2.5.2 1979 Total Trihalomethane Rule Under the Total Trihalomethane Rule ( 44 FR 68624 November 1979), EPA set an MCL for total trihalomethanes ( TTHM), the sum of chloroform, bromoform, bromodichloromethane, dibromochloromethane, of 0.10 mg/ L as a running annual average ( RAA) of quarterly samples. This standard applied to CWSs using surface or ground water that served at least 10,000 people and that added a disinfectant to the drinking water during any part of the treatment process. This 1979 rule was superseded by the 1998 Stage 1 DBPR ( section 2.5.7).

2.5.3 1989 Total Coliform Rule The Total Coliform Rule ( TCR) ( 54 FR 27544 June 1989) applies to all PWSs. Because monitoring PWSs for every possible pathogenic organism is not feasible, coliform organisms are used as indicators of possible distribution system contamination. Coliforms are easily detected in water and are used to indicate a system's vulnerability to pathogens. In the TCR, EPA set an MCLG of zero for total coliforms. EPA also set a monthly MCL for total coliforms and required testing of total coliform positive cultures for the presence of E. coli or fecal coliforms. E. coli and fecal coliforms indicate more immediate health risks from sewage or fecal contamination and result in an acute MCL violation ( acute MCL violations require immediate public notification). Coliform monitoring frequency is determined by population served, the type of system ( community or noncommunity) and they type of source water ( surface water or GWUDI, or ground water). In addition, the TCR required sanitary surveys every 5 years ( or 10 years for NCWSs using disinfected ground water) for systems that collect fewer than 5 routine total coliform samples per month ( typically serving fewer than 4,100 people).

2.5.4 1989 Surface Water Treatment Rule Under the SWTR ( 54 FR 27486 June 1989), EPA set MCLGs of zero for Giardia lamblia, viruses, and Legionella, and established treatment requirements for all PWSs using surface water or GWUDI as a source. The SWTR includes treatment technique requirements for filtered and unfiltered systems that are intended to protect against the adverse health effects associated with Giardia lamblia, viruses, and Legionella, as well as many other pathogenic organisms. These requirements include the following:

° Maintenance of a disinfectant residual in water entering and within the distribution system.

° Removal/ inactivation of at least 99.9 percent ( 3 log) of Giardia and 99.99 percent ( 4 log) of viruses.

° Filtration, unless systems meet specified avoidance criteria.

° For filtered systems, meet a turbidity performance standard for the combined filter effluent consisting of a 5 NTU maximum and 95 percent of measurements in one month not to exceed 0.5 NTU, based on 4 hour monitoring for treatment plants using conventional treatment or Economic Analysis for the LT2ESWTR Proposal 2 9 June 2003 direct filtration ( with separate standards for other filtration technologies). These particular requirements were superseded by the 1998 IESWTR and the 2002 LT1ESWTR.

° Watershed control programs and water quality requirements for unfiltered systems.

2.5.5 1996 Information Collection Rule The ICR ( 61 FR 24354 May 1996) applied to PWSs serving more than 100,000 people. A more limited set of ICR requirements pertained to ground water systems serving 50,000 to 100,000 people.

The ICR authorized EPA to collect occurrence and treatment information to help evaluate the need for possible changes to the microbial requirements and microbial treatment practices and to help evaluate the need for future regulation of disinfectants and DBPs. The ICR provided EPA with additional information on the national occurrence in drinking water of ( 1) chemical byproducts that form when disinfectants used for microbial control react with naturally occurring compounds present in source water; and ( 2) disease causing microorganisms, including Cryptosporidium, Giardia, viruses, and coliform bacteria. The ICR also required water systems to collect plant configuration data showing the type of treatment provided. The ICR monthly sampling data provided a total of 18 months of influent and treated water quality data including pH, alkalinity, turbidity, temperature, calcium and total hardness, total organic carbon, UV 254, bromide, ammonia, and disinfectant residual. These data provided an indication of the " treatability" of the water, the occurrence of contaminants, and the potential for DBP formation. The data collected under the ICR were used in analyses supporting development of the LT2ESWTR and Stage 2 DBPR.

2.5.6 1998 Interim Enhanced Surface Water Treatment Rule The IESWTR ( 63 FR 69478 December 1998) updated the 1989 SWTR for large systems. It applies to PWSs serving at least 10,000 people and using surface water or GWUDI as a source. These systems were to comply with the IESWTR by January 2002. The primary purpose of the IESWTR is to improve control of Cryptosporidium and to address tradeoffs between the risks from microbial pathogens and those from DBPs. The requirements and guidelines include:

° An MCLG of zero for Cryptosporidium ° Removal of 99 percent ( 2 log) of Cryptosporidium for systems that provide filtration ° For treatment plants using conventional treatment or direct filtration, a turbidity performance standard for the combined filter effluent consising of a 1 NTU maximum and 95 percent of measurements in one month not to exceed 0.3 NTU, based on 4 hour monitoring ° Continuous monitoring of individual filter effluent turbidity in conventional and direct filtration plants and recording turbidity readings every 15 minutes when these filters are on line ° A disinfection benchmark to assess the level of microbial protection provided before facilities change their disinfection practices to meet the requirements of the Stage 1 DBPR ° Inclusion of Cryptosporidium in the definition of GWUDI and in the watershed control requirements for unfiltered PWSs Economic Analysis for the LT2ESWTR Proposal 2 10 June 2003 ° Covers for all new finished water storage facilities ° A primacy provision that requires States to conduct sanitary surveys with a minimum frequency for all surface water systems, including those serving fewer than 10,000 people EPA promulgated the IESWTR concurrently with the Stage 1 DBPR so that systems could coordinate their response to the risks posed by DBPs and microbial pathogens.

2.5.7 1998 Stage 1 Disinfectants and Disinfection Byproducts Rule The Stage 1 DBPR ( 63 FR 69390 December 1998) applies to all CWSs and NTNCWSs that add a chemical disinfectant to their water. Certain requirements designed to provide protection against acute health effects from chlorine dioxide also apply to transient noncommunity water systems ( TNCWSs). Surface water and GWUDI systems serving at least 10,000 people were required to comply with the rule by January 2002. Surface water and GWUDI systems serving fewer than 10,000 people and all ground water systems must comply by January 2004.

The Stage 1 DBPR sets maximum residual disinfectant level goals ( MRDLGs) for chlorine ( 4 mg/ L as Cl 2), chloramines ( 4 mg/ L as Cl 2), and chlorine dioxide ( 0.8 mg/ L as ClO 2); and MCLGs for bromodichloromethane ( 0 mg/ L), bromoform ( 0 mg/ L), dibromochloromethane ( 0.06 mg/ L), dichloroacetic acid ( 0 mg/ L), trichloroacetic acid ( 0.3 mg/ L), bromate ( 0 mg/ L), and chlorite ( 0.8 mg/ L). The rule sets MRDLs for chlorine ( 4 mg/ L as Cl 2), chloramines ( 4 mg/ L as Cl 2), and chlorine dioxide ( 0.8 mg/ L as ClO 2); and MCLs for TTHM ( 0.080 mg/ L), HAA5 ( 0.060 mg/ L), bromate ( 0.010 mg/ L), and chlorite ( 1.0 mg/ L). The MRDLs and MCLs, except those for chlorite and chlorine dioxide, are calculated as RAAs. For conventional surface water and GWUDI systems, a treatment technique   enhanced coagulation/ softening   is specified for the removal of DBP precursors.

As noted in section 2.5.6, the Stage 1 DBPR was promulgated concurrently with the IESWTR to coordinate the control of DBPs and microbial contaminants.

2.5.8 2000 Proposed Ground Water Rule The proposed Ground Water Rule ( 65 FR 30194 May 2000) addresses fecal contamination in ground water systems. The rule also builds on the TCR through provisions based on further evaluation of E. coli monitoring results measured under the TCR. Key components of the multibarrier approach for protection of ground water included in the proposed rule are:

° Sanitary surveys for all ground water systems conducted at the same frequency as in surface water systems ° Hydrogeologic sensitivity assessments to identify ground water sources that are susceptible to fecal contamination ° Source water monitoring for an indicator of fecal contamination for systems drawing from susceptible ground water sources Economic Analysis for the LT2ESWTR Proposal 2 11 June 2003 ° Correction of significant deficiencies and fecal contamination by eliminating the source of contamination, correcting the deficiency, providing an alternative source of water, or providing inactivation and/ or removal of 99.99 percent ( 4 log) of viruses ° Compliance monitoring to ensure that disinfection treatment is reliably operated when it is used 2.5.9 2001 Filter Backwash Recycling Rule The Filter Backwash Recycling Rule ( FBRR) ( 66 FR 31086 June 2001) regulates systems where filter backwash is returned to the treatment process. The rule applies to surface water and GWUDI systems that use direct or conventional filtration and recycle spent filter backwash water, sludge thickener supernatant, or liquids from dewatering processes. The rule requires that these recycled liquids be returned to a location such that all steps of a system's conventional or direct filtration are employed. The rule also requires systems to notify the State that they practice recycling. Finally, systems must collect and maintain information for review by the State.

2.5.10 2002 Long Term 1 Enhanced Surface Water Treatment Rule The LT1ESWTR ( 67 FR 1812 January 2002) is an extension of the 1998 IESWTR to small systems. LT1ESWTR extends control of Cryptosporidium and other disease causing microbes to surface water and GWUDI systems that serve fewer than 10,000 people. Key provisions in the LT1ESWTR are very similar to those for the IESWTR, but provide additional flexibility for small systems.

2.5.11 2003 Proposed Stage 2 Disinfectants and Disinfection Byproducts Rule The requirements of the Stage 2 DBPR apply to all CWSs and NTNCWSs that add a disinfectant other than UV or that deliver water that has been treated with a disinfectant other than UV. The Stage 2 DBPR builds on the 1979 Total Trihalomethane Rule and the 1998 Stage 1 DBPR by requiring reduced levels of DBPs in distribution systems. Each rule activity for the Preferred Regulatory Alternative and the associated rule schedule are described below.

The Stage 2 DBPR is designed to reduce DBP occurrence peaks. in the distribution system by changing compliance monitoring requirements. Compliance monitoring will be preceded by an initial distribution system evaluation ( IDSE) to identify distribution system locations that represent high total trihalomethane ( TTHM) and haloacetic acids ( HAA5) levels. Systems may perform an IDSE either by completing a system specific study ( SSS) or a standard monitoring program ( SMP). NTNCWSs serving fewer than 10,000 people are not required to conduct an IDSE, and other systems may receive waivers from the IDSE requirement.

The Stage 2 DBPR changes the way sampling results are averaged to determine compliance. The compliance determination for the Stage 2 DBPR is based on a locational running annual average ( LRAA) instead of the system wide RAA used under the Stage 1 DBPR. LRAAs are RAAs calculated separately for each sample location in the distribution system. With the Stage 2 LRAA requirement, the Economic Analysis for the LT2ESWTR Proposal 2 12 June 2003 TTHM and HAA5 MCLs must be met at each monitoring location, while the Stage 1 RAA requires a system to average results over all monitoring locations.

The Stage 2 DBPR, which is being promulgated simultaneously with the LT2ESWTR to address complex risk trade offs between DBPs and microbial pathogens, will be implemented in two stages.

Stage 2A: Starting [ 3 years after rule promulgation], all systems must comply with TTHM/ HAA5 MCLs of 120/ 100 µ g/ L measured as LRAAs at each Stage 1 DBPR monitoring site and must continue to comply with the Stage 1 DBPR TTHM/ HAA5 MCLs of 80/ 60 µ g/ L, measured as RAAs.

Stage 2B: Starting [ 6 years after rule promulgation], systems serving at least 10,000 people must comply with TTHM/ HAA5 MCLs of 80/ 60 µ g/ L measured as LRAAs at the monitoring sites identified during an IDSE. For small systems required to perform Cryptosporidium monitoring under the LT2ESWTR, compliance with the 80/ 60 µ g/ L MCLs, measured as LRAAs, will begin [ 8.5 years after rule promulgation]. For all other small systems, compliance with the 80/ 60 µ g/ L MCLs, measured as LRAAs, will begin [ 7.5 years after rule promulgation]. If a system requires capital improvements, the State/ Primacy Agency may grant up to an additional 24 months for compliance.

2.6 Economic Rationale for Regulation This section addresses the economic rationale for choosing a regulatory approach. Such a rationale for the rule is required by Executive Order Number 12866, Regulatory Planning and Review ( The White House 1993), which states the following:

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

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

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

First, many water systems are natural monopolies. A natural monopoly exists when it is impossible for more than one firm in each area to recover the costs of production and survive. There are high fixed costs associated with reservoirs and wells, transmission and distribution systems, treatment plants, and other facilities. For other potential suppliers to enter the market, they would have to provide Economic Analysis for the LT2ESWTR Proposal 2 13 June 2003 the same extensive infrastructure to realize similar economies of scale and be competitive. A splitting of the market with increased fixed costs ( for example, two supplier networks in a single market) usually makes this situation unprofitable for one or both suppliers. The result is a market suitable for a single supplier and one that is hostile to alternative suppliers. In such natural monopolies, suppliers have fewer incentives for providing high quality service or maintaining competitive prices. In these situations, governments often intervene to help protect the public interest.

