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 Peak 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 Adverse 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 Potential 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 Reductions 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 Significantly 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, and 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, Attributable 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 Disinfection Under the Ground Water Rule

Appendix N: Cost Effectiveness Analysis Using 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 for 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 Occurrence 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 Data 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 Occurrence 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 Compliance 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 LRAAMAX - ICR LRAA2ndHI for Stage 2 Non-Compliant

Plants (TTHM data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14

Exhibit 5.4c Cumulative Distribution of ICR LRAAMAX - ICR LRAA2ndHI for Stage 2 Non-Compliant

Plants (HAA5 Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15

Exhibit 5.5 Compliance Analysis of ICR Screened 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 Incorporating 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 Deltas 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 Deltas 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 DBPR Preferred Alternative (80/60 LRAA, IDSE)- 25

Percent Safety Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39

Exhibit 5.16c ICR Matrix Method for 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 Stage 2-Compliant Ground Water Plants Using

Chloramines and/or an Advanced Treatment Technology . . . . . . . . . . . . . . . . . . . . . . 5-47

Exhibit 5.21a ICR Matrix Method for Ground Water Plants for the Stage 2 DBPR Preferred 

Alternative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-48

Exhibit 5.21b ICR Matrix Method for Ground Water 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 Uncertainties 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 for 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 Water 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 Values Associated with Average TTHM Exposures 

for Both Sexes from Villanueva et al. (2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-17

Exhibit 6.8 Detailed Data on OR as a Function 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 Assessments 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 and 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 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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51

Exhibit 6.16 Availability of Reproductive and Developmental Toxicology Studies for 

Specific DBPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-55

Exhibit 6.17 Reproductive and Developmental Health 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 TTHM1

 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 Cessation 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 Avoided 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 Concentrations 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 Benefits 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 Benefits 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 Stage 2 DBPR, Preferred Regulatory Alternative 

(Millions, 2003$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-90

Exhibit 6.28 Benefits Summary for the Stage 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 Value1

 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/Year) 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 Forecasts for Surface Water Systems . . . . . . . . . . . . 7-33

Exhibit 7.13 Total Initial Capital Costs ($Millions) and Yearly O&M Costs ($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 Stage 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 Compliance 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 Determining 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 Estimates 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 for the Stage 2 Preferred Regulatory Alternative 

($Millions) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-12

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) . . . . . . 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 Annualized 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 Benefits for All Regulatory Alternatives ($Millions) . . . . 9-18

Exhibit 9.13 Incremental Net Benefits for All Regulatory Alternatives ($ Millions) . . . . . . . . . . . 9-19

Exhibit 9.14 Incremental Cost Per Case Avoided1 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

ED10 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

LED10 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/cm2 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

O3 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 regulation 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 requirements, all of which are addressed in this Economic

Analysis (EA) for the Stage 2 Disinfectants and Disinfection 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 effort 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 analysis of their impacts. This two-stage approach was

subsequently incorporated into the 1996 Safe Drinking Water Act (SDWA) Amendments. The first stage of the

M-DBP rulemaking process culminated with the joint promulgation 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 rules 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 disinfection, 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 other cancers. While there are uncertainties in the

quantitative relationship between the incidence of these 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 link has been established, EPA believes the evidence

supports concern for these potential hazards 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 control 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 extensive 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 [total 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 locational 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 compliance 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 Alternative 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 equivalent 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 (NTNCWSs)—both ground and surface water systems2

—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 Stage 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 Preferred Regulatory Alternative is described below and

illustrated in the flow chart in Exhibit ES.1. Exhibit 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 compliance monitoring locations that represent distribution

system sites with high TTHM and HAA5 levels. The IDSE 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 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. 

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 system-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 Requirements
Final Economic Analysis for the Stage 2 DBPR ES-5 

Schedule 1Systems serving> 100,000 1

IDSE Plan Due IDSE Report Due Begin ComplianceOctober 1, 2006 January 1, 2009 April 1, 2012

Schedule 2Systems serving 50,000 to 99,999 1

IDSE Plan Due IDSE Report Due Begin ComplianceApril 1, 2007 July 1, 2009 October 1, 2012

Schedule 3Systems serving 10,000 to 49,999 1

IDSE Plan Due IDSE Report Due Begin CompliancOctober 1, 2007 January 1, 2010 October 1, 2013

Schedule 4Systems serving < 10,000 1

IDSE Plan Due IDSE Report DueApril 1, 2008 July 1, 2010 Begin Complianc

October 1, 2013

2006 2007 2008 2009 2010 2011 2012

Crypto mon.3

Treatment Installation Possib

2013 20

Treatment Installation Possible Extension 2

Treatment Installation Possible Extension 2

2006 2007 2008 2009 20

2010 2011 2012 2013

Treatment Installation Possib

LT2 Crypto monitoring

IDSE mon.

LT2 Crypto monitoring

LT2 Crypto monitoring

IDSE mon.

IDSE mon.

IDSE mon.

E. Coli mon.

Exhibit ES.2 Implementation Timeline for the Stage 2 DBPR

1 Includes all systems that are part of a combined distribution system that have a largest system with this population.2 A State may grant up to an additional 2 years for systems to comply if the State determines that additional time is necessary for capital improvements.3 Subpart H systems that must conduct Cryptosporidium monitoring have an additional 12 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 is either 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 monitoring approach for the Stage 2 DBPR, where

compliance and IDSE monitoring requirements are based only on source water type and retail population

served. This is a change from the plant-based approach used in the 1979 TTHM rule and the Stage 1

DBPR. EPA’s decision to use a population-based approach for all systems is based on improved public

health protection, flexibility, and simplified implementation. 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 could 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 concentrations 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 evaluation 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 analytical results that caused the excursion.

Requirements for Consecutive Systems

The Stage 2 DBPR includes provisions for consecutive 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, and 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 Population-Based Compliance Monitoring

Requirements

System Size 

(Population Served )

Distribution System Sample

Locations1

Total

Sample

Locations per

System5

Monitoring

Frequency3

Highest

TTHM

Locations

Highest

HAA5

Locations

Existing

Stage 1

Compliance 

Locations2

Systems Using Surface Water in Whole or in Part4

< 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/Primacy Agency requires different or additional locations.

Locations should be distributed throughout the distribution system to the extent possible. 2

 Alternate between highest HAA5 LRAA and highest TTHM 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, alternate 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” systems 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, respectively. 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 Applicable
3

 The baseline number of plants–as opposed to systems–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 systems subject to the rule and the estimated number

that will perform various rule activities (implementation, IDSE monitoring, monitoring plans, and

operational evaluations).3

 This baseline is derived from EPA’s Safe Drinking Water Information System

(SDWIS) inventory, 4th 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 all 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 monitoring 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 may 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 operational 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 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 install disinfection after the IDSE requirement period is complete. Systems installing

disinfection for the GWR will, however, be required to prepare monitoring plans and in some cases,

monitor DBPs for the first time under Stage 2. Exhibit ES.4b shows that all newly disinfecting ground

water systems will prepare monitoring plans. All newly 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 

IDSE Monitoring Plans

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. Some 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. Overall 

the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. See Exhibit H.8a, 

column H, for the change in total samples for the different size categories.

Exhibit ES.4a Number of Disinfecting 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 Activities for Systems Installing Disinfection

to Comply with the Ground Water Rule

ES.5 National Benefits and Costs of the Stage 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 benefits that EPA has quantified is the expected range of

avoided new cases of bladder cancer each year, including both fatal and non-fatal cases. In addition, EPA

has estimated the monetized value of avoiding these fatal and non-fatal bladder cancer cases. 

The major steps in deriving and characterizing 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 exposure; and (3) Estimate the reduction in future cases

corresponding to anticipated reductions in DBP occurrence and exposure due to the Stage 2 DBPR. For

step 2, 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

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 estimates of willingness-to-pay to avoid non-fatal cases

(one based on curable lymphoma and the other based on chronic bronchitis). Exhibit ES.5 summarizes
4

 For the Stage 2 DBPR cost and benefit analyses, calculations are made using two discount rates–3 and 7

percent–to represent current policy evaluation methodologies. 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 and 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 concentrations that result from making treatment technology

changes. 

There are several categories of unquantified potential 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 cancers, and reproductive and developmental effects. In

addition to bladder cancer, human epidemiology studies 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 studies on colon and rectal cancers continues to support

a possible association, but evidence remains mixed. For these reasons, EPA performed a sensitivity

analysis to determine potential reductions in rectal and colon cancer cases as a result of the Stage 2

DBPR. 

Scientific knowledge about the association of reproductive 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 adverse reproductive or developmental effects and

chlorinated drinking water, a weight of evidence evaluation 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 and 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 costs 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 Preferred Regulatory Alternative. Note that the exhibit

presents two estimates for national costs and monetized 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 

Surface Water Disinfecting Ground Water

 < 10,000 > 10,000 < 10,000 > 10,000

9.0 260.5 3.5 5.8 

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 smoking/lung cancer cessation lag model (90% confidence bounds) ($7.5 - $113.8) ($217.6 - $3304.0) ($2.9 - $44.6) ($4.9 - $73.7) WTP for Chronic Bronchitis as the basis for non-fatal cases, $24.5 $712.7 $9.6 $15.9 smoking/lung cancer cessation lag model (90% confidence bounds) ($5.3 - $54.5) ($154.3 - $1581.2) ($2.1 - $21.4) ($3.4 - $35.3) 

Annualized Total Costs (90% confidence bounds) $12.2 $35.7 $18.1 $11.1 

($7.2 - $17.3) ($21.0 - $51.1) ($16.1 - $20.2) ($10.2 - $11.9) 

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 smoking/lung cancer cessation lag model (90% confidence bounds) ($6.0 - $91.4) ($177.4 - $2690.6) ($2.4 - $35.8) ($3.9 - $59.9) 

WTP for Chronic Bronchitis as the basis for non-fatal cases, $19.7 $580.3 $7.7 $12.9 smoking/lung cancer cessation lag model (90% confidence bounds) ($4.3 - $43.7) ($85.5 - $873.2) ($1.7 - $17.1) ($2.8 - $28.6) 

Annualized Total Costs (90% confidence bounds) $11.5 $36.1 $17.0 $10.5 

($7.0 - $16.2) ($21.3 - $51.1) ($15.0 - $19.0) ($9.6 - $11.3) 

Notes:Sources:

Estimated Number of Bladder Cancer Cases Avoided per YearIncludes both fatal and non-fatal cases. Expected value (90% confidence bounds) Based on Villanvueva et al. 2003 for baseline risk estimates, smoking/lung cancer cessation lag model, and TTHM as an indicator of all DBPs.

Type of Cost or Benefit

Totals 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 and Costs Based on Annualization Discount Rate of 3 %

Benefits for the Stage 2 DBPR are estimated using three approaches for estimating baseline risk, three different cessation lag models, and either TTHMs or 

benefits presented here are estimated using Villanueva et al. (2003) for baseline risk, TTHMs as an indicator, and the Smoking/Lung Cancer cessation lag m

indicator for all DBPs produces similar results. Because Villaneuva et al. (2003) and the Smoking/Lung Cancer cessation lag model result in benefits estimaalternatives, results are presented as a representative comparison to costs. Chapter 6 presents results for the full range of alternative approaches to estimatThe 90 percent confidence bounds around cost address uncertainty in the compliance forecast methodology, potential impacts of the IDSE, and unit costs. 9cases avoided accounts for uncertainty in the compliance forecast, PAR, cessation lag model form, and predicted DBP reduction from the compliance forecaaround monetized benefits also reflect uncertainty in VSL and WTP inputs. Causality has not been established, so lower bound of potential risks may be as Detail may not add due to independent rounding. Monetized benefits and costs are discounted and annualized in 2003 dollars. Costs are for CWSs and NTtreatment costs. 

Benefits and Costs Based on Annualization Discount Rate of 7 %

Exhibit ES.5 Summary of Estimated National Benefits and Costs of the Stage 2 D

Preferred 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 Compliance Forecast and Consequent Reductions in DBPs

Changes in concentrations of DBPs are the direct result of changes in treatment technologies. 

Therefore, it is important that EPA uses consistent 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 analysis). This section summarizes the tools used and key

assumptions for the Stage 2 DBPR compliance forecasts and the consequent reductions in TTHM and

HAA5 concentrations.

Since the rule was proposed, EPA has modified 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 concentration, 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 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 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 forecasts 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. SWAT uses a series of decision rules and algorithms to

predict the number of plants making treatment technology changes and the type of treatment they will

install for a specific regulatory alternative, based on source water quality and existing treatment as

reported in the ICR database. The ICR Matrix Method 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 was generated using the ICR Ground Water Delphi

process, which convened a group of experts to evaluate plant configurations 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 water 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 information 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 potential developmental and reproductive health benefits

is based on reductions in occurrences of peak concentrations. 

To predict changes in average TTHM and HAA5 levels 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 and 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 and 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 distribution systems. As with the compliance forecast,

SWAT and the ICR Matrix Method produce different results for DBP levels; thus, both are incorporated

into a Monte Carlo simulation model to further quantify uncertainty in the national benefits estimate. 

ICR ground water plant data were not robust enough 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 treatment technology predictions, it was not calibrated to plantlevel DBP predictions and, thus, could not be used to assess changes in occurrence of peak levels. See Appendix A

for more information on SWAT.

6

 There is uncertainty in using the ICR data to represent pre-Stage 1 conditions because some plants may

have begun making changes prior to the ICR in anticipation 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 occurrence of peak TTHM and HAA5 concentrations, EPA used only

the ICR Matrix Method5

. Similar to the way in which it is used to evaluate changes in average TTHM

and HAA5 concentrations, 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 occurrence of peaks after treatment technology changes, EPA analyzed TTHM and HAA5

occurrence data for those surface water plants already 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 characterize large plants are from the ICR, which were

collected before the Stage 1 DBPR compliance deadlines and likely represent pre-Stage 1 conditions6

. 

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. Similarly, the post-Stage

2 TTHM and HAA5 predictions need to take into account 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 method 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 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 TTHM and HAA5 levels are predicted 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 from 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. The delta approach compensates, at the national level, for

potential errors in treatment technology selection 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 systems are not as complex when compared to large systems. EPA also believes

that the safety margin accounts 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 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 Stage 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 suggests 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 percent 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 systems 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 chloramines7

. EPA believes,

however, that the 20 percent safety 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 are greatest in these 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 percent 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 adsorption 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 national 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 Treatment 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 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*

GW

System Size 

(Population Served)

Pre-Stage 2 Post-Stage 2 Level (μg/L) Percent Reduction

Level (μg/L)

F G = (E - F) / E

Source Water 

Type

HAA5

SW

Source Water 

Type

System Size 

(Population Served)

Pre-Stage 2 

Population Level (μg/L)

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 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 incidence of colon 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 annual colon and rectal cancer avoided (the sensitivity

analysis), and the number of annual fetal losses avoided (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 attributable 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 value 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 fetal 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, and 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-age 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 after age 85, the incidence of new bladder cancers does,

which represents the overall decline in the number of individuals 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 attributable to DBP exposure, EPA used three different

approaches: 

• Using the range of Population Attributable Risk (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 Villanueva et al. (2003) meta-analysis that reflects

both sexes, ever exposed population from the studies considered (yields a pre-Stage 1 best

estimate for PAR of ~16%)

• Using the Villanueva et al. (2004) pooled data analysis to develop a dose-response

relationship for OR as a function of Average TTHM. The dose-response relationship was

modeled 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 reasonable 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 that the existing epidemiological evidence has not

conclusively established causality between DBP exposure 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 bladder cancer cases attributable to DBPs. Predicted

reductions in national average TTHM and 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 annual bladder cancer cases “ultimately avoidable” for the Stage 1

and Stage 2 rules.

Over the long run, the annual 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 benefits 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, cigarette smoke and bladder cancer, and arsenic and

bladder cancer) 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 lifetime. 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 reduce DBPs based on the rule implementation schedule. 

Although a 100-year cessation lag period is modeled, 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 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 and deaths. EPA has estimated that 74 percent of

bladder cancer cases are non-fatal (USEPA, 1999a). The 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 avoiding non-fatal bladder cancer are not available, EPA

estimated values from two other non-fatal illnesses: curable lymphoma and chronic bronchitis. Both are

considered valid estimates of WTP for non-fatal cancer.

For fatal bladder cancer cases, VSL is used to capture 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 individual’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 calculating 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 respect to quantified benefits for bladder cancer. 

Many are described qualitatively in the analysis, while others are incorporated explicitly as follows: 

• There is uncertainty in the percent reduction 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 combined are the third most common type of new cancer

cases and deaths in both men and women in the U.S., excluding 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 colon 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 from the SEER cancer registry and two quality studies

were chosen to estimate a PAR value for DBPs. Using 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 benefits 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 benefits 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 exist 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 attributable to DBP exposure by reducing DBP levels

may result in a significant number of avoided fetal losses.

EPA estimated the reduction in potential fetal losses in a similar manner to bladder cancer cases. 

A range of possible PAR values for relating annual fetal losses to DBP exposure was obtained from

available epidemiological studies. Reductions in the number of peak DBP events due to the Stage 1

DBPR and the Stage 2 DBPR were estimated. Reductions in exposure to peak DBPs were assumed to be

proportional to reductions in peak DBP events. Like the 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 values used in the analysis. There are other important

uncertainties in this illustrative calculation, including 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 Stage 2 DBPR, EPA calculated the incremental costs to

be incurred by PWSs and States/Primacy Agencies from the Stage 1 DBPR to the Stage 2 DBPR. Cost

analyses for PWSs include identifying treatment process 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 evaluations (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, followed 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 the 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 those 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 plus the estimate of additional ground water systems

expected to install disinfection for the GWR as shown 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 mean values for the various input parameters are

adequate to generate EPA’s best estimate of national costs for the rule.

EPA has quantified several large areas of uncertainty in costs by quantifying uncertainty in

compliance forecasts, as noted in section ES.5.1. There 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 2.83 9,060,119 $ 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 5.97 509,562 $ 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 be 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 

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 possible, pass cost increases on to their customers

through increases in water rates. Exhibit ES.8 presents 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 costs, such as for rule implementation or additional

routine monitoring. The bottom half shows statistics 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 insight 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 90th percentile annual household cost increases

for all systems are $0.62, $0.03, and $0.36 per year, respectively. The mean, median, and 90th 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 variances 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, system 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 chloramines 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 (including 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 more 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 income. The results of EPA's analysis show that, for

each of the three categories of small systems, there 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 alternatives 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 established between exposure to chlorinated water and

bladder cancer.

The regulatory alternative EPA chose, which specifically 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 approximately three times those for the Preferred

Alternative because of the more stringent bromate standard. Quantified benefits based on bladder cancer

cases avoided are nearly the same for Alternative 1 and the Preferred Alternative (Exhibit ES.9) because

the benefits of avoiding potential cancer cases by lowering the bromate standard in Alternative 1 are

unquantified. EPA did not favor this alternative because 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 Alternatives 2 and 3. However, these alternatives do

not include the risk targeting strategy of the Preferred 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 comply with these alternatives. The associated costs

presented in ES.10 show mean estimated values between $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 confidence 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 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). 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 3%, 25 Years Discounted at 7%, 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 for All Regulatory Alternatives ($Millions)

Exhibit ES.10 Comparison of Costs for All Regulatory Alternatives ($Millions)
11Alternative 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 ES-26 December 2005

3%, 25 

Years

7%, 25 

Years

Preferred 1,452 $ 1,170 $ 

A1 1 $ 885 1,122 $ 

A2 4,746 $ 3,821 $ 

A3 6,495 $ 5,219 $ 

Preferred $ 684 $ 544 

A1 1 $ 319 432 $ 

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 benefits. Exhibit ES.11 shows that the Preferred

Alternative has higher net benefits than Alternative 111, but lower net benefits than Alternatives 2 and 3

using either estimate of WTP for non-fatal bladder cancer. These net benefits do not include the

unquantified benefits. 

Exhibit ES.11 Comparison of Annualized Mean Net Benefits for All Regulatory

Alternatives ($Millions)
12The WTP values, shown here in 2003$, could be used to develop an annualized WTP value for the most

accurate comparison to the annualized cost per case avoided values 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 period 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 would 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 increase the threshold used in this analysis; therefore

annualized WTP values are not calculated because the Preferred 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 effectiveness 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 acceptable 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 bladder 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 alternative, 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 Exhibit 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 threshold 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 includes 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 $ 0.41 0.33 $ 

Alternative 1 $ 1.42 1.18 $ 

Alternative 2 $ 0.63 0.52 $ 

Alternative 3 $ 0.69 0.57 $ 

Source: Exhibit 9.14

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. 

 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 effectiveness of the alternatives or change their 

rankings, because it involves the subtraction 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 Case Avoided, by Discount Rate and

Regulatory Alternative ($Millions) 

An alternative type of CEA considers the cost 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, Exhibit ES.13 presents an approach to QALYs termed

Morbidity Inclusive Life Years (MILYs), which incorporates 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, which 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 specialists 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 compared to the highest of these thresholds ($100,000)

at a discount rate of 3 percent; it is not cost effective 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 * 106

/C E = B * 106/C

Preferred $ 45 43 $ 725 $ 59,946 $ 62,001 $ 

Alternative 11 $ 224 222 $ 652 $ 340,584 $ 342,641 $ 

Alternative 2 $ 307 302 $ 2,449 $ 123,259 $ 125,315 $ 

Alternative 3 $ 476 469 $ 3,379 $ 138,754 $ 140,810 $ 

Preferred $ 61 60 $ 510 $ 118,394 $ 119,540 $ 

Alternative 11 $ 227 227 $ 462 $ 491,295 $ 492,449 $ 

Alternative 2 $ 352 350 $ 1,733 $ 202,150 $ 203,303 $ 

Alternative 3 $ 538 536 $ 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 smoking/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 established between exposure to chlorinated water and 

bladder cancer. 

7 Percent

3 Percent

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.8 Conclusions

EPA is finalizing the Stage 2 DBPR to reduce the 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 are potentially associated with increased incidence

of bladder and possibly other cancers. DBPs may also 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 literature regarding the extent to which DBPs contribute to

the incidence of these adverse effects in the 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 that 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 impact of rules costing over $100 million annually in an

Economic Analysis (EA) and to submit the analysis 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 Section 1.3 provides information regarding 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) 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 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 another 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 distribution systems. Each rule activity for the Preferred

Regulatory Alternative and the associated rule schedule are described below. Note that consecutive

systems of any size must comply with the requirements of the Stage 2 DBPR at the same time as the

largest system in the combined distribution system. 

The numerical maximum contaminant levels (MCLs) 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 compliance 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 for 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 which 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 plants 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 generally 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 approach 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 Stage 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 requirements. EPA believes that the new Stage 2

population-based approach makes monitoring requirements 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 simultaneously with the Long Term 2 Enhanced Surface

Water Treatment Rule (LT2ESWTR) to address complex risk trade-offs between DBPs and microbial

pathogens. The schedule for the Stage 2 DBPR is summarized in Exhibit 1.4. Note that the compliance

deadlines are based on population served. For consecutive and wholesale systems, the compliance

schedule is based on the population served by the largest 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 could 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 concentrations 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 evaluation 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 analytical results 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 and 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 Sites1

 (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 Part3

< 500 consecutive

systems 1 - 1- 2 1 (during peakhistorical month)4

< 500 non-consecutive

systems - - 11 2 1 (during peakhistorical 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 peakhistorical month)4

< 500 non-consecutive

systems - - 11 2 1 (during peakhistorical 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 monitoring location during each monitoring period. Sampling

location must be distributed throughout the distribution system. 2 If the number of entry points to the distribution system is fewer than the specified 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, samples must be taken at entry points to the distribution

system having the highest water flows. 3

 For the purposes of this EA, “surface water” systems 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 month 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 Population-Based Monitoring Requirements

System Size 

(Population Served )

Distribution System Sample

Locations1

Total

Sample

Locations per

System5

Monitoring

Frequency3

Highest

TTHM

Locations

Highest

HAA5

Locations

Existing

Stage 1

Compliance 

Locations2

Systems Using Surface Water in Whole or in Part4

< 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/Primacy Agency requires different or additional locations.

Locations should be distributed throughout the distribution system to the extent possible. 2

 Alternate between highest HAA5 LRAA and highest TTHM 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, alternate 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” systems 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, respectively. 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 

Schedule 1Systems serving> 100,000 1

IDSE Plan Due IDSE Report Due Begin ComplianceOctober 1, 2006 January 1, 2009 April 1, 2012

Schedule 2Systems serving 50,000 to 99,999 1

IDSE Plan Due IDSE Report Due Begin ComplianceApril 1, 2007 July 1, 2009 October 1, 2012

Schedule 3Systems serving 10,000 to 49,999 1

IDSE Plan Due IDSE Report Due Begin CompliancOctober 1, 2007 January 1, 2010 October 1, 2013

Schedule 4Systems serving < 10,000 1

IDSE Plan Due IDSE Report DueApril 1, 2008 July 1, 2010 Begin Complianc

October 1, 2013

2006 2007 2008 2009 2010 2011 2012

Crypto mon.3

Treatment Installation Possibl

2013 201

Treatment Installation Possible Extension 2

Treatment Installation Possible Extension 2

2006 2007 2008 2009 201

2010 2011 2012 2013

Treatment Installation Possibl

LT2 Crypto monitoring

IDSE mon.

LT2 Crypto monitoring

LT2 Crypto monitoring

IDSE mon.

IDSE mon.

IDSE mon.

E. Coli mon.

Exhibit 1.4 Stage 2 DBPR Implementation Schedule

1 Includes all systems that are part of a combined distribution system that have a largest system with this population.2 A State may grant up to an additional 2 years for systems to comply if the State determines that additional time is necessary for capital improvements.3 Subpart H systems that must conduct Cryptosporidium monitoring have an additional 12 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 for 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 rationale 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 contains compliance forecast results for the Stage 1

DBPR and the Stage 2 DBPR Preferred Regulatory Alternative.

• Chapter 6 reviews available epidemiological and toxicological data related to DBPs. The

public health and economic benefits 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 evaluate the effects of the rule on different

segments of the population and considers various executive orders and requirements,

including the Regulatory Flexibility Act (RFA) 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 Preferred 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 complex 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 effort 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 analysis of their impacts. This two-stage approach was

subsequently incorporated into the 1996 Safe Drinking Water Act (SDWA) Amendments. The first stage of the

M-DBP rulemaking process culminated with the joint promulgation 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 rationale 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 disinfection, 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 other cancers. While there are uncertainties in the

quantitative relationship between the incidence of these 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 link has been established, EPA believes the evidence

supports concern for these potential hazards 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 control 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 based on incomplete or uncertain information. The

Agency believes it is appropriate and prudent to take 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. Additionally, 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 health 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 are served by public water systems (PWSs) that add

chemical disinfectants (see Exhibit 3.3 for the 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, dichloroacetic acid, and bromate) to be carcinogenic

in laboratory animals (see Chapter 6 for details).

Other epidemiological studies indicate a potential link between DBP exposure and adverse

reproductive and developmental health effects, particularly 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 animals. EPA believes that these studies together provide

evidence that DBPs may present potential public health risks to pregnant women and their fetuses.

Research, therefore, supports EPA’s conclusion that chlorinated drinking water could potentially

be a source of health risk to the general public. There is uncertainty in the scientific literature regarding

the extent to which DBPs contribute to the incidence of these adverse effects in the exposed population. 

Nevertheless, EPA believes that the weight of evidence 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 quality 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 contaminants allowed in drinking water.

Section 1412(b)(1) of the 1996 SDWA reauthorization mandated new drinking water

requirements. EPA’s general authority to set Maximum Contaminant Level Goals (MCLGs) and develop
Final Economic Analysis for the Stage 2 DBPR 2-3 December 2005

the National Primary Drinking Water Regulations (NPDWRs) was modified to apply to contaminants that

“may have an adverse effect on the health of persons,” are “known to occur or there is a substantial

likelihood that the contaminant will occur in public water systems with a frequency and at levels of public

health concern,” and for which, “in the sole judgment of the Administrator, regulation of such

contaminant presents a meaningful opportunity for health 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 maximum 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 above, SDWA 1412(b)(2)(C) requires specifically that

EPA promulgate the Stage 2 DBPR.

The Administrator shall promulgate an Interim Enhanced Surface Water Treatment Rule,

a Final Enhanced Surface Water Treatment Rule, a Stage 1 Disinfectants and Disinfection

Byproducts Rule, and a Stage 2 Disinfectants 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 Federal Register (FR) 68624, November 29, 1979),

EPA set an MCL for TTHM (the sum of the concentrations of chloroform, bromoform, bromodichloromethane, and dibromochloromethane) of 0.10 milligrams 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 disinfectant 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, June 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 required 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 the 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 (surface 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 routine 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 (SWTR) (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 protect 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 water 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 performance 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 possible need for changes to microbial requirements and microbial treatment

practices and to help evaluate the need for future regulation 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 occurring 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 water systems currently treat for contaminants. The

ICR monthly sampling data provided 18 months of information on the quality of the influent and treated

water, including pH, alkalinity, turbidity, temperature, calcium, total hardness, total organic carbon,

ultraviolet254 (UV) absorbency, bromide, ammonia, and disinfectant residual. These data provide some

indication of the “treatability” of the water, the occurrence 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 purpose of the IESWTR is to improve control of the

protozoan Cryptosporidium and to address tradeoffs between the risks 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 standard 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 plants using conventional treatment or direct

filtration.

• Continuous monitoring of individual filter effluent in conventional and direct filtration plants

and recording turbidity readings every 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. Certain 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 systems 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 chlorine (4 mg/L as chlorine (Cl2)), chloramines (4.0 mg/L

as Cl2), and chlorine dioxide (0.8 mg/L as ClO2); 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 Cl2), chloramines (4.0 mg/L as Cl2), and chlorine dioxide (0.8 mg/L as ClO2); 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 dioxide, are calculated as RAAs. For conventional surface

water and GWUDI systems, a treatment technique—enhanced coagulation/softening—is specified for the

removal of DBP precursors.

As noted in section 2.3.6, the Stage 1 DBPR was 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 identify 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, providing an alternative source of water, or

providing inactivation and/or removal of 99.99 percent (4 logs) of viruses.

• Compliance monitoring to ensure that disinfection 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. The 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 inorganic 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 recycle 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 they 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 water or GWUDI as a source. It incorporates

system-specific treatment requirements based on a “Microbial 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 distribution 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 for 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 that it intends to address (including, where

applicable, the failures of the private markets 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 Budget (OMB) guidance dated January 11, 1996, states

that “in order to establish the need for the proposed action, the analysis should discuss whether the

problem constitutes a significant market failure” (USEPA 1996b).

In a perfectly competitive market, prices and quantities 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 decisions cannot affect the price. Several properties of

the public water supply do not satisfy the conditions for 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 recover 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 suppliers to enter the market, they would have to provide

the same extensive infrastructure to realize similar 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 suppliers. The result is a market suitable for a single

supplier and one that is hostile to alternative suppliers. In such natural monopolies, suppliers have fewer

incentives for providing high-quality services or maintaining 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 customers may demand improvements in water quality, the

regulatory regime may not transmit that demand to the 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 water utility. Other options for obtaining safe drinking

water (e.g., buying bottled water or installing point-of-use filtration) most often cost consumers more than

the purchase from public water supplies. Therefore, 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 potentially 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 requires 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 water 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 level 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 reduce the high information and transaction costs by acting

on behalf of consumers in balancing 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 compare the set of regulatory alternatives. The baseline

is a characterization of the industry and its operations under the conditions expected to exist before

systems make changes to meet requirements of the 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 advanced 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 compliance 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. Thus, the observed water quality data and occurrence

of treatment technologies presented in this chapter represent 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. This 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 and tools used to characterize the pre-Stage 1

baseline. Section 3.4 characterizes the water industry, including the baseline estimates of treatment plants

and population subject to the Stage 2 DBPR. Influent water quality is summarized in section 3.5, and

section 3.6 describes the types of treatment technologies used by systems prior to the Stage 1 DBPR. 

Treated water quality, as it relates to the pre-Stage 1 baseline, 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 level 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 industry, source waters, or industry practices. 
1

 Throughout this document, the acronym “SDWIS/FED” is shortened 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 alternatives. The Safe Drinking Water Information

System-Federal Version (SDWIS/FED1

) data (4th 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 drinking water regulations. A second key source 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 compiles 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 Water 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 comprehensive 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 people, 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 information 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 chemical 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 analyses described in this chapter.

The American Water Works Association (AWWA) submitted several comments in response to

the proposed Stage 1 DBPR that underscore the necessity 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 ESWTR 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 determine 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 used 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 discuss 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 pre-Stage 1 conditions, finished water quality is

modeled to give a consistent basis to compare with 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 regulatory 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 comply 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 and
3

 Empirical equations are used to estimate DBP formation 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 from 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 changes, 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 train 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 Tree Program modifies the process train to meet the

specified water quality objectives using a least-cost 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 higher-cost option) in the decision tree, and the Water

Treatment Plant Model generates a new DBP prediction. 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 predicted the DBP levels based on the inputted process train

and water quality data. This run was to serve as a comparison 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–December 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 characterization 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 terminology 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)

• Ultraviolet254 (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, CO2, NaOH, Cl2 (gas), ClO2, ferric chloride, lime, ozone,

potassium permanganate, soda ash, SO2, and H2SO4)

• 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 systems 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 which 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 according 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 that 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 are 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 water (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: Surface 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 “mixed 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. For example, the ICR Auxiliary Database 1 (AUX1)

classifies systems that receive more than 80 percent 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 provides 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 systems are owned by private corporations or individuals. 

Public systems are owned by public entities such as municipalities, counties, or special districts and have

access to capital and means of financing that are not available 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 regulations. 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 categories, 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 subsection, 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 the 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 ground 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 source 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 System 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 accurate 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 individual 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 systems to their retail customers is actually treated by other systems. It is

important to recognize that the total flow of surface water 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 flows 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 number 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 performed to “link” consecutive surface water systems to

their respective wholesale system using data from 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 system 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 (System C) purchases from a non-purchased system

(System B1) and a purchased system (System 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 referred 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 System 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 inventory. These include surface water systems that could

not be linked (e.g., many surface water NTNCWSs purchase 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 system 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 systems 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 these 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 disinfect their water. Therefore systems which

do not disinfect were removed from the baseline. The 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 derived 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 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. 

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 water plants. This is mainly because ground water

sources generally have lower DBP precursor (e.g., TOC) 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 Report (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 classified 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 the 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), originally 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 Stage 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 ground 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 Stage 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 inventory 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 Stage 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 are 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 similar 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 businesses such as restaurants, schools, campgrounds,

etc., their population generally duplicates the population 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 System Inventory by Linking Buyers and Sellers

As with the plant baseline, EPA has modified the 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 detailed 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 disinfect 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 percent 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 the ground water CWS category. 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.
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 Stage 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 Stage 2 DBPR Population Baseline (Continued)
5

 Equations are from the 3nd 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 MGD. 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 annual 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 X0.97757 / 1,000

Average Daily Flow (MGD) = 0.10540 X1.02058 / 1,000

Ground Water: Design Flow (MGD) = 0.39639 X0.97708 / 1,000

Average Daily Flow (MGD) = 0.06428 X1.07652 / 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 served 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 estimate 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 total 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 

Average Daily Flow (MGD) Per 

Plant

X Y Z = 0.36971 X0.97757/1000Y AA = 0.10540 X1.02058/1000Y

<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 X0.97708/1000Y AA = 0.06428 X1.07652/1000Y

<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 X0.97757/1000Y AA = 0.10540 X1.02058/1000Y

<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 X0.97708/1000Y AA = 0.06428 X1.07652/1000Y

<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 category 

(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 population 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 selection 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 equations to NTNCWSs, recognizing that this may

over-estimate flows and, therefore, costs. This over-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 expected 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 shown 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 served (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 alternatives 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 data that were collected under the ICR for large

surface and ground water systems. These data represent the parameters that most affect DBP formation in

disinfected waters. The median, 90th 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 months 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 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

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 characterization 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

90th Percentile of

Plant Means

Range of Plant

Means

Alkalinity

(mg/L as CaCO3)

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 CaCO3)

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

UV254 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 milligrams per liter (mg/L), is not shown. This value was

calculated based on a one-month reported bromide concentration of 28 mg/L, which EPA assumes to be a reporting

error. (Laboratories often report bromide 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 the last 12 months of the ICR were below 0.1 mg/L. 

Source: ICR AUX1 database (USEPA 2000h). Represents distribution 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 generally 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 concentrations 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, bromide can react with ozone and chlorine dioxide to

form bromate, another byproduct of concern. As shown in Exhibit 3.6, most of the plant-mean bromide

levels are relatively low (the 90th percentiles were 0.122 and 0.190 mg/L for surface water and ground

water sources, respectively). Exhibit 3.8 shows the distribution of mean bromide concentrations for large

surface water and ground water plants.

There is no extensive data set similar to the ICR 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 (ICRSS), NRWA data, AWWA WATER:\Stats data, and

data from individual states. The ICRSS is a survey meant 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 medium and small surface and ground water systems. 

Appendices A and B provide additional detail regarding influent water quality 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 water systems, respectively, using ICR data. Exhibit

3.12 shows average TOC concentrations by State 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 Distribution of Bromide in Influent Water of Large System

ICR Plant-Mean Data

Source: Each data point in the distribution represents the mean value of monthly data collected at a single plant over

a 12-month period (January 1998–December 1998). Only plants with reported data for at least 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 of

PlantMeans

Median of

PlantMeans

90th

Percentile of

Plant-Means

Range of

Plant-Means

Source Water Alkalinity (mg/L as CaCO3)

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-fourths 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 Distribution for ICR Surface Water Systems

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

Exhibit 3.11 Influent Water TOC Distribution for ICR Ground Water Systems

Source: ICR AUX1 Database (USEPA 2000h); mean 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 Distribution 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 predicted 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 technology
6

As described in Appendix A, the treatment technologies used to characterize the pre-Stage 1and pre-Stage

2 baselines are different than those presented in the Stage 1 RIA. New tools to characterize 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 characterizations 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, respectively6

. 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-Stage 2 and post-Stage 2 data. (If observed data were

used for pre-Stage 1 treatment technology-in-place estimates, differences between pre-Stage 1 and preStage 2 results would represent potential inconsistencies 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 (approximately 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 

Chlorine Dioxide UV Ozone MF/UF 

CL2 CLM CL2 CLM CL2 CLM CL2 CL

A B CDE FG H I J 

<100 96.4% 346 0.0% 0 3.6% 13 0.0% 100-499 96.4% 739 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 28 0.0% 500-999 96.4% 466 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 17 0.0% 1,000-3,299 96.4% 1,089 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 41 0.0% 3,300-9,999 96.4% 1,213 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 45 0.0% 10,000-49,999 53.4% 689 31.6% 408 5.1% 65 3.0% 39 3.2% 42 1.9% 25 0.2% 3 0.1% 50,000-99,999 53.4% 309 31.6% 183 5.1% 29 3.0% 17 3.2% 19 1.9% 11 0.2% 1 0.1% 100,000-999,999 53.4% 326 31.6% 193 5.1% 31 3.0% 18 3.2% 20 1.9% 12 0.2% 1 0.1% =1,000,000 53.4% 39 31.6% 23 5.1% 4 3.0% 2 3.2% 2 1.9% 1 0.2% 0 0.1% Total 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% 

GAC10 + AD GAC20 GAC20 + AD Membranes 

CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 

M N OP

Q RS T A+C+E+G+I+K+M+O

<100 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0100-499 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0500-999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 01,000-3,299 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 03,300-9,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 010,000-49,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 050,000-99,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0100,000-999,999 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% 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% 0

Note: 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).

