Appendices to the Economic
Analysis for the Final Stage 2
Disinfectants and Disinfection
Byproducts Rule
Volume I (A-E2)
-------
Office of Water (4606-M) EPA 815-R-05-010 December 2005 www.epa.gov/safewater
-------
Appendix A
Surface Water Compliance Forecasts
-------
-------
Appendix A
Surface Water Compliance Forecasts
The Surface Water Analytical Tool (SWAT) is the primary tool used by EPA to predict treatment
technology changes in surface water systems to achieve compliance with the Stage 2 Disinfection and
Disinfectants Byproducts Rule (DBPR). Treatment technology changes are the basis for calculating
national cost estimates in this Economic Analysis (EA). SWAT is also one of the primary tools used to
predict changes in national chlorination disinfection byproduct (DBF) occurrence levels as a result of the
treatment technology changes. Changes in DBF occurrence levels are used to quantify benefits
(specifically, reduced bladder cancer) of the Stage 2 DBPR.
The purpose of this appendix is to review the major components in SWAT; summarize its
operations; itemize the uncertainties in SWAT and discuss their potential impact on cost and benefits
estimates; present an alternative compliance forecast methodology for comparison to SWAT; and present
detailed compliance forecast results for all sizes of surface water systems. It is organized as follows:
Part I: SWAT Operations
A.I SWAT: An Introduction
A.2 Model Configuration
A.3 User Inputs for Stage 2 DBPR Model Runs
A.4 Model Operation
A. 5 Description of WTP Model Calibration Process and Results
Part H: Evaluation of SWAT Predictions
A. 6 Uncertainties in SWAT Results
Part III: Compliance Forecasts
A. 7 SWAT-based Compliance Forecasts for Large Surface Water Systems
A.8 SWAT based Compliance Forecasts for Medium Surface Water Systems
A.9 SWAT based Compliance Forecasts for Small Surface Water Systems
Part I: SWAT Operations
A.I SWAT: An Introduction
One of the major tools developed in conjunction with the Microbial-Disinfectants/Disinfection
Byproducts Federal Advisory Committees Act (M-DBP FACA) process is the SWAT. SWAT is a
decision support computational model designed to predict treatment technology choices and resulting
changes in water quality for different rule alternatives and input conditions based on the Information
Collection Rule (ICR) data. SWAT model outputs are used to generate compliance forecasts and DBP
exposure estimates. The Environmental Protection Agency (EPA) used SWAT outputs to estimate costs
and benefits of the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR) regulatory
alternatives.
Final Economic Analysis for the Stage 2 DBPR A-l December 2005
-------
A.1.1 Overview
This section presents an overview of how SWAT predicts DBFs and treatment technology
selections for a given rule alternative1. The steps of a SWAT model run that predict DBFs and treatment
technology selections for regulatory alternatives include the following (also shown in Exhibit A.I).
• DBF occurrence estimates are a function of total organic carbon (TOC), Ultraviolet-254
Absorbance (UVA), bromide, pH, temperature, residence time, and primary and secondary
disinfectants. These data, from each valid month used in the SWAT analysis, are input from
Auxiliary Database 8 (AUX8) into the Water Treatment Plant (WTP) Model.
• The WTP Model calculates trihalomethanes (THMs), haloacetic acids (HAAs), bromate, and
chlorite concentrations with empirical equations at three different residence times—one
representing finished water, one representing distribution system average, and one
representing distribution system maximum.
• Based on an input compliance scheme (usually involving Maximum Contaminant Levels
[MCLs] and a compliance aggregation method, such as running annual average), the Decision
Tree Program assesses whether the plant meets the compliance criteria.
• If the plant meets the criteria, the WTP Model results are stored and no further change is
made to the treatment process of the plant.
• If the plant fails to meet the criteria, the Decision Tree Program selects the next least cost
treatment technology feasible for that plant (see Exhibits A. 5 and A. 6).
• The WTP Model is then run with the same influent water characteristics, but with the new
treatment technology added to the plant record.
• The resulting DBP predictions are then compared with the compliance scheme.
• The process is repeated until either compliance is achieved or the end of the treatment
technology tree is reached.
For details on SWAT components or operation beyond the descriptions in this appendix, refer to
Surface Water Analytical Tool (SWAT) Version 1.1—Program Descriptions and Assumptions
(USEPA 2000a).
'The SWAT program can also be run in a mode to evaluate all possible treatment technology choices for
each plant and the resulting DBP concentrations (called "Monster" SWAT runs). This section, however, focuses on
regulatory compliance analyses
Final Economic Analysis for the Stage 2 DBPR A-2 December 2005
-------
Exhibit A.1 Diagram of SWAT Process
Loads raw water quality and processes train data for all monthly records
of the WTP Model from AUX8 database
Runs WTP Model for each initial plant-month to generate treated water
quality predictions
Yes
No
Implements next level of process improvement, per Decision Tree order
Runs WTP Model for each modified plant-month to generate treated
water quality predictions
Stores Results in
AUX8 and Moves to
Next Plant
Sends output to
AUX8 database
A.2 Model Configuration
This section provides an overview of SWAT's configuration. Exhibit A.2 shows the four main
components and how they interact. These components can be grouped into two categories:
• The input/output components, i.e., the user interface and the AUX8 database
• The computational/analytical components, i.e., the Decision Tree Program, and the WTP
Model
Sections A.2.1 through A.2.4 describe these components in more detail.
Final Economic Analysis for the Stage 2 DBPR
A-3
December 2005
-------
Exhibit A.2 SWAT Components
User Interface
Inputs
(eg., MCL criteria)
ICR Auxfliaiy Database 8
Treatment Plants and Water
Quality (ICRData)
Modified Plants and Selected
Outputs (eg, DBF Levels)
Water Treatment
Rant Model
Decision Tree
Program
A.2.1 User Interface
A Microsoft Windows™ interface enables the user to specify the disinfection and DBF
regulatory criteria, as well as numerous other assumptions for a SWAT run (e.g., use of disinfection
benchmarking, use of ultraviolet light [UV]). It also allows the user to run the WTP Model, which
predicts DBF occurrence, and the Decision Tree Program, which selects treatment technologies to meet
specified compliance options. The SWAT Version 1.1 program description document (USEPA 2000a)
shows all input screens for the SWAT user interface. Section A.4 describes the user inputs and SWAT
assumptions for the Stage 2 DBPR model runs.
A.2.2 Auxiliary Database 8
AUX8 is a Microsoft Access™ database that holds inputs and outputs for SWAT analyses. The
database contains only the data from AUX 1 (the primary ICR database) that was need to run the SWAT
model. Only the last 12 months of the 18-month ICR collection period were used in SWAT in order to
avoid seasonal bias.2 Ground water plants generally did not have as much information as surface water
plants and thus were not modeled in SWAT. The surface water plants with at least one month of all
required SWAT input data in AUX1 were screened into the AUX8 database. SWAT inputs from AUX8
are grouped into five categories—source water quality, treatment plant characteristics, unit processes,
chemical additions, and distribution system characteristics—and are summarized below.
(1) Source Water Quality
• pH
• Temperature (average and annual minimum)
• TOC
2 All of the 12-month series (months 1 to 12, 2 to 13, etc.) were examined during the M-DBP FACA process
and determined to be similar.
Final Economic Analysis for the Stage 2 DBPR
A-4
December 2005
-------
• UVA3
• Bromide
Alkalinity
• Hardness (total and calcium)
• Ammonia
• Turbidity
(2) Treatment Plant Characteristics
• Flow (average and design)
• Sequence of unit processes and parameters influencing their performance such as volumes,
flow, detention times, baffling characteristics and other process specific parameters.
(3) Unit Processes
• Conventional processes such as rapid mix, flocculation, sedimentation, and rapid sand filtration
• Granular activated carbon
• Microfiltration
• Nanofiltration
• Ozonation
(4) Chemical Additions
• Coagulation/Softening related chemicals: alum, carbon dioxide, sodium hydroxide, ferric
chloride, lime, soda ash, and sulfuric acid.
• Oxidation/Disinfection related chemicals: chlorine (gas), sodium hypochlorite, chloramines,
chlorine dioxide, ozone, ammonia, ammonium sulfate, potassium permanganate, and sulfur
dioxide.
(5) Distribution System Characteristics
• Average and maximum distribution system residence times
In some cases, plants reported changes in their unit processes or chemical addition inputs during
the ICR period. For example, some plants installed ozone during the ICR collection period. Also, many
plants change disinfectant type from chlorine to chloramines during the year. The initial treatment
technology level determination and disinfectant type for a plant was always based on the treatment
technology or disinfectant that was reported most often.
Unlike user inputs described in Section A.2.1, ICR data in AUX8 is not intended to be modified
by the user or varied from run to run. Each run creates a series of additional records in the AUX8
database. Each run is saved in a separate version of the AUX8 database. The databases are then
compiled by a summary program.
To increase the number of plant-months that could be processed by SWAT, some missing raw
water quality data were estimated. For example, missing monthly values for influent pH, hardness,
alkalinity, and ammonia were estimated based on the average of values that were reported in AUX1 for
the other months. Missing monthly raw water temperature data were estimated based on reported
3 UV-254 absorbance measures the extent of absorbance of UV light (having a wavelength of 254
nanometers) by the natural organic matter (NOM) present/remaining in untreated/treated waters. It is sometimes
referred to as UV254, and it's units are cm"1. In conjunction with TOC, it yields important insights into the
characteristics of the NOM.
Final Economic Analysis for the Stage 2 DBPR A-5 December 2005
-------
temperature data from other points in the plant or distribution system for the same month. TOC and
UVA were determined to be too critical to the calculations to be estimated if neither value was provided
for a given month. If either TOC or UVA data existed for a plant month, the missing value was
estimated using the ratio of UVA to TOC for the rest of the plant-months.
Of the 350 surface water plants in the ICR, 273, or approximately 78 percent, had at least one
month with all required data for SWAT analyses. There is a potential bias resulting from the exclusion of
ICR plants from the analysis. The M-DBP Technical Expert Working Group (TWO) determined,
however, that the 273 plants evaluated in SWAT adequately capture treatment configuration and water
quality conditions of all ICR surface water plants.
Plants only needed to report one valid month of data (i.e., one month with all required parameters)
to be used in SWAT, so many of the 273 plants used do not have complete records for all months. Exhibit
A. shows the extent to which there are complete plant-month records in SWAT. Note that over 70
percent of plants have at least 10 months of data, and more than 90 percent have at least eight months of
data.
Exhibit A.3 Extent of Plant-Month Data in SWAT
No. of
Months
1
2
3
4
5
6
7
8
9
10
11
12
TOTAL
No. of ICR Plants
With Corresponding
Months of Data in
AUX8
3
3
1
3
5
2
8
15
38
35
65
95
273
Percent of Plants with
at Least That Many
Months of Data in
Aux8
100%
99%
98%
97%
96%
95%
94%
91%
85%
71%
59%
35%
Source: SWAT Run Summaries (USEPA 2001 b).
Outputs from the computational components in SWAT (the WTP model and Decision Tree
Program) are also stored in AUX8 and consist of the following for each plant:
• Treatment technology level at compliance
• Modified process train at compliance (e.g., modified chemical doses)
Final Economic Analysis for the Stage 2 DBPR
A-6
December 2005
-------
• Water quality at compliance for finished water, average distribution system residence time,
maximum distribution system residence time locations (see Section A3 for a complete
description of these locations in SWAT):
Disinfection Byproduct:
- Chloroform (CHC13)
Bromodichloromethane (BDCM)
Dibromochloromethane (DBCM)
- Bromoform (CHBr3)
- Total trihalomethanes (TTHM)
Monochloracetic acid (MCAA)
Dichloroacetic acid (DCAA)
Trichloroacetic acid (TCAA)
Monobromoacetic acid (MBAA)
Dibromoacetic acid (DBAA)
Bromochloroacetic acid (BCAA)
- Haloacetic Acid (HAAS) (sum of MCAA, DCAA, TCAA, MBAA, and DBAA)
- HAA6 (sum of HAAS and BCAA)
- HAA9 (sum of HAA6 and BDCAA, CDBAA, and TBAA)
where: BDCAA = Bromodichloroacetic acid
CDBAA = Chlorodibromoacetic acid
TBAA = Tribromoacetic acid
Other Water Quality Parameters
Bromate
Chlorite
Temperature
- pH
Alkalinity
- TOC
- UV254
Bromide
Calcium
Magnesium
Ammonia
Disinfectant Residuals
Pathogen Inactivation
SWAT outputs are discussed further in the next two sections.
A.2.3 Water Treatment Plant Model
The WTP Model predicts the formation of DBFs given source water quality conditions and water
treatment plant configuration. It consists of several empirical equations that predict DBF precursor and
disinfection behavior, the impact of water treatment plant processes on water quality, and concentrations
of DBFs in the distribution system. The original version of the WTP Model was developed in 1992
(Water Treatment Plant Simulation Program Version 1.21 User's Manual, Malcolm Pirnie Inc., June
1992). In 2000, the WTP Model was thoroughly revised to incorporate new research in the areas of DBP
Final Economic Analysis for the Stage 2 DBPR A-7 December 2005
-------
precursor removal and DBF formation during chlorination, ozonation, and chlorine dioxide addition. The
extensions and modifications to the original model have been documented in Solarik et al. (2000).
The purpose of this section is to describe how DBF precursors and other related parameters
were modeled through a treatment plant and to present the final equations used by the WTP Model to
predict DBF concentrations. DBF precursors need to be model as accurately as possible as the impact
the amount of DBF formation. Since chlorination DBF's are formed by the interaction of chlorine with
organic and inorganic matter, TOC, a measure of the organic content of water, is a key factor in
predicting chlorination DBFs.
The last subsection includes a description of how the final DBF equations are used for different
treatment plant scenarios. Section A.5 builds on this section by explaining how the DBF equations were
calibrated using ICR data.
A.2.3.1 Predicting Changes in pH
The WTP Model predicts pH changes as a result of chemical addition during coagulation and
softening using thermodynamic equilibrium assumptions in a closed system (with respect to carbon dioxide
equilibrium). This may not be an entirely accurate assumption since a water treatment plant is neither a
perfectly closed system because it is open to the atmosphere, nor a perfectly open system because of the
depths of the basins. The WTP Model equations that predict pH changes due to softening do not account
for the kinetics of processes such as calcium carbonate precipitation or carbon dioxide dissolution.
Consequently, predictions are not always completely accurate. In general, the WTP Model is believed to
slightly over-predict the depression of pH due to coagulant addition (Solarik et al. 2000).
Coagulation pH is an input parameter for the algorithms that calculate settled water TOC and
UVA. The over-prediction of the depression in pH could result in the propagation of error in the settled
water quality. However, based on observed data from several water treatment plants, these errors are not
large (see section A.5, Model Calibration).
A.2.3.2 Predicting TOC Removal
In the earlier (1992) version of the Model, TOC removal by coagulation was predicted using an
empirically-derived equation based on the raw water TOC, coagulant dose, and the coagulation pH. In
the current version of the Model, TOC removal is predicted using a semi-empirical sorption model
published by Edwards (1997). Though the semi-empirical sorption model is applicable specifically for
dissolved organic carbon (DOC) removal, it has been shown to predict TOC removal nearly as well
(Edwards 1997). The major differences in the 1992 model equations and the current semi-empirical
sorption model are:
• The current model divides the TOC into fractions that are sorbable and non-sorbable by the
coagulant, and attributes TOC removal to the sorbable fraction alone.
• In addition to TOC, coagulant dose, and the coagulation pH, the current model uses certain
calculated model coefficients and the Specific UVA (SUVA - the ratio of UVA to the DOC
concentration) of the raw water as inputs.
Final Economic Analysis for the Stage 2 DBPR A-8 December 2005
-------
A.2.3.3 Predicting UVA Reduction
In the 1992 version of the WTP Model, the precision of the equations used to predict UVA
removal was limited by the small data sets used in their derivation. The new equations are based on data
analysis performed on the more extensive American Water Works Association (AWWA)/Water Industry
Technical Action Fund (WITAF) database (Tseng et al. 1996), thereby improving their precision.
An analysis of predictive errors for the UVA removal equations was performed using raw water
data from the AWWA/WITAF database as inputs to the equations and comparing the WTP Model
results to those from the database. The analysis concluded that the equations tend to over-predict UVA
removal. Further, the errors in settled water UVA predictions are greater for softening than for
coagulation. However, it must be noted that the data set used for verification of UVA removal by
softening (i.e., from the AWWA/WITAF database) is very limited.
A.2.3.4 Predicting Chlorine Decay
In the current version of the WTP model, chlorine decay is predicted using a single equation
based on bench scale data and work published by Koechling et al. (1998). The general form of the
equation is:
Ct = [«, x ln(Co/Ct)] - [k2 x SUVA,, x t] + C0
where:
Ct = chlorine residual concentration at any reaction time t
C0 = initial chlorine dose
al = a kinetic rate parameter related to the initial dissolved organic carbon (i.e., DOC0) and the
initial UVA (i.e., UVAg), for a given chlorine-to-TOC ratio.
k2 = -[a x (UVAo15)], where a and b are fitted parameters that depend on the treatment and the
chlorine dose
SUVAo = Initial Specific UVA = (UVA0/TOC0), where TOC0 = initial TOC
t = reaction time
The derivation of al was originally performed at a chlorine-to-TOC ratio of 2:
«1@2 = 4.98*UVAo - 1.91*DOC
A correction factor was developed for «13 making it applicable for other chlorine-to-TOC ratios (Solarik et
al. 2000):
a1/a1&2 = 0.503 (CL/TOC)
A.2.3.5 WTP Model Equations for DBF Formation
During the development of the WTP simulation model in 1992, only a limited number of research
reports were available to derive predictive equations for THM formation during chlorination. As a result,
the 1992 version used an empirical THM formation equation that was based on chlorination experiments
Final Economic Analysis for the Stage 2 DBPR A-9 December 2005
-------
of raw (i.e., no coagulation or filtration) waters only. The equation was originally used in the model
irrespective of chlorine application locations throughout the water treatment plant. Chlorination conditions
on which this original THM predictive equation was based included conditions that are experienced in
water plants as well as some more severe chlorination conditions that are beyond normal practice at
water plants.
At the time of developing the revised WTP simulation model in 2000, predictive equations for
THM were available from the literature that represented more realistic chlorination conditions at various
stages of treatment. Consequently, different predictive equations were used for predicting THM
formation in raw water and in waters after various levels of treatment. This section discusses the
different sets of equations used by the WTP Model to predict DBF formation. It includes two sets of
equations used to model DBF formation as a result of (1) raw water chlorination (i.e., water not subjected
to any treatment other than chlorination), and (2) chlorination of treated water (i.e., water subjected to
full-scale treatment process(es) besides chlorination).
DBF Formation as a Result of Chlorination of Raw Water
"Raw water" model equations were empirically derived from studies documenting the chlorination
of untreated/raw waters under laboratory conditions.
raw = 0.0412(TOCraw)1-098(Cl2)0-152(Brraw)0-0
-------
TTHM = 23.9(TOCxUVA)0-403(Cl2)0-225(Br)ai41(1.027)(T-20)(l. 156)(pH-7-5)(t)°-264
HAA5=41.6(TOCxUVA)0328(Cl2)0-585(Br)-ai2(1.021)fr-20'(0.932)(PH-7-5'(t)0-150
where:
TTHM = treated water TTHM (ug/L): 13 < TTHM < 690
HAAS = treated water HAAS (ug/L): 12 < HAA6 < 643
TOC = treated water TOC (mg/L): 1.00 < TOC < 7.77
UVA = treated water UVA (cm'1): 0.016 < UVA < 0.215
C12 = applied chlorine dose (mg/L): 1.11 < C12 < 24.75
Br = treated water bromide concentration (ug/L): 23 < Br < 308
T = temperature (degrees centigrade): 15 < T < 25 4
pH = treated water pH: 6.5 < pH < 8.5 3
t = reaction time (hour): 2 < t < 168
The treated water TTHM and HAA5 equations were verified by plotting modeled results against
observed values from 47 coagulated waters and 4 softened waters and analyzing the residuals (i.e., the
predicted value minus the observed value) and average errors. In general, results indicated that the WTP
Model slightly under-predicted the formation of TTHMs and slightly over-predicted the formation of
HAA5s for coagulated waters. For TTHMs, ninety percent of the residuals were within ±24 ug/L of the
measured values. For HAA5s, ninety percent of the residuals were within ±18 ug/L of the measured
values. Due to the limited number of data points, the results from the analysis of the softened waters
were not as conclusive as those from the coagulated waters.
A.2.3.6 Using the DBF Formation Equations for Different Chlorinating Scenarios
DBF formation is modeled as the cumulative formation through the treatment plant. This section
describes how the two sets of equations presented above can be applied to different treatment plant
chlorination scenarios. The following scenarios are discussed:
• Pre-chlorination only (i.e., chlorine added just prior to coagulation)
• Post-chlorination only (i.e., a single point of chlorination just prior to filtration, after the
combined treatment of coagulation, flocculation, and sedimentation)
• Pre- and Post-chlorination (i.e., two points of chlorination -just prior to coagulation and just
prior to filtration)
Exhibit A.4 (presented at the end of this subsection) shows where the chlorine is assumed to be applied
within the treatment plant for the pre- and post-chlorination scenarios and summarizes how DBP
formation is modeled. Note that separate equations for DBP formation in distribution systems were not
developed—the distribution system is considered as an extension of the treatment plant, and formation is
assumed to follow the same kinetics and rates.
Sufficient pH and temperature-dependent data were not available to model their effect on DBP formation
for treated waters. Therefore, pH and temperature factors from the raw water equations were applied to treated water
conditions. These factors are valid in the temperature range of 15-25°C and a pH range of 6.5-8.5.
Final Economic Analysis for the Stage 2 DBPR A-11 December 2005
-------
Pre-Chlorination Only
The raw water model equations were originally used to predict DBF formation for plants that pre-
chlorinated only. However, research by Summers et al. (1998) indicates that pre-chlorination just before
or after rapid mixing results in less DBF formation than chlorination of raw water as shown in the original
studies. To better predict DBF formation post-coagulation/flocculation, an empirical pre-chlorination
factor was developed to account for the decrease in DBF formation that occurs as a result of adding
chlorine just prior to the rapid mixers relative to the DBF formation that occurs as a result of adding
chlorine to the raw water:
Decrease in TTHM Formation = 85.3 % of raw water model results
Decrease in HAA5 Formation = 79.4 % of raw water model results
As shown by Exhibit A.4, the raw water equations, adjusted using the pre-chlorination factors, are used to
model DBF formation through the sedimentation process (prior to the filters). The treated water model is
used to predict DBF formation through the filtration process and into the distribution system, using settled
water quality (including settled water chlorine residual) as input parameters.
Post-Chlorination Only
For post-chlorination (prior to filtration), the treated water model was applied, with the settled
water quality and chlorine residual after sedimentation being the inputs to the model equations.
Pre- and Post-Chlorination
As shown in Exhibit A.4, the raw water equations, adjusted using the pre-chlorination factors, are
used to model DBF formation from the raw water through the sedimentation process (prior to the filters).
The treated water model is used to predict DBF formation starting after sedimentation. The treated
water model is adjusted because pre-chlorination will result in lowering the UVA of the settled water due
to the oxidation of the UVA by the chlorine. The settled UVA after prechlorination (i.e., UVApre.cl2)
was estimated from the settled UVA without prechlorination (i.e., UVANo C12) using the following
equation:
UVApre.cl2 = 0.7437 (UVANo C12) + 0.0042
where the UVA concentrations are expressed in cm"1.
Final Economic Analysis for the Stage 2 DBPR A-12 December 2005
-------
Exhibit A.4 Application of DBF Formation Equations for Three Chlorinating
Scenarios
1) PRE-CHLORINATION ONLY
Alum
Cl,
Floc/Sed
r liter
Distribution
System
DBFs
Raw Water Model
adjusted with
Prechlorination Factor
Treated Water Model
(using settled water
quality and C12 residual)
Time (or Location in Plant)
Final Economic Analysis for the Stage 2 DBPR
A-13
December 2005
-------
Exhibit A.4 Application of DBF Formation Equations for Three Chlorinating
Scenarios (Continued)
2) POST-CHLORINATION ONLY
Alum
Floc/Sed
4 r
Filter
Distribution
System
DBFs
Treated Water Model for
settled water quality and
C12 dose
Time (or Location in Plant)
3) PRE- AND POST-CHLORINATION
Alum
I
CL
C12
1 ,
Filter
Distribution
System
DBFs
Raw Water Model
adjusted with
Prechlorination Factor
Treated Water Model
(with time = 0 at 2nd
C12 addition point and
UVA decreased)
Time (or Location in Plant)
Final Economic Analysis for the Stage 2 DBPR
A-14
December 2005
-------
A.2.4 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 least-cost treatment technology. This process continues until the
plant achieves compliance. The treatment technology selection algorithm can therefore be described as a
"least cost" based approach. The program receives inputs from the database (AUX8), uses the WTP
Model to estimate treated water quality before and after predicted treatment technology changes, and
sends the results back to the database.
The steps involved with using the Decision Tree Program are presented in Exhibits A. 5 and A. 6
in flow chart and table format. The starting point is at the top of the tree, and the process improvement
order is from the top row to the bottom row and from left to right in any row.
For each treatment technology starting with Enhanced Coagulation/Enhanced Softening (EC/ES)
there is an additional option of chloramine secondary disinfection with that treatment technology. For
example, if the tree starts at EC/ES treatment technology and that treatment technology does not yield
compliance, then the next option is EC/ES with chloramines. One important aspect of the decision tree is
how it accounts for existing disinfection credit. To implement an advanced disinfectant in a process train,
SWAT credits the train with the levels of inactivation specified by the user (see section A. 3 for user
inputs) and adjusts the existing primary disinfectant to achieve the necessary CT credit. Any other
chlorine additions contributing to CT are decreased, if necessary.
Final Economic Analysis for the Stage 2 DBPR A-15 December 2005
-------
Exhibit A.5 SWAT Decision Tree
(Compliance Selection Sequence)
Adjust Disinfection
Optional
2Ozone (raw)
2Ozone (sed.)
3GAC10+CIO,
3GAC20+CI00
1'3GAC10+UV
, •3GAC20+UV j
3GAC10+03(raw)
3GAC20+03(raw)
•
»
3GAC10+03(sed)
3GAC20+03(sed)
MF+NF50
MF+NF75
MF+NF100
'Optional steps that the user determines whether to include in the tree. For Stage 1 and Stage 2 runs, turbo
coagulation was an available treatment technology. UV was "turned off" for Stage 1 but "turned on" for Stage 2
runs. See Section A.3, User Inputs for Stage 2 DBPR Model Runs, for more information.
'With EC/ES.
3Not applicable for plants that initially soften via precipitation.
Notes: Order is top to bottom, and left to right. The granular activated carbon (GAC)10/20 + O3(raw/sed) treatment
technology can be implemented with or without pH adjustment. Chloramines can be used at any point in the
decision tree (including initial plant).
Final Economic Analysis for the Stage 2 DBPR
A-16
December 2005
-------
Exhibit A.6 Abbreviations Used and Description of Decision Tree Steps
Abbreviation
Initial Plant
Adjust
Disinfection
EC/ES
TC
Move C\2
CI02
UV
Ozone (raw)
Ozone (sed.)
MF/UF
GAG 10
GAC20
Descriotion
Unmodified Plant
Adjust Disinfection
Enhanced Coagulation/
Enhanced Softenina
Turbo Coagulation
Move Chlorination Point
Chlorine Dioxide
UV Disinfection
Ozonation (raw water)
Ozonation (settled water)
M icrof i Itrati on/I) Itraf i Itrati o n
GAC(10-min. EBCT)
GAG (20-min. EBCT)
Abbreviation
GAC10 + CIO2
GAC10 + UV
GAC10 + O3(raw)
GAC10 + O3(sed.)
GAC20 + CIO2
GAC20 + UV
GAC20 + O3(raw)
GAC20 + O3(sed.)
MF + NF50
MF + NF75
MF + NF100
Descriotion
GAC10 with Chlorine Dioxide
GAC10 with UV Disinfection
GAC1 0 with Ozonation of raw water
GAC10 with Ozonation of settled
water
GAC20 with Chlorine Dioxide
GAC20 with UV Disinfection
GAC20 with Ozonation of raw water
GAC20 with Ozonation of settled
water
MF/UF with 50% of flow treated by
Nanofiltration
MF/UF with 75% of flow treated by
Nanofiltration
MF/UF with 100% of flow treated by
Nanofiltration
The least cost decision approach, as used in SWAT, has two inherent limitations that contribute to
uncertainty in national cost and benefit estimates:
• The decision tree does not include operational or design modifications of the distribution
system that could reduce DBFs and allow the plant to achieve compliance without a
treatment technology change.
• The model cannot take into account site specific factors (e.g., taste and odor) that could
cause a system to choose a more expensive treatment technology than the SWAT least cost
algorithms say is necessary.
Uncertainties are discussed further in Section A.6.
A.2.5 Improvement in Decision Tree for Stage 2 versus Stage 1
In the Stage 1 DBPR Regulatory Impact Analysis (RIA) (USEPA 1998a), EPA estimated
treatment technologies in place at treatment plants prior to the Stage 1 DBPR, as well as treatment
technology changes that systems would make to comply with the Stage 1 DBPR. This estimate of
treatment technologies in place for the pre-Stage 1 baseline is not the same as the pre-Stage 1 baseline
derived in this EA. The two estimates differ because new information and treatment technologies, such
as UV disinfection, have become available since the promulgation of the Stage 1 DBPR. For the Stage 2
DBPR analyses, new tools and processes were used to forecast the costs of complying with the Stage 1
DBPR. These tools and processes, summarized in Chapter 7, included:
Final Economic Analysis for the Stage 2 DBPR
A-17
December 2005
-------
• SWAT
• ICR Ground Water Delphi process
• Expert opinion process for small systems (both surface and ground water)
These tools and processes provided a larger and more detailed set of treatment technology choices than
those used in the Stage 1 DBPR RIA. Consequently, the estimate of treatment technologies in place for
both the pre-Stage 1 and post-Stage 1 baselines, while different from those in the Stage 1 DBPR RIA, are
based on a more complete set of compliance options and a more rigorous analysis. Exhibit A.7 compares
the treatment technology choices used in the Stage 1 DBPR RIA to those used in the Stage 2 DBPR EA.
The detailed treatment technology choices evaluated for the Stage 2 DBPR EA were aggregated
into more general categories for the purposes of estimating national costs. The final 12 major treatment
technology categories evaluated in this EA are summarized in Exhibit A.8. They are generally ordered
according to cost, with the most expensive at the bottom of the exhibit. With each treatment technology,
systems are expected to use either free chlorine or combined chlorine (chloramines) as the residual
disinfectant. Conversion from free chlorine to chloramine residual disinfection is a relatively inexpensive
way for systems to reduce DBP levels.
The first four treatment technologies (in italic font in Exhibit A. 8) represent operational changes
to existing treatment configurations. Although these changes may result in small increases in chemical
usage or minor capital improvements, EPA assumes their costs to be negligible when compared to the
costs of the advanced treatment technologies (e.g., UV, ozone, granulated activated carbon,
microfiltration/ultra-filtration) shown in Exhibit A. 8 (refer to Technologies and Costs for Control of
Microbial Contaminants and Disinfection Byproducts [USEPA 2003o] for comparison). Also, most
systems that are able to use these treatment technologies are predicted to do so to meet the Stage 1
DBPR. For these reasons, the predicted costs for the Stage 2 DBPR do not include costs for operational
changes. (Section A.6 and Chapter 7 further explain that this uncertainty may lead to an underestimate in
national costs.)
Because UV is an emerging treatment technology, it was not considered an option for most
systems for the Stage 1 DBPR. For the Stage 2 DBPR, UV is an advanced disinfection option for all
surface water systems and small ground water systems. Adjustments to the compliance forecast to
account for use of UV are discussed in Chapter 5 and Appendices A and B.
As indicated in Exhibit A. 8, fewer treatment technologies are listed for ground water plants than
for surface water plants. As summarized in Appendix B, section B.2.2, the ICR Ground Water Delphi
Group concluded that large ground water systems would choose primarily from four treatment
technologies: conversion to chloramines, ozone, granular activated carbon - 20-minute contact time
(GAC20), or nanofiltration; small ground water systems would also consider UV. The selection of
treatment technologies as a function of source water types and small systems' constraints are
summarized in Chapter 5 and discussed in detail in the compliance forecasts for surface and ground water
plants, as described in Appendices A and B, respectively.
Final Economic Analysis for the Stage 2 DBPR A-18 December 2005
-------
Exhibit A.7 Treatment Technologies Considered for the Stage 1 DBPR in the
Stage 1 DBPR RIA and their Stage 2 DBPR EA Equivalent
Stage 1 DBPR RIA Treatment
Technologies
Stage 2 DBPR EA Treatment Technologies
Chlorine/Chloramine
Adjust Primary Disinfection
Move Points of Disinfection with Chloramines
Enhanced Coagulation
Enhanced Coagulation with Chlorine
Turbo Coagulation with Chlorine
Enhanced Coagulation with
Chloramines
Enhanced Coagulation with Chloramines
Turbo Coagulation with Chloramines
Chlorine Dioxide
Chlorine Dioxide with Chlorine
Chlorine Dioxide with Chloramines
Ozone with Chloramines
Ozone with Chlorine
Ozone with Chloramines
GAC10
GAC10 with Chlorine
GAC10 with Chloramines
GAC10 + Chlorine Dioxide with Chlorine
GAC10 + Chlorine Dioxide with Chloramines
GAC10 + UV (Small Systems)
GAC20
GAC20 with Chlorine
GAC20 with Chloramines
GAC20 + Chlorine Dioxide with Chlorine (Large and Medium
Systems)
GAC20 + Chlorine Dioxide with Chloramines (Large and Medium
Systems)
GAC20 + Ozone with Chlorine (Small Systems)
GAC20 + Ozone with Chloramines (Small Systems)
GAC20 + UV (Small Systems)
Membranes
Microfiltration/Ultrafiltration with Chlorine
Microfiltration/Ultrafiltration with Chloramines
Integrated Membranes with Chlorine (Surface Water Systems)
Integrated Membranes with Chloramines (Surface Water
Systems)
Nanofiltration with Chlorine (Ground Water Systems)
Nanofiltration with Chloramines (Ground Water Systems)
Source: Stage 1 DBPR RIA(USEPA 1998a) for Stage 1 treatment technologies; Federal Advisory Committees Act
(FACA) deliberations for Stage 2 treatment technologies (USEPA 2000n).
Final Economic Analysis for the Stage 2 DBPR
A-19
December 2005
-------
Exhibit A.8 Aggregated Treatment Technology Categories for Stage 1 DBPR Used
for the Stage 2 DBPR EA
Treatment Technology
Category
Adjust Primary
Disinfectant Dose
Enhanced
Coagulation/Enhanced
Softening
Turbo Coagulation
Moving Point of
Disinfection
Chlorine Dioxide
Ozone
MF/UF
GAC10
GAC10 + Advanced
Disinfectants
GAC20
GAC20 + Advanced
Disinfectants
Membranes
Explanation of Technology for
Surface Water Plants
Reduce primary disinfectant dose
(usually chlorine)
Increased TOC removal through
increased coagulant addition to meet
Stage 1 DBPR requirements
Increased TOC removal through
increased coagulant addition, but
higher than that required by
enhanced coagulation
Move point of disinfection
downstream to minimize formation of
DBFs
Chlorine dioxide instead of chlorine
for primary disinfection
Ozone instead of chlorine for primary
disinfection, applied to raw or settled
water
Microfiltration or ultrafiltration as the
particle removal process
Granular activated carbon with a
1 0-minute Empty Bed Contact Time
(EBCT)
GAC10 + chlorine dioxide (large and
medium systems)
GAC10 + UV (small systems)
Granular activated carbon with a 20-
minute EBCT
GAC20 + UV or ozone
Integrated membranes as the
particle removal process (MF/UF and
nanofiltration)
Explanation of Technology for
Ground Water Plants
NA
NA
NA
NA
NA
Ozone instead of chlorine for primary
disinfection, applied to raw or settled
water
NA
NA
NA
Granular activated carbon with a 20-
minute EBCT
NA
Nanofiltration alone as the particle
removal process
Notes: NA = Not applicable plant type. Italic font indicates that treatment technology was not considered in
estimating costs of rule alternatives.
Source: Technology and Cost Document (USEPA 2003o); applicability to ground water systems discussed in
Chapter 5 and Appendix B of this EA.
Final Economic Analysis for the Stage 2 DBPR
A-20
December 2005
-------
A.3 User Inputs for SWAT Model Runs
This section summarizes the inputs and settings (as entered into the SWAT user interface) used
for the Stage 2 DBPR regulatory alternatives. SWAT was also used to support the development of the
Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR). The inputs presented here,
however, are specific the Stage 2 DBPR development process. Those specific to the LT2ESWTR are
described in the Economic Analysis for the LT2ESWTR (USEPA 2003c). A complete listing of the user
inputs for each SWAT Run used in the Stage 2 DBPR can be found in the Access databases that contain
the results for each run. The compliance scheme, and compliance aggregation method, are also inputs to
the SWAT Model and are described in Section A. 4.
Average and Maximum Residence Times
SWAT computes DBP concentrations at theoretical locations representing average and maximum
residence times in the distribution system. The inputs for the average residence time location (DS
Average) and the maximum residence time location (DS Maximum) are based on ICR data from four
distribution system residence times reported by the system as follows.
• Distribution System Equivalent (DSE)—a sample point in the distribution system that has a
residence time equivalent to a laboratory sample.
• Average 1 and Average 2 (AVG1 and AVG2)—two locations having average residence
times in the distribution system, as designated by the system.
• Distribution System Maximum (MAX)—the location having the longest residence time in the
distribution system, as designated by the system.
The input for the DS Average is the average of those four residence times. The input for DS
Maximum is the highest residence time reported for those four locations.
Flowrate Conditions Used
Three flowrate conditions are available for SWAT execution: 1) flow at time of ICR sampling; 2)
average monthly flow for a given ICR period; and 3) plant design flow. All calculations of DBP
concentrations were completed using the average monthly flow. All new unit processes "built" by SWAT
were sized using the design flow condition.
Inclusion of Eiofiltration
All Stage 2 DBPR regulatory evaluations included biofiltration processes for ozone treatment
technologies. This assumed that the filters downstream of ozonation would achieve enhanced DBP
precursor removal.
Surface Water Treatment Rule Disinfection Requirements
For all regulatory alternatives, the plants must meet, at a minimum, the Surface Water Treatment
Rule (SWTR) Giardia and virus log removal requirements of 3 and 4 logs, respectively. The "Initial
Plant Run" did not have this requirement since it represents pre-Stage 1 or existing conditions. Therefore,
Final Economic Analysis for the Stage 2 DBPR A-21 December 2005
-------
all systems are not assumed to be compliant with the SWTR. In other words, if SWAT predicted a plant
to achieve lower Giardia or virus log removals, the plant was not modified for this run.
Log Removal Credits for Pathogens
Log removal credits for pathogens were based on (1) the recommended credits contained in the
Guidance Manual for Compliance with the Filtration and Disinfection Requirements for Public
Water Systems Using Surface Water Sources (USEPA 1990), and (2) as recommended by the
Microbial Treatment subcommittee of the TWO (Exhibit A.9). Cryptosporidium inactivation/removal
requirements were not included (they are considered under the LT2ESWTR). If the removal credits used
in SWAT are overstated (i.e., the credits are greater than the treatment provides), then the estimates
provided would under-specify treatment selection and consequently under-predict national compliance
costs and benefits. Likewise, if the removal credits used in SWAT are understated, then the treatment
technology selection could be over-specified and both the national compliance costs and benefits over-
predicted.
Exhibit A.9 Log Removal Credits Used as Default Values in SWAT
Unit Process
Microfiltration/Ultrafiltration
Nanofiltration
Sedimentation
Filtration
Log Removal Credits (logs)
Giardia
3.0
3.0
0.5
2.0
Virus
2.0
2.0
1.0
1.0
Source: Guidance Manual for Compliance with the Filtration and Disinfection Requirements for Public Water
Systems Using Surface Water Sources (USEPA 1990)
Use of Disinfection Benchmarking
Disinfection benchmarking is the lowest monthly average of microbial inactivation during the
disinfection profile period. Benchmarking is used to ensure a plant does not compromise microbial
protection when changing treatment technologies. If "Benchmarking OFF" is selected, then SWAT
selects disinfectant doses to meet the most stringent of the log removal and/or inactivation requirements
set for the regulatory option. If "Benchmarking ON" is selected, SWAT determines the minimum
monthly level of log removal plus inactivation for each plant under existing conditions and sets these as the
log removal plus inactivation requirements for that plant for all process modifications. If the benchmark is
less stringent than the disinfection requirements set for that SWAT run, SWAT will default to the most
stringent requirements.
All Stage 2 DBPR regulatory evaluations, as well as the Stage 1 baseline evaluation, were
conducted with "Benchmarking ON." Maximum benchmark levels for Giardia and viruses were set at
8.0 and 9.0 logs, respectively. Cryptosporidium disinfection was not benchmarked because most
systems currently don't achieve any Cryptosporidium inactivation. Using the "Benchmarking ON"
option most likely causes an overall higher treatment technology selection estimate. Some systems may
use a high dose of oxidant for other reasons (e.g., taste and odor control); the high level of disinfection is a
secondary benefit. In the SWAT model, if a plant currently has a high oxidant dose and its DBF
estimates are above the user-defined MCLs, then the next treatment technology in the decision tree is
selected and the same high level of inactivation corresponding to the annual high oxidant dose must be
Final Economic Analysis for the Stage 2 DBPR A-22 December 2005
-------
maintained. (However, in implementation of the DBPR the State may allow lower disinfection for
improved DBF control, as long as the level of disinfection is higher than the existing standards.)
Chloramine Conversion Rate
SWAT can evaluate three settings to represent whether treatment plants that initially use free
chlorine for secondary or residual disinfection will convert to chloramines.
• All free chlorine plants can convert
• No free chlorine plants can convert
• A specified percentage of free chlorine plants can convert, and are assigned randomly
through a Monte Carlo probability function
For regulatory evaluation, 77 percent of free chlorine plants were randomly allowed to convert to
chloramines. This was set as the maximum possible conversion rate expected for all free chlorine plants
in the United States. This percentage rate was recommended by the TWO during the M-DBP FACA.
This maximum national chloramine usage level is intended to incorporate site-specific circumstances and
other local factors that would preclude chloramine usage at some plants for reasons other than technical
suitability. The maximum chloramine conversion rate was approached only when more stringent
regulatory alternatives (i.e., 40/30 Running Annual Average (RAA)) were evaluated.
Use ofUV
Adding UV disinfection to a treatment process is an optional step in the SWAT decision tree.
Because UV is an emerging treatment technology for drinking water treatment it was not considered a
viable option for Stage 1 compliance. However, EPA believes the treatment technology and necessary
regulations will be available for systems to use UV to achieve compliance with the Stage 2 DBPR.
Therefore, the UV option was "turned off" for the Stage 1 DBPR run and "turned on" for the Stage 2
DBPR runs. (Part III of this Appendix for further discussion on the inclusion of UV for the Stage 2
runs.)
Clearwell Baffling Improvement Rate
For regulatory evaluation, 90 percent of plants were assumed able to make improvements to
clearwell baffling. The TWO assumed that a 0.70 value for the clearwell baffling factor (the ratio of the
time required for 10 percent of a system's flow to pass through the clearwell to the theoretical detention
time in the clearwell) was a reasonable upper limit for improvements to hydraulic retention through such
basins. An analysis of the ICR data on clearwell baffling factors showed that 10 percent of ICR plants
had baffling factors at or above 0.70. Therefore, the remaining 90 percent of the plants could improve
their clearwell hydraulic regime to attain such a baffling factor. While SWAT allowed 90 percent of the
plants to increase the hydraulic retention time performance of clearwells, it did not require plants to do so
in evaluating regulatory alternatives. The clearwell baffling factor was considered only when increased
disinfection performance was necessary and could be achieved by such measures.
Final Economic Analysis for the Stage 2 DBPR A-23 December 2005
-------
Nanofiltration Performance for Precursors
Nanofiltration performance for precursors was assigned based on ICR Treatment Studies data,
representing the median performance of nanofilters for precursor control. The performance and
operating parameters were assigned as follows.
• TOC removal = 92 percent
• UVA removal = 87 percent
• Bromide removal = 78 percent
• Molecular weight cutoff = 200 daltons
• Water recovery = 85 percent
GAC10 and GAC20 Regeneration Frequency
When the decision tree program chooses GAC10 or GAC20 as the next feasible treatment
technology to achieve compliance, it adopts the following sequence of reactivation frequencies to check
for compliance: An initial evaluation with a reactivation frequency of 360 days, followed by reactivation
frequencies of 300, 240, 180, 120, and 90 days in that order, until the plant is in compliance. The TWG
verified that the cost hierarchy of the compliance decision tree was maintained under this sequence.
Turbo Coagulation
Turbo coagulation achieves increased TOC removal using coagulant doses higher than those
required by enhanced coagulation. A (4x3) matrix of raw water TOC and alkalinity defines the percent
TOC removal in SWAT. The default turbo coagulation setting used in SWAT represents the 75th
percentile ICR values for a given raw water TOC-alkalinity category (i.e., 25 percent of ICR water
treatment plants in a given raw water TOC-alkalinity category achieved TOC removal greater than or
equal to the specified level). Exhibit A. 10 shows the additional TOC removal achieved with turbo
coagulation at these settings.
To determine if turbo coagulation was a viable treatment alternative, the ICR data were analyzed
to see if additional TOC removal was possible. For surface water plants with conventional treatment
(non-softening plants), the TOC removal was found for each month where available data existed. Each
plant was characterized within the Stage 1 DBPR enhanced coagulation matrix for TOC removal, based
on the annual average source water alkalinity and TOC. The distribution of annual average TOC removal
for ICR plants was determined for each alkalinity and TOC category in the matrix. The median
performance of the plants within each of the categories was found to be very close to the TOC removal
requirements in the Stage 1 DBPR. Therefore, the ability of such plants to achieve even more TOC
reduction by further enhancing their treatment performance was considered a viable treatment
alternative.
SWAT did not require any plants to meet the TOC removal performance criteria contained in the
turbo coagulation step, but allowed conventional plants to further optimize TOC removal as a means of
meeting DBF requirements. The inclusion of the turbo coagulation treatment step contributes to more
Final Economic Analysis for the Stage 2 DBPR A-24 December 2005
-------
realistic national compliance costs by reducing the number of plants requiring more advanced, but possibly
unnecessary, treatment technologies to meet DBF standards.
Exhibit A.10 Additional Increase in TOC Removal for the Turbo Coagulation
Treatment Step
100
90 -
80 -
f- ™-
* 60 -
o
S 50 -
tf 40 -
g 30 -I
H 20 -I
34
n
34
10
15
R
TOC<2 TOC2-4, TOC 2-4, TOC 2-4, TOC 4-8, TOC 4-8, TOC 4-8, TOO8,
Alk<60 Alk60- Alk>120 Alk<60 Alk60- Alk>120 Alk<60
120 120
D Required Removal • ICR 75%ile Annual Avg
Numbers above bars indicate additional TOC
percent removal with Turbo Coagulation
A.4 Model Operation
This section explains how compliance is determined and lists several uncertainties associated with
SWAT's compliance determination methodology.
A.4.1 Compliance Determination
Each plant's compliance was determined in one of three ways:
• RAA is the calculated average of all distribution system samples. For SWAT, the RAA was
calculated by averaging the SWAT-predicted monthly concentrations at the DS Average
location, as described in Section A.3, over the 1-year period.
• Locational Running Annual Average (LRAA) is the average of four quarters of data from
each distribution system location. For SWAT, the LRAA was calculated by averaging the
Final Economic Analysis for the Stage 2 DBPR A-25 December 2005
-------
SWAT-predicted monthly concentration at the DS Maximum location, as described in Section
A3, over the 1-year period.
• Single high is the highest concentration of the four distribution system samples collected. For
SWAT, the single high value was determined by selecting the maximum of the SWAT-
predicted monthly concentrations at the distribution system maximum location.
In addition, SWAT determines compliance for bromate and chlorite. The bromate MCL was
determined using an annual average of predicted bromate at the finished water sample point. The chlorite
MCL was determined as a single high concentration of chlorite predicted in the finished water.
The M-DBP TWO recommended that a mean 20 percent operational safety margin be used for
DBF MCLs (TTHM, HAAS, bromate, and chlorite) when evaluating all regulatory alternatives. This
safety margin is consistent with practices in prior DBF regulatory development efforts and is intended to
represent the level at which systems typically take some action to ensure consistent compliance with a
new drinking water standard. In addition to representing industry practices, the safety margin also is
intended to account for year-to-year fluctuations in DBF data (ICR data are limited to one year and might
not represent the highest DBF concentrations that occur in a system). There is uncertainty, however, in
the concentration below the MCL value at which systems are confident operating (in other words, the
safety margin may be more or less in some specific cases). A 25 percent operation safety margin run
was also conducted for the Preferred Regulatory Alternative to estimate the impacts of the IDSE. See
Chapter 5 for more information.
A.5 Description of WTP Model Calibration Process and Results
The WTP Model was calibrated using observed data to improve its ability to predict the central
tendency of the ICR data and to better general national level predictions. The methodology and results of
the calibration process can be found in Chapter 8 of the report, Information Collection Request Data
Analysis (McGuire et al. 2002). It is important to summarize results of the calibration in this economic
analysis, however, to help characterize the uncertainties in SWAT (see Section A. 6). The remainder of
this section summarizes the WTP Model calibration process and presents the results.
A.5.1 Calibration Methodology
Water Quality Parameters that were calibrated. The calibration process focused on the
following parameters:
• pH adjustment (in softening and non-softening plants)
• TOC removal (in softening and non-softening plants)
• Free chlorine decay
• Chloramine decay
• THM and HAA formation with free chlorine (in treatment plant and distribution systems)
Final Economic Analysis for the Stage 2 DBPR A-26 December 2005
-------
• THM and HAA formation with chloramines
The Model algorithms were calibrated starting with pH and ending with DBFs since the algorithms in
some of the processes in the above list use the results of algorithms for processes preceding them.
Note that calibration was not performed for DBF formation for plants using chlorine dioxide or
ozone due to the lack of sufficient data sets. This introduces uncertainty in compliance forecasts for
systems using these treatment technologies (see Section A.6 for a summary of uncertainties associated
with the SWAT).
Data Set Used for Calibration: Although the ICR database contains data from 350 large
surface water treatment plants across the US, only a subset of those records were used for calibrating the
WTP Model. The following rules were applied to this subset of ICR plants, which further reduced the
number of plants/plant-month records used for the calibration analysis:
1) To avoid seasonal bias, the calibration analysis used the last 12 months of ICR data (i.e., from
January to December 1998), instead of all 18 months.
2) Plants using unit processes such as air stripping or process configurations such as mid-stream
blending were excluded, since the WTP Model was unable to handle those.
3) Plant-month records with missing water quality or treatment train parameters were excluded
from the analysis.
4) Plant-months with predicted finished water alkalinities less than zero were excluded from
further consideration (see step 1 of the calibration approach discussed below). A finished
water alkalinity of less than zero indicated erroneous chemical dosages (most likely errors
with the units). Hence, these plant-months were excluded.
Calibration Approach'. The calibration approach is summarized by the following steps:
1) Generate uncalibrated model predictions, which are stored in AUX8 along with the observed
data. Plant-months with predicted finished water alkalinity less than zero were eliminated
from further consideration.
2) Calculate absolute residuals, i.e., the absolute value of the difference between observed and
predicted data for a particular parameter.
3) Exclude observed and predicted data pairs having the highest 10 percent of absolute residuals
for the parameter being calibrated from further consideration. This was done to ensure that
the extreme outliers in the ICR data didn't skew the calibration of the WTP Model.
4) Generate scatter plots of predicted versus observed data for a given parameter to identify if
calibration adjustments were required. To determine whether a calibration factor was
required, a line of best fit forced through the origin was applied to the scatter plot. If the
slope of that line was within 5 percent of unity, no calibration factor was applied. If the
above was not true, one of the following two calibration adjustments was applied:
Final Economic Analysis for the Stage 2 DBPR A-27 December 2005
-------
(a) Slope-based adjustment: This was applied when the best-fit line not forced through the
origin had an intercept close to zero. Calibration was then performed using the best-fit line
forced through the origin. If the slope of this line was beyond 5 percent of unity, a
multiplicative calibration factor equal to the inverse of this slope was applied to the
appropriate WTP algorithm.
(b) Slope and intercept-based adjustment: This was applied when a clear linear relationship
existed between the observed and predicted values and the best-fit line not forced through the
origin did not have an intercept close to zero. In such cases, there was a clear trend of
under-prediction at one end and over-prediction at the other end. The slope and intercept of
the best-fit line were then used to calibrate the appropriate WTP algorithm.
Model Performance Evaluation'. After the Model was calibrated, its performance was
evaluated as follows:
1) The WTP Model was re-run to generate a set of calibrated predictions.
2) Observed and predicted (new) data were queried from AUX8 for the same plant subsets, and
scatter plots were constructed. The square of the correlation coefficient (i.e., r2) was
calculated for the scatter plots to assess the predictive performance of the Model. An r2
value of close to unity indicates a strong correlation between the observed and predicted data,
and thus a better predictive performance of the Model.
3) Cumulative distributions of all data observed (without the exclusion of any data pairs as
described in step 5 above) were compared to cumulative distributions of predicted data to
assess the ability of the Model to predict full-scale treatment performance on a national level.
4) Paired data were analyzed to investigate the Model's correlation with site-specific ICR
observations. This was achieved by calculating residuals (i.e., SWAT predicted minus ICR
observed value) for paired data for each water quality parameter.
A.5.2 Calibration Results
A summary of the calibration results for all the parameters is presented in Exhibit A. 11. The
exhibit summarizes:
• The calibration adjustment factor for each parameter (refer to step 5 of "Calibration
Approach")
• The r2 value of the scatter plots after calibration (refer to step 2 of "Model Performance
Evaluation")
• The 5th, 50th, and 95th percentile of the actual residuals for each parameter after calibration
(refer to step 4 of "Model Performance Evaluation").
Box plots showing distributions of observed and predicted data after calibration (refer to step 3 of
"Model Performance Evaluation") are not presented here but are included in chapter 8 of the ICR data
analysis book (McGuire et al. 2002).
Final Economic Analysis for the Stage 2 DBPR A-28 December 2005
-------
A.5.3 Discussion of the Calibration Results for each Parameter
Softening plants: An adjustment in the slope and the intercept was required in this case (i.e.,
- 1.86) -^ 0.71). After calibration, the r2 of the scatter plot increased from 0.33 to 0.37. The
slope of the best-fit line, forced through the origin, was within 5 percent of unity. This indicated that the
observed and predicted data pairs were more symmetrically distributed around the line with a slope of
unity, after calibration.
Non-softening plants: No calibration was required since the slope of the best-fit line, forced
through the origin, was very close to unity (i.e., 0.98). The r2 of the scatter plot was substantially higher
than that of the softening plants (i.e., 0.69), indicating a strong correlation between the data pairs.
TOC
Softening plants: A slope adjustment was required in this case (i.e.,TOCcal = TOCong -^ 0.87).
After the calibration, the r2 of the scatter plot was 0.58, thus indicating a fairly strong correlation between
the data pairs.
Non-softening plants: No calibration was required since the slope of the best-fit line, forced
through the origin, for the unconnected predicted data, was very close to unity. The r2 of the scatter plot
was the highest among all the parameters investigated (i.e., 0.84), indicating a very strong correlation
between the data pairs.
A comparison of the distributions of the observed and predicted (after calibration) data (including
data from both softening and non-softening plants) indicated that:
• Predicted values at the 75th percentile or below exceeded observed values by only 0.1-0.2
mg/L.
• The Model predictions were generally slightly higher than the observed values.
Free Chlorine
No calibration was required since the slope of the best-fit line, forced through the origin, for the
uncorrected predicted data, was within 5 percent of unity. The r2 of the scatter plot was 0.49, indicating a
reasonable correlation between the data pairs.
Final Economic Analysis for the Stage 2 DBPR A-29 December 2005
-------
Exhibit A.11 Summary of Calibration Results
Parameter
PH
TOC
Free Chlorine
Chloramine
TTHM:
Finished
TTHM:
DS_AVG
TTHM:
DS_AVG
HAAS:
Finished
Sampling Locations Included
in Analysis
Any in-plant site but mainly
settled, filtered, and finished water
Any in-plant site but mainly
settled, filtered, and finished water
Any in-plant site but mainly
settled, filtered, and finished water
Any in-plant site but mainly
settled, filtered, and finished water
Finished water
Location in distribution system
corresponding to average res.
time
Location in distribution system
corresponding to average res.
time
Finished water
Treatment
Conditions
Softening
Non softening
Softening
Non softening
Plants using free
chlorine as primary
disinfectant
Plants using
chloramines within the
plant
Free chlorine only in
plant and distribution
system
Free chlorine only in
plant and distribution
system
Chloramine in
distribution system
Free chlorine only in
plant and distribution
system
Calibration
Adjustment
pHc, = (PHortg-
1.86) -0.71
None
TOCcal = TOCwig
-0.87
None
None
None
TTHMcal =
TTHMortg^0.77
TTHMcal =
TTHMortg-0.77
TTHMclm = 0.3
xTTHMcaUeeCI
None
Result with
Calibration
Slope = 0.97, r2=0.37
Slope = 0.98, r2=0.69
Slope = 0.95, r2=0.58
Slope = 1.05, r2=0.84
Slope = 0.95, r2=0.49
Slope = 0.87, r2=0.21
Slope = 0.96, r2=0.50
Slope = 1.04, r2=0.52
Slope = 0.99, r2=0.27
Slope = 0.98, r2=0.47
Cumulative Distribution of
Residuals (Calibrated Results)
5th %ile
-1.8
50th %ile
-0.2
95th %ile
1.6
Not reported
-1.0
0.2
1.2
Not reported
-1.4
-2.9
0.0
0.1
1.8
3.0
Not reported
-43
1.7
69
Not reported
Not reported
Final Economic Analysis for the Stage 2 DBPR
A-30
December 2005
-------
Parameter
HAAS:
DS_AVG
HAAS:
DS_AVG
Sampling Locations Included
in Analysis
Location in distribution system
corresponding to average res.
time
Location in distribution system
corresponding to average res.
time
Treatment
Conditions
Free chlorine only in
plant and distribution
system
Chloramine in
distribution system
Calibration
Adjustment
None
HAA5clm=0.35
xHAA5cal,taeCI
Result with
Calibration
Slope = 1.00, r2=0.37
Slope = 1.02, r2=0.27
Cumulative Distribution of
Residuals (Calibrated Results)
5th %ile
-30
50th %ile
1.7
95th %ile
55
Not reported
Notes: "cal" = calibrated predicted value of a parameter; "orig" = uncalibrated predicted value of a parameter; TTHMc,m = calibrated value of predicted TTHM
concentration with chloramines; HAA5am = calibrated value of predicted HAAS concentration with chloramines; TTHMca, taea = calibrated value of predicted TTHM
with free chlorine; HAA5caUeeC, = calibrated value of predicted HAAS with free chlorine
Source: McGuire et al. 2002, Chapter 8
Final Economic Analysis for the Stage 2 DBPR
A-31
December 2005
-------
Chloramine
No calibration adjustment was made in this case even though the slope of the best-fit line forced
through the origin (for the unconnected predicted data) was not within 5 percent of unity. The reasons for
this are:
• The predicted and observed data were weakly correlated to start with (since r2 = 0.21).
Consequently, multiple attempts at calibration failed to produce a desirable improvement.
• The combined effects of the errors in reported dosages of chlorine and ammonia (required for
chloramine formation) compounded the errors in the predicted chloramine residual.
• Chloramine residual is not a critical parameter and is rarely used to achieve disinfection
credit.
Paired data analysis indicated that a substantial spread in the distribution of the residuals (see
Exhibit A. 11), although an evaluation of the observed and predicted distributions indicated that the median
values matched reasonably.
TTHM
For plants using chlorine in the distribution system, modeled TTHM formation was calibrated
using observed ICR data from the finished water location and calculated distribution system average (or
RAA). For plants using chloramines, the DBF formation is estimated as a percent of the predicted
TTHM in plants using free chlorine. Results from the calibration of TTHM formation under different
disinfection scenarios is summarized below:
• TTHM formation at the finished water location when disinfecting with chlorine in the
treatment plant and the distribution system: A slope adjustment was required in this case
(i.e.,TTHMcal = TTHMong - 0.77). After the calibration, the r2 of the scatter plot was 0.50,
indicating a reasonable correlation between the data pairs.
• TTHM formation at the DS Average location when disinfecting with chlorine in the treatment
plant and the distribution system: The slope adjustment factor of 0.77 (from the TTHM in
finished water case described above) was applied to the data set for the DS_AVG location
(i.e.,TTHMcal = TTHMong + 0.77). After the calibration adjustment, the r2 and the slope of
the scatter plot were found to be 0.52 and 1.04 respectively, indicating a reasonable
correlation between the data pairs.
• TTHM formation at the DS Average location when disinfecting with chloramine in the
distribution system: The calibration analysis for the chloramine condition indicated that
TTHM formation with chloramine = 0.30 x TTHM formation with free chlorine.
Final Economic Analysis for the Stage 2 DBPR A-32 December 2005
-------
HAAS
Like TTHM, HAAS was calibrated based on finished water and RAA results for chlorine plants,
and RAA results for chloramine plants. Results from the calibration of HAAS formation under the
following disinfection scenarios is summarized below:
• Chlorine in treatment plant and distribution system (finished water location): The r2 of the
scatter plot for the uncorrected predicted data was marginally lower than that in the case of
TTHMs (i.e., 0.47). However, no calibration was required since the slope of the best-fit line
forced through the origin (for the uncorrected predicted data), was within 2 percent of unity.
• Chlorine in treatment plant and distribution system (DS_AVG location): The r2 of the scatter
plot for the uncorrected predicted data was marginally lower than that in the case of TTHMs
(i.e., 0.37). However, no calibration was required since the slope of the best-fit line, forced
through the origin, for the uncorrected predicted data was nearly unity.
• Chloramine in distribution system (DS_AVG location): The calibration analysis for the
chloramine condition indicated that HAAS formation with chloramine = 0.35 x HAAS
formation with free chlorine.
The middle 50 percent of the observed and predicted distributions of both TTHM and HAA5
show a very good match. However, the predicted values beyond the 90th percentile are significantly
higher than those of the observed values (approximately 25-30 ug/L higher). There is a progressive
increase in disparity at the tails of the two distributions as one moves from pH, to TOC, to chlorine
residual, and finally to TTHM or HAAS. Since the parameters at the beginning of this list serve as inputs
to the algorithms for TTHM and HAAS formation, the predictive errors propagate from the pH algorithm
to the DBF algorithms. Thus the probability of generating outlier predictions increases accordingly. This
coupled with the fact that there are large uncertainties in the distribution system residence time estimates,
results in the DBF predictions exhibiting the greatest spread in residuals of all the parameters.
Part H: Evaluation of SWAT Predictions
A.6 Uncertainties in SWAT Compliance Forecasts
EPA has identified 12 areas of uncertainty in SWAT compliance prediction, as listed in Exhibit
A. 12, that can be grouped into four main categories:
• Uncertainty in ICR observed data, upon which the SWAT model is based
• Uncertainty in predictive equations for DBF formation
• Uncertainty in the SWAT compliance determination
• Uncertainty in SWAT treatment technology selection
There may be others, but EPA believes this list captures the ones that have the largest impact on costs
and benefits.
Final Economic Analysis for the Stage 2 DBPR A-33 December 2005
-------
Exhibit A. 12 includes information on the potential effect of each source of uncertainty on the cost
and benefit estimates. Note that the direction of the potential bias resulting from each uncertainty source
(i.e., whether it results in an over- or under-estimate) is the same for both costs and benefits in every
case. The direction of the impact of the uncertainty is unknown for a majority of the cases.
Final Economic Analysis for the Stage 2 DBPR A-34 December 2005
-------
Exhibit A.12 Summary of Uncertainties and Their Impact On Costs and Benefits
Uncertainty
Effect on Benefit
Estimate
Under-
estimate
Over-
estimate
Unknown
Impact
Effect on Cost
Estimates
Under-
estimate
Over-
estimate
Unknown
Impact
Uncertainty in ICR Observed Data as SWAT Inputs
1
2
3
4
There are possible reporting errors during the ICR
and the ICR data may not be representative.
The residence times reported for the four ICR
distribution system locations may not represent the
actual residence times.
A single quarterly DBP sample may not represent
average water quality conditions in that quarter.
Distribution system samples were not required to be
evenly spaced.
Water quality records were not available for all
months in the ICR database. These were "filled in" in
Aux8.
X
X
X
X
X
X
X
X
Uncertainty in Predictive Equations for DBF Formation
5
6
7
Generic treatment process configurations were used
to represent real ICR plants.
Empirical model equations are based on bench-
scale tests and may not represent site-specific plant
conditions.
WTP algorithms for predicting DBP occurrence for
CIO2 and Ozone plants were not calibrated using ICR
observed data.
X
X
X
X
X
X
Uncertainty in the SWAT Compliance Determination
8
9
10
The IDSE may impact the maximum residence times
and predicted DBP values.
Compliance determinations are based on plant-level
rather than system-level analyses for RAA compliance
determinations.
Some plants that switch from surface water to ground
water during certain times of the year can affect RAA
and LRAA calculations.
X
X
X
X
X
X
Uncertainty in SWAT Treatment Technology Selection
11
12
The maximum chloramine conversion rate was set at
77 percent based on best professional judgement.
Actual limitations on chloramine use could be lower
or higher.
Benchmarking was turned "on" for all Stage 1 and
Stage 2 runs, meaning that plants had to maintain
their initial level of inactivation when switching
disinfectants.
X
X
X
X
Final Economic Analysis for the Stage 2 DBPR
A-35
December 2005
-------
A discussion of each of the 15 areas of uncertainty is given in Section A.6.1. Validation of
SWAT treatment technology selections as performed during the M-DBP FACA is described in Section
A.6.2
EPA has developed an approach to account explicitly for two key areas of uncertainty in the
surface water compliance forecast: the potential impacts of the IDSE (# 8), and uncertainty in predictive
equations for DBF formation (#'s 5 through 7). Chapter 5 provides details on how these uncertainties are
addressed quantitatively in the final compliance forecast estimates.
A.6.1 Discussion of Individual Areas of Uncertainty
Uncertainty in ICR Observed Data as SWAT Inputs
1. Possible reporting errors during the ICR
The are several sources of uncertainty in the DBF data collected under the ICR. The American
Water Works Association Research Foundation (AWWARF) has compiled a description of the 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. Weather and rainfall during the ICR sampling
period were compared to historical data to make this assessment (see Chapter 3, section 3.8 for additional
data on weather and rainfall patterns). On a nationwide basis, 1998 was hotter and wetter than normal,
although several mid-Atlantic states experienced severe droughts during the summer.
It is unknown how year-to-year variability in source water quality will affect estimated DBF
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
DBF occurrence in future years than what is represented in the ICR database.