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

Second, the public may not understand the health and safety issues associated with drinking water quality. Understanding the health risks posed by trace quantities of drinking water contaminants involves analysis and synthesis of complex toxicological and health sciences data. Therefore, the public may not be aware of the risks it faces. For example, cases of waterborne disease are likely to be under reported since a significant portion may be endemic, making them more difficult to recognize. There is, therefore, a lack of occurrence data and related cost information on endemic waterborne disease available to the public. EPA has implemented a Consumer Confidence Report ( CCR) Rule ( USEPA 1998) that makes water quality information more easily available to consumers. This rule requires CWSs to publish an annual report on Local drinking water quality. Consumers, however, still have to analyze this information for its health risk implications. Even if informed consumers are able to engage water systems in a dialogue about health issues, the costs of such interaction ( measured in personal time and monetary outlays) present a significant impediment to consumer expression of risk reduction preferences. Moreover, these reports typically contain no information about the risk associated with Cryptosporidium and most other microbial pathogens, because PWSs are not required to analyze for them.

SDWA regulations are intended to provide a level of protection from exposure to drinking water contaminants by setting minimum performance requirements. These regulations are not intended to restructure market mechanisms or to establish competition in supply; rather, they establish the level of service to be provided that best reflects public preference for safety. The Federal regulations reduce the high information and transaction costs by acting on behalf of consumers in balancing risk reduction and the social costs of achieving this risk reduction. 1 The Stage 2 M DBP Advisory Committee was composed of representatives from a variety of stakeholder organizations. A complete list of participating members ( as well as a summary of committee findings) is included in the docket for the LT2ESWTR.

Economic Analysis for the LT2ESWTR Proposal 3 1 June 2003 3. Consideration of Regulatory Alternatives 3.1 Introduction The U. S. Environmental Protection Agency ( EPA or the Agency) evaluated a number of regulatory alternatives that could mitigate the health concerns addressed by the Long Term 2 Enhanced Surface Water Treatment Rule ( LT2ESWTR). These evaluations took place during a regulatory negotiation process that began in the Spring of 1999, and included consultation with the Stage 2 Microbial and Disinfection Byproducts ( Stage 2 M DBP) Advisory Committee that was convened under the Federal Advisory Committees Act ( FACA). This chapter summarizes the alternatives considered and develops a context for the proposed regulatory approach. The remainder of the chapter is organized as follows:

3.2 Development Process for Regulatory Alternatives 3.3 Specific Regulatory Alternatives Considered in this EA 3.3.1 Summary of Bin Classification and Treatment Requirements for Regulatory Alternatives 3.3.2 Additional Treatment for Direct Filtration Systems 3.4 Alternative Monitoring Approaches Considered 3.4.1 Indicators of Microbial Contamination 3.4.2 Cryptosporidium Monitoring Strategies for Bin Classification 3.2 Development Process for Regulatory Alternatives Two efforts in the regulatory development process for the LT2ESWTR are particularly relevant to evaluation of alternatives discussed in the Economic Analysis ( EA): ( 1) the data synthesis and analysis resulting from the Information Collection Rule ( ICR) and ICR Supplementary Surveys ( ICRSSs), and ( 2) the deliberations and recommendations of the Stage 2 M DBP Advisory Committee. 1 EPA held 14 formal negotiation meetings of the Stage 2 M DBP Advisory Committee, between March 1999 and September 2000. Before convening the committee, EPA also held three preparatory stakeholder meetings on pathogen and disinfection byproduct ( DBP) health effects, occurrence, and treatment. The objective of the committee meetings was to reach a consensus regarding recommended provisions for the two rules ( Stage 2 Disinfectants and Disinfection Byproducts Rule ( DBPR) and LT2ESWTR).

Technical support for the Stage 2 M DBP negotiation meetings was provided by the Technical Work Group ( TWG), which was established by the committee at its first meeting and which was composed of EPA and drinking water industry experts. The TWG's activities resulted in the collection, development, evaluation, and presentation of key data related to the LT2ESWTR, including new data on pathogenicity, occurrence, and treatment of microbial contaminants, specifically Cryptosporidium. Economic Analysis for the LT2ESWTR Proposal 3 2 June 2003 The ICR database provided much of the information evaluated for the LT2ESWTR. EPA promulgated the ICR in 1996 pursuant to the Safe Drinking Water Act ( SDWA) requirements. The ICR required approximately 300 large public water systems ( PWSs) with approximately 500 separate water treatment plants to conduct 18 months of sampling for water quality and treatment parameters related to DBP formation and the occurrence of microbial pathogens. Subsequent to the ICR data collection, EPA obtained additional data on pathogen occurrence through the ICRSSs. These involved 127 water treatment systems, including 40 small systems. Large and medium systems collected semi monthly samples for Cryptosporidium, Giardia, and other water quality parameters for 1 year. Small systems ( those serving fewer than 10,000 people) collected monthly water quality data, but did not sample for protozoa.

EPA, in consultation with nationally recognized experts in statistics, evaluated ICR and ICRSS data to generate estimates of national occurrence of Cryptosporidium in surface water and finished water. These data were evaluated under various regulatory scenarios to estimate the reduction of Cryptosporidium reaching consumers.

3.3 Specific Regulatory Alternatives Considered in this EA The recommendations of the Advisory Committee are described in a document called the Agreement in Principle ( USEPA 2000g). The Advisory Committee reached consensus on the issues of uncovered finished water reservoirs and treatment of unfiltered water without formally identifying regulatory alternatives other than the proposed approaches. Consequently, no formal alternatives were presented for these requirements. The committee's recommendations to address these issues are reflected in the proposed LT2ESWTR. For control of Cryptosporidium in filtered systems, however, several alternatives were considered. The committee discussed, but quickly found impractical, alternatives based on monitoring for Cryptosporidium in finished water. The occurrence of Cryptosporidium in finished water is so low that the volume of water required for analysis would make monitoring costs prohibitive. Thus, all the alternatives based on monitoring directed that monitoring be performed on the source water.

The following subsections detail the differences between the alternatives and explain the rationale behind EPA's selection of the Preferred Alternative. Section 3.3.2 also describes the additional treatment requirements proposed for direct filtration systems.

3.3.1 Summary of Bin Classification and Treatment Requirements for Regulatory Alternatives In considering different approaches for filtered systems under the LT2ESWTR, the M DBP Advisory Committee focused on four regulatory alternatives ( hereafter referred to as Alternatives A1 through A4). Alternative A1 requires the same amount of reduction of Cryptosporidium for all systems, while the other three base their treatment requirements on the amount of Cryptosporidium found in a system's source water through monitoring. These measurements place a system in one of several " bins," ranging from " no action" to an additional 2.5 log crypto treatment. Further, all alternatives allow systems to select treatment technologies based on the amount of crypto treatment needed to meet requirements and the effectiveness of each technology.

In evaluating each binning scenario, the committee asked the following questions: Economic Analysis for the LT2ESWTR Proposal 3 3 June 2003 ° Do the treatment requirements adequately reduce Cryptosporidium concentrations in finished water?

° How many systems would be required to add treatment?

° What is the likelihood of bin misclassification?

° What are the chances that systems with high source water concentrations would be placed in the bin requiring no action?

The predicted finished water Cryptosporidium concentrations and percentages of plants adding treatment are shown for the Preferred Regulatory Alternative in Chapter 4. The likelihood of classification in a certain bin for a given concentration and the predicted percentages of plants in each bin for all the regulatory alternatives are shown in Appendix B.

Exhibit 3.1 summarizes binning and treatment scenarios for each specific regulatory alternative. These alternatives were defined by two criteria: ( 1) bin boundaries, as defined by the results of Cryptosporidium monitoring, and ( 2) the treatment scenarios ( log reduction requirements for Cryptosporidium) required for each bin. The alternatives were compared within the context of the economic analysis to assist EPA in selecting a Preferred Regulatory Alternative.

On the basis of preliminary cost benefit analyses, the Advisory Committee chose Alternative A3 as the Preferred Alternative. This EA continues to support Alternative A3 as the best choice and EPA is proposing Alternative A3 for this reason. Alternative A3 was shown to be the most cost effective and the best value. Comparisons of the net benefits of each alternative are summarized in the executive summary and described in more detail in Chapter 8. Economic Analysis for the LT2ESWTR Proposal 3 4 June 2003 Exhibit 3.1 Summary of Bin Requirements for Filtered Systems Source Water Cryptosporidium Monitoring Results ( oocysts/ L) Additional Treatment Requirements Alternative A1 2.0 log inactivation required for all systems Alternative A2 < 0.03 No action $ 0.03 and < 0.1 0.5 log $ 0.1 and < 1.0 1.5 log $ 1.0 2.5 log Alternative A3  Preferred Alternative < 0.075 No action $ 0.075 and < 1.0 1 log $ 1.0 and < 3.0 2 log $ 3.0 2.5 log Alternative A4 < 0.1 No action $ 0.1 and < 1.0 0.5 log $ 1.0 1.0 log Note: " Additional treatment requirements" are for systems with conventional treatment in full compliance with existing rules ( the IESWTR and LT1ESWTR).

3.3.2 Additional Treatment for Direct Filtration Systems The Agreement in Principle ( USEPA 2000g) recommended that EPA address direct filtration systems in connection with Bins 2 4 of Alternative A3 in the proposed LT2ESWTR. Direct filtration plants lack sedimentation basins; their treatment processes move directly from addition of coagulant and mixing to filtration. Conventional filtration plants use coagulation, sedimentation, and filtration. Sedimentation reduces the Cryptosporidium load on the filters and helps plants respond to sudden changes in influent water quality.

EPA considered the effectiveness of direct filtration in removing Cryptosporidium when determining how to apply the Advisory Committee's treatment technique recommendations for conventional plants to direct filtration plants. EPA has consistently recognized the value of employing multiple barriers for pathogen removal to provide redundancy and reliability. Studies have shown that a well operated sedimentation basin can reduce Cryptosporidium levels by 0.5 log or greater ( Dugan et al. 1999; Edzwald and Kelley 1998; and Patania et al. 1995). The SWTR Guidance Manual ( USEPA 1991) also supports giving less credit to direct filtration systems; these systems are eligible for 0.5 log less credit for Giardia than conventional filtration systems. Economic Analysis for the LT2ESWTR Proposal 3 5 June 2003 Based on these studies, EPA's prior consideration of the effectiveness of direct filtration systems for Giardia, and the Agency's belief in the multiple barrier approach, EPA concluded that direct filtration plants should provide an additional 0.5 log of treatment beyond that required for conventional treatment plants. A more detailed discussion of Cryptosporidium removal by conventional and direct filtration can be found in the EPA document, Occurrence and Exposure Assessment for the Long Term 2 Enhanced Surface Water Treatment Rule ( USEPA 2003c).

3.4 Alternative Monitoring Approaches Considered EPA considered a variety of monitoring approaches while developing LT2ESWTR regulatory alternatives. These include evaluating other water quality parameters as surrogates for Cryptosporidium and alternative monitoring strategies to minimize monitoring costs, especially for small drinking water systems. These issues are described in the subsections below.

3.4.1 Indicators of Microbial Contamination Due to the cost associated with Cryptosporidium monitoring, the Stage 2 M DBP Advisory Committee evaluated alternative source water quality parameters to determine if they could be used to identify water sources with high Cryptosporidium levels. The committee assessed the 12 month means of monitoring data for turbidity, total organic carbon ( TOC), E. coli, fecal coliform, and total coliform bacteria as surrogates for Cryptosporidium. Specifically, the committee evaluated whether these potential surrogates could accurately assign plants to LT2ESWTR microbial framework bins. None of these parameters correlated well with Cryptosporidium levels at all concentrations. Evidence indicated that E. coli would be somewhat effective in identifying plants with Cryptosporidium levels above or below 0.075 oocysts/ L in reservoirs, lakes, and flowing streams ( USEPA 2003c). Under the Preferred Alternative, this level is the cutoff between the no action and the action bins ( Bins 2, 3, and 4). Thus, the committee recommended that E. coli be used as a screening test for small systems under the Preferred Alternative, A3.

The selection of the E. coli level for determining when additional Cryptosporidium monitoring in small systems should be conducted was based on limited data. Therefore, the Advisory Committee agreed that additional data should be collected to evaluate the E. coli indicator criteria and to develop alternative criteria, if appropriate. Accordingly, the Advisory Committee agreed that large systems would measure E. coli and turbidity in their source water when they sample for Cryptosporidium. The composite data will be submitted to EPA. This will give EPA time to develop possible alternative indicator levels or indicator parameters ( e. g., turbidity in combination with E. coli) prior to the date when small systems are required to begin source water monitoring for E. coli. Following the completion of 1 year of monitoring under the LT2ESWTR, EPA will determine if alternative indicators ( to the E. coli levels prescribed in the rule) are appropriate for determining classification into bins. Depending upon its findings, EPA will issue guidance for States to consider prescribing alternative indicator requirements for small systems. Therefore, the LT2ESWTR allows for alternative indicators to be considered by the Primacy Agency.

The use of E. coli as a screen for Cryptosporidium monitoring is applicable only to Alternatives A3 and A4. Using E. coli levels to predict a mean Cryptosporidium concentration of less than 0.03 oocysts per liter   the action cutoff level for Alternative A2   was less reliable ( USEPA 2003c). Thus, Economic Analysis for the LT2ESWTR Proposal 3 6 June 2003 under Alternative A2, no screening test is available, and all small systems must monitor for Cryptosporidium. Since Alternative A1 requires the same level of treatment for all systems, no monitoring provisions for large or small systems are included for Alternative A1.

3.4.2 Cryptosporidium Monitoring Strategies for Bin Classification EPA and the Advisory Committee also evaluated alternative monitoring strategies to ensure that levels of source water contamination would be adequately characterized, while minimizing the monitoring burden. Approaches considered included taking 24, 48, or 72 source water samples to determine bin classification using the bin boundaries in Alternative A3.

