2-log virus inactivation, to achieve the desired 4-log virus removal. A dose of 200 millijoules per centimeter square (mJ/cm 2 

### ) is assumed necessary to achieve 2-log virus inactivati on, much higher than the UV dose of 100 mJ/cm 2 

### required by the LT2 ESWTR for 2 log viral inactiv ation (USEPA 2005n). The unit cost estimates shown in Exhibit 7.11 for UV ($/plant) for small ground wate r plants represent new estimates for two reactors in series with a higher dose (200 mJ/cm 2 

### ). 3 

### The cost assumptions for ground water systems to achieve compliance with the Stage 2 DBPR are therefore probably overstated.

### Final Economic Analysis for the Stage 2 DBPR 7-23 December 2005 

## Exhibit 7.8b Treatment Technologi es for Disinfecting Ground Water Plants

Treatment Technology Constraints Design Crit eria and Operating Conditions Switching to CLM as a Residual Disinfectant This advanced treatment technology can be used alone or in conjunction with all of the following treatment technologies Ammonia dose = 0.15 mg/L 1



### UV 2 

### Was considered only for small systems with populations of fewer than 10,000 people. Requires two reactors in series. Median water quality parameters— UV 254 

### = 0.051 cm -1 

### , turbidity = 0.1 NTU, alkalinity = 60 mg/L as CaCO 3 

### , hardness = 100 mg/L as CaCO 3 

### Dose = approximately 200 mJ/cm

Ozone Assumed not practical for systems serving fewer than 100 people Design dose = approximately 3.2 mg/L, contact time = 12 minutes

GAC20 None Reactivation frequency = 240 days 3 

### On-site regeneration used for systems serving > 10,000 Media replacement used for systems < 10,000

Nanofiltration None Median water quality parameters— Temperature=10°C, ocean discharge
Notes: 1 Dose based on decisions from t he ICR Ground Water Delphi Group. 2 Available for Stage 2 DBPR regulatory alternatives only; not considered available for the Stage 1 DBPR. 3 Reactivation frequency based on decisions fr om the ICR Ground Water Delphi Group. Source: T&C document (USEPA 2005n), FACA deliberati ons for Stage 2 treatment technologies (USEPA 2000n), and ICR and Small Ground Water Delphi Groups (Appendix B).



### Unit Costs for the Stage 2 DBPR

Capital and O&M unit costs for each treatment technology are derived from the T&C document (USEPA 2005n). The T&C document contains between 16 and 20 point estimates of capital and O&M costs over the range of expected design and average fl ow rates. Appendix I displays these point estimates for the treatment technologies, design criteria, and opera ting conditions listed in Exhibits 7.8a and b. In previous T&C drafts, non-linear cost curves were generated for specific flow ranges based on a more limited set of point estimates. Because the number of point estimates for the unit costs was increased to better represent the full range of expected flows, EPA believes that direct straight-line interpolation between the point values is adequate for character izing the changes in unit costs as flow increases or decreases. Along with the 16 to 20 point estimates, A ppendix I graphically shows the relationship of unit cost and flow (i.e., the point estimates connected by straight lines).

Unit treatment costs for each system type and si ze category are estimated using (1) the capital and O&M cost data in Appendix I and (2) the mean desi gn and average daily flow values presented in Exhibit 3.4. For example, the design flow for UV for su rface water plants in CWSs serving between 10,000 and 49,999 people is estimated to be 5.324565 millions of ga llons per day (MGD) (the value in Exhibit 3.4 is rounded to 5.325 MGD). Exhibit I.5 shows that the capital cost for UV surface water plants is $362,965 for a design flow of 3.5 MGD and $544,728 for a design flow of 7.0 MGD. The cost for a 5.325 MGD plant can be calculated by linear interpolation as:

Unit Cost = $362,965 + ($544,728 - $362,965) * (5.324565 MGD - 3.5 MGD)/(7.0 MGD - 3.5 MGD)4 

### Cost-of-capital estimates are used to account for interest payments that PWSs may incur and pass along to customers in the form of water bill increases. These rate s may be different than social discount rates (3 and 7 percent) used elsewhere in the economic analysis. So cial discount rates are more appropriate for estimating economic impacts on a national level. See EPA’s Guidelines for Preparing Economic Analyses (USEPA 2000j) for a full discussion of the use of social discount rates in the evaluation of policy decisions.

5 

### Note that household usage rates for the affordability analysis in Chapter 8 are median values from 1995 CWSS data, whereas the values in Exhibit 7.13 are mean values



### .

Final Economic Analysis for the Stage 2 DBPR 7-24 December 2005 

### Unit Cost = $457,719 (Note, detail may not exactly equal value in Exhibit 7.10a due to independent rounding.)

Household unit treatment costs ($/HH/year) are estimated in order to provide a potential and approximate measure of the increase in water bills that is expected to result from the Stage 2 DBPR. Steps for deriving household unit costs are outlined below.

• Step 1: Capital costs of treatment are amortized over a 20 year period using the cost-ofcapital rates summarized in Exhibit 7.9. These rates are derived from Development of Cost of Capital Estimates for Public Water Systems, Final Report (USEPA 2001e). 4 

### For each treatment technology, amortized capital costs are added to annual O&M costs to produce annual treatment costs in units of dollars per plant per year ($/plant/yr).

• Step 2: Results from step 1 ($/plant/yr) ar e converted to dollars per 1000 gallons per year ($/kgal/yr) using the following formula:

C HH 

### = C P 

### /(ADF*365*1000)

where: C HH 

### = unit cost per household in $/kgal C P 

### = unit cost per plant $/plant/yr ADF = average daily flow in mgd

• Step 3: Estimated household usage rate (in un its of 1,000 gallons per year, or kgal/yr) is used to convert results from step 2 ($/kgal/yr) to dollars per household per year ($/HH/yr) according to the formula:

C AHH 

### = C HH 

### *Q HH



### where: C AHH 

### = annual household unit cost ($/HH/yr) Q HH 

### = annual household usage rate (kgal/yr)

Exhibit 7.9 summarizes the annual household usage ra tes used to estimate household costs in this chapter. These are from the Baseline Handbook, as derived from the 1995 CWSS 5 

### . EPA recognizes that there may be significant variation in household water usage between specific PWSs (table C.4.2.3 of the handbook presents confidence intervals around these estimat es), but believes that mean usage rate values are adequate for characterizing household costs.

### Final Economic Analysis for the Stage 2 DBPR 7-25 December 2005 Mean Annual Water Usage per Household (kgal/year) Public Cost of Capital

Rate Private Cost of Capital

Rate A B C <100 83 5.31% 6.22% 100-499 83 5.31% 6.22% 500-999 104 5.58% 6.22% 1,000-3,299 87 5.58% 6.22% 3,300-9,999 97 5.58% 6.22% 10,000-49,999 109 5.20% 5.66% 50,000-99,999 119 5.24% 6.27% 100,000-999,999 125 5.24% 6.27% 1,000,000+ 125 5.24% 6.27%
Note: Data derived from 1995 CWSS Sources: (A) Derived from the Third Edition of the Baseline Handbook (USEPA, 2001c) Table C.4.2.3, all systems. Rates for systems serving < 500 people revised based on further analysis by EPA. (B) and (C) Development of Cost of Capital Estimates for Public Water Systems, Final Report (USEPA, 2001d). 

### System Size (Population

Served)



## Exhibit 7.9 Household Cost Inputs

As an example, consider the derivation of the household unit costs for UV for a CWS surface water system serving between 10,000 and 49,999 peopl e. The capital cost is $457,719 as calculated above. The O&M cost from Exhibit 7.10b is $15,274/plant/year. In that size category 90.03 percent of households are served by public systems and 9.97 percent by private systems (see Exhibit 3.3). So the discount rate to be used for amortizing the capital is then:

i = .9003*(0.052) + .0997*(0.0566) = 0.0525

Amortizing the capital at a 5.25 percent discount rates gives:

C cap-ann 

### = $457,719*0.0525*(1 + 0.0525) 20 

### /((1 + 0.0525) 20 

### - 1) = $37,511.12/plant/year

C p 

### = $37,511.12 + $15,274.16 = $52,785.28/plant/year

This is the result of step 1 listed above. Step 2 i nvolves multiplying the cost by the average daily flow to obtain a unit cost per thousand gallons of water.

C HH 

### = ($52,785.28/plant/year)/[(2.34Mgal/plan t/d)*(365d/yr)*(1000kgal/Mgal)] = $0.0618/kgal/yr.

Step 3 involves multiplying by the household usage rate, which from Exhibit 7.9 is 109 kgal/yr

C AHH 

### = $0.06/kgal/yr*109kgal/HH/yr = $6.73

This is the value shown in Exhibit 7.10c.

Exhibits 7.10a-b and 7.11a-b summarize annua l O&M costs ($/plant/year) and capital costs ($/plant) for surface and ground water treatment technol ogies, respectively. Note that unit costs are

### Final Economic Analysis for the Stage 2 DBPR 7-26 December 2005 

### different for surface and ground water plants in each size category because they are based on different mean design and average daily flows per plant as shown in Exhibit 3.4. Costs are provided for each treatment technology and for each of the nine population size categories for plants in CWSs. Unit costs for NTNCWSs are not presented, but can be derived from the data in Appendix I using NTNCWS flows, as summarized in Exhibit 3.4.

Exhibits 7.10c and 7.11c summarize household unit treatment costs ($/HH/year) for surface and ground water treatment technologies, respectively. Th e household unit costs in Exhibits 7.10c and 7.11c represent only the household costs for installation a nd operation of the treatment technology and do not include costs for other items such as the IDSE or monitoring. A description of the process used to generate distributions of possible household tr eatment costs is provided in Section 7.5.3.Final Economic Analysis for the Stage 2 DBPR 7-27 December 2005System Size(PopulationServed)Plant DesignFlow (MGD) CLM CLO2 UV Ozone MF/UF GAC10 GAC10+AD GAC20 GAC20+AD Membranes<100 0.02 $29,104.44 $12,389.73 $195,605.59 $49,239.18 $49,239.18 $260,909.86100-499 0.08 $29,104.44 $23,031.80 $373,422.89 $120,214.53 $120,214.53 $511,246.10500-999 0.21 $33,047.01 $38,637.25 $45,188.94 $401,494.03 $668,228.42 $274,674.40 $319,863.34 $922,064.411,000-3,299 0.57 $40,954.40 $41,950.61 $87,869.72 $610,467.18 $1,032,468.83 $630,258.56 $1,240,725.74 $1,585,978.033,300-9,999 1.42 $83,772.20 $80,980.80 $318,958.94 $989,688.20 $2,002,992.32 $1,726,876.12 $2,716,564.32 $3,336,150.8210,000-49,999 5.32 $83,772.20 $201,714.84 $457,719.08 $1,992,626.96 $5,831,476.20 $2,662,884.51 $2,864,599.35 $4,388,911.73 $4,590,626.57$10,977,349.6550,000-99,999 8.26 $85,666.74 $218,640.92 $645,428.85 $2,558,175.82 $8,469,691.08 $3,622,545.65 $3,841,186.57 $6,043,134.98 $6,261,775.90$16,315,154.73100,000-999,999 29.89 $172,356.04 $341,382.88 $2,142,573.70 $5,726,786.46 $26,059,504.89 $8,993,844.94 $9,335,227.82 $15,370,784.57 $15,712,167.45 $51,427,679.241,000,000+ 200.95 $485,633.73 $877,596.16 $7,756,654.16 $25,393,578.52 $146,969,927.83 $36,851,865.10 $37,729,461.26 $64,692,915.84 $65,570,511.99 $271,760,784.19System Size(PopulationServed)Plant-AverageDaily Flow(MGD) CLM CLO2 UV Ozone MF/UF GAC10 GAC10+AD GAC20 GAC20+AD Membranes<100 0.01 $1,366.97 $3,465.33 $6,788.93 $19,849.99 $19,849.99 $14,964.21100-499 0.03 $1,486.13 $14,200.27 $4,567.48 $55,555.11 $10,560.66 $47,768.56 $61,968.83 $25,798.47500-999 0.08 $2,786.70 $16,491.53 $5,906.07 $58,860.42 $25,696.45 $59,595.71 $65,501.78 $62,334.661,000-3,299 0.22 $3,075.12 $17,767.16 $8,060.73 $61,074.17 $40,850.24 $120,988.58 $182,062.75 $110,067.203,300-9,999 0.59 $6,367.05 $20,288.25 $10,462.98 $65,448.37 $90,783.00 $188,170.95 $253,619.32 $256,285.8210,000-49,999 2.34 $9,532.26 $23,993.48 $15,274.16 $88,075.83 $258,314.24 $103,214.63 $127,208.11 $292,402.22 $316,395.70 $817,602.6950,000-99,999 3.80 $11,365.79 $27,157.89 $17,309.16 $103,776.82 $401,754.12 $138,118.16 $165,276.06 $385,208.70 $412,366.60 $1,291,782.17100,000-999,999 14.61 $20,636.42 $48,315.25 $32,774.25 $216,130.20 $1,337,846.76 $337,930.01 $386,245.26 $1,074,985.73 $1,123,300.98 $4,500,839.351,000,000+ 107.80 $73,080.99 $197,514.28 $170,152.92 $1,241,600.91 $8,908,719.65 $1,767,266.63 $1,964,780.90 $6,091,016.57 $6,288,530.85 $30,384,366.43Exhibit 7.10a Capital Unit Costs ($/Plant) for CWS Surface Water PlantsSource: Design flows from Exhibit 3.4, and unit costs from Appendix I.Exhibit 7.10b Annual O&M Unit Costs ($/Plant/Year) for CWS Surface Water PlantsSource: Average daily flows from Exhibit 3.4, and unit costs from Appendix I.Final Economic Analysis for the Stage 2 DBPR 7-28 December 2005System Size(PopulationServed)Mean AnnualWater Usageper HH (kgal) CLM CLO2 UV Ozone MF/UF GAC10 GAC10+AD GAC20 GAC20+AD Membranes<10083$139.50 $163.48 $851.00 $869.14 $869.14 $1,348.43100-499 83 $32.32 $116.11 $53.30 $454.26 $345.06 $473.88 $589.99 $565.14500-999 104 $20.10 $70.95 $34.99 $333.82 $296.45 $297.68 $332.68 $505.441,000-3,299 87 $6.99 $22.76 $16.56 $120.46 $137.12 $186.18 $306.64 $261.113,300-9,999 97 $6.09 $12.29 $16.97 $67.58 $117.89 $151.41 $218.99 $243.9510,000-49,999 109 $2.09 $5.17 $6.73 $32.07 $93.93 $41.01 $46.18 $83.20 $88.37 $219.1050,000-99,999 119 $1.58 $3.88 $6.07 $27.07 $94.61 $37.58 $41.46 $75.95 $79.84 $226.64100,000-999,999 125 $0.82 $1.80 $4.93 $16.18 $81.94 $25.38 $27.17 $55.03 $56.83 $205.321,000,000+ 125 $0.36 $0.86 $2.58 $10.62 $66.95 $15.31 $16.16 $36.36 $37.22 $167.99Exhibit 7.10c Household Unit Treatment Costs($/Household/Year) for CWS Surface Water PlantsSource: Mean water usage per household derived from theThird Edition of the Baseline Handbook (USEPA, 2001c)

### Final Economic Analysis for the Stage 2 DBPR 7-29 December 2005 System Size (Population Served) Plant Design Flow (MGD) CLM UV Ozone GAC20 NF <100 0.02 $29,104.44 $47,108.15 $0.00 $51,560.52 $67,676.53 100-499 0.06 $29,104.44 $70,701.54 $0.00 $102,166.90 $119,226.85 500-999 0.16 $30,298.78 $126,977.04 $370,630.22 $220,628.98 $209,226.96 1,000-3,299 0.38 $39,102.18 $252,548.67 $507,022.28 $457,866.77 $379,952.47 3,300-9,999 0.89 $49,613.33 $724,535.96 $761,428.90 $1,064,410.99 $828,831.05 10,000-49,999 1.64 $83,772.20 $1,081,364.49 $1,671,400.95 $1,598,457.82 50,000-99,999 4.12 $83,772.20 $1,730,048.55 $3,239,129.48 $3,994,609.23 100,000-999,999 7.68 $84,799.17 $2,466,239.92 $5,162,317.80 $7,341,646.08 1,000,000+ 41.35 $98,772.20 $7,443,344.25 $18,067,167.43 $33,373,795.86

System Size (Population Served) Plant-Average Daily Flow

(MGD) CLM UV Ozone GAC20 NF <100 0.01 $1,361.57 $7,837.50 $0.00 $10,760.70 $7,835.50 100-499 0.02 $1,414.02 $10,317.16 $0.00 $19,848.70 $11,411.69 500-999 0.05 $1,470.58 $13,703.26 $55,839.34 $33,800.85 $27,593.43 1,000-3,299 0.13 $2,958.23 $18,540.93 $59,990.54 $54,578.39 $48,144.08 3,300-9,999 0.34 $4,184.39 $22,261.81 $62,469.10 $97,058.06 $109,460.54 10,000-49,999 0.72 $6,186.52 $66,975.28 $108,465.68 $193,835.38 50,000-99,999 2.01 $7,788.54 $84,465.80 $157,248.60 $484,005.34 100,000-999,999 4.26 $9,383.04 $108,576.27 $239,037.74 $992,000.90 1,000,000+ 27.22 $19,308.42 $350,166.93 $965,369.32 $5,723,540.30



## Exhibit 7.11a Capital Cost ($/Plant) for CWS Disinfecting Ground Water Plants

Source: Design flows from Exhibit 3.4, and unit costs from Appendix I.



## Exhibit 7.11b Annual O&M Costs ($/P lant/Year) for CWS Disinfecting Ground Water Plants

Source: Average daily flows from Exhi bit 3.4, and unit costs from Appendix I.

### Final Economic Analysis for the Stage 2 DBPR 7-30 December 2005 System Size (Population Served) Mean Annual Water Usage per HH (kgal) CLM UV Ozone GAC20 NF <100 83 $177.77 $543.11 $0.00 $693.37 $625.03 100-499 83 $51.26 $214.42 $0.00 $374.38 $283.38 500-999 104 $22.99 $138.94 $495.03 $297.85 $257.26 1,000-3,299 87 $11.40 $72.63 $187.08 $169.68 $145.98 3,300-9,999 97 $6.53 $65.17 $98.87 $145.77 $139.86 10,000-49,999 109 $5.45 $65.00 $102.54 $135.66 50,000-99,999 119 $2.39 $37.03 $69.19 $132.32 100,000-999,999 125 $1.32 $25.16 $53.61 $128.64 1,000,000+ 125 $0.34 $12.08 $30.76 $106.41 

## Exhibit 7.11c Household Unit Treatme nt Costs ($/Household/Year) for CWS Disinfecting Ground Water Plants

Source: Mean water usage per household derived from the Third Edition of the Baseline Handbook (USEPA, 2001c)



### 7.4.2 Alternatives to Treatment

During the FACA process, the M-DBP TWG identified many ways systems could reduce the residence time of the water in their distribution systems, thereby reducing TTHM and HAA5 concentrations. These included:

• Flushing more frequently, or looping sections of the distribution system to eliminate dead ends.

• Modifying portions of the distribution system with problematic DBP levels.

• Optimizing storage to minimize retention time in the distribution system.

The costs for these activities could range from close to zero (e.g., changing tank operations without making capital improvements) to more substa ntial costs for reconfiguring storage facilities or looping distribution system networks. The benefits from distribution system activities that reduce DBP concentrations also vary widely and are depende nt on system-specific conditions. Therefore, these activities were not evaluated in this EA (see S ection 7.7 for a discussion of unquantifiable costs).

Other alternatives to treatment that could re duce TTHM and HAA5 levels in the distribution system include connecting to a nearby water system or identifying another water source that has lower DBP precursor levels. While the latter may not be feasible for some remote systems, EPA estimates that more than 22 percent of all small systems are located within metropolitan regions where distances between neighboring utilities will not present a prohib itive barrier. To estimate this percentage, EPA used the April 2000 Safe Drinking Water Informati on System (SDWIS) database to compare the ZIP codes for CWSs that serve 100 or fewer people and me dium and large CWSs. EPA then determined that out of the 446 surface water CWSs that serve 100 or fewer people, 98 (22 percent) have ZIP codes that are identical to those for medium and large CWSs (however, size of the ZIP code zone was not considered). Consolidation with another water sy stem may represent the least-cost alternative for many small systems.

### Final Economic Analysis for the Stage 2 DBPR 7-31 December 2005 

### 7.4.3 Uncertainty in Unit Costs

In developing the unit costs used in this EA , the design criteria for the compliance treatment technologies were selected to represent typical, or averag e, conditions for the universe of systems. As a result, there is uncertainty in these unit cost es timates, as they are based on designs and quotes assuming average conditions, rather than on an detailed aggrega tion of State, regional, or local estimates based on actual field conditions. To model the uncertainty ar ound unit costs in this EA, the national average unit cost factors are characterized as triangular distributions with minimum and maximum values set at the following percentages relative to the best estimate:

• Capital costs: ± 30%

• O&M costs: ± 15%

These percentages were developed by EPA based on input from engineering professionals and represent recommendations from the National Dri nking Water Advisory Council (NDWAC) (2001) in their review of the national cost estimation methodol ogy for the Arsenic Rule. EPA believes that the uncertainties in capital and O&M costs for a given tr eatment technology are independent of one another and that uncertainties across all treatment technologies are independent.



## 7.5 The Stage 2 DBPR Cost Model

The cost model combines complia nce forecasts, as described in Chapter 5, with the technology unit costs, as described in Section 7.4, to obtai n national treatment costs. The model accounts for uncertainty in the national costs in several ways, as e xplained in Section 7.5.1. Once the treatment costs and the uncertainty surrounding them are determined, they are combined with the non-treatment costs from Section 7.3. The costs are then projected over time and discounted to obtain present value and annualized costs for the rule as descr ibed in Section 7.6.2. Lastly, household costs are determined as described in Section 7.6.3. A detailed list of all cost model files and flow charts is in Appendix K.

7.5.1 Probability Analysis to Estimate Nominal Treatment Costs

Inputs to Incorporate Uncertainty in National Costs

EPA recognizes that there is uncertainty in the i nputs used to determine the national costs of this rule. Ideally, the model would quantify each uncerta inty. Data regarding the quantitative impact of each element of uncertainty, however, are not available. EPA has developed an approach to quantifying the more significant areas of uncertainty that poten tially have a large influence on national costs:

• Uncertainty in potential impact of the IDSE on the compliance forecast for large and medium surface water systems, discussed in Chapter 5. • Uncertainty in methods used to develop th e compliance forecast (i.e., SWAT and the ICR Matrix Method), as discussed in Chapter 5 and Appendix A. • Uncertainty in average unit costs (capita l and O&M), as discussed in Section 7.4.3.

### Final Economic Analysis for the Stage 2 DBPR 7-32 December 2005 

### As described in Chapter 5, it is possible that systems may measure higher DBP levels at Stage 2 compliance monitoring sites than were measured under the ICR. EPA believes that the 20 percent safety margin on compliance (used throughout the primary cost analysis) already accounts for the potential impacts of the IDSE for small surface water systems, ground water systems, and chloramine systems. The Agency believes, however, that the 20 percent safety margin is not sufficient to account for the potential impacts of the IDSE on all large and medi um surface water systems because spatial variability of DBP levels and distribution system complexity ar e greatest in these systems. Based on the analysis of spatial variability in DBP distribution system data and other factors, EPA developed an approach to quantify the potential impacts of the IDSE by prepar ing an alternative compliance forecast using a 25 percent safety margin (see Section 5.3.4 for details ). Because a 20 percent safety margin may already account for the IDSE for some large a medium surface water systems, results based on both a 20 and 25 percent safety margin are used in the cost model.

Since the proposal, EPA developed an second method, called the ICR Matrix Method, to estimate the percent of plants making treatment technology changes to meet the Stage 2 DBPR. The percent of plants making treatment technology changes from Stage 1 to Stage 2 are different for the ICR Matrix Method and SWAT. Because both SWAT and the ICR Matrix Method have associated uncertainty, results from both are used to generate the co mpliance forecast for surface water systems.

The ICR Matrix Method does not predict the specific treatment technologies that plants will install. Thus, results from the ICR matrix method ar e incorporated by comparing the predicted percent ofplants making treatment technology changes with SWAT results to create a ICR Matrix Method-toSWAT multiplier. EPA generated a uniform distribution with 1.0 as the 5 th 

### percentile value and the ICR Matrix Method-to-SWAT multiplier for plants making treatment technology changes as the 95 th 

### percentile value. Two separate distributions were used for la rge and medium surface water systems, one for the 20 percent safety margin and one for the 25 percent safety margin. Exhibit 7.12 provides a graphical depiction of the two uniform distributions for the Pr eferred Alternative. The implications of using one method as opposed to combining results from both is discussed in Section 7.8.

Uncertainty in technology unit costs is described in Section 7.4.3. In summary, EPA developed triangular distributions for both capital (± 30 percent) and O&M costs (±15 percent) to incorporate the uncertainty in unit treatment costs.

### Final Economic Analysis for the Stage 2 DBPR 7-33 December 2005 (2.91) (1.98) 

### 1 2 3Probability



### Multiplier 1 2 3Probability



### Multiplier 

## Uniform Distribution for a 20% Safety Margin Uniform Distribution for a 25% Safety Margin

(2.91) (1.98) 

### 1 2 3Probability



### Multiplier 1 2 3Probability



### Multiplier 

## Uniform Distribution for a 20% Safety Margin Uniform Distribution for a 25% Safety Margin 

## Exhibit 7.12 Uniform Distributions for Incorporating the ICR Matrix Method-toSWAT Multiplier into the Compliance Forecasts for Surface Water Systems

Source: Exhibit 5.6



### Probability Analysis in the Cost Model

The cost model incorporates uncertainty using a Monte Carlo simulation. The model follows four basic steps, the first three of which are consistent w ith the probability analysis to generate the compliance forecast as described in Section 5.3.6:

• Step 1: For large and medium surface water systems, the model randomly selects the SWAT-predicted treatment technology selection delta for either the 20 or 25 percent safety margin runs. Each safety margin h as an equal (50 percent) chance of being selected. For small surface water systems and all ground water systems, the treatment technology selection delta for the 20 percent safety margin is always selected.

• Step 2: For large, medium, and small surface water systems, the model randomly selects the ICR-to-SWAT multiplier from the appropriate uniform distribution from Exhibit 7.12 for the safety margin selected in Step 1.

• Step 3: For large, medium, and small surface water systems, the model multiplies the result from Step 2 by the treatment technology selection delta results identified in Step 1 to calculate the percent and number of plan ts making treatment technology changes from Stage 1 to Stage 2. For ground water systems, the treatment technology selection delta for a 20 percent safety margin is always used to calculate the percent of plants changing from Stage 1 to Stage 2.

• Step 4: The model randomly selects a number from 0.7 to 1.3 (± 30%) to represent uncertainty in national capital costs for all technologies. Then it randomly selects a number from 0.85 to 1.15 (± 15%) to represent uncertainty in national O&M costs for all technologies. The model multiplies the treatment technology selection delta (step 3) by6 

### For purposes of analyses in this EA, all present va lue figures are presented at a year 2003 price level. Present value calculations are performed to th e expected year rule implementation (2005).

7 

### See EPA’s Guidelines for Preparing Economic Analyses (USEPA 2000j) for a full discussion of the use of social discount rates in the evaluation of policy decisions.

Final Economic Analysis for the Stage 2 DBPR 7-34 December 2005 

### the technology unit costs and the two uncertainty factors to estimate national treatment costs (O&M)

The process is repeated 10,000 times to produce a distribution of plants making treatment technology changes from Stage 1 to Stage 2 (results from Step 3, same as reported in Chapter 5) and national treatment costs (results from Step 4). For mo re detail on this simulation, see the flow charts and file description in Appendix K.

7.5.2 Projections and Discounting to Produce Annualized Costs

There are two kinds of nominal cost estimates for both treatment and non-treatment activities: (1) one-time costs that occur near the beginning of the ru le implementation period, and (2) yearly costs that systems and States/Primacy Agencies will incur afte r systems have made necessary changes to treatment and/or monitoring to comply with the Stage 2 DBPR. For the purposes of this EA, one-time and yearly costs were projected over a 25-year time period to coincide with the estimated life span of capital equipment and a time lag of 5 to 10 years for treatme nt technology installation after rule promulgation. The projected schedules for all rule activities are summarized in Appendix D.

As described previously in this chapter and in the discussion of benefits in Chapter 6, it is common practice to adjust benefits and costs to a present value 6 

### using a social discount rate so that they can be compared to one another. This process ta kes into account the time preference that society places on expenditures and allows comparison of cost and be nefit streams that are variable over a given time period. 7 

### Similar to calculating the present value of bene fits (see Section 6.5), the present value of costs for any future period can be calculated using the following equation:

PV = V(t) / (1 + R) t



### Where: t = The number of years from the reference period R = Social discount rate V(t) = The cost occurring t years from the reference period

There is much discussion among economists of the proper social discount rate to use for policy analysis. For Stage 2 DBPR cost analyses, present value calculations are made using two social discount rates thought to best represent current policy evaluati on methodologies, 3 and 7 percent. Historically, the use of 3 percent is based on rates of return on re latively risk-free investment s, as described in the Guidelines for Preparing Economic Analyses (USEPA 2000j). The rate of 7 percent is a recommendation of the Office of Management and Budget (OMB) as an estimate of “before-tax rate of return to incremental private investment” (USEPA 1996b). For an y future cost, the higher the discount rate, the lower the present value. Specifically, a future cost (or stream of costs) evaluated at a 7 percent social discount rate will always result in a lower total present value cost than the same future cost evaluated at a 3 percent rate.

### Final Economic Analysis for the Stage 2 DBPR 7-35 December 2005 

### To allow evaluation of alternatives on an annua l basis, their total present value costs are annualized using the same social discount rates (3 and 7 percent) over 25 years. When applying social discount rates to annualize costs, the higher the disc ount rate, the higher the annualized cost. Thus, the magnitudes of the discount rates influence costs in the opposite direction (i.e., a present value cost annualized at a 7 percent rate will always result in higher values than the same present value cost annualized at a 3 percent rate). The final relations hip between annualized costs at 3 and 7 percent is dependent on the time frame for annualization, as well as when the costs are incurred (as set forth in the Rule Activity Schedule in Appendix D). Given a long enough time frame, the 7 percent annualized value will eventually be greater than the 3 percent annualized value.

In summary, the methodology for projecting and discounting costs is as follows:

• Project all nominal costs (treatment, non-treatment, and State) over a 25-year time horizon based on the rule implementation schedule in Appendix D.

• Calculate total present value costs using social discount rates. The same rates were used as for the benefits calculation: 3 and 7 percent (see Section 6.5.3).

• Annualize the costs over 25 years using the same social discount rates.

Values derived using this methodology are presen ted in subsequent sections of Chapter 7. Detailed spreadsheets of all cost calculations are provided in Appendix J.

7.5.3 Methodology for Estimating Household Costs

EPA assumes that generally systems will pass some or all of the costs of a new regulation onto their customers in the form of rate increases. As noted in Section 7.4.1, household costs, which are in units of dollars per household per year ($/HH/yr) , are estimated in this chapter to provide a measure of potential increases in water bills as a result of the St age 2 DBPR, if all costs are passed onto consumers.

A distribution of possible household costs is developed for each system type and population sizecategory. The distribution reflects that some households are served by systems that will incur only nontreatment costs (e.g., implementation, IDSE, mon itoring plans, additional routine monitoring, and operational evaluation costs) as a result of the Stage 2 DBPR, while others are served by systems that incur treatment costs as well. Treatment activities can range from converting to chloramines, which has a relatively low cost, to installing membra nes, which has a relatively high cost.

Data inputs specific to household cost calculations are shown in Exhibit 7.9, and a description of the household cost methodology is provided below. Section 7.4.1 provides a detailed description of the derivation of household unit treatment costs, a nd Exhibits 7.10c and 7.11c present household unit treatment costs for surface and ground water treatment technologies. Appendix K provides a detailed list of files and flow charts for the Stage 2 DBPR household cost model.

• Step 1: The average number of households serve d per plant is calculated by dividing the total households served (Exhibit 3.5) by the total number of plants (Exhibit 3.2) in each system size category.

• Step 2: The number of households incurring different types of costs is based on the number of plants incurring costs, as derived in Appendix H and Section 3.5, respectively. The percent of systems performing non-treatment rela ted rule activities is derived in Appendix H8 

### Only plants in CWSs are used to generate HH co st distributions. NTNCWSs do not typically provide water to households.

Final Economic Analysis for the Stage 2 DBPR 7-36 December 2005 

### and summarized in Exhibit 7.2. EPA assumes that the percent of systems performing these activities is equivalent to the percent of plan ts performing activities. The number of plants making treatment technology changes to meet St age 2 is shown in Exhibits 5.14a and 5.14c 8



### . The number of plants incurring costs is multiplied by the average number of households served per plant to estimate the total number of households incurring different types of costs.

• Step 3: For each type of cost (treatment and non-treatment), a household unit cost is computed using the method described in Section 7.4.

• Step 4: The annual household unit costs ($/HH/yr) are combined with the number of households incurring each type of cost (results from step 2) to generate distributions of possible household treatment costs. The differe nt types of costs are combined assuming that treatment and non-treatment costs are independent of one another. This might not always be the case, but EPA believes it is a realistic approximation.



## 7.6 Results

This section presents the results of the cost m odel described in the preceding sections. Section 7.6.1 shows the number of plants performing various ru le activities. Section 7.6.2 displays the one-time capital and non-treatment costs as calculated by the model. Section 7.6.3 displays the results after the cost projections and discounting are performed. Sec tion 7.6.4 displays the household cost distributions.

7.6.1 Number of Plants Making Treatment Technology Changes

The number of plants making treatment technology changes is determined from the compliance forecasts as described in Chapter 5. The model pr oduces a distribution of the number of plants making treatment technology changes to represent uncertain ty in the compliance forecast for surface water systems. As shown in Exhibit 7.3, the mean estimated number of plants making treatment technology changes is 2,261 plants.

7.6.2 One-Time Costs

Exhibit 7.13 summarizes the estimated initial capital investment and yearly O&M costs. This exhibit is broken out by system type, source wate r type, and system size category. Appendix J and Exhibits J.1b through J.1d provide similar cost info rmation (total initial capital and yearly O&M costs) for the Stage 2 DBPR regulatory alternatives.

7.6.3 Total Annual Costs

Appendix J contains results from each step of the cost projection and discounting process described in Section 7.6.2 for each regulatory alterna tive. For the Preferred Alternative, Exhibits J.2a through J.2ar show the nominal costs projected over th e rule schedule, and Exhibits J.2as through J.2cf

### Final Economic Analysis for the Stage 2 DBPR 7-37 December 2005 

### show the present value of each cost calculated to the expected year of rule implementation (2005). The annualization step is shown at the bottom of the present value exhibits.