2. Uncertainty in the residence time reported at the four ICR distribution system locations
The accuracy of residence time estimates for ICR distribution system sample locations depends
on operator experience with the system and the extent to which distribution system modeling or tracer
studies have been conducted. Moreover, residence time fluctuates at any given location in the distribution
system, and the ICR sample may not represent the typical or average residence time at that location.
Because modeled DBF formation (particularly TTHM formation) is highly dependent on the residence
time, uncertainty in residence time inputs would result in inaccurate estimates of DBF concentration by
the WTP Model.
There is also reason to suspect that the uncertainty in the maximum residence time input in
SWAT is greater than the uncertainty in the average residence time input in SWAT. As explained in
Section A.3, the average residence time in the SWAT model is based on the mean of the four distribution
system residence times reported in the ICR (for the DSE, AVE1, AVE2, and MAX locations). The
maximum residence time is the largest residence time reported (usually at the MAX location). The MAX
Final Economic Analysis for the Stage 2 DBPR A-36 December 2005
-------
residence times reported in the ICR have already been shown in the Occurrence Document (USEPA
2003h) not to be predictive of the highest DBF levels. Therefore, they may not, in fact, represent the
maximum residence time in the distribution system. Exhibit A. 13 shows that only 53 percent of ICR
plants have the highest TTHM LRAA concentration occurring at the maximum residence time monitoring
site. The highest HAAS LRAA occurred at the maximum residence time monitoring site in only 41
percent of the plants.
Exhibit A.13 Percentage of Highest TTHM or HAAS Value Occurring at a
Given Location
60%
50%
40%
30%
20%
10%
niTHM(N=213)
•HAA5(N=213)
DSE AVG1
Distribution System Location
AVG2
MAX
Source: ICR data analysis. Detailed source information provided in the Stage 2 DBPR Occurrence Document
(USEPA 2003h).
3. Uncertainty that a single quarterly sample represents average water quality conditions in
that quarter
ICR quarterly samples were not necessarily collected at evenly spaced intervals. (A minimum of
two months was required between quarterly samples; however, samples were not required to be taken
approximately 90 days apart, as required in the Stage 2 DBPR.) Thus, a single sample may not be
representative of that quarter, especially if the seasonal influence is strong.
Final Economic Analysis for the Stage 2 DBPR
A-37
December 2005
-------
4. Water quality records were "filled in " in Aux 1 for missing months
Missing records in the ICR resulted in fewer plant-months being estimated by SWAT. In order to
increase the number of data points available as input to SWAT, missing values were estimated based on
the average of values for the other months. Influent pH, hardness, alkalinity, and ammonia levels were
among the parameters that were "filled in" (see Section A.2.2 for more information on how plants were
screened and how some missing data were "filled in" in AUX8).
Uncertainty in Predictive Equations for DBF Formation
5. Generic treatment process configurations were used to represent real ICR plants
The WTP Model uses generic treatment process configurations to represent real ICR plants. For
example, it represents a conventional treatment process train using a specific configuration of the
pertinent unit processes. However, ICR plants employing conventional treatment could have a slightly
different configuration from the generic conventional treatment plant used by the WTP Model.
6. Empirical model equations may not represent site specific plant conditions
The WTP Model uses empirical equations (based mainly on bench-scale tests) to predict DBP
concentrations. However, it does not take into account site-specific factors such as non-uniform flow
within a plant, actions of microbes, etc. As a result, the predicted finished water DBP concentration is
likely to be different from the ICR observed data.
7. WTP algorithms for predicting DBP occurrence for CIO2 and Ozone plants were not
calibrated using ICR observed data.
There were not enough data on plants using chlorine dioxide or ozone disinfection in the ICR to
conduct an appropriate calibration of the SWAT model for these parameters. The model may be
inaccurately predicting the formation of DBFs in plants using these treatment technologies. If the model
over-predicts the DBP reduction in these types of plants, the treatment technology selection may be
biased in favor of selecting these plants. If the model under-predicts the DBP reduction in these plants,
the treatment technology selection would be biased in favor of higher-performing treatment technologies,
such as UV for chlorine dioxide plants, or GAC and membrane treatment technologies for both chorine
dioxide and ozone plants. However, the direction of this bias is not known.
Note that EPA explicitly accounts for uncertainty in SWAT predictive equations (uncertainties 5
through 7) by using an alternative approach to estimate the percent of plants changing treatment
technology. The alternative approach is presented in Chapter 5. The ways in which the results from the
alternative approach are incorporated into the Stage 2 benefit and cost models are discussed in Chapters 6
and 7 respectively.
Uncertainty in the SWAT Compliance Determination
8. Effects of the Initial Distribution System Evaluation on the compliance forecast
The purpose of the IDSE is to identify compliance monitoring sites that are representative of high
TTHM and HAAS concentrations in the distribution system. The IDSE may result in systems finding
sites with higher residence times and, thus, higher TTHM and HAAS concentrations than predicted by
Final Economic Analysis for the Stage 2 DBPR A-38 December 2005
-------
SWAT. The IDSE could ultimately result in more systems making treatment technology changes than
estimated by SWAT. A discussion of how EPA accounts for the uncertainty in the impacts of the IDSE
is provided in Chapter 5.
The likelihood of finding a site with higher TTHM and HAAS concentrations depends on many
system-specific factors. First, the overall variability of DBF levels affects whether systems will find
higher DBF levels at a new site. This variability is influenced by the source water type (surface water
versus ground water) and the type of disinfectant used in the distribution system. Analysis of the ICR
data has shown that systems employing chloramines as the distribution system disinfectant have more
stable DBFs that chloramine systems.
Second, the configuration of the distribution system will affect the likelihood of find a new site
with higher DBF levels. Distribution systems that are non-linear, which including looping and circuitous
routes to establish new connections instead of extension of the nearest line, make finding the highest site
difficult. In addition, systems with multiple storage facilities and booster disinfection pumping stations may
find site with higher residence times during the IDSE. This is more likely to be an issue with large system
than with small systems.
Finally, the technical resources employed during the ICR and Stage 1 selection of monitoring sites
may help to eliminate the likelihood of finding a higher site. Any system that has extensive information of
residual data, DBF data, employs hydraulic models, or has employed tracer studies should have a better
idea of their maximum residence time sites.
9. Compliance determinations are based on plant-level rather than system-level analysis
(Stage 1 only).
Stage 1 requires utilities to sample from a certain number of distribution system monitoring
locations for each plant in their distribution system. The required number of monitoring locations varies by
source water type and system size (e.g., 4 monitoring locations are required for large surface water
systems). Although monitoring requirements are specified on a per-plant basis, compliance with Stage 1
MCLs is based on system-wide TTHM and HAA5 monitoring results. Because not all plants in a given
system were available for SWAT modeling, SWAT-predicted DBF results for each plant are evaluated
separately to determine regulatory compliance.
In systems having multiple plants, high DBF results from one plant could be averaged with low
DBF results from other plants to produce a system-level RAA that is below the MCL, even if the one
plant would exceed the MCL if evaluated alone. For example, say that plant A is a surface water plant
with a TTHM RAA of 85 |ig/L. Plants B and C are ground water plants with much lower TTHM
RAA's of 40 and 45 |ig/L respectively. Assuming that each plant had an equal number of DBF
monitoring sites and samples, the system-wide RAA would be (85+40+45) /3 = 56.6 |ig/L. Since SWAT
evaluates compliance for each plant separately, SWAT could potentially predict that a plant needed to
change treatment technology when in fact, it is part of a system that is in compliance.
A potential overestimate of the percentage of plants changing treatment technology affects the
compliance predictions for the Stage 1 Baseline and Alternative 3 (40/30 RAA). The Unadjusted
Preferred Alternative, Alternative 1 (80/60 LRAA with Bromate of 10 ug/L), and Alternative 2 (80/60
single highest) are not affected because compliance with the MCLs is based on sample results from each
location individually. If this phenomenon causes the Stage 1 predictions to be overestimated but not the
Stage 2 predictions, there could be an underestimation of the incremental costs and benefits of Stage 2.
Final Economic Analysis for the Stage 2 DBPR A-39 December 2005
-------
10. The Effect of Switching From Surface Water to Ground Water on Compliance
Determination
Some ICR plants reportedly switch from surface to ground water sources during different times
of the year. DBF results for the ground water use periods were not included in SWAT. Switching from
a surface to a ground water source would most likely decrease TTHM and HAA5 formation and would
impact RAA and LRAA compliance calculations. Not accounting for ground water use periods could
result in an over-prediction in the compliance forecast predicted by SWAT.
Uncertainty in SWAT Treatment Technology Selection
11. Setting the Maximum Chloramine Conversion Rate at 77 Percent
The rate of 77 percent was assumed to be the maximum percentage of systems in the United
States that would be able to convert to chloramines. This rate was set by the TWO in order to
accommodate plants that may not be able to use chloramines due to site-specific circumstances or local
factors other than technical suitability. This rate may be too high or too low, and represents an unknown
impact on the SWAT estimates.
12. Benchmarking was used for all Stage 1 and Stage 2 Runs
Plants were assumed to maintain their initial level of pathogen inactivation when switching
disinfectants. The disinfectant level may be set high for reasons other than disinfection, such as taste and
odor control. Forcing plants to maintain their disinfectant levels could lead to selection of higher-
performing treatment technologies in order to avoid DBF non-compliance. It is possible that the State
would allow a system to lower its disinfectant levels to avoid higher DBFs, provided that the disinfectant
level still meets existing standards.
Final Economic Analysis for the Stage 2 DBPR A-40 December 2005
-------
A.6.2 Validation of SWAT Treatment Technology Selection Results
To validate the reasonableness of the SWAT treatment technology selection methodology,
including the decision tree, the TWO compared two independent analyses of treatment technology
forecasts to SWAT's pre-Stage 2 (post-Stage 1) DBPR predictions.5 The two independent analyses are
referred to as the "Delphi Poll" and the "Utility Poll" and are described below. A discussion of results
follows.
ICR Surface Water Expert Poll (Delphi Poll)
The TWO conducted an expert, or "Delphi," poll to obtain Stage 1 DBPR impact estimates, based
on technical expertise. Experts were provided with detailed water quality and treatment process
characteristics from the AUX1 database for all ICR plants that appeared not to meet the MCLs for the
Stage 1 DBPR (based on the ICR data, assuming a 20 percent safety margin for compliance). The
experts then reviewed each plant to determine the most likely treatment technology choice to meet the
Stage 1 DBPR. They were also asked to choose the least-cost treatment technology option. If an expert
had knowledge about a specific plant that would lead him or her to choose a treatment technology other
than the least-cost, the expert was asked to identify that treatment technology and the reasons for the
choice. The results were collected from the experts, summarized, and presented to the M-DBP FACA
(USEPA 2000n, TWO Presentation to FACA Committee, March 29, 2000).
ICR Surface Water Industry Poll (Utility Poll)
The industry poll was developed by the AWWA and served a similar role as the expert poll. It
compared SWAT results to the Stage 1 DBPR impacts anticipated by industry representatives. In this
process, AWWA asked ICR systems to identify the treatment technology they were planning to
implement in response to the Stage 1 DBPR. The summarized results were presented to the M-DBP
FACA and compared with the other predictions (USEPA 2000n).
Results
Exhibit A. 14 compares the treatment technology selection forecasts predicted by SWAT, the
Delphi poll (both expected and least-cost results), and the utility poll. In general, the distributions of Post-
Stage 1 treatment technologies-in-place predicted by the polls and by SWAT are in good agreement with
each other. Relative to the two polls, SWAT does not significantly over-predict or under-predict the
expected prevalence of any treatment technology following the implementation of the Stage 1 rule. Based
on these comparisons, the M-DBP FACA determined that SWAT was sufficiently reliable to serve as the
basis for Stage 2 treatment technology selection forecasts and relied upon SWAT outputs to compare and
evaluate regulatory options during its deliberations.
'Although validation of Post-Stage 2 results would have been preferable, the validation was done for post-
Stage 1 because, at this time of this analysis, there were many potential Stage 2 DBPR regulatory alternatives still
being evaluated. Performing the independent analyses for several compliance alternatives was considered by the
TWO to be too time intensive.
Final Economic Analysis for the Stage 2 DBPR A-41 December 2005
-------
Exhibit A.14 Comparison of Predicted Post-Stage 1 Treatment Technologies-in-
Place
0.
•5
5% •
5% •
I
-
1-1
~~
I
1-1
d Utility Poll
[
[
[
• Delp
3Delp
DSWfl
li Poll (Best Professional Judgement)
hi Poll (Least Cost)
T
— 1
i-i
1 m - --TI
-
Technology
Part III: Compliance Forecasts
To estimate total benefits and costs of the rule, accurate forecasting of the compliance of
surface water systems with the Stage 2 DBPR is critical. The compliance forecasts for large surface
water systems were derived from ICR data using SWAT. Comprehensive data on operational
parameters and water quality, similar to those gathered for large systems under the ICR, were not
available for medium and small systems. Because the quality of the source water and the operational
capabilities of medium and small systems were anticipated to differ from those of large systems, a
detailed evaluation was performed to accurately estimate impacts of the Stage 2 DBPR on medium and
small systems. A Non-ICR Subgroup of the TWO for the Microbial-Disinfection Byproducts Advisory
Committee (the Subgroup) was charged with understanding the nature of medium and small systems and
developing methodologies for further analysis. Detailed descriptions of the methodologies used in
developing compliance forecasts for each system size category are provided in the latter sections of this
appendix.
Final Economic Analysis for the Stage 2 DBPR
A-42
December 2005
-------
A.7 SWAT-based Compliance Forecasts for Large Surface Water Systems
Converting SWAT Results to the "Screening" Database
The compliance forecasts for large surface water systems were derived primarily using SWAT.
Plant-level results from SWAT were converted to a "screening" database using a SAS program
developed during the M-DBP FACA deliberations. The SAS screening program compiled individual plant
results and makes adjustments based on knowledge of specific system practices. It also removed plants
making minor treatment technology changes (enhanced coagulation, enhanced softening, moving point of
chlorination, adjusting chlorine dose) because these are all implemented during Stage 1, so there is no
change from Stage 1 to Stage 2.
The SWAT screening database provides three primary outputs: DBF Exposures, Treatment
Technology Selection Forecasts, and Ending Treatment Technologies. DBF Exposures provides the
predicted values of TTHM, HAAS, chlorite, and bromate for each rule option being examined. Treatment
Technology Selection describes the distribution of treatment technologies only for those plants predicted to
change to chloramine or an advanced treatment technology. Ending Treatment Technologies predicts the
percentages of all plants using each type of treatment technology after the rule option is implemented.
(The Treatment Technology Selection cannot be used for this purpose as some plants not making
treatment technology changes already use advanced treatment technologies.) Only the Treatment
Technology Selection results are presented below. Ending Treatment Technology results are presented in
Appendix C and DBF Exposures are presented in Chapter 5.
Adjustments for the Stage 1 Baseline
SWAT cannot take compliance with the Stage 1 DBPR into account when predicting compliance
forecasts for Stage 2. Hence, treatment technology shifts from Stage 1 to Stage 2 are estimated by
subtracting the treatment technology shift between pre-Stage 1 and Stage 1 from the treatment
technology shift between pre-Stage 1 and Stage 2. Different treatment technologies, however, were
assumed to be available to meet the regulatory requirements of the Stage 1 and Stage 2 DBPRs. UV
was not a proven disinfectant for Cryptosporidium, Giardia, or viruses at the time of the ICR or when
plants were expected to make treatment decisions to meet Stage 1 DBPR requirements. EPA now
considers UV a viable alternative disinfectant to chlorine to meet Stage 2 DBPR regulatory alternatives.
Because UV is considered an available treatment technology for the Stage 2 DBPR, some plants
are predicted to use UV instead of more expensive treatment technologies such as ozone,
microfiltration/ultrafiltration (MF/UF), or GAC. If the compliance forecasts for the Stage 1 and Stage 2
DBPRs were used independently, more expensive treatment technologies installed to meet Stage 1 would
effectively be removed from the plant to install less expensive treatment technologies under Stage 2. This
is not realistic. In reality, systems that added treatment technology for Stage 1 may not need to add
another treatment technology for Stage 2.
To account for the effect of UV, a less expensive treatment technology, becoming available after
Stage 1 came into effect, EPA used the following approach to adjust the Stage 2 compliance forecast:
• Model Stage 1 without UV. Model the Stage 2 regulatory alternatives with and without UV
as an available treatment technology.
Final Economic Analysis for the Stage 2 DBPR A-43 December 2005
-------
• Use the Stage 1 DBPR estimates of ozone, MF/UF, and GAC10 usage if they are higher
than the Stage 2 results with UV, since systems are predicted to use these treatment
technologies for Stage 1 and will not remove them to install UV.
• Decrease the percentage of plants using UV accordingly.
• To obtain the percentage of plants adding chloramine, use the percentage from the Stage 2
run without UV as an available treatment technology. This percentage decreases when UV
is an available treatment technology. Since the percentage of plants changing to UV to
comply with Stage 2 has been reduce, the estimate from the Stage 2 DBPR without the UV
option is taken for the adjusted option.
These steps are displayed in Exhibit A. 15 a, and an example calculation for the Unadjusted
Preferred Alternative is presented in Exhibit A.15b. Final adjusted compliance forecasts for large surface
water systems are presented in Exhibit A. 16.
Final Economic Analysis for the Stage 2 DBPR A-44 December 2005
-------
Exhibit A.15a Adjustments to Stage 2 Treatment Technology Selection Forecasts
for the Stage 1 Baseline
Stage 2 technical
selection with UV
Is Stage 2
GAG 10 with
UV greater
than Stage 1
GAG10?
Is Stage 2
MF/UF with
UV greater
than Stage
1 MF/UF?
Is Stage 2
Ozone with
UV greater
than Stage
1 Ozone?
Yes
• Adjust Stage 2 GAC10 with UV to the
Stage 1 number
• Change Stage 2 UV by A = (Stage 2
GAC10 with UV- Stage 1 GAC10)
Yes
• Adjust Stage 2 MF/UF with UV to the
Stage 1 number
• Change Stage 2 UV by B = (Stage 2
MF/UF with UV- Stage 1 MF/UF)
Yes
• Adjust Stage 2 Ozone with UV to
the Stage 1 number
• Change Stage 2 UV by C = (Stage
2 Ozone with UV- Stage 1 Ozone)
Adjusted Stage 2 UV = Predicted Stage 2UV + A + B + C
Note: A = Adjustment to Stage 2/UV percentage for GAC10.
B = Adjustment to Stage 2/UV percentage for MF/UF.
C = Adjustment to Stage 2/UV percentage for Ozone.
Final Economic Analysis for the Stage 2 DBPR
A-45
December 2005
-------
Exhibit A.15b Illustration of the Adjustment Steps to Stage 2 Compliance Forecasts for the Stage 1 Baseline
Step 1: GAC10 Adjustment
Staae 1 DBPR
Option w/o UV
Option vi 1 UV
Step 1 Subtotal
Switch to CLM
A1
A2
A3
A4 = A3
Switch to
CLM Only
B1
B2
B3
B4 = B3
Chlorine
Dioxide
C1
C2
C3
C4 = C3
UV
D1
D2
D3
D4 = lfG1>G3
ThenD3-(G1-G3)
Else D3
Ozone
E1
E2
E3
E4 = E3
MF/UF
F1
F2
F3
F4 = F3
GAC10
G1
G2
G3
G4=lfG1>G3
Then G1 Else
G3
GAC10 + Advanced
Disinfectant
H1
H2
H3
H4=H3
GAC20
11
12
13
I4 = I3
GAC20 + Advanced
Disinfectant
J1
J2
J3
J4 = J3
Membranes
K1
K2
K3
K4 = K3
Step 2: MF/UF Adjustment
Staae 1 DBPR
Option w/o UV
Option vi 1 UV
Step 2 Subtotal
Switch to CLM
A1
A2
A3
A4
Switch to
CLM Onlv
B1
B2
B3
B4
Chlorine
Dioxide
C1
C2
C3
C4
UV
D1
D2
D3
D5 = lfF1>F3
ThenD4-(F1-F3)
Else D4
Ozone
E1
E2
E3
E4
MF/UF
F1
F2
F3
F5 = lfF1>F3
Then F1 Else
F4
GAC10
G1
G2
G3
G4
GAC10 + Advanced
Disinfectant
H1
H2
H3
H4
GAC20
11
12
13
14
GAC20 + Advanced
Disinfectant
J1
J2
J3
J4
Membranes
K1
K2
K3
K4
Step 3: Ozone Adjustment
Staae 1 DBPR
Option w/o UV
Option vil UV
Step 3 Subtotal
Switch to CLM
A1
A2
A3
A4
Switch to
CLM Onlv
B1
B2
B3
B4
Chlorine
Dioxide
C1
C2
C3
C4
UV
D1
D2
D3
D6 = lfE1>E3
ThenD5-(E1-E3)
Else D5
Ozone
E1
E2
E3
E5 = lf E1>E3
Then E1 Else
E4
MF/UF
F1
F2
F3
F5
GAC10
G1
G2
G3
G4
GAC10 + Advanced
Disinfectant
H1
H2
H3
H4
GAC20
11
12
13
14
GAC20 + Advanced
Disinfectant
J1
J2
J3
J4
Membranes
K1
K2
K3
K4
Step 4: CLM Adjustment
Staae 1 DBPR
Option w/o UV
Option vi 1 UV
Step 3 Subtotal
Switch to CLM
A1
A2
A3
A5 = lfA2>A3
Then A2 Else
A3
Switch to
CLM Onlv
B1
B2
B3
B4
Chlorine
Dioxide
C1
C2
C3
C4
UV
D1
D2
D3
D6
Ozone
E1
E2
E3
E5
MF/UF
F1
F2
F3
F5
GAC10
G1
G2
G3
G4
GAC10 + Advanced
Disinfectant
H1
H2
H3
H4
GAC20
11
12
13
14
GAC20 + Advanced
Disinfectant
J1
J2
J3
J4
Membranes
K1
K2
K3
K4
Final Economic Analysis for the Stage 2 DBPR
A-46
December 2005
-------
Exhibit A.16 Final Adjusted Compliance Forecasts for Surface Water Systems Serving > 10,000
(Percent of Systems Changing Treatment Technologies from the Pre-Stage 1 Baseline to Stage 2)
Stage 2 Preferred Alternative, 20 Percent Safety Margin: 80 ug/L TTHM as LRAA, 60 ug/L HAAS as LRAA, Bromate
10 ug/L
Stage 1
DBPR
Stage 2
Option w/o
UV
Stage 2
Option w/UV
Stage 2
Option
adjusted
Switch to
CLM
13.92%
19.05%
18.68%
19.05%
Switch to
CLM only
78.39%
76.19%
76.19%
76.19%
Chlorine
Dioxide
5.13%
5.49%
5.49%
5.49%
UV
0.00%
0.00%
7.33%
0.75%
Ozone
10.99%
1 1 .72%
6.23%
10.99%
MF/UF
1 .83%
1 .83%
0.37%
1 .83%
GAG 10
1 .83%
1 .83%
1 .47%
1 .83%
GAC1 0 + Advanced
Disinfectant
1.10%
1 .83%
1 .83%
1 .83%
GAC20
0.37%
0.73%
0.73%
0.73%
GAC20 + Advanced
Disinfectant
0.00%
0.00%
0.00%
0.00%
Membranes
0.37%
0.37%
0.37%
0.37%
Stage 2 Preferred Alternative, 25 Percent Safety Margin: 80 ug/L TTHM as LRAA, 60 ug/L HAAS as LRAA, Bromate
10 ug/L
Stage 1
DBPR
Stage 2
Option w/o
UV
Stage 2
Option w/UV
Stage 2
Option
adjusted
Switch to
CLM
13.92%
22.34%
21 .25%
22.34%
Switch to
CLM only
78.39%
72.53%
72.53%
72.53%
Chlorine
Dioxide
5.13%
4.76%
4.76%
5.13%
UV
0.00%
15.02%
8.79%
4.40%
Ozone
10.99%
2.56%
8.06%
10.99%
MF/UF
1 .83%
1 .83%
0.73%
1 .83%
GAC10
1 .83%
2.56%
1 .83%
1 .83%
GAC10 + Advanced
Disinfectant
1.10%
0.37%
2.56%
2.56%
GAC20
0.37%
0.37%
0.37%
0.37%
GAC20 + Advanced
Disinfectant
0.00%
0.00%
0.00%
0.00%
Membranes
0.37%
0.00%
0.37%
0.37%
Final Economic Analysis for the Stage 2 DBPR
A-47
December 2005
-------
Stage 2 Rule Alternative 1: 80 ug/L TTHM as LRAA, 60 ug/L HAAS as LRAA, Bromate 5 ug/L
Stage 1
DBPR
Stage 2
Option w/o
UV
Stage 2
Option w/UV
Stage 2
Option
adjusted
Switch to
CLM
13.92%
19.05%
18.68%
19.05%
Switch to
CLM only
78.39%
75.82%
75.82%
75.82%
Chlorine
Dioxide
5.13%
5.49%
5.49%
5.49%
UV
0.00%
0.00%
6.96%
0.37%
Ozone
10.99%
10.99%
6.23%
10.99%
MF/UF
1 .83%
2.20%
0.37%
1 .83%
GAG 10
1 .83%
1 .83%
1 .47%
1 .83%
GAC1 0 + Advanced
Disinfectant
1.10%
1 .47%
1 .47%
1 .47%
GAC20
0.37%
0.73%
0.73%
0.73%
GAC20 + Advanced
Disinfectant
0.00%
0.00%
0.00%
0.00%
Membranes
0.37%
1 .47%
1 .47%
1 .47%
Stage 2 Rule Alternative 2: 80 ug/L TTHM as Single Highest, 60 ug/L HAAS as Single Highest, Bromate 10 ug/L
Stage 1
DBPR
Stage 2
Option w/o
UV
Stage 2
Option w/UV
Stage 2
Option
adjusted
Switch to
CLM
13.92%
28.94%
29.30%
28.94%
Switch to
CLM only
78.39%
54.58%
54.58%
54.58%
Chlorine
Dioxide
5.13%
10.62%
10.62%
10.62%
UV
0.00%
0.00%
5.49%
2.93%
Ozone
10.99%
12.45%
8.79%
10.99%
MF/UF
1 .83%
2.56%
1 .47%
1 .83%
GAG 10
1 .83%
10.62%
10.26%
10.26%
GAC1 0 + Advanced
Disinfectant
1.10%
6.59%
6.23%
6.23%
GAC20
0.37%
1.10%
1.10%
1.10%
GAC20 + Advanced
Disinfectant
0.00%
0.37%
0.37%
0.37%
Membranes
0.37%
1.10%
1.10%
1.10%
Final Economic Analysis for the Stage 2 DBPR
A-48
December 2005
-------
Stage 2 Rule Alternative 3: 40 ug/L TTHM as RAA, 30 ug/L HAAS as RAA, Bromate 10 ug/L
Stage 1
DBPR
Stage 2
Option w/o
UV
Stage 2
Option w/UV
Stage 2
Option
adjusted
Switch to
CLM
13.92%
29 67%
30.77%
29.67%
Switch to
CLM only
78.39%
42.12%
42.12%
42.12%
Chlorine
Dioxide
5.13%
13.19%
13.19%
13.19%
UV
0.00%
0.00%
7.33%
3.30%
Ozone
10.99%
12.45%
6.96%
10.99%
MF/UF
1 .83%
4.03%
2.93%
2.93%
GAG 10
1 .83%
17.58%
17.22%
17.22%
GAC1 0 + Advanced
Disinfectant
1.10%
7.69%
7.33%
7.33%
GAC20
0.37%
1 .47%
1 .47%
1 .47%
GAC20 + Advanced
Disinfectant
0.00%
0.37%
0.37%
0.37%
Membranes
0.37%
1.10%
1.10%
1.10%
Final Economic Analysis for the Stage 2 DBPR
A-49
December 2005
-------
A.8 SWAT based Compliance Forecasts for Medium Surface Water Systems
After a detailed review of available data, the TWO Small/Medium Systems Subgroup concluded
that the influent water quality, treatment characterization, and DBF occurrence for medium surface water
plants are similar to large surface water plants. This section describes and examines the data that support
this conclusion.
The Water Utility Database (WATERASTATS [AWWA 2000]), developed by AWWA, was
used in this analysis. Its data were collected during a 1996 survey of approximately 900 primarily medium
and large systems. This database includes information on influent water quality, treatment, and the
occurrence of DBFs in finished water for all system sizes.
Exhibit A. 17 compares source water types for medium and large surface water systems. Further
information is provided in the Stage 2 DBPR Occurrence Document (USEPA 2003h). Given the
similarities in the distribution of large and medium systems using each type of surface water, the Subgroup
expected to find only minor differences in source water quality. Exhibits A. 18 through A.20, which
compare source water TOC, turbidity, and alkalinity, respectively, confirm this hypothesis.
Exhibit A.21 shows that the disinfectant usage of medium and large systems is similar. Exhibits
A. 22 and A. 23 show that the distribution of TTHM values was similar between large and medium
systems for measurements at finished water and distribution system sampling points.
Final Economic Analysis for the Stage 2 DBPR A-50 December 2005
-------
Exhibit A.17 Percentages of Medium and Large Surface Water Systems Using
Different Source Water Types
60%
Reservoir/Lake
Flowing Stream
Mix
Source: WATERASTATS (AWWA 2000).
Exhibit A.18 Comparison of Source Water TOC for Medium and Large Surface
Water Systems
100%
» Medium Surface Water Systems (N = 102)
A Large Surface Water Systems (N = 196)
10 15 20
Plant-Mean TOC (mg/L as C)
25
30
Source: WATERASTATS (AWWA 2000).
Final Economic Analysis for the Stage 2 DBPR
A-51
December 2005
-------
Exhibit A.19 Comparison of Source Water Turbidity For Medium and Large
Surface Water Systems
V
I
E
O
40%
30%
20%
10%
0%
*Medium Surface Water Systems (N = 243)
A Large Surface Water Systems (N = 240)
100 150 200
Plant-Mean Turbidity (NTU)
250
300
Source: WATERASTATS (AWWA 2000).
Exhibit A.20 Comparison of Source Water Alkalinity for Medium and Large
Surface Water Systems
100%
» Medium Surface Water Systems (N=224)
A Large Surface Water Systems (N=234)
100 150 200 250
Plant-Mean Alkalinity (mg CaCOa/L)
300
350
Source: WATERASTATS (AWWA 2000).
Final Economic Analysis for the Stage 2 DBPR
A-52
December 2005
-------
Exhibit A.21 Comparison of Disinfectant Type for Medium and Large Surface
Water Systems Using Conventional Filtration
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Medium Surface Water Systems (N = 266)
Large Surface Water Systems (N = 249)
PreCI2/ Post CI2 Only PreCI2/
PostCI2 PostNH2CI
w/03
W/CIO2
Other
Source: WATERASTATS (AWWA 2000).
Exhibit A.22 Comparison of Finished Water Annual Average TTHM for Medium
and Large Surface Water Systems
100%
90%
80%
a>
'c
Q.
I
^
JS
3
3
o
* Medium Surface Water Systems (N=211)
A Large Surface Water Systems (N=210)
20
40
60
TTHM (ug/L)
80
100
120
Source: WATERASTATS (AWWA 2000).
Final Economic Analysis for the Stage 2 DBPR
A-53
December 2005
-------
Exhibit A.23 Comparison of Distribution System Annual Average TTHM for
Medium and Large Surface Water Systems
I
*Medium Surface Water Systems (N = 207)
A Large Surface Water Systems (N = 131)
20
40
60
TTHM (ug/L)
80
100
120
Source: WATERASTATS (AWWA 2000).
Because of the similarities between large and medium surface water systems, the Subgroup
assumed that ICR data on DBF occurrence and the results of the SWAT analysis were also applicable to
medium surface water systems. Thus, the Subgroup assumed that medium surface water systems
treatment technology selection was identical to the large surface water system treatment technology
selection for pre-Stage 1, Stage 1, and the Stage 2 alternatives.
For this proportional allocation to be valid, some similarity must exist between the nationwide
geographical distribution of ICR surface water systems and that of medium surface water systems. The
Subgroup compared the distribution of ICR surface water systems by State to the distribution of medium
surface water systems by State, using the Baseline Handbook (USEPA 2001c). This effort established
that there is no significant difference in overall geographic distribution (as shown in Exhibit A.24),
although there is some variation in the distribution of systems in different size categories.
To ensure that the distribution assumptions did not mask differences that may affect DBF
formation, additional analyses were performed. In particular, the distribution of systems with high levels
of DBF precursors (TOC in Florida, bromide in Texas; based on State data and ICR data analysis) within
certain States was examined. No significant difference was found between the percentages of medium
and large systems having high precursor levels. The Subgroup concluded that SWAT predictions of
occurrence for large systems could be directly applied to the universe of medium surface water plants.
Final Economic Analysis for the Stage 2 DBPR
A-54
December 2005
-------
Exhibit A.24 Distribution of Large and Medium Surface Water Plants by EPA
Region
EPA Region
1
2
3
4
5
6
7
8
9
10
Total
Percent of Large Systems
5.83%
12.55
11.22
16.60
13.46
11.67
5.38
4.93
14.80
3.60
100%
Percent of Medium
Systems
9.00%
6.35
12.60
25.20
14.22
12.51
4.14
6.06
7.48
3.22
100%
Note: Detail may not add due to independent rounding.
Source: Baseline Handbook (USEPA 2001 c).
A.9
SWAT based Compliance Forecasts for Small Surface Water Systems
Small surface water systems differ in many ways from medium and large surface water systems.
Small systems are exempt from the 1979 Total Trihalomethane Rule, which set the TTHM MCL at 100
ug/L. Source water quality is somewhat better in small systems than in larger systems, as demonstrated
by the ICR Supplemental and National Rural Water Association (NRWA) Survey data, discussed below,
and the Stage 2 DBPR Occurrence and Exposure Assessment (USEPA 2003h). Unit cost estimates for
new treatment technologies are higher in small systems than larger systems, which may drive small
systems to take different treatment approaches. In addition, some treatment technologies predicted for
use in large and medium systems may not be feasible in small systems.
Due to these considerations, the Technical Workgroup used an expert review process to extract
the predicted compliance forecast for large systems to small system subgroups. The method, or the
Delphi Poll process, consisted of a group of experts who provided their best professional judgement to
identify likely treatment technologies for affected plants. The expert opinions were consolidated for a
best estimate of the treatment technology selection response of compliance affected systems. This
provided a compliance forecast for a given regulatory option.
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
The participating experts included members of the NRWA (a federation of 45 State rural water
associations, representing over 19,000 water and wastewater utilities), EPA staff, and consulting
engineers with many years of experience in small surface water systems. The review process for small
surface water systems integrated technical analyses of source water characteristics and experts'
predictions of anticipated treatment technologies changes and DBF formation. The experts' responses
were then aggregated for further analysis.
A.9.1 Data Sources and Uncertainties
Because the small surface water system compliance forecast is extracted from SWAT model
runs, many of the uncertainties in the SWAT model as discussed in Section A. 6 apply to the small surface
water system compliance forecast. One of the key areas of uncertainty, uncertainty in SWAT predictive
equations, is quantified for small surface water systems as it is for large surface water systems. The
derivation of alternative compliance forecasts to quantify uncertainty in SWAT predictive equations are
presented in Chapter 5.
The ICR Supplemental Survey is a survey meant to compliment the ICR data set. It is a survey
of raw source water quality and DBF concentrations from 40 random plants each from the small,
medium, and large size categories. This is a small data set when compared to the nearly 4,000 small
surface water system. The same is true of the NRWA data set, which consists of 117 randomly
surveyed small plants nationwide to determined treatment process, source water quality, and DBF
concentrations. Thus, adjustments to the large compliance forecast based on these data sets are
uncertain.
The compliance forecasts of small systems are not adjusted to account for the IDSE. Small
systems typically have distribution systems that are less complex than those of large surface water
systems. As a result, they are more likely to already know the maximum residence time location in their
distribution system.
A.9.2 Decisions from the Delphi Poll Process
For the expert review process, small surface water systems were subdivided into three size
categories: systems serving fewer than 100 people, systems serving 100 to 999 people, and systems
serving between 1,000 and 9,999 people. The Subgroup expected systems in each category to make
different treatment choices.
The following sections detail the results of the Subgroup's deliberation of specific treatment
technologies. The flowchart describing the analytical process is shown in Exhibit A.25.
Systems Serving 1,000 to 9,999 People
A review of ICR Supplemental Survey and NRWA Survey data indicated that source water
quality at small systems was better than that at large systems. NRWA Survey results showed slightly
higher TOC concentrations; however, NRWA results may be biased, as discussed in Section A.9.1.
Based on Supplemental Survey data shown in Exhibit A.25, the Subgroup predicted that a smaller
proportion of small systems would change to advanced treatment technologies as a result of the Stage 1
and Stage 2 DBPRs than the proportion of large systems predicted by SWAT.
Final Economic Analysis for the Stage 2 DBPR A-56 December 2005
-------
The Subgroup adjusted the percentage of small systems using conventional or nonconventional
treatment (i.e., not switching to advanced treatment) in the following manner:
• If the percentage of large systems employing conventional and nonconventional treatment
technologies, as predicted by SWAT, exceeded or equaled 65 percent, then the corresponding
percentage for small systems were to be adjusted upward to 75 percent.
• If the percentage of systems employing conventional and nonconventional treatment
technologies was predicted to be less than 65 percent, then the corresponding percentage for
small systems were to be adjusted by adding 10 percent to the SWAT output.
Exhibit A.25 Average TOC Levels in Surface Water Systems
lative Percent of Systems
^
E
3
O
iuu/o
nno/ _
yUvo
ono/ _
oUvb
vno/ _
/Uvb
cno/ _
DUvo
cno/ _
/\r\o/ -
4Uyo
ono/
oUvb
ono/ _
zUvo
1 n% -
I u /o
no/. ,
• A^
4," • *»
•*** '
* A^ •
* * X • *
* & *
A •
9 •
/*
•A
^
A^T
* A A •
1 /" : " A Large (N=47)
• A 9
^ • /* , • • Medium (N=4Uj
* & • » Small (N-40)
• . A •
* AA •
A m
* A •
» /T •
* A^ •
A A (
* ,A«
* V*
7f .
234567
Average TOC Concentration (mg/L as C)
Source: 12 months from the ICR Supplemental Survey Data (USEPA2000b).
Final Economic Analysis for the Stage 2 DBPR
A-57
December 2005
-------
SWAT predicted that the percentage of large systems using conventional or nonconventional
treatment would exceed 65 percent, so the percentage for small systems was increased to 75. The
Subgroup correspondingly removed systems from other treatment categories, including chlorine dioxide,
UV, and ozone. The Subgroup assumed that the conventional treatment category included some systems
modifying treatment by increasing coagulant dose, installing a pre-sedimentation basin, or moving the point
of chlorination. While these activities pose a smaller cost impact to large systems than implementing an
advanced treatment technology does, some of these modifications (e.g., installing a pre-sedimentation
basin) could constitute a substantial burden for a few small systems. However, the Subgroup was of the
opinion that on a national scale the effects would not be significant, and hence did not account for it.
The Subgroup then imposed additional constraints that further affected the Stage 1 and 2 DBPR
analyses and increased the number of systems predicted to change to advanced treatment technologies.
Because SWAT predictions are based on large systems, they do not account for small systems
that were known to be using microfiltration or ultrafiltration before the Stage 1 DBPR was implemented
(no large systems were using these treatment technologies during the ICR period). According to the
NRWA Survey, microfiltration and ultrafiltration were used by 3.6 percent of small systems before the
Stage 1 DBPR went into effect. As a result, the experts added 3.6 percent to the percentage of small
systems predicted to be using microfiltration and ultrafiltration after the Stage 1 and Stage 2 DBPRs.
These extra systems were subtracted from the systems predicted to use chlorine dioxide, ozone, and UV,
as predicted by SWAT.
The SWAT model includes four options for systems using GAC:
• GAC10 (10-minute empty bed contact time)
• GAC 10 plus advanced disinfectants
• GAC20 (20-minute empty bed contact time)
• GAC20 plus advanced disinfectants
Costs for GAC systems include frequent replacement or regeneration of the carbon media. The
Subgroup believed that surface water systems serving more than 1,000 people would choose to replace
rather than regenerate their GAC media. Because unit costs for GAC20 with replacement are lower than
unit costs for GAC 10 with regeneration of the media (for small systems), the Subgroup assumed that the
systems using GAC 10 or GAC 10 plus advanced oxidants, based on the large system prediction, would
instead use GAC20 or GAC20 plus advanced disinfectants, respectively.
Systems Serving 100 to 999 People
For systems serving 100 to 999 people, the starting point for treatment technology selection was
the treatment technology distribution predicted for systems serving 1,000 to 9,999 people. These
predictions were further modified to account for the difficulties systems of this size might have with
disinfectants such as ozone, chlorine dioxide, and chloramines. Predictions for systems using GAC20
were adjusted as well.
In general, the Subgroup established that many small systems would probably not use chlorine
dioxide, because it is difficult to handle and must be generated on site. The application of chlorine dioxide
Final Economic Analysis for the Stage 2 DBPR A-58 December 2005
-------
also requires daily testing for chlorite, a regulated DBF. The effort or expertise required for this testing
may be beyond the capability of many small systems. Therefore, the Subgroup constrained chlorine
dioxide use in the 100-999 size category to half that of the 1,000 to 9,999 category, allocating the rest to
UV, ozone, and MF/UF in proportion to the existing numbers for these treatment technologies.
The preceding constraints on the treatment technologies available to small systems necessitated
predicting the treatment technology to which each small system will switch. The only difference between
the SWAT Decision Tree and the one used for small surface water systems is that GAC10 is not an
option for the small surface water systems. The Subgroup also assumed that systems predicted to modify
their primary treatment would continue to use the same residual disinfectant.
The Subgroup next adjusted the compliance forecast to account for a small portion of smaller
systems that may not be able to apply GAC20 treatment technologies. The Subgroup subtracted 10
percent from the percentage of systems predicted to use GAC20. The systems removed from GAC20
were then added to NF (microfiltration followed by nanofiltration), the next available treatment technology
on the decision tree.
Chloramine use may be difficult for some small systems, especially if an operator is not always
present. Chloramine use was adjusted in a two-step process. First, the percentage of systems predicted
to use chloramine as a residual disinfectant was reduced to 90 percent of the value predicted for systems
serving 1,000 to 9,999 people. These systems instead were predicted to use chlorine as a residual
disinfectant. Second, the Subgroup predicted that systems using chlorine would switch to different
primary treatment technologies. This reallocation was necessary because chlorine contributes more to
DBF formation than chloramine does, thereby forcing systems to use a higher cost treatment technology
in order to meet the DBF standards of the Stage 2 DBPR.
Systems Serving Fewer than 100 People
For systems serving 100 or fewer people, the starting point for treatment technology selection
was the treatment technology distribution predicted for systems serving 100 to 999 people. These
predictions were modified to account for the additional difficulties systems of this size might have with
disinfectants such as ozone, chlorine dioxide, and chloramine. Predictions for systems using GAC20 were
adjusted as well.
The Subgroup assumed that no systems in this size category would use chlorine dioxide or ozone.
Consequently, the Subgroup allocated to conventional treatment two-thirds of the systems that were
predicted to use chlorine dioxide and ozone. The remaining one-third of chlorine dioxide systems were
allocated to UV, MF/UF, GAC20, GAC20 with UV, and NF, and the remaining one-third of ozone
systems were allocated to MF/UF, GAC20, GAC20 with UV, and NF, all in proportion to existing
numbers for these treatment technologies.
As with systems serving 100 to 999 people, the percentage of systems predicted to use GAC20
was decreased by 10. The systems removed from GAC20 were then added to NF, the next available
treatment technology on the decision tree.
The Subgroup adjusted chloramine usage using the same process as it did for systems serving 100
to 999 people, except that the percentage of systems predicted to use chloramine as a residual disinfectant
was reduced to 75 percent, rather than 90 percent.
Final Economic Analysis for the Stage 2 DBPR A-59 December 2005
-------
The most significant effect of the chloramine constraint was that systems using less expensive
treatment technologies were predicted to move toward more expensive treatment technologies. This
effectively neutralizes the cost savings small systems might have achieved through better source water
quality. A review of the compliance forecasts shows that when the Stage 1 DBPR predictions for both
large and small surface water systems are compared, there is no significant difference in the percentage
of systems using advanced treatment technologies to comply with the Stage 1 DBPR. Small systems
have better source water quality than large systems do, but this is outweighed by the fact that they must
install more expensive treatment technologies to comply with DBF regulations and by the fact that large
systems are already complying with the 1979 TTHM Rule.
Adjustments for the Stage 1 DBPR
To account for the effect of less expensive treatment technologies becoming available to meet the
Stage 2 DBPR requirements for small surface water systems, the following adjustments were made to
the Stage 2 ending treatment technology predictions made by the Delphi subgroup:
• Start with SWAT/Delphi subgroup treatment technology selection predictions for the Stage 1
DBPR and Stage 2 DBPR options (with and without UV) for the small surface water
systems.
• Check the Stage 2 small surface water predictions for NF (i.e., the most expensive treatment
technology). Use the Stage 1 DBPR estimates for NF usage if they are higher than the
Stage 2 NF usage estimates. This is because systems predicted to use NF for Stage 1 will
not remove it to shift to a lower-performing treatment technology, even if the actual Stage 2
predictions specify the latter.
• Repeat the above step with the next most expensive treatment technology (i.e., GAC20 &
UV or advanced oxidants (AO)). Continue this procedure for each succeeding treatment
technology, moving all the way down to chlorine dioxide.
These steps are outlined in Exhibit A.26 (see "Adjusting for Stage 1 Baseline"), and an example
of the adjustments made for each size category is presented in Exhibit A.27.
In addition to the treatment technology abbreviations commonly used in this EA, the following
acronyms are used in Exhibit A.26:
• C/S - Conventional filtration with softening
• NC - Nonconventional filtration
A.9.3 Results
Exhibits A.28a, A.28b, and A.28c summarize the treatment technology selection results for small
surface water systems, for all Stage 2 DBPR regulatory alternatives and sensitivity options.
Final Economic Analysis for the Stage 2 DBPR A-60 December 2005
-------
Exhibit A.26 Small Surface Water Forecast Flowchart
I
SWAT
output
Initial
Adjustments
(1,000-9,999)
Copy SWAT output
If C/S +
NC=0
No adjustment to C/S and NC
If C/S +
NC>0
but <= 65
Add a total of 10% to C/S (CL2 &
CLM combined) and remove in
prop, from CIO2, UV and O3.
N
Adjust sum of NC and C/S to 75% taking in proportion
from CIO2, UV and O3 and adding to C/S
Add 3.6% to MF/UF in prop, to Cb and CLM; remove
in prop. CIO2, UV and O3.
| GAC20 + UV/O3 and NF
•Move 1/3 of O3to MF/UF, GAC20, GAC20
+ UV/O3and NF
s
c
Final Economic Analysis for the Stage 2 DBPR
A-61
December 2005
-------
GAC
Adjustments
100-999:
Move 10%ofGAC20
&GAC20 + UVto NF
<100:
Move 10%ofGAC20
&GAC20 + UVto NF
CLM Adjustments
100-999:
•Reduce NC + CLM & C/S + CLM to 90%; move to UV, O3&
MF/UF + CI2
•Reduce CIO, + CLM to 90%; move to UV, O3, & MF/UF + CI2
•Reduce UV + CLM to 90%; move to O3 & MF/UF + CI2
•Reduce O3 + CLM to 90%; move to MF/UF + CL,
•Reduce MF/UF + CLM to 90%; if GAC20 + CI2<> 0 then move
to GAC20 + CI2 , else move to GAC20 + UV + CI2
•Reduce GAC20 + CLM to 90%; move to GAC20 + UV + CI2
•Reduce GAC20 + UV + CLM to 90%; move to NF + CI2
•Reduce NF + CLM to 90%; move to NF + CI2
<100:
•Reduce NC & C/S CLM to 75%; move to UV &
MF/UF+ CI2
•Reduce MF + CLM to 75%; move to GAC20 +
CI2 if GAC20 + CI2 <> 0, else move to GAC20 +
UV + CI2
•Reduce GAC20 + CLM to 75%; move to
GAC20 + UV + CI2
•Reduce GAC20 + UV + CLM to 75%; move to
NF + Cb
•Reduce NF + CLM to 75%; move to NF + CI2
Adjusting for "Negatives"
1) Start with NF and move in reverse order to CIO2, adjusting for "Negatives"
2) If stage 2 # < stage 1 # Then
Adjust stage 2 # upwards to stage 1
Remove balance from the next less expensive tech. (i.e., from GAC20 + UV)
Else
No adjustment to stage 2 # required
3) Go to GAC20 + UV and follow the above procedure
Stop when CIO2 is adjusted
STOP
Final Economic Analysis for the Stage 2 DBPR
A-62
December 2005
-------
Exhibit A.27 Small Surface Water Adjustments Example
Initial Adjustments
SWAT for ICR S
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
CL2
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
/stems
CLM
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
Serving 1,000-9,999
CL2
C1 =A1
C2 = If A1 +A2+B1 +B2=0 Then A2 Else
If A1+A2+B1+B2 AND A1+A2+B1+B2O.65 Then
A2+(A2/(A2+B2))*0.1 Else
If A1 +A2+B1 +B2>0.65 AND A1 +A2+B1 +B2O.75
ThenA2+(0.75-(A1+A2+B1+B2))*(A2/(A2+B2)))
Else A2
C3 = (A3-((C2-A3)*(A3/(A3+A4+A5)))-
(0.036*(A6/(A6+B6))*(A3/(A3+A4+A5))))
C4 = (A4-((C2-A4)*(A4/(A3+A4+A5)))-
(0.036*(A6/(A6+B6»*(A4/(A3+A4+A5»«
C5 = (A5-((C2-A5)*(A5/(A3+A4+A5)))-
(0.036*(A6/(A6+B6))*(A5/(A3+A4+A5))))
C6 = A6+0.036*(A6/(A6+B6))
C7 = 0
C8 = 0
C9 = A9+A7
C10 = A10+A8
C1 1 = A1 1
CLM
D1 =B1
D2 = If A1 +A2+B1 +B2=0 Then B2 Else
If A1 +A2+B1 +B2 AND A1 +A2+B1 +B2O.65 Then
B2+(B2/(A2+B2))*0.1Else
If A1 +A2+B1 +B2>0.65 AND A1 +A2+B1 +B2O.75
Then B2+(0.75-(A1 +A2+B1 +B2))*(B2/(A2+B2)))
Else B2
D3 = (B3-((D2-B3)*(B3/(B3+B4+B5)))-
(0.036*(B6/(A6+B6))*(B3/(B3+B4+B5))))
D4 = (B4-((D2-B4)*(B4/(B3+B4+B5)))-
(0.036*(B6/(A6+B6»*(B4/(B3+B4+B5«»
D5 = (B5-((D2-B5)*(B5/(B3+B4+B5)))-
(0.036*(B6/(A6+B6))*(B5/(B3+B4+B5))))
D6 = B6+0.036*(B6/(A6+B6))
D7 = 0
D8 = 0
D9 = B8+B7
D10 = B10+B8
D11 =B11
Serving 100 -999
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
CL2
E1 =C1
E2 = C2
E3 = 50%*C3
E4 = C4+(0.5*C3)* (C4/(C4+C5+C6))
E5 = C5+(0.5*C3)* (C5/(C4+C5+C6))
E6 = C6+(0.5*C3)* (C6/(C4+C5+C6))
E7 = C7
E8 = C8
E9 = C9
E10 = C10
E11 = C11
CLM
F1 =D1
F2 = D2
F3 = 50%*D3
F4 = D4+(0.5*D3)*
(D4/(D4+D5+D6))
F5 = D5+(0.5*D3)*
(D5/(D4+D5+D6»
F6 = D6+(0.5*D3)*
(D6/(D4+D5+D6))
F7 = D7
F8 = D8
F9 = D9
F10 = D10
F11 =D11
Serving <1 00
CL2
G1 =E1
G2 = E2+0.67*(E3+E5)
G3 = 0
G4 = E4+0.33*E3*(E4/(E4+E6+E9+E10+E11))
G5 = 0
G6 = E6+0.33*E5*(E6/(E6+E9+E10+E11))+
0.33*E3*(E6/(E4+E6+E9+E10+E11))
G7 = 0
G8 = 0
G9 = E9+0.33*E5*(E9/(E6+E9+E10+E11))+
0.33*E3*(E9/(E4+E6+E9+E10+E11))
G10 = E10+0.33*E5*(E10/(E6+E9+E10+E11))+
0.33*E3*(E 1 0/(E4+E6+E9+E 1 0+E 1 1 ))
G1 1 = E1 1+0.33*E5*(E1 1/(E6+E9+E10+E1 1))+
0.33*E3*(E 1 1 /(E4+E6+E9+E 1 0+E 1 1 ))
CLM
H1 =F1
H2 = F2+0.67*(F3+F5)
H3 = 0
H4 = F4+0.33*F3*(F4/(F4+F6+F9+F1 0+F1 1 ))
H5 = 0
H6 = F6+0.33*F5*(F6/(F6+F9+F10+F11))+
0.33*F3*(F6/(F4+F6+F9+F10+F1 1))
H7 = 0
H8 = 0
H9 = F9+0.33*F5*(F9/(F6+F9+F10+F11))+
0.33*F3*(F9/(F4+F6+F9+F10+F1 1))
H1 0 = F1 0+0.33*F5*(F1 0/(F6+F9+F1 0+F1 1 ))+
0.33*F3*(F1 0/(F4+F6+F9+F1 0+F1 1 ))
H11 = F11+0.33*F5*(F11/(F6+F9+F10+F11))+
0.33*F3*(F1 1/(F4+F6+F9+F1 0+F1 1 ))
Final Economic Analysis for the Stage 2 DBPR
A-63
December 2005
-------
Exhibit A.27 Small Surface Water Adjustments Example (Continued)
GAC20 Adjustments
Serving 100 -999
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
CL2
E1
E2
E3
E4
E5
E6
E7
E8
19 = 90%*E9
I10 = 90%*E10
111 =E11+10%*(E9+E10)
CLM
F1
F2
F3
F4
F5
F6
F7
F8
J9 = 90%*F9
J10 = 90%*F10
J11 =F11 +10%*(F9+F10)
Serving <100
CL2
G1
G2
G3
G4
G5
G6
G7
G8
K9 = 90%*G9
K10 = 90%*G10
K11 =G11+10%*(G9+G10)
CLM
H1
H2
H3
H4
H5
H6
H7
H8
L9 = 90%*H9
L10 = 90%*H10
L11 =H11+10%*(H9+H10)
CLM Adjustments
Serving 100 -999
Nonconventional
Conventional/Softening
CI02
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
CL2
E1
E2
E3
M4 = E4+10%*(F1+F2)*
(E4/(E4+E5+E6))+
1 0%*F3*(E4/(E4+E5+E6))
M5 = E5+10%*(F1+F2)*
(E5/(E4+E5+E6))+
1 0%*F3*(E5/(E4+E5+E6))+
10%*F4*(E5/(E5+E6))
M6 = E6+10%*(F1+F2)*
(E6/SUM(E4+E5+E6))+
1 0%*F3*(E6/(E4+E5+E6))+
10%*F4*(E6/(E5+E6))+ 10%*F5
E7
E8
M9 = If 19=0 Then 0 Else I9+10%*F6
M10 = IF 19=0 Then I10+10%*J9+
10%*F6Else I10+10%*J9
M11 =l11+10%*fJ1CH-J111
CLM
N1 = 90%*F1
N2 = 90%*F2
N3 = 90%*F3
N4 = 90%*F4
N5 = 90%*F5
N6 = 90%*F6
F7
F8
N9 = 90%*J9
N10 = 90%*J10
N11 =90%*J11
Serving <1 00
CL2
G1
G2
G3
04 = G4+25%*(H1+H2)*(G4/(G4+G6))
G5
O6 = G6+25%*(H1+H2)*(G6/(G4+G6))+25%*H4
G7
G8
09 = If K9=0 Then 0 Else K9+25%*H6
010 = If K9=0 Then K10+25%*H6+25%*L9 Else
K10+25%*L9
O11 = K11+25%*(L10+L11)
CLM
P1 = 75%*H1
P2 = 75%*H2
H3
P4 = 75%*H4
H5
P6 = 75%*H6
H7
H8
P9 = 75%*H9
P10 = 75%*H10
P1 1 = 75%*H1 1
Final Economic Analysis for the Stage 2 DBPR
A-64
December 2005
-------
Exhibit A.27 Small Surface Water Adjustments Example (Continued)
Adjusting for "Nee
Check if NF is below Stag
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
Check if GAC20 & UV is b
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
Check if GAC20 is below
Nonconventional
Conventional/Softening
CI02
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
jatives"
e1
Stage 1
CL2
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
Baseline
CLM
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
elow Stage 1
Stage 1 Baseline
CL2 CLM
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
Stage 1
Stage 1
CL2
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
Baseline
CLM
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
Stage 2 Alternative
CL2 CLM
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
Stage 2 Alternative
CL2 CLM
C1
C2
C3
C4
C5
C6
C7
C8
C9
E10
E11
D1
D2
D3
D4
D5
D6
D7
D8
D9
F10
F11
Stage 2 Alternative
CL2 CLM
C1
C2
C3
C4
C5
C6
C7
C8
C9
E10
E11
D1
D2
D3
D4
D5
D6
D7
D8
D9
F10
F11
Stage 2 Alternative, after Adjustment
CL2
C1
C2
C3
C4
C5
C6
C7
C8
C9
E10=lfC1KA11 Then C10-ABS(A11-C11) Else C10
E1 1 = If C1 1
-------
Exhibit A.27 Small Surface Water Adjustments Example (Continued)
Check if MF/UF is below Stage 1
Stage 1 Baseline Stage 2 Alternative Stage 2 Alternative, after Adjustment
CL2 CLM CL2 CLM CL2 CLM
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
A1 B1
A2 B2
A3 B3
A4 B4
A5 B5
A6 B6
A7 B7
A8 B8
A9 B9
A10 B10
A11 B11
C1 D1
C2 D2
C3 D3
C4 D4
C5 D5
C6 D6
C7 D7
C8 D8
C9 D9
E10 F10
E11 F11
C1 D1
C2 D2
C3 D3
C4 D4
K5 = If I6
-------
Exhibit A.27 Small Surface Water Adjustments Example (Continued)
Check if CIO2 is below Stage 1
Stage 1 Baseline
CL2 CLM
Nonconventional
Conventional/Softening
CIO2
UV
Ozone
MF/UF
GAC10
GAC10&UV
GAC20
GAC20 & UV
Membranes (NF)
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
B11
Stage 2 Alternative
CL2 CLM
Stage 2 Alternative, after Adjustment
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
Q1 =
Q2 =
CL2
If O3
-------
Exhibit A.28a Small Surface Water Treatment Technology Selection Results (Serving Populations <100)
Rule Option
Staae 1 Baseline
Stage 2 Preferred,
20% SM
Alternative 1
Alternative 2
Alternative 3
Description of Ru
Compliance
Calculation
80/60 RAA
80/60 LRAA
80/60 LRAA
80/60 SH
40/30 RAA
Bromate
MCL
10
10
5
10
10
e Option
UV
Considered?
No
Yes
Yes
Yes
Yes
Cb
Converting
toCLM
39.56%
42.58%
42.58%
50.55%
51.10%
Non
Conventional
9.98%
9.80%
9.80%
6.48%
4.17%
Conventional/
Softenina
65.21%
60.76%
60.50%
47.68%
39.39%
CIO2
0.00%
0.00%
0.00%
0.00%
0.00%
UV
0.00%
3.98%
3.32%
2.44%
3.49%
Ozone
0.00%
0.00%
0.00%
0.00%
0.00%
MF UF
18.03%
18.03%
18.03%
21.25%
21.93%
GAC10
0.00%
0.00%
0.00%
0.00%
0.00%
GAC10
& UV
0.00%
0.00%
0.00%
0.00%
0.00%
GAC20
3.25%
3.25%
3.25%
1 1 .39%
17.87%
GAC20
& UV
0.00%
0.66%
1.41%
6.38%
7.77%
Membranes
3.52%
3.52%
3.69%
4.37%
5.38%
Exhibit A.28b Small Surface Water Treatment Technology Selection Results (Serving Populations 100-999)
Rule Option
Staae 1 Baseline
Stage 2 Preferred,
20% SM
Alternative 1
Alternative 2
Alternative 3
Description of Ru
Compliance
Calculation
80/60 RAA
80/60 LRAA
80/60 LRAA
80/60 SH
40/30 RAA
Bromate
MCL
10
10
5
10
10
e Option
UV
Considered?
No
Yes
Yes
Yes
Yes
CI2
Converting
toCLM
47.47%
51.10%
51.10%
60.66%
61 .32%
Non
Conventional
10.59%
10.51%
10.50%
7.24%
4.73%
Conventional/
Softenina
64.03%
61.71%
61.33%
47.23%
39.75%
CI02
1.83%
2.10%
2.10%
1 .83%
2.35%
UV
0.00%
1.40%
1.05%
0.00%
0.00%
Ozone
9.65%
9.65%
9.65%
9.65%
9.65%
MF UF
10.11%
10.11%
10.11%
14.40%
15.33%
GAC10
0.00%
0.00%
0.00%
0.00%
0.00%
GAC10
& UV
0.00%
0.00%
0.00%
0.00%
0.00%
GAC20
2.01%
2.01%
2.01%
10.43%
16.93%
GAC20
& UV
0.92%
1.62%
1.35%
6.00%
7.02%
Membranes
0.86%
0.89%
1 .90%
3.22%
4.23%
Exhibit A.28c Small Surface Water Treatment Technology Selection Results (Serving Populations 1,000-9,999)
Rule Option
Staae 1 Baseline
Stage 2 Preferred,
20% SM
Alternative 1
Alternative 2
Alternative 3
Description of Ru
Compliance
Calculation
80/60 RAA
80/60 LRAA
80/60 LRAA
80/60 SH
40/30 RAA
Bromate
MCL
10
10
5
10
10
e Option
UV
Considered?
No
Yes
Yes
Yes
Yes
CI2
Converting
toCLM
52.75%
56.78%
56.78%
67.40%
68.13%
Non
Conventional
1 0.99%
1 0.93%
10.93%
7.98%
5.14%
Conventional/
Softenina
67.40%
64.90%
64.53%
50.96%
43.05%
CI02
4.03%
4.63%
4.63%
4.12%
5.79%
UV
0.00%
1 .23%
0.87%
0.00%
0.00%
Ozone
8.49%
8.49%
8.49%
8.49%
8.49%
MF UF
5.43%
5.43%
5.43%
9.41%
10.06%
GAC10
0.00%
0.00%
0.00%
0.00%
0.00%
GAC10
& UV
0.00%
0.00%
0.00%
0.00%
0.00%
GAC20
2.20%
2.20%
2.20%
1 1 .36%
18.68%
GAC20
& UV
1.10%
1 .83%
1.47%
6.59%
7.69%
Membranes
0.37%
0.37%
1 .47%
1.10%
1.10%
Final Economic Analysis for the Stage 2 DBPR
A-68
December 2005
-------
Appendix B
Ground Water Plant Compliance Forecasts
-------
-------
Appendix B
Ground Water Plant Compliance Forecasts
B.I Introduction
This appendix documents the derivation of the compliance forecasts for ground water plants.
These forecasts are used in the Economic Analysis (EA) for the Stage 2 Disinfectants and Disinfection
Byproducts Rule (DBPR). The forecast for large ground water plants was generated using the
Information Collection Rule (ICR) Ground Water Delphi process, which convened a group of ground
water system experts. Medium plants were evaluated in a similar manner as large plants. Forecasts for
small plants were developed under the small ground water system expert review process. The following
sections provide the methodology for developing compliance forecasts for all ground water plants.
B.2 Compliance Forecast for Large and Medium Ground Water Plants
Unlike the compliance forecast for surface water plants generated by the Surface Water
Analytical Tool (SWAT), the forecast for ground water plants in large and medium systems (those
serving over 10,000 people) was developed in two steps described below (and summarized in Exhibit B.I).
• Estimate the percentage of plants not in compliance: First, the ICR Ground Water Delphi Group
used ICR data to evaluate each plant for compliance under various regulatory alternatives.
However, most of the large plants predicted to be out of compliance were located in Florida.
Florida systems make up a significantly larger proportion of ICR data than is the proportion of all
United States ground water systems made up by Florida. Therefore, the Environmental
Protection Agency (EPA) applied a "Florida/Non-Florida" stratification when extrapolating the
results of the Delphi Group to the universe of ground water systems.
• Apply treatment technology selection forecasts to the plants not in compliance: The Delphi Group
predicted treatment technology selection for each non-compliant large ground water plant. These
plant-level analyses were aggregated into national-level compliance treatment technology
forecasts, which were then applied to the percent of medium and large systems not in
compliance.
Section B.2.1 explains the rationale for using ICR Delphi results for medium ground water systems.
Final Economic Analysis for the Stage 2 DBPR B-l December 2005
-------
At the time of the Delphi process, EPA was still evaluating a large number of regulatory
alternatives and had not been advised by the Federal Advisory Committees Act (FACA) on the Preferred
Regulatory Alternative. Therefore, the Delphi group analyzed four "bounding" alternatives to address the
variety in the MCL levels (80 micrograms per liter (//g/L) for total trihalomethanes (TTHM), 60 //g/L for
haloacetic acids (HAAS), and 40 //g/L for TTHM, 30 //g/L for HAA5), and measurement methods
(running annual average (RAA), single highest (SH) values, and locational running annual average
(LRAA)) being considered. The original bounding alternatives considered by the Delphi group were
• 80/60 //g/LRAA (The Stage 1 DBPR)
• 80/60 //g/L SH (Alternative 2)
• 40/30 //g/L RAA (Alternative 3)
• 40/30 //g/L SH (Bounding Alternative 4, not considered in this EA)
Two additional regulatory alternatives were identified after the original Delphi group analysis was
completed:
• 80/60 //g/L LRAA (The Preferred Alternative)
• 80/60 //g/L LRAA with reduced Bromate maximum contaminant level (MCL) of 5 //g/L
(Alternative 1)
Unlike the large surface water systems, no sensitivity analysis was performed to quantify the
potential effects of the Initial Distribution System Evaluation (IDSE) on the Preferred Alternative.
Ground water sources have more stable water quality than surface water systems. As a result, ground
water systems will more likely operate their treatment with a much lower safety margin than 20 percent.
Therefore, the ground water system compliance forecasts are conservative enough to estimate the
potential effects of the IDSE.
Sections B.2.2 and B.2.3 provide the detailed process for estimating the percent of plants not in
compliance for each of the 4 alternatives described above and predicting the treatment technologies they
may select to meet compliance.
Final Economic Analysis for the Stage 2 DBPR B-2 December 2005
-------
Exhibit B.1 Compliance Forecast for Medium and Large Ground Water Plants
Baseline Data for
Large GW Plants:
Baseline
Handbook
Determine baseline number
of disinfecting FL ground
water plants.
