United States
Environmental Protection
Agency
Office of Water
(WH-550)
EPA811/R-92-008
November 1992
&EPA ANALYSIS OF POTENTIAL
TRADE-OFFS IN REGULATION
OF DISINFECTION BY-PRODUCTS
Recycled/Recyclable
Printed on paper that contains
at least 50% recycled fiber
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Analysis of Potential Trade-offs In
Regulation of Disinfection By-Products
John E. Cromwell, III & Xin Zhang
Wade Miller Associates, Inc.
Frank Letkiewicz
Abt Associates, Inc.
Stig Regli & Brace Macler
U.S. Environmental Protection Agency
1.0 Introduction
Executive Order 12291 requires the preparation of a Regulatory Impact Analysis (RIA) on all new
major federal regulations. The U.S. Environmental Protection Agency (EPA) guidelines for performing
regulatory impact analysis state:
The goal of regulatory impact analysis is to develop and organize information on benefits,
costs, and economic impacts so as to clarify trade-offs among alternative regulatory
options.1
Potential trade-offs between microbial and disinfection by-product risks are technically complex
and fraught with uncertainties. In addition to meeting the requirement for a Regulatory Impact Analysis,
the research effort reported in this paper is intended to establish a systematic analytical framework for
appreciating these factors. Explicit methodology for assessing the technical potential for risk-risk trade-
offs is developed (termed, the Disinfection By-Products Regulatory Analysis Model, [DBF-RAM]). The
strategies used to cope with complexities and uncertainties in developing the DBP-RAM are explained.
Results are presented and discussed in light of uncertainties, and in light of the analytical requirements
for regulatory impact analysis.
Technical complexities stem from several competing relationships between microbial and by-
product treatment objectives that are inherent in water treatment processes. These relationships are
described in Section 2.
There is current exposure to both microbial and disinfection by-product risks. Neither of these
is well-characterized in baseline data. Moreover, the baseline is changing. Section 3 presents an
assessment of available baseline data.
Section 4 presents a statement of the specific research objectives and of the overall approach
pursued in this analysis. The overall objective is to bound the range of possibilities implied by the
competing treatment relationships in terms of the potential for trade-offs between microbial and by-product
risks under different regulatory scenarios.
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Section 5 describes the analytical approach that has been adopted in developing the DBF-RAM
and the supporting rationale. Profiles of the changes in microbial and by-product risks associated with
different regulatory scenarios are generated through Monte Carlo simulation of the competing treatment
relationships. Specific methodology and assumptions of the DBF-RAM are presented. Section 6 presents
results and discussion. Section 7 presents summary observations relevant to the research questions posed.
2.0 Competing Relationships In Microbial and By-Product Control
Exhibit 1 presents an overview of the key factors involved in the competing relationships which
result in exposure to microbial contaminants and disinfection by-products at the consumer's tap. As
illustrated in the diagram, these factors can be sorted into four groups:
o
o
o
o
raw water quality;
treatment combinations applied;
treatment process control strategies applied; and
distribution system characteristics.
2.1.1 Raw Water Quality
The effectiveness of treatment technologies to control pathogens and disinfection by-products is
affected by the interaction of multiple influent water quality parameters. Many water quality parameters
affect both microbial and by-product treatment simultaneously (e.g., TOC, UV absorbance, temperature,
pH, alkalinity, and hardness). Other parameters such as bromide and pH can strongly influence the
formation of one type of disinfection by-product versus another.
Chemical disinfectants are oxidants which destroy or inactivate microorganisms by oxidation.
They also react with naturally occuring dissolved organics (grossly represented by TOC ~ Total Organic
Carbon) and with inorganics (such as bromide) to produce disinfection by-products, some of which may
be carcinogenic. Higher doses of disinfectants are required in high TOC waters to achieve a desired level
of microbial kill because some of the disinfectant is consumed by organic compounds comprising the
TOC. But, higher disinfectant dosages lead to higher disinfection by-product formation unless TOC is
removed before the disinfectant is applied. Coagulation and filtration processes, granular activated carbon,
and membrane filtration remove TOC. However, such processes may also shift the chemical balance
towards more brominated by-products, depending upon the bromide concentration and the amount of
disinfectant applied.
The chemical reactions that kill microorganisms and those that form halogenated by-products will
proceed to greater or lesser extent of completion depending upon the pH (often correlated to alkalinity and
hardness); and, at a faster or slower rate depending upon the temperature of the water. Microbial kill is
increased at higher temperature but so is disinfection by-product formation. At different levels of pH,
different groups of by-products are favored. Trihalomethane formation is favored at high pH, while
trichloroacetic acid formation is favored at low pH.
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2,1.2 Treatment Processes Applied
Of the possible treatment processes indicated in the diagram in Exhibit 1, some (e.g., coagulation
and filtration) are appropriate mainly in surface water systems. While there are some groundwaters that
also use these treatments (e.g., in lime softening), surface waters generally present a far more complicated
treatment problem where the conflict between the competing objectives (controlling for both pathogens
and disinfection by-products) is much more pronounced.
Surface waters contain protozoa whereas groundwaters (that are not under the direct influence of
surface waters) may only contain viruses or bacteria. Since protozoa are much more resistant to
disinfection than bacteria or viruses, higher levels of disinfection are needed in surface waters than in
groundwaters. Because of this feature, the analysis presented in this paper focuses exclusively on surface
water systems. The number of factors involved is so great, that an excessive amount of analysis would
be required to assess every conceivable type of treatment situation. By focusing on the surface water
situation, which is likely to be the most highly constrained, alternative regulatory scenarios can be
subjected to the toughest test. Whatever by-product control levels are achievable in surface water systems
without unacceptable trade-offs are probably achievable in generally less constrained groundwaters without
unacceptable trade-offs.
The nature of treatment for microbial and disinfection by-product control is complex in that
several treatment steps may be added in succession to achieve successively greater levels of control. Thus,
there is not a single solution to these treatment problems, but rather a menu of choices. This menu feature
adds a considerable degree of complication to the evaluation of regulatory alternatives because impact
assessment requires a method for forecasting these complex treatment choices.
2.1.3 Process Control Strategies Applied
The box labelled "treatment process control strategies" in Exhibit 1 is perhaps the most important
part of the diagram. In striving to meet any set of microbial and by-product treatment targets, the plant
operator has a broad array of choices. The following control options are available:
control TOC
• control pH
select type of disinfectant
control disinfectant dose
• control contact time
In addition to influent water quality parameters such as TOC and pH, another major factor
influencing the extent of microbial kill and by-product formation is the amount of time the disinfectant
is in contact with the water. For any given disinfectant dose, both the level of microbial kill and the level
of by-product formation will increase as the contact time is increased. Thus, the treatment process may
be optimized through a combination of strategies intended to: 1) change the water quality parameters that
affect the relevant reactions, 2) change the disinfectant or disinfectant dose, or 3) change the amount of
contact time.
Temperature is one key variable that is beyond the operator's direct control. However, it is
possible to vary winter and summer strategies to take advantage of the temperature effect. For example,
a higher disinfectant residual might be needed within the plant in the winter to maintain the same level
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of inactivalion as in the summer. This may be possible without significant increased by-product formation
since by-product formation is slowed at colder temperatures. Additional flexibility would be made
available if compliance with by-product MCLs is determined on the basis of the running average of the
last four quarterly samples, as under the current Tl'HM standard.
2.2.4 Distribution System Characteristics
A final key variable that is generally beyond the plant operator's direct control is the residence
time in the distribution system — the amount of time elapsing from the moment the water leaves the plant
to when it is drawn from the consumer's tap. The residence time may vary enormously between the
customer nearest to the plant and the customer farthest from the plant. The resulting microbial and by-
product risk may also vary greatly between first and last customer. The plant operator must account for
this additional contact time at the plant, adjusting the available controls to simultaneously assure: 1) an
acceptable level of microbial kill at the first customer's tap, 2) additional microbial kill sufficient to
protect the distribution system from bacterial growth and external contamination all the way to the last
customer's tap, and 3) compliance with by-product MCLs on average across the total distribution system
(i.e., at the average customer).
Thus, the competition between microbial and by-product control strategies is driven by
performance standards that must be met in the distribution system rather than at the plant effluent.
Because there are so many control options available to the plant operator, there is no unique solution to
the control problem — i.e., within each plant, there may be a number of different control strategies that
can meet a given set of performance targets. Viewing the variability between plants, there is an even
broader range of possible solutions since raw water and distribution system characteristics vary greatly.
2.7-5 Implications of Variability
The inherent variability in possible treatment and control configurations has two profound effects
on the assessment of prospective impacts of regulatory alternatives. First, it is extraordinarily difficult to
fully define the baseline, or pre-regulatory condition. This difficulty is a combined result of the fact that
current microbial performance requirements are in the process of being changed through implementation
of the Surface Water Treatment Rule (SWTR) and the Total Coliform Rule (TCR), and the fact that plants
can meet performance standards with vastly different treatment and control strategies, depending on their
individual circumstances. Also, the recent promulgation of the Lead rule will result in still further changes
in water treatment that influence microbial control and disinfection by-product formation. Even if a
complete characterization of baseline process settings were available, it would still be extremely difficult
to predict impacts in terms of the increment of change likely to be induced by alternative by-product
MCLs because the process control options are so numerous. Baseline issues are discussed further in
Section 3.
The second major analytical implication is that for any set of performance targets, some plants
will be driven to more extreme treatment and control strategies than others due to the inherent variability
in raw water conditions. This range of strategies is significant because the competition between treatment
objectives results from side-effects — inadvertent increases in either microbial or by-product risks ~ that
are inherent in the underlying raw water chemistry. Many of these side-effects become more pronounced
as control strategies become more extreme. Thus a regulatory alternative could appear to represent a
happy medium between microbial and carcinogenic risks, on average (i.e., across all plants), but in fact
entail significant trade-offs due to side-effects induced in the extreme cases. As a result, a complete
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assessment of the trade-offs entailed in any set of microbial and by-product performance standards must
incorporate an analysis of the downside risks that could result from side-effects at the extremes. The
approach to coping with this analytical requirement is via the technique of Monte Carlo simulation as
described in Section 4. In the remainder of this section, treatment and control strategies are individually
characterized, including an identification of the side-effects potentially associated with each of them.
2.2 Microbial Control
There are two distinct objectives in controlling microbial contamination in water supplies: 1)
removing or killing pathogens present in the source water, and 2) preventing contamination of the treated
water during storage and distribution. The Surface Water Treatment Rule (SWTR) specifies performance
targets for both of these objectives which must be met simultaneously. The Total Coliform Rule (TCR)
specifies additional performance requirements that must be met in the distribution system. Both the
SWTR and the TCR are currently being implemented.
2.2.1 Source Water Treatment
Microbial contamination in water sources presents a significant treatment challenge because the
level of contamination can change sporadically and logarithmically (by several orders of magnitude).
Available monitoring protocols are subject to significant sources of sampling error and measurement error,
making reliable real-time process control impossible. The only effective strategy is to assure a level of
over-kill sufficient to meet the most extreme condition at all times. However, this strategy conflicts with
by-product control objectives.
In general, individual unit microbial treatment processes are not 100 percent effective. While
coagulation/filtration processes and disinfection can each remove and inactivate a high proportion of
organisms present, neither treatment is completely successful by itself (unless the source water is
exceptional). The best means of assuring the necessary high degree of over-kill in microbial treatment
is through the multiple barriers approach, combining watershed protection, coagulation/filtration processes,
and disinfection. In the SWTR, the multiple barriers approach is advocated with focus on a "target
organism," Giardia lamblia.
Giardia is used as a target organism in the SWTR because it has been implicated as causing the
most waterborne disease outbreaks of any specific organism and Giardia cysts are much more resistant
to disinfection than bacteria and viruses. Control of Giardia therefore implies significant risk reduction
for bacteria and viruses that are responsible for the most notorious and lethal forms of waterborne disease
(typhoid, cholera, hepatitis, etc.). Control of Giardia. however, may not provide adequate protection from
Cryptosporidium. Crvptosporidium is also transmitted in the form of cysts, but these are much more
resistant to disinfection than Giardia cysts.
The SWTR imposes a filtration requirement for all surface waters unless the system has an
effective watershed control program and the source water is of exceptional quality. Associated with the
filtration requirement are design and operating conditions specified by the state, and performance standards
for effluent turbidity. In combination, these requirements are believed to represent reduction of the
influent concentration of Giardia cysts by two-and-one-half orders of magnitude (2.5 "logs"). (Note: plants
that do not employ a sedimentation step are assumed to achieve only a 2.0 log reduction of Giardia cysts.")
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In addition to the filtration and turbidity requirements, the SWTR also specifies the need for plants
with sedimentation and filtration to provide an additional half an order of magnitude reduction (0.5 "log")
in Giardia cyst concentration through the provision of adequate disinfection. The adequacy of this
additional disinfection is determined via equations, as follows:
Total Log Reduction = filtration removal credit + X logs of inactivation
Where: X = /{disinfectant dose, contact time, temp, pH}
Methods to predict the level of inactivation of Giardia achieved through disinfection (C-T
calculations) have been developed. Also, a dose response function exists for Giardia (derived from human
data) which has received some empirical validation through study of actual waterborne disease outbreak
events.2 Knowledge sufficient to develop comparable risk assessment methodology for Crvptosporidium
does not exist, so it remains an unknown at present.
The filtration and disinfection requirements of the SWTR provide for a minimum 3-log total
reduction in Giardia cyst concentrations in surface waters by the time the water reaches the first customer
in the distribution system. Based on risk assessment, this level of reduction was intended to provide
protection equivalent to a giardiasis incidence rate (endemic rate) of less than 1 infection per 10,000
persons per year in systems with good source water quality. The SWTR Guidance Document3 also
stipulated that systems with source waters containing higher than average levels of contamination should
provide an additional log of inactivation through disinfection for each additional log of Giardia present
in the source water. This guidance is difficult to implement because practical methods for measuring
Giardia cysts and for determining whether they are viable or infectious to humans are not yet available.
It is significant that, at this time, the criterion for achieving higher levels of treatment for poorer source
water quality is not a requirement, but stipulated only as guidance. Recent Giardia occurrence data4
indicate that many more systems may require such additional inactivation than was believed necessary at
the time the SWTR was promulgated. These data are discussed further in the baseline analysis presented
in section 3.
Surface water systems meeting the requirements of the SWTR are typically not expected to
experience epidemic outbreaks of giardiasis or of any other waterbome disease. However, recent data on
Giardia occurrence5 indicate that plants having extremely poor source water quality, and only meeting
the minimal requirements of the SWTR (i.e., not providing treatment according to the guidance) may still
be vulnerable to outbreaks or significant levels of endemic illness. Another key point is that provision
of additional logs of disinfection in accordance with the SWTR guidance (e.g., replacing chloramine
contact time with chlorine contact time) can result in significant by-product formation when there is also
a high level of TOC in the water.
2.22 Protection from Contamination During Distribution and Storage
The SWTR specifies that at least a trace level of disinfectant residual be maintained in the
distribution system to contain "colonization" and "regrowth" of bacteria which might have survived the
multiple treatment barriers. Even if less than one-in-a-million bacteria survives, it can conceivably attach
to the wall of a pipe in an area where deposits -- potentially containing trace nutrients — have collected.
Such colonization can ultimately result in the presence of significant numbers of bacteria in localized
areas. The disinfectant residual is intended, in-part, to limit such growth.
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The other function of the disinfectant residual is to kill microorganisms that may enter the
distribution system through breaks, leaks, cross connections, or other violations of its integrity. The
absence of a residual indicates when such contamination may be occurring. This requirement to maintain
a disinfectant residual complements the Total Coliform Rule (TCR) which specifies a monitoring protocol
that must be followed to regularly sample the distribution system for coliform bacteria — an indicator of
either inadequate disinfection or a violation of system integrity. Under the SWTR, monitoring for
disinfectant residual is required at the same locations where coliform samples are taken. The TCR,
promulgated in 1989 and currently being implemented, imposes more stringent coliform monitoring than
previous requirements and a more sensitive threshold for determining a violation. Many systems are
expected to compensate by increasing the level of disinfectant residual they maintain in the distribution
system, with a likely side effect of some increased by-product formation.
Like source water microbial treatment, protection from contamination during distribution and
storage is not an exact science. Several competing relationships enter into the picture. First, the extent
to which the disinfectant can remain active all the way to the ends of the distribution system is, in part,
a function of the amount of TOC in the water. The higher the TOC, the higher the disinfectant dose
required. The higher the disinfectant dose, in the presence of high TOC, the greater will be the by-product
formation. Secondly, as described earlier, chlorine — the most commonly used disinfectant — is more
effective at low pH, whereas corrosion control treatment required to comply with the lead rule results in
higher pH in distributed water. Higher disinfectant dosages are required to compensate for higher pH,
increasing formation of certain by-products such as trihalomethanes.
Adding to the significance of these potential side effects, there is some evidence which suggests
that levels of over-kill implied by the SWTR and TCR distribution system requirements may not be as
protective as had previously been believed. Recent epidemiological evidence on this topic is reviewed
in the baseline discussion presented in section 3.
2.3 Disinfection By-Product Control
The chemistry of disinfection by-product formation has been theoretically defined and empirically
described for trihalomethanes and haloacetic acids with equations6 of the following form:
DBFs =/{[Cy, contact time, [TOC], UV absorbance, [Bf], temp, pH, alkalinity, hardness}
All disinfection by-product control strategies have potential side effects which pose certain risks,
some more than others. The most practical strategy is to limit formation through precursor removal and/or
through the use of alternatives to chlorine disinfection.
23.1 Adjusting Pre-Disinfection Practices
A first instinct in attempting to lower DBF formation is to relocate the point of disinfection, or
to discontinue the practice of pre-disinfection — disinfection prior to settling or filtration. Such relocation
permits the coagulation and filtration steps of the treatment process to remove a substantial amount of the
TOC in the water before the introduction of chlorine. The extent of such removal and its significance for
reducing by-product formation will largely depend upon the water quality characteristic to the plant and
the extent to which clarification processes are optimized.
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There are compromises in moving the first point of disinfection further into the treatment train.
Abandonment of pre-disinfection may reduce the disinfectant contact time prior to the first customer and
make it necessary to add storage to the end of the treatment process as a means of making up the lost
contact time under the SWTR, especially if the guidance formula is followed. Plant operators also may
be reluctant to give up pre-disinfection because of its benefit of sometimes improving coagulation or taste
and odor control, and of keeping the filter from becoming fouled with microbial growths.
233, Use of Chloramines
In complying with the present Total Trihalomethanes MCL of 100 ug/1, it appears that many plant
operators opted to switch to chloramines in lieu of abandoning pre-disinfection. It is estimated that as
many as one-third of the large surface water systems (> 10,000 population) that are subject to the TTHM
standard use chloramines. Prior to the TTHM standard as few as five percent of these systems are
believed to have used chloramines. The low cost of making such a change, the effectiveness of
chloramines in reducing TTHMs and their ability to persist as a residual in the distribution system makes
this an attractive option.
Chloramines are formed through the addition of ammonia which suppresses many of the by-
product formation reactions while preserving some, albeit weaker, disinfecting capability. When by-
product control problems are only moderately constraining, ammonia may be added after a brief period
of contact with the free chlorine. Allowing the free contact time preserves some of the benefits of pre-
disinfection. In more severely constrained cases, ammonia is added from the start. A side effect of the
switch to chloramines is that the weaker disinfectant may not be capable of providing the level of log
reduction required at the first customer under the SWTR and/or recommended in the SWTR guidance.
Some plants may have to re-evaluate their use of chloramines in complying with the SWTR.
Use of chloramines also entails potential side effects on microbial protection of distributed water.
Through chemical interactions, the ammonia may eventually become a source of nitrogen for bacterial
growth and may promote nitrification which can lead to poor tasting water. Chloramines may also offset
the need to remove TOC to some extent, leaving some available carbon in the water. If corrosion control
happens to involve iron pipes and the addition of phosphate inhibitors, the biochemical setting for
microbial growth in the distribution system may be further established. Coupled with an inherently weaker
disinfectant, there is potential for negative side effects of a chloramines DBF control strategy on microbial
control in distributed water. However, the fact that the chloramine residual may be more persistent in
some systems holds the possibility that chloramines may provide more bacterial protection than chlorine
where maintenance of residual is a problem. Microbial regrowth is a known problem in many systems
using chloramines presently. To compensate, some systems have increased chloramine residuals to the
point where they may not meet potential MCLs for chloramines.
233 Optimization of Precursor Removal
In the succession of likely treatments available for by-product control, the plant operator will
probably turn next to optimization of TOC removal through fine-tuning of the coagulation and filtration
processes. Removal of TOC directly reduces the potential for by-product formation. Coagulation and
filtration are very effective, and depending on source water quality, may be relatively inexpensive
compared to other treatments such as addition of granular activated carbon (GAC) or membrane filtration.
Much of the added expense is due to increased sludge disposal. In some plants, high alkalinity could
make enhanced precursor removal very expensive due to the need for pH reduction.
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Optimized precursor removal has two potentially significant side effects. First, the lower TOC
makes it possible to meet the disinfectant residual requirement of the SWTR with much lower free
chlorine concentrations leaving the plant. However, the reduction in TOC may have a perverse effect of
allowing the system to have less C-T (less inactivation) prior to the first customer, conceivably increasing
the risk from Giardia in poorer source waters, unless additional inactivation is provided as recommended
in the SWTR guidance document. Under the minimum requirements of the SWTR, attainment of a level
of log-reduction at the first customer greater than or equal to the 1/10,000 risk level may be resulting
incidentally from the need to overcome the competing effect of TOC in meeting the residual requirement.
The second potential side effect of optimizing TOC removal is a change in the chemical balances
with respect to the different classes of by-products. Removal of organic precursors does not reduce
bromide concentrations. As TOC removal increases, the formation of brominated by-products, some of
which may be more carcinogenic, may be favored depending upon the molar ratios of bromide, free
chlorine, and organic precursors.
23.4 Use of Ozone
Modifying pre-disinfection practice, adding ammonia, and optimizing TOC removal are relatively
inexpensive, conventional technologies that are likely to be tried first. The next threshold is switching
to alternative technologies that are relatively novel and relatively more expensive. The first of these is
the use of ozone as a primary disinfectant. Ozone is an extremely powerful oxidant. From a process
perspective, it can be very beneficial (depending on source water quality) in achieving a high level of
microbial kill and in enhancing the performance of coagulation processes, sometimes with enhanced
precursor removal. Because ozone is so reactive, however, it does not leave a detectable trace residual
in the distributed water. To provide a measurable residual, chloramines or chlorine are generally added
at the end of the plant.