Exhibits 7.14a and 7.14b present the stream of pr esent value costs and the total annualized costs for the Stage 2 DBPR Preferred Alternative at 3 and 7 percent discount rates, respectively. These tables are equivalent to Exhibits J.2as and J.2aw in Appendix J.

### Final Economic Analysis for the Stage 2 DBPR 7-38 December 2005 Lower (5th %tile) Upper (95th %tile) Lower (5th %tile) Upper (95th %tile)

<100 1.09 $ 1.07 $ 0.58 $ 1.68 $ 0.20 $ 0.20 $ 0.11 $ 0.29 $

100-499 3.27 $ 3.22 $ 1.77 $ 4.94 $ 0.82 $ 0.82 $ 0.46 $ 1.19 $

500-999 3.86 $ 3.78 $ 2.08 $ 5.89 $ 0.61 $ 0.61 $ 0.34 $ 0.88 $

1,000-3,299 24.39 $ 24.27 $ 13.37 $ 36.07 $ 3.36 $ 3.36 $ 1.88 $ 4.86 $

3,300-9,999 62.23 $ 61.92 $ 34.42 $ 91.81 $ 5.32 $ 5.34 $ 2.97 $ 7.70 $

10,000-49,999 113.20 $ 113.98 $ 62.72 $ 157.05 $ 6.04 $ 6.00 $ 3.74 $ 8.66 $

50,000-99,999 67.40 $ 68.08 $ 37.41 $ 93.50 $ 3.41 $ 3.36 $ 2.13 $ 4.95 $

100,000-999,999 183.98 $ 186.24 $ 98.21 $ 257.75 $ 8.17 $ 7.87 $ 5.21 $ 12.52 $

1,000,000+ 86.04 $ 86.46 $ 47.14 $ 120.41 $ 4.91 $ 4.65 $ 3.11 $ 7.73 $

All Sizes 545.44 $ 549.03 $ 297.70 $ 769.10 $ 32.84 $ 32.21 $ 19.95 $ 48.78 $

<100 0.67 $ 0.66 $ 0.36 $ 1.03 $ 0.12 $ 0.12 $ 0.07 $ 0.17 $

100-499 1.32 $ 1.31 $ 0.72 $ 2.00 $ 0.33 $ 0.33 $ 0.19 $ 0.48 $

500-999 0.85 $ 0.84 $ 0.46 $ 1.30 $ 0.13 $ 0.13 $ 0.07 $ 0.20 $

1,000-3,299 1.89 $ 1.88 $ 1.04 $ 2.80 $ 0.26 $ 0.26 $ 0.15 $ 0.38 $

3,300-9,999 1.29 $ 1.28 $ 0.71 $ 1.90 $ 0.11 $ 0.11 $ 0.06 $ 0.16 $

10,000-49,999 0.55 $ 0.55 $ 0.30 $ 0.76 $ 0.03 $ 0.03 $ 0.02 $ 0.04 $

50,000-99,999 - $ - $ - $ - $ - $ - $ - $ - $

100,000-999,999 0.41 $ 0.41 $ 0.22 $ 0.57 $ 0.02 $ 0.02 $ 0.01 $ 0.03 $

1,000,000+ - $ - $ - $ - $ - $ - $ - $ - $

All Sizes 6.99 $ 6.95 $ 3.82 $ 10.36 $ 1.00 $ 1.00 $ 0.56 $ 1.46 $

552.43 $ 555.97 $ 301.52 $ 779.46 $ 33.85 $ 33.22 $ 20.52 $ 50.24 $

<100 8.34 $ 8.34 $ 7.19 $ 9.53 $ 0.98 $ 0.98 $ 0.91 $ 1.05 $

100-499 33.19 $ 33.18 $ 28.04 $ 38.38 $ 3.68 $ 3.68 $ 3.38 $ 3.98 $

500-999 20.18 $ 20.18 $ 17.00 $ 23.34 $ 1.96 $ 1.96 $ 1.80 $ 2.12 $

1,000-3,299 39.43 $ 39.42 $ 32.35 $ 46.54 $ 3.00 $ 3.00 $ 2.73 $ 3.26 $

3,300-9,999 65.91 $ 65.86 $ 53.53 $ 78.34 $ 2.55 $ 2.55 $ 2.33 $ 2.76 $

10,000-49,999 59.09 $ 59.08 $ 53.39 $ 64.79 $ 5.03 $ 5.03 $ 4.76 $ 5.30 $

50,000-99,999 14.96 $ 14.96 $ 13.38 $ 16.53 $ 1.28 $ 1.28 $ 1.20 $ 1.36 $

100,000-999,999 29.70 $ 29.71 $ 26.43 $ 32.95 $ 2.83 $ 2.83 $ 2.64 $ 3.02 $

1,000,000+ 3.38 $ 3.38 $ 2.97 $ 3.79 $ 0.43 $ 0.43 $ 0.40 $ 0.46 $

All Sizes 274.18 $ 274.11 $ 234.29 $ 314.20 $ 21.73 $ 21.73 $ 20.16 $ 23.31 $

<100 3.17 $ 3.17 $ 2.73 $ 3.62 $ 0.37 $ 0.37 $ 0.35 $ 0.40 $

100-499 5.04 $ 5.04 $ 4.25 $ 5.81 $ 0.55 $ 0.55 $ 0.51 $ 0.60 $

500-999 2.47 $ 2.47 $ 2.07 $ 2.87 $ 0.23 $ 0.23 $ 0.21 $ 0.25 $

1,000-3,299 1.61 $ 1.61 $ 1.32 $ 1.90 $ 0.10 $ 0.10 $ 0.09 $ 0.11 $

3,300-9,999 0.46 $ 0.46 $ 0.38 $ 0.55 $ 0.01 $ 0.01 $ 0.01 $ 0.02 $

10,000-49,999 0.10 $ 0.10 $ 0.09 $ 0.11 $ 0.01 $ 0.01 $ 0.01 $ 0.01 $

50,000-99,999 0.02 $ 0.02 $ 0.02 $ 0.02 $ 0.00 $ 0.00 $ 0.00 $ 0.00 $

100,000-999,999 0.03 $ 0.03 $ 0.03 $ 0.03 $ 0.00 $ 0.00 $ 0.00 $ 0.00 $

1,000,000+ - $ - $ - $ - $ - $ - $ - $ - $

All Sizes 12.90 $ 12.90 $ 10.87 $ 14.91 $ 1.29 $ 1.29 $ 1.18 $ 1.39 $

287.08 $ 287.01 $ 245.16 $ 329.11 $ 23.02 $ 23.02 $ 21.34 $ 24.70 $

839.51 $ 842.98 $ 546.68 $ 1,108.57 $ 56.86 $ 56.23 $ 41.86 $ 74.94 $

Notes:

Source: This exhibit is identical to Exhibit J.1a All values in millions of year 2003 dollars. Detail may not add exactly to totals due to independent rounding. System

Size (population

served) Capital Costs

CWSs

NTNCWSs

Subtotal Subtotal Median Value 90 Percent Confidence Bound

Total Ground Water Surface Water CWSs

NTNCWSs O&M Costs

Mean Value Median Value 90 Percent Confidence Bound

Source Mean Value System Classification 

## Exhibit 7.13 Total Initial Capital Costs ($Millions) and Yearly O&M Costs ($Millions/Year)Final Economic Analysis for the Stage 2 DBPR 7-39 December 2005Preferred AlternativePrimacy AgenciesLower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)20050.8$ 0.8$ 0.8$ 0.0$ 0.0$ 0.0$ 0.2$ 0.2$ 0.2$ 0.0$ 0.0$ 0.0$ 3.7$ 4.7$ 4.7$ 4.7$200610.8$ 10.8$ 10.8$ 0.1$ 0.1$ 0.1$ 3.1$ 3.1$ 3.1$ 0.5$ 0.5$ 0.5$ 3.6$ 18.1$ 18.1$ 18.1$200720.6$ 20.6$ 20.6$ 0.0$ 0.0$ 0.0$ 1.0$ 1.0$ 1.0$ 0.0$ 0.0$ 0.0$ 0.1$ 21.8$ 21.8$ 21.8$200815.0$ 15.0$ 15.0$ 0.0$ 0.0$ 0.0$ 6.0$ 6.0$ 6.0$ 0.0$ 0.0$ 0.0$ 1.8$ 22.9$ 22.9$ 22.9$200999.7$ 54.8$ 139.8$ 1.0$ 0.6$ 1.4$ 43.1$ 37.4$ 48.8$ 2.1$ 1.8$ 2.3$ 0.7$ 146.6$ 95.4$ 193.0$2010101.6$ 55.9$ 143.2$ 1.1$ 0.6$ 1.6$ 43.6$ 37.8$ 49.3$ 2.0$ 1.7$ 2.3$ -$ 148.2$ 96.1$ 196.3$2011103.8$ 57.4$ 146.8$ 1.1$ 0.6$ 1.7$ 44.0$ 38.2$ 49.7$ 1.9$ 1.6$ 2.2$ 1.3$ 152.1$ 99.2$ 201.7$2012105.1$ 58.0$ 149.3$ 1.2$ 0.7$ 1.8$ 46.4$ 40.6$ 52.2$ 2.3$ 2.0$ 2.5$ 1.3$ 156.2$ 102.6$ 207.2$201350.3$ 28.4$ 73.5$ 1.3$ 0.7$ 1.8$ 41.2$ 36.2$ 46.3$ 2.6$ 2.3$ 2.9$ 1.3$ 96.6$ 68.9$ 125.7$201438.2$ 21.8$ 56.8$ 1.3$ 0.7$ 1.9$ 37.1$ 32.5$ 41.6$ 2.7$ 2.4$ 2.9$ 1.2$ 80.5$ 58.6$ 104.4$201526.3$ 15.2$ 39.7$ 1.0$ 0.6$ 1.5$ 24.8$ 22.2$ 27.4$ 2.0$ 1.8$ 2.2$ 1.2$ 55.3$ 41.0$ 72.0$201621.1$ 12.3$ 32.0$ 0.7$ 0.4$ 1.0$ 16.0$ 14.9$ 17.1$ 1.4$ 1.3$ 1.4$ 1.2$ 40.3$ 30.1$ 52.6$201720.5$ 12.0$ 31.0$ 0.7$ 0.4$ 1.0$ 15.5$ 14.5$ 16.6$ 1.3$ 1.3$ 1.4$ 1.1$ 39.1$ 29.2$ 51.1$201819.9$ 11.6$ 30.1$ 0.7$ 0.4$ 1.0$ 15.1$ 14.1$ 16.1$ 1.3$ 1.2$ 1.3$ 1.1$ 38.0$ 28.4$ 49.6$201919.3$ 11.3$ 29.2$ 0.6$ 0.4$ 0.9$ 14.6$ 13.7$ 15.6$ 1.2$ 1.2$ 1.3$ 1.1$ 36.9$ 27.5$ 48.2$202018.7$ 10.9$ 28.4$ 0.6$ 0.4$ 0.9$ 14.2$ 13.3$ 15.2$ 1.2$ 1.1$ 1.3$ 1.0$ 35.8$ 26.7$ 46.8$202118.2$ 10.6$ 27.6$ 0.6$ 0.3$ 0.9$ 13.8$ 12.9$ 14.7$ 1.2$ 1.1$ 1.2$ 1.0$ 34.8$ 26.0$ 45.4$202217.7$ 10.3$ 26.8$ 0.6$ 0.3$ 0.8$ 13.4$ 12.5$ 14.3$ 1.1$ 1.1$ 1.2$ 1.0$ 33.8$ 25.2$ 44.1$202317.2$ 10.0$ 26.0$ 0.6$ 0.3$ 0.8$ 13.0$ 12.1$ 13.9$ 1.1$ 1.0$ 1.2$ 0.9$ 32.8$ 24.5$ 42.8$202416.7$ 9.7$ 25.2$ 0.6$ 0.3$ 0.8$ 12.6$ 11.8$ 13.5$ 1.1$ 1.0$ 1.1$ 0.9$ 31.8$ 23.8$ 41.5$202516.2$ 9.4$ 24.5$ 0.5$ 0.3$ 0.8$ 12.3$ 11.4$ 13.1$ 1.0$ 1.0$ 1.1$ 0.9$ 30.9$ 23.1$ 40.3$202615.7$ 9.2$ 23.8$ 0.5$ 0.3$ 0.8$ 11.9$ 11.1$ 12.7$ 1.0$ 1.0$ 1.1$ 0.9$ 30.0$ 22.4$ 39.2$202715.2$ 8.9$ 23.1$ 0.5$ 0.3$ 0.7$ 11.6$ 10.8$ 12.3$ 1.0$ 0.9$ 1.0$ 0.8$ 29.1$ 21.7$ 38.0$202814.8$ 8.6$ 22.4$ 0.5$ 0.3$ 0.7$ 11.2$ 10.5$ 12.0$ 1.0$ 0.9$ 1.0$ 0.8$ 28.3$ 21.1$ 36.9$202914.4$ 8.4$ 21.8$ 0.5$ 0.3$ 0.7$ 10.9$ 10.2$ 11.6$ 0.9$ 0.9$ 1.0$ 0.8$ 27.5$ 20.5$ 35.8$Total 817.6$ 482.2$ 1,167.9$ 16.3$ 9.3$ 23.6$ 476.5$ 428.8$ 524.2$ 31.8$ 29.2$ 34.4$ 29.8$ 1,372.1$ 979.4$ 1,780.0$Ann. 47.0$ 27.7$ 67.1$ 0.9$ 0.5$ 1.4$ 27.4$ 24.6$ 30.1$ 1.8$ 1.7$ 2.0$ 1.7$ 78.8$ 56.2$ 102.2$Notes:Ann = value of total annualized at discount rate.Source: This exhibit is identical to Exhibit J.2as, which is derived from Exhibits J.2a through rr.Detail may not add exactly to totals due to independent rounding.MeanValueMeanValue90 PercentConfidence BoundPoint EstimateMeanValue90 PercentConfidence Bound90 PercentConfidence BoundTotal90 PercentConfidence Bound90 PercentConfidence BoundMeanValueDisinfecting Ground Water NTNCWSSurface Water CWS Surface Water NTNCWSMeanValueDisinfecting Ground Water CWSPresent values in millions of 2003 dollars. Estimates are discounted to 2005.Exhibit 7.14a Total Annualized Costs at 3Percent Social Discount Rate ($Millions)Final Economic Analysis for the Stage 2 DBPR 7-40 December 2005Preferred AlternativePrimacy AgenciesLower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)Lower(5th %tile)Upper(95th %tile)20050.8$ 0.8$ 0.8$ 0.0$ 0.0$ 0.0$ 0.2$ 0.2$ 0.2$ 0.0$ 0.0$ 0.0$ 3.4$ 4.3$ 4.3$ 4.3$20069.6$ 9.6$ 9.6$ 0.1$ 0.1$ 0.1$ 2.8$ 2.8$ 2.8$ 0.5$ 0.5$ 0.5$ 3.2$ 16.1$ 16.1$ 16.1$200717.7$ 17.7$ 17.7$ 0.0$ 0.0$ 0.0$ 0.8$ 0.8$ 0.8$ 0.0$ 0.0$ 0.0$ 0.1$ 18.7$ 18.7$ 18.7$200812.4$ 12.4$ 12.4$ 0.0$ 0.0$ 0.0$ 5.0$ 5.0$ 5.0$ 0.0$ 0.0$ 0.0$ 1.5$ 18.9$ 18.9$ 18.9$200979.3$ 43.6$ 111.2$ 0.8$ 0.4$ 1.1$ 34.3$ 29.8$ 38.8$ 1.7$ 1.4$ 1.9$ 0.6$ 116.6$ 75.9$ 153.6$201077.8$ 42.8$ 109.6$ 0.8$ 0.5$ 1.2$ 33.4$ 29.0$ 37.8$ 1.5$ 1.3$ 1.7$ -$ 113.5$ 73.6$ 150.3$201176.5$ 42.3$ 108.2$ 0.8$ 0.5$ 1.2$ 32.4$ 28.2$ 36.7$ 1.4$ 1.2$ 1.6$ 1.0$ 112.2$ 73.1$ 148.7$201274.6$ 41.2$ 106.0$ 0.9$ 0.5$ 1.3$ 32.9$ 28.8$ 37.1$ 1.6$ 1.4$ 1.8$ 0.9$ 110.9$ 72.8$ 147.0$201334.4$ 19.4$ 50.2$ 0.9$ 0.5$ 1.3$ 28.2$ 24.7$ 31.6$ 1.8$ 1.6$ 2.0$ 0.9$ 66.0$ 47.0$ 85.9$201425.1$ 14.3$ 37.3$ 0.8$ 0.5$ 1.2$ 24.4$ 21.4$ 27.4$ 1.7$ 1.6$ 1.9$ 0.8$ 52.9$ 38.6$ 68.7$201516.6$ 9.6$ 25.1$ 0.6$ 0.4$ 0.9$ 15.7$ 14.1$ 17.4$ 1.3$ 1.2$ 1.4$ 0.8$ 35.0$ 26.0$ 45.6$201612.9$ 7.5$ 19.5$ 0.4$ 0.2$ 0.6$ 9.7$ 9.1$ 10.4$ 0.8$ 0.8$ 0.9$ 0.7$ 24.6$ 18.3$ 32.1$201712.0$ 7.0$ 18.2$ 0.4$ 0.2$ 0.6$ 9.1$ 8.5$ 9.7$ 0.8$ 0.7$ 0.8$ 0.7$ 23.0$ 17.1$ 30.0$201811.2$ 6.6$ 17.0$ 0.4$ 0.2$ 0.5$ 8.5$ 7.9$ 9.1$ 0.7$ 0.7$ 0.8$ 0.6$ 21.5$ 16.0$ 28.0$201910.5$ 6.1$ 15.9$ 0.4$ 0.2$ 0.5$ 8.0$ 7.4$ 8.5$ 0.7$ 0.6$ 0.7$ 0.6$ 20.1$ 15.0$ 26.2$20209.8$ 5.7$ 14.9$ 0.3$ 0.2$ 0.5$ 7.4$ 6.9$ 7.9$ 0.6$ 0.6$ 0.7$ 0.5$ 18.7$ 14.0$ 24.5$20219.2$ 5.4$ 13.9$ 0.3$ 0.2$ 0.4$ 6.9$ 6.5$ 7.4$ 0.6$ 0.6$ 0.6$ 0.5$ 17.5$ 13.1$ 22.9$20228.6$ 5.0$ 13.0$ 0.3$ 0.2$ 0.4$ 6.5$ 6.1$ 6.9$ 0.6$ 0.5$ 0.6$ 0.5$ 16.4$ 12.2$ 21.4$20238.0$ 4.7$ 12.1$ 0.3$ 0.2$ 0.4$ 6.1$ 5.7$ 6.5$ 0.5$ 0.5$ 0.5$ 0.4$ 15.3$ 11.4$ 20.0$20247.5$ 4.4$ 11.3$ 0.2$ 0.1$ 0.4$ 5.7$ 5.3$ 6.1$ 0.5$ 0.5$ 0.5$ 0.4$ 14.3$ 10.7$ 18.7$20257.0$ 4.1$ 10.6$ 0.2$ 0.1$ 0.3$ 5.3$ 4.9$ 5.7$ 0.5$ 0.4$ 0.5$ 0.4$ 13.4$ 10.0$ 17.4$20266.5$ 3.8$ 9.9$ 0.2$ 0.1$ 0.3$ 5.0$ 4.6$ 5.3$ 0.4$ 0.4$ 0.4$ 0.4$ 12.5$ 9.3$ 16.3$20276.1$ 3.6$ 9.3$ 0.2$ 0.1$ 0.3$ 4.6$ 4.3$ 4.9$ 0.4$ 0.4$ 0.4$ 0.3$ 11.7$ 8.7$ 15.2$20285.7$ 3.3$ 8.6$ 0.2$ 0.1$ 0.3$ 4.3$ 4.0$ 4.6$ 0.4$ 0.3$ 0.4$ 0.3$ 10.9$ 8.1$ 14.2$20295.3$ 3.1$ 8.1$ 0.2$ 0.1$ 0.3$ 4.0$ 3.8$ 4.3$ 0.3$ 0.3$ 0.4$ 0.3$ 10.2$ 7.6$ 13.3$Total 545.1$ 324.1$ 770.4$ 9.8$ 5.6$ 14.2$ 301.2$ 269.7$ 332.7$ 19.2$ 17.5$ 20.9$ 19.8$ 895.1$ 636.7$ 1,157.9$Ann. 46.8$ 27.8$ 66.1$ 0.8$ 0.5$ 1.2$ 25.8$ 23.1$ 28.6$ 1.6$ 1.5$ 1.8$ 1.7$ 76.8$ 54.6$ 99.4$Notes:Ann = value of total annualized at discount rate.Source: This exhibit is identical to Exhibit J.2aw, which is derived from Exhibits J.2a through rr.Total90 PercentConfidence Bound90 PercentConfidence BoundSurface Water NTNCWS Disinfecting Ground Water NTNCWSMeanValueMeanValueDisinfecting Ground Water CWS90 PercentConfidence BoundSurface Water CWSPresent values in millions of 2003 dollars. Estimates are discounted to 2005.Detail may not add exactly to totals due to independent rounding.MeanValueMeanValueMeanValue90 PercentConfidence Bound90 PercentConfidence BoundPoint EstimateExhibit 7.14b Total Annualized Costs at7 Percent Social Discount Rate ($Millions)

### Final Economic Analysis for the Stage 2 DBPR 7-41 December 2005 

### 7.6.4 Household Cost Results

Exhibit 7.15 presents the mean, median, 90 th 

### percentile, and 95 th 

### percentile of expected rate increases, along with the percent of households that are expected to face an increase of $1 or less per month ($12 or less per year) or $10 or less each month ($120 or less per year). Data are provided for all systems subject to the rule and for only the subset of systems making treatment technology changes. Note that household cost increases in Exhibit 7.15 in clude costs for non-treatment-related rule activities (implementation, IDSE, additional routin e monitoring, and operational evaluations).

Exhibits 7.16a and b show the cumulative distribution of household cost increases for all surface water and ground water systems, and Exhibits 7.17a and b shows the distribution of household cost increases for only those systems making treatment technology changes. Additionally, Exhibit 7.17c shows the cumulative distributions in systems maki ng treatment technology changes for the five small system size categories.

As shown in Exhibit 7.15, the mean, median, and 90 th 

### percentile household costs increase for all systems (including those that do not make treatment technology changes) are $0.62, $0.03, and $0.36, respectively. The mean, median, and 90 th 

### percentile household cost increases for systems that install new treatment technologies are $5.53, $0.80 and $10.04, resp ectively. Note that the number of households affected by plants installing treatment could be greater than shown in Exhibit 7.15 because an entire system would most likely incur costs even if only so me of the plants for that system make treatment technology changes (this would result in lower house hold costs, however). EPA assumes that systems will pass some or all of the costs of a new regulation on to their customers in the form of rate increases. It should also be noted that these are very conser vative estimates, assuming that cheaper compliance alternatives are not used, and that systems do not receive any financial assistance.

EPA estimates that, as a whole, households subject to the Stage 2 DBPR face minimal increases in their annual costs. Approximately 86 percent of the households potentially subject to the rule are served by systems serving at least 10,000 people; th ese systems experience the lowest increases in costs due to significant economies of scale. Households served by small systems that make treatment technology changes will face the greatest increases in annual costs. Exhibit 7.17c provides additional detail regarding the distribution of household costs for small systems.

Although cost model results predict that a few very small systems will experience large household cost increases as a result of adding advanced treatment technology for the Stage 2 DBPR, these predictions are probably not realistic because small systems have other alternatives available to them besides making treatment technology changes. For example, some of these systems currently may be operated on a part-time basis; therefore, they may be able to modify the current operational schedule or use excess capacity to avoid installing a costly treatme nt technology to comply with the Stage 2 DBPR. The system also may identify another water sour ce that has lower TTHM and HAA5 precursor levels. Systems that can identify such an alternate water source may not have to treat that water as intensely as their current source, resulting in lower treatment cost s. Systems may elect to connect to a neighboring water system. While this may not be feasible for so me remote systems, EPA estimates that more than 22 percent of all small water systems are located within metropolitan regions (USEPA 2003t) where distances between neighboring systems will not present a prohibitive barrier.

### Final Economic Analysis for the Stage 2 DBPR 7-42 December 2005 Total Number of Households Served Mean Annual Household Cost Increase Median Annual Household Cost Increase 90th Percentile Annual Household Cost Increase 95th Percentile Annual Household Cost Increase Percentage of Annual Household Cost Increase < $12 Percentage of Annual Household Cost Increase < $120 All Systems 101,553,868 0.62 $ 0.03 $ 0.36 $ 0.98 $ 99% 100% All Small Systems 14,261,241 2.20 $ 0.10 $ 0.79 $ 2.57 $ 97% 100% SW < 10,000 3,251,893 4.58 $ 0.79 $ 2.69 $ 7.24 $ 95% 99% SW > 10,000 62,137,350 0.46 $ 0.02 $ 0.35 $ 1.81 $ 99% 100% GW < 10,000 11,009,348 1.49 $ 0.02 $ 0.39 $ 0.99 $ 98% 100% GW > 10,000 25,155,277 0.13 $ 0.00 $ 0.03 $ 0.08 $ 100% 100%

Total Number of Households Served Mean Annual Household Cost Increase Median Annual Household Cost Increase 90th Percentile Annual Household Cost Increase 95th Percentile Annual Household Cost Increase Percentage of Household Cost Increase < $12 Percentage of Household Cost Increase < $120 All Systems 10,161,304 5.53 $ 0.80 $ 10.04 $ 22.40 $ 92% 99% All Small Systems 591,623 46.48 $ 18.47 $ 168.85 $ 197.62 $ 38% 89% SW < 10,000 285,911 43.05 $ 13.79 $ 173.53 $ 177.93 $ 47% 85% SW > 10,000 9,060,119 2.83 $ 0.80 $ 6.98 $ 11.31 $ 96% 100% GW < 10,000 305,712 49.69 $ 16.65 $ 109.86 $ 197.62 $ 31% 92% GW > 10,000 509,562 5.97 $ 1.37 $ 26.82 $ 33.84 $ 79% 100%

Source: Results represent the sum of treatm ent and non-treatment costs. Household costs for treatm ent are derived from househo ld unit costs in Exhibits 7.10c and 7.11c combined with technology selection deltas, shown in Chapter 5. Household costs for non-treatment-related rule a ctivites are derived from mean costs for each system size category for implementation, ID SE, monitoring plans, additional routine monitoring, and signifi cant excursion (as derived in Appendix H). See section 7.5.3 for additional in formation on the derivation of household costs. Notes: Detail may not add to total due to independent rounding. Number of households served by systems adding treatment will b e higher than households served by plants adding treatment because an entire system will incur costs even if only some of the plants for that system add treatment (this would result in lower household costs, however). Households Served by Plants Adding Treatment Households Served by All Plants 

## Exhibit 7.15 Annual Household Cost Increases

### Final Economic Analysis for the Stage 2 DBPR 7-43 December 2005 0 200 400 600 800 1000 1200

0% 20% 40% 60% 80% 100 % Cumulative Percent of Households (N=65,389,243)Annual Household Cost ($/HH/Year)

0 200 400 600 800 1000 1200

99.5% 99.6% 99.7% 99.8% 99.9% 100.0% 

## Exhibit 7.16a Househol d Cost Distributions, All Surface Water Systems Subject to the Rule

### Final Economic Analysis for the Stage 2 DBPR 7-44 December 2005 0 100 200 300 400 500 600 700 800

0% 20% 40% 60% 80% 100 % Cumulative Percent of Households (N=36,164,625)Annual Household Cost ($/HH/Year)

0 100 200 300 400 500 600 700 800

99.5% 99.6% 99.7% 99.8% 99.9% 100.0% 

## Exhibit 7.16b Househol d Cost Distributions, All Ground Water Systems Subject to the Rule

### Final Economic Analysis for the Stage 2 DBPR 7-45 December 2005 0 200 400 600 800 1000 1200

0% 20% 40% 60% 80% 100 % Cumulative Percent of Households (N=9,346,030)Annual Household Cost ($/HH/Year)

0 200 400 600 800 1000 1200

99.5% 99.6% 99.7% 99.8% 99.9% 100.0% 

## Exhibit 7.17a Househol d Cost Distributions, Surface Water Systems Making Treatment Technology Changes

### Final Economic Analysis for the Stage 2 DBPR 7-46 December 2005 0 100 200 300 400 500 600 700 800

0% 20% 40% 60% 80% 100 % Cumulative Percent of Households (N=815,274)Annual Household Cost ($/HH/Year)

0 100 200 300 400 500 600 700 800

99.5% 99.6% 99.7% 99.8% 99.9% 100.0% 

## Exhibit 7.17b Househol d Cost Distributions, Ground Water Systems Making Treatment Technology Changes

### Final Economic Analysis for the Stage 2 DBPR 7-47 December 2005 $0 $200 $400 $600 $800 $1,000 $1,200

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

### Cumulative Percent Affected (N=591,623)Annual Household Costs ($/HH/Year) 4,198 HHs (<100) 39,058 HHs (100-499) 45,637 HHs (500-999) 160,205 HHs (1,000-3,299) 342,525 HHs (3,300 - 9,999) 

## Exhibit 7.17c Household Cost Distri butions, Small Systems Making Treatment Technology Changes (Surface and Ground)

7.7 Non-Quantified Costs

All significant costs that EPA has identified have been quantified. In some instances, EPA did not include a potential cost element because its effects are relatively minor and difficult to estimate. For example, it may be less costly for a small system to merge with neighboring systems than to add advanced treatment. Such changes have both costs (l egal fees and connecting infrastructure) and benefits (economies of scale). Likewise, procuring a new sour ce of water would have costs for new infrastructure, but could result in lower treatment costs. Opera tional costs such as changing storage tank operation were not considered as alternatives to treatment. These might be options for systems with a single problem area with a long residence time. In the absence of deta iled information needed to evaluate situations such as these, EPA has included a discussion of possible eff ects where appropriate. In general, however, the expected net effect of such situations is lowe r costs to PWSs. Thus, the EA tends to present conservatively high estimates of costs in relation to non-quantified costs.



## 7.8 Uncertainty Analysis

Many factors contribute to uncertainty in nati onal cost estimates. Uncertainty in baseline data inputs, such as the total number of disinfecting plants and their typical average and design flow rates, are described in detail in Section 3.8. Other cost model inputs such as labor rates and laboratory fees also contain uncertainties. In these cases, EPA has evaluated available data and estimated a cost input value to

### Final Economic Analysis for the Stage 2 DBPR 7-48 December 2005 

### represent the average of all water systems nationally. EPA recognizes that there is uncertainty in this average, and variability in the characteristics of indi vidual systems. The influence of these uncertainties on national cost estimates is expected to be minor.

Key areas of uncertainty and their potential effects on the estimate of national costs are presented in Exhibit 7.18. EPA believes that uncertainty in the compliance forecast has a potentially large influence on cost (and benefit) estimates in this EA. Thus, th e Agency has attempted to quantify the uncertainty by giving equal weight to two different compliance fo recast approaches. One co mpliance forecast approach is based on the SWAT predictions, and the other is based on the ICR matrix method. The ICR Matrix Method uses the same basic approach as SWAT, but uses TTHM and HAA5 data from the ICR directly to estimate the percent of plants changing technology to comply with the Stage 2 DBPR and the resulting DBP reduction (see Chapter 5 for more information on these approaches). To characterize the uncertainty of the compliance forecast results, EPA assumes a uni form distribution between SWAT and ICR Matrix Method results. That is, the national cost estimat es presented in this chapter represent the midpoint between costs estimated using the SWAT model, and those estimated using the ICR Matrix Method. Cost estimates using the SWAT model are about 25% lower than the midpoint estimates while those using the ICR Matrix Method are about 25% higher.

In addition to quantifying some uncertainties in the compliance forecasts, EPA has explicitly accounted for uncertainty in estimated treatment t echnology unit costs. Treatment costs are modeled using a triangular distribution of ± 30 percent for Ca pital, and ± 15 percent for O&M costs to recognize uncertainty in the assumptions used to produce the national average unit costs.

### Final Economic Analysis for the Stage 2 DBPR 7-49 December 2005 

## Exhibit 7.18 Cost Uncertainty Summary

Uncertainty Section With Full Discussion of Uncertainty Effect on Estimate of National Costs

Underestimate Overestimate Unknown Impact
Uncertainty in the industry baseline (SDWIS and 1995 CWSS data) 3.4 X

Uncertainty in observed data and predictive tools used to characterize DBP occurrence and advanced treatment technology use for the pre-Stage 1 baseline 3 X

Uncertainty in predictive tools used to develop the compliance forecast for surface water systems (SWAT and ICR Matrix Method) Chapter 5, Appendix A Quantified in primary analysis (addresses potential overestimate or underestimate)

Uncertainty in ground water and small surface water compliance forecast methodologies Appendices A and B X

Uncertainty in the potential impact of the IDSE on the compliance forecast for large and medium surface water systems Chapter 5 Quantified in primary analysis (addresses potential underestimate)

Treatment costs do not include costs for minor operational changes predicted by SWAT 7.4.1 X

Median operational and water quality parameters considered for treatment technology unit costs 7.4.1 X

Economies of scale for combination treatment technologies not considered 7.4.1 X

UV dose assumptions needed for viral inactivation 7.4.1 X

Potential low-cost alternatives to treatment not considered 7.4.2 X

Uncertainties in unit costs 7.4.3 Quantified in primary analysis (addresses potential overestimate or underestimate)

### Final Economic Analysis for the Stage 2 DBPR 7-50 December 2005 Preferred 78.8 $ 76.8 $

Alt. 1 254.1 $ 241.8 $

Alt. 2 421.7 $ 406.4 $

Alt. 3 634.2 $ 613.1 $ 

### Source: Appendix J. For the Preferred Alternative, see Exhibit J.2as for 3% and J.2aw for 7%. For Alternative 1, see Exhibit J.3i for 3% and J.3m for 7%. For Alternative 2, see Exhibit J.4i for 3% and J.4m for 7%. For Alternative 3, see Exhibit J.5i for 3% and J.5m for 7%. Total Annualized Cost ($Millions)

7 Percent Discount Rate Mean Estimate Rule Alternative 3 Percent Discount Rate Mean Estimate 

## 7.9 Comparison of Regulatory Alternatives

During the development of the Stage 2 DBPR, many regulatory alternatives were considered. Of these alternatives, four (including the Preferred Altern ative analyzed in this chapter) were chosen for further, in-depth analysis. Chapter 4 provides a description of each alternative. Chapter 5 present compliance forecasts for the Preferred Alternative, and Appendix C presents the compliance forecasts for the other three alternatives, including the treatment technology selection deltas.