Large GW
characteristics
ICR data
Determine baseline number
of disinfecting Non-FL ground
water plants.
Determine ICR
Non-FL
percent non-
complying.
Apply percent non-complying
to the baseline FL plants in
each of the four large
population size categories, to
get the respective number of
non-compliers.
Apply percent non-complying to
the baseline Non-FL plants in
each of the four large population
size categories, to get the
respective number of non-
compliers.
Combine the FL and Non-FL non-
compliers from above appropriately to
get the national percent non-compliers
for the medium (i.e., 10K-100K) and
large (i.e., >100K) systems.
Apply the "Delphi" Treatment Selection percentages to
the national percent non-compliers for the medium and
large systems.
Large GW
"Delphi" results
for treatment
selection
Final Compliance
Forecast
Final Economic Analysis for the Stage 2 DBPR
B-3
December 2005
-------
B.2.1 Rationale for Using ICR Delphi Results for Medium Ground Water Systems
To determine if results from the ICR Ground Water Delphi Group could be used for medium
ground water systems, EPA compared data on disinfection byproducts (DBFs) and DBF precursors from
large ground water systems to data from medium ground water systems. The most relevant information
for assessing precursor and byproduct occurrence and treatment technology distribution in medium ground
water systems is that provided in the WATERASTATS database (AWWA 2000). Exhibits B.2 to B.4
provide comparisons of average influent total organic carbon (TOC) levels, treatment technology used,
and average TTHM levels for medium and large ground water systems in the WATERASTATS data set.
Based on this data, the treatment technology configurations and well fields of large and medium ground
water systems are believed to be similar. Therefore, the percent of plants not in compliance (stratified by
Florida/Non-Florida) and compliance treatment technology selections projected for the large ground water
plants were used for the medium ground water plants.
For more details on medium ground water systems, refer to Chapter 3 of Stage 2 Occurrence
Assessment for Disinfectants and Disinfection Byproducts (USEPA 2005k).
Exhibit B.2 Annual Average Raw Water TOC for Medium and Large Ground Water
Systems
ive Percent
ra
E
O
90% -
80% -
70% •
60% •
50% •
40% •
30% •
20% •
j
10% •
I
noi 1
.*. •«* **
J*
/
/
*f
1
A
*
J * Medium Ground Water Systems (N=51)
A Large Ground Water Systems (N=38)
10 15 20
Plant-Mean TOC (mg C/L)
25
30
Source: WATERASTATS (AWWA 2000).
Final Economic Analysis for the Stage 2 DBPR
B-4
December 2005
-------
Exhibit B.3 Annual Average Finished Water TTHM for Medium and Large Ground
Water Systems
# Medium Ground Water Systems (N=169)
A Large Ground Water Systems (N=68)
0%
20
40
60
TTHM (jig/L)
80
100
120
Source: WATERASTATS (AWWA 2000).
Exhibit B.4 Treatment Technology Summary for Medium and Large Ground Water
Systems (Chlorinating and Non-Chlorinating)
45%
40%
35%
• Medium systems 10-100K (N=364)
n Large systems >100K(N=110)
Source: WATERASTATS (AWWA 2000).
Final Economic Analysis for the Stage 2 DBPR
B-5
December 2005
-------
B.2.2 Uncertainties in Compliance Forecasts for Medium and Large Ground Water Systems
There are uncertainties in the ground water compliance forecast. Only 130 ICR ground water
plants were used for the Ground Water Delphi process. This only 2 percent of the roughly 8,400 medium
and large disinfection ground water systems to which these estimates directly apply. In addition, the
Ground Water Delphi is based on expert opinion, and is not as reproducible as the SWAT predictions used
for the surface water compliance forecast. It is unknown as to whether expert opinion is more or less
accurate than a model, although independent Delphi Polls for the surface water systems found agreement
between the two methods.
B.2.3 Estimating the Percentage of Systems Not in Compliance
Total Percent Plants not in Compliance from ICR Data
ICR data (USEPA 2000h) were evaluated to estimate the number of plants that would currently
exceed MCL requirements of the Stage 1 DBPR and each of the Stage 2 DBPR regulatory alternatives.1
Plants were initially classified as not in compliance if ICR data showed that they exceeded the MCLs,
taking into account a 20 percent safety margin for all alternatives. For example, the Preferred Alternative
for the Stage 2 DBPR is 80 ug/L measured as an LRAA for TTHM and 60 ug/L measured as an LRAA
for HAA5. Compliance, therefore, is evaluated at 64 ug/L for TTHM and 48 ug/L for HAAS, both
measured as LRAAs.
Next, EPA checked to see if water from ground water plants was being blended with water from
surface water plants in the distribution system. This may have resulted in higher TTHM and HAAS
concentrations than would normally be associated with an individual ground water plant. If plants with
blended water were included in the compliance forecast assessment, the percent of ground water plants
not in compliance may be overstated. Therefore, ground water plants that had a surface water plant with
the same public water system ID number were considered in compliance for all regulatory alternatives
(i.e., compliance would most likely be achieved by modifying the surface water plant rather than the
ground water plant).
For regulatory alternatives based on LRAA and RAA calculations, EPA further reviewed ICR
data to evaluate the variance in individual distribution system measurements. Influent water quality does
not typically fluctuate in ground water systems as much as it does in surface water systems. Distribution
system TTHM and HAAS concentrations may not vary much, and, thus, some ground water systems may
not need a safety margin as large a 20 percent. EPA evaluated the SH value of each system predicted to
be out of compliance. If the SH value was below the true regulatory limit (without the safety margin),
EPA assumed that it was unlikely that the ground water plant would add a treatment technology to
comply with the rule. These plants were considered in compliance for all regulatory alternatives. Exhibit
B.5 shows an example of two plants (ICR plants 281 and 287) that were initially considered not in
1 A total of 130 large ground water plants were evaluated using the last 12 months of ICR data. Based on
data in the ICR applicability database, there is a higher total number of ground water plants in large systems than
contained in the ICR (see Chapter 4 for the baseline number of large plants used in this analysis). These plants were
not included in the ICR as they were medium or small plants (serving fewer than 100,000 people). The EA accounted
for this discrepancy by using the total plant estimate from the ICR applicability database to adjust the flow per plant
for large ground water systems.
Final Economic Analysis for the Stage 2 DBPR B-6 December 2005
-------
compliance (based on 20 percent safety margin), but were changed to in compliance based on their SH
values.
Exhibit B.5 Evaluation of RAA, LRAA and SH (ug/L)
ICR
WTPID
281
287
RAA*
TTHM
54X
59.f
HAAS
10.8
37.4
LRAA*
TTHM
64.6
66.3
HAAS
11.5
44.6
SH*
TTHM
75.4
75.7
HAAS
16.0
46.5
Source: ICRAuxl (USEPA2000h), 12 months of data.
Florida/Non-Florida Stratification
EPA evaluated the regional characteristics of those plants exceeding MCLs for each alternative.
Large ground water plants in Florida comprise the majority of large ground water plants predicted to be
out of compliance with all regulatory scenarios. However, the national proportion of ground water
systems in Florida is lower than in the ICR data. This is because Florida requires their ground water
systems to disinfect their water due to the high influent TOC concentrations (see Chapter 3 for a
discussion of regional impacts). To avoid inappropriately extrapolating national estimates of non-
compliance from the heavily Florida-weighted ICR results, EPA evaluated Florida and Non-Florida plants
separately and then aggregated the results together to produce national estimates. Below is a step-by-
step explanation of how the percent of plants not in compliance was calculated using the Florida/Non-
Florida stratification.
Step 1: Determine the baseline number of Florida and Non-Florida ground water plants
Exhibit B.6 shows the number of plants by size category, presented separately for Florida and
Non-Florida plants. The total number of Florida ground water systems was derived from SDWIS
(USEPA 2003t). EPA assumes that all Florida ground water systems disinfect (USEPA 1996a). Also,
surface water systems in Florida that derive the majority of their flow from ground water were moved to
the Florida primarily ground water source category (see Chapter 3 for an explanation of how EPA altered
system inventories so that they are classified by primary water source). Numbers of systems were
converted to numbers of plants using plant per system ratios presented in Chapter 3, with the exception of
the systems serving 100,000 to 1 million people. The ICR Applicability database was used to determine
the relative plants per system ratio for Florida/Non-Florida systems. The analysis showed that Florida
systems had a lower plant per system ratio than Non-Florida systems. The national plant per system
number was weighted to incorporate this difference.
Step 2 : Estimate the percent of plants not in compliance in Florida
The percent of plants not in compliance in Florida was based on an evaluation of ICR ground
water plant data for non-surface water influenced plants (as previously noted, ground water distribution
systems were determined to be potentially under the influence of surface water if systems included a
surface water plant). The percent not in compliance is applied to the baseline of both large and medium
plants.
Final Economic Analysis for the Stage 2 DBPR
B-7
December 2005
-------
Step 3: Estimate the percent of plants not in compliance outside of Florida
The percent of non-Florida plants not in compliance was based on an evaluation of ICR ground
water plant data for non-surface water influenced plants. The same methodology was used, as described
in Step 2, to obtain the percent plants not in compliance for Non-Florida plants. This percentage was
applied to both medium and large plants.
Step 4: Estimate the total national percent of plants not in compliance
For each medium and large size category, the total number of plants not in compliance was
estimated by multiplying the percentages in Steps 2 and 3 by the baseline numbers from Exhibit B.6 of
Florida and non-Florida plants, respectively. The Florida and non-Florida plants not in compliance were
then summed and divided by the total number of plants (Florida plus non-Florida). By using this method,
EPA was able to estimate a more accurate national percentage of plants out of compliance with the Stage
2 DBPR.
Exhibits B.7 through B.ll present a summary of the Florida/non-Florida stratification described
above for the Stage 1 DBPR, Stage 2 DBPR Preferred Alternative for 20 percent safety margin,
Alternatives 2 and 3, and the Bounding Alternative 4, respectively. Results are presented for both large
and medium ground water systems. Regulatory Alternative 1 (80/60 ug/L LRAA with reduced Bromate
MCL of 5 ug/L) is not presented separately; the results for that case are equivalent to the Preferred
Alternative (Exhibit B.8), because the Delphi Group assumed that no ground water plants would use
ozone with an MCL of 5 ppb.
Final Economic Analysis for the Stage 2 DBPR B-8 December 2005
-------
Exhibit B.6a Baseline Number of Florida and Non-Florida Plants, CWSs
System Size
(Population Served)
<100
1 00-499
500-999
1 ,000-3,299
3,300-9,999
10,000-49,999
50,000-99,999
100,000-999,999
i1 ,000,000
Total
Florida
Number of
Disinfecting
GW Systems
A
416
650
184
258
135
147
39
21
1
1.851
Number of
SW/GWUDI
Systems
B
2
2
2
7
6
8
1
8
0
36
Percent
SW/GWUDI that
are Primarily
Ground Water
C
3.70%
9.60%
0.00%
5.90%
12.00%
10.00%
8.90%
14.00%
0.00%
Number of
Disinfecting
Systems,
Primarily GW
D = A+B*C
416
650
184
258
136
148
39
22
1
1.854
Plants
Per
System
E
1.0
1.3
1.5
1.6
2.1
4.0
4.9
4.6
9.1
1.7
Number
of Plants
F = D*E
424
858
276
413
280
591
192
101
9
3.145
Non-Florida
Number of
Disinfecting
Systems, Primarily
GW
G
5,881
10,897
3,878
4,484
2,306
1,198
107
79
2
28.831
Plants Per
System
H
1.0
1.3
1.5
1.6
2.1
4.0
4.9
10.4
9.1
1.5
Number
of
Plants
I = G*H
5,999
14,384
5,817
7,174
4,750
4,791
525
817
18
44.275
Exhibit B.6b Baseline Number of Florida and Non-Florida Plants, NTNCWSs
System Size
(Peculation Served)
<100
1 00-499
500-999
1 ,000-3,299
3,300-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>1 ,000,000
Total
Florida
Number of
Disinfecting
GW Systems
A
626
298
81
32
5
2
0
0
0
1,044
Number of
SW/GWUDI
Systems
B
0
0
1
0
0
0
0
0
0
1
Percent
SW/GWUDI that
are Primarily
Ground Water
C
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
Number of
Disinfecting
Systems,
Primarily GW
D = A+B*C
626
298
81
32
5
2
0
0
0
1,044
Plants
Per
System
E
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Number
of Plants
F = D*E
626
298
81
32
5
2
0
0
0
1,044
Non-Florida
Number of
Disinfecting
Systems, Primarily
GW
G
1,867
1,831
508
215
16
1
0
0
0
4,439
Plants Per
System
H
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Number
of
Plants
I = G*H
1,867
1,831
508
215
16
1
0
0
0
4,439
Note: Detail may not add due to independent rounding.
Sources:
(A & B) SDWIS 4th quarter freeze (2003).
(C) Florida surface water systems are moved to the Florida GW system category if > 50% of their flow comes from GW. The percentages from Exhibit
3.4, Column F were used to approximate percentages for Florida.
(E & H) Plants per system for Florida were assumed to be equal to plants per system found in Exhibit 3.4, Column L, except for systems serving 2100,000.
For large systems, ICR data was evaluated to determine if the number of GW plants/system was lower in Florida because they have so many large ground
water plants. The relationship of plants/system from ICR data was maintained for the national analysis (in other words, the ratio of plants per system of
Florida systems to non-Florida systems was used to adjust the entry point estimates.
(G) The number of disinfecting, primarily GW systems is from the Exhibit 3.4, minus the number of disinfecting ground water systems in Florida from
Column A.
Final Economic Analysis for the Stage 2 DBPR
B-9
December 2005
-------
Exhibit B.7 Percentage of Plants Not In Compliance with the
Stage 1 DBPR (80/60 RAA)
Stage 1
80 M9/L TTHM RAA, 60 [igIL HAAS RAA,
10 ycj/L Bromate RAA
Florida
System Size
(Pooulation Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
Plants
A
591
192
101
9
Number of ICR Plants
Not Complying with
Staae 1
B
8
8
8
8
Percent of Florida
Plants Not Complying
with Staae 1
C = B/33
24%
24%
24%
24%
Non-Florida
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Number of
Plants
D
4791
525
817
18
Number of ICR Plants
Not Complying with
Staqe 1
E
0
0
0
0
Percent of Non-Florida
Plants Not Complying
with Staae 1
F = E/97
0%
0%
0%
0%
National
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
All Plants
G=A+D
5,382
716
918
27
Number of ICR Plants
Not Complying with
Staae 1
H = B+E
8
8
8
8
Percent of All Plants
Not Complying with
Staae 1
I=((A*C)+(D*F))/G
3%
6%
3%
8%
Total Percentage Not
Complvinq
J =SumProduct(G*l)/Sum(G)
3.1%
2.8%
Note: Totals may not add due to independent rounding.
Sources: A& D from Exhibit B.6.
B, C, E, & F are based on evaluation of ICR data for ground water plants without surface water
influence. Note that a total of 33 ICR Florida plants and 97 ICR non-Florida plants were evaluated.
Final Economic Analysis for the Stage 2 DBPR
B-10
December 2005
-------
Exhibit B.8 Percentage of Plants Not In Compliance with the
Preferred Alternative, 20 Percent Safety Margin (80/60 LRAA)
Stage 2, Preferred Option
80 H9/L TTHM LRAA, 60 [igIL HAAS LRAA,
10 ycj/L Bromate RAA
Florida
System Size
(Pooulation Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
Plants
A
591
192
101
9
Number of ICR Plants
Not Complying with
Staae 2
B
11
11
11
11
Percent of Florida Plants
Not Complying with
Staae 2
C = B/33
33%
33%
33%
33%
Non-Florida
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Number of
Plants
D
4791
525
817
18
Number of ICR Plants
Not Complying with
Staqe 2
E
1
1
1
1
Percent of Non-Florida
Plants Not Complying
with Staae 2
F = E/97
1%
1%
1%
1%
National
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
All Plants
G=A+D
5,382
716
918
27
Number of ICR Plants
Not Complying with
Staae 2
H = B+E
12
12
12
12
Percent of All Plants Not
Complvina with Staae 2
I=((A*C)+(D*F))/G
5%
10%
5%
12%
Total Percentage Not
Complvinq
J =SumProduct(G*l)/Sum(G)
5.2%
4.8%
Note: Totals may not add due to independent rounding.
Sources: A& D from Exhibit B.6.
B, C, E, & F are based on evaluation of ICR data for ground water plants without surface water
influence. Note that a total of 33 ICR Florida plants and 97 ICR non-Florida plants were evaluated.
Final Economic Analysis for the Stage 2 DBPR
B-ll
December 2005
-------
Exhibit B.9 Percentage of Plants Not In Compliance with
Regulatory Alternative 2 (80/60 SH)
Stage 2, Alternative 2
80 M9/L TTHM SH, 60 [igIL HAAS SH,
10 ycj/L Bromate RAA
Florida
System Size
(Pooulation Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
Plants
A
591
192
101
9
Number of ICR Plants
Not Complying with
Staae 2
B
19
19
19
19
Percent of Florida
Plants Not Complying
with Staae 2
C = B/33
58%
58%
58%
58%
Non-Florida
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Number of
Plants
D
4791
525
817
18
Number of ICR Plants
Not Complying with
Staqe 2
E
3
3
3
3
Percent of Non-Florida
Plants Not Complying
with Staae 2
F = E/97
3%
3%
3%
3%
National
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
All Plants
G=A+D
5,382
716
918
27
Number of ICR Plants
Not Complying with
Staae 2
H = B+E
22
22
22
22
Percent of All Plants
Not Complying with
Staae 2
I=((A*C)+(D*F))/G
9%
18%
9%
21%
Total Percentage Not
Complvina
J =SumProduct(G*l)/Sum(G)
10.1%
9.5%
Note: Totals may not add due to independent rounding.
Sources: A& D from Exhibit B.6.
B, C, E, & F are based on evaluation of ICR data for ground water plants without surface water
influence. Note that a total of 33 ICR Florida plants and 97 ICR non-Florida plants were evaluated.
Final Economic Analysis for the Stage 2 DBPR
B-12
December 2005
-------
Exhibit B.10 Percentage of Plants Not In Compliance with
Regulatory Alternative 3 (40/30 RAA)
Stage 2, Alternative 3
40 M9/L TTHM RAA, 30 [igIL HAAS RAA,
10 ycj/L Bromate RAA
Florida
System Size
(Pooulation Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
Plants
A
591
192
101
9
Number of ICR Plants
Not Complying with
Staae 2
B
18
18
18
18
Percent of Florida
Plants Not Complying
with Staae 2
C = B/33
55%
55%
55%
55%
Non-Florida
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Number of
Plants
D
4791
525
817
18
Number of ICR Plants
Not Complying with
Staqe 2
E
1
1
1
1
Percent of Non-Florida
Plants Not Complying
with Staae 2
F = E/97
1%
1%
1%
1%
National
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
All Plants
G=A+D
5,382
716
918
27
Number of ICR Plants
Not Complying with
Staae 2
H = B+E
19
19
19
19
Percent of All Plants
Not Complying with
Staae 2
I=((A*C)+(D*F))/G
7%
15%
7%
19%
Total Percentage Not
Complvina
J =SumProduct(G*l)/Sum(G)
7.9%
7.3%
Note: Totals may not add due to independent rounding.
Sources: A& D from Exhibit B.6.
B, C, E, & F are based on evaluation of ICR data for ground water plants without surface water
influence. Note that a total of 33 ICR Florida plants and 97 ICR non-Florida plants were evaluated.
Final Economic Analysis for the Stage 2 DBPR
B-13
December 2005
-------
Exhibit B.11 Percentage of Plants Not In Compliance with
Bounding Alternative 4 (40/30 SH)
Stage 2, Alternative 4
40 M9/L TTHM SH, 30 [igIL HAAS SH,
10 ycj/L Bromate RAA
Florida
System Size
(Pooulation Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
Plants
A
591
192
101
9
Number of ICR Plants
Not Complying with
Staae 2
B
27
27
27
27
Percent of Florida Plants
Not Complying with
Staae 2
C = B/33
82%
82%
82%
82%
Non-Florida
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Number of
Plants
D
4791
525
817
18
Number of ICR Plants
Not Complying with
Staqe 2
E
8
8
8
8
Percent of Non-Florida
Plants Not Complying
with Staae 2
F = E/97
8%
8%
8%
8%
National
System Size
(Population Served)
10,000-49,999
50,000-99,999
100,000-999,999
>=1. 000.000
Number of
All Plants
G=A+D
5,382
716
918
27
Number of ICR Plants
Not Complying with
Staae 2
H = B+E
35
35
35
35
Percent of All Plants Not
Complvina with Staae 2
I=((A*C)+(D*F))/G
16%
28%
16%
33%
Total Percentage Not
Complvina
J =SumProduct(G*l)/Sum(G)
17.7%
16.8%
Note: Totals may not add due to independent rounding.
Sources: A& D from Exhibit B.6.
B, C, E, & F are based on evaluation of ICR data for ground water plants without surface water
influence. Note that a total of 33 ICR Florida plants and 97 ICR non-Florida plants were evaluated.
Final Economic Analysis for the Stage 2 DBPR
B-14
December 2005
-------
B.2.4 Treatment Technology Selection
Original "Bounding " Alternatives
The Delphi Group used a multi-step process to develop the compliance forecasts for those large
ground water plants out of compliance with the four original regulatory alternatives.
First, the Delphi participants were given ICR data (such as plant type, residual disinfectant, and
water quality) for ground water plants unable to meet the MCLs of each alternative. Second, Delphi
participants selected a treatment technology from a list of 16 treatment technologies and a residual
disinfectant (chlorine or chloramines) for each plant and rated their confidence in their treatment
technology selections. Judging by the response provided, it appears that each participant focused on
different information to select the treatment technology required by each plant. Some participants gave
greater importance to water quality aspects, while others emphasized design issues. There were four
general approaches that appear to have guided the participants selections:
• Assess the use of chloramines-If the use of chloramines is not feasible, then look for another
treatment technology that better addresses ground water-specific needs, such as multiple
small entry points. Evaluate whether these entry points would be best served by treatment
technologies such as nanofiltration (NF) and Granular Activated Carbon (GAC) rather than
an advanced oxidant (ozone).
• Always maintain a consistent residual in the distribution system-If other plants in the system
use chlorine as a residual, the plant cannot select chloramines as its treatment technology. In
addition, chloramines cannot be selected when TOC is above a certain level.
• Microfiltration/ultrafiltration (MF/UF) cannot be selected as a treatment technology because
ground water plants are not subject to the high removal or inactivation requirements of
surface water plants. Other treatment technologies are selected as needed.
• Assess how far the plant is from compliance with the MCLs. Determine whether the plant
already uses chloramines. If chloramines are not used, and up to a 20 to 30 percent reduction
of DBFs results in compliance, select chloramines as the final treatment technology. If
chloramines cannot be used based on specific water quality conditions, eliminate treatment
technologies that are not feasible and select the least expensive treatment technology that
meets the compliance criteria.
Third, the completed treatment technology selection results from each participant were
aggregated. Quality control and quality assurance steps were performed to ensure a consistent and
usable data entry format. For example, notes provided by each participant were checked against the
treatment technologies they selected to ensure they were consistent. In many cases, multiple treatment
technologies were selected by a participant for one plant. In these circumstances, most expensive
treatment technology was chosen as a conservative estimate. A Microsoft Access™ database was used
to consolidate the participants' responses. Finally, the results were weighted, with higher confidence
responses receiving an additional weighting of 25 percent.
Final Economic Analysis for the Stage 2 DBPR B-15 December 2005
-------
The Delphi process concluded that ground water systems that could not comply with the levels
specified in the Regulatory Alternative would choose primarily from four advanced treatment
technologies:
• Conventional treatment (with chloramines)
• Advanced disinfectants (ozone)
• GAC with an empty bed contact time of 20 minutes (GAC20)
• NF
The use of chloramines with each treatment technology also was calculated for these four
advanced treatment technologies. Exhibit B. 12 presents the proportion of treatment technologies
predicted by the Delphi Group to be selected for the four bounding alternatives. The Delphi results from
the bounding alternatives were also used to develop treatment technology selections for the additional
regulatory alternatives (discussed later in this appendix).
Additional Regulatory Alternatives
Following the initial Delphi process, the Microbial-Disinfectants and Disinfection Byproducts
Advisory Committee (M-DBP Advisory Committee) asked the Delphi group to consider regulatory
alternatives in addition to the original "bounding" alternatives. These new alternatives considered a
bromate MCL, as well as TTHM and HAAS MCLs. Two of these new alternatives were considered in
this EA (the Preferred Regulatory Alternative and Alternative 1).
Because these alternatives were identified late in the process, the Delphi group decided not to
repeat the full evaluation to develop new treatment technology selections (a time-consuming process), but
instead evaluated the new alternatives using the treatment technology selections for the original four
alternatives. A straight interpolation between the 80/60 RAA (the Stage 1 DBPR) and the 40/30 RAA
(Regulatory Alternative 3) was originally used to estimate the treatment technology selection for the 80/60
LRAA alternative. However, EPA later estimated that because water quality in ground water plants does
not generally fluctuate as much as it does in surface water plants and they monitor at only one point for
Stage 1, treatment technologies identified for the 80/60 RAA would most likely be appropriate for
maintaining an 80/60 LRAA. Therefore, the treatment technology selection for the subset of plants not in
compliance with the 80/60 RAA was maintained for the 80/60 LRAA alternative. A straight interpolation
between the 80/60 RAA and the 40/30 RAA regulatory alternatives was used to estimate the treatment
technology selection for all other alternatives (i.e., those complying with 80/60 RAA but not 80/60
LRAA).
Final Results
The percentage of plants not in compliance (Exhibits B.7 through B.ll) is multiplied by the
proportion of plants predicted to select various treatment technologies. This gives the final treatment
technology selection results for each regulatory alternative and sensitivity analyses (Bounding Alternative
4 is not included). Exhibit B.13a presents results for large ground water plants, and B.13b presents results
for medium ground water plants.
Final Economic Analysis for the Stage 2 DBPR B-16 December 2005
-------
For Regulatory Alternative 1, the compliance forecast was adjusted so that the compliance
forecast delta from Stage 1 to Stage 2 did not show any systems removing treatment technologies
(negative forecasts). This is consistent with the methodology used for surface water system compliance
forecasts.
Final Economic Analysis for the Stage 2 DBPR B-l 7 December 2005
-------
Exhibit B.12 Proportion of Treatment Technologies Selected by Non-compliant Large Ground Water Plants as
Predicted by the Delphi Group
Scenario
Bounding Alternative 1 :
RAA 80/60 (Stage 1)
Bounding Alternative 2:
RAA 40/30 (Regulatory
Alternative 3)
Bounding Alternative 3:
SH 80/60 (Regulatory
Alternative 21
Bounding Alternative 4:
SH 40/30
Converting to
CLM onlv
A
59.3%
69.5%
77.5%
63.5%
Advanced
Disinfectants
B
2.5%
2.6%
2.1%
4.1%
Advanced
Disinfectants +
CLM
C
24.8%
7.9%
7.4%
9.5%
GAC20
D
0.0%
0.0%
0.0%
1 .0%
GAC20 + CLM
E
1 .3%
8.5%
4.5%
8.5%
NF
F
4.0%
0.9%
0.7%
1 .9%
NF + CLM
G
8.2%
10.6%
7.8%
1 1 .6%
Total
H = SUM(A:G)
100.00%
100.00%
100.00%
100.00%
2. Extrapolation for Preferred Alternative and Regulatory Alternative 1
Alternative 5: LRAA 80/60
(Preferred Regulatory
Alternative)
Alternative 6: LRAA 80/60,
reduced Bromate MCL of 5
ug/L (Regulatory
Alternative 1)
62.7%
62.7%
2.5%
0.0%
19.2%
0.0%
0.0%
1 .0%
3.7%
1 1 .5%
3.0%
4.5%
9.0%
20.3%
100.0%
100.0%
Notes: Totals may not add due to rounding.
The original Delphi Group Results were adjusted slightly form the original numbers reported during the Technical Working Group (TWG), to make
the total equal to 100 percent.
Sources: ICR Ground Water Delphi Group Results
Final Economic Analysis for the Stage 2 DBPR
B-18
December 2005
-------
Exhibit B.13a Final Treatment Technology Selection Results for Large Ground Water Plants
Stage 2 Regulatory Alternatives
Regulatory Alternative
Stage 1 DBPR
80|jg/LTTHM RAA
60 ug/L HAAS RAA
Unadjusted Stage 2 Preferred Alternative,
20% Safety Margin
80|jg/LTTHM LRAA
60 ug/L HAAS LRAA
Alternative 1
80|jg/LTTHM LRAA
60 ug/L HAAS LRAA
5 ug/L Bromate MCL
Alternative 2
80 ug/L TTH WISH
60 ug/L HAAS SH
Alternative 3
40 ug/L TTH M RAA
30 ug/L HAAS RAA
Converting to
CLMOnly
1.68%
3.01%
2.24%
7.27%
4.88%
Advanced
Disinfectants
0.07%
0.12%
0.07%
0.20%
0.19%
Advanced
Disinfectants +
CLM
0.70%
0.92%
0.70%
0.70%
0.70%
GAC20
0.00%
0.00%
0.05%
0.00%
0.00%
GAC20 + CLM
0.04%
0.18%
0.55%
0.43%
0.62%
NF
0.11%
0.14%
0 22%
0.11%
0.11%
NF + CLM
0.23%
0.43%
0.97%
0.74%
0.77%
Total Percent
Non-Complying
2.83%
4.80%
4.80%
9.45%
7.28%
Sources: Percentage of plant not in compliance derived from Exhibits B.7 through B.12. Percentage of plants adding each treatment technology was
calculated by multiplying the percentage of plants not in compliance by the proportion selecting each treatment technology (Exhibit B. 13).
Notes: [1] Totals may not add due to rounding.
[2] The treatment technology selection for Regulatory Alternative 1 was adjusted to ensure that the compliance forecast delta (compliance forecast
for Alternative 1 minus the compliance forecast for the Stage 1 DBPR) did not have any negative predictions.
[3] The Preferred Alternative row in Exhibit B.13 is used for both Preferred Alternative safety margin rows in this exhibit.
Final Economic Analysis for the Stage 2 DBPR
B-19
December 2005
-------
Exhibit B.13b Final Treatment Technology Selection Results for Medium Ground Water Plants
Stage 2 Regulatory Alternatives
Regulatory Alternative
Stage 1 DBPR
80|jg/LTTHM RAA
60 ug/L HAAS RAA
Unadjusted Stage 2 Preferred Alternative,
20% Safety Margin
80|jg/LTTHM LRAA
60 ug/L HAAS LRAA
Alternative 1
80|jg/LTTHM LRAA
60 ug/L HAAS LRAA
5 ug/L Bromate MCL
Alternative 2
80 ug/L TTH WISH
60 ug/L HAAS SH
Alternative 3
40 ug/L TTH M RAA
30 ug/L HAAS RAA
Converting to
CLMOnly
1.84%
3.24%
2.40%
7.73%
5.29%
Advanced
Disinfectants
0.08%
0.13%
0.08%
0.21%
0.21%
Advanced
Disinfectants +
CLM
0.77%
0.99%
0.77%
0.77%
0.77%
GAC20
0.00%
0.00%
0.05%
0.00%
0.00%
GAC20 + CLM
0.04%
0.19%
0.60%
0.45%
0.67%
NF
0.13%
0.16%
0.23%
0.13%
0.13%
NF + CLM
0.26%
0.47%
1 .05%
0.79%
0.84%
Total Percent
Non-Complying
3.11%
5.18%
5.18%
10.09%
7.90%
Sources: Percentage of plant not in compliance derived from Exhibits B.7 through B.12. Percentage of plants adding each treatment technology was
calculated by multiplying the percentage of plants not in compliance by the proportion selecting each treatment technology (Exhibit B. 13).
Notes: [1] Totals may not add due to rounding.
[2] The treatment technology selection for Regulatory Alternative 1 was adjusted to ensure that the compliance forecast delta (compliance forecast
for Alternative 1 minus the compliance forecast for the Stage 1 DBPR) did not have any negative predictions.
[3] The Preferred Alternative row in Exhibit B.13 is used for both Preferred Alternative safety margin rows in this exhibit.
Final Economic Analysis for the Stage 2 DBPR
B-20
December 2005
-------
B.3 Compliance Forecast for Small Ground Water Plants
Because of differences in water quality, location, and economies of scale, the compliance
treatment technologies predicted for large and medium plants do not represent those that small plants
would select (see Stage 2 Occurrence Assessment for Disinfectants and Disinfection Byproducts
(USEPA 20031) for a comparison of large and small systems). Instead, EPA and experts on small water
systems estimated compliance forecasts by beginning with the compliance forecasts for large plants and
making adjustments based on expert knowledge and data evaluation. A discussion of the adjustments
made to the large ground water system forecasts to produce the forecasts for small systems is presented
in this section.
To further recognize differences in treatment technology use, treatment technology capability, and
water quality among the small systems, the small ground water system group prepared compliance
forecasts separately for the following size categories:
• Systems serving between 1,000 and 9,999 people
• Systems serving between 100 and 999 people
• Systems serving fewer than 100 people
Exhibit B.14 summarizes the derivation of the small ground water compliance forecast via a
flowchart, consisting of two steps:
• Estimation of percent of plants not in compliance
• Treatment technology forecasts for plants not in compliance
B.3.1 Estimation of Percent of Plants Not In Compliance
Exhibits B.7 through B. 11 show the percent of large ground water systems that were judged to be
not in compliance for each rule alternative, based on the evaluation of ICR data. Several adjustments
were made to these estimates to make them applicable to small ground water plants.
Florida and Non-Florida stratification: One of the most significant influences on the regulatory
alternatives considered was plant location. Florida systems (which have higher TOC levels than those of
other States) account for a substantial fraction of all large ground water systems, whereas the proportion
of all small ground water systems located in Florida is much smaller. Without adjusting for this, the
national forecast of small ground water system non-compliance would be overstated. The large and small
ground water systems were analyzed separately to the mitigate potential biases of the large system
compliance and treatment technology forecasts.
Final Economic Analysis for the Stage 2 DBPR B-21 December 2005
-------
Exhibit B.14 Compliance Forecast for Small Ground Water Plants
Small GW Plant
Characteristics
GWSS: FL and Non-FL
Large GW Plant
Characteristics
ICR:FL and Non-FL
Stratify ICR FL Non-Compliers
1. Obtain number of ICR Stage 1 and 2 FL non-
compliers after adjusting for 1979 TTHM Rule.
2. Assign them to the four TOC/Soft.
categories.
3. Calculate percent ICR FL Stage 1 and 2 non-
compliers in each category.
I
Stratify ICR Non -FL Non-Compliers
1. Obtain number of ICR Stage 1 and 2 Non-FL
non-compliers after adjusting for 1979 TTHM
Rule.
2. Assign them to the four TOC/Soft. categories.
3. Calculate percent ICR Non-FL Stage 1 and 2
non-compliers into each category.
Determine FL Stage 1 and 2 Non-
Compliers for Small Systems
Apply the respective FL non-complier
percents from above to the total no. of small
FL plants in each TOC/Soft. category, for all
three small population size categories.
Determine Non-FL Stage 1 and 2 Non-
Compliers for Small Systems
Apply the respective Non-FL non-complier
percents from above to the total no. of small
Non-FL plants in each TOC/Soft. category, for
all three small population size categories.
Determine Stage 1 and 2 National Percent Non-Compliers for Small
Systems
Combine the FL and Non-FL non-compliers for all three small population size
categories to obtain the percent national non-compliers.
Stage 1 Treatment Selection
1. Adjust Stage 1 large GW "Delphi"
treatment selections for ozone
usage.
2. Apply these to the Stage 1
percent non-compliers in each
population size category.
3. Apply chloramine usage
adjustments to the results from 2.
Stage 2 Treatment Selection
1. Adjust Stage 2 large GW "Delphi"
treatment selections for ozone usage.
2. Adjust for Stage 1 "negatives".
3. Adjust for UV usage.
4. Re-adjust for Stage 1 "negatives".
5. Apply these to the Stage 2 percent
non-compliers in each population size
category.
6. Apply chloramine usage
adjustments to the results from 5.
Final Compliance
Forecast
Final Economic Analysis for the Stage 2 DBPR
B-22
December 2005
-------
The 1979 TTHMRule Adjustment The percentage of small ground water plants not in
compliance is expected to be greater than the percentage of large plants not in compliance because small
plants have not had to meet the 1979 TTHM standards. As a proxy for estimating the additional number
of small plants that would currently exceed regulatory targets, EPA assumed that large plants using
chloramines and meeting regulatory targets probably would not have met the targets without chloramines.
The percentage of these large plants (based on ICR data) not meeting the targets (adjusted to remove
those plants with surface water influence) was used to obtain a more accurate estimate of the number of
small systems not meeting the targets.
TOC/Softening Adjustment The compliance forecast was further adjusted by taking into the
account the differences in source water TOC levels and softening use in small plants compared to large
plants.
Exhibit B.I 5 illustrates the procedure for obtaining the percent of plants not in compliance in small
ground water universe using the ICR data for large ground water systems as a starting point. The
descriptions of steps 1 through 5 in Exhibit B.15 are presented below.
Step 1) Obtain the number of ICR ground water plants not in compliance with Stage 1 and Stage 2
from Exhibits B.7 through B.ll. Using this percentage yields a net increase (delta) of plants
changing treatment technology from Stage 1 to Stage 2 of 3.08% for all size categories.
However, the ICR data, as stated previously is comprised of a greater proportion of ground
water plants from Florida than exist in the nation as a whole, especially when compared to
small ground water systems.
Step 2) Next, both the number of ICR ground water plants and small ground water plants are
stratified into a Florida or non-Florida category. This step is done because Florida ground
waters typically have higher DBF precursor levels compared to other states, and Florida has
a proportionately higher number of ICR ground water plants compared to other states. This
simple stratification lowers the delta to 1.48 percent, a little more than half of the percentage
obtained during Step 1.
Step 3) Next, we need to take in several factors that make small ground water plants unique from
medium and large ground water plants. Small ground water plants were not subject to the
1979 TTHM Rule whereas medium and large ground water plants were subject to the rule.
In order to adjust for this fact, EPA assumed that large ground water plants that used
chloramines did so to meet the 1979 TTHM rule, and thus were added to plants out of
compliance with Stage 1 and Stage 2.
Step 4) Next we take into account the differences in treatment and influent water quality between
ICR ground water plans and small ground water plants. We do this by stratifying both lare
and small plants according to whether or not they have softening, and by TOC concentration
(TOC< 1 mg/L, or TOC > 1 mg/L). Softening adjustments increase the delta to 1.82%The
inclusion of TOC helps separate plants that have high TOC from those without, as high TOC
is a leading factor in increased DBF levels. Adding TOC to the adjustments made in Step 3
raises the delta to 2.35 percent.
Step 5) Finally, all factors (Florida, Chloramine compliers, Softening, TOC) are combined to create
the final percent of plants not complying. The delta after this method is 2.90 percent. Exhibit
Final Economic Analysis for the Stage 2 DBPR B-23 December 2005
-------
B.15 also shows the breakout of plants not in compliance for all three population categories
combined.
Exhibit B.15 Steps for Estimating National Percentage of Plants Not in
Compliance for Small Ground Water Systems
Step 1) Initial ICR GW Non Complying Extrapolation
ICR GW Plants
A
130
ICRGW
Noncompllers -
Staqel
B
8
ICRGW
Noncompllers -
Stage 2
C
12
System Size
(Population
Served)
<100
100-999
1,000-9,999
Total
# of All Plants
D
6,423
21,336
12,617
40,376
% of All Plants
Not Complying
with Stage 1
E=B/A
6.15%
6.15%
6.15%
# of All Plants
Not Complying
with Stage 1
F=D*E
395
1,313
776
2,485
% of All Plants
Not Complying
with Stage 2
G=C/A
9.23%
9.23%
9.23%
# of All Plants
Not Complying
with Stage 2
H=D*G
593
1,969
1,165
3,727
Stage 2 to Stage 1
Delta
I = G-E
198
656
388
1,242
J=I/D
3.08%
3.08%
3.08%
3.08%
Sources:
(A-C) ICR Aux 1 Database.
(D) Exhibit 3.2, Column AB
Step 2) Florida/Non-Florida Stratification (FL)
ICR Plants Total
Florida I Non-Florida
A I B
33 I 97
ICRGW Noncompllers -
Staae 1
Florida
C
8
Non-Florida
D
0
ICR GW Noncompllers -
Staae 2
Florida
E
11
Non-Florida
F
1
System Size
(Population
Served)
<100
100-999
1,000-9,999
Total
Florida Plant
Number of
Plants
G
424
1,134
693
2,252
% Not
Complying
with Stage 1
H=C/A
24.24%
24.24%
24.24%
s
% Not
Complying
with Stage 2
I=E/A
33.33%
33.33%
33.33%
t
Number of
Plants
J
5,999
20,201
11,924
38,124
on-Florida Plan
% Not
Complying
with Stage 1
K=D/B
0.00%
0.00%
0.00%
ts
% Not
Complying
with Stage 2
L=F/B
1 .03%
1 .03%
1 .03%
Plants Not
Complying
with Staqe 1
M=G*H+J*K
103
275
168
546
Plants Not
Complying
with Staqe 2
N=G*I+J*L
203
586
354
1.144
Stage 2 to Stage 1
Delta
0=N-M
100
311
186
598
P=0/G
1 .48%
1 .48%
Sources: (A-B) ICR Aux 1 Database.
(C) Exhibit B.7, column B.
(D) Exhibit B.7, column E.
(E) Exhibits.8, column B.
(F) Exhibit B.8, column E.
(G) Exhibit B.6a, Column F
(J) Exhibit B.6a, Column I
Final Economic Analysis for the Stage 2 DBPR
B-24
December 2005
-------
Exhibit B.15 Steps for Estimating National Percentage of Plants Not in
Compliance for Small Ground Water Systems (continued)
Step 3) Inclusion of ICR GW Compilers using Chloramines
ICR Noncomoliers
ICR CLM Compilers
ICR Pla
Florida
A
33
nts Total
Non-Florida
B
97
ICR GW Nor
Stac
Florida
C
8
9
compilers -
e 1
Non-Florida
D
0
2
ICR GW No
Sta
Florida
E
11
9
ncompllers -
ae2
Non-Florida
F
1
2
System Size
(Population
Served)
<100
100-999
1,000-9,999
Total
Florida Plan
Number of
Plants
G
424
1,134
693
2,252
% Not
Complying
with Staae 1
H=(C1+C2)/A
51.52%
51.52%
51.52%
s
% Not
Complying
with Staae 2
I=(E1+E2)/A
60.61%
60.61%
60.61%
N
Number of
Plants
J
5,999
20,201
11,924
38,124
an-Florlda Pla
% Not
Complying
with Staae 1
K=(D1+D2)/B
2.06%
2.06%
2.06%
its
% Not
Complying
with Staae 2
L=(F1+F2)/B
3.09%
3.09%
3.09%
Plants Not
Complying
with Staae 1
M=G*H+J*K
342
1001
603
1,946
Plants Not
Complying
with Staae 2
N=G*I+J*L
443
1312
789
2,544
Stage 2 to Stage 1
Delta
0=N-M
100
311
186
598
P=0/(G+J)
1.56%
1.46%
1.47%
1.48%
Sources: (A-B) ICR Aux 1 Database.
(C) Exhibit B.7, column B for noncompliers, ICR chloramine compilers derived from the ICR database.
(D) Exhibit B.7, column E for noncompliers, ICR chloramine compilers derived from the ICR database.
(E) Exhibit B.8, column B for noncompliers, ICR chloramine compilers derived from the ICR database.
(F) Exhibit B.8, column E for noncompliers, ICR chloramine compilers derived from the ICR database.
(G) Exhibit B.6a, Column F
(J) Exhibit B.6a, Column I
Step 4a) Inclusion of Softening/Non-Softening Stratification Only
Total ICR GW Plants
Florida
Soft
A
14
Non-
Softenlna
B
19
Non-Florida
Soft
C
4
Non-
Softenlna
D
93
ICR GW Noncom
Florida
Soft
E
12
Non-
Softenlna
F
5
oilers - Staae 1
Non-Florida
Soft
G
1
Non-
Softenlna
H
1
ICR GW Noncon
Florida
Soft
I
12
Non-
Softenlna
J
8
nailers - Staae 2
Non-Florida
Soft
K
1
Non-
Softenlna
L
2
System Size
(Population
Served)
<100
100-999
1,000-9,999
Total
Florida
Total
Plants
M
424
1,134
693
7 75?
Percent
Softening
N
4.3%
4.0%
4.1%
4 1%
% Not Com
Sta
Soft
0=E/A
85.71%
85.71%
85.71%
plying with
cieT
Non-
Softeninci
P=F/B
26.32%
26.32%
26.32%
% Not Com
Sta
Soft
Q=I/A
85.71%
85.71%
85.71%
plying with
K2
Non-
Softeninci
R=J/B
42.11%
42.11%
42.11%
Non-Florida
Total
Plants
S
5,99£
20,201
11,92'
38 1?'
Percent
Softening
T
3.9%
4.0%
4.1%
40%
% Not Complying
with Staae 1
Soft
U=G/C
25.00%
25.00%
25.00%
Non-
Softenina
V=H/D
1.08%
1.08%
1.08%
% Not Complying
with Staae 2
Soft
W=K/C
25.00%
25.00%
25.00%
Non-
Softenina
X=L/D
2.15%
2.15%
2.15%
Plan
Com
Staae 1
Y
243
737
444
1 475
tsNot
plying
Staae 2
Z
369
1,117
672
7 159
Stage 2 to Stage 1 Delta
Plants
AA=Z-Y
126
380
228
734
Percent
AB=AA/(M+S)
1.96%
1.78%
1.81%
1 87%
Sources: (A-L) ICR Aux 1 Database.
(M) Exhibit B.6a, Column F
(N) Derived from the GWSS 1983 data.
(S) Exhibit B.6a, Column
(T) Derived from the GWSS 1983 data.
(Y) M*N*O + M*(1-N)*P + S*T*U + S*(1-T)*V
(Z) M*N*Q + M*(1-N)*R + S*T*W + S*(1-T)*X
Final Economic Analysis for the Stage 2 DBPR
B-25
December 2005
-------
Exhibit B.15 Steps for Estimating National Percentage of Plants Not in Compliance
for Small Ground Water Systems (continued)
Step4b) Inclusion of TOC<1/TOC>1 Stratification Only (FL+CLM+TOC)
System Size
(Population
Served)
<100
100-999
1 000-9 999
Total
Total 1C
FIc
TOC<1
A
5
rida
TOO1
B
28
R GW Plants
Non-Florida
TOC<1
C
78
TOO 1
D
19
ICR GW Noncom
Flo
TOC< 1
E
0
Ida
TOO1
F
17
Florida
Total
Plants
M
424
1,134
693
2,252
Percent
with TOC
<=1
N
40.0%
38.5%
41 4%
39.7%
% Not Complying with
Staae 1
TOCS.1
O=E/A
0.00%
0.00%
0 00%
TOO 1
P=F/B
60.71%
60.71%
6071%
% Not Complying with
Staae 2
TOC<_1
Q=I/A
0.00%
0.00%
0 00%
TOO1
R=J/B
71.43%
71.43%
71 43%
liers - Staae 1
Non-
TOC <1
G
0
Morida
TOO1
H
2
ICR GW NoncomDliers - Staae 2
FIc
TOC <1
I
0
rida 1 Non-
TOC > 1 1 TOC < 1
J I K
20 1 0
lorida
TOO1
L
3
Non-Florida
Total
Plants
S
5,999
20,201
11 974
38,124
Percent
with TOC
<=1
T
69.1%
55.5%
6? 8%
59.9%
% Not Complying
with Staae 1
TOC £.1
U=G/C
0.00%
0.00%
0 00%
TOO1
V=H/D
10.53%
10.53%
10 53%
% Not Complying
with Staae 2
TOC<_1
W=K/C
0.00%
0.00%
0 00%
TOO1
X=L/D
15.79%
15.79%
1 5 79%
Total PI
Com
Staae 1
Y
350
1,370
714
2,433
ants Not
lying
Staae 2
Z
474
1,918
991
3,383
Stage 2 to Stage 1 Delta
Plants
AA=Z-Y
125
548
777
950
Percent
AB=AA/(M+S)
1.94%
2.57%
7 70%
2.35%
(A-L) ICR Aux 1 Database.
(M) Exhibit B.6a, Column F
(N) Derived from the GWSS 1983 data.
(T) Derived from the GWSS 1983 data.
(Y) M*N*O + M*(1-N)*P + ST*U + S*(1-T)*V
(Z) M*N*Q + M*(1-N)*R + S*T*W + S*(1-T)*X
Step 5) Final Stratification by Florida/Non-Florida, TOC<1/TOC>1 & Softening/Non-Softening Stratifications
(FL+CLM+TOC+SOFT)
Total ICR GW Plants
ICR Stage 1 Noncompliers
ICR Staae 2 NoncomDliers
System Size (Population
Served)
<100
100-999
1,000-9,999
<100
100-999
1,000-9,999
<100
100-999
1 000-9 999
Total
Florida
TOC<1
Soft
A
1
0
0
Non-
Softening
B
4
0
0
TOO1
Soft
C
13
12
12
Non-
Softening
D
15
5
8
Non-Florida
TOC<1
Soft
E
0
0
0
Non-
Softening
F
78
0
0
TOO1
Soft
G
4
1
1
Non-Softeninc
H
15
1
2
Florida
Total
Plants
I
424
1,134
693
Percentac
TOC<1
Soft
J
0.0%
0.0%
0.0%
Non-
Softening
K
40.0%
38.5%
41.4%
es in Bins
TOO 1
Soft
L
4.3%
4.0%
4.1%
Non-
Softening
M
55.7%
57.5%
54.5%
% Not Complying with Stage 1
TOC<1
Soft
N=A2/A1
0.00%
0.00%
0.00%
Non-
Softening
O=B2/B1
0.00%
0.00%
0.00%
TOO 1
Soft
P=C2/C1
92.31%
92.31%
92.31%
Non-
Softening
Q=D2/D1
33.33%
33.33%
33.33%
% Not Complying with Stage 2
TOC< 1
Non-
Soft Softening
R=A3/A1 S=B3/B1
0.00% 0.00%
0.00% 0.00%
0.00% 0.00%
TOO1
Soft
T=C3/C1
92.31%
92.31%
92.31%
Non-Florida
V
5,999
20,201
11,924
w
0.0%
0.0%
0.0%
Florida
Stage 1
Al
96
259
152
507
Stage 2
AJ
143
390
228
76C
X
69.1%
55.5%
62.8%
Y
3.9%
4.0%
4.1%
Plants Not Complyin
Non-Florida
Stage 1
AK
167
748
386
1,300
Stage 2
AL
274
1,293
649
2,217
Z
27.0%
40.5%
33.1%
AA=E2/E1
0.00%
0.00%
0.00%
g
Total
Stage 1
AM=AI+AK
262
1,007
538
1,807
Stage 2
AN=AJ+AL
417
1,683
877
2,977
AB=F2/F1
0.00%
0.00%
0.00%
AC=G2/G1
25.00%
25.00%
25.00%
Stage 2 to Stage 1 Delta
Plants
AO=AN,AM
155
676
339
1,169
Percent
AP=AO/(I+V]
2.41%
3.17%
2 69%
2.90%
AD=H2/H1
6.67%
6.67%
6.67%
AE=E3/E1 AF=F3/F1
0.00% 0.00%
0.00% 0.00%
0.00% 0.00%
AG=G3/G1
25.00%
25.00%
25.00%
Non-
Softening
U=D3/D1
53.33%
53.33%
53.33%
AH=H3/H1
13.33%
13.33%
13.33%
(A-H)ICRAuxl Database.
(I) Exhibit B.6a, Column F
(J-M) Derived from the GWSS 1983 data.
(V) Exhibit B.6a, Column I
(W-Z) Derived from the GWSS 1983 data.
(Al) I*J*N + I*K*O + I*L*P + I*M*Q
(AJ) I*J*R + I*K*S + I*L*T + I*M*U
(AK) V*WAA + V*X*AB + V*Y*AC + V*Z*AD
(AL) V*WAE + V*X*AF + V*Y*AG + V*Z*AH
Final Economic Analysis for the Stage 2 DBPR
B-26
December 2005
-------
Exhibit B.16 illustrates the individual effect of the three adjustments on the estimate of the
number of small ground water plants not in compliance. The first column, "FL," displays the change from
Stage 1 to Stage 2 if no adjustments were made from large to small ground water systems. This results in
a difference of 1.48 percent. The second column, "FL + CLM," displays the results of adding the large
ICR GW systems that are in compliance but use chloramine (CLM). This is a surrogate for the fact that
large GW systems were subject to the 1979 TTHM rule but small ground waters are not subject to the
1979 TTHM Rule. Note the change from Stage 1 to Stage 2 is the same, only the total number of plants
affected has changed.
The third column, "FL + CLM + Soft," displays the results if systems are stratified based on
whether they use softening at their plants. The change from Stage 1 to Stage 2 for this step is 1.79
percent as opposed to 1.48 percent. The fourth column, "FL + CLM + TOC," displays the results if
systems are stratified based on whether their TOC is greater than 1 milligrams per liter (mg/L). The
difference is now 2.35 percent, almost a full percentage point higher than the softening. Finally, the fifth
column, "FL + CLM + TOC/Soft," shows the results if one combines the stratification of softening with
TOC. The difference increases again to 2.90 percent. The stratification of small ground water plants
results in more plants changing treatment technology, representing the unique situation with regard to EPA
regulations and the differences in Florida systems between small and large ground water systems.
Exhibit B.16 Effect of the Adjustment Steps on the Change
from Stage 1 to Stage 2
4,000
3,500
• 3,000
Q Stage 1 Small Ground Water
Systems Not Complying
H Stage 2 Small Ground Water
Systems Not Complying
D Delta
FL + CLM
FL + CLM + Soft FL + CLM + TOC
Type of Stratification
FL + CLM + TOC/Soft
Final Economic Analysis for the Stage 2 DBPR
B-27
December 2005
-------
B.3.2 Uncertainties in Compliance Forecasts for Small Ground Water Systems
The biggest source of uncertainty for the compliance forecasts for small ground water systems
exists in the extrapolation from the large ground water compliance forecasts. As mentioned previously,
the compliance forecasts for medium and large systems is based on a relatively small subset of total
plants. The extrapolation does attempt to factor in difference in geography by adjusting for the
percentage of systems in Florida.
B.3.3 Treatment Technology Forecasts for Systems Not in Compliance
The treatment technology forecasts for small ground water systems were generated by adjusting
the large ground water compliance forecast. As with small surface water systems, chloramine and ozone
were assumed to be less feasible treatment technologies for small ground water systems than for large
systems. The assumed use of these disinfectants was adjusted for each small system size category. The
steps for generating the Stage 1 and Stage 2 forecasts are summarized below.
Adjustments for the Stage 1 treatment technology forecasts:
Step 1: Start with the Stage 1 (i.e., 80/60 RAA, Bromate 10) compliance forecast for large ground water
systems from Exhibit B.12.
Step 2: For the two smaller population size categories, adjust the percentage of ozone selected as follows:
• 100-999: 50 percent reduction in ozone use; the remaining 50 percent is allocated to GAC.
• <100: 100 percent reduction in ozone use; the 100 percent is allocated to GAC.
Step 3: Multiply the results from Step 2 by the percent of plants not in compliance for each population
category of small ground water systems.
Step 4: Obtain the treatment technology selection showing the CLM use breakout for each treatment
technology, for each population category, as follows:
• 1,000-9,999:
1. Start with results from Step 3.
2. Converting to chloramine: No change from Step 3.
3. Ozone: 75 percent of the original ozone shifts to ozone+CLM, 25 percent remains in
ozone.
4. GAC: All original GAC shifts to GAC+CLM.
5. Membranes: 90 percent of the original membranes shifts to membranes+CLM, 10
percent remains in membranes.
• 100-999:
1. Start with results from Step 3.
2. Converting to chloramine: No change from Step 3.
3. Ozone: 75 percent of the original ozone shifts to ozone+CLM, 25 percent remains in
ozone.
4. GAC: All original GAC shifts to GAC+CLM.
Final Economic Analysis for the Stage 2 DBPR B-28 December 2005
-------
5. Membranes: 90 percent of the original membranes shifts to membranes+CLM, 10
percent remains in membranes.
6. Final chloramine adjustment: 10 percent of GAC+CLM shifts to membranes.
• <100:
1. Start with results from Step 3.
2. Converting to chloramine: No change from Step 3.
3. Ozone: Not selected.
4. GAC: All original GAC shifts to GAC+CLM.
5. Membranes: 90 percent of the original membranes shifts to membranes+CLM, 10
percent remains in membranes.
6. Final chloramine adjustment: 25 percent of GAC+CLM shifts to membranes.
Adjustments for the Stage 2 treatment technology forecasts:
Step 1: Start with the Stage 2 (i.e., 80/60 LRAA, Bromate 10) compliance forecast for large ground water
systems from Exhibit B.12.
Step 2: For the two smaller population size categories, adjust the percentage of ozone selected as follows:
• 100-999: 50 percent reduction in ozone use; the remaining 50 percent is allocated to GAC.
• <100: 100 percent reduction in ozone use; the 100 percent is allocated to GAC.
Step 3: Adjust the numbers from Step 2 for "negatives": This ensures that the overall percentages of
systems using advanced treatment technologies do not fall below those forecasted for the Stage 1 DBPR.
Step 4: Adjust the numbers from Step 3 for Ultraviolet disinfection (UV): UV is available as a treatment
technology option for all Stage 2 DBPR alternatives. Small systems are assumed to be able to achieve 4-
logs of virus inactivation by installing 2, 2-log UV reactors in series. Even with the 2 reactor series, UV is
less expensive than other advanced treatment technologies. For the Stage 2 DBPR alternatives, EPA
assumed that 60 percent of the advanced treatment technology selections of ozone, GAC, and membranes
would instead be UV. UV was not included as a viable treatment technology for the Stage 1 DBPR, so
EPA assumed that all of the systems adding advanced treatment technology for the Stage 1 DBPR would
stay with that treatment technology for the Stage 2 DBPR, while additional systems adding treatment
technology for the Stage 2 DBPR can use UV. As a result, EPA apportioned a fraction (i.e., 60 percent)
of the systems moving to advanced treatment technologies, to UV.
Step 5: Re-adjust the numbers from Step 4 for "negatives": This ensures that the overall percentages of
systems using advanced treatment technologies do not fall below those forecasted for the Stage 1 DBPR.
Step 6: Multiply the results from Step 2 by the percent of plants not in compliance for each population
category of small ground water systems.
Step 7: Chloramine adjustments: Obtain the treatment technology selection showing the chloramine use
breakout for each treatment technology, for each population category, as follows:
Final Economic Analysis for the Stage 2 DBPR B-29 December 2005
-------
1,001-10,000:
1. Start with the results from Step 6.
2. Converting to chloramine: No change from Step 6.
3. UV: All shift to UV+CLM.
4. Ozone: 75 percent of the original ozone shifts to ozone+CLM, 25 percent remains in
ozone.
5. GAC: All original GAC shifts to GAC+CLM.
6. Membranes: 90 percent of the original membranes shifts to membranes+CLM, 10
percent remains in membranes.
101-1,000:
1. Start with the results from Step 6.
2. Converting to chloramine: No change from Step 6.
3. UV: 90 percent of the original UV shifts to UV+CLM, 0% remains in UV.
4. Ozone: 75 percent of the original ozone shifts to ozone+CLM, 25 percent remains in
ozone.
5. GAC: All original GAC shifts to GAC+CLM, 10% of original UV shifts to GAC.
6. Membranes: 90 percent of the original membranes shifts to membranes+CLM, 10
percent remains in membranes.
7. Final chloramine adjustment: 10 percent of GAC+CLM shifts to membranes.
< 100:
1. Start with the results from Step 6.
2. Converting to chloramine: No change from Step 6.
3. UV: 75 percent of the original UV shifts to UV+CLM, 0% remains in UV.
4. Ozone: Not selected.
5. GAC: All original GAC shifts to GAC+CLM, 25% of original UV shifts to GAC.
6. Membranes: 90 percent of the original membranes shifts to membranes+CLM, 10
percent remains in membranes.
7. Final chloramine adjustment: 25 percent of GAC+CLM shifts to membranes.
B.3.3 Results
Exhibits B.I7 and B.I8 illustrate the adjustments discussed in section B.3.2. for the Stage 1 (i.e.,
80/60 RAA, Bromate 10) and the Stage 2 DBPR Preferred Alternative (i.e., 80/60 LRAA, Bromate 10)
respectively. In addition to conducting the above analysis for the Stage 2 DBPR Preferred Alternative,
similar analyses were performed for all regulatory alternatives considered during the development of the
Stage 2 DBPR. Results are summarized in Chapter 5 and Appendix C for all regulatory alternatives.
Exhibit B.19 summarizes the treatment technology selection results for small ground water systems, for all
Stage 2 DBPR regulatory alternatives and sensitivity options.
Final Economic Analysis for the Stage 2 DBPR B-30 December 2005
-------
Exhibit B.17 Stage 1 (80/60 RAA, Bromate 10) Treatment Technology Selection Forecasts
Adjustments
% Disinfecting
non-compliers
Converting
to CLM only
CONV
Ozone
Ozone+
CLM
GAC
GAC+
CLM
MEM
MEM+
CLM
Comments
1,001-10,000 category
1 . Large GW treatment selection for
noncomoliers (DelDhh
2. Treatment selection for noncompliers
after aoolvina ozone adjustments to 1
3. Treatment selection from 2 applied to
the oercent noncomoliers
4. Final treatment selection showing
chloramine use breakout within each
technology
4.26%
4.26%
4.26%
4.26%
2.52%
2.52%
59.25%
59.25%
27.25%
27.25%
1.16%
0.29%
0.87%
1 .25%
1 .25%
0.05%
0.00%
0.26%
12.25%
12.25%
0.52%
0.05%
0.47%
From larae GW delohi.
No adjustments to ozone usage in this cateaorv.
All plants predicted to be CONV have to switch to
CLM to be compliant. Example calculation
fOzone^: 27.25% of 4.26% = 1.16%.
(1) Start with results from 3. (2) Convert to CLM:
No change. (3) Ozone: 75% to Ozone+CLM, 25%
to Ozone. (4) GAC: All go to GAC+CLM. (5) MEM:
90% to MEM+CLM, 10% remains in MEM.
101-1,000 category
1 . Large GW treatment selection for
noncomoliers (DelDhh
2. Treatment selection for noncompliers
after aDDlvina ozone adjustments to 1
3. Treatment selection from 2 applied to
the oercent noncomoliers
4. Final treatment selection showing
chloramine use breakout within each
technology
4.72%
477%
4.72%
4.72%
2.80%
2.80%
59.25%
59 75%
27.25%
1 3 63%
0.64%
0.16%
0.48%
1 .25%
1 4 RR%
0.70%
0.22%
0.63%
12.25%
17 75%
0.58%
0.15%
0.70%
From larae GW delohi.
50% reduction in ozone, balance aoes to GAC.
All plants predicted to be CONV have to switch to
CLM to be compliant. Example calculation
fOzone^: 13.63% of 4.72% = 0.64%.
(1) Start with results from 3. (2) Convert to CLM:
No change. (2) Ozone: 75% to Ozone+CLM, 25%
to Ozone. (3) GAC: All go to GAC+CLM. (4) MEM:
90% to MEM+CLM, 10% remain in MEM. (5) Final
CLM adjustment: 10% of GAC+CLM to MEM.
<= 100 category
1 . Large GW treatment selection for
noncomoliers (DelDhh
2. Treatment selection for noncompliers
after aoolvina ozone adjustments to 1
3. Treatment selection from 2 applied to
the oercent noncomoliers
4. Final treatment selection showing
chloramine use breakout within each
technology
408%
4.08%
4.08%
4.08%
2.42%
2.42%
59 75%
59.25%
77 75%
0.00%
0.00%
0.00%
0.00%
1 75%
28.50%
1.16%
0.00%
0.87%
17 75%
12.25%
0.50%
0.34%
0.45%
From larae GW delohi.
100% reduction in ozone, balance aoes to GAC.
All plants predicted to be CONV have to switch to
CLM to be compliant. Example calculation (GAC):
28.50% of 4.08% = 1.1 6%.
(1) Start with results from 3. (2) Convert to CLM:
No change. (3) Ozone: 0%. (4) GAC: All go to
GAC+CLM. (5) MEM: 90% to MEM+CLM, 10%
remain in MEM. (6) Final CLM adjustment: 25% of
GAC+CLM to MEM.
Final Economic Analysis for the Stage 2 DBPR
B-31
December 2005
-------
Exhibit B.18 Stage 2 Preferred Alternative (80/60 LRAA, Bromate 10) Treatment Technology Selection Forecast
Adiustments
% Disinfecting
non-compliers
Converting to
CLM only
CONV
UV
UV+
CLM
Ozone
Ozone+C
LM
GAC
GAC+CI
LM | MEM
MEM+C
LM
Comments
<= 100 category
1. Large GWtreatment selection
for noncompliers (Delphi)
2. Treatment selection for
noncompliers after applying ozone
adiustmentsto 1
3. Treatment selection after
adjusting 2 for "negatives"
4. Treatment selection after UV
adjustments to 3
5. Treatment selection after
adjusting 4 for "negatives"
6. Treatment selection from 5
applied to noncompliers
7. Finaltreatment selection
showing chloramine use breakout
within each technology
6.50%
6.50%
6.50%
6.50%
6.50%
6.50%
6.50%
3.38%
3.03%
62.67%
62.67%
62.67%
62.67%
51.99%
22.40%
22.40%
1.46%
0.00%
1.25%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
25.33%
25.33%
25.33%
10.13%
17.91%
1.16%
0.42%
0.87%
12.00%
12.00%
12.00%
4.80%
7.70%
0.50%
0.36%
0.58%
From large GWdelphi.
100% reduction in ozone, balance goes to GAC.
To ensure that treatment selection for a technology is not below
the Stage 1 selection.
Assumes that 60% of (Ozone+GAC+MEM) switch to UV, the
balance 40% is distrbuted among Ozone, GAC, and MEM in
their existing proportions.
To ensure that treatment selection for a technology is not below
the Stage 1 selection.
All plants predicted to be CONV have to switch to CLM to be
compliant. Example calculation (GAC): 17.91% of 6.50% =
1.16%.
(1) Start with results from 6. (2) Convert to CLM: No change. (3)
UV: 75% of original UV to UV+CLM, 0% to UV. (4) Ozone: 0%.
(5) GAC: All original GAC to GAC+CLM, balance 25% of
original UV to GAC. (6) MEM: 90% to MEM+CLM, 10% remains
in MEM. (7) Final CLM adjustment: 25% of GAC+CLM to MEM.