62.8%62.8%85.5%

100.0%100.0%100.0%

62.8%

System Size

(Population 

Served)

y

(Population 

Served)

100.0%100.0%

No Advanced 

Treatment 

Technologies1 CL2

No Advanced 

Treatment 

Technologies1 CLM

Source: Surface water systems serving <10,000 people: National Rural Water Survey (USEPA 2001a). Surface water systems serving 10,000 or more people: SWAT initial plant run (U

62.8% 

Exhibit 3.13a Pre-Stage 1 DBPR Treatment Technologies-in-Place for CWS Surface Wa
Final Economic Analysis for the Stage 2 DBPR Proposal 3-34 

Chlorine Dioxide UV Ozone MF/UF 

CL2 CLM CL2 CLM CL2 CLM CL2 CLM 

A B C DE FGH I J 

<100 96.4% 218 0.0% 0 3.6% 8 0.0% 100-499 96.4% 301 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 11 0.0% 500-999 96.4% 102 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 4 0.0% 1,000-3,299 96.4% 89 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 3 0.0% 3,300-9,999 96.4% 24 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 1 0.0% 10,000-49,999 53.4% 3 31.6% 2 5.1% 0 3.0% 0 3.2% 0 1.9% 0 0.2% 0 0.1% 50,000-99,999 53.4% 0 31.6% 0 5.1% 0 3.0% 0 3.2% 0 1.9% 0 0.2% 0 0.1% 100,000-999,999 53.4% 1 31.6% 0 5.1% 0 3.0% 0 3.2% 0 1.9% 0 0.2% 0 0.1% =1,000,000 53.4% 0 31.6% 0 5.1% 0 3.0% 0 3.2% 0 1.9% 0 0.2% 0 0.1% Total Plants, % 96.1% 737 0.2% 2 0.0% 0 0.0% 0 0.0% 0 0.0% 0 3.6% 27 0.0% 

GAC10 + AD GAC20 GAC20 + AD Membranes 

CL2 CLM CL2 CLM CL2 CLM CL2 CLM CL2 

M N OP

Q RST A+C+E+G+I+K+M+O+Q

<100 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0100-499 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0500-999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 01,000-3,299 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 03,300-9,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 010,000-49,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 050,000-99,999 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0100,000-999,999 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% 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% 0

Note: 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 same as the CWS technology distribution presented in Exhibit 3.13a.

Percentage using chloramine is taken from the Occurrence Document (USEPA 2005k) and ICR AUX1 data (USEPA 2000h).

99.7%

System Size

(Population 

Served)

y

(Population 

Served)

100.0%100.0%100.0%100.0%100.0%

62.8%62.8%62.8%62.8%

Source: Surface water systems serving <10,000 people: National Rural Water Survey (USEPA 2001a). Surface water systems serving 10,000 or more people: SWAT initial plant run (USEPA 2

No Advanced 

Treatment 

Technologies1 CL2

No Advanced 

Treatment 

Technologies1 CLM

Exhibit 3.13b Pre-Stage 1 DBPR Treatment Technologies-in-Place for NTNCWS Surface W
Final Economic Analysis for the Stage 2 DBPR Proposal 3-35 

System Size

(Population Served) No Adv1 CL2 No Adv1 CLM UV CL2 UV CLM Ozone CL2 Ozone CLM GAC20 CL2 GAC20 CLM Membranes CL2

Membranes 

CLM TOTAL 

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

<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% 0 100.0100-499 100.0% 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% 0 100.0500-999 100.0% 6,093 0.0% 0 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

1,000-3,299 100.0% 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% 0 100.03,300-9,999 100.0% 5,030 0.0% 0 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

10,000-49,999 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 94.650,000-99,999 92.3% 661 5.4% 39 0.8% 6 0.0% 0 0.0% 0 0.0% 0 1.5% 11 0.0% 0 94.6

100,000-999,999 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?1,000,000 92.3% 25 5.4% 1 0.8% 0 0.0% 0 0.0% 0 0.0% 0 1.5% 0 0.0% 0 94.6

Total 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 99.2Note: Detail may not add to totals due to independent rounding

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 - b

plants.

System Size

(Population Served) No Adv1 CL2 No Adv1 CLM UV CL2 UV CLM Ozone CL2 Ozone CLM GAC20 CL2 GAC20 CLM Membranes CL2

Membranes 

CLM TOTAL 

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

<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% 0 100.0100-499 100.0% 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% 0 100.0500-999 100.0% 589 0.0% 0 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

1,000-3,299 100.0% 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% 0 100.03,300-9,999 100.0% 21 0.0% 0 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

10,000-49,999 92.3% 3 5.4% 0 0.8% 0 0.0% 0 0.0% 0 0.0% 0 1.5% 0 0.0% 0 94.650,000-99,999 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

100,000-999,999 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=1,000,000 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

Total 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% 0 100.0Note: Detail may not add to totals due to independent rounding

The NTNCWS technology distribution is assumed to be the same as 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 - b

plants.

Exhibit 3.14a Pre-Stage 1 DBPR Treatment Technologies-in-Place for CWS Ground Wa

Exhibit 3.14b Pre-Stage 1 DBPR Treatment Technologies-in-Place for NTNCWS Ground W
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 Chapter 5, SWAT-modeled DBP occurrence is used instead

of observed (ICR) values so that any changes shown in the analysis would be a result of treatment

technology changes and operating conditions and not differences between 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 section 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 Appendix 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 collected during the first 6 months of the survey. In

addition, 6 quarters were not used because 2 quarters 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 residence 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 additional 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 plants 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 points 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 balance 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 occurrence 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 shown for both observed ICR data and SWAT-predicted

data. Exhibits 3.16 through 3.19 show the cumulative distributions 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 residence 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 concentrations from both ICR and SWAT data sets, although ICR

chlorite data show the maximum finished-water concentration at each plant, rather than the plant-mean. 

Statistical calculations of individual observations are for SWAT monthly data and ICR quarterly data.

Exhibit 3.15 reveals differences between SWAT-predicted and ICR observed data. Although the

predicted SWAT data is calibrated to the national averages reported in the ICR, differences still exist

because of differences in calculated versus actual residence 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, since 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 summarized in Exhibit 3.20. DBP levels in ground

water are significantly less than in large surface water plants (see Exhibit 3.15); mean TTHM levels in

ground water plants are less than half those in large 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

90th

%ile Range N Mean Median

90th

%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 data 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 are used. All SWAT data are based on

monthly predicted observations, ICR TTHM and HAA5 data are based on quarterly observations, and ICR chlorite

and bromate data are based on monthly observations. For ICR data, only individual observations used to calculate

plant means are shown. For ICR data, only plants that have data for 3 of the last 4 quarters were included, and, for

ICR TTHM and HAA5 data, only plants with at least 3 of the 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

90th

%ile Range N Mean Median

90th

%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 database compiled by the American Water Works

Association (AWWA 2000). Graphs of WATER:\STATS data 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 submitted to EPA from eight

States to assess pre-Stage 1 DBP occurrence levels for 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 State surface water data. (For further characterization of small surface

and ground water plant data sets, refer to Chapter 3 of the Occurrence Document (USEPA 2005k)). 

Therefore, NRWA observed data were used to describe occurrence for small 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

90th

%ile Range N Mean Median

90th

%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. Average 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 periods 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 surface 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 analysis due to measurement error and incomplete

information that could result in either an over-estimate or under-estimate of the benefits and/or costs as

presented in Chapters 6 and 7. These uncertainties were 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 Uncertainties Affecting Stage 2 DBPR

Baseline Estimates

Uncertainty

Section

Where

Estimates

are

Presented

Effect on Benefit Estimate Effect on Cost Estimates

UnderestimateOverestimateUnknown

Impact

UnderestimateOverestimateUnknown

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 system, including name, ID number, number of people

served, type of system (year-round or seasonal), and 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 SDWIS. 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 uncertainty 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 water 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 population served (presented in section 3.4.3)

Because the CWSS is a survey of CWSs, estimates 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 confidence 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 disinfecting), 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 between 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 overestimation in treatment technology costs and therefore,

national costs of the rule. The number of NTNCWSs 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 advanced treatment technologies prior to the

implementation of the Stage 1 DBPR is based on different 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 large systems has a relatively high degree of certainty

because the ICR database represents a census of all 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 than 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 DBP 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 ultimate 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 experts 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 collection (1998) represent past years and adequately be

used to predict future DBP occurrence? Weather 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 than normal. Approximately 75 percent of the

country experienced warmer than usual temperatures 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 conditions. 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) could 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 what 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 that DBP levels can fluctuate even on a daily basis. 

Over a 1 week sampling period where samples were taken 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 the ICR does not represent this potential variability. In

addition to hourly variations, the ICR data were not required to be collected at evenly spaced intervals. 

Thus, there is uncertainty in assuming that a single data 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 the 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 compliance 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 4-1 December 2005

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 4-2 December 2005

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 4-3 December 2005

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 4-4 December 2005

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 4-5 December 2005

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 4-6 December 2005

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 4-7 December 2005

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. 11Loc. 21Loc. 3 1Loc. 41

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 Consequent 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 Rule (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 methodologies used to develop the compliance forecasts

and consequent reduction in the levels of DBPs. Section 5.3 provides details on the derivation of the

compliance forecast, and Section 5.4 presents the forecast 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 forecasts 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 percentage of plants predicted to change treatment

technology, along with the specific technologies those plants 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 direct result of changes in treatment technologies. 

Therefore, it is important that EPA use consistent 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 analysis). This section summarizes the tools used and key

assumptions for the Stage 2 DBPR compliance forecasts and the consequent reductions in TTHM and

HAA5 concentrations.

Since the rule was proposed, EPA has modified 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 making treatment technology changes and consequent

reductions in TTHM and HAA5 concentration, which supplements the Surface Water Analytical Tool

(SWAT) predictions. EPA has also quantified uncertainty 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 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 change technology for a specific regulatory alternative. SWAT

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. 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 large 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 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 operational and water quality

characteristics in medium and small systems 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 treatment technology predictions, it was not calibrated to plantlevel DBP predictions and, thus, could not be used to assess 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 information 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 health benefits is based on reductions in occurrences of peak

concentrations. 

To predict changes in average TTHM and HAA5 levels 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 and 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 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 distribution systems. As with the compliance forecast,

SWAT and the ICR Matrix Method produce different results; 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 enough 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 occurrence of peak TTHM and HAA5 concentrations, EPA used only

the ICR Matrix Method1

. Similar to the way in which it is used to evaluate changes in average TTHM

and HAA5 concentrations, 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 occurrence of peaks after treatment technology changes, EPA analyzed TTHM and HAA5

occurrence data for those surface water plants already 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 characterize large plants are from the ICR, which were
2

 There is uncertainty in using the ICR data to represent pre-Stage 1 conditions because some plants may

have begun making changes prior to the ICR in anticipation 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 conditions2. 

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. Similarly, the post-Stage

2 TTHM and HAA5 predictions need to take into account 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 and HAA5 occurrence for the Pre-Stage 1 baseline. 

Second, EPA predicts treatment technology changes 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 TTHM 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 TTHM/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. The 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 Stage 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 suggests 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 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 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 percent 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 systems 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 chloramines3

. EPA believes,

however, that the 20 percent safety 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 are greatest in these 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 treatment 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 using the ICR Ground Water Delphi Process. Exhibit

5.1 summarizes the tools used to develop the compliance 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 treatment 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 model 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 uses a decision tree, arranged from lowest-cost to the

highest-cost treatment technology, to determine which new technology is selected for the plant. A total of
4

As will be explained in Sections 5.5 and 5.6, revised 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 ensure 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 screened plants into “bins”

based on their running annual average (RAA) and locational running annual average (LRAA) TTHM and

HAA5 concentrations. Plants in bins that are non-compliant with Stage 1 are moved into compliant bins4

. 

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 Method are used in the primary analysis for all

regulatory alternatives. Section 5.3.6 compares the results 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 quality 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 experts 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 extrapolated 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 compliance 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 extrapolation; 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 compliance forecast 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 characterize large plants are from the ICR, which were collected before the Stage 1 DBPR
5

 There is uncertainty in using the ICR data to represent pre-Stage 1 conditions because some plants may

have begun making changes prior to the ICR in anticipation 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 conditions5. 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 because new data and tools became available since the

Stage 1 RIA was developed (namely ICR and SWAT) that 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 during the M-DBP FACA deliberations. Under this

approach, SWAT would be used to predict treatment technology use and TTHM or HAA5 levels for each

plant for post-Stage 1 conditions (i.e., after plants make 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 words, 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 ICR-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 individual plants were uncertain. Because of this

uncertainty, the M-DBP TWG decided that it was inappropriate to evaluate SWAT-predicted post-Stage 1

TTHM and HAA5 occurrence for each plant separately to assess compliance with the Stage 2 DBPR.

To minimize the impacts of uncertainty in SWAT plant-level predictions on the Stage 2 DBPR

compliance forecast, the M-DBP FACA developed a four-step, or “delta,” approach for SWAT: 

• Step 1: Model TTHM and HAA5 occurrence for 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 baseline was used to evaluate compliance 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 robust enough to allow for modeling in SWAT, ICR observed data were

used as the pre-Stage 1 baseline. Using the ICR Ground 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 the implicit assumption that plants making treatment

technology changes to comply with the Stage 1 DBPR also meet the Stage 2 DBPR MCLs (Stage 1

forecasts are subtracted from Stage 2; only the delta is considered for costs and benefits). To illustrate the

mechanisms of the delta approach and this uncertainty, 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 with 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 the Safe Drinking Water Act (SDWA) Amendments

of 1996. Details of the Stage 2 DBPR were published in the Agreement in Principle, which

includes the Stage 2 MCLs, in December 2000, which 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 spatial 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/L

Maximum 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/L

Maximum 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 technologies 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. The 77 percent cap on chloramine conversion was

developed during the M-DBP FACA deliberations to represent site-specific circumstances

and other local factors that would preclude chloramine 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 Stage 1 compliance, but is allowed to use it for

Stage 2 compliance.

• EPA considered ultraviolet (UV) an available 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 shown 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 once 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 availability, however, causes the delta from Stage 1 to

Stage 2 to be negative for some advanced technologies. EPA developed an approach to adjust the

technology selection forecast to correct for the negatives (called the “adjustment for negatives” step). In

summary, plants selecting UV for the Stage 2 DBPR were reallocated to advanced technologies (ozone,

MF/UF, and GAC-10-Minute Contact Time (GAC10)) when overall predictions were lower for Stage 2

than for Stage 1. Details on the adjustment-for-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 represent the 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 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 and 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 consistent 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 system. Not all systems must perform an IDSE. 

Nontransient noncommunity water system (NTNCWSs) 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 compliance 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 levels. Whether an individual system finds sites

with higher TTHM and HAA5 levels than those reported 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 average 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, systems 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 Stage 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 forecast 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 with
6

This method was selected over other alternatives since 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 the 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 ICR 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 limitation is, in part, accounted for by the use of a 20

percent safety margin. EPA believes that the 20 percent 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 believes 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 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. 

EPA believes, however, that the 20 percent safety 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 are 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 surface 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 the 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 formula6

:

post-IDSE LRAAmax = ICR LRAAmax + (ICR LRAAmax - ICR LRAA2ndHi) (Equation 5.1)

Where:

post-IDSE LRAAmax = the maximum LRAA value found after the system has conduced the IDSE

ICR LRAAmax = the maximum LRAA value as reported for the last four quarters of the ICR

ICR LRAA2ndHi = 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 the 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

LRAAmax and ICR LRAA2ndHi 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 213 36 16.9% 15 7.0%

Notes: CL2 = free chlorine, CLM = chloramines

LRAAMAX = the maximum LRAA value for each plant.

LRAA2ndHI = 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 LRAAMAX + 

(LRAAMAX - LRAA2ndHI)

S1-Compliant Plants that are 

already S2 Non-Compliant

TTHM HAA5

CL2 CLM All CL2 CLM All

Number of Screened Stage 2 non-compliant Plants 1 40 11 51 40 11 51

Average of LRAAMAX 64.50 57.63 63.02 37.57 38.97 37.87

Average of LRAA2ndHI 49.79 52.31 50.33 32.93 35.58 33.50

Delta of Average of LRAAMAX and Average of LRAA2ndHI 14.71 5.31 12.68 4.64 3.39 4.37

Notes

LRAAMAX = the maximum LRAA value for each plant.

LRAA2ndHI = 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 difference between the ICR

LRAAmax and ICR LRAA2ndHi is 12.7 :g/L for TTHM and 4.4 :g/L for HAA5. The cumulative

distribution for ICR LRAAmax - LRAA2ndHi for TTHM and HAA5 are shown in Exhibits 5.4b and 5.4c,

respectively.

Exhibit 5.3 Predicted Increase in Percent Making Treatment Technology Changes

based on Spatial Variability Analysis

Exhibit 5.4a Analysis of Variability 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

LRAAMAX - LRAA2ndHI for TTHM (μg/L)

Cumulative Percentage (N=51)

Exhibit 5.4b Cumulative Distribution of ICR LRAAMAX - ICR LRAA2ndHI 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

LRAAMAX - LRAA2ndHI for HAA5 (μg/L)

Cumulative Percentage (N=51)

Exhibit 5.4c Cumulative Distribution of ICR LRAAMAX - ICR LRAA2ndHI 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 generally based on the difference between a calculated LRAA

from a maximum residence time and an average residence 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 the ICR. Systems that used advanced tools are not

expected to find drastic differences between their ICR LRAAmax and their post-IDSE LRAAmax. Third,

systems using chloramines for residual disinfection generally 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 LRAAmax and the LRAA 2ndHi for

screened ICR surface water plants that are in compliance with Stage 1 but out of compliance with Stage 2,

based on the post-IDSE LRAAmax calculation (see equation 5.1). The average between the TTHM

LRAAmax 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 forecast 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 increased safety margin could result in plants predicted to

install higher-cost treatment technologies to meet compliance with a lower numeric MCL value. The

decrease in TTHM and HAA5 levels resulting from the 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 variability analysis in Exhibit 5.3 predicts that an

additional 7 percent of all plants could make treatment technology changes as a result of the IDSE. This

prediction falls in between the compliance analysis 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 previously, 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 analysis 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

Incremental Percent 

More Plants 

Compared to 20 

Percent SM for 

Stage 2

Ratio of

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 Screened 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 safety 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 percent safety margin accounts for potential impacts of the

IDSE for small systems because their distribution systems are less complex than those of large systems. 

EPA also believes that the safety margin accounts 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 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 that the compliance forecast for some large and medium surface water systems

using a 20 percent safety margin is already conservative 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 Stage 2 as compared to Stage 1. However, for the reasons given in

Section 5.3.4, EPA believes that a compliance forecast 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 plausible, 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 direction. 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 analysis 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 reported 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 especially 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), average 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 tools to predict changes in treatment technology and

resulting reductions in DBP levels: SWAT and the ICR Matrix Method. Exhibit 5.6 compares the SWAT

and ICR Matrix Method predictions of plants changing treatment technology for Stage 1 and Stage 2. 

(Comparisons of the predicted reductions in TTHM and HAA5 concentrations are presented in Sections

5.6 and 5.7.) Exhibit 5.6 shows that although the total 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 expected given the inherent differences in the two

methods and uncertainties associated with each7

. 

 

Because both SWAT and the ICR Matrix Method have associated uncertainty, results from both

are used to generate the compliance 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 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 5th percentile value and the ICR Matrix

Method-to-SWAT multiplier for plants making treatment technology changes as the 95th 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 DBPR 5-19 

ICR Matrix Method SWAT

% Changing 

from pre-S1 

to pre-S2

% Changing 

fom pre-S1 

to post-S2

% Changing 

from pre-S2 

to post-S2

% Changing 

from pre-S1 

to pre-S2

% Changing 

fom pre-S1 

to post-S2

% 

f

A B C = B - A D E 

Preferred Reg. Alternative (80/60 LRAA, IDSE), 20% SM 19.2% 36.2% 16.9% 32.5% 39.1% Preferred Reg. Alternative (80/60 LRAA, IDSE), 25% SM 19.2% 41.3% 22.1% 32.5% 45.4% Reg. Alternative 1 (Bromate = 5), 20% SM 19.2% 36.2% 16.9% 32.5% 39.4% Reg. Alternative 2 (80/60 Single High), 20% SM 19.2% 58.2% 39.0% 32.5% 63.1% Reg. Alternative 3 (40/30 RAA), 20% SM 19.2% 72.8% 53.5% 32.5% 72.1% 

Regulatory Alternative

Exhibit 5.6 Predicted Percent of Plants Making Treatment Technology Changes to Meet Stag

Regulatory Alternatives for the ICR Matrix Method and SWAT

Notes: 1) The operational safety margin for 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 Matrix Method results for Reg. Alternative 1 are the sam

Alternative, assuming a 20 percent safety margin. (B) The sum of percent of plants changing in bins B2 and A2 from Exhibits 5.16b through 5.16d. ICR Matrix Method results 

the 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 3

Probability

Multiplier

1 2 3

Probability

Multiplier

Uniform Distribution for a 20% 

Safety Margin

Uniform Distribution for a 25% 

Safety Margin

(2.57) (1.71)

1 2 3

Probability

Multiplier

1 2 3

Probability

Multiplier

Uniform Distribution for a 20% 

Safety Margin

Uniform Distribution for a 25% 

Safety Margin

Exhibit 5.7 Uniform Distributions for Incorporating Results from SWAT and the

ICR Matrix Method into the Compliance Forecast for Surface Water Systems

Note: The uniform distributions are used when generating the Stage 2 DBPR compliance forecasts for surface water

systems for the Preferred Alternative. The same method is applied to the other 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 compliance 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 calculate 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 treatment 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 results 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 5.142

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 DBPR baseline). The treatment technologies-in-place

for small surface and ground water systems can be derived 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 already 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 technology selection deltas for large and medium surface

water systems represent equal probability of a 20 and 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 baseline is discussed in Section 3.6.2. For plants in

large and medium ground water systems, ICR treatment data were used to derive the predicted percent of

plants using each treatment technology. The percentage 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 baseline). 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 treatment 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 predicted 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 and NTNCWSs Making Treatment Technology

Changes From Stage 1 For Stage 2 DBPR Regulatory Alternatives
Exhibit 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 Technologies1 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 Technologies1 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

403 47.5% 364

229

596

664

681

306

322

39

51.0% 3,342

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

100.0% 0

47.3%

47.3%

47.3% 43

12

89

148

50

49

164 47.5%

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

5-25
Exhibit 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

5-26
Exhibit 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

5-27
Exhibit 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 CL21

No Advanced Treatment 

Technologies CLM1

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

1No 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 CL21

No Advanced Treatment 

Technologies CLM1

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

1No 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

5-28
Exhibit 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 CL21

No Advanced Treatment 

Technologies CLM1

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

1No 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 CL21

No Advanced Treatment 

Technologies CLM1

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

1No 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 CL21

No Advanced 

Treatment 

Technologies CLM1 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

1No 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 CL21

No Advanced 

Treatment 

Technologies CLM1 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

1No 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

5-30
Exhibit 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

5-31
Exhibit 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

5-32
Exhibit 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 

CL21

No Advanced 

Treatment 

Technologies 

CLM1 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.

1No 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 

CL21

No Advanced 

Treatment 

Technologies 

CLM1 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.

1No 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

5-33
Exhibit 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 

CL21

No Advanced 

Treatment 

Technologies 

CLM1 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.

1No 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 

CL21

No Advanced 

Treatment 

Technologies 

CLM1 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.

1No 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 reductions 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 percent 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 predict percent reduction in average DBPs. The first

section summarizes the methodology using SWAT. Section 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 plants 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 consistent 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 pre-Stage 2 or post-Stage 2 results would reflect
Final Economic Analysis for the Stage 2 DBPR December 2005 5-36

potential inconsistencies between observed 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. The ICR Matrix Method for the Preferred Regulatory

Alternative with safety margins of 20 and 25 percent is presented in Exhibits 5.16a and 5.16b (the 25

percent safety margin is used to account for the potential impacts of the IDSE). Exhibits 5.16c and 5.16d

show the ICR Matrix Method for Regulatory Alternatives 2 and 3. Regulatory Alternative 1 was not

analyzed separately, as it contains the same MCLs for TTHM and HAA5 and therefore has the same

matrix as the Preferred Regulatory Alternative. 

To illustrate how the ICR matrix method works, consider 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 under 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 consistent with the SWAT methodology, which considers

only the delta or additional plants that need to make 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 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). 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 indication 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 levels for Stage 2-compliant plants that use

advanced treatment technologies and/or chloramines during the ICR are summarized in Exhibit 5.17. The

average of the plant-average TTHM and HAA5 concentrations 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 average 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 

Number (ug/L)

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 model future changes in DBP occurrence but believes it

provides a plausible result. 

Exhibit 5.16a ICR Matrix Method for the 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 the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines a plant’s bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant 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 

Number (ug/L)

Bin of Plants

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 the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines a plant’s bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant 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 

Number (ug/L)

Bin of Plants

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 the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines a plant’s bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant 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 

Number (ug/L)

Bin of Plants

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 for Regulatory Alternative 3 (40/30 RAA)

Notes: 1) In the first table on the left, A1 through B2 are the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines a plant’s bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant 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 

(μg/L)

Mean HAA5 

(μg/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 

(μg/L)

Mean HAA5 

(μg/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 

(μg/L)

Mean HAA5 

(μg/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 

(μg/L)

Mean HAA5 

(μg/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 mean 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 predictions for a 20 and 25 percent safety margin are shown

separately for the Preferred Alternative. Both predictions 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 two 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 uncertainty, results from both are used to generate the

estimated percent reduction in TTHM and HAA5 concentrations 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 multipliers. The distributions use 1.0 as the 5th percentile

value and the ICR Matrix Method-to-SWAT multiplier as the 95th 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 the 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 reduction 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 systems. 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 reduction 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 DBPR 5-43 

ICR Matrix Method SWAT

Mean of Plant 

Means preS2 (μg/L)

Mean of Plant 

Means post-S2 

(μg/L)

% Reduction 

from pre-S2 

to post-S2

Mean of Plant 

Means preS2 (μg/L)

Mean of Plant 

Means postS2 (μg/L)

% 

fro

to 

A B C = (B - A)/A D E F 

TTHM

Preferred Reg. Alternative (80/60 LRAA, IDSE), 20% SM 35.0 31.6 9.7% 35.5 33.8 Preferred Reg. Alternative (80/60 LRAA, IDSE), 25% SM 34.6 29.8 13.8% 35.5 32.5 Reg. Alternative 1 (Bromate = 5), 20% SM 35.0 31.6 9.7% 35.5 33.0 Reg. Alternative 2 (80/60 Single High), 20% SM 33.7 25.1 25.7% 35.5 23.7 Reg. Alternative 3 (40/30 RAA), 20% SM 32.7 19.7 39.7% 35.5 21.0 

HAA5

Preferred Reg. Alternative (80/60 LRAA, IDSE), 20% SM 22.5 20.1 10.5% 25.0 23.8 Preferred Reg. Alternative (80/60 LRAA, IDSE), 25% SM 22.2 18.6 16.0% 25.0 22.9 Reg. Alternative 1 (BR = 5), 20% SM 22.5 20.1 10.5% 25.0 23.6 Reg. Alternative 2 (80/60 SH), 20% SM 21.7 15.7 27.6% 25.0 16.5 Reg. Alternative 3 (40/30 RAA), 20% SM 21.3 13.0 39.0% 25.0 13.9 

Regulatory Alternative

Exhibit 5.18 Inputs to Monte Carlo Simulation Model: Estimated DBP Reduction from SWAT 

Method

Sources: SWAT run summaries (USEPA 2001b), ICR Matrix Method Results (USEPA 2005a)

(A) Average of plant-average values for all plants, pre-Stage 2 conditions from Exhibit 5.16a. ICR Matrix Method results fosame 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 M

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 3

Probability

Multiplier

1 2 3

Probability

Multiplier

Uniform Distribution for a 20% 

Safety Margin

Uniform Distribution for a 25% 

Safety Margin

(2.06) (1.65)

1 2 3

Probability

Multiplier

1 2 3

Probability

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 the 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 systems (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 reduction in TTHM and HAA5 levels for small surface water

systems. Note that EPA believes that the 20 percent safety margin already accounts for potential impacts

of the IDSE for small surface water systems because 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 systems, as it was for large and medium surface water

systems.

Final estimated percent reductions in average 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 used to characterize TTHM and HAA5 levels for the

ground water system pre-Stage 1 baseline. EPA used 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 ICR data collection is shown in Exhibit 5.20. TTHM

and HAA5 levels for these plants are used to estimate 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 advanced 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 for the Stage 2 DBPR Preferred Alternative. 

Exhibits 5.21b and 5.21c show the method for Alternatives 2 and 3. Note that the ICR Matrix Method for

ground water plants is not performed for the 25 percent 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). Section 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 used to characterize TTHM and HAA5 levels for small ground water

systems (USEPA 2000d). To derive the percent reduction 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 Matrix Method for large ground water systems (GWL) is

multiplied by the ratio of small ground water plants changing treatment technology (GCTs) to large

ground water plants changing treatment technology (GCTL), or

GWS = GWL * (GCTS/GCTL)

where,

GWL and GWS = predicted percent DBP reduction for large and small ground water systems,

respectively.

GCTL and GCTS = 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 

(μg/L)

Mean HAA5 

(μg/L)

CLM only 10 29.0 19.4

Adv. tech with CLM 2 19.9 16.5

Adv. tech w/o CLM 0 0.0 0.0

Total 12 27.5 18.9

No. of 

Plants

Mean TTHM 

(μg/L)

Mean HAA5 

(μg/L)

CLM only 6 21.7 14.2

Adv. tech with CLM 2 19.9 16.5

Adv. tech w/o CLM 0 0.0 0.0

Total 8 21.2 14.8

No. of 

Plants

Mean TTHM 

(μg/L)

Mean HAA5 

(μg/L)

CLM only 6 18.5 11.3

Adv. tech with CLM 2 19.9 16.5

Adv. 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 mean of plant-means.

ICR Matrix Method results for Reg. Alternative 1 are the same as for the 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 

Number (ug/L)

Bin of Plants

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 the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines the bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant with Stage 1 and Stage 2 and use 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 

Number (ug/L)

Bin of Plants

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 the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines the bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant with Stage 1 and Stage 2 and use 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 

Number (ug/L)

Bin of Plants

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 the number of ICR plants that meet the criteria for each bin

under pre-Stage 1 conditions. Their calculated average 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 into the bin). The maximum TTHM or HAA5 result

determines the bin placement.

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. The TTHM and HAA5 concentrations for these plants are the averages of the values for

those ICR plants that are compliant with Stage 1 and Stage 2 and use 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 water 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 percent 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 reduction in TTHM and HAA5 concentrations from preStage 2 to post-Stage 2 DBPR conditions for the Preferred 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 Alternatives 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 changes 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 predicted 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 

(μg/L)

Pre-Stage 2 

(μg/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 

(μg/L)

Pre-Stage 2 

(μg/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 

Type

System Size 

(Population 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 

Type

System Size 

(Population Served)

Exhibit 5.22 Reduction in Average TTHM and HAA5 Concentrations 

from Pre-Stage 1 to Pre-Stage 2
Final Economic Analysis for the Stage 2 DBPR 5-52 

Mean 5th 95th Mean 

A B

Large (> 10,000) 160,935,736 35.5 32.2 33.7 30.7 9.2% 

Small (< 10,000) 8,422,403 35.5 32.9 33.8 32.0 7.2% 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% 

Mean 5th 95th Mean 

A E

Large (> 10,000) 160,935,736 25.0 22.5 23.7 21.1 9.9% 

Small (< 10,000) 8,422,403 25.0 23.1 23.8 22.4 7.6% 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% 

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 Mont* 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 account for unforecast methodologies and potential impacts of the IDSE. Confidence bounds for small SW systems account for uncertainties in complian

SW

GW

All 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)

C

TTHM

Perce

D = 

SW

Source Water 

Type

System Size 

(Population Served)

Pre-Stage 2 

Population Level (μg/L)

All Systems*

GW

System Size 

(Population Served)

Pre-Stage 2 Post-Stage 2 Level (μg/L) Perce

Level (μg/L)

F G = 

Source Water 

Type

HAA5

Exhibit 5.23 Reduction in Average TTHM and HAA5 Concentrations from 

Pre-Stage 2 to Post-Stage 2, Preferred Alternative 
Final Economic Analysis for the Stage 2 DBPR 5-53 

Mean 5th 95th Mean 

A B

Large (= 10,000) 160,935,736 35.5 32.5 33.0 32.0 8.3% Small (< 10,000) 8,422,403 35.5 32.5 33.0 32.0 8.3% 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% 

Mean 5th 95th Mean 

A E

Large (= 10,000) 160,935,736 25.0 23.0 23.6 22.4 8.1% Small (< 10,000) 8,422,403 25.0 23.0 23.6 22.4 8.1% 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% 

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 Mont* 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 

(Population Served)

Pre-Stage 2 Post-Stage 2 Level (μg/L) Perce

Level (μg/L)

F G = 

Source Water 

Type

HAA5

SW

Source Water 

Type

System Size 

(Population Served)

Pre-Stage 2 

Population Level (μg/L)

Post-Stage 2 Level (μg/L)

C

TTHM

Perce

D = 

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 uncertainties in compmethodologies.

SW

GW

All Systems*

Exhibit 5.24a Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to 

Regulatory Alternative 1
Final Economic Analysis for the Stage 2 DBPR 5-54 

Mean 5th 95th Mean 

A B

Large (= 10,000) 160,935,736 35.5 25.0 26.4 23.7 29.4% Small (< 10,000) 8,422,403 35.5 25.0 26.4 23.7 29.4% 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% 

Mean 5th 95th Mean 

A E

Large (= 10,000) 160,935,736 25.0 17.3 18.1 16.5 30.7% Small (< 10,000) 8,422,403 25.0 17.3 18.1 16.5 30.7% 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% 

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 Mont* 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 uncertainties in compmethodologies. 

SW

GW

All Systems*

Post-Stage 2 Level (μg/L)

C

TTHM

Perce

D = 

SW

Source Water 

Type

System Size 

(Population Served)

Pre-Stage 2 

Population Level (μg/L)

All Systems*

GW

System Size 

(Population Served)

Pre-Stage 2 Post-Stage 2 Level (μg/L) Perce

Level (μg/L)

F G = 

Source Water 

Type

HAA5

Exhibit 5.24b Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to 

Regulatory Alternative 2
Final Economic Analysis for the Stage 2 DBPR 5-55 

Mean 5th 95th Mean 

A B

Large (= 10,000) 160,935,736 35.5 21.2 21.4 21.0 40.2% Small (< 10,000) 8,422,403 35.5 21.2 21.4 21.0 40.2% 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% 

Mean 5th 95th Mean 

A E

Large (= 10,000) 160,935,736 25.0 14.6 15.3 13.9 41.6% Small (< 10,000) 8,422,403 25.0 14.6 15.3 13.9 41.6% 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% 

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 Mont* 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

SW

Source Water 

Type

System Size 

(Population Served)

Pre-Stage 2 

Population Level (μg/L)

Post-Stage 2 Level (μg/L)

C

TTHM

Perce

D = 

(C) and (F) Outputs from the benefits Monte Carlo simulation model. Confidence bounds for SW systems account for uncertainties in compmethodologies .

SW

GW

All Systems*

Source Water 

Type

HAA5

Population

(B) and (E) Exhibit 5.22

System Size 

(Population Served)

Pre-Stage 2 Post-Stage 2 Level (μg/L) Perce

Level (μg/L)

F G = 

Exhibit 5.24c Reduction in Average TTHM and HAA5 Concentrations from Pre-Stage 2 to 

Regulatory Alternative 3
Final Economic Analysis for the Stage 2 DBPR December 2005 5-56

5.6 Reduction in Frequency of Peak TTHM and HAA5 Concentrations

Treatment technology changes to meet the Stage 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 concentration. The level does not have to be sustained

over any period of time to be considered a peak measurement, and it can be a measurement taken at any

time in the year. As discussed in Chapter 6, the health 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 information 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 changes as a result of the Stage 1 and Stage 2 rules. 