The same process used for developing costs for th e Preferred Alternative was used to develop costs for the other alternatives (see section 7.5 for a description of the Stage 2 DBPR cost model). Exhibit 7.19 presents the summary of annualized co sts for each alternative (detailed costs for each alternative are presented in Appendix J). While the total annualized costs in Exhibit 7.19 include costs for non-treatment-related rule activities (implementa tion, IDSE, monitoring plans, additional routine monitoring, and operational evaluations), any increase in cost for the regulatory alternatives is fully attributable to treatment costs.



## Exhibit 7.19 Total Annualized Cost for the Stage 2 DBPR Regulatory Alternatives ($Millions)

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-1 

# 8. Economic Impact Analysis

8.1 Introduction

As part of the rulemaking process, the Envir onmental Protection Agency (EPA) is required to address the potential direct and indirect burdens that the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR) may place on certain typ es of governments, businesses, and populations. This chapter presents the analyses EPA has performed in accordance with the following 12 Federal requirements:

1) The Regulatory Flexibility Act (RFA) of 1980, as amended by the Small Business Regulatory Enforcement Fairness Act (SBREFA) of 1996. 2) An analysis of small system affordability to determine variance treatment technologies in accordance with Section 1415(e)(1) of the 1996 Safe Drinking Water Act (SDWA) Amendments. 3) Feasible treatment technologies available to all systems as required by Section 1412(b)(4)(E) of the 1996 SDWA Amendments. 4) A Technical, Financial, and Managerial Capacity Assessment as required by Section 1420(d)(3) of the 1996 SDWA Amendments. 5) The Paperwork Reduction Act (A separate Information Collection Request document contains the complete analysis). 6) The Unfunded Mandates Reform Act (UMRA) of 1995. 7) Executive Order 13175 (Consultation and Coordina tion with Indian Tribal Governments). 8) Impacts on sensitive subpopulations as requi red by Section 1412(b)(3)(c)(i) of the 1996 SDWA Amendments. 9) Executive Order 13045 (Protection of Children from Environmental Health Risks and Safety Risks). 10) Executive Order 12898 (Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations). 11) Executive Order 13132 (Federalism). 12) Executive Order 13211 (Actions Concerning Regul ations That Significantly Affect Energy Supply, Distribution, or Use).

Many of the requirements and executive orders lis ted above call for an explanation of why the rule is necessary, the statutory authority for the rule, and the primary objectives that the rule is intended to achieve (refer to Chapter 2 for more information rega rding the objectives of the rule). More specifically, they are designed to assess the financial and health eff ects of the rule on sensitive, low-income, and Tribal populations as well as on small systems. The chap ter also examines how much additional capacity systems will need to meet Stage 2 DBPR requirement s and whether there are existing, feasible treatment technologies and treatment techniques available to meet rule requirements.



## 8.2 Regulatory Flexibility Act and Small Business Regulatory Enforcement Fairness Act

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a regulatory flexibility analysis for any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or other statute, unless the Agency certifies that the rule will not have a significant economic impact on a substantial number of small entities (5 U.S.C. 603(a)). Small entities include small businesses, small organizations , and small governmental jurisdictions.1 

### Revenue information was used whenever available. When it was not available, different measures, such as sales or annual operating expenditures, were used.

2 

### System information in this chapter is from the system baseline presented in Chapter 3 (Exhibit 3.2). For discussions of plants making treatment technology changes, refer to the Stage 2 DBPR treatment plants baseline (Exhibit 3.2). Systems conducting rule activities are presen ted in Exhibit 7.2, and plants adding treatment are presented in Exhibit 7.3. Final Economic Analysis for the Stage 2 DBPR December 2005 8-2 

### 8.2.1 Determining Significant Impacts on Small Entities

EPA conducted a screening analysis to determine if the Stage 2 DBPR would have a significant economic impact on a substantial number of small entiti es. In this analysis, EPA evaluated the potential economic impact of the rule on small entities by comp aring annualized compliance cost as a percentage of annual revenues 1 

### for different small-entity classifications. Chapter 3 of this Economic Analysis (EA) provides data on the small entities potentially subj ect to the Stage 2 DBPR, and Chapter 7 discusses changes systems would need to make, as well as the likely costs. 2 

### Using information from these two chapters, along with additional information from the Safe Drinking Water Information System (SDWIS), the Community Water System Survey (CWSS), a nd the U.S. Census, EPA conducted a quantitative analysis of small-system impacts resulting from the rule.

Defining Small Entities Affected by the Rule

The RFA provides default definitions for each type of small entity. Small entities are defined as: (1) a small business as defined by the Small Business Administration (SBA) regulations at 13 CFR 121.201; (2) a small governmental jurisdiction that is a government of a city, county, town, school districtor special district with a population of less than 50,000; or (3) a small organization that is any “not-forprofit enterprise which is independen tly owned and operated a nd is not dominant in its field.” However, the RFA authorizes an agency to adopt alternative defi nitions that “are appropriate to the activities of the Agency after proposing the alternative definition(s) in the Federal Register and taking comment” (5 U.S.C. §601(3)-(5)) for each category of small entity. In addition, to establish an alternative small business definition, agencies must consult with the SBA’s Chief Council for Advocacy.

The RFA references the definition of “small bus iness” found in the Small Business Act, which authorizes the SBA to define “small business” furthe r by regulation. The SBA defines small businesses by category using the North American Industry Classification System (NAICS). The NAICS code for public water supplies (PWSs) is 22131 (Water Supply and Irrigation Systems), and State agencies that include drinking water programs are classified as 92411 (Administration of Air and Water Resource and Solid Waste Management Programs) or 923120 (Adminis tration of Public Health Programs). Ancillary systems (i.e., those that supplement the function of other establishments like factories, power plants, mobile home parks, etc.) cannot be categorized in a single NAICS code. For ancillary systems, the NAICS code is that of the primary establishment or industry. Examples of small businesses include small, privately owned PWSs and for-profit busin esses where provision of water may be ancillary, such as mobile home parks or day-care centers. Examples of small organizations include churches, schools, and homeowner associations.

The Stage 2 DBPR applies to all community water systems (CWSs) and nontransient noncommunity water systems (NTNCWSs) that add a disi nfectant other than ultraviolet light (UV) or that deliver water that has been treated with a disinf ectant other than UV. A small CWS can be a business, government, or organization, since all entity types may supply water to the same population year round. A NTNCWS can be a business (other than a water utility ) or an organization, since both types of entities may regularly supply water to at least 25 of the same people at least 6 months per year, but not year3 

### Methodology recommended by Bruce E. Baker, State and Local Governments, Government Division, U.S. Bureau of Economic Analysis. Final Economic Analysis for the Stage 2 DBPR December 2005 8-3 

### round. Although the SBA and the RFA provide clear definitions for small businesses, organizations, and governmental jurisdictions, small entities are not necessar ily small water systems. The size of the entity has no relation to the number of people it serves as a wate r supply. Furthermore, data are not collected on businesses, organizations, and governmental jurisdictions in terms of the number of water customers they serve. Therefore, EPA chose to use an alternative definition for small entities.

For purposes of assessing the impacts of the Stage 2 DBPR on small entities, EPA considered small entities to be PWSs serving 10,000 or fewer peopl e, which is the cut-off level Congress specified in the 1996 Amendments to SDWA for small system flexibility provisions. Because this definition does not correspond to the RFA’s definitions for small bus inesses, governments, and nonprofit organizations, EPA requested comment on an alternative definition of a small entity in the preamble to the proposed Consumer Confidence Report (CCR) regulation (63 FR 7620 February 13, 1998). In the preamble to the final CCR regulation (63 FR 44511 August 19, 1998), EPA st ated its intent to establish this alternative definition for regulatory flexibility assessments under th e RFA for all drinking water regulations and has, therefore, used it for the Stage 2 DBPR.

Obtaining Data on the Number of Small Entities and Their Revenues or Expenditures

EPA obtained data on the number of small entities in each category, which are presented in Column A of Exhibit 8.1. The numbers of entities and their distribution among categories are derived from EPA’s Baseline Handbook (USEPA 2001c) and the 1995 CWSS (USEPA 1997c), respectively. Approximately 43.0 percent of small entities are owned by governments, 37.3 percent are owned by businesses and ancillary systems, and 19.7 percent are owned by organizations.

EPA also estimated the annual revenues or expend itures of small entities, presented in Column B of Exhibit 8.1. PWS inventories, managed by EPA and other organizations, have traditionally been categorized by size and by the characteristics of the population served (i.e., CWSs, NTNCWSs, and transient noncommunity water systems (TNCWSs)) ra ther than by NAICS code. Revenues by NAICS code are not readily applicable to EPA’s categorizati on of systems. Therefore, alternative methods for determining revenue were de veloped, as discussed below.

The estimated revenues for small entities in Exhib it 8.1 are from the Bureau of the Census (U.S. Department of Commerce 1992), the SDWIS, and add itional data on independent privately owned CWSs, special districts, and authorities from the 1995 CWSS (USEPA 1997c). Column A of Exhibit 8.1 shows the numbers of entities classified as small businesses, governments, and organi zations, obtained using information from the Third Edition of the B aseline Handbook (USEPA 2001c). These numbers were used to determine the weighted averages of estimat ed revenue. Column C shows the estimated revenues.

Small-government entities include municipal, county, state, federal, military, and special district systems. Data on all revenue for townsh ips and municipalities were obtained from the 1992 Census of Governments (U.S. Department of Commerce 1992) and converted to 2003 dollars by applying a conversion factor calculated from the national income and product account tables of the U.S. Bureau of Economic Analysis. 3 

### Specifically, the price deflators for 1992 and 2003 were obtained from Chain-Type Price Indexes for State and Local Governments (U.S. Department of Commerce BEA 2004a). The average revenue for all small government s with PWSs was calculated at $2,649,186.

Small-business entities in Exhibit 8.1 include both CWSs and NTNCWSs, such as privately owned CWSs, mobile home parks, country clubs , hotels, manufacturers, hospitals, and other establishments. For this analysis, all hospitals a nd day care centers are assumed to be businesses, as are4 

### The “other” category contains systems that do not yet have a specific function identified.

Final Economic Analysis for the Stage 2 DBPR December 2005 8-4 

### 50 percent of systems classified as “other.” 4 

### Estimated average revenue for the small businesses affected by the Stage 2 DBPR is $2,555,888.

Small organizations include primarily nonprofit NTNCWS such as schools and homeowner associations. The revenue estimates for small nonpr ofit organizations serving 500 or more people are actually higher than those for small businesses because th e total number of such systems is small, and a large proportion of these organizations are schools a nd colleges with large budgets. This category also includes 50 percent of systems classified as “other .” The average estimated revenue for small organizations affected by the Stage 2 DBPR is $4,750,838.

EPA also calculated the average estimated reve nue for all small entities. This estimate is weighted to account for the number of small en tities in each category (government, business, and organization) affected by the Stage 2 DBPR. This overall average is $2,981,331.

Measuring Significant Impacts

To evaluate the impact that a small entity is exp ected to incur as a result of the rule, this analysis calculates the entity’s annualized compliance cost as a percentage of sales (for privately owned entities) or the entity’s annualized compliance cost as a percentage of annual governmental revenue or expenditures (for publicly owned entities). The Inte rim Guidance for EPA Rulewriters for the RFA as amended by the SBREFA (March 1999) suggests us ing 1 percent as a threshold for determining significance, although additional factors may be consider ed. If compliance costs are less than 1 percent of sales or revenues for fewer than 1,000 entities, which re present less than 20 percent of all affected small entities, then in most cases there is no significant impact . In addition, the guidance suggests that if fewer than 100 entities experience economic impacts of 3 per cent of their revenues or greater, then in most cases there is no significant impact.

Exhibit 8.1 presents the data that EPA used fo r the screening analysis. The numbers of entities expected to incur costs of more than 1 and 3 per cent of their revenues are presented in Columns D and F, respectively. The numbers of entities experiencing imp acts of more than 1 and 3 percent of their revenues were compared to the total number of entities in each size category to calculate percentages, shown in Columns E and G.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-5 Number of Systems Percent of Systems Number of Systems Percent of Systems A B C D=A*E E F=A*G G

Small Governments 1,827 43% $2,649,186 41 2.24% 18 1.00% Small Businesses 1,584 37% $2,555,888 36 2.25% 16 1.03% Small Organizations 838 20% $4,750,838 15 1.84% 6 0.66% All Small Entities Using Primarily Surface Water or GWUDI 4,250 100% $2,981,331 92 2.16% 40 0.94%

Small Governments 14,865 43% $2,649,186 176 1.18% 18 0.12% Small Businesses 12,888 37% $2,555,888 153 1.18% 18 0.14% Small Organizations 6,817 20% $4,750,838 25 0.37% 8 0.12% All Small Entities Using Primarily Ground Water 34,570 100% $2,981,331 354 1.02% 45 0.13% Primarily Ground Water Systems Systems Experiencing Costs of > 1% of their
Revenues 2,3

Primarily Surface Water and GWUDI Systems Number of

Small Systems Average Annual Estimated Revenues 1 per

System ($) Percent of

Small Systems Small Systems by Source of Water and T y pe of Entit y Systems Experiencing Costs of > 3% of their
Revenues 2,4 

## Exhibit 8.1 Annualized Compliance Cost as a Percentage of Revenues for All Small Entities

Note: Detail may not add due to independent rounding.

1 

### Revenue information was used whenever available. When it wa s not available, other measures such as sales or annual operating expenditures were used. Data were not av ailable to differentiate revenue for small entities by system sizes or by source water type. The revenue estima tes reflect ground water as well as surface water systems.

2 

### Compliance costs were compared to average annual rev enue to determine whether 20 per cent of small entities would incur costs exceeding 1 percent of their average annual revenues. Thres holds to determine whether a rule has a significant impact on a substantial num ber of small entities are taken from the Interim Guidance for EPA Rulewriters for the RFA as amended by the SBREFA (March 1999).

3 

### Compliance costs incurred by each entity were compar ed to 1 percent of average annual revenues to determine whether 1,000 or more entities will experience an impact of 1 percent or greater of average annual revenues.

4 

### Compliance costs incurred by each entity were compar ed to 3 percent of average annual revenues to determine whether 100 or more entities will ex perience an impact of 3 percent or greater of average annual revenues.

Sources: (A) Number of disinfecting CWSs and NTNCWSs servi ng fewer than 10,000 people from the system baseline in Exhibit 3.2 (Column V), multiplied by 43%, 37.3%, and 19.7% to obtain number of small government, small businesses, and small organizations, respectively. (B) Percent of small governments, businesses, and organizations derived from the 1995 CWSS (USEPA 1997c). (C) Small Governments: Revenues from 1992 Census of Governments, GC92(4)-4: Finances of Municipal and Township Governments, U.S. Dept. of Commerce, Bureau of the Census; price deflators from Table 8.11, Chain-Type Quantity and Price Indexes for Governmen t. All other price adjustments were calculated using the Consumer Price Index. (E, G) Derived from the Stage 2 DBPR Cost Model (USEPA 2005i).



### To consider whether 1,000 or more entities will experience an impact of 1 percent or greater of average annual revenues, EPA compared compliance cost s incurred by each entity to 1 percent of average annual revenue. EPA estimated that a total of 92 small entities using surface water or ground water under the influence of surface water (GWUDI) and 354 sm all entities using ground water, representing 2.16 and 1.02 percent of all small entities affected by the Stag e 2 DBPR, respectively, will experience an impact of 1 percent or greater of average annual revenues. Th is is less than the criteria of 1,000 entities used to determine significant impact.

Using a similar methodology, EPA also consider ed whether 100 or more entities will experience an impact of 3 percent or greater of average annua l revenues. The Agency determined that 40 small

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-6 

### entities using surface water or GWUDI and 45 sma ll entities using ground water, representing 0.94 and 0.13 percent of all small entities subject to the Stage 2 DBPR, respectively, will experience an impact of 3 percent or greater of average annual revenues. Th is is less than the criterion of 100 entities used to determine significant impact.

Based on the large number of small entities, the nature of the economics for community water systems, and the information presented in Exhibits 8.1, EPA is certifying that the Stage 2 DBPR will not lead to significant economic impacts for a substa ntial number of small entities. CWSs have many resources available to them that other industries do not have. For example, financial assistance to small systems may be available from programs administered by EPA or other Federal agencies, as described in sections 8.2.2 and 8.3.3.

Because EPA is certifying that the Stage 2 DBPR will not lead to significant economic impacts for a substantial number of small entities, EPA is not required by the RFA, as amended by SBREFA, to conduct a final regulatory flexibility analysis (FRFA). Nevertheless, EPA has tried to reduce the impact of this rule on small systems.

8.2.2 Summary of the SBREFA Process

The RFA, as amended by SBREFA, and Section 203 of UMRA require EPA to provide small governments with an opportunity for timely and meani ngful participation in the regulatory development process. EPA provided stakeholders, including sma ll governments, with several opportunities to provide input on the Stage 2 DBPR. For example, EPA conducted three conference calls in Washington, DC to solicit feedback and information from the Small En tity Representatives (SERs) on Stage 2 DBPR impacts on small systems. SERs included small-system operators, local government officials, and small nonprofit organizations.

During the first call, held on January 28, 2000, EPA presented an overview of SDWA, as amended in 1996, and SBREFA. Issues and schedules for the Stage 2 DBPR rules were also discussed. The second call was held on February 25, 2000. EPA pr esented the stakeholders with an overview of the EPA regulatory development process and background on the development of the Stage 2 MicrobialDisinfectants/Disinfection Byproduct (M-DBP) Rules, particularly regarding health risks, issues and options identified by the Federal Advisory Comm ittees Act (FACA) Committee, and DBP and microbial occurrence in small systems. The third meeting w as held on April 7, 2000. EPA presented SERs with a cost estimate and an impact analysis for selected regulatory options. In addition, EPA presented SERs with schedules for the FACA and SBREFA processes.

These three conference calls generated a wide range of information, issues, and technical input from SERs. In general, the SERs were concerned a bout the impact of these proposed rules on small water systems (because of their small staff and limited budgets), small systems’ ability to acquire the technical and financial capability to implement requirements, maintaining the flexibility to tailor requirements to their needs, and other limitations of small systems. The Agency used the feedback received during these meetings in developing the Stage 2 DBPR. EPA also mailed a draft version of the rule’s preamble to the attendees of these meetings.

The Agency convened a Small Business Advocacy Review (SBAR) Panel to obtain advice and recommendations of representatives of the regulated small entities, including those of small local governments, in accordance with Section 609(b) of th e RFA. The small entity stakeholders received background on the need for the rule and the possible co mponents of the rule to assist them with their deliberations. EPA convened the SB AR Panel after completing the c onsultation meetings with SERs on

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-7 

### the Stage 2 DBPR. Eight of the small entities were governments. SER’s concerns were provided to the SBAR Panel when the Panel convened on April 25, 2000.



## 8.3 Small-System Affordability

Section 1415(e)(1) of SDWA allows States/Primacy Agencies to grant variances to small water systems (i.e., those serving fewer than 10,000 people) in lieu of complying with a maximum contaminant level (MCL) if EPA determines that no nationally a ffordable compliance treatment technologies exist for that combination of system size and water quality. These variances may be granted only when EPA has identified a variance treatment technology under Section 1412(b)(15) for the contaminant, system size, and source water quality in question. To list vari ance treatment technologies, three showings must be made.

1) EPA must determine, on a national level, that there are no compliance treatment technologies that are available and affordable for the give n combination of system size and source water quality.

2) If there is no nationally affordable compliance treatment technology, EPA must identify a variance treatment technology that may not reach the MCL, but that will achieve the maximum contaminant reduction affordable. Th is treatment technology must be listed as a small-system variance treatment technology by EPA for small systems to be able to rely on it for regulatory purposes.

3) EPA must make a finding, on a national level, that the use of the variance treatment technology would be protective of public health.

The State/Primacy Agency must then make a de termination for each system as to whether the system can afford to meet the MCL based on a ffordability criteria developed by the State/Primacy Agency. If the State/Primacy Agency determines that compliance is not affordable for the system, it may grant a variance, but it must establish terms and c onditions, as necessary, to ensure that the variance adequately protects human health.

The 1996 SDWA Amendments identify three categori es of small PWSs that need to be addressed: (1) those serving a population of 3,301 to 10,000; (2) those serving a population of 501 to 3,300; and (3) those serving a population of 25 to 500. SDWA requi res EPA to make determinations of available compliance treatment technologies and, if needed, va riance treatment technologies for each size category. A compliance treatment technology is a technology that is affordable and that achieves compliance with the MCL and/or treatment technique. Compliance treatment technologies can include point-of-entry (POE) or point-of-use (POU) treatment units. Vari ance treatment technologies are specified only for those system size/source water quality combinations for which there are no listed compliance treatment technologies.

EPA determined that affordable compliance treatment technologies for each of the three categories of small systems are available for small systems for the Stage 2 DBPR. Therefore, variance treatment technologies are not required. The followi ng sections show how small system affordability was evaluated for the Stage 2 DBPR. The analysis is c onsistent with the current methodology used in the document National-Level Affordability Criteria Under the 1996 Amendments to the Safe Drinking Water Act (USEPA 1998c) and the Variance Technology Findings for Contaminants Regulated Before 1996 (USEPA 1998d).5 

### To adjust the baseline water bills to account for costs, EPA spread total annual or annualized costs attributable to regulations promulgated after 1996 (at a 3 percent discount rate) over the total number of households by size category.

Final Economic Analysis for the Stage 2 DBPR December 2005 8-8 

### 8.3.1 Affordability Threshold

EPA discussed its draft national-level affo rdability criteria in the August 6, 1998, Federal Register for the contaminants regulated before 1996. Na tional-level affordability criteria were developed by identifying an “affordability threshold” (i.e., the total annual household water bill that would be considered affordable). In developing this thr eshold, EPA considered the percentage of median household income (MHI) spent by an average household on comparable goods and services, including housing (28 percent), transportation (16 percent), f ood (12 percent), energy and fuels (3.3 percent), telephone (1.9 percent), water and other public services (0.7 percent), entertainment (4.4 percent), and alcohol and tobacco (1.5 percent). Another key factor that EPA used to select an affordability threshold was cost comparisons with other risk reduction activ ities for drinking water. Section 1412(b)(4)(E)(ii) of SDWA identifies both POU and POE devices as compliance technologies for small systems. EPA examined the projected costs of these options, and al so investigated the costs associated with supplying bottled water for drinking and cooking. The median income percentages asso ciated with these riskreduction activities were more than 2.5 percent for POE devices and bottled water, and 2 percent for POU devices. Based on the foregoing analysis, EPA develope d an affordability criterion of 2.5 percent of MHI for the affordability threshold (USEPA 1998c).

The median water bill for households in each sma ll system size category was subtracted from the affordability threshold to obtain the affordable leve l of expenditure per household for new treatment. This difference is referred to as the “available expe nditure margin.” Based on EPA’s 1995 CWSS, median water bills were about $250 per year for small system customers. However, the available expenditure margins are expected to change because water rates and MHI have increased. The 1995 MHI was updated to 2003 dollars using the Consumer Price Index (CPI) (BLS 2004). The results are shown in Exhibit 8.2.

The baseline for annual water bills (median water bill from the 1995 CWSS) also increased to account for regulations promulgated after 1996, but before the Stage 2 DBPR is promulgated. For each rule promulgated after 1996, the total national costs for each small size category were averaged over the number of households within that size category. The mean costs per household for each size category were added to the national median annual household water bills. This was done for the following rules: the Arsenic Rule, Long Term 1 Enhanced Surface Wa ter Treatment Rule (LT1ESWTR), Stage 1 DBPR, Filter Backwash Recycling Rule, and Radionuclides Rule. 5 

### The adjusted water bill may underestimate costs, which were spread over the total number of households by size category, as opposed to only the households affected by each rule. Exhibit 8.2 presents the analysis before and after the baseline water bill was adjusted for regulations promulgated after 1996.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-9 MHI (2003) Pre-1996 Median Waterbills (2003) Adjusted Waterbills %MHI for Waterbills

A B C D=C/A E=A*2.5% F=E-C 0 - 500 40,734 $ 255 $ 285 $ 0.70% 1,018 $ 733 $ 501 - 3,300 38,655 $ 222 $ 243 $ 0.63% 966 $ 724 $ 3,301 - 10,000 39,536 $ 219 $ 239 $ 0.60% 988 $ 750 $ Population

Served Small System Available Expenditures Baseline (2003$)

Affordability Threshold Available Expenditure

Margin 

## Exhibit 8.2 Derivation of Available Expenditure Margin

Sources: A) 1995 CWSS and 2000 Census data, adjusted to 2003$ using the Consumer Pr ice Index (BLS 2004). B) 1995 CWSS (as reported in the document "Vari ance Technology Findings for Contaminants Regulated Before 1996), adjusted to 2003$ using t he Consumer Price Index (BLS 2004). C) Pre-1996 Median Water Bills (2003) from Column B plus mean HH costs for drinking water rules since 1996.



### 8.3.2 Affordable Compliance Treatment Technologies

Section 1412(b)(4)(E)(ii) of SDWA, as amended in 1996, requires EPA to list treatment technologies that achieve compliance with MCLs est ablished under the Act that are affordable and applicable to typical small drinking water systems. Owners and operators may choose any treatment technology or technique that best suits their conditions, as long as the MCL is met.

This section presents household costs ($ pe r household per year) for various treatment technologies. The methodology for generating household cost estimates is explained in detail in section 7.6.4. In general, the analysis in this section followed the methodology in section 7.6.4; however, some inputs for household cost calculations in this section ar e different and, in some cases, are conservatively high compared to data used to generate household cost distributions in Chapter 7. A conservatively high estimate of household costs is used to more accura tely represent the high-end variability in household costs. This allows affordability of the Stage 2 DB PR to be more confidently assessed across the range of all affected small systems.

The size categories specified in SDWA for afford able treatment technology determinations are different than the nine standard size categories used in the majority of this EA, and subsequently, mean design and average daily flows for each category are different. The values for design and average flows (shown in Exhibit 8.2) are derived from the 1995 CWSS (USEPA 1997c).6 

### Although the size categories specified by SDWA for th e affordability analysis do not specifically include systems serving fewer than 25 people (per SDWA), these syst ems are included in all other analyses in this EA and are accounted for in Exhibit 8.4c. Thus, the estimate of the number of systems and households experiencing cost increases in Exhibit 8.4c is conservatively high.

Final Economic Analysis for the Stage 2 DBPR December 2005 8-10 Median Average Daily Flow Design Flow

a b c d = 1.15*c 25-500 0.015 0.058 72 83 501-3,300 0.17 0.5 74 85 3,301-10,000 0.7 1.8 77 89 System Size (Population

Served) Flows (mgd) Median HH Consumption Rates (kgal/yr) HH Consumption Rate Used for Costing 

## Exhibit 8.3 Affordability Analysis Inputs

Sources: A, B, and C: 1995 CWSS (USEPA 1997c). D: Consumption rates were adjusted upward by 15 per cent to account for distribution system leaks.



### For each treatment technology, unit treatment costs ($ per 1,000 gallons) are estimated using the flow rates shown in Exhibit 8.3 and the technology unit costs in Appendix I (see Exhibit I.27 for details on the derivation of unit treatment costs). As suggested in Variance Technology Findings for Contaminants Regulated Before 1996 (USEPA 1998d), capital costs were annualized using a 7 percent discount rate rather than the cost-of-capital rates u sed to generate the distribution of household costs in Chapter 7. The unit treatment costs ($ per 1,000 gallons) were multip lied by annual household consumption rates to determine the annual househol d cost increase ($ per household) for each treatment technology. Annual consumption rates are show n in Exhibit 8.3 and represent median yearly consumption derived from the 1995 CWSS (USEPA 1997c). (Note that mean yearly household consumption are shown in Exhibit 7.9 and are used for the household cost estimates in Chapter 7.) The values shown in Exhibit 8.3 were adjusted upward by 15 percent to account for water lost in the distribution system due to leaks, as suggested in Variance Technology Findings for Contaminants Regulated Before 1996 (USEPA 1998d).

Exhibits 8.4a and 8.4b show the compliance treatment technologies for the Stage 2 DBPR for surface water and ground water systems along with their mean annual household costs for each of the three size categories. Exhibit 8.4c presents annua l household cost increases for all households served by plants installing treatment to comply with the Stage 2 DBPR for systems serving 0 to 500, 501 to 3,300 and 3,301 to 10,000 people 6 

### . The mean, median, 90 th 

### percentile, and 95 th 

### percentile values are shown as well as the available expenditure margin and the num ber of households and plants that will experience annual cost increases above the available expenditure margin.

For a $733 affordability threshold in the 0 to 500 category, integrated membranes with chloramines and granulated activated carbon (GAC ) with advanced disinfectants are above the affordability threshold. Fourteen plants are expect ed to install GAC20 with advanced disinfectants and one plant is expected to install integrated membranes with chloramines to comply with the rule. In the 500 to 3,300 category and the 3,300 to 10,000 cate gory, no treatment technologies are above the affordability thresholds for these size categories.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-11 

### 0-500 501-3,300 3,301-10,000 Chloramines (0.15 mg/L) 62.88 $ 9.41 $ 5.03 $ Chlorine Dioxide (1.25 mg/L) 1 

### 121.92 $ 29.22 $ 9.99 $ UV (40mJ/cm2) 89.21 $ 20.55 $ 14.59 $ MF/UF 1 

### 583.74 $ 174.74 $ 115.86 $ GAC20 (EBCT=20 min, 90 day regeneration) 1 

### 644.69 $ 209.42 $ 134.39 $ GAC20 + Advanced Disinfectants 733.90 $ 366.53 $ 195.02 $ Integrated Membranes 907.52 $ 316.45 $ 240.22 $ System Size (Population Served) Compliance Technologies

0-500 501-3,300 3,301-10,000 Chloramines (0.15 mg/L) 62.70 $ 9.28 $ 4.88 $ UV (200mJ/cm2) 236.32 $ 65.81 $ 50.38 $ Ozone (0.5-log dose) 1 

### 1,300.43 $ 157.11 $ 60.62 $ GAC20 (EBCT=20 min, 240 day regeneration) 1 

### 414.74 $ 159.38 $ 95.81 $ Nanofiltration 1 

### 323.78 $ 141.71 $ 124.36 $ System Size (Population Served) Compliance Technologies 

## Exhibit 8.4a Affordable Compliance Treatment Technologies and Household Unit Treatment Costs ($/HH/Year) for Surface Water Systems

Exhibit 8.4b Affordable Compliance Treatment Technologies and Household Unit Treatment Costs ($/HH/Year) for Ground Water Systems

1 

### Zero percent of plants were predicted to select this treatment technology.
Source: Exhibit I.27.Final Economic Analysis for the Stage 2 DBPRDecember 20058-12Systems Size(populationserved)Number of HouseholdsServed by PlantsAdding Treatment(Percent of allHouseholds Subject tothe Stage 2 DBPR)Mean AnnualHouseholdCost IncreaseMedian AnnualHouseholdCost Increase90th PercentileAnnualHouseholdCost Increase95th PercentileAnnualHouseholdCost IncreaseAvailableExpenditureMargin($/hh/yr)Number ofHouseholds withAnnual CostIncreases Greaterthen the AvailableExpenditure MarginNumber of SurfaceWater Plants withAnnual CostIncreases Greaterthan the AvailableExpenditure MarginNumber ofGroundwater Plantswith Annual CostIncreases Greaterthan the AvailableExpenditure MarginTotal Number ofPlants with AnnualCost IncreasesGreater than theAvailableExpenditure MarginAB C D E F G H I J = H + I0 - 500 43045 ( 3% ) $201.55 $299.01 $299.01 $414.74 $733 964 15 0 15501 - 3,300 205842 ( 4% ) $58.41 $29.96 $75.09 $366.53 $72400003,301 - 10,000 342525 ( 5% ) $37.05 $14.59 $55.25 $200.05 $7500000Exhibit 8.4c Distribution ofHousehold Unit Treatment Costs for Plants Adding TreatmentNotes: Household unit costs represent treatment costs only, as presented in Exhibits 8.4a and 8.4b.Source: Household unit costs in Exhibits 8.4a and 8.4b combined with treatment technology selection deltas in Exhibits 5.11 and5.14.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-13 

### EPA believes, however, that the number of plants in small systems predicted to add advanced treatment technologies is overstated for two reasons : 1) distribution system modifications are not considered in the compliance forecast, and 2) Stage 2 DBPR requirements for small systems are similar to Stage 1 DBPR requirements and might not trigger compliance violations.

Very few households will experience cost increases above the available expenditure margin as a result of adding advanced treatment technology to comply with the Stage 2 DBPR. However, these predictions are likely overestimated because small syst ems have lower-cost alternatives available to them besides adding treatment, such as operating on a part- time basis, identifying an alternative water source, and connecting to a neighboring water system. Low- cost alternatives to reduce total trihalomethanes (TTHM) and haloacetic acid (HAA5) levels also include distribution system modification such as reducing average residence time include flushing di stribution mains more frequently, eliminating loops and dead ends, and optimizing storage to minimize rete ntion time. Refer to Chapter 7 for a more detailed discussion of household cost increases.

Under the Stage 1 DBPR, surface and ground water systems serving fewer than 500 people must have one site for TTHM and HAA5 monitoring. Unde r the Stage 2 DBPR, systems have to add a site only if their highest TTHM and HAA5 concentrations are at different locations. EPA estimates that approximately 3/4 of systems serving fewer than 100 people will have one site representing both their highest TTHM and HAA5 concentrations for the Stage 2 DBPR. Systems with one site for Stage 1 and one site for the Stage 2 DBPR would produce the sam e measure of compliance whether calculated using a running annual average (RAA) (Stage 1 compliance) or a locational running annual average (LRAA) (Stage 2 compliance).

In addition, it is anticipated that systems cu rrently predicted by EPA to select higher-cost technologies may be able to use less expensive treatment technologies by the time the Stage 2 DBPR is implemented. This is because the compliance deci sion tree (summarized in Chapter 3 and described in detail in Appendices A and B) represents current limitations on the use of inexpensive treatment technologies (e.g., chloramines and UV) taking into acc ount operational and constr uctability constraints. These limitations may not exist by the time the rule is implemented due to advances in treatment and innovations by manufacturers.