Final Economic Analysis for the Stage 2 DBPR
B-32
December 2005
-------
Exhibit B.18 Stage 2 Preferred Alternative (80/60 LRAA, Bromate 10) Treatment Technology Selection Forecast
(continued)
Adjustments
% Disinfecting
non-compilers
Converting to
CLM only
CONV
UV
UV+
CLM
Ozone
Ozone*
CLM
GAC
GAC+
CLM
MEM
MEM+
CLM
Comments
1,001-10,000 category
1. Large GW treatment selection
for noncompliers (Delphi)
2. Treatment selection for
noncompliers after applying
ozone adjustments to 1
3. Treatment selection after
adjusting 2 for "negatives"
4. Treatment selection after UV
adjustments to 3
5. Treatment selection after
adjusting 4 for "negatives"
6. Treatment selection from 5
applied to noncompliers
7. Final treatment selection
showing chloramine use
breakout within each technology
6.95%
6.95%
6.95%
6.95%
6.95%
6.95%
6.95%
3.61%
0.00%
62.67%
62.67%
62.67%
62.67%
51.90%
22.40%
22.40%
1.56%
0.00%
0.00%
21.67%
21.67%
21.67%
8.67%
16.72%
1.16%
0.00%
0.00%
3.67%
3.67%
3.67%
1.47%
1.47%
0.10%
0.00%
0.00%
12.00%
12.00%
12.00%
4.80%
7.51%
0.52%
0.00%
0.00%
From large GWdelphi.
No adjustments to ozone usage in this category.
To ensure that treatment selection for a technology is not below the
Stage 1 selection.
Assumes that 60% of (Ozone+GAC+MEM) switch to UV, the balance
40% is distrbuted among Ozone, GAC, and MEM in their existing
proportions.
To ensure that treatment selection for a technology is not below the
Stage 1 selection.
All plants predicted to be CONV have to switch to CLM to be
compliant. Example calculation (UV): 22.40% of 6.95% = 1.56%.
(1 ) Start with results from 6. (2) Convert to CLM : No change. (3) UV:
All go to UV+CLM. (4) Ozone: 75% of original to Ozone+CLM, 25% to
Ozone. (5) GAC: All go to GAC+CLM. (6) MEM: 90% to MEM+CLM,
10% remains in MEM.
101-1,000 category
1. Large GW treatment selection
for noncompliers (Delphi)
2. Treatment selection for
noncompliers after applying
ozone adiustments to 1
3. Treatment selection after
adiustina 2 for "neaatives"
4. Treatment selection after UV
adjustments to 3
5. Treatment selection after
adiustina 4 for "neaatives"
6. Treatment selection from 5
applied to noncompliers
7. Final treatment selection
showing chloramine use
breakout within each technology
7.89%
7.89%
7.89%
7.89%
7.89%
7.89%
7.89%
4.20%
4.90%
62.67%
62.67%
62.67%
62.67%
53.21%
22.40%
22.40%
1.77%
0.00%
1.51%
10.83%
5.42%
5.42%
2.17%
8.16%
0.64%
0.16%
0.48%
14.50%
19.92%
19.92%
7.97%
8.90%
0.70%
0.17%
0.63%
12.00%
12.00%
12.00%
4.80%
7.33%
0.58%
0.13%
0.52%
From large GWdelphi.
50% reduction in ozone, balance goes to GAC.
To ensure that treatment selection for a technology is not below the
Staae 1 selection.
Assumes that 60% of (Ozone+GAC+MEM) switch to UV, the balance
40% is distrbuted among Ozone, GAC, and MEM in their existing
DroDortions.
To ensure that treatment selection for a technology is not below the
Staae 1 selection.
All plants predicted to be CONV have to switch to CLM to be
compliant. Example calculation (Ozone): 8.16% of 7.89% = 0.64%.
(1 ) Start with results from 6. (2) Convert to CLM : No change. (3) UV:
90% of original UV to UV+CLM, 0% to UV. (4) Ozone: 75% of original
to Ozone+CLM, 25% to Ozone. (5) GAC: All original GAC go to
GAC+CLM, balance 10% of original UV to GAC. (6) MEM: 90% to
MEM+CLM, 10% remains in MEM. (7) Final CLM adjustment:10% of
GAC+CLM to MEM.
Final Economic Analysis for the Stage 2 DBPR
B-33
December 2005
-------
Exhibit B.19 Small Ground Water Treatment Technology Selection Results Summary
Regulatory Option
Converting to
CLM only
UV
UV + CLM
Ozone
Ozone +
CLM
GAC20
GAC20 +
CLM
NF
NF + CLM
Total %
Changing
Tech.
1,001-10,000 category
Stage 1 Baseline, 80/60 RAA, BROS = 10, UV = OFF
Preferred Alternative, 20% Safety Margin, 80/60 LRAA,
BRO3= 10, UV = ON
Stage 2 Alternative 1 , 80/60 LRAA, BROS = 5, UV = ON
Stage 2 Alternative 2, 80/60 SH, BROS = 10, UV=ON
Stage 2 Alternative 3, 40/30 RAA, BROS = 10, UV = ON
2.52%
3.61%
2.50%
6.00%
4.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1 .56%
2.25%
1 .42%
1 .60%
0.29%
0.29%
0.29%
0.29%
0.29%
0.87%
0.87%
0.87%
0.87%
0.87%
0.00%
0.00%
0.00%
0.00%
0.00%
0.05%
0.10%
0.35%
0.17%
0.26%
0.05%
0.05%
0.07%
0.05%
0.05%
0.47%
0.47%
0.62%
0.47%
0.47%
4.26%
6.95%
6.95%
9.27%
7.55%
101 -1,000 category
Stage 1 Baseline, 80/60 RAA, BROS = 10, UV = OFF
Preferred Alternative, 20% Safety Margin, 80/60 LRAA,
BR03= 10, UV = ON
Stage 2 Alternative 1 , 80/60 LRAA, BROS = 5, UV = ON
Stage 2 Alternative 2, 80/60 SH, BROS = 10, UV=ON
Stage 2 Alternative 3, 40/30 RAA, BROS = 10, UV = ON
2.80%
4.20%
3.61%
6.67%
4.90%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1 .59%
1 .94%
1 .31 %
1 .51 %
0.16%
0.16%
0.16%
0.16%
0.16%
0.48%
0.48%
0.48%
0.48%
0.48%
0.00%
0.18%
0.22%
0.15%
0.17%
0.63%
0.63%
0.63%
0.63%
0.63%
0.13%
0.13%
0.15%
0.13%
0.13%
0.52%
0.52%
0.70%
0.52%
0.52%
4.72%
7.89%
7.89%
10.05%
8.50%
<= 100 category
Stage 1 Baseline, 80/60 RAA, BROS = 10, UV = OFF
Preferred Alternative, 20% Safety Margin, 80/60 LRAA,
BRO3= 10, UV = ON
Stage 2 Alternative 1 , 80/60 LRAA, BROS = 5, UV = ON
Stage 2 Alternative 2. 80/60 SH. BROS = 10. UV=ON
Stage 2 Alternative 3, 40/30 RAA, BROS = 10, UV = ON
2.42%
3.38%
3.03%
6.24%
4.25%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1 .09%
1 .25%
0.92%
0.99%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.36%
0.42%
0.31%
0.33%
0.87%
0.87%
0.87%
0.87%
0.87%
0.34%
0.34%
0.36%
0.34%
0.34%
0.45%
0.45%
0.58%
0.45%
0.45%
4.08%
6.50%
6.50%
9.13%
7.23%
Final Economic Analysis for the Stage 2 DBPR
B-34
December 2005
-------
Appendix C
Supplemental Compliance Forecasts
-------
-------
Appendix C
Supplemental Compliance Forecasts
This appendix presents the Stage 1 and Stage 2 Disinfectants and Disinfection Byproducts Rule
(DBPR) compliance forecast results for both surface water and ground water systems. There are three
basic types of compliance forecasts presented:
• Treatment Technology Selection—The treatment technology selection tables represent the
number and percent of systems that have to add a treatment technology to comply with the
rule. These results include only the number of systems that exceed rule maximum
contaminant levels (MCLs) and must add treatment technology to comply with the rule.
Those plants that are already using a treatment technology prior to the rule and do not have to
add an additional treatment technology to comply are not included in this table. The
treatment technology selection numbers are based on the pre-Stage 1 treatment technology
baseline.
• Treatment Technology Selection Deltas—The treatment technology selection delta tables
represent the incremental number of plants that must add a treatment technology to meet
Stage 2 DBPR regulatory alternatives after predicted changes to meet the Stage 1 DBPR.
These tables are calculated by subtracting the Stage 1 DBPR treatment technology selection
tables from the Stage 2 DBPR treatment technology selection tables. These tables are used
for costing.
• Treatment Technologies in Place—The treatment technologies in place tables show the
number and percent of systems that are using a treatment technology, once systems are in
compliance with the rule. This includes the systems predicted to add a treatment technology
to comply with the rule, and those systems that were already using the treatment technology
before rule promulgation.
This Appendix presents the treatment technology selection tables for the Stage 1 DBPR and the
Stage 2 DBPR, and the treatment technology selection, treatment technology selection deltas, and
treatment technologies in place tables for the other regulatory alternatives and the sensitivity analyses.
Compliance forecasts are organized as follows (see next page).
Note'. Some compliance forecasts are presented in the main body of the Economic Analysis (i.e., Exhibits 3.13a through 3.14b,
7.14a through 7.19b), and are thus not repeated in this Appendix.
Final Economic Analysis for the Stage 2 DBPR C-l December 2005
-------
Rule Option
Pre-Stage 1
Pre-Stage 2
(Post-Stage 1)
Stage 2
Preferred
Alternative
Stage 2
Alternative 1
Stage 2
Alternative 2
Stage 2
Alternative 3
Stage 2
Preferred
Alternative, 20%
Safety Margin
Stage 2
Preferred
Alternative, 25%
Safety Margin
Compliance Forecast
Type
Treatment
Technologies in Place
Selection
Treatment
Technologies in Place
Delta
Treatment
Technologies in Place
Delta
Treatment
Technologies in Place
Delta
Treatment
Technologies in Place
Delta
Treatment
Technologies in Place
Delta
Treatment
Technologies in Place
Delta
Treatment
Technologies in Place
Source
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
Surface Water
Ground Water
System
Type
cws
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
CWS
NTNCWS
Exhibit Number
Chapters, Exhibit 3. 13a
Chapters, Exhibit 3. 13b
Chapters, Exhibit 3. 14a
Chapters, Exhibit 3. 14b
Exhibit C.1 a
Exhibit C.1b
Exhibit C.2a
Exhibit C.2b
Chapter?, Exhibit 7. 14a
Chapter?, Exhibit 7. 14b
Chapter?, Exhibit 7. 17a
Chapter 7, Exhibit 7. 17b
Chapter 7, Exhibit 7. 15a & 7.15b
Chapter 7, Exhibit 7.15c & 7.15d
Chapter 7, Exhibit 7. 18a & 7.18b
Chapter 7, Exhibit 7.18c & 7.18d
Chapter 7, Exhibit 7. 16a & 7.16b
Chapter 7, Exhibit 7.16c & 7.16d
Chapter 7, Exhibit 7. 19a & 7.19b
Chapter 7, Exhibit 7.19c & 7.19d
Exhibits C.3a&C.3b
Exhibits C.3c & C.3d
Exhibits C.4a&C.4b
Exhibits C.4c & C.4d
Exhibits C.5a&C.5b
Exhibits C.5c & C.5d
Exhibits C.6a&C.6b
Exhibits C.6c & C.6d
Exhibits C.7a&C.7b
Exhibits C.8a&C.8b
Exhibits C.8c & C.8d
Exhibits C.9a&C.9b
Exhibits C.9c & C.9d
Exhibits C.10a&C.10b
Exhibits C.10c&C.10d
Exhibits C.1 1a&C.11b
Exhibits C.1 1c& C.1 1d
Exhibits C.12a&C.12b
Exhibits C.12c&C.12d
Exhibits C.13a&C.13b
Exhibits C.13c& C.1 3d
Exhibits C.14a&C.14b
Exhibits C.14c&C.14d
Exhibits C.15a&C.15b
Exhibits C.15c&C.15d
Exhibits C.16a&C.16b
Exhibits C.16c&C.16d
Exhibits C.17a&C.17b
Exhibits C.17c&C.17d
Exhibits C.18a&C.18b
Exhibits C.18c&C.18d
Exhibits C.19a&C.19b
Exhibits C.19c&C.19d
Exhibits C.20a & C.20b
Exhibits C.20c & C.20d
Exhibits C.21 a & C. 21 b
Exhibits C.21c&C.21d
Exhibits C.22a & C.22b
Exhibits C.22c & C.22d
Page Number
C-3
C-4
C-5
C-6
7-41
7-41
7-46
7-46
7-42
7-43
7-47
7-48
7-44
7-45
7-49
7-50
C-7
C-8
C-9
C-10
C-11
C-12
C-13
C-14
C-15
C-17
C-18
C-19
C-20
C-21
C-22
C-23
C-24
C-25
C-26
C-27
C-28
C-29
C-30
C-31
C-32
C-33
C-34
C-35
C-36
C-37
C-38
C-39
C-40
C-41
C-42
C-43
C-44
C-45
C-46
Economic Analysis for the Stage 2 DBPR
C-2
December 2005
-------
Stage 1 I
Exhibit C.1a
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3301-9999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Adding CLH only
A
29.7% 107
35.4% 272
171
41.3% 467
520
10.9% 141
63
10.9% 67
8
27.7% 1,816
Adding Advanced Treatment Treatment Technologies
Chlorine Dioxide
CL2
CLH
B
1.0% 7
5
1.9% 22
24
4.4% 57
26
4.4% 27
3
2.6% 170
0.9% 7
4
2.1% 24
27
0.7% 9
4
0.7% 4
1
1 .2% 80
UV
CL2
CLH
C
Ozone
CL2
CLU
D
5.1% 39
24
4.0% 45
50
9.5% 122
55
9.5% 58
7
6.1% 401
4.6% 35
22
4.5% 51
56
1 .5% 20
9
1 .5% 9
1
3.1% 203
MBUF
CL2
CLH
E
10.9% 39
5.3% 41
26
2.6% 29
32
1.6% 20
9
1.6% 10
1
3.2% 207
7.1% 26
4.8% 37
23
2.9% 32
36
0.3% 3
1
0.3% 2
0
2.5% 161
GAC10
CL2
CLU
F
1.6% 20
9
1.6% 10
1
0.6% 40
0.3% 3
1
0.3% 2
0
0.1% 7
GAC10 + Advanced
Disinfectants
CL2
CLM
G
0.9% 12
5
0.9% 6
1
0.4% 24
0.2% 2
1
0.2% 1
0
0.1% 4
GAC20
CL2
CLM
H
2.0% 7
1.1% 8
5
1.0% 12
13
0.3% 4
2
0.3% 2
0
0.8% 53
1.3% 5
1.0% 7
5
1.2% 13
15
0.1% 1
0
0.1% 0
0
0.7% 46
GAC20 -(-Advanced
Disinfectants
CL2
0.0% 0
0.5% 4
2
0.5% 6
7
0.0% 0
0
0.0% 0
0
0.3% 18
CLM
0.0% 0
0.4% 3
2
0.6% 7
7
0.0% 0
0
0.0% 0
0
0.3% 19
Membranes
CL2
CLM
J
2.1% 8
0.5% 3
2
0.2% 2
2
0.3% 4
2
0.3% 2
0
0.4% 25
1 .4% 5
0.4% 3
2
0.2% 2
2
0.1% 1
0
0.1% 0
0
0.2% 16
Total Converting
to CLM
K
39.6% 142
47.5% 364
229
52.7% 596
664
13.9% 1 80
81
13.9% 85
10
35.9% 2,350
Total Adding
Treatment
Technology
L = SUM(A:J)
54.6% 1 96
60.8% 466
294
63.0% 711
792
32.5% 420
188
32.5% 199
24
50.2% 3,290
Note: Detail may not add to totals due to independent rounding
Source: Percent of plants from Appendix A, A.19a for systems serving <100 people, A.19b for systems
ing 100 to 999 people, A.19c for systems serving 1,000 to 9,999 people, and Exhibit A.7c for systems serving 10,000 o
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Stage 1 I
Exhibit C.1b
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Adding CLH only
A
29.7% 67
35.4% 111
38
41.3% 38
10
10.9% 1
0
10.9% 0
0
34.5% 264
Adding Advanced Treatment Treatment Technologies
Chlorine Dioxide
CL2
CLH
B
1.0% 3
1
1.9% 2
0
4.4% 0
0
4.4% 0
0
0.8% 7
0.9% 3
1
2.1% 2
1
0.7% 0
0
0.7% 0
0
0.8% 6
UV
CL2
CLH
C
Ozone
CL2
CLU
D
5.1% 16
5
4.0% 4
1
9.5% 0
0
9.5% 0
0
3.4% 26
4.6% 14
5
4.5% 4
1
1 .5% 0
0
1 .5% 0
0
3.2% 24
MBUF
CL2
CLH
E
10.9% 25
5.3% 17
6
2.6% 2
1
1.6% 0
0
1.6% 0
0
6.5% 50
7.1% 16
4.8% 15
5
2.9% 3
1
0.3% 0
0
0.3% 0
0
5.2% 40
GAC10
CL2
CLU
F
1.6% 0
0
1.6% 0
0
0.0% 0
0.3% 0
0
0.3% 0
0
0.0% 0
GAC10 + Advanced
Disinfectants
CL2
CLM
G
0.9% 0
0
0.9% 0
0
0.0% 0
0.2% 0
0
0.2% 0
0
0.0% 0
GAC20
CL2
CLM
H
2.0% 4
1.1% 3
1
1.0% 1
0
0.3% 0
0
0.3% 0
0
1.3% 10
1.3% 3
1.0% 3
1
1.2% 1
0
0.1% 0
0
0.1% 0
0
1.1% 8
GAC20 -(-Advanced
Disinfectants
CL2
0.0% 0
0.5% 2
1
0.5% 0
0
0.0% 0
0
0.0% 0
0
0.3% 3
CLM
0.0% 0
0.4% 1
0
0.6% 1
0
0.0% 0
0
0.0% 0
0
0.3% 3
Membranes
CL2
CLM
J
2.1% 5
0.5% 1
0
0.2% 0
0
0.3% 0
0
0.3% 0
0
0.9% 7
1 .4% 3
0.4% 1
0
0.2% 0
0
0.1% 0
0
0.1% 0
0
0.7% 5
Total Converting
to CLM
K
39.6% 89
47.5% 148
50
52.7% 49
13
13.9% 1
0
13.9% 0
0
45.7% 350
Total Adding
Treatment
Technology
L = SUM(A:J)
54.6% 1 23
60.8% 190
64
63.0% 58
16
32.5% 2
0
32.5% 0
0
59.1% 453
Note: Detail may not add to totals due to independent rounding
Source: Percent of plants from Appendix A, A.19a for systems serving <100 people, A.19b for systems
ing 100 to 999 people, A.19c for systems serving 1,000 to 9,999 people, and Exhibit A.7c for systems serving 10,000 o
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.2a
Stage 1 DBPR Treatment Technology Selection for CWS Groundwater Plants (Percent and Number of Plants, by Residual
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total Plants
CLM Only
A
2.4% 155
2.8% 426
170
2.5% 192
127
1 .8% 99
13
1.7% 15
0
2.5% 1,199
UVCL2
B
0.0% 0
0.0% 0
0
0.0% 0
0
0.0% 0
UVCLM
C
0.0% 0
0.0% 0
0
0.0% 0
0
0.0% 0
Ozone CL2
D
0.0% 0
0.2% 25
10
0.3% 22
15
0.1% 4
1
0.1% 1
0
0.2% 76
Ozone CLM
GAC20
CL2
E F
0.0% 0
0.5% 74
29
0.9% 66
44
0.8% 42
6
0.7% 6
0
0.6% 267
0.0% 0
0.0% 0
0
0.0% 0
0
0.0% 0
0
0.0% 0
0
0.0% 0
GAC20 CLM
G
0.9% 56
0.6% 96
39
0.1% 4
3
0.0% 2
0
0.0% 0
0
0.4% 200
Membranes
CL2
H
0.3% 22
0.1% 20
8
0.1% 4
3
0.1% 7
1
0.1% 1
0
0.1% 65
Membranes
CLM
I
0.5% 29
0.5% 79
32
0.5% 36
24
0.3% 14
2
0.2% 2
0
0.5% 217
Total Converting to
CLM
J = A+C+E+G+I
3.7% 240
4.4% 676
270
3.9% 297
197
2.9% 157
21
2.6% 24
1
4.0% 1,883
Total Adding
Treatment
Technology
K = SUM(A:I)
4.1%
4.7%
4.3%
3.1%
2.8%
4.3%
Note: Detail may not add to totals due to independent rounding
Source: Percent of plants from Appendix B, Exhibit B.34a for systems serving <100 people, B.34b for systems serving 100 to 999 people, B.34c for systems serving 1,000 to 9,999 people,
Exhibit B.11b for systems serving 10,000 to 99,999 people, and B.11a for systems serving 100,000 or more people.
Final Economic Analysis for the Stage 2 DBPR
C-5
December 2005
-------
Exhibit C.2b
Stage 1 DBPR Treatment Technology Selection for NTNCWS Groundwater Plants (Percent and Number of Plants, by Residual
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total Plants
CLM Only
A
2.4% 60
2.8% 60
16
2.5% 6
1
1 .8% 0
0
1 .7% 0
0
2.6% 143
UVCL2
B
0.0% 0
0.0% 0
0
0.0% 0
0
0.0% 0
UVCLM
C
0.0% 0
0.0% 0
0
0.0% 0
0
0.0% 0
Ozone CL2
D
0.0% 0
0.2% 3
1
0.3% 1
0
0.1% 0
0
0.1% 0
0
0.1% 5
Ozone CLM
GAC20
CL2
E F
0.0% 0
0.5% 10
3
0.9% 2
0
0.8% 0
0
0.7% 0
0
0.3% 15
0.0% 0
0.0% 0
0
0.0% 0
0
0.0% 0
0
0.0% 0
0
0.0% 0
GAC20 CLM
G
0.9% 22
0.6% 13
4
0.1% 0
0
0.0% 0
0
0.0% 0
0
0.7% 39
Membranes
CL2
H
0.3% 8
0.1% 3
1
0.1% 0
0
0.1% 0
0
0.1% 0
0
0.2% 12
Membranes
CLM
I
0.5% 1 1
0.5% 1 1
3
0.5% 1
0
0.3% 0
0
0.2% 0
0
0.5% 27
Total Converting to
CLM
J = A+C+E+G+I
3.7% 93
4.4% 94
26
3.9% 10
1
2.9% 0
0
2.6% 0
0
4.1% 224
Total Adding
Treatment
Technology
K = SUM(A:I)
4.1%
4.7%
4.3%
3.1%
2.8%
4.4%
Note: Detail may not add to totals due to independent rounding
Source: Percent of plants from Appendix B, Exhibit B.34a for systems serving <100 people, B.34b for systems serving 100 to 999 people, B.34c for systems serving 1,000 to 9,999 people,
Exhibit B.11b for systems serving 10,000 to 99,999 people, and B.11a for systems serving 100,000 or more people.
Final Economic Analysis for the Stage 2 DBPR
C-6
December 2005
-------
Exhibit C.3a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population
Served)
Converting to CLM Only
Chlorine Dioxide
Mean 5th 95th
Mean 5th 95th
Mean 5th 95th
1.5% 0.8% 2.1%
3.3% 1.9% 4.7%
2.5% 1.4% 3.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
100-499
500-999
3.6% 2.1%
3.6% 2.1%
5.1%
5.1%
0.2%
0.2%
0.5%
0.5%
0.5%
0.5%
0.9% 0.5% 1.;
0.9% 0.5% 1.;
1,000-3,300
3,301-9,999
3.7% 2.2%
3.7% 2.2%
5.3%
5.3%
0.5%
0.5%
0.7%
0.7%
0.5% 1.2%
0.5% 1.2%
10,000-49,999
50,000-99,999
7.4%
7.4%
10.6%
10.6%
0.3%
0.3%
0.5%
0.5%
0.3%
0.3%
0.7%
0.7%
100,000-999,999
•=1,000,000
7.4%
7.4%
10.6%
10.6%
0.3%
0.3%
0.5%
0.5%
0.3%
0.3%
0.7%
0.7%
5.0% 2.9% 7.2%
0.6% 0.4% 0.9%
0.8% 0.5% 1.1%
0.7% 0.4% 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% 0.0% 0.0% 0.0% 0.0%
System Size
(Population
Served)
GAC10 +Advanc
ed Disinfectants
GAC20 + Advan
ced Disinfectants
Total Converting to CLM
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th Mean 5th
95th I Mean 5th
T=A+C+E+G+I+K+M+O+
Q+S
0.3% 0.2% 0.4?
5.3% 3.0% 7.5%
10.0% 5.7% 14.2
100-499
500-999
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.2% 0.4?
0.3% 0.2% 0.4?
0.4% 0.3% 0.6%
0.4% 0.3% 0.6%
0.5%
0.5%
1.0% 0.6% 1.4?
3.7%
3.7%
8.4% 4.9% 12.0%
8.4% 4.9% 12.0%
8.8% 5.1% 12.5%
1,000-3,300
3,301-9,999
0.4% 0.3% 0.6%
0.4% 0.3% 0.6%
0.5%
0.5%
7.0% 4.1%
7.0% 4.1%
10.0%
10.0%
10,000-49,999
50,000-99,999
0.4%
0.4%
0.2%
0.2%
0.5% 0.3%
0.5% 0.3%
0.7%
0.7%
0.2%
0.2%
2.2%
2.2%
0.2%
0.2%
12.7%
12.7%
100,000-999,999
•=1,000,000
0.4%
0.4%
0.2%
0.2%
0.5% 0.3%
0.5% 0.3%
0.7%
0.7%
0.2%
0.2%
2.2%
2.2%
0.2%
0.2%
12.7%
12.7%
0.2% 0.1% 0.2%
0.1% 0.0% 0.1%
0.2% 0.1% 0.3%
0.1% 0.0% 0.1%
0.2% 0.1% 0.3%
0.3% 0.2% 0.4?
1.0% 0.6% 1.5%
0.8% 0.4% 1.1%
7.5% 4.4% 10.7%
10.0% 5.8% 14.2%
10.0% 5.8% 14.2%
Exhibit C.3b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
538
28 16 39
17 10 25
42 24 60
47 27 67
96 56 137
43 25 61
45 26 65
538
330 191 469
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
1 1 1
1 0 1
2 1 3
2 1 3
1 0 1
000
0 0 1
000
7410
ed Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
538
2 1 3
3 1 4
000
11 6 15
324
1 1 2
1 1 2
000
638
CLM
Mean 5th 95th
B
324
2 1 2
10 6 14
11 6 16
7411
325
425
0 0 1
40 23 57
GAC
CL2
Mean 5th 95th
UV
CL2
Mean 5th 95th
12 7 17
7410
436
7410
8512
749
324
324
0 0 1
52 30 74
20
CLM
Mean 5th 95th
H
000
000
000
000
000
649
324
324
0 0 1
12 7 18
000
000
000
000
000
2 1 3
1 1 1
1 1 1
000
426
CLM
Mean 5th 95th
C
9 513
7410
536
10 6 14
11 6 15
2 1 2
1 0 1
1 0 1
000
44 26 63
GAC20 + Advan
CL2
Mean 5th 95th
537
2 1 3
1 1 2
2 1 3
2 1 4
000
000
000
000
14 8 19
Ozone
CL2
Mean 5th 95th
[
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
425
325
2 1 3
537
638
000
000
000
000
20 11 28
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
000
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
649
426
9 513
10 6 14
20 11 28
9 512
9 513
1 1 2
68 40 97
1 1 1
7411
537
13 7 18
14 8 20
537
2 1 3
2 1 3
000
50 29 71
CLM
Mean 5th 95th
E
000
000
000
000
000
000
000
000
000
000
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
19 11 27
48 28 69
30 18 43
79 46 112
88 51 125
115 67 164
52 30 73
54 32 77
749
493 286 701
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
36 21 51
65 38 92
41 24 58
100 58 142
111 65 158
154 89 219
69 40 98
73 42 103
9512
656 381 933
352 204 501
304 1 77 432
656 381 933
Note: Detail may not add to totals due to independent rounding
Source: Above table with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-7
December 2005
-------
Exhibit C.3c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population
Served)
Converting to CLM Only
Chlorine Dioxide
Mean 5th 95th
Mean 5th 95th
Mean 5th 95th
Mean 5th 95th
1.5% 0.8% 2.1%
3.3% 1.9% 4.7%
2.5% 1.4% 3.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
100-499
500-999
3.6% 2.1% 5.1%
3.6% 2.1% 5.1%
0.2%
0.2%
0.5%
0.5%
1,000-3,300
3,301-9,999
3.7% 2.2% 5.3%
3.7% 2.2% 5.3%
0.7% 0.4%
0.7% 0.4%
10,000-49,999
50,000-99,999
0.5% 0.3% 0.7%
0.1% 0.1% 0.2%
100,000-999,999
•=1,000,000
0.5% 0.3% 0.7%
0.1% 0.1% 0.2%
3.0% 1.7% 4.30/
0.1% 0.1% 0.1%
0.3% 0.2% 0.5%
1.6% 0.9% 2.2%
1.4% 0.8% 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%
System Size
(Population
Served)
GAC10 +Advanc
ed Disinfectants
GAC20 + Advan
ced Disinfectants
Total Converting to CLM
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th Mean 5th
95th I Mean 5th
T=A+C+E+G+I+K+M+O+
Q+S
0.3% 0.2% 0.4?
5.3% 3.0% 7.5%
10.0% 5.7% 14.2
100-499
500-999
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.2%
0.3% 0.2%
0.4%
0.4'
0.4% 0.3% 0.6%
0.4% 0.3% 0.6%
0.5% 1.2%
0.5% 1.2%
1.0% 0.6% 1 A",
3.7%
3.7%
8.4% 4.9% 12.0%
8.4% 4.9% 12.0%
9.0% 5.2% 12.7%
1,000-3,300
3,301-9,999
0.3%
0.3%
0.4% 0.3% 0.6%
0.4% 0.3% 0.6%
0.5%
0.5%
7.0% 4.1% 10.0%
7.0% 4.1% 10.0%
10,000-49,999
50,000-99,999
0.5% 0.3% 0.7%
0.2% 0.1% 0.2%
8.9% 5.2% 12.7%
100,000-999,999
•=1,000,000
0.5% 0.3% 0.7%
0.2% 0.1% 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%
0.6% 0.4% 0.9%
0.6% 0.4% 0.9%
0.6% 0.3% 0.8%
0.8% 0.5% 1.1%
6.1% 3.5% 8.7%
Exhibit C.3d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
325
11 7 16
425
325
1 1 1
0 0 1
000
000
000
23 13 33
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
0 0 1
000
000
000
000
000
000
000
1 0 1
ed Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
B
1 1 2
0 0 1
1 0 1
000
000
000
000
000
3 1 4
GAC
CL2
Mean 5th 95th
uv
CL2
Mean 5th 95th
7411
324
1 1 1
1 0 1
000
000
000
000
000
12 7 17
20
CLM
Mean 5th 95th
H
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
C
638
324
1 1 1
1 0 1
000
000
000
000
000
10 6 15
GAC20 + Advan
CL2
Mean 5th 95th
325
1 1 1
000
000
000
000
000
000
000
537
Ozone
CL2
Mean 5th 95th
[
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
2 1 3
1 1 2
0 0 1
0 0 1
000
000
000
000
000
537
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
000
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
324
1 1 1
1 0 1
000
000
000
000
000
436
1 0 1
324
1 1 1
1 1 1
000
000
000
000
000
649
CLM
Mean 5th 95th
E
000
000
000
000
000
000
000
000
000
000
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
12 7 17
20 11 28
7410
649
2 1 2
0 0 1
000
000
000
47 27 67
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
23 13 32
26 15 37
9513
8512
2 1 3
1 0 1
000
000
000
69 40 98
68 40 97
1 0 1
69 40 98
Note: Detail may not add to totals due to independent rounding
Source: Above table with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-8
December 2005
-------
Exhibit C.4a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
0.6%
0.8%
0.8%
0.0%
0.0%
0.6%
0.6%
0.6%
0.6%
0.5%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1 .2%
1 .9%
1 .9%
2.3%
2.3%
1 .6%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
GAC20
CL2
F
0.4%
0.2%
0.2%
0.0%
0.0%
0.1%
0.1%
0.0%
0.0%
0.2%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.3%
0.3%
0.6%
0.6%
0.5%
0.5%
0.2%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.1%
0.0%
Membranes
CLM
I
0.1%
0.2%
0.2%
0.2%
0.2%
0.8%
0.8%
0.7%
0.7%
0.3%
Total Converting
to CLM
J = A+C+E+G+I
2.0%
2.9%
2.9%
2.7%
2.7%
1 .9%
1 .9%
1 .8%
1 .8%
2.6%
Total Adding
Treatment
Technology
K = SUM(A:I)
2.4%
3.2%
3.2%
2.7%
2.7%
2.1%
2.1%
2.0%
2.0%
2.8%
2.9%
2.1%
2.8%
Note: Detail may not add to totals due to independent rounding
Exhibit C.4b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
3890.3%
123
49
0
0
30
4
5
0
UVCL2
B
0
0
0
0
0
UVCLM
C
80
295
118
171
113
Ozone
CL2
D
0
0
0
0
0
0
0
0
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
27
33
13
0
0
3
0
0
0
GAC20
CLM
G
0
0
0
22
15
30
4
5
0
Membranes
CL2
H
1
3
1
1
1
6
1
1
0
Membranes
CLM
I
8
28
11
11
8
43
6
7
0
Total Converting
to CLM
J = A+C+E+G+I
127
447
179
205
136
103
14
17
0
Total Adding
Treatment
Technology
K = SUM(A:I)
155
483
193
206
137
111
15
18
1
1,173
145
Final Economic Analysis for the Stage 2 DBPR
C-9
December 2005
-------
Exhibit C.4c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
0.6%
0.8%
0.8%
0.0%
0.0%
0.6%
0.6%
0.6%
0.0%
0.7%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1 .2%
1 .9%
1 .9%
2.3%
2.3%
1 .6%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
GAC20
CL2
F
0.4%
0.2%
0.2%
0.0%
0.0%
0.1%
0.1%
0.0%
0.0%
0.3%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.3%
0.3%
0.6%
0.6%
0.5%
0.0%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
0.0%
Membranes
CLM
I
0.1%
0.2%
0.2%
0.2%
0.2%
0.8%
0.8%
0.7%
0.0%
0.2%
Total Converting
to CLM
J = A+C+E+G+I
2.0%
2.9%
2.9%
2.7%
2.7%
1 .9%
1 .9%
1 .8%
0.0%
2.5%
Total Adding
Treatment
Technology
K = SUM(A:I)
2.4%
3.2%
3.2%
2.7%
2.7%
2.1%
2.1%
2.0%
0.0%
2.8%
2.8%
2.1%
2.8%
Note: Detail may not add to totals due to independent rounding
Exhibit C.4d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
1509.9%
17
5
0
°
UVCL2
B
0
0
0
0
0
o
1
UVCLM
C
31
41
11
6
0
Ozone
CL2
D
0
0
0
0
0
0
0
0
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
10
5
1
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
1
0
0
0
0
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
3
4
1
0
0
0
0
0
0
Total Converting
to CLM
J = A+C+E+G+I
49
62
17
7
1
0
0
0
0
Total Adding
Treatment
Technology
K = SUM(A:I)
60
67
19
7
1
0
0
0
0
154
0
Final Economic Analysis for the Stage 2 DBPR
C-10
December 2005
-------
Exhibit C.5a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population Served)
<100
10M99
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
System Size
(Population Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
31.9% 27.6% 36.1%
27.1% 23.6% 30.7%
27.1% 23.6% 30.7%
24.6% 20.9% 28.3%
24.6% 20.9% 28.3%
31.2% 31.2% 31.2%
31.2% 31.2% 31.2%
31.2% 31.2% 31.2%
31.2% 31.2% 31.2%
28.1% 25.8% 30.3%
GAC10 + ADCL2
Mean 5th 95th
M
Mean
A
0.6% 0.6% 0.6%
0.6% 0.6% 0.6%
0.6% 0.6% 0.6%
0.6% 0.6% 0.6%
0.2% 0.2% 0.2%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
31.2% 30.6% 31.9%
39.0% 37.5% 40.5%
39.0% 37.5% 40.5%
5.1% 43.5% 46.6%
5.1% 43.5% 46.6%
.0% 41.0% 41.0%
.0% 41.0% 41.0%
.0% 41.0% 41.0%
.0% 41.0% 41.0%
.6% 40.7% 42.5%
GAC10 + ADCLM
Mean 5th 95th
N
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.3% 0.3% 0.3%
Chlorine Dioxide
CL2
Mean 5th 95th
C
1.1% 1.0% 1.1%
1.1% 1.0% 1.1%
2.1% 2.0% 2.1%
2.1% 2.0% 2.1%
3.0% 3.0% 3.0%
3.0% 3.0% 3.0%
3.0% 3.0% 3.0%
3.0% 3.0% 3.0%
2.1% 2.1% 2.2%
GAC20 CL2
Mean 5th 95th
O
2.0% 2.0% 2.0%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.0% 1 .0% 1 .0%
1.0% 1.0% 1.0%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.8% 0.8% 0.8%
Chlorine Dioxide
CLM
Mean 5th 95th
D
1.2% 1.1% 1.4%
1.2% 1.1% 1.4%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
2.9% 2.7% 3.0%
GAC20 CLM
Mean 5th 95th
P
1 .3% .3% .3%
1 .0% .0% .0%
1 .0% .0% .0%
1 .2% .2% .2%
1 .2% .2% .2%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.8% 0.8% 0.8%
UVCL2
Mean 5th 95th
E
3.3% 1.9% 4.7%
0.9% 0.5% 1 .3%
0.9% 0.5% 1.3%
0.7% 0.4% 0.9%
0.7% 0.4% 0.9%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.6% 0.3% 0.8%
GAC20 + AD CL2
Mean 5th 95th
Q
1.4% 0.8% 2.0%
0.8% 0.7% 0.9%
0.8% 0.7% 0.9%
0.7% 0.6% 0.8%
0.7% 0.6% 0.8%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.5% 0.4% 0.6%
UVCLM
Mean 5th 95th
F
2.5% 1.4% 3.5%
0.9% 0.5% 1.3%
0.9% 0.5% 1.3%
0.9% 0.5% 1.2%
0.9% 0.5% 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%
0.6% 0.4% 0.9%
GAC20 + ADCLM
Mean 5th 95th
R
1.0% 0.6% 1.5%
0.9% 0.7% 1.1%
0.9% 0.7% 1.1%
1.0% 0.8% 1.2%
1.0% 0.8% 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%
0.6% 0.5% 0.7%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
4.6% 4.6% 4.6%
Membranes CL2
Mean 5th 95th
S
2.1% 2.1% 2.1%
1.3% 0.9% 1.6%
1.3% 0.9% 1.6%
1.0% 0.6% 1.3%
1.0% 0.6% 1.3%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
1.0% 0.8% 1.2%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
7.3% 7.3% 7.3%
7.3% 7.3% 7.3%
7.3% 7.3% 7.3%
7.3% 7.3% 7.3%
5.3% 5.3% 5.3%
Membranes CLM
Mean 5th 95th
T
1.7% 1.6% 1.8%
1.4% 1.0% 1.8%
1.4% 1.0% 1.8%
1.3% 0.8% 1.8%
1.3% 0.8% 1.8%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
1 .0% 1.0% 1 .0%
1.0% 1.0% 1.0%
1.2% 1.0% 1.5%
MF/UFCL2
Mean 5th 95th
I
14.5% 14.5% 14.5%
8.9% 8.9% 8.9%
8.9% 8.9% 8.9%
6.2% 6.2% 6.2%
6.2% 6.2% 6.2%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
5.1% 5.1% 5.1%
MF/UFCLM
Mean 5th 95th
J
7.1% 7.1% 7.1%
4.8% 4.8% 4.8%
4.8% 4.8% 4.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
2.8% 2.8% 2.8%
TOTAL CL2
Mean 5th 95th
U = A+C+E+G+I+K+M+O+Q+S
55.2% 49.0% 61 .4%
46.2% 41 .8% 50.7%
46.2% 41 .8% 50.7%
40.2% 35.8% 44.7%
40.2% 35.8% 44.7%
43.2% 43.2% 43.2%
43.2% 43.2% 43.2%
43.2% 43.2% 43.2%
43.2% 43.2% 43.2%
43.4% 40.5% 46.2%
GAC10CL2
Mean 5th 95th
K
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.4% 0.4% 0.4%
GAC10CLM
Mean 5th 95th
L
1 .2% 1.2% 1 .2%
1 .2% 1.2% 1 .2%
1 .2% 1.2% 1 .2%
1 .2% 1.2% 1 .2%
0.5% 0.5% 0.5%
TOTAL CLM
Mean 5th 95th
V= B+D+F+H+J+L+N+P+R+T
44.8% 42.6% 47.1%
53.8% 51.1% 56.4%
53.8% 51.1% 56.4%
59.8% 56.8% 62.7%
59.8% 56.8% 62.7%
56.8% 56.8% 56.8%
56.8% 56.8% 56.8%
56.8% 56.8% 56.8%
56.8% 56.8% 56.8%
56.6% 54.9% 58.3%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 1. Surface water systems serving 10,000 or more people: Use ending technology predictions from SWAT (FACA
Screen SeriesS v3.0 Database) for the Alternative 1.
Exhibit C.5b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
System Size
(Population Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
114 99 130
208 181 235
131 114 148
278 236 320
310 263 356
403 403 403
181 181 181
190 190 190
23 23 23
1 ,838 1 ,690 1 ,986
GAC10 + ADCL2
Mean 5th 95th
M
888
444
444
000
16 16 16
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
112 110 115
299 288 311
188 181 196
509 491 527
567 548 587
529 529 529
237 237 237
250 250 250
30 30 30
2,723 2,664 2,782
GAC10 + ADCLM
Mean 5th 95th
N
11 11 11
555
555
1 1 1
21 21 21
Chlorine Dioxide
CL2
Mean 5th 95th
C
889
555
23 23 24
26 25 27
39 39 39
17 17 17
18 18 18
222
140 137 142
GAC20 CL2
Mean 5th 95th
O
777
888
555
12 12 12
13 13 13
444
222
222
000
53 53 53
Chlorine Dioxide
CLM
Mean 5th 95th
D
9811
657
34 30 38
38 33 42
51 51 51
23 23 23
24 24 24
333
188 177 199
GAC20 CLM
Mean 5th 95th
P
555
777
555
13 13 13
15 15 15
555
222
333
000
55 55 55
UVCL2
Mean 5th 95th
E
12 7 17
7 4 10
436
7 4 10
8 5 12
000
000
000
000
39 22 55
GAC20 + AD CL2
Mean 5th 95th
Q
537
657
434
879
9 8 10
000
000
000
000
32 26 38
UVCLM
Mean 5th 95th
F
9 5 13
7410
536
10 6 14
11 6 15
000
000
000
000
41 24 58
GAC20 + ADCLM
Mean 5th 95th
R
425
758
435
11 9 14
13 11 15
000
000
000
000
39 31 47
Ozone CL2
Mean 5th 95th
G
39 39 39
24 24 24
45 45 45
50 50 50
72 72 72
32 32 32
34 34 34
444
301 301 301
Membranes CL2
Mean 5th 95th
S
888
10 7 12
658
11 7 15
12 8 16
10 10 10
555
555
1 1 1
67 55 80
Ozone CLM
Mean 5th 95th
H
35 35 35
22 22 22
51 51 51
56 56 56
94 94 94
42 42 42
44 44 44
555
350 350 350
Membranes CLM
Mean 5th 95th
T
666
11 7 14
759
15 9 20
16 11 22
13 13 13
666
666
1 1 1
81 64 98
MF/UFCL2
Mean 5th 95th
I
52 52 52
68 68 68
43 43 43
70 70 70
78 78 78
10 10 10
555
555
1 1 1
331 331 331
MF/UFCLM
Mean 5th 95th
J
26 26 26
37 37 37
23 23 23
32 32 32
36 36 36
13 13 13
666
666
1 1 1
181 181 181
TOTAL CL2
Mean 5th 95th
U = A+C+E+G+I+K+M+O+Q+S
198 176 221
354 320 388
223 202 245
454 404 505
506 450 563
558 558 558
250 250 250
264 264 264
32 32 32
2,841 2,656 3,025
GAC10CL2
Mean 5th 95th
K
12 12 12
666
666
1 1 1
24 24 24
GAC10CLM
Mean 5th 95th
L
16 16 16
777
888
1 1 1
32 32 32
TOTAL CLM
Mean 5th 95th
V= B+D+F+H+J+L+N+P+R+T
161 153 169
412 392 433
260 247 273
675 642 708
752 715 789
733 733 733
329 329 329
347 347 347
42 42 42
3,710 3,599 3,822
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 1. Surface water systems serving 10,000 or more people: Use ending technology predictions from SWAT (FACA
Screen SeriesS v3.0 Database) for the Alternative 1.
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.5c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population Served)
<100
10M99
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
System Size
(Population Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
31.9% 27.6% 36.1%
27.1% 23.6% 30.7%
27.1% 23.6% 30.7%
24.6% 20.9% 28.3%
24.6% 20.9% 28.3%
31.2% 31.2% 31.2%
0.0% 0.0% 0.0%
31.2% 31.2% 31.2%
0.0% 0.0% 0.0%
28.2% 24.4% 31.9%
GAC10 + ADCL2
Mean 5th 95th
M
Mean
A
0.6% 0.6% 0.6%
0.0% 0.0% 0.0%
0.6% 0.6% 0.6%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
31.2% 30.6% 31.9%
39.0% 37.5% 40.5%
39.0% 37.5% 40.5%
45.1% 43.5% 46.6%
45.1% 43.5% 46.6%
41.0% 41.0% 41.0%
0.0% 0.0% 0.0%
41.0% 41.0% 41.0%
0.0% 0.0% 0.0%
37.7% 36.4% 38.9%
GAC10 + ADCLM
Mean 5th 95th
N
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Chlorine Dioxide
CL2
Mean 5th 95th
C
1.1% 1.0% 1.1%
1.1% 1.0% 1.1%
2.1% 2.0% 2.1%
2.1% 2.0% 2.1%
3.0% 3.0% 3.0%
0.0% 0.0% 0.0%
3.0% 3.0% 3.0%
0.0% 0.0% 0.0%
0.9% 0.9% 1.0%
GAC20 CL2
Mean 5th 95th
O
2.0% 2.0% 2.0%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.0% 1 .0% 1 .0%
1.0% 1.0% 1.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
1.3% 1.3% 1.3%
Chlorine Dioxide
CLM
Mean 5th 95th
D
1.2% 1.1% 1.4%
1.2% 1.1% 1.4%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
4.0% 4.0% 4.0%
0.0% 0.0% 0.0%
4.0% 4.0% 4.0%
0.0% 0.0% 0.0%
1.2% 1.0% 1.3%
GAC20 CLM
Mean 5th 95th
P
1 .3% .3% .3%
1 .0% .0% .0%
1 .0% .0% .0%
1 .2% .2% .2%
1 .2% .2% .2%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
1.1% 1.1% 1.1%
UVCL2
Mean 5th 95th
E
3.3% 1.9% 4.7%
0.9% 0.5% 1.3%
0.9% 0.5% 1.3%
0.7% 0.4% 0.9%
0.7% 0.4% 0.9%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
1.6% 0.9% 2.2%
GAC20 + AD CL2
Mean 5th 95th
Q
1.4% 0.8% 2.0%
0.8% 0.7% 0.9%
0.8% 0.7% 0.9%
0.7% 0.6% 0.8%
0.7% 0.6% 0.8%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
1.0% 0.7% 1.2%
UVCLM
Mean 5th 95th
F
2.5% 1.4% 3.5%
0.9% 0.5% 1.3%
0.9% 0.5% 1.3%
0.9% 0.5% 1.2%
0.9% 0.5% 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%
1.4% 0.8% 1.9%
GAC20 + ADCLM
Mean 5th 95th
R
1.0% 0.6% 1.5%
0.9% 0.7% 1.1%
0.9% 0.7% 1.1%
1.0% 0.8% 1.2%
1.0% 0.8% 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%
0.9% 0.7% 1.2%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
5.5% 5.5% 5.5%
0.0% 0.0% 0.0%
5.5% 5.5% 5.5%
0.0% 0.0% 0.0%
3.4% 3.4% 3.4%
Membranes CL2
Mean 5th 95th
S
2.1% 2.1% 2.1%
1.3% 0.9% 1.6%
1.3% 0.9% 1.6%
1.0% 0.6% 1.3%
1.0% 0.6% 1.3%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
1.5% 1.2% 1.7%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
7.3% 7.3% 7.3%
0.0% 0.0% 0.0%
7.3% 7.3% 7.3%
0.0% 0.0% 0.0%
3.2% 3.2% 3.2%
Membranes CLM
Mean 5th 95th
T
1.7% 1.6% .8%
1.4% 1.0% .8%
1.4% 1.0% .8%
1.3% 0.8% .8%
1.3% 0.8% .8%
1.0% 1.0% .0%
0.0% 0.0% 0.0%
1 .0% 1.0% 1 .0%
0.0% 0.0% 0.0%
1.5% 1.1% 1.8%
MF/UFCL2
Mean 5th 95th
I
14.5% 14.5% 14.5%
8.9% 8.9% 8.9%
8.9% 8.9% 8.9%
6.2% 6.2% 6.2%
6.2% 6.2% 6.2%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
10.1% 10.1% 10.1%
MF/UFCLM
Mean 5th 95th
J
7.1% 7.1% 7.1%
4.8% 4.8% 4.8%
4.8% 4.8% 4.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
1.0% 1.0% 1.0%
0.0% 0.0% 0.0%
1.0% 1.0% 1.0%
0.0% 0.0% 0.0%
5.2% 5.2% 5.2%
TOTAL CL2
Mean 5th 95th
U = A+C+E+G+I+K+M+O+Q+S
55.2% 49.0% 61 .4%
46.2% 41 .8% 50.7%
46.2% 41 .8% 50.7%
40.2% 35.8% 44.7%
40.2% 35.8% 44.7%
43.2% 43.2% 43.2%
0.0% 0.0% 0.0%
43.2% 43.2% 43.2%
0.0% 0.0% 0.0%
47.9% 43.0% 52.9%
GAC10CL2
Mean 5th 95th
K
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
GAC10CLM
Mean 5th 95th
L
1 .2% 1.2% 1 .2%
0.0% 0.0% 0.0%
1 .2% 1.2% 1 .2%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
TOTAL CLM
Mean 5th 95th
V= B+D+F+H+J+L+N+P+R+T
44.8% 42.6% 47.1%
53.8% 51.1% 56.4%
53.8% 51.1% 56.4%
59.8% 56.8% 62.7%
59.8% 56.8% 62.7%
56.8% 56.8% 56.8%
0.0% 0.0% 0.0%
56.8% 56.8% 56.8%
0.0% 0.0% 0.0%
52.1% 49.5% 54.6%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 1. Surface water systems serving 10,000 or more people: Use ending technology predictions from SWAT (FACA
Screen SeriesS v3.0 Database) for the Alternative 1.
Exhibit C.5d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 1
System Size
(Population Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
System Size
(Population Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
72 62 82
85 74 96
29 25 33
23 19 26
657
222
000
000
000
216 187 245
GAC10 + ADCL2
Mean 5th 95th
M
000
000
000
000
000
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
71 69 72
122 117 127
41 40 43
41 40 43
11 11 12
222
000
000
000
289 279 299
GAC10 + ADCLM
Mean 5th 95th
N
000
000
000
000
000
Chlorine Dioxide
CL2
Mean 5th 95th
C
333
1 1 1
222
1 1 1
000
000
000
000
777
GAC20 CL2
Mean 5th 95th
O
444
333
1 1 1
1 1 1
000
000
000
000
000
10 10 10
Chlorine Dioxide
CLM
Mean 5th 95th
D
434
1 1 1
323
1 1 1
000
000
000
000
9 8 10
GAC20 CLM
Mean 5th 95th
P
333
333
1 1 1
1 1 1
000
000
000
000
000
888
UVCL2
Mean 5th 95th
E
7 411
324
1 1 1
1 0 1
000
000
000
000
000
12 7 17
GAC20 + AD CL2
Mean 5th 95th
Q
325
223
1 1 1
1 1 1
000
000
000
000
000
759
UVCLM
Mean 5th 95th
F
638
324
1 1 1
1 0 1
000
000
000
000
000
10 6 15
GAC20 + ADCLM
Mean 5th 95th
R
2 1 3
323
1 1 1
1 1 1
000
000
000
000
000
759
Ozone CL2
Mean 5th 95th
G
16 16 16
555
1 1 1
000
000
000
000
26 26 26
Membranes CL2
Mean 5th 95th
S
555
435
1 1 2
1 1 1
000
000
000
000
000
11 10 13
Ozone CLM
Mean 5th 95th
H
14 14 14
555
1 1 1
000
000
000
000
25 25 25
Membranes CLM
Mean 5th 95th
T
444
436
1 1 2
1 1 2
000
000
000
000
000
11 9 14
MF/UFCL2
Mean 5th 95th
I
33 33 33
28 28 28
999
666
222
000
000
000
000
77 77 77
MF/UFCLM
Mean 5th 95th
J
16 16 16
15 15 15
555
333
1 1 1
000
000
000
000
40 40 40
TOTAL CL2
Mean 5th 95th
U = A+C+E+G+I+K+M+O+Q+S
125 111 139
144 130 158
49 44 54
37 33 41
10 9 11
222
000
000
000
368 330 405
GAC10CL2
Mean 5th 95th
K
000
000
000
000
000
GAC10CLM
Mean 5th 95th
L
000
000
000
000
000
TOTAL CLM
Mean 5th 95th
V= B+D+F+H+J+L+N+P+R+T
101 96 106
168 160 176
57 54 60
55 52 58
15 14 16
333
000
1 1 1
000
399 380 419
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 1. Surface water systems serving 10,000 or more people: Use ending technology predictions from SWAT (FACA
Screen SeriesS v3.0 Database) for the Alternative 1.
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
ExhibitC.Ga
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
93.5%
92.1%
92.1%
93.0%
93.0%
87.1%
87.1%
87.5%
87.5%
91 .8%
No Advanced
Treatment
Technologies
CLM1
B
3.0%
3.6%
3.6%
2.5%
2.5%
7.8%
7.8%
7.6%
7.6%
3.9%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1 .2%
1 .9%
1 .9%
2.3%
2.3%
1 .6%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
0.8%
0.8%
0.8%
0.8%
0.3%
Ozone
CLM
F
GAC20
CL2
G
0.0% 0.4%
0.5% 0.2%
0.5% 0.2%
0.9% 0.0%
0.9% 0.0%
0.8% 0.1%
0.8% 0.1%
0.7% 0.0%
0.7% 0.0%
0.6%
0.2%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.3%
0.3%
0.6%
0.6%
0.6%
0.6%
0.6%
Membranes
CL2
I
0.4%
0.1%
0.1%
0.1%
0.1%
1 .8%
1 .8%
1 .8%
1 .8%
0.4%
Membranes
CLM
J
0.6%
0.7%
0.7%
0.6%
0.6%
1.1%
1.1%
1 .0%
1 .0%
0.7%
Total Using CL2
K = A+C+E+G+I
94.3%
92.6%
92.6%
93.4%
93.4%
89.8%
89.8%
90.1%
90.1%
92.6%
Total Using CLM
L = B+D+F+H+J
5.7%
7.4%
7.4%
6.6%
6.6%
10.2%
10.2%
9.9%
9.9%
7.4%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative
1.
Exhibit C.6b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
ichnology CL21
A
6,006
14,040
5,613
7,058
4,679
4,690
624
No Advanced
Treatment
Technologies
CLM1
B
194
550
220
192
127
419
56
UVCL2
C
0
0
0
0
0
UVCLM
D
80
295
118
171
113
Ozone
CL2
E
0
25
10
22
15
46
6
Ozone
CLM
F
0
74
29
66
44
42
6
GAC20
CL2
G
27
33
13
0
0
3
0
GAC20
CLM
H
56
96
39
26
18
32
4
Membranes
CL2
I
23
23
9
5
3
95
13
Membranes
CLM
J
37
107
43
47
31
57
8
Total Using CL2
K = A+C+E+G+I
6,055
14,120
5,645
7,085
4,697
4,833
643
Total Using CLM
L = B+D+F+H+J
368
1,122
449
502
333
549
73
Total Plants
43,536
1,829| 0| 778
130
267
76 1 277
188 1 339| 43,930| 3,489
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 1.
Final Economic Analysis for the Stage 2 DBPR
C-13
December 2005
-------
ExhibitC.Gc
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
93.5%
92.1%
92.1%
93.0%
93.0%
87.1%
87.1%
87.5%
0.0%
92.8%
No Advanced
Treatment
Technologies
CLM1
B
3.0%
3.6%
3.6%
2.5%
2.5%
7.8%
7.8%
7.6%
0.0%
3.3%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1 .2%
1 .9%
1 .9%
2.3%
2.3%
1 .6%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
0.8%
0.8%
0.8%
0.0%
Ozone
CLM
F
0.0%
0.5%
0.5%
0.9%
0.9%
0.8%
0.8%
0.7%
0.0%
0.1% 0.3%
GAC20
CL2
G
0.4%
0.2%
0.2%
0.0%
0.0%
0.1%
0.1%
0.0%
0.0%
0.3%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.3%
0.3%
0.6%
0.6%
0.6%
0.0%
0.7%
Membranes
CL2
I
0.4%
0.1%
0.1%
0.1%
0.1%
1 .8%
1 .8%
1 .8%
0.0%
0.2%
Membranes
CLM
J
0.6%
0.7%
0.7%
0.6%
0.6%
1.1%
1.1%
1.0%
0.0%
0.6%
Total Using CL2
K = A+C+E+G+I
94.3%
92.6%
92.6%
93.4%
93.4%
89.8%
89.8%
90.1%
0.0%
93.4%
Total Using CLM
L = B+D+F+H+J
5.7%
7.4%
7.4%
6.6%
6.6%
10.2%
10.2%
9.9%
0.0%
6.6%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 1.
ExhibitC.Gd
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 1
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
schnology CL21
A
2,331
1,961
543
230
20
3
0
No Advanced
Treatment
Technologies
CLM1
B
75
77
21
6
1
0
0
UVCL2
C
0
0
0
0
0
UVCLM
D
31
41
11
6
0
Ozone
CL2
E
0
3
1
1
0
0
0
Ozone
CLM
F
0
10
3
2
0
0
0
GAC20
CL2
G
10
5
1
0
0
0
0
GAC20
CLM
H
22
13
4
1
0
0
0
Membranes
CL2
I
9
3
1
0
0
0
0
Membranes
CLM
J
14
15
4
2
0
0
0
Total Using CL2
K = A+C+E+G+I
2,350
1,972
546
231
20
3
0
Total Using CLM
L = B+D+F+H+J
143
157
43
16
1
0
0
Total Plants
5,088
181
0
90
5
15
16
40| 13
35| 5,1 22| 361
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 1.