For surface water plants, ICR-observed data is used instead of SWAT-predicted data because, as

explained in Appendix A, SWAT-predicted TTHM 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 compliant 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 DBPR 5-57 

Pre-Stage 1

Bin

Number of 

Locations

Locations with 

TTHM >60 ug/L

Locations with 

TTHM >75 ug/L

Locations with 

TTHM >80 ug/L

Locations with 

TTHM >100 ug/L

Locations with 

HAA5 >45 ug/L

H

A1 880 122 31 19 4 80 

<64/48 >=64/48 (S1 non- compliant) A2 155 96 65 54 17 82 

<64/48 A1 B2 195 173 151 142 83 136 >= 64/48 (S2 noncompliant) A2 B2 All Plants 1,230 391 247 215 104 298 

Pre-Stage 2

Bin

Number of 

Locations

Locations with 

TTHM >60 ug/L

Locations with 

TTHM >75 ug/L

Locations with 

TTHM >80 ug/L

Locations with 

TTHM >100 ug/L

Locations with 

HAA5 >45 ug/L

H

A1 880 122 31 19 4 80 

<64/48 >=64/48 (S1 non- compliant) A2 155 96 65 54 17 82 

<64/48 A1+B2 B2 195 40 5 1 0 21 >= 64/48 (S2 noncompliant) A2 All Plants 1,230 258 101 74 21 183 

Post-Stage 2

Bin

Number of 

Locations

Locations with 

TTHM >60 ug/L

Locations with 

TTHM >75 ug/L

Locations with 

TTHM >80 ug/L

Locations with 

TTHM >100 ug/L

Locations with 

HAA5 >45 ug/L

H

A1 880 122 31 19 4 80 

<64/48 >=64/48 (S1 non- compliant) A2 155 32 4 1 0 17 

<64/48 A1+B2+A2 B2 195 40 5 1 0 21 >= 64/48 (S2 noncompliant) All Plants 1,230 193 40 21 4 117 

Max 

LRAA

RAA

Max 

LRAA

RAA

Max 

LRAA

RAA

Exhibit 5.25a ICR Matrix Method for Peak Locations for the Stage 2 DBPR, 20 Percent Safet

Surface and Ground Water Plants

Notes: 1) In the Pre-Stage 1 tables, A1 through B2 are the number of locations for ICR plants that meet the criteria for each bin under p

total 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 HAA5 RAA concentrations and Maximum LRAA concentrations in :g/L (

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 into the bin). ThHAA5 result determines the bin placement. Note that bins are based on a 20 percent safety margin on the Stage 1 and Stage 2 

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. ThTTHM and HAA5 concentrations above each study level is the percent of locations above the study level for those ICR plants thStage 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 5-58 

Pre-Stage 1

Bin

Number of 

Locations

Locations with 

TTHM >60 ug/L

Locations with 

TTHM >75 ug/L

Locations with 

TTHM >80 ug/L

Locations with 

TTHM >100 ug/L

Locations with 

HAA5 >45 ug/L

A1 832 90 18 11 2 52 

<64/48 >=64/48 (S1 non- compliant) A2 203 128 78 62 19 110 

<60/45 A1 B2 195 173 151 142 83 136 >= 60/45 (S2 noncompliant) A2 B2 All Plants 1,230 391 247 215 104 298 

Pre-Stage 2

Bin

Number of 

Locations

Locations with 

TTHM >60 ug/L

Locations with 

TTHM >75 ug/L

Locations with 

TTHM >80 ug/L

Locations with 

TTHM >100 ug/L

Locations with 

HAA5 >45 ug/L

A1 832 90 18 11 2 52 

<64/48 >=64/48 (S1 non- compliant) A2 203 128 78 62 19 110 

<60/45 A1+B2 B2 195 40 5 1 0 21 >= 60/45 (S2 noncompliant) A2 All Plants 1,230 258 101 74 21 183 

Post-Stage 2

Bin

Number of 

Locations

Locations with 

TTHM >60 ug/L

Locations with 

TTHM >75 ug/L

Locations with 

TTHM >80 ug/L

Locations with 

TTHM >100 ug/L

Locations with 

HAA5 >45 ug/L

A1 832 90 18 11 2 52 

<64/48 >=64/48 (S1 non- compliant) A2 203 41 5 1 0 22 

<60/45 A1+B2+A2 B2 195 40 5 1 0 21 >= 60/45 (S2 noncompliant) All Plants 1,230 171 28 14 2 94 

Max 

LRAA

RAA

Max 

LRAA

RAA

Max 

LRAA

RAA

Exhibit 5.25b ICR Matrix Method for Peak Locations for the Stage 2 DBPR, 25 Percent Safet

Surface and Ground Water Plants

Notes: 1) In the Pre-Stage 1 tables, A1 through B2 are the number of locations for ICR plants that meet the criteria for each bin under p

total 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 HAA5 RAA concentrations and Maximum LRAA concentrations in :g/L (

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 into the bin). ThHAA5 result determines the bin placement. Note that bins are based on a 20 percent safety margin on the Stage 1 and on a 25 

the 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. ThTTHM and HAA5 concentrations above each study level is the percent of locations above the study level for those ICR plants thStage 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 

Category

Percent of Locations with 

Number of HAA5 Peaks Above

Locations

Characterization of peak TTHM and HAA5 levels for 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 locations 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 technologies 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 ground water plants.

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 Approach, 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 conservatively 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 conservatively high because the analysis includes ground water

systems. 

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

5.7 Uncertainties in the Compliance Forecast and Subsequent DBP Reduction

There are numerous sources of uncertainty in the 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 methodology to quantify and incorporate both of these

uncertainties into the compliance forecast 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 sensitivity analyses. Exhibit 5.29 shows the predicted

percent of all plants changing technology and resulting decrease in average TTHM concentration for the

primary analysis and four sensitivity analyses. Note 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 water systems, respectively. The impact of the safety

margin is more pronounced for DBP reduction than for 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 tool has a greater impact on the compliance forecast

results compared to the operational safety margin. The percent of all plants changing technology for the

ICR Matrix Method is approximately 15 percent higher than the primary analysis. The percent DBP

reduction for the ICR Matrix Method is about 30 percent 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 national costs, estimates using the SWAT model are

about 25 percent lower then the midpoint estimate, while 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 are 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 the compliance forecasts affect cost and benefits

similarly. If more plants make treatment technology 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 technology 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

UnderestimateOverestimateUnknown

Impact

UnderestimateOverestimateUnknown

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 forecast 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 changes 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 lower 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 sample results are uncertain due to the nature of

distribution system monitoring. Research has shown 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 the ICR does not represent this potential variability. In

addition to hourly variations, the ICR data were not required to be collected at evenly spaced intervals. 

Thus, there is uncertainty in assuming that a single data 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 contains 213 of the 353 surface water plants in the

ICR database, or roughly 60 percent. To evaluate the representativeness of the ICR Matrix Method

subset, EPA evaluated source water TOC and distribution 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 relatively little bias from source water quality.

TTHM and HAA5 data for plants that passed the data 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 higher overall DBP levels, and their 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 larger uncertainties in the compliance forecast. 

Uncertainties associated with SWAT are discussed 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 simultaneously 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 alternatives. 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 consistent 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 represent 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 contaminant 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 likelihood 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 persons 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 are 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 apply 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 presented in this chapter follow the requirements of

the executive order and related Office of Management and Budget (OMB) and EPA guidance, and

provide a reasonable basis for estimating potential health benefits using the best available science.

EPA has quantified the benefits associated with expected reductions in the incidence of bladder

cancer. EPA also includes a sensitivity analysis for 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 risks 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 drinking 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 could come from reducing potential reproductive and

developmental risks and that it is important to provide 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. Section 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 uncertainties of national benefits estimates. Section 6.7

contains a sensitivity analysis for benefits from avoiding colon and rectal cancers. Potential benefits from

reductions in one reproductive and developmental endpoint—fetal loss—are evaluated through an

illustrative calculation in Section 6.8. 

The following provide additional details 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 activities (this information is used as input for the

quantified benefits calculation).

• Appendix E provides general background information 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, Appendix 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 with 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 incidence of colon 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 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 DBPR
1

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 value of a statistical life (VSL) was used for fatal

bladder cancers, while two alternate estimates of willingness-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 fetal 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, and 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-age 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 after age 85, the number of new bladder cancer cases does,

which represents the overall decline in the number of individuals 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 attributable to DBP exposure, EPA used three different

approaches: 

• Using the range of Population Attributable Risk (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 Villanueva et al. (2003) meta-analysis that reflects

both sexes, ever exposed population from the studies considered (yields a pre-Stage 1 best

estimate for PAR of ~16%)

• Using the Villanueva et al. (2004) pooled data analysis to develop a dose-response

relationship for OR as a function of Average TTHM. The dose-response relationship was

modeled 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 reasonable 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 bladder cancer. Thus, percent reductions in national

average DBPs are used to determine the percent reductions in bladder cancer cases attributable to DBPs. 

Predicted reductions in national average TTHM and 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 annual 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 benefits 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 lifetime. 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 adjusted 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, 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 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 and deaths. EPA has estimated that 74 percent of

bladder cancer cases are non-fatal (USEPA, 1999a). The 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 avoiding non-fatal bladder cancer are not available, EPA

estimated values from two other non-fatal illnesses: curable lymphoma and chronic bronchitis. Both are

considered valid estimates of WTP for non-fatal cancer.

For fatal bladder cancer cases, VSL is used to capture 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 individual’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 calculating 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 respect to quantified benefits for bladder cancer. 

Many are described qualitatively in the analysis, while other are incorporated explicitly as follows: 

• There is uncertainty in the percent reduction 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 combined are the third most common type of new cancer

cases and deaths in both men and women in the U.S., excluding 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 accrued 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 from 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 and 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 additional benefits of this rule could come from

reductions in potential developmental 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), avoiding 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 losses in a similar manner to bladder cancer cases. 

A range of possible PAR values for relating annual fetal losses to DBP exposure was obtained from

available epidemiological studies. Reductions in the number of peak DBP events due to the Stage 1

DBPR and the Stage 2 DBPR were estimated. Reductions 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

Annualized 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 the 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 calculation, 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 Exhibit 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 represent 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 avoided 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 expected 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 calculated 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 benefits reflect uncertainty in monetization inputs relative to mean cases. 

Values are discounted and annualized in 2003$. 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.

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 associated with exposure to DBPs. In addition to the

studies and reviews presented here, EPA has addressed 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 dichloroacetic 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 effects, 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 toxicology 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 association 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 the 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 from 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 misclassification, 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 sufficient 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 comparable 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 following 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 bladder cancer in humans are Cohen et al. (2000) and

Fukushima and Wanibuchi (2000). The following is derived largely from these reviews.

Bladder cancer involves a heterogenous group of tumors, 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 endemic schistosomiasis involve squamous cell carcinomas

rather than transitional cell carcinomas, and also tend 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 malignancies. In countries without schistosomiasis, over 95

percent of bladder cancers involve transitional cell tumors. There is a relatively long history of

association of these bladder cancers with specific environmental 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 bladder 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 been 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 relationship 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, there do not appear to be any dietary factors associated

with bladder cancer beyond the associations made between cancer in general and diets high in fats, red

meats and fried foods. In a systematic literature review, 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 consumption 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 some 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 attributable factor for about 50 percent of bladder cancers

in men and 30 percent in women (NCI 2004). The NCI also indicates that up to 25 percent of bladder

cancers may be attributable to occupational factors, 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 bladder 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 annual 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 water consumed prior to these rules maybe responsible

for between 2 and 17 percent of bladder cancers.

It is not appropriate simply to add the estimates of percent contribution to total bladder cancer

estimated separately for each of the individual causes described above, as there may be overlap among

them. The Venn diagram shown in Exhibit 6.2 is intended to provide a schematic depiction of possible

overlaps among the major attributable causes of bladder 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 Bladder 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 fully 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 less 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 possible association between bladder cancer and

chlorinated water or DBP exposure than for other cancers. The Stage 1 DBPR Regulatory Impact

Assessment (USEPA 1998a) presents EPA’s review of the large body of epidemiology literature for

bladder cancer and its association with DBPs in drinking 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 understanding include the reason for inconsistent results across subpopulations

in the different studies, especially for males versus females and smokers 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 calculate a range of PAR values for bladder cancer

associated with exposure to chlorinated drinking water. 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 information 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 details on the PAR values derived from these studies by

EPA.

All of these studies include adjustments in their 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 percentages from the studies range from 2 percent to

17 percent. Those values are “best estimates” derived from the study data as described in Appendix E. 

EPA has also estimated 95 percent confidence intervals 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 plausibility considerations. That is, notwithstanding

statistical indications of PAR values < 0 percent implied 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 μg/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-year3

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 plausibility. The actual lower confidence 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 criteria 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 bladder cancer related to exposure to chlorinated

drinking water. The Villanueva et al. (2003) meta-analysis included most of the studies that EPA had

used for Stage 1 and the Stage 2 proposal (see Exhibit 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), separately 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 data 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 analysis, EPA has chosen to use the estimated

OR from Villanueva et al. (2003) for the "ever-exposed, 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 individual 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 inclusion criteria and adjustments for confounders relative to the analysis performed in the 1985

study.

2

 Freedman et al. (1997) is a subset of the Wilkins and Comstock 1981 study and was used in Stage 1 and the Stage 2 proposal for

calculation of bladder cancer 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 Villanueva et al. (2003) study of 15.8 percent (95

percent CI = 8.5 percent - 27.2 percent). See Appendix E for details of the PAR calculation.

Subsequent to the Stage 2 DBPR proposal, Villanueva 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, occupation, 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 TTHM exposure. The authors also evaluated the

relationship between OR for bladder cancer and duration of exposure to chlorinated water.

For support of the Stage 2 DBPR, EPA is using 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 detailed data on the relationship between 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 Villanueva (2005), and an estimate of a national average Pre-Stage 1 TTHM

concentration of 38.05 μg/L, EPA has estimated a pre-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 levels 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 reasonable 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-Stage 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 Proposal1

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 percent 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 exposure. 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 Cantor et al. (1985). Several studies published since the

Stage 1 DBPR continue to support an association between 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 reviews 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 determine causality between chlorinated water and any

health endpoint, although they concluded that the evidence is better for bladder cancer than for other

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. The database of studies on colon

and rectal cancers continues to support a possible association, 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 chlorination DBPs than for colon cancer (Mills et al.

1998). The WHO (2000) review reported that studies showed weak to moderate associations with colon

and rectal cancers and chlorinated surface water or THMs 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 these endpoints that definitive conclusions cannot be

made. Studies on leukemia found little or no association 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 (Vinceti et al. 2004). In the development of the

Stage 2 DBPR, EPA has evaluated several key bladder cancer studies. Summary information on these

studies can be found in Exhibit 6.10.
Final Economic Analysis for the Stage 2 DBPR 6-20 

Exhibit 6.10 Summary of Bladder Cancer Epidemiology Studies and Review/Meta-analys

Reviewed for Stage 2 DBPR

Author(s) Study Type Exposure(s)

Studied

Outcome(s)

Measured

Findings

Do et al. 2005 Case-control

study inCanada, 1994-1997.

Estimated chlorinatedDBPs, chloroform,BDCM concentrations.

Pancreaticcancer.

No association was found between pancreatexposure to chlorinated DBPs, chloroform, o

Chevrier et al.2004

Case-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 blfound as duration of exposure to ozonated wwas evident with and without adjustment for measures. A small association was detectecancer risk and duration of exposure to chloand with the estimated THM content of the wstatistical significance only when adjusted fowater exposures. Effect modification by genadjusted analyses.

Vinceti et al.2004

Retrospectivecohort 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 stasmall increase for males consuming drinking THMs. For females, an increased mortality was seen but was not statistically significantmen was the only individual cancer in which significant excess in mortality was detected drinking water with high THMs.

Ranmuthugalaet al. 2003

Cohort 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 bdose metrics were near 1.0. The study provTHMs are associated with DNA damage to band dose-response patterns were not detect

Infante-Rivardet al. 2002

Populationbased casecontrol study inQuebec, 1980-1993.

Estimated prenataland postnatalexposure to THMs andpolymorphisms in twogenes.

Acutelymphoblasticleukemia.

Data are suggestive, but imprecise, linking Dof acute lymphoblastic leukemia associated DBPs. The number of genotyped subjects f

CYP2E1 genes was too small to be conclus
Author(s) Study Type Exposure(s)

Studied

Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-21 

Infante-Rivardet al. 2001

Populationbased casecontrol study inQuebec, 1980-1993.

Compared waterchlorination (never,sometimes, always)and exposure toTTHMs, metals, andnitrates.

Acutelymphoblasticleukemia.

No increased risk for lymphoblastic leukemia prenatal exposure at average levels of TTHMHowever, a non-statistically significant, smaseen for postnatal cumulative exposure to T(both at above the 95th exposure percentile o

cases and controls), for zinc, cadmium, and metals or nitrates.

King et al.2000a

Populationbased 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 associateterm exposure to THMs, chlorinated surface consumption metrics among males only. Exrelationships were evident for exposure meaduration and THM levels. Associations betwmeasures and rectal cancer were not observ

Cantor et al.1999

Populationbased casecontrol study inIowa, 1984-1987.

Compared level andduration of THMexposure (cumulativeand average), sourceof water, chlorination,and waterconsumption.

Brain cancer. Among males, a statistically significant incre

cancer was detected for duration of chlorinachlorinated source water, especially among of tap water. An increased risk of brain cancintake level was found in men. No associatiwomen for any of the exposure metrics exam

Cantor et al.1998

Populationbased casecontrol study inIowa, 1986-1989.

Compared level andduration of THMexposure (cumulativeand average), sourceof water, chlorination,and waterconsumption.

Bladdercancer.

A statistically significant positive association bladder cancer and exposure to chlorinated surface water reported for men and for smokassociation found for male/female non-smokoverall. Limited evidence was found for an atapwater consumption and bladder cancer rievidence existed for exposure-response effewater and lifetime THM measures on bladde

Hildesheim etal. 1998

Populationbased 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 associaexposure to chlorinated surface water and awith evidence of an exposure-response relatcancer is statistically significant increased wexposure to THMs in drinking water, and risk individuals with low dietary fiber intake. Riskand women and no effects were observed foNo associations were detected for water exprisk of colon cancer.
Author(s) Study Type Exposure(s)

Studied

Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-22 

Koivusalo etal. 1998

Populationbased casecontrol study inFinland, 1991-1992.

Estimated residentialduration of exposureand level of drinkingwater mutagenicity.

Bladder andkidneycancer.

Drinking water mutagenicity was associated statistically significant, exposure-related excbladder cancers among men; weaker assocfor mutagenic water and bladder or kidney cThe effect of mutagenicity on bladder cancesmoking status, with an increased risk amon

Yang et al.1998

Cross-sectionalstudy in Taiwan, 1982-1991.

Examined residence in chlorinated (mainlysurface water sources)relative to nonchlorinated (mainlyprivate well) water.

Cancer ofrectum, lung,bladder,kidney, colon,and 11others.

Residence in chlorinating municipalities (vs. statistically significantly associated with the fcancer in both males and females: rectal, luncancer. Liver cancer and all cancers were asignificantly elevated in chlorinated towns forates for cancers of the esophagus, stomachprostate, brain, breast, cervix uteri and uterucomparable for chlorinated and non-chlorina

Doyle et al.1997

Prospectivecohort study inIowa, 1987-1993.

Examined chloroformlevels and source ofdrinking water.

Colon,rectum,bladder, and8 othercancers inwomen.

Statistically significant increased risk of colocancer and all cancers combined was obserexposed to chloroform in drinking water, with exposure-response effects. No associations between chloroform and bladder, rectum, kidorgans, lung, ovary, endometrium, or breast melanomas or non-Hodgkin’s lymphoma. S(compared to ground water users) was also of colon and breast cancer risk.

Freedman etal. 1997

Populationbased casecontrol study inMaryland,1975-1992.

Estimated duration ofexposure tochlorinated water. Compared exposure tochlorinated municipalwater (yes/no).

Bladdercancer.

There was a weak association between bladduration of exposure to municipal water for msmokers, as well as an exposure-response rassociation was seen for those with no histosuggesting that smoking may modify a posschlorinated surface water on the risk of blad

King andMarrett 1996

Case-controlstudy inOntario,Canada, 1992-1994.

Compared source ofdrinking water andchlorination status. Estimated TTHMlevels, duration ofexposure, and tapwater consumption.

Bladdercancer.

Statistically significant associations were detcancer and chlorinated surface water, duratiTHM levels and tap water consumption metrattributable risks were estimated at 14 to 16 response relationship was observed for estimTHM exposures and risk of bladder cancer.
Author(s) Study Type Exposure(s)

Studied

Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-23 

Cordier et al.1993

Hospital-basedcase-controlstudy inFrance, 1984-1987.

Estimated duration ofexposure to TTHMs. 

Bladdercancer.

No associations were not detected for bladdexposure to TTHMs. 

McGeehin etal. 1993

Populationbased 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 detcancer and duration of exposure to chlorinatrisk was similar for males and females and aand smokers. The attributable risk was estimHigh tap water intake was associated with ria exposure-response fashion. No associatiobetween bladder cancer and levels of TTHMresidual chlorine.

Vena et al.

1993

Case-controlstudy inwestern NY,1979-1985.

Comparedconsumption of fluids,including chlorinatedtap water.

Bladdercancer.

Bladder cancer and consumption of total fluishowed a significant finding for both age catyears and over 65 years). A dose-response observed for consumption of tap water and tassociated with tap water consumption were nonsmokers.

Lynch et al.1989

Case-controlstudy in Iowa,1894-1979.

Compared source ofdrinking water. Estimated chlorinatedwater consumptionand duration ofexposure.

Bladdercancer.

Bladder cancer was statistically associated wexposure to chlorinated drinking water sourc

Cantor et al.1987 (andCantor et 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 wexposure to chlorinated surface water for woof both sexes. The largest risks were seen wduration and level of tap water ingestion werassociation was seen for total beverage con

Wilkins andComstock1981

Cohort study inWashingtonCounty, MD,1963-1975

Compared chlorinatedsurface water.

Bladdercancer.

Incidence rates for bladder cancer among mhigher in the chlorinated surface water group group (results not statistically significant).
Author(s) Study Type Exposure(s)

Studied

Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-24 

Reviews/Meta-analysesVillanueva etal. 2004

Review andmeta-analysisof 6 casecontrol studies.

Individual-basedexposure estimates toTHMs and waterconsumption over a40-year period.

Bladdercancer.

The meta-analysis suggests that risk of bladincreases with long-term exposure to TTHMresponse pattern was observed among men with statistically significant risk seen at expo

:g/L. No association between TTHMs and b

seen for women.

Villanueva etal. 2003 (andGoebell et 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 moderbladder cancer attributable to long-term consdrinking water for both genders, particularly significance seen with men and combined bwas higher when exposure exceeded 40 yea

Villanueva etal. 2001

Qualitativereview 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 cancewere not always statistically significant, evidcancer is strongest, and all 10 of the bladdeshowed increased cancer risks with ingestioThe authors felt associations with chlorinatethe colon, rectum, pancreas, esophagus, brawere inconsistent.

WHO 2000 Qualitative

reviews ofvarious studiesin Finland,U.S., andCanada.

Various exposures toTHMs.

Variouscancers.

Studies reviewed reported weak to moderate relative risks of bladder, colon, rectal, pancrelung cancer associated with long-term exposdrinking water. The authors felt evidence is association between colon cancer and long-THMs, that evidence is insufficient to evaluarelationship between THMs and rectal, bladdThey found no association between THMs acardiovascular disease.
Author(s) Study Type Exposure(s)

Studied

Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-25 

Mills et al.1998

Qualitativereview 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 exposure to chlorinated drinking water. The for increased risk of colon and rectal cancerthough evidence is stronger for rectal cance
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 information 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 descriptions of cancer risk assessments that EPA has

performed for seven DBPs. Included on IRIS are weight-of-evidence characterizations of the

carcinogenic potential of those seven DBPs and lifetime 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 establish 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 the 1999 EPA Proposed Guidelines for Carcinogenic

Risk Assessment (USEPA 1999b). In March 2005, EPA updated and finalized the Cancer Guidelines and

a Supplementary Children’s Guidance, which include new considerations on mode of action for cancer

risk determination and additional potential risks 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 exception 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 Children’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, and 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-Stage 2 baseline cancer cases, the pre-Stage 1 concentrations of these

compounds, the changes in those concentrations following Stage 1, and the estimated number of people

exposed. As described in the Criteria Documents, cancer risk values were developed by fitting the key

animal toxicological data to linearized multistage 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 data. The risk factors shown in Exhibit 6.12 are then

computed from the linearized multistage dose-response 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 ED10). (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 risks 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 LED10).

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 Probable1 2.3 X 10-7 (:g/L)-1 1993 (IRIS)

Bromodichloromethane Probable1 1.8 X 10-6 (:g/L)-1 1993 (IRIS)

Chloroform Probable1

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 Possible1 2.4 X 10-6 (:g/L)-1 1992 (IRIS)

Dichloroacetic Acid Likely2 1.4 X 10-6 (:g/L)-1 2003 (IRIS)

Trichloroacetic Acid Possible1 Not Available 1996 (IRIS)

Bromate Probable1

Likely to be carcinogenic via

oral route of exposure2

2 X 10-5 (:g/L)-1 2001 (IRIS)

1 EPA’s Human Carcinogen Assessment reported, as classified 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 ED10 risk factors and 91 cases for

the LED10 risk factors. Assuming that DBP risk reductions 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 conjunction 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 available 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. Lastly, 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 

Lifetime unit 

risk (cases/ 

person)/ 

(mg/kg-day)

Lifetime unit risk 

conc. (cases/ 

person)/ (ug/L)

Annual unit risk 

conc. (cases/ 

person)/ (ug/L)

Baseline 

Cases

Lifetime unit 

risk (cases/ 

person)/ 

(mg/kg-day)

Lifetime unit risk 

conc. (cases/ 

person)/ (ug/L)

ABC

D=C*(1/1000)*

(2L/day)*(1/70 kD) E=D/70 years F=A*B*E G

H=G*(1/1000)*

(2L/day)*(1/70 kD) 

BDCMSW 8.20 169,358,139 2.2E-02 6.29E-07 8.98E-09 12.5 3.4E-02 9.71E-07 GW 3.05 93,666,379 2.2E-02 6.29E-07 8.98E-09 2.6 3.4E-02 9.71E-07 Total 263,024,518 15.0 BromoformSW 2.69 169,358,139 3.4E-03 9.71E-08 1.39E-09 0.6 4.5E-03 1.29E-07 GW 2.22 93,666,379 3.4E-03 9.71E-08 1.39E-09 0.3 4.5E-03 1.29E-07 Total 263,024,518 0.9 DBCMSW 5.50 169,358,139 1.7E-02 4.86E-07 6.94E-09 6.5 4.0E-02 1.14E-06 GW 3.13 93,666,379 1.7E-02 4.86E-07 6.94E-09 2.0 4.0E-02 1.14E-06 Total 263,024,518 8.5 DCA

A

SW 11.98 169,358,139 1.5E-02 4.29E-07 6.12E-09 12.4 4.8E-02 1.36E-06 GW 4.28 93,666,379 1.5E-02 4.29E-07 6.12E-09 2.5 4.8E-02 1.36E-06 Total 263,024,518 14.9 Grand Total 39.3 

Derivation of cases using ED10 Derivation of cases 

Source Water 

Type Population

Pre-Stage 2 

Conc (ug/L), 

Mean of Plant 

Means, 

DS Average

Exhibit 6.12 Quantification of Cancer Risk for BDCM, Bromoform, DBCM, and DCAA, Pre-St

Note: Unit risk factors are different from Exhibit 6.5 - see text for discussion.Sources: A) SW: SWAT DBP Summary Statistics, Run 300 (Pre-Stage 2); GW: Stage 2 Benefits Model

B) Stage 2 Population Baseline: Exhibit 3.3C) The unit risk factor is based on the ED10 (effective dose for 10 percent response) based on the Maximum Likelihood Est(provided by Nancy Chiu of EPA's Health and Ecological Criteria Division in email (5/22/03)). 

D) This calculation assumes a 70 kg person with the average drinking water consumption rate of 2L/day. kD = kilogram-DaG) The unit risk factor is based on the LED10 (lower 95 percent confidence bound on effective dose for 10 percent response

method (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 and 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 drinking 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 administered 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 long-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 nonlinear 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 (ILSI) 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), the 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 (simple) 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 are 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 result 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 analytical minimum reporting levels; plant-average

concentrations of iodo-THM and halonitromethane 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 species, was also measured in the ICR with a median

concentration of 0.0002 mg/L across all surface water 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 and 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, further 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 occurrence 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 NDMA forms in both chlorinated and chloraminated

systems. Barrett et al. (2003) reported median concentrations 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 the 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 determine the relative exposure of NDMA in drinking

water to these other sources.

Other DBPs

Some systems, depending on bromide and organic 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 concentration may decrease. In some instances where

alternative disinfectants are used, levels of chlorite 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 removal is a highly effective strategy to reduce levels of

DBPs.

Other toxicological effects

The Agency has modified the reference dose (RfD) values of the chlorinated 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 Animal 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 concordance 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 credence and support in the analysis of mode of action,

it is not necessary to cite animal and human tumor site 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 similarities and differences across species when conducting

such a cross-species concordance evaluation for bladder 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 given species. In addition, it is important to consider

the differences in exposure routes for the animal studies (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, concordance on the general tissue type may be present in

different organs. Kidney cancer in the renal pelvis, which has been reported for a few DBPs, may be

linked with bladder cancer, as the renal pelvis is lined with the same transitional cell epithelium as found

in the bladder (Cohen et al. 1988). Further, the pathogenesis of transitional cell carcinomas in the urinary

bladder appears to be similar throughout the renal pelvis, 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 (Komulainen et al. 1997). A review of occupational cancer of

the urinary tract reported that approximately 15 percent of kidney neoplasms are in the renal pelvis and

appear to be caused by the same carcinogens as bladder neoplasms (Schulte et al. 1987). Finally, over 90

percent of human bladder cancers involve the transitional cell epithelium or urothelium (Silverman et al.

1996), suggesting that a component of the kidney and bladder can be considered part of the same target

tissue, known as the urinary tract. 

DBPs may not be unique with respect to an apparent 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 (Cohen 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 animals 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 disinfection 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 bladder cancer risk, the specific causative constituents

have not been identified. Since there is no definitive support provided by studies to date for or against the

generalization of adverse effects across different organs or between or within species, EPA concludes that

target organ concordance for cancer is not a prerequisite 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-exposure conditions that lead to cytotoxicity

and regenerative hyperplasia.

• Epidemiological data from individual investigations and from a meta-analysis

(Villanueva et al. 2003) links exposure to chlorinated 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 specific DBPs with cancer risk because of

difficulties in characterizing the exposure. Exposure in some epidemiology studies was

monitored purely in terms of chlorinated water, 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 mixture 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 women, 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 planned 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 reproductive and developmental health effects. Based

on the weight-of-evidence evaluation of the reproductive and developmental epidemiology data EPA

concludes that a causal link between adverse reproductive 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 recent studies continue to suggest associations between

DBP exposure and various adverse reproductive and developmental effects. In addition, data from a

number of toxicology studies, although the majority 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 causal 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 exposure to chlorinated drinking water or disinfection

byproducts cause adverse reproductive and developmental health effects, but do provide an indication of a

potential health hazard concern that warrants incremental 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 Reproductive and Developmental Health Effects

As discussed previously, epidemiology studies have the strength of relating human exposure to

DBP mixtures through multiple intake routes. Although 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 and developmental epidemiology studies. Exposure

assessment uncertainties and possible exposure classification errors arise from limited data on DBP

concentrations and maternal water usage and source over the course of the pregnancy. However,

classification errors typically push the true risk estimate 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 underestimates 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 moderate 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 reproductive 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 inconsistencies 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, studies that use THM levels to estimate exposure tend

to be higher quality than studies that define exposure by source or treatment. These factors were taken

into account by EPA when comparing and making conclusions on the reproductive and developmental

epidemiology literature. What follows is a summary of available epidemiology literature on reproductive

and developmental endpoints such as spontaneous abortion, stillbirth, neural tube and other birth defects,

low birth weight, and intrauterine growth retardation. 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 fetal growth (mainly small for gestational age,

low birth weight, and pre-term delivery) and chlorinated 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 adverse 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 increased risk of small for gestational age and low

birth weight.

Fetal viability

While the database of epidemiology studies for 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. Various studies support the possibility that exposure to

chlorinated water or DBPs is associated with decreased 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 fetal 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 exposures, 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 fetal 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 DBPs 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 inconclusive results (Magnus et al. 1999) between

chlorinated water or DBP exposure and fetal malformations. Birth defects most consistently identified as

being associated with DBPs include neural tube defects and urinary tract malformations.

Other endpoints have also been examined in recent epidemiology studies. One study suggests an

association between DBPs and decreased menstrual cycle length (Windham et al. 2003), which, if

corroborated, could be linked to the biological basis 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 inadequate to support an association between reproductive

and developmental health effects and THM exposure (Reif et al. 1996, Craun 1998, WHO 2000). Mills et

al. (1998) examined data on and found support for an association between fetal viability and

malformations and THMs. Another review presented to the Stage 2 MDBP FACA found some evidence

for an association with fetal viability and some fetal 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 evidence 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 hazard identification stage. Nieuwenhuijsen et al. (2000)

found some evidence for an association between fetal growth and THM exposure and concluded evidence

for associations with other fetal endpoints is weak 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 supported 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. Exhibit 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 

Exhibit 6.13 Summary of Reproductive/Developmental Epidemiology Studies

Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Porter et al.2005

Crosssectional studyin Maryland,1998-2002.

Estimated trimesterspecific andpregnancy-averageexposures to THMsand HAAs, includingindividual DBPs.

Intrauterinegrowthretardation.

No consistent association or dose-responfound between exposure to either TTHM intrauterine growth retardation. Results srisk of intrauterine growth retardation assand HAA5 exposure in the third trimesterresults were statistically significant.

Savitz et al.2005

Populationbasedprospectivecohort 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 sexposures were compared to low exposuindividual THMs, a statistically significant between bromodichloromethane (BDCM) Although non-statistically significant, an imagnitude was seen between dibromochand pregnancy loss. Some increased rislosses at greater than 12 weeks’ gestatioand TOX (total organic halide), but most not provide support for an association. Psmall inverse relationship with DBP expoexposures were less likely to have a pretassociation was weak. TTHM exposure osignificantly associated with twice the risk gestational age during the third trimester.

Toledano et al.2005

Large 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 low birth weight, and very low birth weighof the three regions. When all three regiosmall, but non-significant, excess risks wthree outcomes and TTHM and chloroforwere observed between reproductive riskbrominated THMs.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-38 

Dodds et 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 

stillbirths and exposure to total THM, BDCAssociations were also detected for metrwater consumption, showering and bathinrelative risks were observed for intermedHAA and DCAA measures; TCAA and brexposures showed no association. No stassociations or dose-response relationshand stillbirth were detected after controllin

Infante-Rivard2004

Case-controlstudy ofnewborns inMontreal,1998-2000.

Estimated THM levelsand waterconsumption duringpregnancy. Exposurefrom showering andpresence of twogeneticpolymorphisms.

Intrauterinegrowthretardation.

No associations were found between expintrauterine growth retardation. Howeverwas observed between THM exposure anretardation for newborns with the CYP2EFindings suggest that exposure to THMs can affect fetal growth but only in geneticnewborns.

Wright et al.2004

Large 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 observed for BDCM, chloroform, and mutexposure-response relationship was founexposure and reductions in mean birth wfor gestational age. There was no assocdelivery and elevated levels of HAAs, MXreduced risk of preterm delivery was obseexposures. Gestational age was associaTHMs and mutagenicity.

Yang 2004 (andYang et al.2000)

Large crosssectionalstudies inTaiwan, 1994-1996.

Compared maternalconsumption ofchlorinated drinkingwater (yes/no).

Low birthweight, pretermdelivery.

Residence in area supplied with chlorinatshowed a statistically significant associatdelivery. No association was seen betwedrinking water and low birth weight.

Fenster et al.2003

Smallprospectivestudy inCalifornia,1990-1991.

Examined TTHMlevels within the 90days preceding semencollection.

Sperm motility,spermmorphology.

No association between TTHM level and morphology. BDCM was inversely assocsperm motion. There was some suggestconsumption and other ingestion metrics with different indicators of semen quality.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-39 

Shaw et al. 2003 2 case-control

maternalinterviewstudies: CA,1987-1991.

Estimated THM levelsfor mothers'residences from beforeconception throughearly pregnancy.

Neural tubedefects, oralclefts, selectedheart defects.

No associations or exposure-response rebetween malformations and TTHMs in eit

Windham et al.2003

Prospectivestudy: 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 function. All brominated THM compoundsignificantly shorter menstrual cycles with for chlorodibromomethane. There was litbetween TTHM exposure and luteal phaslength, or cycle variability.

Wright et al.2003

Crosssectionalstudy:Massachusetts, 1990.

Estimated TTHMexposure in womenduring pregnancy(average forpregnancy and duringeach trimester).

Birth weight,small forgestational age,pretermdelivery,gestational age.

Statistically significant associations betwe

pregnancy average TTHM exposure and age and fetal birth weight were detected. significant increases in gestational duratioat increased TTHM levels, but there was association between TTHM and preterm weight.

Cedergren et al.2002

Retrospectivecase-controlstudy:Sweden,1982-1997.

Examined maternalpericonceptional DBPlevels and used GIS toassign water supplies.

Cardiacdefects.

Exposure to chlorine dioxide in drinking wsignificance for cardiac defects. THM co

and higher were significantly associated wNo excess risk for cardiac defect and nitr

Hwang et al.2002

Large 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, respiratotract defects were significantly associated chlorination. Exposure to chlorinated drinstatistically significantly associated with rdefects, and an exposure-response pattespecific defects were associated with the examined.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-40 

Dodds and King2001

Populationbasedretrospectivecohort in NovaScotia, 1988-1995.

Estimated THM,chloroform, andbromodichloromethane(BDCM) exposure.

Neural tubedefects,cardiovasculardefects, cleftdefects,chromosomalabnormalities.

Exposure to BDCM was associated with neural tube defects, cardiovascular anomnot associated with neural tube defects, bwith chromosomal abnormalities. No assand cleft defects were detected.

Jaakkola et al.2001

Large 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 betweto chlorinated drinking water and low birth gestational age. A reduced risk of preterfor exposure to chlorinated water with hig

Källén andRobert 2000

Large 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 fogestational duration and low birth weight mother resided in areas using sodium hychlorine dioxide. Sodium hypochlorite waother indices of fetal development but nodefects. No other effects were observed childhood cancer, infant mortality, low Apjaundice, or neonatal hypothyroidism in redisinfection method.

Dodds et 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 fototal THMs and specific THMs during prewith higher risks observed among asphyxBromodichloromethane had the strongesexhibited an exposure-response pattern. evidence of an association between THM reproductive outcomes. No congenital anassociated with THM exposure, except fosignificant association with chromosomal 

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 betweand neural tube defects. No associations neural tube defects and haloacetic acids 
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-41 

Magnus et al.1999

Large 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 tract defects and chlorination and high waof organic compounds). No associations other outcomes or all birth defects combisignificant, overall excess risk of birth defmunicipalities with chlorination and high wto municipalities with no chlorination and 

Gallagher et al.1998

Retrospectivecohort study ofnewborns inColorado,1990-1993.

Estimated THM levelsin drinking waterduring third trimesterof pregnancy.

Low birthweight, termlow birthweight,and pretermdelivery.

Weak, non-statistically significant associaweight and TTHM exposure during the thstatistically significant increase for term lohighest THM exposure levels. No associdelivery and THM exposure.

Swan et al. 1998 Prospective

study inalifornia, 1990-1991.

Comparedconsumption of coldtap water to bottledwater during earlypregnancy.

Spontaneousabortion.

Pregnant women who drank cold tap watwho consumed no cold tap water showed for spontaneous abortion at one of three 

Waller et al.1998 (andWaller et al.2001)

Prospectivecohort inCalifornia,1989-1991.

Estimated TTHMlevels during firsttrimester of pregnancyvia ingestion andshowering.

Spontaneousabortion.

Statistically significant increased risk betwTTHMs and spontaneous abortion compaBDCM statistically associated with increaabortion; other THMs not. Reanalysis of exposure misclassification and relative rismagnitude to earlier study. An exposure-was seen between spontaneous abortion exposure to TTHMs.