Under section 1416(a), EPA or a State may exempt a PWS from any requirements related to an MCL or treatment technique of an NPDWR, if it finds that (1) due to compelling factors (which may include economic factors such as qualification of the PWS as serving a disadvantaged community), the PWS is unable to comply with the requirement or im plement measure to develop an alternative source of water supply; (2) the exemption will not result in an unreasonable risk to health; and; (3) the PWS was in operation on the effective date of the NPDWR, or for a system that was not in operation by that date, only if no reasonable alternative source of drinking water is available to the new system; and (4) management or restructuring changes (or both) cannot reasonably result in compliance with the SDWA or improve the quality of drinking water.

8.3.3 Funding Options for Disadvantaged Systems

EPA believes that there is another mechanism in SDWA to address cost impacts on small systems that serve primarily low-income households. Systems that meet criteria established by the State/Primacy Agency could be classified as disadvantaged communities under §1452(d) of SDWA. They can receive additional subsidies through the Drinking Water St ate Revolving Fund (DWSRF), including forgiveness of principal. Under DWSRF, States/Primacy Agencies must provide a minimum of 15 percent of the available funds for infrastructure loans to system s serving fewer than 10,000 or fewer people. Two percent of the State’s/Primacy Agency’s grant is set-aside funding that can only be used to provide

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-14 

### technical assistance to small systems. In addition, up to 14 percent of the State’s/Primacy Agency’s grant may be used to provide technical, managerial, and financial assistance to all system sizes. For small systems that are disadvantaged, as defined by the State/Primacy Agency, up to 30 percent of a State’s/Primacy Agency’s DWSRF may be used for incr eased loan subsidies. This assistance can take the form of lower interest rates, principal forgiveness, or negative interest rate loans. The State may also extend repayment terms of loans for disa dvantaged communities to up to 30 years.

Small systems will be encouraged to discuss thei r infrastructure needs for complying with the Stage 2 DBPR with their State/Primacy Agency to determine their eligibility for DWSRF loans, and, if eligible, to ask for assistan ce in applying for the loans.

In addition to the DWSRF, money is available from the Department of Agriculture’s Rural Utility Service (RUS) and Housing and Urban Developmen t’s Community Development Block Grant (CDBG) Program. RUS provides loans, guaranteed loans, and gr ants to improve, repair, or construct water supply and distribution systems in rural areas and towns with a population of up to 10,000 people. In fiscal year 2003, RUS had over $1.5 billion of available funds for water and environmental programs. Also, three sources of funding exist under the CDBG program to finance building and improvements of public faculties such as water systems. These include: 1) direct grants to communities with populations over 200,000; 2) direct grants to States/Primacy Agencies, which in turn are awarded to smaller communities, rural areas, and coloñas in Arizona, California, New Mexico, and Texas; and 3) direct grants to U.S. territories and trusts. The CDBG budget for the fo rmula program for fiscal year 2003 totaled over $4.4 billion.



## 8.4 Feasible Treatment Technologies for All Systems

In accordance with Section 1412(b)(4)(E) of the 1996 SDWA Amendments, EPA examined whether there were existing, feasible treatment technologies and treatment techniques available that would allow systems to meet the Stage 2 DBPR re quirements. EPA examined alternatives for best available technologies (BATs) using two methods: In formation Collection Rule (ICR) treatment studies and Surface Water Analytical Tool (SWAT) predicti ons. A discussion of the evaluation is provided in sections 8.4.1 and 8.4.2. Results from these two eval uations show that all systems can meet the TTHM and HAA5 LRAA MCLs (80 micrograms per liter (μg/L) and 60 μg/L, respectively) using one of the three following treatment technologies:

1) GAC adsorbers with at least 10 minutes of empty-bed contact time and an annual average reactivation/replacement frequency no greater th an 120 days, plus enhanced coagulation or enhanced softening.

2) Nanofiltration using a membrane with a mol ecular weight cutoff of 1000 Daltons or less.

3) GAC adsorbers with at least 20 minutes of empty-bed contact time and an annual average reactivation/replacement frequency no greater than 240 days.

Section 8.4.3 discusses BATs specifically for consecutive systems.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-15 

### 8.4.1 ICR Treatment Studies

The ICR treatment studies were designed to evaluate the technical feasibility of using GAC and nanofiltration to remove DBP precursors prior to the addition of chlorine-based disinfectants (USEPA 2000o, Hooper and Allgeier 2002). EPA used TOC levels in the source or finished water to determine whether the ICR treatment study requirement applied to plants. Specifically, surface water plants with annual average source water TOC concentrations gr eater than 4 milligrams per liter (mg/L) and ground water plants with annual average finished water TOC concentrations greater than 2 mg/L were required to conduct treatment studies. Thus, the plants require d to conduct treatment studies generally had waters with organic DBP precursor levels that were signifi cantly higher than the national means of 3.2 mg/L and 1.5 mg/L for ICR surface and ground water plants, respectively (USEPA 2005l).

Plants that used GAC typically evaluated performance at two empty-bed contact times, 10 and 20 minutes, and over a range of operational times to eval uate the unsteady nature of TOC removal by GAC. This allowed GAC performance to be assessed with respect to empty-bed contact time, as well as reactivation/replacement frequency. Plants that c onducted membrane treatment studies evaluated one or two nanofiltration membranes with molecular weight cutoffs less than 1,000 Daltons. Regardless of the treatment technology evaluated, all treatment st udies evaluated post-treatment DBP formation under distribution system conditions representative of the fu ll-scale plant at the average residence time, using free chlorine as the primary and residual disinfectant.

The results of the ICR treatment study suggest that GAC would be an appropriate treatment technology for surface water systems and some ground water systems with influent TOC concentrations below approximately 6 mg/L (USEPA 2000o). (The ICR and National Rural Water Association (NRWA) data indicate that over 90 percent of plants have av erage influent TOC levels below 6 mg/L.) Larger systems would likely realize an economic benefit fro m on-site reactivation, which could allow them to use smaller, 10-minute empty-bed contact time contact ors with more frequent reactivation (i.e., 120 days or less). Most small utilities would not find it ec onomically advantageous to install on-site carbon reactivation facilities, and thus would opt for larg er, 20-minute empty-bed contact time contactors, with less frequent carbon replacement (i.e., 240 days or less). EPA recognizes that some small systems attempting to implement GAC20 may face GAC supply challenges.

Theoretically, there is a linear relationship be tween empty-bed contact time and reactivation interval. Assuming equivalent performance, a doubli ng of the empty-bed contact time would be expected to result in a doubling of the reactivation interval. If this is the case, the 10-minute empty-bed contact time contactor reactivated at 120 days should result in equivalent performance to a 20-minute empty-bed contact time contactor reactivated at 240 days. Howeve r, the ICR treatment study data demonstrated that the 20-minute contactors generally outperform the 10-minute contactors. On the other hand, larger systems will typically operate with a larger number of parallel contactors compared to small systems, resulting in improved performance. Thus, the benefit th at small systems gain by using a larger empty-bed contact time will be offset by use of a smaller number of parallel contactors. Based on these considerations, the proposed reactivation/replacemen t interval for the 20-minute contactor is simply double the reactivation/replacement interval for a 10-minute contactor.

The ICR treatment study demonstrated that approximately 70 percent of the surface water plants that conducted GAC studies could meet the 80/60 μg/L TTHM/HAA5 MCLs with a 20 percent safety factor (i.e., 64 μg/L and 48 μg/L, respectively) using GAC with 10 minutes of empty-bed contact time and a 120-day reactivation frequency. It also showed that 78 percent of the plants could meet the MCLs using GAC with 20 minutes of empty-bed contact time and a 240-day reactivation frequency. As discussed previously, the treatment studies were c onducted at plants having poorer source and finished water quality than the national average. Therefore, EPA believes that the percentages of plants in the

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-16 

### GAC studies that could meet the MCLs with the BATs tran slate to much higher percentages of plants nationwide.

The ICR treatment study results also demonstrat ed that nanofiltration was the better DBP control treatment technology for ground water sources with hi gh TOC concentrations (i .e., above approximately 6 mg/L). The results of the membrane treatment st udies showed that all ground water plants could meet the 80/60 μg/L TTHM/HAA5 MCLs with a 20 percent safety factor (i.e., 64 μg/L and 48 μg/L, respectively) at the average distribution system residence time using nanofiltration (USEPA 2000o, Hooper and Allgeier 2002). Although nanofiltration is generally more expensive than GAC, it would be less expensive than GAC for high TOC ground waters that require minimal pretreatment. Also, nanofiltration is an accepted treatment technology fo r treatment of the high-TOC ground waters in areas of the country, such as Florida and parts of the southwest.

8.4.2 BAT Evaluation Using SWAT

The second method that EPA used to examine alternatives for BAT was SWAT, which was developed to compare alternative regulatory stra tegies as part of the Stage 1 and Stage 2 M-DBP Advisory Committee deliberations (Seidel 2001). EPA considered the following BAT options:

• Enhanced coagulation (EC)/softening with chlorine

• EC/softening with chlorine and no pre-disinfection

• EC and GAC10

• EC and GAC20

• EC and chloramines

EC/softening is required under the Stage 1 DBPR for conventional plants. In the model, GAC10 was defined as granular activated carbon with an em pty-bed contact time of 10 minutes and a reactivation frequency of no more than 90 days. GAC20 was define d as granular activated carbon with an empty-bed contact time of 20 minutes and a reactivation or re placement frequency of no more than 90 days. EPA assumed that systems would be operating to achieve both the Stage 2 MCLs of 80 μg/L TTHM and 60 μg/L HAA5 as an LRAA and the Surface Water Treat ment Rule (SWTR) removal and inactivation requirements of 3-log for Giardia and 4-log for viruses. EPA also evaluated the BAT options under the assumption that plants operate to achieve DBP levels 20 percent below the MCL (i.e., a 20 percent safety factor). These assumptions along with other inputs for the SWAT runs are consistent with those specified in Appendix A.

The compliance percentages forecasted by SWAT are indicated in Exhibit 8.5. EPA estimates that over 97 percent of large systems will be able to achieve the Stage 2 MCLs, regardless of postdisinfection choice, if they apply the BAT (i.e., EC and GAC10). As shown in the current Occurrence Document (USEPA 2005k), the source water quality (e.g ., DBP precursor levels) in medium and small systems is comparable to or better than that for la rge systems. Using the large-system estimate as a proxy for medium and small systems, EPA believes it is con servative to assume that at least 90 percent of these systems will be able to achieve the Stage 2 MCLs if they were to apply one of the GAC BATs. EPA realizes that it may not be economically feasible for small systems to install and operate an on-site GACreactivation facility. Thus, it is assumed that small systems may adopt GAC20 (with 240 days of emptybed contact time) in a replacement mode over GAC10. Some small systems may find that another defined BAT, like nanofiltration, will be cheaper than the GAC20 in a replacement mode because their specific geographic locations may make routine GAC shipment expensive.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-17 

## Exhibit 8.5 SWAT Model Predictions of Percent of Large Plants in Compliance

with TTHM and HAA5 Stage 2 MCLs after Application of Specified Treatment Technologies

Treatment Technology Compliance with 80/60 LRAA Compliance with 64/48 LRAA (20% Safety Factor)

Residual Disinfectant All Systems Residual Disinfectant All Systems Chlorine Chloramine Chlorine Chloramine
(EC) 73.5% 76.9% 74.8% 57.2% 65.4% 60.4%
EC (no pre-disinfection) 73.4% 88.0% 78.4% 44.1% 62.7% 50.5%

EC & GAC10 100% 97.1% 99.1% 100% 95.7% 98.6%

EC & GAC20 100% 100% 100% 100% 100% 100%

EC & All Chloramines NA 83.9% NA NA 73.6% NA Source: Seidel (2001).



### 8.4.3 BATs for Consecutive Systems

EPA is also proposing a BAT for consecutive systems to meet the TTHM and HAA5 MCLs of 80 and 60 μg/L, respectively. Presumably, consecutive systems that may need to employ the BAT are receiving water from their wholesaler(s) that barely meets or does not meet the MCLs. Removal of TTHM and HAA5 is difficult after they have formed. EPA believes that the best compliance strategy for consecutive systems is to collaborate with wholesalers on the water quality they need. However, this is a private agreement over which EPA does not have juri sdiction. There are expected to be wholesalers treating water with whom consecutive systems cannot work out agreements to enable the consecutive systems to meet the MCLs.

EPA is specifying chloramination with management of hydraulic flow and storage to minimize residence time in the distribution system as a BAT for consecutive systems that serve at least 10,000 people. Chloramination has been used for residual di sinfection for many years to minimize the formation of chlorination DBPs, including TTHM and HAA5 (U SEPA 2005l). EPA estimates that over 50 percent of large subpart H systems serving at least 10,000 people use chloramination for Stage 1 DBPR. The BAT provision to manage hydraulic flow and minimize residence time in the distribution system is intended to facilitate the maintenance of the chloramine residual and minimize the likelihood of nitrification. If consecutive systems receive chlorinate d water that is close to, but lower than, the MCLs, they should in most cases be able to use chloramination to stop the formation of TTHM and HAA5 in their distribution system and thereby meet the MCL. If consecutive systems are already receiving chloraminated water from the wholesaler that is mee ting the MCLs, the consecutive system should also be able to meet the MCL. In either of these situations , distribution system flow maintenance is important for maintaining the chloramine residual.

For those consecutive systems serving fewer th an 10,000 people, EPA believes that the best compliance strategy for consecutive systems is to collaborate with wholesalers on the water quality they need. For consecutive systems that are having diffi culty meeting the MCLs, EPA is specifying a BAT of chloramination with management of hydraulic flow and storage to minimize residence time in the

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-18 

### distribution system for systems serving at least 10,000 and management of hydraulic flow and storage to minimize residence time in the distribution system for systems serving fewer than 10,000. EPA believes that small consecutive systems can u se this BAT to comply with the Stage 2 DBPR, but if they cannot, then they can apply to the State for a variance. Chloramines are not included as a BAT for consecutive systems serving fewer than 10,000 people due to concerns about the ability of systems to properly control the process, given that many have no treatment capability or expertise. EPA is also concerned about such systems having operational difficulties such as distribution system nitrification.

EPA believes that the various BATs for non- consecutive systems are not appropriate for consecutive systems because their efficacy in cont rolling DBPs is based on precursor removal. Consecutive systems face the unique challenge of recei ving waters in which DBPs are already present if the wholesale systems has used a residual disinfect ant, which BATs for non-consecutive systems do not effectively remove. GAC is not cost-effective fo r removing DBPs. Dioxin is a potent carcinogen and a byproduct of GAC regeneration when GAC has been used to adsorb DBPs. Nanofiltration can be moderately effective at removing TTHM and HAA5, but only with membranes that have a very low molecular weight cutoff and very high cost of ope ration. Therefore, EPA believes that GAC and nanofiltration are not appropriate BATs for consecutive systems.



## 8.5 Effect of Compliance with the Stage 2 DBPR on the Technical, Managerial, and Financial Capacity of Public Water Systems

Section 1420(d)(3) of SDWA, as amended, requir es that, in promulgating a National Primary Drinking Water Regulation (NPDWR), the Administrator shall include an analysis of the likely effect of compliance with the regulation on the technical, manage rial, and financial (TMF) capacity of PWSs. The following analysis fulfills this statutory obligation by identifying the incremental impact that the Stage 2 DBPR will have on the TMF of regulated water syst ems. Analyses presented in this document represent only the impact of new or revised requirements, as established by the Stage 2 DBPR; the impacts of previously established requirements on system capacity are not considered.

Overall water system capacity is defined in Guidance on Implementing the Capacity Development Provisions of the Safe Drinking Water Act Amendments of 1996 (USEPA 1998e) as the ability to plan for, achieve, and maintain compliance with applicable dr inking water standards. Capacity encompasses three components: technical, managerial, and financial. T echnical capacity is the operational ability of a water system to meet SDWA requirements. Key issues of technical capacity include:

• Source water adequacy—Does the system have a reliable source of water with adequate quantity? Is the source generally of good quality and adequately protected?

• Infrastructure adequacy—Can the system provi de water that meets SDWA standards? What is the condition of its infrastructure, including wells or source water intakes, treatment and storage facilities, and distribution systems? What is the infrastructure’s life expectancy? Does the system have a capital improvement plan?

• Technical knowledge and implementation—Are th e system’s operators certified? Do the operators have sufficient knowledge of applicable standards? Can the operators effectively implement this technical knowledge? Do the ope rators understand the system’s technical and operational characteristics? Does the system ha ve an effective Operations and Maintenance (O&M) program?

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-19 

### Managerial capacity is the ability of a water system ’s managers to make fi nancial, operating, and staffing decisions that enable the system to achie ve and maintain compliance with SDWA requirements. Key issues include:

• Ownership accountability—Are the owners clearly identified? Can they be held accountable for the system?

• Staffing and organization—Are the operators and managers clearly identified? Is the system properly organized and staffed? Do pe rsonnel understand the management aspects of regulatory requirements and system operations? Do they have adequate expertise to manage water system operations (i.e., to conduct implementation, IDSE, additional routine monitoring, and operational evaluation activities to meet the Stage 2 DBPR requirements)? Do personnel have the necessary licenses and certifications?

• Effective external linkages—Does the system in teract well with customers, regulators, and other entities? Is the system aware of availa ble external resources, such as technical and financial assistance?

Financial capacity is a water system’s ability to acquire and manage sufficient financial resources to allow the system to achieve and maintain comp liance with SDWA requirements. Key issues include:

• Revenue sufficiency—Do revenues cover costs?

• Creditworthiness—Is the system financially healthy? Does it have access to capital through public or private sources?

• Fiscal management and controls—Are ade quate books and records maintained? Are appropriate budgeting, accounting, and financial planning methods used? Does the system manage its revenues effectively?

8.5.1 Requirements of the Stage 2 DPBR

The Stage 2 DBPR establishes five new requirements that may affect the TMF capacity of affected PWSs:

1) Compliance with MCLs established for TTHM and HAA5: MCLs of 80μg/L and 60μg/L for TTHM and HAA5, respectively, measured as LRAAs at the monitoring sites identified as a result of the IDSEs required under the Stage 2 DBPR.

2) Conducting an IDSE to identify sample locati ons for Stage 2 compliance monitoring that represent distribution system sites with high TTHM and HAA5 levels.

3) Preparing a monitoring plan, based on inform ation in the IDSE and consultation with the State/Primacy Agency, that details th e sites and times for compliance sampling.

4) Additional routine monitoring for DBPs.

5) If the operational evaluation level is ex ceeded, systems must conduct an operational evaluation and submit a report to the State/Primacy Agency no later than 90 days after being notified of the analytical result that exceeded the operational evaluation level.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-20 

### In addition, personnel from systems regulated under the Stage 2 DBPR will need to familiarize themselves with the rule and its requirements.

8.5.2 Systems Subject to the Stage 2 DBPR

The Stage 2 DBPR will apply to all CWSs and NTNCWSs that add a primary or residual disinfectant other than UV, or that deliver water that has been treated with a disinfectant other than UV. The Stage 2 DBPR may affect 42,032 CWSs and 6,260 NTNCWSs—48,293 systems in all. While most will not, some systems may require increased TMF capacity to comply with the new requirements, or will need to tailor their choices to match their capacities.

8.5.3 Impact of the Stage 2 DBPR on System Capacity

The estimates presented in Exhibits 8.6 and 8.7 re present the anticipated impact of the Stage 2 DBPR on small and large system capacity as a result of the measures that systems are expected to adopt to meet the requirements of the rule (e.g., selecting monitoring sites for the IDSE, installing/upgrading treatment, operator training, communication with regulators and the service community, etc.). The extent of the impact of a particular requirement on system capacity is estimated using a scale of 0-5, where 0 represents a requirement that is not anticipated to have any impact on system capacity, 1 represents a requirement that is expected to have a minimal im pact on system capacity, and 5 represents a requirement that is anticipated to have a very significant impact on system capacity.

Criteria used to develop the scores and associated impacts are discussed further in section 8.5.5.

8.5.4 Rationale for Scores

The baseline assumed for the purposes of this analys is, which identifies the incremental impact of the Stage 2 DBPR on the TMF capacity of systems, is complete implementation of the Stage 1 DBPR, the Interim Enhanced Surface Water Treatment Rule (IESWTR), and the Long Term 1 Enhanced Surface Water Treatment Rule (LT1ESWTR). As a result, it is anticipated that many of the systems facing the most difficult DBP challenges will have made appropria te modifications to their treatment process (e.g., changed point of disinfection, installed membrane technologies, etc.) to achieve compliance with these rules, and, therefore, will not need to install a dditional treatment technology to achieve compliance with the Stage 2 DBPR. However, the revised methodol ogy for measuring system compliance with the MCLs for TTHM and HAA5 (i.e., LRAA) will require systems to reduce peak levels in DBP concentrations within their distribution systems. Since an LRAA re presents a more stringent testing standard than an RAA, it is likely that some systems that previous ly met requirements established by the Stage 1 DBPR will be required to make changes to their treatment processes to comply with the Stage 2 DBPR. The derivation of the scores assigned in Exhibits 8.5 and 8. 6 and the rationale behind them is described in the next section.Final Economic Analysis for the Stage 2 DBPRDecember 20058-21Exhibit 8.6 Estimated Impact of the Stage 2 DBPR on Small System Capacity(0 = no impact, 1 = minimal impact, and 5 = very significant impact)RequirementNumber ofSystems/Plants(Percent)Technical Capacity Managerial Capacity Financial Capacity

Source Water Adequacy Infrastructure Adequacy Technical Knowledge & Implementation Ownership Accountability Staffing & Organization Effective External Linkages Revenue Sufficiency Credit Worthiness Fiscal Mgmt. & ControlsFamiliarization withrequirements of the rule44,453 systems(100%)001010000Conducting IDSE monitoring10,478 systems(24%)002102301Plants with major treatmenttechnology changes1,743 plants(3%)244223553Stage 2 monitoring plan122,499 systems(48%)001101100Additional routine monitoring1001000300Operational evaluations97 systems(0.2%)0011110001Some systems are expected to take moresamples and some are expected to takeless from Stage 1 to Stage 2, depending on the number of plants in theirsystems. Overall, the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. Exhibit H.8a presents thechange in total samples for different system size categories.Notes: (1) To analyze the impact of theserequirements on system capacity, the requirements believedto have the most and the least impact on affectedsystems (i.e., the installation of treatment to ensure compliance with the LRAA MCLs for TTHM and HAA5, and familiarization with the requirements of therule, respectively), were analyzed first. These initial analyseswere then used as the bases against which the relative impactof the remainingrequirements were assessed. The impact estimates developed foreach requirement were also compared to those developed for theLong Term 2Enhanced Surface Water Treatment Rule (LT2ESWTR) to ensure cross-rule consistency and to enable cross-rule comparisons.(2) The scores presented above represent the worst-case scenario; the requirementsof this rule are expected to have less impact on the capacity of mostsystems affected by each requirement.Source: Number and percent of small systems subject to familiarization of rule, IDSE monitoring, and operational evaluations from Exhibit 7.2a Columns B, D, andJ (sum of surface water/GWUDI, and ground water CWSs and NTNCWSsserving fewer than 10,000 people), respectively. Number and percent of smallsystems subject to Stage 2 monitoring plan is the sum of Exhibit7.2a Column F and Exhibit 7.2b Column B. Number of small plants making treatmenttechnology changes from Exhibit 7.3 Column B (sum of surface water/GWUDI, and ground water CWSs and NTNCWSs serving fewer than10,000people). Impact on capacity is determined relative to previous regulations as a function of thecost and number of systems/plants that require additionalcapacity to comply with each requirement, as described in section 8.5.5.Final Economic Analysis for the Stage 2 DBPRDecember 20058-22Exhibit 8.7 Estimated Impact of the Stage 2 DBPR on Large System Capacity(0 = no impact, 1 = minimal impact, and 5 = very significant impact)RequirementNumber ofSystems/Plants(Percent)Technical Capacity Managerial Capacity Financial Capacity

Source Water Adequacy Infrastructure Adequacy Technical Knowledge & Implementation Ownership Accountability Staffing & Organization Effective External Linkages Revenue Sufficiency Credit Worthiness Fiscal Mgmt. & ControlsFamiliarization withrequirements of the rule3,839 systems(100%)001010000Conducting IDSE monitoring2,302 systems(60%)002101100Plants with major treatmenttechnology changes518 plants(5%)233222443Stage 2 monitoring plan13,853 systems(100%)001101100Additional routine monitoring1000000100Operational evaluations327 systems(9%)0001010001Some systems are expected to take moresamples and some are expected to takeless from Stage 1 to Stage 2, depending on the number of plants in theirsystems. Overall, the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. Exhibit H.8a presents thechange in total samples for different system size categories.Notes: (1) To analyze the impact of theserequirements on system capacity, the requirements believedto have the most and the least impact on affectedsystems (i.e., the installation of treatment to ensure compliance with the LRAA MCLs for TTHM and HAA5, and familiarization with the requirements of therule, respectively), were analyzed first. These initial analyseswere then used as the bases against which the relative impactof the remainingrequirements were assessed. The impact estimates developed foreach requirement were also compared to those developed for theLT2ESWTR toensure cross-rule consistency andto enable cross-rule comparisons.(2) The scores presented above represent the worst case scenario; the requirementsof this rule are expected to have less impact on the capacity of mostsystems affected by each requirement.Source: Number and percent of large systems subject to familiarization of rule, IDSE monitoring, and operational evaluations from Exhibit 7.2a Columns B, D, andJ (sum of surface water/GWUDI, and ground water CWSs and NTNCWSsserving 10,000 or more people), respectively. Number and percent of largesystems subject to Stage 2 monitoring plan is the sum of Exhibit7.2a Column F and Exhibit 7.2b Column B. Number of large plants making treatmenttechnology changes from Exhibit 7.3 Column B (sum of surface water/GWUDI, and ground water CWSs and NTNCWSs serving 10,000 or more people).Impact on capacity is determined relative toprevious regulations as a function of the cost and number of systems/plants that require additional capacity tocomply with each requirement, as described in section 8.5.5.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-23 

### 8.5.5 Derivation of Stage 2 DBPR Scores

EPA developed a 5-point scoring system to analyze the likely effect of compliance with an NPDWR on the technical, managerial, and financia l capacity of PWSs. For each regulation, it is necessary to complete the following steps:

1) Determine the type and number of PW Ss to which the regulation applies.

2) List all of the requirements of the regulation.

3) Determine the type and number of PW Ss to which each requirement applies.

4) Evaluate the impact of each requirement on the capacity of affected PWSs.

The determination of the universe of affected systems and the evaluation of the capacity impact of individual requirements requires the use of the co st and technical information contained in SDWIS, EAs developed for other rules, information collec tion requests, and other supporting documentation for the rule. These data sources are also used to deve lop a qualitative description of the expected response of affected systems to each requirement.

The overall evaluation of the impact of a requirement on the affected systems, presented in Exhibits 8.6 and 8.7, is determined by the im pact of each requirement on nine sub-categories of capacity—three sub-categories under each of the broader divisions of technical, managerial, and financial capacity. Within these sub-categories, a professional engineer with extensive water system experience reviewed the costs, number of systems affected, a nd complexity of each requirement. After estimating the technical, managerial, and financial impacts within each sub-category, the professional engineer assigned the scores using best professional judgment. Costs were considered cumulatively for each requirement for small and large systems. This score represents the additional capacity that systems will need to develop to comply with each requirement. Due to a lack of available information on operating budgets, this analysis does not include a quantitative component.

To ensure the ability to make cross-rule comp arisons, to standardize the assignment of numerical scores, and to minimize the subjectivity of the sco ring system, the professional engineer compared the Stage 2 DBPR requirements to requirements of re gulations for which capacity impact analyses have already been conducted (e.g., Ground Water Rule, LT1ESWTR). Similar requirements are assigned similar impact scores.

These group assignments are reviewed by the EPA Rule Manager and other EPA staff cognizant of small system issues to ensure that they accu rately represent the cumulative impact of the rule requirements on system capacity. Any disagreements over the assignments are discussed. The EPA Rule Manager and other EPA staff discuss the rationale for the disagreement and evaluate whether the assignments need to be adjusted. EPA adjusts th e assignments only after review of the rule support documents and an analysis of the expected system response to the rule requirements.

8.5.5.1 Familiarization with the Stage 2 DBPR

The requirements established under the Stage 2 DBPR are straightforward (use of LRAA instead of RAA to determine compliance with the MCLs fo r DBPs) and are grounded in requirements previously established under the Stage 1 DBPR. As a result, EPA does not expect that small or large systems regulated under this rule will face more than a minima l challenge to their technical or managerial capacity as a result of efforts to familiarize themselves with the Stage 2 DBPR. Furthermore, familiarization with the rule will not impact the financial capacity of either large or small systems.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-24 

### 8.5.5.2 Conducting an Initial Distribution System Evaluation

The IDSE was incorporated into the Stage 2 DBPR to ensure that systems monitor for DBPs at the location where TTHM and HAA5 values are highest. IDSEs are required for most PWSs under the Stage 2 DBPR, but will not affect the capacity of all systems subject to the requirement to the same extent. Some systems will be able to meet this requirement without conduc ting extensive additional monitoring, through an IDSE waiver. CWSs servi ng fewer than 500 people that have TTHM and HAA5 data qualify for a very small system waiver. Systems for which every Stage 1 DBPR TTHM and HAA5 compliance sample is less than or equal to 40/30 μg /L, respectively, and which did not have TTHM or HAA5 violations during this period may qualify for the 40/30 certification and not perform the IDSE. NTNCWSs that serve fewer than 10,000 people are not subject to the IDSE requirement. It is expected that large surface water systems will typically be required to conduct the most monitoring for the IDSE, while small ground water systems will be required to conduct the least.

Before doing an IDSE, systems required to mon itor for DBPs will need to select monitoring locations. Identifying appropriate sampling locations is expected to require a modest improvement in the technical and managerial capacity of many systems. This requirement will have a much smaller impact on the capacity of systems that do not have to monito r than on those that do. While the former may need to contact their regulatory agencies to obtain waiv ers for the IDSE requirement and to meet reporting requirements, they will not be required to conduct as many new technical analyses of their distribution system and its impact on finished water quality. Re gardless of whether a system must conduct additional monitoring as part of an IDSE, this requirement will have an impact on ownership accountability, since all new or historic monitoring data must be logge d and submitted to the appropriate regulatory agency.

It is expected that large surface water systems will typically be required to conduct the greatest amount of monitoring for the IDSE, while small gr ound water systems will be required to conduct the least. Lab analysis of samples for TTHM and HAA5 are expensive (estimated at $200 per sample plus $10 to $40 for shipping costs, depending on system size; see Chapter 7 for discussion of laboratory costs). While these costs are not typically prohibitive for large systems, they may represent a significant challenge to the financial capacity of small systems. Some of these systems will need to revisit their current budgeting practices and fee structures to meet these additional costs.

8.5.5.3 Compliance with MCLs for TTHM and HAA5

The impact of the revised DBP MCLs on the manage rial capacity of systems is not anticipated to be as great as the technical and financial challenges. However, system managers will need to review the implications of the revised method for measuring compliance with the MCLs for TTHM and HAA5, and may need to hire more highly certified operators or provide additional traini ng for existing operators to ensure that system staff can safely and effectively opera te all new elements of the system’s treatment train at all times. In addition, systems will need to rely on, and improve upon, their communication with regulators, technical and financial assistance providers, and their service community.

Systems whose finished water does meet the MCLs for TTHM or HAA5 calculated using LRAAs will need to adjust, change, or enhance their treat ment practices. The installation, operation, and maintenance of new treatment technologies will requi re a substantial enhancement of these systems’ technical capacity, particularly for small systems. Specifically, source water adequacy may be reduced if marginal sources are no longer viable. The system ma y also need to improve its infrastructure, and system operators will require correspondingly greater technical expertise to operate new treatment
processes.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-25 

### Note, however, that based on the recommendations of the Small Surface Water System Delphi Group convened by the Agency, EPA assumed for the purpo ses of the capacity impact analysis that small surface water systems would not install treatment technologies that were beyond their technical or managerial capacity in order to meet the requirement s of this rule. For example, very small systems (those serving fewer than 500 people) would not install chlorine dioxide treatment because it requires a system operator to be in the plant every day. In stead, EPA assumed these smallest systems would install UV or membrane technologies, which do not require a sy stem operator to be present each day. While the operation and maintenance of membrane treatment elements may challenge the technical capacity of small system operators, UV treatment is easy to implement, does not require the same level of technical know-how, and is relatively low cost. As a result, onl y some of those small systems that must install treatment to meet the MCLs for TTHM and HAA5 are expected to experience the full impact detailed in Exhibit 8.6.

While some small systems that must install new treatment to meet the revised MCL requirement will face a substantial challenge to their capacity, it is expected that this requirement will not have as dramatic an impact on large system s for the reasons described below.

Management for both large and small systems will need to work closely with regulatory agencies to receive approval on proposed design/treatment modifi cations. Since larger systems tend to have more developed relationships with re gulatory personnel, as well as more established means of communicating with their customers, the impact on the managerial capacity of small systems is expected to be greater than the impact on the managerial capacity of large sy stems. Further, large system operators tend to have a higher level of expertise than their small system c ounterparts. As a result, large system operators will not require as much training to adequately operate and maintain any new treatment that must be installed. This requirement will not pose as great of a technical or managerial challenge to large systems.

The impact of the Stage 2 DBPR on the financial capacity of regulated systems is closely tied to the rule’s impact on the technical capacity of these systems. Systems that must install additional treatment processes or upgrade their current treatme nt processes will face high costs. These costs may pose particular difficulties for many of the affected systems since the majority are relatively small (i.e., serving fewer than 3,300 customers), and therefore typically have a smaller revenue base and fewer households over which they can distribute additional co sts. In addition, large systems may take better advantage of economies of scale than smaller systems because they buy larger quantities of chemicals and equipment. However, it is anticipated that some sy stems may elect to develop an alternative source (e.g., one with lower levels of naturally-occurring organic material) or interconnect with a nearby system if treatment costs prove prohibitive.

To obtain funding from either public or private sources, systems will need to demonstrate sound financial accounting and budgeting practices, and the ability to repay their debts. As a result, many of the smallest systems that do not currently charge exp licitly for water service (e.g., mobile home parks, camp grounds, etc.) may need to begin billing their customer s. Those systems that already charge for water service will likely need to increase their rates (freque ntly requiring approval of the local public utilities commission or public services commission, board approval, or vote within the service boundary), and improve their recordkeeping procedures. Again, this poses less of a challenge to large systems that have established billing practices and have developed cl ose relationships with public utilities commissions.

Therefore, on the basis of the TMF challenges posed by this requirement, it is anticipated that the implementation of the revised monitoring methodology will have a substantial impact on the capacity of the 1,743 small plants and 518 large plants that are expected to make treatment technology changes to reduce DBP concentrations to comply with this rule (see Exhibit 7.3). The other systems are expected to experience only minor TMF impacts.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-26 

### 8.5.5.4 Stage 2 Monitoring Plan

Most systems are required to prepare a Stage 2 DBPR monitoring plan that includes monitoring locations, monitoring dates, and compliance calculation procedures. Most systems will have to base the new monitoring plan on the IDSE results and Stage 1 compliance monitoring locations. Some systems will only need to update existing monitoring plans. The Stage 2 DBPR monitoring plan will be similar to monitoring plans already in existence. Therefore, th e requirement is expected to have a minimal impact.