EPA chose to allow systems serving at least 10,000 people to collect and analyze 24 monthly samples over a 2 year period and base the bin assignment on the maximum running annual average ( RAA). ( The first RAA will be the average of the results of the first 12 months of monitoring; the second RAA will be the average of results from months 2   13, the third will be the average of concentrations from months 3   14, etc.) Alternatively, systems may collect two or more samples per month over the 2 year period, at regular intervals, and use the simple average ( the average of all 48 samples) to determine bin placement.

The following paragraphs discuss the methodology for choosing these monitoring frequencies.

EPA knew that the measured amount of Cryptosporidium in each sample might be different from the actual or " true" concentration because of error inherent in sampling and analytical methods. For example, method error can be introduced by oocyst recovery, false detections, and analyst error. Sampling error is affected by the sample size and the fact that the concentration in a given sample may misrepresent the concentration in the larger water body. EPA was primarily concerned that highoccurrence sources could possibly be placed in either the no action bin ( Bin 1, for mean occurrence below 0.075 oocysts per liter) or bins that provided insufficient remedies. This could result in insufficient protection of public health. A secondary concern was that systems could be assigned to a higher bin than was warranted by their true concentration, resulting in unnecessary costs for systems.

EPA performed a Monte Carlo analysis to determine the probabilities of misclassification based on different monitoring scenarios ( see Appendix B for more details). The analysis accounted for the volume assayed, variation in source water Cryptosporidium occurrence, and variable method recovery.

The analysis specifically considered the likelihood that a system with a true mean Cryptosporidium concentration a factor of 3.16 ( 0.5 log) above or below a bin boundary would be assigned to the wrong bin. Probabilities were assessed for two cases:

° Misclassification low: a system with a mean concentration of 0.24 oocysts/ L ( i. e., factor of 3.16 above the Bin 1 boundary of 0.075 oocysts/ L) is misclassified low in Bin 1.

° Misclassification high: a system with a mean concentration of 0.024 oocysts/ L ( i. e., factor of 3.16 below the Bin 1 boundary of 0.075 oocysts/ L) is misclassified high in Bin 2. Economic Analysis for the LT2ESWTR Proposal 3 7 June 2003 Exhibit 3.2 shows the error rates the model predicts at concentrations of 0.24 oocysts/ L and 0.024 oocysts/ L ( a factor of 3.16 above and below the Bin 1 boundary of 0.075 oocysts/ L) under different monitoring scenarios.

Exhibit 3.2 Probability of Misclassification for Monitoring and Binning Strategies Considered for the LT2ESWTR Monitoring Strategy Probability of Misclassification High Probability of Misclassification Low 48 sample simple mean 1.7% 1.4%

24 sample maximum RAA 5.3% 1.7%

24 sample simple mean 2.8% 6.2%

12 sample second highest value 47% 1.1%

8 sample maximum value 66% 1.0%

Note: Probability of misclassification high into Bin 2 was calculated for systems with true Cryptosporidium concentrations of 0.024 oocysts/ L, or 0.5 log below the Bin 1 boundary of 0.075 oocysts/ L. Probability of misclassification low into Bin 1 was calculated for systems with Cryptosporidium concentrations of 0.24 oocysts/ L, or 0.5 log above the Bin 1 boundary.

Source: Appendix B.

The first two of the approaches shown in Exhibit 3.2, the 48 sample simple mean and 24 sample maximum RAA, were recommended by the Advisory Committee and are proposed for bin classification under the LT2ESWTR because they have low misclassification rates. As shown in Exhibit 3.2, these strategies have misclassification low rates of 1 to 2 percent, meaning there is a 98 to 99 percent likelihood that a plant with an oocyst concentration 0.5 log above the Bin 1 boundary would be correctly assigned to Bin 2. The misclassification high rate is near 2 percent for the 48 sample simple mean and 5 percent for the 24 sample maximum RAA. These rates indicate that a plant with an oocyst concentration 0.5 log below the Bin 1 boundary of would have a 95 to 98 percent probability of being correctly assigned to Bin 1. Bin misclassification rates across a wide range of concentrations are shown in Appendix B.

The 24 sample simple mean had a slightly lower misclassification high rate than the 24 sample maximum RAA ( 2.8 vs. 5.3 percent) but the misclassification low rate of the simple mean was almost 4 times greater. Consequently, a plant with a mean Cryptosporidium level above the Bin 1 boundary would be much more likely to be misclassified in Bin 1 using a 24 sample simple mean than with a 24 sample maximum RAA. To increase the probability that systems with mean Cryptosporidium concentrations above 0.075 oocysts/ L will provide additional treatment, EPA is proposing that if only 24 samples are taken, the maximum RAA be used to determine bin assignment.

EPA evaluated monitoring strategies involving only 12 and 8 samples to determine if lower frequency monitoring could provide satisfactory bin classification; these lower numbers of samples are not adequate. For example, Exhibit 3.2 shows that if plants were classified in bins based on the second highest concentration of 12 samples or the highest concentration of eight samples, then low misclassification low rates could be achieved. A system with a mean Cryptosporidium level 0.5 log above the Bin 1 boundary would have a 99 percent chance of being appropriately classified in a bin requiring additional treatment under either strategy. However, a system with a mean oocyst Economic Analysis for the LT2ESWTR Proposal 3 8 June 2003 concentration 0.5 log below the Bin 1 boundary would have a 47 percent chance of being incorrectly classified in Bin 2 using the second highest result among 12 samples, or a 66 percent likelihood of being misclassified in Bin 2 using the maximum result among 8 samples. Therefore, these strategies were not proposed. Increasing the number of samples used to compute the maximum RAA above 24 also increased the number of annual averages computed, so it did not reduce the likelihood of misclassification high. Computing a simple mean based on more than 48 samples did reduce bin misclassification rates, but the rates were already very small ( 1 to 2 percent for plants with levels 0.5 log above or below bin boundaries). For sources with Cryptosporidium concentrations very near or at bin boundaries, increasing the number of samples did not markedly improve the error rates, which remained near 50 percent at the bin boundaries.

In summary, EPA believes that the prescribed sampling designs in the Preferred Alternative perform well for the purpose of assigning source waters to bins. More costly designs, involving more frequent sampling and analysis, provide only marginally improved performance, while placing a greater burden on limited laboratory capacity. Economic Analysis for the LT2ESWTR Proposal June 2003 4 1 4. Baseline Conditions 4.1 Introduction To estimate the impact of the LT2ESWTR regulatory alternatives on the water supply industry, it is necessary to first establish the conditions that exist within the industry just before the regulatory requirements take effect. This baseline allows a consistent comparison of public health impacts ( developed in Chapter 5) and the economic and financial impacts ( developed in Chapters 6 and 7) across regulatory alternatives.

Because the compliance deadlines of recently promulgated and proposed rules will occur after the date EPA is required to promulgate the LT2ESWTR, many of the baseline conditions must be estimated rather than directly measured. Thus, data on existing conditions are combined with projections of changes to those conditions to estimate the baseline for the LT2ESWTR. The steps required to determine the baseline conditions are as follows:

° Compile an industry profile   identify and collect information on the segments of the water supply industry subject to the rule.

° Characterize influent water quality   summarize the relevant characteristics of the raw water treated by the industry.

° Characterize treatment for other rules   predict what the industry will do to comply with the provisions of rules that will precede the LT2ESWTR and that may generate changes relevant to this rule, specifically the IESWTR, LT1ESWTR, Stage 1 DBPR, and Stage 2 DBPR.

° Predict occurrence following implementation of other rules   estimate what the treated water quality will be after the rules preceding the LT2ESWTR are implemented.

This chapter presents an analysis that is at a level of detail and precision appropriate to support subsequent analyses and regulatory decisions for the LT2ESWTR. An exhaustive review of the water supply industry, source waters, or industry practices was not needed to conduct the analysis. The remainder of this chapter is organized as follows:

4.2 Data, Tools, and Processes Used in Baseline Development 4.2.1 ICR and ICRSS Observed Data 4.2.2 ICR and ICRSS Modeled Data and Method for Predicting Source Water Occurrence 4.2.3 Surface Water Analytical Tool ( SWAT) 4.3 Industry Profile 4.3.1 Public Water System Characterization 4.3.2 Systems, Plants, and Population Subject to the LT2ESWTR 4.3.3 Water Treatment Plant Design and Average Daily Flows 4.4 Baseline for Unfiltered Plants ( Pre LT2ESWTR) 4.4.1 Treatment Characterization for Unfiltered Plants 4.4.2 Number of Unfiltered Systems, Plants, and Population Served 4.4.3 Source Water Cryptosporidium Occurrence for Unfiltered Plants 4.4.4 Finished Water Cryptosporidium Occurrence for Unfiltered Plants 1 Throughout this document, the acronym " SDWIS" represents " SDWIS   Federal Version."

2 All industry baseline data reflect revisions to SDWIS 4th Quarter Year 2000 Freeze to account for reporting errors in Massachusetts and Montana.

Economic Analysis for the LT2ESWTR Proposal June 2003 4 2 4.5 Baselines for Filtered Plants ( Pre LT2ESWTR) 4.5.1 Treatment Characterization for Filtered Plants 4.5.2 Number of Filtered Plants and Population Served 4.5.3 Source Water Cryptosporidium Occurrence for Filtered Plants 4.5.4 Finished Water Cryptosporidium Occurrence for Filtered Plants 4.5.5 Comparison of EPA Estimates with Aboytes et al. ( 2000) 4.5.6 Predicted Bin Classification for Filtered Plants 4.6 Baseline for Uncovered Finished Water Reservoirs 4.7 Households Incurring Costs Due to the LT2ESWTR 4.8 Summary of Uncertainties in Development of LT2ESWTR Baselines 4.2 Data, Tools, and Processes Used in Baseline Development Several data sources were used to characterize the baseline and to predict changes in treatment technologies and water quality for different regulatory alternatives. The Safe Drinking Water Information System   Federal Version ( SDWIS1) ( 4th Quarter Freeze Year 2000 data2) is used to create system and population baselines ( USEPA 2000f). SDWIS is EPA's national regulatory compliance database for the drinking water program. It includes information on the nation's 170,000 public water systems ( PWSs) and on violations of drinking water regulations. EPA's web site provides more information on SDWIS ( http:// www. epa. gov/ safewater/ sdwisfed/ sdwis. htm). A second key source of data used to develop the industry profile is the Third Edition of the Water Industry Baseline Handbook ( Baseline Handbook) ( USEPA 2001c) published in May 2001, which compiles data derived from the 1995 Community Water System Survey ( CWSS) and SDWIS. The 1995 CWSS was a mail survey that covered ground and surface water systems of all sizes ( based on population served). The survey was based on a two phase, stratified sample design. Phase 1 was a telephone screening survey that provided a sampling frame for the main data collection in Phase 2. The survey sample in Phase 2 was stratified according to water system size ( residential population served), ownership ( public, private, or ancillary), and primary water source ( ground or surface). A total of 3,681 systems covering a range of source water types and system sizes were randomly selected to receive the main survey questionnaire. Of these, 1,980 systems responded. See Community Water System Survey, Volume 2 ( USEPA 1997c), for more information on the 1995 CWSS sample design and data evaluation.

EPA also used Geometries and Characteristics of Water Systems ( also called the Model Systems Report) ( USEPA 2000b). In this document, EPA used CWSS data to develop equations to predict flows based on system populations.

To characterize the influent water quality, treatment processes, and finished water quality, EPA primarily used data from the 1996 Information Collection Rule ( ICR), for which Cryptosporidium monitoring requirements applied to all PWSs serving at least 100,000 people and using surface water or ground water under the direct influence of surface water ( GWUDI) as a source. The purpose of the ICR was to collect DBP and microbial occurrence and treatment information to help evaluate the need for further microbial and DBP rules. The ICR gathered plant level data from about 300 water systems over 3 Method 1622 was used for the first 4 months of data collection, at which time Method 1623 replaced Method 1622. The primary difference between Method 1622 and Method 1623 is that the Method 1622 immunomagnetic separation ( IMS) kit includes only reagents for Cryptosporidium purification, whereas Method 1623 uses the Giardia/ Cryptosporidium combination kit, which includes reagents for both Cryptosporidium and Giardia purification.

4 Forty small plants also were included in the survey, but they did not monitor protozoa concentrations.

Economic Analysis for the LT2ESWTR Proposal June 2003 4 3 18 months ( July 1997 December 1998). These data characterize the source water occurrence of Cryptosporidium, Giardia, viruses, and indicators of microbial contamination, along with types of treatment in place. The data used in this economic analysis ( EA) are from the Auxiliary 1 ( AUX1) database ( USEPA 2000j).

The Information Collection Rule Supplemental Surveys were voluntary surveys, for which 40 medium ( serving 10,000   99,999) and 40 large plants ( serving 100,000 or more) collected Cryptosporidium and Giardia source water occurrence data. These data are presented in the Occurrence and Exposure Assessment for the Long Term 2 Enhanced Surface Water Treatment Rule ( USEPA 2003c). Treatment characterization and other information were obtained from industry and State sources.