Kanitz et al.1996

Crosssectional 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 circstatistical significant association with matchlorinated drinking water. Neonatal jaunto prenatal exposure to drinking water tredioxide. Length of pregnancy, type of deshowed no association.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-42 

Bove et al. 1995(and Bove et 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 riTTHM levels with small for gestational agcentral nervous system defects, oral cleft cardiac defects. Some association with hand low birth weight. No effect seen for pbirth weight, or fetal deaths.

Savitz et al.1995

Populationbased casecontrol study:NorthCarolina,1988-1991.

Examined TTHMconcentration atresidences and waterconsumption (duringfirst and thirdtrimesters).

Spontaneousabortion,pretermdelivery, lowbirth weight.

There was a statistically significant increawith high THM concentration, but THM inconcentration times consumption level) wpregnancy outcome. No associations wedelivery or low birth weight. Water sourcpregnancy outcome either, with the excepsignificant, increased risk of spontaneous water users. There was a non-statistically reduced risk with increased consumption outcomes.

Aschengrau etal. 1993

Case-controlstudy inMassachusetts, 1977-1980.

Source of water and 2types of watertreatment (chlorination,chloramination).

Neonatal death,stillbirth,congenitalanomalies.

There was a non-significant, increased asfrequency of stillbirths and maternal expoversus chloraminated surface water. An urinary track and respiratory track defects was detected. Neonatal death and other showed no association. No increased rispregnancy outcomes for surface water vemixed water use.

Kramer et al.1992

Populationbased 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 iretardation effects from chloroform exposNon-significant increased risks were obseweight and chloroform and for intrauterine and DCBM. No intrauterine growth retardweight effects were seen for the other THprematurity were observed for any of the 
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-43 

Shaw et al. 1991

(and Shaw et 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 betwanomalies and TTHM level were observe

Aschengrau etal. 1989

Case-controlstudy inMassachusetts, 1976-1978.

Source of water andexposure to metalsand othercontaminants.

Spontaneousabortion.

A statistically significantly association wasurface water source and frequency of sp

Reviews/Meta-analysesHwang andJakkola 2003

Review 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 associatioto chlorination by-products and the risk oparticularly the risk of neural tube defects defects.

Bove et al. 2002 Qualitative

review 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 aprovide moderate evidence for associatiogestational age, neural tube defects, and abortions. Authors felt risks may have beand exposure-response relationships distmisclassification.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-44 

Graves et al.2001

Review 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 asexposure for growth retardation such as sage or intrauterine growth retardation and Review found no support for an associatiexposure and low birth weight, preterm dbirth defects, and neonatal death. The reinconsistent findings for all birth defects, system defects, neural tube defects, sponstillbirth.

Villanueva et al.2001

Qualitativereview 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 betwespontaneous abortion, low birth weight, sage, and neural tube defects and drinking most studies although not always with sta

Nieuwenhuijsenet al. 2000

Qualitativereview ofnumeroustoxicological andepidemiological 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 asTHMs and low birth weight, but inconclusevidence of association between THMs aand that associations for other outcomes abortions, stillbirth, and birth defects) werweight.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Final Economic Analysis for the Stage 2 DBPR 6-45 

Reif et al. 2000 Qualitative

reviews 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 in humans under appropriate exposure coreports findings between TTHMs and effefetal viability, and congenital anomalies aReviewers felt data are at the stage of hadid not suggest a dose-response pattern increasing TTHM concentration.

WHO 2000 Qualitative

reviews ofvariousstudies inFinland, U.S.,and Canada.

Various exposures toTHMs.

Variousreproductiveanddevelopmentaleffects.

Review found some support for an associncreased risks of neural tube defects and THM exposure. Other associations have the authors believed insufficient data existhese associations.

Craun, ed. 1998 Qualitative

review of 10studies, focuson Californiacohort study.

Examined THM levelsand waterconsumption, andcompared source ofwater and watertreatment (chlorine,chloramines, chlorinedioxide).

Stillbirth,neonatal death,spontaneousabortion, lowbirth weight,pretermdelivery,intrauterine growthretardation,neonataljaundice, birthdefects.

Associations between DBPs and various were seen in some epidemiological studiethese results do not provide convincing erelationship between DBPs and reproduc
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Economic Analysis for the Stage 2 DBPR 6-46 

Mills et al. 1998 Qualitative

review of 22studies.

Examined TTHMlevels and waterconsumption. Compared source ofwater and 2 types ofwater treatment(chlorination andchloramination).

Variousreproductiveanddevelopmentaleffects.

Review found studies suggest possible inreproductive and developmental effects, spontaneous abortion rates, small for gesanomalies, but that insufficient evidence causal relationship.

Reif et al. 1996 Review of 3

case-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 intrauterine growth retardation, neural tubdefects, and oral cleft defects. Review foevidence supporting associations betweeand adverse pregnancy outcomes to be san inadequate basis to identify DBPs as a developmental hazard.
Author(s) Study Type Exposure(s) Studied Outcome(s)

Measured

Findings

Economic Analysis for the Stage 2 DBPR 6-47 

Swan et al.

1992

Qualitativereview of 5studies inSanta ClaraCounty, CA(Deane et al.1992,Wrensch et al.1992, HertzPicciotto et al.1992,Windham etal. 1992,Fenster et al.1992). 

Compared maternalconsumption ofresidence tap water tobottled water.

Spontaneousabortion.

Four of the studies reviewed suggest thabottled water during the first trimester of preduced risk of spontaneous abortion relawater. No association seen in the fifth stconcluded that if findings are causal and bias, data suggest a 10-50% increase in risk for pregnant women drinking tap wat
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 review of the literature was important in this process. 

The review included 16 peer-reviewed scientific manuscripts and published reports 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 fetus]); (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 growth. Studies using TTHM concentrations reached

differing conclusions. Some studies found weak but statistically 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 similarly 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 exists for an increased risk

of spontaneous abortion and stillbirth. Increased rates of spontaneous abortion associated with TTHM

levels of 75 μg/L or more were reported by Waller et al. (1998). Aschengrau et al. (1989) reported a

doubling of risk of spontaneous abortion for exposure to surface water compared to ground and mixed

water. Although Savitz et al. (1995) found an association between high levels of THMs and spontaneous

abortion, no relationship with dose or water source was discovered. As discussed previously, an

increased risk of stillbirth was found to be associated 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 authors 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 exposure, Reif et al. (2000) reported that the

relatively few studies available in the technical literature 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 conceded 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 the 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 differences 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 increasing risk with increasing concentration of TTHM,

but they did observe a general trend of small increases 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 discussion 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 from various studies are compared in Exhibit 6.14 and

PAR values with 95 percent confidence intervals (truncated 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 studies 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 eliminating not only TTHM exposure 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 and 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 findings presented in Exhibit 6.14. The Waller et al.

(1998) study presents higher ORs for spontaneous abortions than Savitz et al. (1995). The ORs for

spontaneous abortion varied from region 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. There 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 thresholds 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: 1Lower 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: 1Lower confidence limit is truncated at zero

N/A indicates that data for that health endpoint were not presented in the study. 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 review 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 defects, and cardiac effects), and spontaneous abortions. 

Studies that evaluated the end point of “small for gestational 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 rate. 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 exposure 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 misclassification, and further strengthened the relationship found

in the Denver study between TTHM and small for gestational age (OR = 5.9; no confidence intervals

reported).

Bove et al. (2002) believed that there was some consistency 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 for 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 other found an association with chloroform, but not

TTHM (Dodds and King 2001).

The California prospective cohort study (Waller et al. 1998) found a correlation between

spontaneous abortion and specific THMs, especially BDCM. An association was also reported for

spontaneous abortion when TTHM levels were evaluated, but this relationship disappeared when water

consumption habits were taken into account. Bove et al. (1995) noted that this study’s low participation

rate was a notable weakness. In addition, because the 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 toxicological 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 routinely 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 from 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 difficult 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 toxicological and epidemiological evidence for various

reproductive and developmental effects based on outcome, using a weight-of-evidence procedure. The

studies included in the review examined water consumption, 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 delivery (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 anomalies (1 article), integument anomalies (1 article),

musculoskeletal anomalies (1 article), chromosomal 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 circumference (1 article), and urinary tract defects (2

articles). The authors note that the exposure characterization 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 maternal 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, respiratory defects, and oral clefts were inconsistent.

EPA’s epidemiology research program

EPA’s epidemiology research program continues to examine the potential relationship between

exposure to DBPs and adverse developmental and reproductive effects. The Agency is supporting several

studies using improved study designs to provide better information for characterizing potential risks.

6.2.2.2 Toxicological Evidence of Adverse Reproductive and Developmental Health Effects

EPA has evaluated published studies of the potential adverse effects of DBPs on the reproductive

and developmental health of laboratory animals. Especially 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 Water Contaminants: DBPs” and by the World Health

Organization (2000): “Environmental Health Criteria 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 performed 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 approach to the analysis of the available,

relevant literature, that “some of the DBPs have the 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 identified and associated with exposure to various DBPs are

summarized in Exhibit 6.17.

Notwithstanding the evidence supporting the identification of developmental and reproductive

effects from DBP exposure, Tyl also concluded that 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 that 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 particular, there was an observed temporal relationship

between the exposures in toxicological studies and the 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. The effects were consistent across study designs. The

effects observed in animal toxicological studies were comparable to those observed in some human

epidemiological studies (e.g., embryonic heart and neural 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 Reproductive 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 the literature; P = In planning stage; WEC = 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; Multi-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,” however, for the effects listed as suggestive of positive

associations or producing mixed, inconsistent, 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 Safety (IPCS) of the World Health Organization

(WHO) published an evaluation of Disinfectants and DBPs in its Environmental Health Criteria

monograph series (WHO 2000). In this review of the toxicology data on reproductive 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 health concern. They also conclude that reproductive

effects in females have been principally embryolethality 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 testicular toxicity and spermatoxic effects.

New Toxicology Data Since the Stage 1 DBPR

Since promulgating the Stage 1 DBPR, more research 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 cultures. 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 the 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 developmental 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 significant, 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 exposed to BDCM in the drinking water at concentrations of 0, 50, 150,

450, and 900 ppm on gestation days 6 to 21. The concentration-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 correspond to doses of 18.4 mg/kg-day and 45.0 mg/kg-day,

respectively. The concentration-based developmental 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 metatarsals 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-Dawley (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, respectively. The parental NOAEL and LOAEL were 50 and

150 ppm, respectively, based on statistically significant 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 provided to the dams. Body weight and body weight gain

were also reduced in the 150 and 450 ppm F1 generation 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 maturation occurred at 150 ppm (males) and 450 ppm (males

and females) as determined by timing of vaginal patency 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 experiment, 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-treated 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 toxicity, 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 F344 rats that resorbed their litters. The rats were

allowed to deliver and pups were examined on postnatal 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 luteinizing hormone (LH)-mediated (Bielmeier et al. 2004). 

In this more recent study, Bielmeier et al. hypothesizes 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 pregnancy 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 increased risk of spontaneous abortion associated with

consumption of BDCM (Waller et al. 1998, 2001). 

Christian et al. (2002b) performed a two-generation 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 continuously from initiation of exposure of the parental (P)

generation male and female rats through weaning of the F2 offspring. Based on testicular

histomorphology indicative of abnormal spermatogenesis in P and F1 males, the parental and

reproductive/developmental toxicity LOAEL and NOAEL are 250 and 50 ppm, respectively.

Previous studies by EPA have reported adverse effects 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 reduced 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 using the body weight on the day of

acquisition (at postnatal day 45) as the co-variant. This was not found by Christian et al. (2002b) using

the body weight at weaning as the statistical covariant. However, the authors analyzed the data for

preputial separation and vaginal opening 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 significant reproductive alterations, but effects were seen

with continuous exposure until adulthood. Males and females exposed to 400 ppm DBAA were reported

to have delayed preputial separation and vaginal openings by 4 and 3 days, and also an increased

responsiveness of both the testis ad ovary to human 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 sophisticated measures into recent intramural and
Final Economic Analysis for the Stage 2 DBPR 6-59 December 2005

extramural studies that have not yet been incorporated into the testing guidelines. Such measures include

changes in specific proteins in the sperm membrane 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 incomplete 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 supporting documents that provide detailed summaries

of the relevant new information, as well as an overall 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 indicate a possible reproductive/developmental health

hazard although they are preliminary in nature for the majority of DBPs, and the dose-response

characteristics of most DBPs have not been quantified. 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 high 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 health 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. Although scientific knowledge about the association of

reproductive and developmental health effects with DBP exposure is not known well enough to quantify

these risks or the benefits of reduced DBP exposure, EPA concludes that the data are sufficient to

determine that a concern exists that warrants additional 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, spontaneous 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 risks 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 potential adverse reproductive and developmental 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 regularly 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, processed 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 ranges from 0.9 to 1.2 liters per day, with an upper 95th

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 showering, bathing, washing dishes,

washing clothes, or swimming. An international 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 section focuses on routes of exposure other than direct

ingestion.

Some studies have found that exposure due to inhalation and skin absorption during showering

may actually be higher than ingestion-related exposure (Kuo et al. 1998, Backer et al. 2000, Miles et al.

2002). Backer et al. (2000) found that when volunteers 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 who 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 the 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. showering, 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 over 10 percent of the ingestion dose during a shower. 

Weisel et al. (1999) found a significant correlation between 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 breath concentrations, except for chloroform, were below

the detection limit, most likely due to low water concentrations. Weisel et al. also found a link between

urinary trichloroacetic acid (TCAA) excretion rates and TCAA exposure, calculated as the water’s TCAA

concentration multiplied by the volume of water consumed by volunteers over a 48 hour period, adjusted

for home filters and boiling. TCAA exposure generally fell below 10 ng over 48 hours, but ranged up to

50 ng. In addition, Batterman et al. (2000) quantified 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 females, with males absorbing more chloroform

than females (Corley et al. 2000). Most significantly, 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 (Corley et al. 2000, Gordon et al. 1998). Measuring the

chloroform breath concentrations of test subjects immersed 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 increased to 44 μg for men and 40 μg/L for women. (This

includes chloroform exhaled, metabolized, and maintained in the body.) At 30°C, on the other hand,

most subjects had chloroform breath levels below detection limits. For example in comparison to dermal

absorption, if 2 liters of water with the same chloroform 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 developmental effects of DBPs, pregnant women

represent a population subset of special concern with respect 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 pregnant 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 women bathed more often and for a

longer duration than non-pregnant women but showering 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 and 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 systems 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 concern 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 encouraged to drink more water than the average person

and who may be especially sensitive to the potential effects of DBPs, as explained in the previous section. 

Under the Stage 2 DBPR preferred regulatory alternative (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 (LRAA). Exhibits 1.3 and 4.1 illustrate how the

LRAA and RAA are calculated. This revised compliance 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 events 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 Alternative is expected to yield health benefits by

achieving the following effects in those systems subject to the rule:

1) Reducing exposures to all DBPs levels.

2) Reducing exposures to single peak occurrences or repeated peak occurrence 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 analysis 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 (Singer 1999), TTHM and HAA5 reductions are assumed to be

reasonable indicators of the overall chlorination DBP reductions. Separate evaluations for TTHM and

HAA5 are carried throughout the analyses.

The average reduction in TTHM and HAA5 concentration 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 details the estimation of the benefits of the Stage 2

DBP Rule.

Exhibit 6.19 National Average TTHM1 Reduction Estimates

Note: Estimates of mean and 90% confidence 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 Information 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 used to generate these estimates and presents results

in Exhibit 5.27. Section G.3 presents an analysis 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 several 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 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. To translate estimated changes

in peak DBP occurrence as a result of the Stage 2 DBPR 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 overestimate the population exposed to peaks. This

potential overestimate, however, is minimized because 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 Document (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 comparisons 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 comply 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 caused 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-locations with peaks as a result of the Stage 2 DBPR

(shown in Exhibit 5.27) can be taken to represent the 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 expected 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 incidence 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, Villanueva 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 relationship exists between exposure to chlorinated

surface water and bladder cancer. However, EPA and the international bodies (e.g. WHO) that classify

risk recognize that such causality has not yet been established. 

Other important assumptions regarding the bladder 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 exposure 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 basis 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 incidence of bladder cancer attributable to DBPs remaining

after Stage 1 is X cases, and the Stage 2 DBPR is determined 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 monetized benefits discussed in subsequent

sections) was calculated using TTHM and HAA5 as indicators 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 TTHM as an indicator are similar to those calculated

using HAA5. Detailed results for all analyses using 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 begin to occur. Individual cancer risks at any point

in time generally represent lifetime exposure levels and 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-Stage 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 taking 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 subsection 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; Villanueva 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 attributable risk (PAR). However, for the sake of

simplicity, one method for estimating PAR based on Villanueva et al. (2003) is carried through the full

benefits analysis to account for cessation lag, implementation 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 implemented, the incidence of bladder cancer cases

annually is anticipated to decline to a new, lower 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 more influenced by the lower, post-Stage 2 levels. 

Over the long-term, the lower incidence of new bladder 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 the “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 attributable cases that a specific reduction in average DBP

concentrations would eliminate.

To calculate the post-Stage 2 ultimately-avoidable 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 determine the cases ultimately avoidable by Stage 2. This

is done as follows:

• Estimate of the total cases attributable to DBPs (under pre-Stage 1 conditions) in drinking

water by applying the age-based PAR values 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 percent reduction in average DBP levels due to

Stage 2.

As discussed in Section 6.2.1, EPA has developed three approaches for estimating the total

attributable pre-Stage 1 cases: 

• Using the range of Population Attributable Risk (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 considered (yields a pre-Stage 1 best estimate for

PAR of ~16%)

• Using the Villanueva et al. (2004) pooled data analysis to develop a dose-response

relationship for OR as a function of Average TTHM. The dose-response relationship was

modeled 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 approaches 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 exposure 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 exposure 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 individual studies separate and presenting them as a full

range of plausible results is that an explicit depiction 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 the 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 multiple 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 combining multiple epidemiology studies than is

pooled data analysis. If heterogeneity is not properly 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 Olkin 2001). Although the results of meta-analysis can

also be affected by heterogeneity across the studies 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-analysis 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 specifically 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), both of which combine the results of multiple select

studies, offer reasonable approaches to arriving at a single, 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 addition to two other lower weighted studies, obtained

results that are consistent with the five study estimates. The meta-analysis found a relationship between

duration of exposure to DBPs (or chlorinated water) 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 characteristic 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) paper 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 estimate 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 variety of methods, including modeling, were used to

estimate TTHM concentrations. In using the Villanueva 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 ultimately 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 average DBP concentration and bladder cancer risks. 

Because of this, EPA considers these estimates to be an upper bound on the annual reduction in bladder

cancer cases due to the rule.

There are two major sources of uncertainty reflected in the estimates presented in Exhibit 6.20 for

each of the three PAR estimate sources. 

First, each shows three “rows” indicating the uncertainty 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 lower 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 reductions, 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 estimate of percent DBP reductions ranges from 275 to 874,

with a best estimate of 506. For the estimates derived 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 obtained 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 and 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 model using the Villanueva et

al. (2003) inputs for PAR and incorporating all of the 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 

Abbreviation: PAR = Population Attributable Risk (values shown are best estimates). CB = Confidence Bound Notes: Estimated annual cases ultimately avoidable are based on predicted DBP reduction from Stage 1 to Stage 2. Results shown assume that percent reduction in concentrations is an indicator of percent reduction in concentrations of all DBPs. Three contributions to the uncertainty in the estimate of the annual cases ultimately adisplayed in this exhibit: (1) uncertainty in the approach used to estimate PAR; (2) uncertainty in the underlying data used to derive the PAR estimates for each appro95 percent confidence intervals displayed in each horizontal bar; and (3) uncertainty in the percent reduction in the national average DBP levels achieved by Stage 2 90 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 rfrom the five separate studies considered.in the Odds Ratios underlying the PAR values. The estimates in boxes are the overall mean (best) estimates for each appro

Approach 2

Villanueva et al. 

(2003)

(PAR = 15.7%)

Approach 3

Villanueva et al.

(2004)

(PAR = 17.1%)

Annual Cases Ultimately Avoidable

1,000 

37 317 615

64 546 1,060

92 782 1,518

Approach 1

“5 Studies”

(PAR = 2%-17%)

159 293 507

275 874 506

393 724 1,252

47 319 617

80 550 1,064

115 787 1,523

DBP Red. = 4.5%

(lower CB)

DBP Red. = 7.8%

(best estimate)

DBP Red. = 11.2%

(upper CB)

0

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)

Abbreviation: PAR = Population Attributable Risk (values shown are best estimates). CB = Confidence Bound Notes: Estimated annual cases ultimately avoidable are based on predicted DBP reduction from Stage 1 to Stage 2. Results shown assume that percent reduction in concentrations is an indicator of percent reduction in concentrations of all DBPs. Three contributions to the uncertainty in the estimate of the annual cases ultimately adisplayed in this exhibit: (1) uncertainty in the approach used to estimate PAR; (2) uncertainty in the underlying data used to derive the PAR estimates for each appro95 percent confidence intervals displayed in each horizontal bar; and (3) uncertainty in the percent reduction in the national average DBP levels achieved by Stage 2 90 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 rfrom the five separate studies considered.in the Odds Ratios underlying the PAR values. The estimates in boxes are the overall mean (best) estimates for each appro

Approach 2

Villanueva et al. 

(2003)

(PAR = 15.7%)

Approach 3

Villanueva et al.

(2004)

(PAR = 17.1%)

Annual Cases Ultimately Avoidable

1,000 

37 317 615

64 546 1,060

92 782 1,518

Approach 1

“5 Studies”

(PAR = 2%-17%)

159 293 507

275 874 506

393 724 1,252

47 319 617

80 550 1,064

115 787 1,523

DBP Red. = 4.5%

(lower CB)

DBP Red. = 7.8%

(best estimate)

DBP Red. = 11.2%

(upper CB)

0

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)

Approach 2

Villanueva et al. 

(2003)

(PAR = 15.7%)

Approach 3

Villanueva et al.

(2004)

(PAR = 17.1%)

Annual Cases Ultimately Avoidable

1,000 

37 317 615

64 546 1,060

92 782 1,518

Approach 1

“5 Studies”

(PAR = 2%-17%)

159 293 507

275 874 506

393 724 1,252

47 319 617

80 550 1,064

115 787 1,523

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)

0

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)

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 Avoid

for Three PAR Approaches and DBP Reductions
3

 The SAB included the following statement in its report 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 example is cigarette smoking: the latency between initiation of

exposure and an increase in lung cancer risk is approximately 20 years. However, after cessation of exposure, risk

for lung cancer begins to decline rather quickly. A benefits 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 Panel (ARBRP) of EPA’s Science Advisory Board

(SAB) addressed cessation lag in detail and provided guidance 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 risk 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 increase 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 consideration of this issue had more general applications beyond

the arsenic issue they were considering at that 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 mechanism by which cancer occurs should be used in

estimating the cessation lag (noting that late-stage 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 mechanisms 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 calculating benefits for the arsenic regulation. EPA

recognized, however, that the concept of cessation lag may 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 SAB 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 characterize 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 database was available for characterizing cessation

lag was for cigarette smoking. EPA examined several extensive epidemiological studies on the risks of

adverse health effects, including lung cancer and bladder 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 substantial portion of their lives changes over time toward the

risk observed for other individuals who have experienced some lower level 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 comprehensive 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 histories. In addition, EPA selected Chen and Gibb

(2003) to develop a cessation lag model based on arsenic 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 related to bladder cancer. Thus, EPA assumes that the mode of action may be

adequately represented by the mixed initiator and promoter aspects of the cigarette smoking model. As

discussed in the SAB report and the EPA Cessation 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 derived 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 important in differentiating between smoking/lung cancer and

DBPs/bladder cancer. The route of exposure for smoking/bladder cancer is similar to DBP ingestion in

that there is expected to be a first pass through the liver followed by entry into the bloodstream prior to

reaching the bladder. 

In the case of dermal exposure, which is considered a major route, DBPs enter the bloodstream

directly and can reach the bladder without that “first 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 smoking/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 smoking/bladder cancer data is a reasonable approach. 

EPA has therefore judged that these two models, plus 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 estimate 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 percent. 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 cessation lag model. The results from the cessation lag

models show that the majority of the potential cases avoided occur within the first fifteen years after initial

reduced exposure to DBPs. For example, fifteen years after the exposure reduction has occurred, the

annual cases avoided will be 489 for the smoking / lung cancer cessation lag model, 329 for the smoking /

bladder cancer cessation lag model, and 534 cases for the 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) PAR estimates in the preceding section. The 581 number

is generated by the full Monte Carlo simulation benefits 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 Avoided to Account for the Rule Implementation

Schedule

In addition to the delay in reaching a steady-state level of risk reduction as a result of cessation

lag, there is a delay in attaining maximum exposure 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 regarding 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 percent of bladder cancers are fatal and 74 percent

are non-fatal (USEPA 1999a). Annual cases avoided were 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 Begins

Annual Baldder Cancer Cases Avoided

Arsenic / Bladder Cancerr Model

Smoking / Lung Cancer Model

Smoking / Bladder Cancer Model

Exhibit 6.21a Comparison of Alternative 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 Percent1 Total Percent1 Total Percent1

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 Cases Avoided by Year For Three Cessation

Lag Models, Considering Rule Implementation 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 ultimately 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 toxicological 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 potential adverse reproductive and developmental effects is not

known and no causal link has been established, EPA believes that lowering the overall levels of DBPs in

distribution systems and in particular by reducing the incidence of peak levels is important from a public

health perspective.

EPA believes that the current scientific knowledge on reproductive and developmental health

effects is not strong enough to quantify risk in the primary 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 developmental 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 DBPs may be related to health effects other than

reproductive, developmental, and bladder cancer effects. Some studies have indicated an association

between consumption of chlorinated 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 rectal cancer. As TTHM and HAA5 levels are reduced

under the Stage 2 DBPR, other potentially carcinogenic 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 indicate 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 presented 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, including their degree of risk aversion, their perceptions

about drinking water quality, and the expected probability 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 dioxide, 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 resulting 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-tasting and smelling water. Installation of certain

advanced treatment technologies can remove many contaminants 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) that EPA is in the process of regulating. GAC lowers

nutrient availability for bacterial growth, produces a biologically 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 contaminants 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 between the risks from DBPs and those from microbial

pathogens in drinking water. The Microbial-Disinfectants/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 described 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 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 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 other disinfectants. Exhibits 6.22 and 6.23 compare the

SWAT-predicted monthly average and plant-mean average concentrations for chlorite (a potential

byproduct of chlorine dioxide disinfection) and bromate (a potential byproduct 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-Mean Concentration for Pre-Stage 2 and

Post-Stage 2

Exhibit 6.22b Predicted Chlorite 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 6-80 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 literature, EPA incorporated into the Stage 2 DBPR

analyses recommendations from reviews of previous regulations. 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 Stage 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., incorporation 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 attributable 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 Reproductive 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 the 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, suffering, and loss. Another potential benefit is that the

life of the fetus is saved. Avoiding these costs represents 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-birth-weight infants and children. It estimates that

such children are almost 50 percent more likely than 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 birth defects is high. For example, the lifetime cost

for a case of spina bifida is estimated at $300,000. Estimates 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 attributed 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 potential for substantial savings to society. Although

uncertainties in the estimation of potentially avoided adverse reproductive and 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 possible 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 percent of bladder cancers are fatal and 74 percent

are non-fatal (USEPA 1999a). Annual cases avoided were 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 adjusted 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 combined 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 statistical life (VSL) is used to calculate the value of

benefits. The VSL represents an estimate of the monetary 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 statistical 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 Arsenic Rule) used the WTP value for avoiding a case of

chronic bronchitis for benefits transfer calculations. The SAB review of the Arsenic benefits analysis identified the

curable lymphoma WTP value as another metric that could 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 Increment 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 associated 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 increment 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 (USEPA 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 discomfort, productivity losses, and any other adverse

consequences related to contraction of a non-fatal case of bladder cancer. As is the case with VSL

valuation, the cumulative WTP for this risk reduction across an exposed population can be used to

represent the statistical value of avoiding the illness itself. WTP is a more comprehensive measure of the

total value that a person would place on avoiding a cancer case than the much simpler cost of illness (COI)

measure.

A review of the available literature did not reveal any studies that specifically measured the WTP

to avoid the risk of non-fatal bladder cancer. Instead, 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

bronchitis4

. Results using both WTP estimates are presented throughout the remainder of the analyses.

The WTP to avoid the risk of contracting curable lymphoma is derived from a survey by Magat et

al. (1996) that evaluates the risk-risk trade-off between curable lymphoma and death using a reference

lottery metric. A reference lottery is a methodology that educates survey respondents of the health

consequences of a particular disease (in this case curable lymphoma) and, based on this information,

presents them with choices related to health outcomes. The choices in health outcomes made by the

respondents can be further evaluated to derive quantitative measures of relative risk aversion. Based on

the outcomes of the Magat et al. study, it was determined that the median risk-risk 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 average person would pay 58.3 percent of what they

would pay to reduce the risk of sudden death to achieve 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 bronchitis 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 

Valuation Parameter Factor

Values at Year 2003 Price Level

(Millions)

Although the WTP to avoid curable lymphoma or chronic 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 implications, 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 values 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 morbidity increment used in the model are updated based on

adjustment factors derived from Bureau of Labor Statistics (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 constant throughout the benefits model, projections of

benefits in future years are subject to income elasticity adjustments. These represent 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 increases by 1 percent, this would be represented by an

income elasticity of 1. For most items, income elasticity 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 and 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 that 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 calculated using real per capita personal income growth.

However, real per capita GDP is used as a proxy for real 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 average 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. These 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 estimate 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 represent 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 = (eI1 - eI2 - I2 - I1) / (eI2 - eI1 - I2 - I1)

where: e = income elasticity

I1 = real income (per-capita GDP) in the base year

I2 = real income (per-capita GDP) in the year of analysis

Income elasticity adjustment factors are calculated from the same base year as the values subject to

adjustment. For example, income elasticity factors for fatal cancers are calculated from a 1990 base year

because that is the base year used in the study from which VSL estimates are derived.7

 The mean values of

the income adjustment factors calculated for the Stage 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 calculations). 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-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 based 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 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 

Point 

Estimate 5th %tile 95th %tile 5th %tile 95th %tile 

2005 0.12 7.76 6.73 1.19 17.91 4.43 3.85 0.68 10.13 0.80 0.74 2006 0.12 7.85 6.81 1.20 18.12 4.48 3.89 0.69 10.24 0.81 0.75 2007 0.12 7.95 6.88 1.21 18.33 4.53 3.93 0.70 10.36 0.82 0.75 2008 0.12 8.03 6.95 1.23 18.55 4.57 3.97 0.70 10.45 0.82 0.76 2009 0.12 8.12 7.03 1.24 18.76 4.61 4.00 0.71 10.57 0.83 0.77 2010 0.12 8.22 7.10 1.25 18.99 4.66 4.04 0.71 10.67 0.84 0.78 2011 0.12 8.31 7.18 1.26 19.24 4.70 4.08 0.72 10.76 0.85 0.78 2012 0.12 8.40 7.25 1.28 19.43 4.74 4.12 0.73 10.85 0.86 0.79 2013 0.12 8.49 7.33 1.29 19.64 4.79 4.16 0.74 10.95 0.86 0.80 2014 0.12 8.59 7.41 1.30 19.87 4.83 4.20 0.74 11.07 0.87 0.81 2015 0.12 8.68 7.48 1.31 20.13 4.88 4.24 0.75 11.16 0.88 0.81 2016 0.12 8.78 7.57 1.32 20.34 4.92 4.28 0.76 11.27 0.89 0.82 2017 0.12 8.88 7.64 1.34 20.62 4.97 4.32 0.77 11.38 0.90 0.83 2018 0.12 8.98 7.72 1.35 20.86 5.02 4.36 0.77 11.49 0.91 0.84 2019 0.12 9.08 7.81 1.36 21.15 5.06 4.40 0.78 11.61 0.91 0.85 2020 0.12 9.18 7.89 1.37 21.40 5.11 4.44 0.79 11.72 0.92 0.85 2021 0.12 9.28 7.97 1.38 21.67 5.16 4.48 0.79 11.82 0.93 0.86 2022 0.12 9.38 8.05 1.40 21.95 5.20 4.52 0.80 11.94 0.94 0.87 2023 0.12 9.49 8.15 1.41 22.20 5.25 4.56 0.81 12.06 0.95 0.88 2024 0.12 9.59 8.23 1.42 22.45 5.30 4.60 0.82 12.17 0.96 0.89 2025 0.12 9.70 8.32 1.43 22.71 5.34 4.64 0.82 12.28 0.97 0.89 2026 0.12 9.80 8.41 1.45 22.96 5.39 4.69 0.83 12.40 0.98 0.90 2027 0.12 9.91 8.49 1.46 23.28 5.44 4.73 0.84 12.52 0.99 0.91 2028 0.12 9.87 8.46 1.46 23.17 5.42 4.71 0.84 12.48 0.98 0.91 2029 0.12 9.95 8.52 1.47 23.40 5.46 4.74 0.84 12.56 0.99 0.91 

WTP 

Mean 

Value 

Median 

Value

WTP - Non-Fatal Lymphoma

Mean 

Value 

Median 

Value

90% Confidence Interval

Morbidity 

Increment

Fatal Cancer Cases Non-Fatal Cancer Cases

Year

90% Confidence Interval

VSL 

Mean 

Value 

Median 

Value

Exhibit 6.25 Value of Morbidity Increment, VSL, and WTP by Year, Adjusted for Income 

Notes: All values in millions of year 2003 dollars. Detail may not add exactly due to independent rounding.Source: Exhibit F.1f
10 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 considered. Although information is not sufficient to quantify the value of

preventing potential adverse reproductive and developmental 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 benefits could be substantial, for example reduction in

risk of other cancers and reductions in other regulated 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 derived from reductions in bladder cancer cases as a result

of the Stage 2 DBPR, a stream of monetary benefits 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 distributions 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 cessation lag models and for both WTP estimates (for

curable lymphoma and chronic bronchitis) were calculated and carried through the Stage 2 DBPR benefits

model. The results for fatal, non-fatal, and total benefits are presented in Exhibits 6.26 (note that the

alternative analysis assumptions, e.g. Arsenic/Bladder 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 allows 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 proper 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 risk-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 private investment” (USEPA 1996b). To allow evaluation on an

annual basis, the PV of benefits is annualized using 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. $ 266 1,749 $ 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

Year Mean Value 

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 $ 15 67 $ 146 $ 63 $ 14 $ 138 $ 125 $ 27 $ 274 $ 

2011 $ 38 172 $ 380 $ 149 $ 33 $ 329 $ 306 $ 67 $ 673 $ 

2012 $ 69 312 $ 687 $ 255 $ 56 $ 560 $ 528 $ 116 $ 1,162 $ 

2013 $ 106 484 $ 1,065 $ 377 $ 83 $ 829 $ 784 $ 172 $ 1,725 $ 

2014 $ 139 632 $ 1,392 $ 462 $ 101 $ 1,017 $ 962 $ 211 $ 2,118 $ 

2015 $ 169 772 $ 1,702 $ 535 $ 117 $ 1,180 $ 1,103 $ 241 $ 2,432 $ 

2016 $ 196 898 $ 1,980 $ 598 $ 131 $ 1,319 $ 1,212 $ 265 $ 2,671 $ 

2017 $ 221 1,013 $ 2,238 $ 657 $ 143 $ 1,451 $ 1,301 $ 284 $ 2,874 $ 

2018 $ 243 1,114 $ 2,462 $ 712 $ 155 $ 1,572 $ 1,377 $ 300 $ 3,042 $ 

2019 $ 261 1,201 $ 2,660 $ 764 $ 166 $ 1,691 $ 1,442 $ 313 $ 3,193 $ 

2020 $ 277 1,277 $ 2,830 $ 814 $ 177 $ 1,803 $ 1,499 $ 325 $ 3,321 $ 

2021 $ 291 1,344 $ 2,983 $ 861 $ 187 $ 1,911 $ 1,549 $ 336 $ 3,437 $ 

2022 $ 304 1,404 $ 3,122 $ 908 $ 196 $ 2,018 $ 1,595 $ 345 $ 3,545 $ 

2023 $ 315 1,459 $ 3,244 $ 952 $ 206 $ 2,118 $ 1,636 $ 353 $ 3,638 $ 

2024 $ 325 1,509 $ 3,358 $ 996 $ 215 $ 2,216 $ 1,675 $ 361 $ 3,725 $ 

2025 $ 335 1,556 $ 3,464 $ 1,038 $ 223 $ 2,311 $ 1,711 $ 368 $ 3,808 $ 

2026 $ 344 1,600 $ 3,562 $ 1,080 $ 232 $ 2,404 $ 1,744 $ 375 $ 3,884 $ 

2027 $ 352 1,641 $ 3,663 $ 1,120 $ 240 $ 2,501 $ 1,776 $ 381 $ 3,965 $ 

2028 $ 355 1,656 $ 3,693 $ 1,143 $ 245 $ 2,550 $ 1,782 $ 382 $ 3,974 $ 

2029 $ 362 1,688 $ 3,772 $ 1,179 $ 253 $ 2,633 $ 1,806 $ 387 $ 4,035 $ 

Ann. 

Avg. $ 189 872 $ 1,936 $ 587 $ 127 $ 1,302 $ 1,036 $ 224 $ 2,300 $ 

Year

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 Systems, 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

Best Estimate 

Annual Cases 

Ultimately 

Avoided1

Average 

Annualized 

Cases Avoided 

for 25 years

Annualized

Expected Benefits 

3%2

(Millions, 2003$)

Annualized

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 benefits 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 uncertainty 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 for 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 case, 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 quantified 

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 value 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 established between exposure to chlorinated water and bladder

cancer. 

2

 The 90 percent confidence interval for cases incorporates 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 Benefits for Regulatory Alternatives

This section compares the benefits of decreasing DBP occurrence under the Stage 2 DBPR

preferred alternative with the three evaluated alternatives. The alternatives 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 

Avoided3

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 adverse potential reproductive and developmental health

effects, the exhibit also shows that the quantified 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 analysis 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 Alternatives, Villanueva et al. (2003) for

Baseline Risk (Millions, 2003$)1

Notes: 1Based on TTHM as indicator, and the smoking and 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. 

2

 The 90 percent confidence interval for cases incorporates 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 which the IDSE is considered, resulting in

increased benefits. However, Alternative 1 is the only alternative requiring a lower bromate standard, whose

benefits are not considered in this analysis. Should potential cancer cases averted by lowering the bromate

standard be quantified, Alternative 1 would capture more benefits.

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.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 independent cessation lag models derived from three different

epidemiological studies are used. 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 (see Appendix E for more

detail). For monetization of benefits, EPA uses two alternatives for valuing non-fatal bladder cancer. 