8.5.5.5 Additional Routine Monitoring

It is anticipated that the additional routine monitoring required for some systems will have a relatively limited impact on TMF capacity. These sy stems already have experience sampling for DBPs and only a small number of additional samples may be required. Some systems may take fewer or the same number of samples under Stage 2 than Stage 1. Nonetheless, it is important to consider that the monitoring costs may strain the financial capacity of some small systems, especially since the sampling costs are high for TTHM and HAA5. Additional routine monitoring is expected to have minimal impact on the capacity of large systems since costs will be spread across a large population base.

8.5.5.6 Operational Evaluations

The operational evaluation level is exceeded wh en a sample result (when multiplied by 2 and added to the previous two quarters and then divided by 4) results in a concentration greater than 80 : 

### g/L for TTHM or 60 : 

### g/L for HAA5. If a system has exceeded the operational evaluation level, it must conduct a operational evaluation and submit a report to the State/Primacy Agency no later than 90 days after being notified of the analytical result that ex ceeded the operational evaluation level. The evaluation involves an examination of system treatment and distribution operational practices, including storage tank operations, excess storage capacity, distribution system flushing, changes in sources or source water quality, and treatment technology changes or problems that may contribute to TTHM and HAA5 formation and what steps could be considered to minimize future exceedances. Based on the conclusions of their evaluation, some systems may install additional treatment or modify their distribution system (e.g., improve tank mixing by adding a recirculation sy stem). However, since such modifications are not required, they are not considered for the purposes of this analysis.

An operational evaluation will require system operators to have a thorough knowledge of the components of their treatment train and distribution system. Further, some system operators, particularly small system operators, may require additional training or input from State/Primacy Agency or local extension agents. This requirement may also have a small impact on the managerial capacity of some systems, since owners will be required to ensure that these evaluations are conducted. Note, however, that this requirement does not require systems to no tify the public or provide an explanation in their CCRs—minimizing the need for them to further devel op their public outreach efforts. Finally, given that an operational evaluation is expected to take only 2 to 16 hours (depending on system size), this requirement will probably not have any impact on the fi nancial capacity of large or small systems. Large systems with more complex distribution networks are expected to spend more time per exceedance than smaller, simpler systems. The frequency of exceedan ces is also expected to decrease over time as systems begin identifying the causes and working with th eir States/Primacy Agencies to reduce future exceedances.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-27 

### 8.5.6 Summary

The Stage 2 DBPR may have a substantial impact on the capacity of the 1,743 plants in small systems and 518 plants in large systems that must ma ke changes to their treatment process to meet the Stage 2 DBPR requirements. However, while the im pact to these systems is potentially significant, only 3.8 percent of all plants regulated under the Stage 2 DBPR (2,261 of 60,220) will be affected by this requirement. Since individual systems may employ more than one plant, it is likely that fewer than 1,620 systems (3.4 percent of systems) will be affected by this requirement. The new IDSE and monitoring requirements are expected to have a small impact on the technical and managerial capacity of small systems, a moderate impact on the financial capacity of some small systems, and a much smaller impact on large systems. The capacity of systems that must conduct an operational evaluation will only be impacted in a minor way, while those systems that must only familiarize themselves with the rule (the large majority of systems) will not face any cap acity impact as a result of the Stage 2 DBPR.



## 8.6 Paperwork Reduction Act

The information collected as a result of the St age 2 DBPR allows the States/Primacy Agencies and EPA to determine appropriate requirements for sp ecific systems and to evaluate compliance with the rule. The Paperwork Reduction Act requires EPA to estimate the burden of complying with the rule on PWSs, States, and territories. Burden means the to tal time, effort, and financial resources required to generate, maintain, retain, disclose, or provide info rmation to or for a Federal agency. This burden includes the time needed to:

• Review instructions

• Develop, acquire, install, and utilize treatment technology and systems for the purposes of collecting, validating, and verifying information

• Process and maintain information, and disclose and provide information

• Adjust the existing ways to comply with an y previously applicable instructions and requirements

• Train personnel to be able to r espond to a collection of information

• Search data sources

• Complete and review the collection of information

• Transmit or otherwise disclose the information

For the first 3 years following promulgation of the final rule, the major information requirements involve developing and submitting a monitoring plan , conducting the IDSE Standard Monitoring Program or the System Specific Study, and submitting the IDSE report. The information collection requirements are mandatory under Part 141, the NPDWRs, and the in formation collected is not confidential. This information will allow the systems to determine appropriate treatment requirements and will allow States/Primacy Agencies and EPA to evaluate system s’ compliance with the rule. The calculation of Stage 2 DBPR burden and costs can be found in Information Collection Request for the Stage 2 Disinfectants and Disinfection Byproducts Rule (USEPA 2005l). Exhibit 8.8 provides a summary of the results of the Information Collection Request calculations.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-28 Average Annual Burden (Hours) A verage Annual Labor Costs ($Millions) A verage Annual O&M Costs ($Millions) A verage Annual Capital Costs

($Millions) Average Annual Costs

($Millions) NTNCWSs 9,506 $0.2 $0.0 $0 $0.2 CWSs 139,802 $3.7 $9.8 $0 $13.5 States and Territories 79,221 $2.7 $0.0 $0 $2.7 Total 228,529 $6.6 $9.8 $0.0 $16.4



## Exhibit 8.8 Summary of Average Annual Burden Hours and Labor Costs

Note: Figures represent burden and cost for the 3-year Information Collection Request approval only. Detail may not add due to independent rounding.
Source: Information Collection Request for the Stage 2 Disinfectants and Disinfection Byproducts Rule (USEPA 2005l).



### The estimate of annual average burden for St age 2 DBPR for States/Primacy Agencies and systems is 228,529 hours. This estimate covers the first 3 years of the Stage 2 DBPR and includes implementation of a portion of the IDSE (small system reports are not due until the fifth year). The annual average aggregate cost estimate is $9.8 million for operation and maintenance as a purchase of service for lab work, and $6.6 million is associated with labor. EPA assumes systems affected by the Stage 2 DBPR have already purchased the basic equipmen t to record chlorine concentrations, disinfection efficacy, and other parameters to comply with the Stage 1 DBPR. Therefore, there are no capital start-up costs associated with information collection under th is rule. The annual burden per response is 4.18 hours, and the frequency for response (average res ponses per respondent) is 7.59 annually. Respondents include 57 States/Primacy Agencies and 21,549 PWSs. The estimated number of likely respondents is 7,202 per year (the product of burden hours per res ponse, frequency, and respondents may not total the annual average burden hours due to rounding).



## 8.7 Unfunded Mandates Reform Act Analysis

8.7.1 UMRA Requirements and their Impact on the Stage 2 DBPR

The UMRA of 1995, Public Law 104-4, consists of four Titles and numerous sections. Sections 201 through 205 of Title II, entitled “Regulatory Accountab ility and Reform,” are relevant to the Stage 2 DBPR and are discussed in this section. Title II, Section 201 of the UMRA, requires Federal agencies to assess the effects of their regulatory actions on State, local, and Tribal governments, and the privatesector. Under UMRA Section 202, EPA generally must prepare a written statement, including a costbenefit analysis, for proposed and final rules with “Federal mandates” that may result in expenditures by State, local, and Tribal governments, in the aggregate, or by the private sector, of $100 million or more in any 1 year. Section 203 requires the Agency to establish a small government agency plan before establishing any regulatory requirements that may significantly or uniquely affect small governments.

Section 204 of the UMRA requires the Agency to develop an effective process to permit elected officers of State, local, and Tribal governments to provide meaningful and timely input in the development of regulatory proposals that contain significant Federal intergovernmental mandates. Finally, Section 205 generally requires EPA to identif y and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-eff ective, or least burdensome alternative that achieves the objectives of the rule before promulgating a ru le for which a written statement is needed under Section 202. The provisions of Section 205 do not apply when they are inconsistent with applicable law. Moreover, Section 205 allows EPA to adopt an alterna tive other than the least costly, most cost-effective,

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-29 

### or least burdensome alternative if the Administrator publishes with the final rule an explanation why that alternative was not adopted.

EPA has determined that this rule may contain a Federal mandate that results in expenditures of $100 million or more for State, Local and Tribal governments, in the aggregate, or for the private sector in any one year. While the annualized costs fall below the $100 million threshold (as shown in Exhibit 8.9), the costs in some future years may be above the $100 million mark as PWSs make capital investments and finance these through bonds, loans, and other means. EPA’s year by year cost tables do not reflect that financed investments spread out these co sts over many years. In addition, the cost analysis does not consider that some systems may be eligible for financial assistance (e.g ., low-interest loans and grants) through such programs as EPA’s DWSRF.

The Stage 2 DBPR is promulgated pursuant to Section 1412 (b)(1)(A) of the SDWA, as amended in 1996, which directs EPA to promulgate a NPDWR for a contaminant if EPA determines that the contaminant may have an adverse effect on the health of persons, occurs in PW Ss with a frequency and at levels of public health concern, and regulation presents a meaningful opportunity for health risk reduction.

Pursuant to the requirements of Sections 202 and 205 of UMRA, EPA prepared a written statement addressing the following items:

• The authorizing legislation

• Benefit-cost analysis, including an analysis of th e extent to which the costs of State, local and Tribal governments will be paid for by the Federal government

• Estimates of future compliance costs and disproportionate budgetary effects

• Macroeconomic effects

• A summary of EPA’s consultation with Stat e, local, and Tribal governments and their concerns, including a summary of the Agency’s evaluation of those comments and concerns

• Identification and consideration of regulatory alternatives and the selection of the least costly, most cost-effective, or least burdensome alte rnative that achieves th e objectives of the rule

The legislative authority for the Stage 2 DBPR is discussed in Chapter 2. The remaining items are discussed below, but are also addressed in other ch apters of this EA, such as Chapters 4 and 7.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-30 

### 3% Discount Rate 7% Discount Rate Percent of 3% Grand Total Costs Percent of 7% Grand Total Costs Surface Water Systems Costs 41.4 $ 41.2 $ 53% 54% Ground Water Systems Costs 20.3 $ 19.2 $ 26% 25% State Costs 1.7 $ 1.7 $ 2% 2% Tribal Costs 0.4 $ 0.4 $ 1% 0% Total Public 63.8 $ 62.5 $ 81% 81% Surface Water Systems Costs 6.4 $ 6.3 $ 8% 8% Ground Water Systems Costs 8.5 $ 8.0 $ 11% 10% Total Private 15.0 $ 14.3 $ 19% 19%
GRAND TOTAL 78.8 $ 76.8 $ 100% 100% 

## Exhibit 8.9 Public and Private Costs for the Stage 2 DBPR (Annualized at 3 and 7 Percent, $Millions)

Note: Detail may not add due to independent rounding. Source: Derived from Exhibit 7.5 (costs ) and Exhibit 3.2 (public/private breakout).



### 8.7.2 Social Benefits and Costs

The social benefits are those that accrue prim arily to the public through increased protection from cancer and reproductive and developmental effects. To assign a monetary value to the reductions in fatal and non-fatal cancer cases, EPA estimated the current and future annual cases of bladder cancer from all causes, the number of cases attributed to DBP occurre nce and exposure, and the reduction in future cases corresponding to anticipated reductions in DBP o ccurrence and exposure due to the Stage 2 DBPR. Mortalities from cancer were valued using a value of st atistical life estimate consistent with EPA’s policy. EPA also used two alternate (but equally valid) es timates of willingness-to-pay to avoid non-fatal bladder cancer (one based on avoiding a case of curable lymphoma and the other based on avoiding a case of chronic bronchitis).

Chapter 6 presents the benefits analysis, whic h includes both qualitative and monetized benefits of the rule. Although EPA estimated the number of a voided incidence of fetal loss, it is only presented as an illustrative example. The potential nonquantifiable benefits may include reproductive health effects, developmental health effects, reduction in other can cers, and benefits from reduction of other DBPs, cooccurring contaminants, or emerging contaminants. In addition, certain non-hea lth-related benefits may exist, such as perceptions of drinking wa ter quality, ecological, and other unknown effects.

Measuring the social costs of the rule requires identifying affected entities by ownership (public or private), considering regulatory alternatives, cal culating regulatory compliance costs, and estimating any disproportionate impacts. Chapter 7 of this doc ument details the cost analysis performed for the Stage 2 DBPR. The likely compliance scenario is expected to result in total annualized costs of approximately 78.8 million using a 3-percent discount rate (or 76.8 million using a 7-percent discount rate). Exhibit 8.10 summarizes the annualized cost s and benefits for each regulatory alternative.7 

### “...[T]he agency shall prepare a written statement contai ning. . . (3) estimates by the agency, if and to the extent that the agency determines that accurate estimat es are reasonably feasible, of. . . (B) any disproportionate budgetary effects of the Federal mandate upon any particular re gions of the nation or partic ular State, local, or Tribal government, urban or rural or other types of communitie s, or particular segments of the private sector...” Final Economic Analysis for the Stage 2 DBPR December 2005 8-31 

## Exhibit 8.10 Total Annualized Benefits and Costs of Regulatory Alternatives ($Millions, 2003$)

Various Federal programs exist to provide fi nancial assistance to State, local, and Tribal governments in complying with this rule. The Fe deral government provides funding to States that have primary enforcement responsibility for their drinking water programs through the Public Water Systems Supervision (PWSS) Grants Program. States may use these funds to develop primacy programs or to contract with other State agencies to assist in the development or implementation of their primacy programs. However, they may not use these funds to contract with regulated entities (i.e., water systems). States may use PWSS Grants to set up and administer a State program that includes such activities as public education, testing, training, technical assistan ce, development and administration of a remediation grant and loan or incentive program (excluding the act ual grant or loan funds), or other regulatory or nonregulatory measures.

Additional funding is available from other progr ams administered by EPA or other Federal agencies. These include EPA’s DWSRF, the U.S. Depa rtment of Agriculture’s Rural Utilities’ Loan and Grant Program, and the Department of Housing and Urban Development’s Community Development Block Grant (CDBG) Program. Refer to section 8.3.3 for a more detailed discussion on funding.

8.7.3 Disproportionate Budgetary Effects

UMRA is intended to reduce the burden on Stat e, local, and Tribal governments of Federal mandates that are not accompanied by adequate Fe deral funding. Section 202 of UMRA requires an analysis of possible disproportionate budgetary effect s of certain classes of rules, in which Stage 2 DBPR falls. 7 

### Such an analysis is required if EPA determ ines that accurate estimates are reasonably feasible. The specific concern is disproportionate budgetary effects of the Stage 2 DBPR upon certain areas or industries:

• Any particular regions of the United States Regulatory Alternative Mean Annualized Benefits, Lymphoma (3%) Mean Annualized Benefits, Lymphoma (7%) Mean Annualized Benefits, Bronchitis (3%) Mean Annualized Benefits, Bronchitis (7%) Mean Annualized Costs (3%) Mean Annualized Costs (7%) Preferred Alternative 1,530.79 $ 1,246.50 $ 762.76 $ 620.66 $ 78.80 $ 76.81 $ Alternative A1 1 

### 1,376.60 $ 1,126.39 $ 685.87 $ 560.80 $ 254.14 $ 241.81 $ Alternative A2 5,167.40 $ 4,227.19 $ 2,574.59 $ 2,104.60 $ 421.71 $ 406.45 $ Alternative A3 7,129.61 $ 5,832.37 $ 3,552.24 $ 2,903.78 $ 634.20 $ 613.07 $

Source: Benefits from Appendix F. Costs from Appendix J: For the Preferred Alternative, see Exhibit J.2as for 3% and J.2aw for 7%. For Alternative 1, see Exhibit J.3i for 3% and J.3m for 7%. For Alternative 2, see Exhibit J.4i for 3% and J.4m for 7%. For Alter native 3, see Exhibit J.5i for 3% and J.5m for 7%. Footnote 1: Alternative 1 appears to have fewer benefits (MILYs) than the Preferred Alternative because it does not incorporat e the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentiall y associated cancer cases), a requirement that is included only in Alternative 1.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-32 

### • Any particular State, local, or Tribal government

• Urban or rural or other types of communities

• Any segment of the private sector

This EA has considered how best to interp ret and comply with these requirements. The remainder of this section describes ways to consider these requirements, whether meaningful data can be provided, and whether accurate estimates are possible. The general conclusion for this section, however, is that there are little basis and insufficient data to make accurate estimates of budgetary impacts that differ among groups, governments, types of communities, or segments of the private sector.

Most of the following analyses begin with nationa l data and then disaggregate those data, when possible, using other measures. Because the data a nd estimates are national in scope, parameters tend to be merely proportional extensions based on a ch aracteristic, not “bottom-up” estimates of actual differences among types of systems, communities, or economic sectors. Thus, the analyses may not reveal true differences attributable to the impact s of the rule alternatives on various regions. Local conditions at each regulated entity will drive the actua l cost impacts of the rule (e.g., areas with high levels of DBP precursors).

When considering disproportionate impacts, it is necessary to consider whom the Stage 2 DBPR affects. The rule, by definition, covers some communities and a segment of the private sector. Most CWSs and NTNCWSs that add a primary or residual disinfectant other than UV, or that deliver water than has been treated with a disi nfectant other than UV, will incur so me costs. In an economic sense, differences between communities and utilities do not disadvantage one group over the other because the systems are not in a national market that allows for direct competition for customers. In general, those systems are better considered local natural monopolies.

Regions

EPA determined that the Stage 2 DBPR may ha ve disproportionate budgetary effects on certain geographic regions. Higher TOC levels in source wa ter present special challenges to some areas of the country (for detailed analysis, see section 3.5.2 and Appendix B). Ground water systems in Florida, in particular, will be heavily impacted by the Stage 2 DBPR due to the high levels of TOC in their source water and the large number of ground water systems locat ed in the State. Other areas with high levels of precursors, such as TOC or bromide, will also be a dversely affected. However, those systems with high precursor and DBP levels are also the ones most lik ely to receive the greatest benefit from the rule.

State, Local, or Tribal Governments

There is no expectation that there will be disp roportionate budgetary effects upon State, local, or Tribal governments. Costs are expected to be propor tional to the risk posed by DBPs, even if unevenly distributed among systems and perhaps types of system s. Furthermore, there are no accurate estimates to address the differing budgetary effects of the Stage 2 DBPR on State, local or Tribal governments.

There are few data available that bear on this issu e. Exhibit 8.8 breaks out national-level costs for PWSs, Tribal costs, and State costs, but only allocat es costs to these categories ra ther than revealing any disproportionate impacts on the budgets of these groups . Exhibits 8.11a and 8.11b imply that State impacts may be larger to the extent that States contain a greater proportion of small disinfecting systems (particularly Texas, Florida, and New York).

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-33 297

268

359 1,435

746

112 1,368 107 366

6,286 5,547

1,976

822 1,038 1,834

442 373

311 231 178 256

364

132 605

679

845

979

696 1,035 598 1,828 658

6,168 879 1,823 725 364
905

699 1,962 4,231

975 180

535 476

90

157 479 22

516

Number of Small Disinfecting CWSs and NTNCWSs

800 to 1,999 (15) 400 to 799 (14) 0 to 399 (24) D.C. 0

American Samoa
Guam
N. Mariana Islands
Puerto Rico 21

9

127

441 Virgin Islands 350 2,000 or more (4)

Palau 18 297

268

359 1,435

746

112 1,368 107 366

6,286 5,547

1,976

822 1,038 1,834

442 373

311 231 178 256

364

132 605

679

845

979

696 1,035 598 1,828 658

6,168 879 1,823 725 364
905

699 1,962 4,231

975 180

535 476

90

157 479 22

516

Number of Small Disinfecting CWSs and NTNCWSs

800 to 1,999 (15) 400 to 799 (14) 0 to 399 (24) D.C. 0

American Samoa
Guam
N. Mariana Islands
Puerto Rico 21

9

127

441 Virgin Islands 350 2,000 or more (4)

Palau 18 

## Exhibit 8.11a Number of Small Disinfecting Systems by State

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-34 1%

<1%

1% 3%

1%

<1% <1% 1%

11% 10%

4%

2% 2% 3%

1% 1% 1% <1% <1% <1%

1%

<1% 1%

1%

2%

2%

1% 2% 1% 3% 1%

11% 2% 3% 1% 1% 2%

1% 4% 8%

2% <1%

1% 1%
<1 % <1% 1% <1 %

1%

Percentages of Small Disinfecting CWSs and NTNCWSs
9% or more (3) 4-8% (4) 0-3% (50) D.C. 0%

American Samoa
Guam
N. Mariana Islands
Puerto Rico <1%

<1%

<1%

1% Virgin Islands 1% 2%

Palau <1% 1%

<1%

1% 3%

1%

<1% <1% 1%

11% 10%

4%

2% 2% 3%

1% 1% 1% <1% <1% <1%

1%

<1% 1%

1%

2%

2%

1% 2% 1% 3% 1%

11% 2% 3% 1% 1% 2%

1% 4% 8%

2% <1%

1% 1%
<1 % <1% 1% <1 %

1%

Percentages of Small Disinfecting CWSs and NTNCWSs
9% or more (3) 4-8% (4) 0-3% (50) D.C. 0%

American Samoa
Guam
N. Mariana Islands
Puerto Rico <1%

<1%

<1%

1% Virgin Islands 1% 2%

Palau <1% 

## Exhibit 8.11b Percent of Sma ll Disinfecting Systems by State

Source: Derived from SDWIS 4 th 

### Quarter Year 2003 Freeze data (USEPA 2003t). Percent of ground water systems disinfecting derived from Ground Water Disinfection and Protective Practices in the U.S. (USEPA 1996a).



### Urban and Rural

In addition to the analyses summarized above , EPA assessed an additional area of potential disproportionate impact: the impacts on urban versus rural communities. The costs presented in Exhibit 8.12 demonstrate that there is no such disproportionate impact. For this analysis, small water systems (i.e., systems serving fewer than 10,000 people) were assumed to represent rural areas since they are generally located in more rural communities. Larg e water systems (i.e., systems serving at least 10,000 people) were assumed to represent urban areas since most urban areas are served by a few large systems. As seen by the figures in Exhibit 8.12a and 8.12b, the costs of the rule are similar for small and large systems. However, rural systems may lack the same economies of scale and technical, managerial, and financial capacity as urban systems, making it more challenging for them to meet rule requirements. These issues are discussed in greater detail in section 8.5.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-35 

### Total Annual Cost to Systems Serving < 10,000 People ($ Millions) Total Annual Cost to Systems Serving > 10,000 People ($ Millions) Source Water Category 3 Percent 7 Percent 3 Percent 7 Percent Surface Water Systems 11.3 $ 10.7 $ 35.6 $ 36.1 $ Ground Water Systems 16.1 $ 15.1 $ 11.0 $ 10.4 $ Tribal Systems 0.3 $ 0.3 $ 0.0 $ 0.0 $ Total 27.7 $ 26.1 $ 46.6 $ 46.5 $

Total Annual Cost to Systems Serving < 10,000 People ($ Millions) Total Annual Cost to Systems Serving > 10,000 People ($ Millions) Source Water Category 3 Percent 7 Percent 3 Percent 7 Percent Surface Water Systems 0.9 $ 0.8 $ 0.1 $ 0.1 $ Ground Water Systems 1.8 $ 1.6 $ 0.0 $ 0.0 $ Tribal Systems 0.1 $ 0.0 $ - $ - $ Total 2.7 $ 2.4 $ 0.1 $ 0.1 $ 

## Exhibit 8.12a Total Annualized Cost of Compliance for CWSs (3 and 7 Percent Discount Rates) ($Millions)

Exhibit 8.12b Annualized Cost of Compliance for NTNCWSs (3 and 7 Percent Discount Rates) ($Millions)

Note: Detail may not add due to independent rounding (some data are rounded to zero if less than $0.05 million).
Source: Derived from Exhibit 7.5; for th is exhibit, Tribal system costs are appor tioned by the percent of Tribal systems in each size category and source water type (see Exhibit 8.13).



### Segments of the Private Sector

EPA performed an impact analysis for public and private systems (Exhibit 8.9 and 8.13). The percent public and private systems shown in Exhi bit 8.13 indicate that publically owned CWSs are expected to incur greater costs than privately ow ned CWSs. However, costs to individual public and private water systems will not differ substantially.

Discount rates for capital costs of private system s, as presented in Exhibit 7.9, are approximately 1 percent higher than public systems. Based on these discount rates, a private system may owe $7,000 more than a public system for a $1 million loan. Sin ce the water industry is regulated, rates of public and private systems are monitored and are not likely to fl uctuate substantially over time. However, the rates can be adjusted to help a system qualify for a low-co st bond. Overall, the Stage 2 DBPR is not expected to have a disproportionate impact on public and private systems.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-36 % Public % Private Total Cost for Public Systems (3%) Total Cost for Private Systems (3%) Total Cost for Public Systems (7%) Total Cost for Private Systems (7%)

<100 45.7% 54.3% 0.08 $ 0.09 $ 0.08 $ 0.09 $ 100-499 62.6% 37.4% 0.40 $ 0.24 $ 0.37 $ 0.22 $ 500-999 77.0% 23.0% 0.40 $ 0.12 $ 0.42 $ 0.13 $ 1,000-3,299 84.7% 15.3% 2.58 $ 0.47 $ 2.44 $ 0.44 $ 3,300-9,999 90.7% 9.3% 6.31 $ 0.65 $ 5.92 $ 0.61 $ 10,000-49,999 90.1% 9.9% 7.53 $ 0.83 $ 7.83 $ 0.86 $ 50,000-99,999 85.8% 14.2% 4.93 $ 0.82 $ 4.95 $ 0.82 $ 100,000-999,999 87.1% 12.9% 12.45 $ 1.84 $ 12.60 $ 1.86 $ > 1 Million 87.3% 12.7% 6.29 $ 0.91 $ 6.23 $ 0.91 $ Subtotal 40.98 $ 5.98 $ 40.84 $ 5.94 $

<100 45.4% 54.6% 0.05 $ 0.06 $ 0.04 $ 0.05 $ 100-499 48.5% 51.5% 0.13 $ 0.13 $ 0.11 $ 0.12 $ 500-999 45.8% 54.2% 0.05 $ 0.06 $ 0.05 $ 0.06 $ 1,000-3,299 48.4% 51.6% 0.12 $ 0.12 $ 0.10 $ 0.11 $ 3,300-9,999 58.8% 41.2% 0.08 $ 0.06 $ 0.07 $ 0.05 $ 10,000-49,999 56.2% 43.8% 0.02 $ 0.02 $ 0.02 $ 0.02 $ 50,000-99,999 0.0% 0.0% - $ - $ - $ - $ 100,000-999,999 100.0% 0.0% 0.03 $ - $ 0.03 $ - $ > 1 Million 0.0% 0.0% - $ - $ - $ - $ Subtotal 0.48 $ 0.45 $ 0.44 $ 0.41 $

<100 12.1% 87.9% 0.13 $ 0.97 $ 0.12 $ 0.90 $ 100-499 39.0% 61.0% 1.50 $ 2.34 $ 1.37 $ 2.15 $ 500-999 65.7% 34.3% 1.68 $ 0.88 $ 1.55 $ 0.81 $ 1,000-3,299 78.3% 21.7% 3.23 $ 0.89 $ 3.03 $ 0.84 $ 3,300-9,999 84.1% 15.9% 3.96 $ 0.75 $ 3.87 $ 0.73 $ 10,000-49,999 84.4% 15.6% 5.02 $ 0.93 $ 4.75 $ 0.88 $ 50,000-99,999 80.0% 20.0% 1.21 $ 0.30 $ 1.16 $ 0.29 $ 100,000-999,999 85.6% 14.4% 2.69 $ 0.45 $ 2.57 $ 0.43 $ > 1 Million 100.0% 0.0% 0.42 $ - $ 0.39 $ - $ Subtotal 19.85 $ 7.52 $ 18.81 $ 7.03 $

<100 20.4% 79.6% 0.10 $ 0.39 $ 0.09 $ 0.36 $ 100-499 48.9% 51.1% 0.32 $ 0.34 $ 0.29 $ 0.31 $ 500-999 60.7% 39.3% 0.25 $ 0.16 $ 0.22 $ 0.14 $ 1,000-3,299 50.8% 49.2% 0.10 $ 0.10 $ 0.09 $ 0.09 $ 3,300-9,999 44.4% 55.6% 0.02 $ 0.02 $ 0.01 $ 0.02 $ 10,000-49,999 84.2% 15.8% 0.01 $ 0.00 $ 0.01 $ 0.00 $ 50,000-99,999 100.0% 0.0% 0.00 $ - $ 0.00 $ - $ 100,000-999,999 0.0% 100.0% - $ 0.00 $ - $ 0.00 $ > 1 Million 0.0% 0.0% - $ - $ - $ - $ Subtotal 0.81 $ 1.02 $ 0.73 $ 0.92 $ Grand Total 62.12 $ 14.97 $ 60.81 $ 14.30 $ Surface Water CWSs

Surface Water NTNCWSs

Ground Water CWSs

Ground Water NTNCWSs 

## Exhibit 8.13 Percentages and Costs by Public and Private Sector (Costs Annualized at 3 and 7 Percent)

Source: Derived from Exhibit 3.2 ( public/private) and Exhibit 7.5 (costs).

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-37 

### 8.7.4 Macroeconomic Effects

Under UMRA Section 202, EPA is required to estimate the potential macroeconomic effects of the regulation. These include effects on productivity , economic growth, full employment, and creation of Gross Domestic Product (GDP) (USEPA 2000j). M acroeconomic effects tend to be measurable in nationwide econometric models only if the economic im pact of the regulation reaches 0.25 percent to 0.5 percent of GDP. In 2003, real GDP was $10,321 billion (U.S. Department of Commerce BEA 2004b); thus, a rule would have to cost at least $26 billion annually to have a measurable effect. A regulation with a smaller aggregate effect is unlikely to have any measurable impact, unless it is highly focused on a particular geographic region or economic sector. Th e Stage 2 DBPR should not have a measurable effect on the national economy; the total annualized costs for the rule range from $78.8 million to $76.8 million using a 3 and 7 percent discount rate, respectivel y. Using these annualized figures as a measure, the annual cost of the Stage 2 DBPR is an insignifican t fraction of a $26 billion annual cost that would be considered a measurable macroeconomic impact. Thus, annualized Stage 2 DBPR costs measured as a percentage of the national GDP will only decline over time as GDP grows.

8.7.5 Consultation with Small Governments

Before the Agency establishes any regulatory requirements that may significantly or uniquely affect small governments, including Tribal governme nts, it must develop, under Section 203 of the UMRA, a small government agency plan. The plan must provide notice of rule requirements to potentially affected small governments, enabling their officials to have meaningful and timely input in the development of EPA regulatory proposals with signifi cant Federal intergovernmental mandates. The plan must also inform, educate, and advise sma ll governments on compliance with the regulatory requirements.

EPA has determined that the Stage 2 DBPR does not contain regulatory requirements that would significantly or uniquely affect a substantia l number of small governments (see section 8.2). Nevertheless, EPA consulted with small governments to address impacts of the rule that might uniquely affect them. As described in section 8.2.2, a vari ety of stakeholders, including small governments, had the opportunity for timely and meaningful particip ation in the regulatory development process through the SBREFA process, public stakeholder meetings, and Tribal meetings.

8.7.6 Consultation with State, Local, and Tribal Governments

Section 204 of the UMRA requires the Agency to develop an effective process to permit elected officers of State, local, and Tribal governments (or their designated authorized employees) to provide meaningful and timely input in the development of regulatory proposals that contain significant Federal intergovernmental mandates. Consistent with these provisions, EPA held consultations with the governmental entities affected by this rule prior to proposal, as described in sections 8.2.2 and 8.8.

Representatives from State, local, and Tribal gove rnments were involved in the development of the Agreement in Principle, which was created ear ly in the regulatory process. EPA provided the Association of State Drinking Water Administrators (ASDWA) with an opportunity to comment before officially proposing the Stage 2 DBPR. EPA accepted comments from ASDWA and other FACA members, such as the National League of Cities (NLC), on a draft of the Stage 2 DBPR posted on their Web site.

In addition to these efforts, EPA will educate, inform, and advise small systems, including those run by small governments, about the Stage 2 DBPR requirements. The Agency is developing plainEnglish guidance that will explain what actions a small entity must take to comply with the rule. Also,

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-38 

### the Agency has developed fact sheets that conci sely describe various aspects and requirements of the Stage 2 DBPR. Additional details on Tribal invol vement in the rulemaking process can be found in section 8.8.

8.7.7 Regulatory Alternatives Considered

As required under Section 205 of UMRA, EPA cons idered several regulatory alternatives and numerous approaches to ensure safe levels of DBPs throughout a system’s entire distribution system. Chapter 4 provides a detailed discussion of these alte rnatives. EPA chose the Preferred Regulatory Alternative because it provided substan tial benefits at an acceptable level of costs. In addition, the FACA Committee recommended the Preferred Regulatory A lternative in the Stage 2 M-DBP Agreement in Principle.

8.7.8 Impacts on Small Governments

In developing this rule, EPA consulted with small governments pursuant to Section 203 of UMRA to address impacts of regulatory requirements in the rule that might significantly or uniquely affect small governments. In preparation for th e Stage 2 DBPR, EPA conducted an analysis on small government impacts and included small government o fficials or their designated representatives in the rulemaking process. A variety of stakeholders, including small governments, had the opportunity for timely and meaningful participation in the regul atory development process through the SBREFA process, public stakeholder meetings, and Tribal meetings. Representatives of small governments took part in the SBREFA process for this rulemaking and attended pub lic stakeholder meetings. Through participation and exchange in the SBREFA process and various mee tings, EPA notified some potentially affected small governments of requirements under consideration.

As previously stated, EPA has determined that this rule does not contain regulatory requirements that would significantly or uniquely affect small govern ments. As described in section 8.2, EPA certified that the Stage 2 DBPR will not have significant economic impact on a substantial number of small entities. As shown in Exhibit 8.12, estimated annua l expenditures per small system for the Stage 2 DBPR are $27.7 for CWSs and $2.7 for NTNCWSs (at a 3 percent discount rate).



## 8.8 Indian Tribal Governments

Executive Order 13175, entitled “Consultation and Coordination with Indian Tribal Governments” (65 FR 67249; November 9, 2000), re quires EPA to develop “an accountable process to ensure meaningful and timely input by Tribal officials in the development of regulatory policies that have Tribal implications.” The Executive Order defines “ policies that have Tribal implications” to include regulations that have “substantial direct effects on one or more Indian Tribes, on the relationship between the Federal government and the Indian Tribes, or on the distribution of power and responsibilities between the Federal government and Indian Tribes.”