Several analytical tools ( models) also were used to estimate the following:

° Source water Cryptosporidium occurrence ° Treatment changes due to the Stage 1 and Stage 2 DBPRs ° Number of plants falling into treatment bins, based on predicted source water occurrence ° Finished water occurrence under Pre LT2ESWTR and LT2ESWTR conditions 4.2.1 ICR and ICRSS Observed Data Three sets of monitoring data are used to characterize Cryptosporidium source water occurrence: the ICR data set and the ICRSS data sets for medium and large systems. Microbial analyses for the ICR were conducted according to the ICR Method ( USEPA 1996a). The ICRSSs evaluated source water for Cryptosporidium, Giardia, and coliforms at a sample of medium surface water systems ( those serving 10,000 to 99,999 people) as well as a sample of large surface water systems. EPA Methods 1622 ( USEPA 1999a) and 16233 ( USEPA 1999b), summarized in the Occurrence and Exposure Assessment, were used for Cryptosporidium and Giardia analyses. With Methods 1622 and 1623, the volume of water analyzed was on the average larger than the volume analyzed with the ICR Method, yielding better estimates of Cryptosporidium occurrence on a per sample basis ( the volume analyzed with the ICR Method depended on the sample pellet volume after centrifugation and was based on the volume needed to meet detection limits). The ICRSS data consist of semi monthly observations taken over a 12 month period at 40 randomly selected large plants and 40 randomly selected medium plants4. Exhibit 4.1 summarizes the differences between the ICR and ICRSS data collection methods. Economic Analysis for the LT2ESWTR Proposal June 2003 4 4 Exhibit 4.1 ICR and ICRSS Comparison ICR ICRSSM ICRSSL System size of plants participating ( population served) > 100,000 10,000 99,999 > 100,000 Number of surface water plants participating 350 40 40 Sample frequency Monthly Semi monthly Semi monthly Sampling period July 1997 December 1998 March 1999 February 2000 March 1999 February 2000 Required sample volume 100 L 10 L 10 L Median sample volume analyzed 3.2 L 10 L 10 L Average recovery rates for lab method 12% 43%

Percentage of samples where Cryptosporidium was not detected 93% 83% 87%

Percentage of plants with at least one positive Cryptosporidium sample 44% 85% 85%

Source: USEPA 2003c, USEPA 2000j, and USEPA 2000k.

The ICR monitoring program resulted in nearly 6,000 Cryptosporidium measurements from 350 water sources ( USEPA 2000j). The ICRSS monitoring produced approximately 1,900 measurements from 80 source waters. Cryptosporidium was not detected in most of the samples ( 93 percent of ICR samples and 86 percent of ICRSS samples). Approximately 44 percent of plants participating in the ICR program had at least one positive sample, while the increased sensitivity of the methods under the ICRSS led to a much higher percentage of plants ( approximately 85 percent) having at least one positive sample.

The detection of Cryptosporidium oocysts is complex. Because of the low occurrence of Cryptosporidium in source waters, a sample may not contain any oocysts even though the source water does. Thus, a non detection in a test volume is not definitive evidence against occurrence in the source. In addition, the laboratory method is inefficient and may not recover all the oocysts that were in a sample. While underestimation is much more likely, when detections do occur, sample concentrations also may overestimate influent concentrations because of the small volume of sample involved ( i. e., one oocyst identified in a 10 liter sample may not represent the true proportion of oocysts in the much larger source water volume).

During the ICR collection period, EPA implemented the ICR Lab Spiking Program ( LSP) to assess the recovery of Cryptosporidium oocysts from field samples analyzed with the ICR Method. At the time of the ICR sample collection, duplicate 100 liter samples were collected and spiked with a known quantity of Cryptosporidium oocysts. Recovery of the oocysts ( the detection of known oocysts) by laboratories was very low, with an average of 12 percent of the known quantity being recovered per sample. The ICRSS Matrix Spike Program was used to assess the recovery of oocysts from field samples using Methods 1622/ 1623 during the ICRSS. Duplicate samples were spiked with a known quantity of Cryptosporidium oocysts, filtered, and analyzed using Methods 1622/ 1623. The average Economic Analysis for the LT2ESWTR Proposal June 2003 4 5 recovery for the ICRSS was 43 percent. These spiking programs are further described in the LT2ESWTR Occurrence and Exposure Assessment ( USEPA 2003c).

Each set of data has its advantages; for instance, the ICR data set contains data from more plants than the ICRSS data sets do, but the ICR data set does not include data for plants in medium sized systems. The ICRSS data set does not include enough data for unfiltered plants to be useful for modeling, while the ICR set does. The ICRSS sets, on average, had higher recovery rates and larger sample volumes than the ICR set. The ICR collected data for a longer time period, but the ICRSS data were collected more frequently. In considering which data set best represents the national distribution of Cryptosporidium occurrence, none was judged superior to the others; that is, no one set was considered to have a greater likelihood of representing " true" occurrence. In view of this, each data set was kept separate, and a weighting of their relative values to allow them to be combined was not attempted.

Cryptosporidium observations were characterized according to oocyst structure as observed under a microscope, as defined below:

° Empty: Oocyst type walls, but not presently containing internal material.

° Non Empty: Includes oocysts with internal structure and with amorphous structures. Oocysts with amorphous structures have walls and internal material characteristic of Cryptosporidium, but the material cannot be confirmed as Cryptosporidium.

° With Internal Structures: Oocysts that have a cell wall and recognizable internal structures consistent with Cryptosporidium; subset of non empty category.

° Amorphous Structures: Oocysts that have a cell wall and internal material but no recognizable internal structures; subset of non empty category.

° Total: The combined count of empty oocysts and non empty oocysts ( those with either internal or amorphous structures).

At meetings of the Technical Working Group of the Microbial Disinfection Byproducts Advisory Committee, participants agreed that the type of oocyst observed gives information about the level of confidence that the oocyst is actually Cryptosporidium. The presence of internal structures may increase the confidence that the observed object is indeed a Cryptosporidium oocyst, and not some other item or organism with similar gross morphology. While oocysts that are empty are unlikely to be viable or infectious at the time of the laboratory analysis, they still are indicators of the presence of Cryptosporidium Oocysts with amorphous structures give a level of confidence between that of empty oocysts and those with internal structures. A detailed presentation of observed Cryptosporidium occurrence and evaluation of results from the ICR and ICRSS is provided in the Occurrence and Exposure Assessment ( USEPA 2003c).

The analysis presented in this document assumes that total Cryptosporidium counts are the most representative of the presence of Cryptosporidium in source waters. While some of these oocysts may not have been infectious at the time of analysis, they likely represent Cryptosporidium that were present in the water. The probability of oocyst infectivity is addressed in the risk assessment model in section 5.2.3. Economic Analysis for the LT2ESWTR Proposal June 2003 4 6 To account for the limitations in observed data in the three data sets, modeled estimates of the range of underlying total " true" Cryptosporidium occurrence, consistent with the observed occurrence, were developed.

4.2.2 ICR and ICRSS Modeled Data and Method for Estimating Source Water Occurrence An accurate representation of Cryptosporidium concentrations in source water is important for estimating both costs and benefits. The LT2ESWTR treatment requirements are based on the results of source water Cryptosporidium monitoring. Therefore, occurrence estimates determine the number of plants requiring treatment and the level of treatment, which in turn determines cost. The population affected and the estimated reduction in illness also derive from those same occurrence estimates. This section provides the rationale for using model based estimates over observed data and summarizes the modeled results.

The ICR and ICRSS provide source water quality data for developing this estimated national occurrence distribution. The raw data include the number of oocysts detected and the associated sample volume analyzed. A straightforward approach to modeling is to first divide the counts by the volumes to obtain concentrations and then model the distribution of estimated concentrations. However, there are limitations in the data that negate the usefulness of this approach.

First, sample volumes are low relative to the volume needed to consistently calculate representative concentrations of Cryptosporidium in source water. The volumes collected also are inconsistent from location to location or even month to month at a given location. For example, the majority of sample counts in the ICR and ICRSS were zeros ( no Cryptosporidium detected), but these zero counts are based on widely varying sample volumes. Common sense suggests that a zero count from a 10 liter sample should be weighted more heavily than a zero count from a 1 liter sample. Based on a straightforward sample concentration calculation, however, both concentrations would be considered the same.

Second, the majority of Cryptosporidium oocysts captured in samples likely were not detected in testing, due to a lack of precision in analytical methods. Therefore, straightforward concentration estimates would systematically under estimate the true concentration of Cryptosporidium in national source waters.

Finally, there are limitations in the counts or concentrations that can be reported due to the rare occurrence of oocysts. Oocyst counts can only be whole numbers   it is impossible to detect half an oocyst   and most counts were zeros or ones. Since the volume analyzed in each sample was generally small, a limited number of concentrations could be calculated from the count and volume data. For instance, one oocyst in 10 liters gives a concentration of 0.1 oocysts/ L, while one oocyst in 5 liters gives a concentration of 0.2 oocysts/ L. The volume analyzed would have to be quite large or the number of oocysts present greater to enable more precise calculations of concentration.

These limitations, when the observed data are used to calculate concentrations, result in a large number of individual sample values that may each over or under represent the true Cryptosporidium concentrations. To account for these limitations and other sources of variability in the data and to be able to estimate national occurrence, model based occurrence estimates are chosen over observed data. A more detailed discussion of this estimation procedure is provided in the Occurrence and Exposure Assessment ( USEPA 2003c). Economic Analysis for the LT2ESWTR Proposal June 2003 4 7 The benefits of using model based estimates are that a model can properly account for the following conditions:

° Variability in the data, based on location, sampling technique, turbidity dependence, and other factors ° The low and variable recovery rates of the measurement methods ° The small volumes assayed and their adequacy in representing the much larger volume of source water at the time of sampling ° The small number of samples assayed at each location and their ability to represent the average concentration in that location's water during the 18 months of the survey EPA developed a probability model that links the survey data ( sample volumes and laboratory oocyst counts) to the unknown source water concentrations, the quantities of interest. The model accomplishes two tasks. First, it adjusts concentration estimates to account for varying sample volumes and test method recovery rates. Second, the assumed probability structure makes it possible to quantify uncertainty in the concentration estimates.

To account for varying sample volumes and recovery rates, the model defines an expected count for each survey sample. This is the average number of oocysts detected in repeated sampling from a given survey location, assuming a particular source water concentration, sample volume, and test method recovery rate:

Average Count = Concentration × Volume × Recovery or, in terms of units:

oocysts detected = ( oocysts present/ liter) × ( liters) × ( oocysts detected/ oocysts present)

Concentration is the unknown, and estimating it from data is the goal of the modeling. Count and Volume are known. They are measured directly and reported for each survey sample. Recovery is the ratio of oocysts detected in laboratory testing to oocysts present in the sample. Since Recovery cannot be measured directly for an individual test, there are no sample by sample data available for it. For a particular test method, however, the typical range of recovery values can be estimated from designed experiments using " spiked" samples with known concentrations ( see Chapter 3). This was done for each of the Cryptosporidium lab methods used in the ICR and ICRSS, and recovery rate probability distributions were fit to the results. Simulated Recovery values were drawn from these probability distributions.

As the formula shows, the average oocyst count is directly related to source water concentration, but also to sample volume and recovery rate. For example, increasing the sample volume will result in a higher average oocyst count even if the source water concentration is fixed. Because Count, Volume, and Recovery are known or can be simulated, however, fitting the data to this formula results in estimates for the unknown Concentration that account for the variation in these other variables.

The second part of the model is a probability structure for the observed sample counts. Each observed count is assumed to come from a Poisson probability distribution, a fundamental probability Economic Analysis for the LT2ESWTR Proposal June 2003 4 8 model for counting rare events ( this is the distribution that results from the fact that only zero or one oocyst is usually present in a sample). The mean of each distribution is the average count as defined above. Incorporating these probability distributions in the model allows for the calculation of uncertainty bounds for the concentration estimates.

A Markov Chain Monte Carlo ( MCMC) approach was employed to fit this model to the data. MCMC is an iterative technique for fitting statistical models to data. The Markov Chain is a sequence of joint probability distributions that converges to a stable distribution for likely model parameter values. Monte Carlo is a computational technique for solving intractable integrals through extensive, simulated sampling.

For each primary data set ( ICR, ICRSSM, and ICRSSL), rather than estimating a single mean and standard deviation that captures the log normal distribution of plant mean concentrations, the model produces distributions of possible means and variances from which one can sample. ( EPA believes the distribution of plant mean concentrations is log normal; this is described in section <IP_ADDRESS> of the Occurrence and Exposure Assessment.)

The results of the occurrence models employed in this EA are documented in sections 4.4.3 and 4.5.3 for unfiltered and filtered plants. As described above, there was no single occurrence distribution that served as input but, instead, a collection of plausible occurrence distributions. Summary plots in these sections, then, show both the mean occurrence distribution   which represents the " middle" distribution   and confidence bounds that capture the range of occurrence distributions in a given collection.

4.2.3 Surface Water Analytical Tool ( SWAT)

SWAT used source water and treatment data collected under the ICR to estimate the percentage of large surface water plants that would require advanced technologies to meet Stage 1 and Stage 2 DBPR limits for disinfection byproducts ( DBPs). Several advanced technologies, namely chlorine dioxide, ozone, ultraviolet light ( UV) disinfection, and membrane processes, not only achieve DBP reduction, but also provide varying degrees of Cryptosporidium removal or inactivation. The characteristics, costs, and effectiveness of these technologies are taken into account in developing the baselines for this EA.

A more detailed description of SWAT and how it was used to predict changes in treatment technologies under the Stage 2 DBPR is provided in the Draft Economic Analysis for the Stage 2 Disinfectants and Disinfection Byproducts Rule ( USEPA 2003d). In addition, a detailed description of the SWAT components and its operation can be found in the document Surface Water Analytical Tool ( SWAT) Version 1.1   Program Descriptions and Assumptions ( USEPA 2000c).