There are numerous potential side effects associated with the use of ozone. First, ozone is so
powerful an oxidant that it produces very small organic fragments that can evade coagulation and filtration
processes, ending up in the distribution system in a form that is readily assimilable to bacteria. The
increase in assimilable organic carbon (AOC) can lead to significant baterial growth in distribution
systems. Some of these bacteria may be pathogenic and especially harmful to immune compromised
individuals. The biochemical setting for bacterial growth may be further enhanced with the addition of
ammonia as a nitrogen source.
In addition to the microbial side effects, the oxidizing properties of ozone also result in the
formation of entirely different mixes of other chemical by-products which could be carcinogenic.
Toxicological research on ozone by-products is in infancy; many unknowns remain in this area. A
potential remedy to minimize these side effects, employed in Europe, is to follow ozone treatment with
a biological activated carbon filter. This provides a means of biologically removing AOC while simulta-
neously removing organic precursors to by-product formation. It represents another technology shift,
however, as well as another level of expense.
Another concern of ozonation is the extent to which bromate is formed. Bromate appears to be
a potent carcinogen. At the moment, there is insufficient understanding of the rate at which it is formed
during ozonation and the extent to which its formation can be controlled by chemical treatment processes
such as pH adjustment or ammonia addition. Occurrence data are also slight due to limitations in present
analytical methods of detection.
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233 Use of Chlorine Dioxide
Another alternative disinfectant is chlorine dioxide. Although there is concern about health effects
caused by exposure to chlorine dioxide and its by-products (chlorite and chlorate), its use in preventing
or limiting other disinfection by-products may make it attractive in certain water quality situations. Unlike
chlorine, chlorine dioxide is a very effective disinfectant at high pH. It does not react by itself with
organics to form trihalomethanes or haloacetic acids. Also, it apparently does not react with bromide to
form bromate. Systems with high levels of pathogens, TOC, and bromide in their source water which also
have high hardness and alkalinity and use lime softening, may have difficulty in simultaneously achieving
low by-product levels and compliance with the SWTR guidance. Use of reducing agents such as ferrous
chloride, to convert chlorine dioxide to chloride may make use of chlorine dioxide a possible means of
controlling disinfection by-products in such systems.
23.6 Use of Granular Activated Carbon or Membrane Filtration
Depending upon the source water quality, attainment of low levels of disinfection by-products may
ultimately require deep bed Granular Activated Carbon (GAC) (following conventional filtration processes
and the use of alternate disinfectants) or membrane filtration. Due to the expense of GAC and membrane
filtration, these treatments are likely to be selected as a last resort compliance option.
When GAC is used, application of chlorine as a residual disinfectant for the distribution system
should be delayed until after the water's passage through the GAC filter. This strategy optimizes the
efficiency of biological removal of by-product precursors and prevents desorption of chlorinated by-
products that might otherwise be fqrmed if chlorine were added prior to GAC. Although GAC can
remove by-products by adsorption, the frequency of carbon regeneration required would not be practical.
Chlorine, if used, should therefore be added after GAC. Ozone, on the other hand, can be used prior to
GAC to enhance removal of by-product precursors and AOC, and to provide substantial disinfection
(depending upon the ozone demand in the water).
There are, however, several side effects of GAC. Deep bed carbon following conventional
filtration processes may not remove significant levels of pathogens still remaining. Thus plants with high
pathogen levels in their source water and currently predisinfecting prior to sedimentation may need to
install substantial disinfectant contact time following GAC to prevent significant increases in risks from
protozoa to populations near the first customer. Since GAC will significantly reduce disinfectant demand,
aslant would be able to have very low levels of disinfectant dose and still maintain a residual in the
distribution system. While use of GAC would likely decrease risk from pathogenic bacteria that could
grow in the distribution system, it could increase risk from pathogens in the source water if less
disinfectant or contact time is used prior to the first customer.
Membrane technologies can remove greater than 90 percent of disinfection by-product precursors
and essentially all pathogens which might be present in the source water. Thus, there is little or no
downside risk concerning pathogens with this by-product control technology. It is however, the most
expensive approach. Solid waste and water wastage could increase costs even more. In surface waters,
membrane technology would have to follow other filtration processes in most cases to prevent clogging
of the membranes.
Both GAC and membranes are subject to the same uncertainties as previously mentioned for
optimized coagulation/filtration precursor removal. Depending largely upon the bromide concentration,
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there is potential for a shift in the mix of resulting by-products towards more brominated and more
carcinogenic compounds. The shift is likely to be considerably less significant in terms of overall risk
for most waters, however, because of the much greater levels of precursor removal achieved by these
technologies, especially membrane filtration.
3.0 Baseline Assessment ofMicrobiat and By-Product Exposures And Risks
The existing exposure to microbial and by-product risks is shaped by microbial performance
requirements (applicable to all systems) and an MCL of 100 ug/1 for Total Trihalomethanes (applicable
to systems serving more than 10,000 persons) which have both been in place for over a decade. In
addition, as noted in the foregoing, the SWTR and TCR, promulgated in 1989, have begun to set changes
in motion in the level of microbial control being achieved from the first customer to the last. Inescapable
changes in by-product exposures are also occurring as a result of these changes.
As discussed above, it is virtually impossible to predict the precise nature of the process
adaptations that are presently taking place due to the enormous variability from one case to the next and
the broad range of process control strategies available. It is possible to develop some characterization of
the baseline that existed before these changes were put in motion, however, from data collected prior to
these latest adaptations.
3.1 Microbial Contaminant Exposure And Risk
As mentioned in Section 2, the analysis presented in this paper focuses on surface water systems,
since these systems are believed to be the group most highly constrained by the competing relationships
between microbial and by-product control. More specifically, the analysis in this paper will focus on
filtered surface water systems for which there are more data with which to characterize influent levels of
contamination with the target organism, Giardia.
In the SWTR regulatory impact analysis (RIA) prepared in 1989,7 data available at that time8
were used to derive an assumption of 3.3 Giardia cysts per 100 liters as an average influent concentration
in filtered surface water systems. Based in part on data collected by the American Water Works
Association,9 an average of 3.33 logs of total reduction at the first customer was assumed to represent
pre-SWTR treatment practices in filtered systems. Combining these two crude assumptions, via the
dose/response function for Giardia infection,10 with an estimated 133,560,000 population exposure in
filtered surface water systems yielded an estimated pre-SWTR baseline incidence rate of 27,839 infections
per year (2 per 10,000 population), assuming no additional inactivation is achieved in the distribution
system. It was then projected that the SWTR would increase the total log reduction achieved in such
systems to an average of 4.5 logs at the first customer, due mainly to improved efficiency of filtration
(since average turbidities were expected to drop from 0.7 MTU to 0.3 NTU), but also due to some systems
increasing their level of disinfection. EPA estimated a post-SWTR incidence rate of 1,882 infections per
year in filtered systems (1 per 100,000 population).
The minimum 3-logs of total reduction required in the SWTR was believed to represent less than
a 1/10,000 giardiasis risk level at the first customer for systems with good source water quality (i.e., < 1
cyst/100 liters). It is important to note that predicted infections are a worst case representation of
predicted illness since many infected people are asymptomatic. The rationale supporting the use of a
1/10,000 giardiasis risk level as a regulatory target is further explored by Macler and Regli.11
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The SWTR Guidance Document recommends an additional log of inactivation be applied for every
additional log of influent Giardia in order to maintain the 1/10,000 risk level at the first customer. As
indicated by the assumptions used in the SWTR RIA, it was believed that most plants would exceed the
minimum requirements through unproved filtration and additional disinfection. It was further believed
that systems meeting a 1/10,000 endemic incidence rate would be below the threshold at which epidemic
outbreaks occur. The true extent of outbreaks of giardiasis and of other forms of waterborne disease is
subject to great uncertainty. The common gastrointestinal symptoms are so easily associated with other
causes that it is suspected that much waterborne disease goes unreported. It is known, however, that
outbreaks are not reported in systems which both filter and disinfect except when there is an identifiable
failure in one of these two barriers.
In a study published in 1991, LeChevallier, et.al.12 collected comprehensive data including
measurements of influent Giardia concentrations and filter effluent Giardia concentrations, as well as
computations (via CxT equations) of additional inactivation achieved through disinfection at the first
customer. Eliminating results where the Giardia concentrations were estimated on the basis of the
detection limit of the monitoring technique, the LeChevallier study provides 62 complete sets of data,
representing 46 different plants.
The LeChevallier study employed new, state-of-the-art monitoring techniques for Giardia.
Corrected for recovery efficiency (.48) and conservatively estimated cyst viability (.13) (and, dropping data
points computed on the basis of the method detection limit rather than actually measured), the results
indicate an average influent concentration of 203 cysts per 100 liters. The median is 89 cysts per 100
liters. These results are considerably higher than the average contamination levels of 3.3 cysts/100 liters
assumed to exist in such systems in the SWTR regulatory impact analysis.
In addition to noting the generally higher level of influent Giardia implied by the results, the
LeChevallier study presents an analysis computing the minimum total log reduction necessary at each plant
in order to produce an effluent concentration of 0.0007 cysts/100 liters, equivalent (based on the same
dose/response function employed here and in the SWTR RIA) to the 1/10,000 population risk level at the
first customer sought by the SWTR. The conclusion is that the plants studied would have to achieve, on
average, a 5-log total reduction — compared to the 3-log minimum required in the SWTR to achieve, on
average, the 1/10,000 risk level at the first customer. This difference is consistent with the two-log
difference between the average influent Giardia concentration found by LeChevallier and the average
influent concentration assumed in the SWTR RIA. An apparent contradiction is raised, however, by the
fact that the average of the total (winter) log-reductions calculated to be actually achieved across the 62
data sets is 7.0 logs, whereas the average Giardia concentration at the first customer is 0.0016 cysts per
100 liters. Despite having a very high average log-reduction, the average Giardia concentration remains
significantly higher than the 1/10,000 risk level.
Exhibit 2 provides some further illumination through a plot of influent Giardia versus total log
reduction achieved for the 62 data sets. A critical underlying feature of the pre-SWTR baseline, made
clear by the random scatter pattern in this plot, is the fact that there appears to be no correlation between
the influent Giardia concentration and the total log reduction achieved. The diagonal line through the
points represents the formula prescribed in the SWTR Guidance Document; points to the right are plants
achieving more total log reduction than needed to meet the 1/10,000 risk target and points to the left are
plants achieving less total log reduction than needed to meet the 1/10,000 risk target.
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The lack of correlation between the influent Giardia concentration and the total log reduction
achieved could be attributable to numerous factors: 1) the lack of monitoring capability for Giardia and
appropriately adjusted treatment; 2) attempts to comply with the current TTHM MCL (Significantly, many
of the plants to the left of the line are using chloramines.); 3) attempts to comply with current standards
for coliforms and state chlorine residual requirements (e.g., Ten States Standards); or 4) attempts to meet
other treatment objectives served by pre-disinfection. Whatever the reason, much of the 7.0 logs of total
reduction that is achieved, on average, does not translate into reduced risk of microbial contamination.
The LeChevallier study reveals that it is the unique relationship between the influent Giardia level
and the total log reduction achieved at each individual plant which matters rather than the average level
across all plants. Indeed, it is made clear that such simple arithmetic as was performed in the SWTR RIA
(in the absence of data such as that provided by LeChevallier) is subject to significant error.
Performing simple arithmetic based upon assumptions of average influent concentrations and
average total log reductions may produce very deceiving results. For example, if the average influent
concentration (203 cysts/lOOL) and average total log reduction (7.0) of the 62 data sets in the LeChevallier
study are applied to the same exposed population used in the SWTR RIA, a pre-SWTR endemic incidence
rate of 353 infections per year is the result (3 per 100,000 population).
If, in contrast, the 133,560,000 persons served by filtered surface water plants are distributed
equally across the 62 pairs of influent Giardia and total log-reduction estimates, the results indicate a pre-
SWTR disease incidence rate of 2,801,396 infections per year (2 per 100 population). While this rate of
endemic incidence is not beyond belief, given the problems of reporting and the similarity of symptoms
to those of other diseases, it is probably higher than what most knowledgeable observers would offer as
a best estimate. If it is instead assumed that only the first 10 percent of customers are exposed to
minimally treated water - the rest receiving water having substantially more logs of inactivation through
contact with residual disinfectant in the distribution system — then the estimate of pre-SWTR incidence
of infection would be 280,000 per year.
The clear implication of the LeCevallier data is that the minimum 3-log reduction requirement of
the SWTR does not appear sufficient to reliably achieve the target 1/10,000 risk level in all systems. The
higher levels of total reduction specified in the SWTR Guidance Document appear to be necessary in order
to assure meeting the target risk level.
Epidemiological evidence confirms the suspicion that the extent of waterborne disease may be
greater than previously realized. Bennett, et al present analysis of data collected by the Centers for
Disease Control (CDC) which suggest that about 70,000 of 940,000 cases of waterbome disease per year
are attributable to giardiasis.13
Another study published in 1991 provides evidence suggesting significantly greater incidence of
waterborne disease than previously suspected. Payment14 conducted a 15-month tracking study in
Montreal comparing large samplings of families using reverse osmosis treatment units and families using
tap water from a water system using very poor source water but apparently meeting the SWTR criteria.
Treatment included coagulation, sedimentation, filtration, and residual disinfection. His findings indicate
a 35 percent greater incidence of gastrointestinal illness among tap water users. In interpreting these
results, it is impossible to determine if the increased incidence is attributable to inadequacies in source
water treatment or due to inadequacies in the protection provided from contamination during storage and
distribution. In either case, the results indicate more residual microbial risk than previously believed,
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although the significance of these findings as they pertain to systems with different source water qualities
and levels of treatment still needs to be addressed.
3.2 Disinfection By-Product Exposure And Risk
Despite the fact that an MCL for Total Trihalomethanes (TTHMs) has been in effect for over a
decade, there is no resulting national data base with which to characterize exposure because the EPA
Federal Reporting Data System records only compliance status, not monitoring results. Many of the large
water utilities (those serving more than 10,000 persons) do have records, however, and they have been
polled on several occasions by the American Water Works Association (AWWA).
In a 1987 effort sponsored by the AWWA Research Foundation, the Metropolitan Water District
of Southern California (MWDSC) conducted a national mail survey of water systems serving more than
10,000 persons.15 Completed questionnaires were received from 910 respondents. This consisted of 286
surface water systems, 243 groundwater systems, 127 purchased water systems, and 254 mixed source
systems. Quarterly sampling data on TTHMs were received from over half of the respondents covering
1984, 1985, and 1986. MWDSC summarized the data in terms of the cross-utility medians (i.e., half of
all utilities higher; half lower) of the maximum, minimum, and mean results obtained by individual utili-
ties in each quarter. The summary, presented in Exhibit 3, shows that while utilities are achieving levels
in a 30 to 44 ug/1 range, on average, half of them have maximum values higher than 65 ug/1 in the third
quarter. Five percent of utilities reported having been required to make at least one public notification
regarding violation of the TTHM standard. Groundwater systems had markedly lower TTHM levels,
averaging around 10 ug/1.
Thirteen percent of respondents to the MWDSC survey indicated they had changed chlorine
dosages to comply with the TTHM standard. Sixteen percent indicated they had moved the point of
disinfection. Eleven percent of all respondents indicated use of chloramines. Among users of large lake
and flowing surface sources, 25 percent reported use of chloramines. Improved clarification was reported
by five percent of respondents. Very few other TTHM compliance adaptations were reported by
significant proportions of the respondents.
In 1989, the AWWA Research Foundation sponsored a survey to establish the first phase of a
standing Water Industry Data Base (WIDE).16 The effort covered all utilities serving more than 50,000
persons. The Phase I WIDE contains data for 442 surface water treatment plants (a second phase of the
WIDE project has extended the data base down to systems serving more than 10,000 persons and includes
over a thousand records).
The Phase I WIDE data for plants that presently filter shows that 65 percent of softening plants
make at least some use of alternate disinfectants (56 of the 65 percent use chloramines) compared to 37
percent for plants that do not soften (25 of the 37 percent use chloramines). For softening plants, the
median of the average annual TTHM concentrations was 26 ug/1 for 27 plants using chloramines versus
60 ug/1 for 19 plants using chlorine. For plants that do not soften, the median of the average annual
TTHM concentrations was 36 ug/1 for 43 plants using chloramines versus 45 ug/1 for 113 plants using
chlorine. These data suggest that softening plants are more tightly constrained by the current TTHM
standard than non-softening plants.
Among the filtered surface water plants which practice softening, those using chloramines
answered yes to the practice of pre-disinfection 91 percent of the time while those using chlorine answered
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yes only 54 percent of the time. Similarly, in filtered surface water plants which do not soften, those
using chloramines answered yes to the practice of pre-disinfection 91 percent of the time compared to 80
percent for those using chlorine. These data suggest that chloramines may be being used as a means of
clinging to some of the benefits of pre-disinfection while also maintaining lower levels of TTHMs than
if chlorine were used.
Other noteworthy relationships in the Phase I WIDB data are that plants using chloramines have
higher influent TOC concentrations, higher average temperatures, and longer distribution system residence
times than plants using chlorine. All of these factors, which cause higher TTHM formation, have probably
influenced the shift to chloramines.
In 1989-90, the AWWA Disinfection Committee performed a survey of disinfection practices17
which drew responses from 283 respondents, mostly (96%) systems serving more than 10,000 persons.
About half were predominantly surface water systems and the other half were predominantly groundwater
systems. Of 186 respondents (surface and ground) describing measures taken to comply with the TTHM
MCL of 100 ug/1, the top categories of responses were:
44% no changes needed
24% moved first point of chlorination downstream
23% ceased prechlorinating
20% decreased prechlorination dose
19% add ammonia after some contact time
16% improve coagulation by increasing pH
11% modify coagulation by other method
Of these respondents, 39 utilities reported problems as a result of modifications made to attain
TTHM compliance. The top categories of modifications producing operational problems were:
56% add ammonia after some contact time
44% moved first point of chlorination downstream
36% improve coagulation by decreasing pH
36% ceased prechlorinating
28% decreased prechlorination dose
18% modify coagulation by other method
Of the systems reporting problems, the categories of problems reported were as follows:
15% increased heterotrophic plate counts in treated water
21% increased heterotrophic plate counts in tap water
13% coliforms in distribution system
For the 134 predominantly surface water systems that responded to the question regarding
treatment modifications to meet the TTHM MCL of 100 ug/1, the responses were as follows:
20% moved chlorination point
19% ceased prechlorination
16% decrease prechlorination
16% add ammonia after some contact time
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16% improve coagulation
13% change preoxidation
The results of the AWWA Disinfection Survey appear to confirm that compliance with the current
TTHM standard can be achieved with relatively simple and inexpensive treatment modifications.
However, evidence of side effects on microbial treatment objectives are also apparent.
Although the three studies discussed above provide evidence of progress made in complying with
the TTHM MCL of 100 ug/1, assessment of the baseline cancer risk posed by the remaining by-product
exposure requires data on the concentrations of the individual compounds, encompassing not only the
individual trihalomethanes but the individual haloacetic acids as well. Such data does not exist in great
abundance. The two such data sets available have been utilized to produce high and low estimates of
baseline cancer incidence (computed from a lexicological perspective; i.e., assumed exposure factored by
estimated dose/response relationships), summarized in Exhibit 4.
The first data set has been collected by the EPA Technical Support Division (TSD)18 and is
probably biased towards worst cases which were the original focus of the EPA investigations. Results are
summarized in Exhibit 4 in the column labelled "High Case Estimate." As a point of comparison, the
TSD data indicate an average TTHM concentration in systems serving more than 10,000 persons of 84
ug/1, roughly twice the average levels reported by such systems in the AWWARF survey and the WIDE
survey discussed above. The high case cancer incidence estimates are therefore considered a high case,
being based on this data set.
Cancer incidence estimates are presented in two forms, based on maximum likelihood estimates
of the dose-response relationship and based on a 95th percentile estimate of the dose-response relationship.
WhUe the 95th percentile is the basis that has been used in most previous assessments of drinking water
cancer risks, the microbial risk assessment methodology provided for assessing incidence of giardiasis
infections is based upon a maximum likelihood procedure. Thus, the maximum likelihood estimates are
a better basis for comparison. As shown in Exhibit 4, the total baseline cancer incidence in the high case,
based on the maximum likelihood estimates, is on the order of 159 cases per year (0.4+2.4+20+136-
=158.8). Several significant footnotes must accompany this result. First, it is significant that all but three
of these cases are attributable not to trihalomethanes, but to haloacetic acids. The greater impact of
haloacetic acids is a result of the use of the maximum likelihood procedure and the higher cancer potency
factors attributed to dichloroacetic acid and trichloroacetic acid than to the THMs. On a maximum
likelihood basis, the cancer risk from trihalomethanes becomes less significant. A second footnote
concerns the fact that cancer risk estimates are presently available for only two of the haloacetic acids.
No risk factors are available for the brominated haloacetic acids. A third footnote is that TCAA and
dibromochloromethane are tentatively being classified by EPA as Class C (possible) carcinogens, rather
than Class B (probable) carcinogens. As a conservative assumption, this analysis has assigned
carcinogenic potential equal to that of Group B. Finally, it is noted that of the total incidence, 20 cases
are projected in systems serving fewer than 10,000 persons.
The Low Case Estimates presented in Exhibit 4 are based on a study by Krasner performed by
the Metropolitan Water District of Southern California (MWDSC)19 in which data on concentrations of
individual compounds were collected at the clearwell prior to entry into the distribution system. Higher
concentration levels could be expected if the samples had been collected in the distribution system. The
median TTHM concentration of the plants surveyed in this study was 25 ug/1, somewhat below the levels
typifying the other surveys reviewed above. If these data were assumed to represent a lower bound of
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exposure, the total baseline cancer incidence, as indicated in Exhibit 4, would be 40 cases per year, on
a maximum likelihood basis.
A recent "meta" analysis of epidemiological data20 has produced estimates that as many as 10,000
cases of cancer per year may be attributable to chlorinated drinking water. There is considerable
controversy among epidemiologists on the appropriateness of applying "meta" analysis on the data that
were used, and on the interpretation of the results. The results of the study differ significantly from the
estimates yielded by the above toxicological approach (exposure/dose-response) to estimating baseline
cancer risk. The analysis in this paper has been developed using the toxicological approach.