Other uncertainties, such as the linear relationship between DBP reductions and reductions in bladder

cancer cases avoided, are discussed qualitatively. A summary 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 compliance 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 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 uniform distribution between SWAT and ICR Matrix

Method results. That is, the national benefits estimates 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 benefits of the Stage 2 DBPR are related to nonquantified benefits estimates. Both of these factors result in an underestimation of quantified Stage 2

DBPR benefits. To inform the reader of the potential magnitude of these benefits, Section 6.7 provides

results from a sensitivity analysis for colon and rectal cancers combined. An illustrative analysis of

potential developmental and reproductive 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 than one rule in a short period of time are also not

quantified. EPA has taken into account compliance with 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 Possible Effect on Estimate of Benefits

Uncertainty

Section with

Full Discussion

of Uncertainty

Effect on Benefit Estimate

UnderestimateOverestimateUnknown

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 value1 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 recognizes 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 most 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 chlorinated 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 prospective cohort studies had been published that

evaluated the association between consumption of chlorinated 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 users. For rectal cancer, a relative risk (RR) of

1.42 was reported, but this was not statistically significant. No difference was observed for colon cancer. 

Bean et al. (1982) examined cancer incidence data 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 observed

for 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 exposure assessment. Doyle et al. (1997), a prospective

cohort study, indicated that in comparison with women who used municipal ground water sources, women

with municipal surface water sources were at an increased 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 cohort study of 621,431individuals

in 56 towns in Finland and compared cancer incidence between locations based on a measure of raw water

and chlorination treatment. Comparing locations representing exposure to chlorinated surface water and

no exposure to chlorinated surface water, they observed a statistically significant increased risk of rectal

cancer for women (RR = 1.38), but no increased risk for 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 epidemiology studies have been published that

investigated the potential relationship between colon 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 duration of exposure to chlorinated surface water. No associations

were observed between exposure measures and rectal cancer. Yang et al. (1998) conducted a crosssectional mortality study in Taiwan which compared 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 females 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 associations 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-based case-control study and found an association

between duration of chlorinated surface water use and 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 main 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 exposure such as whether the study group was exposed to

chlorinated surface, ground water, or any chlorinated 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-control 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-analysis. 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 between rectal cancer risk by any of the measures of

exposure to disinfection byproducts in this study. This 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 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 2 of the King et al. (2000) study reflecting years of exposure to chlorinated water resulted in a PAR

value estimate of 24.5%. Note again that this only applies to males since the authors did not detect

associations for females.

PAR for rectal cancer

EPA estimated a PAR value for rectal cancer for 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 and the American Cancer Society for colon and

rectal cancer incidence, EPA estimated the annualized 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 value of the bladder cancer cases avoided from Stage 2. 

These results are based on TTHM reductions and include 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 potentially 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 Value1 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: 1The 90 percent confidence bounds shown in the exhibit incorporate uncertainty in the VSL, WTP, and

income elasticity adjustment.

2 Based on TTHM as indicator for the Preferred Regulatory Alternative. EPA recognizes that the benefits may

be as low as zero since causality has not yet been established between exposure to chlorinated water and

bladder, colon, or rectal cancer. 

Source: Quantitative values are summarized 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 cancer. 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; and 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 rectal but not for colon cancer) are significant and

difficult to explain. Therefore, the estimates from this 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 provides an adequate basis

for quantifying potential reproductive or developmental risks in the main benefits analysis. Furthermore,

no causal link between DBPs and these risks has yet been 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 society 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 risk in these categories. To do this, PAR calculations

from several studies on the relationship between chlorinated water exposure and fetal loss have been

adapted and applied to national statistics 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 

Analysis

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 Cases
11Use of unadjusted OR or RR estimates has the effect of including possible biases from known

confounders; however, EPA believes the unadjusted estimates are adequate for purposes of the illustrative

calculations presented here. It was not possible to calculate ORs adjusted for the same confounders from the four

studies. 12The calculated lower 95 percent confidence intervals on PAR for all four studies were less than zero. This

means the possibility of no effect cannot be ruled out with 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 confounders11, 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 results using ICR data to derive PAR values that are

more relevant to the general population (Appendix G).

The four studies (using unadjusted data to allow 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 percent12. 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 chlorinated drinking water could range 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 percent 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 above 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 loss 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 populations and the national population (e.g., different types

of DBP mixtures having similar TTHM levels). The 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 risk implications of some of the published results. 

EPA reiterates that causality has not yet been established 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 Results1

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 for

unweighted 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 developmental and reproductive health in general. See Section 6.5.1 for a

discussion of the value of reducing reproductive and developmental health risks.

The agency is considering further work specific to fetal loss valuation. One possible area of

further research is the value that prospective parents have for reducing risks during pregnancy. In this

regard, the substantial lifestyle changes that prospective 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 endpoints of a regulation are lacking, the Agency

typically draws upon existing studies of similar health endpoints 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 further 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 technology 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 Works 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 inputs 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 Chapter 5). Section 7.5 discusses the cost model itself, including how it

accounts for uncertainty in some inputs, the calculation of the costs, and projection and discounting of

those costs. The results of the cost model are 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 rule 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 explanation 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 value estimates, and annualization of costs for

all Stage 2 DBPR regulatory alternatives and sensitivity analyses.

• Appendix K contains documentation for the Stage 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 systems (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 costs
1

For the purposes of this Economic Analysis (EA), rule implementation activities are assumed to include

activities such as reading the rule and training personnel. 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 rule1, conduct IDSEs, prepare monitoring plans, perform additional routine monitoring,

and perform operational evaluations. System costs were 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 categories 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 programmed in SAS version 9.1. Exhibit 7.1

summarizes the model inputs and outputs. The model combines baseline data discussed 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 for the rule. All costs are calculated in 2003 dollars. 

Costs are then distributed in the years in which they 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 rate2

. 

Household costs are also generated by the cost model using assumptions regarding household

water usage and total households served per plant.

Exhibit 7.1 Stage 2 DBPR Cost 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, and 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 and 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 choose to perform studies, meet the criteria for a 40/30

certification, or receive waivers from IDSE requirements. 

EPA has established a population-based monitoring 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 operational 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 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 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 install disinfection after the IDSE requirement is complete. Systems installing disinfection

for the GWR will, however, be required to prepare monitoring 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 monitoring for the first time. It should be noted that

actual monitoring-related costs for newly disinfecting 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 

IDSE Monitoring Stage 2 Monitoring Plans

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 addition to those shown in the table, also include routine compliance monitoring. Some 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. Overall 

the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. See Exhibit H.8a, 

column H, for the change in total samples for the 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 and 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 percent for all systems, with a much higher percentage

for surface water plants. The baseline number of ground water 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 mean for the surface water plant estimate accounts

for alternative compliance forecast methodologies (SWAT 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 capital, 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 initial 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 annualized 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 estimate of approximately $79 million per year. At a 7

percent discount rate, system costs range from approximately $55 to $99 million annually, with a mean

estimate of approximately $77 million per year. State 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 DBPR
Final Economic Analysis for the Stage 2 DBPR 7-8 

Surface Water Systems Disinfecting Ground Water Syst

Serving < 10,000 Serving > 10,000 Serving < 10,000 Serving > 

Total Initial Capital Costs for the Rule $ 451.57 100.9 $ 179.8 $ 

$ 

(90% Confidence Bounds) (55.51 - 149.42) (246.01 - 630.04) (148.85 - 210.88) (96.3CWS Total Initial Capital $ 450.6 94.83 $ 167.1 $ 

$ 

(90% Confidence Bounds) (52.22 - 140.40) (245.48 - 628.71) (138.11 - 196.13) (96.1NTNCWS Total Initial Capital $ 1.0 6.03 $ 12.8 $ 

$ 

(90% Confidence Bounds) (3.29 - 9.02) (0.53 - 1.33) (10.74 - 14.75) (0CWS One-Time Costs $ 33.5 21.8 $ 13.5 $ 

$ 

Implementation $ 1.7 2.4 $ 6.0 $ 

$ 

IDSE $ 31.1 18.4 $ 5.8 $ 

$ 

Monitoring Plans $ 0.7 1.0 $ 1.6 $ 

$ 

NTNCWS One-Time Costs $ 0.1 0.2 $ 1.3 $ 

$ 

Implementation $ 0.0 0.2 $ 1.1 $ 

$ 

IDSE $ 0.1 - $ - $ 

$ 

Monitoring Plans $ 0.0 0.0 $ 0.2 $ 

$ 

State/Primacy Agency One-Time Costs Implementation IDSE Monitoring Plans 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 Monitoring Plan costs from Exhibit 7.7.

Stage/Primacy Agency One-Time Costs from Exhibit H.21

Exhibit 7.4 Initial Capital and One-Time Costs for the Stage 2 DBPR ($Millions
Final Economic Analysis for the Stage 2 DBPR 7-9 

Total System Costs

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Implementation IDSE Monitoring Plans Moni- toring Operational Evalations 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 Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecast and unit treatment 

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.2as

Ground Water NTNCWSs

System Size 

(Population 

Served)

Non-Treatment Costs 

(Point Estimate)

Mean

Value

Mean

Value

90 Percent

Confidence Bound

System Costs

90 Percent

Confidence Bound

St

Co

Surface Water CWSsSurface Water NTNCWSs

90 Percent 

Confidence Bound

Capital Costs O&M Costs

Mean

Value

Ground Water CWSs

Exhibit 7.5a Total Annualized Costs for Stage 2 DBPR Rule Activities ($Millions/Year, 3 Perce
Final Economic Analysis for the Stage 2 DBPR 7-10 

Total System Costs

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Implementation IDSE Monitoring Plans Moni- toring Operational Evaluations 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 Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecast and unit treatment c

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.2aw

Surface Water CWSsSurface Water NTNCWSs

90 Percent 

Confidence Bound

Capital Costs O&M Costs

Mean

Value

Ground Water CWSs

System Costs

90 Percent

Confidence Bound

Sta

Co

Ground Water NTNCWSs

System Size 

(Population 

Served)

Non-Treatment Costs 

(Point Estimate)

Mean

Value

Mean

Value

90 Percent

Confidence Bound

Exhibit 7.5b Total Annualized Costs for Stage 2 DBPR Rule Activities ($Millions/Year, 7 Perce
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 Water Rules, Exhibits 20 and 21 (USEPA, 2003s)

System Size (Population Labor Rate (2003$/hr)

Served)

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.

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 requirements. 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 managerial 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 geographical region. In the absence of such data and

because analyses in this EA are performed 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 administrative tasks are estimated based on an average

annual full-time-equivalent (FTE) labor cost, including overhead and fringe benefits, of $65,255 (2001$). 

This rate was established based on data from the 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 expected 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 costs 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 calculations 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 were dependent on the system type (NTNCWS or

CWS), the source water type (surface or ground) and the number of plants per system. EPA has

identified several potential issues when requirements 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, particularly 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 included 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 whereby 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 incur one-time costs for staff to read the rule and

become familiar with its provisions and to be trained 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 labor 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 estimated by EPA based on previous experience

with other rules. State/Primacy Agency activities 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 results. 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 compliance samples are less than or equal to 40 :g/L for

TTHM and 30 :g/L for HAA5, or (2) the system serves fewer than 500 people and qualifies for the very

small system waiver.

Systems performing an IDSE will incur costs for evaluating 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 detailed 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, determining which systems cannot receive a very small

system waiver, consulting with PWSs, and IDSE recordkeeping. 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 monitoring 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 promulgated, 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, however, 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 monitoring 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, therefore, incur no additional costs for this activity. 

In other cases, incremental costs will be negative (that 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 under 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 Stage 2. The derivation of routine monitoring costs for

systems that add disinfection to achieve compliance 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 require 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 (COL ) is:

COL = (Q1 + Q2 + 2*Q3)/4

where Q1 is the concentration of the of the DBP two quarters ago, Q2 is the concentration of the DBP in

the previous quarter, and Q3 is the concentration of the DBP in the current quarter. If COL 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 μg/L for TTHM and

had a third-quarter result of 90 μg/L, then the calculation would yield:

(75 μg/L +75 μg/L + 2*(90 μg/L))/4 = 82.5 μg/L,

meaning the operational evaluation level is exceeded.

If an operational evaluation level is exceeded, systems 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 evaluation level and the associated costs.

States/Primacy Agencies

States will incur some costs to review operational evaluations submitted by systems. Appendix H

estimates the time and costs that will be spent by States 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 majority 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 costs 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 Exhibit 7.7, additional routine monitoring costs are

positive for some systems and negative for others (because of the redefined monitoring requirements from

a plant-based to a population-based approach). One-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 $ 447,582 244,635 $ 76,408 $ 768,625 $ (52,103) $ 534 $ (51,568) $ 

100-499 $ 912,489 498,740 $ 155,774 $ 1,567,003 $ (106,222) $ 1,089 $ (105,133) $ 

500-999 $ 3,140,721 363,678 $ 181,839 $ 3,686,237 $ (331,698) $ 3,011 $ (328,686) $ 

1,000-3,299 $ 5,529,388 640,272 $ 320,136 $ 6,489,795 $ (583,969) $ 5,301 $ (578,668) $ 

3,300-9,999 $ 8,379,826 623,055 $ 258,721 $ 9,261,602 $ 953,611 $ 20,870 $ 974,481 $ 

10,000-49,999 $ 17,851,398 1,212,306 $ 461,867 $ 19,525,571 $ (2,477,619) $ 98,959 $ (2,378,660) $ 

50,000-99,999 $ 6,426,075 239,846 $ 93,524 $ 6,759,446 $ 216,219 $ 39,199 $ 255,418 $ 

100,000-999,999 $ 6,113,574 212,143 $ 93,984 $ 6,419,700 $ 276,429 $ 38,699 $ 315,128 $ 

1,000,000+ $ 731,365 15,227 $ 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 $ 221,266 1,596,365 $ 77,140 $ 1,894,770 $ 96,758 $ - $ 96,758 $ 

100-499 $ 273,849 1,975,736 $ 95,472 $ 2,345,057 $ 119,753 $ - $ 119,753 $ 

500-999 $ 1,931,945 894,469 $ 507,572 $ 3,333,986 $ 553,626 $ - $ 553,626 $ 

1,000-3,299 $ 2,344,617 1,085,531 $ 615,991 $ 4,046,139 $ 671,883 $ - $ 671,883 $ 

3,300-9,999 $ 1,063,047 492,179 $ 279,290 $ 1,834,515 $ 304,631 $ - $ 304,631 $ 

10,000-49,999 $ 1,642,671 797,681 $ 320,895 $ 2,761,247 $ 117,333 $ - $ 117,333 $ 

50,000-99,999 $ 182,233 88,492 $ 35,599 $ 306,325 $ 13,017 $ - $ 13,017 $ 

100,000-999,999 $ 166,938 46,421 $ 30,689 $ 244,048 $ (92,140) $ - $ (92,140) $ 

1,000,000+ $ 5,964 2,180 $ 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 $ 46,876 3,263 $ 1,303 $ 51,442 $ - $ - $ - $ 

50,000-99,999 $ - - $ - $ - $ - $ - $ - $ 

100,000-999,999 $ 23,725 740 $ 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 $ 855 2,082 $ 1,143 $ 4,080 $ 13,563 $ - $ 13,563 $ 

50,000-99,999 $ 78 189 $ 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 for 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 alternative 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 chloramines 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 costs 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 Exhibit 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 process and explains why these processes are not included

in the cost analysis for the Stage 2 DBPR. Section 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 available 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 derivation of unit costs over the entire range of expected

plant flows.

Treatment Technologies for Surface Water Plants

Exhibit 7.8a lists the treatment technologies that are available to surface water plants for

complying with Stage 1 DBPR and Stage 2 DBPR regulatory 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 are 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 the Stage 2 DBPR do not include costs for operational

changes (Section 7.8 summarizes uncertainties in national 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, summarized 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 treatment 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) selected 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 capture reasonable estimates of national costs. The

uncertainties associated with selecting these operational 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 considered 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 Activated 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 Criteria 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

ClO2 dose = 1.25 mg/L

Ultraviolet light

disinfection (UV)1 None

Median water quality parameters—

UV254 = 0.051 cm-1, turbidity = 0.1 NTU, alkalinity = 60

mg/L as CaCO3, hardness = 100 mg/L as CaCO3

Dose = 40 mJ/cm2

Ozone

Assumed not practical for

systems serving fewer

than 100 people

Design dose = 3.2 mg/L, contact time = 12 minutes2

Microfiltration /

Ultrafiltration (MF/UF) None Median water quality parameters—Temperature=10°C, disposal to sewer

Granular activated

carbon filtration, emptybed contact time of 10

minutes (GAC10)

Assumed not practical for

small systems serving

fewer than 10,000 people

Reactivation frequency = 360 days3

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 days3

On-site regeneration

Granular activated

carbon filtration, emptybed contact time of 20

minutes (GAC20)

None

Reactivation frequency = 90 days3

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 chlorine 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 deliberations 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/cm2 UV unit costs based on data obtained for recent installations of this

technology. Similar data for 200 mJ/cm2

 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 water 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—conversion 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/cm2

) is

assumed necessary to achieve 2-log virus inactivation, much higher than the UV dose of 100 mJ/cm2

required by the LT2 ESWTR for 2 log viral inactivation (USEPA 2005n). The unit cost estimates shown

in Exhibit 7.11 for UV ($/plant) for small ground water plants represent new estimates for two reactors in

series with a higher dose (200 mJ/cm2

).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 Technologies for Disinfecting Ground Water Plants

Treatment

Technology Constraints Design Criteria 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/L1

UV2

Was considered only for small

systems with populations of fewer

than 10,000 people. Requires two

reactors in series.

Median water quality parameters—

UV254 = 0.051 cm-1, turbidity = 0.1 NTU, alkalinity =

60 mg/L as CaCO3, hardness = 100 mg/L as CaCO3

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 days3

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 the 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 from the ICR Ground Water Delphi Group.

Source: T&C document (USEPA 2005n), FACA deliberations 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 flow rates. Appendix I displays these point estimates

for the treatment technologies, design criteria, and operating 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 characterizing the changes in unit costs as flow increases or

decreases. Along with the 16 to 20 point estimates, Appendix 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 size category are estimated using (1) the capital and

O&M cost data in Appendix I and (2) the mean design and average daily flow values presented in Exhibit

3.4. For example, the design flow for UV for surface water plants in CWSs serving between 10,000 and

49,999 people is estimated to be 5.324565 millions of gallons 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 rates may be different than social discount rates (3 and 7

percent) used elsewhere in the economic analysis. Social 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) are converted to dollars per 1000 gallons per year

($/kgal/yr) using the following formula: 

CHH = CP/(ADF*365*1000)

where: CHH = unit cost per household in $/kgal

CP = unit cost per plant $/plant/yr

ADF = average daily flow in mgd

• Step 3: Estimated household usage rate (in units 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:

CAHH = CHH*QHH

where: CAHH = annual household unit cost ($/HH/yr)

QHH = annual household usage rate (kgal/yr) 

Exhibit 7.9 summarizes the annual household usage rates used to estimate household costs in this

chapter. These are from the Baseline Handbook, as derived from the 1995 CWSS5

. 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 estimates), 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 people. 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:

Ccap-ann = $457,719*0.0525*(1 + 0.0525)20/((1 + 0.0525)20 - 1) = $37,511.12/plant/year

Cp = $37,511.12 + $15,274.16 = $52,785.28/plant/year

This is the result of step 1 listed above. Step 2 involves multiplying the cost by the average daily flow to

obtain a unit cost per thousand gallons of water. 

CHH = ($52,785.28/plant/year)/[(2.34Mgal/plant/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

CAHH = $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 annual O&M costs ($/plant/year) and capital costs

($/plant) for surface and ground water treatment technologies, 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. The household unit costs in Exhibits 7.10c and 7.11c

represent only the household costs for installation and 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 treatment costs is provided in Section 7.5.3.
Final Economic Analysis for the Stage 2 DBPR 7-27 

System Size

(Population 

Served)

Plant Design 

Flow (MGD) CLM CLO2 UV Ozone MF/UF GAC10 GAC10+AD GAC20 

<100 0.02 $29,104.44 $12,389.73 $195,605.59 $49,239.18 100-499 0.08 $29,104.44 $23,031.80 $373,422.89 $120,214.53 500-999 0.21 $33,047.01 $38,637.25 $45,188.94 $401,494.03 $668,228.42 $274,674.40 1,000-3,299 0.57 $40,954.40 $41,950.61 $87,869.72 $610,467.18 $1,032,468.83 $630,258.56 3,300-9,999 1.42 $83,772.20 $80,980.80 $318,958.94 $989,688.20 $2,002,992.32 $1,726,876.12 10,000-49,999 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 

50,000-99,999 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 $6,043,134.98 100,000-999,999 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 1,000,000+ 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 

System Size

(Population 

Served)

Plant-Average 

Daily Flow 

(MGD) CLM CLO2 UV Ozone MF/UF GAC10 GAC10+AD GAC20 

<100 0.01 $1,366.97 $3,465.33 $6,788.93 $19,849.99 100-499 0.03 $1,486.13 $14,200.27 $4,567.48 $55,555.11 $10,560.66 $47,768.56 500-999 0.08 $2,786.70 $16,491.53 $5,906.07 $58,860.42 $25,696.45 $59,595.71 1,000-3,299 0.22 $3,075.12 $17,767.16 $8,060.73 $61,074.17 $40,850.24 $120,988.58 3,300-9,999 0.59 $6,367.05 $20,288.25 $10,462.98 $65,448.37 $90,783.00 $188,170.95 10,000-49,999 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 50,000-99,999 3.80 $11,365.79 $27,157.89 $17,309.16 $103,776.82 $401,754.12 $138,118.16 $165,276.06 $385,208.70 100,000-999,999 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 1,000,000+ 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 

Exhibit 7.10a Capital Unit Costs ($/Plant) for CWS Surface Water Plants

Source: 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 Pla

Source: Average daily flows from Exhibit 3.4, and unit costs from Appendix I.
Final Economic Analysis for the Stage 2 DBPR 7-28 

System Size

(Population 

Served)

Mean Annual 

Water Usage 

per HH (kgal) CLM CLO2 UV Ozone MF/UF GAC10 GAC10+AD GAC20 

<100 83 $139.50 $163.48 $851.00 $869.14 

100-499 83 $32.32 $116.11 $53.30 $454.26 $345.06 $473.88 500-999 104 $20.10 $70.95 $34.99 $333.82 $296.45 $297.68 1,000-3,299 87 $6.99 $22.76 $16.56 $120.46 $137.12 $186.18 3,300-9,999 97 $6.09 $12.29 $16.97 $67.58 $117.89 $151.41 10,000-49,999 109 $2.09 $5.17 $6.73 $32.07 $93.93 $41.01 $46.18 $83.20 50,000-99,999 119 $1.58 $3.88 $6.07 $27.07 $94.61 $37.58 $41.46 $75.95 100,000-999,999 125 $0.82 $1.80 $4.93 $16.18 $81.94 $25.38 $27.17 $55.03 1,000,000+ 125 $0.36 $0.86 $2.58 $10.62 $66.95 $15.31 $16.16 $36.36 

Exhibit 7.10c Household Unit Treatment Costs ($/Household/Year) for CWS Surface Wa

Source: Mean water usage per household derived from the Third 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 ($/Plant/Year) for CWS Disinfecting Ground

Water Plants

Source: Average daily flows from Exhibit 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 Treatment 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 substantial 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 dependent on system-specific conditions. Therefore, these

activities were not evaluated in this EA (see Section 7.7 for a discussion of unquantifiable costs).

Other alternatives to treatment that could reduce 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 prohibitive barrier. To estimate this percentage, EPA

used the April 2000 Safe Drinking Water Information System (SDWIS) database to compare the ZIP

codes for CWSs that serve 100 or fewer people and medium 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 system 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 average, conditions for the universe of systems. As a

result, there is uncertainty in these unit cost estimates, as they are based on designs and quotes assuming

average conditions, rather than on an detailed aggregation of State, regional, or local estimates based on

actual field conditions. To model the uncertainty around 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 Drinking Water Advisory Council (NDWAC) (2001) in

their review of the national cost estimation methodology for the Arsenic Rule. EPA believes that the

uncertainties in capital and O&M costs for a given treatment 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 compliance forecasts, as described in Chapter 5, with the technology

unit costs, as described in Section 7.4, to obtain national treatment costs. The model accounts for

uncertainty in the national costs in several ways, as explained 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 described 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 inputs used to determine the national costs of this

rule. Ideally, the model would quantify each uncertainty. 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 potentially 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 the compliance forecast (i.e., SWAT and the ICR

Matrix Method), as discussed in Chapter 5 and Appendix A.

• Uncertainty in average unit costs (capital 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 medium surface water systems because spatial variability

of DBP levels and distribution system complexity are 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 preparing 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 compliance 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 are incorporated by comparing the predicted percent of

plants 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 5th percentile value and the ICR

Matrix Method-to-SWAT multiplier for plants making treatment technology changes as the 95th percentile

value. Two separate distributions were used for large 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 Preferred 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 3

Probability

Multiplier

1 2 3

Probability

Multiplier

Uniform Distribution for a 20% 

Safety Margin

Uniform Distribution for a 25% 

Safety Margin

(2.91) (1.98)

1 2 3

Probability

Multiplier

1 2 3

Probability

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 with 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 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 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 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 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) by
6 For purposes of analyses in this EA, all present value figures are presented at a year 2003 price level. 

Present value calculations are performed to the 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 more 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 rule implementation period, and (2) yearly costs that

systems and States/Primacy Agencies will incur after 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 treatment 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 value6

 using a social discount rate so that they

can be compared to one another. This process takes into account the time preference that society places

on expenditures and allows comparison of cost and benefit streams that are variable over a given time

period.7

 Similar to calculating the present value of benefits (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 evaluation methodologies, 3 and 7 percent. Historically, the

use of 3 percent is based on rates of return on relatively risk-free investments, 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 any 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 annual 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 discount 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 relationship 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 presented 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 Stage 2 DBPR, if all costs are passed onto consumers.

A distribution of possible household costs is developed for each system type and population size

category. The distribution reflects that some households are served by systems that will incur only nontreatment costs (e.g., implementation, IDSE, monitoring 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 membranes, 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, and 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 served 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 related rule activities is derived in Appendix H
8

Only plants in CWSs are used to generate HH cost 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 plants performing activities. The number of plants

making treatment technology changes to meet Stage 2 is shown in Exhibits 5.14a and 5.14c8

. 

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 different 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 model described in the preceding sections. Section

7.6.1 shows the number of plants performing various rule 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. Section 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 produces a distribution of the number of plants making

treatment technology changes to represent uncertainty 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 water type, and system size category. Appendix J and

Exhibits J.1b through J.1d provide similar cost information (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 alternative. For the Preferred Alternative, Exhibits J.2a

through J.2ar show the nominal costs projected over the 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 present 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 $ 

$ 555.97 552.43 $ 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 $ 

$ 287.01 287.08 $ 245.16 $ 329.11 $ 23.02 $ 23.02 $ 21.34 $ 24.70 $ 

$ 842.98 839.51 $ 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 

Preferred Alternative

Primacy Agencie

 Lower

(5th %tile) 

 Upper

(95th %tile) 

 Lower

(5th %tile) 

 Upper

(95th %tile) 

 Lower

(5th %tile) 

 Upper

(95th %tile) 

 Lower

(5th %tile) 

 Upper

(95th %tile) 

2005 $ 0.8 0.8 $ 0.8 $ 0.0 $ 0.0 $ 0.0 $ 0.2 $ 0.2 $ 0.2 $ 0.0 $ 0.0 $ 0.0 $ 

$ 

2006 $ 10.8 10.8 $ 10.8 $ 0.1 $ 0.1 $ 0.1 $ 3.1 $ 3.1 $ 3.1 $ 0.5 $ 0.5 $ 0.5 $ 

$ 

2007 $ 20.6 20.6 $ 20.6 $ 0.0 $ 0.0 $ 0.0 $ 1.0 $ 1.0 $ 1.0 $ 0.0 $ 0.0 $ 0.0 $ 

$ 

2008 $ 15.0 15.0 $ 15.0 $ 0.0 $ 0.0 $ 0.0 $ 6.0 $ 6.0 $ 6.0 $ 0.0 $ 0.0 $ 0.0 $ 

$ 

2009 $ 54.8 99.7 $ 139.8 $ 1.0 $ 0.6 $ 1.4 $ 43.1 $ 37.4 $ 48.8 $ 2.1 $ 1.8 $ 2.3 $ 

$ 

2010 $ 55.9 101.6 $ 143.2 $ 1.1 $ 0.6 $ 1.6 $ 43.6 $ 37.8 $ 49.3 $ 2.0 $ 1.7 $ 2.3 $ 

$ 

2011 $ 57.4 103.8 $ 146.8 $ 1.1 $ 0.6 $ 1.7 $ 44.0 $ 38.2 $ 49.7 $ 1.9 $ 1.6 $ 2.2 $ 

$ 

2012 $ 58.0 105.1 $ 149.3 $ 1.2 $ 0.7 $ 1.8 $ 46.4 $ 40.6 $ 52.2 $ 2.3 $ 2.0 $ 2.5 $ 

$ 

2013 $ 28.4 50.3 $ 73.5 $ 1.3 $ 0.7 $ 1.8 $ 41.2 $ 36.2 $ 46.3 $ 2.6 $ 2.3 $ 2.9 $ 

$ 

2014 $ 21.8 38.2 $ 56.8 $ 1.3 $ 0.7 $ 1.9 $ 37.1 $ 32.5 $ 41.6 $ 2.7 $ 2.4 $ 2.9 $ 

$ 

2015 $ 15.2 26.3 $ 39.7 $ 1.0 $ 0.6 $ 1.5 $ 24.8 $ 22.2 $ 27.4 $ 2.0 $ 1.8 $ 2.2 $ 

$ 

2016 $ 12.3 21.1 $ 32.0 $ 0.7 $ 0.4 $ 1.0 $ 16.0 $ 14.9 $ 17.1 $ 1.4 $ 1.3 $ 1.4 $ 

$ 

2017 $ 12.0 20.5 $ 31.0 $ 0.7 $ 0.4 $ 1.0 $ 15.5 $ 14.5 $ 16.6 $ 1.3 $ 1.3 $ 1.4 $ 

$ 

2018 $ 11.6 19.9 $ 30.1 $ 0.7 $ 0.4 $ 1.0 $ 15.1 $ 14.1 $ 16.1 $ 1.3 $ 1.2 $ 1.3 $ 

$ 

2019 $ 11.3 19.3 $ 29.2 $ 0.6 $ 0.4 $ 0.9 $ 14.6 $ 13.7 $ 15.6 $ 1.2 $ 1.2 $ 1.3 $ 

$ 

2020 $ 10.9 18.7 $ 28.4 $ 0.6 $ 0.4 $ 0.9 $ 14.2 $ 13.3 $ 15.2 $ 1.2 $ 1.1 $ 1.3 $ 

$ 

2021 $ 10.6 18.2 $ 27.6 $ 0.6 $ 0.3 $ 0.9 $ 13.8 $ 12.9 $ 14.7 $ 1.2 $ 1.1 $ 1.2 $ 

$ 

2022 $ 10.3 17.7 $ 26.8 $ 0.6 $ 0.3 $ 0.8 $ 13.4 $ 12.5 $ 14.3 $ 1.1 $ 1.1 $ 1.2 $ 

$ 

2023 $ 10.0 17.2 $ 26.0 $ 0.6 $ 0.3 $ 0.8 $ 13.0 $ 12.1 $ 13.9 $ 1.1 $ 1.0 $ 1.2 $ 

$ 

2024 $ 9.7 16.7 $ 25.2 $ 0.6 $ 0.3 $ 0.8 $ 12.6 $ 11.8 $ 13.5 $ 1.1 $ 1.0 $ 1.1 $ 

$ 

2025 $ 9.4 16.2 $ 24.5 $ 0.5 $ 0.3 $ 0.8 $ 12.3 $ 11.4 $ 13.1 $ 1.0 $ 1.0 $ 1.1 $ 

$ 

2026 $ 9.2 15.7 $ 23.8 $ 0.5 $ 0.3 $ 0.8 $ 11.9 $ 11.1 $ 12.7 $ 1.0 $ 1.0 $ 1.1 $ 

$ 

2027 $ 8.9 15.2 $ 23.1 $ 0.5 $ 0.3 $ 0.7 $ 11.6 $ 10.8 $ 12.3 $ 1.0 $ 0.9 $ 1.0 $ 

$ 

2028 $ 8.6 14.8 $ 22.4 $ 0.5 $ 0.3 $ 0.7 $ 11.2 $ 10.5 $ 12.0 $ 1.0 $ 0.9 $ 1.0 $ 

$ 

2029 $ 8.4 14.4 $ 21.8 $ 0.5 $ 0.3 $ 0.7 $ 10.9 $ 10.2 $ 11.6 $ 0.9 $ 0.9 $ 1.0 $ 

$ 

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 $ 2

$ 

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 $ 

$ 

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.

Mean

Value

90 Percent

Confidence Bound

Point Estimate

Mean

Value

90 Percent

Confidence Bound

90 Percent

Confidence Bound

90 Percent

Confidence Bound

Mean

Value

Surface Water CWS Surface Water NTNCWS Disinfecting Ground Water NTNCWS

Mean

Value

Disinfecting Ground Water CWS

Present values in millions of 2003 dollars. Estimates are discounted to 2005.

Exhibit 7.14a Total Annualized Costs at 3 Percent Social Discount Rate ($Millio
Final Economic Analysis for the Stage 2 DBPR 7-40 

Preferred Alternative

Primacy Agencie

 Lower

(5th %tile) 

 Upper

(95th %tile) 

 Lower

(5th %tile) 

 Upper

(95th %tile) 

 Lower

(5th %tile) 

 Upper

(95th %tile) 

 Lower

(5th %tile) 

 Upper

(95th %tile) 

2005 $ 0.8 0.8 $ 0.8 $ 0.0 $ 0.0 $ 0.0 $ 0.2 $ 0.2 $ 0.2 $ 0.0 $ 0.0 $ 0.0 $ 3

$ 

2006 $ 9.6 9.6 $ 9.6 $ 0.1 $ 0.1 $ 0.1 $ 2.8 $ 2.8 $ 2.8 $ 0.5 $ 0.5 $ 0.5 $ 3

$ 

2007 $ 17.7 17.7 $ 17.7 $ 0.0 $ 0.0 $ 0.0 $ 0.8 $ 0.8 $ 0.8 $ 0.0 $ 0.0 $ 0.0 $ 0

$ 

2008 $ 12.4 12.4 $ 12.4 $ 0.0 $ 0.0 $ 0.0 $ 5.0 $ 5.0 $ 5.0 $ 0.0 $ 0.0 $ 0.0 $ 

$ 

2009 $ 43.6 79.3 $ 111.2 $ 0.8 $ 0.4 $ 1.1 $ 34.3 $ 29.8 $ 38.8 $ 1.7 $ 1.4 $ 1.9 $ 0

$ 

2010 $ 42.8 77.8 $ 109.6 $ 0.8 $ 0.5 $ 1.2 $ 33.4 $ 29.0 $ 37.8 $ 1.5 $ 1.3 $ 1.7 $ - $ 

2011 $ 42.3 76.5 $ 108.2 $ 0.8 $ 0.5 $ 1.2 $ 32.4 $ 28.2 $ 36.7 $ 1.4 $ 1.2 $ 1.6 $ 

$ 

2012 $ 41.2 74.6 $ 106.0 $ 0.9 $ 0.5 $ 1.3 $ 32.9 $ 28.8 $ 37.1 $ 1.6 $ 1.4 $ 1.8 $ 0

$ 

2013 $ 19.4 34.4 $ 50.2 $ 0.9 $ 0.5 $ 1.3 $ 28.2 $ 24.7 $ 31.6 $ 1.8 $ 1.6 $ 2.0 $ 0

$ 

2014 $ 14.3 25.1 $ 37.3 $ 0.8 $ 0.5 $ 1.2 $ 24.4 $ 21.4 $ 27.4 $ 1.7 $ 1.6 $ 1.9 $ 0

$ 

2015 $ 9.6 16.6 $ 25.1 $ 0.6 $ 0.4 $ 0.9 $ 15.7 $ 14.1 $ 17.4 $ 1.3 $ 1.2 $ 1.4 $ 0

$ 

2016 $ 7.5 12.9 $ 19.5 $ 0.4 $ 0.2 $ 0.6 $ 9.7 $ 9.1 $ 10.4 $ 0.8 $ 0.8 $ 0.9 $ 0

$ 

2017 $ 7.0 12.0 $ 18.2 $ 0.4 $ 0.2 $ 0.6 $ 9.1 $ 8.5 $ 9.7 $ 0.8 $ 0.7 $ 0.8 $ 0

$ 

2018 $ 6.6 11.2 $ 17.0 $ 0.4 $ 0.2 $ 0.5 $ 8.5 $ 7.9 $ 9.1 $ 0.7 $ 0.7 $ 0.8 $ 0

$ 

2019 $ 6.1 10.5 $ 15.9 $ 0.4 $ 0.2 $ 0.5 $ 8.0 $ 7.4 $ 8.5 $ 0.7 $ 0.6 $ 0.7 $ 0

$ 

2020 $ 5.7 9.8 $ 14.9 $ 0.3 $ 0.2 $ 0.5 $ 7.4 $ 6.9 $ 7.9 $ 0.6 $ 0.6 $ 0.7 $ 0

$ 

2021 $ 5.4 9.2 $ 13.9 $ 0.3 $ 0.2 $ 0.4 $ 6.9 $ 6.5 $ 7.4 $ 0.6 $ 0.6 $ 0.6 $ 0

$ 

2022 $ 5.0 8.6 $ 13.0 $ 0.3 $ 0.2 $ 0.4 $ 6.5 $ 6.1 $ 6.9 $ 0.6 $ 0.5 $ 0.6 $ 0

$ 

2023 $ 4.7 8.0 $ 12.1 $ 0.3 $ 0.2 $ 0.4 $ 6.1 $ 5.7 $ 6.5 $ 0.5 $ 0.5 $ 0.5 $ 0

$ 

2024 $ 4.4 7.5 $ 11.3 $ 0.2 $ 0.1 $ 0.4 $ 5.7 $ 5.3 $ 6.1 $ 0.5 $ 0.5 $ 0.5 $ 0

$ 

2025 $ 4.1 7.0 $ 10.6 $ 0.2 $ 0.1 $ 0.3 $ 5.3 $ 4.9 $ 5.7 $ 0.5 $ 0.4 $ 0.5 $ 0

$ 

2026 $ 3.8 6.5 $ 9.9 $ 0.2 $ 0.1 $ 0.3 $ 5.0 $ 4.6 $ 5.3 $ 0.4 $ 0.4 $ 0.4 $ 0

$ 

2027 $ 3.6 6.1 $ 9.3 $ 0.2 $ 0.1 $ 0.3 $ 4.6 $ 4.3 $ 4.9 $ 0.4 $ 0.4 $ 0.4 $ 0

$ 

2028 $ 3.3 5.7 $ 8.6 $ 0.2 $ 0.1 $ 0.3 $ 4.3 $ 4.0 $ 4.6 $ 0.4 $ 0.3 $ 0.4 $ 0

$ 

2029 $ 3.1 5.3 $ 8.1 $ 0.2 $ 0.1 $ 0.3 $ 4.0 $ 3.8 $ 4.3 $ 0.3 $ 0.3 $ 0.4 $ 0

$ 

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

$ 

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

$ 

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.