4.3 Industry Profile This section identifies the PWSs subject to the LT2ESWTR. Subsequent sections identify the subset of systems subject to specific provisions of the rule ( e. g., baselines for unfiltered and filtered systems). The water system baseline characterizations presented here are key inputs to the cost and benefit assessments described in this EA. 5 Although the language in EPA rules generally does not include systems serving exactly 10,000 people in the small category ( or refers to small systems as serving fewer than 10,000 people), analyses in this document place them in the small category to be consistent with the system and population data categories from SDWIS and the Baseline Handbook.

Economic Analysis for the LT2ESWTR Proposal June 2003 4 9 EPA's categorization scheme for water systems is first summarized, followed by a presentation of systems, plants, and populations subject to the LT2ESWTR. A summary of water treatment plant design flows and average daily flows concludes the industry profile section.

4.3.1 Public Water System Characterization Categorizing water systems allows EPA to determine the impacts of this rule on different types of systems. For this EA, EPA sorted systems on the basis of size, ownership, and retail/ wholesale relationships provided in SDWIS. This section explains the classifications used.

PWS Type As defined by the Safe Drinking Water Act ( SDWA), a PWS is a water system that provides piped water for human consumption and has at least 15 service connections or regularly serves an average of at least 25 individuals per day for at least 60 days per year. EPA classifies PWSs into two broad groups:

° Community Water Systems ( CWSs) have at least 15 service connections used by yearround residents or regularly serve at least 25 year round residents.

° Noncommunity Water Systems ( NCWSs) are PWSs that are not classified as CWSs.

EPA further classifies NCWSs into two types:

° Nontransient Noncommunity Water Systems ( NTNCWSs) regularly serve at least 25 of the same people more than 6 months per year.

° Transient Noncommunity Water Systems ( TNCWSs) do not regularly serve at least 25 of the same people more than 6 months per year.

Population Served Under the LT2ESWTR, as with some previous rules, small systems ( those serving fewer than 10,000 people, as defined in the rule) will face somewhat different requirements than larger systems5. System size is important in the regulatory analysis as well. Costs are estimated using the size of systems as a factor, household costs are derived in part from the number of households in a system size category, and separate technology decision trees are used for different sizes of systems. 6 In the first part of the cost model, cost equations are specified for all nine subcategories. Later these subcategories are combined into four categories.

Economic Analysis for the LT2ESWTR Proposal June 2003 4 10 In some of the analyses that follow, nine size categories are used ( these are based on SDWIS categories):

C Small systems, which include those serving 10,000 or fewer people and are broken down into five subcategories: C < 25 C 26   100 C 101   500 C 501   3,300 C 3,301   10,000 C Medium systems, which are broken down into two subcategories: C 10,001   50,000 C 50,001   100,000 C Large systems, which are broken down into two subcategories: C 100,001   1 million C > 1 million In other analyses, four sizes are used ( medium and large categories include the same systems, but smalls are subdivided into very small (< 500) and small ( 501   10,000)) 6. To calculate net benefits, only two categories are used ( small and large systems   those serving < 10,000 people and those serving > 10,000 people).

Source Water Systems are classified by the source water from which they draw. Surface water systems typically draw from reservoirs, natural lakes, or flowing streams. Ground water systems draw from wells. Some ground water sources are under the direct influence of surface water sources. These systems, called GWUDI systems, are considered directly influenced if surface water microorganisms are present. This category is important to the extent that pathogens, such as Giardia cysts and Cryptosporidium oocysts, can contaminate the ground water source. Some systems may have multiple source types and are referred to as " mixed systems." In SDWIS and the Baseline Handbook, a mixed system is categorized as a surface water system if it gets any portion of its flow from surface water. Based on the analysis in the Model Systems Report ( USEPA 2000b), it is estimated that 21 percent of surface water systems obtain some of their water from ground water sources. Of these systems, one third ( 8 percent of all surface water systems) get the majority of their flow from ground water. Mixed systems may either be systems that have some plants that are solely supplied by ground water and other plants that are solely supplied by surface water, or they may have one or more plants in which both types of source waters are mixed. Economic Analysis for the LT2ESWTR Proposal June 2003 4 11 Ownership Systems also are categorized by ownership. Private systems are owned by private corporations, associations, or individuals. Private systems are still PWSs. Public systems are owned by public entities, such as a municipality, county, or special district. Ownership distinctions are important to the analysis since differences exist between public and private systems in their access to capital and other means of financing. This distinction becomes important in calculating household costs in Chapter 6 and the Unfunded Mandates analysis presented in Chapter 7.

Purchased and Nonpurchased Systems Systems are categorized according to whether they provide or treat water themselves or whether they purchase it from other systems. Treatment modifications are not expected to be made by purchased systems under the LT2ESWTR; instead, purchased systems will absorb ( through rate increases) the costs of additional treatment installed by the sellers. On the other hand, nonpurchased systems collect and treat the water themselves and distribute it to their retail and wholesale customers. These systems are subject to most of the provisions of the LT2ESWTR.

4.3.2 Systems, Plants, and Population Subject to the LT2ESWTR This section estimates the baseline number of systems subject to the LT2ESWTR. The proposed LT2ESWTR applies to all PWSs, regardless of type or size, that use surface water or GWUDI, since a person may need only ingest one Cryptosporidium oocyst to become infected. However, the economic impact of the LT2ESWTR may vary across the types of surface water systems ( e. g., some systems may purchase all their water from other systems and will not be subject to monitoring or treatment requirements).

The baseline presented in this section is used to estimate implementation costs, such as those for training and becoming familiar with the rule ( see Appendix D). Not all of the systems incurring implementation costs will incur costs for other provisions of the rule. Based on analysis of existing treatment technology, size, and other factors, only subsets of the systems presented in this section are subject to specific provisions of the rule. The numbers of unfiltered plants, filtered plants, and uncovered finished water reservoirs subject to specific rule provisions are presented in sections 4.4 through 4.6.

Number of Systems Systems in SDWIS are listed according to systems' retail populations. The advantage of classifying them this way is that it appropriately accounts for both the total number of individual PWSs in the United States and the total population served by all of those systems. However, a disadvantage of this method ( especially for surface water CWSs) when estimating national costs of regulations is that it does not directly account for the fact that the water delivered by purchased systems to their retail customers is actually treated by other systems. It is important to recognize that the total flow for systems supplying surface water is actually treated by fewer than half of the surface water systems accounted for in SDWIS. Because of economies of scale, the cost of treatment ( in cents per gallon) is less for systems treating larger flows than it is for systems treating smaller flows. For example, it is typically more expensive to build and operate two treatment plants serving 5,000 people than one treatment plant serving 10,000 people. Failing to account for the fact that surface water is actually treated in larger quantities at a smaller number of systems than SDWIS suggests could result in an upward bias in national cost estimates of rules that affect a substantial portion of surface water systems. Economic Analysis for the LT2ESWTR Proposal June 2003 4 12 To rectify this bias, an analysis was performed to " link" consecutive, or purchased, surface water CWSs and NTNCWSs to their respective wholesale systems using SDWIS data. CWSs were only linked to other CWSs and NTNCWSs were linked only to other NTNCWSs. TNCWSs were not linked, since they usually do not purchase water from other TNCWSs. If a consecutive system could be linked to a wholesaler, that system was removed from the system count and its population was added to the population of the wholesale system. Consecutive systems that could not be definitively linked to their wholesalers were considered stand alone purchased systems. Although consecutive systems do not treat their water, these systems are included in the treatment baseline because their populations and flows must be accounted for in estimating treatment costs. EPA recognizes that including them as separate plants overestimates treatment costs. The decision process for this analysis is shown in Exhibit 4.2.

The number of surface water ( including mixed systems) and GWUDI systems per size category in SDWIS ( pre linking) is shown on the left side of Exhibit 4.3. These numbers are modified to correct obvious population errors, such as excluding populations that were counted by both purchasing and selling systems. Additionally, systems whose ownership category was listed as " other" in SDWIS were reallocated to private and public and purchased and nonpurchased categories based on the existing proportion of each category to the total number of systems. Note that the total number of nonpurchased systems in columns H and I is the same as the total number of nonpurchased systems before linking in columns C and D. However, the numbers of public and private nonpurchased systems changed slightly because of how the systems with " other" ownership types are allocated within each size category. The inventory of nonpurchased systems, unlinked, or pre linking, is used as the baseline for determining implementation costs. Purchased systems are not included in this baseline because they do not have their own source water, so they will not be subject to monitoring or treatment requirements and do not need to conduct implementation activities. This baseline is also used with minor modifications to determine the number of systems ( and plants) subject to monitoring costs. Economic Analysis for the LT2ESWTR Proposal June 2003 4 13 Exhibit 4.2 Methodology for " Linking" Consecutive Surface Water CWSs and NTNCWSs to Their Selling Systems ° If a system has multiple sources, ( e. g., a system has a primary source of surface water in addition to a purchased surface water source), it is assumed to be adequately represented as a nonpurchased surface water system, and was not linked to its seller ( i. e., only 100 percent purchased surface water systems were linked).

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

° If the purchased surface water system buys water from multiple nonpurchased systems, it was assigned to the most directly related nonpurchased seller with the largest population. For example, a purchased system ( System C) purchases from a nonpurchased system ( System B1) and a purchased system ( System B2), which in turn purchases from a nonpurchased system ( System A). In this case, System C was linked to System B1, and the population of System C was added to that of System B1.

° When the purchased system and its seller were not of the same type ( e. g., a CWS purchasing from a NTNCWS), they were not linked. Systems purchasing from sellers of a different system type were counted as separate, unlinked purchased systems.

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

° Some purchased systems have what is referred to as " cascading provider relationships." For instance, a purchased system ( System C) may purchase water from another system ( System B). This system ( System B) does not treat its own water, but instead purchases water from another system ( System A). For this analysis, the populations of both Systems B and C were added to the population of System A, and Systems B and C were removed from the inventory of purchased systems.

° In a few cases, the seller could not be found, i. e., a purchased system ( e. g., System C) could not be linked to a nonpurchased system ( e. g., System A). These purchased systems were counted as separate, unlinked, purchased systems. Economic Analysis for the LT2ESWTR Proposal June 2003 4 14 Number of Systems Number of Systems Purchased Nonpurchased Purchased Nonpurchased Public Private Public Private Public Private Public Private A B C D E = A+ B+ C+ D F G H I J= F+ G+ H+ I K L= J* K M N < 100 445 308 222 308 1,283 18 14 135 321 488 1.0 488 28,999 0.02% 101 500 850 513 426 331 2,120 47 22 396 338 803 1.1 883 224,777 0.13% 501 1,000 614 243 372 84 1,313 20 9 344 86 459 1.0 459 353,113 0.20% 1,001 3,300 1,199 181 977 110 2,467 51 5 904 106 1,066 1.0 1066 2,158,022 1.24% 3,301 10,000 831 80 961 56 1,928 45 1 951 64 1,061 1.2 1273 6,343,982 3.65% 10,001 50,000 672 59 880 79 1,690 32 6 984 78 1,100 1.1 1210 25,722,417 14.80% 50,001 100,000 106 14 165 28 313 6 0 210 30 246 1.8 443 16,976,910 9.77% 100,001 1 Million 60 11 176 29 276 8 0 215 33 256 1.6 410 70,801,601 40.74% > 1Million 0 0 13 0 13 0 0 20 2 22 2.8 62 51,175,720 29.45% Total 4,776 1,410 4,192 1,025 11,403 227 57 4,159 1,058 5,501 6,293 173,785,541 100% Population Served Total No. of Systems Total No. of Systems System Inventory Before Linking Linked System Inventory Treatment Baseline Plants per System Total Plants Population Served Percent of Total Population Number of Systems Number of Systems Purchased Nonpurchased Purchased Nonpurchased Public Private Public Private Public Private Public Private A B C D E = A+ B+ C+ D F G H I J= F+ G+ H+ I K L= J* K M N < 100 21 80 76 126 303 21 74 76 126 297 1.0 297 15,319 1.71% 101 500 28 38 60 176 302 28 36 61 174 299 1.0 299 81,388 9.06% 501 1,000 11 14 19 65 109 12 12 19 66 108 1.0 108 77,627 8.64% 1,001 3,300 11 8 15 40 74 9 9 14 40 71 1.0 71 124,176 13.83% 3,301 10,000 5 4 4 9 22 5 4 6 8 23 1.0 23 122,305 13.62% 10,001 50,000 3 3 2 1 9 2 3 2 1 8 1.0 8 214,174 23.85% 50,001 100,000 1 0 0 0 1 1 0 0 0 1 1.0 1 93,204 10.38% 100,001 1 Million 1 0 0 0 1 1 0 0 0 1 1.0 1 169,846 18.91% > 1Million 0 0 0 0 0 0 0 0 0 0 1.0 0 0 0.00% Total 81 147 177 416 821 77 138 177 416 808 808 898,039 100% System Inventory Before Linking Linked System Inventory Treatment Baseline Population Served Plants per System Total Plants Population Served Percent of Total Population Total No. of Systems Total No. of Systems Exhibit 4.3a Number of Unlinked and Linked Surface Water and GWUDI CWSs, Number of Plants, and Population Served, by Ownership and System Size Exhibit 4.3b Number of Unlinked and Linked Surface Water and GWUDI NTNCWSs, Number of Plants, and Population Served, by Ownership and System Size Economic Analysis for the LT2ESWTR Proposal June 2003 4 15 Number of Systems Number of Systems Purchased Nonpurchased Purchased Nonpurchased Public Private Public Private Public Private Public Private A B C D E = A+ B+ C+ D F G H I J= F+ G+ H+ I K L= J* K M N < 100 88 415 172 559 1,234 88 415 172 559 1,234 1.0 1234 53,903 5.69% 101 500 23 83 137 243 486 23 83 137 243 486 1.0 486 117,344 12.38% 501 1,000 11 14 35 22 81 11 14 35 22 81 1.0 81 61,426 6.48% 1,001 3,300 12 2 36 16 67 12 2 36 16 67 1.0 67 155,500 16.41% 3,301 10,000 5 1 22 2 31 5 1 22 2 31 1.0 31 176,789 18.66% 10,001 50,000 3 0 8 0 10 3 0 8 0 10 1.0 10 203,456 21.47% 50,001 100,000 0 0 3 0 3 0 0 3 0 3 1.0 3 179,240 18.91% 100,001 1 Million 0 0 0 0 0 0 0 0 0 0 1.0 0 0 0.00% > 1Million 0 0 0 0 0 0 0 0 0 0 1.0 0 0 0.00% Total 141 515 413 843 1,912 141 515 413 843 1,912 1,912 947,658 100% System Inventory Before Linking Linked System Inventory Treatment Baseline Population Served Plants per System Total Plants Population Served Percent of Total Population Total No. of Systems Total No. of Systems Exhibit 4.3c Number of Unlinked and Linked Surface Water and GWUDI TNCWSs, Number of Plants, and Population Served, by Ownership and System Size Note: For TNCWSs, " population served" is actually the population served at a given time. These numbers are used for calculating treatment costs. The total number of people served over 1 year ( or whichever length of time the TNCWSs is in operation) is generally much larger. To calculate benefits, EPA adjusted the population to account for the total number of people served per year and for the fact that each customer would be served by the system for a shorter period of time than 1 year. These adjustments are described in Chapter 5.