Under Executive Order 13175, EPA may not issue a regulation that has Tribal implications, that imposes substantial direct compliance costs, and th at is not required by statute, unless the Federal government provides the funds necessary to pay th e direct compliance costs incurred by Tribal governments, or EPA consults with Tribal offici als early in the process of developing the proposed regulation and develops a Tribal summary impact st atement. EPA has concluded that the Stage 2 DBPR may have Tribal implications, because it may have substantial direct compliance costs on Tribal governments, as specified in Executive Order 13175.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-39 

### Total Tribal costs are estimated to be appr oximately $391,773 per year (at a 3 percent discount rate), distributed across 755 water systems owned by Tribes. The costs for individual systems depend on system size and source water type. Of the 755 Tribal water systems that may be affected by the Stage 2 DBPR, 654 use ground water as a source and 101 systems use surface water or GWUDI. Since the majority of Tribal water systems are ground water systems serving fewer than 500 people, only 15.6 percent of all Tribal water systems will likely have to conduct an IDSE. As a result, the Stage 2 DBPR is most likely to have an impact on Tribes using surface water or GWUDI serving more than 500 people. Accordingly, EPA provides the following Tribal su mmary impact statement, as required by Section 5(b)of Executive Order 13175. The results of the analysis conducted for the Tribal summary impact statement are presented in Exhibit 8.14

EPA consulted with Tribal officials early in th e development of the Stage 2 DBPR to permit them to have meaningful and timely input. Tribes were able to have long-term input in the rule by participating in the Federal Advisory Committee. During the Las Vegas EPA/Inter-Tribal Council of Arizona in February 1999, a number of Tribal representatives requ ested that the All Indian Pueblo Council (AIPC) representative be the FACA representative for Federa l Tribes, given his knowledge of drinking water systems. Approximately 20 Tribes are associated with the AIPC.

In addition to obtaining FACA Tribal input, EPA presented the Stage 2 DBPR at three conferences: the 16 th 

### Annual Consumer Conference of the Nati onal Indian Health Board, the National Tribal Environmental Council’s Annual Conference in April 2000, and the EPA/Inter-Tribal Council of Arizona, Inc. Tribal consultation meeting. Over 900 attendees representing Tribes from across the country attended the National Indian Health Boar d’s Consumer Conference, and representatives from over 100 Tribes attended the annual conference of the National Tribal Environmental Council. Finally, representatives from 15 Tribes participated at the EPA/I nter-Tribal Council of Arizona meeting. At the first two conferences, an EPA representative conduc ted two workshops on their drinking water program and upcoming regulations, including the Stage 2 DBPR. The presentation materials and meeting summary were sent to over 500 Tribes and Tribal organizations.

EPA distributed fact sheets describing the re quirements of the Stage 2 DBPR and requested Tribal input at an annual EPA Tribal meeting in San Francisco and a Native American Water Works Association meeting in Scottsdale, Arizona. EPA al so worked through its Regional Indian Coordinators and the National Tribal Operations Committee to ma il fact sheets on the Stage 2 DBPR to all of the Federally recognized Tribes in November 2000.

After reviewing the fact sheets, a few Tribes re quested more information and expressed concern about having to implement too many regulations. Th ey were also concerned about infrastructure costs and the lack of funding attached to the rule. In response to a Tribal representative’s comments, EPA explained the health protection benefits associated with the Stage 2 DBPR, which some members of the Tribal Caucus also noted. EPA directed Tribes to the Agreement in Principle on the EPA Web site for more information.

On January 24, 2002, EPA held a teleconference for Tribal representatives as another step in Tribal consultation. Prior to the teleconference, EPA sent invitations to all Federally-recognized Tribes, along with fact sheets explaining the rule. Twel ve Tribal representatives and four regional Tribal Program Coordinators attended the teleconference, requested further explanation of the rule, and expressed concerns about funding sources. Tribes al so called EPA after the teleconference to provide additional feedback.

In the spirit of Executive Order 13175, and c onsistent with EPA’s policy to promote communications between EPA and Tribal governme nts, EPA specifically solicited comment on the propose rule from Tribal officials. As required by section 7(a) of Executive Order 13175, when EPA sent the draft of the final rule to Office of Manageme nt and Budget (OMB) for review pursuant to Executive

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-40 

### Order 12866, EPA included a certification from its tribal consultation official stating that EPA had met the Executive Order’s requirements in a meaningful and timely manner.Final Economic Analysis for the Stage 2 DBPRDecember 20058-41Percent Number Percent Number Percent Number Percent Number Percent NumberA B C = B * A D E = D * A F G = F * A H I = H * A J K = J * A L M = A * LPrimarily Surface Water CWSs10019 100% 19 62% 12 0% 0 0% 0 62% 12 160$ 3,038$100-499 46 100% 46 62% 29 0% 0 0% 0 62% 29 292$ 13,423$500-999 13 100% 13 94% 12 0% 0 1% 0 100% 13 353$ 4,595$1,000-3,299 14 100% 14 94% 13 0% 0 1% 0 100% 14 1,177$ 16,476$3,300-9,999 5 100% 5 92% 5 66% 3 3% 100% 5 3,409$ 17,043$10,000-49,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$50,000-99,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$100,000-999,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$>1 Million0 NA NA NA NA NA NA NA NA NA NA -$ -$National Subtotal97 97 71 3 0 73 54,575$Primarily Disinfecting Ground Water CWSs100149 100% 149 4% 6 0% 0 0% 0 4% 6 139$ 20,712$100-499 233 100% 233 4% 10 0% 0 0% 0 4% 10 391$ 91,055$500-999 78 100% 78 18% 14 56% 44 0% 0 100% 78 639$ 49,813$1,000-3,299 71 100% 71 18% 13 56% 40 0% 0 100% 71 850$ 60,380$3,300-9,999 21 100% 21 18% 4 56% 12 0% 0 100% 21 2,139$ 44,916$10,000-49,999 4 100% 4 18% 1 57% 2 0% 0 100% 4 4,871$ 19,483$50,000-99,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$100,000-999,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$>1 Million0 NA NA NA NA NA NA NA NA NA NA -$ -$National Subtotal556 556 47 97 0 0 286,358$Systems ConductingStage 2 MonitoringPlansSystem Size/TypeNumber ofTribalSystemsAffected bythe Stage 2DBPRMeanAnnualizedCost perSystemEstimatedTotal TribalCostsSystems ConductingImplementationActivitiesSystems ConductingIDSE MonitoringSystems ConductingAdditional RoutineMonitoringSystems ConductingSignificant ExcursionActivitiesExhibit 8.14 Annual Cost of Compliance for Tribal Systems by System Type and Size (Annualized at 3 Percent)Final Economic Analysis for the Stage 2 DBPRDecember 20058-42Percent Number Percent Number Percent Number Percent Number Percent NumberA B C = B * A D E = D * A F G = F * A H I = H * A J K = J * A L M = A * LPrimarily Surface Water NTNCWSs1002 100% 2 0% 0 0% 0 0% 0 0% 0 448$ 897$100-499 1 100% 1 0% 0 0% 0 0% 0 0% 0 818$ 818$500-999 0 NA NA NA NA NA NA NA NA NA NA -$ -$1,000-3,299 1 100% 1 0% 0 0% 0 0% 0 0% 0 2,590$ 2,590$3,300-9,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$10,000-49,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$50,000-99,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$100,000-999,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$>1 Million0 NA NA NA NA NA NA NA NA NA NA -$ -$National Subtotal4 4 0 0 0 0 4,304$Primarily Disinfecting Ground Water NTNCWSs10031 100% 31 0% 0 0% 0 0% 0 0% 0 199$ 6,161$100-499 28 100% 28 0% 0 0% 0 0% 0 0% 0 312$ 8,746$500-999 13 100% 13 0% 0 100% 13 0% 0 0% 0 692$ 8,994$1,000-3,299 24 100% 24 0% 0 100% 24 0% 0 0% 0 808$ 19,397$3,300-9,999 2 100% 2 0% 0 100% 2 0% 0 0% 0 1,619$ 3,238$10,000-49,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$50,000-99,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$100,000-999,999 0 NA NA NA NA NA NA NA NA NA NA -$ -$>1 Million0 NA NA NA NA NA NA NA NA NA NA -$ -$National Subtotal98 98 0 39 0 0 46,536$TOTALS755 755 118 140 0 73391,773$System Size/TypeNumber ofTribalSystemsAffected bythe Stage 2DBPRSystems ConductingImplementationActivitiesSystems ConductingIDSE MonitoringEstimatedTotal TribalCostsSystems ConductingAdditional RoutineMonitoringSystems ConductingSignificant ExcursionActivitiesSystems ConductingStage 2 MonitoringPlansMeanAnnualizedCost perSystemExhibit 8.14 Annual Cost of Compliance for Tribal Systems by System Type and Size (Annualized at 3 Percent)(Continued)Sources: (A) Number of Indian Landsfrom SDWIS 4th Quarter FY2003 data.(B, D, F, and H) Derived from Exhibit H.12.(J) Mean costs are total annualized costs (at3 percent) (Exhibits J.2ba,J.2be, J.2bi, J.2bm) divided bythe number of primarily ground or primarilysurface water CWSs or NTNCWSs inthe size category (Exhibit 3.2).

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-43 

## 8.9 Impacts on Sensitive Subpopulations

EPA’s Office of Water has historically consid ered risks to sensitive subpopulations (including fetuses, infants, and children) when establishi ng drinking water assessments, advisories and other guidance, and standards (USEPA 1989, USEPA 1991a). The disinfection of public drinking water supplies to prevent waterborne disease is the most successful public health program in U.S. history (USEPA 1991a). However, numerous DBPs that resu lt from chemical disinfection may have potential health risks. Thus, maximizing health protection for sensitive subpopulations requires balancing risks to achieve the recognized benefits of controlling water borne pathogens while minimizing risk of potential DBP toxicity. Experience shows that waterborne disease from pathogens in drinking water is a major concern for children and other subgroups (e.g., the elderly, immunocompromised, and pregnant women) because of their greater vulnerabilities (Gerba et al . 1996). EPA believes that, based on animal studies, DBPs may also potentially pose risks to fetuses and pregnant women (USEPA 1998f). In addition, because the elderly population (age 65 and above) is na turally at a higher risk of developing bladder cancer, their health risks may further increase as a result of long-term DBP exposure (National Cancer Institute 2002).

In developing this rule, risks to sensitive s ubpopulations, including children, were taken into account in the assessments of disinfectants and DBPs (see sections 6.2.1 and 6.3). For each of the DBPs included in the Stage 2 DBPR, the MCLGs are derived using the most sensitive endpoint among all available data and an intraspecies uncertainty f actor of 10, which accounts for human variability, including sensitive subpopulations like children. The Ag ency has evaluated alternative regulatory options and selected the one that balances cost with significant benefits, including those for sensitive subpopulations. The Stage 2 DBPR will result in a reduction in cancer risk and a potential reduction in reproductive and developmental risk to fetuses and pr egnant women. It should be noted that the LT2ESWTR, which accompanies this rule, reduces pat hogens in drinking water and further protects sensitive subpopulations.

SDWA identifies pregnant women as a sensitive subpopulation. Epidemiological and toxicological research suggests a potential associ ation between exposure to DBPs and adverse reproductive and developmental health effects such as spontaneous abortion, stillbirth, neural tube defects, cardiovascular effects, and low birth weight. Stage 2 DBPR will help to protect pregnant women and their fetuses from adverse health effects that may be caused by exposure to elevated DBP levels. In this respect, any benefits derived from implemen tation of Stage 2 DBPR provisions should have a positive health impact on this sensitive subpopulation. Research outlining the potential health benefits of the Stage 2 DBPR to both sensitive subpopulations and the general public is discussed in greater detail in Chapter 6 of this EA.

8.9.1 Protecting Children from Environm ental Health Risks and Safety Risks

Executive Order 13045 (62 FR 19885 April 23, 1997) a pplies to any rule initiated after April 21, 1998, that (1) is determined to be “economica lly significant” as defined under Executive Order 12866; and (2) concerns an environmental, health, or safet y risk that EPA has reason to believe may have a disproportionate effect on children. If the regulator y action meets both criteria, EPA must evaluate the environmental health or safety effects of the pla nned rule on children, and explain why the planned regulation is preferable to other potentially effec tive and reasonably feasible alternatives considered by the Agency.

The Stage 2 DBPR is not subject to the Executiv e Order because it is not economically significant as defined in Executive Order 12866. EPA has consisten tly and explicitly considered risks to infants and children in all assessments developed for this rule making and presents the environmental health and safety effects of DBPs on children in sections 6.2.1 and 6.5.1. For each of the DBPs included in the Stage

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-44 

### 2 DBPR, EPA has compiled analyses of the available data used for deriving the maximum contaminant level goal (MCLG) to determine if these va lues are protective for fetuses and children.

The Agency concluded that the Stage 2 DBPR w ill result in greater risk reduction for children than for the general population. The MCLGs of all DB Ps in the rule help protect fetuses, infants, and children from potential adverse developmental/reproductive effects.



## 8.10 Environmental Justice

Executive Order 12898 (59 FR 7629) establishes a Federal policy for incorporating environmental justice into Federal agency missi ons by directing agencies to identify and address disproportionately high and adverse human health or e nvironmental effects of its programs, policies, and activities on minority and low-income populations. Th e Agency has considered environmental-justicerelated issues concerning the potential impacts of this action and consulted with minority and low-income stakeholders.

Two aspects of the Stage 2 DBPR comply with th e order that requires the Agency to consider environmental justice issues in the rulemaking and to consult with stakeholders representing a variety of economic and ethnic backgrounds. These include: (1) the overall nature of the rule, and (2) the convening of a stakeholder meeting specifically to address environmental justice issues.

The Agency has built on the efforts conducted dur ing the Stage 1 DBPR development to comply with Executive Order 12898. On March 12, 1998, EPA held a stakeholder meeting to address various components of pending drinking wate r regulations and how they might impact sensitive subpopulations, minority populations, and low-income populations. Th is meeting was a continuation of stakeholder meetings that started in 1995 to obtain input on the Agency’s Drinking Water Programs. Topics discussed included treatment techniques, costs and benef its, data quality, health effects, and the regulatory process. Participants were national, State, Tr ibal, municipal, and individual stakeholders. EPA conducted the meeting by video conference call betw een 11 cities. The major objectives for the March 12, 1998, meeting included the following:

• To solicit ideas from stakeholders on known issues concerning current drinking water regulatory efforts.

• To identify key areas of concern to stakeholders.

• To receive suggestions from stakeholders concerning ways to increase representation of communities in the Office of Ground Water and Drinking Water (OGWDW) regulatory efforts.

In addition, EPA developed a plain-English guide for this meeting to assist stakeholders in understanding the multiple, and sometimes complex, issues surrounding drinking water regulations.

The Stage 2 DBPR and other drinking water regul ations promulgated or under development are expected to have a positive effect on human health regardless of the social or economic status of a specific population. The Stage 2 DBPR serves to provi de a similar level of drinking water protection to all groups. Where water systems have high DBP leve ls, they must reduce levels to meet the MCLs. Further, to the extent that DBP levels in dri nking water might be disproportionately high now among minority or low-income populations (which is unknown) , the Stage 2 DBPR will work to remove those differences. Thus, the Stage 2 DBPR meets the in tent of Federal policy requiring incorporation of environmental justice into Federal agency missions.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-45 

### The Stage 2 DBPR applies uniformly to CWSs and NTNCWSs that add a disinfectant other than UV light or that deliver water that has been chemica lly disinfected. Consequently, the health protection from DBP exposure that this rule provides is e qual across all income and minority groups served by systems regulated by this rule.



## 8.11 Federalism

Executive Order 13132, “Federalism” (64 FR 43255 August 10, 1999), requires EPA to develop an accountable process to ensure “meaningful and tim ely input by State and local officials in the development of regulatory policies that have federalis m implications.” “Policies that have federalism implications” are defined in the executive order to incl ude regulations that have “substantial direct effects on the States, on the relationship between the national government and the States, or on the distribution of power and responsibilities among the various levels of government.”

Under Section 6(b) of Executive Order 13132, EPA may not issue a regulation that has federalism implications, imposes substantial direct compliance costs, and is not required by statute, unless the Federal government provides the funds necessary to pay the direct compliance costs incurred by State and local governments, or consults with State and local officials early in the process of developing the proposed regulation.

EPA has concluded that the Stage 2 DPBR will not have federalism implications. It will not impose substantial direct effects on the States, on the relationship between the national government and the States, or on the distribution of power and responsibilities among various levels of government, as specified in Executive Order 13132. The Stage 2 DBPR has total annualized costs ranging from $78.8 million to $76.8 million using a 3 and 7 percent discount rate, respectively. Thus, the requirements of Sections 6(b) and 6(c) of the executive order do not apply to this rule.

Although Executive Order 13132 does not apply to this rule, EPA did consult with State and local officials early in the process of developing the Stag e 2 DPBR to permit them to have meaningful and timely input into its development. On February 20, 2001, EPA held a dialogue on both the Stage 2 DBPR and LT2ESWTR with representatives of State and local governmental organizations including those that represent elected officials. Representatives from th e following organizations attended the meeting: ASDWA, the National Governors’ Association (NGA), the National Conference of State Legislatures (NCSL), the International City/County Management Association (ICMA), NLC, the County Executives of America, and health departments. Questions ranged from a basic inquiry into how Cryptosporidium gets into water to more detailed queries about an ticipated implementation guidance, procedures, and schedules. No concerns were expressed. Some of the State and local organizations that attended this meeting were also participants in the Stage 2 M-DBP Federal Advisory Committee and signed the Agreement in Principle. In addition, EPA consu lted with a mayor in the SBREFA consultation. EPA considered all input from these consultations in the development of the Stage 2 DBPR.

In the spirit of Executive Order 13132 and c onsistent with EPA’s policy to promote communications between EPA and State and local governments, EPA specifically solicited comment on the proposed rule from State and local officials.



## 8.12 Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use

Executive Order 13211, “Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use” (66 FR 28355 May 22, 2001), provides that agencies shall prepare and submit to the Administrator of the Office of Information and Regulatory Affairs, OMB, a Statement of

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-46 

### Energy Effects for certain actions identified as “signi ficant energy actions.” Section 4(b) of Executive Order 13211 defines “significant energy actions” as “any action by an agency (normally published in the Federal Register ) that promulgates or is expected to lead to the promulgation of a final rule or regulation, including notices of inquiry, advance notices of proposed rulemaking, and notices of proposed rulemaking: (1)(i) that is a significant regulatory action under Executive Order 12866 or any successor order, and (ii) is likely to have a significant adverse effect on the supply, distribution, or use of energy; or (2) that is designated by the Administrator of the Office of Information and Regulatory Affairs as a significant energy action.”

The Stage 2 DBPR has not been designated by the Administrator of the Office of Information and Regulatory Affairs as a significant energy action because it is not likely to have a significant adverse effect on the supply, distribution, or use of energy. This determination represents the analysis presented below.

Energy Supply

The first consideration is whether the Stage 2 DB PR would adversely affect the supply of energy. The Stage 2 DBPR does not regulate power generation, either directly or indirectly and the public and private utilities that the Stage 2 DBPR regulates do not , as a rule, generate power. Further, the cost increases borne by customers of water utilities as a result of the Stage 2 DBPR are a small percentage of the total cost of water, except for a few small system s that will need to spread the cost of installing advanced treatment technologies over a narrow customer base. Therefore, those customers that are power generation utilities are unlikely to face any significan t effects as a result of the Stage 2 DBPR. In summary, the Stage 2 DBPR does not regulate the supply of energy, does not generally regulate the utilities that supply energy, and is unlikely to significan tly affect the customer base of energy suppliers. Thus, the Stage 2 DBPR would not adversely affect the supply of energy.

In response to the Stage 2 DBPR, some water u tilities are expected to increase their energy use, and those impacts are discussed later in this section.

Energy Distribution

The second consideration is whether the Stage 2 DB PR would adversely affect the distribution of energy. The Stage 2 DBPR does not regulate any aspect of energy distribution. PWSs that are regulated by the Stage 2 DBPR already have electrical service. As derived later in this section, the Stage 2 DBPR is projected to increase peak electricity demand at water utilities by only 0.008 percent. Therefore, EPA estimates that the existing connections are adequa te and that the Stage 2 DBPR has no discernable adverse effect on energy distribution.

Energy Use

The third consideration is whether the Stage 2 DB PR would adversely affect the use of energy. Because some PWSs are expected to add treatment technologies that use electrical power, this potential impact of the Stage 2 DBPR on the use of energy re quires further evaluation. The analyses that underlay the estimation of costs in Chapter 7 for the Stag e 2 DBPR are national in scope and do not identify specific plants or utilities that may install treatment in response to the rule. As a result, no analysis of the effect on specific energy suppliers is possible with th e available data. The approach used to estimate the impact of energy use, therefore, focuses on national- level impacts. The analysis estimates the additional energy use due to the Stage 2 DBPR, and compares th at to the national levels of power generation in terms of average and peak loads.

The first step in the analysis is to estimate th e energy used by the treatment technologies expected to be installed as a result of the Stage 2 DBPR. Energy use is not directly stated in Technologies and8 

### EPA is aware that DOE has updated its 2003 “average national cost of electricity per kilowatt hour per year” from $0.076 to $0.074. However, EPA continues to use the $0.076 value to maintain consistency with the Technologies and Cost Document (USEPA 2005n). EPA is aware that DOE has updated its 2003 average national cost of electricity per kilowatt hour per year from $0. 076 to $0.074. However, EPA continues to use the $0.076 value to maintain consistency with the Technologies and Cost Document.

9 

### EPA is aware that DOE has updated its estimate of total electricity produced in 2003 from 3,848 million to 3,883 million. However, EPA continues to use the 3, 848 million estimate to maintain consistency with related electricity estimates used in this EA and the Technologies and Cost Document. Final Economic Analysis for the Stage 2 DBPR December 2005 8-47 

### Costs for Control of Microbial Contaminants and Disinfection By-Products (USEPA 2005n), but the annual cost of energy for each treatment technology addition or upgrade necessitated by the Stage 2 DBPR is provided. An estimate of plant-level energy use is derived by dividing the total energy cost per plant for a range of flows by an average national co st of electricity of $0.076 per kilowatt hour per year (kWh/y) (U.S. DOE EIA 2004a 8 

### ). The energy use per plant for each flow range and treatment technology is then multiplied by the number of plants predicted to install each treatment technology in a given flow range (treatment technology selection forecasts are presented in Chapter 7). The energy requirements for each flow range are then added to produce a national tota l. No electricity use is subtracted to account for the treatment technologies that may be replaced by new treatment technologies, resulting in a conservative estimate of the increase in energy use. Exhibit 8.14 shows the estimated energy use for each Stage 2 DBPR compliance treatment technology in kWh/y. The incremental national annual energy usage is approximately 0.12 million megawatt-hours (MWh). Although the energy usage after implementing the Stage 2 DBPR is expected to be greater than before implementation (advanced treatment technologies typically require more energy than conventional treatment technologies), the net increase in energy usage is not expected to be significant.

Exhibit 8.16 provides a sample calculation for chlo ramines showing the increase in energy usage as a result of the Stage 2 DBPR.

To determine if the additional energy required for systems to comply with the rule would have a significant adverse effect on the use of energy, EPA co mpared the numbers in Exhibit 8.14 to the national production figures for electricity. According to the U.S. Department of Energy’s Energy Information Administration, electricity producers generated 3, 848 million MWh of electricity in 2003 (USDOE EIA 2004b 9 

### ). Therefore, even using the highest assumed energy use for the Stage 2 DBPR (i.e., 116,302,140 kWh/y, or 116,302 MWh/y), the rule when fully implemented would result in only a 0.003 percent increase in annual average energy use. This calculation is shown below:

116,302 MWh/y ÷ 3,848,000,000 MWh/y * 100 = 0.003%

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-48 (a) (b) Chloramines (with and without advanced tech.) 1,971 1,764 3,476,461 Chlorine Dioxide 43 3,578 154,809 UV 912 34,588 31,545,906 Ozone (0.5 log) 19 98,182 1,890,156 GAC10 + Adv. Disinfectants 42 835,330 35,321,879 GAC20 100 162,411 16,264,624 GAC20 + Adv. Disinfectants 61 132,168 8,001,027 Membranes 18 1,103,656 19,647,278 TOTAL 3,167 36,727 116,302,140 Total Increase in Energy Usage as a Result of the Stage 2 DBPR (kWh/yr) A

verage Energy Usage per

Plant per Year for all Plants Selecting the Technology (kWh/plant/yr) Number of Plants

Selecting the Technology

Technolo gy 

## Exhibit 8.15 Increase in Energy Usage as a Result of the Stage 2 DBPR

Notes: Detail may not add due to independent rounding. Sources: (a) Number of plants select ing each treatment technology is deriv ed from Exhibits 5.11b, 5.11d, 5.14b, 5.14d. Note that the number of pl ants selecting chloramines is the num ber of plants selecting chloramines only PLUS the number selecting chloramines with adv anced treatment technology (making the total in this exhibit higher than the total number of plants maki ng treatment technology changes in Exhibit 7.3). (b) Energy costs derived from the Technologies and Costs Document (USEPA 2005n) for the treatment plant design conditions listed in Exhi bit 7.8. Energy costs were conv erted to energy usage by dividing the costs by the unit costs for energy listed in Table 4-3 of the Technologies and Costs Document. Energy usage is different for different size categories; the average per plant is the we ighted average for all plants selecting the treatment technology.

### Final Economic Analysis for the Stage 2 DBPR December 2005 8-49 Number of Total Energy Usage Average Daily Total No. Plants Annual Energy Annual Energy for Plants Selecting Flow per Plant of Selecting Cost per Plant Requirement Chloramines (mgd) Plants Chloramines ($/plant/yr) (kWhr/plant/yr) (kWhr/year)

A B C D E = D/$0.076 per kWhr F = C*E

Primarily Surface Water CWSs
<100 0.01 359 19 67 876 16,999 100-499 0.03 767 50 67 876 43,541 500-999 0.08 483 31 115 1,518 47,538 1,000-3,299 0.22 1,129 81 124 1,633 132,846 3,300-9,999 0.59 1,258 91 200 2,632 238,544 10,000-49,999 2.34 1,292 133 200 2,632 350,559 50,000-99,999 3.80 579 60 200 2,632 157,247 100,000-999,999 14.61 610 63 300 3,947 248,503

> 1 Million 107.80 74 8 4,550 59,875 454,475 Primarily Ground Water CWSs <100 0.01 6,423 132 67 876 115,378 100-499 0.02 15,242 456 67 876 399,194 500-999 0.05 6,093 182 67 876 159,600 1,000-3,299 0.13 7,587 204 124 1,633 332,829 3,300-9,999 0.34 5,030 135 124 1,633 220,669 10,000-49,999 0.72 5,382 107 200 2,632 280,834 50,000-99,999 2.01 716 14 200 2,632 37,424 100,000-999,999 4.26 918 17 200 2,632 45,746

> 1 Million 27.22 27 1 200 2,632 1,431 Primarily Surface Water NTNCWSs <100 0.01 226 12 67 876 10,696 100-499 0.03 312 20 67 876 17,719 500-999 0.08 106 7 124 1,633 11,223 1,000-3,299 0.21 91 7 124 1,633 10,823 3,300-9,999 0.63 25 2 200 2,632 4,739 10,000-49,999 3.33 5 1 200 2,632 1,357 50,000-99,999 - 0 0 0 0 0 100,000-999,999 22.94 1 0 300 3,947 407

> 1 Million - 0 0 - - - Primarily Ground Water NTNCWSs <100 0.00 2,493 51 67 876 44,771 100-499 0.02 2,129 64 67 876 55,752 500-999 0.07 589 18 100 1,309 23,066 1,000-3,299 0.18 247 7 124 1,633 10,837 3,300-9,999 0.64 21 1 200 2,632 1,517 10,000-49,999 2.86 3 0 200 2,632 166 50,000-99,999 9.92 0 0 200 2,632 15 100,000-999,999 17.19 0 0 200 2,632 14

> 1 Million - 0 0 - - - TOTALS 60,220 1,971 3,476,461 System Size (Population

Served ) Chloramines (Ground Water, Ammonia Dose = 0.15 mg/l; Surface Water, Ammonia Dose = 0.55 mg/l) 

## Exhibit 8.16 Sample Calculation for Determining Increase in Energy Usage: Chloramines

Notes: Detail may not add due to independent rounding. Sources: (A) The flows are taken from Exhibit 3.4. (B) The baseline numbers of plant s are taken from Exhibit 3.2. (C) Numbers of plants selecting chloramines ar e taken from Exhibits 5.11b, 5.11d, 5.14b, 5.14d. (D) The electricity cost per plant is taken fr om the Technologies and Costs Document (USEPA 2005n). (E) Electricity cost is $0.076/kW h, as presented in the Technologi es and Costs Document (USEPA 2005n).



### In addition to average energy use, the impact at times of peak power demand is important. To examine whether increased energy usage might signi ficantly affect the capacity margins of energy suppliers, their peak-season generating capacity reserve was compared to an estimate of peak incremental10 

### EPA is aware that DOE has updated its estimate of capacity exceeding consumption in the summer of 2003 from 160,000 to 159,000 MW. However, EPA continues to use the estimate of 160,000 MW to maintain consistency with related electricity estimates used in this EA and the Technologies and Cost Document (USEPA 2005n). Final Economic Analysis for the Stage 2 DBPR December 2005 8-50 

### power demand by water utilities. Both energy use a nd water use peak in the summer months, so the most significant effects on supply would be seen then. In the summer of 2003, U.S. generation capacity exceeded consumption by 15 percent, or appr oximately 160,000 megawatts (MW) (USDOE EIA 2004b 10 

### ). Assuming around-the-clock operation of water treatment plants, the total energy requirement for the Stage 2 DBPR (Exhibit 8.15) can be divided by 8,760 hours per year to obtain an average power demand of 13.28 MW. This is only 0.008 percent of the capacity margin available at peak use. This calculation is presented below:

1. 116,302,140 kWh/y * (y/8,760 hr) * (MW/1,000 kW) = 13.28 MW

2. 13.28 MW ÷ 160,000 MW * 100 = 0.008%

Assuming that power demand is proportional to wa ter flow through the plant and that peak flow can be as high as twice the average daily flow during the summer months, about 26.55 MW (13.28 MW x 2) could be needed to operate the treatment tec hnologies installed to comply with the Stage 2 DBPR. This is still only a very small fraction (0.017 percent) of the U.S. capacity margin available at peak use (160,000 MW).

Although EPA recognizes that not all regions have a 15 percent capacity margin and that this margin varies across regions and over time, this anal ysis represents the effect of the rule on national energy supply, distribution, and use. While certa in areas have experienced shortfalls in generating capacity in the recent past, a peak incremental power requirement of 26.55 MW nationwide is not likely to significantly change the energy supply, distribution, or use in any given area.

Conclusion

The Stage 2 DBPR is not a “significant energy action” as defined in Executive Order 13211, “Actions Concerning Regulations That Significantly A ffect Energy Supply, Distribution, or Use” (66 FR 28355 May 22, 2001) because it is not likely to have a significant adverse effect on the supply, distribution, or use of energy (as a function of a nnual average use and conditions of peak power demand).

The total increase in energy usage by water system s as a result of the Stage 2 DBPR is predicted to approximately 116 million kWh/y, which is less than three one-thousandths of 1 percent of the total energy produced in 2003. While the rule may have some adverse energy effects, EPA does not believe that this constitutes a significant adverse effect on the energy supply.

### Final Economic Analysis for the Stage 2 DBPR 9-1 December 2005 

# 9. Comparison of Benefits and Costs of the Stage 2 DBPR

9.1 Introduction

This chapter presents a summary a nd comparison of the benefits and costs of the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBP R). Evaluation on a national level shows that the benefits derived from the Stage 2 DBPR are likely to exceed the costs. The following sections present summary results from the body of the economic analys is (EA), followed by a discussion of the results. The first sections focus on analysis of the Stage 2 DB PR Preferred Regulatory Alternative, followed by a comparison of this alternative to the other alternatives considered.

For comparison purposes, this chapter sometimes presents only mean estimates of benefits and costs. These estimates are discussed in Chapters 6 and 7, respectively. To avoid repetition, the following discussion assumes the reader is familiar with those chap ters. The remaining sections of this chapter are organized as follows:

9.2 Summary of National Benefits, Costs and Ne t Benefits of the Stage 2 DBPR Preferred Regulatory Alternative 9.2.1 National Benefits Summary 9.2.2 National Cost Summary 9.2.3 National Net Benefits 9.3 Comparison of Regulatory Alternatives 9.3.1 Comparison of Reductions in DBP Occurrence 9.3.2 Comparison of Benefits and Costs 9.3.3 Cost-Effectiveness 9.4 Effect of Uncertainties on the Estimation of Net National Benefits 9.5 Summary of Conclusions

As described in Chapter 6 and Appendix E, bene fits for the Stage 2 DBPR are estimated using three different cessation lag models, and either TTHMs or HAA5s as an indicator of chlorination DBPs. Note that because the maximum rate of risk re duction derived from the Smoking/Lung Cancer cessation model falls in between the maximum rate of risk reduction derived from the Smoking/Bladder Cancer and Arsenic/Bladder Cancer cessation lag models, only benefits derived from the Smoking/Lung Cancer cessation lag model are presented as a representative comparison to costs. TTHM is used as a chlorination DBP indicator unless otherwise noted.



## 9.2 Summary of National Benefits, Costs, and Net Benefits of the Stage 2 Preferred Regulatory Alternative

This section summarizes national benefits, costs, a nd net benefits of the Stage 2 DBPR Preferred Regulatory Alternative.

The rule will be implemented over time, not instantaneously, and therefore, the treatment costs incurred and benefits realized by the affected sy stems and population they serve will vary by year. Exhibits 9.1a and 9.1b summarize the undiscounted benefit and cost estimates incurred by systems, according to the implementation schedule (see Appendix D), over the 25 year period analyzed in this EA. Exhibit 9.1a shows benefits calculated using a w illingness to pay (WTP) surrogate of curable lymphoma for valuing avoided non-fatal bladder cancer and 9.1b presents estimates using chronic bronchitis. As

### Final Economic Analysis for the Stage 2 DBPR 9-2 December 2005 

### explained in Chapter 6 of this EA, WTP surrogates provide an estimate of the dollar amount society is willing to pay to avoid a case of non-fatal bladder cancer.