4.0 Research Objective and Approach
Clearly, assessing the impacts of alternative regulatory options for disinfection by-products is not
straightforward. Additional difficulty results because the objective of this research is to devise a means
of analyzing the problem in a summary form that permits drawing generalized conclusions to serve as a
basis for national level analysis. At least four features of the problem contribute significant additional
complications in elevating analysis to an aggregate level:
The baseline is in motion. Water systems are presently in the process of complying with SWTR
and TCR requirements subject to the existing by-product constraint embodied in the TTHM MCL
of 100 ug/1. Analysis of microbial and by-product exposures under the currently defined
regulatory regime indicates that ~ under various interpretations — both types of exposure have a
wide range of possible risk.
The two types of exposures are dynamically interrelated in a manner which is not completely
revealed through simple intuition. Inadvertent side-effects are accentuated under various
combinations of extreme conditions, making the average condition a deceiving benchmark. The
central tendencies are not as important as the interactions between the tails in assessing potential
trade-offs.
• There is a factorial number of combinations of raw water, treatment, process control, and
distribution conditions that present an unmanageable number of conceivable "extremes" to be
considered in analyzing potential interactions of the exposures. There are, in fact, many times
more combinations than there are public water systems. There is also a factorial number of con-
ceivable combinations of regulatory options to consider (e.g., TTHMs 100, SWTR 3-logs; TTHMs
50, SWTR 4-logs; etc.).
• Finally, there are significant uncertainties inherent in the current state-of-the-art of both exposure
and risk assessment pertaining to both types of health risks. Many of the key areas of uncertainty
can be addressed through "what-if" analysis to test the sensitivity of results to uncertain estimating
procedures. However, such extensive "what-ifs" add to the factorial burden of analysis.
Moreover, in addition to areas of uncertainty, there are several areas where so little is known of
potential side-effects that even provisional quantification of exposure and risk is infeasible (e.g.,
formation and health risk from brominated haloacetic acids, ozone by-products, changes in
bacterial populations in the distribution system, Cryptosporidium. etc.), requiring them to be
treated as complete unknowns at this point.
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Analysis of by-product regulatory options is a unique problem. Most drinking water regulations
present much simpler analytical problems, where national impacts can be fully characterized in a way that
is representative of the overall national exposure and risk. Because of the factorial dimensions of the by-
products problem, and the extensive range of uncertainties and unknowns characterizing the available
information and understanding, analysis of by-products regulation cannot be approached in the same
comprehensive manner. Even if the outstanding uncertainties and unknowns were resolved, the factorial
dimensions would still place practical limits on the extent of the analysis.
4.1 Defining A Manageable And Meaningful "Core Problem "
Recognizing the practical constraints, the specific research approach adopted in this analysis is to
define the nature of the trade-offs by focusing on a core problem; a manageable segment of the problem
where the most conflicting interactions and the most significant population exposures are likely to be
found. A manageable and meaningful core problem can be defined by two means. First, some of the
factorial dimensions may be collapsed into a smaller number by focusing on those relationships which are
perceived to be most influential in, causing trade-offs to occur. Fortunately, this approach is feasible
because, although there are many dimensions conceivably involved, there is sufficient understanding to
suggest which are the strongest relationships (i.e., where extreme treatments, and therefore trade-offs, are
most likely).
Surface water systems are generally much more likely to have to resort to extreme treatments than
groundwater systems. They also serve a much larger segment of the overall population. Though there
are locations (e.g., FL, CA) characterized by high TOC or high bromide groundwaters, unless they are
groundwater under the direct influence of surface water, they are not vulnerable to protozoa and therefore
do not need as much disinfection. Any set of regulatory requirements achievable by most surface water
systems without adverse trade-offs Will probably also be achievable by most groundwater systems. Exhibit
5 illustrates the relative proportions of population exposure between surface and ground water systems.
In addition to collapsing factorial dimensions to define a manageable and meaningful core
problem, there must also be a strategy to maintain an accurate summary representation of the variability
introduced by site-specific conditions. Site-specific variability affects the analysis in two ways. First, the
multidimensional baseline configuration of factors affecting microbial and by-product risks is unique in
every local situation. Second, the baseline condition is changing in every local circumstance in response
to implementation of the SWTR and TCR. The various conceivable combinations of site-specific
conditions must be factored into the analysis in a way that is somehow representative. This site-specific
variability can be analyzed statistically through the technique of Monte Carlo simulation, described in
section 5.0.
4.2 Coping With Uncertainties and Unknowns: Scenarios And Place-Holders
The second part of the research strategy for defining a manageable and meaningful core problem
is to collapse additional dimensions of complexity through the use of scenarios and place-holder
assumptions. If the above-described complex of interrelated factors and processes that simultaneously
contribute to by-product and microbial risks is envisioned as one great multi-dimensional matrix, the
scenarios and place-holder assumptions provide a limited selection of cross-sections through it. A factorial
number of other cross-sections are feasible, but the selected views are intended to offer a meaningful
starting point — a benchmark from which other "what-if' excursions can be launched.
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Even within the core problem category of surface water systems, the number of conceivable
combinations of microbial and by-product risk targets that could be considered is factorially large. The
approach to coping with this large number of combinations is to bracket the range of microbial protection
through evaluation of two scenarios: a SWTR scenario, and an Enhanced SWTR scenario (ESWTR). The
SWTR scenario assumes that all surface water systems will be constrained to meet a total reduction target
for Giardia of only 3-logs at the first customer. Additional log-reduction for poorer quality source water
that is recommended in the SWTR Guidance Document is assumed to be ignored. The ESWTR scenario
presumes an enhanced SWTR is enacted which would convert the prescription for additional total
reduction presently made in guidance into a mandatory requirement. Due to lack of adequate monitoring
techniques, an easily implementable enhanced SWTR does not appear to be a near-term possibility, but
is nonetheless an important benchmark. Both scenarios assume the SWTR disinfectant residual
requirements, the coliform rule, and the lead rule are met in the distribution system.
Another significant challenge in defining a core problem is presented by the fact that quantitative
exposure and risk assessment is feasible for some of the microbial and by-product risks (albeit, subject
to significant uncertainties), but is not feasible for others, leaving them as pure unknowns. The estimable
aspects of the problem include risks associated with Giardia. trihalomethanes, dichloroacetic acid and
trichloroacetic acid. Exposure and risk assessment models have been pieced together for these
contaminants. The assumptions required are regarded as place-holders. As implied by the name, place-
holder assumptions are intended to be continually refined as better information becomes available. They
can also be systematically varied as the object of "what-if" analysis to examine the sensitivity of the
results to uncertainties associated with them.
As a result of its use as a target organism in the SWTR, methods to predict the level of
inactivation of Giardia achieved through disinfection (C-T calculations) have been developed. Also as a
result of its recent popularity, a dose response function exists for Giardia.
Chemical equations with which to predict the formation of disinfection by-products have been the
focus of much recent research effort. As a result, equations are available with which to predict formation
of trihalomethanes and haloacetic acids as a function of the concentrations of organic precursors and other
raw water and treatment conditions. The research in this area is far from complete. The equations utilized
in the present analysis are clearly place holders. Although the equations have been validated within
numerous individual plants, their validity when extrapolated outside the boundary conditions of those cases
remains a question mark which can only be removed through broader validation studies. The equations
used to predict the brominated species are particularly prone to such error. The equations utilized for
haloacetic acids base predictions on concentrations of THMs rather than directly as a function of water
treatment and precursor parameters. The caveats associated with all the formation equations have been
documented in the users manual which accompanies the USEPA Water Treatment Plant Model.
The cancer risks attributable to three of the four trihalomethanes (chloroform, bromodichloro-
methane (BDCM), and bromoform) have been estimated according to standard protocols used by EPA in
establishing MCLGs. The final carcinogenic status of dibromochloromethane (DBCM) is unclear at this
time, however. As a conservative place holder assumption, the cancer risk estimate for BDCM has been
used for DBCM. (NB: the estimated risk for BDCM has been increased since this analysis was finalized.)
Cancer risk estimates have been developed for the chlorinated haloacetic acids (dichloroacetic acid
(DCAA) and trichloroacetic acid (TCAA)) following standard protocols used by EPA in establishing
MCLGs. Although a cancer risk estimate has been developed for TCAA. its carcinogenic status has not
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been finally established. As a conservative place holder assumption, the cancer risk estimate for TCAA
has been used in the analysis as though it were a B2 carcinogen. While formation equations exist for five
of the haloacetic acids (mono-, di-, and trichloroacetic acids and mono- and dibromoacetic acid), cancer
risk estimates have been developed only for DCAA and TCAA (MCAA does not appear to be a
carcinogen). Thus the present analysis predicts concentrations of two brominated haloacetic acids, but
does not quantify associated risks. Finally, to further condense dimensions, summary results are presented
in terms of hypothetical Total Trihalomethane (TTHM) MCL alternatives and hypothetical Total
Haloacetic Acid (THAA) MCLs, despite the fact that calculations are performed for individual compounds.
Results for individual compounds are provided in appendices.
In focusing on those contaminants for which exposures and risks are estimable, it is possible to
begin to quantify the core problem and learn as much about the potential risk-risk trade-offs as possible
given current information. Insights regarding the potential significance of yet inestimable, or unknown,
risks can be obtained by further segmenting the analysis into two additional scenarios. Since more
unknown risks are associated with the use of alternate disinfectants than with the use of chlorine, the
analysts has been structured into two scenarios: with and without alternate disinfectants.
The without scenario permits evaluation of the limits of risk reduction possibilities for Giardia.
trihalomethanes, and chlorinated acetic acids without introducing other unknown by-product or microbial
risks associated with alternate disinfectants. The with scenario permits evaluation of the potential for
further reductions in Giardia, trihalomethane, and chlorinated acetic acid risks at the expense of
introducing other unknown risks. Results for the with scenario can be further subdivided to differentiate
between a scenario allowing only chloramines and a scenario allowing both chloramines and ozone.
Comparison of the results of with versus without scenarios should make it possible to evaluate the
potential significance of the unknowns (i.e., how many plants might switch to alternate disinfectants as
a means for meeting TTHM and THAA regulatory requirements). Cost estimates can also be made to
compare complying with and without alternate disinfectants.
The guiding principle in this attempt to develop exposure and risk assessment models is the
scientific method of inquiry. Models are constructed using the best information available as place-holders.
Hypotheses are substituted as place-holders for missing information or understanding. Scenarios are
structured to bound the range of possibilities introduced by complete unknowns. The models are exercised
under a range of conditions to examine the plausibility of results and to explore sensitivity to the major
uncertainties and unknowns. Using the exposure and risk assessment models in this way, significant areas
of research need can be identified and prioritized in a systematic manner. The models can be refined as
research results provide new information and understanding.
5.0 Analytical Methodology
There are four steps to the overall methodology for analyzing potential trade-offs.
• step 1: model the mix of raw water and distribution system characteristics in a manner
that is representative of the variability in site-specific conditions;
• step 2: predict the compliance choices that plants will make in attempting to meet a given
set of regulatory targets, given their raw water and distribution system characteristics;
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• step 3: predict the plant effluent (1st customer) and distribution system exposures that will
result from the given characteristics and compliance choices;
• step 4: predict the microbial and by-product risks that will result from these exposures.
Exhibit 6 illustrates these steps and summarizes the different analytical strategies that have been
applied to each. These are discussed below.
5.1 Step 1: Characterize Raw Water and Site-Specific Variability
Step 1 requires a means of modelling influent variability that is representative of conditions nation-
wide. As described in earlier sections, both microbial and by-product control techniques have side effects
which are exacerbated under extreme combinations of influent water quality conditions. It is these side
effects that are responsible for the existence of trade-offs between microbial and by-product treatment
objectives. The analytical objective for a national exposure assessment is therefore to build a model that
permits analysis of side effects resulting from treating extreme raw water conditions.
Analyzing a core problem focusing on surface waters provides a practical strategy for reducing
the scope of this analytical need to just one category of raw waters. The most fundamental relationship
in the core problem is that between influent TOC, influent bromide, and influent Giardia concentrations.
Although many other factors are included in the model, as described below, these three are assumed to
be the fundamental influences - the "drivers." High-TOC/high-Bromide/high-Giardia source waters are
likely to be much more susceptible to adverse trade-offs than low-TOC/low-bromide/low-Giardia source
waters. In addition, for any given combination of influent concentrations, the extent of the trade-offs will
be a function of the interplay between influent conditions and alternative combinations of regulatory
constraints. There are three different regulatory targets: a microbial target at the plant effluent (or 1st
customer); a microbial target in the distribution system (the disinfectant residual requirement); and a by-
products target in the distribution system. Significant trade-offs can result depending upon how the
regulatory regime specified at these three points is constrained. Modelling the core problem under a range
of assumptions about influent conditions and regulatory alternatives permits an assessment of the possible
extent to which trade-offs may be encountered.
Even when limiting analysis to focus on the core problem, a difficulty still arises in coping with
the enormous variability in site-specific conditions. A first instinct in approaching an analysis of by-
product regulatory options might be to perform case studies of plants representing different types of raw
water conditions. Upon closer scrutiny of the factors involved, however, it becomes apparent that an
unmanageable number of different cases would be required in order to capture the diversity that exists in
water supplies. Moreover, significant leaps of faith would be needed for determining the influence that
individual cases should have in the overall analysis and in the ultimate selection of appropriate regulatory
options.
Another approach would be to convene a panel of "experts" to render a judgement regarding the
effects of different regulatory options. Experts provide a concentrated source of case studies, drawn from
their collective experiences. While this feature is very efficient, it is likely that such a panel would also
have difficulty weighting different cases in arriving at the type of generalized conclusion required in this
unusual rulemaking. The panel of experts approach has been used in development of EPA regulatory
impact analyses involving straightforward contamination problems such as Flouride, VOCs, lOCs, SOCs,
and Radionuclides. It was also used - with much greater difficulty - in analyses of the Surface Water
21
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Treatment Rule and the Lead and Copper Rule, since these rulemakings involved optimization of entire
treatment and distribution systems as a function of variations in raw water quality. The treatment and
distribution optimizations implied by the by-products regulatory options are several times more complex
than previous rulemakings.
Another analytical option is to characterize site-specific variability through statistical modelling
(simulation) of the variability in raw water and distribution system characteristics. The diversity of site-
specific conditions is represented through the technique of Monte Carlo simulation applied to the core
problem.
The key question is: what percent of the time can the most extreme combinations of circumstances
be expected to co-occur? Monte Carlo simulation provides an explicit methodology through which the
frequency of such co-occurrences can be assessed. In this procedure (fully described in a companion
paper),21 numerous parameters that are critical to predicting microbial and by-product treatment perfor-
mance are characterized as probability distributions. Next, interdependencies between related parameters
(e.g., TOC and UV; pH and alkalinity) are examined and joint occurrence relationships are statistically
defined. A significant assumption made in this procedure is that influent Giardia occurrence is indepen-
dent of all other influent parameters, (Since the approach to simulating influent Giardia levels is integral
to the approach taken to the Giardia risk assessment, it is discussed under step 4, below).
In the actual simulation step, individual and joint distributions are sampled randomly to create 100
different simulated raw water quality profiles which, depending on the quality of the data and the statisti-
cal analysis, should be representative of the variability that will confront surface water systems. In other
words, the critical analytical step of weighting, or assigning a frequency to different combinations of
extreme raw water and distribution system conditions to form a representative composite picture is handled
by this procedure, based on an explicit statistical analysis. In effect, the statistical approach provides a
means of considering 100 case studies at a time, that are randomly selected via a procedure that should
be broadly representative of the inherent variability between individual sites, and thus of the extent to
which extreme conditions co-occur.
The characterization of influent raw water quality and distribution system residence time, was
made possible through the extensiveness of the AWWA Water Industry Data Base (WIDB) for fundamen-
tal parameters such as pH, hardness, alkalinity, temperature, and many others. There are important
weaknesses, however, affecting the three driving variables in the core problem. The WIDB contains only
just enough data on TOC (84 observations), to permit a statistical approach. Furthermore, the WIDB does
not provide data on bromide or Giardia. Thirty bromide observations from a national study by Krasner,
et al were utilized to define a statistical distribution for influent bromide concentrations.22 (An ongoing
study by the AWWA Research Foundation is developing new data on TOC and bromide which may be
used to improve current place holder assumptions.) The LeChevallier study of Giardia concentrations
(representing 46 plants), discussed above, was utilized to model the variability in influent Giardia
concentrations between plants. Another data base was used to model the day-to-day variability of Giardia
cysts within individual plants (Hibler23). A companion paper describes these statistical analyses
developed to characterize Giardia occurrence as a function of both these considerations.24
The provision of explicit methodology for assessing site-specific variability enables explicit
inspection of the uncertainties inherent in this key contributor to the overall exposure assessment. All
distributional assumptions are regarded as place-holders in the same sense discussed earlier. The Monte
Carlo framework can support sensitivity and what-if analyses that can shed light on the potential
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significance of major areas of uncertainty. Exhibit 7 presents a three-panel summary of the simulated
values TOC, bromide and Giardia as compared to the respective observations in the data sets on which
the simulations are based. Exhibit 8 presents plots of simulated influent TOC versus simulated influent
bromide and versus simulated influent Giardia. The assumption of total independence between these
"driving" variables is evident in the scatter patterns.
5.2 Step 2: Predict Compliance Choices
The second step, predicting the compliance response to individual site specific circumstances in
terms of treatments deployed and process control settings, is far more troublesome. It represents another
source of factorial possibilities that must somehow be contained within a manageable analytical
framework. As mentioned earlier, there is often no unique solution to the treatment and process control
problem. There may be several different combinations of strategies that will serve to achieve compliance
in any given circumstance. As noted in Section 2.0, this feature has two profound analytical implications:
1) it is virtually impossible to construct a model that can precisely replicate the existing baseline condition;
and 2) the extent to which the most extreme compliance options (e.g., GAC or membrane filtration) are
relied upon depends on the extent to which less extreme options (e.g., use of alternate disinfectants) are
adequate, given raw water conditions.
A baseline "calibration" of the Monte Carlo simulation procedure was undertaken during prototype
development of the modelling methods employed here. Results of that effort, summarized in the next few
paragraphs, are documented in more detail in a separate paper.25 The AWWA WIDE is unique in that
it enables a correlation of existing treatment practices with data on influent water quality, plant effluent
quality, and distributed water quality. The uniquely comprehensive coverage of the WIDE data across
the treatment train suggests the potential to obtain a baseline "calibration" of the Monte Carlo procedure.
Such calibration might be achieved by adjusting treatment and process control assumptions to produce
model results that replicate the statistical distributions exhibited in the WIDB finished water and
distributed water data. In the end, it became clear that process control assumptions regarding factors not
included hi the WIDB (e.g., chlorine dosages, basin contact times, and points of chlorination) are more
important to matching the observed distributions of effluent and distributed water quality than the
distributional assumptions applied to the raw water parameters. It was shown to be possible to achieve
a number of different calibrations through a number of different combinations of these fixed settings —
i.e., since different process control assumptions could be used to achieve the same target used for
calibration, one could not conclude which assumptions would be most appropriate.
It was possible to match the means of the predicted total trihalomethane and chlorine residual
distributions for 100 randomly synthesized plants to the means of the observed distributions in the WIDB
data under a number of different settings. The predicted and observed distributions for chlorine residual
were shown not to be statistically different as measured by a Kolomogorov-Smirnoff test. The predicted
and observed distributions for total trihalomethanes did not compare favorably, however. Since the
chlorine dosages were fixed by calibrating to the chlorine residual (via a chlorine decay equation), they
reflected the log-normal distribution of the chlorine residual data. The total trihalomethane levels predict-
ed from these dosages were then also log-normal. However, the observed distribution for total
trihalomethanes in the WIDB data is normal — a reflection of the fact it is a regulated parameter. In
reality, the plants in the upper tail of the log-normal TTHM distribution predicted by the model would
have undertaken various process refinements to meet the current TTHM standard. Such refinements were
not accounted for in the model.
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It was concluded that true model "calibration" could eventually require process data at such a level
of detail that the exercise becomes indistinguishable from that required for case-by-case "validation"
studies. The rule development process might stimulate the development of additional validation through
the release of EPA's Water Treatment Plant Model and accompanying users manual.26 Despite the
inherent difficulties, some measure of calibration was achieved. The revised version of the model
produces predictions that appear to approximate national occurrence data on average and that, starting from
a pre-TTHM standard condition, reflect treatment changes comparable to those that actually occurred to
meet the TTHM MCL of 100 ug/1. These results are discussed in section 6.0 and confirm that the Monte
Carlo procedure involving distributional representation of the variability in raw water conditions is
reasonable, on average. But, the imperfect match to observed by-product distributions is an important
source of error inherent in the process control assumptions applied to the simulated raw waters. The tails
of the distributions of chlorine dosages, chlorine residuals, and by-products are ultimately the determining
influences in characterizing what percent of plants can meet a given microbial or by-product risk target.
Results show that process control assumptions and model equations are over-predicting TTHMs in the
upper tail of the distribution and under-predicting in the lower tail. Thus conclusions regarding the
attainability of relatively less stringent TTHM MCLs are likely to be overly pessimistic while conclusions
regarding the attainability of relatively more stringent MCLs are likely to be overly optimistic.
Without a baseline characterization of the configuration and performance of treatments and process
controls, it is not possible to approach regulatory impact analysis in the conventional manner of comparing
pre- and post-regulatory conditions. The approach adopted therefore is one of assessing the attainability
of different treatment objectives as if working from a "clean slate." Microbial treatment objectives are
evaluated in terms of two scenarios, as described above: 1) the SWTR scenario, assuming minimalist
adherence to the existing SWTR (i.e., ignoring the advice of the Guidance Document for poorer quality
source waters); and 2) the Enhanced SWTR scenario (ESWTR), assuming adherence to the SWTR
Guidance as if it were a firm requirement. Both the SWTR and the ESWTR scenarios assume disinfectant
dosages sufficient to meet the distribution system residual requirements of the SWTR, as well as pH
conditions assumed sufficient to meet the requirements of the Lead Rule. A full range of alternative by-
product MCLs are evaluated under each microbial scenario.