The total number of nonpurchased systems remains the same before and after linking; however, the number of such systems in a size category may change. This is because a nonpurchased system's population changes if a purchased system is linked to the nonpurchased system, and the population may change enough to move the system to the next size category.

The number of purchased systems left after the linking process and the population associated with these systems are included in the baseline for plants subject to treatment requirements because their population must be accounted for in determining treatment costs ( EPA realizes that including the systems themselves does result in an over estimate of the number of systems requiring treatment under LT2ESWTR).

Sources: [ A][ D] SDWIS September 2000 data adjusted for reporting errors in Massachusetts and Montana ( USEPA 2000f). Also includes systems not categorized as " public" or " private" in SDWIS, which were redistributed among public and private purchased and nonpurchased water systems according to the proportions of systems in these categories. [ F][ I] Data from Columns A D modified using linking methodology described in Exhibit 4.2, except for TNCWSs, which were not linked. [ K] Derived from CWSS data ( USEPA 1997c) and Model Systems Report ( USEPA 2000b), modified to exclude ground water plants and weighted for representativeness of each system to all CWSs. [ M] Includes SDWIS population served by surface water and GWUDI systems for nonpurchased and purchased systems in columns F   I based on SDWIS ( USEPA 2000f). Original SDWIS population distribution was modified using linking methodology described in Exhibit 4.2, except for TNCWSs, which were not linked. Economic Analysis for the LT2ESWTR Proposal June 2003 4 16 The right side of Exhibit 4.3 shows the baseline number of systems, after the linking process, that are subject to the treatment requirements of the LT2ESWTR ( actual treatment requirements will be determined by results of Cryptosporidium monitoring). This baseline includes both purchased and nonpurchased systems. The baseline number of systems was converted to plants as described below.

Number of Plants Water systems can consist of one treatment plant supplying all of the water received by the population, or multiple plants treating water, possibly from different sources, or even different source types such as surface and ground water sources. Many of the costs in Chapter 6 are developed by estimating the costs of installing additional treatment at existing water treatment plants. Therefore, for this baseline analysis, EPA needed to calculate the number of plants from the number of systems reported in existing databases. To determine the appropriate number of plants per system, EPA used the 1995 CWSS data. All systems reporting some surface water use were extracted from the database. Each system's plants were then examined to determine the number of plants that would be subject to the LT2ESWTR. Any plant that reported treatment of any amount of surface water was included. Plants that treated solely ground water were eliminated. The CWSS does not differentiate between ground water and GWUDI, so GWUDI plants could not be included in the baseline. Because GWUDI systems are generally less than 10 percent of the total number of systems subject to the rule, it is assumed that excluding them will introduce only a small error.

Using the number of surface water plants and the number of systems, a weighted analysis was performed to determine the average number of surface water treatment plants per system for each size category. ( The sampling weights were developed in the CWSS to account for the representativeness of each system to the nationwide population of systems.)

The ratios of plants to systems are shown in Exhibit 4.3. For surface water and GWUDI CWSs, the number of surface water treatment plants per system varies from 1.0 to 2.8.

Plant information is not available for noncommunity water systems. Because they typically serve a single building or are located in a small area, this analysis assumes that the ratio of plants per system is 1: 1 for all size categories. Exhibit 4.3 summarizes the total number of plants for CWSs, NTNCWSs, and TNCWSs. The total number of systems displayed in Exhibit 4.3 is the baseline from which regulatory impacts are estimated.

Population The total population affected by the LT2ESWTR is derived from SDWIS data ( USEPA 2000f). For purposes of estimating the number of households served ( one method for determining the impact of the rule is calculated in terms of costs per household), the total population affected by the LT2ESWTR is estimated to be 174 million people, the population served by CWSs. This number includes modifications to SDWIS data to correct double counted populations. The breakdown of population by size category is shown in Exhibit 4.3a; this breakdown includes adjustments made to populations during the linking process ( see Exhibit 4.2).

The population served by NTNCWSs and TNCWSs was not included in the determination of the number of households. People served by NTNCWSs and TNCWSs when working, attending school, or traveling also are served on a regular basis by another source, such as a private well or a CWS. Their consumption from a NCWS is an incidental use in addition to their regular service. Adding the population Economic Analysis for the LT2ESWTR Proposal June 2003 4 17 served by NCWSs to that served by CWSs would lead to double counting in cases where the same person was served by both types of systems. If some people served by private wells receive water from NTNCWSs for part of the year, the error introduced by ignoring this consumption from this source is less than if the population served by NCWSs was included in the baseline.

For determining the benefits of the LT2ESWTR, the populations served by CWSs and both types of NCWSs are included, since people receive the benefits of the rule at each system they use.

Uncertainty in Baseline Input Data EPA recognizes that there is uncertainty in the data sources used to define the system inventory for the LT2ESWTR. The uncertainty is not quantified in this EA; however, a qualitative discussion of the identified uncertainties is provided below.

As noted above, SDWIS and the 1995 CWSS are the primary sources of system inventory data. SDWIS is EPA's primary drinking water database. SDWIS stores State reported information on each water system, including name, ID number, number of people served, type of system ( year round or seasonal), and source of water ( ground water or surface water), along with monitoring and violation information. In 1998, EPA began a major effort to assess the quality of its drinking water data in SDWIS. The results, published in Data Reliability Analysis of the EPA SDWIS/ FED, found that the quality of the required inventory data was high ( USEPA 2000d). Thus, EPA believes that uncertainty in the system inventory data from SDWIS with respect to numbers of systems, source information, and size classification is low.

The 1995 CWSS was developed to gather data on water systems in the United States. Of the 3,681 systems statistically selected to receive the main survey questionnaire, 1,980 responded. These responses were weighted to maintain statistical representation of the total universe of CWSs ( USEPA 1997c).

The 1995 CWSS was the primary data source used to develop estimates of the number of treatment plants per system and of average and design flows as a function of population served ( presented in section 4.3.3). Because the CWSS is a survey of CWSs, estimates based on the data will contain uncertainty because of sampling errors. To help define these uncertainties, the CWSS report provides the confidence intervals on certain parameters. The report does not, however, contain data for the percentage of systems disinfecting, the percentage of SDWIS surface water systems providing ground water, and the number of treatment plants per system that are used in this EA. The confidence intervals for similar parameters can provide some information on uncertainty. For example, an analysis of the percent of ground water systems with no treatment ( which uses similar data to the analysis of percent disinfecting), yielded 95 percent confidence intervals of less than +/ 10 percent. Economic Analysis for the LT2ESWTR Proposal June 2003 4 18 4.3.3 Water Treatment Plant Design and Average Daily Flows Treatment technology costs are based on the volume of water treated per day. The cost analysis described in Chapter 6 uses two types of treatment plant flow:

° Design flow   the maximum capacity at which the plant was intended to operate, expressed in millions of gallons per day ( mgd).

° Average daily flow   the flow produced by a treatment plant in 1 year, averaged over 365 days, expressed in mgd.

Design flows are used to estimate the capital costs of the technology that will be installed to meet the requirements of the LT2ESWTR. Average daily flows are used to estimate the annual cost of ongoing operations and maintenance ( O& M). Average daily flows give a better indication of chemical usage and operational costs than do design flows. The flows presented in this section are used to estimate costs for both unfiltered and filtered plants.

To derive flow information for different sized plants, EPA developed the following regression equations relating design and average daily flow to population served for surface water systems using data from the 1995 CWSS:

Design Flow ( MGD) = 0.36971 X0.97757 / 1,000 Average Daily Flow ( MGD) = 0.10540 X1.02058/ 1,000 Where X = mean population served.

The derivation of these equations is presented in detail in the Model Systems Report ( USEPA 2000b) and summarized in the Baseline Handbook ( USEPA 2001c). EPA used these equations to estimate mean flows per system, based on the population per system shown in column A of Exhibit 4.4, and then divided by the number of plants per system to determine the flow per plant.

Exhibit 4.4a summarizes these populations and design and average daily flows for filtered plants at CWSs. Exhibit 4.4b shows the flows and populations for unfiltered plants ( all unfiltered plants are CWSs except for one TNCWS, which was grouped with the CWSs). EPA recognizes that there is a range of design and average daily flows within each category, but believes that using mean flow values is adequate for the cost and benefit analysis in this EA.

An equivalent regression analysis relating NCWS flows to population served was not done in the Model Systems Report. Therefore, average daily and design flows for NCWSs were estimated using mean population served per plant for NCWSs substituted into the CWS regression equations. Flows are summarized in Exhibit 4.4a for filtered NTNCWSs and TNCWSs. Plant flows for filtered CWSs, NTNCWSs, and TNCWSs differ from each other because of the difference in mean population per plant for each of the three categories, and the volume of water delivered to commercial and industrial customers. EPA recognizes that using CWS equations to determine NCWS flows may overestimate flows and, therefore, benefits and costs. This overestimation is addressed as part of the uncertainties summarized in section 4.8. Economic Analysis for the LT2ESWTR Proposal June 2003 4 19 For this rulemaking, EPA considered estimating flows for NCWSs according to service category ( e. g., schools, restaurants, hotels, industry) instead of size as has been done in some other rules. EPA decided against an approach based on service category for the following reasons:

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

° More critical to the LT2ESWTR, the prediction of technology selection in Chapter 6 is a function of population served, and does not directly apply to service categories that may include a wide range of water system sizes ( e. g., schools can be very small local buildings or large universities). Economic Analysis for the LT2ESWTR Proposal June 2003 4 20 System Size Population per System Plants per System Average Daily Flow ( MGD) per Plant Design Flow ( MGD) per Plant A B ( 0.10540* A1.02058)/( B* 1,000) ( 0.36971* A0.97757)/( B* 1,000)

< 100 59 1.0 0.01 0.02 101 500 280 1.1 0.03 0.08 501 1,000 769 1.0 0.09 0.25 1,001 3,300 2,030 1.0 0.25 0.63 3,301 10,000 5,961 1.2 0.63 1.51 10,001 50,000 23,403 1.1 2.76 6.28 50,001 100,000 69,010 1.8 5.08 11.04 100,001 1 Million 277,629 1.6 23.67 48.43 > 1Million 2,158,693 2.8 109.71 205.50 < 100 52 1.0 0.01 0.02 101 500 272 1.0 0.03 0.09 501 1,000 719 1.0 0.09 0.23 1,001 3,300 1,749 1.0 0.21 0.55 3,301 10,000 5,318 1.0 0.67 1.62 10,001 50,000 26,772 1.0 3.48 7.87 50,001 100,000 93,204 1.0 12.43 26.66 100,001 1 Million 169,846 1.0 22.94 47.93 > 1Million 0 1.0

< 100 44 1.0 0.00 0.01 101 500 241 1.0 0.03 0.08 501 1,000 758 1.0 0.09 0.24 1,001 3,300 2,321 1.0 0.29 0.72 3,301 10,000 5,703 1.0 0.72 1.74 10,001 50,000 20,346 1.0 2.63 6.02 50,001 100,000 59,747 1.0 7.90 17.26 100,001 1 Million 0 1.0   > 1Million 0 1.0   CWSs NTNCWSs TNCWSs Exhibit 4.4a Filtered Plant Average Daily and Design Flow by System Size Source: [ A] Population served for each size category ( Exhibit 4.11, Column I) divided by number of systems in each category ( Exhibit 4.11 Column G), based on the treatment baseline. [ B], [ C] USEPA 2000b. Economic Analysis for the LT2ESWTR Proposal June 2003 4 21 System Size Population per System Plants per System Average Daily Flow ( MGD) per Plant Design Flow ( MGD) per Plant A B C = ( 0.10540* A1.02058)/( B* 1,000) D = ( 0.36971* A0.97757)/( B* 1,000)

< 100 75 1.0 0.01 0.03 101 500 269 1.1 0.03 0.09 501 1,000 774 1.0 0.09 0.25 1,001 3,300 1,592 1.0 0.20 0.50 3,301 10,000 6,881 1.2 0.87 2.09 10,001 50,000 22,097 1.1 2.86 6.53 50,001 100,000 69,138 1.8 9.17 19.91 100,001 1 Million 209,787 1.6 28.45 58.92 > 1Million 3,386,848 2.8 486.43 893.70 CWS Exhibit 4.4b Unfiltered Plant Average Daily and Design Flow by System Size Source: [ A] Population served for each size category ( Exhibit 4.5, Column D) divided by number of systems in each category ( Exhibit 4.5, Column A), based on the treatment baseline. [ B], [ C] USEPA 2000b.