## Exhibit 9.1a Summary of Benefit and Cost Estimates by Year for the Stage 2 Preferred Regulatory Alternati ve Using Lymphoma WTP ($Millions)

Benefits Systems Cost Benefits Systems Cost Benefits Total Cost
A B C D E F=A+C G=B+D+E
2005 - $ 0.4 $ - $ 0.7 $ 3.9 $ - $ 4.9 $ 2006 - $ 6.4 $ - $ 9.4 $ 3.9 $ - $ 19.7 $ 2007 - $ 5.0 $ - $ 19.4 $ 0.2 $ - $ 24.5 $ 2008 - $ 18.0 $ - $ 6.5 $ 2.1 $ - $ 26.5 $ 2009 - $ 47.9 $ - $ 126.3 $ 0.8 $ - $ 175.0 $ 2010 4.6 $ 49.2 $ 130.2 $ 133.1 $ - $ 134.8 $ 182.3 $ 2011 12.1 $ 50.8 $ 336.8 $ 140.3 $ 1.7 $ 348.9 $ 192.7 $ 2012 21.9 $ 55.7 $ 609.5 $ 146.4 $ 1.7 $ 631.3 $ 203.9 $ 2013 33.9 $ 60.7 $ 944.2 $ 67.4 $ 1.7 $ 978.0 $ 129.9 $ 2014 48.0 $ 64.5 $ 1,228.6 $ 45.1 $ 1.7 $ 1,276.7 $ 111.4 $ 2015 64.3 $ 46.8 $ 1,492.7 $ 30.4 $ 1.7 $ 1,557.0 $ 78.9 $ 2016 77.3 $ 27.1 $ 1,733.1 $ 30.4 $ 1.7 $ 1,810.5 $ 59.2 $ 2017 89.0 $ 27.1 $ 1,951.4 $ 30.4 $ 1.7 $ 2,040.4 $ 59.2 $ 2018 99.5 $ 27.1 $ 2,143.0 $ 30.4 $ 1.7 $ 2,242.5 $ 59.2 $ 2019 108.9 $ 27.1 $ 2,306.7 $ 30.4 $ 1.7 $ 2,415.6 $ 59.2 $ 2020 117.2 $ 27.1 $ 2,448.8 $ 30.4 $ 1.7 $ 2,566.0 $ 59.2 $ 2021 124.3 $ 27.1 $ 2,574.1 $ 30.4 $ 1.7 $ 2,698.5 $ 59.2 $ 2022 130.6 $ 27.1 $ 2,686.4 $ 30.4 $ 1.7 $ 2,817.0 $ 59.2 $ 2023 136.3 $ 27.1 $ 2,788.0 $ 30.4 $ 1.7 $ 2,924.3 $ 59.2 $ 2024 141.5 $ 27.1 $ 2,881.1 $ 30.4 $ 1.7 $ 3,022.5 $ 59.2 $ 2025 146.2 $ 27.1 $ 2,967.0 $ 30.4 $ 1.7 $ 3,113.2 $ 59.2 $ 2026 150.5 $ 27.1 $ 3,047.2 $ 30.4 $ 1.7 $ 3,197.7 $ 59.2 $ 2027 154.6 $ 27.1 $ 3,122.5 $ 30.4 $ 1.7 $ 3,277.1 $ 59.2 $ 2028 156.4 $ 27.1 $ 3,152.3 $ 30.4 $ 1.7 $ 3,308.6 $ 59.2 $ 2029 159.6 $ 27.1 $ 3,211.4 $ 30.4 $ 1.7 $ 3,370.9 $ 59.2 $

Sources: Benefits: Appendix F.2a - F.2i, F.2k - F2.s Costs: Appendix J.2a - J.2i, J.2k - J. 2s, J.2v - J.2ad, J.2af - J.2an, J.2ar Notes: Values are discoun ted and annualized in 2003 $. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Some numbers may not add correctly due to rounding. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. State Costs Systems <10,000 Systems > 10,000 All Systems & State

Year

### Final Economic Analysis for the Stage 2 DBPR 9-3 December 2005 

## Exhibit 9.1b Summary of Benefit and Cost Estimates by Year for the Stage 2 Preferred Regulatory Alternative Using Bronchitis WTP ($Millions)

The analyses in this EA assume that implementation of this rule will begin in 2006. If implementation of the rule were to begin a year or two later, the fundamental conclusions of the analysis would not be significantly changed. In the first fe w years, before systems have installed treatment, no benefits are realized, although some costs are incurre d for rule implementation and the Initial Distribution System Evaluation (IDSE). Some bladder cancer cases are projected to be avoided in the year following installation of treatment and each year thereafter, adjusted to account for the cessation lag (see Section 6.4.2 for a description of adjustments made to projected cancer cases avoided to account for cessation lag). By 2016, all treatment is projected to be insta lled, and yearly system costs thereafter are constant, representing only operations and maintenance (O&M), monitoring, and yearly operational evaluation costs. Using the rule implementation schedule pr esented in Appendix D of this EA, approximately 87% of the cases ultimately avoidable will be avoided by 25 years after promulgation, and 100% of the cases will be avoided by 100 years after promulgation. Benefits Cost Benefits Cost Benefits Cost A B C D E F=A+C G=B+D+E
2005 - $ 0.4 $ - $ 0.7 $ 3.9 $ - $ 4.9 $ 2006 - $ 6.4 $ - $ 9.4 $ 3.9 $ - $ 19.7 $ 2007 - $ 5.0 $ - $ 19.4 $ 0.2 $ - $ 24.5 $ 2008 - $ 18.0 $ - $ 6.5 $ 2.1 $ - $ 26.5 $ 2009 - $ 47.9 $ - $ 126.3 $ 0.8 $ - $ 175.0 $ 2010 2.3 $ 49.2 $ 64.3 $ 133.1 $ - $ 66.6 $ 182.3 $ 2011 6.0 $ 50.8 $ 166.5 $ 140.3 $ 1.7 $ 172.5 $ 192.7 $ 2012 10.8 $ 55.7 $ 301.5 $ 146.4 $ 1.7 $ 312.3 $ 203.9 $ 2013 16.8 $ 60.7 $ 467.4 $ 67.4 $ 1.7 $ 484.2 $ 129.9 $ 2014 23.8 $ 64.5 $ 608.7 $ 45.1 $ 1.7 $ 632.5 $ 111.4 $ 2015 31.9 $ 46.8 $ 740.0 $ 30.4 $ 1.7 $ 771.9 $ 78.9 $ 2016 38.4 $ 27.1 $ 859.9 $ 30.4 $ 1.7 $ 898.3 $ 59.2 $ 2017 44.2 $ 27.1 $ 968.9 $ 30.4 $ 1.7 $ 1,013.1 $ 59.2 $ 2018 49.4 $ 27.1 $ 1,064.9 $ 30.4 $ 1.7 $ 1,114.4 $ 59.2 $ 2019 54.2 $ 27.1 $ 1,147.2 $ 30.4 $ 1.7 $ 1,201.3 $ 59.2 $ 2020 58.3 $ 27.1 $ 1,218.8 $ 30.4 $ 1.7 $ 1,277.2 $ 59.2 $ 2021 61.9 $ 27.1 $ 1,282.3 $ 30.4 $ 1.7 $ 1,344.2 $ 59.2 $ 2022 65.1 $ 27.1 $ 1,339.3 $ 30.4 $ 1.7 $ 1,404.4 $ 59.2 $ 2023 68.0 $ 27.1 $ 1,391.1 $ 30.4 $ 1.7 $ 1,459.1 $ 59.2 $ 2024 70.6 $ 27.1 $ 1,438.8 $ 30.4 $ 1.7 $ 1,509.4 $ 59.2 $ 2025 73.1 $ 27.1 $ 1,483.0 $ 30.4 $ 1.7 $ 1,556.0 $ 59.2 $ 2026 75.3 $ 27.1 $ 1,524.4 $ 30.4 $ 1.7 $ 1,599.7 $ 59.2 $ 2027 77.4 $ 27.1 $ 1,563.4 $ 30.4 $ 1.7 $ 1,640.8 $ 59.2 $ 2028 78.3 $ 27.1 $ 1,577.8 $ 30.4 $ 1.7 $ 1,656.1 $ 59.2 $ 2029 79.9 $ 27.1 $ 1,608.5 $ 30.4 $ 1.7 $ 1,688.4 $ 59.2 $

Sources: Benefits: Appendix F.2a - F.2i, F.2k - F2.s Costs: Appendix J.2a - J.2i, J.2k - J.2s , J.2v - J.2ad, J.2af - J.2an, J.2ar Notes: Values are discounted and annualiz ed in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Some numbers may not add correctly due to rounding. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. State Costs Systems <10,000 Systems > 10,000 All systems

Year

### Final Economic Analysis for the Stage 2 DBPR 9-4 December 2005 

### 9.2.1 National Benefits Summary

The Environmental Protection Agency (EPA) has de termined from its analysis of the available animal toxicological studies and human epidemiological studies that the Stage 2 DBPR could provide benefits resulting from reduced incidence of cancer, pa rticularly bladder cancer, and reduced incidence of adverse reproductive and developmental effects.

Because of limitations in the available data, it is not possible to quantify all of the health benefits of the Stage 2 DBPR. In particular, the science is not strong enough to quantify the risk of reproductive and developmental health effects resulting from DBP exposure. Nevertheless, although the results from different studies are mixed, a weight of evidence eval uation of the health effects data suggests a potential association between DBP exposure and various adve rse reproductive and developmental outcomes. To help inform the assessment of the Stage 2 DBPR benef its, EPA has prepared an illustrative calculation for one specific reproductive effects endpoint (fetal loss). Results from this analysis show that 0 to 3,700 fetal losses could potentially be avoided annually as a result of the Stage 2 DBPR. More detail on this analysis can be found in Appendix G. Additionally , EPA reviewed the literature on colon and rectal cancers, which combined are the third most common s ite (excluding skin) of new cases and deaths in both men and women in the U.S. Human epidemiology studies on chlorinated surface water have reported associations with colon and rectal cancers, although there still remains some conflicting evidence. Since Stage 1, new studies have become available that provi de for an estimation of population attributable risk (PAR), explained further in Chapter 6 of this EA. Hence, EPA chose to perform a sensitivity analysis on benefits from avoiding colon and rectal cancers, th e results of which are shown in Exhibit 6.31 and detailed in Appendix F. An additional analysis in Appendix N presents the results of a cost effectiveness analysis in terms of quality-adjusted life years (QALYs) for avoided cases of bladder cancer; it also incorporates QALYs that would be saved for potentia lly avoided cases of colon and rectal cancers in a sensitivity analysis (section 3.0 of Appendix N).

Other nonquantified health and non-h ealth benefits derived from rule implementation also could contribute to the overall value of benefits. Nonquantif ied benefits are discussed in detail in Chapter 6 and are summarized below in Exhibit 9.2.1 

### Causality has not been clearly established in the a ssociation between consumption of DBPs in drinking water and bladder cancer and therefore the number of bl adder cancer cases avoided could be as low as zero.

2 

### Because specific estimates of WTP for avoiding non-fa tal bladder cancer are not available, EPA estimated the WTP from two other non-fatal illnesses: chronic bronchitis and curable lymphoma.

3 

### The number of cases avoided and the resulting bene fits were calculated using both TTHM and HAA5 as indicators of exposure to all chlorination DBPs. However, because results for both indicators were similar, only the results of calculations using TTHM as an indicator are presented, to simplify presentation. Detailed results for all analyses using both TTHM and HAA5 as indicators are presented in Appendix F



### .

Final Economic Analysis for the Stage 2 DBPR 9-5 December 2005 

## Exhibit 9.2 Summary of Nonquantified National Benefits of the Stage 2 DBPR

Nonquantified Benefit Group(s) Affected Type of Cost Avoided

Medical

Care Pain & Suffering Lifetime Care Other 1



### Adverse Reproductive Health Effects Avoided Women (and men) of reproductive age 

## TT T

Pregnant women 

## TT T

Developmental Health Effects Avoided (e.g., congenital anomalies) Fetuses 

## T T

Children/adults with birth defects 

## TTT

Other Adverse Health Effects Avoided (Reduction in other cancers, including colon and rectal cancers, and benefits fromreduction of other DBPs, cooccurring contaminants, or emerging contaminants) All individuals exposed to elevated levels of DBPs in drinking water 

## TTTT

Adverse Non-Health Effects Avoided (Perceptions of drinking water quality, ecological, and other unknown effects) All individuals 

## T

Note: Discussions of these health effects are presented in Chapter 6. Footnote 1: Includes costs such as potential changes to life plans due to feta l loss and lost opportunities for family life. For patients of cancer or ot her diseases potentially induced by DBP ex posure this includes opportunity costs in reduced work opportunities and reduced parti cipation in social and family life. Adverse non-health effect costs potentially include reduced enjoym ent of drinking water through perception of water as undesirable in taste or odor, and costs for bottled water or home filters.



### EPA has quantified the expected range of avoided new cases of bladder cancer each year, including both fatal and non-fatal cases. 1 

### In addition, EPA has estimated the monetized value of avoiding these cases using estimates of willingness to pay (WTP) for non-fatal cancer 2 

### and the value of a statistical life (VSL) for fatal cancer cases. Exhi bits 9.3 and 9.4 summarize, respectively, these quantified and monetized benefits estimates based on total trihalomethane (TTHM) and haloacetic acid (HAA5) reduction as an indicator 3 

### for reduced levels of all chlo rination DBPs. Data are generally summarized using an estimate of the mean and 90 percent confidence intervals, which address some

### Final Economic Analysis for the Stage 2 DBPR 9-6 December 2005 

### Mean 5th 95th

TTHM 581 232 1084 HAA5 680 261 1288

Mean 5th 95th
TTHM 279 103 541 HAA5 325 115 642

Source: Appendix E, Exhibits E.12, E.38d, E.39d Footnotes: 1) Benefits (avoided cases) are estimated using Villanueva et al. (2003) for baseline risk. Ultimately avoidable annual cases represents the number of cases to be avoided annually following the cessation lag, and its presentation is consistent with OMB recommendations to extend the horizon of analysis to include significant benefits. The ultimately avoidable number is reached approximately 100 years from the start of implementation of the Stage 2 regulation, although about 61% of the ultimate number of case avoided occurs by year 25.
2) Average annual avoided cases is based upon the 25-year period of analysis and so is much lower than the ultimate number of cases to be avoided following the cessation lag period. The cessation lag is explained in detail in Appendix E of the Stage 2 EA. 

### DBP Indicator Bladder Cancer Cases

Annual Average Ultimately Avoidable 1



### Annual Average Avoidable Over 25 Years 2



### Notes: Estimates are discounted and annualized. The 90 percent confidence bounds around benefits reflect uncertainty in monetization inputs relative to mean cases. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. 

### elements of uncertainty and variability, such as th e distribution of bladder cancer cases obtained from a Monte Carlo simulation.



## Exhibit 9.3 Summary of Annual Bladder Cancer Cases Ultimately Avoidable for the Stage 2 DBPR Preferred Regulatory Alternative

Exhibit 9.3 shows two kinds of estimates. One is the annual average of bladder cancer cases that will be avoided following full installation of any tr eatment technology changes and completion of the cessation lag period; this is the maximum, or steady state number of cases that will be avoided. The second set of numbers is lower because it is the average number of cases avoided annually over the 25 year period of analysis, incorporating the years prio r to full installation of treatment technology changes4 

### In the Stage 2 benefits analysis the income-adjusted VSL estimates are applied to the year in which cases have been avoided. An alternative approach supported by some economists, and used in other EPA analyses, is for the income adjustments to be applied only up to the time that exposures are reduced rather than over the cessation lag. Because of the shorter time period over which income growth would be calculated the alternative would result in smaller income adjustment. To use the alternative EPA would need to link the year cancers are avoided to a specific year of exposure reduction. This cannot be done with the risk assessment and cessation lag application in the Stage 2 analysis, where estimated cases avoided are ba sed on a transition from one steady state to another. The VSL income adjustment approach used in this EA will tend to overstate benefits somewhat relative to the alternative described above. EPA recognizes this potential bias, but not es that is small in comparison to other uncertainties in valuation, as well as uncertainties in the risk assessment and estimates of cases avoided.

Final Economic Analysis for the Stage 2 DBPR 9-7 December 2005 

### resulting from the Stage 2 DBPR and prior to completion of the cessation lag period. The cessation lag is explained further in Appendix E and Chapter 6 of this EA.

Exhibit 9.4 (below) presents monetized estimates of the value of avoided non-fatal cases of bladder cancer using lymphoma and bronchitis WTP estimates as surrogates for the costs of illness. The VSL is applied to estimates of fatal cases avoided, and includes factors for income growth and income elasticity that vary by year 4 

### . For fatal and non-fatal cases, the data in Exhibit 9.4 represent the monetized values from each year, discounted to the year 2003 (to obtain present values) and annualized over the 25 year period. These figures represent the annualized value of the estimated annual number of illnesses and deaths avoided according to the rule schedule; th ey are also the annualized values of the undiscounted benefits data presented in Exhibit 9.1.



## Exhibit 9.4 Estimated Annualized National Benefits for the Stage 2 Preferred Regulatory Alternative ($Millions)

L ower (5th %ile) U pper (95th %ile) L ower (5th %ile) U pper (95th %ile)

1,531 $ 233 $ 3,536 $ 763 $ 165 $ 1,692 $

1,246 $ 190 $ 2,878 $ 621 $ 135 $ 1,376 $

Sources: Appendix F.2v, 2w, 3v, 3w Appendix J.2as and J.2aw Notes: Values are discounted and annualized in 2003$. The 90 percent confidence bounds around benefits reflect uncertainty in monetization inputs relative to mean cases. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Some numbers may not add correctly due to rounding. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (US EPA 1999a). EPA rec ognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. WTP for Non-Fatal Bladder Cancer Cases, Bronchitis as Surrogate WTP for Non-Fatal Bladder Cancer

Cases, Lymphoma as Surrogate

Mean 90% Confidence Bound

3% Discount Rate

7% Discount Rate Mean 90% Confidence Bound

### Final Economic Analysis for the Stage 2 DBPR 9-8 December 2005 

### 9.2.2 National Cost Summary

The national annual costs of the Stage 2 DB PR result from activities associated with rule implementation, IDSEs, monitoring plans, additiona l routine monitoring, operational evaluations, and changes in treatment technologies by some water systems. Cost analysis methodology is thoroughly discussed in Chapter 7 and Appendix J. These costs are summarized below in Exhibits 9.5a and 9.5b.

As with the benefits, these estimates of cost are best characterized by distributions. Uncertainty in the predictions of plants making treatment tec hnology changes, together with uncertainty in the estimates of capital and O&M unit costs, contribute to th e uncertainty in estimates of national costs. See Section 7.8 for further explanation of the distribution of cost estimates.

Exhibits 9.5a and 9.5b summarize the cost information by its two main components: capital and one-time costs, and O&M. Both cost components o ccur over time, hence, the present value is calculated at 3 and 7 percent discount rates and those amounts are annualized over the 25 year period of analysis. The estimated mean annualized cost of the Stage 2 DBPR Preferred Regulatory Alternative is $76.8 million at a 3 percent discount rate and $78. 8 million at a 7 percent discount rate.Final Economic Analysis for the Stage 2 DBPR 9-9 December 2005Exhibit 9.5a Annualized Costs for Stage 2 DBPR Preferred Regulatory Alternative Rule Activities($Millions/Year, 3% Discount Rate)Total System CostsLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)ImplementationIDSEMonitoringPlansMoni-toringSignificantExcursionLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)< 10,000 $4.21 $2.32 $6.23 $6.10 $3.41 $8.83 $0.12 $0.93 $0.05 -$0.07 $0.02 $11.34 $6.76 $16.10>10,000$20.60 $11.22 $28.75 $14.33 $9.03 $21.55 $0.09 $1.59 $0.03 -$1.14 $0.11 $35.61 $20.93 $50.97< 10,000 $0.27 $0.15 $0.40 $0.57 $0.32 $0.82 $0.01 $0.00 $0.00 $0.02 $0.00 $0.86 $0.49 $1.25>10,000$0.04 $0.02 $0.06 $0.03 $0.02 $0.04 $0.00 $0.00 $0.00 $0.00 $0.00 $0.08 $0.05 $0.11< 10,000 $7.41 $6.13 $8.70 $7.20 $6.60 $7.79 $0.30 $0.29 $0.08 $1.05 $0.00 $16.33 $14.45 $18.21>10,000$4.87 $4.37 $5.36 $6.00 $5.64 $6.37 $0.05 $0.10 $0.02 $0.00 $0.00 $11.04 $10.18 $11.90< 10,000 $0.57 $0.48 $0.65 $0.75 $0.69 $0.81 $0.06 $0.00 $0.01 $0.42 $0.00 $1.80 $1.65 $1.95>10,000$0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.00 $0.00 $0.00 $0.01 $0.00 $0.03 $0.02 $0.03TOTAL $37.97 $24.69 $50.17 $34.98 $25.72 $46.22 $0.62 $2.91 $0.19 $0.28 $0.12 $77.08 $54.53 $100.51 $1.71 $78.80 $56.24 $102.22Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecasts and unit treatment costs.Sources: Exhibit 7.5aCapital Costs: SW CWS, Exhibit J.2bb; SW NTNCWS, Exhibit J.2bf; GW CWS, Exhibit J.2bj; GW NTNCWS, Exhibit J.2bn.O&M Costs: SW CWS, Exhibit J.2bc; SW NTNCWS, ExhibitJ.2bg; GW CWS, Exhibit J.2bk; GW NTNCWS, Exhibit J.2bo.Non-Treatment Costs: SW CWS, Exhibit J.2bd; SW NTNCWS, Exhibit J.2bh; GW CWS, Exhibit J.2bl; GW NTNCWS, Exhibit J.2bp.State Costs; Appendix J, Exhibit J.2asNotes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator.Total Costs of the RuleSurface Water CWSsSurface Water NTNCWSsMeanValue90 PercentConfidence BoundCapital Costs O&M CostsMeanValue90 PercentConfidence BoundSystem Costs90 PercentConfidence BoundStateCostsGround Water NTNCWSsSystem Size(PopulationServed)Non-Treatment Costs(Point Estimate)MeanValueMeanValue90 PercentConfidence BoundGround Water CWSsFinal Economic Analysis for the Stage 2 DBPR 9-10 December 2005Exhibit 9.5b Annualized Costs for Stage 2 DBPRPreferred Regulatory Alternative Rule Activities($Millions/Year, 7 Percent Discount Rate)Total System CostsLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)ImplementationIDSEMonitoringPlansMoni-toringSignificantExcursionLower(5th %tile)Upper(95th%tile)Lower(5th %tile)Upper(95th%tile)< 10,000 $4.53 $2.50 $6.71 $4.86 $2.72 $7.04 $0.15 $1.16 $0.06 -$0.06 $0.01 $10.72 $6.54 $15.08>10,000$23.00 $12.53 $32.10 $11.66 $7.35 $17.54 $0.11 $2.06 $0.04 -$0.90 $0.08 $36.06 $21.27 $51.03< 10,000 $0.29 $0.16 $0.43 $0.45 $0.25 $0.66 $0.01 $0.00 $0.00 $0.01 $0.00 $0.76 $0.43 $1.11>10,000$0.05 $0.03 $0.07 $0.02 $0.01 $0.03 $0.00 $0.00 $0.00 $0.00 $0.00 $0.08 $0.05 $0.11< 10,000 $7.98 $6.60 $9.37 $5.74 $5.26 $6.21 $0.38 $0.36 $0.09 $0.84 $0.00 $15.38 $13.53 $17.24>10,000$5.39 $4.84 $5.94 $4.87 $4.57 $5.16 $0.06 $0.13 $0.02 $0.00 $0.00 $10.46 $9.62 $11.31< 10,000 $0.61 $0.51 $0.70 $0.60 $0.55 $0.65 $0.07 $0.00 $0.01 $0.33 $0.00 $1.62 $1.48 $1.77>10,000$0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.00 $0.00 $0.00 $0.01 $0.00 $0.02 $0.02 $0.02TOTAL $41.86 $27.16 $55.33 $28.21 $20.73 $37.29 $0.78 $3.71 $0.23 $0.23 $0.10 $75.11 $52.94 $97.67 $1.70 $76.81 $54.64 $99.36Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecasts and unit treatment costs.Sources: Exhibit 7.5bCapital Costs: SW CWS, Exhibit J.2br; SW NTNCWS, Exhibit J.2bv; GW CWS, Exhibit J.2bz; GW NTNCWS, Exhibit J.2cd.O&M Costs: SW CWS, Exhibit J.2bs; SW NTNCWS, Exhibit J.2bw; GW CWS, Exhibit J.2ca; GW NTNCWS, Exhibit J.2ce.Non-Treatment Costs: SW CWS, Exhibit J.2bt; SW NTNCWS, Exhibit J.2bx; GW CWS, Exhibit J.2cb; GW NTNCWS, Exhibit J.2cf.State Costs: Appendix J, Exhibit J.2awNotes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator.Ground Water NTNCWSsSystem Size(PopulationServed)Non-Treatment Costs(Point Estimate)MeanValueMeanValue90 PercentConfidence BoundSurface Water CWSsCapital Costs O&M CostsMeanValue90 PercentConfidence BoundSystem Costs90 PercentConfidence BoundStateCostsTotal Costs of the RuleGround Water CWSsSurface Water NTNCWSsMeanValue90 PercentConfidence Bound

### Final Economic Analysis for the Stage 2 DBPR 9-11 December 2005 

### 9.2.3 National Net Benefits

Net benefits are the difference between the es timated value of avoided cases of bladder cancer resulting from the Stage 2 DBPR and the estimated cost s of complying with the rule. The Stage 2 DBPR will be implemented over time and, therefore, the treatment technology changes that PWSs implement and the benefits realized by the populations they serve will vary year to year until implementation is complete; the benefits will be at their maximum, steady state following the cessation lag period (explained further in Chapter 6 and Appendix E). E xhibit 9.6 takes the present value of the costs and benefits listed by year in Exhibit 9.1a-b, and annualizes them over the 25 year period of analysis at 3 and 7 percent discount rates. Exhibit 9.6 shows that, in using either the 3 or 7 percent discount rates, net benefits for the Stage 2 DBPR Preferred Regulatory A lternative are positive, indicating that the regulation results in a net benefit to society.

When the magnitude of uncertainty around the costs and benefits estimates differs significantly, another approach to evaluating net benefits is a “break even analysis.” In Exhibit 9.7, costs and benefits are adjusted to present value (based on 2003 dollars) and annualized over 25 years at a 3 and 7 percent discount rate to generate the average annuali zed values. The upper bound on costs and lower bound on benefits are presented as a percentage of the corresponding mean estimates, since these bounds will determine if the alternative is acceptable at the lowest estimate of its net benefits. Exhibit 9.7 shows that the amount of uncertainty around the upper bound on the co st estimate is approximately 30 percent larger than the estimated mean cost; the lower bound on the be nefits estimate is approximately 85 percent lower than the estimated mean benefit. Furthermore, EPA recognizes that the quantified benefits, based on reduced cases of bladder cancer as shown in Exhibit 9. 3, could be zero for all alternatives since causality has not yet been established between exposure to chlorinated water and bladder cancer.

Using the estimate with less uncertainty, in this case, costs, a calculation is made of the minimum level of benefits which, if achieved, would cause the rule to break even. Comparing this breakeven level of benefits with the actual benefits estimate provi des a measure of the likelihood that the Stage 2 DBPR Preferred Regulatory Alternative will have positive net benefits. Exhibit 9.8 presents the breakeven analysis for average annual cases of bladder cancer avoi ded for the 25 year period of analysis. Based on the estimated cost of the rule ($78 million/year at a 3 percent discount rate) shown in Exhibit 9.7, the number of bladder cancer cases that must be avoided annually to break even is a mean of 18 cases (range of 13 to 23 cases based on a 90% confidence interval ), using the WTP for non-fatal lymphoma as the basis for valuing non-fatal cancer cases. Based on the WTP for avoiding chronic bronchitis, the rule must avoid a mean of 99 cases to break even (range of 70 to 128 using a 90% confidence interval).

By comparison, Exhibit 9.8 shows that the estimated mean of bladder cancer cases avoided through promulgation of the Stage 2 DBPR Prefe rred Regulatory Alternative is 279; the lower 5 th 

### percentile and upper 95 th 

### percentile bounds are 103 and 541 cases, respectively. Using a 3 percent discount rate, the mean annual number of bladder can cer cases avoided (over the 25 year analysis period) is more than 14 times the mean break even estimate using lymphoma WTP, and more than 2.5 times the mean break even estimate using chronic bronchitis WTP. Furthermore, the lower bound (5 th 

### percentile = 103) on the estimated mean number of cases avoided is greater than the upper bound on the breakeven estimate using the non-fatal lymphoma WTP (95 th 

### percentile = 23), and greater than the mean estimate using chronic bronchitis WTP (mean = 99). Results for the same calculations using a 7 percent discount rate are also shown in Exhibit 9.8.

The break even analysis indicates that the Stage 2 DBPR is likely to have more benefits than costs based upon implementation of the Preferred Regul atory Alternative. However, EPA recognizes that causality has not yet been established between DBPs and bladder cancer.

### Final Economic Analysis for the Stage 2 DBPR 9-12 December 2005 

### It is also important to note that the non-quantif ied benefits (e.g., reducti on in developmental and reproductive risk) are not included in the primary benefits analysis but could be substantial. This means that the number of bladder cancer cases that must be avoided to break even could potentially be less than shown in Exhibit 9.8, if benefits are also achieve d by a reduction in developmental and reproductive risks.



## Exhibit 9.6 Annualized Mean Net Benefi ts for the Stage 2 Preferred Regulatory Alternative ($Millions)

WTP for NonFatal Bladder Cancer Cases Mean Benefits Mean Costs Mean Net Benefits

Lymphoma 1,531 $ 79 $ 1,452 $ Bronchitis 763 $ 79 $ 684 $

Lymphoma 1,246 $ 77 $ 1,170 $ Bronchitis 621 $ 77 $ 544 $

Sources: Costs - Appendix J, Exhibits J.2as, J.2aw Benefits - Appendix F, Exhibits F.2v, F.2w, F.3v, F.3w Notes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Some numbers may not add correctly due to rounding. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. 7 Percent, 25 Years 3 Percent, 25 Years

### Final Economic Analysis for the Stage 2 DBPR 9-13 December 2005 

## Exhibit 9.7 Estimated Annualized National Costs and Benefits for the Stage 2 Preferred Regulatory Alternative with Uncertainty Measured as a Percent of the Mean ($Millions)

Exhibit 9.8 Estimated Breakeven Points (Number of Bladder Cancer Cases Avoided) for the Stage 2 Preferred Regulatory Alternative Lower (5th %ile) Upper (95th %ile) Lower (5th %ile) Upper (95th %ile)

1,531 $ 233 $ 3,536 $ 79 $ 56 $ 102 $ 15% 130%

1,246 $ 190 $ 2,878 $ 77 $ 55 $ 99 $ 15% 129% Sources: Appendix F.2v, 2w, 3v, 3w Appendix J.2as and J.2aw Mean 3% Discount Rate

7% Discount Rate

Notes: Values are discounted and annualized in 2003$. 90 percent confidence bounds for benefits reflect uncertainty in monetization inputs relative to mean cases; around costs reflect uncertainty in technology compliance forecasts and unit treatment costs. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. Mean 90% Confidence Bound 

### Benefits Costs Lower Bound of Benefits as % of Mean

Benefits Estimate Upper Bound of Costs as %

of Mean

Cost Estimate 90% Confidence Bound



### 90 Percent Confidence Bound 90 Percent Confidence Bound 90 Percent Confidence Bound

Lower (5th %ile) Upper (95th %ile) Lower (5th %ile) Upper (95th %ile) Lower (5th %ile) Upper (95th %ile) Lymphoma 18 13 23 17 12 22 Bronchitis 99 70 128 96 68 124

Sources:

Cases avoided: from Appendix E, Exhibits E.24, E.38d Notes: Breakeven cases are derived by dividing the regulation cost by the WTP estimates. Values are discounted and annualized in 2003$. The 90 percent confidence bounds for cases avoided reflect uncertainty in PAR, in reduction in average TTHM and HAA5 concentrat ions, and in cessation lag. The 90 percent confidence bounds around the estimates of break even cases result from the 90 percent confide nce bounds around the cost estimates. Ba sed on TTHM as an indicato r, Villanueva et al. ( 2003) for baseline risk, and smoking/lung cancer cessation lag model. Some numbers may not add correctly due to rounding. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (U SEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to ch lorinated water and bladder cancer. 

### 541 Breakeven Cases (at 3 Percent, 25 Years)

Mean Mean WTP for Non

Fatal Bladder Cancer Cases Mean Breakeven Cases (at 7 Percent, 25 Years) Average Annual Avoided Cases

Breakeven cases: Total regulation cost from Appendix J, Exhibits J.2as and J.2aw; WTP estimates from "Stage 2 Valuation Inputs Model, Values by Year" 

### 279 103

### Final Economic Analysis for the Stage 2 DBPR 9-14 December 2005 

## 9.3 Comparison of Regulatory Alternatives

As discussed in Chapter 4, the development and evaluation of regulatory alternatives was undertaken as part of a consultation process c onvened under the Federal Advisory Committees Act (FACA). The FACA process narrowed hundreds of regul atory options down to four major alternatives for further evaluation, one of which was designated, in an Agreement in Principle (65 FR 83015 December 2000), as the Stage 2 DBPR Preferred Regulat ory Alternative. These four alternatives are summarized below.

Preferred Alternative: 80 μg/L TTHM and 60 μg/L HAA5 as an LRAA; bromate MCL of 10 μg/L as an RAA based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR for bromate). Compliance monitoring preceded by IDSE..

Alternative 1: 80 μg/L TTHM and 60 μg/L HAA5 as an LRAA; bromate MCL of 5μg/L as an RAA based on monthly samples taken at the finished water point.

Alternative 2: 80 μg/L TTHM and 60 μg/L HAA5 as the single maximum value for any sample taken during the year; bromate MCL of 10 μg/L as an RAA based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR for bromate).

Alternative 3: 40 μg/L TTHM and 30 μg/L HAA5 as an RAA of all distribution samples taken; bromate MCL of 10 μg/L as an RAA based on monthly samples taken at the finished water point (no change from the Stage 1 DBPR for bromate).

Detailed benefit and cost analyses for each of th e alternatives are presented in Chapters 6 and 7, respectively, and are summarized in this chapter. The following sections present several analyses that compare the Preferred Regulatory Altern ative to the other three alternatives.

9.3.1 Comparison of Reductions in DBP Occurrence

Given that the goal of the Stage 2 DBPR is to reduce adverse health effects by reducing exposure to DBPs in drinking water, a useful starting point for comparing regulatory alternatives is a comparison of reduction in DBPs estimated for each alternative. E xhibit 9.9 presents percent reductions in DBPs for the Stage 2 DBPR regulatory alternatives. These reducti ons are calculated as the average of annual plant means at the point representing average residence tim e in the distribution system for each regulatory alternative.