The answer to the question of what is ultimately attainable should be the same regardless of
whether the baseline is understood. Similarly, the trade-offs between the different treatment objectives
can be revealed just as clearly through the "clean slate" approach to assessing treatment performance under
different regulatory scenarios. At some point it will become necessary to "net out" the treatments already
applied to comply with current standards from the treatments required to comply with alternative standards
in order to pinpoint the regulatory impact. Fortunately, studies such as those reviewed in Section 3.0
indicate that most of the adaptations undertaken in response to the current TTHM standard have involved
simple low cost process control modifications (moving points of chlorination, adjusting dosages, etc.) that
would not amount to significant costs at an aggregate level. The largest adaptation in evidence is the
indication from WIDE data that perhaps as many as one-third of surface water plants are using chloramin-
es, which is also relatively inexpensive.
The "clean slate" approach is analyzed in terms of a With Alternate Disinfectants Scenario and a
Without Alternate Disinfectants Scenario (described in Section 4.0). The Without Scenario would not be
expected to bear any resemblance to the baseline treatment configuration in any case, since chioramines
and other alternate disinfectants are already in use. In both the With and Without Scenarios, a progression
of successively more extreme treatment and process control adaptations is assumed with increasingly
stringent MCLs. The progression of treatments follows a least-cost criteria; the less costly compliance
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options being tried first before progressing to more extreme and more costly measures. This sequence is
illustrated in the decision tree illustrated in Exhibit 9. As noted in Section 3.0, there appears to be a
tendency of some plant operators to add ammonia as a means of preserving the use of pre-chlorination
in the face of by-product control requirements. The least-cost criteria of the decision tree produces results
(Section 6.0) that indicate less extensive use of chloramines in the With Alternate Disinfectants Scenario
than the proportion shown in the WIDE and AWWARF data. Despite this divergence from reality, the
least-cost sequence, as illustrated in Exhibit 9 is believed to be an intuitively sound place holder
assumption to assess likely treatment adaptations. The decision tree is discussed further in a separate
paper
27
A related problem arises in the "clean slate" approach regarding the need to make an assumption
about the presence or absence of pre-chlorination in the starting condition. As shown in Exhibit 9,
elimination of pre-chlorination is the first step in the compliance decision tree. It has the potential to
greatly reduce by-product formation, but can also compromise microbial treatment under certain
conditions. For this analysis, the presence or absence of pre-chlorination was simulated as a random
variable based on the prevalence of the practice indicated in the AWWA WIDB which shows about 80
percent of surface water plants still practicing pre-disinfection as of 1988 (Pre-SWTR). This proportion
seems high by comparison to the Disinfection Survey conducted by the AWWA Disinfection Committee
in 1990 which shows, at most, two-thirds. However, the wording of the WIDB question was somewhat
vague since the term "predisinfection" was not more precisely defined. Ideally, pre-TTHM standard
baseline data on the extensiveness of pre-chlorination would be required in order to truly start with a clean
slate. The WIDB estimate is used with the knowledge that it may be on the high side. Alternatively, it
may be an accurate reflection of the fact that many plants have followed a different decision tree in their
compliance choices, favoring the introduction of chloramines as a strategy for preserving the benefits of
pre-chlorination.
In addition to defining the likely sequence of treatments resulting from alternative regulatory
scenarios, it is also necessary to specify a wide range of specific process control assumptions. A long list
of place holder assumptions is required here, as discussed at length in a separate paper.28 In general,
the assumptions fall into three categories: best estimates, constraints, and bounding assumptions. Best
estimates are employed in places (e.g., initial dosages and unit process contact times) where the range of
variability is considered reasonably estimable based on engineering judgement and experience. Constraints
are performance requirements that relate to treatment objectives other than microbial or by-product objec-
tives. The need to simultaneously meet these other objectives forces more extreme microbial and by-
product treatment solutions in some instances. The most significant constraints are pH conditions assumed
necessary to comply with the Lead Rule, and maximum disinfectant residual concentrations required to
satisfy the taste constraint for chlorine and a possible MCL of 3.0 mg/1 for chloramines.
Bounding assumptions were applied at several key junctures where it was deemed prudent to
estimate conservatively in order to minimize the chances of under-estimating the potential for trade-offs
between the competing treatment objectives. An important set of bounding assumptions concerns the
engineering design margins assumed in making treatment choices (described more fully in a companion
paper29). The CxT equations used to assess compliance with SWTR requirements for disinfection are
applied with a 20 percent over-design factor. Similarly, compliance with by-product MCLs is evaluated
with a 20 percent over-design factor. In combination, these two bounding assumptions are expected to
reflect the upperbound of possibilities for conflicts between treatment objectives, minimizing the possibility
that the conflicts between them will be under-estimated.
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Similarly, the effect of seasonal temperature variations on compliance choices and resulting
exposure estimates has been incorporated into the model via a set of bounding assumptions. Partially to
avoid the necessity of building a much more complex model capable of simulating seasonal treatment
adaptations, and partially to minimize the possibility of under-estimating the potential for trade-offs
between treatment objectives, the model equations are structured to predict Giardia concentrations on the
basis of average winter temperatures and by-product concentrations on the basis of average annual
temperatures. The two temperatures are simulated based on the joint distribution they exhibit in the
AWWA WIDE data.
S3 Step 3: Predict Resulting Microbial &. By-Product Exposures
Step 3, predicting microbial and by-product exposures resulting from the different combinations
of raw water, distribution system, and treatment characteristics, is accomplished through application of a
number of chemical equations that have been brought together in a comprehensive modelling framework
in the USEPA Water Treatment Plant Model.30 Among these are the CxT equations utilized to compute
disinfection requirements, trihalomethane formation equations, haloacetic acid formation equations, and
chlorine decay equations.
All of these relationships are considered place holder assumptions. They have all received some
measure of validation and yet many uncertainties remain. The major ones are as follows:
• The CxT equations were developed to assess inactivation at the first customer. In the model
employed in this analysis, they are linearly extrapolated assuming first order kinetics to estimate
total log inactivation through the distribution system. Thus, for a given concentration of chlorine,
pH, and water temperature, if a CT of 300 achieves a 3-log inactivation of Giardia cysts, a CT
of 900 (which may occur through a point in the distribution system) would be estimated to
achieve a 9-log inactivation. No data are available to assess the validity of this assumption for
Giardia inactivation, although for viruses there appears to be reduced inactivation efficiency
relative to CT, as the level of inactivation increases.
• Giardia has been selected as the target organism for analysis. The rationale for this is described
in more detail in a companion paper.31 Since Cryptosporidium is much more resistant to
disinfection than Giardia, and is ubiquitous in surface waters, it may be a more appropria*" *arget
organism, but it is not sufficiently understood to support all the analytical needs.
• The trihalomethane formation equations have been the focus of considerable research and have
been empirically validated to some extent. A major source of potential error concerns use of these
equations to extrapolate outside the boundaries of the raw water and treatment conditions that
characterized the plants in which they were originally developed and tested. Although predicted
results for total THMs seem to fall within a plausible range of occurrence, results for individual
THMs, especially regarding the brominated compounds, may have large uncertainties.
• The haloacetic acid formation equations are at a less refined stage of development. The equations
predict haloacetic acids as a function of predicted trihalomethane concentrations rather than as a
direct function of raw water parameters and treatment conditions. Much less validation research
has been performed on haloacetic acid formation equations. Although results for THAAs seem
to fall within a plausible range of occurrence, there could be large uncertainties in the predictive
capability for individual compounds, especially the brominated compounds. New equations have
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recently been developed and are being incorporated into the DBF-RAM for future iterations of the
analysis.
While the collection of equations being utilized produces results in plausible ranges, all equations
are subject to error. Significantly, extrapolations outside the boundaries of the conditions where equations
were estimated and verified may be most susceptible to error. Unfortunately, such results are also most
likely to represent the types of extreme conditions that are critical to the analysis. Until further
characterization of the error in the selected model equations or replacement with better equations, the
results presented herein represent best estimates.
Presently, trihalomethane and haloacetic acid formation equations have been developed only for
surface water plants that do not soften. Owing to the different chemical relationships that characterize a
softening plant, a different set of by-product formation equations is required. EPA is currently developing
such equations for incorporation into the Water Treatment Plant Model and DBP-RAM.
The initial analytical effort has focused only on large (>10,000 population) surface water systems
that presently filter but do not soften. This portion of the universe represents a substantial proportion of
the total population exposed to the highest microbial and by-product risk levels. They serve a total of 103
million persons, equivalent to 64 percent of the total population served by community surface water
systems (162 million) and 43 percent of the total population served by all community water supplies (242
million; not including non-transient, non-community systems). Under some conditions, surface water
plants which soften could be more highly constrained. This question bears further investigation. By
comparison, the total population served by large (> 10,000) surface water softening plants is about 20
million.
Although the initial modelling work has focused on large (> 10,000 population) surface water
systems that do not soften, it is believed that results of this analysis can be applied to smaller systems of
the same type. The raw water characteristics of the large systems represented in the principal data source -
- the AWWA WIDE — should not be vastly different from raw water characteristics facing small water
systems of the same type.
5.4 Step 4: Predict Resulting Microbial and By-Product Risks
Step 4, predicting microbial and by-product risks resulting from the projected exposures, is
performed through the application of risk assessment models for Giardia and carcinogenic by-products,
respectively. These models are of the nature of best estimates. In more precise statistical terms, the risk
models are based upon "maximum likelihood" estimates of the dose-response relationships. Use of
maximum likelihood estimates is a departure from EPA's usual practice in evaluating carcinogens.
Carcinogenic risks in drinking water have more typically been evaluated on the basis of the upper 95-
percent confidence bound of the dose-response relationship. Since risk assessment methods for Giardia
are developed on a maximum likelihood basis, however, the cancer risk has been evaluated on a similar
basis to facilitate comparison. Cancer risk estimates are also presented in summary print-outs for the 95-
percent confidence interval in order to provide a point of comparison to other drinking water rules that
have been assessed by that procedure. However, the maximum likelihood estimates are considered the
best estimates. The methods employed in derivation of cancer risk estimates are summarized in Technical
Note 1.
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Risk assessment methodology for Giardia has been previously defined32 and has received some
empirical validation.33 Exhibit 10 presents the results of previous attempts to validate the Giardia risk
assessment procedure using data from epidemic outbreaks of the disease. In the analysis supporting
Exhibit 10, the following assumptions were made:
• the dose-response function (developed from data on human subjects) is representative for all
populations;
• all cysts recovered by the monitoring technique are viable;
• all viable cysts are infective in humans;
• a single cyst can cause an infection (the dose-response function estimates that only about 2
percent will cause an infection upon ingestion, but the point is it only takes one);
• infection = illness (i.e., all infections are symptomatic);
• cysts recovered by the analytical technique represent all cysts which are present; and
• the error caused by assuming all cysts are recoverable and infectious to humans (an under-
estimate) is equivalent to the error in assuming that there is 100 percent recovery of cysts by the
analytical method (an over-estimate).
These are the same assumptions that were used in the Surface Water Treatment Rule Regulatory
Impact Analysis prepared in 1989.34 At that time, they were considered to be conservative assumptions.
The results of the validation studies suggest that the assumptions may not be significantly conservative
since the results they generate replicate the empirical data fairly well.
In approaching the assessment of by-product regulatory alternatives, a number of refinements were
made in the existing risk assessment methodology for Giardia. These refinements are fully described in
a companion paper.35 In considering analysis of waterborne disease risk within a simulation framework,
there are two important dimensions of variability in influent concentrations: 1) variation between different
plants, and 2) temporal (day-to-day) variation within individual plants. The first dimension is important
for accurately characterizing the risk at an aggregate or national level. The second dimension is critical
to assessing the risk of epidemic occurrence of giardiasis.
The SWTR endemic risk target of one case per 10,000 persons per year was believed to be
equivalent to a level of outbreak risk that would be trivially small. The recent findings of LeChevallier
discussed in Section 3.0, indicate that the minimum treatment requirements specified in the SWTR would
not be sufficient to achieve the target risk level for many systems. If by-product regulatory alternatives
further erode the level of microbial treatment provided, the threshold of significant outbreak risk may be
crossed. The refinement of predictive methods for assessing outbreak risk is therefore important.
There are two primary sources of data regarding the raw water occurrence of Giardia. Both were
used to support the analysis. Hibler collected data for 73 plants which includes multiple samples at the
same plants to give an indication of the variation from day to day within the same plant. These data have
been utilized (adjusted by a recovery efficiency factor of two) to develop a statistical characterization of
the day-to-day within-plant variation in terms of a delta negative binomial distribution which is described
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in an accompanying paper. LeChevallier collected data for 46 plants using state-of-the-art monitoring
methods. These data have been used to simulate the raw water variability in influent Giardia between
different plants. Since LeChevallier provides empirical estimates of the recovery efficiency (48%) and
percentage of viable cysts (13%), these estimates were adopted in the analysis. All the other pertinent
assumptions, listed above, from previous Giardia risk modelling efforts are kept the same.
A few other best estimate, place-holder assumptions have been added to the Giardia risk
assessment methodology. To reflect the day-to-day variability in operations, the level of Giardia present
in the simulated effluents is assumed to drop by 1-log five percent of the time.37 In addition, to the
primary infections estimated by the dose-response function, a secondary infection rate of 25 percent is
assumed. Finally, the outbreak threshold, the level at which outbreaks are considered to be detected and
reported, resulting in a chain reaction of societal coping costs, is assumed to be the. point at which 1
percent of the population is ill (defined, in this analysis, as infection of 1 percent of the population
exposed in the vicinity of the first customer) within any 30-day period. The overall Giardia risk
assessment methodology is illustrated in Exhibit 11.
5.5 Summary of Analytical Methodology
The overall analytical methodology is summarized in the diagram in Exhibit 12. The modelling
apparatus is structured in a "batch mode." A batch of 100 simulated input profiles, characterizing influent,
distribution system, and some treatment parameters are run through the EPA treatment model as a separate
batch run for an initial (clean slate status quo) treatment benchmark and again for each treatment
configuration indicated by the successive nodes of the decision tree (Exhibit 9). In this way, the same 100
input files are run as a batch through every one of the conceivable treatment configurations.
These batch results are then interpreted by a compliance sorting routine. Compliance with either
the present SWTR or an Enhanced SWTR is assured by the structure of the scenarios. The compliance
sorting routine evaluates the extent to which more extreme treatments are required in order to meet
alternative by-product MCLs. (For economy of presentation, these are summarized in terms of
hypothetical total THM MCLs and in terms of total haloacetic acid MCLs. Detailed results are also
generated for each individual compound, however.)
The compliance sorting routine begins by reading the 100 effluent by-product concentrations for
the initial, status quo, treatment configuration. For each alternative MCL being evaluated, it selects the
records for those plants which meet the MCL and saves their output files to the master output file for that
MCL. It then reads the model results at the next treatment node on the decision tree and performs the
same selection routine, but working only on the remainder of the 100 plants ~ i.e., those that did not
achieve compliance at the previous node. Exhibit 13 presents a two-panel summary of the procedure.
The flow diagram in the top panel summarizes the logic. The bottom panel summarizes the form of the
output.
As illustrated in the bottom panel, the sorting routine produces a complete assessment of the core
problem. For any given combination of microbial treatment targets and by-product MCLs, it produces
an assessment of the extent to which relatively more extreme treatments will be required. These treatment
deployment profiles are matched to the appropriate unit cost vectors to produce estimates of the total
national treatment cost of alternative by-product MCLs for the subset of the universe being studied (large
surface water plants that filter but do not soften). In addition to the treatment percentages, the output files
assembled by the sorting routine provide a quantitative profile of the exposures to Giardia. trihalo-
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methanes, and baloacetic acids at each benchmark. The exposures are then converted into estimates of
disease incidence via the risk models described above. Taken together, these elements of methodology
provide a complete basis from which to characterize the trade-offs between regulatory alternatives — i.e.,
a complete basis for regulatory impact analysis.
6,0 Results & Discussion
The results generated by the above-described DBF-RAM methodology are presented in a series
of tables in Appendix A. These tables present results in a form similar to the bottom panel of Exhibit 13,
showing the degree of treatment required to meet a specific combination of regulatory targets. In addition,
the appendix tables also present quantitative estimates of the microbial and cancer risks associated with
each combination of regulatory targets.
Several different tabulations are provided, representing different scenarios. Results are summarized
in terms of the SWTR Scenario With Alternate Disinfectants versus the SWTR Scenario Without Alternate
Disinfectants. Equivalent summaries are provided for the Enhanced SWTR Scenario.
For each of the scenarios evaluated, two complete sets of tables are provided; one in which by-
product control is dimensioned in terms of total trihalomethane (TTHM) MCLs and the other in terms of
total haloacctic acid (THAA) MCLs. To facilitate comparisons between hypothetical TTHM MCLs and
comparable THAA MCLs, tables comparing the two are provided in Appendix B. Appendix C presents
tables which document the associated concentrations of individual trihalomethanes and individual
baloacetic acids under the different scenarios.
The results are evaluated and discussed at a summary level in the remainder of this section. There
are two broad questions which must be addressed in reviewing these results:
1) Are the DBF-RAM outputs a plausible representation of reality?
2) What is implied about the trade-offs between microbial and by-product risks?
The first question is covered in Section 6.1; the second in Section 6.2.
6.1 Assessment of Model Performance
Before examining model results in terms of the potential trade-offs between microbial and by-
product control objectives, it is necessary to first compare model outputs to available studies of actual
conditions in order to assess the realism of model predictions. Such comparisons can be made in terms
of: 1) by-product occurrence, 2) treatment adaptations to meet the current trihalomethane standard, 3)
cancer incidence, and 4) Giardia incidence. Given the "clean slate" approach outlined in Section 5, the
SWTR With Alternate Disinfectants Scenario, evaluated at a TTHM MCL of 100 ug/1, should demonstrate
a reasonable degree of replication of the pre-SWTR/TTHM MCL=100 conditions reflected in a number
of available studies.
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6.1.1 Comparison of DBF-RAM Results to By-Product Occurrence Data
In the SWTR Scenario (with alternate disinfectants) at a TTHM MCL of 100 ug/1, the 100
simulated plants exhibit a mean TTHM concentration at the average customer of 40 ug/1, with a 95th
percentile of 78 ug/1. These results compare favorably with data reported by Krasner et al38 which shows
a median of 25 ug/1 and a 75th percentile of 59 ug/1, measured at the plant effluent. It also compares
favorably with the AWWARF TTHM survey39 which reported the mean of quarterly distribution system
samples, averaged across all plants, to be in a range of 30 to 45 ug/1. Similarly, the AWWA Water
Industry Data Base shows annual means of distribution system samples to lie within a range of 30 to 60
ug/1. In contrast, data collected by EPA at plants selected to reflect more extreme conditions exhibited
a mean of 84 ug/1.40
As noted in section 5..3, the place holder assumptions used to predict haloacetic acid
concentrations are based on a relationship to TTHMs rather than on chemical formation relationships. It
is important therefore to check the realism of THAA predictions also. Based on only dichloroactic acid
and trichloroacetic acid, average customer levels predicted in the SWTR scenario (with alternate
disinfectants) for a TTHM MCL of 100 ug/1 exhibit means of 10 ug/1 and 8 ug/1, respectively. 95th
percentiles are 24 ug/1 and 20 ug/1, respectively. These model results compare favorably to data reported
by Krasner et al41 which have medians of 6 ug/1 and 75th percentiles of 12 to 15 ug/1, measured at the
plant effluent. In contrast, data collected by EPA at plants selected to reflect more extreme conditions
exhibits a mean of 39 ug/1.42
6.1 J Comparison of DBF-RAM Compliance Predictions to Actual Compliance Patterns
While it is good that mean values are replicated by the modeling apparatus, the true test of validity
is in the prediction of performance at the extreme ends of the range ~ the interactions between the tails
of the different random variables. Some indication of this critical aspect of model performance is provided
by a comparison of the treatment deployment projected by the "clean slate" model results at a TTHM
MCL of 100 ug/1 versus the treatment deployment that actually resulted from the current TTHM standard.
The need to resort to higher levels of treatment to meet a TTHM MCL is driven by the tails of the
distributions ~ by the more extreme conditions. The model results show the following treatment
deployment required to meet a TTHM MCL of 100 ug/1 in the SWTR Scenario with alternate
disinfectants:
No Further Treatment 61%
Eliminate Pre-chlor 21%
Eliminate Pre-chlor & Add NH3 18%
Data on treatment changes made to comply with the TTHM MCL of 100 ug/1 were collected in
the AWWA Disinfection Survey43 and in the AWWARF TTHM Survey44. Unfortunately, both studies
tabulate the data in a manner which does not permit direct comparison because the treatment percentages
(presented in section 3.2) do not sum to 100 percent. It is nonetheless possible to make some
comparisons.
Compared to the model prediction that 61 percent of plants require no treatment changes to meet
a TTHM MCL of 100 ug/1, the AWWA Disinfection Survey reported 44 percent. The survey included
the option of moving the first point of chlorination as well as a second option of eliminating
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prechlorination, versus the model algorithm which offers only elimination of prechlorination as the first
option. Both surveys indicate that compliance with the present TTHM standard was achieved predomi-
nantly through changes in chlorination practice; and, at the most extreme, addition of ammonia. As in
the model results, more significant treatment modifications were generally not rquired to meet TTHM
MCL of 100 ug/1. The model indicates 18 percent of systems had to resort to chloramines versus survey
estimates of 25 percent (AWWARF) and 16 percent (AWWA). The AWWA Water Industry Data Base
provides another data point, showing 25 percent of surface water plants that filter, but do not soften, using
chloramines. As discussed in Section 3.2, some plants may be using chloramines as a means of clinging
to the benefits of pre-disinfection. This may account for survey estimates of chloramines usage exceeding
estimates produced by the DBF-RAM algorithm.
6.13 Comparison of DBF-RAM Estimates of Cancer Incidence to Other Estimates
As the tables in Appendix A indicate, the model results suggest that both cancer risks and
microbial risks lie in significant ranges. The potential for significant trade-offs to occur appears to be
confirmed. On a maximum likelihood basis, however, the risk posed by trihalomethanes becomes very
small while the cancer risk of dichloroacetic acid becomes the predominant concern. DCAA is shown to
account for most of the total by-product cancer risk. In the period since the input assumptions used in
the current model were fixed, the cancer risk factors that EPA believes should be used for the brominated
THMs have been increased, so this result may change somewhat in the next iteration.