4.4 Baseline for Unfiltered Plants ( Pre LT2ESWTR)

Unfiltered plants are subject to different LT2ESWTR provisions than filtered plants ( e. g., all unfiltered plants must achieve some inactivation of Cryptosporidium; binning only determines the degree of inactivation required). Therefore, the baselines for unfiltered and filtered plants must be developed separately. The following sections summarize the existing treatment; system, plant, and population data; source water Cryptosporidium occurrence; and predicted finished water Cryptosporidium occurrence for unfiltered plants.

4.4.1 Treatment Characterization for Unfiltered Plants EPA estimates that a number of plants that currently are not required to filter will need to install an advanced disinfectant to meet the 2 log or 3 log removal requirement for LT2ESWTR. Some treatment data collected over time by EPA regional offices are available on these plants. Most of these plants are not predicted to add treatment to meet the Stage 1 or Stage 2 DBPR requirements, because they generally have low turbidity source water and, thus, low levels of precursors for DBP formation. Unfiltered plants are not subject to IESWTR or LT1ESWTR filtration requirements. Thus, EPA used the existing regional data to develop the treatment characterization for these unfiltered plants.

A review of these data reveals that unfiltered plants use a variety of treatments to disinfect or control other water quality problems. Plants serving 3,300 or fewer people generally use chlorine as the primary disinfectant, although at least one plant serving 501 to 1,000 people uses ozone. Some of these plants may also employ corrosion control, as well as manganese and iron removal. Plants serving 3,301 or more people mainly use chlorination for disinfection, and at least one plant serving 3,301 to 10,000 people uses chlorine dioxide. Other treatment processes used in medium and large unfiltered plants include corrosion control, softening, fluoridation, DBP control, taste and odor control, as well as organics and iron removal. Some plants avoiding filtration may have already installed treatment equivalent to filtration, and some systems may filter water from some but not all of their sources. Economic Analysis for the LT2ESWTR Proposal June 2003 4 22 The unfiltered plant database contains treatment data for less than 50 percent of unfiltered plants. The ICR database also was examined. Only one unfiltered plant participating in the ICR was found to use ozone ( USEPA 2000j), and its dose levels were not high enough to meet the LT2ESWTR requirements. No ICR unfiltered plants used chlorine dioxide. Because of the high doses of ozone or chlorine dioxide required to inactivate Cryptosporidium, it appears unlikely that any unfiltered plants are currently meeting the requirements of the LT2ESWTR. Therefore, for this EA, EPA estimates that all unfiltered systems will have to add advanced disinfection to achieve the 2 log Cryptosporidium inactivation minimum requirement.

4.4.2 Number of Unfiltered Systems, Plants, and Population Served Systems that operate unfiltered plants can be placed in one of two categories:

° Systems with plants that are now unfiltered but are required to filter under the 1989 Surface Water Treatment Rule ( SWTR)

° Systems that meet the filtration avoidance criteria of the SWTR Three of the 12 unfiltered plants in the ICR are currently unfiltered, but will be changing to filtration in the future. These systems are subject to requirements of the proposed LT2ESWTR for filtered systems because they do not meet avoidance criteria under the SWTR and are, therefore, included in the baseline for filtered systems ( section 4.5). In addition, a fourth plant, the Massachusetts Water Resources Authority, was omitted from the unfiltered baseline ( and not moved to filtered baseline) because of the uncertainties regarding its filtration avoidance status due to ongoing litigation at the time of calculation. The purchased plants associated with this plant were also removed from the baseline.

The baseline for unfiltered plants, therefore, includes only systems that meet the SWTR avoidance criteria. The criteria include the following:

° Disinfect to achieve 3 and 4 log reduction of Giardia and viruses, respectively ° Have watershed control measures in place ° Are below source water limits on fecal coliform occurrence ( 20/ 100 ml) and turbidity ( 5 nephelometric turbidity units ( NTU))

Exhibit 4.5 presents the baseline for unfiltered systems and plants that is used for estimating costs and benefits in this EA. Data on population and the number of systems are derived from SDWIS and from the ICR for large systems ( USEPA 2000f, 2000j). The number of plants is calculated using the plants per system ratios given earlier. There is only one TNCWS unfiltered system, and there are no unfiltered NTNCWSs. Maintaining a separate category in subsequent analyses for one system was unlikely to add precision, so this TNCWS was grouped with CWSs in all subsequent analyses. These baseline values, in conjunction with flows presented in section 4.3.3, are used to estimate costs for unfiltered systems ( see Chapter 5). Economic Analysis for the LT2ESWTR Proposal June 2003 4 23 System Size ( Population Served) Number of Systems Plants per System Number of Plants Population Served Percent of Total Population Served by Surface and GWUDI CWSs A B C= A* B D E < 100 1 1.0 1 75 0.00% 101 500 5 1.1 6 1,343 0.00% 501 1,000 7 1.0 7 5,418 0.00% 1,001 3,300 14 1.0 14 22,281 0.01% 3,301 10,000 22 1.2 26 150,000 0.09% 10,001 50,000 16 1.1 18 353,552 0.20% 50,001 100,000 3 1.8 5 207,415 0.12% 100,001 1 Million 4 1.6 6 839,148 0.48% > 1Million 3 2.8 8 10,160,545 5.85%

Total 75 91 11,739,777 6.76% Exhibit 4.5 Treatment Baseline for Unfiltered Plants By System Size Notes: All systems are CWSs except one TNCWS, which was grouped with the CWSs for analysis.

Sources: [ A] SDWIS ( USEPA 2000f) data adjusted by EPA to exclude systems that do not meet filtration avoidance criteria. [ B] Exhibit 4.3, Column K. [ D] SDWIS data for the systems in Column A ( 2000h), modified to include populations added in the linking process ( see Exhibit 4.1). [ E] Population served ( Column D) divided by total population served by surface water and GWUDI CWSs ( Exhibit 4.3a, bottom of Column M).

4.4.3 Source Water Cryptosporidium Occurrence for Unfiltered Plants ICR data from 12 plants that are classified as unfiltered surface water are used to characterize Cryptosporidium occurrence ( USEPA 2000j). The results of the ICR Cryptosporidium monitoring were evaluated using the model described in section 4.2.2. Because a few of these plants do not meet the filtration avoidance criteria, a sensitivity analysis was performed to see if results would be significantly different if they were excluded. The occurrence distributions were nearly identical; therefore, the original results were used in the analysis.

Observed Cryptosporidium Occurrence Observed results for ICR unfiltered plants are shown in Exhibit 4.6. A comparison of these results to those for filtered plants in the ICR ( Exhibit 4.12) shows both a lower rate of positive samples and a lower average concentration of Cryptosporidium for unfiltered plants. While too few unfiltered plants were sampled in the ICRSS to carry out a meaningful analysis, the few that were sampled also were on the low side of the ICR unfiltered occurrence distribution. Economic Analysis for the LT2ESWTR Proposal June 2003 4 24 Exhibit 4.6 Observed ICR Source Water Total Oocyst Occurrence for Unfiltered Plants Total Number of Plants Number of Plants with at Least One Positive Sample ( Percent) Observed Plant Mean Data ( oocysts/ L)

Mean Median 90th Percentile 12 7 ( 58%) 0.002 0.001 0.005 Notes: Total Cryptosporidium includes non empty and empty oocysts. Non empty includes oocysts with internal structures and with amorphous structures. For each plant, all monthly observations were averaged over the sampling period ( 18 months) to produce plant mean data. The mean, median, and 90th percentile shown summarize plant mean Cryptosporidium for all unfiltered plants.

Source: USEPA 2000j.

Modeled Cryptosporidium Occurrence The data from these 12 plants are used to fit the unfiltered plants occurrence model, which serves as input to the EA. The modeling was carried out using the approach outlined in section 4.2.2. As explained in that section, the modeling produces a collection of plausible occurrence distributions. Exhibit 4.7 summarizes this collection of distributions. The solid center curve represents the mean distribution across the collection, and the dotted lines give a 90 percent confidence bound for the unfiltered occurrence distribution.

4.4.4 Finished Water Cryptosporidium Occurrence for Unfiltered Plants As mentioned in section 4.4.1, because unfiltered plants do not have advanced treatment technologies in place that are capable of meeting 2.0 log Cryptosporidium removal or inactivation, they are not expected to remove or inactivate any Cryptosporidium from their source water. Although most unfiltered systems chlorinate their water ( a few may use other disinfectants besides chlorine), chlorination is ineffective for inactivation of Cryptosporidium. Therefore, the finished water occurrence of Cryptosporidium for unfiltered plants is assumed to be the same as the source water occurrence shown in Exhibit 4.7.

This occurrence distribution is derived from the ICR data set ( USEPA 2000j). Although there were unfiltered plants in the ICRSS data sets, they were too few to use successfully in the model to derive national distributions. Thus, there are no explicit estimated occurrence distributions for the unfiltered ICRSS data sets. In order to develop national benefit estimates for the ICRSS data sets, EPA estimated unfiltered results based on the ratios between ICR and ICRSSM, and ICR and ICRSSL, results for filtered plants. Thus, although no explicit occurrence distributions were estimated for the ICRSS data sets, the likely differences in Cryptosporidium occurrence between the ICR and ICRSS unfiltered data sets are reflected in later analyses. Economic Analysis for the LT2ESWTR Proposal June 2003 4 25 1e 005 0.0001 0.001 0.01 0.1 1 10 Plant Mean Cryptosporidium Concentration ( Total oocysts/ L) 0% 20% 40% 60% 80% 100%

Cumulative Percentage of Plants 5th/ 95th % tile Median Example:

Modeled Percent of Plants with Average Source Water Cryptosporidium Concentration Below 0.01 oocysts/ L:

5th % tile Bound = 74%

Median Estimate = 59%

95 % tile Bound = 39% This graph shows modeled variability and uncertainty in source water Cryptosporidium occurrence for unfiltered systems. It summarizes 1,000 plausible curves used in the Economic Analysis.

Ninety percent of the modeled curve values fall within the dashed line uncertainty bounds. The heavy center line reflects the central tendency across all 1,000 curves.

Among the 1,000 modeled curves, any single curve describes variability in occurrence across all filtered systems. Steeper curves indicate less variability from system to system.

Variability Uncertainty Exhibit 4.7 Modeled Source Water Cryptosporidium Occurrence ICR Data for Unfiltered Systems Source: USEPA 2003c.

Under the LT2ESWTR, all unfiltered systems must provide treatment for Cryptosporidium. Systems with Cryptosporidium concentrations < 0.01 oocysts/ L must provide 2 log treatment, while systems with concentrations > 0.01 oocysts/ L must provide 3 log treatment. The predicted binning for unfiltered systems, derived from the occurrence distribution in Exhibit 4.7, is shown in Exhibit 4.8. Percentages represent averages over 250 simulated assignments of unfiltered systems to 2 or 3 log treatment bins. In the case of systems serving fewer than 100,000 people, the simulated binning was based on 1,000 values drawn from the modeled unfiltered occurrence distribution. For the small number of systems serving 100,000 or more, the binning simulation drew Cryptosporidium concentrations directly from ICR survey results. In both cases, recovery was simulated to match recovery rates expected in Cryptosporidium monitoring employing EPA Methods 1622/ 1623. Economic Analysis for the LT2ESWTR Proposal June 2003 4 26 Exhibit 4.8 Predicted System Binning for Unfiltered Systems, Based on Central Tendency of Cryptosporidium Occurrence System Size ( Population Served) 2.0 Log Treatment 3.0 Log Treatment < 100,000 79.2% 20.8%

> 100,000 81.2% 18.8%

Source: Monte Carlo simulation. For systems serving less than 100,000, percentages based on modeled occurrence distribution and lab method recovery rate distribution. For systems serving 100,000 or more, percentages are based on actual ICR results and modeled lab method recovery distribution.

The percentages in Exhibit 4.8 were used to determine treatment costs incurred by each unfiltered system.

4.5 Baselines for Filtered Plants ( Pre LT2ESWTR)

This section first presents the Pre LT2ESWTR treatment characterization for filtered plants. It includes estimates of number of systems, plants, and population served for filtered systems. It also contains source water and finished water Cryptosporidium occurrence and predicted bin classification.

4.5.1 Treatment Characterization for Filtered Plants The treatment characterization for the LT2ESWTR must take into account projected treatment modifications made to comply with other existing and soon to be promulgated rules. This adjustment allows treatment changes attributable only to LT2ESWTR requirements to be isolated for benefit and cost analysis. In addition, systems with certain treatments already in place prior to the implementation of the LT2ESWTR may qualify for Pre LT2ESWTR credit, meaning they can get credit towards the log treatment requirements of the LT2ESWTR. The rest of this subsection explains how treatment changes attributable to the IESWTR, LT1ESWTR, Stage 1 DBPR, and Stage 2 DBPR have been accounted for in developing the LT2ESWTR baseline. Although other rules have been promulgated recently or are scheduled to be promulgated before this rule, they either do not affect surface water supplies or do not involve installation of treatment that is expected to significantly remove or inactivate Cryptosporidium. Therefore, such rules were not considered further.