90 Percent

Confidence Bound

Surface Water NTNCWS Disinfecting Ground Water NTNCWS

Mean

Value

Mean

Value

Disinfecting Ground Water CWS

90 Percent

Confidence Bound

Surface Water CWS

Present values in millions of 2003 dollars. Estimates are discounted to 2005.Detail may not add exactly to totals due to independent rounding.

Mean

Value

Mean

Value

90 Percent

Confidence Bound

90 Percent

Confidence Bound

Point Estimate

Exhibit 7.14b Total Annualized Costs at 7 Percent Social Discount Rate ($Millio
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, 90th percentile, and 95th 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 include costs for non-treatment-related rule activities

(implementation, IDSE, additional routine 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 making treatment technology changes for the five small

system size categories.

As shown in Exhibit 7.15, the mean, median, and 90th 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 90th percentile household cost increases for systems that install new

treatment technologies are $5.53, $0.80 and $10.04, respectively. 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 some of the plants for that system make treatment

technology changes (this would result in lower household 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 conservative 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; these 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 treatment technology to comply with the Stage 2 DBPR. 

The system also may identify another water source 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 costs. Systems may elect to connect to a neighboring

water system. While this may not be feasible for some 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 2.83 9,060,119 $ 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 5.97 509,562 $ 1.37 $ 26.82 $ 33.84 $ 79% 100%

Source: Results represent the sum of treatment and non-treatment costs. Household costs for treatment are derived from household 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 activites are derived from 

mean costs for each system size category for implementation, IDSE, monitoring plans, additional routine monitoring, and significant excursion (as derived in 

Appendix H). See section 7.5.3 for additional information 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 be 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 Household 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 Household 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 Household 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 Household 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 Distributions, 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 (legal fees and connecting infrastructure) and benefits

(economies of scale). Likewise, procuring a new source of water would have costs for new infrastructure,

but could result in lower treatment costs. Operational 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 detailed information needed to evaluate situations such

as these, EPA has included a discussion of possible effects where appropriate. In general, however, the

expected net effect of such situations is lower 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 national 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 individual 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, the Agency has attempted to quantify the uncertainty by

giving 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 uniform distribution between SWAT and ICR Matrix

Method results. That is, the national cost estimates 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 technology unit costs. Treatment costs are modeled

using a triangular distribution of ± 30 percent for Capital, 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 $ 76.8 78.8 $ 

Alt. 1 $ 241.8 254.1 $ 

Alt. 2 $ 406.4 421.7 $ 

Alt. 3 $ 613.1 634.2 $ 

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

Rule Alternative Mean Estimate

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 Alternative 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 the 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 costs 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 (implementation, 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 Environmental 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 types 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 Coordination with Indian Tribal Governments).

8) Impacts on sensitive subpopulations as required 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 Regulations That Significantly Affect Energy

Supply, Distribution, or Use).

Many of the requirements and executive orders listed 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 regarding the objectives of the rule). More specifically,

they are designed to assess the financial and health effects of the rule on sensitive, low-income, and Tribal

populations as well as on small systems. The chapter also examines how much additional capacity

systems will need to meet Stage 2 DBPR requirements 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 presented 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 entities. In this analysis, EPA evaluated the potential

economic impact of the rule on small entities by comparing annualized compliance cost as a percentage of

annual revenues1

 for different small-entity classifications. Chapter 3 of this Economic Analysis (EA)

provides data on the small entities potentially subject 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), and 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 district

or special district with a population of less than 50,000; or (3) a small organization that is any “not-forprofit enterprise which is independently owned and operated and is not dominant in its field.” However,

the RFA authorizes an agency to adopt alternative definitions 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 business” found in the Small Business Act, which

authorizes the SBA to define “small business” further 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 (Administration 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 businesses 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 disinfectant other than ultraviolet light (UV) or that

deliver water that has been treated with a disinfectant 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 year
3

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 necessarily small water systems. The size of the entity

has no relation to the number of people it serves as a water 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 people, 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 businesses, 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 stated its intent to establish this alternative

definition for regulatory flexibility assessments under the 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 expenditures 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)) rather than by NAICS code. Revenues by NAICS

code are not readily applicable to EPA’s categorization of systems. Therefore, alternative methods for

determining revenue were developed, as discussed below.

The estimated revenues for small entities in Exhibit 8.1 are from the Bureau of the Census (U.S.

Department of Commerce 1992), the SDWIS, and additional 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 organizations, obtained using

information from the Third Edition of the Baseline Handbook (USEPA 2001c). These numbers were

used to determine the weighted averages of estimated 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 townships 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 governments 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 and day care centers are assumed to be businesses, as are
4

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 nonprofit organizations serving 500 or more people are

actually higher than those for small businesses because the total number of such systems is small, and a

large proportion of these organizations are schools and 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 revenue for all small entities. This estimate is

weighted to account for the number of small entities 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 expected 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 Interim Guidance for EPA Rulewriters for the RFA as

amended by the SBREFA (March 1999) suggests using 1 percent as a threshold for determining

significance, although additional factors may be considered. If compliance costs are less than 1 percent of

sales or revenues for fewer than 1,000 entities, which represent 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 percent of their revenues or greater, then in most

cases there is no significant impact.

Exhibit 8.1 presents the data that EPA used for the screening analysis. The numbers of entities

expected to incur costs of more than 1 and 3 percent of their revenues are presented in Columns D and F,

respectively. The numbers of entities experiencing impacts 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 

Revenues1

 per 

System ($)

Percent of 

Small 

Systems

Small Systems by 

Source of Water and

Type of Entity

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 was not available, other measures such as sales or

annual operating expenditures were used. Data were not available to differentiate revenue for small entities by

system sizes or by source water type. The revenue estimates reflect ground water as well as surface water systems. 2

 Compliance costs were compared to average annual revenue to determine whether 20 percent of small entities

would incur costs exceeding 1 percent of their average annual revenues. Thresholds to determine whether a rule has

a significant impact on a substantial number 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 compared 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 compared to 3 percent of average annual revenues to determine

whether 100 or more entities will experience an impact of 3 percent or greater of average annual revenues. 

Sources:

(A) Number of disinfecting CWSs and NTNCWSs serving 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 Government. 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 costs 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 small entities using ground water, representing 2.16 and

1.02 percent of all small entities affected by the Stage 2 DBPR, respectively, will experience an impact of

1 percent or greater of average annual revenues. This is less than the criteria of 1,000 entities used to

determine significant impact.

Using a similar methodology, EPA also considered whether 100 or more entities will experience

an impact of 3 percent or greater of average annual 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 small 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. This 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 substantial 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 meaningful participation in the regulatory development

process. EPA provided stakeholders, including small 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 Entity 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 presented 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 Committees Act (FACA) Committee, and DBP and microbial

occurrence in small systems. The third meeting was 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 about 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 the RFA. The small entity stakeholders received

background on the need for the rule and the possible components of the rule to assist them with their

deliberations. EPA convened the SBAR Panel after completing the consultation 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 affordable 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 variance 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 given 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. This 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 determination for each system as to whether the

system can afford to meet the MCL based on affordability 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 conditions, as necessary, to ensure that the variance

adequately protects human health.

The 1996 SDWA Amendments identify three categories 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 requires EPA to make determinations of available

compliance treatment technologies and, if needed, variance 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. Variance 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 following sections show how small system affordability was

evaluated for the Stage 2 DBPR. The analysis is consistent 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 affordability criteria in the August 6, 1998, Federal

Register for the contaminants regulated before 1996. National-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 threshold, 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), food (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 activities 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 also investigated the costs associated with supplying

bottled water for drinking and cooking. The median income percentages associated 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 developed an affordability criterion of 2.5 percent of MHI

for the affordability threshold (USEPA 1998c). 

The median water bill for households in each small system size category was subtracted from the

affordability threshold to obtain the affordable level of expenditure per household for new treatment. This

difference is referred to as the “available expenditure 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 Water 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 Price Index (BLS 2004).

B) 1995 CWSS (as reported in the document "Variance Technology Findings for Contaminants Regulated

Before 1996), adjusted to 2003$ using the 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 established 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 ($ per 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 are 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 accurately represent the high-end variability in household

costs. This allows affordability of the Stage 2 DBPR to be more confidently assessed across the range of

all affected small systems.

The size categories specified in SDWA for affordable 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 the affordability analysis do not specifically include

systems serving fewer than 25 people (per SDWA), these systems 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 percent 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 used to generate the distribution of household costs in

Chapter 7. The unit treatment costs ($ per 1,000 gallons) were multiplied by annual household

consumption rates to determine the annual household cost increase ($ per household) for each treatment

technology. Annual consumption rates are shown 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 annual 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 people6

. The mean, median, 90th percentile, and 95th percentile values are shown as

well as the available expenditure margin and the number 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 expected 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 category, 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) $ 9.41 62.88 $ 5.03 $ 

Chlorine Dioxide (1.25 mg/L) 1 $ 29.22 121.92 $ 9.99 $ 

UV (40mJ/cm2) $ 20.55 89.21 $ 14.59 $ 

MF/UF 1 $ 174.74 583.74 $ 115.86 $ 

GAC20 (EBCT=20 min, 90 day regeneration) 1 $ 209.42 644.69 $ 134.39 $ 

GAC20 + Advanced Disinfectants $ 366.53 733.90 $ 195.02 $ 

Integrated Membranes $ 316.45 907.52 $ 240.22 $ 

System Size (Population Served)

Compliance Technologies

0-500 501-3,300 3,301-10,000

Chloramines (0.15 mg/L) $ 9.28 62.70 $ 4.88 $ 

UV (200mJ/cm2) $ 65.81 236.32 $ 50.38 $ 

Ozone (0.5-log dose) 1 $ 157.11 1,300.43 $ 60.62 $ 

GAC20 (EBCT=20 min, 240 day regeneration) 1 $ 159.38 414.74 $ 95.81 $ 

Nanofiltration 1 $ 141.71 323.78 $ 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 DBPR 8-12 

Systems Size 

(population 

served)

Number of Households 

Served by Plants 

Adding Treatment 

(Percent of all 

Households Subject to 

the Stage 2 DBPR)

Mean Annual 

Household 

Cost Increase

Median Annual 

Household 

Cost Increase

90th Percentile 

Annual 

Household 

Cost Increase

95th Percentile 

Annual 

Household 

Cost Increase

Available 

Expenditure 

Margin 

($/hh/yr)

Number of 

Households with 

Annual Cost 

Increases Greater 

then the Available 

Expenditure Margin

Number of Surface 

Water Plants with 

Annual Cost 

Increases Greater 

than the Available 

Expenditure Margin

Num

Groundw

with An

Increas

than th

Expend

A B C D E F G H 

0 - 500 43045 ( 3% ) $201.55 $299.01 $299.01 $414.74 $733 964 15 501 - 3,300 205842 ( 4% ) $58.41 $29.96 $75.09 $366.53 $724003,301 - 10,000 342525 ( 5% ) $37.05 $14.59 $55.25 $200.05 $75000

Exhibit 8.4c Distribution of Household Unit Treatment Costs for Plants Adding Tre

Notes: 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 and 5.14.
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 systems 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 distribution mains more frequently, eliminating loops

and dead ends, and optimizing storage to minimize retention 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. Under 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 same 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 currently 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 decision 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 account operational and constructability 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 implement 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 State 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 systems 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 increased 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 disadvantaged communities to up to 30 years. 

Small systems will be encouraged to discuss their 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 assistance 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 Development’s Community Development Block Grant (CDBG)

Program. RUS provides loans, guaranteed loans, and grants 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 formula 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 requirements. EPA examined alternatives for best

available technologies (BATs) using two methods: Information Collection Rule (ICR) treatment studies

and Surface Water Analytical Tool (SWAT) predictions. A discussion of the evaluation is provided in

sections 8.4.1 and 8.4.2. Results from these two evaluations 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 than 120 days, plus enhanced coagulation or

enhanced softening.

2) Nanofiltration using a membrane with a molecular 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 greater 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 required to conduct treatment studies generally had waters

with organic DBP precursor levels that were significantly 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 evaluate 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 conducted 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 studies evaluated post-treatment DBP formation under

distribution system conditions representative of the full-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 average influent TOC levels below 6 mg/L.) Larger

systems would likely realize an economic benefit from on-site reactivation, which could allow them to

use smaller, 10-minute empty-bed contact time contactors with more frequent reactivation (i.e., 120 days

or less). Most small utilities would not find it economically advantageous to install on-site carbon

reactivation facilities, and thus would opt for larger, 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 between empty-bed contact time and reactivation

interval. Assuming equivalent performance, a doubling 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. However, 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 that 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/replacement 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 conducted 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 translate to much higher percentages of plants nationwide.

The ICR treatment study results also demonstrated that nanofiltration was the better DBP control

treatment technology for ground water sources with high TOC concentrations (i.e., above approximately

6 mg/L). The results of the membrane treatment studies 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 for 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 strategies 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 empty-bed contact time of 10 minutes and a reactivation

frequency of no more than 90 days. GAC20 was defined as granular activated carbon with an empty-bed

contact time of 20 minutes and a reactivation or replacement 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 Treatment 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 large systems. Using the large-system estimate as a proxy

for medium and small systems, EPA believes it is conservative 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 GAC

reactivation 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

Chlorine Chloramine Chlorine Chloramine Systems

 (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 jurisdiction. 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 disinfection for many years to minimize the formation

of chlorination DBPs, including TTHM and HAA5 (USEPA 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 chlorinated 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 meeting 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 than 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 difficulty 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 use 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 controlling DBPs is based on precursor removal. 

Consecutive systems face the unique challenge of receiving waters in which DBPs are already present if

the wholesale systems has used a residual disinfectant, which BATs for non-consecutive systems do not

effectively remove. GAC is not cost-effective for 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 operation. 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, requires 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, managerial, 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 systems. 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 drinking water standards. Capacity encompasses three

components: technical, managerial, and financial. Technical 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 provide 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 the system’s operators certified? Do the

operators have sufficient knowledge of applicable standards? Can the operators effectively

implement this technical knowledge? Do the operators understand the system’s technical and

operational characteristics? Does the system have 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 financial, operating, and

staffing decisions that enable the system to achieve 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 personnel 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 interact well with customers, regulators, and

other entities? Is the system aware of available 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 compliance 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 adequate 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 locations for Stage 2 compliance monitoring that

represent distribution system sites with high TTHM and HAA5 levels. 

3) Preparing a monitoring plan, based on information in the IDSE and consultation with the

State/Primacy Agency, that details the sites and times for compliance sampling.

4) Additional routine monitoring for DBPs.

5) If the operational evaluation level is exceeded, 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 represent 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 impact 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 analysis, 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 appropriate 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 additional treatment technology to achieve compliance with

the Stage 2 DBPR. However, the revised methodology 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 represents a more stringent testing standard than an

RAA, it is likely that some systems that previously 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 DBPR 8-21 

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)

Requirement

Number of

Systems/

Plants

(Percent)

Technical Capacity Managerial Capacity F

Source Water

Adequacy

Infrastructure

Adequacy

Technical

Knowledge &

Implementation

Ownership

Accountability

Staffing &

Organization

Effective External

Linkages

Revenue

Sufficiency

Familiarization withrequirements of the rule

44,453 systems

(100%) 0010100

Conducting IDSE monitoring 10,478 systems (24%) 0021023Plants with major treatmenttechnology changes

1,743 plants

(3%) 2442235

Stage 2 monitoring plan1 22,499 systems (48%) 0011011

Additional routine monitoring1 0010003

Operational evaluations 97 systems (0.2%) 0011110

1Some systems are expected to take more samples and some are expected to take less from Stage 1 to Stage 2, depending on the num

systems. Overall, the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. Echange in total samples for different system size categories.Notes: (1) To analyze the impact of these requirements on system capacity, the requirements believed to have the most and the least i

systems (i.e., the installation of treatment to ensure compliance with the LRAA MCLs for TTHM and HAA5, and familiarization wrule, respectively), were analyzed first. These initial analyses were then used as the bases against which the relative impact of requirements were assessed. The impact estimates developed for each requirement were also compared to those developed foEnhanced 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 requirements of this rule are expected to have less impa

systems affected by each requirement.

Source: Number and percent of small systems subject to familiarization of rule, IDSE monitoring, and operational evaluations from Exhib

J (sum of surface water/GWUDI, and ground water CWSs and NTNCWSs serving fewer than 10,000 people), respectively. Num

systems subject to Stage 2 monitoring plan is the sum of Exhibit 7.2a Column F and Exhibit 7.2b Column B. Number of small p

technology changes from Exhibit 7.3 Column B (sum of surface water/GWUDI, and ground water CWSs and NTNCWSs serving people). Impact on capacity is determined relative to previous regulations as a function of the cost and number of systems/plan

capacity to comply with each requirement, as described in section 8.5.5.
Final Economic Analysis for the Stage 2 DBPR 8-22 

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)

Requirement

Number of

Systems/

Plants

(Percent)

Technical Capacity Managerial Capacity Fin

Source Water

Adequacy

Infrastructure

Adequacy

Technical

Knowledge &

Implementation

Ownership

Accountability

Staffing &

Organization

Effective

External

Linkages

Revenue

Sufficiency

Familiarization withrequirements of the rule

3,839 systems

(100%) 0010100

Conducting IDSE monitoring 2,302 systems (60%) 0021011Plants with major treatmenttechnology changes

518 plants

(5%) 2332224

Stage 2 monitoring plan1 3,853 systems (100%) 0011011

Additional routine monitoring1 0000001

Operational evaluations 327 systems (9%) 0001010

1Some systems are expected to take more samples and some are expected to take less from Stage 1 to Stage 2, depending on the num

systems. Overall, the Stage 2 DBPR results in an increase in the total number of compliance samples taken from the Stage 1 DBPR. Ex

change in total samples for different system size categories.Notes: (1) To analyze the impact of these requirements on system capacity, the requirements believed to have the most and the least i

systems (i.e., the installation of treatment to ensure compliance with the LRAA MCLs for TTHM and HAA5, and familiarization wrule, respectively), were analyzed first. These initial analyses were then used as the bases against which the relative impact of requirements were assessed. The impact estimates developed for each requirement were also compared to those developed foensure cross-rule consistency and to enable cross-rule comparisons.(2) The scores presented above represent the worst case scenario; the requirements of this rule are expected to have less impa

systems affected by each requirement.

Source: Number and percent of large systems subject to familiarization of rule, IDSE monitoring, and operational evaluations from Exhib

J (sum of surface water/GWUDI, and ground water CWSs and NTNCWSs serving 10,000 or more people), respectively. Numb

systems subject to Stage 2 monitoring plan is the sum of Exhibit 7.2a Column F and Exhibit 7.2b Column B. Number of large p

technology changes from Exhibit 7.3 Column B (sum of surface water/GWUDI, and ground water CWSs and NTNCWSs serving 

Impact on capacity is determined relative to previous regulations as a function of the cost and number of systems/plants that reqcomply 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 financial capacity of PWSs. For each regulation, it is

necessary to complete the following steps:

1) Determine the type and number of PWSs to which the regulation applies.

2) List all of the requirements of the regulation.

3) Determine the type and number of PWSs 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 cost and technical information contained in SDWIS,

EAs developed for other rules, information collection requests, and other supporting documentation for

the rule. These data sources are also used to develop 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 impact 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, and 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 comparisons, to standardize the assignment of numerical

scores, and to minimize the subjectivity of the scoring system, the professional engineer compared the

Stage 2 DBPR requirements to requirements of regulations 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 accurately 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 the 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 for 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 minimal 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 conducting extensive additional

monitoring, through an IDSE waiver. CWSs serving 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 monitor 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 monitor than on those that do. While the former may need

to contact their regulatory agencies to obtain waivers 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. Regardless 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 logged 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 ground 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 managerial 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 training for existing operators to

ensure that system staff can safely and effectively operate 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 treatment practices. The installation, operation, and

maintenance of new treatment technologies will require 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 may 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 purposes 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 requirements 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. Instead, EPA assumed these smallest systems would install

UV or membrane technologies, which do not require a system 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, only 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 systems 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 modifications. Since larger systems tend to have more

developed relationships with regulatory 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 systems. Further, large system operators tend to have

a higher level of expertise than their small system counterparts. 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 treatment 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 costs. 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 systems 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 explicitly for water service (e.g., mobile home parks, camp

grounds, etc.) may need to begin billing their customers. Those systems that already charge for water

service will likely need to increase their rates (frequently 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 close 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, the 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 systems 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 when 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 exceeded 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 system). 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 notify the public or provide an explanation in their

CCRs—minimizing the need for them to further develop 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 financial 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 exceedances is also expected to decrease over time as systems

begin identifying the causes and working with their 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 make changes to their treatment process to meet the

Stage 2 DBPR requirements. However, while the impact 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 capacity impact as a result of the Stage 2 DBPR. 

8.6 Paperwork Reduction Act

The information collected as a result of the Stage 2 DBPR allows the States/Primacy Agencies

and EPA to determine appropriate requirements for specific 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 total time, effort, and financial resources required to

generate, maintain, retain, disclose, or provide information 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 any previously applicable instructions and

requirements

• Train personnel to be able to respond 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 information 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 systems’ 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)

Average 

Annual Labor 

Costs 

($Millions)

Average 

Annual O&M 

Costs 

($Millions)

Average 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 Stage 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 equipment 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 this rule. The annual burden per response is 4.18

hours, and the frequency for response (average responses 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 response, 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 Accountability 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 private

sector. 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 identify and consider a reasonable number of regulatory

alternatives and adopt the least costly, most cost-effective, or least burdensome alternative that achieves

the objectives of the rule before promulgating a rule 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 alternative 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 costs 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 PWSs 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 the 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 State, 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 alternative that achieves the 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 chapters 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 primarily 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 occurrence and exposure, and the reduction in future cases

corresponding to anticipated reductions in DBP occurrence and exposure due to the Stage 2 DBPR. 

Mortalities from cancer were valued using a value of statistical life estimate consistent with EPA’s policy. 

EPA also used two alternate (but equally valid) estimates 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, which includes both qualitative and monetized benefits

of the rule. Although EPA estimated the number of avoided 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 cancers, and benefits from reduction of other DBPs, cooccurring contaminants, or emerging contaminants. In addition, certain non-health-related benefits may

exist, such as perceptions of drinking water 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, calculating regulatory compliance costs, and estimating

any disproportionate impacts. Chapter 7 of this document 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 costs and benefits for each regulatory alternative.
7 “...[T]he agency shall prepare a written statement containing. . . (3) estimates by the agency, if and to the

extent that the agency determines that accurate estimates are reasonably feasible, of. . . (B) any disproportionate

budgetary effects of the Federal mandate upon any particular regions of the nation or particular State, local, or Tribal

government, urban or rural or other types of communities, 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 financial assistance to State, local, and Tribal

governments in complying with this rule. The Federal 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 assistance, development and administration of a remediation

grant and loan or incentive program (excluding the actual grant or loan funds), or other regulatory or

nonregulatory measures.

Additional funding is available from other programs administered by EPA or other Federal

agencies. These include EPA’s DWSRF, the U.S. Department 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 State, local, and Tribal governments of Federal

mandates that are not accompanied by adequate Federal funding. Section 202 of UMRA requires an

analysis of possible disproportionate budgetary effects of certain classes of rules, in which Stage 2 DBPR

falls.7

 Such an analysis is required if EPA determines 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 $ 1,246.50 1,530.79 $ 762.76 $ 620.66 $ 78.80 $ 76.81 $ 

Alternative A11 $ 1,126.39 1,376.60 $ 685.87 $ 560.80 $ 254.14 $ 241.81 $ 

Alternative A2 $ 4,227.19 5,167.40 $ 2,574.59 $ 2,104.60 $ 421.71 $ 406.45 $ 

Alternative A3 $ 5,832.37 7,129.61 $ 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 Alternative 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 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 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 interpret 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 national data and then disaggregate those data, when

possible, using other measures. Because the data and estimates are national in scope, parameters tend to

be merely proportional extensions based on a characteristic, 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 impacts of the rule alternatives on various regions. Local

conditions at each regulated entity will drive the actual 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 disinfectant other than UV, will incur some 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 have disproportionate budgetary effects on certain

geographic regions. Higher TOC levels in source water 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 located in the State. Other areas with high levels of

precursors, such as TOC or bromide, will also be adversely affected. However, those systems with high

precursor and DBP levels are also the ones most likely to receive the greatest benefit from the rule.

State, Local, or Tribal Governments

There is no expectation that there will be disproportionate budgetary effects upon State, local, or

Tribal governments. Costs are expected to be proportional to the risk posed by DBPs, even if unevenly

distributed among systems and perhaps types of systems. 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 issue. Exhibit 8.8 breaks out national-level costs for

PWSs, Tribal costs, and State costs, but only allocates costs to these categories rather 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)

0 D.C.

American Samoa

Guam

N. Mariana Islands

Puerto Rico

21

9

127

441

350 Virgin Islands

2,000 or more (4)

18 Palau

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)

0 D.C.

American Samoa

Guam

N. Mariana Islands

Puerto Rico

21

9

127

441

350 Virgin Islands

2,000 or more (4)

18 Palau

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)

0% D.C.

American Samoa

Guam

N. Mariana Islands

Puerto Rico

<1%

<1%

<1%

1%

1% Virgin Islands

2%

<1% Palau

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)

0% D.C.

American Samoa

Guam

N. Mariana Islands

Puerto Rico

<1%

<1%

<1%

1%

1% Virgin Islands

2%

<1% Palau

Exhibit 8.11b Percent of Small Disinfecting Systems by State

Source: Derived from SDWIS 4th 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. Large 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 this exhibit, Tribal system costs are apportioned 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 Exhibit 8.13 indicate that publically owned CWSs are

expected to incur greater costs than privately owned CWSs. However, costs to individual public and

private water systems will not differ substantially. 

Discount rates for capital costs of private systems, 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. Since the water industry is regulated, rates of public and

private systems are monitored and are not likely to fluctuate substantially over time. However, the rates

can be adjusted to help a system qualify for a low-cost 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). Macroeconomic effects tend to be measurable in

nationwide econometric models only if the economic impact 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. The 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, respectively. Using these annualized figures as a measure, the

annual cost of the Stage 2 DBPR is an insignificant 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 governments, 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 significant Federal intergovernmental mandates. The plan

must also inform, educate, and advise small 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 substantial 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 variety of stakeholders, including small governments, had

the opportunity for timely and meaningful participation 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 governments were involved in the development of

the Agreement in Principle, which was created early 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 concisely describe various aspects and requirements of the

Stage 2 DBPR. Additional details on Tribal involvement 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 considered 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 alternatives. EPA chose the Preferred Regulatory

Alternative because it provided substantial benefits at an acceptable level of costs. In addition, the FACA

Committee recommended the Preferred Regulatory Alternative 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 the Stage 2 DBPR, EPA conducted an analysis on small

government impacts and included small government officials 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 regulatory 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 public stakeholder meetings. Through participation

and exchange in the SBREFA process and various meetings, 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 governments. 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 annual 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), requires 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 that is not required by statute, unless the Federal

government provides the funds necessary to pay the direct compliance costs incurred by Tribal

governments, or EPA consults with Tribal officials early in the process of developing the proposed

regulation and develops a Tribal summary impact statement. 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 approximately $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 summary 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 the 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 requested that the All Indian Pueblo Council (AIPC)

representative be the FACA representative for Federal 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 16th Annual Consumer Conference of the National 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 Board’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/Inter-Tribal Council of Arizona meeting. At the

first two conferences, an EPA representative conducted 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 requirements 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 also worked through its Regional Indian Coordinators

and the National Tribal Operations Committee to mail 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 requested more information and expressed concern

about having to implement too many regulations. They 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. Twelve Tribal representatives and four regional Tribal

Program Coordinators attended the teleconference, requested further explanation of the rule, and

expressed concerns about funding sources. Tribes also called EPA after the teleconference to provide

additional feedback.

In the spirit of Executive Order 13175, and consistent with EPA’s policy to promote

communications between EPA and Tribal governments, 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 Management 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 DBPR 8-41 

Percent Number Percent Number Percent Number Percent Number Percent Numbe

A B C = B * A D E = D * A F G = F * A H I = H * A J K = J * 

Primarily Surface Water CWSs

100 19 100% 19 62% 12 0% 0 0% 0 62% 

100-499 46 100% 46 62% 29 0% 0 0% 0 62% 2500-999 13 100% 13 94% 12 0% 0 1% 0 100% 1,000-3,299 14 100% 14 94% 13 0% 0 1% 0 100% 3,300-9,999 5 100% 5 92% 5 66% 3 3% 100% 10,000-49,999 0 NA NA NA NA NA NA NA NA NA N

50,000-99,999 0 NA NA NA NA NA NA NA NA NA N

100,000-999,999 0 NA NA NA NA NA NA NA NA NA N

> 1 Million 0 NA NA NA NA NA NA NA NA NA N

National Subtotal 97 97 71 3 0 

Primarily Disinfecting Ground Water CWSs

100 149 100% 149 4% 6 0% 0 0% 0 4% 

100-499 233 100% 233 4% 10 0% 0 0% 0 4% 500-999 78 100% 78 18% 14 56% 44 0% 0 100% 1,000-3,299 71 100% 71 18% 13 56% 40 0% 0 100% 3,300-9,999 21 100% 21 18% 4 56% 12 0% 0 100% 210,000-49,999 4 100% 4 18% 1 57% 2 0% 0 100% 50,000-99,999 0 NA NA NA NA NA NA NA NA NA N

100,000-999,999 0 NA NA NA NA NA NA NA NA NA N

> 1 Million 0 NA NA NA NA NA NA NA NA NA N

National Subtotal 556 556 47 97 0 

Systems Conductin

Stage 2 Monitorin

Plans

System Size/Type

Number of 

Tribal 

Systems 

Affected by 

the Stage 2 

DBPR

Systems Conducting 

Implementation 

Activities

Systems Conducting 

IDSE Monitoring

Systems Conducting 

Additional Routine 

Monitoring

Systems Conducting 

Significant Excursion 

Activities

Exhibit 8.14 Annual Cost of Compliance for Tribal Systems by System Type and Size (Annual
Final Economic Analysis for the Stage 2 DBPR 8-42 

Percent Number Percent Number Percent Number Percent Number Percent Numbe

A B C = B * A D E = D * A F G = F * A H I = H * A J K = J * 

Primarily Surface Water NTNCWSs

100 2 100% 2 0% 0 0% 0 0% 0 0% 

100-499 1 100% 1 0% 0 0% 0 0% 0 0% 

500-999 0 NA NA NA NA NA NA NA NA NA N

1,000-3,299 1 100% 1 0% 0 0% 0 0% 0 0% 

3,300-9,999 0 NA NA NA NA NA NA NA NA NA N

10,000-49,999 0 NA NA NA NA NA NA NA NA NA N

50,000-99,999 0 NA NA NA NA NA NA NA NA NA N

100,000-999,999 0 NA NA NA NA NA NA NA NA NA N

> 1 Million 0 NA NA NA NA NA NA NA NA NA N

National Subtotal 4 4 0 0 0 

Primarily Disinfecting Ground Water NTNCWSs

100 31 100% 31 0% 0 0% 0 0% 0 0% 

100-499 28 100% 28 0% 0 0% 0 0% 0 0% 

500-999 13 100% 13 0% 0 100% 13 0% 0 0% 

1,000-3,299 24 100% 24 0% 0 100% 24 0% 0 0% 

3,300-9,999 2 100% 2 0% 0 100% 2 0% 0 0% 

10,000-49,999 0 NA NA NA NA NA NA NA NA NA N

50,000-99,999 0 NA NA NA NA NA NA NA NA NA N

100,000-999,999 0 NA NA NA NA NA NA NA NA NA N

> 1 Million 0 NA NA NA NA NA NA NA NA NA N

National Subtotal 98 98 0 39 0 

TOTALS 755 755 118 140 0 

System Size/Type

Number of 

Tribal 

Systems 

Affected by 

the Stage 2 

DBPR

Systems Conducting 

Implementation 

Activities

Systems Conducting 

IDSE Monitoring

Systems Conducting 

Additional Routine 

Monitoring

Systems Conducting 

Significant Excursion 

Activities

Systems Conductin

Stage 2 Monitorin

Plans

Exhibit 8.14 Annual Cost of Compliance for Tribal Systems by System Type and Size (Annual

(Continued)

Sources: (A) Number of Indian Lands from SDWIS 4th Quarter FY2003 data.

(B, D, F, and H) Derived from Exhibit H.12.(J) Mean costs are total annualized costs (at 3 percent) (Exhibits J.2ba, J.2be, J.2bi, J.2bm) divided by the number of prima

surface water CWSs or NTNCWSs in the 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 considered risks to sensitive subpopulations (including

fetuses, infants, and children) when establishing 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 result 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 waterborne 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 naturally 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 subpopulations, 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 factor of 10, which accounts for human variability,

including sensitive subpopulations like children. The Agency 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 pregnant women. It should be noted that the

LT2ESWTR, which accompanies this rule, reduces pathogens in drinking water and further protects

sensitive subpopulations.

SDWA identifies pregnant women as a sensitive subpopulation. Epidemiological and

toxicological research suggests a potential association 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 implementation 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 Environmental Health Risks and Safety Risks

Executive Order 13045 (62 FR 19885 April 23, 1997) applies to any rule initiated after April 21,

1998, that (1) is determined to be “economically significant” as defined under Executive Order 12866;

and (2) concerns an environmental, health, or safety risk that EPA has reason to believe may have a

disproportionate effect on children. If the regulatory action meets both criteria, EPA must evaluate the

environmental health or safety effects of the planned rule on children, and explain why the planned

regulation is preferable to other potentially effective and reasonably feasible alternatives considered by

the Agency.

The Stage 2 DBPR is not subject to the Executive Order because it is not economically significant

as defined in Executive Order 12866. EPA has consistently and explicitly considered risks to infants and

children in all assessments developed for this rulemaking 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 values are protective for fetuses and children.

The Agency concluded that the Stage 2 DBPR will result in greater risk reduction for children

than for the general population. The MCLGs of all DBPs 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 missions by directing agencies to identify and address

disproportionately high and adverse human health or environmental effects of its programs, policies, and

activities on minority and low-income populations. The 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 the 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 during 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 water regulations and how they might impact sensitive subpopulations,

minority populations, and low-income populations. This 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 benefits, data quality, health effects, and the regulatory

process. Participants were national, State, Tribal, municipal, and individual stakeholders. EPA

conducted the meeting by video conference call between 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 regulations 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 provide a similar level of drinking water protection to

all groups. Where water systems have high DBP levels, they must reduce levels to meet the MCLs. 

Further, to the extent that DBP levels in drinking 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 intent 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 chemically disinfected. Consequently, the health protection

from DBP exposure that this rule provides is equal 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 timely input by State and local officials in the

development of regulatory policies that have federalism implications.” “Policies that have federalism

implications” are defined in the executive order to include 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 Stage 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 the 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 anticipated 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 consulted 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 consistent 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 “significant 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 DBPR 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 systems 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 significant 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 significantly 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 utilities 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 DBPR 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 adequate and that the Stage 2 DBPR has no discernable

adverse effect on energy distribution.

Energy Use

The third consideration is whether the Stage 2 DBPR 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 requires further evaluation. The analyses that underlay

the estimation of costs in Chapter 7 for the Stage 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 the 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 that to the national levels of power generation in

terms of average and peak loads.

The first step in the analysis is to estimate the 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 and
8 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 cost of electricity of $0.076 per kilowatt hour per year

(kWh/y) (U.S. DOE EIA 2004a8

). 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 total. 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 chloramines 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 compared 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

2004b9

). 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.) 1,764 1,971 3,476,461 

Chlorine Dioxide 3,578 43 154,809 

UV 34,588 912 31,545,906 

Ozone (0.5 log) 98,182 19 1,890,156 

GAC10 + Adv. Disinfectants 835,330 42 35,321,879 

GAC20 162,411 100 16,264,624 

GAC20 + Adv. Disinfectants 132,168 61 8,001,027 

Membranes 1,103,656 18 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)

Average Energy Usage per 

Plant per Year for all 

Plants Selecting the 

Technology

(kWh/plant/yr)

Number of Plants 

Selecting the 

Technology

Technology

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 selecting each treatment technology is derived from Exhibits 5.11b, 5.11d, 5.14b,

5.14d. Note that the number of plants selecting chloramines is the number of plants selecting chloramines

only PLUS the number selecting chloramines with advanced treatment technology (making the total in this

exhibit higher than the total number of plants making 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 Exhibit 7.8. Energy costs were converted 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 weighted 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 74 8 4,550 59,875 454,475 107.80

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 plants are taken from Exhibit 3.2.

(C) Numbers of plants selecting chloramines are taken from Exhibits 5.11b, 5.11d, 5.14b, 5.14d.

(D) The electricity cost per plant is taken from the Technologies and Costs Document (USEPA 2005n).

(E) Electricity cost is $0.076/kWh, as presented in the Technologies 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 significantly affect the capacity margins of energy

suppliers, their peak-season generating capacity reserve was compared to an estimate of peak incremental
10 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 and 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 approximately 160,000 megawatts (MW) (USDOE EIA

2004b10). 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 water 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 technologies 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 analysis represents the effect of the rule on national

energy supply, distribution, and use. While certain 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 Affect 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 annual average use and conditions of peak power demand).

The total increase in energy usage by water systems 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 and comparison of the benefits and costs of the Stage 2

Disinfectants and Disinfection Byproducts Rule (DBPR). 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 analysis (EA), followed by a discussion of the results. 

The first sections focus on analysis of the Stage 2 DBPR 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 chapters. The remaining sections of this chapter are

organized as follows:

9.2 Summary of National Benefits, Costs and Net 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, benefits 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 reduction 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, and 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 systems 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 willingness 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 Alternative 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 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.