Within the 103 million population covered by the current model, DCAA would be responsible for
38 cases of cancer per year. Because of the widespread exposure, this would make DCAA one of the
most significant carcinogens ever encountered in the SDWA regulatory program. As discussed in Section
5, the DBF-RAM predictions of cancer incidence are based on dose-response relationships derived from
toxicological research. There is debate in the epidemiological literature over suggestions that cancer
incidence attributable to disinfection by-products may be on the order of thousands of cases per
year/5'46'47 EPA does not believe that it is appropriate to derive quantitative cancer risk estimates from
the available studies because the weight of evidence is not strong enough to indicate a causal relationship
between exposure to chlorinated drinking water and cancer incidence.
6.1.4 Comparison of DBF-RAM Results to Estimates of Waterborne Disease Incidence
The Appendix A tables show significant incidence of Giardia infections at the benchmark TTHM
MCL of 100 ug/1 in the SWTR Scenario (with alternate disinfectants). Within the 103 million population
represented by the model, 340,000 infections per year are indicated. These results are two orders of
magnitude higher than the post-SWTR predictions presented in the SWTR Regulatory Impact Analysis.
As discussed in section 3.1, the difference results from use of Giardia data collected recently by
LeChevallier18 which indicate occurrence levels about two orders of magnitude higher than that estimated
in the SWTR RIA. As the TTHM MCL is changed from 100 ug/1 to 25 ug/1, the DBP-RAM predicts a
near doubling over this baseline level of Giardia infections.
In the present analysis, all predicted Giardia infections occur at the first customer which is
assumed to represent 10 percent of the total 103 million population exposed hi the model. The C-T
equations have been extrapolated to predict additional log-kill achieved in the distribution system. The
resulting log-kills are sufficiently high that Giardia infections are indicated only at the first customer. As
described in an accompanying paper by Grubbs. et. al.,49 use of the Giardia dose/response model
employed in this analysis includes several new aspects of analysis beyond that developed originally by
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Rose et. al.50 The same dose/response relationship is utilized, but the daily variation in the influent
concentration is simulated over 365 days rather than based on a single annual average. In addition, a
treatment failure rate is incorporated by assuming a 1-log drop in the total reduction achieved five percent
of the time. Also, a secondary infection rate of 25 percent is assumed. The collective result of these
refinements is a model which predicts approximately twice the level of infections estimated by using only
the arithmetic mean with the Rose equation.
In addition to predicting endemic incidence of giardiasis, the modelling framework developed by
Grubbs et. al. also includes an outbreak simulation feature. As shown in the results in Appendix A, the
outbreak risk is shown to be significant at the current TTHM MCL, a finding that does not appear to
match reality. This prediction is based, however, on the conservative assumption that infection equals
illness and that infection of one percent of the population at the first customer (versus the total population)
would lead to community awareness of waterborne disease and formal recognition of an outbreak event.
On balance, both the endemic and epidemic predictions for Giardia risk are believed to be
plausible considering that there are great uncertainties regarding accurate reporting of disease and outbreak
incidence and considering the uncertainty in the relationship between infection and symptomatic illness.
Reconciling the model results against epidemiological evidence on endemic incidence of waterborne
disease31, it is possible that the target organism methodology used in DBF-RAM may be a conservative
estimate of the total microbial risk.
A flaw in the current Giardia modelling exists in the evaluation of the more extreme MCL
scenarios where ozone is employed to obtain compliance. The present treatment model assumes that if
ozone is used for primary disinfection, then Giardia inactivation will be achieved to meet only the
minimum SWTR requirements in the SWTR Scenario, or the 1/10,000 risk level in the Enhanced SWTR
Scenario. This must be refined in future development of the model. Due to concerns over unknown
dimensions of ozone by-products and ozone decay, the current framework focuses more keenly on chlorine
and chloramines. Ultimately, analysis of the "ozone paradigm"52 will have to be fully developed to the
same level of sophistication as the "chlorine paradigm."
6.2 Assessment of Potential Trade-offs
As discussed in Section 4, the analysis of trade-offs between competing risk reduction objectives
presents a problem that has infinite dimensions, making it necessary to focus on a smaller core problem
for purposes of analysis. Analysis of the core problem is intended to illuminate both the nature of the
trade-offs involved and the potential significance of uncertainties and unknowns. There are three
conceivable types of trade-offs to be evaluated:
• As by-product MCLs are imposed, the level of microbial protection may be compromised. As a
result, the reduction in known cancer risks (THMs and HAAs) may be offset by increased
microbial risk.
• If the level of microbial control is first enhanced to prevent adverse side effects on the level of
microbial protection, the increased level of microbial control could result in greater by-product
formation and higher costs for by-product control.
• If by-product control is pursued with the use of alternate disinfectants, the incidence of known
cancer risks (THMs and HAAs) will be reduced, but unknown cancer risks (e.g., bromate, other
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DBFs) and unknown microbial risks (e.g., bacterial growth from increased AOC with use of
ozone, or reduced inactivation with use of chloramines) may be introduced. Weighing against
these concerns, alternative disinfectants may be less expensive than other advanced by-product
control strategies.
A structured approach to evaluating these three types of trade-offs is provided through the
consideration of four scenarios:
• The SWTR Scenario evaluates the potential for increased microbial risk when by-product MCLs
are imposed with the currently required level of microbial control. The decrease in known cancer
risks may be compared to the increase in microbial risk.
• The Enhanced SWTR (ESWTR) Scenario assumes the microbial risk level is fixed at a 1/10,000
endemic incidence rate for giardiasis. Alternative by-product MCLs are evaluated with this
microbial constraint. Results can be compared to the SWTR Scenario to determine 1) how much
additional known cancer risk results from the higher level of microbial control; and, 2) how much
additional treatment cost is entailed in by-product control to compensate for the higher level of
microbial protection.
• Both the SWTR Scenario and the ESWTR Scenario are evaluated in two variations: 1) with
alternate disinfectants; and 2) without alternate disinfectants. Comparing the results of the two
variants permits an assessment of the treatment cost savings made possible by the use of alternate
disinfectants. This cost savings must then be weighed against the unknown cancer risks and
unknown microbial risks that may be introduced through the use of alternate disinfectants. The
unknown risks cannot be quantified, but the comparison to cost savings provides a framework
within which to consider these unknowns.
6.2.1 Potential for Increased Microbial Risks
Exhibit 14 presents a graphic illustration of the process through which by-product controls might
result in inadvertent increases in microbial risks. Coagulation and filtration have the joint effect of
removing Giardia and reducing TOC. Reducing TOC decreases the overall potential for by-product forma-
tion. It also decreases the chlorine demand in the water and permits the SWTR disinfectant residual
requirements in the distribution system to be met with lower chlorine dosages. While the diminished use
of chlorine may reduce the overall potential for by-product formation, it may also decrease the total level
of Giardia inactivation achieved at the first customer, unless compensating treatment is added.
Reducing TOC may not seem like a problem since the SWTR requires a minimum of 3-logs of
inactivation at the first customer. New Giardia occurrence data collected by LeChevallier, however,
indicate that an average of at least 5-logs may be required by many utilities in order to meet the 1/10,000
endemic incidence goal sought by the SWTR (as discussed in Section 3). Many plants may be achieving
adequate log-reduction at the first customer as an accidental result of the need to meet the distribution
system residual requirement in the presence of high chlorine demand. Removal of TOC to meet by-
product constraints may allow the log-reduction at the first customer to drop back to the 3-log minimum
which now appears inadequate. This phenomenon is observed in the modelling results.
Exhibit 2 summarizes the LeChevallier data in terms of a plot of influent Giardia versus the total
log reduction achieved. The pattern of the data indicate a complete lack of correlation between the
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influent Giardia concentration and the log reduction achieved. This lack of correlation is perhaps the
result of the dominant influence of the residual requirement, in addition to other factors. Model results
appear to support this explanation. The Monte Carlo simulation framework established in the DBF-RAM
is well-suited to analyzing the relationship between influent Giardia levels and total log-reductions
achieved at each individual plant. Since the influent Giardia distribution used to drive the simulation was
developed from the LeChevallier data, some direct similarities to the LeChevallier findings are to be
expected in the model results. Unfortunately a complete calibration check of the model against the
LeChevallier results is not possible because the LeChevallier data was collected in a pre-SWTR
compliance environment whereas the model is constructed to include the SWTR requirements as initial
constraints. Some similarities are evident, nonetheless, as discussed further below.
There are many possible reasons why the LeChevallier data indicate a lack of correlation between
the influent Giardia level and the total log reduction achieved. In the over-simplified framework of the
DBF-RAM, a few assumptions dominate this relationship. Exhibit 15 presents a plot similar to that of
Exhibit 2 showing simulation results for a scenario that assumes implementation of the SWTR (with
alternate disinfectants) and a TTHM standard of 100 ug/1. Of 100 simulated plants, only 39 were
determined by the DBF-RAM algorithm to require treatment adjustments in order to meet a TTHM MCL
of 100 ug/1. These 39 points are plotted in Exhibit 15. The plotted points reflect the relationship between
influent Giardia and total log reduction after compliance with these scenario constraints.
Many of the 39 points in Exhibit 15 appear in a vertical line reflecting the SWTR requirement for
a minimum of 3-logs of total reduction. In contrast to the pre-SWTR baseline reflected in the
LeChevallier data in Exhibit 2, some of the points along the 3-log line in Exhibit 15 represent a higher
level of total reduction than in the pre-SWTR condition while others represent a lower level. In the
simplified model logic, those plants that were not previously achieving a 3-log level would have increased
their level of total reduction to equal 3-logs, while some of those achieving more than a 3-log reduction
would have dropped their level of total reduction to the 3-log minimum, where they could still meet the
residual requirement. Notably, all the points aligned vertically along the 3-log minimum are to the left
of the diagonal line that defines the 1/10,000 risk threshold that the SWTR sought to achieve, indicating
that they are incurring risks greater than this target level.
The remaining points plotted in Exhibit 15 show more than 3-logs of total reduction. Some of
them are above the 1/10,000 risk threshold (to the right of the diagonal) while others are below it (to the
left of the diagonal). In tracing the source of this result through the model logic, it appears that the reason
these plants exhibit more than the 3-log minimum is due to the additional SWTR constraint of maintaining
a disinfectant residual in the distribution system. The simulated plants that are achieving more than a 3-
log total reduction at the first customer in Exhibit 15 represent conditions where there is enough TOC to
require elevated chlorine dosages to overcome the chlorine demand of the TOC in the distribution system,
but yet where the resulting contribution to TTHM formation is not significant enough to require additional
TOC removal.
Exhibit 16 illustrates the effect of a tighter by-product MCL of 25 ug/1. In this scenario, 83 plants
would be required to change treatment in order to comply. As shown in the graph, the result is that all
the points are aligned along the 3-log minimum line. Those plants that had more than 3-logs of total
reduction in Exhibit 15 have been guided by the treatment algorithm into TOC removal strategies which
in turn reduced the chlorine demands that had to be overcome in order to meet the SWTR residual
requirement in the distribution system. As a result, the model predicts that all plants required to change
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treatment to meet a TTHM standard of 25 ug/1 would" reduce their total log reduction to the 3-log
minimum.
As shown in Exhibit 16, the convergence to the 3-log minimum results in a risk level higher than
the 1/10,000 target for all of the 83 plants compelled to modify treatment in meeting a TTHM MCL of
25 ug/1. The resulting change in endemic incidence of Giardia infections will be larger for some plants
than for others. In particular, there may be some plants for which the co-occurrence of high influent TOC
and high influent Giardia result in a very large increase in incidence of Giardia infections. This increase
results from significant drops in chlorine dosages for compliance with SWTR residual requirement after
TOC removal has been triggered by the need to meet a TTHM MCL. The increase in incidence of
Giardia infections has been computed based on the change in total log-reduction between one MCL and
another, as computed by the DBF-RAM model. Results of this analysis are presented in Exhibits 17 and
18.
Exhibit 17 plots the cumulative distribution of the 100 simulated plants in terms of the increase
in annual Giardia infections per 1 million persons when moving from a TTHM MCL of 100 ug/1 to an
MCL of 75 ug/1. The plot confirms that there are a few plants that would face extreme conditions,
resulting in significant increases in Giardia infection rates. Nearly 10 percent of plants could experience
increases of between 1,000 and 10,000 infections per 1 million population. The top end of this range
approaches outbreak proportions. Exhibit 18 shows that the impact of a shift from a TTHM MCL of 100
ug/1 to a TTHM MCL of 25 ug/1 would produce increases in Giardia infection rates on this same order
of magnitude for over 30 percent of plants. Exhibits 19 and 20 present the same results that are plotted
in Exhibits 17 and 18 in terms of bar charts that emphasize the impacts at the extreme ends of the range.
Ultimately, a comparison must be made between the increase in incidence of Giardia infections
and the corresponding decrease in cancer incidence that might result from alternative MCLs. This is
achieved by combining the above analysis of predicted Giardia infections with predictions of cancer
incidence resulting from exposure to by-product concentrations that are simultaneously projected by the
DBF-RAM. Results are summarized graphically in the log-log plots presented in Exhibits 21 and 22.
Exhibit 21 plots increased incidence of Giardia infections per 1 million persons per year versus decreased
cancer incidence per 1 million persons per year for the plants having to change treatment when the TTHM
MCL is changed from 100 ug/l to 75 ug/1 in a SWTR scenario (with alternate disinfectants).
Exhibit 22 presents the same picture for the plants that would have to change treatment if the
TTHM MCL is changed from 100 ug/1 to 25 ug/1. The pattern of the points plotted in these diagrams
indicates that the degree of change in cancer incidence generally spans only one order of magnitude (less
than one case per 1 million persons per year) while the degree of change in incidence of Giardia infections
spans several orders of magnitude (from one to more than 10,000 infections per 1 million persons per
year). This points to a finding that changes in microbial risk are more sensitive than changes hi cancer
risk to treatment changes needed to meet TTHM MCLs.
Exhibits 23 through 30 present the same series of graphs as Exhibits 15 through 22 except that
the entire analysis is structured in terms of alternative MCLs for total haloacetic acids. The interpretation
is identical to that described above.
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622 Potential to Avoid Increased Microbial Risk
One obvious direction indicated by the above results would be to strengthen the SWTR before
proceeding with requirements for additional by-product controls. An enhanced SWTR has been evaluated
using the DBF-RAM. It was assumed that the portion of the SWTR Guidance Document which
recommends an additional log of inactivation for each additional log of influent Giardia is converted to
a mandatory requirement. If an enhanced SWTR were in place, the graphs presented in Exhibits 2, 16
and 17, would show plants lined up along the diagonal line representing the 1/10,000 target risk level
rather than along the vertical line representing the 3-log minimum required by the present SWTR. By
definition of the 1/10,000 risk level as the target, there would therefore be no adverse risk trade-offs in
terms of Giardia. (Trade-offs with respect to other pathogens such as Cryptosporidium may still occur,
depending on the efficiency of using Giardia as a target organism for defining adequate treatment.) Such
an Enhanced SWTR may not be feasible due to the lack of practical monitoring protocols available for
Giardia. but there is promising research in this area.53
The evaluation of the Enhanced SWTR with the DBF-RAM showed that adherence to the 1/10,000
risk level resulted in very little additional cancer incidence, very little modification in treatment and, there-
fore, very little additional cost. This minimal cost impact resulted from the model assumption that the
additional inactivation requirements at the first customer would be met with additional contact time at the
plant effluent. Additional contact time allows inactivation requirements to be met without significant
increased chlorine dosages. While additional contact time contributes to inactivation, it does not contribute
very much to additional by-product formation, because the additional increment of contact time is trivial
in proportion to the total residence time in the distribution system.
6.2 3 Potential Trade-Offs Involved In The Use of Alternate Disinfectants
The most striking aspect of the cancer incidence predicted by the DBF-RAM is the order of
magnitude of the results (tabulated in Exhibits A-l through A-8). Exhibit A-l presents an evaluation of
the SWTR Scenario in the presence of alternate disinfectants. This exhibit most closely resembles the
status quo. Using maximum likelihood estimates of the dose response functions, results indicate 45.5
cases of cancer per year (trihalomethanes: 0.9 cases; di- and tri- chloroacetic acids: 44.6 cases) at the
current TTHM standard of 100 ug/1. Using the upper 95 percent confidence bound of the dose response
functions, results indicate 163 cases of cancer per year (trihalomethanes: 24.6 cases; di- and tri-
chloroacetic acids: 138.7 cases) at the current TTHM standard of 100 ug/1. About 70-85% of the total
projected cancer incidence is attributable to dichloroacetic acid.
The most promising aspect of the cancer incidence results is the small difference between SWTR
Scenarios and Enhanced SWTR Scenarios. Comparison of results in Exhibits A-l and A-2 indicate that
an Enhanced SWTR, versus the SWTR, would produce about 1 additional case of cancer per year at the
current TTHM standard of 100 ug/1.
Exhibits 31 and 32 present an analysis of the total and incremental costs of cancer risk reduction.
The analysis is presented in terms of different Total Haloacetic Acid MCLs in recognition of the fact that
dichloroacetic acid accounts for most of the projected cancer risk. The tables in Appendix B permit
various comparisons between THAA MCLs and comparable TTHM MCLs. The total and incremental
cost analysis presented in Exhibits 31 and 32 is developed from the treatment profiles assigned by the
DBF-RAM to meet different regulatory targets and from unit cost estimates derived from the Water Cost
Model.54
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Exhibits 33 and 34 present vectors of compliance percentages for the with and without alternate
disinfectants variants of the SWTR and the ESWTR scenarios. Each column in each of the tables contains
a compliance vector for a given THAA MCL. The percentages in each vector total 100 percent. Taken
together, the percentages comprising each vector indicate the mix of treatments that are projected to be
required to comply with each MCL alternative. For example, in Exhibit 34, compliance with an MCL of
60 ug/1 in the without alternate disinfectants variant of the SWTR scenario is projected to require no
further treatment by 70 percent of plants; elimination of pre-chlorination by 12 percent of plants;
elimination of pre-chlorination and optimization of alum coagulation by 11 percent of plants; and
elimination of pre-chlorination, optimization of alum coagulation, and installation of granular activated
carbon (GAC) by 7 percent of plants.
These compliance vectors are an input to the computation of the aggregate treatment cost of each
MCL alternative. The percentages are used to split the total number of plants into treatment categories.
The corresponding unit costs for each treatment are then multiplied by the number of plants assigned to
the category. The resulting products in each category are summed to produce the estimate of the total
aggregate treatment cost. (Because of the clean slate approach adopted in the DBF-RAM (see Section
5), some of the costs counted by this procedure may have already been incurred by systems complying
with the current TTHM standard. These costs are trivially small, however, because they involve low cost
techniques such as moving the point of chlorination and adding ammonia.)
It is important to note that although the vectors of compliance percentages total 100 percent, a
portion of the plants assigned to the final treatment category, involving GAC, may not actually be able
to comply with the MCL. This is particularly true at the more stringent MCLs. For purposes of
estimating aggregate treatment costs, it makes no difference. Once GAC is installed, the plant would
either be in compliance or would be eligible for a variance. The cost computed by the DBF-RAM is the
same either way. The inability to attain compliance even with GAC is taken into account, however, in
the analysis of the change in by-product exposures. Appendix A presents a more detailed print-out of the
modelling results which contains a graphical summary of the extent of variances that would be implied
with increasing stringency of regulation.
Results in Exhibits 33 and 34 indicate that (assuming some proportion of variances is accepted)
stringent by-product MCLs are technically feasible if the use of alternate disinfectants and/or GAC is
accepted. In without alternate disinfectants scenarios, 57 to 58 percent of plants are projected to require
GAC to comply with a THAA MCL of 10 ug/1. Nearly one-third of the plants driven to GAC at this
MCL would not be able to meet it, but would obtain variances. In the with alternate disinfectants
scenarios, 50 percent of plants would still require GAC to comply with a THAA MCL of 10 ug/1. About
one-fifth of these plants would not meet the MCL, but would obtain variances. A THAA MCL of 10 ug/1
would eliminate roughly three-quarters of the total quantifiable by-product cancer risk.
For the segment of the water industry studied (representing 103 million persons; 43 percent of the
total population served by community water supplies), the aggregate treatment cost of a THAA MCL of
10 ug/1 is estimated to be $957 million (SWTR Scenario) to $974 million (ESWTR Scenario) per year in
the without alternate disinfectant scenarios and $922 million (SWTR Scenario) to $963 million (ESWTR
Scenario) per year in the with alternate disinfectant scenarios. These results do not indicate significant
cost savings from the use of alternate disinfectants because an MCL of this stringency would drive nearly
the same percentage of plants to GAC, as noted above.
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An alternative regulatory strategy might attempt to reduce by-product cancer incidence to the
maximum extent possible without triggering irreversible and expensive technology shifts, such as switching
to ozone and GAC. The results in Exhibits 33 and 34 indicate that standards equivalent to a THAA MCL
of 30 ug/1 would force 20 to 21 percent of systems to GAC if alternate disinfectants were not permitted
(without scenarios). If chloramines are permitted, the with scenario results show that only 4 to 11 percent
of systems would have to resort to ozone or GAC to meet a THAA MCL of 30 ug/1. Results for the with
scenarios indicate 59 to 60 percent of plants could achieve a THAA MCL of 30 ug/1 with either no
treatment changes at all or with elimination of pre-chlorination. An additional 30 to 36 percent of plants
could achieve this level with elimination of pre-chlorination, chloramines and optimization of coagulation.
This proportion of chloramines usage is about equivalent to the current penetration of this technology in
systems subject to the interim TTHM standard.
If alternate disinfectants are permitted (with scenario), the total treatment cost of a 30 ug/1 MCL
for the segment of the industry under study (surface water systems serving > 10,000 that filter but do not
soften) would be roughly 10 percent as much as for an MCL of 10 ug/1 ($71 to $120 million per year for
the SWTR and ESWTR, respectively vs. $1 billion per year). If chloramines are not used (without scenar-
io), a THAA MCL of 30 ug/1 would cost about one-third as much as a THAA MCL of 10 ug/1 ($336 to
$353 million per year vs. $1 billion per year). In the without scenarios, a THAA MCL of 30 ug/1 achieves
50 percent as much reduction in cancer incidence as a THAA MCL of 10 ug/1. In the with scenarios, a
THAA MCL of 30 ug/1 achieves 38 percent as much reduction in cancer incidence as a THAA MCL of
10 ug/1. The difference results from the reliance on GAC as a last resort in the without scenarios. Once
use of GAC is triggered, it over-shoots a target of 30 ug/1.