Final Economic Analysis for the Stage 2 DBPR
C-14
December 2005
-------
Exhibit C.7a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population
Served)
<100
10M99
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
System Size
(Population
Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-2.3% -2.6% -1.9%
-1.0% -1.5% -0.5%
-1.0% -1.5% -0.5%
0.2% -0.2% 0.7%
0.2% -0.2% 0.7%
7.7% 6.9% 8.6%
7.7% 6.9% 8.6%
7.7% 6.9% 8.6%
7.7% 6.9% 8.6%
2.8% 2.2% 3.4%
GAC10 + Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
3.6% 3.2% 4.0%
3.6% 3.2% 4.0%
3.6% 3.2% 4.0%
3.6% 3.2% 4.0%
1.4% 1.2% 1.6%
d Disinfectants
CLM
Mean 5th 95th
ting to
CLM M
Only
4.0% 3.5% 4.4%
4.0% 3.5% 4.4%
4.0% 3.5% 4.4%
4.0% 3.5% 4.4%
1 .5% 1 .4% 1 .7%
000
1.9% 1.7% 2.1%
1.9% 1.7% 2.1%
1.9% 1.7% 2.1%
1.9% 1.7% 2.1%
0.7% 0.6% 0.8%
000
CLM
Mean 5th 95th
C
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
2.7% 2.4% 3.0%
2.7% 2.4% 3.0%
2.7% 2.4% 3.0%
2.7% 2.4% 3.0%
1.1% 1.0% 1.2%
GAC
CL2
Mean 5th 95th
N
4.2% 3.7% 4.7%
3.5% 3.1% 3.9%
3.5% 3.1% 3.9%
3.0% 2.7% 3.4%
3.0% 2.7% 3.4%
0.5% 0.5% 0.6%
0.5% 0.5% 0.6%
0.5% 0.5% 0.6%
0.5% 0.5% 0.6%
2.2% 1 .9% 2.5%
000
UV
CL2
Mean 5th 95th
D
1.4% 1.2% 1.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
2.4% 2.1% 2.6%
2.4% 2.1% 2.6%
2.4% 2.1% 2.6%
2.4% 2.1% 2.6%
1.0% 0.9% 1.1%
20
CLM
Mean 5th 95th
O
5.1% 4.5% 5.7%
6.1% 5.4% 6.8%
6.1% 5.4% 6.8%
7.4% 6.5% 8.2%
7.4% 6.5% 8.2%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
4.3% 3.8% 4.8%
000
CLM
Mean 5th 95th
E
1.4% 1.2% 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%
1.0% 0.9% 1.1%
1.0% 0.9% 1.1%
1.0% 0.9% 1.1%
1.0% 0.9% 1.1%
0.5% 0.4% 0.5%
GAC20 + Advan
CL2
Mean 5th 95th
p
3.6% 3.2% 4.0%
2.1% 1.9% 2.4%
2.1% 1.9% 2.4%
1.9% 1.6% 2.1%
1.9% 1.6% 2.1%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
1 .4% 1.2% 1 .6%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ed Disinfectants
CLM
Mean 5th 95th
Q
3.7% 3.2% 4.1%
3.6% 3.2% 4.1%
3.6% 3.2% 4.1%
4.4% 3.9% 4.9%
4.4% 3.9% 4.9%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
2.5% 2.3% 2.8%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.6% 0.5% 0.6%
0.6% 0.5% 0.6%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.0% 0.0% 0.0%
0.9% 0.8% 1.0%
0.9% 0.8% 1.0%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.5% 0.5% 0.6%
0.5% 0.5% 0.6%
0.5% 0.5% 0.6%
0.5% 0.5% 0.6%
0.5% 0.4% 0.5%
000
0.9% 0.8% 1.0%
1.8% 1.6% 2.0%
1.8% 1.6% 2.0%
0.6% 0.6% 0.7%
0.6% 0.6% 0.7%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.7% 0.6% 0.8%
000
CLM
Mean 5th 95th
I
3.7% 3.2% 4.1%
4.5% 4.0% 5.0%
4.5% 4.0% 5.0%
4.0% 3.5% 4.4%
4.0% 3.5% 4.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
2.5% 2.2% 2.8%
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O
+Q+S
12.5% 10.4% 4.6%
15.0% 12.6% 7.4%
15.0% 12.6% 7.4%
16.7% 14.3% 9.0%
16.7% 14.3% 9.0%
17.1% 15. % 9.1%
17.1% 15. % 9.1%
17.1% 15. % 9.1%
17.1% 15. % 9.1%
16.3% 14. % 8.5%
000
GAC10
CL2
Mean 5th 95th
J
6.5% 5.8% 7.3%
6.5% 5.8% 7.3%
6.5% 5.8% 7.3%
6.5% 5.8% 7.3%
2.5% 2.2% 2.8%
CLM
Mean 5th 95th
K
3.1% 2.7% 3.4%
3.1% 2.7% 3.4%
3.1% 2.7% 3.4%
3.1% 2.7% 3.4%
1.2% 1.1% 1.3%
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
21.7% 18.6% 24.8%
21.9% 18.8% 25.1%
21.9% 18.8% 25.1%
22.3% 19.3% 25.4%
22.3% 19.3% 25.4%
34.8% 30.8% 38.9%
34.8% 30.8% 38.9%
34.8% 30.8% 38.9%
34.8% 30.8% 38.9%
27.1% 23.7% 30.6%
000
22.2% 19.1% 25.2%
34.8% 30.8% 38.9%
27.1% 23.7% 30.6%
000
Exhibit C.7b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population
Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
System Size
(Population
Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-8 -9 -7
-8 -1 1 -4
-5 -7 -2
2-37
3-38
100 89 112
45 40 50
47 42 53
656
183 141 224
GAC10 + Advanc
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
000
000
000
000
46 41 51
21 18 23
22 19 24
323
91 81 102
d Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
51 45 57
23 20 26
24 21 27
333
101 89 113
24 21 27
11 10 12
11 10 13
1 1 2
48 42 54
CLM
Mean 5th 95th
B
000
000
1 1 1
1 1 1
35 31 39
16 14 17
16 15 18
222
71 63 79
GAC
CL2
Mean 5th 95th
UV
CL2
Mean 5th 95th
546
000
000
000
000
31 27 34
14 12 15
14 13 16
222
65 58 73
20
CLM
Mean 5th 95th
H
15 13 17
27 24 30
17 15 19
34 30 38
38 34 43
768
333
334
000
144 128 161
18 16 20
47 41 52
30 26 33
83 74 93
93 82 104
222
222
000
279 247 311
CLM
Mean 5th 95th
C
546
000
000
000
000
12 11 14
656
657
1 1 1
30 26 33
GAC20 + Advan
CL2
Mean 5th 95th
Ozone
CL2
Mean 5th 95th
CLM
Mean 5th 95th
D
000
000
000
000
000
000
000
000
000
ed Disinfectants
CLM
Mean 5th 95th
I
13 11 14
16 14 18
10 9 12
21 19 23
23 21 26
222
222
000
91 81 102
13 12 15
28 25 31
18 16 20
50 44 55
55 49 62
2 1 2
1 1 1
1 1 1
000
167 148 186
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
333
222
667
768
000
000
000
000
19 16 21
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
768
445
223
323
768
333
334
000
30 27 34
334
13 12 15
989
768
879
434
222
222
000
48 42 54
CLM
Mean 5th 95th
E
13 12 15
34 30 38
22 19 24
45 40 50
50 44 56
000
000
000
000
164 145 183
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O
+Q+S
45 37 52
115 97 133
72 61 84
188 162 215
210 180 240
221 195 246
99 88 111
104 92 116
13 11 14
1,067 924 1,212
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
84 74 94
38 33 42
40 35 44
545
40 35 45
18 16 20
19 17 21
223
166 147 185 79 70 88
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
78 67 89
168 144 193
106 91 121
252 218 286
281 243 319
450 398 502
202 179 225
213 188 237
26 23 29
1,776 1,551 2,003
886 763 1 ,009
890 788 994
1,776 1,551 2,003
Note: Detail may not add to totals due to independent rounding
Source: Above table with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.7c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population
Served)
<100
10M99
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
System Size
(Population
Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-2.3% -2.6% -1.9%
-1 .0% -1 .5% -0.5%
-1.0% -1.5% -0.5%
0.2% -0.2% 0.7%
0.2% -0.2% 0.7%
7.7% 6.9% 8.6%
0.0% 0.0% 0.0%
7.7% 6.9% 8.6%
0.0% 0.0% 0.0%
-1.1% -1.6% -0.6%
GAC10 + Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
3.6% 3.2% 4.0%
0.0% 0.0% 0.0%
3.6% 3.2% 4.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
d Disinfectants
CLM
Mean 5th 95th
ing to
CLM M
Only
4.0% 3.5% 4.4%
0.0% 0.0% 0.0%
4.0% 3.5% 4.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
1.9% 1.7% 2.1%
0.0% 0.0% 0.0%
1.9% 1.7% 2.1%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
C
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
2.7% 2.4% 3.0%
0.0% 0.0% 0.0%
2.7% 2.4% 3.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
GAC
CL2
Mean 5th 95th
N
4.2% 3.7% 4.7%
3.5% 3.1% 3.9%
3.5% 3.1% 3.9%
3.0% 2.7% 3.4%
3.0% 2.7% 3.4%
0.5% 0.5% 0.6%
0.0% 0.0% 0.0%
0.5% 0.5% 0.6%
0.0% 0.0% 0.0%
3.6% 3.2% 4.0%
000
UV
CL2
Mean 5th 95th
D
1.4% 1.2% 1.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
2.4% 2.1% 2.6%
0.0% 0.0% 0.0%
2.4% 2.1% 2.6%
0.0% 0.0% 0.0%
0.4% 0.4% 0.5%
20
CLM
Mean 5th 95th
O
5.1% 4.5% 5.7%
6.1% 5.4% 6.8%
6.1% 5.4% 6.8%
7.4% 6.5% 8.2%
7.4% 6.5% 8.2%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
6.0% 5.3% 6.7%
000
CLM
Mean 5th 95th
E
1.4% 1.2% 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%
1.0% 0.9% 1.1%
0.0% 0.0% 0.0%
1.0% 0.9% 1.1%
0.0% 0.0% 0.0%
0.4% 0.4% 0.5%
GAC20 + Advan
CL2
Mean 5th 95th
p
3.6% 3.2% 4.0%
2.1% 1.9% 2.4%
2.1% 1.9% 2.4%
1.9% 1.6% 2.1%
1.9% 1.6% 2.1%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
2.5% 2.2% 2.8%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ed Disinfectants
CLM
Mean 5th 95th
Q
3.7% 3.2% .1%
3.6% 3.2% .1%
3.6% 3.2% .1%
4.4% 3.9% .9%
4.4% 3.9% .9%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
3.7% 3.3% 4.2%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.6% 0.5% 0.6%
0.6% 0.5% 0.6%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.0% 0.0% 0.0%
0.9% 0.8% 1.0%
0.9% 0.8% 1.0%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.5% 0.5% 0.6%
0.0% 0.0% 0.0%
0.5% 0.5% 0.6%
0.0% 0.0% 0.0%
0.6% 0.5% 0.6%
000
0.9% 0.8% 1.0%
1.8% 1.6% 2.0%
1.8% 1.6% 2.0%
0.6% 0.6% 0.7%
0.6% 0.6% 0.7%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
1.3% 1.2% 1.5%
000
CLM
Mean 5th 95th
I
3.7% 3.2% 4.1%
4.5% 4.0% 5.0%
4.5% 4.0% 5.0%
4.0% 3.5% 4.4%
4.0% 3.5% 4.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
4.1% 3.6% 4.6%
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
12.5% 0.4% 14.6%
15.0% 2.6% 17.4%
15.0% 2.6% 17.4%
16.7% 4.3% 19.0%
16.7% 4.3% 19.0%
17.1% 5.1% 19.1%
0.0% 0.0% 0.0%
17.1% 15.1% 19.1%
0.0% 0.0% 0.0%
14.5% 12.3% 16.8%
000
GAC10
CL2
Mean 5th 95th
J
6.5% 5.8% 7.3%
0.0% 0.0% 0.0%
6.5% 5.8% 7.3%
0.0% 0.0% 0.0%
0.1% 0.0% 0.1%
CLM
Mean 5th 95th
K
3.1% 2.7% 3.4%
0.0% 0.0% 0.0%
3.1% 2.7% 3.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
21.7% 18.6% 24.8%
21.9% 18.8% 25.1%
21.9% 18.8% 25.1%
22.3% 19.3% 25.4%
22.3% 19.3% 25.4%
34.8% 30.8% 38.9%
0.0% 0.0% 0.0%
34.8% 30.8% 38.9%
0.0% 0.0% 0.0%
22.0% 18.9% 25.2%
000
21.9% 18.8% 25.1%
34.8% 30.8% 38.9%
22.0% 18.9% 25.2%
000
Exhibit C.7d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population
Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
System Size
(Population
Served)
<100
100^199
500-999
1 ,000-3,300
3,301-9,999
10,000^19,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-5 -6 -4
-3 -5 -1
-1 -2 -1
0 0 1
000
000
000
000
000
-9 -12 -5
GAC10 + Advanc
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
d Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
B
000
000
000
000
000
000
000
000
000
GAC
CL2
Mean 5th 95th
UV
CL2
Mean 5th 95th
333
000
000
000
000
000
000
000
000
334
20
CLM
Mean 5th 95th
H
9811
11 10 12
434
323
1 1 1
000
000
000
000
28 24 31
11 10 13
19 17 21
667
768
222
000
000
000
000
46 40 51
CLM
Mean 5th 95th
C
334
000
000
000
000
000
000
000
000
334
GAC20 + Advan
CL2
Mean 5th 95th
879
767
223
222
0 0 1
000
000
000
000
19 17 21
Ozone
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
ed Disinfectants
CLM
Mean 5th 95th
879
11 10 13
434
445
1 1 1
000
000
000
000
29 25 32
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
CLM
Mean 5th 95th
E
000
1 1 1
000
1 0 1
000
000
000
000
000
223
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
333
1 1 1
000
000
000
000
000
000
445
222
556
222
1 1 1
000
000
000
000
000
10 9 11
879
14 12 16
545
434
1 1 1
000
000
000
000
32 28 35
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
28 24 33
47 39 54
16 13 18
15 13 18
445
1 1 1
000
000
000
112 94 129
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
49 42 56
68 59 78
23 20 27
21 18 23
656
222
000
000
000
169 145 193
167 143 191
222
169 145 193
Note: Detail may not add to totals due to independent rounding
Source: Above table with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only colum
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.8a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
3.8%
3.9%
3.9%
3.5%
3.5%
5.9%
5.9%
5.6%
5.6%
4.1%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
0.9%
1 .3%
1 .3%
1 .4%
1 .4%
1.1%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.1%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
GAC20
CL2
F
0.3%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.1%
0.1%
0.4%
0.4%
0.4%
0.4%
0.1%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.5%
0.5%
0.5%
0.5%
0.1%
Total Converting
to CLM
J = A+C+E+G+I
4.7%
5.2%
5.2%
5.0%
5.0%
6.8%
6.8%
6.5%
6.5%
5.3%
Total Adding
Treatment
Technology
K = SUM(A:I)
5.1%
5.3%
5.3%
5.0%
5.0%
7.0%
7.0%
6.6%
6.6%
5.4%
5.2%
6.9%
5.4%
Note: Detail may not add to totals due to independent rounding
Exhibit C.8b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
24520.6%
590
236
263
175
317
42
51
2
UVCL2
B
0
0
0
0
0
UVCLM
C
59
200
80
108
71
Ozone
CL2
D
0
0
0
0
0
7
1
1
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
20
22
9
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
9
6
22
3
4
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
0
0
0
0
0
29
4
5
0
Total Converting
to CLM
J = A+C+E+G+I
305
790
316
380
252
368
49
60
2
Total Adding
Treatment
Technology
K = SUM(A:I)
324
812
325
380
252
375
50
61
2
2,093
488
Final Economic Analysis for the Stage 2 DBPR
C-17
December 2005
-------
Exhibit C.8c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
3.8%
3.9%
3.9%
3.5%
3.5%
5.9%
5.9%
5.6%
0.0%
3.8%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
0.9%
1 .3%
1 .3%
1 .4%
1 .4%
1.1%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
GAC20
CL2
F
0.3%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.1%
0.1%
0.4%
0.4%
0.4%
0.0%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.5%
0.5%
0.5%
0.0%
0.0%
Total Converting
to CLM
J = A+C+E+G+I
4.7%
5.2%
5.2%
5.0%
5.0%
6.8%
6.8%
6.5%
0.0%
5.0%
Total Adding
Treatment
Technology
K = SUM(A:I)
5.1%
5.3%
5.3%
5.0%
5.0%
7.0%
7.0%
6.6%
0.0%
5.2%
5.2%
6.9%
5.2%
Note: Detail may not add to totals due to independent rounding
Exhibit C.8d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
9516.8%
82
23
9
1
0
0
0
0
UVCL2
B
0
0
0
0
0
UVCLM
C
23
28
8
4
0
Ozone
CL2
D
0
0
0
0
0
0
0
0
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
8
3
1
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
0
0
0
0
0
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
0
0
0
0
0
0
0
0
0
Total Converting
to CLM
J = A+C+E+G+I
118
110
31
12
1
0
0
0
0
Total Adding
Treatment
Technology
K = SUM(A:I)
126
113
31
12
1
0
0
0
0
284
0
Final Economic Analysis for the Stage 2 DBPR
C-18
December 2005
-------
Exhibit C.9a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
20.2% 17.0% 23.3%
3.7% 10.5% 16.8%
3.7% 10.5% 16.8%
1.1% 8.1% 14.1%
1.1% 8.1% 14.1%
6.7% 16.7% 16.7%
6.7% 16.7% 16.7%
6.7% 16.7% 16.7%
6.7% 16.7% 16.7%
4.3% 12.4% 16.1%
Mean 5th 95th
M
Mean
A
2.0% 2.0% 2.0%
2.0% 2.0% 2.0%
2.0% 2.0% 2.0%
2.0% 2.0% 2.0%
0.8% 0.8% 0.8%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
27.5% 27.1% 27.9%
34.4% 33.9% 34.9%
34.4% 33.9% 34.9%
41.6% 41.1% 42.0%
41.6% 41.1% 42.0%
34.6% 34.6% 34.6%
34.6% 34.6% 34.6%
34.6% 34.6% 34.6%
34.6% 34.6% 34.6%
36.7% 36.4% 37.0%
Mean 5th 95th
N
4.2% .2% 4.2%
4.2% .2% 4.2%
4.2% .2% 4.2%
4.2% .2% 4.2%
1 .6% .6% 1 .6%
Chlorine Dioxide CL2
Mean 5th 95th
C
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
1.9% 1.9% 1.9%
1.9% 1.9% 1.9%
3.9% 3.9% 3.9%
3.9% 3.9% 3.9%
3.9% 3.9% 3.9%
3.9% 3.9% 3.9%
2.4% 2.4% 2.4%
Mean 5th 95th
0
6.1% 5.7% 6.6%
4.5% 4.1% 4.9%
4.5% 4.1% 4.9%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
2.8% 2.6% 3.1%
Chlorine Dioxide CLM
Mean 5th 95th
D
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
2.2% 2.2% 2.2%
2.2% 2.2% 2.2%
8.1% 8.1% 8.1%
8.1% 8.1% 8.1%
8.1% 8.1% 8.1%
8.1% 8.1% 8.1%
4.2% 4.1% 4.2%
Mean 5th 95th
P
6.4% 5.8% 7.0%
0.7% 0.7% 0.7%
0.7% 0.7% 0.7%
0.7% 0.7% 0.7%
0.7% 0.7% 0.7%
5.1% 4.6% 5.6%
UVCL2
Mean 5th 95th
E
1.4% 1.2% 1.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.4% 0.4% 0.4%
Mean 5th 95th
Q
3.6% 3.2% 4.0%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
1.6% 1.5% 1.8%
UVCLM
Mean 5th 95th
F
1.4% 1.2% 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%
1.7% 1.7% 1.7%
1.7% 1.7% 1.7%
1.7% 1.7% 1.7%
1.7% 1.7% 1.7%
0.8% 0.7% 0.8%
Mean 5th 95th
R
3.7% 3.2% 4.1%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
2.9% 2.6% 3.2%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
4.2% 4.2% 4.2%
4.2% 4.2% 4.2%
4.2% 4.2% 4.2%
4.2% 4.2% 4.2%
4.1% 4.1% 4.1%
Mean 5th 95th
S
2.2% 2.2% 2.2%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.7% 0.7% 0.7%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
4.5% 4.5% 4.5%
8.6% 8.6% 8.6%
8.6% 8.6% 8.6%
8.6% 8.6% 8.6%
8.6% 8.6% 8.6%
5.9% 5.9% 5.9%
Mean 5th 95th
T
2.3% 2.2% 2.4%
2.2% 2.0% 2.4%
2.2% 2.0% 2.4%
0.8% 0.7% 0.9%
0.8% 0.7% 0.9%
.0% .0% .0%
.0% .0% .0%
.0% .0% .0%
.0% .0% .0%
.2% .2% .3%
MF/UFCL2
Mean 5th 95th
I
14.5% 14.5% 14.5%
9.3% 9.3% 9.4%
9.3% 9.3% 9.4%
6.7% 6.7% 6.8%
6.7% 6.7% 6.8%
0.6% 0.6% 0.6%
0.6% 0.6% 0.6%
0.6% 0.6% 0.6%
0.6% 0.6% 0.6%
5.3% 5.2% 5.3%
MF/UFCLM
Mean 5th 95th
J
10.8% 10.4% 11.2%
9.3% 8.8% 9.8%
9.3% 8.8% 9.8%
6.8% 6.4% 7.3%
6.8% 6.4% 7.3%
1 .2% 1 .2% 1 .2%
1 .2% 1 .2% 1 .2%
1 .2% 1 .2% 1 .2%
1 .2% 1 .2% 1 .2%
5.3% 5.0% 5.6%
Mean 5th 95th
U = A+C+E-K3+I+K+M+O+Q+S
47.9% 43.7% 52.1%
37.5% 33.5% 41.5%
37.5% 33.5% 41.5%
30.6% 26.9% 34.2%
30.6% 26.9% 34.2%
32.6% 32.6% 32.6%
32.6% 32.6% 32.6%
32.6% 32.6% 32.6%
32.6% 32.6% 32.6%
33.6% 31.3% 36.0%
GAC10CL2
Mean 5th 95th
K
3.3% 3.3% 3.3%
3.3% 3.3% 3.3%
3.3% 3.3% 3.3%
3.3% 3.3% 3.3%
1.3% 1.3% 1.3%
GAC10CLM
Mean 5th 95th
L
6.9% 6.9% 6.9%
6.9% 6.9% 6.9%
6.9% 6.9% 6.9%
6.9% 6.9% 6.9%
2.7% 2.7% 2.7%
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
52.1% 50.0% 54.1%
62.5% 60.1% 64.9%
62.5% 60.1% 64.9%
69.4% 67.1% 71.8%
69.4% 67.1% 71.8%
67.4% 67.4% 67.4%
67.4% 67.4% 67.4%
67.4% 67.4% 67.4%
67.4% 67.4% 67.4%
66.4% 64.9% 67.8%
Note: Detail may not add to totals due to independent rounding.
'No advanced Treatment Technologies includes conventional, n on- conventional, and softening plants.
Source: Surface water systems serving <1 0,000 people: Add Technologies- n-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 1. Surface water systems serving 10, 000 or more people: Use ending technology predictions from SWAT (FACA Screen
SeriesS v3.0 Database) for the Alternative 1 .
Exhibit C.9b
Post-Stage 2 DBPR Treatment Technologles-ln-Place for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
72 61 84
105 80 129
66 51 81
125 91 159
140 102 177
216 216 216
97 97 97
102 102 102
12 12 12
935 812 1,058
GAC10 + ADCL2
Mean 5th 95th
M
26 26 26
12 12 12
12 12 12
1 1 1
52 52 52
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
99 97 100
264 260 268
166 164 169
469 464 474
523 517 529
446 446 446
200 200 200
211 211 211
25 25 25
2,404 2,386 2,423
GAC10 + ADCLM
Mean 5th 95th
N
54 54 54
24 24 24
26 26 26
333
107 107 107
Chlorine Dioxide CL2
Mean 5th 95th
C
111
555
22 22 22
24 24 24
51 51 51
23 23 23
24 24 24
333
158 158 158
GAC20 CL2
Mean 5th 95th
O
22 20 24
35 32 38
22 20 24
46 42 50
51 47 56
555
222
222
000
185 170 200
Chlorine Dioxide CLM
Mean 5th 95th
D
111
444
25 25 25
28 28 28
105 105 105
47 47 47
50 50 50
666
272 272 272
GAC20 CLM
Mean 5th 95th
P
23 21 25
54 49 60
34 31 38
97 87 1 06
108 97 118
10 10 10
444
555
1 1 1
334 303 366
UVCL2
Mean 5th 95th
E
546
000
000
000
000
11 11 11
555
555
1 1 1
26 26 27
GAC20 + AD CL2
Mean 5th 95th
Q
13 11 14
20 18 22
13 11 14
27 24 29
30 27 33
222
1 1 1
1 1 1
000
105 96 115
UVCLM
Mean 5th 95th
F
546
000
000
000
000
22 22 22
10 10 10
11 11 11
1 1 1
49 49 50
GAC20 + AD CLM
Mean 5th 95th
R
13 12 15
31 28 35
20 18 22
56 50 62
63 56 69
333
1 1 1
222
000
189 170 208
Ozone CL2
Mean 5th 95th
G
39 39 39
24 24 24
45 45 45
50 50 50
54 54 54
24 24 24
26 26 26
333
266 266 266
Membranes CL2
Mean 5th 95th
S
888
11 10 11
767
445
555
666
333
333
000
46 44 48
Ozone CLM
Mean 5th 95th
H
35 35 35
22 22 22
51 51 51
56 56 56
112 112 112
50 50 50
53 53 53
666
385 385 385
Membranes CLM
Mean 5th 95th
T
889
17 15 18
10 9 11
9810
10 9 11
13 13 13
666
666
1 1 1
80 76 85
MFAJFCL2
Mean 5th 95th
I
52 52 52
71 71 72
45 45 45
76 75 77
85 84 86
888
333
444
000
345 342 347
MFAJFCLM
Mean 5th 95th
J
39 37 40
71 67 75
45 42 47
77 72 82
86 80 92
16 16 16
111
888
1 1 1
349 330 368
TOTAL CL2
Mean 5th 95th
U = A+C+E4G+I+K+M+O+Q+S
172 157 187
288 257 318
181 162 200
345 304 387
385 338 431
421 421 421
189 189 189
199 199 199
24 24 24
2,204 2,051 2,356
GAC 10CL2
Mean 5th 95th
K
43 43 43
19 19 19
20 20 20
222
85 85 85
GAC 10 CLM
Mean 5th 95th
L
89 89 89
40 40 40
42 42 42
555
177 177 177
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
187 180 194
479 461 497
302 290 313
784 757 81 1
874 844 903
871 871 871
391 391 391
41 1 41 1 41 1
50 50 50
4,348 4,254 4,441
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 2. Surface water systems s
e people: Use ending technolo
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.9c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
(Population Served)
<100
100-499
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
20.2% 17.0% 23.3%
3.7% 10.5% 16.8%
3.7% 10.5% 16.8%
1.1% 8.1% 14.1%
1.1% 8.1% 14.1%
6.7% 16.7% 16.7%
0.0% 0.0% 0.0%
16.7% 16.7% 16.7%
0.0% 0.0% 0.0%
15.2% 12.1% 18.3%
Mean 5th 95th
M
2.0% 2.0% 2.0%
0.0% 0.0% 0.0%
2.0% 2.0% 2.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
27.5% 27.1% 27.9%
34.4% 33.9% 34.9%
34.4% 33.9% 34.9%
41.6% 41.1% 42.0%
41.6% 41.1% 42.0%
34.6% 34.6% 34.6%
0.0% 0.0% 0.0%
34.6% 34.6% 34.6%
0.0% 0.0% 0.0%
33.5% 33.0% 33.9%
Mean 5th 95th
N
4.2% 4.2% 4.2%
0.0% 0.0% 0.0%
4.2% 4.2% 4.2%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Chlorine Dioxide CL2
Mean 5th 95th
C
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .9% 1 .9% 1 .9%
1 .9% 1 .9% 1 .9%
3.9% 3.9% 3.9%
0.0% 0.0% 0.0%
3.9% 3.9% 3.9%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
Mean 5th 95th
0
6.1% 5.7% 6.6%
4.5% 4.1% 4.9%
4.5% 4.1% 4.9%
4.1% 3.7% 4.4%
4.1% 3.7% 4.4%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
Chlorine Dioxide CLM
Mean 5th 95th
D
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
2.2% 2.2% 2.2%
2.2% 2.2% 2.2%
8.1% 8.1% 8.1%
0.0% 0.0% 0.0%
8.1% 8.1% 8.1%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
Mean 5th 95th
P
6.4% 5.8% 7.0%
7.1% 6.4% 7.8%
7.1% 6.4% 7.8%
8.6% 7.7% 9.4%
8.6% 7.7% 9.4%
0.7% 0.7% 0.7%
0.0% 0.0% 0.0%
0.7% 0.7% 0.7%
0.0% 0.0% 0.0%
UVCL2
Mean 5th 95th
E
1 .4% 1 .2% 1 .5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.4% 0.4% 0.5%
Mean 5th 95th
Q
3.6% 3.2% 4.0%
2.6% 2.4% 2.9%
2.6% 2.4% 2.9%
2.4% 2.2% 2.6%
2.4% 2.2% 2.6%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
UVCLM
Mean 5th 95th
F
1 .4% 1 .2% 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%
1 .7% 1 .7% 1 .7%
0.0% 0.0% 0.0%
1 .7% 1 .7% 1 .7%
0.0% 0.0% 0.0%
0.4% 0.4% 0.5%
Mean 5th 95th
R
3.7% 3.2% 4.1%
4.1% 3.7% 4.5%
4.1% 3.7% 4.5%
5.0% 4.5% 5.5%
5.0% 4.5% 5.5%
0.2% 0.2% 0.2%
0.0% 0.0% 0.0%
0.2% 0.2% 0.2%
0.0% 0.0% 0.0%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
4.2% 4.2% 4.2%
0.0% 0.0% 0.0%
4.2% 4.2% 4.2%
0.0% 0.0% 0.0%
3.4% 3.4% 3.4%
Mean 5th 95th
S
2.2% 2.2% 2.2%
1 .4% 1 .3% 1 .5%
1 .4% 1 .3% 1 .5%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.5% 0.5% 0.5%
0.0% 0.0% 0.0%
0.5% 0.5% 0.5%
0.0% 0.0% 0.0%
1 .5% 1 .4% 1 .5%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
4.5% 4.5% 4.5%
8.6% 8.6% 8.6%
0.0% 0.0% 0.0%
8.6% 8.6% 8.6%
0.0% 0.0% 0.0%
3.2% 3.2% 3.2%
Mean 5th 95th
T
2.3% 2.2% 2.4%
2.2% 2.0% 2.4%
2.2% 2.0% 2.4%
0.8% 0.7% 0.9%
0.8% 0.7% 0.9%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
2.0% 1.8% 2.2%
MF/UF CL2
Mean 5th 95th
I
14.5% 14.5% 14.5%
9.3% 9.3% 9.4%
9.3% 9.3% 9.4%
6.7% 6.7% 6.8%
6.7% 6.7% 6.8%
0.6% 0.6% 0.6%
0.0% 0.0% 0.0%
0.6% 0.6% 0.6%
0.0% 0.0% 0.0%
10.4% 10.3% 10.4%
MF/UF CLM
Mean 5th 95th
J
10.8% 10.4% 11.2%
9.3% 8.8% 9.8%
9.3% 8.8% 9.8%
6.8% 6.4% 7.3%
6.8% 6.4% 7.3%
1 .2% 1 .2% 1 .2%
0.0% 0.0% 0.0%
1 .2% 1 .2% 1 .2%
0.0% 0.0% 0.0%
9.3% 8.8% 9.8%
Mean 5th 95th
U =A+C+E+G+I+K+M4O+Q+S
47.9% 43.7% 52.1%
37.5% 33.5% 41.5%
37.5% 33.5% 41.5%
30.6% 26.9% 34.2%
30.6% 26.9% 34.2%
32.6% 32.6% 32.6%
0.0% 0.0% 0.0%
32.6% 32.6% 32.6%
0.0% 0.0% 0.0%
39.5% 35.5% 43.4%
GAC10CL2
Mean 5th 95th
K
3.3% 3.3% 3.3%
0.0% 0.0% 0.0%
3.3% 3.3% 3.3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
GAC10CLM
Mean 5th 95th
L
6.9% 6.9% 6.9%
0.0% 0.0% 0.0%
6.9% 6.9% 6.9%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
52.1% 50.0% 54.1%
62.5% 60.1% 64.9%
62.5% 60.1% 64.9%
69.4% 67.1% 71.8%
69.4% 67.1% 71.8%
67.4% 67.4% 67.4%
0.0% 0.0% 0.0%
67.4% 67.4% 67.4%
0.0% 0.0% 0.0%
60.5% 58.3% 62.8%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 2. Surface water systems serving 10,000 or more people: Use ending technolo
Exhibit C.9d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
46 38 53
43 33 52
14 11 18
10 7 13
324
1 1 1
000
000
000
117 93 140
GAC10 + ADCL2
Mean 5th 95th
M
000
000
000
000
000
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
62 61 63
107 106 109
37 36 37
38 38 39
10 10 11
222
000
000
000
257 253 260
GAC10 + ADCLM
Mean 5th 95th
N
000
000
000
000
000
Chlorine Dioxide CL2
Mean 5th 95th
C
333
1 1 1
222
000
000
000
000
000
666
GAC20 CL2
Mean 5th 95th
O
14 13 15
14 13 15
545
434
1 1 1
000
000
000
000
38 34 41
Chlorine Dioxide CLM
Mean 5th 95th
D
333
1 1 1
222
1 1 1
000
000
000
000
111
GAC20 CLM
Mean 5th 95th
P
14 13 16
22 20 24
778
879
222
000
000
000
000
54 49 59
UVCL2
Mean 5th 95th
E
333
000
000
000
000
000
000
000
000
334
GAC20 + AD CL2
Mean 5th 95th
Q
879
879
333
222
1 1 1
000
000
000
000
22 20 24
UVCLM
Mean 5th 95th
F
334
000
000
000
000
000
000
000
000
334
GAC20 + AD CLM
Mean 5th 95th
R
879
13 11 14
445
545
1 1 1
000
000
000
000
31 28 35
Ozone CL2
Mean 5th 95th
G
16 16 16
555
1 1 1
000
000
000
000
26 26 26
Membranes CL2
Mean 5th 95th
S
555
445
1 1 2
000
000
000
000
000
000
11 11 12
Ozone CLM
Mean 5th 95th
H
14 14 14
555
1 1 1
000
000
000
000
25 25 25
Membranes CLM
Mean 5th 95th
T
555
767
223
1 1 1
000
000
000
000
000
15 14 17
MFAJF CL2
Mean 5th 95th
I
33 33 33
29 29 29
10 10 10
666
222
000
000
000
000
80 79 80
MFAJF CLM
Mean 5th 95th
J
24 23 25
29 27 31
10 9 10
667
222
000
000
000
000
71 68 75
TOTAL CL2
Mean 5th 95th
U =A+C+E+G+I+K+M4O+Q+S
108 99 118
117 105 129
40 36 44
28 25 32
879
222
000
000
000
303 272 333
GAC 10CL2
Mean 5th 95th
K
000
000
000
000
000
GAC 10 CLM
Mean 5th 95th
L
000
000
000
000
000
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
118 113 122
195 188 202
66 64 69
64 62 66
17 17 18
333
000
1 1 1
000
464 447 481
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternative 2. Surface water systems s
e people: Use ending technolo
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.10a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
90.9%
89.9%
89.9%
90.7%
90.7%
82.2%
82.2%
82.9%
82.9%
89.1%
No Advanced
Treatment
Technologies
CLM1
B
6.2%
6.7%
6.7%
6.0%
6.0%
13.1%
13.1%
12.7%
12.7%
7.4%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
0.9%
1 .3%
1 .3%
1 .4%
1 .4%
1.1%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
1 .0%
1 .0%
1 .0%
1 .0%
0.3%
Ozone
CLM
F
GAC20
CL2
G
0.0% 0.3%
0.5% 0.1%
0.5% 0.1%
0.9% 0.0%
0.9% 0.0%
0.8% 0.0%
0.8% 0.0%
0.7% 0.0%
0.7% 0.0%
0.6%
0.1%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.2%
0.2%
0.5%
0.5%
0.4%
0.4%
0.5%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1.7%
1.7%
1.7%
1.7%
0.4%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.8%
0.8%
0.7%
0.7%
0.5%
Total Using CL2
K = A+C+E+G+I
91.5%
90.4%
90.4%
91.1%
91.1%
84.9%
84.9%
85.5%
85.5%
89.9%
Total Using CLM
L = B+D+F+H+J
8.5%
9.6%
9.6%
8.9%
8.9%
15.1%
15.1%
14.5%
14.5%
10.1%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 2.
Exhibit C.10b
System Size
(Population Served)
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
No Advanced
Treatment
Technologies
CL21
A
5,836
13,710
5,481
6,884
4,564
4,426
589
No Advanced
Treatment
Technologies
CLM1
B
401
1,017
406
455
302
706
94
UVCL2
C
0
0
0
0
0
UVCLM
D
59
200
80
108
71
Ozone
CL2
E
0
25
10
22
15
53
7
Ozone
CLM
F
0
74
29
66
44
42
6
GAC20
CL2
G
20
22
9
0
0
0
0
GAC20
CLM
H
56
96
39
13
8
24
3
Membranes
CL2
I
22
20
8
4
3
90
12
Membranes
CLM
J
29
79
32
36
24
42
6
Total Using CL2
K = A+C+E+G+I
5,878
13,776
5,507
6,910
4,581
4,568
608
Total Using CLM
L = B+D+F+H+J
545
1,466
586
677
449
815
108
Total Plants
42,273
3,500
0
518
140
267
51
244
173
254
42,637
4,783
Note: Detail may not add to totals due to independent rounding
nNo advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 2.
Final Economic Analysis for the Stage 2 DBPR
C-21
December 2005
-------
Exhibit C.10c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 2
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
90.9%
89.9%
89.9%
90.7%
90.7%
82.2%
82.2%
82.9%
0.0%
90.4%
No Advanced
Treatment
Technologies
CLM1
B
6.2%
6.7%
6.7%
6.0%
6.0%
13.1%
1 2.7%
0.0%
6.4%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
0.9%
1.3%
1.3%
1.4%
1.4%
1.1%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
1.0%
1.0%
1.0%
0.0%
Ozone
CLM
F
0.0%
0.5%
0.5%
0.9%
0.9%
0.8%
0.8%
0.7%
0.0%
0.1% 0.3%
GAC20
CL2
G
0.3%
0.1%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.2%
0.2%
0.5%
0.5%
0.4%
0.0%
0.7%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1.7%
1.7%
1.7%
0.0%
0.2%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.8%
0.8%
0.7%
0.0%
0.5%
Total Using CL2
K = A+C+E+G+I
91 .5%
90.4%
90.4%
91.1%
91.1%
84.9%
84.9%
85.5%
0.0%
90.9%
Total Using CLM
L = B+D+F+H+J
8.5%
9.6%
9.6%
8.9%
8.9%
15.1%
15.1%
14.5%
0.0%
9.1%
Note: Detail may not add to totals due to independent rounding
nNo advanced Treatment Technologies includes conventional, non-conventional, and
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the
softening plants.
Technology Selection Delta for the Alternative 2.
Exhibit C.10d
System Size
(Population Served)
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
No Advanced
Treatment
Technologies
CL21
A
2,265
1,915
530
224
19
3
0
No Advanced
Treatment
Technologies
CLM1
B
155
142
39
15
1
0
0
UVCL2
C
0
0
0
0
0
UVCLM
D
23
28
8
4
0
Ozone
CL2
E
0
3
1
1
0
0
0
Ozone
CLM
F
0
10
3
2
0
0
0
GAC20
CL2
G
8
3
1
0
0
0
0
GAC20
CLM
H
22
13
4
0
0
0
0
Membranes
CL2
I
8
3
1
0
0
0
0
Membranes
CLM
J
11
11
3
1
0
0
0
Total Using CL2
K = A+C+E+G+I
2,281
1,924
533
225
20
3
0
Total Using CLM
L= B+D+F+H+J
212
205
57
22
2
0
0
[Total Plants
I
4,957
3531
62
15
12
39
12
.M.
4,986|
4971
Note: Detail may not add to totals due to independent rounding
nNo advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 2.
Final Economic Analysis for the Stage 2 DBPR
C-22
December 2005
-------
Exhibit C.11a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population
Served)
Converting to CLM Only
Chlorine Dioxide
Mean 5th 95th Mean
-8.6% -9.9% -7.3%
2.0% 1.7% 2.3%
0.0% 0.0% 0.0%
100-499
500-999
-8.0%
-8.0%
0.7%
0.7%
0.7%
0.7%
5.4%
5.4%
1,000-3,300
3,301-9,999
-8.4%
-8.4%
-10.1% -6.7%
-10.1% -6.7%
2.1%
2.1%
2.4?
2.4?
5.4?
5.4?
10,000-49,999
50,000-99,999
3.3%
3.3%
5.7%
5.7%
2.7% 2.3% 3.1%
2.7% 2.3% 3.1%
0.5%
0.5%
0.7%
0.7%
12.4% 10.6% 14
12.4% 10.6% 14.2%
5.7%
5.7%
100,000-999,999
•=1,000,000
3.3%
3.3%
5.7%
5.7%
2.7%
2.7%
2.3%
2.3%
0.5%
0.5%
0.7%
0.7%
0.7%
0.7%
12.4% 10.6% 14.2
12.4% 10.6% 14.2
5.7%
5.7%
-3.5% -4.8% -2.3% 2.2% 1.9% 2
1.2% 1.0% 1.3°/
0.6% 0.5% 0.6%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0% 0.6% 0.5% 0.7%
4.8% 4.1% 5.5%
System Size
(Population
Served)
GAC10 +Advanced Disinfectants
GAC20 + Advan
ced Disinfectants
Total Converting to CLM
Total Adding Treatment Technology
95th I Mean
Mean 5th 95th Mean
System Size
(Population
Served)
100-499
500-999
:+E+G+l+K+M+O+
Q+S
8.0% 6.8% 9.1%
9.2% 7.9% 10.6%
4.5% 3.8% 5.1%
0.6% 0.5% 0.7%
5.5%
5.5%
2.6%
2.6%
2.2%
2.2%
3.0%
3.0%
13.6% 9.0% 18.2%
2.6%
2.6%
2.2%
2.2%
3.0%
3.0%
16.3%
16.3%
11.1% 21
11.1% 21.5%
27.5% 20.7% 34.4?
27.5% 20.7% 34.4?
27.4% 20.5% 34.3%
1,000-3,300
3,301-9,999
13.6%
13.6%
11.6% 15.6%
11.6% 15.6%
2.3%
2.3%
2.6%
2.6%
5.5%
5.5%
0.2%
0.2%
0.2%
0.2%
0.7%
0.7%
18.1%
18.1%
12.5% 23.7%
12.5% 23.7%
27.1% 20.2% 34.0%
27.1% 20.2% 34.0%
10,000-49,999
50,000-99,999
5.0%
5.0%
5.7%
5.7%
2.4% 2.0% 2.7%
2.4% 2.0% 2.7%
0.7%
0.7%
0.5%
0.5%
0.5%
0.5%
0.3%
0.3%
0.3%
0.3%
0.3%
0.3%
0.5%
0.5%
0.5%
0.5%
0.3%
0.3%
0.3%
0.3%
18.5%
18.5%
15.8% 21.;
15.8% 21.;
53.4?
53.4?
46.6% 39.8% 53.4?
100,000-999,999
•=1,000,000
5.0%
5.0%
5.7%
5.7%
2.4% 2.0% 2.7%
2.4% 2.0% 2.7%
0.7%
0.7%
0.5%
0.5%
0.5%
0.5%
0.3%
0.3%
0.3%
0.3%
0.3%
0.3%
0.5%
0.5%
0.5%
0.5%
0.3%
0.3%
0.3%
0.3%
18.5%
18.5%
15.8% 21.;
15.8% 21.;
53.4?
53.4?
1.9% 1.6% 2.2%
0.9% 0.8% 1.1%
4.1% 3.5% 4.7%
3.2% 2.7% 3.6%
0.6% 0.5% 0.7%
0.9% 0.8% 1.1%
17.7% 13.4% 22.0%
34.9% 28.0% 41.7%
34.9% 28.0% 41.7%
Exhibit C.11b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-31 -36 -26
-61 -74 -48
-38 -46 -31
-95 -114 -76
-106 -127 -85
50 43 58
23 19 26
24 20 27
323
-232 -312 -151
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
000
000
000
000
74 63 85
33 28 38
35 30 40
445
146 125 168
d Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
64 55 73
29 25 33
30 26 35
434
127 108 145
31 26 35
14 12 16
15 12 17
2 1 2
61 52 70
CLM
Mean 5th 95th
B
545
333
23 20 27
26 22 30
49 42 56
22 19 25
23 20 26
323
153 131 176
G/
CL2
Mean 5th 95th
UV
CL2
Mean 5th 95th
CLM
Mean 5th 95th
C
768
000
000
000
000
35 30 40
16 14 18
17 14 19
222
77 66 88
C20
CLM
Mean 5th 95th
H
29 24 33
50 42 57
31 27 36
65 56 75
73 62 83
11 9 12
546
546
1 1 1
269 230 309
33 28 38
85 73 98
54 46 61
1 54 131 1 76
171 146 197
657
323
323
000
508 434 583
869
000
000
000
000
15 13 17
768
768
1 1 1
37 32 42
Ozone
CL2
Mean 5th 95th
CLM
Mean 5th 95th
D
000
000
000
000
000
000
000
000
000
GAC20 + Advan ced Disinfectants
CL2
Mean 5th 95th
16 14 18
20 17 23
13 11 15
26 22 29
29 24 33
434
222
222
000
111 95 127
CLM
Mean 5th 95th
17 14 19
35 30 40
22 19 25
62 53 71
69 59 79
2 1 2
1 1 1
1 1 1
000
208 178 238
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
CLM
Mean 5th 95th
E
000
656
434
8 710
9 811
768
000
42 36 48
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
222
11 9 12
768
223
323
768
334
334
000
38 33 44
657
20 17 23
12 11 14
768
879
445
222
222
000
62 53 71
16 14 19
42 35 48
26 22 30
53 45 61
59 50 68
9811
445
445
1 0 1
215 183 246
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
49 32 65
125 85 165
79 54 104
204 142 268
228 158 298
239 205 275
107 92 123
113 97 130
14 12 16
1,159 875 1,443
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
160 137 184
72 61 82
76 65 87
9810
74 63 85
33 28 38
35 30 40
445
317 271 363 146 125 167
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
103 79 127
211 159 263
133 100 166
306 229 384
341 255 428
602 514 690
270 231 310
284 243 326
34 29 39
2,285 1,837 2,735
1,094 820 1,369
1,190 1,017 1,365
2,285 1,837 2,735
Note: Detail may not add to totals due to independent rounding
Source: Above table with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-23
December 2005
-------
Exhibit C.11c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
System Size
(Population
Served)
System Size
(Population
Served)
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-8.6% -9.9% -7.3%
-8.0% -9.6% -6.3%
-8.0% -9.6% -6.3%
-8.4% -10.1% -6.7%
-8.4% -10.1% -6.7%
3.9% 3.3% 4.5%
0.0% 0.0% 0.0%
3.9% 3.3% 4.5%
0.0% 0.0% 0.0%
-8.1% -9.7% -6.6%
GAC10 +Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
5.7% 4.9% 6.6%
0.0% 0.0% 0.0%
5.7% 4.9% 6.6%
0.0% 0.0% 0.0%
0.0% 0.0% 0.1%
ed Disinfectants
CLM
Mean 5th 95th
L M
5.0% 4.2% 5.7%
0.0% 0.0% 0.0%
5.0% 4.2% 5.7%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
2.4% 2.0% 2.7%
0.0% 0.0% 0.0%
2.4% 2.0% 2.7%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
C
0.6% 0.5% 0.7%
0.6% 0.5% 0.7%
2.1% 1.8% 2.4%
2.1% 1.8% 2.4%
3.8% 3.2% 4.3%
0.0% 0.0% 0.0%
3.8% 3.2% 4.3%
0.0% 0.0% 0.0%
0.7% 0.6% 0.8%
Gt
CL2
Mean 5th 95th
N
8.0% 6.8% 9.1%
6.5% 5.5% 7.4%
6.5% 5.5% 7.4%
5.8% 4.9% 6.6%
5.8% 4.9% 6.6%
0.8% 0.7% 1 .0%
0.0% 0.0% 0.0%
0.8% 0.7% 1 .0%
0.0% 0.0% 0.0%
6.8% 5.8% 7.8%
000
UV
CL2
Mean 5th 95th
D
2.0% 1 .7% 2.3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
2.7% 2.3% 3.1%
0.0% 0.0% 0.0%
2.7% 2.3% 3.1%
0.0% 0.0% 0.0%
0.6% 0.5% 0.7%
C20
CLM
Mean 5th 95th
O
9.2% 7.9% 10.6%
11.1% 9.5% 12.7%
11.1% 9.5% 12.7%
13.6% 11.6% 15.6%
13.6% 11.6% 15.6%
0.5% 0.4% 0.5%
0.0% 0.0% 0.0%
0.5% 0.4% 0.5%
0.0% 0.0% 0.0%
10.8% 9.3% 12.4%
000
CLM
Mean 5th 95th
E
2.1% 1.8% 2.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
1.2% 1 .0% 1 .3%
0.0% 0.0% 0.0%
1.2% 1 .0% 1 .3%
0.0% 0.0% 0.0%
0.6% 0.5% 0.7%
GAC20 + Advan
CL2
Mean 5th 95th
P
4.5% 3.8% 5.1%
2.6% 2.2% 3.0%
2.6% 2.2% 3.0%
2.3% 1 .9% 2.6%
2.3% 1 .9% 2.6%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
3.1% 2.6% 3.6%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ced Disinfectants
CLM
Mean 5th 95th
Q
4.7% 4.0% 5.4%
4.6% 3.9% 5.2%
4.6% 3.9% 5.2%
5.5% 4.7% 6.3%
5.5% 4.7% 6.3%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
4.7% 4.0% 5.4%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.7% 0.6% 0.8%
0.7% 0.6% 0.8%
0.8% 0.6% 0.9%
0.8% 0.6% 0.9%
0.6% 0.5% 0.7%
0.0% 0.0% 0.0%
0.6% 0.5% 0.7%
0.0% 0.0% 0.0%
0.5% 0.4% 0.6%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.6% 0.5% 0.7%
1 .4% 1.2% 1 .6%
1 .4% 1.2% 1 .6%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.5% 0.5% 0.6%
0.0% 0.0% 0.0%
0.5% 0.5% 0.6%
0.0% 0.0% 0.0%
1.0% 0.8% 1.1%
000
1.6% 1 .4% 1 .8%
2.6% 2.2% 3.0%
2.6% 2.2% 3.0%
0.7% 0.6% 0.7%
0.7% 0.6% 0.7%
0.3% 0.3% 0.4%
0.0% 0.0% 0.0%
0.3% 0.3% 0.4%
0.0% 0.0% 0.0%
2.0% 1 .7% 2.3%
000
CLM
Mean 5th 95th
I
4.6% 3.9% 5.3%
5.4% 4.6% 6.2%
5.4% 4.6% 6.2%
4.7% 4.0% 5.4%
4.7% 4.0% 5.4%
0.7% 0.6% 0.8%
0.0% 0.0% 0.0%
0.7% 0.6% 0.8%
0.0% 0.0% 0.0%
5.0% 4.3% 5.8%
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
13.6% 9.0% 18.2%
16.3% 11.1% 21.5%
16.3% 11.1% 21.5%
18.1% 12.5% 23.7%
18.1% 12.5% 23.7%
18.5% 15.8% 21.3%
0.0% 0.0% 0.0%
18.5% 15.8% 21.3%
0.0% 0.0% 0.0%
15.8% 10.7% 20.8%
000
GAC10
CL2
Mean 5th 95th
J
12.4% 10.6% 14.2%
0.0% 0.0% 0.0%
12.4% 10.6% 14.2%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
CLM
Mean 5th 95th
K
5.7% 4.9% 6.6%
0.0% 0.0% 0.0%
5.7% 4.9% 6.6%
0.0% 0.0% 0.0%
0.0% 0.0% 0.1%
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
28.6% 21.9% 35.4%
27.5% 20.7% 34.4%
27.5% 20.7% 34.4%
27.1% 20.2% 34.0%
27.1% 20.2% 34.0%
46.6% 39.8% 53.4%
0.0% 0.0% 0.0%
46.6% 39.8% 53.4%
0.0% 0.0% 0.0%
27.9% 21.1% 34.8%
000
27.8% 21.0% 34.6%
46.6% 39.8% 53.4%
27.9% 21.1% 34.8%
000
Exhibit C.11d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
-19 -22 -16
-25 -30 -20
-8 -10 -7
-8 -9 -6
-2 -3 -2
000
000
000
000
-62 -74 -50
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
ed Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
B
222
1 1 1
222
1 0 1
000
000
000
000
546
Gf
CL2
Mean 5th 95th
UV
CL2
Mean 5th 95th
C
545
000
000
000
000
000
000
000
000
545
C20
CLM
Mean 5th 95th
H
18 15 21
20 17 23
768
556
1 1 2
000
000
000
000
52 44 59
21 18 24
35 30 40
12 10 13
13 11 14
334
000
000
000
000
83 71 95
CLM
Mean 5th 95th
545
000
000
000
000
000
000
000
000
546
GAC20 + Advan
CL2
Mean 5th 95th
Ozone
CL2
Mean 5th 95th
:
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
I
10 9 12
879
323
222
1 0 1
000
000
000
000
24 20 27
11 9 12
14 12 16
546
546
1 1 2
000
000
000
000
36 31 41
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
223
1 1 1
1 1 1
000
000
000
000
000
435
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
1 1 2
445
1 1 2
000
000
000
000
000
000
769
434
879
323
1 1 1
000
000
000
000
000
15 13 17
CLM
Mean 5th 95th
E
10 9 12
17 14 19
657
445
1 1 1
000
000
000
000
39 33 44
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
31 20 41
51 35 67
17 12 23
17 12 22
536
1 1 1
000
000
000
121 82 160
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
1 1 1
000
000
000
000
000
000
000
111000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
65 49 80
86 65 107
29 22 36
25 19 31
759
223
000
0 0 1
000
214 162 267
211 160 263
323
214 162 267
Note: Detail may
Source: Above t;
lot add to totals due to independent rounding
ible with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-24
December 2005
-------
Exhibit C.12a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
1 .8%
2.1%
2.1%
1 .5%
1 .5%
3.4%
3.4%
3.2%
3.2%
2.1%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1 .0%
1 .5%
1 .5%
1 .6%
1 .6%
1 .2%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.1%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
GAC20
CL2
F
0.3%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.2%
0.2%
0.6%
0.6%
0.6%
0.6%
0.1%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.6%
0.6%
0.5%
0.5%
0.1%
Total Converting
to CLM
J = A+C+E+G+I
2.8%
3.6%
3.6%
3.3%
3.3%
4.7%
4.7%
4.3%
4.3%
3.6%
Total Adding
Treatment
Technology
K = SUM(A:I)
3.2%
3.8%
3.8%
3.3%
3.3%
4.8%
4.8%
4.4%
4.4%
3.7%
3.5%
4.7%
3.7%
Note: Detail may not add to totals due to independent rounding
Exhibit C.12b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
117
321
128
112
74
185
25
29
1
UVCL2
B
0
0
0
0
0
UVCLM
C
64
230
92
122
81
Ozone
CL2
D
0
0
0
0
0
7
1
1
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
21
26
10
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
15
10
34
5
5
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
0
0
0
0
0
31
4
5
0
Total Converting
to CLM
J = A+C+E+G+I
181
550
220
249
165
251
33
40
1
Total Adding
Treatment
Technology
K = SUM(A:I)
202
576
230
249
165
258
34
41
1
1,423
334
Final Economic Analysis for the Stage 2 DBPR
C-25
December 2005
-------
Exhibit C.12c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
CLM Only
A
1 .8%
2.1%
2.1%
1 .5%
1 .5%
3.4%
3.4%
3.2%
0.0%
1 .9%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1 .0%
1 .5%
1 .5%
Ozone
CL2
D
0.0%
0.0%
0.0%
1 .6% 0.0%
1 .6% 0.0%
0.1%
0.1%
0.1%
0.0%
1 .3% 0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
GAC20
CL2
F
0.3%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.2%
0.2%
0.6%
0.6%
0.6%
0.0%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.6%
0.6%
0.5%
0.0%
0.0%
Total Converting
to CLM
J = A+C+E+G+I
2.8%
3.6%
3.6%
3.3%
3.3%
4.7%
4.7%
4.3%
0.0%
3.2%
Total Adding
Treatment
Technology
K=SUM(A:I)
3.2%
3.8%
3.8%
3.3%
3.3%
4.8%
4.8%
4.4%
0.0%
3.5%
3.5%
4.8%
3.5%
Note: Detail may not add to totals due to independent rounding
Exhibit C.12d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
CLM Only
A
4552.9%
45
12
4
0
0
0
0
0
UVCL2
B
0
0
0
0
0
UVCLM
C
25
32
9
4
0
Ozone
CL2
D
0
0
0
0
0
0
0
0
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
8
4
1
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
1
0
0
0
0
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
0
0
0
0
0
0
0
0
0
Total Converting
to CLM
J = A+C+E+G+I
70
77
21
8
1
0
0
0
0
Total Adding
Treatment
Technology
K=SUM(A:I)
79
80
22
8
1
0
0
0
0
190
0
Final Economic Analysis for the Stage 2 DBPR
C-26
December 2005
-------
Exhibit C.13a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
13.2% 6.4% 20.0%
8.1% 1.2% 4.9%
8.1% 1.2% 4.9%
6.3% -0.6% 3.2%
6.3% -0.6% 3.2%
12.6% 12.6% 2.6%
12.6% 12.6% 2.6%
12.6% 12.6% 2.6%
12.6% 12.6% 2.6%
9.5% 5.3% 13.7%
GAC10+ADCL2
Mean 5th 95th
M
Mean
A
2.3% 2.3% 2.3%
2.3% 2.3% 2.3%
2.3% 2.3% 2.3%
2.3% 2.3% 2.3%
0.9% 0.9% 0.9%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
21.1% 19.8% 22.5%
27.5% 25.8% 29.1%
27.5% 25.8% 29.1%
32.9% 31.2% 34.6%
32.9% 31.2% 34.6%
27.0% 27.0% 27.0%
27.0% 27.0% 27.0%
27.0% 27.0% 27.0%
27.0% 27.0% 27.0%
28.9% 27.9% 29.9%
GAC10+ADCLM
Mean 5th 95th
N
5.0% 5.0% 5.0%
5.0% 5.0% 5.0%
5.0% 5.0% 5.0%
5.0% 5.0% 5.0%
1 .9% 1 .9% 1 .9%
Chlorine Dioxide CL2
Mean 5th 95th
C
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .9% 1 .9% 1 .9%
1 .9% 1 .9% 1 .9%
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
2.7% 2.7% 2.7%
GAC20 CL2
Mean 5th 95th
0
9.9% 8.8% 11.1%
7.5% 6.6% 8.5%
7.5% 6.6% 8.5%
6.8% 6.0% 7.7%
6.8% 6.0% 7.7%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
4.6% 4.1% 5.2%
Chlorine Dioxide CLM
Mean 5th 95th
D
1 .5% 1 .4% 1 .6%
1 .5% 1 .4% 1 .6%
4.2% 3.9% 4.5%
4.2% 3.9% 4.5%
9.7% 9.7% 9.7%
9.7% 9.7% 9.7%
9.7% 9.7% 9.7%
9.7% 9.7% 9.7%
5.6% 5.5% 5.7%
GAC20 CLM
Mean 5th 95th
P
0.5% 9.2% 1.9%
2.1% 10.4% 3.7%
2.1% 10.4% 3.7%
.8% 12.8% 6.8%
.8% 12.8% 6.8%
.0% 1 .0% 1 .0%
.0% 1 .0% 1 .0%
.0% 1 .0% 1 .0%
.0% 1 .0% 1 .0%
8.6% 7.5% 9.8%
UVCL2
Mean 5th 95th
E
2.0% 1.7% 2.3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.4% 0.4% 0.4%
GAC20 + AD CL2
Mean 5th 95th
Q
4.5% 3.8% 5.1%
3.1% 2.7% 3.5%
3.1% 2.7% 3.5%
2.8% 2.5% 3.1%
2.8% 2.5% 3.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
1.9% 1.7% 2.1%
UVCLM
Mean 5th 95th
F
2.1% 1.8% 2.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
1.7% 1.7% 1.7%
1.7% 1.7% 1.7%
1.7% 1.7% 1.7%
1.7% 1.7% 1.7%
0.8% 0.8% 0.8%
GAC20 + AD CLM
Mean 5th 95th
R
4.7% 4.0% 5.4%
5.0% 4.3% 5.7%
5.0% 4.3% 5.7%
6.1% 5.3% 6.9%
6.1% 5.3% 6.9%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
3.5% 3.1% 4.0%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.1% 4.1% 4.1%
4.1% 4.1% 4.1%
4.1% 4.1% 4.1%
4.1% 4.1% 4.1%
4.0% 4.0% 4.0%
Membranes CL2
Mean 5th 95th
S
2.7% 2.6% 2.8%
1.8% 1.6% 2.1%
1.8% 1.6% 2.1%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.8% 0.8% 0.9%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
8.7% 8.7% 8.7%
8.7% 8.7% 8.7%
8.7% 8.7% 8.7%
8.7% 8.7% 8.7%
5.9% 5.9% 5.9%
Membranes CLM
Mean 5th 95th
T
3.0% 2.8% 3.2%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
0.8% 0.8% 0.9%
0.8% 0.8% 0.9%
.0% .0% .0%
.0% .0% .0%
.0% .0% .0%
.0% .0% .0%
.4% .3% .6%
MF/UF CL2
Mean 5th 95th
I
14.5% 14.5% 14.5%
9.6% 9.5% 9.7%
9.6% 9.5% 9.7%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
5.5% 5.5% 5.6%
MF/UF CLM
Mean 5th 95th
J
11.7% 11.1% 12.4%
10.2% 9.4% 11.0%
10.2% 9.4% 11.0%
2.0% 2.0% 2.0%
2.0% 2.0% 2.0%
2.0% 2.0% 2.0%
2.0% 2.0% 2.0%
6.1% 5.7% 6.6%
TOTAL CL2
Mean 5th 95th
U =A+C+E+G+I+K+M4O+Q+S
46.9% 37.9% 55.8%
36.2% 27.7% 44.7%
36.2% 27.7% 44.7%
29.1% 20.9% 37.4%
29.1% 20.9% 37.4%
31 .9% 31 .9% 31 .9%
31 .9% 31 .9% 31 .9%
31 .9% 31 .9% 31 .9%
31 .9% 31 .9% 31 .9%
32.5% 27.4% 37.6%
GAC10CL2
Mean 5th 95th
K
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
2.1% 2.1% 2.1%
GAC10CLM
Mean 5th 95th
L
1 1 .7% 1 1 .7% 1 1 .7%
1 1 .7% 1 1 .7% 1 1 .7%
1 1 .7% 1 1 .7% 1 1 .7%
1 1 .7% 1 1 .7% 1 1 .7%
4.6% 4.6% 4.6%
TOTAL CLM
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
53.1% 48.6% 57.7%
63.8% 58.6% 69.0%
63.8% 58.6% 69.0%
70.9% 65.3% 76.4%
70.9% 65.3% 76.4%
68.1% 68.1% 68.1%
68.1% 68.1% 68.1%
68.1% 68.1% 68.1%
68.1% 68.1% 68.1%
67.5% 64.2% 70.8%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternatives. Surface water systems serving 10,000 or more people: Use ending technolo
Exhibit C.13b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
47 23 72
62 9 114
39 6 72
71 -7 149
79 -8 166
163 163 163
73 73 73
77 77 77
999
621 346 895
GAC10+ADCL2
Mean 5th 95th
M
30 30 30
14 14 14
14 14 14
222
60 60 60
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
76 71 81
211 198 223
133 125 141
372 353 391
414 393 436
348 348 348
156 156 156
165 165 165
20 20 20
1,894 1,828 1,960
GAC10+ADCLM
Mean 5th 95th
N
64 64 64
29 29 29
30 30 30
444
128 128 128
Chlorine Dioxide CL2
Mean 5th 95th
C
111
555
22 22 22
24 24 24
59 59 59
26 26 26
28 28 28
333
174 174 174
GAC20 CL2
Mean 5th 95th
O
36 32 40
58 50 65
36 32 41
77 68 87
86 75 97
666
333
333
000
305 268 341
Chlorine Dioxide CLM
Mean 5th 95th
D
11 11 12
778
47 44 51
53 49 57
126 126 126
56 56 56
59 59 59
777
367 359 376
GAC20 CLM
Mean 5th 95th
P
38 33 43
92 80 105
58 50 66
167 144 189
186 161 211
13 13 13
666
666
1 1 1
567 494 640
UVCL2
Mean 5th 95th
E
768
000
000
000
000
11 11 11
555
555
1 1 1
28 27 29
GAC20 + AD CL2
Mean 5th 95th
Q
16 14 18
24 21 27
15 13 17
32 28 35
35 31 39
222
1 1 1
1 1 1
000
125 110 140
UVCLM
Mean 5th 95th
F
869
000
000
000
000
23 23 23
10 10 10
11 11 11
1 1 1
52 51 53
GAC20 + AD CLM
Mean 5th 95th
R
17 14 19
38 33 43
24 21 27
69 59 78
76 66 86
333
1 1 1
222
000
230 201 260
Ozone CL2
Mean 5th 95th
G
39 39 39
24 24 24
45 45 45
50 50 50
53 53 53
24 24 24
25 25 25
333
263 263 263
Membranes CL2
Mean 5th 95th
S
10 9 10
14 13 16
9810
445
545
666
333
333
000
54 50 57
Ozone CLM
Mean 5th 95th
H
35 35 35
22 22 22
51 51 51
56 56 56
113 113 113
51 51 51
53 53 53
666
387 387 387
Membranes CLM
Mean 5th 95th
T
11 10 12
23 20 26
14 13 16
10 8 11
11 9 12
13 13 13
666
1 1 1
94 86 102
MFAJF CL2
Mean 5th 95th
I
52 52 52
74 73 75
47 46 47
78 77 79
87 86 88
12 12 12
555
666
1 1 1
362 358 366
MFAJF CLM
Mean 5th 95th
J
42 40 45
78 72 84
49 46 53
85 78 93
95 87 104
26 26 26
12 12 12
12 12 12
1 1 1
401 373 430
TOTAL CL2
Mean 5th 95th
U =A+C+E+G+I+K+M4O+Q+S
168 136 201
278 213 343
175 134 216
329 236 422
367 263 470
412 412 412
185 185 185
195 195 195
23 23 23
2,131 1,796 2,465
GAC10CL2
Mean 5th 95th
K
71 71 71
32 32 32
33 33 33
444
140 140 140
GAC 10 CLM
Mean 5th 95th
L
152 152 152
68 68 68
72 72 72
999
300 300 300
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
191 174 207
489 449 529
308 283 333
800 737 863
892 821 962
880 880 880
395 395 395
416 416 416
50 50 50
4,420 4,206 4,635
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternatives. Surface water systems si
e people: Use ending technolo
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.13c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
System Size
(Population Served)
System Size
(Population Served)
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
13.2% 6.4% 20.0%
8.1% 1.2% 14.9%
8.1% 1.2% 14.9%
.3% -0.6% 13.2%
.3% -0.6% 13.2%
1 .6% 12.6% 12.6%
.0% 0.0% 0.0%
1 .6% 12.6% 12.6%
.0% 0.0% 0.0%
.4% 2.6% 16.1%
GAC10+AD CL2
Mean 5th 95th
M
2.3% 2.3% 2.3%
0.0% 0.0% 0.0%
2.3% 2.3% 2.3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
No Advanced Treatment
Technologies CLM
Mean 5th 95th
B
21.1% 19.8% 22.5%
27.5% 25.8% 29.1%
27.5% 25.8% 29.1%
32.9% 31.2% 34.6%
32.9% 31.2% 34.6%
27.0% 27.0% 27.0%
0.0% 0.0% 0.0%
27.0% 27.0% 27.0%
0.0% 0.0% 0.0%
26.4% 24.9% 28.0%
GAC10 +AD CLM
Mean 5th 95th
N
5.0% 5.0% 5.0%
0.0% 0.0% 0.0%
5.0% 5.0% 5.0%
0.0% 0.0% 0.0%
Chlorine Dioxide CL2
Mean 5th 95th
C
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .9% 1 .9% 1 .9%
1 .9% 1 .9% 1 .9%
4.6% 4.6% 4.6%
0.0% 0.0% 0.0%
4.6% 4.6% 4.6%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
GAC20 CL2
Mean 5th 95th
O
7.5% 6.6% 8.5%
6.8% 6.0% 7.7%
0.5% 0.5% 0.5%
0.0% 0.0% 0.0%
0.5% 0.5% 0.5%
0.0% 0.0% 0.0%
Chlorine Dioxide CLM
Mean 5th 95th
D
1 .5% 1 .4% 1 .6%
1 .5% 1 .4% 1 .6%
4.2% 3.9% 4.5%
9.7% 9.7% 9.7%
0.0% 0.0% 0.0%
9.7% 9.7% 9.7%
0.0% 0.0% 0.0%
1 .5% 1 .4% 1 .6%
Mean 5th 95th
P
4.8% 12.8% 16.8%
1.0% 1.0% 1.0%
0.0% 0.0% 0.0%
1.0% 1.0% 1.0%
0.0% 0.0% 0.0%
UVCL2
Mean 5th 95th
E
2.0% 1.7% 2.3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.6% 0.5% 0.7%
Mean 5th 95th
Q
2.8% 2.5% 3.1%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
UVCLM
F
2.1% 1.8% 2.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
1 .7% 1 .7% 1 .7%
0.0% 0.0% 0.0%
1 .7% 1 .7% 1 .7%
0.0% 0.0% 0.0%
0.6% 0.5% 0.7%
Mean 5th 95th
R
6.1% 5.3% 6.9%
0.2% 0.2% 0.2%
0.0% 0.0% 0.0%
0.2% 0.2% 0.2%
0.0% 0.0% 0.0%
Ozone CL2
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.1% 4.1% 4.1%
0.0% 0.0% 0.0%
4.1% 4.1% 4.1%
0.0% 0.0% 0.0%
3.4% 3.4% 3.4%
Mean 5th 95th
S
1.8% 1.6% 2.1%
0.4% 0.4% 0.4%
0.5% 0.5% 0.5%
0.0% 0.0% 0.0%
0.5% 0.5% 0.5%
0.0% 0.0% 0.0%
Ozone CLM
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
8.7% 8.7% 8.7%
0.0% 0.0% 0.0%
8.7% 8.7% 8.7%
0.0% 0.0% 0.0%
3.2% 3.2% 3.2%
Membranes CLM
Mean 5th 95th
3.0% 2.6% 3.4%
0.8% 0.8% 0.9%
0.8% 0.8% 0.9%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
MF/UF CL2
Mean 5th 95th
I
14.5% 14.5% 14.5%
9.6% 9.5% 9.7%
9.6% 9.5% 9.7%
6.9% 6.8% 7.0%
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
10.6% 10.5% 10.7%
MF/UF CLM
Mean 5th 95th
J
11.7% 11.1% 12.4%
10.2% 9.4% 11.0%
10.2% 9.4% 11.0%
7.6% 6.9% 8.2%
2.0% 2.0% 2.0%
0.0% 0.0% 0.0%
2.0% 2.0% 2.0%
0.0% 0.0% 0.0%
10.2% 9.5% 10.9%
TOTAL CL2
Mean 5th 95th
U = A4C4E-K3-H4K4M4O+Q+S
36.2% 27.7% 44.7%
36.2% 27.7% 44.7%
29.1% 20.9% 37.4%
29.1% 20.9% 37.4%
31.9% 31.9% 31.9%
0.0% 0.0% 0.0%
31.9% 31.9% 31.9%
0.0% 0.0% 0.0%
38.2% 29.7% 46.8%
GAC 10 CL2
Mean 5th 95th
K
5.5% 5.5% 5.5%
0.0% 0.0% 0.0%
5.5% 5.5% 5.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
GAC 10 CLM
Mean 5th 95th
L
11.7% 11.7% 11.7%
0.0% 0.0% 0.0%
11.7% 11.7% 11.7%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
TOTAL CLM
Mean 5th 95th
V = B4O4F-HH+J+L+N4P4R+1
63.8% 58.6% 69.0%
63.8% 58.6% 69.0%
70.9% 65.3% 76.4%
70.9% 65.3% 76.4%
68.1% 68.1% 68.1%
0.0% 0.0% 0.0%
68.1% 68.1% 68.1%
0.0% 0.0% 0.0%
61.8% 56.7% 66.8%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plar
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Alternatives. Surface water syster
tople: Use ending technolo
Exhibit C.13d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Alternative 3
System Size
(Population Served)
100-499
500-999
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
25 4 46
9 1 16
203
1 1 1
000
000
000
72 20 124
Mean 5th 95th
M
000
000
000
000
000
No Advanced Treatment
Technologies CLM
Mean 5th 95th
B
29 27 31
889
1 1 1
000
000
000
203 191 215
Mean 5th 95th
N
000
000
000
000
000
Chlorine Dioxide CL2
Mean 5th 95th
C
1 1 1
000
000
000
000
000
777
Mean 5th 95th
O
22 20 25
879
667
2 1 2
000
000
000
000
62 54 70
Chlorine Dioxide CLM
Mean 5th 95th
D
2 1 2
1 1 1
000
000
000
000
12 11 12
Mean 5th 95th
P
24 21 27
13 11 15
14 12 15
434
000
000
000
000
91 79 104
UVCL2
Mean 5th 95th
E
000
000
000
000
000
000
000
Mean 5th 95th
Q
10 9 12
334
323
1 1 1
000
000
000
000
26 23 30
UVCLM
Mean 5th 95th
F
000
000
000
000
000
000
000
Mean 5th 95th
R
11 9 12
16 13 18
556
656
2 1 2
000
000
000
000
39 33 44
Ozone CL2
Mean 5th 95th
G
555
1 1 1
000
000
000
000
Mean 5th 95th
S
666
656
222
000
000
000
000
000
000
14 13 15
Ozone CLM
Mean 5th 95th
H
555
1 1 1
000
000
000
000
Mean 5th 95th
T
767
9 8 10
334
1 1 1
000
000
000
000
000
20 18 22
MF/UF CL2
Mean 5th 95th
I
10 10 10
222
000
000
000
000
MF/UF CLM
Mean 5th 95th
J
11 10 12
222
000
000
000
000
TOTAL CL2
Mean 5th 95th
U =A4C4E-K3-H4K4M4O+Q+S
106 86 126
113 87 139
38 29 47
27 19 34
759
222
000
000
000
293 228 359
GAC 10 CL2
Mean 5th 95th
K
000
000
000
000
000
GAC 10 CLM
Mean 5th 95th
L
1 1 1
000
000
000
1 1 1
TOTAL CLM
Mean 5th 95th
V = B4O4F-HH+J+L+N4P4R+1
120 110 130
199 183 215
68 62 73
65 60 70
18 16 19
333
000
1 1 1
000
474 435 512
Note: Detail may not add to totals due to independi
'No advanced Treatment Technologies includes
Source: Surface water systems serving <10,000
lent rounding
onventional, non-conventional, and softening plar
leople: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) I
the Technology Selection Delta for the Alternative 3. Surface water syster
tople: Use ending technolo
Final Economic Analysis lor the Stage 2 DBPR
December 2005
-------
Exhibit C.14a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
92.8%
91 .5%
91 .5%
92.5%
92.5%
84.4%
84.4%
85.0%
85.0%
90.9%
No Advanced
Treatment
Technologies
CLM1
B
4.2%
4.9%
4.9%
4.0%
4.0%
10.7%
10.7%
10.3%
10.3%
5.4%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1 .0%
1 .5%
1 .5%
1 .6%
1 .6%
1 .2%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
1 .0%
1 .0%
1 .0%
1 .0%
0.3%
Ozone
CLM
F
GAC20
CL2
G
0.0% 0.3%
0.5% 0.2%
0.5% 0.2%
0.9% 0.0%
0.9% 0.0%
0.8% 0.0%
0.8% 0.0%
0.7% 0.0%
0.7% 0.0%
0.6%
0.1%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.3%
0.3%
0.7%
0.7%
0.6%
0.6%
0.6%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1 .7%
1 .7%
1.7%
1 .7%
0.4%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.8%
0.8%
0.8%
0.8%
0.5%
Total Using CL2
K = A+C+E+G+I
93.4%
92.0%
92.0%
92.8%
92.8%
87.0%
87.0%
87.6%
87.6%
91 .7%
Total Using CLM
L = B+D+F+H+J
6.6%
8.0%
8.0%
7.2%
7.2%
13.0%
13.0%
12.4%
12.4%
8.3%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative
3.