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 few years, before systems have installed treatment, no

benefits are realized, although some costs are incurred 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 installed, 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 presented 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 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

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 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

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 evaluation of the health effects data suggests a potential

association between DBP exposure and various adverse reproductive and developmental outcomes. To

help inform the assessment of the Stage 2 DBPR benefits, 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 site (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 provide 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, the 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 potentially avoided cases of colon and rectal cancers in a

sensitivity analysis (section 3.0 of Appendix N). 

Other nonquantified health and non-health benefits derived from rule implementation also could

contribute to the overall value of benefits. Nonquantified 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 association between consumption of DBPs in drinking

water and bladder cancer and therefore the number of bladder cancer cases avoided could be as low as zero.

2

Because specific estimates of WTP for avoiding non-fatal 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 benefits 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 Other1

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 from

reduction 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 fetal loss and lost opportunities for family

life. For patients of cancer or other diseases potentially induced by DBP exposure this includes opportunity costs in

reduced work opportunities and reduced participation in social and family life. Adverse non-health effect costs

potentially include reduced enjoyment 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 cancer2

 and the value of a

statistical life (VSL) for fatal cancer cases. Exhibits 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 indicator3

 for reduced levels of all chlorination 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 Avoidable1

Annual Average Avoidable Over 25 Years2

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 the 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 treatment 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 prior to full installation of treatment technology changes
4

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 based 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 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 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 year4

. 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; they 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)

Lower

(5th %ile)

Upper

(95th %ile)

Lower

(5th %ile)

Upper

(95th %ile)

$ 233 1,531 $ 3,536 $ 763 $ 165 $ 1,692 $ 

$ 190 1,246 $ 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 (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.

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 DBPR result from activities associated with rule

implementation, IDSEs, monitoring plans, additional 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 technology changes, together with uncertainty in the

estimates of capital and O&M unit costs, contribute to the 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 occur 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 

Exhibit 9.5a Annualized Costs for Stage 2 DBPR Preferred Regulatory Alternative Rule 

($Millions/Year, 3% Discount Rate)

Total System Costs

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Implementation IDSE Monitoring Plans Moni- toring Significant Excursion 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 $16.1> 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 $50.9

< 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 $1.2> 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 $0.1< 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 $18.2> 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 $11.9< 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 $1.9> 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 $0.0

TOTAL $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 $100.5

Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecasts and unit treatment cosSources: Exhibit 7.5a

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.2asNotes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator.

Surface Water CWSsSurface Water NTNCWSs

90 Percent 

Confidence Bound

Capital Costs O&M Costs

Mean

Value

System Costs

90 Percent

Confidence Bound

Ground Water NTNCWSs

System Size 

(Population 

Served)

Non-Treatment Costs 

(Point Estimate)

Mean

Value

Mean

Value

90 Percent

Confidence Bound

Ground Water CWSs
Final Economic Analysis for the Stage 2 DBPR 9-10 

Exhibit 9.5b Annualized Costs for Stage 2 DBPR Preferred Regulatory Alternative Rule 

($Millions/Year, 7 Percent Discount Rate)

Total System Costs

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Lower

(5th %tile) 

 Upper

(95th 

%tile) 

 Implementation IDSE Monitoring Plans Moni- toring Significant Excursion Lower (5th %tile) 

 Uppe

(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 $15.0> 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 $51.0

< 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.> 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.

< 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 $17.2> 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 $11.3< 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 $1.7> 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 $0.0TOTAL $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 $97.6

Notes: Detail may not add due to independent rounding. 90 percent confidence bounds reflect uncertainty in technology compliance forecasts and unit treatment coSources: Exhibit 7.5b

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.2awNotes: Values are discounted and annualized in 2003$. Based on TTHM as an indicator.

Ground Water NTNCWSs

System Size 

(Population 

Served)

Non-Treatment Costs 

(Point Estimate)

Mean

Value

Mean

Value

90 Percent

Confidence Bound

Surface Water CWSs

Capital Costs O&M Costs

Mean

Value

System Costs

90 Percent

Confidence Bound

Ground Water CWSsSurface Water NTNCWSs

90 Percent 

Confidence Boun
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 estimated value of avoided cases of bladder cancer

resulting from the Stage 2 DBPR and the estimated costs 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). Exhibit 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 Alternative 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 “breakeven 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 annualized 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 cost estimate is approximately 30 percent larger

than the estimated mean cost; the lower bound on the benefits 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 provides 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 avoided 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 Preferred Regulatory Alternative is 279; the lower 5th

percentile and upper 95th percentile bounds are 103 and 541 cases, respectively. Using a 3 percent

discount rate, the mean annual number of bladder cancer 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 (5th 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 (95th 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 Regulatory 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-quantified benefits (e.g., reduction 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 achieved by a reduction in developmental and reproductive risks.

Exhibit 9.6 Annualized Mean Net Benefits for the Stage 2 Preferred Regulatory

Alternative ($Millions)

WTP for NonFatal Bladder 

Cancer Cases

Mean 

Benefits

Mean 

Costs

Mean Net 

Benefits

Lymphoma $ 79 1,531 $ 1,452 $ 

Bronchitis $ 79 763 $ 684 $ 

Lymphoma $ 77 1,246 $ 1,170 $ 

Bronchitis $ 77 621 $ 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)

$ 233 1,531 $ 3,536 $ $ 56 79 $ 102 $ 15% 130%

$ 190 1,246 $ 2,878 $ $ 55 77 $ 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 13 18 23 17 12 22 

Bronchitis 70 99 128 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 concentrations, and 

in cessation lag. The 90 percent confidence bounds around the estimates of break even cases result from the 90 percent confidence bounds 

around the cost estimates. 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.

 541

Breakeven Cases 

(at 3 Percent, 25 Years)

Mean Mean

WTP for NonFatal 

Bladder 

Cancer 

Mean Cases

Breakeven Cases

Average Annual Avoided Cases (at 7 Percent, 25 Years)

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"

 103 279 
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 convened under the Federal Advisory Committees Act

(FACA). The FACA process narrowed hundreds of regulatory 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 Regulatory 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 the 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 Alternative 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. Exhibit 9.9 presents percent reductions in DBPs for the

Stage 2 DBPR regulatory alternatives. These reductions are calculated as the average of annual plant

means at the point representing average residence time 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%

Regulatory 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 quantified and monetized annualized benefits for each

alternative considered. The average annual number 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 following completion of the cessation lag period. The

benefits derive from PWS treatment adjustments and a predicted subsequent reduction 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 rule include costs for rule implementation, IDSEs,

monitoring plans, additional routine monitoring, operational evaluations, and changes in treatment

technologies by some water systems; these cost estimates include costs for PWSs and the State agencies.

 

The regulatory alternatives are arranged in Exhibits 9.10 and 9.11 in order of increasing cost from

left to right and top to bottom, respectively. Exhibit 9.10 shows that the Preferred Regulatory Alternative

avoids more cases of bladder cancer than the next least expensive alternative (Alternative 1). This is

because incorporating the IDSE, which increases benefits, is only considered under the Preferred

Alternative (explained further in Chapter 4). Additionally, 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 Byproducts (M-DBP) Advisory Committee did not favor

alternative A1 because they were concerned that lowering the bromate level to 5 μg/L could have adverse

effects on microbial protection (see Chapter 4 for a full 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 alternatives A2 and A3. Exhibit 9.11 shows that the costs

of the Preferred Regulatory Alternative are far less than 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 11 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 percent confidence bounds for cases avoided reflect uncertainty 

in PAR, reduction in average TTHM and HAA5 concentrations, and cessation 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. 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. 

Footnotes: 1) Alternative 1 has lower benefits than the Preferred Alternative because it does not incorporate the IDSE. 

Additionally, benefits of Alternative 1 would be higher if the value of reduced bromate was quantified. 2) The estimate of 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. Estimates of ultimately 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

Discount Rate, Regulatory Alternative

WTP for NonFatal Cases

Average Annual Number of 

Cases Avoided2 
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 difference 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 lymphoma and bronchitis as a WTP surrogate for bladder

cancer. Accounting for the unquantified benefits that may be achieved through reduction of

developmental and reproductive health risks would raise 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 should 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 rates, the Preferred Regulatory Alternative achieves more net

benefits than Alternative 1, while Alternatives 2 and 3 achieve more net benefits than the Preferred

Regulatory Alternative. However, 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.

Incremental Net Benefits

The goal in comparing incremental net benefits is generally to identify the regulatory alternative,

in a series of increasingly stringent alternatives, having marginal net benefits that come the closest to

zero while still being positive. Protection to the level of this alternative captures the most benefits

possible among the alternatives without experiencing diminishing 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 Alternative 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

Value

Discounted at 3%, 25 

Years

Detail may not add due to independent rounding. The 90 percent confidence bounds reflect uncertainty in compliance forecasts and 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

Value

Mean

Value

90 Percent

Confidence 

Bound

Mean

Value
5

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 9-18 December 2005

Alternative in this analysis5 (costs more but produces fewer benefits) 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 proportion to cost as compared to Alternatives 2 and 3. 

Exhibit 9.12 Comparison of Mean Net Benefits for All Regulatory Alternatives

($Millions)

3% 7%

Preferred 1,452 $ 1,170 $ 

A11 $ 885 1,122 $ 

A2 4,746 $ 3,821 $ 

A3 6,495 $ 5,219 $ 

Preferred $ 684 $ 544 

A11 $ 319 432 $ 

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 for All Regulatory Alternatives ($ Millions)

Annual 

Costs

Annual 

Benefits

Incremental 

Costs

Incremental 

Benefits

Incremental 

Net Benefits

A B C D E=D-C

Preferred $ 1,531 79 $ 79 $ 1,531 $ 1,452 $ 

Alternative 11 $ 1,377 254 $ 

Alternative 2 $ 5,167 422 $ 343 $ 3,637 $ 3,294 $ 

Alternative 3 $ 7,130 634 $ 212 $ 1,962 $ 1,750 $ 

Preferred $ 763 79 $ 79 $ 763 $ 684 $ 

Alternative 11 $ 686 254 $ 

Alternative 2 $ 2,575 422 $ 343 $ 1,812 $ 1,469 $ 

Alternative 3 $ 3,552 634 $ 212 $ 978 $ 765 $ 

Preferred $ 1,246 77 $ 77 $ 1,246 $ 1,170 $ 

Alternative 11 $ 1,126 242 $ 

Alternative 2 $ 4,227 406 $ 330 $ 2,981 $ 2,651 $ 

Alternative 3 $ 5,832 613 $ 207 $ 1,605 $ 1,399 $ 

Preferred $ 621 77 $ 77 $ 621 $ 544 $ 

Alternative 11 $ 561 242 $ 

Alternative 2 $ 2,105 406 $ 330 $ 1,484 $ 1,154 $ 

Alternative 3 $ 2,904 613 $ 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. 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. This means that Alternative 1 is dominated by the Preferred 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 effectiveness 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 that 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 values 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 period 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 would 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 increase the threshold used in this analysis; therefore

annualized WTP values are not calculated because the Preferred 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 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 9-20 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 level 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 evaluated in terms of the cost per case of bladder cancer

avoided (fatal and non-fatal) reviewed 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 benefits comparable to the discounted costs. OMB

Circular A-4 provides that “there is professional consensus 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 based 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 cancer. 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 bronchitis 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 than the WTP for chronic bronchitis. Alternative 1 is

not competitive for reasons explained previously.7
8

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 Alternative 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 alternative 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 has 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 comparisons 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 cost is less than the WTP values to avoid the risk

(mean of $4.49 million and $0.80 million for lymphoma and 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 Exhibit 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 Alternative and Alternative 2 are also cost effective

compared to the lowest threshold (WTP to avoid chronic 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 all of the benefits of implementing any reasonable

regulation over maintaining the status quo, captures the largest portion of benefits and has a

lower"incremental" cost than 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 comparison among the alternatives (setting aside Alternative

1, for reasons explained previously), the Preferred 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 Avoided1 for All Regulatory Alternatives

by Discount Rate ($Millions)

Preferred $ 0.41 0.33 $ 0.33 $ 0.41 $ 

Alternative 1 $ 1.42 1.18 $ 

Alternative 2 $ 0.63 0.52 $ 0.60 $ 0.73 $ 

Alternative 3 $ 0.69 0.57 $ 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 

Rule Per Case Avoided 

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. Exhibit 9.15 shows that each regulatory alternative of the Stage

2 DBPR has a benefit cost ratio greater than 1. However, as in the case of the CEA ratio, the information

provided by the benefit cost ratios does not allow for 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 Estimation of Net National Benefits

Detailed discussions of the assumptions and uncertainties 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 listing of assumptions for which there is uncertainty.

EPA is aware that there is uncertainty in the prediction 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 11 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 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. 

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 uniform distribution between SWAT and ICR Matrix

Method results. That is, the national cost estimates 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. Benefits estimates using the SWAT model are about 30

percent lower than the midpoint estimate, while those 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 treatment technology installed and the level of costs

incurred and benefits achieved by the rule. The 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 magnitude of this uncertainty, EPA conducted a sensitivity

analysis to compare the predicted percentage of plants changing technology and 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 treatment technology. This implies that uncertainty in the

IDSE will have a larger impact on the national benefits 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 DBP 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

UnderestimateOverestimate

Unknown

Impact

UnderestimateOverestimate

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

UnderestimateOverestimate

Unknown

Impact

UnderestimateOverestimate

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 potentially understated because it does not include the

benefits for reductions in adverse 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. Alternatives 2 and 3 have a greater net benefit than

the Preferred Alternative. However, net benefits do not include the unquantified benefits. 

Because the Preferred Alternative uses a risk targeting strategy, unquantified 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 Preferred Regulatory Alternative shows it to be the

most cost effective according to the measures evaluated (Exhibit 9.14). 

5) The number of cases of cancer that must be avoided to break even with the cost of the

Preferred Alternative rule are approximately 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, respectively. 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 believes that the annualized benefits of the Stage 2

DBPR will likely exceed the annualized national costs 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

10. References

American Cancer Society (ACS) Website. 2004. What are the Key Statistics for Bladder Cancer? Cancer

Reference Information. http://www.cancer.org/. Accessed 2004.

American Cancer Society Website. 2005. Overview: Colon and Rectum Cancer. How Many People Get

Colorectal Cancer? 

http://www.cancer.org/docroot/CRI/content/CRI_2_2_1X_How_Many_People_Get_Colorectal_

Cancer.asp?sitearea=

American Water Works Association (AWWA). 2000. WATER:\STATS. Database containing 1996

AWWA survey of water systems. AWWA: Denver, CO. June, 2000.

Amy, G., M. Siddiqui, K. Ozelcin, H.W. Zhu, C.Wang. 1998. Empirically based models for predicting

chlorination and ozonation by-products: haloacetic acids, chloralhydrate, and bromate. EPA

Report CX 819579.

Arbuckle, T.E., S.E. Hrudey, S.W. Krasner, J.R. Nuckols, S.D. Richardson, P. Singer, P. Mendola, L.

Dodds, C. Weisel, D.L. Ashley, K.L. Froese, R.A. Pegram, I.R. Schultz, J. Reif, A.M. Bachand,

F.M. Benoit, M. Lynberg, C. Poole, and K. Waller. 2002. Assessing exposure in epidemiologic

studies to disinfection by-products in drinking water: report from an international workshop.

Environmental Health Perspectives. 110(Suppl. 1):53-60.

Aschengrau, A., S. Zierler, and A. Cohen. 1989. Quality of Community Drinking Water and the

Occurrence of Spontaneous Abortions. Archives of Environmental Health. 44:283-290.

Aschengrau, A, Zierler, S., and Cohen, A. 1993. Quality of Community Drinking Water and the

Occurrence of Late Adverse Pregnancy Outcomes. Archives of Environmental Health. 48:105-

113.

Association of State Drinking Water Administrators (ASDWA). 2001. ASDWA Needs Analysis.

Backer, L.C., D.L. Ashley, M.A. Bonin, F.L. Cardinali, S.M. Kieszak, and J.V. Wooten. 2000. 

Household Exposures to Drinking Water Disinfection By-products: Whole Blood Trihalomethane

Levels. Journal of Exposure Analysis and Environmental Epidemiology. 10(4):321-326.

Ballester, N.A., and J.P. Malley. 2004. Journal AWWA - Sequential Disinfection of Adenovirus Type 2

with UV-Chlorine-Chloramine. American Water Works Association, October, 2004. p. 97.

Barrett S., C. Hwang, Y. Guo, S.A. Andrews, and R. Valentine. 2003. Occurrence of NDMA in

Drinking Water: A North American Survey, 2001- 2002. Proceedings of 2003 AWWA Annual

Conference, Anaheim, CA.

Batterman, S., A.T. Huang, S.G. Wang, and L. Zhang. 2000. Reduction of ingestion exposure to

trihalomethanes due to volatilization. Environmental Science and Technology. 34(20):4418-

4424.

Bean, J.A., P. Isacson, W.J. Hausler, Jr., and J. Kohler. 1982. Drinking water and cancer incidence in

Iowa. I. Trends and incidence by source of drinking water and size of municipality. American

Journal of Epidemiology. 116(6):912-923.
Final Economic Analysis for the Stage 2 DBPR R-2 December 2005

Bielmeier, S.R., D.S. Best, D.L. Guidici, and M.G. Narotsky. 2001. Pregnancy Loss in the Rat Caused

by Bromodichloromethane. Toxicological Sciences. 59(2):309-315.

Bielmeier, S.R., D.S. Best, and M.G. Narotsky. 2004. Serum Hormone Characterization and Exogenous

Hormone Rescue of Bromodicholoromethane-Induced Pregnancy Loss in the F344 Rat. 

Toxicological Sciences. 77(1):101-108.

Boardman, Anthony E., David H. Greenberg, Aidan R. 1996. Vining and David L. Weimer, Cost-Benefit

Analysis Concepts and Practice. p. 405.

Bove, F.J., M.C. Fulcomer, J.B. Klotz, J. Esmart, E.M. Dufficy, R. Zagraniski, and J.E. Savrin. 1992a. 

Public Drinking Water Contamination and Birthweight, Fetal Deaths, and Birth Defects: A CrossSectional Study (Phase IV-A). New Jersey Department of Health. April 1992.

Bove, F.J., M.C. Fulcomer, J.B. Klotz, J. Esmart, E.M. Dufficy, R. Zagraniski, and J.E. Savrin. 1992b. 

Public Drinking Water Contamination and Birthweight, and Selected Birth Defects: A CaseControl Study (Phase IV-B). New Jersey Department of Health. May 1992.

Bove, F.J., M.C. Fulcomer, J.B. Klotz, J. Esmart, E.M. Dufficy, and J.E. Savrin. 1995. Public Drinking

Water Contamination and Birth Outcomes. American Journal of Epidemiology. 141(9):850-862.

Bove F.J., Y. Shim, and P. Zeitz. 2002. Drinking Water Contaminants and Adverse Pregnancy

Outcomes: A Review. Environmental Health Perspectives. 110(Suppl. 1):61-74.

Bravata, D.M. and I. Olkin. 2001. Simple pooling versus combining in meta-analysis. Evaluation and

the Health Professions. 24(2):218-230.

Cantor, K.P., R. Hoover, and P. Hartge. 1985. Drinking Water Source and Bladder Cancer: A CaseControl Study. In: Water Chlorination: Chemistry, Environmental Impact and Health Effects,

vol. 5, R.L. Jolley, R.J. Bull, and W.P. Davis (eds.). 1:145-152. Chelsea, MI: Lewis Publishers,

Inc.

Cantor, K.P., R. Hoover, P. Hartge, T.J. Mason, D.T. Silverman, R. Altman, D.F. Austin, M.A. Child,

C.R. Key, L.D. Marrett, M.H. Myers, A.S. Narayana, L.I. Levin, J.W. Sullivan, G.M. Swanson,

D.B. Thomas, and D.W. West. 1987. Bladder Cancer, Drinking Water Source, and Tap Water

Consumption: A Case-Control Study. Journal of the National Cancer Institute. 79(6):1269-1279.

Cantor, K.P., C.F. Lunch, M. Hildesheim, M. Dosemeci, J. Lubin, M. Alavanja, and G.F. Craun. 1998. 

Drinking Water Source and Chlorination Byproducts I. Risk of Bladder Cancer. Epidemiology. 

9(1):21-28.

Cantor, K.P, C.F. Lynch, M.E. Hildesheim, M. Dosemeci, J. Lubin, M. Alavanja, and G. Craun. 1999. 

Drinking water source and chlorination byproducts in Iowa. III. Risk of brain cancer. American

Journal of Epidemiology. 150(6):552-560.

Cedergren, M.I., A.J. Selbing, O. Lofman, and B.A.J. Källén. 2002. Chlorination byproducts and nitrate

in drinking water and risk for congenital cardiac defects. Environmental Research. 

89(2):124-130.

CDC. 1995. Economic Costs of Birth Defects and Cerebral Palsy–United States, 1992. 

http://www.cdc.gov/mmwr/preview/mmwrhtml/00038946.htm. 
Final Economic Analysis for the Stage 2 DBPR R-3 December 2005

CDC. 2005. Birthweight and Gestation Fact Sheet for 2002. 

http://www.cdc.gov/nchs/fastats/birthwt.htm

Chen, C.W., and H. Gibb. 2003. Procedures for calculating cessation lag. Regulatory Toxicology and

Pharmacology. 38(2):157-65.

Chen, J., G.C. Douglas, T.L. Thirkill, P.N. Lohstroh, S.R. Bielmeir, M.G. Narotsky, D.S. Best, R.A.

Harrison, K. Natarajan, R.A. Pegram, J.W. Overstreet, and B.L. Lasley. 2003. Effect of

bromodichloromethane on chorionic gonadotropin secretion by human placental trophoblast

cultures. Toxicological Sciences. 76(1):75-82.

Chen, J., T.L. Thirkill, P.N. Lohstroh, S.R. Bielmeir, M.G. Narotsky, D.S. Best, R.A. Harrison, K.

Natarajan, R.A. Pegram, J.W. Overstreet, B. L. Lasley, and G.C. Douglas. 2004. 

Bromodichloromethane inhibits human placental trophoblast differentiation. Toxicological

Sciences. 78(1):166-174.

Chevrier, C., B. Junod, and S. Cordier. 2004. Does ozonation of drinking water reduce the risk of

bladder cancer? Epidemiology. 15(5):605-614.

Christian, M.S., R.G. York, A.M. Hoberman, R.M. Diener, and L.C. Fisher. 2001. Oral (drinking water)

developmental toxicity studies of bromodichloromethane (BDCM) in rats and rabbits. 

International Journal of Toxicology. 20(4):225-237.

Christian M.S., R.G. York, A.M. Hoberman, R.M. Diener, and L.C. Fisher. 2002a. Oral (drinking water)

Two Generation Reproductive Toxicity Study of Bromodichloromethane (BDCM) in Rats. 

International Journal of Toxicology. 21(2):115-146.

Christian, M.S., R.G. York, A.M. Hoberman, L.C. Frazee, L.C. Fisher, W.R. Brown, and D.M. Creasy.

2002b. Oral (drinking water) Two Generation Reproductive Toxicity Study of Dibromoacetic

Acid (DBA) in Rats. International Journal of Toxicology. 21(4):237-76. 

Clark, R.M., J.Q. Adams, and B.W. Lykins. 1994. DBP Control in Drinking Water: Cost and

Performance. Journal of Environmental Engineering. 120(4):759-782.

Cohen, S.M., M. Cano, T. Sakata, and S.L. Johansson. 1988. Ultrastructural characteristics of the fetal

and neonatal rat urinary bladder. Journal of Scanning Micros. 2:2091-2104.

Cohen, S.M., and S.L. Johansson. 1992. Epidemiology and Etiology of Bladder Cancer. Urologic

Clinics of North America. 19(3): 421-428.

Cohen, S.M., T. Shirai, and G. Steineck. 2000. Epidemiology and etiology of premalignant and

malignant urothelial changes. Scandinavian Journal of Urology and Nephrology. 

Suppl.(205):105-15.

Cordier, S., J. Clavel, J.C. Limasset, L. Boccon-Gibod, N. Le Moual, L. Mandereau, and D. Hemon. 

1993. Occupational risks of bladder cancer in France: a multicentre case-control study. 

International Journal of Epidemiology. 22(3):403-11.

Corley, R.A., S.M. Gordon, and L.A. Wallace. 2000. Physiologically Based Pharmacokinetic Modeling

of the Temperature-dependent Dermal Absorption of Chloroform by Humans Following Bath

Water Exposures. Toxicological Science. 53(1):13-23.
Final Economic Analysis for the Stage 2 DBPR R-4 December 2005

Cragle, D.L., C.M. Shy, R.J. Struba, and E.J. Siff. 1985. A Case-Control Study of Colon Cancer and

Water Chlorination in North Carolina. In: Water Chlorination: Chemistry, Environmental Impact

and Health Effects, vol. 5. R.L. Jolley, R.J. Bull, and W.P. Davis (eds.). Chelsea, MI: Lewis

Publishers, Inc.

Craun GC, ed. 1998. EPA Panel Report and Recommendations for Conducting Epidemiological

Research on Possible Reproductive and Developmental Effects of Exposure to Disinfected

Drinking Water. U.S. EPA, NHEERL. Research Triangle Park, NC.

DeAngelo, A.B., F.B. Daniel, B.M. Most, and G.R.Olson. 1997. Failure of Monochloroacetic Acid and

Trichloroacetic Acid Administered in the Drinking Water to Produce Liver Cancer in Male

F344/N rats. Journal of Toxicology and Environmental Health. 52(5):425-445.

DeAngelo, A.B., M.H. George, and D.E. House. 1999. Hepatocarcinogenicity in the male B6C3F1

mouse following a lifetime exposure to dichloroacetic acid in the drinking water: dose-response

determination and modes of action. Journal of Toxicology and Environmental Health. 

58(8):485-507.

Do, M.T., N.J. Birkett, K.C. Johnson, D. Krewski, P. Villeneuva, and the Canadian Cancer Registries

Epidemiology Research Group. 2005. Chlorination Disinfection By-products and Pancreatic

Cancer Risk. Environmental Health Perspectives. 113(4):418-424.

Dodds, L., W. King, C. Wolcott, and J. Pole. 1999. Trihalomethanes in public water supplies and

adverse birth outcomes. Epidemiology. 10(3): 233-237.

Dodds, L. and W.D. King. 2001. Relation between trihalomethane compounds and birth defects. Journal

for Occupational and Environmental Medicine. 58(7): 443-46.

Dodds, L., W. King, A.C. Allen, B.A. Armson, D.B. Deshayne, and C. Nimrod. 2004. Trihalomethanes

in public water supplies and risk of stillbirth. Epidemiology. 15(2):179-186.

Dominitz, J.A. and Provenzale, D., “Patient Preferences and Quality of Life Associated with Colorectal

Cancer Screening,” Am J Gastroenterol., 92: 2171-2178, 1997.

Doyle, T.J., W. Sheng, J.R. Cerhan,C.P. Hong, T.A. Sellers, L.H. Kushi, and A.R. Folsom. 1997. The

Association of Drinking Water Source and Chlorination By-Products with Cancer Incidence

Among Postmenopausal Women in Iowa: A Prospective Cohort Study. American Journal of

Public Health. 87(7).

Edwards, M. 1997. Predicting DOC removal during enhanced coagulation. Journal of the American 

Water Works Association. 89(5):78-89.

Fenster, L., K. Waller, G. Windham, T. Henneman, M. Anderson, P. Mendola, J.W. Overstreet and S.H.

Swan. 2003. Trihalomethane levels in home tap water and semen quality. Epidemiology. 

14:650-658.

Freedman, M., K.P. Cantor, N.L. Lee, L.S. Chen, H.H. Lei, C.E. Ruhl, and S.S. Wang. 1997. Bladder

Cancer and Drinking Water: A Population-Based Case Control Study in Washington County,

Maryland. Cancer Causes and Control. 8(5):738-744.

Fukushima, S. and H. Wanibuchi. 2000. Prevention of urinary bladder cancer: The interface between

experimental and human studies. Asian Pacific Journal of Cancer Prevention. 1:15-33.
Final Economic Analysis for the Stage 2 DBPR R-5 December 2005

Gallagher, M.D., J.R. Nuckols, L. Stallones, and D.A. Savitz. 1998. Exposure to trihalomethanes and

adverse pregnancy outcomes. Epidemiology. 9:484-489.

George, M.H., G.R. Olson, D. Doerfler, T. Moore, S. Kilburn, and A.B. DeAngelo. 2002. 

Carcinogenicity of bromodichloromethane administered in drinking water to male F344/N rats

and B6C3F(1) mice. International Journal of Toxicology. 21(3):219-230.

Gerba, C.P., J.B. Rose, and C.N.Haas. 1996. Sensitive Populations: Who is at the Greatest Risk. 

International Journal of Food and Microbiology. 30:113-123.

Goebell, P.J., C.M. Villanueva, and A.W. Rettenmeier. 2004. Environmental exposure, chlorinated

drinking water, and bladder cancer. World Journal of Urology. 21(6):424-432. 

Goetzel, R.Z., S.R. Long, R.J. Ozminkowski, K. Hawkins, S. Wang, and W. Lynch. 2004. Health,

Absence, Disability, and Presenteeism Cost Estimtes of Certain Physical and Mental Health

Conditions Affecting U.S. Employers. J Occup Environ Med. 46 (4): 1-15. April 2004.

Gold, M.R., J.E. Siegel, L.B. Russel, and M.C. Weinstein. 1996. Cost-effectiveness in Health and

Medicine. New York: Oxford University Press.

Gold, M.R., D. Stevenson, and D.G. Fryback. 2002. HALYS and QALYS and DALYS, Oh My: 

Similarities and Differences in Summary Measures of Population Health. Annual Review of

Public Health. 23:115–34.

Gordis, L. 2000. Epidemiology. Second Edition. W.B. Saunders Company. Philadelphia, PA.

Gordon, S.M., L.A. Wallace, P.J. Callahan, D.V. Kenny, and M.C. Brinkman. 1998. Effect of Water

Temperature on Dermal Exposure to Chloroform. Environ Health Perspectives. 106(6):337-345.

Graves, C.G., G.M. Matanoski and R.G. Tardiff. 2001. Weight of evidence for an association between

adverse reproductive and developmental effects and exposure to disinfection by-products: a

critical review. Regulatory Toxicology and Pharmacology. 34:103–124.

Grunberg, S.M., Boutin, N., Ireland, A., Miner, S., Silvera, J., and Ashikaga, T. Impact of

Nausea/Vomiting on Quality of Life as a Visual Analogue Scale-Derived Utility Score. Support

Care Cancer. 4(6): 435-439. November 1996.

Grunberg, S.M., Srivastava, A., Grunberg, K.J., Weeks, J. 2002. Intensity of Chemotherapy-Induced

Emesis and Overall Survival as Determinants of a Global Utility Score. Support Care Cancer.

10(8): 624-629. November 2002. 

Halpern, M.T., B.W. Gillespie, and K.E. Warner. 1993. Patterns of Absolute Risk of Lung Cancer

Mortality in Former Smokers. Journal of the National Cancer Institute. 85(6): 457-464.

Hartge, P., D. Silberman, R. Hoover, C. Schairer, R. Altman, D. Austin, K. Cantor, M. Child, C. Key, and

L.D. Marrett. 1987. Changing Cigarette Habits and Bladder Cancer Risk: A Case-Control Study. 

Journal of the National Cancer Institute. 78(6): 1119-1125.

Harvard School of Public Health, Cost-Effectiveness Analysis Database (accessed January 2005):

www.hcra.harvard.edu/pdf/preferencescores.pdf 

Havelaar, A.H., de Hollander, A.E.M., Teunis, P.F.M., Evers, E.G., van Kranen, H.J., Versteegh, F.M.,

van Koten, J.E.M., and Slob, W. 2000. Balancing the Risks and Benefits of Drinking Water

Disinfection: Disability Adjusted Life-Years on the Scale. Environmental Health Perspectives,

108(4): 315-321. April 2000.
Final Economic Analysis for the Stage 2 DBPR R-6 December 2005

Hildesheim, M.E., K.P. Canbor, C.F. Lynch, M. Dosemeci, J. Lubin, M. Alavanja, and G.F. Craun. 1998. 

Drinking Water Source and Chlorination Byproducts: Risk of Colon and Rectal Cancers. 

Epidemiology. 9(1):29-35.

Hooper, S.M. and S.C. Allgeier. 2002. GAC and Membrane Treatment Studies Data Analysis. In:

Information Collection Rule Data Analysis Report. McGuire, M.J., J. McLain, and A. Obolensky

(eds.). Denver, CO: AWWARF.

Hristova, L. and Hakama, M. 1997. Effect of Screening for Cancer in the Nordic Countries on Deaths,

Cost, and Quality of Life up to the Year 2017. Acta Oncology. 36S9: 1-60. 1997.

Hrubec, Z. and McLaughlin, J.K. 1997b. Former cigarette smoking and mortality among U.S. veterans: a

26-year follow-up. In: Changes in cigarette related disease risks and their implication for

prevention and control. D.M. Burns, L. Garfinkel, J.M. Samet (eds.). NIH Monograph No. 8,

National Institutes of Health. 501-530. Washington, DC: National Cancer Institute. 

Hwang, B., P. Magnus, and J.K. Jaakkola. 2002. Risk of specific birth defects in relation to chlorination

and the amount of natural organic matter in the water supply. American Journal of

Epidemiology. 156(4):374-382. 

Hwang, B.F. and J.J.K. Jaakkola. 2003. Water chlorination and birth defects: A systematic review and

meta-analysis. Archives of Environmental Health. 58(2):83-91.

Industrial Economics, Incorporated. 2002. Valuing Time Losses Due to Illness under the 1996

Amendments to the Safe Drinking Water Act (Draft Final). Prepared for the Office of Ground

Water and Drinking Water, U.S. Environmental Protection Agency. October 2002.

Infante-Rivard, C., E. Olson, L. Jacques, and P. Ayotte. 2001. Drinking Water Contaminants and

Childhood Leukemia. Epidemiology. 12(1):3-9.

Infante-Rivard, C., D. Amre, and D. Sinnett. 2002. GSTT1 and CYP2E1 polymorphisms and

trihalomethanes in drinking water: effect on childhood leukemia. Environmental Health

Perspective. 110(6):591-593.

Infante-Rivard, C. 2004. Drinking water contaminants, gene polymorphisms, and fetal growth. 

Environmental Health Perspectives. 112(11):1213-1216.

International Life Sciences Institutes (ILSI). 1997. An evaluation of EPA’s proposed guidelines for

carcinogen risk assessment using chloroform and dichloroacetate as case studies: Report of an

expert panel. Washington, D.C., November.

International Life Science Institute and Risk Science Institute (ILSI and RSI). 1998. Assessing the

Toxicity of the Exposure of Mixture to Disinfection Byproducts. Published as part of the Research

Recommendations under a cooperative agreement with U.S. EPA’s Office of Water, March.

IRIS. 1993. Integrated Risk Information System (IRIS). N-nitrosodimethylamine (NDMA). 

Washington, DC: U.S. EPA. http://www.epa.gov/iris/subst/0045.htm

IRIS. 2000. Integrated Risk Information System (IRIS). Toxicological Review of Chlorine Dioxide and

Chlorite. Washington, DC: U.S. EPA. EPA/635/R-00-007. 

http://www.epa.gov/iris/toxreviews/0496-tr.pdf
Final Economic Analysis for the Stage 2 DBPR R-7 December 2005

IRIS. 2001a. Integrated Risk Information System (IRIS). Toxicological Review Bromate. Washington,

DC: U.S. EPA. EPA/635/R-01/002. http://www.epa.gov/iris/toxreviews/1002-tr.pdf

IRIS. 2001b. Integrated Risk Information System (IRIS). Toxicological Review of Chloroform. 

Washington, DC: U.S. EPA. EPA/635/R-01/001. 

http://www.epa.gov/iris/toxreviews/0025-tr.pdf

IRIS. 2003. Integrated Risk Information System (IRIS). Toxicological Review for Dichloroacetic Acid:

Consensus Review Draft. Washington, DC: U.S. EPA. EPA/635/R-03-007. 

http://www.epa.gov/iris/subst/0654.htm

Jaakkola, J.J.K., P .Magnus, A. Skrondal, B.F. Hwang, G. Becher, and E. Dybing. 2001. Fetal growth

and duration of gestation relative to water chlorination. Journal of Occupational and

Environmental Medicine. 58(7):437-442.

Källén, B.A.J .and E. Robert. 2000. Drinking water Chlorination and Delivery Outcome - a Registry

Based Study in Sweden. Reproductive Toxicology. 14:303-309.

Kanitz, S, Y. Franco, V. Patrone, M. Caltabellotta, E. Raffo, C. Riggi, D. Timitilli, and G. Ravera. 1996. 

Association between drinking water disinfection and somatic parameters at birth. Environmental

Health Perspectives. 104(5):516-520.

Kaydos, E.H., J.D. Suarez, N.L.,Roberts, K. Bobseine, R. Zucker, J. Laskey, and G.R. Klinefelter. 2004. 

Haloacid Induced Alterations in Fertility and the Sperm Biomarker SP22 in the Rat Are Additive:

Validation of an ELISA. Toxicological Sciences. 8:430-442.

King, W.D. and L.D. Marrett. 1996. Case-Control Study of Bladder Cancer and Chlorination ByProducts in Treated Water (Ontario, Canada). Cancer Causes Control. 7:596-604.

King, W.D., L.D. Marrett, and C.G. Woolcott. 2000a. Case-Control Study of Colon and Rectal Cancers

and Chlorination Byproducts in Treated Water. Cancer Epidemiology, Biomarkers and

Prevention. 9(8):813–818.

King, W., L. Dodds and A. Allen. 2000b. Relation between Stillbirth and Specific Chlorination Byproducts in Public Water Supplies. Environmental Health Perspectives. 108(9):883-886.

King, W.D., L. Dodds, A.C. Allen, B.A. Armson, D. Fell, and C. Nimrod. 2005. Haloacetic acids in

drinking water and risk for stillbirth. Journal of Occupational and Environmental Medicine. 

62(2):124-127.

Kleckner, N. and J. Neuman. 2000. Update to Recommended Approach to Adjusting WTP Estimates to

Reflect Changes in Real Income. Industrial Economics, Incorporated. Memorandum to Jim

DeMocker. U.S. Environmental Protection Agency. September 30, 2000.

Klinefelter, G.R., E.S. Hunter, and M. Narotsky. 2001. Reproductive and Developmental Toxicity

Associated with Disinfection By-Products of Drinking Water. In: Microbial Pathogens and

Disinfection By-Products of Drinking Water. ILSI Press, 309–323.

Klinefelter, G.R., L.F. Strader, J.D. Suarez, N.L. Roberts, J.M. Goldman, and A.S. Murr. 2004. 