6.2.4 Evaluation of Trade-Offs Within The Regulatory Impact Analysis Framework
The framework for regulatory decisionmaking is defined in the Safe Drinking Water Act. The
application of that statutory framework to the problem of disinfection by-products regulation, using DBF-
RAM results, is described in a companion paper.55 A totally separate mandate, Executive Order 12291,
specifies a cost-benefit framework for analyzing trade-offs during the rule development process. As stated
at the outset of this paper, the goal of regulatory impact analysis is to develop and organize information
on benefits, costs, and economic impacts so as to clarify trade-offs among alternative regulatory
options.*6 EPA guidance for developing regulatory impact analyses (RIAs) specifies that the comparison
of regulatory options is to be achieved by quantifying and monetizing benefits and costs to the extent
possible.
This section presents DBF-RAM results in the context of the RIA requirement. The discussion
is presented in three parts. The first part (6.2.5.1) briefly describes the conventional approach that has
been followed in assessing the benefits of SDWA regulations in previous RIAs. The second part (6.2.5.2)
discusses how the development of DBF regulations presents a completely different type of problem due
to the trade-offs between health risks, and describes the solution that has been devised to cope with this
analytical complication. The third part (6.2.5.3) presents DBF-RAM results in the context of this RIA
framework.
6.2.4.1 Economic Benefits Relevant to SDWA Regulations
The benefits of SDWA regulations are usually evaluated in terms of two approaches: 1) damages
avoided, and 2) willingness-to-pay. It is possible to quantify the monetary value of direct economic
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damages, such as lost income and medical costs, that are associated with the incidence of adverse health
effects. Such damage estimates can be combined with exposure and risk models to yield aggregate
national estimates of the expected value of monetary damages that will result at alternative levels of
regulation. The difference in the expected value of damages between one level of regulation and the next
most stringent level (the damages avoided) may be compared to the difference in the associated treatment
and monitoring costs. As long as the expected value of the damages avoided exceeds the difference in
the treatment and monitoring costs, the aggregate net benefits of regulation are positive.
The concept of economic benefits is not merely the expected value of the avoided damages, but
the wittingness-to-pay for a reduction in the risk of incurring those damages? The willingness-to-pay
reflects the intuitive notion that damages are, afterall, disagreeable. Therefore, it usually exceeds the
expected value of avoided damages. It is presumed that people are willing to pay something extra for a
margin of safety depending upon the degree of damages involved, their assessment of the degree of risk
(i.e., of the uncertainties affecting the estimate of the expected value of the damages), and their individual
risk tolerance for the type of damage involved. It has been suggested that altruism provides another
motive for additional willingness-to-pay. Studies have shown that the safety of water supplies is perceived
to have a societal value to consumers, reflecting concerns for sensitive populations and civic pride.58
Thus, an extra measure of insurance is part of the benefit being derived from SDWA regulations,
supplemental to the expected value of the avoided health damages. This willingness-to-pay concept of
benefits is quite consistent with the structure of the SDWA which stipulates that MCLs be established as
close as feasible to the no adverse effect level (i.e., as close as feasible to the MCLG), plus a margin of
safety.
In the Surface Water Treatment Rule Regulatory Impact Analysis,59 for example, economic
damages associated with contracting giardiasis were estimated to lie within a range of $1400 to $1800 per
case (1986 dollars) based on valuation of medical costs and lost work.60 Using this estimate of the cost
of illness together with estimates of the costs of coping with waterborne disease outbreaks, the expected
value of damages avoided was compared to the projected costs of installing filtration. The results
indicated that net benefits might be negative in the small system size categories, depending upon the
assumptions made regarding the risk of infection.61 However, viewing the same results in light of the
uncertainties not captured in the analysis, and in light of the presumed willingness to pay for an extra
margin of safety, it is possible to make a subjective judgement that net benefits are in fact positive. For
example, the above damage estimate does not take account of the fact that about 0.1 percent of cases of
waterborne disease may result in a fatality.62 In addition, there are significant economic impacts
accompanying the epidemic occurrence of waterborne disease -- instances in which a substantial portion
of the population is simultaneously infected. Quantitative valuation of "willingness-to-pay" is inherently
more difficult than valuation of direct damages and is ultimately somewhat subjective. (NB: neither the
SDWA, nor EO 12291 require EPA to demonstrate that net benefits are positive.)
Similarly, regulatory impact analyses (RIAs) developed for SDWA regulations governing
carcinogens, have often shown negative net benefits in the small system size categories. Under the
damages avoided approach, cancer incidence is valued at $400,000 per case, reflecting lost income and
medical costs. Under the willingness-to-pay approach, cancer incidence is valued at $8,000,000 per case.
The willingness-to-pay figure is taken from economic research on the value of life which draws its conclu-
sions from data on wage rate differentials in high risk occupations and other evidence.63 The $8,000,000
per case figure is not literally intended as an estimate of the value of life, but rather of the willingness-to-
pay to reduce the risk of premature death. Typically, RIAs on SDWA regulations governing carcinogens
have used the $8,000,000 figure. Using this figure, the net benefits of proposed MCLs for many
40
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contaminants are negative in the small system size categories and are sometimes negative in the larger
system size categories as well. In the final analysis, determining the margin of safety for carcinogens is
a judgement call and the SDWA does not constrain that judgement to adhere to a cost-benefit criterion.
6.2.42 Benefits Concepts Relevant to Disinfection By-Products Regulation
The regulation of disinfection by-products presents a distinctly different type of benefits valuation
problem, unlike that posed by any other drinking water contaminant or group of contaminants. The
valuation of benefits is complicated because there are multiple health risks involved and measures to
reduce one type of risk may increase another. The ideal solution would be one which minimizes the total
incidence of adverse health effects. Since the incidences of the different health effects are inversely
related to each other, a means of trading-off one benefit for another is needed.
Exhibit 35 illustrates a transformation of the conventional benefit/cost framework into a
theoretically equivalent form that is more suited to the multiple trade-offs involved. It shows that
maximizing health damages avoided per dollar of treatment expenditure is the same as minimizing health
damages incurred per dollar of treatment expenditure. Mathematically, minimizing total social costs is
the "dual" ~ the mirror image of maximizing net benefits. As described in Technical Note 2, both
formulations yield the same answer, but the dual is easier to work with since treatment costs and the costs
stemming from both types of health damages can be simply added together to compute the total cost.
A means of capturing the extra willingness to pay for a margin of safety within such a "damages
avoided" context has been suggested by envisioning a category of "anxiety damages" (i.e., without the
insurance benefit inherent in a safety margin, there would be comparable anxiety damages).64 Thus, total
willingness-to-pay equals the total damages, where total damages are conceived as the sum of direct
economic damages (lost work and medical expenses) plus anxiety damages. Within this framework, the
health damages incurred are valued in this analysis on the basis of willingness-to-pay estimates: $3,000
per case of giardiasis and .$8,000,000 per case of cancer.
The willingness-to-pay rationale implicit in this economic framework serves to clarify an important
aspect of the nature of potential trade-offs; they may consist of conflicts between competing safety margins
as much as trade-offs of actual health effects.
6.2.43 Analysis of Trade-offs In Terms of Total Social Costs
Viewed in the minimum total cost framework, the evaluation of disinfection by-product regulatory
alternatives entails trade-offs between five different dimensions of costs. As by-product controls are
applied, they impose treatment and monitoring costs (dimension 1) while reducing by-product cancer risks
(dimension 2) and potentially increasing endemic and epidemic microbial risks from source water
contaminants (dimension 3). As more stringent by-product controls are introduced, they may generate
additional cancer risks (e.g., bromate) and microbial risks (e.g., Cryptosporidium. or pathogenic bacterial
growth in the distribution system) as side effects (dimensions 4 and 5).
In order to clarify the nature of the trade-offs within this minimum total cost framework, it is
necessary to model how the five different dimensions change simultaneously; one or more types of costs
decreasing while others increase as a result of the opposing relationships. Currently, the DBF-RAM
provides quantification covering only the first three dimensions (the core problem). Exhibits 36 through
39 present results for the four different scenarios defined to analyze the core problem, showing a family
41
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of cost curves for each. Plotted as a function of THAA MCLs are: by-product treatment costs (dimension
1), costs of cancer cases incurred (dimension 2), costs of Giardia cases incurred (dimension 3), and the
total cost. With respect to dimension 2, the current DBF-RAM does not include quantification of cancer
risk associated with brominated haloacetic acids. With respect to dimension 3, the DBF-RAM quantifies
increased Giardia outbreak risk, but the expected value of damages associated with outbreaks have not
been included in the total cost results to simplify the comparison in this first iteration of the analysis.
While the additional damages associated with increased outbreak risks would be significant, they would
not change the shape of the resulting total cost curves, but would steepen the slope. Costs of unknown
cancer risks (dimension 4) and unknown microbial risks (dimension 5) introduced by use of alternate
disinfectants are unqualified in the current DBF-RAM.
Both the cancer and giardiasis incidence estimates are based on maximum likelihood risk
assessment procedures, as described in Section 5.4 and Technical Note 1. As noted in Section 5.4, the
dose-response relationships presently being utilized produce aggregate results for both cancer and
waterbome disease incidence that are much lower than the levels potentially implied by recent
epidcmiological studies in both areas. Conceivably, both types of health risks could be much greater than
indicated by the current dose-response functions.
6.2 A3.1 MCL Alternatives
The total cost curves for the SWTR scenarios shown in Exhibits 36 and 38 have an assymetric
shape which suggests that the lowest total cost will result from the least stringent THAA MCL (60 ug/1).
This result implies that the side-effect of increased giardiasis risk outweighs the gains in cancer risk
reduction. The trade-off is a reflection of the apparent flaw in the current SWTR with regard to the level
of risk reduction assured at the first customer, discussed at length above. Moreover, if the potentially
significant giardiasis outbreak risk were also reflected in the current set of total cost curves, the picture
would be even more assymetric.
Exhibits 37 and 39 present total cost curves for the ESWTR scenarios. These are also assymetric,
but have minimum points suggesting least cost THAA MCLs lie in a range of 40 to 50 ug/1. The reason
there is a potential for by-product reduction in these two scenarios is that an enhanced SWTR would
eliminate the trade-off with respect to Giardia risk while introducing very little additional treatment cost
and contributing only a small offsetting increase in cancer risk, according to the DBF-RAM. Significantly,
detailed results tabulated in Appendix A indicate that the increased outbreak risks induced by by-product
controls would be eliminated in the ESWTR scenarios.
Comparing results of with vs. without alternate disinfectant scenarios (Exhibits 36 vs. 38, and 37
vs. 39), indicates that the nature of the trade-offs is not markedly different between the two scenarios.
As discussed in section 6.2.3, above, the treatment costs are higher in the without alternate disinfectant
scenarios, but the difference is less significant at the most stringent MCLs because similar proportions of
plants would be driven to GAC in both scenarios. It is at the most stringent MCLs, however, where the
trade-offs implied by the with alternate disinfectant scenarios would be different in ways that are not
shown in the present results due to the inability to incorporate quantitative assessment of additional side
effects introduced by alternate disinfectants: carcinogenic by-products of alternate disinfectants and
inadvertent microbial risks introduced by alternate disinfectants.
The results summarized in Exhibits 36 through 39 point to a conclusion that the potential for
trade-offs between cancer risk (dimension 2) and microbial risk (dimension 3) is significant without an
42
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enhanced SWTR in place. The uncertainties associated with the risk assessment procedures assumed in
DBF-RAM and the exposure assessment methodology of DBF-RAM are so great that there is no confident
means of defining an error bound around these results. The individual microbial and cancer risk
assessments are subject to a wide range of potential error. It is conceivable that either of the two could
be in error in ways which would cause the total cost curves to exhibit very different shapes (e.g.,
significant underestimation of cancer risk). The exposure assessment produced by DBF-RAM, while
producing roughly plausible results in terms of mean values, could be in much greater error at the
extremes where trade-offs may be more acute. Consideration of the additional uncertainty presented by
unquantified cancer and microbial risks introduced by use of alternate disinfectants (dimensions 4 and 5)
further complicates the picture.
6.2.432 Technology-Based Alternatives
The current set of DBF-RAM results indicates there is a significant potential for trade-offs between
microbial and cancer risks if by-product MCLs are established in the absence of an enhanced SWTR. In
the alternative, there has been some mention of technology-based approaches in the hope that trade-offs
may be more avoidable with such approaches. The alternative to establishment of MCLs under the SDWA
is the specification of a treatment technique. Normally, treatment techniques can be used only when
measurement problems prevent the establishment of MCLs. A rationalization might be made that, in this
unique case, the difficulty of relating MCLs to resulting health risks could be considered a measurement
problem.
One treatment technique approach would be to specify a membrane technology capable of reducing
both microbial and cancer risks simultaneously. Because membrane filtration removes microbial
contaminants, it might provide a means of progressing without an enhanced SWTR. The cost of installing
nanofiltration in all the large surface water systems that filter but do not soften has been very roughly
estimated to be on the order of $5 billion per year ($150/household/yr. x [103 million persons/3 persons
per household]). By comparison, the results in Exhibit 36 indicate that the total social cost of a THAA
MCL of 60 ug/1 in the SWTR With Alternate Disinfectants Scenario ~ a rough proxy for the status quo -
- is about $1 billion per year, nearly all in the form of cancer damages ($358 million/yr) and Giardia
damages ($639 million/yr). In addition to the unfavorable economics, it is incorrect to conceive of
membrane technology as being free of trade-offs. Membrane technologies would not remove bromide and
may result in a shift to brominated by-products (in some systems) which may be more potent carcinogens.
In addition the solid waste and water wastage impacts of these technologies would pose significant cost
and environmental issues.
Another technology-based alternative would be to adopt an approach similar to that used in the
Lead and Copper Rule. The objective would be to require optimized application of the simplest and most
inexpensive by-product control strategies by all plants above a specified action level. Under this approach,
systems above the action level would be required to demonstrate that they have eliminated prechlorination
and optimized coagulation processes, relative to their unique raw water and treatment conditions. DBF-
RAM analysis of a similar option (81% not prechorinating and optimizing coagulation; see Exhibits 40
and 41) shows that at the most stringent MCL of 10 ug/1, about one-third of the known cancer risk
(dimension 2) would be removed at a fairly low cost cost ($122 million per year). The apparent effective-
ness of an option requiring elimination of prechlorination and optimization of coagulation as the only
available treatments reflects an inherent feature of the influent simulation — that most of the aggregate
cancer risk is in the large number of plants that have average influent conditions, not in the small percent-
age of plants that have extreme influent conditions. In this way, the technology-based approach applies
43
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the low cost treatments more extensively to more of they total known cancer risk, achieving significant
reduction without introducing the higher costs and unknown risks associated with alternate disinfectants.
Such an approach would not be free of significant trade-offs, however. One downside is that it
would provide uneven protection, leaving much cancer risk untouched in the small percentage of plants
that have extreme influent conditions. Another major inescapable trade-off is that the elimination of
prcchlorination would generate offsetting increases in Giardia risks in about the same proportions in this
scenario as in the other DBF-RAM scenarios. In fact, results in Exhibits 40 and 41 indicate that it is these
two first steps in the least cost algorithm that are responsible for most of the increased risk of giardiasis.
The development of an enhanced SWTR would therefore also be a requisite for implementation of this
strategy.
An additional drawback is the fact that the optimization of coagulation could cause a shift to more
brominated by-products that are potentially more potent carcinogens. Furthermore, there are some plants
in which influent conditions (e.g., alkalinity) are particularly unsuited to optimization of coagulation, or
in which site conditions are particularly unsuited to the addition of contact time to compensate for the loss
of prechlorination. As with the membrane technologies, the cost and environmental consequences of the
solid waste and water wastage impacts of such a strategy would have to be fully evaluated. All these
factors would have to be weighed by states in making individual optimization decisions. A final obstacle,
therefore, would be the capacity of state primacy agencies to make plant-by-plant determinations regarding
the optimization of treatment.
7.0 Summary Observations
The DBF-RAM may be viewed as simply an explicit, systematic framework for testing the
hypothesis that a specified regulatory strategy will result in a net decrease in residual health risk. In the
current analysis, the uncertainties and unknowns dominate. The only clear conclusion seems to be that
an enhanced SWTR could eliminate increased microbial risk; and that such enhancement might not
contribute significant additional treatment cost or cancer risk (in systems already chlorinating).
The current DBF-RAM results are intended as starting points in this attempt to define and analyze
a manageable and meaningful core problem. The broader utility of the DBF-RAM framework lies in the
fact that modelling such a process requires explicit specification of the methodology and assumptions that
underly the trade-off functions. Within this explicit framework, it is possible to investigate the many
remaining unknowns and the sensitivity of the results to uncertainties in the current place holder assump-
tions.
The "what-if' phase of analysis that lies ahead has potential to reveal which unknowns and which
place holders have the greatest influence on the results. The importance of missing information can be
evaluated by examining various additional place holder hypotheses regarding unknown processes, or by
simply accentuating the pure unknowns that lie outside the model, in the manner adopted in this paper.
The DBF-RAM provides a frame of reference for evaluating, or anticipating the implications of
new scientific understanding or technology. For example, the technology to practically implement an
enhanced SWTR may not yet exist, but the potential of such a development has been usefully evaluated.
In this way, the DBF-RAM can serve as a framework for evaluating the value of research as well as the
relative importance of different research objectives. Significantly, in the current DBF-RAM results, the
44
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total social cost curve most closely approximating the status quo has an intercept that implies existing
health damages are on the order of one billion dollars per year. This provides a benchmark against which
the value of research and of technology development can be assessed.
A number of sensitivity tests of current place holder assumptions are presently underway in
response to comments offerred by the EPA Science Advisory Board. The sensitivity of current results is
being evaluated with respect to: influent TOC assumptions, influent bromide assumptions, seasonal
variation, over-design assumptions, prechlorination assumptions, chlorine dose and contact basin sizing
assumptions, chlorine decay assumptions, trihalomethane formation equations, influent Giardia
assumptions, and Giardia infection modelling. In addition to sensitivity tests, the modelling framework
is presently being expanded to encompass surface water plants which employ lime softening and to
examine differences in the implied trade-offs when model assumptions are modified to reflect the
circumstances faced by small water systems, serving fewer than 10,000 persons. In addition, a formation
equation to predict bromate occurrence and exposure is being added.
Ultimately, the utility of "what-if' analysis will diminish. The number of conceivable variations
of assumptions far exceeds the number of public water supplies. At some point, it would make more
sense to apply the EPA Water Treatment Plant Model to each individual plant than it would to continue
to guess about them in aggregate. Efficient selection of follow-up DBP-RAM analyses should still offer
further improvements in the understanding of the problem before that point is reached. Beyond that point,
however, there must be a recognition that no amount of analysis can overcome many of the uncertainties
inherent in this problem. A modelling framework such as that of the DBP-RAM is useful in gaining
understanding of the potential significance of uncertainties, unknowns, and variability, but it cannot supply
the measure of judgement that will eventually be required to devise a solution in the presence of such
factors.
45
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References
1. USEPA, Office of Policy Analysis, Guidelines for Performing
Regulatory Impact Analysis, December 1983, OPA EPA-230-01-84-
003.
2. Regli, S., Rose, J.B., Haas, C.N., and Gerba, C.P., Modeling
the Risk from Giardia and Viruses in Drinking Water, JAWWA,
November 1991, 76-84.
3. USEPA, Guidance Manual for Compliance With the Filtration
Requirements for Public Water Systems Using Surface Water
Sources, March 31, 1989.
4. LeChevallier, M.W., Norton, W.D., and Lee, R.G., Occurrence
of Giardia and Cryptosporidium spp. in Surface Water Supplies,
Applied and Environmental Microbiology 57 (September 1991):
2610-2616.
5. Ibid.
6. Amy G.L., Chadik, P.A. and Chowdhury, Z.A., Developing Models
for Predicting Trihalomethane Formation Potential and Kinet-
ics, JAWWA, 79(7).
7. USEPA, Regulatory Impact Analysis: Benefits and Costs of Final
Surface Water Treatment Rule, prepared by Wade Miller Associ-
ates, Inc., February 17, 1989.
8. Rose, Joan B., Haas, Charles N., and Regli, Stig, Risk
Assessment and Control of Waterborne Giardiasis, American
Journal of Public Health 81 (June 1991): 709-713.
9. American Water Works Association, Government Affairs Office,
Surface Water Treatment Rule Evaluation Project, Final Report,
December 1987.
10. Op. cit. note 8.
11. Macler, B.A. and Regli, S., Use of Microbial Risk Assessment
In Setting U.S. Drinking Water Standards, USEPA, 1992.
12. LeChevallier, M.W., Norton, W.D., Lee, R.G. and Rose, J.B.,
Detection and Treatment of Giardia and Cryptosporidium In
Water Supplies, American Water Works Association Research
Foundation, January 1991.
13. Bennett, J.V., Holmberg, S.D., Rogers, M.F., and Solomon,
S.L., Infectious and Parasitic Diseases in Closing the Gap;
46
-------
The Burden of Unecessarv Illness, Edited by Amler, R.w. and
Dull, H.B., Oxford University Press, New York, Oxford, 1987.
14. Payment, P., Richardson, L., Siemiatycki, J., Dewar, R.,
Edwardes, M. and Franco, E., A Randomized Trial to Evaluate
the Risk of Gastrointestinal Disease due to Consumptionm of
Drinking Water Meeting Current Microbiological Standards,
American Journal of Public Health 81 (June 1991): 703-708.
15. Decision Research, American Water Works Association Research
Foundation National Trihalomethane Survey Report, prepared for
Metropolitan Water District of Southern California, April 27,
1987.
16. Cromwell, J.E., III, Lee, R.G., Kawczynski, E., Development of
the AWWA Water Industry Data Base: A "Facts" Machine for the
Future, Annual Conference of the American Water Works Associa-
tion, Cincinnati, OH, June 1990.
17.
18.
19.
20.
21.
22
23
24
25
Haas, C.N., Final Report of the 1990 AWWA Disinfection Survey,
AWWA Disinfection Committee, May 20, 1991.
Fair, P.S., TSD Plant Data Base, unpublished data, disk
transmitted July 31, 1991.
Krasner, S.W., McGuire, M.J.7 Jacangelo, J.G., Patania, N.L.,
Reagan, K.M., Aieta, E.M., The Occurrence of Disinfection By-
products In U.S. Drinking Water, Journal AWWA, August 1989.