Exhibit C.14b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
No Advanced
Treatment
Technologies
ichnology CL21 CLM1
A
5,958
13,947
5,575
7,015
4,650
4,543
604
B
273
747
299
304
201
574
76
UVCL2
C
0
0
0
0
0
UVCLM
D
64
230
92
122
81
Ozone
CL2
E
0
25
10
22
15
53
7
Ozone
CLM
F
0
74
29
66
44
42
6
GAC20
CL2
G
21
26
10
0
0
0
0
GAC20
CLM
H
56
96
39
19
13
36
5
Membranes
CL2
I
22
20
8
4
3
90
12
Membranes
CLM
J
29
79
32
36
24
45
6
Total Using CL2
K = A+C+E+G+I
6,001
14,016
5,603
7,041
4,668
4,685
623
Total Using CLM
L = B+D+F+H+J
421
1,226
490
547
362
697
93
Total Plants
43,097
2,572| 0| 587
140
267
57
270
173 1 258| 43,466| 3,953
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 3.
Final Economic Analysis for the Stage 2 DBPR
C-29
December 2005
-------
Exhibit C.14c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
92.8%
91 .5%
91 .5%
92.5%
92.5%
84.4%
84.4%
85.0%
0.0%
92.1%
No Advanced
Treatment
Technologies
CLM1
B
4.2%
4.9%
4.9%
4.0%
4.0%
10.7%
10.7%
10.3%
0.0%
4.6%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1 .0%
1 .5%
1 .5%
1 .6%
1 .6%
1 .3%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
1 .0%
1 .0%
1 .0%
0.0%
Ozone
CLM
F
0.0%
0.5%
0.5%
0.9%
0.9%
0.8%
0.8%
0.7%
0.0%
0.1% 0.3%
GAC20
CL2
G
0.3%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.3%
0.3%
0.7%
0.7%
0.6%
0.0%
0.7%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1 .7%
1 .7%
1 .7%
0.0%
0.2%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.8%
0.8%
0.8%
0.0%
0.5%
Total Using CL2
K = A+C+E+G+I
93.4%
92.0%
92.0%
92.8%
92.8%
87.0%
87.0%
87.6%
0.0%
92.7%
Total Using CLM
L = B+D+F+H+J
6.6%
8.0%
8.0%
7.2%
7.2%
13.0%
13.0%
12.4%
0.0%
7.3%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 3.
Exhibit C.14d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Alternative 3
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
No Advanced
Treatment
Technologies
schnology CL21 CLM1
A
2,313
1,948
539
228
20
3
0
B
106
104
29
10
1
0
0
UVCL2
C
0
0
0
0
0
UVCLM
D
25
32
9
4
0
Ozone
CL2
E
0
3
1
1
0
0
0
Ozone
CLM
F
0
10
3
2
0
0
0
GAC20
CL2
G
8
4
1
0
0
0
0
GAC20
CLM
H
22
13
4
1
0
0
0
Membranes
CL2
I
8
3
1
0
0
0
0
Membranes
CLM
J
11
11
3
1
0
0
0
Total Using CL2
K = A+C+E+G+I
2,329
1,958
542
229
20
3
0
Total Using CLM
L = B+D+F+H+J
164
171
47
18
2
0
0
Total Plants
5,051
250 1 0
70
5
15
13
40| 12
27| 5,081
402
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Alternative 3.
Final Economic Analysis for the Stage 2 DBPR
C-30
December 2005
-------
Exhibit C.15a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
1.9% 1.1% 2.7%
4.1% 2.3% 5.9%
4.1% 2.3% 5.9%
4.2% 2. % 6.1%
4.2% 2. % 6.1%
7.8% 4. % 11.2%
7.8% 4. % 11.2%
7.8% 4. % 11.2%
7.8% 4. % 11.2%
5.5% 3. % 7.9%
GAC10 +Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.1% 0.1% 0.2%
0.1% 0.1% 0.2%
0.2% 0.1% 0.2%
0.2% 0.1% 0.2%
0.1% 0.0% 0.1%
0.1% 0.0% 0.1%
0.1% 0.0% 0.1%
0.1% 0.0% 0.1%
0.1% 0.1% 0.2%
ed Disinfectants
CLM
Mean 5th 95th
L M
Mean
1 .0% 0.5% 1 .4%
1 .0% 0.5% 1 .4%
1 .0% 0.5% 1 .4%
1 .0% 0.5% 1 .4%
0.4% 0.2% 0.5%
000
0.3% 0.2% 0.5%
0.3% 0.2% 0.5%
0.3% 0.2% 0.5%
0.3% 0.2% 0.5%
0.1% 0.1% 0.2%
000
CLM
Mean 5th 95th
C
0.4% 0.2% 0.5%
0.4% 0.2% 0.5%
0.9% 0.5% 1 .3%
0.9% 0.5% 1 .3%
0.6% 0.3% 0.9%
0.6% 0.3% 0.9%
0.6% 0.3% 0.9%
0.6% 0.3% 0.9%
0.6% 0.4% 0.9%
Gt
CL2
Mean 5th 95th
N
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.2% 0.1% 0.3%
000
UV
CL2
Mean 5th 95th
D
4.1% 2.3% 5.9%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
0.9% 0.5% 1 .4%
0.9% 0.5% 1 .4%
1.1% 0.6% 1.5%
1.1% 0.6% 1.5%
1.1% 0.6% 1.5%
1.1% 0.6% 1.5%
1 .2% 0.7% 1 .7%
C20
CLM
Mean 5th 95th
O
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.2% 0.1% 0.2%
0.2% 0.1% 0.2%
0.2% 0.1% 0.2%
0.2% 0.1% 0.2%
0.1% 0.0% 0.1%
000
CLM
Mean 5th 95th
E
3.0% 1 .7% 4.3%
1 .3% 0.7% 1 .8%
1 .3% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
0.2% 0.1% 0.4%
0.2% 0.1% 0.4%
0.2% 0.1% 0.4%
0.2% 0.1% 0.4%
1 .0% 0.5% 1 .4%
GAC20 + Advan
CL2
Mean 5th 95th
P
0.7% 0.4% 1 .0%
0.6% 0.3% 0.8%
0.6% 0.3% 0.8%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.2% 0.5%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ced Disinfectants
CLM
Mean 5th 95th
Q
0.5% 0.3% 0.7%
0.7% 0.4% 1 .0%
0.7% 0.4% 1 .0%
0.8% 0.5% 1 .2%
0.8% 0.5% 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%
0.5% 0.3% 0.7%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
0.0% 0.0% 0.0%
0.1% 0.0% 0.1%
0.1% 0.0% 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%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
I
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
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 Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
5.4% 3.0% 7.8%
6.5% 3.6% 9.3%
6.5% 3.6% 9.3%
7.2% 4.0% 10.3%
7.2% 4.0% 10.3%
9.1% 5.1% 13.2%
9.1% 5.1% 13.2%
9.1% 5.1% 13.2%
9.1% 5.1% 13.2%
7.7% 4.3% 11.1%
000
GAC10
CL2
Mean 5th 95th
J
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
CLM
Mean 5th 95th
K
0.0% 0.0% 0.0%
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 Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
10.1% 5.7% 14.6%
8.4% 4.7% 12.0%
8.4% 4.7% 12.0%
8.8% 4.9% 12.7%
8.8% 4.9% 12.7%
11.7% 6.6% 16.9%
11.7% 6.6% 16.9%
11.7% 6.6% 16.9%
11.7% 6.6% 16.9%
9.9% 5.6% 14.3%
000
8.8% 4.9% 12.6%
11.7% 6.6% 16.9%
9.9% 5.6% 14.3%
000
Exhibit C.15b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
7410
31 18 45
20 11 28
48 27 69
53 30 76
101 57 145
45 25 65
48 27 69
638
358 201 515
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
1 1 1
1 0 1
2 1 3
2 1 3
1 0 1
000
0 0 1
000
7410
ed Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
12 7 18
638
638
1 0 1
24 14 35
536
2 1 3
2 1 3
000
9 513
CLM
Mean 5th 95th
B
324
2 1 3
10 6 15
11 6 16
8 4 11
325
425
0 0 1
41 23 59
Gf
CL2
Mean 5th 95th
UV
CL2
Mean 5th 95th
C
15 8 21
9513
638
11 6 15
12 7 17
14 8 20
639
649
1 0 1
80 45 115
C20
CLM
Mean 5th 95th
H
000
000
000
000
000
649
324
324
0 0 1
13 7 18
000
000
000
000
000
2 1 3
1 1 1
1 1 1
000
426
CLM
Mean 5th 95th
11 6 16
10 5 14
639
14 8 20
16 9 23
325
1 1 2
2 1 2
000
63 35 91
GAC20 + Advan
CL2
Mean 5th 95th
Ozone
CL2
Mean 5th 95th
:
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
I
2 1 3
426
3 1 4
538
639
000
000
000
000
21 12 30
2 1 3
538
325
9 513
10 6 15
000
000
000
000
30 17 43
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
000
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
000
000
000
000
000
000
000
000
000
000
0 0 1
000
000
000
000
000
000
000
1 0 1
CLM
Mean 5th 95th
E
000
000
000
000
000
000
000
000
000
000
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
19 11 28
50 28 71
31 18 45
81 46 117
90 51 130
118 66 170
53 30 76
56 31 80
7410
505 285 728
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
36 20 52
64 36 92
40 23 58
99 56 143
111 62 159
151 85 218
68 38 98
72 40 103
9512
650 366 936
351 197 505
299 169 431
650 366 936
Note: Detail may
Source: Above t;
lot add to totals due to independent rounding
ible with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-31
December 2005
-------
Exhibit C.15c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
1.9% 1.1% 2.7%
4.1% 2.3% 5.9%
4.1% 2.3% 5.9%
4.2% 2.4% 6.1%
4.2% 2.4% 6.1%
7.8% 4.4% 11.2%
0.0% 0.0% 0.0%
7.8% 4.4% 11.2%
0.0% 0.0% 0.0%
3.5% 2.0% 5.0%
GAC10 +Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.1% 0.1% 0.2%
0.1% 0.1% 0.2%
0.2% 0.1% 0.2%
0.2% 0.1% 0.2%
0.1% 0.0% 0.1%
0.0% 0.0% 0.0%
0.1% 0.0% 0.1%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
ed Disinfectants
CLM
Mean 5th 95th
L M
Mean
1 .0% 0.5% 1 .4%
0.0% 0.0% 0.0%
1 .0% 0.5% 1 .4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
0.3% 0.2% 0.5%
0.0% 0.0% 0.0%
0.3% 0.2% 0.5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
C
0.4% 0.2% 0.5%
0.4% 0.2% 0.5%
0.9% 0.5% 1 .3%
0.9% 0.5% 1 .3%
0.6% 0.3% 0.9%
0.0% 0.0% 0.0%
0.6% 0.3% 0.9%
0.0% 0.0% 0.0%
0.3% 0.2% 0.5%
Gt
CL2
Mean 5th 95th
N
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.5% 0.3% 0.7%
0.0% 0.0% 0.0%
0.5% 0.3% 0.7%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
uv
CL2
Mean 5th 95th
D
4.1% 2.3% 5.9%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
0.9% 0.5% 1 .4%
0.9% 0.5% 1 .4%
1.1% 0.6% 1.5%
0.0% 0.0% 0.0%
1.1% 0.6% 1.5%
0.0% 0.0% 0.0%
2.0% 1.1% 2.9%
C20
CLM
Mean 5th 95th
O
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.2% 0.1% 0.2%
0.0% 0.0% 0.0%
0.2% 0.1% 0.2%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
E
3.0% 1 .7% 4.3%
1 .3% 0.7% 1 .8%
1 .3% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
0.2% 0.1% 0.4%
0.0% 0.0% 0.0%
0.2% 0.1% 0.4%
0.0% 0.0% 0.0%
1.8% 1 .0% 2.6%
GAC20 + Advan
CL2
Mean 5th 95th
P
0.7% 0.4% 1 .0%
0.6% 0.3% 0.8%
0.6% 0.3% 0.8%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.0% 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%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ced Disinfectants
CLM
Mean 5th 95th
Q
0.5% 0.3% 0.7%
0.7% 0.4% 1 .0%
0.7% 0.4% 1 .0%
0.8% 0.5% 1 .2%
0.8% 0.5% 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%
0.7% 0.4% 0.9%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
0.0% 0.0% 0.0%
0.1% 0.0% 0.1%
0.1% 0.0% 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%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
I
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
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 Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
5.4% 3.0% 7.8%
6.5% 3.6% 9.3%
6.5% 3.6% 9.3%
7.2% 4.0% 10.3%
7.2% 4.0% 10.3%
9.1% 5.1% 13.2%
0.0% 0.0% 0.0%
9.1% 5.1% 13.2%
0.0% 0.0% 0.0%
6.3% 3.5% 9.0%
000
GAC10
CL2
Mean 5th 95th
J
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
CLM
Mean 5th 95th
K
0.0% 0.0% 0.0%
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 Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
10.1% 5.7% 14.6%
8.4% 4.7% 12.0%
8.4% 4.7% 12.0%
8.8% 4.9% 12.7%
8.8% 4.9% 12.7%
11.7% 6.6% 16.9%
0.0% 0.0% 0.0%
11.7% 6.6% 16.9%
0.0% 0.0% 0.0%
9.0% 5.0% 12.9%
000
8.9% 5.0% 12.9%
11.7% 6.6% 16.9%
9.0% 5.0% 12.9%
000
Exhibit C.15d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
426
13 7 18
426
426
1 1 2
0 0 1
000
000
000
27 15 38
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
0 0 1
000
000
000
000
000
000
000
1 0 1
ed Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
B
1 1 2
0 0 1
1 0 1
000
000
000
000
000
3 1 4
Gf
CL2
Mean 5th 95th
uv
CL2
Mean 5th 95th
C
9513
425
1 1 2
1 0 1
000
000
000
000
000
15 9 22
C20
CLM
Mean 5th 95th
H
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
7410
426
1 1 2
1 1 2
000
000
000
000
000
14 8 20
GAC20 + Advan
CL2
Mean 5th 95th
Ozone
CL2
Mean 5th 95th
:
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
I
2 1 2
2 1 2
1 0 1
0 0 1
000
000
000
000
000
426
1 1 2
2 1 3
1 0 1
1 0 1
000
000
000
000
000
537
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
000
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
E
000
000
000
000
000
000
000
000
000
000
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
12 7 18
20 11 29
7410
7410
2 1 3
0 0 1
000
000
000
48 27 69
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
23 13 33
26 15 38
9513
8512
2 1 3
1 0 1
000
000
000
69 39 99
68 38 98
1 0 1
69 39 99
Note: Detail may
Source: Above t;
lot add to totals due to independent rounding
ible with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-32
December 2005
-------
Exhibit C.16a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
1 .0%
1 .4%
1 .4%
1.1%
1.1%
1 .4%
1 .4%
1 .3%
1 .4%
1 .3%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .3%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.1%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.2%
0.0%
GAC20
CL2
F
0.4%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.1%
0.1%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.2%
0.0%
Total Converting
to CLM
J = A+C+E+G+I
2.1%
3.0%
3.0%
2.7%
2.7%
2.0%
2.0%
1 .9%
2.0%
2.6%
Total Adding
Treatment
Technology
K = SUM(A:I)
2.4%
3.2%
3.2%
2.7%
2.7%
2.1%
2.1%
2.0%
2.1%
2.8%
2.9%
2.1%
2.8%
Note: Detail may not add to totals due to independent rounding
Exhibit C.16b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
6159.5%
213
85
82
54
75
10
12
0
UVCL2
B
0
0
0
0
0
UVCLM
C
70
242
97
118
78
Ozone
CL2
D
0
0
0
0
0
3
0
0
0
Ozone
CLM
E
0
0
0
0
0
12
2
2
0
GAC20
CL2
F
23
27
11
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
4
2
8
1
1
0
Membranes
CL2
H
0
0
0
0
0
2
0
0
0
Membranes
CLM
I
0
0
0
0
0
11
2
2
0
Total Converting
to CLM
J = A+C+E+G+I
132
456
182
204
135
107
14
17
1
Total Adding
Treatment
Technology
K = SUM(A:I)
155
483
193
204
135
111
15
18
1
1,170
145
Final Economic Analysis for the Stage 2 DBPR
C-33
December 2005
-------
Exhibit C.16c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
1 .0%
1 .4%
1 .4%
1.1%
1.1%
1 .4%
1 .4%
1 .3%
0.0%
1 .2%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .4%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.0%
0.0%
GAC20
CL2
F
0.4%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.1%
0.0%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.0%
0.0%
Total Converting
to CLM
J = A+C+E+G+I
2.1%
3.0%
3.0%
2.7%
2.7%
2.0%
2.0%
1.9%
0.0%
2.5%
Total Adding
Treatment
Technology
K = SUM(A:I)
2.4%
3.2%
3.2%
2.7%
2.7%
2.1%
2.1%
2.0%
0.0%
2.8%
2.8%
2.1%
2.8%
Note: Detail may not add to totals due to independent rounding
Exhibit C.16d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
2389.6%
30
8
3
0
0
0
0
0
UVCL2
B
0
0
0
0
0
UVCLM
C
27
34
9
4
0
Ozone
CL2
D
0
0
0
0
0
0
0
0
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
9
4
1
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
0
0
0
0
0
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
0
0
0
0
0
0
0
0
0
Total Converting
to CLM
J = A+C+E+G+I
51
64
18
7
1
0
0
0
0
Total Adding
Treatment
Technology
K = SUM(A:I)
60
67
19
7
1
0
0
0
0
153
0
Final Economic Analysis for the Stage 2 DBPR
C-34
December 2005
-------
Exhibit C.17a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
31.7% 27.3% 36.1%
27.2% 23.6% 30.9%
27.2% 23.6% 30.9%
24.6% 20.8% 28.5%
24.6% 20.8% 28.5%
31.2% 31.2% 31.2%
31.2% 31.2% 31.2%
31.2% 31.2% 31.2%
31.2% 31.2% 31.2%
28.1% 25.7% 30.4%
Mean 5th 95th
M
Mean
A
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.3% 0.3% 0.3%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
31.6% 30.8% 32.4%
39.5% 37.7% 41.3%
39.5% 37.7% 41.3%
45.6% 43.7% 47.4%
45.6% 43.7% 47.4%
41.0% 41.0% 41.0%
41.0% 41.0% 41.0%
41.0% 41.0% 41.0%
41.0% 41.0% 41.0%
41.9% 40.8% 42.9%
Mean 5th 95th
N
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
0.4% 0.4% 0.4%
Chlorine Dioxide CL2
Mean 5th 95th
C
1.1% 1.0% 1.1%
1.1% 1.0% 1.1%
2.1% 2.0% 2.2%
2.1% 2.0% 2.2%
3.0% 3.0% 3.0%
3.0% 3.0% 3.0%
3.0% 3.0% 3.0%
3.0% 3.0% 3.0%
2.1% 2.1% 2.2%
Mean 5th 95th
0
2.0% 2.0% 2.0%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.8% 0.8% 0.8%
Chlorine Dioxide CLM
Mean 5th 95th
D
1.2% 1.1% 1.4%
1.2% 1.1% 1.4%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
2.9% 2.7% 3.1%
Mean 5th 95th
P
.3% .3% 1 .3%
.0% .0% 1 .0%
.0% .0% 1 .0%
.2% .2% 1 .2%
.2% .2% 1 .2%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.8% 0.8% 0.8%
UVCL2
Mean 5th 95th
E
4.1% 2.3% 5.9%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
0.9% 0.5% 1.4%
0.9% 0.5% 1.4%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.9% 0.6% 1.3%
Mean 5th 95th
Q
0.7% 0.4% 1.0%
1.0% 0.8% 1.3%
1.0% 0.8% 1.3%
1.0% 0.8% 1.2%
1.0% 0.8% 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%
0.6% 0.5% 0.7%
UVCLM
Mean 5th 95th
F
3.0% 1.7% 4.3%
1.3% 0.7% 1.8%
1.3% 0.7% 1.8%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
1.0% 0.6% 1.4%
Mean 5th 95th
R
0.5% 0.3% 0.7%
1.1% 0.8% 1.4%
1.1% 0.8% 1.4%
1.4% 1.0% 1.8%
1.4% 1.0% 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%
0.8% 0.6% 1.0%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
5.5% 5.5% 5.5%
4.6% 4.6% 4.6%
Mean 5th 95th
S
2.1% 2.1% 2.1%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.4% 0.4% 0.4%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
4.5% 4.5% 4.5%
7.3% 7.3% 7.3%
7.3% 7.3% 7.3%
7.3% 7.3% 7.3%
7.3% 7.3% 7.3%
5.3% 5.3% 5.3%
Mean 5th 95th
T
1.4% 1.4% 1.4%
0.5% 0.4% 0.5%
0.5% 0.4% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
MF/UFCL2
Mean 5th 95th
I
8.9% 8.9% 8.9%
8.9% 8.9% 8.9%
6.2% 6.2% 6.2%
6.2% 6.2% 6.2%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
0.8% 0.8% 0.8%
5.1% 5.1% 5.1%
MF/UFCLM
Mean 5th 95th
J
4.8% 4.8% 4.8%
4.8% 4.8% 4.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
2.8% 2.8% 2.8%
Mean 5th 95th
U = A+C+E-K3+I+K+M+O+Q+S
55. % 48.5% 61.6%
46. % 41.6% 50.5%
46. % 41.6% 50.5%
40. % 35.5% 44.6%
40. % 35.5% 44.6%
43.2% 43.2% 43.2%
43.2% 43.2% 43.2%
43.2% 43.2% 43.2%
43.2% 43.2% 43.2%
43.3% 40.4% 46.1%
GAC10CL2
Mean 5th 95th
K
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.4% 0.4% 0.4%
GAC 10 CLM
Mean 5th 95th
L
1.2% 1.2% 1.2%
1.2% 1.2% 1.2%
1.2% 1.2% 1.2%
1.2% 1.2% 1.2%
0.5% 0.5% 0.5%
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
44.9% 42.6% 47.3%
53.9% 51.1% 56.8%
53.9% 51.1% 56.8%
59.9% 56.8% 63.1%
59.9% 56.8% 63.1%
56.8% 56.8% 56.8%
56.8% 56.8% 56.8%
56.8% 56.8% 56.8%
56.8% 56.8% 56.8%
56.7% 54.9% 58.6%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologi
Source: Surface water systems serving
icludes conventional, non-conventional, and softening plants.
10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative. Surface water systems serving 10,000 or more p
Exhibit C.17b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
114 98 130
209 181 237
132 114 149
278 235 322
310 262 359
403 403 403
181 181 181
190 190 190
23 23 23
1,840 1,686 1,993
GAC10 + ADCL2
Mean 5th 95th
M
10 10 10
555
555
1 1 1
20 20 20
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
114 111 117
303 289 317
191 182 199
515 494 536
573 550 597
529 529 529
237 237 237
250 250 250
30 30 30
2,742 2,673 2,812
GAC10 + ADCLM
Mean 5th 95th
N
13 13 13
666
666
1 1 1
27 27 27
Chlorine Dioxide CL2
Mean 5th 95th
C
889
555
23 23 24
26 25 27
39 39 39
17 17 17
18 18 18
222
140 137 142
GAC20 CL2
Mean 5th 95th
O
111
888
555
12 12 12
13 13 13
444
222
222
000
53 53 53
Chlorine Dioxide CLM
Mean 5th 95th
D
9 8 11
657
34 30 39
38 33 43
51 51 51
23 23 23
24 24 24
333
189 177 200
GAC20 CLM
Mean 5th 95th
P
555
111
555
13 13 13
15 15 15
555
222
333
000
55 55 55
UVCL2
Mean 5th 95th
E
15 8 21
9 5 13
638
11 6 15
12 7 17
222
222
000
61 38 84
GAC20 + AD CL2
Mean 5th 95th
Q
2 1 3
8 6 10
546
11 9 14
13 10 15
000
000
000
000
39 30 49
UVCLM
Mean 5th 95th
F
11 6 16
10 5 14
639
14 8 20
16 9 23
555
222
333
000
67 42 92
GAC20 + AD CLM
Mean 5th 95th
R
2 1 3
9611
547
16 12 20
18 13 22
000
000
000
000
49 36 63
Ozone CL2
Mean 5th 95th
G
39 39 39
24 24 24
45 45 45
50 50 50
72 72 72
32 32 32
34 34 34
444
301 301 301
Membranes CL2
Mean 5th 95th
S
888
333
222
222
222
444
222
222
000
26 26 26
Ozone CLM
Mean 5th 95th
H
35 35 35
22 22 22
51 51 51
56 56 56
94 94 94
42 42 42
44 44 44
555
350 350 350
Membranes CLM
Mean 5th 95th
T
555
434
222
222
222
555
222
333
000
26 26 26
MFAJFCL2
Mean 5th 95th
I
52 52 52
68 68 68
43 43 43
70 70 70
78 78 78
10 10 10
555
555
1 1 1
331 331 331
MFAJFCLM
Mean 5th 95th
J
26 26 26
37 37 37
23 23 23
32 32 32
36 36 36
13 13 13
666
666
1 1 1
181 181 181
TOTAL CL2
Mean 5th 95th
U = A+C+E4G+I+K+M+O+Q+S
198 174 221
353 319 387
222 201 244
452 401 504
504 447 561
558 558 558
250 250 250
264 264 264
32 32 32
2,834 2,646 3,022
GAC 10CL2
Mean 5th 95th
K
12 12 12
666
666
1 1 1
24 24 24
GAC 10 CLM
Mean 5th 95th
L
16 16 16
777
888
1 1 1
32 32 32
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
161 153 170
414 392 435
260 247 274
677 641 712
754 715 794
733 733 733
329 329 329
347 347 347
42 42 42
3,717 3,598 3,837
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative. Surface water systems si
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.17c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
31.7% 27.3% 36.1%
27.2% 23.6% 30.9%
27.2% 23.6% 30.9%
24.6% 20.8% 28.5%
24.6% 20.8% 28.5%
31.2% 31.2% 31.2%
0.0% 0.0% 0.0%
31.2% 31.2% 31.2%
0.0% 0.0% 0.0%
28.2% 24.3% 32.1%
Mean 5th 95th
M
Mean
A
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
31.6% 30.8% 32.4%
39.5% 37.7% 41.3%
39.5% 37.7% 41.3%
45.6% 43.7% 47.4%
45.6% 43.7% 47.4%
41.0% 41.0% 41.0%
0.0% 0.0% 0.0%
41.0% 41.0% 41.0%
0.0% 0.0% 0.0%
38.1% 36.6% 39.6%
Mean 5th 95th
N
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Chlorine Dioxide CL2
Mean 5th 95th
C
1.1% 1.0% 1.1%
1.1% 1.0% 1.1%
2.1% 2.0% 2.2%
2.1% 2.0% 2.2%
3.0% 3.0% 3.0%
0.0% 0.0% 0.0%
3.0% 3.0% 3.0%
0.0% 0.0% 0.0%
0.9% 0.9% 1.0%
Mean 5th 95th
0
2.0% 2.0% 2.0%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
1.3% 1.3% 1.3%
Chlorine Dioxide CLM
Mean 5th 95th
D
1.2% 1.1% 1.4%
1.2% 1.1% 1.4%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
4.0% 4.0% 4.0%
0.0% 0.0% 0.0%
4.0% 4.0% 4.0%
0.0% 0.0% 0.0%
1.2% 1.0% 1.3%
Mean 5th 95th
P
.3% .3% 1 .3%
.0% .0% 1 .0%
.0% .0% 1 .0%
.2% .2% 1 .2%
.2% .2% 1 .2%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
1.1% 1.1% 1.1%
UVCL2
Mean 5th 95th
E
4.1% 2.3% 5.9%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
0.9% 0.5% 1.4%
0.9% 0.5% 1.4%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
2.0% 1.1% 2.9%
Mean 5th 95th
Q
0.7% 0.4% 1.0%
1.0% 0.8% 1.3%
1.0% 0.8% 1.3%
1.0% 0.8% 1.2%
1.0% 0.8% 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%
0.9% 0.7% 1.2%
UVCLM
Mean 5th 95th
F
3.0% 1.7% 4.3%
1.3% 0.7% 1.8%
1.3% 0.7% 1.8%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
1.8% 1.0% 2.6%
Mean 5th 95th
R
0.5% 0.3% 0.7%
1.1% 0.8% 1.4%
1.1% 0.8% 1.4%
1.4% 1.0% 1.8%
1.4% 1.0% 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%
1.0% 0.7% 1.3%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
5.5% 5.5% 5.5%
0.0% 0.0% 0.0%
5.5% 5.5% 5.5%
0.0% 0.0% 0.0%
3.4% 3.4% 3.4%
Mean 5th 95th
S
2.1% 2.1% 2.1%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
4.5% 4.5% 4.5%
7.3% 7.3% 7.3%
0.0% 0.0% 0.0%
7.3% 7.3% 7.3%
0.0% 0.0% 0.0%
3.2% 3.2% 3.2%
Mean 5th 95th
T
1.4% 1.4% 1.4%
0.5% 0.4% 0.5%
0.5% 0.4% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.7% 0.7% 0.7%
MF/UFCL2
Mean 5th 95th
I
8.9% 8.9% 8.9%
8.9% 8.9% 8.9%
6.2% 6.2% 6.2%
6.2% 6.2% 6.2%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
0.8% 0.8% 0.8%
0.0% 0.0% 0.0%
10.1% 10.1% 10.1%
MF/UFCLM
Mean 5th 95th
J
4.8% 4.8% 4.8%
4.8% 4.8% 4.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
5.2% 5.2% 5.2%
Mean 5th 95th
U = A+C+E-K3+I+K+M+O+Q+S
55. % 48.5% 61.6%
46. % 41.6% 50.5%
46. % 41.6% 50.5%
40. % 35.5% 44.6%
40. % 35.5% 44.6%
43.2% 43.2% 43.2%
0.0% 0.0% 0.0%
43.2% 43.2% 43.2%
0.0% 0.0% 0.0%
47.8% 42.7% 52.8%
GAC10CL2
Mean 5th 95th
K
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
GAC 10 CLM
Mean 5th 95th
L
1.2% 1.2% 1.2%
0.0% 0.0% 0.0%
1.2% 1.2% 1.2%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
44.9% 42.6% 47.3%
53.9% 51.1% 56.8%
53.9% 51.1% 56.8%
59.9% 56.8% 63.1%
59.9% 56.8% 63.1%
56.8% 56.8% 56.8%
0.0% 0.0% 0.0%
56.8% 56.8% 56.8%
0.0% 0.0% 0.0%
52.2% 49.5% 55.0%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologi
Source: Surface water systems serving
icludes conventional, non-conventional, and softening plants.
10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative. Surface water systems serving 10,000 or more p
Exhibit C.17d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
72 62 82
85 74 96
29 25 33
23 19 26
657
222
000
000
000
216 186 246
GAC10 + ADCL2
Mean 5th 95th
M
000
000
000
000
000
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
71 70 73
123 118 129
42 40 44
42 40 44
11 11 12
222
000
000
000
292 281 304
GAC10 + ADCLM
Mean 5th 95th
N
000
000
000
000
000
Chlorine Dioxide CL2
Mean 5th 95th
C
334
1 1 1
222
1 1 1
000
000
000
000
111
GAC20 CL2
Mean 5th 95th
O
444
333
1 1 1
1 1 1
000
000
000
000
000
10 10 10
Chlorine Dioxide CLM
Mean 5th 95th
D
434
1 1 1
323
1 1 1
000
000
000
000
9 8 10
GAC20 CLM
Mean 5th 95th
P
333
333
1 1 1
1 1 1
000
000
000
000
000
888
UVCL2
Mean 5th 95th
E
9 5 13
425
1 1 2
1 0 1
000
000
000
000
000
15 9 22
GAC20 + AD CL2
Mean 5th 95th
Q
2 1 2
324
1 1 1
1 1 1
000
000
000
000
000
759
UVCLM
Mean 5th 95th
F
7410
426
1 1 2
1 1 2
000
000
000
000
000
14 8 20
GAC20 + AD CLM
Mean 5th 95th
R
1 1 2
434
1 1 2
1 1 2
000
000
000
000
000
8 5 10
Ozone CL2
Mean 5th 95th
G
16 16 16
555
1 1 1
000
000
000
000
26 26 26
Membranes CL2
Mean 5th 95th
S
555
1 1 1
000
000
000
000
000
000
000
111
Ozone CLM
Mean 5th 95th
H
14 14 14
555
1 1 1
000
000
000
000
25 25 25
Membranes CLM
Mean 5th 95th
T
333
1 1 2
0 0 1
000
000
000
000
000
000
555
MFAJFCL2
Mean 5th 95th
I
33 33 33
28 28 28
999
666
222
000
000
000
000
77 77 77
MFAJFCLM
Mean 5th 95th
J
16 16 16
15 15 15
555
333
1 1 1
000
000
000
000
40 40 40
TOTAL CL2
Mean 5th 95th
U = A+C+E4G+I+K+M+O+Q+S
124 110 139
144 130 158
49 44 54
37 33 41
10 9 11
222
000
000
000
366 328 405
GAC 10CL2
Mean 5th 95th
K
000
000
000
000
000
GAC 10 CLM
Mean 5th 95th
L
000
000
000
000
000
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
102 96 107
168 159 177
57 54 60
55 52 58
15 14 16
333
000
1 1 1
000
401 380 422
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative. Surface water systems si
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.18a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
93.5%
92.1%
92.1%
93.1%
93.1%
87.1%
87.1%
87.5%
87.4%
91 .8%
No Advanced
Treatment
Technologies
CLM1
B
3.4%
4.2%
4.2%
3.6%
3.6%
8.6%
8.6%
8.4%
8.5%
4.6%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .3%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
0.9%
0.9%
0.9%
0.9%
0.3%
Ozone
CLM
F
GAC20
CL2
G
0.0% 0.4%
0.5% 0.2%
0.5% 0.2%
0.9% 0.0%
0.9% 0.0%
1 .0% 0.0%
1 .0% 0.0%
0.9% 0.0%
0.9% 0.0%
0.6%
0.1%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.1%
0.1%
0.2%
0.2%
0.2%
0.2%
0.5%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1 .7%
1 .7%
1 .7%
1 .7%
0.4%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.4%
0.4%
0.5%
Total Using CL2
K = A+C+E+G+I
94.2%
92.6%
92.6%
93.4%
93.4%
89.7%
89.7%
90.1%
90.0%
92.6%
Total Using CLM
L = B+D+F+H+J
5.8%
7.4%
7.4%
6.6%
6.6%
10.3%
10.3%
9.9%
10.0%
7.4%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative.
Exhibit C.18b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
rred Alternative, 20% I A
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
ichnology CL21
A
6,006
14,040
5,613
7,060
4,681
4,690
624
No Advanced
Treatment
Technologies
CLM1
B
217
640
256
274
181
464
62
UVCL2
C
0
0
0
0
0
UVCLM
D
70
242
97
118
78
Ozone
CL2
E
0
25
10
22
15
48
6
Ozone
CLM
F
0
74
29
66
44
53
7
GAC20
CL2
G
23
27
11
0
0
0
0
GAC20
CLM
H
56
96
39
8
5
10
1
Membranes
CL2
I
22
20
8
4
3
91
12
Membranes
CLM
J
29
79
32
36
24
25
3
Total Using CL2
K = A+C+E+G+I
6,051
14,111
5,641
7,086
4,698
4,829
642
Total Using CLM
L = B+D+F+H+J
372
1,131
452
501
332
553
74
(Total Plants | 43,539
2,173| 0| 606
134
282
61
217
175 1 232| 43,91 0| 3,51 0|
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative.
Final Economic Analysis for the Stage 2 DBPR
C-37
December 2005
-------
Exhibit C.18c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 20% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
93.5%
92.1%
92.1%
93.1%
93.1%
87.1%
87.1%
87.5%
0.0%
92.8%
No Advanced
Treatment
Technologies
CLM1
B
3.4%
4.2%
4.2%
3.6%
3.6%
8.6%
8.6%
8.4%
0.0%
3.8%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .4%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
0.9%
0.9%
0.9%
0.0%
Ozone
CLM
F
0.0%
0.5%
0.5%
0.9%
0.9%
1 .0%
1 .0%
0.9%
0.0%
0.1% 0.3%
GAC20
CL2
G
0.4%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.1%
0.1%
0.2%
0.2%
0.2%
0.0%
0.7%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1 .7%
1 .7%
1 .7%
0.0%
0.2%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.4%
0.0%
0.5%
Total Using CL2
K = A+C+E+G+I
94.2%
92.6%
92.6%
93.4%
93.4%
89.7%
89.7%
90.1%
0.0%
93.4%
Total Using CLM
L = B+D+F+H+J
5.8%
7.4%
7.4%
6.6%
6.6%
10.3%
10.3%
9.9%
0.0%
6.6%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative.
Exhibit C.18d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
irred Alternative, 20% I A
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
schnology CL21
A
2,331
1,961
543
230
20
3
0
No Advanced
Treatment
Technologies
CLM1
B
84
89
25
9
1
0
0
UVCL2
C
0
0
0
0
0
UVCLM
D
27
34
9
4
0
Ozone
CL2
E
0
3
1
1
0
0
0
Ozone
CLM
F
0
10
3
2
0
0
0
GAC20
CL2
G
9
4
1
0
0
0
0
GAC20
CLM
H
22
13
4
0
0
0
0
Membranes
CL2
I
8
3
1
0
0
0
0
Membranes
CLM
J
11
11
3
1
0
0
0
Total Using CL2
K = A+C+E+G+I
2,348
1,971
546
231
20
3
0
Total Using CLM
L = B+D+F+H+J
144
158
44
16
1
0
0
(Total Plants | 5,088
208 1 0
75
5
15
14
39 1 12
27| 5,1 19| 364|
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the Unadjusted Stage 2 Preferred Alternative.
Final Economic Analysis for the Stage 2 DBPR
C-38
December 2005
-------
Exhibit C.19a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
1.9% 1.1% 2.7%
4.1% 2.3% 5.9%
4.1% 2.3% 5.9%
4.2% 2.4% 6.1%
4.2% 2.4% 6.1%
9.6% 7.1% 12.0%
9.6% 7.1% 12.0%
9.6% 7.1% 12.0%
9.6% 7.1% 12.0%
6.1% 4.1% 8.2%
GAC10 +Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.1% 0.1% 0.2%
0.1% 0.1% 0.2%
0.2% 0.1% 0.2%
0.2% 0.1% 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%
0.1% 0.0% 0.1%
ed Disinfectants
CLM
Mean 5th 95th
L M
Mean
1.4% 1.1% 1.8%
1.4% 1.1% 1.8%
1.4% 1.1% 1.8%
1.4% 1.1% 1.8%
0.6% 0.4% 0.7%
000
0.6% 0.4% 0.7%
0.6% 0.4% 0.7%
0.6% 0.4% 0.7%
0.6% 0.4% 0.7%
0.2% 0.2% 0.3%
000
CLM
Mean 5th 95th
C
0.4% 0.2% 0.5%
0.4% 0.2% 0.5%
0.9% 0.5% 1 .3%
0.9% 0.5% 1 .3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.4% 0.2% 0.6%
Gt
CL2
Mean 5th 95th
N
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
uv
CL2
Mean 5th 95th
D
4.1% 2.3% 5.9%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
0.9% 0.5% 1 .4%
0.9% 0.5% 1 .4%
4.6% 3.4% 5.8%
4.6% 3.4% 5.8%
4.6% 3.4% 5.8%
4.6% 3.4% 5.8%
2.6% 1 .8% 3.4%
C20
CLM
Mean 5th 95th
O
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
E
3.0% 1 .7% 4.3%
1 .3% 0.7% 1 .8%
1 .3% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
1.3% 1 .0% 1 .7%
1.3% 1 .0% 1 .7%
1.3% 1 .0% 1 .7%
1.3% 1 .0% 1 .7%
1 .4% 0.9% 1 .9%
GAC20 + Advan
CL2
Mean 5th 95th
P
0.7% 0.4% 1 .0%
0.6% 0.3% 0.8%
0.6% 0.3% 0.8%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.2% 0.5%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ced Disinfectants
CLM
Mean 5th 95th
Q
0.5% 0.3% 0.7%
0.7% 0.4% 1 .0%
0.7% 0.4% 1 .0%
0.8% 0.5% 1 .2%
0.8% 0.5% 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%
0.5% 0.3% 0.7%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
0.0% 0.0% 0.0%
0.1% 0.0% 0.1%
0.1% 0.0% 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%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
I
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
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 Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
5.4% 3.0% 7.8%
6.5% 3.6% 9.3%
6.5% 3.6% 9.3%
7.2% 4.0% 10.3%
7.2% 4.0% 10.3%
11.5% 8.5% 14.4%
11.5% 8.5% 14.4%
11.5% 8.5% 14.4%
11.5% 8.5% 14.4%
8.6% 5.6% 11 .6%
000
GAC10
CL2
Mean 5th 95th
J
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
CLM
Mean 5th 95th
K
0.0% 0.0% 0.0%
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 Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
10.1% 5.7% 14.6%
8.4% 4.7% 12.0%
8.4% 4.7% 12.0%
8.8% 4.9% 12.7%
8.8% 4.9% 12.7%
17.5% 13.0% 22.0%
17.5% 13.0% 22.0%
17.5% 13.0% 22.0%
17.5% 13.0% 22.0%
12.2% 8.1% 16.3%
000
8.8% 4.9% 12.6%
17.5% 13.0% 22.0%
12.2% 8.1% 16.3%
000
Exhibit C.19b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
7410
31 18 45
20 11 28
48 27 69
53 30 76
123 91 155
55 41 70
58 43 73
759
403 270 535
GAC10 + Advan c
CL2
Chlorine Dioxide
CL2
Mean 5th 95th
1 1 1
1 0 1
2 1 3
2 1 3
000
000
000
000
638
ed Disinfectants
CLM
Mean 5th 95th Mean 5th 95th
G
18 14 23
8610
9611
1 1 1
36 27 46
759
324
334
0 0 1
15 11 18
CLM
Mean 5th 95th
B
324
2 1 3
10 6 15
11 6 16
000
000
000
000
26 15 37
Gf
CL2
Mean 5th 95th
uv
CL2
Mean 5th 95th
C
15 8 21
9513
638
11 6 15
12 7 17
60 44 75
27 20 34
28 21 36
334
171 117 225
C20
CLM
Mean 5th 95th
H
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
11 6 16
10 5 14
639
14 8 20
16 9 23
17 13 22
8 610
8 610
1 1 1
91 57 124
GAC20 + Advan
CL2
Mean 5th 95th
Ozone
CL2
Mean 5th 95th
:
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
I
2 1 3
426
3 1 4
538
639
000
000
000
000
21 12 30
2 1 3
538
325
9 513
10 6 15
000
000
000
000
30 17 43
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
000
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
000
000
000
000
000
000
000
000
000
000
0 0 1
000
000
000
000
000
000
000
1 0 1
CLM
Mean 5th 95th
E
000
000
000
000
000
000
000
000
000
000
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
19 11 28
50 28 71
31 18 45
81 46 117
90 51 130
148 110 186
66 49 83
70 52 88
8611
565 370 759
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
36 20 52
64 36 92
40 23 58
99 56 143
111 62 159
226 168 285
102 75 128
1 07 79 1 34
13 10 16
799 529 1,068
351 197 505
448 332 563
799 529 1,068
Note: Detail may
Source: Above t;
lot add to totals due to independent rounding
ible with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-39
December 2005
-------
Exhibit C.19c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total %
Total Plants
Converting to CLM Only
Mean 5th 95th
A
1.9% 1.1% 2.7%
4.1% 2.3% 5.9%
4.1% 2.3% 5.9%
4.2% 2.4% 6.1%
4.2% 2.4% 6.1%
9.6% 7.1% 12.0%
0.0% 0.0% 0.0%
9.6% 7.1% 12.0%
0.0% 0.0% 0.0%
3.5% 2.0% 5.0%
GAC10 +Advanc
CL2
Mean 5th 95th
Chlorine Dioxide
CL2
Mean 5th 95th
B
0.1% 0.1% 0.2%
0.1% 0.1% 0.2%
0.2% 0.1% 0.2%
0.2% 0.1% 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%
0.1% 0.1% 0.1%
ed Disinfectants
CLM
Mean 5th 95th
L M
Mean
1.4% 1.1% 1.8%
0.0% 0.0% 0.0%
1.4% 1.1% 1.8%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
0.6% 0.4% 0.7%
0.0% 0.0% 0.0%
0.6% 0.4% 0.7%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
C
0.4% 0.2% 0.5%
0.4% 0.2% 0.5%
0.9% 0.5% 1 .3%
0.9% 0.5% 1 .3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.3% 0.2% 0.5%
Gt
CL2
Mean 5th 95th
N
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
uv
CL2
Mean 5th 95th
D
4.1% 2.3% 5.9%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
0.9% 0.5% 1 .4%
0.9% 0.5% 1 .4%
4.6% 3.4% 5.8%
0.0% 0.0% 0.0%
4.6% 3.4% 5.8%
0.0% 0.0% 0.0%
2.0% 1 .2% 2.9%
C20
CLM
Mean 5th 95th
O
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
E
3.0% 1 .7% 4.3%
1 .3% 0.7% 1 .8%
1 .3% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
1 .2% 0.7% 1 .8%
1.3% 1 .0% 1 .7%
0.0% 0.0% 0.0%
1.3% 1 .0% 1 .7%
0.0% 0.0% 0.0%
1.8% 1 .0% 2.6%
GAC20 + Advan
CL2
Mean 5th 95th
P
0.7% 0.4% 1 .0%
0.6% 0.3% 0.8%
0.6% 0.3% 0.8%
0.5% 0.3% 0.7%
0.5% 0.3% 0.7%
0.0% 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%
000
Ozone
CL2
Mean 5th 95th
F
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
ced Disinfectants
CLM
Mean 5th 95th
Q
0.5% 0.3% 0.7%
0.7% 0.4% 1 .0%
0.7% 0.4% 1 .0%
0.8% 0.5% 1 .2%
0.8% 0.5% 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%
0.7% 0.4% 0.9%
000
CLM
Mean 5th 95th
G
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
MF/UF
CL2
Mean 5th 95th
H
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
R S
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
0.0% 0.0% 0.0%
0.1% 0.0% 0.1%
0.1% 0.0% 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%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
000
CLM
Mean 5th 95th
I
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
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 Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
5.4% 3.0% 7.8%
6.5% 3.6% 9.3%
6.5% 3.6% 9.3%
7.2% 4.0% 10.3%
7.2% 4.0% 10.3%
11.5% 8.5% 14.4%
0.0% 0.0% 0.0%
11.5% 8.5% 14.4%
0.0% 0.0% 0.0%
6.3% 3.6% 9.1%
000
GAC10
CL2
Mean 5th 95th
J
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
CLM
Mean 5th 95th
K
0.0% 0.0% 0.0%
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 Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
10.1% 5.7% 14.6%
8.4% 4.7% 12.0%
8.4% 4.7% 12.0%
8.8% 4.9% 12.7%
8.8% 4.9% 12.7%
17.5% 13.0% 22.0%
0.0% 0.0% 0.0%
17.5% 13.0% 22.0%
0.0% 0.0% 0.0%
9.0% 5.1% 13.0%
000
8.9% 5.0% 12.9%
17.5% 13.0% 22.0%
9.0% 5.1% 13.0%
000
Exhibit C.19d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population
Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
Converting to CLM Only
Mean 5th 95th
A
426
13 7 18
426
426
1 1 2
0 0 1
000
000
000
27 15 38
Chlorine Dioxide
CL2
Mean 5th 95th
0 0 1
000
000
000
000
000
000
000
1 0 1
GAC10 +Advanced Disinfectants
CL2
CLM
Mean 5th 95th Mean 5th 95th
G
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
B
1 1 2
0 0 1
1 0 1
000
000
000
000
000
3 1 4
Gt
CL2
Mean 5th 95th
uv
CL2
Mean 5th 95th
C
9513
425
1 1 2
1 0 1
000
000
000
000
000
16 9 23
C20
CLM
Mean 5th 95th
H
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
7410
426
1 1 2
1 1 2
000
000
000
000
000
14 8 20
GAC20 + Advan
CL2
Mean 5th 95th
Ozone
CL2
Mean 5th 95th
:
000
000
000
000
000
000
000
000
000
ced Disinfectants
CLM
Mean 5th 95th
I
2 1 2
2 1 2
1 0 1
0 0 1
000
000
000
000
000
426
1 1 2
2 1 3
1 0 1
1 0 1
000
000
000
000
000
537
CLM
Mean 5th 95th
000
000
000
000
000
000
000
000
000
MF/UF
CL2
Mean 5th 95th
000
000
000
000
000
000
000
000
000
000
Membranes
CL2
CLM
Mean 5th 95th Mean 5th 95th
J
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
000
CLM
Mean 5th 95th
E
000
000
000
000
000
000
000
000
000
000
Total Converting to CLM
Mean 5th 95th
T=A+C+E+G+I+K+M+O+
Q+S
12 7 18
20 11 29
7410
7410
2 1 3
1 0 1
000
000
000
48 27 69
GAC10
CL2
Mean 5th 95th
CLM
Mean 5th 95th
F
000
000
000
000
000
000
000
000
000000
Total Adding Treatment Technology
Mean 5th 95th
Mean 5th 95th
L = SUM(A:S)
23 13 33
26 15 38
9513
8512
2 1 3
1 1 1
000
000
000
69 39 99
68 38 98
1 1 1
69 39 99
Note: Detail may noi
Source: Above tabl
it add to totals due to independent rounding
le with technologies switching from an advanced technology with CI2 to the same advanced technology with CLM being moved into the CLM only column
Final Economic Analysis for the Stage 2 DBPR
C-40
December 2005
-------
Exhibit C.20a
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
1 .0%
1 .4%
1 .4%
1.1%
1.1%
1 .4%
1 .4%
1 .3%
1 .4%
1 .3%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .3%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.1%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.2%
0.0%
GAC20
CL2
F
0.4%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.1%
0.1%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.2%
0.0%
Total Converting
to CLM
J = A+C+E+G+I
2.1%
3.0%
3.0%
2.7%
2.7%
2.0%
2.0%
1 .9%
2.0%
2.6%
Total Adding
Treatment
Technology
K = SUM(A:I)
2.4%
3.2%
3.2%
2.7%
2.7%
2.1%
2.1%
2.0%
2.1%
2.8%
2.9%
2.1%
2.8%
Note: Detail may not add to totals due to independent rounding
Exhibit C.20b
Stage 2 DBPR Treatment Technology Selection Deltas for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
6160.1%
213
85
82
54
75
10
12
0
UVCL2
B
0
0
0
0
0
UVCLM
C
70
242
97
118
78
Ozone
CL2
D
0
0
0
0
0
3
0
0
0
Ozone
CLM
E
0
0
0
0
0
12
2
2
0
GAC20
CL2
F
23
27
11
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
4
2
8
1
1
0
Membranes
CL2
H
0
0
0
0
0
2
0
0
0
Membranes
CLM
I
0
0
0
0
0
11
2
2
0
Total Converting
to CLM
J = A+C+E+G+I
132
456
182
204
135
107
14
17
1
Total Adding
Treatment
Technology
K = SUM(A:I)
155
483
193
204
135
111
15
18
1
1,170
145
Final Economic Analysis for the Stage 2 DBPR
C-41
December 2005
-------
Exhibit C.20c
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
CLM Only
A
1 .0%
1 .4%
1 .4%
1.1%
1.1%
1 .4%
1 .4%
1 .3%
0.0%
1 .2%
UVCL2
B
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
C
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .4%
Ozone
CL2
D
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.1%
0.1%
0.0%
0.0%
Ozone
CLM
E
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.0%
0.0%
GAC20
CL2
F
0.4%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
GAC20
CLM
G
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.1%
0.0%
0.0%
Membranes
CL2
H
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Membranes
CLM
I
0.0%
0.0%
0.0%
0.0%
0.0%
0.2%
0.2%
0.2%
0.0%
0.0%
Total Converting
to CLM
J = A+C+E+G+I
2.1%
3.0%
3.0%
2.7%
2.7%
2.0%
2.0%
1.9%
0.0%
2.5%
Total Adding
Treatment
Technology
K = SUM(A:I)
2.4%
3.2%
3.2%
2.7%
2.7%
2.1%
2.1%
2.0%
0.0%
2.8%
2.8%
2.1%
2.8%
Note: Detail may not add to totals due to independent rounding
Exhibit C.20d
Stage 2 DBPR Treatment Technology Selection Deltas for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
CLM Only
A
2389.6%
30
8
3
0
0
0
0
0
UVCL2
B
0
0
0
0
0
UVCLM
C
27
34
9
4
0
Ozone
CL2
D
0
0
0
0
0
0
0
0
0
Ozone
CLM
E
0
0
0
0
0
0
0
0
0
GAC20
CL2
F
9
4
1
0
0
0
0
0
0
GAC20
CLM
G
0
0
0
0
0
0
0
0
0
Membranes
CL2
H
0
0
0
0
0
0
0
0
0
Membranes
CLM
I
0
0
0
0
0
0
0
0
0
Total Converting
to CLM
J = A+C+E+G+I
51
64
18
7
1
0
0
0
0
Total Adding
Treatment
Technology
K = SUM(A:I)
60
67
19
7
1
0
0
0
0
153
0
Final Economic Analysis for the Stage 2 DBPR
C-42
December 2005
-------
Exhibit C.21a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
31.7% 27.3% 36.1%
27.2% 23.6% 30.9%
27.2% 23.6% 30.9%
24.6% 20.8% 28.5%
24.6% 20.8% 28.5%
27.5% 27.5% 27.5%
27.5% 27.5% 27.5%
27.5% 27.5% 27.5%
27.5% 27.5% 27.5%
26.6% 24.3% 29.0%
Mean 5th 95th
M
Mean
A
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
1 .0% 1 .0% 1 .0%
0.4% 0.4% 0.4%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
31.6% 30.8% 32.4%
39.5% 37.7% 41.3%
39.5% 37.7% 41.3%
45.6% 43.7% 47.4%
45.6% 43.7% 47.4%
414% 414% 414%
414% 414% 414%
42.0% 41.0% 43.1%
Mean 5th 95th
N
1 .5% 1 .5% 1 .5%
1 .5% 1 .5% 1 .5%
1 .5% 1 .5% 1 .5%
1 .5% 1 .5% 1 .5%
0.6% 0.6% 0.6%
Chlorine Dioxide CL2
Mean 5th 95th
C
1.1% 1.0% 1.1%
1.1% 1.0% 1.1%
2.1% 2.0% 2.2%
2.1% 2.0% 2.2%
2.3% 2.3% 2.3%
2.3% 2.3% 2.3%
2.3% 2.3% 2.3%
2.3% 2.3% 2.3%
1.9% 1.8% 1.9%
Mean 5th 95th
0
2.0% 2.0% 2.0%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.1% 0.1% 0.1%
0.7% 0.7% 0.7%
Chlorine Dioxide CLM
Mean 5th 95th
D
1.2% 1.1% 1.4%
1.2% 1.1% 1.4%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
3.5% 3.5% 3.5%
3.5% 3.5% 3.5%
3.5% 3.5% 3.5%
3.5% 3.5% 3.5%
2.7% 2.5% 2.9%
Mean 5th 95th
P
.3% 1 .3% 1 .3%
.0% 1 .0% 1 .0%
.0% 1 .0% 1 .0%
.2% 1 .2% 1 .2%
.2% 1 .2% 1 .2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.8% 0.8% 0.8%
UVCL2
Mean 5th 95th
E
4.1% 2.3% 5.9%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
0.9% 0.5% 1.4%
0.9% 0.5% 1.4%
1.9% 1.9% 1.9%
1.9% 1.9% 1.9%
1.9% 1.9% 1.9%
1.9% 1.9% 1.9%
1.5% 1.2% 1.9%
Mean 5th 95th
Q
0.7% 0.4% 1.0%
1.0% 0.8% 1.3%
1.0% 0.8% 1.3%
1.0% 0.8% 1.2%
1.0% 0.8% 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%
0.6% 0.5% 0.7%
UVCLM
Mean 5th 95th
F
3.0% 1.7% 4.3%
1.3% 0.7% 1.8%
1.3% 0.7% 1.8%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
2.0% 1.6% 2.4%
Mean 5th 95th
R
0.5% 0.3% 0.7%
1.1% 0.8% 1.4%
1.1% 0.8% 1.4%
1.4% 1.0% 1.8%
1.4% 1.0% 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%
0.8% 0.6% 1.0%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.4% 4.4% 4.4%
Mean 5th 95th
S
2.1% 2.1% 2.1%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.3% 0.3% 0.3%
0.4% 0.4% 0.4%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
4.5% 4.5% 4.5%
7.7% 7.7% 7.7%
7.7% 7.7% 7.7%
7.7% 7.7% 7.7%
7.7% 7.7% 7.7%
5.5% 5.5% 5.5%
Mean 5th 95th
T
1.4% 1.4% 1.4%
0.5% 0.4% 0.5%
0.5% 0.4% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
0.4% 0.4% 0.4%
MF/UFCL2
Mean 5th 95th
I
8.9% 8.9% 8.9%
8.9% 8.9% 8.9%
6.2% 6.2% 6.2%
6.2% 6.2% 6.2%
0.7% 0.7% 0.7%
0.7% 0.7% 0.7%
0.7% 0.7% 0.7%
0.7% 0.7% 0.7%
5.0% 5.0% 5.0%
MF/UFCLM
Mean 5th 95th
J
4.8% 4.8% 4.8%
4.8% 4.8% 4.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
2.8% 2.8% 2.8%
Mean 5th 95th
U = A+C+E-K3+I+K+M+O+Q+S
55.1% 48.5% 61.6%
46.1% 41.6% 50.5%
46.1% 41.6% 50.5%
40.1% 35.5% 44.6%
40.1% 35.5% 44.6%
39.9% 39.9% 39.9%
39.9% 39.9% 39.9%
39.9% 39.9% 39.9%
39.9% 39.9% 39.9%
42.0% 39.1% 44.8%
GAC10CL2
Mean 5th 95th
K
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.9% 0.9% 0.9%
0.3% 0.3% 0.3%
GAC 10 CLM
Mean 5th 95th
L
1.3% 1.3% 1.3%
1.3% 1.3% 1.3%
1.3% 1.3% 1.3%
1.3% 1.3% 1.3%
0.5% 0.5% 0.5%
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
44.9% 42.6% 47.3%
53.9% 51.1% 56.8%
53.9% 51.1% 56.8%
59.9% 56.8% 63. %
59.9% 56.8% 63. %
60.1% 60.1% 60. %
60.1% 60.1% 60. %
60.1% 60.1% 60. %
60.1% 60.1% 60. %
58.0% 56.2% 59.8%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologi
Source: Surface water systems serving
icludes conventional, non-conventional, and softening plants.
:10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative. Surface water systems serving 10,000 or
Exhibit C.21b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
114 98 130
209 181 237
132 114 149
278 235 322
310 262 359
355 355 355
159 159 159
168 168 168
20 20 20
1,745 1,591 1,899
GAC10 + ADCL2
Mean 5th 95th
M
13 13 13
666
666
1 1 1
26 26 26
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
114 111 117
303 289 317
191 182 199
515 494 536
573 550 597
534 534 534
240 240 240
253 253 253
30 30 30
2,752 2,683 2,822
GAC10 + ADCLM
Mean 5th 95th
N
20 20 20
999
999
1 1 1
39 39 39
Chlorine Dioxide CL2
Mean 5th 95th
C
889
555
23 23 24
26 25 27
30 30 30
14 14 14
14 14 14
222
123 120 125
GAC20 CL2
Mean 5th 95th
O
111
888
555
12 12 12
13 13 13
222
1 1 1
1 1 1
000
49 49 49
Chlorine Dioxide CLM
Mean 5th 95th
D
9 8 11
657
34 30 39
38 33 43
45 45 45
20 20 20
21 21 21
333
178 166 189
GAC20 CLM
Mean 5th 95th
P
555
111
555
13 13 13
15 15 15
333
1 1 1
1 1 1
000
50 50 50
UVCL2
Mean 5th 95th
E
15 8 21
9 5 13
638
11 6 15
12 7 17
25 25 25
11 11 11
12 12 12
1 1 1
101 78 124
GAC20 + AD CL2
Mean 5th 95th
Q
2 1 3
8 6 10
546
11 9 14
13 10 15
000
000
000
000
39 30 49
UVCLM
Mean 5th 95th
F
11 6 16
10 5 14
639
14 8 20
16 9 23
37 37 37
17 17 17
17 17 17
222
130 105 154
GAC20 + AD CLM
Mean 5th 95th
R
2 1 3
9611
547
16 12 20
18 13 22
000
000
000
000
49 36 63
Ozone CL2
Mean 5th 95th
G
39 39 39
24 24 24
45 45 45
50 50 50
66 66 66
30 30 30
31 31 31
444
290 290 290
Membranes CL2
Mean 5th 95th
S
888
333
222
222
222
444
222
222
000
25 25 25
Ozone CLM
Mean 5th 95th
H
35 35 35
22 22 22
51 51 51
56 56 56
99 99 99
45 45 45
666
361 361 361
Membranes CLM
Mean 5th 95th
T
555
434
222
222
222
666
333
333
000
27 26 27
MFAJFCL2
Mean 5th 95th
I
52 52 52
68 68 68
43 43 43
70 70 70
78 78 78
999
1 1 1
329 329 329
MFAJFCLM
Mean 5th 95th
J
26 26 26
37 37 37
23 23 23
32 32 32
36 36 36
14 14 14
666
777
1 1 1
182 182 182
TOTAL CL2
Mean 5th 95th
U = A+C+E4G+I+K+M+O+Q+S
198 174 221
353 319 387
222 201 244
452 401 504
504 447 561
516 516 516
231 231 231
244 244 244
29 29 29
2,750 2,561 2,938
GAC 10CL2
Mean 5th 95th
K
11 11 11
555
555
1 1 1
22 22 22
GAC 10 CLM
Mean 5th 95th
L
17 17 17
888
888
1 1 1
34 34 34
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
161 153 170
414 392 435
260 247 274
677 641 712
754 715 794
776 776 776
348 348 348
367 367 367
44 44 44
3,801 3,683 3,921
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative. Surface water systems si
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.21c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Percent of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
(Population Served)
<100
1 00-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>= 1,000, 000
Total %
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
31.7% 27.3% 36.1%
27.2% 23.6% 30.9%
27.2% 23.6% 30.9%
24.6% 20.8% 28.5%
24.6% 20.8% 28.5%
27.5% 27.5% 27.5%
0.0% 0.0% 0.0%
27.5% 27.5% 27.5%
0.0% 0.0% 0.0%
28.2% 24.3% 32.0%
Mean 5th 95th
M
Mean
A
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
1 .0% 1 .0% 1 .0%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
31.6% 30.8% 32.4%
39.5% 37.7% 41.3%
39.5% 37.7% 41.3%
45.6% 43.7% 47.4%
45.6% 43.7% 47.4%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
38.1% 36.6% 39.6%
Mean 5th 95th
N
1 .5% 1 .5% 1 .5%
0.0% 0.0% 0.0%
1 .5% 1 .5% 1 .5%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Chlorine Dioxide CL2
Mean 5th 95th
C
1.1% 1.0% 1.1%
1.1% 1.0% 1.1%
2.1% 2.0% 2.2%
2.1% 2.0% 2.2%
2.3% 2.3% 2.3%
0.0% 0.0% 0.0%
2.3% 2.3% 2.3%
0.0% 0.0% 0.0%
0.9% 0.9% 1.0%
Mean 5th 95th
0
2.0% 2.0% 2.0%
1.1% 1.1% 1.1%
1.1% 1.1% 1.1%
1.0% 1.0% 1.0%
1.0% 1.0% 1.0%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
0.1% 0.1% 0.1%
0.0% 0.0% 0.0%
1.3% 1.3% 1.3%
Chlorine Dioxide CLM
Mean 5th 95th
D
1.2% 1.1% 1.4%
1.2% 1.1% 1.4%
3.0% 2.6% 3.4%
3.0% 2.6% 3.4%
3.5% 3.5% 3.5%
0.0% 0.0% 0.0%
3.5% 3.5% 3.5%
0.0% 0.0% 0.0%
1.2% 1.0% 1.3%
Mean 5th 95th
P
.3% 1 .3% 1 .3%
.0% 1 .0% 1 .0%
.0% 1 .0% 1 .0%
.2% 1 .2% 1 .2%
.2% 1 .2% 1 .2%
0.2% 0.2% 0.2%
0.0% 0.0% 0.0%
0.2% 0.2% 0.2%
0.0% 0.0% 0.0%
1.1% 1.1% 1.1%
UVCL2
Mean 5th 95th
E
4.1% 2.3% 5.9%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
0.9% 0.5% 1.4%
0.9% 0.5% 1.4%
1.9% 1.9% 1.9%
0.0% 0.0% 0.0%
1.9% 1.9% 1.9%
0.0% 0.0% 0.0%
2.0% 1.1% 2.9%
Mean 5th 95th
Q
0.7% 0.4% 1.0%
1.0% 0.8% 1.3%
1.0% 0.8% 1.3%
1.0% 0.8% 1.2%
1.0% 0.8% 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%
0.9% 0.7% 1.2%
UVCLM
Mean 5th 95th
F
3.0% 1.7% 4.3%
1.3% 0.7% 1.8%
1.3% 0.7% 1.8%
1.2% 0.7% 1.8%
1.2% 0.7% 1.8%
2.9% 2.9% 2.9%
0.0% 0.0% 0.0%
2.9% 2.9% 2.9%
0.0% 0.0% 0.0%
1.8% 1.0% 2.6%
Mean 5th 95th
R
0.5% 0.3% 0.7%
1.1% 0.8% 1.4%
1.1% 0.8% 1.4%
1.4% 1.0% 1.8%
1.4% 1.0% 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%
1.0% 0.7% 1.3%
Ozone CL2
Mean 5th 95th
G
5.1% 5.1% 5.1%
5.1% 5.1% 5.1%
4.0% 4.0% 4.0%
4.0% 4.0% 4.0%
5.1% 5.1% 5.1%
0.0% 0.0% 0.0%
5.1% 5.1% 5.1%
0.0% 0.0% 0.0%
3.4% 3.4% 3.4%
Mean 5th 95th
S
2.1% 2.1% 2.1%
0.5% 0.5% 0.5%
0.5% 0.5% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.3% 0.3% 0.3%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
Ozone CLM
Mean 5th 95th
H
4.6% 4.6% 4.6%
4.6% 4.6% 4.6%
4.5% 4.5% 4.5%
4.5% 4.5% 4.5%
7.7% 7.7% 7.7%
0.0% 0.0% 0.0%
7.7% 7.7% 7.7%
0.0% 0.0% 0.0%
3.2% 3.2% 3.2%
Mean 5th 95th
T
1.4% 1.4% 1.4%
0.5% 0.4% 0.5%
0.5% 0.4% 0.5%
0.2% 0.2% 0.2%
0.2% 0.2% 0.2%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.4% 0.4% 0.4%
0.0% 0.0% 0.0%
0.7% 0.7% 0.7%
MF/UFCL2
Mean 5th 95th
I
8.9% 8.9% 8.9%
8.9% 8.9% 8.9%
6.2% 6.2% 6.2%
6.2% 6.2% 6.2%
0.7% 0.7% 0.7%
0.0% 0.0% 0.0%
0.7% 0.7% 0.7%
0.0% 0.0% 0.0%
10.1% 10.1% 10.1%
MF/UFCLM
Mean 5th 95th
J
4.8% 4.8% 4.8%
4.8% 4.8% 4.8%
2.9% 2.9% 2.9%
2.9% 2.9% 2.9%
1.1% 1.1% 1.1%
0.0% 0.0% 0.0%
1.1% 1.1% 1.1%
0.0% 0.0% 0.0%
5.2% 5.2% 5.2%
Mean 5th 95th
U = A+C+E-K3+I+K+M+O+Q+S
55. % 48.5% 61.6%
46. % 41.6% 50.5%
46. % 41.6% 50.5%
40. % 35.5% 44.6%
40. % 35.5% 44.6%
39.9% 39.9% 39.9%
0.0% 0.0% 0.0%
39.9% 39.9% 39.9%
0.0% 0.0% 0.0%
47.7% 42.7% 52.8%
GAC10CL2
Mean 5th 95th
K
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.9% 0.9% 0.9%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
GAC 10 CLM
Mean 5th 95th
L
1.3% 1.3% 1.3%
0.0% 0.0% 0.0%
1.3% 1.3% 1.3%
0.0% 0.0% 0.0%
0.0% 0.0% 0.0%
Mean 5th 95th
V= B+D+F-HH+J+L+N+P+R+T
44.9% 42.6% 47.3%
53.9% 51.1% 56.8%
53.9% 51.1% 56.8%
59.9% 56.8% 63.1%
59.9% 56.8% 63.1%
60.1% 60.1% 60.1%
0.0% 0.0% 0.0%
60.1% 60.1% 60.1%
0.0% 0.0% 0.0%
52.3% 49.5% 55.0%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologi
Source: Surface water systems serving
icludes conventional, non-conventional, and softening plants.