Continuous Exposure to Dibromoacetic Acid Delays Pubertal Development and Compromises

Sperm Quality in the Rat. Toxicological Science. 81:49-429.
Final Economic Analysis for the Stage 2 DBPR R-8 December 2005

Klotz, J.B. and L.A. Pyrch. 1998. A Case Control Study of Neural Tube Defects and Drinking Water

Contaminants. U.S. Department of Health and Human Services, Agency for Toxic Substances

and Disease Registry (ATSDR).

Klotz, J.B. and L.A. Pyrch. 1999. Neural tube defects and drinking water disinfection byproducts. 

Epidemiology. 10(4):383-390.

Koechling, M.T., A.N. Rajbhandari, and R.S. Summers. 1998. Proceedings. American Water Works

Association Annual Conference, Dallas, TX. June 1998, 363-373.

Kogevinas, M. 2005. The importance of cultural factors in the recognition of occupational disease. 

Journal of Occupational and Environmental Medicine. 62(5):286.

Kogevinas, M., F. Fernandez, and C. Villanueva. 2005. Personal communication regarding Odds Ratios

as a function of exposure duration to DBPs in Villanueva et al (2003). February 14, 2005.

Kogevinas, M. and C. Villanueva.. 2005. Personal communication regarding THM exposure levels

presented in Villanueva et al (2004). July 15, 2005.

Koivusalo, M., Hakulinen, T., Vartiainen, T., Pukkala, E., Jaakkola, J.J., and Tuomisto, J. 1998. 

Drinking water mutagenicity and urinary tract cancers: a population-based case-control study in

Finland. American Journal of Epidemiology. 148(7):704-12.

Koivusalo, M., E. Pukkala, T. Vartiainen, J. J. K. Jaakkola, and T. Hakulinen. 1997. Drinking water

chlorination and cancer–a historical cohort study in Finland. Cancer Causes and Control.

8(2):192-200.

Komulainen, H., V.M. Kosma, S.L. Vaittinen, T. Vartiainen, E. Kaliste-Korhonen, S. Lotjonen, R.K.

Tuominen, and J. Tuomisto. 1997. Carcinogenicity of the drinking water mutagen 3-chloro-4-

(dichloromethyl)-5-hydroxy-2(5H)-furanone in the rat. Journal of the National Cancer Institute. 

89(12):848-56.

Kramer M.D., C.F. Lynch, P. Isacson, and J.W. Hanson. 1992. The Association of waterborne

chloroform with intrauterine growth retardation. Epidemiology. 3(5):407-413.

Kundu, B., S.D. Richardson, P.D. Swartz, P.P. Matthews, A.M. Richard, and D.M. DeMarini. 2004. 

Mutagenicity in Salmonella of halonitromethanes: a recently recognized class of disinfection byproducts in drinking water. Mutation Research. 562:39-65.

Kuo, H.W., T.F. Chiang, I.I. Lo, J.S. Lai, C.C. Chan, and J.D. Wang. 1998. Estimates of Cancer Risk

from Chloroform Exposure During Showering in Taiwan. Science of the Total Environment. 

218(1): 1-7.

Lewitt, E.M., L.S. Baker, H. Corman, and P.H. Shiono. 1995. The Direct Cost of Low Birth Weight. 

The Future of Children. 5(1).

Linder, R.E., G.R. Klinefelter, L.F. Strader, J.D. Suarez, C.J. Dyer. 1994. Acute spermatogenic effects

of bromoacetic acids. Fundamental and Applied Toxicology. 22(3):422-30.

Linder, R.E., G.R. Klinefelter, L.F. Strader, M.G. Narotsky, J.D. Suarez, N.L. Roberts, and S.D.

Perreault. 1995. Dibromoacetic acid affects reproductive competence and sperm quality in the

male rat. Fundamental and Applied Toxicology. 28(1):9-17.
Final Economic Analysis for the Stage 2 DBPR R-9 December 2005

Linder, R.E., G.R. Klinefelter, L.F. Strader, J.D. Suarez, and N.L. Roberts NL. 1997. Spermatotoxicity

of dichloroacetic acid. Reproductive Toxicology. 11(5):681-8.

Lynch, C.F., R.F. Woolson, T. O’Gorman, and K.P. Cantor. 1989. Chlorinated drinking water and

bladder cancer: effect of misclassification on risk estimates. Archives of Environmental Health. 

44(4):252-9.

MacLehose, R. 2005. Personal communication on spontaneous abortion data in Savitz et al. (2005). 

November 8, 2005.

Magat, W.A., W.K. Viscusi, and J. Huber. 1996. A Reference Lottery Metric for Valuing Health. 

Management Science. 42:1118-1130.

Magnus, P., J.J.K. Jaakkola, A. Skrondal, J. Alexander, G. Becher, T. Krogh, and E. Dybing. 1999. 

Water chlorination and birth defects. Epidemiology. 10(5):513-517.

Malcolm Pirnie Inc. 1992. Water Treatment Plant Simulation Program Version 1.21 User’s Manual. 

Malcolm Pirnie Inc. June 1992.

March of Dimes Birth Defects Foundation. 1999. Deliver the Best. http://www.modimes.org.

Mauskopf, J.A. and French, M.T. 1991. Estimating the Value of Avoiding Morbidity and Mortality from

Foodborne Illnesses. Risk Analysis. 11(4):619-631. December 1991.

Mayo Clinic Website. 2004. Bladder Cancer.

http://www.mayoclinic.com/invoke.cfm?id=DS00177&dsection=3. Accessed 2004.

McDowell, I. and Newell, C. 1996. Measuring Health: A Guide to Rating Scales and Questionnaires,

Oxford University Press: New York. 1996.

McGeehin, M.A., J.S. Reif, J.C. Becher, and E.J. Mangione. 1993. Case Control Study of Bladder

Cancer and Water Disinfection Methods in Colorado. American Journal of Epidemiology. 138.

McGuire, M.J., J.L. McLain, and A. Obolensky. 2002. Information Collection Rule Data Analysis. 

Awwa Research Foundation and AWWA, Denver.

Miles, A.M., P.C. Singer, D.L. Ashley, M.C. Lynberg, P. Mendola, P.H. Langlois and J.R. Nuckols. 

2002. Comparison of trihalomethanes in tap water and blood. Environmental Science and

Technology. 36(8):1692-1698.

Mills, C.J., R.J. Bull, K.P. Cantor, J. Reif, S.E. Hrudey, and P. Huston. 1998. Workshop report. Health

risks of drinking water chlorination by-products: report of an expert working group. Chronic

Disease in Canada. 19(3):91-102.

Morris, R.D., Audet, A.M., Angelillo, I.F. Chalmers, T.C. Mosteller, F. 1992. Chlorination, chlorination

by-products, and cancer: A meta-analysis. American Journal of Public Health. 82:955-63.

Narotsky, M.G., R.A. Pegram, and R.J. Kavlock. 1992. Full-litter resorptions caused by low-molecular

weight halocarbons in F-344 rats. Teratology. 45:472-473.
Final Economic Analysis for the Stage 2 DBPR R-10 December 2005

National Cancer Institute (NCI) website. 2002. What You Need to Know About Bladder Cancer. 

http://www.cancer.gov/cancertopics/wyntk/bladder/page4. Posted 09/07/2001, Updated

09/16/2002. Accessed 2004.

National Drinking Water Advisory Council. 2001. Report of the Arsenic Cost Working Group to the

National Drinking Water Advisory Council. Final Report. August 14, 2001.

National Research Council (NRC). 1990 Arsenic in Drinking Water. National Research Council,

Committee on Toxicology. National Academy Press: Washington.

National Toxicology Program. 1987. Toxicity and carcinogenesis studies of bromodichloromethane

(CAS No. 75-27-4) in F344/N rats and B6C3F1 mice (gavage studies). Technical Report Series

No. 321. Research Triangle Park, NC: U.S. Department of Health and Human Services.

National Toxicology Program. 2004. Toxicology and Carcinogenesis Studies of Sodium Chlorate (CAS

No. 7775-09-9) in F344/N Rats and B6C3F1 Mice (Drinking Water Studies) - Draft Abstract . 

TR-517. 

http://ntp-server.niehs.nih.gov/index.cfm?objectid=00132319-F1F6-975E-778A4E6504EB9191

National Toxicology Program. 2005a. Toxicology and carcinogenesis studies of bromodichloromethane

(CAS No. 75-27-4) in male F344/N rats and female B6C3F1 mice (Drinking Water Studies) -

Draft Abstract. TR-532. 

http://ntp.niehs.nih.gov/INDEX.CFM?OBJECTID=00271EF5-F1F6-975E-73E6FE7AEE1A1A31

National Toxicology Program (NTP). 2005b. Water disinfection byproducts (dibromoacetic acid). 

CASNO: 631-64-1. 

http://ntp.niehs.nih.gov/index.cfm?objectid=071A45CC-A74F-C13F-1AFDE911CEC2FBDC

(accessed April 1, 2005).

Ness, R.M., Holmes, A.M., Klein, R. and Dittus, R. 1999. Utility Valuations for Outcome States of

Colorectal Cancer. American Journal of Gastroenterology. 94 (6): 1650-1657. June 1999.

Nieuwenhuijsen, M.J., M.B. Toledano, N.E. Eaton, J. Fawell, and P. Elliott. 2000. Chlorination

disinfection by-products in water and their association with adverse reproductive outcomes: a

review. Journal of Occupational and Environmental Medicine. 57(2):73-85.

Norum, J. and Olsen, J.A.. 1997. A Cost-effectiveness Approach to the Norwegian Follow-up

Programme in Colorectal Cancer. Annals of Oncology, 8(11): 1081-1087. 

Office of Management and Budget. 2003. Circular A-4. September 2003.

Pereira, V.J., H.S. Weinberg, and P.C. Singer. 2004. Temporal and spatial variability of DBPs in a

choraminated distribution system. Journal of the American Water Works Association. 

96(11):91-102.

Plewa, M.J., S.D. Richardson and P. Jazwierska. 2004a. Halonitromethane drinking water disinfection

byproducts: chemical characterization and mammalian cell cytotoxicity and genotoxicity. 

Environmental Science and Technology. 38(1): 62-68.

Plewa, M.J., E.D. Wagner, S.D. Richardson, A.D. Thruston Jr, Y.-T. Woo, and A.B. McKague. 2004b. 

Chemical and biological characterization of newly discovered iodo-acid drinking water

disinfection by-products. Environmental Science and Technology. 38(18): 4713-4722.
Final Economic Analysis for the Stage 2 DBPR R-11 December 2005

Poole, C.A. 1997. Analytical meta-analysis of epidemiological studies of chlorinated surface water and

cancer: Quantitative review and reanalysis of the work published by Morris et al., Am J Public

Health 1992. 82:955-963. Prepared for USEPA, National Center for Environmental Assessment,

Cincinnati.

Porru S, Scotto di Carlo A, Carta A, Placidi D. 2003. Bladder cancer and occupational activity. G Ital

Med Lav Ergon. 25(3):298-300.

Porter, C.K., S.D. Putnam, K.L. Hunting, and M.R. Riddle. 2005. The Effect of Trihalomethane and

Haloacetic Acid Exposure on Fetal Growth in a Maryland County. American Journal of

Epidemiology. 162(4):334-344.

Ramsey, S.D., Andersen, M.R., Etzioni, R., Moinpour, C., Peacock, S., Potosky, A., and Urban, N., 

Quality of Life in Survivors of Colorectal Carcinoma, Cancer, 88(6):1294-1303, March 15, 2000.

Ranmuthugala, G., L. Pilotto, W. Smith, T. Vimalasiri, K. Dear, and R. Douglas. 2003. Chlorinated

drinking water and micronuclei in urinary bladder epithelial cells. Epidemiology. 

14(5):617–622.

Reif, J.S., M.C. Hatch, M. Bracken, L. Holmes, B. Schwetz, and P.C. Singer. 1996. Reproductive and

developmental effects of disinfection byproducts in drinking water. Environmental Health

Perspectives. 104:1056-1061.

Reif, J.S., A. Bachand and M. Andersen. 2000. Reproductive and Developmental Effects of Disinfection

By-Products. Bureau of Reproductive and Child Health, Health Canada, Ottawa, Ontario,

Canada. Executive summary available at http://www.hc-sc.gc.ca/pphbdgspsp/publicat/reif/index.html.

Rice J.M., R.A. Baan, M. Blettner, C. Genevois-Charmeau, Y. Grosse, D.B. McGregor, C. Partensky, and

J.D. Wilbourn. 1999. Rodent tumors of urinary bladder, renal cortex, and thyroid gland in IARC

Monographs evaluations of carcinogenic risk to humans. Toxicological Sciences. 49(2):166-71.

Richardson, S.D. 2003. Disinfection by-products and other emerging contaminants in drinking water. 

Trends in Analytical Chemistry. 22(10):666-684.

Richardson, S.D., J.E. Simmons and G. Rice. 2002. Disinfection by-products: the next generation. 

Environmental Science and Technology. 36(9):198A-205A

Rockhill, B., B. Newman, and C. Weinberg. 1998. Use and Misuse of Population Attributable Fractions. 

American Journal of Public Health. 88(1):15-19.

R.S. Means Company Inc. 1999. RSMeans Facilities Construction Cost Data. 14th annual ed. Kingston,

MA.

Savitz, D.A., K.W. Andrews, and L.M. Pastore. 1995. Drinking water and pregnancy outcome in central

North Carolina: Source, Amount, and Trihalomethane levels. Environmental Health

Perspectives. 103(6), 592-596.

Savitz, D.A., Singer, P.C., Hartmann, K.E., Herring, A.H., Weinberg, H.S., Makarushka, C., Hoffman, C.,

Chan, R. and Maclehose, R. 2005. Drinking Water Disinfection By-Products and Pregnancy

Outcome. Sponsored by Microbial/Disinfection By-Products Research Council. Jointly funded

by Awwa Research Foundation and U.S. Environmental Protection Agency.
Final Economic Analysis for the Stage 2 DBPR R-12 December 2005

Schreiber, I.M. and W.A. Mitch. 2005. Influence of the order of reagent addition on NDMA formation

during chloramination. Environmental Science and Technology. 39(10):3811-8.

Schulman, A., M. Manibusan, C. Abernathy, M. Messner, J. Donohue, and D. Gaylor. 2004. Arsenic in

Drinking Water: Cessation Lag Model. Toxicological Sciences. 

Schulte, P.A., K. Ringen, G.P. Hemstreet, E. Ward. 1987. Occupational cancer of the urinary tract. 

Journal of Occupational Medicine. 2(1):85-107.

Seidel, C. 2001. BAT Memorandum on SWAT Runs for Stage 2 BAT Evaluation. June 25, 2001.

Shaw, G.M., S.H. Swan, J.A. Harris, and L.H. Malcoe. 1990. Maternal water consumption during

pregnancy and congenital cardiac anomalies. Epidemiology. 1(3):206-211.

Shaw, G.M., L.H. Malcoe, A. Milea, and S.H. Swan. 1991. Chlorinated water exposures and cardiac

anomalies. Epidemiology. 2:459-460.

Shaw, G.M., D. Ranatunga, T. Quach, E. Neri, A. Correa, and R.R. Neutra. 2003. Trihalomethane

exposure from municipal water supplies and selected congenital malformations. Epidemiology. 

14(2):191-199.

Silverman, D.T., L.I. Levin, R.N. Hoover, and P. Hartge. 1989a. Occupational Risks of Bladder Cancer

in the United States: I. White Men. Journal of the National Cancer Institute. 81: 1472-1480.

Silverman, D.T., L.I. Levin, and R.N. Hoover. 1989b. Occupational Risks of Bladder Cancer in the

United States: II. Nonwhite Men. Journal of the National Cancer Institute. 81: 1480-1483.

Silverman, D.T., L.I. Levin, and R.N. Hoover. 1990. Occupational Risks of Bladder Cancer Among

White Women in the United States. American Journal of Epidemiology. 132(3): 453-461.

Silverman, D.T., A.S. Morrison, and S.S. Devesa. 1996. Bladder cancer. In: Cancer Epidemiology and

Prevention. 2nd edition. Schottenfeld, D. and J.F. Fraumeni Jr. (eds.). 1156-1179. New York:

Oxford University Press.

Singer, P.C. 1999. Formation and Control of Disinfection By-Products in Drinking Water. 13-18.

American Water Works Association, Denver, Colorado.

Singer, P.C., E. DePaz, D.L. Ashley, B. Blount, J.R. Nuckols, C.R. Wilkes, D. Cade, C. Lyu, S. Gordon,

J. Masters, and M. Brinkman. 2003. Impact of Trihalomethane Concentrations in Tap Water and

Water Use Activities on Biological Levels of Trihalomethanes. AWWA WQTC Conference.

Smith, V.K., G. Van Houtven, and S.K. Pattanayak. 2002. Benefit transfer via preference calibration:

‘Prudential algebra’ for policy. Land Economics. 78(1):132-152.

Solarik, G., R.S. Summers, J. Sohn, W.J. Swanson, Z.K. Chowdhury, and G.L. Amy. 2000. Extensions

and Verification of the Water Treatment Plant Model for DBP Formation. In: Natural Organic

Matter and Disinfection Byproducts Characterization and Control in Drinking Water. Amy, G.L.,

S. Huang, and S. Krasner (eds.). American Chemical Society Symposium Series, volume 761. 

Washington, D.C.: American Chemical Society. 
Final Economic Analysis for the Stage 2 DBPR R-13 December 2005

Stiggelbout, A.M., Kiebert, G.M., Kievit, J., Leer, J.W.H., Habbema, J.D.F., and De Haes, J.D.J.M. 1995. 

 The ‘Utility’ of the Time Trade-off Method in Cancer Patients: Feasibility and Proportional

Trade-off, Journal of Clinical Epidemiology. 48(10): 1207-1214.

Summers, R.S., G. Solarik, V.A. Hatcher, R.S. Isabel, and J.F. Stile. 1998. Impact of Point of Chlorine

Addition and Coagulation. Final Project Report. USEPA Office of Ground Water and Drinking

Water: Cincinnati, OH.

Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov). 2004. 

SEER*Stat Databases: Incidence - SEER 11 Regs + AK Public-Use, Nov 2003 Sub for Expanded

Races (1992-2001) and Incidence - SEER 11 Regs Public-Use, Nov 2003 Sub for Hispanics

(1992-2001), National Cancer Institute, DCCPS, Surveillance Research Program, Cancer

Statistics Branch, released April 2004, based on the November 2003 submission.

Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov). 2005. Ries,

L.A.G., M.P. Eisner, C.L. Kosary, B.F. Hankey, B.A. Miller, L. Clegg, A. Mariotto, E.J. Feuer,

B.K. Edwards (eds.). SEER Cancer Statistics Review, 1975-2002. National Cancer Institute. 

Bethesda, MD, released 2005, based on November 2004 submission.

Swan, S.H., R.R. Neutra, M. Wrensch, I. Hertz-Picciotto, G.C. Windham, L. Fenster, D.M. Epstein, and

M. Deane. 1992. Is drinking water related to spontaneous abortion? Reviewing the evidence

from the California Department of Health Services Studies. Epidemiology. 3:83-93.

Swan, S.H., K. Waller, B. Hopkins, G. Windham, L. Fenster, C. Schaefer, and R. Neutra. 1998. A

prospective study of spontaneous abortion; relation to amount and source of drinking water

consumed in early pregnancy. Epidemiology. 9:126-133.

Toledano, M.B., M.J. Nieuwenhuijsen, N. Best, H. Whitaker, P. Hambly, C. de Hoogh, J. Fawell, L.

Jarup, and P. Elliott. 2005. Relation of trihalomethane concentrations in public water supplies to

stillbirth and birth weight in three water regions in England. Environmental Health Perspectives. 

113(2):225-232.

Torrance, G.W., Zhang, Y., Feeny, D., Furlong, W., Barr, R.. Multi-attribute preference functions for a

comprehensive health status classification system. CHEPA Working Paper Series No. 92-18,

McMaster University, 1992.

Tseng, T., M. Edwards, and Z.K. Chowdhury. 1996. American Water Works Association National

Enhanced Coagulation and Softening Database.

Tyl, R.W. 2000. Review of Animal Studies for Reproductive and Developmental Toxicity Assessment

of Drinking Water Contaminants: Disinfection By-Products (DBPs). RTI Project No. 07639. 

Research Triangle Institute.

U.S. Bureau of Labor Statistics. 2003. Employment Cost Index. http://www.bls.gov

U.S. Bureau of Labor Statistics. 2004. Consumer Price Index. http://www.bls.gov

U.S. Census Bureau. 2001. Households and Families: 2000. Census 2000 Brief. C2KBR/01-8.

USDA (U.S. Department of Agriculture). 1997. 1994-1996 USDA Continuing Survey of Food Intakes

by Individuals.
Final Economic Analysis for the Stage 2 DBPR R-14 December 2005

U.S. Department of Commerce, Bureau of the Census. 1992. 1992 Census of Governments, GC92(4)-4:

Finances of Municipal and Township Governments.

U.S. Department of Commerce, Bureau of Economic Analysis. 2004a. Table 1.1.6. Real Gross

Domestic Product, Chained Dollars, Billions of chained (2000) dollars. http://www.bea.gov/

U.S. Department of Commerce, Bureau of Economic Analysis. 2004b. Table 3.9.4. Price Indexes for

Government Consumption Expenditures and Gross Investment. http://www.bea.gov/

USDOE, Energy Information Administration. 2004a. Table 1d. Average Retail Price for Bundled and

Unbundled. http://www.eia.doe.gov/cneaf/electricity/esr/esr_tabs.html

USDOE, Energy Information Administration. 2004b. Table ES. Summary Statistics for the United

States, 1992 through 2003.

http://www.eia.doe.gov/cneaf/electricity/page/at_a_glance/gen_tabs.html

USEPA. 1983. Ground Water Supply Survey: Summary of Volatile Organic Contaminant Occurrence

Data. Office of Drinking Water, January 1983.

USEPA. 1986. Guidelines for Carcinogen Risk Assessment. Federal Register, 51(185):33992-34003. 

Risk Assessment Forum. EPA 630-R-00-004. September 1986.

USEPA. 1989. Review of Environmental Contaminants and Toxicology. Office of Drinking Water

Health Advisories, 106:225.

USEPA. 1990. Guidance Manual for Compliance with the Filtration and Disinfection Requirements for

Public Water Systems Using Surface Water Sources. Science and Technology Branch Criteria

and Standards Division Office of Drinking Water.

USEPA. 1991a. National Primary Drinking Water Regulations; Synthetic Organic Chemicals and

Inorganic Chemicals; Monitoring for Unregulated Contaminants; National Primary Drinking

Water Regulations Implementation; National Secondary Drinking Water Regulations; Final Rule. 

Federal Register, 56:20:3526-3597. January 31, 1991.

USEPA. 1991b. Part V, Environmental Protection Agency: Guidelines for Developmental Toxicity Risk

Assessment; Notice. Federal Register, 56:234:63798-63826. December 5, 1991.

USEPA. 1993. Executive Order 12866. Regulatory Planning and Review. Federal Register,

58:190:51735-51744. October 4, 1993. 

USEPA. 1994a. Carcinogenicity peer review of cacodylic acid. Office of Pesticide Programs. U.S.

Environmental Protection Agency: Washington, D.C. 20 pp.

USEPA. 1994b. Comment Response Database for the Stage 1 Disinfection Byproducts Rule. Comment

ID M1.065-001.

USEPA. 1996a. Ground Water Disinfection and Protective Practices in the United States. Prepared by

the EPA and Science Applications International Corporation. November 1996.

USEPA. 1996b. Economic Analysis of Federal Regulations Under Executive Order 12866. Office of

Management and Budget. January 11, 1996.
Final Economic Analysis for the Stage 2 DBPR R-15 December 2005

USEPA. 1996c. Proposed Guidelines for Carcinogen Risk Assessment. Office of Research and

Development. EPA 600-P-92-003C. Federal Register, 61:79:17960-18011. April 23, 1996. 

USEPA. 1996d. Part II, Environmental Protection Agency: Reproductive Toxicity Risk Assessment

Guidelines; Notice. Federal Register, 61:212:56274-56322. October 31, 1996.

USEPA. 1997a. Summaries of New Health Effects Data. Office of Science and Technology, Office of

Water. Washington, D.C. October 1997.

USEPA. 1997b. The Benefits and Costs of the Clean Air Act, 1970-1990. Prepared for U.S. Congress.

USEPA. 1997c. Community Water Systems Survey (CWSS), Volumes I and II. Office of Water,

Washington, D.C. EPA 815-R-97-001a and -001b.

USEPA. 1997d. Comment Response Database for the Stage 1 Disinfection Byproducts Rule. Comment

ID J1.057-002.

USEPA. 1998a. Regulatory Impact Analysis for the Stage 1 Disinfectants/Disinfection Byproducts Rule. 

Washington, D.C. EPA-815-B-98-002. PB 99-111304. November 12, 1998.

USEPA. 1998b. Comment Response Database for the Stage 1 Disinfection Byproducts Rule. Comment

ID D.009-025.

USEPA. 1998c. National-Level Affordability Criteria Under the 1996 Amendments to the Safe Drinking

Water Act. Prepared by International Consultants, Inc., Hagler Bailly Services, Inc and Janice A.

Beecher, Ph.D. for the EPA. August, 1998.

USEPA. 1998d. Variance Technology Findings for Contaminants Regulated Before 1996. Office of

Water. EPA 815-R-98-003. September, 1998

USEPA. 1998e. Guidance on Implementing the Capacity Development Provisions of the Safe Drinking

Water Act Amendments of 1996. Office of Water. July, 1998. EPA 816-R-98-006.

USEPA. 1998f. National Primary Drinking Water Regulations; Disinfectants and Disinfection

Byproducts; Notice of Data Availability; Proposed Rule. Federal Register, 63:61:15606-15692. 

March 31, 1998.

USEPA. 1998g. Quantification of Cancer Risk from Exposure to Chlorinated Water. Office of Science

and Technology, Office of Water. Washington, DC. March 13, 1998.

USEPA. 1999a. Cost of Illness Handbook. Office of Pollution Prevention and Toxics. Chapter 1 II.8. 

Cost of Bladder Cancer. September, 1999. 54 pp.

USEPA. 1999b. Guidelines for Carcinogen Risk Assessment. July SAB Review draft. Office of

Research and Development, Washington, DC. USEPA NCEA-F-0644. 

http://www.epa.gov/ncea/raf/crasab.htm.

USEPA. 1999c. Treatment Technologies. M-DBP Federal Advisory Committee (FACA2), Meeting #5. 

http://www.epa.gov/safewater/
Final Economic Analysis for the Stage 2 DBPR R-16 December 2005

USEPA. 2000a. Surface Water Analytical Tool (SWAT) Version 1.1 - Program Design and

Assumptions. Prepared by Malcolm Pirnie, Inc.

USEPA. 2000b. ICR Supplemental Survey Database. Prepared by DynCorp, Inc.

USEPA. 2000c. Geometries and Characteristics of Water Systems Report. Office of Ground Water and

Drinking Water. EPA 815-R-00-024. December, 2000.

USEPA. 2000d. TTHM Plant-Mean Data from Seven States. EPA Office of Ground Water and

Drinking Water.

USEPA. 2000e. Data Reliability Analysis of the EPA Safe Drinking Water Information System/Federal

Version (SDWIS/FED). Office of Water. 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, Office 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. Contract 68-C-99-206 and 245. April 5, 2000.

USEPA. 2000h. ICR AUX1 Database. April, 2000 version.

USEPA. 2000i. Estimated per Capita Water Ingestion in the United States. Based on Data Collected by

the United States Department of Agriculture’s 1994-96 Continuing Survey of Food Intakes by

Individuals. Office of Science and Technology. EPA Contracts 68-C4-0046 and 68-C-99-233.

USEPA. 2000j. Guidelines for Preparing Economic Analysis. Office of the Administrator. EPA-240-R00-003. September, 2000.

USEPA. 2000m. Stage 2 Microbial and Disinfection Byproducts Federal Advisory Committee Agreement

in Principle. FR 65:251:83015–83024. December 29, 2000. http://www.epa.gov/fedrgstr/EPAWATER/2000/December/Day-29/w33306.htm and

http://www.epa.gov/safewater/disinfection/st2agreement.html .

USEPA. 2000n. Stage 2 M/DBP FACA Meeting Summaries. November 1997 to June 2000. 

http://www.epa.gov/safewater/

USEPA. 2000o. ICR Treatment Study Database CD-ROM, Version 1.0. EPA 815-C-00-003.

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.

USEPA. 2001e. Development of Cost of Capital Estimates for Public Water Systems, Final Report. 

November, 2001.

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.

USEPA. 2003s. Labor Costs for National Drinking Water Rules.

USEPA. 2003t. SDWIS Database. 4th quarter SDWIS freeze data.USEPA. 2005a. ICR Matrix Method

for the Stage 2 DBPR EA. Prepared by The Cadmus Group, Inc. 

USEPA. 2005b. Drinking Water Criteria Document for Brominated Trihalomethanes. Washington, DC. 

EPA 822-R-05-011. 

USEPA. 2005c. Drinking Water Criteria Document for Brominated Acetic Acids. Washington, DC. 

EPA 822-R-05-007. 

USEPA. 2005d. Drinking Water Addendum to the Criteria Document for Trichloroacetic Acid. 

Washington, DC. EPA 822-R-05-010. 

USEPA. 2005e. Drinking Water Addendum to the Criteria Document for Monochloroacetic Acid. 

Washington, DC. EPA 822-R-05-008. 

USEPA. 2005f. Guidelines for carcinogen risk assessment. Office of Research and Development,

Washington, DC. EPA/630/P-03/001F. Available online at http://cfpub.epa.gov/ncea/.

USEPA. 2005g. Supplemental guidance for assessing susceptibility from early-life exposure to

carcinogens. Office of Research and Development, Washington, DC. EPA/630/R-03/003F. 

Available online at http://cfpub.epa.gov/ncea/.

USEPA. 2005h. Stage 2 Benefits Model. Prepared by the Cadmus Group, Inc.

USEPA. 2005i. Stage 2 Cost Model. Prepared by the Cadmus Group, Inc.

USEPA. 2005j. Drinking Water Addendum to the IRIS Toxicological Review of Dichloroacetic Acid. 

Washington, D.C. EPA 822-R-05-009. 

USEPA. 2005k. Occurrence Assessment for the Final Stage 2 Disinfectants and Disinfection Byproducts

(D/DBPs) Rule. Prepared by The Cadmus Group, Inc. Contract 68-C-99-206. December 2005.

EPA 815-R-05-011.

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.

USEPA. 2005m. Economic Analysis for Long Term 2 Enhanced Surface Water Treatment Rule. 

Prepared by The Cadmus Group, Inc., Arlington, VA. Contract 68-C-02-026. December 2005.

USEPA. 2005n. Technologies and Costs for the Final Long term 2 Enhanced Surface Water Treatment

Rule and Final Stage 2 Disinfectants and Disinfection Byproducts Rule. Prepared by The

Cadmus Group, Inc. Contract 68-C-99-206. December 2005. EPA 815-R-05-012.

USEPA. 2005o. Regulatory Impact Analysis for the Final Clean Air Interstate Rule. 

EPA-452/R-05-002. Appendix G, pp. 369 – 420. March 2005.
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.

USFDA. Preliminary Regulatory Impact Analysis and Initial Regulatory Flexibility Analysis of the

Proposed Rules to Ensure the Safety of Juice and Juice Products; Proposed Rule. 63 FR 24253;

May 1, 1998.

USFDA. Hazard Analysis and Critical Control Point (HAACP); Procedures for the Safe and Sanitary

Processing and Importing of Juice; Final Rule. 66 FR 6137; January 19, 2001.

USFDA. Establishment and Maintenance of Records Under the Public Health and Security and

Bioterrorism Preparedness and Response Act of 2002; Establishment and Maintenance of

Records for Foods; Notice of Public Meeting; Availability of Draft Guidance for Records Access

Authority; Final Rules and Notice. 69 FR 71561; December 9, 2004.

Veeramachaneni, D.N.R., J.S. Palmer, C.M. Kane, and T.T. Higuchi. 2000. Dibromoacetic acid, a

disinfection by-product in drinking water, impairs sexual function and fertility in male rabbits. 

Biology of Reproduction. 62:246.

Vena, J.E., S. Graham, J. Freudenheim, J. Marshall, M. Zielezny, M. Swanson, and G. Sufrin. 1993. 

Drinking Water, Fluid Intake, and Bladder Cancer in Western New York. Archives of

Environmental Health. 48(3):191-197.

Ventura, S.J., W.D. Mosher, S.C. Curtin, J.C. Abma, and S. Henshaw. 2000. Trends in Pregnancies and

Pregnancy Rates by Outcome: Estimates for the Unites States, 1976-96. National Center for

Health Statistics. Vital Health Statistics. 21(56).

Villanueva, C.M., M. Kogevinas and J.O. Grimalt. 2001a. Drinking water chlorination and adverse

health effects: a review of epidemiological studies. Medicina Clinica. 117(1): 27-35. [Spanish]

Villanueva, C.M., Fernandez, F., Malats, N., Grimalt, J.O., and M. Kogenvinas. 2003b. Meta-analysis of

Studies on Individual Consumption of Chlorinated Drinking Water and Bladder Cancer. Journal

of Epidemiology Community Health. 57:166-173.

Villanueva, C.M., K.P. Cantor, S. Cordier, J.J.K. Jaakkola, W.D. King, C.F. Lynch, S. Porru, and M.

Kogevinas. 2004. Disinfection byproducts and bladder cancer a pooled analysis. Epidemiology. 

15(3):357-367.

Vinceti, M., G. Fantuzzi, L. Monici, M. Cassinadri, G. Predieri, and G. Aggazzotti. 2004. A

retrospective cohort study of trihalomethane exposure through drinking water and cancer

mortality in northern Italy. Science of the Total Environment. 330(1-3):47-53.

Vineis. 2004. A self-fulling prophecy: are we underestimating the role of the environment in the geneenvironment interaction research? International Journal of Epidemiology. 33:945-946.

Viscusi, W.K.,W.A. Magat, and J. Huber. 1991. Pricing Environmental Health Risks: Survey

Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis. Journal of

Environmental Economics and Management. 21.
Final Economic Analysis for the Stage 2 DBPR R-19 December 2005

Waller, K., S.H. Swan, G. DeLorenze, and B. Hopkins. 1998. Trihalomethanes in Drinking Water and

Spontaneous Abortion. Epidemiology. 9(2):134-140.

Waller, K., S.H. Swan, G.C. Windham, and L. Fenster. 2001. Influence of exposure assessment methods

on risk estimates in an epidemiologic study of total trihalomethane exposure and spontaneous

abortion. Journal of Exposure Analysis and Environmental Epidemiology. 11(6):522-31.

Weinberg, H.S., S.W. Krasner, S.D. Richardson, and A.D. Thruston, Jr. 2002. The Occurrence of

Disinfection By-Products (DBPs) of Health Concern in Drinking Water: Results of a Nationwide

DBP Occurrence Study, U.S. Environmental Protection Agency, National Exposure Research

Laboratory, Athens, GA. EPA/600/R-02/068. http://www.epa.gov/athens/publications/

reports/EPA_600_R02_068.pdf

Weinstein, M.C., Siegel, J.E., Garber, A.M., Lipscomb, J., Luce, B.R., Manning, Jr., W.G., and Torrance,

G.W. Productivity Costs, Time Costs and Health-related Quality of Life: A Response to the

Erasmus Group. Health Economics. 6:505-510. 1997.

Weisel, C.P., H. Kim, P. Haltemeier, and J.B. Klotz. 1999. Exposure Estimates to Disinfection Byproducts of Chlorinated Drinking Water. Environmental Health Perspectives. 107(2).

Whynes, D.K. and Neilson, A.R. 1993. Convergent Validity of Two Measures of the Quality of Life,

Health Economics, 2: 229-235.

Wilkins, J.R., III and G.W. Comstock. 1981. Source of drinking water at home and site-specific cancer

incidence in Washington County, Maryland. American Journal of Epidemiology. 114(2):178-

190.

Windham, G.C., K. Waller, M. Anderson, L. Fenster, P. Mendola, and S. Swan. 2003. Chlorination

Byproducts in Drinking Water and Menstrual Cycle Function. Environmental Health

Perspectives. doi:10.1289/ehp.5922.

WHO. 2000. World Health Organization, International Programme on Chemical Safety (IPCS).

Environmental Health Criteria 216: Disinfectants and Disinfectant By-products.

Wright, J.M., J. Schwartz and D.W. Dockery. 2003. Effect of trihalomethane exposure on fetal

development. Occupational and Environmental Medicine. 60(3):173-180.

Wright, J.M., J. Schwartz and D.W. Dockery. 2004. The effect of disinfection by-products and

mutagenic activity on birth weight and gestational duration. Environmental Health Perspectives. 

112(8):920-925.

Xu, X., T.M. Marino, J.D. Laskin, and C.P. Weisel. 2002. Pericutaneous absorption of trihalomethanes,

haloacetic acids, and haloketones. Toxicology and Applied Pharmacology. 184(1):19-26.

Xu, X. and C.P. Weisel. 2003. Inhalation exposure to haloacetic acids and haloketones during

showering. Environmental Science and Technology. 37(3):569-576.

Xu, X. and C.P. Weisel. 2004. Dermal uptake of chloroform and haloketones during bathing. Journal of

Exposure Analysis and Environmental Epidemiology. 1-8.

Xu, X. and C.P. Weisel. 2005. Human respiratory uptake of chloroform and haloketones showering. 

Journal of Exposure Analysis and Environmental Epidemiology. 15:6-16.
Final Economic Analysis for the Stage 2 DBPR R-20 December 2005

Yang, C.Y., H.F. Chiu, M.F. Cheng, and S.S. Tsai. 1998. Chlorination of Drinking Water and Cancer

Mortality in Taiwan. Environmental Research. 78:1-6.

Yang, C.Y., B. Cheng, S. Tsai, T. Wu, M. Lin, and K. Lin. 2000. Association between Chlorination of

Drinking Water and Adverse Pregnancy Outcome in Taiwan. Environmental Health

Perspectives. 108(8).

Yang, C.Y. 2004. Drinking water chlorination and adverse birth outcomes in Taiwan. Toxicology. 

198:249-254. 

Young, T.B., D.A. Wolf, and M.S. Kanarek. 1987. Case-Control Study of Colon Cancer and Drinking

Water Trihalomethanes in Wisconsin. International Journal of Epidemiology. 16(190).

Zeegers, M.P., E. Kellen, F. Buntinx, and P.A. van den Brandt. 2004. The association between smoking,

beverage consumption, diet and bladder cancer: a systematic literature review. World Journal of

Urology. 21(6):392-401.

Zender, R., A.M. Bachand, and J.S. Reif. 2001. Exposure to tap water during pregnancy. Journal of

Exposure Analysis and Environmental Epidemiology. 11(3):224-230.