Morris, R.D., Auder, A.M., Angelillo, I.F., Chalmers, T.C.,
Mosteller, F., CMorination, Chlorination By-Products, and
Cancer: A Meta Analysis, Am. J. Public Health, 1992; 82-955-
963.
Letkiewicz, F.J., Grubbs, W., Lustik, M., Cromwell, J.,
Mosher, J., Zhang, X, and Regli, S., Simulation of Raw Water
and Treatment Parameters In Support of the Disinfection By-
products Regulatory Impact Analysis, 1992, EPA-811-R-92-001.
Op. cit. note 19.
Hibler, C.P., Analysis of Municipal Water Samples for Cysts of
Giardia, Advances in Giardia Research (1988): 237-245.
Grubbs, W.D., Macler, B., and Regli, S., Simulation of
Microbial Occurrence, Exposure and Health Risks After Drinking
Water Treatment Processes, (1992), EPA-811-B-92-005.
Gelderloos, A.B., Harrington, G.W., Schaefer, J.K., and Regli,
S., Simulation of Compliance Choices to Meet Both Microbial
and Disinfection By-Product Treatment Objectives, AWWA Water
Quality Technology Conference, Orlando, FL, November 1991.
47
-------
26. Malcolm Pirnie, Inc., Water Treatment Plant Simulation Program
— tfsers Manual, March 1992, prepared for USEPA Office of
Ground Water and Drinking Water., EPA-811-C-92-001.
27. Gelderloos, A.B., Harrington, G.W., Owen, D.W., Regli, S.,
Schaefer, J.K., Cromwell, J.E., and Zhang, X., Simulation of
Compliance Choices for Regulatory Impact Analysis, 1992.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
Ib:
Ib:
Op
Re<
Grt
T\f*t
u&\
Op
Op
op
op
op
Coi
Dr-
L/X.
A
19!
Op
Op
Op
Op
Op
Op
Op
Op
Cr,
Op. cit. note 26.
Regli, S., Cromwell, J.E., Zhang, X., Gelderloos, A.B.,
,s, W., Letkiewicz, F., Macler, B.A., FramevorJc for
Decision Makings EPA Perspective, 1992.
cit. note 8.
cit. note 2.
cit. note 7.
cit. note 24.
cit. note 24.
v,«~3onery, P.J., and Greenfield, D.N., Pennsylvania Safe
Drinking Water Program Filter Plant Performance Evaluations;
A Summary of Procedures and Results, January 1988-December
1990, Pennsylvania Department of Environmental Resources.
cit. note 19.
cit. note 15.
Cit. note 18.
cit. note 19.
cit. note 18.
cit. note 17.
cit. note 15.
cit. note 20.
Craun, G.F., Epidemiologic Studies of Organic Micro-pollutants
in Drinking Water, in The Handbook of Environmental Chemistry,
48
-------
Vol 5: Water Pollution, O. Hatzinger (ed)f Springer-Verlog,
Berlin, 1991.
47. Cantor, K.P., Hoover, R., Hartge P., Mason, T.J., Silverman,
D.T., Altman, R., Austin, D.F., Child, M.A., Key, C.R.,
Marrett, L.D., Myers, M.H., Narayana, A.S., Levin, L.I.,
Sullivan, J.W., Swanson, G.M., Thomas, O.B., West, D.W., J.
National Cancer Institute, 1987; 79;1269.
48. Op. cit. note 12.
49. Op. cit. note 24.
50. Op. cit. note 8.
51. Op. cit. note 14.
52. Glaze, W., The Dilemma of Competing Risks In Drinking Water,
AWWA Annual Conference, Vancouver, BC, 1992.
53. LeChevallier, M.W. and Norton, W.D., Relationship Between
Treatment of Giardia, Cryptosporidium, Turbidity, and Particle
Counts, American Water Works Service Company, Belleville, IL,
January 1992.
54. Malcolm Pirnie, Inc., Technologies and Costs for Control of
Disinfection By-Products, July 15, 1992, prepared for USEPA
Office of Ground Water and Drinking Water.
55. Op. cit. note 31
56. Op. cit. note 1.
57. Freeman, M.A., The Benefits of Environmental Improvement,
Resources for the Future, 1979, Washington, D.C.
58. Mitchell, R. and Carson, R., Valuing Drinking Water Risk
Reductions Using the Contingent Valuation Method: A Method-
ological Study of Risks from THMs and Giardia, Resources for
the Future, report to USEPA, Cooperative Agreement Grant# CR
810 466 016, p 107.
59. Op. cit. note 7.
60. Harrington, W., Krupnick, A., Spofford, W., The Benefits of
Preventing An Outbreak of Giardiasis Due to Drinking Water
Contamination, Resources for the Future, September 1985, Draft
Final Report to the USEPA, Cooperative Agreement Grant# CR 810
466 010.
49
-------
61. Cromwell, J.E., III, Costs and Benefits of Filtration of
Surface Water Supplies, AWWA Annual Conference, Kansas City,
MO., June 1987.
62. Op. cit. note 13.
63. Fisher, A, Chesnut, L.G., Violette, D.M., The Value of
Reducing Risks of Death: A Note On New Evidence, Journal of
Policy Analysis and Management, Vol. 8, No.l, 88-100 (1989)
64. Op. cit. note 58, p9-6.
SO
-------
Technical Note 1
Cancer Risk Assessment Procedures
The cancer risk estimates for the trihalomethanes and haioacetic acids included in the
DBF-RAM are based on dose-response relationships developed from animal bioassays for
carcinogenicity using the linearized multistage model for low dose extrapolations. In the DBF-
RAM, both upper limit and maximum likelihood estimate values for the dose-response
relationships for these substances have been used, as discussed further below.
The EPA carcinogen risk assessment guidelines provide a discussion and rationale for
computing and using an upper limit value to characterize the carcinogenicity dose-response
relationship for use in risk assessments. While noting the need to examine and compare dose-
response results from various extrapolation models, these guidelines recommend the use of the
linearized multistage model as the model of choice, emphasizing that this model provides a
plausible upper limit to the risk that is consistent with some proposed mechanisms of
carcinogenicity.
It is noteworthy that the EPA guidelines also state that such an upper limit estimate "does
not necessarily give a realistic prediction of the risk. The true value of the risk is unknown, and
may be as low as zero. The range of risks, defined by the upper limit of the chosen model and
the lower limit which may be as low as zero, should be explicitly stated. An established
procedure does not yet exist for making "most likely" or "best" estimates."
The linearized multistage model produces values for the "slope" of a line relating the
"extra" incidence of cancer over background rates (the y axis value) with the dose of a
contaminant received (the x axis value). The dose is usually expressed in terms of mg/kg-day
(milligrams of the substance received per kilogram of body weight per day), and the incidence
is usually treated as a unitless value. Therefore, the units for this slope describing the risk are
(mg/kg-day)"1. (Note that the "unitless" incidence could also be expressed in units of "cases per
individual per lifetime.")
The mathematical form of the multistage model for determining the extra risk is:
A(d) = l-
where A(d) is the extra risk at dose d (see, e.g., Anderson et al., 1983). In this model, the "point
estimates" of the coefficients qt (and therefore the extra risk function A(d) at any dose d) are
calculated by maximizing the likelihood function of the data. The upper 95% confidence limits
on the extra risk are then determined from the upper 95% confidence limit on the parameter q^
which is denoted as "
-------
As noted above, the risk value is presented in units of (mg/kg-day)'1. It is often
convenient to express the risk in terms of an exposure value that corresponds to specified risk
levels, such as 104, 10'5, or 10"* (that is, exposure corresponding to a 1 in 10,000, 1 in 100,000
or 1 in 1,000,000 chance of a carcinogenic response, respectively). For drinking water
contaminants, these risk levels are usually expressed in terms of a drinking water concentration
that an individual is assumed to experience for a lifetime. Most often for organics, the drinking
water concentrations corresponding to these risk levels are expressed in units of ug/I. The
arithmetic for converting the q2* and ql values to corresponding drinking water concentrations
associated with various risk levels is detailed in the attached Exhibit. As noted there, these
conversions involve the assumptions of a 70 kg individual consuming 2 liters of drinking water
per day for a lifetime.
Notwithstanding the EPA guidelines recommending the use of the upper limit for risk
assessments involving carcinogens, the DBF RAM uses, as previously noted, both the "maximum
likelihood estimate" (
-------
to 600 ftg/L. That is, an individual exposed to drinking water with approximately 600 /ig/L of
chloroform present for his/her lifetime, and assuming the 70 kg body weight and 2 L/day
consumption, is estimated to have a one in ten-thousand chance of cancer (above background
cancer rates).. The chloroform drinking water concentrations corresponding to the 10"4, 10"5, and
10"* risk levels for both the MLE and upper 95% confidence interval unit risks obtained by
similar calculations, including rounding, are shown below.
Risk Level
10-4
10"5
10-*
MLE
10,000
1,000
100
Upper 95% CI
600
60
6
(Units^
fig/L
//g/L
References:
Anderson, EL and the Carcinogen Assessment Group of the U.S. Environmental Protection
Agency. 1983. Quantitative Approaches in Use to Assess Cancer Risk. Risk Analysis, 3(4):
277-295.
USEPA, Guidelines for Carcinogenic Risk Assessment. Federal Register, 1986, 51(185): 33992-
34003.
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o •r tri
in
o o o o
O T-
T- ^ rr
« CM CM Cl «- CM
O O O O O O
• in
cn c
c cv cvi
o c
o o
CO CO CO
CO O
-------
Technical Note 2
Equivalence of "Maximum Net Benefit" and "Minimum Total Social Cost" Objectives
Introduction
The issue of trade-offs among alternative regulatory options may be approached by either
minimizing total social cost or maximizing the net benefit. Total social cost includes 1) the cost
of treatment and monitoring, and 2) the value of health damages incurred. The net benefit includes
1) value of direct health damages avoided, 2) "willingness-to-pay" for a reduction in the risk of
incurring health damages, and minus 3) the cost of treatment and monitoring. The following
discussion provides an intuitive method of showing the equivalence of two approaches without using
a rigid mathematical proof. First, necessary and sufficient conditions for maximizing the net benefit
are derived. Second, the optimal degree of treatment maximizing the net benefit is shown to be the
same as the optimal degree of treatment minimizing total social cost. Therefore, the equivalence
of maximizing the net benefit and minimizing total social cost objectives can be established.
Maximizing Net Benefit
The health effects associated with microbial contaminants and disinfection by-products in
drinking water are waterborne diseases and cancer, respectively. Depending upon the degree of
treatment applied, both types of health damages may be assessed by estimating the "willingness to
pay" and direct damages.
1. Value of Health Damages Incurred
The total value of health damages incurred at any time can be defined as the sum of the
value of waterborne diseases and cancer cases incurred at that time. This definition can also be
expressed as
Dt(6) = Gt(6)+ Ct(5)
where t = any time instant, and t a 0,
5 = degree of treatment, and 5 a 0,
D,(6) = value of health damages incurred at time t,
G,(5) = value of waterborne disease incurred at time t, and
C,(o) = value of cancer cases incurred at time t.
2. Value of Health Damages Avoided
Similarly, the value of health damages avoided at any time is expressed as
= Dt=0(6=0) - Dt(6) = [Gt=0(5=0) - G,(6)] + [Ct=0(5=0) -
(1)
(2)
where A,(o) = value of health damages avoided at time t,
Dt=0(6=0) = value of health damages incurred at time 0 (baseline),
Gt=0(S=0) = value of waterborne disease incurred at time 0 (baseline), and
Ct=0(6=0) = value of cancer cases incurred at time 0 (baseline).
-------
3. Cost of Treatment and Monitoring
The cost of treatment and monitoring under the regulation may be denoted by R,(8).
Assuming that no treatment is installed before the regulation (i.e., a "clean slate"), the baseline cost
of treatment and monitoring is zero, or
Rt=0(6=0) = 0
(3)
4. Net Benefit
The net benefit from the regulation is the difference between value of health damages
avoided and the cost of treatment and monitoring. It can be defined as
Bt(8) = A,(8) - Rt(8)
(4)
where B,(8) = the net benefit at time t.
5. Maximization of Net Benefit
The objective of the trade-off analysis is to solve for the optimal degree of treatment that
maximizes the net benefit. The optimal degree of treatment 8* is found when
(5)
!,(8') = Max Bt(8) = A,(8") - Rt(8*)
8*0
Assuming that B,(8) has finite first order derivative and that second order derivative exists, 8* may
be found by applying the first order condition
as
a=8'
as
l«=8
. =0
(6)
and second order condition
6=6'
as2
8=8'
as2
(7)
Equation (6) is the necessary condition for optimization. The set of solutions to this equation
includes all the possible values of 8* that maximize the net benefit Bt(8), except when B,(8) is
maximized at the boundary 8 = 0. Equation (7) is the sufficient condition for maximizing B,(8),
meaning that if 8" satisfies both equations (6) and (7) then 8* is the optimal degree of treatment to
maximize the net benefit, provided that B,(8) is not maximized at 6=0.
Assuming that the degree of treatment 8 = 0 is not the optimal solution, equation (6) implies
that the marginal value of health damages avoided is equal to the marginal treatment expenditure
with respect to the degree of treatment when the net benefit is maximized.
-------
Minimizing Total Social Cost
Total social cost is the sum of the value of health damages incurred and the cost of treatment
and monitoring. Let St(5) represent the total social cost, it is defined as
St(5) = Dt(5) + R,(5) (8)
Total social cost is minimized if the optimal degree of treatment 5" can be found for
St(6") = Min St(5) = Dt(5") + ^(5") (9)
6*0
By applying the first order condition, the value of 6* may be solved from
as
aa
ad
... =0
To minimize S,(8), the optimal solution 6" must also satisfy the second order condition
From equation (2), it is known that
Dt(5) = Dt=0(6=0) - A.C5)
Substituting D,(5) from equation (12), equations (10) and (11) become
3S,(o)
36 '
as
35
l8=ft"
!»=»
.. = o
and
3o2
ao2
3o2
(10)
(ii)
(12)
(13)
(14)
Recalling that Dt=0(6=0) is a constant describing the baseline condition, it has zero derivative.
-------
The equations (13) and (14) can be rearranged as
as
18=8*
18=8*
and
«=8"
18-6
.. =0
.2 "«=»
.. s 0
(15)
(16)
Equivalence of the Two Optimization Processes
Note that the equations (15) and (16) are of the same form as equations (6) and (7),
respectively. It follows that the sets of solutions to equations (5) and (9) are identical for 6 > 0.
In other words, the two optimization processes are equivalent for any degree of treatment other than
zero. To complete the argument, it must be proven that if 5 = 0 is the optimal degree of treatment
for maximizing net benefit B,(8), it also minimizes total social cost S,(5), and vice versa.
If 5 = 0 is maximizing net benefit B,(S), then the net benefit associated with no treatment
is greater than the net benefit associated with any positive degree of treatment, or
B,(6=0) = Max Bt(6) a B,(6>0)
5*0
By substituting for B,(6) from equation (4), the above equation is transformed into
^(6=0) - R,(5=0) & A,(8>0) - R,(5>0)
Replacing for A,(6) from equation (2), the inequality (18) becomes
[Dt=0(5=0) - D,(5=0)]- R,(8=0) * Dt=0(8=0) - Dt(5>0) - R,(6X))
(17)
(18)
(19)
It is true that Dt=0(6=0) is equal to Dt(8=0) because health damages remain constant over time if no
treatment is installed. Thus the above expression can be rearranged as
Dt(5=0) + Rt(8=0) * Dt(5>0) + Rt(5>0)
Recalling equation (8), the above inequality is shown to be
S,(5=0) x St(5>0)
(20)
(21)
This inequality implies that 5=0 also minimizes total social cost S,(5). Similarly, we can show that
if 6=0 minimizes total social cost S,(6), it also maximizes net benefit Bt(6). Therefore, maximizing
the net benefit is equivalent to minimizing total social cost for any degree of treatment.
-------
EXHIBITS AND APPENDIX MATERIAL
-------
Analysis of Potential Trade-offs In
Regulation of Disinfection By-Products
John E. Cromwell, III & Xin Zhang
Wade Miller Associates, Inc.
Frank Letkiewicz
Abt Associates, Inc.
Stig Regli & Bruce Macler
U.S. Environmental Protection Agency
Exhibits & Appendix Material
-------
Exhibit 1
Raw Water
Quality
Treatment Practices
Pre-Disinfection
Coagulation
Filtration —
Softening —
Disinfection
By-Product Removal/Control-
Corrosion Control
Post-Disinfection and Storage-
Process
Control
Strategies
Distribution System
Characteristics (Dwell Time)
1 st Customer
Ava. 'Customer
Last Customer
microbial
risk
DBF
risk
microbiai
risk
DBP
risk
t
microbial
risk
DBP
risk-
-------
Exhibit 2
Influent Giardia vs. Total Log Reduction (Winter) From LeChevailier
10,000
1,000
OJ
.2 100
"S
.2
a
I
10
• I
i I
SWTR Win.
10
Total Log Reduction
-------
Exhibit 3
•<£> i
CO !
Q
'"~> /""">
•M* ««—'
G O
"I
CD
CO
at
E
3
X
O
cn
O O
-------
Exhibit 4
Disinfection By-Products Regulatory Analysis
Baseline Cancer Incidence — Based on Occurrence Data
(Surface Water Systems)
Annual Cases = Population Exposed (persons) x DBF Concentration (f/g/l) x Annual Risk Factor (cases/persons/year/fid
! Total Population (million persons) 3 '•"-
Average Concentrations fr/g/1)
, Chloroform
1 Bromodichloromethane
Dlbromochloromethane
Bromoform
TTHMs
Dichloroacetic Acid
Tichloroacetic Acid
THAAs ,
\ MLE Annual DW Risk Factors * i
| Chloroform ' ;
I Bromodichloromethane ;
! Dibromochloromethane
Bromoform
DichloronceticAcid
Tichloroacelic Acid
j Cancer Incidence based on MLE (cases/yr.)
Chloroform
Bromodichloromethane
Dibromochloromethane
Bromoform
TTHMs
! Dichloroncetic Acid
: Tichloroacetic Acid
THAAs
I Total
Upper 95% Cl Annual DW Risk Factors °
Chloroform
Bromodichloromethane
Dibromochloromethane
Bromoform
Dichloroacetic Acid
Tichloroacetic Acid
Cancer incidence Biased on 95% Cl (cases/yr.)
Chloroform
Bromodichloromethane
Dibromochloromethane
Bromoform
TTHMs:
Dichloroacetic Acid
Tichloroacetic Acid
THAAs
Total
High Case Estimate '
<1 0,000 people
17
77.2
24.8
10.4
1.4
114
27.7
16.6
'•'-' ;.:44
1.43E-10
3.33E-10
3.33E-10
1.78E-10
3.59E-08
8.68E-09
0.19
0.14
0.06
0.00
0.4
17.3
2.5
20
2.49E-09
> 10,000 people
' ;:V:145-
59.7
17.4
6.3
0.8
4.V' , v,"'; ,""84
22.1
17.00
*£. :•:;'" . ., v.'y.so
1.43E-10
3.33E-10
3.33E-10
1.78E-10
3.59E-08
8.68E-09
1.24
0.84
0.30
0.02
-2:4
115.0
21.4
138
2.49E-09
1.02E-08| 1.02E-08
1.02E-08I 1.02E-08
3.22E-09i 3.22E-09
1.13E-07
2.57E-08
3.3
4.4
1.8
0.1
10
54.3
7.4
62
1.13E-07
2.57E-08
21.6
25.7
9.3
0.4
57
361.0
63.4
! 424
Low Case Estimate ^
< 10,000 people
;i • ••>••: -mm
$.?„ .. ..'• /i.'S';":;''-
•':'':' . • ,••' ' ''^Tf'^:1:'-'''^' • '
sii;" ' • ' ".."''••• '*&<-'
:.::#;.:: . • .: ••'/..'
1';
Si:-
> 10,000 people
'•:' ::'^-':.' .1
;::.;i;:. :;:..;. ,.-
q
; ;:ilS': .'!"••-.:'.
1.43E-
3.33E-
3.33E-
1 .79E-
3.59E-
8.68E-
••''•W-'-"
0
0
0
0
.. .':! , ' .' '(
a
2.49E-
1.02E-
1.02E-
3.22E-
1.13E-
2.57E-
10
2
1
1 Band on occurrcno* data from: dsirrftction by-products fiold studies data (EPA.OGWDW.TBO)
: B*Md on occuf r«fx» datt from Krasncn t/tjai.. 1989
1 Soure*: F«d»f«l Reporting D«ta System (FRDS)
4 maximum lik«Ihood •stimat* (MLE)
J upper 85% confidence Intervil
-------
Exhibit 5
CO cc
CO
mmm
3
O
Q.
os ^
o
CO
c
o
oo
QOCO
c
o
§,S'5o,
feg.®?
j^f:
V
-------
Exhibit 6
CO
CO 3
'co
03
CO >*
03
CL
00
Q
03
03
•ma
D)
08
00)
CT3 -^3
03 O .g O
O C ^
-------
Exhibit 7
90%
.a 70%
JQ UuJb [
3 «rvv /
O 20% /
0
SURFACE WATER WITHOUT FILTRATION/SOFTENING
CUMULATIVE PROBABILITY DISTRIBUTIONS
Actual and Model-predicted Influent TOC Concentration
ft LOGNORMAL CURVE
i i"^L_ ' i-'-TT.... 1
! .--•' ^** — r
h'lx^**"
j^ i
^ 1
/? • I
X'' ' i
' .•" I
..
i
5 10 15 20 25
Influent TOC Concentration (mg/l)
30
100% T
& 90% •
(8 7fW .
.a /uvo i
l S ~w
0 n«n/
Actual and Model-predicted Bromide Concentration
FITTED LOGNORMAL CURVE i
I —-^^=~- ,....,..
.-X"
: 1
7 • '
/ I
/ ° '
3 30% 1
0.0
;
!
!
.f
0.5 1.0 1.5 2.0 . 2.5 3.0 3.5
Bromide Concentration (mg/l)
Actual and Model-predicted influent Giardia
100% -
2- 90% -
5 70% i
f\
| 40y^
— LeC
hevallier 46 cities
~ Currently Fitted Lo
1
gnormal Curve i
1
i
••^^^
^
JX?