:10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative. Surface water systems serving 10,000 or
Exhibit C.21d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Surface Water Plants (Number of Plants by Residual Disinfection Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
System Size
(Population Served)
<100
100-499
500-999
1,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1,000,000
Total Plants
No Advanced Treatment
Technologies CL21
Mean 5th 95th
A
72 62 82
85 74 96
29 25 33
23 19 26
657
1 1 1
000
000
000
216 186 246
GAC10 + ADCL2
Mean 5th 95th
M
000
000
000
000
000
No Advanced Treatment
Technologies CLM1
Mean 5th 95th
B
71 70 73
123 118 129
42 40 44
42 40 44
11 11 12
222
000
000
000
292 281 304
GAC10 + ADCLM
Mean 5th 95th
N
000
000
000
000
000
Chlorine Dioxide CL2
Mean 5th 95th
C
334
1 1 1
222
1 1 1
000
000
000
000
111
GAC20 CL2
Mean 5th 95th
O
444
333
1 1 1
1 1 1
000
000
000
000
000
10 10 10
Chlorine Dioxide CLM
Mean 5th 95th
D
434
1 1 1
323
1 1 1
000
000
000
000
9 8 10
GAC20 CLM
Mean 5th 95th
P
333
333
1 1 1
1 1 1
000
000
000
000
000
888
UVCL2
Mean 5th 95th
E
9 5 13
425
1 1 2
1 0 1
000
000
000
000
000
16 9 22
GAC20 + AD CL2
Mean 5th 95th
Q
2 1 2
324
1 1 1
1 1 1
000
000
000
000
000
759
UVCLM
Mean 5th 95th
F
7410
426
1 1 2
1 1 2
000
000
000
000
000
14 8 20
GAC20 + AD CLM
Mean 5th 95th
R
1 1 2
434
1 1 2
1 1 2
000
000
000
000
000
8 5 10
Ozone CL2
Mean 5th 95th
G
16 16 16
555
1 1 1
000
000
000
000
26 26 26
Membranes CL2
Mean 5th 95th
S
555
1 1 1
000
000
000
000
000
000
000
111
Ozone CLM
Mean 5th 95th
H
14 14 14
555
1 1 1
000
000
000
000
25 25 25
Membranes CLM
Mean 5th 95th
T
333
1 1 2
0 0 1
000
000
000
000
000
000
555
MFAJFCL2
Mean 5th 95th
I
33 33 33
28 28 28
999
666
222
000
000
000
000
77 77 77
MFAJFCLM
Mean 5th 95th
J
16 16 16
15 15 15
555
333
1 1 1
000
000
000
000
40 40 40
TOTAL CL2
Mean 5th 95th
U = A+C+E4G+I+K+M+O+Q+S
124 110 139
144 130 158
49 44 54
37 33 41
10 9 11
222
000
000
000
366 327 405
GAC 10CL2
Mean 5th 95th
K
000
000
000
000
000
GAC 10 CLM
Mean 5th 95th
L
000
000
000
000
000
TOTAL CLM
Mean 5th 95th
V= B+D+F4H+J+L+N+P+R+T
102 96 107
168 159 177
57 54 60
55 52 58
15 14 16
333
000
1 1 1
000
401 380 422
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, n on-conventional, and softening plants.
Source: Surface water systems serving <10,000 people: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.16) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative. Surface water systems si
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit C.22a
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1 ,000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
93.5%
92.1%
92.1%
93.1%
93.1%
87.1%
87.1%
87.5%
87.4%
91 .8%
No Advanced
Treatment
Technologies
CLM1
B
3.4%
4.2%
4.2%
3.6%
3.6%
8.6%
8.6%
8.4%
8.5%
4.6%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .3%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
0.9%
0.9%
0.9%
0.9%
0.3%
Ozone
CLM
F
GAC20
CL2
G
0.0% 0.4%
0.5% 0.2%
0.5% 0.2%
0.9% 0.0%
0.9% 0.0%
1 .0% 0.0%
1 .0% 0.0%
0.9% 0.0%
0.9% 0.0%
0.6%
0.1%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.1%
0.1%
0.2%
0.2%
0.2%
0.2%
0.5%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1 .7%
1 .7%
1 .7%
1 .7%
0.4%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.4%
0.4%
0.5%
Total Using CL2
K = A+C+E+G+I
94.2%
92.6%
92.6%
93.4%
93.4%
89.7%
89.7%
90.1%
90.0%
92.6%
Total Using CLM
L = B+D+F+H+J
5.8%
7.4%
7.4%
6.6%
6.6%
10.3%
10.3%
9.9%
10.0%
7.4%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative.
Exhibit C.22b
Post-Stage 2 DBPR Treatment Technologies-in-Place for CWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
rred Alternative, 25% I A
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
ichnology CL21
A
6,006
14,040
5,613
7,060
4,681
4,690
624
No Advanced
Treatment
Technologies
CLM1
B
217
640
256
274
181
464
62
UVCL2
C
0
0
0
0
0
UVCLM
D
70
242
97
118
78
Ozone
CL2
E
0
25
10
22
15
48
6
Ozone
CLM
F
0
74
29
66
44
53
7
GAC20
CL2
G
23
27
11
0
0
0
0
GAC20
CLM
H
56
96
39
8
5
10
1
Membranes
CL2
I
22
20
8
4
3
91
12
Membranes
CLM
J
29
79
32
36
24
25
3
Total Using CL2
K = A+C+E+G+I
6,051
14,111
5,641
7,086
4,698
4,829
642
Total Using CLM
L = B+D+F+H+J
372
1,131
452
501
332
553
74
(Total Plants | 43,539
2,173| 0| 606
134
282
61
217
175 1 232| 43,91 0| 3,51 0|
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative.
Final Economic Analysis for the Stage 2 DBPR
C-45
December 2005
-------
Exhibit C.22c
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Percent of Plants, by Residual Disinfectant Type)
Stage 2 Preferred Alternative, 25% Safety Margin
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
100,000-999,999
>=1, 000,000
Total %
No Advanced
Treatment
Technologies
CL21
A
93.5%
92.1%
92.1%
93.1%
93.1%
87.1%
87.1%
87.5%
0.0%
92.8%
No Advanced
Treatment
Technologies
CLM1
B
3.4%
4.2%
4.2%
3.6%
3.6%
8.6%
8.6%
8.4%
0.0%
3.8%
UVCL2
C
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
UVCLM
D
1.1%
1 .6%
1 .6%
1 .6%
1 .6%
1 .4%
Ozone
CL2
E
0.0%
0.2%
0.2%
0.3%
0.3%
0.9%
0.9%
0.9%
0.0%
Ozone
CLM
F
0.0%
0.5%
0.5%
0.9%
0.9%
1 .0%
1 .0%
0.9%
0.0%
0.1% 0.3%
GAC20
CL2
G
0.4%
0.2%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
GAC20
CLM
H
0.9%
0.6%
0.6%
0.1%
0.1%
0.2%
0.2%
0.2%
0.0%
0.7%
Membranes
CL2
I
0.3%
0.1%
0.1%
0.1%
0.1%
1 .7%
1 .7%
1 .7%
0.0%
0.2%
Membranes
CLM
J
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.4%
0.0%
0.5%
Total Using CL2
K = A+C+E+G+I
94.2%
92.6%
92.6%
93.4%
93.4%
89.7%
89.7%
90.1%
0.0%
93.4%
Total Using CLM
L = B+D+F+H+J
5.8%
7.4%
7.4%
6.6%
6.6%
10.3%
10.3%
9.9%
0.0%
6.6%
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative.
Exhibit C.22d
Post-Stage 2 DBPR Treatment Technologies-in-Place for NTNCWS Ground Water Plants (Number of Plants, by Residual Disinfectant Type)
irred Alternative, 25% I A
System Size
(Population Served)
<100
100-499
500-999
1 ,000-3,300
3,301-9,999
10,000-49,999
50,000-99,999
schnology CL21
A
2,331
1,961
543
230
20
3
0
No Advanced
Treatment
Technologies
CLM1
B
84
89
25
9
1
0
0
UVCL2
C
0
0
0
0
0
UVCLM
D
27
34
9
4
0
Ozone
CL2
E
0
3
1
1
0
0
0
Ozone
CLM
F
0
10
3
2
0
0
0
GAC20
CL2
G
9
4
1
0
0
0
0
GAC20
CLM
H
22
13
4
0
0
0
0
Membranes
CL2
I
8
3
1
0
0
0
0
Membranes
CLM
J
11
11
3
1
0
0
0
Total Using CL2
K = A+C+E+G+I
2,348
1,971
546
231
20
3
0
Total Using CLM
L = B+D+F+H+J
144
158
44
16
1
0
0
(Total Plants | 5,088
208 1 0
75
5
15
14
39 1 12
27| 5,1 19| 364|
Note: Detail may not add to totals due to independent rounding
'No advanced Treatment Technologies includes conventional, non-conventional, and softening plants.
Source: Add Technologies-in-Place for the Pre-Stage 2 Baseline (Exhibit 3.17) to the Technology Selection Delta for the IDSE Alternative Stage 2 Preferred Alternative.
Final Economic Analysis for the Stage 2 DBPR
C-46
December 2005
-------
Appendix D
Rule Activity Schedule
-------
-------
Appendix D
Rule Activity Schedule
This appendix presents the year-by-year schedules for systems for the following rule activities:
capital and operations and maintenance (O&M) treatment technology costs (Exhibits D.3 and D.4),
implementation (Exhibit D.5), Initial Distribution System Evaluation (IDSE) activities (Exhibit D.6),
preparation of monitoring plans (Exhibit D.7), annual routine monitoring (Exhibit D.8), and operational
evaluations (Exhibit D.9). Schedules for State/Primacy Agency activities are presented in Exhibit D.10.
These schedules are based on the Stage 2 implementation timeline, as presented in Exhibit D. 1. When
systems and States had several years within which to complete a rule activity, the Environmental
Protection Agency (EPA) assumed that the same proportion of systems would perform the activity in
each year. EPA recognizes that more systems may start in early or later years, but believes that a
uniform schedule is still a reasonable approximation nationally.
Exhibit D.1 Schedule of Rule Activities
Schedule 1
Systems serving
> 100,000 1
Schedule 2
Systems serving
50,000 to 99,999
Schedule 3
Systems serving
10,000 to 49,999 1
Schedule 4
Systems serving
< 10,000 1
2006
2007
2008
| LT2 Crypto monitoring
I
IDSEPIa
October '
ID
Ap
2006
| IDSEmon.
i Due
, 2006
| LT2 Cn
3E Plan Due
ril 1, 2007
IDSEPIa
October •
ID,
Ap
2007
IDSE
Janua
2009
=ter.
y 1
oto monitorina
1 IDSE
mon.
2010
2011
2012 I 2013 I 2014
1
Treatment Installation j Possible Extension
ort Due
, 2009
»
Be
Ap
rr T
gin Compliance
111,2012 1
]
Treatment Installation i Possible Extension 2 i
i n
IDSE Report
July 1, 2009
Due
1 LT2 Crypto monitoring I
n Due
,2007
3E Plan D
ril 1, 2008
IDSE
mon.
£. Co// mon.
je
2008
ID,
Ja
I
3E Report
luary 1, 2
\Crypto
IDSEmon. I 4
2009
I
IDSE Rep
July 1,20
1 1
Begin Compliance
October 1,201 2
1
2015
Treatment Installation I Possible Extension2 I
Out
310
!
mon.l
n —
Begin Compliance
October 1,201 3
Treatment Installation I Possible Extension2 '
ort
0
2010
Due
2011
2012
1
Begin Co
October 1
2013
npliance
2013
2014
2015
Includes all systems that are part of a combined distribution system that has a largest system with this population.
2 A State may grant up to a two year extension for systems to comply if the State determines that additional time is necessary
for capital improvements needed for compliance.
3 Subpart H systems serving fewer than 10,000 that must conduct Crypto monitoring have an additional 12 months to comply with
Stage 2 DBPR MCLs.
D.I Estimate of Small and Medium Systems on Early Implementation Schedules
Systems are required to perform IDSE and routine monitoring on the same schedule as the largest
system in their combined distribution system. For the Stage 2 DBPR, a combined distribution system
encompasses all systems that are connected by common buyers and sellers. Exhibit D.2 presents an
estimate of surface water CWSs that will be on early implementation schedules based on the linking
analysis.
Final Economic Analysis for the Stage 2 DBPR
D-l
December 2005
-------
There are uncertainties in using the results of the linking exercise to estimate the number of small
systems on accelerated schedules. The analysis was performed by obtaining data from EPA regions and
States on which systems would be considered in a combined distribution system. The data was collected
in 2005 and was used as it more accurately portrays how Primacy Agencies will handle consecutive
systems. Although SDWIS contains information on consecutive systems it does not differentiate between
regular connections and emergency connections. It also cannot give information on the multiple levels of
buyers and sellers that often exist.
Exhibit D.2 Numbers of Surface Water CWSs on Accelerated Schedules
Type of
System
SWCWS
SW
NTNCWS
GWCWS
GW
NTNCWS
Size
Category
(People
Served)
Small
Medium 1
Medium 2
Large
Small
Medium 1
Medium 2
Large
Small
Medium 1
Medium 2
Large
Small
Medium 1
Medium 2
Large
Total
Systems
A
9,136
1,758
339
298
713
0
0
0
39,519
1,313
147
75
18,528
0
0
0
Number of
Smaller
Systems
Buying from
or Selling to
Medium 1
Category
B
1,666
875
0
0
64
0
0
0
886
1149
0
0
35
0
0
0
Number of
Smaller
Systems
Buying from
or Selling to
Medium 2
Category
C
585
130
196
0
22
0
0
0
234
35
127
0
28
0
0
0
Number of
Smaller
Systems
Buying from
or Selling to
Large
Category
D
1,989
753
143
298
75
0
0
0
515
129
20
75
31
0
0
0
Percent
Systems on
Medium 1
Schedule
E = B/A1
18.24%
49.77%
8.98%
0.00%
2.24%
87.51 %
0.19%
0.00%
Percent
Systems on
Medium 2
Schedule
F = C/A1
6.40%
7.39%
57.82%
3.09%
0.00%
0.00%
0.59%
2.67%
86.39%
0.15%
0.00%
0.00%
Percent
Systems
on Large
Schedule
G = D/A
21 .77%
42.83%
42.18%
100.00%
10.52%
0.00%
0.00%
0.00%
1 .30%
9.82%
13.61%
100.00%
0.17%
0.00%
0.00%
0.00%
Notes:
Small serves < 10,000 retail population
Medium 1 serves from 10,000 to 49,999 retail population
Medium 2 serves from 50,000 to 99,999 retail population
Large serves 100,000 or more retail population
For medium 1 E = 1 - F - G, for medium 2 F = 1 - G
Sources:
(A) - (D) SDWIS 4th quarter 2003 frozen database - IDSE4 analysis 10/14/2004
D.2 Capital and Operation and Maintenance Schedule
The schedule for making treatment technology changes is based on the rule schedule. EPA
assumed that systems will start making capital improvements soon after their IDSE monitoring and report
are complete. EPA assumes that large systems would start making capital improvements one year after
the IDSE is complete. As a simplifying assumption, EPA spreads capital costs equally through the end of
the possible 2-year extension period. Capital costs are spread over 5 years for systems serving between
Final Economic Analysis for the Stage 2 DBPR
D-2
December 2005
-------
50,000 and 99,999 people, 6 years for systems serving between 10,000 and 49,999 people, and 7 years for
small systems. This reflects that these systems have longer to comply with the rule. It also reflects the
fact that some of these systems will be required to monitor on the same schedule with the large systems
and may begin installing treatment at the same time as the large systems. For simplicity, the installation of
treatment for the smaller systems is distributed evenly over the period from when the large systems begin
installing treatment until the compliance deadline. For small systems the schedule also reflects the fact
that some of them will have additional time for compliance because of Cryptosporidium monitoring.1
O&M costs for all system sizes lag behind capital costs by 1 year and are incurred annually.
Exhibits D.3a and D.3b display the capital cost schedule for surface and ground water systems,
respectively. Exhibits D.4a and D.4b display the O&M costs for surface and ground water systems,
respectively.
D.3 Implementation and IDSE Schedule
EPA assumed that systems will incur half of their implementation costs the year before they
begin IDSE monitoring and the other half the year after completing their IDSE monitoring. The
implementation and IDSE schedules for small surface water CWSs are adjusted to account for small
systems that are in a combined distribution system with medium and large systems and are thus on an
earlier schedule. See section D.I for a discussion on how EPA estimated the number of systems on an
accelerated schedule. Implementation costs are distributed according to the estimated percentages of
systems on accelerated schedules. For example, for the 50,000 to 99,999 category incurring IDSE costs,
42 percent are expected to be on the greater than 100,000 schedule, and the remaining 58 percent are
expected to stay on the 50,000 to 99,999 schedule, which is delayed by 6 months.
The IDSE schedule applies to costs related to the standard monitoring, System Specific Studies
(SSSs), and 40/30 certification. Although the 40/30 certification will occur before the IDSE and SSSs, the
portion of the costs represented by the 40/30 certification is so small (< 0.1%) that discounting it on a
separate schedule would make no noticeable difference in total costs. Therefore, to simplify the
calculations, EPA discounted the 40/30 costs using the same schedule.
Exhibits D.5a and D.5b present the schedule for implementation costs for surface and ground
water systems, respectively. Exhibits D.6a and D.6b display the schedule for IDSE costs for surface and
ground water systems, respectively.
D.4 Monitoring Plans
The routine monitoring plans indicate the planned locations and schedule on which routine
monitoring will be conducted, based on information collected during the IDSE and provided in the IDSE
report. EPA assumed that the costs for preparing routine monitoring plans will be incurred as soon as the
IDSE ends. This may be a conservative estimate, as systems could potentially delay monitoring plans until
just before the Stage 2 DBPR requirements take effect. Exhibits D.7a and D.7b display the schedule for
monitoring plan preparation for surface and ground water systems, respectively.
'Time periods for capital costs for small and medium systems include a possible 2-year extension for
systems making capital improvements.
Final Economic Analysis for the Stage 2 DBPR D-3 December 2005
-------
D.5 Additional Routine Monitoring
The costs for additional routine monitoring are assumed to begin when Stage 2 DBPR
requirements take effect. The ground water schedule also is assumed to reflect the schedule for systems
that add disinfection for the Ground Water Rule prior to compliance monitoring. Exhibits D.8a and D.8b
display the routine monitoring schedule for surface and ground water systems, respectively.
D.6 Operational Evaluations
An operational evaluation is only triggered when a system exceeds an operational evaluation
level. Since a system needs at least three quarters of data to calculate an operational evaluation level,
EPA assumes that operational evaluations will not begin until 1 year after Stage 2 DBPR requirements
take effect. Exhibits D.9a and D.9b display the operational evaluation level schedule for costs for surface
and ground water systems, respectively.
D.7 Primacy Agency Schedule
EPA assumed that primacy agencies will incur implementation costs during the first 2 years after
promulgation of the Stage 2 DBPR. Since primacy agencies will incur IDSE costs as systems conduct
their IDSEs, cost were weighted according to the number of systems performing the IDSE each year.
EPA assumed that monitoring costs will be incurred annually. Exhibit D.10 displays the schedule for
primacy agency costs.
Final Economic Analysis for the Stage 2 DBPR D-4 December 2005
-------
Exhibit D.3a Schedule for Surface Water Capital Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12-25
Community Water Systems
< 10,000
-
-
-
-
15%
15%
15%
15%
15%
15%
8%
10,000-
49,999
-
-
-
-
18%
18%
18%
18%
18%
9%
-
50,000 -
99,999
-
-
-
-
22%
22%
22%
22%
11%
-
-
100,000+
-
-
-
-
25%
25%
25%
25%
-
-
-
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
15%
15%
15%
15%
15%
15%
8%
10,000-
49,999
-
-
-
-
18%
18%
18%
18%
18%
9%
-
50,000 -
99,999
-
-
-
-
22%
22%
22%
22%
11%
-
-
100,000+
-
-
-
-
25%
25%
25%
25%
-
-
-
No Capital Costs
Source: Derived from rule implementation schedule.
Final Economic Analysis for the Stage 2 DBPR
D-5
December 2005
-------
Exhibit D.3b Schedule for Ground Water Capital Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
11 -25
Community Water Systems
< 10,000
-
-
-
-
15%
15%
15%
15%
15%
15%
8%
10,000-
49,999
-
-
-
-
18%
18%
18%
18%
18%
9%
-
50,000 -
99,999
-
-
-
-
22%
22%
22%
22%
11%
-
-
100,000+
-
-
-
-
25%
25%
25%
25%
-
-
-
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
15%
15%
15%
15%
15%
15%
8%
10,000-
49,999
-
-
-
-
18%
18%
18%
18%
18%
9%
-
50,000 -
99,999
-
-
-
-
22%
22%
22%
22%
11%
-
-
100,000+
-
-
-
-
25%
25%
25%
25%
-
-
-
No Capital Costs
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-6
December 2005
-------
Exhibit D.4a Schedule for Surface Water O&M Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Community Water Systems
< 10,000
-
-
-
-
-
15%
31%
46%
62%
77%
92%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
18%
36%
55%
73%
91%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
22%
44%
67%
89%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
25%
50%
75%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
-
15%
31%
46%
62%
77%
92%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000-
49,999
-
-
-
-
-
18%
36%
55%
73%
91%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
22%
44%
67%
89%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
25%
50%
75%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-7
December 2005
-------
Exhibit D.4b Schedule for Ground Water O&M Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Community Water Systems
< 10,000
-
-
-
-
-
15%
31%
46%
62%
77%
92%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
18%
36%
55%
73%
91%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
22%
44%
67%
89%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
25%
50%
75%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
-
15%
31%
46%
62%
77%
92%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000-
49,999
-
-
-
-
-
18%
36%
55%
73%
91%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
22%
44%
67%
89%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
25%
50%
75%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-8
December 2005
-------
Exhibit D.5a Schedule forSW PWS Implementation Costs
All Alternatives
Year
1
2
3
4
5
6
7-25
Community Water Systems
< 10,000
14%
36%
-
12%
20%
18%
10,000 -
49,999
25%
25%
-
23%
14%
12%
50,000 -
99,999
50%
-
-
36%
14%
-
100,000+
50%
-
-
50%
-
-
Nontransient Noncommunity Water Systems
< 10,000
7%
43%
-
6%
22%
22%
10,000 -
49,999
-
50%
-
-
25%
25%
50,000 -
99,999
50%
-
-
25%
25%
-
100,000+
50%
-
-
50%
-
-
No Implementation Costs
Source: Derived from rule implementation schedule.
The schedule for all systems assumes that they will incur half of implementation costs as they prepare for the IDSE
and the other half as they prepare for compliance with the Stage 2 requirements.
The schedule for small surface water systems has been adjusted to account for consecutive systems that are on a
faster schedule because they buy from or sell to larger systems
Economic Analysis for the Stage 2 DBPR
D-9
December 2005
-------
Exhibit D.5b Schedule for GW PWS Implementation Costs
All Alternatives
Year
1
2
3
4
5
6
7-25
Community Water Systems
< 10,000
1%
49%
-
1%
25%
25%
10,000 -
49,999
6%
44%
-
6%
23%
22%
50,000 -
99,999
50%
-
-
28%
22%
-
100,000+
50%
-
-
50%
-
-
Nontransient Noncommunity Water Systems
< 10,000
0.2%
49.8%
-
0.1%
25.0%
24.9%
10,000 -
49,999
-
50%
-
-
25%
25%
50,000 -
99,999
50%
-
-
25%
25%
-
100,000+
50%
-
-
50%
-
-
No Implementation Costs
Source: Derived from rule implementation schedule.
The schedule for all systems assumes that they will incur half of implementation costs as they prepare for the IDSE
and the other half as they prepare for compliance with the Stage 2 requirements.
The schedule for small surface water systems has been adjusted to account for consecutive systems that are on a
faster schedule because they buy from or sell to larger systems
Economic Analysis for the Stage 2 DBPR
D-10
December 2005
-------
Exhibit D.6a Schedule for SW PWS IDSE Costs
All Alternatives
Year
1
2
3
4
5-25
Community Water Systems
< 10,000
-
11%
26%
63%
10,000 -
49,999
-
21%
54%
25%
50,000 -
99,999
-
21%
79%
-
100,000+
-
50%
50%
-
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
100%
10,000 -
49,999
-
-
50%
50%
50,000 -
99,999
-
-
100%
-
100,000+
50%
50%
-
No IDSE Costs
Source: Derived from rule implementation schedule.
Although 40/30 Certification costs will be incurred earlier, the percent of total costs is so small as to be negligible.
Economic Analysis for the Stage 2 DBPR
D-11
December 2005
-------
Exhibit D.6b Schedule for GW PWS IDSE Costs
All Alternatives
Year
1
2
3
4
5-25
Community Water Systems
< 10,000
-
-
-
100%
10,000 -
49,999
-
-
50%
50%
50,000 -
99,999
-
-
100%
-
100,000+
-
50%
50%
-
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
100%
10,000 -
49,999
-
-
50%
50%
50,000 -
99,999
-
-
100%
-
100,000+
-
50%
50%
-
No IDSE Costs
Source: Derived from rule implementation schedule.
The schedule for small surface water systems has been adjusted to account for consecutive systems that are on a
faster schedule because they buy from or sell to larger systems
Economic Analysis for the Stage 2 DBPR
D-12
December 2005
-------
Exhibit D.7a Schedule for SW PWS Monitoring Plan Costs
All Alternatives
Year
1
2
3
4
5
6-25
Community Water Systems
< 10,000
-
-
11%
26%
63%
10,000 -
49,999
-
-
21%
54%
25%
50,000 -
99,999
-
-
21%
79%
-
100,000+
-
-
50%
50%
-
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
100%
10,000-
49,999
-
-
-
50%
50%
50,000 -
99,999
-
-
-
100%
-
100,000+
-
-
50%
50%
-
No Monitoring Plan Costs
Source: Derived from rule implementation schedule.
The schedule for small surface water systems has been adjusted to account for consecutive systems that are
Economic Analysis for the Stage 2 DBPR
D-13
December 2005
-------
Exhibit D.7b Schedule for GW PWS Monitoring Plan Costs
All Alternatives
Year
1
2
3
4
5
6-25
Community Water Systems
< 10,000
-
-
-
-
100%
10,000-
49,999
-
-
-
50%
50%
50,000 -
99,999
-
-
-
100%
-
100,000+
-
-
50%
50%
-
NonTransient Noncommunity Water Systems
< 10,000
-
-
-
-
100%
10,000 -
49,999
-
-
-
50%
50%
50,000 -
99,999
-
-
-
100%
-
100,000+
-
-
50%
50%
-
No Monitoring Plan Costs
Source: Derived from rule implementation schedule.
The schedule for small surface water systems has been adjusted to account for consecutive systems that are on a faster
Economic Analysis for the Stage 2 DBPR
D-14
December 2005
-------
Exhibit D.8a Schedule for Annual Surface Water Stage 2 Routine Compliance Monitoring Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Community Water Systems
< 10,000
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-15
December 2005
-------
Exhibit D.8b Schedule for Annual Ground Water Routine Stage 2 Compliance Monitoring Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Community Water Systems
< 10,000
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-16
December 2005
-------
Exhibit D.9a Schedule for Annual Surface Water Operational Evaluation Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Community Water Systems
< 10,000
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-17
December 2005
-------
Exhibit D.9b Schedule for Annual Ground Water Operational Evaluation Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Community Water Systems
< 10,000
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Nontransient Noncommunity Water Systems
< 10,000
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
10,000 -
49,999
-
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
50,000 -
99,999
-
-
-
-
-
-
-
50%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100,000+
-
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
Economic Analysis for the Stage 2 DBPR
D-18
December 2005
-------
Exhibit D.10 Schedule for State/Primacy Agency Costs
All Alternatives
Year
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Implementation
Costs
50%
50%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
IDSE Costs
-
2%
7%
91%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Monitoring Plan
Costs
-
-
2%
7%
91%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Compliance
Monitoring Costs
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Significant
Excursion Report
Cost
-
-
-
-
-
-
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Source: Derived from rule implementation schedule.
State implementation will occur in years 1 and 2 as states prepare their primacy packages.
State IDSE activities will lag 6 months behind large system IDSE progress and be concurrent with IDSE work
by small systems.
Economic Analysis for the Stage 2 DBPR
D-19
December 2005
-------
Appendix E
Annual Bladder Cancer Cases Avoided as a
Result of the Stage 2 DBPR
-------
-------
Appendix E
Annual Bladder Cancer Cases Avoided as a Result of the Stage 2 DBPR
E.I Introduction
This appendix presents the assumptions and calculations used to estimate reductions in the
number of bladder cancer cases as a result of the Stage 2 Disinfectants and Disinfection Byproducts Rule
(DBPR), and supports the discussion related to average exposure reduction in Chapter 5. This Appendix
is organized as follows:
Section E.2 describes the number of baseline bladder cancers in the U.S. by age group and in
total.
• Section E.3 explains the derivation of Population Attributable Risk (PAR), Relative Risk
(RR) and Odds Ratios (OR); it explains the derivation of the PAR of bladder cancer
associated with chlorination disinfection byproducts (DBFs); and it presents estimates of the
pre-Stage 1 occurrence of bladder cancer cases attributable to DBFs using three different
approaches.
• Section E.4 defines "Annual bladder cancer cases ultimately avoidable" in relation to
predicted reductions in total trihalomethane (TTHM) and haloacetic acid (HAAS)
concentrations from pre-Stage 1 to pre-Stage 2 and from pre-Stage 2 to post-Stage 2
conditions for all regulatory alternatives.
Section E.5 defines "cessation lag" and discusses how it affects the prediction of avoidable
cases in the population born prior to rule implementation.
• Section E.6 presents the computational procedures for predicting cases of bladder cancer
avoided for each regulatory alternative, along with consideration of model uncertainties. It
also presents the implementation schedule and describes how it affects the computation of
costs and benefits over the 25-year horizon considered in the benefit analysis.
Section E.I presents the results in detail.
All data in this appendix are derived from the Stage 2 DBPR Benefits Model (USEPA 2005).
E.2 Baseline Bladder Cancer Cases in the U.S., in Total and by Age Group
The American Cancer Society (ACS) predicted in 2004 that 60,240 new cases of bladder cancer
would occur in the U.S. population that year, of which approximately 75 percent were expected to occur
in men and 25 percent in women (ACS 2004). To model the incidence of bladder cancer cases
attributable to DBFs and cases avoidable from the Stage 2 DBPR regulations so that information on
cessation lag can be incorporated, it is necessary to use bladder cancer incidence data that represent the
age at which bladder cancer cases occur. (See Sections E.5 and E.6 for how cessation lag is incorporated
into the benefits calculations.)
Final Economic Analysis for the Stage 2 DBPR E-l December 2005
-------
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 in
5-year intervals. 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 by age in one-year steps for ages 1
through 101:
BI, = POP, x Brt (Equation E. 1)
100,000
where for any age /', BIt is the number of new bladder cancer cases per year by age, POPt is the population
for that age, and Bri is the background rate per 100,000 people for that age from the SEER data.
The results of these calculations and the SEER data upon which they are based are shown in
Exhibit E. 1. The number of new bladder cancer cases per year starts to increase at about age 35 and
peaks at 1,500 to over 2,000 cases per one-year age group from about age 66 to 85. Although the annual
rate of bladder cancer does not decline much after age 85, the incidence of bladder cancer does, because
of the overall decline in the number of individuals alive after that age.
Note that the total cases obtained by this procedure, 56,506, is slightly lower than the prediction
for 2004 from the American Cancer Society data noted above. This likely reflects EPA's use of the
census population data from 2000. Though the American Cancer Society data uses more recent
population data, it was necessary to use the U.S. Census population age group breakdown to estimate the
age-group incidence. Using the SEER data with the 2000 census data may be a slight underestimate, but
the impact on the benefits will be small.
E.3 Derivation of PAR and Bladder Cancer Incidence Associated with DBFs
This section first explains the general concepts of PAR, RR and OR.1 It then presents the
derivation of PAR for bladder cancer associated with DBFs and estimates the pre-Stage 1 occurrence of
bladder cancer attributable to DBFs.
E.3.1 Introduction to Concepts of OR, RR and PAR
The risk assessment methodology used to estimate the number of cancer cases that are
attributable to DBFs in chlorinated drinking water involves the estimation of a PAR value. PAR, which is
also referred to frequently and perhaps more appropriately as Population Attributable Fraction, is a
measure of the fraction of a disease that occurs in the population that is attributable to some specified risk
factor. It can also be interpreted as a measure of the fraction of that disease that would be eliminated from
the population if that risk factor were eliminated.
1 Additional background information on the concepts of PAR, OR, and RR is available in Rockhill et al.
(1998) and Gordis (2000)
Final Economic Analysis for the Stage 2 DBPR E-2 December 2005
-------
As stated in the previous section, EPA uses an estimate of 56,506 new cases of bladder cancer
occurring each year for the purposes of modeling benefits. As described in Chapter 6, available
epidemiological data indicate an association between bladder cancer and exposure to chlorinated
(disinfected) drinking water. PAR in this case would be the fraction of those 56,506 new cases of bladder
cancer occurring annually in the entire U.S. population that could be attributed to exposure to disinfected
drinking water (i.e., the risk factor).
For the purposes of illustrating the derivation of PAR values, suppose that the distribution of the
bladder cancer cases in the population were known with respect to those who are exposed to disinfected
water and those who are not. Exhibit E.2 provides a hypothetical example of such a distribution. Several
measures in Exhibit E.2 suggest that exposure to DBFs is a risk factor for cases of bladder cancer. For
example, as shown in the last column, the bladder cancer risk for exposed individuals (2.03 x 10"4) is
higher than that for unexposed individuals (1.81 x 10"4). This is further shown by the RR measure of
1.123 for exposed to unexposed individuals. RR is an important measure in evaluating epidemiological
data.
Another important measure used in evaluating epidemiological data is the OR. The odds of an
event occurring are simply the ratio of the number of events to the number of non-events. So, in the
example used here the odds of a case being exposed is 10.61 (51,632 / 4,868) whereas the odds of a non-
case being exposed is 9.44 (254,426,956 / 26,938,450). The OR for exposed to non-exposed cases is
1.123. If exposure were not related to the event, then we would expect an OR equal to one. If exposure
is positively linked to the event, then the OR will be greater than one, and an odds ratio that is statistically
significantly greater than one indicates that the positive association has not occurred by chance.
It is important to note that the identical value of 1.123 for both the OR and RR in this example
does not imply that they are identical measures. As will be discussed further below, RR is the desired
measure for calculating PAR from sample data; however, an OR is often more readily obtained from
available studies and can under appropriate conditions be used as an approximation of RR (Rockhill et al.
1998, Gordis 2000).
One other indication of a relationship between exposure and increased incidence is that the
probability of having been exposed for someone who has bladder cancer (0.914) is higher than the
probability of having been exposed for someone who does not (0.904).
There are alternative ways to calculate PAR using various measures of risk (Gordis 2000). The
most direct method would be to calculate PAR from the difference between the risk in the entire
population (Rt) and the risk in the unexposed population (Ru) divided by the total risk:
PAR = R.-R.. = (2.01 xlQ-yq 81 xlQ-4^) = 0.0995 ~ 10% (Equation E.2)
Rt 2.01 x 1Q-4
That is, this example would imply that 10% (i.e., approximately 5,650 cases) of the 56,506 bladder cancer
cases are due to exposure to DBFs.
Final Economic Analysis for the Stage 2 DBPR E-3 December 2005
-------
Exhibit E.1 Baseline Incidence of Bladder Cancer, Pre-Stage 1 Conditions
Age
(years)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
Number of
Individuals in
Age Group
A
3,805,648
3,820,582
3,790,446
3,832,799
3,926,323
3,965,103
4,019,705
4,118,147
4,179,230
4,267,320
4,274,056
4,115,093
4,075,842
4,010,850
4,052,231
4,019,404
3,975,021
4,046,012
4,051,598
4,127,855
4,049,448
3,841,082
3,758,648
3,673,582
3,641,241
3,744,539
3,619,660
3,789,800
3,984,812
4,242,525
4,289,970
4,011,575
3,994,121
4,026,573
4,188,149
4,516,118
4,511,168
4,517,060
4,553,814
4,608,504
4,711,434
4,466,676
4,547,220
4,407,870
4,308,663
4,341,460
4,087,563
4,019,692
3,885,145
3,758,544
3,808,515
Background
Incidence Rate
(per 100,000)
B
0.0574
0.0574
0.0574
0.0574
0.0574
0.0274
0.0274
0.0274
0.0274
0.0274
0.0215
0.0215
0.0215
0.0215
0.0215
0.0892
0.0892
0.0892
0.0892
0.0892
0.2299
0.2299
0.2299
0.2299
0.2299
0.4917
0.4917
0.4917
0.4917
0.4917
0.7423
0.7423
0.7423
0.7423
0.7423
1.8064
1.8064
1.8064
1.8064
1.8064
3.8318
3.8318
3.8318
3.8318
3.8318
7.7976
7.7976
7.7976
7.7976
7.7976
15.3155
Baseline Cases
(in Age Group)
C- A "B/
100,000
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
4
4
4
4
4
9
9
9
8
8
18
18
19
20
21
32
30
30
30
31
82
81
82
82
83
181
171
174
169
165
339
319
313
303
293
583
Age
(years)
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
Total
Number of
Individuals in
Age Group
A
3,616,997
3,707,436
3,635,040
2,817,560
2,850,600
2,837,452
2,864,020
2,540,152
2,377,013
2,319,944
2,221,227
2,171,072
2,053,151
2,040,053
2,029,911
1,860,320
1,896,451
1,864,515
1,882,348
1,875,175
1,788,269
1,791,696
1,725,168
1,677,133
1,651,641
1,556,567
1,460,781
1,431,916
1,314,908
1,207,365
1,072,048
981,562
883,063
801,329
730,194
635,154
557,330
465,481
401,659
327,904
266,386
218,217
169,066
130,958
98,095
72,680
52,844
36,003
27,162
50,454
281,421,906
Background
Incidence Rate
(per 100,000)
B
15.3155
15.3155
15.3155
15.3155
28.8233
28.8233
28.8233
28.8233
28.8233
49.3850
49.3850
49.3850
49.3850
49.3850
77.0165
77.0165
77.0165
77.0165
77.0165
111.1442
111.1442
111.1442
111.1442
111.1442
137.7068
137.7068
137.7068
137.7068
137.7068
157.3246
157.3246
157.3246
157.3246
157.3246
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
147.3673
Baseline Cases
(in Age Group)
C- A" B/
100,000
554
568
557
432
822
818
826
732
685
1,146
1,097
1,072
1,014
1,007
1,563
1,433
1,461
1,436
1,450
2,084
1,988
1,991
1,917
1,864
2,274
2,143
2,012
1,972
1,811
1,899
1,687
1,544
1,389
1,261
1,076
936
821
686
592
483
393
322
249
193
145
107
78
53
40
74
56,506
Sources: (A) 2000 U.S. Census data
(B) National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER, 2004)
Final Economic Analysis for the Stage 2 DBPR
December 2005
-------
Exhibit E.2 Hypothetical Data for Example Derivation of PAR
Exposed to
DBPs
Not exposed to
DBPs
Totals
Cases
51,632
(Ce)
4,868
(Cu)
56,500
Probability of
DBP Exposure
for Cases
0.914
(Pe/c = Ce / Ct)
Odds of Cases
Being Exposed
10.61
(Oc = Ce/Cu)
Non-Cases
254,426,956
(Ne)
26,938,450
(NJ
281,365,406
Probability of
DBP Exposure
for Non-Cases
0.904
Odds of Non-
Cases Being
Exposed
9.44
(0N = Ne/Nu)
Odds Ratio
(OR)
1.123
(OR = On / 0N)
Totals
254,478,588
(Te)
26,943,318
OJ
281,421,906
Probability of
Exposure
0.904
(Pe/t=Te/Tt)
Risk
2.03x1 0'4
(Re = Ce/Te)
1.81 x10'4
(RU = CU/TU)
2.01 x10'4
Relative Risk
(RR)
1.123
(RR = Re/Ru)
Final Economic Analysis for the Stage 2 DBPR
E-5
December 2005
-------
One can also calculate PAR from the information provided by the RR and the probability of
exposure in the overall population:
PAR = P.* (RR - 1) = 0.904 x (1.123- 1} = 0.1112 =0.1001 « 10% (EquationE.3)
[Pe/t(RR -!)]+! [0.904 x (1.123 -!)]+! 1.1112
Equation E.3 is essentially a transformation of Equation E.2.
A third method for calculating PAR from these data is:
PAR = Pe/c[(RR-l)/RR] = 0.914 [(1.123-1) /1. 123] = 0.914 x 0.1095 = 0.1001 = 10%
(Equation E. 4)
In this third formulation for calculating PAR, the value obtained from the quantity [(^-1) / RR]
is a direct measure of the attributable fraction within the exposed group. That is, in this example, 10.95%
of the cases within the exposed group are attributable to that exposure, or 0.1095 x Q. The
corresponding fraction of total cases due to exposure is, then, [(0.1095 x Q) / Ct], or [0.1095 x (Ce I Ct)]
which is 0.914 x 0.1095 = 10%.
A more detailed discussion of these alternative methods of calculating PAR is provided in
Rockhill et al. (1998), who also provide some additional information regarding limitations on the use of
these approaches. The major limitation the authors note is that Equations E.2 and E.3 are only valid as
shown here when confounding is controlled for in the study, whereas Equation E.4 can be used to provide
internally valid estimates when confounding exists (examples of possible confounding factors include
age, sex, smoking history, occupation, socioeconomic status). "Confounding" refers to a factor that is
associated with the exposure and independently affects the risk of developing the disease. More detail on
basic epidemiological terms can be found in epidemiological texts, including Gordis (2000).
Of course, having information such as that presented in the hypothetical data above for the entire
population is extremely rare, and PAR values are typically estimated from representative sample data
provided in epidemiological studies. There are two primary types of epidemiological studies that can
provide data for estimating PAR: cohort (prospective) studies and case-control (retrospective) studies.
Prospective cohort studies can most directly provide the data needed for PAR calculations. In
these studies, sample populations are selected at random to be representative of exposure to the risk factor
of interest without any prior consideration of the presence or absence of the disease in the sample. A
major problem with prospective studies is that when the disease of interest is relatively rare, a very large
sample group is required in order to obtain a sufficient number of cases of the disease for subsequent
analysis.
For example, if one were to attempt a prospective study for a disease having a risk factor similar
to those assumed for bladder cancer in this example (approximately 2 x 10"4), it would be necessary to
have a sample population of at least 1,000,000 people (and likely more than that) to ensure observation of
enough cases to be able to estimate RRs and PAR values to a reasonable degree of precision. Exhibit E.3
provides a display of such a prospective study. In this example, the researchers would target a sample of
Final Economic Analysis for the Stage 2 DBPR E-6 December 2005
-------
1,000,000 individuals whose exposure would be representative of the more than 281 million in the overall
population who they are meant to represent.
Exhibit E.3 Hypothetical Data for a Prospective Study
Exposed to
DBFs
Not exposed to
DBFs
Totals
Cases
184
17
201
Non-Cases
905,876
93,923
999,799
Totals
906,060
93,940
1,000,000
Risk
2.03x1 0-4
1.81 x10'4
2.01 x10'4
Assuming also that the observed incidence of cases for the exposed and unexposed groups
represent the actual risks in those underlying populations (as shown in Exhibit E.3), then one would
expect a total of only 201 cases in the entire 1,000,000 sample group - 184 in the exposed subset and a
mere 17 in the unexposed subset.
If one were actually able to carry out such a study, then PAR could be calculated using these data
and the methods described previously. However, it should be obvious from the sample size requirements
alone that prospective studies for diseases with such a low frequency of occurrence are highly impractical,
and indeed they are rarely conducted.
The alternative study approach—and that which has been used in the epidemiological studies
used in this Economic Analysis (EA)—is to use retrospective case-control studies. These have the
advantage of a more practical sample size. Their potential disadvantage, however, is that one cannot
calculate RR values for PAR calculations directly. However, it is possible to calculate an OR from a
case-control study which, under appropriate conditions, can be used as an estimate of RR for PAR
calculations.
In a typical case-control epidemiological study, a researcher would identify a group of cases,
ideally selected in a manner that is unbiased with respect to the underlying exposure factor of interest.
Similarly, a set of controls (non-cases) would be selected in a manner that is also unbiased with respect to
the underlying exposure factor of interest. Exhibit E.4 presents a set of hypothetical data for such a case-
control study. For this example, it is assumed that the study identifies 201 cases and that these are found
(ideally) to be distributed as expected (based on our overall hypothetical data set) with respect to
exposure. The researcher also selects a set of controls not having the disease (1,000 assumed here), also
distributed ideally in a manner that is representative of exposure for non-cases.
Final Economic Analysis for the Stage 2 DBPR
E-7
December 2005
-------
Exhibit E.4 Hypothetical Data for a Case-Control Study
Exposed to
DBFs
Not exposed to
DBFs
Totals
Cases
184
17
201
Probability of
DBF Exposure
for Cases (Pd)
0.915
(184/201)
Odds of Cases
Being Exposed
10.82
(184/17)
Non-Cases
(Controls)
904
96
1,000
Probability of
DBF Exposure
for Non-Cases
0.904
(904 / 1 ,000)
Odds of Non-
Cases Being
Exposed
9.42
(904 / 96)
OR
1.149
(10.82/9.42)
Totals
1,088
113
1,201
Risk
Risk within
exposure
subgroups and
for the entire
sample group
cannot be
calculated.
In a case-control study such as this, "Risk" (and therefore Relative Risk) would be meaningless
and entirely an artifact of the number of cases and controls selected. Therefore, it is not possible to use
Equation E.I to calculate PAR values from a case-control study. However, it is possible to calculate the
OR (that is, the ratio of the odds of a case being exposed to the odds of a non-case being exposed as
shown in these examples) from a case-control study. The OR can be used as an estimate for RR, allowing
PAR to be calculated from the alternative formulations, when the case-control study is designed and
executed in a manner that meets three main conditions (Rockhill et al. 1998, Gordis 2000):
• The disease being considered occurs at a low frequency in the studied population.
The cases have been selected in a manner that is representative with regard to the history of
exposure of all people with the disease in the population from which they are drawn.
• The controls have been selected in a manner that is representative with regard to the history
of exposure of all people without the disease in the population from which they are drawn.
If these conditions are met, then the OR will be a reasonable estimate of the RR and can be used
in place of RR in Equations 3 or 4 for calculating PAR.
Final Economic Analysis for the Stage 2 DBPR
E-&
December 2005
-------
It is important to note, however, that the use of Equation E.3 is limited to circumstances where
there is no confounding and ORs calculated directly, as shown here, are used (Rockhill et al. 1998).
Usually, this is not the case and it is necessary in a case-control study to adjust for confounding factors.
This is often done by computing ORs that take into account the interactions of multiple (potential) risk
factors by the use of logistic regression techniques. In such cases, Equation E.4 is the appropriate
equation to use to calculate PAR. Using the case-control example here, that calculation would be:
PAR = Pd[(OR-l)/OR] = 0.915 x [(1.149- 1) / 1.149] = 0.915 x 0.1297 = 0.1187 = 11.9%
In the foregoing examples of PAR calculations, the population is stratified into two exposure
groups only: those with and those without. More often, multiple exposure groups are used to represent
potential relationships between exposure levels and risk. For PAR calculations involving multiple
exposure groups, the PAR equations shown above as Equations E.3 and E.4 can be modified as follows:
PAR = —^- - (Equation E.5)
(Equation E.6)
The first of these multiple-exposure-group forms of the PAR calculations corresponds to
Equation E.3 and the second to Equation E.4. They both indicate that there are "£" exposure categories,
including an unexposed referent group for which the RR = 1 (or OR = 1 if ORs are being used in place of
RR). These equations are also addressed more fully in Rockhill et al. (1998). As indicated in the next
section, Equation E.6 was used to compute PAR from the epidemiological data for bladder cancer
associated with exposure to chlorinated drinking water.
It is useful to note that calculation of the ORs from epidemiological data where there are multiple
exposure categories and where there is a need to adjust for confounding factors (e.g., age, sex, smoking,
occupation, socioeconomic status, etc.) generally is performed using logistic regression methods rather
than the simple method shown above. As noted in the following section in this Appendix, logistic
regression methods were used to compute the ORs in the specific studies used in this EA to estimate
PARs for pre-Stage 1 bladder cancer incidence.
E.3.2 Data Sources and Methods for the Pre-Stage 1 Bladder Cancer PAR Analysis
The relationship between bladder cancer and chlorinated DBP exposure has historically been the
most strongly supported association among various cancers and chlorinated drinking water. The Stage 1
DBPR RIA (USEPA 1998a) presented EPA's review of the large body of epidemiology literature for
bladder cancer and its association with DBFs in drinking water. From that review, EPA concluded that
although causality has not been established, the data support a weak association that is worthy of concern.
The epidemiological studies used to support the Stage 1 DBPR, the Stage 2 DBPR proposal, and the
Final Economic Analysis for the Stage 2 DBPR E-9 December 2005
-------
Stage 2 DBPR final rule are identified in the next two sections. A more detailed discussion of these
studies is provided in Chapter 6.
The estimates of PAR for DBFs and bladder cancer necessarily reflect Pre-Stage 1 conditions.
This is because the various epidemiology studies that are the sources of data used to estimate PAR were
all conducted prior to promulgation and implementation of the Stage 1 DBPR. The risk and benefits
analysis supporting the Stage 2 DBPR begins with the Pre-Stage 1 estimate of the number of new bladder
cancer cases each year, that is, the annual cases that can be attributed to DBFs given the national
occurrence and exposure conditions prior to the Stage 1 rule. Anticipated reductions in these occurrence
and exposure levels due to the Stage 1 rule are then accounted for, and following that the anticipated
reductions in occurrence and exposure due to the Stage 2 rule are considered in order to estimate the rule's
benefits.
E.3.2.1 Data Sources Used for the Stage 1 and Stage 2 DBF Proposed Rule
Consistent with the approach used for the Stage 1 DBPR, the Stage 2 DBPR proposal (July 2003)
EPA used data provided in five epidemiological studies to calculate the Pre-Stage 1 PAR values for
bladder cancer associated with exposure to chlorinated drinking water:
• Cantor etal. (1985, 1987)2
• McGeehinetal. (1993)
• King and Marrett (1996)
Freedman et al. (1997)
• Cantor etal. (1998)
These five peer-reviewed studies provided a range of estimates of PAR from 2 percent to 17
percent bounded by a 95 percent confidence interval ranging as high as 33 percent and truncated at 0
percent to maintain biological plausibility (USEPA 1998g). As discussed below, EPA is also using the
data from these five studies for one of the approaches for calculating the Pre-Stage 1 PAR values in
support of the Stage 2 Final Rule.
E.3.2.2 Data Sources Used for the Final Rule
Just prior to the publication of the Stage 2 DBPR proposal in 2003, a meta-analysis study of
bladder cancer and the consumption of chlorinated drinking water that was published by Villanueva et al.
(2003). Subsequent to the publication of the Stage 2 proposal, a study group comprised of some of the
same investigators published another study using a pooled analysis that focused more specifically on
bladder cancer related to TTHMs in drinking water.
In support of the final Stage 2 DBPR, EPA has considered three approaches to estimating the
Pre-Stage 1 PAR value. These are based on the three sets of studies noted above:
2Cantor et al. 1985 and Cantor et al. 1987 use the same epidemiological data
Final Economic Analysis for the Stage 2 DBPR E-10 December 2005
-------
• 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 apre-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 pre-
Stage 1 best estimate for PAR of-17%)
EPA considers all three of these approaches to estimating the PAR for DBFs to be equally valid
and to provide plausible quantitative estimates of bladder cancer risk, which are similar to each other.
EPA has long recognized that while the several epidemiology studies described above indicate a potential
association between exposure to DBFs 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.
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
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, all of which were peer-reviewed, that were published in
the 1980's and 1990's (USEPA 1998g). 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 or not 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 and 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 Odds Ratio) 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
Final Economic Analysis for the Stage 2 DBPR E-ll December 2005
-------
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 the same five studies as
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 DBFs (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
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 US.
Additional discussion of the studies included in each of these approaches is provided in Chapter
6. The remainder of this section focuses primarily on the derivation of Pre-Stage 1 PAR estimates from
these studies.
Final Economic Analysis for the Stage 2 DBPR E-12 December 2005
-------
E.3.3 Derivation of Pre-Stage 1 PAR values for the Final Rule
Approach 1: Pre-Stage 1 PAR Range Based on Five Studies
Exhibit E.5 summarizes the key data from the five studies (note that Cantor et al. 1985 and
Cantor et al. 1987 use the same epidemiological data) used to calculate PAR values for pre-Stage 1
bladder cancer incidence. These studies are discussed more fully in Chapter 6 of the EA. The ORs and
their 95% confidence intervals for each exposure group were calculated by the researchers performing
these studies.
EPA calculated PAR values from the data shown in Exhibit E.5 using the multiple-exposure-
group form of Equation E.3 as described in Section E.2.1. These calculations and the resulting PAR
values are shown in Exhibit E.6. The PAR estimates shown in Exhibit E.6 reflect the point estimates of
the ORs for each exposure group in each study. As shown in Exhibit E.5, the researchers for those
studies also presented 95% confidence intervals for those ORs, reflecting uncertainty in the values.
EPA has calculated corresponding 95% confidence intervals on the PAR point estimates shown in
Exhibit E.6 using a Monte Carlo simulation analysis. The confidence intervals on the ORs reported by
the researchers were used to parameterize each OR as a normal distribution. For each study, 10,000
iterations were run, and the OR for each exposure group was selected from its respective uncertainty
distribution assuming independence among the groups (and among the studies). PAR values were
calculated (using the computation as shown in Exhibit E.4) for each of the 10,000 iterations and collected.
Using the 10,000 PAR estimates for each study, lower and upper confidence bounds were
derived. The upper 95% confidence limit is taken from the 97.5 percentile values. The lower limit is
taken from the 2.5 percentile values of the 10,000 values, unless those values are below zero, in which
case the lower confidence interval is assumed to be 0% because it is biologically implausible that the true
PAR value should be less than 0%. The confidence intervals obtained from the Monte Carlo simulation
are summarized in Exhibit E.7.
Final Economic Analysis for the Stage 2 DBPR E-13 December 2005
-------
Exhibit E.5 Summary of Data from the Five Epidemiological Studies Relevant to
PAR Calculations
Study
Cantor et
al. 1985,
1987
Cantor et
al. 1998
Freed man
etal. 1997
King and
Marret
1996
McGeehin
etal. 1993
Location
10
Geographic
areas
Iowa
Washington
County,
Maryland
Ontario,
Canada
Colorado
Sex
Both
Both
Both
Both
Both
Years of
Exposure
0
1-19
20-39
40-59
>59
0
1-19
20-39
40-59
>59
0
0-19
20-39
40-59
>59
0
1-10
11-20
21-30
31-40
>40
0-9
10-19
20-34
>35
0
1-10
11-20
21-30
>30
# of Cases
231
141
324
437
111
Total: 1,244
153
107
236
310
74
Total: 880
689
257
87
61
29
Total: 1,1 23
79
91
56
38
16
13
Total: 293
157
55
169
315
Total: 696
104
37
38
32
116
Total: 327
#of
Controls
570
285
650
849
196
Total: 2,550
345
173
379
430
91
Total: 1,418
1275
428
139
101
40
Total: 1,983
722
701
432
266
107
78
Total: 2,306
413
154
433
545
Total: 1,545
102
46 3
29 3
25 3
50 3
Total: 252
OR1
(95% C.I.)
1.0
1.1 (0.8-1.4)
1.0(0.8-1.3)
1.0(0.8-1.3)
1.1 (0.8-1.5)
1.0
1.2(0.9-1.7)
1.1 (0.8-1.6)
1.3(0.9-1.9)
1 .4 (0.9-2.3)
1.0
1.0(0.8-1.2)
1.1 (0.8-1.4)
1.2(0.8-1.7)
1 .5 (0.9-2.6)
1.0
1.0(0.6-1.5)
1 .0 (0.6-1 .6)
1.1 (0.6-1.8)
1.1 (0.6-2.2)
1 .4 (0.7-2.9)
1.0
1.0(0.7-1.5)
1.2(0.9-1.5)
1.4(1.1-1.8)
1.0
0.7(0.4-1.2)
1.1 (0.6-2.0)
1.3(0.7-2.5)
2.1 (1.4-3.2)
P 2
rc/e(i)
0.186
0.113
0.260
0.351
0.089
0.174
0.122
0.268
0.352
0.084
0.614
0.229
0.077
0.054
0.026
0.270
0.311
0.191
0.130
0.055
0.044
0.226
0.079
0.243
0.453
0.318
0.113
0.116
0.098
0.355
Notes: 10Rs and 95 percent confidence intervals as reported in the studies.
2 Probability of a case being in the indicated years of each ith exposure group.
3 Actual number of controls for McGeehin et al. were not available, proportions were used.
Source: Quantification of Bladder Cancer Risk from Exposure to Chlorinated Surface Water (USEPA 1998h).
Final Economic Analysis for the Stage 2 DBPR
E-14
December 2005
-------
Exhibit E.6 Summary of PAR Calculations from OR Data for
Five Epidemiological Studies
Study
Cantor etal., 1985,
•\ QR7
i yo/
Cantor etal., 1998
Freedman et al.,
1997
King and Marret,
1996
McGeehin et al.,
1993
Years of
Exposure
0
< 19
20-39
40-59
>59
0
< 19
20-39
40-59
>59
0
< 19
20-39
40-59
>59
0
1-10
11-20
21-30
31-40
>40
0-9
10-19
20-34
>35
0
1-10
11-20
21-30
>30
OR
1.0
1.1
1.0
1.0
1.1
1.0
1.2
1.1
1.3
1.4
1.0
1.0
1.1
1.2
1.5
1.0
1.0
1.0
1.1
1.1
1.4
1.0
1.0
1.2
1.4
1.0
0.7
1.1
1.3
2.1
rB/r(i)
0.186
0.113
0.260
0.351
0.089
0.174
0.122
0.268
0.352
0.084
0.614
0.229
0.077
0.054
0.026
0.270
0.311
0.191
0.130
0.055
0.044
0.226
0.079
0.243
0.453
0.318
0.113
0.116
0.098
0.355
PoMix[(OR-1)/OR]
0.000
0.010
0.000
0.000
0.008
Sum = 0.018
0.000
0.020
0.024
0.081
0.024
Sum = 0. 149
0.000
0.000
0.007
0.009
0.009
Sum = 0.025
0.000
0.000
0.000
0.012
0.005
0.013
Sum = 0.029
0.000
0.000
0.040
0.129
Sum = 0.1 69
0.000
-0.048
0.011
0.023
0.186
Sum = 0.1 70
PAR
2%
15%
3%
3%
17%
17%
Final Economic Analysis for the Stage 2 DBPR
E-15
December 2005
-------
Exhibit E.7 Summary of PAR Values with Confidence Intervals Obtained from
Monte Carlo Simulation
Study
Cantor etal., 1985
Cantor etal., 1987
Cantor etal., 1998
Freedman et al., 1997
King and Marret, 1996
McGeehin etal., 1993
PAR Values Obtained from Simulation
Lower 95% Cl
0%
0%
0%
0%
1%
0%
Mean
3%
17%
2%
3%
17%
17%
Upper 95% Cl
15%
31%
8%
22%
28%
33%
Point Estimates
from Studies
2%
15%
3%
3%
17%
17%
In addition to the uncertainty in the PAR values calculated for each of the individual studies as
reflected by the confidence intervals, it is important to consider the uncertainty associated with the use of
those studies—each of which was based upon a specific subset of the entire US population—to represent
the PAR value for the US population as a whole.
One important consideration in this regard is the extent to which exposure in the study population
groups is comparable to exposure in the overall US population. Exhibit E.8 provides an overall summary
of the percent of cases and controls in each study who were in the DBP exposure groups (across all
exposure durations). As shown in this exhibit, the exposure groups typically range from 65 - 80% of the
study populations, with one instance (Cantor 1998) where only about 35 - 40% of the study population
were exposed to DBFs. It is currently estimated that approximately 90% of the US population consumes
water from public water supplies that are disinfecting, and the vast majority of these systems use
chlorination (USEPA 2005k). As a result, it can be argued that the PAR values obtained from these five
epidemiological studies under-represent exposure in the United States, and that the actual PAR values are
higher than suggested by the values calculated and used in this EA.
Lastly, it is important to recognize that, notwithstanding the associations indicated by these
studies, causality has not yet been established between bladder cancer and exposure to chlorinated water.
Therefore, it is possible that the attributable risk from chlorinated water is zero, but not probable.
Final Economic Analysis for the Stage 2 DBPR
E-16
December 2005
-------
Exhibit E.8 Summary of Study Group DBF Exposure for Five Epidemiological
Studies
Study
Cantor etal., 1985
Cantor etal., 1987
Cantor etal., 1998
Freedman et al., 1997
King and Marret, 1996
McGeehin etal., 1993
Total
Cases
(a)
1,244
880
1,123
293
696
327
Cases in
Exposed
Group
(b)
1,013
727
434
214
539
223
% of Cases in
Exposed
Group
(b/a) %
81 .4%
82.6%
38.6%
73.0%
77.4%
68.2%
Odds of Case
Being in
Exposed Group
(b)/(a-b)
4.4
4.8
0.6
2.7
3.4
2.1
%of
Controls in
Exposed
Group
80%
76%
35%
70%
75%
65%
Approach 2: Pre-Stage 1 PAR Based on Villanueva et al. (2003) Meta-Analysis
As discussed in Chapter 6, the Villanueva et al. (2003) meta-analysis generated several estimates
of the OR for bladder cancer as a function of sex (men, women, both) and exposure duration (mid-term,
long-term, ever-exposed). Exhibit E.9 summarizes the OR values for these various combinations of
exposure and population groups.
Of the various OR values shown in Exhibit E.9 from the Villanueva et al. (2003) meta-analysis,
EPA determined that the estimates for the Ever Exposed, Both Sexes was the most appropriate to use for
estimating an overall PAR for the Stage 2 benefits analysis since it includes both men and women, and it
covers of the full range of exposure conditions experienced in the population being addressed by this
analysis.
Using Equation E.3 for the PAR calculation, with the other assumptions noted below, EPA
derived a PAR estimate from these data of 15.7%:
Pex(RR-l) _ 0.935x(l.2-l) _
i jT±J\ — 7 7 v7 — 7 7 v7 — \J. 1 J /
l + (Pex(RR-l)) 1 + (0.935 x(l.2-l))
(Equation E.7)
EPA has used the OR from Villanueva et al. (2003) as the estimate for RR in the PAR
calculations (see earlier discussion) and including an estimate of 0.935 for Pe, the portion of the
Final Economic Analysis for the Stage 2 DBPR
E-17
December 2005
-------
population exposed to chlorinated water obtained from the estimated 263 million people exposed to
chlorinated water (see Chapter 3 for baseline estimates) and a total US population of 281 million (U.S.
Census Bureau 2001).