Although Exhibit 9.9 shows much greater percent reductions in DBPs for Alternatives 2 and 3, these figures tell very little without further evaluation. Specifically, these values need to be evaluated in the context of the costs and benefits associated with achieving them.

### Final Economic Analysis for the Stage 2 DBPR 9-15 December 2005 

### Mean 5th 95th
Preferred 7.81% 4.53% 11.18% Alternative 1 7.15% 5.93% 8.36% Alternative 2 26.91% 23.61% 30.20% Alternative 3 37.12% 36.00% 38.25% Re g ulator y

Alternative Percent TTHM Reduction from PreStage 2 to Post-Stage 2

Note: The 90 percent confidence intervals around the mean estimate represent alternative methodologies (SWAT and the ICR Matrix Method) for surface water systems and the potential impacts of the IDSE.

Source: Exhibit 6.19 

## Exhibit 9.9 Comparison of DBP Reduction (of Annual Plant Mean TTHM Data)

9.3.2 Comparison of Benefits and Costs

Exhibit 9.10 presents a summary of the quantif ied and monetized annualized benefits for each alternative considered. The average annual numbe r of cases avoided as shown in Exhibit 9.10 is based upon the 25 year period of analysis; it represents a portion of the annual average number of cases ultimately avoided, which will be achieved follo wing completion of the cessation lag period. The benefits derive from PWS treatment adjustments and a predicted subsequent re duction in DBP reductions, along with a corresponding reduction in risk for bladder cancer. As stated earlier, the potential nonquantified benefits in the form of reduced risk for developmental and reproductive health effects may be significant, and would be in addition to the benefits quantified in Exhibit 9.10.

Exhibit 9.11 follows with a presentation of the quantified monetized costs of the regulatory alternatives. The annualized total costs of the ru le include costs for rule implementation, IDSEs, monitoring plans, additional routine monitoring, operational evaluations, and changes in treatment technologies by some water systems; these cost estim ates include costs for PWSs and the State agencies.

The regulatory alternatives are arranged in Exhib its 9.10 and 9.11 in order of increasing cost from left to right and top to bottom, respectively. Exhi bit 9.10 shows that the Preferred Regulatory Alternative avoids more cases of bladder cancer than the next l east expensive alternative (Alternative 1). This is because incorporating the IDSE, which increases bene fits, is only considered under the Preferred Alternative (explained further in Chapter 4). Additi onally, Alternative 1 would capture more benefits if potential cancer cases avoided by lowering the bromate standard (included only in Alternative 1) were quantified. The Preferred Alternative avoids far fewer cases than the more expensive Alternatives 2 and 3. The Microbial-Disinfectants/Disinfection By products (M-DBP) Advisory Committee did not favor alternative A1 because they were c oncerned that lowering the bromate le vel to 5 μg/L could have adverse effects on microbial protection (see Chapter 4 for a fu ll discussion). The committee also believed that the current health effects data were not certain enough to warrant the drastic shifts in the Nation’s drinking water treatment practices likely to be caused by altern atives A2 and A3. Exhibit 9.11 shows that the costs of the Preferred Regulatory Alternative are far less th an costs for the three alternatives: approximately 1/3 the cost of A1; less than 1/5 the cost of A2; and less than 1/8 the cost of A3.

### Final Economic Analysis for the Stage 2 DBPR 9-16 December 2005 

## Exhibit 9.10 Comparison of Number and Annualized Value of Estimated Bladder Cancer Cases Avoided for All Regulatory Alternatives ($Millions)

Preferred Alternative 1 1 

### Alternative 2 Alternative 3
279 250 939 1296 (103 - 541) (127 - 397) (483 - 1,466) (675 - 1,988)

$1,531 $1,377 $5,167 $7,130
($233 - 3,536) ($209 - 3,180) ($786 - 11,936) ($1,085 - 16,468) $1,246 $1,126 $4,227 $5,832 ($190 - 2,878) ($172 - 2,600) ($644 - 9,758) ($888 - 13,464) $763 $686 $2,575 $3,552 ($165 - 1,692) ($149 - 1,522) ($558 - 5,712) ($769 - 7,880) $621 $561 $2,105 $2,904 ($135 - 1,376) ($122 - 1,243) ($457 - 4,665) ($630 - 6,436)

Sources: Appendix F.2v, 2w, 3v, 3w, 4d- e, 5d-e, 6b, 7b, 8b, 9b, 10b, 11b Appendix E, Exhibits E.38d, 40d, 41d, 42d Notes: Values are discounted and annualized in 2003$. The 90 per cent confidence bounds for cases avoided reflect uncertainty in PAR, reduction in average TTHM and HAA5 concentrations, and cess ation lag estimates. The 90 percent confidence bounds for benefits reflect uncertainty in monetization inputs relative to mean cases. Based on TTHM as an indicator, Villanueva et al. ( 2003) for baseline risk, and smoking/lung cancer cessation lag model. Assu mes 26 percent of cases are fatal, 74 percent are non-fata l (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. Footnotes: 1) Alternative 1 has lower be nefits than the Preferred Alternative because it does not incorporate the IDSE. Additionally, benefits of Alternative 1 would be higher if the va lue of reduced bromate was quantified. 2) The estimate of ave rage annual avoided cases is based upon the 25-year period of analysis and so is much lower than the ultimate number of cases to be avoided following the cessation lag period. Estimates of ultimate ly avoidable cases are shown in Exhibit 9.3 of this chapter.



### Annualized Mean Benefits of Cases Avoided (90% Confidence Bounds) 3%, Lymphoma

7%, Lymphoma

3%, Bronchitis

7%, Bronchitis Regulatory Alternative Discount Rate,WTP for NonFatal Cases Average Annual Number of Cases Avoided 2

### Final Economic Analysis for the Stage 2 DBPR 9-17 December 2005 

## Exhibit 9.11 Comparison of Costs for All Regulatory Alternatives ($Millions)

Net Benefits

Net benefits are calculated as the diffe rence between the monetized benefits and cost estimates. Exhibit 9.12 presents net benefits based upon the annualized present value of quantified, monetized benefits at 3 and 7 percent discount rates using ly mphoma and bronchitis as a WTP surrogate for bladder cancer. Accounting for the unquantified benef its that may be achieved through reduction of developmental and reproductive health risks would ra ise the overall net benefits. Exhibit 9.12 shows that, based upon the mean values for costs and benefits of each regulatory alternative, all alternatives provide benefits greater than their costs. It s hould be noted that accrual of any of the non-quantified benefits, described in Exhibit 9.2, would increase these net benefits.

Maximum Net Benefits

At both 3 and 7 percent discount rat es, the Preferred Regulatory Alternative achieves more net benefits than Alternative 1, while Alternatives 2 a nd 3 achieve more net benefits than the Preferred Regulatory Alternative. However, The M-DBP A dvisory Committee did not favor Alternatives 2 and 3 because it believed that the health effects data are not certain enough to warrant such a drastic shift in the nation’s drinking water treatment practices.

Incremental Net Benefits

The goal in comparing incremental net benefits is generally to identify the regulatory alternative, in a series of increasingly stringent alternatives, ha ving marginal net benefits that come the closest to zero while still being positive. Protection to the leve l of this alternative captures the most benefits possible among the alternatives without experiencing dimi nishing marginal returns. In Exhibit 9.13, each additional regulatory alternative generally costs more per benefit dollar (monetized value of risk reduction) than its predecessors; the exception is Alte rnative 1, which is dominated by the Preferred Lower

(5th %tile) Upper (95th %tile) Lower (5th %tile) Upper (95th %tile) Implem entatio

n IDSE Monit oring Plans Monitor ing Significant Excursion Lower (5th %tile) Upper (95th %tile) Lower (5th %tile) Upper (95th %tile)
Preferred 

### $840 $547 $1,109 $57 $42 $75 $12 $57 $4 $0 $1,315.1 $13 $79 $56 $102 $77 $55 $99

Alternative 1 

### $2,476 $1,572 $3,453 $215 $142 $290 $12 $57 $4 $0 $1,315.1 $13 $254 $166 $346 $242 $158 $330

Alternative 2 

### $4,708 $4,040 $5,411 $326 $288 $366 $12 $57 $4 $0 $1,315.1 $13 $422 $368 $478 $406 $354 $461
Alternative 3 

### $7,248 $6,047 $8,519 $483 $413 $554 $12 $57 $4 $0 $1,315.1 $13 $634 $536 $736 $613 $518 $713
Notes:

Estimates are discounted to 2003, and shown in 2003 dollars. Sources: Capital Costs: Appendix J, Exhibit J.1a-d O&M Costs: Appendix J, Exhibit J.1a - d Non-Treatment Costs: Appendix J, Exhibit J.2aq State Costs: Appendix J, Exhibit J.1j Total regulation costs: Appendix J, Exhibit J.2 as, aw, J.3-5(i and m) 90 Percent Confidence

Bound

Regulatory Alternative Discounted at 7%, 25 Years PWS Costs

State Costs Undiscounted Costs at Full Implementation, 2003$

Capital Costs O&M Costs

Mean V alue Discounted at 3%, 25 Years

Detail may not add due to independent rounding. The 90 percent confidence bounds reflect uncertainty in compliance forecasts a nd unit treatment costs. Values are in 2003$. 90 Percent Confidence

Bound 90 Percent Confidence

Bound Annualized Total Regulation Costs

Non-Treatment Costs (Point Estimate)

Mean V alue Mean V alue 90 Percent Confidence

Bound

Mean V alue5 

### Alternative 1 appears to have fewer benefits than th e Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a re quirement that is included only in Alternative 1.

Final Economic Analysis for the Stage 2 DBPR 9-18 December 2005 

### Alternative in this analysis 5 

### (costs more but produces fewer benef its) and hence is not included in the comparison (Exhibit 9.13). Alternatives 2 and 3 again look worth pursuing because their incremental benefits are positive. However, net benefits do not include the unquantified benefits. Because the Preferred Alternative uses a risk targeting strategy, the effect of unquantified benefits on the Preferred Alternative is expected to be greater in proporti on to cost as compared to Alternatives 2 and 3.



## Exhibit 9.12 Comparison of Mean Net Be nefits for All Regulatory Alternatives ($Millions)

3% 7%

Preferred 1,452 $ 1,170 $
A1 1 1,122 $ 885 $ A2 4,746 $ 3,821 $ A3 6,495 $ 5,219 $ Preferred $ 684 $ 544
A1 1 432 $ 319 $ A2 2,153 $ 1,698 $ A3 2,918 $ 2,291 $

Sources: Exhibits 9.10 and 9.11 Footnote 1: Alternative 1 appears to have fewer benefits than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a requirement that is included only in Alternative 1. Notes: All values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. Bronchitis Rule Alternative WTP for Non-Fatal

Bladder Cancer Cases

Lymphoma Mean Net Benefits (Million$)

### Final Economic Analysis for the Stage 2 DBPR 9-19 December 2005 

## Exhibit 9.13 Incremental Net Benefits fo r All Regulatory Alternatives ($ Millions)

Annual Costs Annual Benefits Incremental Costs Incremental Benefits Incremental Net Benefits
A B C D E=D-C



### Preferred 79 $ 1,531 $ 79 $ 1,531 $ 1,452 $
Alternative 1 1 

### 254 $ 1,377 $ Alternative 2 422 $ 5,167 $ 343 $ 3,637 $ 3,294 $ Alternative 3 634 $ 7,130 $ 212 $ 1,962 $ 1,750 $ Preferred 79 $ 763 $ 79 $ 763 $ 684 $
Alternative 1 1 

### 254 $ 686 $ Alternative 2 422 $ 2,575 $ 343 $ 1,812 $ 1,469 $ Alternative 3 634 $ 3,552 $ 212 $ 978 $ 765 $

Preferred 77 $ 1,246 $ 77 $ 1,246 $ 1,170 $
Alternative 1 1 

### 242 $ 1,126 $ Alternative 2 406 $ 4,227 $ 330 $ 2,981 $ 2,651 $ Alternative 3 613 $ 5,832 $ 207 $ 1,605 $ 1,399 $ Preferred 77 $ 621 $ 77 $ 621 $ 544 $
Alternative 1 1 

### 242 $ 561 $ Alternative 2 406 $ 2,105 $ 330 $ 1,484 $ 1,154 $ Alternative 3 613 $ 2,904 $ 207 $ 799 $ 593 $

Sources: Costs: Appendix J.2 (as,aw), J.3-5 (i,m) Benefits: Appendix F.2 - 3 (v-w), F.6 - 11 (c,d) Footnote 1 Footnote 1

Footnote 1

Footnote 1

Footnote 1: Alternative 1 appears to have fewer benefits than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furtherm ore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer ca ses), a requirement that is included only in Alternative 1. This means that Alternative 1 is dominated by the Pr eferred Alternative in this analysis (having higher costs than the Preferred Alternative but lower benefits), and so it is not included in the incremental comparison of alternatives (Columns C - E). OMB states this in terms of comparing cost effectivene ss ratios, but the same rule applies to an incremental cost, benefits, or net benefits comparison: "When constructing and comparing incremental cost-effectiveness ratios, [analysts] ... should make sure that inferior alternatives identified by the principles of strong and weak dominance are eliminated from consideration." (OMB Circular A-4, p. 10)



### WTP for NonFatal Bladder Cancer Cases Rule Alternative

Lymphoma

Bronchitis Bronchitis

Lymphoma 3 Percent Discount Rate

7 Percent Discount Rate

Notes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes th at benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer.6 

### The WTP values, shown here in 2003$, could be used to develop an annualized WTP value for the most accurate comparison to the annualized cost per case avoided va lues presented in Exhibit ES.11. First, they would be increased over the period of analysis to reflect the elasticity of WTP in response to increases in real income. Second, the WTP value would be weighted differentially over the pe riod of analysis to reflect in the annualized value the difference in the number of cases avoided, which varies on an annual basis. Third, because the cases avoided in the CEA ratio include both fatal and non-fatal cases, the VSL woul d be incorporated into a weighted average (to reflect that 26% of cases are fatal) with the WTP value after it, too, was increased over time to reflect the above 2 considerations. However, each of these factors would in crease the threshold used in this analysis; therefore annualized WTP values are not calculated because the Preferre d Alternative, and most of the other alternatives, have costs per case avoided that are already below the lowest of the thresholds ($0.80 million in 2003$).

7 

### As mentioned previously, Alternative 1 appears to ha ve fewer benefits than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially a ssociated cancer cases), a requirement that is included only in Alternative 1.

Final Economic Analysis for the Stage 2 DBPR 9-20 December 2005 

### 9.3.3 Cost-Effectiveness

Evaluation of the relative merits of one alternative over another is often made with regard to costeffectiveness. This concept can be defined simply as getting the greatest benefits for a given expenditure, or imposing the least cost while achieving a given le vel of benefits. Although cost effectiveness does not usually account fully for social impacts, it does provide a measure of technical efficiency and can supplement a benefit cost analysis (BCA) or other analyses as part of a comprehensive EA. In a cost effectiveness analysis (CEA), generally either the costs or the benefits will be monetized and the nonmonetized factor will be quantified.

In this CEA, technical efficiency is evaluate d in terms of the cost per case of bladder cancer avoided (fatal and non-fatal) revi ewed in a relative sense (by comparison among alternatives) and an absolute sense (by comparison to thresholds). In calculating the cost per case avoided, the cases of illnesses and fatalities are discounted to make the benef its comparable to the discounted costs. OMB Circular A-4 provides that “there is professional c onsensus that future health effects, including both benefits and costs, should be discounted at the same rate....This consensus applies to both BCA and CEA”(p. 34). Costs and benefits are discounted b ased upon when they accrue in the analysis period, using 3 and 7 percent annual discount rates for both. Although a similar analysis cannot be made for the unquantified benefits of the rule, it is expected that the results would follow the same pattern, with more stringent alternatives generally (with the exception of Alternative 1, See Footnote 4) reaping more benefits at higher total costs than less stringent alternatives.

For the purpose of evaluating cost-effectiveness in an absolute sense, this CEA compares the cost per case avoided, shown in the two left hand columns in Exhibit 9.14, to an established threshold. If an alternative is cost effective, this cost ratio will be equal to or less than the value of the WTP estimate. In this EA, the WTP values for avoidance of non-fatal lymphoma (mean of $4.49 million in 2003$) or chronic bronchitis (mean of $0.80 million in 2003$) serve as surrogates for a measure of what society is willing to pay to avoid a non-fatal case of bladder can cer. For the purpose of this comparison, the cost ratios should be lower than these thresholds. 6 

### The Preferred Alternative meets this criterion for both thresholds: at discount rates of 3 and 7 percent, its cost ratios of $0.35 and $0.43 million, respectively, are less than or equal to the lymphoma and bronc hitis WTP values of $4.49 million and $0.58 million, respectively. Alternatives 1, 2, and 3 meet this criterion with regard to the WTP for avoiding non-fatal lymphoma, and Alternatives 2 and 3 also cost less th an the WTP for chronic bronchitis. Alternative 1 is not competitive for reasons explained previously. 78 

### The incremental gain of a first alternative (in a series of increasingly stringent alternatives) is equivalent to the CEA ratio of that alternative and captures the large amount of benefits achieved by having a rule (compared to the status quo). The differences between subsequent rule alternatives are quite narrow by comparison. Since Alternative 1 is more expensive than the Preferred Alterna tive but this EA does not calculate additional benefits for it, its incremental ratio would be negative, therefore it is excluded from the comparison in Exhibit 9.14. Alternative 1 appears to have fewer benefits than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a requirement that is included only in Alternative 1.

Final Economic Analysis for the Stage 2 DBPR 9-21 December 2005 

### A relative comparison determines the lowest cost per case avoided among the alternatives. Exhibit 9.14 presents each regulatory alternativ e in increasing order of these cost ratios. The estimated cost ratio for the Preferred Alternative is always lower than the cost ratios of the other alternatives, indicating that the Preferred Alternative is the most cost-effective by this measure at both 3 and 7 percent discount rates. Alternatives 2 and 3 have cost ratios closest to those of the Preferred Alternative and are more cost effective than Alternative 1, which h as the highest cost ratio based on current available information (See Footnote 6).

An incremental CEA determines, for a series of increasingly stringent alternatives, the marginal gain for the increase in expenditure from one alternative to the next more stringent alternative. As in the previous paragraphs describing co mparisons using the average cost per case avoided, the incremental analysis can provide information in an absolute sense (by comparing to thresholds) or a relative sense (by comparing among alternatives). If the incremental co st is less than the WTP values to avoid the risk (mean of $4.49 million and $0.80 million for lymphoma a nd bronchitis, respectively), then the regulation is cost effective. An incremental cost that is greater than the WTP value would indicate that an alternative does not avoid enough additional cases beyond the less stringent alternative to justify the additional cost. The two right hand columns in Exhi bit 9.14 show that, setting aside Alternative 1 (See Footnote 5), each alternative is cost effective in an absolute sense

! 

### relative to the WTP value for avoiding non-fatal lymphoma. Additionally, the Preferred Altern ative and Alternative 2 are also cost effective compared to the lowest threshold (WTP to avoid ch ronic bronchitis), and Alternative 3 generally costs less than the lowest threshold at a 3 percent discount rate (at a 7 percent discount rate, the ratio for Alternative 3 slightly exceeds the threshold, with a unit cost ratio of $0.85 million). In a relative comparison, the Preferred Alternative, capturing a ll of the benefits of implementing any reasonable regulation over maintaining the status quo, captu res the largest portion of benefits and has a lower"incremental" cost th an the other alternatives. 8 

### As expected, Alternatives 2 and 3 show a pattern of increasing incremental cost with increasing stringency.

An additional CEA is presented in terms of the quality-adjusted life years (QALYs) saved for avoided cases of bladders cancers in Appendix N to this analysis; a sensitivity analysis in section 3.0 of Appendix N incorporates the QALYs potentially would be saved from avoided cases of colon and rectal

cancers.
In summary, the average cost per case avoided compares favorably to both of the thresholds (WTP surrogate values for avoiding bladder cancer) used in this EA, indicating that the alternatives are all cost effective by this measure. In a relative comp arison among the alternatives (setting aside Alternative 1, for reasons explained previously), the Prefe rred Alternative is the most cost effective.

### Final Economic Analysis for the Stage 2 DBPR 9-22 December 2005 

## Exhibit 9.14 Incremental Cost Per Case Avoided 1 

## for All Regulatory Alternatives by Discount Rate ($Millions)

Preferred 0.33 $ 0.41 $ 0.33 $ 0.41 $
Alternative 1 1.18 $ 1.42 $
Alternative 2 0.52 $ 0.63 $ 0.60 $ 0.73 $ Alternative 3 0.57 $ 0.69 $ 0.69 $ 0.85 $

Sources: Footnotes: 1) The cost effectiveness ratios are a potentially a high estimate in that the regulatory costs in the numerator are not adjusted by subtracting the medical costs associated with cases avoided to produce a net cost numerator. Subtraction of theses costs would not be expected to alter the ranking of alternatives. In the case where thresholds of maximum public expenditure per case avoided are prescribed, defining the numerator more precisely by making such adjustments would be appropriate. 

### Incremental Cost Per Case Avoided Rule Alternative Cost Per Case Avoided
3% 7% 3% 7%

Footnote 2

Discounted costs: Appendix J, Exhibits J.2as,aw; J.3-5, (i,m) Discounted cases avoided: Appendix E, Exhibits E.38a; E.40- E.42,a 2) In reference to conducting incremental CEA, OMB states that the analyst should make sure that "When constructing and comparing incremental cost-effectiveness ratios, [analysts] ... should make sure that inferior alternatives identified by the principles of strong and weak dominance are eliminated from consideration." (OMB Circular A-4, p. 10) Alternative 1 is dominated by the Preferred Alternative and is therefore not included in the incremental analysis. The reason for this domination is mainly that the Preferred Alternative includes IDSE and Alternative 1 does not; and to a lesser degree because the bromate control included in Alternative 1 increases the costs but the benefits of this control are not quantified at this time. Alternative 2 is compared directly to the Preferred Alternative (skipping Alternative 1) in this analysis. Notes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, and smoking/lung cancer cessation lag model. Assumes 26 percent of cases are fatal, 74 percent are non-fatal (USEPA 1999a). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer.

### Final Economic Analysis for the Stage 2 DBPR 9-23 December 2005 

### Benefit Cost Ratios

Benefit cost ratios can be used to rule out an alternative that does not have at least a ratio of benefits to cost equal to or greater than 1. Exhib it 9.15 shows that each regulatory alternative of the Stage 2 DBPR has a benefit cost ratio greater than 1. Ho wever, as in the case of the CEA ratio, the information provided by the benefit cost ratios does not allow fo r comparison across the regulations unless either the benefits or costs are identical for all alternatives, which they are not in this case. A thorough analysis must consider the net benefits, and the absolute costs and benefits in addition to the benefit cost ratio.



## Exhibit 9.15 Benefit Cost Ratios for All Regulatory Alternatives

9.4 Effect of Uncertainties on the Es timation of Net National Benefits

Detailed discussions of the assumptions and uncerta inties associated with national benefits and costs are contained in Chapters 3, 5, 6 and 7. A summary of the key uncertainties and the effects of uncertainty in those assumptions on the benefits and cost analyses are presented in Exhibit 9.16. See Sections 3.8, 5.7, 6.6, and 7.8 for a full listi ng of assumptions for which there is uncertainty.

EPA is aware that there is uncertainty in the pr ediction of the net benefits. Where possible, this uncertainty was incorporated into the cost and benefits models and is incorporated in the range of costs and benefits shown in Exhibits 9.10 and 9.11. In some cases enough information was not available to predict the magnitude of uncertainty in either costs or benefits. Uncertainties that are not quantified are also listed in Exhibit 9.16.

EPA believes that uncertainty in the compliance forecast has a potentially large influence on cost and benefits estimates in this EA. Thus, the Agency has attempted to quantify the uncertainty by giving 3 Percent Discount Rate 7 Percent Discount Rate Preferred 19.4 16.2 Alternative 1 1 

### 5.4 4.7 Alternative 2 12.3 10.4 Alternative 3 11.2 9.5

Source: Exhibit 9.13 

### Benefit Cost Ratio

Rule Alternative

Notes: Based on TTHM as an indicator, Villanueva et al. (2003) for baseline risk, smoking/lung cancer cessation lag model, and lymphoma as WTP estimate. Assumes 26 percent of cases are fatal, 74 percent ar e non-fatal (USEPA 1999a ). EPA recognizes that benefits may be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer. Footnote 1: Alternative 1 appears to have fewer benefits than the Preferred Alternative because it does not incorporate the IDSE, as explained in Chapter 4. Furthermore, this EA does not quantify the benefits of reducing the MCL for bromate (and potentially associated cancer cases), a requirement that is included only in Alternative 1.

### Final Economic Analysis for the Stage 2 DBPR 9-24 December 2005 

### equal weight to two different compliance forecast approaches. One compliance forecast approach is based on the SWAT predictions, and the other is based on the ICR matrix method. The ICR Matrix Method uses the same basic approach as SWAT, but uses TTHM and HAA5 data from the ICR directly to estimate the percent of plants changing technology to comply with the Stage 2 DBPR and the resulting DBP reduction (see Chapter 5 for more information on these approaches). To characterize the uncertainty of the compliance forecast results, EPA assumes a uni form distribution between SWAT and ICR Matrix Method results. That is, the national cost estimat es presented in this chapter represent the midpoint between costs estimated using the SWAT model, and those estimated using the ICR Matrix Method. Cost estimates using the SWAT model are about 25% lower than the midpoint estimates while those using the ICR Matrix Method are about 25% higher. Benef its estimates using the SWAT model are about 30 percent lower than the midpoint estimate, while tho se using the ICR Matrix Method are about 30 percent higher.

A related source of uncertainty in producing net benefits is the potential impacts of the IDSE on the compliance forecast, which will determine the treat ment technology installed and the level of costs incurred and benefits achieved by the rule. Th e primary economic analysis of the Stage 2 DBPR incorporates the uncertainty of the IDSE effect by forecasting treatment technology change for large and medium surface water systems based upon a safety margin that is equally weighted between 20 and 25 percent. To inform the reader of the potential ma gnitude of this uncertainty, EPA conducted a sensitivity analysis to compare the predicted percentage of pl ants changing technology a nd related reduction in DBP occurrence for the 20 and 25 percent safety margin. Results, presented in Exhibit 5.29, show that the impact of the safety margin on the analysis is more pronounced for national DBP reduction estimates than for estimates of total percent of plants changing treatme nt technology. This implies that uncertainty in the IDSE will have a larger impact on the national benef its estimates compared to the national cost estimates.

Another source of uncertainty is the approach used to estimate the number of bladder cancer cases in the baseline that can be attributed to DB P occurrence and exposure, and the number of cases that can be avoided by implementation of the Stage 2 DBPR. EPA has developed three approaches to estimating the number of bladder cancer cases attributable to DBPs. Taken together, the three approaches provide a reasonable estimate of the range of potential risks. For simplicity’s sake, one estimate, based on a 2003 meta-analysis by Villanueva et al., is carried through the full benefits analysis.

### Final Economic Analysis for the Stage 2 DBPR 9-25 December 2005 

## Exhibit 9.16 Effects of Uncertainties on National Estimates

Assumptions for Which

There Is Uncertainty Section with Full Discussion of Uncertainty Potential Effect on Benefit Estimate Potential Effect on Cost Estimates

Underestimate Overestimate Unknown Impact Underestimate Overestimate Unknown Impact
Uncertainty in the industry baseline (SDWIS and 1995 CWSS data) 3.4 X X

Uncertainty in observed data and predictive tools used to characterize DBP occurrence for the pre-Stage 1 baseline 3.7 X X

Uncertainty in predictive tools used to develop the compliance forecast for surface water systems (SWAT and ICR Matrix Method) Chapter 5, Appendix A Quantified in primary analysis (addresses potential underestimate or overestimate) Quantified in primary analysis (addresses potential underestimate or overestimate)

Uncertainty in ground water compliance forecast methodologies Chapter 5, A and B X X

Operational safety margin of 20% 5.2 X X

Impacts of the IDSE on the compliance forecast and predicted DBP reduction for the Preferred Regulatory Alternative 5.7 Quantified in the primary analysis (addresses potential underestimate) Quantified in the primary analysis (addresses potential underestimate)

Uncertainty in the PAR value 6.1.1 Appendix E Quantified in the primary analysis (addresses range of potential effects, but true values could lie outside the range)

Reduction in TTHM and HAA5 used as proxies for all chlorination DBPs 6.3.3 X

DBPs have a linear nothreshold dose-response relationship for bladder cancer effects 6.2.1 X

Uncertainty in benefits valuation inputs 6.5.2 Quantified in the primary analysis (addresses potential underestimate or overestimate)

### Assumptions for Which There Is Uncertainty Section with Full Discussion of Uncertainty Potential Effect on Benefit Estimate Potential Effect on Cost Estimates

Underestimate Overestimate Unknown Impact Underestimate Overestimate Unknown Impact

Final Economic Analysis for the Stage 2 DBPR 9-26 December 2005 Benefits of reduced cancers other than bladder cancer are not included in the quantitative analysis 6.7 Quantified in a sensitivity analysis (addresses potential underestimate)

Value of potential reproductive and developmental health effects avoided is not quantified in the primary analysis 6.8 X

Treatment costs do not include costs for minor operational changes predicted by SWAT 7.4.2 X

Median operational and water quality parameters considered for technology unit costs 7.4.1 X

Economies of scale for combination treatment technologies not considered 7.4.1 X

UV dose assumptions needed for viral inactivation 7.4.1 X

Potential low-cost alternatives to treatment not considered 7.4.2 X

Uncertainties in unit costs 7.4.3 Quantified in primary analysis (addresses potential overestimate or underestimate)

### Final Economic Analysis for the Stage 2 DBPR 9-27 December 2005 

## 9.5 Summary of Conclusions

The following is a summary of the important points that must be considered when weighing the benefits and costs of the Stage 2 DBPR.

1) The quantified benefits estimate is potentia lly understated because it does not include the benefits for reductions in adve rse reproductive and developmental health effects, other health effects, and non-health effects associated with DBP reduction (Exhibits 9.2 - 9.4). At the same time, EPA recognizes that the lower bound of the quantified benefits estimates could be as low as zero since causality has not yet been established between exposure to chlorinated water and bladder cancer.

2) The mean cost estimates of $78.8 million (3 percent discount rate) and $76.8 million (7 percent discount rate) represent EPA’s best estimates of the monetary impacts of the Stage 2 DBPR Preferred Regulatory Alternative.

3) The Stage 2 DBPR Preferred Regulatory Alternative provides the greatest benefits at a cost level that is considered reasonable (i.e., is not cost prohibitive) (Exhibits 9.11 and 9.12) given the uncertainties in health effects data. Alte rnatives 2 and 3 have a greater net benefit than the Preferred Alternative. However, net be nefits do not include th e unquantified benefits. Because the Preferred Alternative uses a risk ta rgeting strategy, unquantif ied benefits effects for the Preferred Alternative are expected to be higher in proportion to cost as compared to Alternatives 2 and 3.

4) Evaluation of the cost-effectiveness of the Pref erred Regulatory Alternative shows it to be the most cost effective according to th e measures evaluated (Exhibit 9.14).

5) The number of cases of cancer that must be a voided to break even with the cost of the Preferred Alternative rule are approximate ly 18 and 17 cases using 3 and 7 percent discount rates, respectively, and the WTP estimate for avoiding non-fatal lymphoma cases. Using the WTP for avoided chronic bronchitis, the break even points are about 99 and 96 cases based on 3 percent and 7 percent discount rates, resp ectively. The mean estimate of 279 bladder cancer cases avoided annually is much higher than (achieves more than) both of these thresholds (Exhibit 9.8).

As a result of all these considerations, EPA believ es that the annualized benefits of the Stage 2 DBPR will likely exceed the annualized national cost s and will be effective in reducing the risks to consumers from exposure to DBPs in drinking water.

### Final Economic Analysis for the Stage 2 DBPR R-1 December 2005 

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USEPA. 2000e. Data Reliability Analysis of the EPA Safe Drinking Water Information System/Federal Version (SDWIS/FED). Office of Wa ter. EPA 816-R-00-020. October, 2000.

USEPA. 2000f. Quantitative Cancer Assessment for MX and Chlorohydroxyfuranones. Contract NO. 68–C–98–195. August 11, 2000. Office of Water, O ffice of Science and Technology, Health and Ecological Criteria Division, Washington, DC.

USEPA. 2000g. Regulatory Impact Analysis for the Proposed Ground Water Rule. Office of Ground Water and Drinking Water. Contr act 68-C-99-206 and 245. April 5, 2000.

USEPA. 2000h. ICR AUX1 Database. April, 2000 version.

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USEPA. 2001a. User Database. Prepared by The Cadmus Group, Inc.

USEPA. 2001b. SWAT run summaries for the Stage 2 DBPR EA. Prepared by The Cadmus Group, Inc.

USEPA. 2001c. Drinking Water Baseline Handbook, Third Edition. Draft. Prepared by International Consultants, Inc. Contract 68-C6-0039. May, 2001.

USEPA. 2001d. Arsenic Rule Benefits Analysis: An SAB Review. Science Advisory Board. EPASAB-RSAC-01-005. May 2001.

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USEPA. 2003g. Draft Ultraviolet Disinfection Guidance Manual , EPA 815-D-03-007, June 2003.

### Final Economic Analysis for the Stage 2 DBPR R-17 December 2005 

### USEPA. 2003r. Arsenic in Drinking Water: Cessation Lag Model. Prepared by Sciences International. Contract No. 68-c-98-195. January 2003.

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USEPA. 2003t. SDWIS Database. 4 th 

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USEPA. 2005l. Information Collection Request for National Primary Drinking Water Regulations: Final Stage 2 Disinfectants and Disinfection Byproducts Rule. Prepared by The Cadmus Group, Inc. Contract 68-C-99-206. December 2005. EPA 815-Z-05-002.

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### Final Economic Analysis for the Stage 2 DBPR R-18 December 2005 

### USEPA. 2005p. The Cost of Illness Handbook. Accessed on-line in January 2005: www.epa.gov/oppt/coi/index.html. 

## USEPA 2005q. Unregulated Contaminant Monitoring Regulation (UCMR) for Public Water Systems Revisions. Proposed Rule. 70 FR 49093-49138, August 22, 2005.

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## . USFDA. Hazard Analysis and Critical Control Poin t (HAACP); Procedures for the Safe and Sanitary Processing and Importing of Juice; Final Rule. 66 FR 6137; January 19, 2001.

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