/.:
S"
t
i
^f i
^x^1' :
rr-~~— K^ ; !
u-i***-**^
1
i
10 100 1,000 10,000 100,000
Influent Giardia Concentration in Logarithmic Seal* (cysts/100 L)
Page
-------
Exhibit 8
-
S"
E
o
2 10 •
c
§
o
O
O
o 1-
4-*
0>
i
Simulated Influent TOC vs. Influent Giardia
i,^-^.— >^— — '
—
a
91
1
p —
-1-
1
i
i
i
o -I
1
_
s
1
100 i==
nt TOC Concentration (mg/1)
—A
_* O
1
0
0
a
-1— «-
•V ' 1 ! ' "" r— rr,
1 'ffl Bf •
Mil' yr^
i|®
i — •*
« 8
"*^j-
^
i : '
III f
8 s ®
fffi T la «
_.±
14 •»
1 i ' 1
ii— H«--
y l a la.
T^1tf:
— r^i
!
3 1-
. h-
— i —
10 100 1,000
Influent Giardia Concentration (cysts/1 00 L)
^ ^^— p «-^
Simulated Influent TOC vs. Influent Bromide
i^— ^^— ^»— — •
.0001
_«^__^_
1
i
i i •: l !
€
£p 1 1 -
>
• | ' ' , <
19"
• ,,> I
i i
' j>> i i a-i
i . ' PL
9^ j ' * 3
S ' i ['
i I
' c, « *
p1 "T
. ' i
TT
^ ^'
/if 1 ^ 1 1
10,000 ;
i
4 i«*
[ «l
0.001 0.01 0.1 1
Influent Bromide Concentration (mg/1)
P i
.
10
-------
Exhibit 9
0
ft\
u/
C
mmtm
*f\
CO
'o
0
0
O
C
CO
"5.
O
2f
£g
fll *""
£R — ^
ZM ^^ta
.E co
UJ
-^
•—p*-
0
m CO
CO 0
coO
i- E
^
2o
/_ CM
•gl
"z
<
0 (
W(
coc
o i
~" <
3>
/)
^
1^
1^
••
3
••
t
_^».
C
o
7^ "***
^ **~
CO
z
^_ X H
T3 O
2o
•§=
•-?
OT0"
^>
.^to.
W
C
O
«^» *•*
CO
•nO
\j v^
^3 ^f
-------
Exhibit 10
U)
= 5
I
=5 tn
o
— o
I
0)
Q.
tn
o
«*^
o
0)
•a
a>
0>
CL
o
o
- ajeu (ssaun;)
-------
Exhibit 11
Overview ofGlardla Modelling
Hibler Data
73 plants
variation between
plants: lognormai
LeChevalier Data
46 plants
variation between
plants: lognormai
t
temporal variation
within plants:
delta negative binomial
distribution used
for influent simulation
Assuming:
• 48% recovery
• 13% viability
• deletion of
values estimated at
limit of detection
100
simulated influents
Treatment Model
100
plant effluents (1st customer)
t
summary statistics
f
mean
90th%
distribution used
for simulation
of within plant
temporal variation
I
compute expected
value of Rose dose
response function
compute expected value
and variance of Rose
dose response function
estimate
cf endemic
incidence
rate
estimate
of outbreak
risk
Assuming:
• 5% failure rate
(loss of 1 log)
• 25% secondary
infection rate
• 1% per 30 days
outbreak threshold
• viable cyst=
infective in humans
• infection=jllness
-------
Exhibit 12
Overview of National-Level Model
Process Characteristics
• SW
• Filtration
• w/out softening
Simulated Raw Water
Quality and Chemical
Dosages for 100
Plants
TOC/UV 254
Removal Model
Alkalinfty/pH
Model
Chlorine/Chloramine
Decay Model
Treatment &
Distribution
Assumptions
• SWTR/ESWTR
• Lead
• Taste
Batch Mode
Treatment
Model
THM Formation
Model
HAA Formation
Model
inactivation
Model
Predicted Plant Effluent
and Distributed
Water Quality
for 100 plants
Compliance Sorting
Routine
(least cost algorithm)
f
Average Customer
By-Product
Concentrations
Treatment Vectors
of Compliance Percentages
for Alternative MCLs
Effluent (1st customer)
Giardia concentrations
for alternative MCLs
I
Cancer
Risk
Model
National
Cost
Models
i
Giardia
Risk
Model
-------
Exhibit 13
Compliance Sorting Routine
MCL Option #1
PLANT #1
Read Predicted By-Product Concentrations
For Status Quo Treatment Configuration
Below MCL
Above MCL
Read Predicted By-Product Concentrations
For Next Least Cost Treatment Configuration
s This ^^^ yes
Last Treatment ?
vailabl
Save Record of Effluent Data to Output File
4,
Compliance Percentages
SWTR Scenario
W/AIt.Disinf.
No further treatment
Eliminate per-chlorination
Eliminate per-chlor & add ammonia
pre-chlor + ammonia + alum dose
pre-chlor + ammonia + alum + ozone
pre-chlor + ammonia + alum + ozone + GAC
100
61
21
18
0
0
0
TTHM
75
49 '
25
23 ,
3
0
0
MCLs (ug/1)
50
33
23
34
9
1
0
25
17
10
30
27
13
3
-------
Exhibit 14
The Core Problem
Influent
Giardia
Influent
TOG
Influent
Bromide
Coagulation
&
Filtration
Giardia
removal
Reduced
TOG
Log
Reduction
at the 1st
customer
Shift to
Brominated
Species
Reduced
Demand
Reduced
Precursors
Giardia
inactivatfon
By-product
formation
C!2
Residual at
the last
customer
-------
Exhibit 15
SWTR Scenario: With Alternate Disinfectants
TTHM MCL= 100 (ug/l) #. of Plants = 39
10,000
1,000
ra
1
OS
5
"£
.3
"2
100
10
Total Log Reduction Achieved
I !
1 • /
1 ! /
i ; ! /
! i i./
i /
I
!
. ,- i |
i ! |
j
10
-------
Exhibit 16
SWTR Scenario: With Alternate Disinfectants
TTHM MCL = 25 (ug/l) #. of Plants Treating = 83
10,000
1,000
to
>>
3-
a
1
jj
(3
i
100
10
z
Total Log Reduction Achieved
I I I
10
-------
Exhibit 17
SWTR Scenario: With Alternate Disinfectants
TTHM MCL = 100 ug/I to 75 ug/l
100%
90%
(0
Q>
S*
o
35
2
I
"3
o
10 100 1,000 10,000
Increase in Annual Giardia infections Per 1 Million People
100,000
-------
Exhibit 18
SWTR Scenario: With Alternate Disinfectants
TTHM MCL = 100 ug/l to 25 ug/l
100%
if)
3
o
10%
0% -T
10 100 1,000 10,000
Increase in Annual Giardia Infections Per 1 Million People
100,000
-------
Exhibit 19
CO
100% —
90% —
80%
70% —
60% —
50% —
85%
O
09
S
-------
Exhibit 20
100%
90%
80%
70%
CO
§ 60% —
I
03
I
0)
50%
30%
47%
SWTR Scenarios: With Alternate Disinfectants
TTHM MCL = 100 ug/i to 25 ug/l
26%
I : --
20% — •
l
\ '
10% — •
i
1
I
n% —
•
7%
- 'I'l'T-l; :!:•*";;' ;'"1 ;";!"''.•
: , ..
•
17%
3%
, lji!!;!i&!':SM-?3!!!!:
1-10 10-100 100-1,000 1,000-10,000 10,000-100,000
Increase in Annual Giardia Infections Per 1 Million People
-------
Exhibit 21
SWTR Scenario: With Alternate Disinfectants
From TTHM MCL = 100 ug/l to 75 ug/l
Change in Risks Per 1 Million People
10
I
— i
0)
I 1
Q>
u
(0
O
13
o
£
u
0>
Q
T—T-TT
! I
i fel I!
fO
I :
TTT
i—r
> ; i
10 100 1,000
Increase in Annual Giardia Infections
10,000
100JOOO
-------
Exhibit 22
SWTR Scenario: With Alternate Disinfectants
From TTHM MCL = 100 ug/l to 25 ug/l
Change in Risks Per 1 Million People
10
I
I
2
I
o
0>
£>
U
0>
a
0.1?
0.01-
i i
I ! t
i 9 | bi
Jfi.
10 100 1,000
Increase in Annual Giardia Infections
10,000
I I
100,000
-------
Exhibit 23
SWTF5 Scenario: With Alternate Disinfectants
THAA MCL = 60 (ug/l) #. of Plants = 29
10.000
1.000
§
_o_
.2 100
ra
o>
-/+•
I / I
1 . w
• /
;•
i
!
;
: / i • ' i
I / . i I
i /
• y
i i
! i
i . /
i./
i /
i
i
Total Log Reduction Achieved
10
-------
Exhibit 24
SWTR Scenario: With Alternate Disinfectants
THAA MCL = 10 (ug/l) #. of Plants Treating = 80
10.000
1,000
i 100
ra
O
i
10
F
*
Total Log Reduction Achieved
to
-------
Exhibit 25
SWTR Scenario: With Alternate Disinfectants
THAA MCL = 60 ug/l to 50 ug/l
100%
10 100 1,000 10.000
Increase in Annual Giardia Infections Per 1 Million People
100,000
-------
Exhibit 26
SWTR Scenario: With Alternate Disinfectants
THAA MCL = 60 ug/l to 10 ug/l
100%
10 100 1,000 10,000
Increase in Annual Giardia Infections Per 1 Million People
100,000
-------
Exhibit 27
100% —
90% —
80% —
70% —
o 60% —
0)
OJ
m
50% —
| 40%-
30% —
20% —
10%
0%
95%
SWTR Scenarios: With Alternate Disinfectants
THAA MCL = 60 ug/1 to 50 ug/l
1%
1%
3%
0%
1-10 10-100 100-1,000 1,000-10,000 10,000-100,000
Increase in Annual Giardia Infections Per 1 Million People
-------
Exhibit 28
100% —
90%
SWTR Scenario: With Alternate Disinfectants
THAA MCL = 60 ug/i to 10 ug/l
80% --'
70% —
CO I
I 60% —
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H 40%
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Increase in Annual Giardia Infections Per 1 Million People
-------
Exhibit 29
SWTR Scenario: With Alternate Disinfectants
From THAA MCL = 60 ug/l to 50 ug/l
Change in Risks Per 1 Million People
10
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Increase in Annual Giardia infections
10,000
100,000
-------
Exhibit 30
SWTR Scenario: With Alternate Disinfectants
THAA MCL = 60 ug/l to 10 ug/l
Change in Risks Per 1 Million People
10
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n Annual Cancer Incid
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Increase in Annual Giardia Infections
-------
Exhibit 31
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Exhibit 32
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Exhibit 33
Compliance Percentages
SWTR Scenario
W/Alt.Disin.
No further treatment
Eliminate pre-chlor
Eliminate pre-chlor & add NH3
pre-chlor + NH3 + alum dose
pre-chlor + NH3 + alum + Oz
pre-chlor + NH3 + alum + Oz + GAG
60
70
12
18
0
0
0
THAAMCLs
50 40 30
64
15
17
4
0
0
56
16
22
5
1
0
48
12
22
14
3
1
(ug/D
20
37
8
8
23
7
17
10
19
3
1
22
5
50
Compliance Percentages
Enhanced SWTR Scenario
W/ Alt. Disin.
THAA MCLs (ug/1)
60 50 40 30 20 10
No further treatment
Eliminate pre-chlor
Eliminate pre-chipr & add NH3
pre-chlor + NH3 + alum dose
pre-chlor + NH3 + alum + Oz
pre-chior -t- NH3 + alum + Oz + GAG
70 62 56 47 -35 17
11 16 16 12
17 18 16 12
10
0
2, 3 10 18 18 15
0 1 2 10 16 15
000 1 17 50
-------
Exhibit 34
Compliance Percentages
SWTR Scenario
W/out Alt Disin.
No further treatment
Eliminate pre-chlor
Eliminate pre-chlor & modify alum
Eliminate pre-chlor + alum + GAG
60
70
12
11
7
THAA MCLs
50 40 30
64
15
14
7
56
16
16
12
48
12
19
21
(ug/1)
20
37
8
20
35
10
19
3
21
57
Compliance Percentages
Enhanced SWTR Scenario
W/out Alt Disin.
No further treatment
Eliminate pre-chlor
Eliminate pre-chlor & modify alum
Eliminate pre-chlor + alum + GAC
60
70
11
12
7
THAA MCLs
50 40 30
62
16
15
7
56
16
15
13
47
12
21
20
(ug/1)
20
35
10
20
35
10
17
3
22
58
-------
Exhibit 35
Equivalence of "Maximum Net Benefit" and
"Minimum Total Social Cost" Objectives
$'s
Benefit: value of health
damages avoided
cost of treatment & monitoring
Degree of
Treatment
$'s
total social cost
minimum total
social cost
cost of
treatment &
monitoring
cost of health
damages
incurred
Degree of
Treatment
Maximizing Health Damages Avoided
Per Dollar of Treatment Expenditure
Minimizing Health Damages Incurred
Per Dollar of Treatment Expenditure
-------
Exhibit 36
Draft: 13-May-\
MODEL OUTPUT (surface w/o softening): SWTR W/ ALTERNATIVE DISINFECTION
Treatment Code:
1 — not requiring further treatment modification
2 — eliminate pre—chlorination
3 - eliminate pre-chlor + add ammonia
4 — pre—chlor + ammonia + alum dose
5 — pre—chlor -t- ammonia + alum + ozone
6 — pre—chlor + ammonia + alum + ozone + GAC
Population =
103,000,000
(persons)
THAA
MCL
fcg/0
60
50
40
30
20
10
Annual
Treatment Cost *
($M)
2
12
21
71
383
922
Cost of Cancer**
@ $8m/case
($M)
358
338
304
259
177
91
Cost of Giardia
@ $3,000/case
($M)
639
714
849
974
1,239
1,729
Total
Costs
($M)
1.0CX
1,0&
1,17^
1,3O
1,79!
2,74i
Capital costs are annualized at 10% interest rate over 20 years.
** MLE of cancer incidence from HAAs.
3.5
CQ
&
CO
O
O
SO 40 3O 20
ALTERNATE THAA MCLs (M9/I)
-------
Exhibit 37
Draft: 13-May-
MODEL OUTPUT (surface w/o softening): ENHANCED SWTR W/ ALTERNATIVE DISINFECTION
Treatment Code:
1 — not requiring further treatment modification
2 — eliminate pre—chlorination
3 — eliminate pre—chlor + add ammonia
4 — pre—chlor + ammonia + alum dose
5 — pre—chlor + ammonia + alum + ozone
6 — pre—chlor + ammonia + alum + ozone + GAC
Population =
103.000.000
(persons)
THAA
MCL
»0
60
50
40
30
20
10
Annual
Treatment Cost *
($M)
7
16
38
120
422
963
Cost of Cancer**
@ $8m/case
($M)
369
348
313
258
178
91
Cost of Giardia
@ $3,000/case
($M)
0.8
0.8
0.9
1.0
1.0
1.0
Total
Costs
($M)
377
364
351
379
601
1,055
* Capital costs are annuaiized at 10% interest rate over 20 years.
** MLE of cancer incidence from HAAs.
1.5r
m
OT
O
o
30
ALTERNATE THAA MCLs
20
10
-------
r
Exhibit 38
Draft: 13-May-\
MODEL OUTPUT (surface w/o softening): SWTR W/O ALTERNATIVE DISINFECTION
Treatment Code:
1 — not requiring further treatment modification
2 — eliminate pre—chlorination
3 — eliminate pre—chlor + modify alum dose
4 — pre-chlor + alum dose + GAC
Population =
103.000.000
(persons)
==r=^=
THAA
MCL
WO
60
50
40
30
20
10
Annual Treatment
Cost *
($M)
,
104
Cost of Cancer**
@ $3m/case
l$M)
334
Cost of Giardia
@ $3,000/case
($M)
740
1181 3201 772
2021 270
831
3531 2061 972
5881 140
957 1 79
I
1,212
1,519
Total
Costs
($M)
1,1
1,2
1,3
1,5
1,9
2,5
* Capital costs are annualized at 10% interest rate over 20 years.
** MLE of cancer incidence from HAAs.
3.5
CO
I
I
CO
8
3.0 r
2.5 r
2.0 r
1.5r
1.0 r
0.5 r
0.0 ^~
TotalCost
Cost of Giardia Cas«s i
Cost of Cancer Cases
60
50 40
ALTERNATE THAA
30
20
10
-------
Drafc 13-May-
Exhibit 39
MODEL OUTPUT (surface w/o softening): ENHANCED SWTR W/O ALTERNATIVE DISINFECT1OI
Treatment Code:
1 — not requiring further treatment modification
2 — eliminate pre—chiorination
3 — eliminate pre—chior + modify alum dose
4 — pre—chlor + alum dose + GAC
Population =
103,000.000
(persons)
THAA
MCL
(M9/I)
60
50
40
30
20
10
Ann.Trt.*
Cost
($M)
118
t 118
218
336
588
974
Cost of Cancer**
@ $8m/case
($M)
337
320
271
211
142
77
Cost of Giardia
@ $3,000/case
($M)
0.8
0.8
0.8
1.0
1.0
1.0
Total
Costs
($M)
455
438
490
548
731
1 ,052
* Capital costs are annuaiized at 10% interest rate over 20 years.
** MLE of cancer incidence from HAAs.
1.5 r
tn
O
co
O
O
Treatment Cost
Cost of Cancer Cases I
60
40 30
ALTERNATE THAA MCLs fr/g/L)
-------
Exhibit 40
Draft: 10/12/92
Model Output (surface water systems w/o softening?: SWTR w/ MODIFYING ALUM DOSE
Treatment Tier Code:
1 - not requiring further treatment modification
2 - eliminating prechlorination + modifying alum dose
Population •
103.000.000 (persons)
Number of Systems •
322
THAA
MCL
(ug/D
60
50
40
30
20
10
Treatment
Code
2
2
2
2
2
2
% of Systems
Modifying
Alum Dose*
30
36
44
52
63
81
Cumulative
% of Systems
<= MCL*
93
93
68
79
65
43
Annual
Treatment
Cost <$M) "
CD
45
54
66
78
95
122
Cost <$M) of
Cancer "*
@$8M/Case
(2)
326
303
275
254
234
215
Cost ($M) of
Gtardiasts
@$3K/Case
(3)
796
853
917
1,086
1,237
1,261
Total
Social
Cost($M)
(1W2)-K3)
1,167
1,210
1,258
1,418
1,565
1,597
* Includes 20% over-design factor.
" Maximum Likelihood Estimates (MLE) of cancer incidence associated with HAAs.
"• Capital costs are annualized at 10% interest rate over 20 years.
Total Social Cost at Alternate THAA MCLs
.Total Social Cost
200 + Treatment Cost
n • •
Cost of Cancer Cases
a
-+-
60
50
40 30
Alternate THAA MCL (ug/Q
20
10
c:\017\dbp\lotus\sensffiv\old\ALMHAAXLS
-------
Exhibit 41
Draff:
Model Output (surface water systems w/o softening): Enhanced SWTR w/ MODIFYING ALUM DOSE
Treatment Tier Code:
1 - not requiring further treatment modification
2 - eliminating prechlorination + modifying alum dose
Population =
103.000.000 (persons)
Number of Systems ••
fZZ
THAA
MCL
(ug/l)
60
50
40
30
20
10
Treatment
Code
2
2
2
2
2
2
% of Systems
Modifying
Alum Dose*
30
38
44
53
65
83
Cumulative
% of Systems
<= MCL*
93
93
87
80
65
42
Annual
Treatment
Cost <$M) **
(1)
45
57
66
80
98
125
Cost <$M) of
Cancer ***
@$8M/Case
(2)
332
300
280
258
235
216
Cost ($M) of
Giardiasis
@$3K/Case
(3)
0.8
0.8
0.9
1.0
1.0
1.0
Total
Social
Cost($M)
(l)-K2)+(3)
378
358
348
339
333
342
" Includes 20% over-design factor.
"* Maximum Likelihood Estimates (MLD of cancer incidence associated with HAAs.
*" Capital costs are annualized at 10% interest rate over 20 years.
O 200 -
a 150 ••
§ 100-•
< 50
Total Social Cost at Alternate THAA MCLs
Total Social Cost
Cost of Cancer Cases
Treatment Cost
40 30
Alternate THAA MCL (ug/0
20
10
c:\017\dbp\lotus\sensitiv\ old\ EALMHAA.XLS
-------
Appendix A
-------
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-------
Appendix B
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2
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Can cat 1
Incidence
0 THAAs
S*
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THAAs D(
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8 5 "* 1
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Treatment Code:
1 - not requiring further treatment modification
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3 - eliminate pre-chlor + modify alum dose
4 - pre-chlor •»• alum dose + GAC
S - pre-chlor -f ammonia -f ahim + ozone
6 - pre-chlor -t- ammonia -I- alum + ozone + GAC
Population = 103.000.000 (parsons)
Cancar 1
MCL MCL Incidence!
(THAAs) (TTHM.) THAA*
Cancar Incidence (Maximum Likelihood Estimate)
TTHMs CF C2B B2C BF THAA* DCAA TCAA
Mean Concentration* of By-Products (Averaga Customer)2
. TTH"-* CF C2B B2C BF THAAs DCAA TCAA
ICumulativa
MCL % of Sy*.
(TTHMs} f_MCL'
IO CM a CM CM i- CM CM to CM CM »- 10 <• O ^ CM «- MJ O » O ^ ^ O O O • ^ <- 0 0 0 0 O *•
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T-
Incidence (Maximum Likelihood Estimate)
C2B B2C BF THAA* DCAA TCAA
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TTHMs CF
Mean Concentration* of By-Products (Average Customer)2
TTHMs CF C2B B2C BF THAAs DCAA TCAA
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-------
Appendix C
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Draft: 01 -Apr~02
Exhibit C-3
Concentrations of Individual THMs and HAAs at Alternate TTHM MCLs
- SWTR w/ Alternate Disinfection -
Treatment Coda: w/ Alternate Disinfection
1 - not requiring further treatment modification
2 - aiminate pra-chlorination
3 - eiminate pra-chlor + add ammonia
4 - pra-chlor + ammonia + alum do**
5 - pre-chtor + ammonia -t- alum dos* + ozone
0 - pre-chtor + ammonia + alum dos* + ozone + GAC
I Concentrations of By— Products (uoA.) (Averag* Customer)*
|
3
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