Using the lower and upper 95% confidence interval estimates on the OR of 1.1 and 1.4,
respectively, yields corresponding lower and upper bound PAR values of 8.5% and 27.2%.
Exhibit E.9 Combined OR Estimates from Villanueva et al. 2003
Exposure Category
Combined OR (95% Cl)
Mid Term (1-40 years)
Both Sexes
Men
Women
1.1 (1.0-1.2)
1.3(1.0-1.7)
1.0(0.7-1.6)
Long Term (> 40 years)
Both Sexes
Men
Women
1.4(1.2-1.7)*
1.6(1.2-2.2)*
1.4(0.6-3.6)
Ever-Exposed
Both Sexes
Men
Women
1.2(1.1 -1.4)*
1.4(1.1 -1.9)*
1.2(0.7-1.8)
Note: The Mid Term and Long Term OR estimates are based on the five case control studies; the Ever
Exposed OR estimates are based on those five studies plus the Wilkins and Comstock cohort study.
* Statistically significant
Approach 3: Pre-Stage 1 PAR Based on Villanueva et al. (2004) Pooled Analysis
As discussed in Chapter 6, the Villanueva et al. (2004) study involved a pooled analysis using
some of the same studies included in their 2003 meta-analysis and included among the "Five Studies"
used for the Stage 1 rule and Stage 2 proposal. One notable aspect of the Villanueva et al. (2004) study is
its focus on the relationship between OR and TTHM exposure measures specifically. Villanueva et al.
(2004) included results showing a dose-response relationship of increasing OR as a function of average
TTHM exposure and as a function of cumulative TTHM exposure.
For this approach to estimating the Pre-Stage 1 PAR value, EPA drew upon the information
relating OR to average TTHM exposure concentrations to develop a dose-response relationship. Exhibit
E.10 provides a summary of the information on this relationship that is presented in the Villanueva et al.
(2004) study.
Final Economic Analysis for the Stage 2 DBPR
E-18
December 2005
-------
Exhibit E.10 Summary of Estimated OR Values Associated with Average TTHM
Exposures for Both Sexes from Villanueva et al. (2004)
Average TTHM (ug/L)
0
>0
0-1
>1
0-1
>1 -5
>5-25
> 25 - 50
>50
OR
1.00
1.18
1.00
1.18
1.00
1.08
1.15
1.22
1.31
95% Cl
NA
1.00-1.39
NA
1.06-1.32
NA
0.93-1.26
0.98-1.35
1 .04 - 1 .42
1.12- 1.54
The authors of the Villanueva et al. (2004) also provided EPA with a more detailed data showing
the relationship between OR and average TTHM level (Kogevinas and Villanueva, 2005). These are
presented in Exhibit E. 11.
Final Economic Analysis for the Stage 2 DBPR
E-19
December 2005
-------
Exhibit E.11 Detailed Data on OR as a Function of Average TTHM Exposure Level
by Kogevinas and Villanueva (2005)
Average
TTHM (ug/L)
0
10
20
30
40
50
60
70
80
90
100
110
120
130
Odds Ratio
1.00
1.13
1.16
1.17
1.19
1.22
1.26
1.32
1.38
1.46
1.55
1.66
1.77
1.90
Lower 95% Cl
-
0.96
0.98
1.00
1.02
1.04
1.08
1.12
1.14
1.13
1.11
1.07
1.03
0.98
Upper 95% Cl
-
1.33
1.38
1.37
1.39
1.43
1.47
1.55
1.68
1.89
2.17
2.55
3.06
3.66
EPA used the detailed data in Exhibit E. 11 to derive a linear relationships between the average
TTHM concentration and the OR. Since the OR at 0 ug/L TTHM is 1.0 by definition, the slope for the
linear relationship was derived with the intercept forced to 1.0 and 0 ug/L. For the best estimates, the
slope of the linear relationship was estimated to be 0.00581. Linear relationships were also derived from
the data in Exhibit E. 11 for the lower and upper 95% Cl values. The slopes for these were estimated to be
0.00072 for the lower confidence bound and 0.01393 forthe upper confidence bound. These linear
relationships are shown in Exhibit E. 12 along with the data used to derive them.
The Pre-Stage 1 OR values were estimated from these linear relationships using the estimated
Pre-Stage 1 average TTHM concentration of 38.05 ug/L and the slopes noted above as OR = 1.0 + (slope
* 38.05). The resulting OR values are shown in Exhibit E.13 below. Also shown are the corresponding
Pre-Stage 1 PAR values for these OR estimates derived from the PAR calculation method show
previously for Approach 2.
Final Economic Analysis for the Stage 2 DBPR
E-20
December 2005
-------
Exhibit E.12 OR as a Function of Average TTHM from Data Provided by Villanueva et al. (2004) Authors
(Linear Regression with Intercept Forced to 1.0)
3.3
2.8
x
-------
Exhibit E.13 Estimates of OR and PAR Values from Villanueva et al. (2004) Data
OR
PAR
Lower 95% Cl
1.03
0.025
Best Estimate
1.22
0.171
Upper 95% Cl
1.53
0.331
E.3.4 Estimates of Pre-Stage 1 Annual Bladder Cancer Cases Attributable to DBFs
Using the Pre-Stage 1 PAR values described in the preceding section, estimates of the Pre-Stage 1
annual bladder cancer cases attributable to DBFs can be made by applying the PAR values to the
estimated 56,506 new cases of bladder cancer per year from all causes. These estimates are shown in
Exhibit E. 14
Exhibit E.14 Estimated Pre-Stage 1 Annual Bladder Cancer Cases Attributable to
DBPs Based on the Three Approaches to PAR
Approach 1
Approach 2
Approach 3
Lower 95% Cl
0
4,830
1,412
Best Estimate
1,130-9,606
8,899
9,670
Upper 95% Cl
18,647
15,376
18,716
Note: The "Best Estimate" for Approach 1 reflects the 2% to 17% range of PAR values from the five
studies used.
E.4 Derivation of Annual Bladder Cancer Cases Ultimately Avoidable
As discussed further in the Section E.5 below, there is an anticipated delay (cessation lag)
between when the reductions in DBP occurrence and exposure levels begin following implementation of
Stage 2 and when the full achievement of the reduction in annual bladder cases expected for that
reduction in exposure occurs. The discussion in Section E.5 focuses on modeling this transition period
from higher risks to lower risks following exposure reduction.
The end-point of that transition period is the realization of the full benefits of the rule in terms of
annual bladder cancer cases avoided. The purpose of this section is to describe how EPA has quantified
that end-point, which is referred to here as the annual bladder cancer cases ultimately avoidable for Stage
2. As discussed here, it is necessary to first determine the expected annual cases avoided from Stage 1,
and then use the post-Stage 1 cases remaining that are attributable to DBPs to derive the annual bladder
cancer cases ultimately avoidable for Stage 2.
Note that the example calculations shown in the text of this section for cases attributable and
cases avoidable are intended to match the actual values shown in the accompanying exhibits. Due to
rounding of some factors, the examples shown in the text do not always produce the exact result shown
Final Economic Analysis for the Stage 2 DBPR
E-22
December 2005
-------
there. The result given, which corresponds with values shown in the exhibits, are the values generated in
and carried thorugh the benefits model.
E.4.1 Relationship of Cases Avoided to Average DBF Reduction
The quantitative benefits calculations in this EA assume that there is a linear relationship between
average DBF concentration and the cases of bladder cancer attributable to DBFs, at least within the
general range of concentrations people will typically be exposed to, on average, before and after the rule.
This implies that for a given percent reduction in the national average DBF concentration (for example,
10%) there will be a similar reduction in the annual cases of bladder cancer attributable to DBF exposure
(that is, also 10% for this example). The amount of time it takes to achieve the full reduction in the
number of attributable cases is called the cessation lag period.
EPA recognizes that this assumption of linearity is uncertain, and that there is limited data to
establish and evaluate this relationship in detail. A key source of supporting data for this assumption is
the Villanueva et al. (2004) pooled data analysis study which provided the basis for the linear dose-
response relationship used in Approach 3 for PAR described in the proceeding section.
In the context of assuming linearity in this range, it is important to note the implications of what a
non-linear relationship would be, relative to the assumption of linearity made here. A dose-response
relationship for a carcinogen that is non-linear in lower dose ranges is typically sublinear. If that is the
case for DBFs, then the assumption of linearity back to zero being used here would be conservative with
respect to the estimation of benefits from the Stage 2 rule. That is, if the relationship is sublinear in this
range, then the slope would be steeper and the estimated cases avoided for a given change in average
DBF levels could be greater than that which is currently being estimated.
On the other hand, if the relationship were markedly supralinear in the range of interest, DBF
reductions expected from the Stage 2 rule might result in a substantially lower reduction in attributable
cases in the DBF concentration range of concern. However, supralinearity would also imply that at some
lower DBF concentrations the reduction in attributable cases relative to the reduction in DBFs would
become quite high as the slope for this relationship becomes very steep again.
EPA concluded that the assumption of a straight linear relationship back to zero, which falls
between these two options of sublinearity and supralinearity, is a reasonable approximation given the
uncertainty in knowing the actual dose-response relationship. This uncertainty is discussed further in
Section 6.6.
To estimate bladder cancer cases avoided as a result of the Stage 2 DBPR, the average reduction
in plant-mean TTHM and HAAS concentrations is assumed to represent the range of reductions for all
chlorination DBFs. A more detailed explanation of the derivation of the estimated reduction in
concentration can be found in Chapter 5. Using these two DBF classes as indicators for all chlorination
DBFs may overestimate or underestimate the true concentration reduction. However, because measurable
halogen-substituted DBF concentrations, comprised primarily of TTHM and HAAS, are estimated to
make up 30 to 60 percent of the measured total organic halide (TOX) concentration (Singer 1999), TTHM
and HAAS reductions are assumed to be reasonable indicators of the overall DBF reductions. Separate
evaluations for TTHM and HAAS are carried throughout the analyses.
Final Economic Analysis for the Stage 2 DBPR E-23 December 2005
-------
The specific calculations to arrive at the annual bladder cancer cases ultimately avoidable from
Stage 1 and Stage 2 for Approaches 1 and 2 are different from those for Approach 3. For Approaches 1
and 2, the linearity assumption used to estimate the effects of DBF reductions for Stage 1 and Stage 2 is
applied to the estimated Pre-Stage 1 cases attributable to DBFs. First, the Pre-Stage 2 cases attributable
are calculated as:
Pre-Stage 2 Cases Attributable = Pre-Stage 1 Cases Attributable * ( 1 - % DBP Reduction for Stage 1)
The % DBP Reduction for Stage 1 is calculated from the estimated Pre-Stage 1 and Post-Stage 1 national
average DBP (either TTHM or HAAS) concentrations. If, for example, the Pre-Stage 1 cases attributable
to DBFs is 8,899 and the %DBP reduction estimate for Stage 1 is 27.21%, the Pre-Stage 2 cases
attributable are 6,477 (= 8,899* 0.7279). The Stage 1 cases avoided are then calculated as the difference
between the Pre-Stage 1 and Pre-Stage 2 attributable cases.
Similarly, to estimate the annual bladder cancer cases ultimately avoidable for Stage 2, the
Post-Stage 2 cases attributable are calculated as:
Stage 2 Cases Attributable = Pre-Stage 2 Attributable Cases * ( 1 - % DBP Reduction for Stage 2)
Using the example, if the % DBP reduction from Stage 1 to Stage 2 is 7.81%, then the Post-Stage 2
attributable cases would be 5,971 ( = 6,477 * 0.9219). The Stage 2 cases avoided are then calculated as
the difference between the Pre-Stage 2 and Post-Stage 2 attributable cases.
For Approach 3, the calculation of annual bladder cancer cases ultimately avoidable from Stage 1
and Stage 2 is different from that for Approaches 1 and 2. Whereas Approaches 1 and 2 can produce a
PAR estimate for Pre-Stage 1 only, the dose-response function derived from the Villanueva et al. (2004)
study used in Approach 3 allows for the PAR to be calculated explicitly for Pre-Stage 1, Pre-Stage 2 and
Post-Stage 2 based on the corresponding estimated national average TTHM concentrations.
To calculate the PAR for these rule stages, it is first necessary to calculate the OR values for the
national average TTHM concentrations estimated for each stage. Using the slope of 0.00581 (see earlier
discussion of the Approach 3 dose-response function), and the indicated estimates of TTHMs, the OR
values for each stage are calculated as:
ORPteStl= 1.0 + (0.00581x38.05) = 1.221
ORprest2 = 1- ° + (°- 00581x27.69) =1.161
ORPostSt2=l.O + (0.00581x25.53) = 1.148
Final Economic Analysis for the Stage 2 DBPR E-24 December 2005
-------
The PAR value is then calculated from the PAR equation as discussed previously (where 0.935 is the
fraction of the population exposed to disinfected drinking water):
PAR - 0-935 '(O^- 1.0) _
rAK-w — - F - 7 - TI — 1 / . 1 /o
1 + [0.935 "(OR^,,- 1.0)]
0.935 *(0/?M1- 1.0) _
— 1 J. 1 /o
- F
1 + [0.935
0.935 "(OJ?^,,- 1.0)
- F - 7 - vj
1 + [0.935 *(0*ftaai- 1.0)]
_
— [2.2/0
For Pre-Stage 1, the attributable cases can be calculated by multiply the total bladder cancer cases
by the Pre-Stage 1 PAR value. If, for example, using the Pre-Stage 1 total cases is 56,506, the
attributable cases would be 9,670 (= 56,506 * 0.171).
The calculation of cases attributable after Stage 1 and after Stage 2 for Approach 3 requires that
the total cases at each stage to which the PAR is applied appropriately reflects reductions in those total
cases resulting from the DBP reductions for the stages. This is done by recognizing that:
PAR = Attributable Cases = Attributable Cases
Total Cases (NonAttributable Cases + Attributable cases)
Rearranging this relationship yields:
Attributable Cases = PAR * NonAttributable Cases
(I-PAR)
If 9,670 of the 56,506 Pre-Stage 1 cases are attributable to DBFs, then 46,836 (= 56,506 - 9,670) are not
attributable to DBFs. Using that
AttribCases^ St2 = °-131*46>836 = 7 063 information and the formula
Prest2 (l-0.131) above, the Pre-Stage 2 and
0 177 * 46 816 Post-Stage 2 attributable cases
AttribCaseSp tst2 = . ', = 6,515 would be calculated as:
Postst2 (1-0.122)
Final Economic Analysis for the Stage 2 DBPR E-25 December 2005
-------
The cases avoided from Stage 1 and Stage 2 are then calculated by subtraction:
Stage 1 Cases Avoided = 9,670 - 7,063 = 2,607
Stage 2 Cases Avoided = 7,063 - 6,515 = 548
E.4.2 Results for Stage 1 and Stage 2
E.4.2.1 Estimates of Cases Attributable and Annual Bladder Cancer Cases Ultimately Avoidable
Using the Three Approaches to Pre-Stage 1 PAR
This section provides detailed estimates of the Pre-Stage 1, Pre-Stage 2 and Post-Stage 2
attributable cases of bladder cancer, and the corresponding annual bladder cancer cases ultimately
avoidable for the Stage 1 and Stage 2 (preferred option) rules. These estimates reflect the three
approaches to estimating PAR described previously.
Exhibit E.I 5 presents estimates of the Pre-Stage 1 cases attributable to DBFs for the three
approaches. As noted, these value are obtained by multiplying the indicated PAR values by 56,506, the
estimated total annual bladder cancer cases due to all causes.
Exhibit E.15 Pre-Stage 1 Cases Attributable to DBFs from Three Approaches to
PAR (Pre-Stage 1 PAR Estimates)
Approach 1 :
Five Studies
Approach 2:
Villanueva et al. (2003)
Approach 3:
Villanueva et al. (2004)
Lower 95%
Cl for PAR
0
(0% PAR)
4,830
(8.5% PAR)
1,412
(2.5% PAR)
Best Estimate for PAR
1,130
(2% PAR)
9,606
(17% PAR)
8,899
(15.7% PAR)
9
(17.1
,670
% PAR)
Upper 95% Cl
for PAR
18,647
(33% PAR)
15,376
(27.2% PAR)
18,716
(33.1% PAR)
Note: Calculated from Pre-Stage 1 PAR * 56,506
Some numbers may reflect rounding
Exhibit E. 16 presents the estimated Pre-Stage 2 attributable cases based on the estimated percent
reduction in the national average TTHM concentration from Stage 1.
Final Economic Analysis for the Stage 2 DBPR
E-26
December 2005
-------
Exhibit E.16 Pre-Stage 2 Cases Attributable to DBFs from Three Approaches to
PAR, Based on Stage 1 TTHM Reduction of 27.2%
Approach 1 :
Five Studies
Approach 2:
Villanueva et al. (2003)
Approach 3:
Villanueva et al. (2004)
Lower 95% Cl for
Post-Stage 1 PAR
0
3,515
1,028
Best Estimate for PAR
823
6,992
6,477
7,063
Upper95%Clfor
Post-Stage 1 PAR
13,572
11,192
13,623
Note: Approaches 1 and 2 are calculated from the Pre-Stage 1 values in Exhibit E.15 multiplied by 0.73 (that is, a
27.0% reduction in TTHMs implying a 27.2% reduction in attributable cases)
Approach 3 is calculated from the Post-Stage 1 PAR based on the OR for TTHM = 27.69 ug/L as described
previously.
Some numbers may reflect rounding
Exhibit E.I 7 provides the estimated Stage 1 cases avoided for the three approaches based on the
estimated Stage 1 TTHM reduction. As described previously, these are obtained by subtracting the Pre-
Stage 2 attributable cases from the Pre-Stage 1 attributable cases.
Exhibit E.17 Stage 1 Cases Avoided from Three Approaches to PAR,
Based on Stage 1 TTHM Reduction of 27.2%
Approach 1 :
Five Studies
Approach 2:
Villanueva et al. (2003)
Approach 3:
Villanueva et al. (2004)
Lower 95% Cl
for Post-Stage 1
PAR
0
1,314
384
Best Estimate for
Post-Stage 1 PAR
308
2
2
2,614
,422
,607
Upper95%Clfor
Post-Stage 1 PAR
5,075
4,185
5,094
Notes: Some numbers may reflect rounding
These represent the difference between the Pre-Stage leases attributable (Exhibit E.15) and the Pre-Stage 2 cases
attributable (Exhibit E.16).
Exhibit E. 18 presents estimates of the Post-Stage 2 attributable cases based on the estimated
percent reduction in the national average TTHM concentration from Stage 2. The % reduction values
shown are the 5th percentile, mean, and 95th percentile values for TTHMs for the range reflecting
uncertainty as described in Chapter 5.
Final Economic Analysis for the Stage 2 DBPR
E-27
December 2005
-------
Exhibit E.18 Post-Stage 2 Cases Attributable to DBFs from Three Approaches to
PAR, Based on Stage 2 TTHM Reductions
Lower 95% Cl for
Post-Stage 2 PAR
Best Estimate for
Post-Stage 2 PAR
Approach 1 : Five Studies
4.5% Reduction
7.8% Reduction
1 1 .2% Reduction
0
0
0
785
758
731
Approach 2:Villanueva et al. (2003)
4.5% Reduction
7.8% Reduction
1 1 .2% Reduction
3,356
3,241
3,122
Upper 95% Cl for
Post-Stage 2 PAR
6,675
6,446
6,210
12,958
12,512
12,055
6,184
5,971
5,753
10,685
10,318
9,940
Approach 3: Villanueva et al. (2004)
4.5% Reduction
7.8% Reduction
1 1 .2% Reduction
981
948
913
6,720
6,515
6,252
13,006
12,559
12,099
Note: Approaches 1 and 2 are calculated from the Post-Stage 1 values in Exhibit E.17 multiplied by 1 minus %
Reduction indicated.
For Approach 3 is calculated from the Post-Stage 2 PAR based on the OR for TTHM = 25.53 ug/L as
described previously.
Some numbers may reflect rounding
Exhibit E.I 9 provides the estimated Stage 2 cases avoided for the three approaches based on the
estimated Stage 2 TTHM % reduction. As described previously, these are obtained by subtracting the
Pre-Stage 2 attributable cases from the Pre-Stage 1 attributable cases.
Final Economic Analysis for the Stage 2 DBPR
E-28
December 2005
-------
Exhibit E.19 Stage 2 Cases Avoided from Three Approaches to PAR,
Based on Stage 2 TTHM Reductions
Lower 95% Cl for
Post-Stage 2 PAR
Best Estimate for
Post-Stage 2 PAR
Approach 1 : Five Studies
4.5% Reduction
7.8% Reduction
1 1 .2% Reduction
0
0
0
37
64
92
Approach 2:Villanueva et al. (2003)
4.5% Reduction
7.8% Reduction
1 1 .2% Reduction
159
275
393
Upper 95% Cl for
Post-Stage 2 PAR
317
546
782
615
1,060
1,518
293
506
724
507
874
1,252
Approach 3: Villanueva et al. (2004)
4.5% Reduction
7.8% Reduction
1 1 .2% Reduction
47
80
115
319
548
787
617
1,064
1,523
Note: Some numbers may reflect rounding
Exhibits E.20 through E.22 provide estimates of the Pre-Stage 2 cases attributable, Post-Stage 2
cases attributable and Stage 2 Cases avoided based on reductions in average HAAS concentrations. As
noted in these tables, Approach 3 is not used since it is based on a dose-response function involving
TTHMs and not HAASs.
Final Economic Analysis for the Stage 2 DBPR
E-29
December 2005
-------
Exhibit E.20 Pre-Stage 2 Cases Attributable to DBFs from Three Approaches to
PAR, Based on Stage 1 HAAS Reduction of 28.8%
Approach 1 :
Five Studies
Approach 2:
Villanueva et al. (2003)
Approach 3:
Villanueva et al. (2004)
Lower 95% Cl
for Post-Stage 1
PAR
0
3,437
Best Estimate for
Pre-Stage 1 PAR
804
6
6,836
,333
Upper95%Clfor
Post-Stage 1 PAR
13,270
10,942
Approach 3 not applicable to HAAS reductions
Notes: Approaches 1 and 2 are calculated from the Pre-Stage 1 values in Exhibit E.19 multiplied by 0.712 (a 28.8%
reduction in HAASs implying a 28.8% reduction in attributable cases).
Some numbers may reflect rounding
Exhibit E.21 Post-Stage 2 Cases Attributable to DBPs from Three Approaches to
PAR, Based on Stage 2 HAAS Reductions
Lower 95% Cl for
Post-Stage 2 PAR
Best Estimate for
Post-Stage 2 PAR
Upper 95% Cl for
Post-Stage 2 PAR
Approach 1 : Five Studies
5.2% Reduction
9.2% Reduction
13.7% Reduction
0
0
0
763
731
694
Approach 2:Villanueva et al. (2003)
5.2% Reduction
9.2% Reduction
13.7% Reduction
3,259
3,122
2,966
6,
5,
5,
6,482
6,210
5,900
12,584
12,054
1 1 ,452
005
753
465
10,376
9,940
9,444
Approach 3: Villanueva et al. (2004)
Approach 3 not applicable to HAA5 reductions
Notes: Approaches 1 & 2 are calculated from the Post-Stage 1 values in Exhibit E.20 multiplied by 1 minus %
Reduction indicated.
Approach 3 is calculated from the Post-Stage 2 PAR based on the OR for the TTHM concentration resulting
from the indicated Stage 2 % reduction
Some numbers may reflect rounding
Final Economic Analysis for the Stage 2 DBPR
E-30
December 2005
-------
Exhibit E.22 Stage 2 Cases Avoided from Three Approaches to PAR,
Based on Stage 2 HAAS Reductions
Lower 95% Cl for
Post-Stage 2 PAR
Best Estimate for
Post-Stage 2 PAR
Upper95%Clfor
Post-Stage 2 PAR
Approach 1 : Five Studies
5.2% Reduction
9.2% Reduction
13.7% Reduction
0
0
0
42
74
110
354
626
936
686
1,216
1,817
Approach 2:Villanueva et al. (2003)
5.2% Reduction
9.2% Reduction
13.7% Reduction
178
315
471
327
580
867
566
1,003
1,499
Approach 3: Villanueva et al. (2004)
Approach 3 not applicable to HAA5 reductions
Note: Some numbers may reflect rounding
E.4.2.2 Annual Bladder Cancer Cases Ultimately Avoidable Estimated in Benefits Model
As discussed in Chapter 6, for the sake of simplicity, EPA has selected Approach 2 based on
Villanueva et al. (2003) to estimate Pre-Stage 1 PAR values to carry through the full benefits modeling.
That is, the Monte Carlo simulation used to generate the benefits of the Stage 2 rule used only the inputs
from Approach 3 to estimate Pre-Stage 1 PAR values. This simulation included uncertainty in the OR
values reported by Villanueva et al. (2003) for the PAR calculations, and also included uncertainty in the
predicted DBP reductions for Stage 2. The resulting estimate of Pre-Stage 1 cases attributable to DBFs
are 10,159 (95% Conf Bounds = 5,575 - 14,642).
Exhibits E.23 and E.24 summarize the estimated annual bladder cancer cases ultimately avoidable
for both Stage 1 and Stage 2 derived from the benefits simulation model.
Exhibit E.23 Annual Bladder Cancer Cases Ultimately Avoidable
for the Stage 1 DBPR
DBP
TTHM
HAAS
Post-Stage 1 (Pre-Stage 2) Cases
Attributable to DBPs
Mean
7,394
7,229
5th
4,058
3,968
95th
10,657
10,420
Annual Cases Ultimately
Avoidable by the Stage 1 DBPR
Mean
2,765
2,929
5th
1,517
1,608
95th
3,985
4,222
Sources: Stage 2 DBPR Benefits Model. The 90 percent confidence bounds reflect
uncertainty in PAR and DBP reduction.
Final Economic Analysis for the Stage 2 DBPR
E-31
December 2005
-------
Exhibit E.24 Annual Bladder Cancer Cases Ultimately Avoidable
for the Stage 2 DBPR
DBP
TTHM
HAAS
Post-Stage 2 Cases
Attributable to DBFs
Mean
6,813
6,550
5th
3,796
3,634
95th
9,765
9,401
Annual Cases Ultimately
Avoidable by the Stage 2 DBPR
Mean
581
680
5th
232
261
95th
1,084
1,288
Sources: Stage 2 DBPR Benefits Model. The 90 percent confidence bounds reflect
uncertainty in PAR and DBP reduction.
E.5 Adjustments to Account for Cessation Lag
E.5.1 Background
If the reduction in bladder cancer risk for individuals exposed to DBFs from drinking water were
to begin immediately when the DBP levels in drinking water are reduced as result of these regulations,
then the benefits of the regulations in terms of annual bladder cancer cases avoided would simply be the
annual bladder cancer cases ultimately avoidable (as described in the preceding section) starting when
those exposure reductions begin and continuing each year thereafter.
Cancer risk reductions (in terms of annual individual risk) are, generally not expected to occur
instantaneously when exposure to a carcinogen is reduced or eliminated. Rather, it is expected that the
risks for those individuals having had previous higher exposures will decline overtime, eventually
reaching or at least approaching the risk level associated with the lower exposure levels. The rate may
depend upon a combination of the carcinogen, its particular end-point and mode of action, and other
factors as mentioned in Chapter 6.
The term "cessation lag" is used to refer to this transition period between higher risks from higher
exposures and lower risks from lower exposures. Cessation lag models, based on available empirical data
of cancer risk reduction following exposure reduction to carcinogens, have been used in this benefits
analysis to quantify the rate of the risk reduction following rule implementation and reduction in exposure
to DBFs from drinking water.
This section of Appendix E provides some additional background on cessation lag and describes
the specific data sources and model-fitting procedures used to derive the cessation lag models included in
the Stage 2 benefits analysis. It also describes the calculations performed in the benefits model to
compute the annual cases avoided each year following exposure reduction that draw upon the cessation
lag models.
When considering cessation lag and its incorporation into the benefits modeling, it is important to
separate the exposed population into two groups: (1) those who are alive at the time that the rule is
implemented and who have, therefore, already been exposed for some portion of their lifetime at the
higher pre-rule DBP levels, and (2) those who are born after the rule is implemented who will only ever
be exposed to the lower post-rule DBP levels.
Cessation lag enters into the calculation of benefits only for the first of these two groups.
Cessation lag does not enter into the calculation of benefits for the second group since there is no change
Final Economic Analysis for the Stage 2 DBPR
E-32
December 2005
-------
from a higher to a lower exposure level for that population, and therefore there is no transition period
from the higher to the lower risk level. (Note: It is to accommodate these two different populations in
each year following the implementation of the rule that it is necessary to have bladder cancer cases from
all causes available as a function of age as presented in Exhibit E. 1.)
At some point following rule implementation, the annual cases avoided will become equal to the
annual bladder cancer cases ultimately avoidable. The time that it takes for this to occur depends mainly
upon the cessation lag model and how it describes the transition to the lower risks. It is also influenced
by the turn-over in the population from being composed primarily of those alive prior to rule
implementation to being composed primarily of those born after rule implementation. It is useful to note
that the absolute upper bound on the time that it will take for the annual cases avoided to become equal to
the annual cases ultimately avoidable described in the preceding section is when the population is
composed solely of those who were born after the rule has gone into effect. For the purposes of the Stage
2 benefits modeling, it is assumed that this will be 100 years after the rule is implemented. At that time
(and from that point forward) the annual bladder cancer cases ultimately avoidable is achieved for the
exposed population.
E.5.2 Data Sources for Cessation Lag Models
As noted above, the bladder cancer risk reductions are not expected to be instantaneous; Rather, it
is assumed that there is a transition period from the risk associated with the higher DBF exposure levels to
the risk associated with the lower exposure levels. The challenge is to estimate the rate at which this
transition occurs.
No epidemiological or other empirical data are available that specifically address the rate or
pattern of achieving the bladder cancer benefits of DBF exposure reductions. In lieu of using data
specific to DBFs, EPA is drawing upon empirical data from three epidemiology studies that address the
rate at which cancer risk reduction occurs for individuals following exposure reduction to other
carcinogens. The three studies used, and the cancer end-points and risk factors they consider, are:
1. Hrubec and McLaughlin (1997a): smoking and lung cancer
2. Hartge et al. (1987): smoking and bladder cancer
3. Chen and Gibb (2003): arsenic (in drinking water) and bladder cancer
Each study 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 lifetime transitions over time to the risk for
individuals at some lower level of exposure following exposure reduction. The first two data sets involve
a change from smoking to not-smoking (complete cessation) while the third involves a change from a
high arsenic exposure level of 50 micrograms per liter (ug/L) in drinking water to a lower exposure level
of lOug/L.
In all cases, the risk reduction in these studies is considered over time in terms of changes in the
RR of cancer where "relative" refers to the lower exposure group (for example, never-smokers for the
first two studies; and those always exposed to 10 ug/L of arsenic forthe third study). For these lower
exposure groups, referred to as the referent group, the RR is set equal to 1.0. That is, the risk for the
exposed individuals is measured relative to the risk of those who have not been exposed (or who are at a
lower exposure). This referent group therefore represents the lowest possible risk that can be reached
following the exposure reduction.
Final Economic Analysis for the Stage 2 DBPR E-33 December 2005
-------
E.5.3 Model Specification Using Cessation Lag
The benefits model incorporates cessation lag by using the concept of % Maximum Relative Risk
Reduction (%MRRR) which is expressed as:
%MRRRj = RRn- RR, x 100 (Equation E.8)
RR0- 1.0
That is, the %MRRR achieved in any year j following exposure cessation or reduction is computed as the
Relative Risk for those at the higher exposure level (RRo) minus the Relative Risk observed in year j for
those whose exposure has been reduced (RRj), divided by the maximum Relative Risk reduction, which is
the Relative Risk for those at the higher exposure (RRo) minus 1.0 (since 1.0 is the lowest value of
Relative Risk that can be achieved under this formulation).
The empirical Relative Risk reduction data in these studies typically provides the changes in RR
for several time periods (usually ranges) representing years following exposure reduction. To be
incorporated in the Stage 2 benefits modeling, continuous functions were fit to the empirical data from
each of the three studies and those functions were then used to calculate the %MRRR for each year after
exposure reduction begins.
E.5.3.1 Model Fitting Process
Based on a set of analyses performed, two general functional forms were found to provide the
most suitable fits to the data from each of these studies. These are a Weibull function and a Pareto
function, as shown below:
Weibull Function:
(Equation E. 9)
Pareto Function:
(Equation E. 10)
As discussed later in this section, EPA initially evaluated nine different functions for the
cessation lag model form from which these two were selected.
Here the term LFj refers to the "Lag Function" value for year j after rule implementation and is
the modeled equivalent to the %MRRR noted above for - and derived from - the empirical data sets. All
LFj values fall between 0 and 1. The parameters q and r in these functions are estimated from the curve
fitting procedures using the data from the individual studies.
All model fitting procedures were carried out in SAS.
Final Economic Analysis for the Stage 2 DBPR E-34 December 2005
-------
Smoking and Bladder Cancer
The smoking and bladder cancer data used to parameterize the cessation lag models for smoking
and bladder cancer is derived from Table 1 of Hartge et al. (1987) and shown in Exhibit E.25. The study
provides values for RR and years following cessation, and %MRRR was calculated from these data using
the RR for never smokers as the referent value (RR = 1.0).
Exhibit E.25 Summary of Smoking / Bladder Cancer Data from Hartge et al. (1987)
Used to Model Cessation Lag
Years After
Cessation
< 1 (RR0)
1 -10
10-20
20-30
30-40
>40
Never Smokers
Estimated RR
(95% Cl)
2.9 (2.6 - 3.3)
2.2(1.9-2.6)
1 .6 ( 1 .4 - 1 .9)
1.7(1.4-2.1)
1.3(1.0-1.7)
1.5(1.1-2.1)
1.0
%MRRR
(Using Estimated RR
Value)
0.0%
36.8%
68.4%
63.2%
84.2%
73.7%
NA
Final Economic Analysis for the Stage 2 DBPR
E-35
December 2005
-------
Exhibit E.26 is a graph of the Weibull form using parameters fit to the best estimates of the RR in
the study and the mid-point of the years after cessation together with the empirical data for those inputs.
The estimated parameters for the Weibull form for these inputs are q = 0.520; r = 17.539.
Exhibit E.26 Graph of the Weibull Form for Smoking / Bladder Cancer Data
on
0.8
0.6
0.4
0.2
10
15
20
25
30
35
40
45
50
Years following cessation
Final Economic Analysis for the Stage 2 DBPR
E-36
December 2005
-------
Exhibit E.27 is a graph of the Pareto form using parameters fit to the best estimates of the RR in
the study and the mid-point of the years after cessation together with the empirical data for those inputs.
The estimated parameters for the Pareto form for these inputs are a=-4.110x 107; b = 7.703 x 10s.
Exhibit E.27 Graph of the Pareto Form for Smoking / Bladder Cancer Data
0.8
0.6
0.4
0.2
.•<*"
I
I
10 15 20 25 30
Years following cessation
35
45
Final Economic Analysis for the Stage 2 DBPR
E-37
December 2005
-------
Smoking and Lung Cancer
The smoking and lung cancer data used to parameterize the cessation lag models for smoking and
lung cancer is derived from Table 4 of Hrubec and McLaughlin (1997a) and are presented in Exhibit
E.28. The study provides values for RR and years following cessation, and %MRRR was calculated from
these data using the RR for never smokers as the referent value (RR = 1.0). The Hrubec and McLaughlin
study did not provide an estimate of RR for current smokers for the RRo value. The range of values used,
as shown in Exhibit E.28, were obtained from two sources: The American Cancer Society (2004) and
Halpernetal. (1993).
Exhibit E.28 Summary of Smoking / Lung Cancer Data from Hrubec and
McLaughlin (1997b) used to Model Cessation Lag
Years After
Cessation
< 1 (RRO)
1 -5
5- 10
10-20
20-30
30-40
>40
Never Smokers
Estimated RR
(95% Cl)
22.1 (16.6-29.5)*
16.1 (10.0-25.2)
7.8(5.6-10.6)
5.1 (4.2-6.1)
3.3(2.8-4.0)
2.0(1.6-2.6)
1.5(1.1-2.0)
1.0
%MRRR
(Using Estimated RR
Value)
0.0%
28.4%
67.8%
80.6%
89.1%
95.3%
97.6%
NA
*RR0 values for current smokers were not provided in Hrubec and McLaughlin (1997b). The values used
here were obtained from relative risks for current smokers reported by American Cancer Society (2004) and
Halpernetal. (1993)
Final Economic Analysis for the Stage 2 DBPR
E-38
December 2005
-------
Exhibit E.29 is a graph of the Weibull form using parameters fit to the best estimates of the RR in
the study and the mid-point of the years after cessation together with the empirical data for those inputs.
The estimated parameters for the Weibull form for these inputs are q = 0.821; r = 7.788.
Exhibit E.29 Graph of the Weibull Form for Smoking / Lung Cancer Data
on
a:
o.s
0.6
0.4
0.2
\ r
O .'
10
J L
20
30
40 45 50
Years following cessation
Final Economic Analysis for the Stage 2 DBPR
E-39
December 2005
-------
Exhibit E.30 is a graph of the Pareto form using parameters fit to the best estimates of the RR in
the study and the mid-point of the years after cessation together with the empirical data for those inputs.
The estimated parameters for the Pareto form for these inputs are q = - 1.597 x 109; r = 1.235 x 1010.
Exhibit E.30 Graph of Pareto Form for Smoking / Lung Cancer Data
on
a:
o.s
0.6
0.4
0.2
I I
10
20
30
40
45
50
Years following cessation
Final Economic Analysis for the Stage 2 DBPR
E-40
December 2005
-------
Arsenic (from drinking water) and Bladder Cancer
The data used to parameterize the cessation lag models for arsenic from drinking water and
bladder cancer is derived from Table 5 of Chen and Gibb (2003) and are shown in Exhibit E.31. Data are
shown separately for the smokers and non-smokers. However, parameters for the Weibull and Pareto
functions were estimated using both the smoker and non-smoker data together. The data were not
weighted to reflect smoking because the results were so similar between the two groups and information
on the proportion of smokers in the study group was not available.
The arsenic and bladder cancer data did not provide ranges for either the RR or the years
following arsenic exposure reduction, and therefore it was not possible to generate uncertainty sets of
parameters for this cessation lag model as was done for the smoking and bladder cancer and the smoking
and lung cancer cessation lag models.
Exhibit E.31 Summary of Arsenic / Bladder Cancer Data from Chen and Gibb
(2003) used to Model Cessation Lag
Years After
Exposure
Reduction from
50to10ug/L
0 (RRO)
8
12
20
22
23
25
Always at 1 0 ug/L
Estimated
RRfor
Smokers
1 .0360
1.0141
1 .0065
1 .0044
1.0050
1.0012
1.0000
1.0
%MRRRfor
Smokers
0.0%
60.80%
81 .85%
87.82%
86.25%
96.74%
100%
NA
Estimated RR
for Non-
Smokers
1.0396
1.0096
1.0087
1.0098
0.9989
1.0000
1.0000
1.0
%MRRRfor
Non-Smokers
0.0%
75.69%
77.89%
75.26%
102.77%
100%
100%
NA
Final Economic Analysis for the Stage 2 DBPR
E-41
December 2005
-------
Exhibit E.32 is a graph of the Weibull form using parameters fit using both the smoker and
non-smoker data on RR in the study and the years after cessation, together with the empirical data for
those inputs (smokers are diamonds; non-smokers are circles). The estimated parameters for the Weibull
form for these inputs are a = 1.079 b = 6.635.
Exhibit E.32 Graph of Weibull Form for Arsenic / Bladder Cancer Data
0.8
0.4
0.2
\ i i i i i i r
o
*/"
/
I o
J L
J
I
L
0 5 10
15 20 25 30
Years following cessation
35
40 45
50
Final Economic Analysis for the Stage 2 DBPR
E-42
December 2005
-------
Exhibit E.33 is a graph of the Pareto form using parameters fit to %MRRR using both the smoker
and non-smoker data on RR in the study and the years after cessation, together with the empirical data for
those inputs (smokers are diamonds; non-smokers are circles). The estimated parameters for the Pareto
form for these inputs are a = -7.224 x 106; b = 4.629 x 107.
Exhibit E.33 Graph of Pareto Form for the Arsenic / Bladder Cancer Data
0.3
0.6
0.4
0.2
* /
.0/ o
/
_L
i
o
I
oo o
_L
_L
_L
10 15 20 25 30
Years following cessation
35
45
50
Final Economic Analysis for the Stage 2 DBPR
E-43
December 2005
-------
E.5.3.2 Other Model Forms Evaluated for the Cessation Lag Function
There were a total of nine functional forms initially considered for the cessation lag models. The
general shape of the cessation lag (as %MRRR over time) was expected to be an increasing function on
the range of 0 to 1 over the domain of years following cessation, reaching or becoming asymptotic to 1 as
the number of years following cessation increases. Therefore, a set of general functional forms were
identified that exhibit this pattern. The specific set of function forms evaluated was (x is time after
cessation, a, b, and c are model parameters):
Weibull (3 parameters):
Weibull (2 parameters):
Pareto I:
Pareto II:
Log n: f(x) = a. ]p(x) +b
Logistic: /v \ i 1
B f(x)=\l+e
~x~\
Exponential: f(x)=a-ebx+C
LgS: f(x)=t
Extreme: /y •-
Final Economic Analysis for the Stage 2 DBPR E-44 December 2005
-------
All of these functions were evaluated using the best estimates of the RR values and the
mid-points of the ranges of years following cessation provided in the three studies. For the Stage 2
benefits modeling, the objective of exploring several various model forms was to select two forms for
each data set rather than a single "best fit" to capture some measure of model uncertainty.
For uniformity in running the benefits analysis, it was desired that the same two models forms be
used for all three cessation lag data sets, so model selection was not strictly the best fits for each data set,
although the two models ultimately selected were among the best fits in all cases. Goodness of fit tests
performed included average-square-residuals, sign test and run test.
Because it was also desired that uncertainty in the parameter values for each of the two model
forms selected be considered in the benefits modeling, it was also necessary that a large set of parameters
for the models reflecting that uncertainty (by considering the reported ranges of values in years following
cessation for each group and the range of RR values reflected by the 95% CI reported for the RR values)
were able to be readily estimated in SAS using its nonlinear curve fitting procedures. Some model forms
were found not to converge or to do so with great difficulty with certain input data; generally, these were
cases where the models also did not fit well.
Another desired characteristic of the cessation lag functions was that the curves that were fit to
the data would pass through the origin - that is, it would predict 0% maximum relative risk reduction at 0
years after cessation. Not all of these model forms did that with the estimated parameters for all of the
data sets.
The parameters for these various functional forms were estimated in SAS using the NLIN SAS
procedure. Estimation of a nonlinear model is an iterative process that begins with a set of initial
parameter value estimates as inputs and explores alternative values around them. The procedure evaluates
the residual sum of squares at each combination of parameter values to determine the set of parameter
values producing the lowest residual sum of squares. The numerical method used obtain the alternative
parameter estimates was the Modified Gauss-Newton for nonlinear least squares (the SAS default
procedure)..
Based on the results of these model fits together with the other general criteria and characteristics
described above, it was determined that the 2-parameter Weibull and the Pareto II model forms were the
most suitable for these data sets.
E.5.3.3 Benefit Model Calculation Using Cessation Lag Function
The number of cases avoided among that part of the population born before the rule goes into
effect for a specific age group i in any j years after implementation is computed in the benefits model as:
CA VS2bl] = (CA VS2A4AXJ x (LFJ for all / >j (Equation E. 11)
Here, the subscript b refers to those born before the Stage 2 rule is implemented, /' refers to each of the
one-year age groups and/ refers to the number of years after exposure reduction. The total cases avoided
across all age groups born before rule implementation in any given year/ is:
100
CAVS2, = y(CAVS2MAX)*(LF)
b'J 4?i (Equation E. 12)
Final Economic Analysis for the Stage 2 DBPR E-45 December 2005
-------
So, for example, 25 years after the rule goes into effect (j = 25) the age groups comprising those born
before the rule went into effect are ages 26 (i = j + 1) to 100. (As noted previously, 25 years after the
rule is implemented those in age groups 25 years old or younger will all have been born after the rule
went into effect.)
The annual bladder cancer cases ultimately avoidable for each age group born before the rule
goes into effect (and exposure reduction begins) is reduced according to the fraction of the maximum
relative risk reduction that is estimated from the Lag Function to be attained j years (25 in this example)
after exposure to the lower levels of DBFs began (based on the particular cessation lag function used).
E.6 Computational Procedures for Predicting Cases of Bladder Cancer Avoided
The purpose of this section is to provide all necessary equations and background information for
computing the final number of annual cancer cases avoided.
E.6.1 Estimating Cases Avoided for Populations Born Before and After the Rule
The calculation of annual benefits for the portion of the population born after the rule is
implemented is relatively straightforward. For any specific age group born after the rule is implemented,
the annual benefits are simply based on the cases ultimately avoidable for that age group. The total for all
age groups born after the rule is implemented is the sum across all the appropriate age groups.
So, for example, 10 years after the rule goes into effect, this part of the population consists only
of those who are 10 years old or younger; the benefit of the rule is calculated as the sum of the cases
ultimately avoidable for each age group 1 through 10. Similarly, 25 years after the rule goes into effect,
the benefits for this portion of the population are the sum of the annual cases ultimately avoidable for
each age group 1 through 25. In the modeling performed for Stage 2, the population is considered in
one-year age groups through age 100. Therefore, 100 years after the rule is implemented, the entire
population is composed of individuals born after the rule is implemented and at that time- at the latest -
and from that time on the cases ultimately avoidable will be achieved.
While the modeling for the Stage 2 benefits is set up for the full 100-year time horizon, the focus
for the comparison of benefits with costs is limited to the first 25 years after the rule is implemented.
Nevertheless, for the sake of completeness, these benefits (cases avoided) are computed in the model for
each year after the rule and are combined with the benefits (cases avoided) obtained for the other portion
of the population: those who are born before the Stage 2 is implemented.
The calculation of annual benefits for the portion of the population born before implementation of
the rule must account for cessation lag. To provide initial insight into how the annual benefits are
computed each year for this part of the population born, consider the group of people who are 50 years
old at the time the rule goes into effect. One year after the rule is implemented, that group has become
the 51-year-old group, two years after the rule they are the 52-year-old group, and so on. For example, if
the annual cases ultimately avoidable from Stage 2 for the 51-year-old age group is 5.3 cases, the number
for the 52-year-old group would be approximately 5.1 cases. Again, if the benefits of the Stage 2
exposure reduction to those who have had some years of exposure to the pre-Stage 2 levels of DBFs (in
this case 50 years of such exposure) were instantaneous, then one year after the rule is implemented the
Final Economic Analysis for the Stage 2 DBPR E-46 December 2005
-------
expected benefits would be all of those 5.3 cases and two years after they would be all of the 5.1 cases -
just as if those individuals had spent their entire lives exposed only to the lower, post-Stage 2 levels.
As we have discussed in Section E.5, however, cancer risk reductions are not instantaneous; there
is a transition period from the risk associated with the higher exposure levels to the risk associated with
the lower exposure levels (referred to as cessation lag). Section E.5 provides a discussion of how
cessation lag is accounted for in the population born before the rule is implemented.
Cases avoided for the two populations (those born before and those born after the rule is
implemented) are added to produce total cases avoided for the rule.
E.6.2 Accounting for Uncertainties in the Benefits Model
The calculation of bladder cancer cases avoided is carried out as a Monte Carlo simulation where
uncertainty in several of the key inputs is considered quantitatively. Three separate benefits estimates are
modeled, each representing the use of one of the three studies serving as the basis for the cessation lag
function as noted above (smoking/lung cancer; smoking/bladder cancer; and arsenic/bladder cancer).
Each model is run independently for percent DBF reduction based on TTHM and HAA5.
Each of these three separate cessation lag models is, as noted, a Monte Carlo simulation in which
several specific inputs will be incorporated as uncertainty variables. These are:
1. Three approaches were used to estimate the baseline number of bladder cancer cases
attributable to DBF exposure. For the sake of simplicity, one approach using data from
Villanueva et al. (2003) was carried through the full benefits model.
2. The PAR value for Pre-Stage 1 that is derived from the Villanueva et al. (2003) study is
input as an uncertain variable. Specifically, the OR and its 95% confidence interval
reported by Villanueva et al. (2003) were used to parameterize a triangular uncertainty
distribution with minumum = 1.0725, mode = 1.2, and maximum = 1.4359. The
minimum was estimated from the lower 95% bound of 1.1 multiplied by 0.975; the
maximum was estimated from the upper 95% confidence bound of 1.4 divided by 0.975;
the mode of 1.2 was taken from the best estimate of the OR reported by the authors. Note
that the expected value of this distribution of 1.24 is higher than the mode of 1.2 because
of the asymmetry of the 95% confidence interval reported by Villanueva et al. (2003).
The confidence bounds from Villanueva et al. (2003) capture a significant portion of the
confidence intervals of the other two approaches.
3. Percent DBP (TTHM or HAA5) reductions for Stage 1 and Stage 2. These values are
derived using the SWAT model and the ICR Matrix Method. For the estimates of DBP
reduction as a result of the Stage 2 DBPR, EPA produces two separate estimates of
percent reduction to account for the potential impact of the IDSE on the compliance
forecast. Also, the uncertainty in SWAT-predicted equations is incorporated into the
model.
4. Model form uncertainty for cessation lag functions. As noted above, two functional
forms have been used to model the Lag Function values: Weibull and Pareto. In the
Final Economic Analysis for the Stage 2 DBPR E-47 December 2005
-------
Monte Carlo simulation, one or the other of these functions is selected randomly (with
equal probability) on a given iteration.
5. Model parameter uncertainty for cessation lag functions. For the Lag Functions based on
the smoking/lung cancer and the smoking/bladder cancer data sets, the two parameters for
the Weibull and Pareto functions (q and r as shown above) are uncertain values; that
uncertainty is accounted for in the simulation. One thousand parameter pairs were
estimated for each function reflecting uncertainty in the time following cessation and in
the reported RR values in those studies. On a given iteration, once one of the two
functional forms has been selected at random, a parameter pair for that function is
selected at random and used for the subsequent calculations in that uncertainty loop.
Note that for the arsenic/bladder cancer data provided in the Chen and Gibb study, there
was insufficient information to estimate the uncertainty around these parameters (Chen
and Gibb 2003). In the model runs using the arsenic/bladder cancer data, only the single
best estimates of those parameters are used once the model function is randomly selected.
E.6.3 Benefits Model Equations
The function and flow of the model is presented in Exhibit E.34. The upper portion presents the
model inputs and distributions for uncertain values. The bottom portion shows the progression of the
model.
The model is run independently to produce PAR values for TTHM and F£AA5 as indicators of
DBP reduction, and for each of three cessation lag functions based on smoking and lung cancer, smoking
and bladder cancer, and arsenic and bladder cancer data (a total of 6 estimates of PAR). The PAR values
are generated by using the triangular distribution of OR values estimated from Villanueva et al. (2003)
and Equation E.3 , as described earlier.
The set of PAR values for each run are used to generate sets of cases attributable to chlorination
DBFs (CATT) as in Equation E. 13 by using the background incidence of bladder cancer (BI) from
Equation E.I.
CATT, = BI, x PAR, (Equation E. 13)
The sets of values for CATT are then used to generate sets of the cases ultimately avoidable due to Stage
1 (CAVSlMax) by using the following equation:
CAVSlMax=CATT x (SIRed) (EquationE.I4)
The percent reduction in average DBP (TTHM or F£AA5) concentration from Pre-Stage 1 to Post-Stage 1
(SIRed) is applied to the cases attributable to DBFs.
These ultimately avoidable values are used to calculate sets of cases avoided for Stage 1. The
total of cases consists of cases avoided for two different populations, those born before the rule and those
born after the rule. Since the group that is born after the rule only experiences post-rule exposure levels,
the cases avoided for this group are equal to the cases ultimately avoidable (CAVSla = CAVSlMax).
For the population alive when the rule is promulgated, there will be a cessation lag effect, as described in
Section E.5. The cases avoided for this group is some fraction of the ultimate value, each year after the
rule is promulgated. This is referred to as the lag function (LF). The cases avoided for this group is
Final Economic Analysis for the Stage 2 DBPR E-48 December 2005
-------
CAVSlb = (CAVSlMax x LF). The lag function is explained in more detail in Section E.5.3.1. To
estimate the total cases avoided by the Stage 1 rule, the cases avoided for each of the two populations is
summed to come up with sets of cases avoided (CAVS1). The model then repeats this process for all 6
combinations of the two DBFs and three cessation lag models.
Final Economic Analysis for the Stage 2 DBPR E-49 December 2005
-------
Exhibit E.34 Benefits Model Process Flow Chart
A.
Define Values for Constant Inputs:
(1) Pe
(2) S1RedTTHM
(3) S1RedHAA5
(4) POPj
(5) BRj
B.
Create Sets of "k" Values for Uncertain
Inputs:
(1) OR (Triangular Dist.)
(2) S2RedTTHM (Uniform Dist.)
(3) S2RedTTHM (Uniform Dist.)
(4) LF form and parameters
(1) Smoking/Bladder
(2) Smoking/Lung
(3) Arsenic/Bladder
C.
Set number of iterations (=k)
Note: For all runs Ages i = 1... 101
and Years After Stage 2 Exposure
Reduction j = 1...100.
Definitions:
Pp Fraction of population exposed to DBFs __
9 OR Odds Ratio to Calculate Pre-Stage 1 PAR
S1 Red % reductions in avg. DBP concentrations from pre- to post- Stage 1 s2Red % reductions in avg DBp concentrations from pre. to post. stage 2
POP; Population at age = i |_p Lag function
BR- Background bladder cancer rate from SEER for age = i k Number of iterations
Model Inputs
D.
Select Model Run: 6 Versions
Based on 2 DSPs' Reduction
(TTHM and HAAS) with 3
Cessation Lag Functions
(Smoking/Bladder; Smoking/Lung;
Arsenic/Bladder)
Generate k sets of PAR,
(Store Results)
J.
Create k Sets of CAVS2,
(Store Results)
Generate k Sets of
CATT,
(Store Results)
Generate k Sets of
CAVS1 Max,
(Store Results)
Create k Sets of:
CAVS2a „
CAVS2b',J
CAVS2,,'
Create k Sets of
CAVS2Max,
(Store Results)
Model Functions and Flow
Final Economic Analysis for the Stage 2 DBPR
E-50
December 2005
-------
A similar process is performed for the annual cases ultimately avoidable due to Stage 2
(CAVS2Max), and is built on the CAVSlMax in the following equation:
CA VS2Max = [CATT - CA VSlMax] x S2Red (Equation E. 15)
The percent reduction in average DBF (TTHM or HAAS) concentration from Pre-Stage 2 to Post-Stage 2
is applied to the cases available after Stage 1 (S2Red). Note that while the percent DBP reduction for
Stage 1 is a point estimate, the percent DBP reduction for Stage 2 incorporates uncertainties (see previous
section).
These estimates of annual cases ultimately avoidable are used to calculate the cases avoided for
Stage 2 following rule implementation. As was the case for Stage 1, the total cases avoided from Stage 2
consist of those for two different populations, those born before the rule and those born after the rule.
Since the group that is born after the rule only experiences post-rule exposure levels, the cases avoided
for this group equal the cases ultimately avoidable (CAVS2a = CAVS2Max). As described for Stage 1
above, the lag function is used to obtain the cases avoided for the population alive when the rule is
promulgated, CAV2b = CAVS2Max * LF. To estimate the total cases avoided by the Stage 2 rule
(CAVS2), the cases avoided for each of the two populations is. The model then repeats this process for all
6 combinations of the two DBFs and three cessation lag models.
Additional details for the Stage 2 DBPR benefits model are provided in Appendix K.
E.6.4 Allocating Cases Avoided to Different System Size and Source Water Categories
The total number of bladder cancer cases avoided as a result of the Stage 2 DBPR includes those
from all system sizes and source water categories. To adjust the projection of cases over 25 years to
account for the rule implementation schedule (see next Section), the total cases are allocated to the
following system categories:
• Large and medium surface water systems
Small surface water systems
• Large and medium ground water systems
Small groundwater systems
The cases are allocated in proportion to 1) total population served in each category and 2) reduction in
TTHM or HAAS concentrations. The percent of cases allocated to the four system categories is shown in
Exhibit E.35 for the Stage 1 DBPR, and Exhibit E.36 for the Stage 2 DBPR.
Final Economic Analysis for the Stage 2 DBPR E-51 December 2005
-------
Exhibit E.35 Allocation of Cases Avoided by the Stage 1 DBPR to System
Categories
System Size and
Type:
Population
Served
A
Population
(Percent of
Total)
B = A/
263,024,518
Pre-Stage 2
DBP
Concentration
(ug/L)
c
Pre-S2
Population
Weighted
Average
Concentration
D = B*C
Percent
Reduction in
DBP
Concentration
E
Amount
Reduced
(ug/L)
F = C*E
Population
Weighted
Amount
Reduced
G = F*B
TTHM
SW>1 0,000
SW< 10,000
GW>1 0,000
GW< 10,000
Total
160,935,736
8,422,403
65,152,168
28,514,211
263,024,518
61 .2%
3.2%
24.8%
10.8%
100.0%
48.70
82.80
15.36
16.53
29.80
2.65
3.80
1.79
27.17%
57.16%
14.31%
1 1 .08%
13.23
47.33
2.20
1.83
8.10
1.52
0.54
0.20
10.35
HAAS
SW>1 0,000
SW< 10,000
GW>1 0,000
GW<1 0,000
Total
160,935,736
8,422,403
65,152,168
28,514,211
263,024,518
61 .2%
3.2%
24.8%
10.8%
100.0%
35.48
45.32
8.45
9.09
21.71
1.45
2.09
0.99
29.54%
44.83%
17.63%
13.65%
10.48
20.32
1.49
1.24
6.41
0.65
0.37
0.13
7.57
Allocation
of Cases
Avoided
H = GIG total
78.2%
14.6%
5.3%
1 .9%
100%
84.7%
8.6%
4.9%
1 .8%
100%
Note:
Sources:
Allocation of cases to system sizes within the size classes noted above (<>10,000) are consistent with the available DBP
information and calculations on a finer level must be based upon population only.
(A) Exhibit 3.3.
(C) & (E) Exhibit 5.22.
Final Economic Analysis for the Stage 2 DBPR
E-52
December 2005
-------
Exhibit E.36 Allocation of Cases Avoided by the Stage 2 DBPR
to System Categories
System Size and
Type:
Population
Served
A
Population
(Percent of
Total)
B = A/
263,024,518
P re-Stage 2
DBP
Concentration
(ug/L)
c
Pre-S2 Population
Weighted Average
Concentration
D = B*C
Percent
Reduction in
DBP
Concentration
E
Amount
Reduced
(ug/L)
F = C*E
Population
Weighted
Amount
Reduced
G = F*B
TTHM (20% SM)
SW> 10,000
SW<1 0,000
GW> 10,000
GW< 10,000
Total
160,935,736
8,422,403
65,152,168
28,514,211
263,024,518
61 .2%
3.2%
24.8%
10.8%
100.0%
35.47
35.47
13.16
14.70
21.70
1.14
3.26
1.59
7.30%
7.30%
1.44%
2.04%
2.59
2.59
0.19
0.30
1.58
0.08
0.05
0.03
1.75
HAAS (20% SM)
SW>1 0,000
SW<1 0,000
GW> 10,000
GW< 10,000
Total
160,935,736
8,422,403
65,152,168
28,514,211
263,024,518
61 .2%
3.2%
24.8%
10.8%
100.0%
25.00
25.00
6.96
7.85
15.30
0.80
1.72
0.85
7.69%
7.69%
4.47%
6.31%
1.92
1.92
0.31
0.50
1.18
0.06
0.08
0.05
1.37
TTHM (25% SM)
SW>1 0,000
SW< 10,000
GW> 10,000
GW< 10,000
Total
160,935,736
8,422,403
65,152,168
28,514,211
263,024,518
61 .2%
3.2%
24.8%
10.8%
100.0%
35.47
35.47
13.16
14.70
21.70
1.14
3.26
1.59
11.16%
7.30%
1.44%
2.04%
3.96
2.59
0.19
0.30
2.42
0.08
0.05
0.03
2.58
HAAS (25% SM)
SW>1 0,000
SW< 10,000
GW> 10,000
GW< 10,000
Total
160,935,736
8,422,403
65,152,168
28,514,211
263,024,518
61 .2%
3.2%
24.8%
10.8%
100.0%
25.00
25.00
6.96
7.85
15.30
0.80
1.72
0.85
12.23%
7.69%
4.47%
6.31%
3.06
1.92
0.31
0.50
1.87
0.06
0.08
0.05
2.06
Allocation
of Cases
Avoided
H = GIG total
90.7%
4.7%
2.7%
1 .9%
100%
85.9%
4.5%
5.6%
3.9%
100%
93.7%
3.2%
1 .8%
1 .3%
100%
90.7%
3.0%
3.7%
2.6%
100%
Note:
Sources:
Allocation of cases to system sizes within the size classes noted above (<>10,000) are consistent
with the available DBP information and calculations on a finer level must be based upon population
only.
(A) Exhibit 3.3.
(C) Exhibit 5.22.
(E) For SW, Percent Reduction = [(SWAT predicted reduction) + ICR/SWAT ratio '
predicted reduction)]/2. See Exhibit 5.18. For GW, see Exhibit 5.23.
(SWAT
E.6.5 Adjusting the 25-year Projection of Cases Avoided to Account for the Rule Implementation
Schedule
Reduction in exposure to DBFs does not begin immediately when the Stage 2 DBPR is
promulgated. Water systems are given a certain amount of time to make treatment technology changes to
come into compliance with the rule. Appendix D shows estimates of when systems will install treatment
technology changes (in the form of cumulative percentages) based on the required compliance schedule.
Exhibit E.37 shows the estimated schedule for large and medium surface water systems, small surface
water systems, large and medium ground water systems, and small ground water systems, as derived from
Appendix D. The projected total estimate of bladder cancer cases avoided is multiplied by the
Final Economic Analysis for the Stage 2 DBPR
E-53
December 2005
-------
percentages in Exhibit E.37 to generate the final stream of bladder cancer cases avoided for 25 years after
the rule is promulgated.
Exhibit E.37 Estimated Schedule for Systems Making Treatment Technology
Changes to Comply with the Stage 2 DBPR
Year after Rule
Promulgation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
% Surface Water Systems
Small
0%
0%
0%
0%
0%
15%
31%
46%
62%
77%
92%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Large
0%
0%
0%
0%
0%
22%
43%
65%
87%
96%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
% Ground Water Systems
Small
0%
0%
0%
0%
0%
15%
31%
46%
62%
77%
92%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Large
0%
0%
0%
0%
0%
24%
47%
71%
95%
99%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Note: Small systems serve less than 10,000 people and large system serve greater than or equal to 10,000
people.
Source: O&M schedule in Appendix D, system size categories combined in proportion to population.
E.7 Detailed Results Output from Models
This section presents detailed results for annual cancer cases avoided (adjusted for cessation lag
and rule implementation schedule) for the Stage 2 DBPR Preferred Regulatory Alternative (includes a
requirement for the IDSE), all other regulatory alternatives, and all sensitivity analyses. Results for
TTHM are shown for each alternative; however, detailed results for HAAS are shown only for the
Preferred Regulatory Alternative. The derivation of results using HAAS occurrence data is exactly the
same as the calculations using TTHM occurrence data. The percent reductions are similar.
Final Economic Analysis for the Stage 2 DBPR
E-54
December 2005
-------
Appendix E2
Calculation of PAR, Attributable Cases and
Cases Avoided for the Colon and Rectal Cancer
Sensitivity Analyses
-------
-------
Appendix E2 Calculation of PAR, Attributable Cases and Cases Avoided for the Colon
and Rectal Cancer Sensitivity Analyses
Section 6.7 of Chapter 6 presents, as a sensitivity analysis, estimates of potential benefits
associated with the reduction of colon and rectal cancer. As indicated there, the colon cancer estimates
are based on a calculation of PAR derived from data presented in the King et al. (2000) study; the rectal
cancer estimates are based on a calculation of PAR derived from data presented in the Hildesheim et al.
(1998) study. The purpose of this appendix is to provide additional information on the calculation of
these PAR values and on the estimation of attributable cases and cases avoided from Stage 1 and Stage 2.
The PAR calculations for colon and rectal cancer were carried out identically to those discussed
in Appendix E Section E.3.3 for bladder cancer using Equation E.6 (for calculating PAR from multiple
exposure groups) as applied to the five bladder cancer epidemiology studies under Approach 1. The form
of that equation, as used here, is:
2=1
where there are k exposure groups and where Pe/c(i) refers to the fraction of all cases observed in the rth
exposure group.
Exhibit E2.1 presents the data from the two studies and the resulting PAR estimates.
Final Economic Analysis for the Stage 2 DBPR E2-1 December 2005
-------
Exhibit E2.1 Data and PAR Calculations for Colon
and Rectal Cancer Sensitivity Analysis
Study
King et al.
(2000)
Colon
Cancer
(Males Only)
Hildesheim
etal. (1998)
Rectal
Cancer
(Both Sexes)
Exposure
Group (Years
of
Chlorinated
Water)
0-9
10-19
20-34
35+
Total:
0
1 -19
20-39
40-59
60+
Total:
Cases
101
41
107
172
421
119
101
136
136
45
537
Pefcfi)
0.240
0.097
0.254
0.409
0.222
0.188
0.253
0.253
0.084
OR;
1.0
1.7
1.33
1.53
PAR (S):
1.0
0.88
1.11
1.41
2.13
PAR (S):
p '[(ORi-IJ/ORJ
^e/c(i)
0.000
0.040
0.063
0.142
0.245
0.000
-0.026
0.025
0.074
0.044
0.118
The SEER data provides an estimate of 148,723 total new colon and rectal cancers per year based
on 1997 - 2001 data. The American Cancer Society (2005) indicates that approximately 72.2% of these
are colon cancers and 27.7% are rectal cancers, or 107,430 and 41,293 respectively. The American
Cancer Society also indicates that of the colon cancers, 46% (i.e., 49,418) occur in men.
Applying the PAR values shown above to these values of colon and rectal cancer incidence from
all causes results in estimate of Pre-Stage 1 DBP attributable cases of 12,093 colon cancers (men only)
and 4,852 rectal cancers (both sexes).
Using TTHM average reductions for Stage 1 of 27.2% results in estimates of Post-Stage 1 colon
cancers (men only) of 8,800 and of rectal cancers of 3,531.
Using TTHM average reductions for Stage 2 of 7.8% results in estimates of Post-Stage 2 colon
cancers (men only) of 8,114 and of rectal cancers (both sexes) of 3,255. Therefore, the estimated annual
cases avoided for Stage 2 are 686 colon cancers and 275 rectal cancers. These estimates are the annual
cancer cases ultimately avoidable as discussed in Chapter 6 and Appendix E. The estimates of the
annualized monetary benefits for reduction of colon and rectal cancers as presented in Exhibit 6.31
include cessation lag based on the smoking / lung cancer cessation lag model.
Final Economic Analysis for the Stage 2 DBPR
E2-2
December 2005
------- |