Post IESWTR and LT1ESWTR Treatment Characterization For this EA, it is assumed that all medium and large systems ( those serving at least 10,000 people) using conventional or direct filtration meet the combined effluent turbidity limit of 0.3 NTU 95 percent of the time, as required under IESWTR and LT1ESWTR. EPA assumed as part of the economic analyses for the IESWTR and LT1ESWTR that systems would achieve less than 0.2 NTU 95 percent of the time in order to operate within a margin of safety. Lower finished water turbidity, with a combined effluent turbidity level of 0.15 NTU, is one of the approaches in the microbial toolbox ( described in detail in Chapter 6) available to systems for achieving an additional 0.5 log removal credit for Cryptosporidium. There are several other toolbox technologies that plants may already have installed that would gain them Pre LT2ESWTR credit of 0.5 log toward Cryptosporidium removal requirements. These include secondary filters and two stage softening. Economic Analysis for the LT2ESWTR Proposal June 2003 4 27 To determine the number of plants that might obtain credit for already using some of these toolbox technologies, several data sources were reviewed. Data from the ICR, the American Water Works Association ( AWWA) ( AWWA 2000), a survey of small systems conducted by the National Rural Water Association ( NRWA) ( USEPA 2001b), and the 1995 CWSS were reviewed to determine the percentage of plants that have these technologies in such a way as to qualify for removal credits. Exhibit 4.9 provides an estimate of the percentage of systems that would be capable of obtaining each of these Pre LT2ESWTR half log removal credits with existing equipment and operations or with equipment and operations predicted to be installed and in use prior to promulgation of this rule. Details on the derivation of these numbers are provided in Appendix A. Some of these plants may be able to obtain credit for having multiple technologies. The percentage of plants estimated to get 1.0 log removal credit is presented in Exhibit 4.9 as well. The estimates of 1.0 log credits are derived from the individual credits for plants receiving the various 0.5 log removal credits, assuming complete independence of the chance of having any one of the 0.5 log credits.

Exhibit 4.9 Percentage of Plants with Pre LT2ESWTR Cryptosporidium Log Reduction Credits for Existing Technologies System Size ( Population Served) Plants with 0.15 NTU Finished Water Turbidity ( 0.5 Log) Plants with Multiple Settling Basins ( Conventional and Softening) ( 0.5 Log) Plants with Multiple Filters ( 0.5 Log) Plants with 0.5 Log Total Credit Plants with 1.0 Log Total Credit A B C D= A + B + C  E E=( A* B)+( A* C)+( B* C)

Small ( # 10k) 34% 3% 0% 36% 1%

Medium ( 10k  100k) 46% 5% 4% 51% 4%

Large (> 100k) 46% 5% 7% 52% 6%

Source: Appendix A, Exhibit A. 7.

Post Stage 2 DBPR Treatment Characterization Under the LT2ESWTR, EPA exempts plants from monitoring, bin classification, and associated treatment requirements if the plants are achieving 5.5 log reduction. EPA estimates that very few plants met this requirement at the time of the ICR. A proportion of surface water plants, however, are expected to implement advanced technologies to meet the DBP requirements of the Stage 1 DBPR and Stage 2 DBPR. Several of these technologies will provide additional logs of Cryptosporidium removal or Economic Analysis for the LT2ESWTR Proposal June 2003 4 28 inactivation in addition to reducing DBPs. Advanced technologies that will provide both DBP and Cryptosporidium control include the following:

° Chlorine dioxide ° UV ° Ozone ° Microfiltration/ ultrafiltration ( MF/ UF)

SWAT ( see section 4.2.3) used source water and treatment data collected through the ICR to predict the percentage of large systems that would have to add treatment to meet Stage 2 DBPR requirements. SWAT was also able to predict plants' technology selection based on source water characteristics, treatment plant configurations, and other factors. SWAT results were extrapolated to medium and small systems using best professional judgment. EPA's Economic Analysis for the Stage 2 Disinfectants and Disinfection Byproducts Rule ( USEPA 2003d) provides a detailed description of Stage 2 DBPR requirements, how SWAT modeled ICR systems, and how SWAT was used to develop the Stage 2 DBPR compliance forecast for plants of all sizes. Exhibit 4.10 presents predictions from SWAT of technology use following Stage 2 DBPR implementation for the four technologies listed above.

Exhibit 4.10 Predicted Percentage of Plants Using Advanced Technologies Following Implementation of the Stage 2 DBPR System Size ( Population Served) Chlorine Dioxide UV Ozone MF/ UF # 100 0 % 3.1 % 0 % 18.6 %

101 500 2.4 % 0.4 % 11.9 % 9.9 %

501 1,000 2.4 % 0.4 % 11.9 % 9.9 %

1,001 3,300 5.1 % 0.5 % 10.1 % 5.4 %

3,301 10,000 5.1 % 0.5 % 10.1 % 5.4 %

10,001 50,000 7.0 % 0.7 % 12.8 % 1.8 %

50,001 100,000 7.0 % 0.7 % 12.8 % 1.8 %

100,001 1 Million 7.0 % 0.7 % 12.8 % 1.8 %

> 1 Million 7.0 % 0.7 % 12.8 % 1.8 %

Source: Economic Analysis for the Stage 2 Disinfectants/ Disinfection Byproducts Rule ( USEPA 2003d), Exhibit 6.15a.

EPA did not adjust the baseline for plants using chlorine dioxide and ozone. Chlorine dioxide and ozone doses required for Cryptosporidium inactivation are higher than SWTR requirements for inactivation of Giardia and viruses. To evaluate the use of these technologies at doses that could inactivate Cryptosporidium, both incremental costs for the increased dose and incremental benefits would need to be evaluated. However, available studies do not allow the quantitative evaluation of inactivation based on dose changes. In the absence of usable data, this analysis assumes that plants Economic Analysis for the LT2ESWTR Proposal June 2003 4 29 predicted to have installed chlorine dioxide and ozone would not meet the monitoring and treatment exemption requirements for the LT2ESWTR. The cost model for this EA assumes that these plants would install the entire technology again for the LT2ESWTR rather than simply increase the dose, resulting in an overestimate of costs. Uncertainties and biases affecting costs are summarized in section 4.8.

Currently, very few plants use UV. This is partly because of the need for higher doses required to inactivate viruses ( compared to that needed for protozoans) and the need to maintain a disinfectant residual. These factors result in only about half a percent of plants nationwide using UV. Since this is such a small number of plants, the LT2ESWTR baseline was not adjusted to reflect plants that already have installed UV.

MF/ UF is the only treatment process assumed to achieve 5.5 log removal. The LT2ESWTR baseline is adjusted in several ways to account for the percentage of plants that may already be using MF/ UF. CWS and NTNCWS plants ( both in small and large systems) predicted to have installed MF/ UF prior to the Stage 2 DBPR are removed entirely from the monitoring and treatment baselines. Plants achieving greater than 5.5 log treatment of Cryptosporidium are meeting the highest level of treatment that could be required based on source water monitoring. These plants would not have to add treatment if they did monitor and were assigned to the most stringent treatment bin. EPA anticipates that some plants will install MF/ UF to meet the requirements of the Stage 2 DBPR. These plants are included in the rule implementation and initial monitoring baselines ( because they are still subject to the LT2ESWTR and because the Stage 2 DBPR schedule is such that they will not have installed MF/ UF before initial Cryptosporidium monitoring begins), but not the future monitoring and treatment baselines. TNCWSs are unlikely to have installed advanced technologies, partly because TNCWSs are not addressed by the Stage 1 or Stage 2 DBPRs.

Treatment plants with different types of filtration systems have been regulated differently under IESWTR and LT1ESWTR and, to a small extent, under LT2ESWTR. The types of filtration included in this analysis are as follows:

° Conventional filtration includes coagulation, flocculation, and sedimentation of particles, followed by granular media filtration.

° Direct filtration involves coagulation and flocculation followed by rapid sand filtration, but no sedimentation basin.

° Slow sand filtration works at very low filtration rates without the use of coagulant in pretreatment.

° Diatomaceous earth filtration works at low filtration rates with the addition of diatomaceous earth.

° Alternative filtration systems include membrane, bag, and cartridge filters.

Plants filtering by slow sand and diatomaceous earth are not required to meet new combined filter effluent provisions under the IESWTR or LT1ESWTR because they can generally achieve higher Cryptosporidium removals at higher effluent turbidity levels. These treatment plants are still subject to additional treatment requirements of the LT2ESWTR based on the results of source water monitoring and, thus, are included in the filtered system baseline. Direct filtration plants also are included in this Economic Analysis for the LT2ESWTR Proposal June 2003 4 30 baseline; however, they have slightly different Cryptosporidium reduction requirements under the LT2ESWTR. The impacts of these additional requirements for direct filtration plants are addressed in Chapter 6.

4.5.2 Number of Filtered Plants and Population Served Exhibit 4.11 presents the baselines for filtered plants. As mentioned previously, plants with MF/ UF are assumed to achieve 5.5 log treatment and, therefore, are not subject to treatment and, in some cases, monitoring requirements. Note that the baseline numbers of plants used to estimate benefits and treatment costs for filtered plants ( columns G and H) include nonpurchased plants and purchased plants that could not be linked with the systems from which they purchased their water. To capture the total flow that must be treated and the population affected by the LT2ESWTR, EPA includes these purchased systems in the analysis as though they are treating water themselves. Effectively, this assumption places more plants in smaller size categories rather than fewer plants in larger size categories, resulting in lower flows and populations per plant. This bias was minimized, however, by the analysis described in section 4.3.1, linking most purchased systems to their sellers. In the process of linking purchased systems to sellers, the population served by the purchased systems is added to that of the sellers. This can result in a population change large enough to bump the seller up to a higher size category. Uncertainties associated with categorization of some purchased systems as retail are summarized in section 4.8.

Source water monitoring costs are not based on flow or population, but are calculated on a perplant basis based on the number of plants in column E of Exhibit 4.11. In estimating monitoring costs, EPA assumed that purchased plants do not treat source water directly and, thus, would not directly incur monitoring costs. Further evaluation of the baseline for filtered plant monitoring is provided in Appendix D. Economic Analysis for the LT2ESWTR Proposal June 2003 4 31 System Size Number of Filtered Systems Number of Filtered Plants Number of Filtered Systems Number of Filtered Plants Number of Filtered Systems Number of Filtered Plants Population Served A B C D= A*( 1 C) E= B*( 1 C) F G H I CWSs < 100 529 529 3.6% 510 510 18.64% 396 396 23,533 101 500 752 827 3.6% 725 797 9.93% 719 791 201,247 501 1,000 449 449 3.6% 433 433 9.93% 407 407 313,169 1,001 3,300 1,073 1,073 3.6% 1,034 1,034 5.44% 995 995 2,019,557 3,301 10,000 995 1,194 3.6% 959 1,151 5.44% 982 1,179 5,857,029 10,001 50,000 943 1,037 0.4% 940 1,034 1.83% 1,064 1,171 24,904,234 50,001 100,000 190 342 0.4% 189 341 1.83% 239 429 16,462,361 100,001 1 Million 201 322 0.4% 200 320 1.83% 247 396 68,681,089 > 1 Million 10 28 0.4% 10 28 1.83% 19 52 40,263,981 Total 5,142 5,801 5,001 5,648 5,068 5,816 158,726,200 NTNCWSs < 100 202 202 3.6% 195 195 18.64% 242 242 12,463 101 500 236 236 3.6% 228 228 9.93% 269 269 73,307 501 1,000 84 84 3.6% 81 81 9.93% 97 97 69,919 1,001 3,300 55 55 3.6% 53 53 5.44% 67 67 117,421 3,301 10,000 13 13 3.6% 13 13 5.44% 22 22 115,651 10,001 50,000 3 3 0.4% 3 3 1.83% 8 8 210,252 50,001 100,000 0 0 0.4% 0 0 1.83% 1 1 91,497 100,001 1 Million 0 0 0.4% 0 0 1.83% 1 1 166,735 > 1 Million 0 0 0.4% 0 0 1.83% 0 0 0 Total 593 593 572 572 707 707 857,244 TNCWSs < 100 731 731 0.0% 731 731 0.00% 1,234 1,234 53,903 101 500 380 380 0.0% 380 380 0.00% 486 486 117,344 501 1,000 57 57 0.0% 57 57 0.00% 81 81 61,426 1,001 3,300 53 53 0.0% 53 53 0.00% 67 67 155,500 3,301 10,000 25 25 0.0% 25 25 0.00% 31 31 176,789 10,001 50,000 8 8 0.0% 8 8 0.00% 10 10 203,456 50,001 100,000 3 3 0.0% 3 3 0.00% 3 3 179,240 100,001 1 Million 0 0 0.0% 0 0 0.00% 0 0 0 > 1 Million 0 0 0.0% 0 0 0.00% 0 0 0 Total 1,256 1,256 1,256 1,256 1,912 1,912 947,658 Treatment Baseline Percent Avoiding Treatment Requirements Implementation Baseline Percent Avoiding Monitoring and Treatment Requirements Monitoring Baseline Exhibit 4.11 Treatment Baseline for Filtered Plants Notes: [ A] Number of filtered systems = nonpurchased unlinked systems ( Exhibit 4.3, Columns C+ D)  unfiltered systems ( Exhibit 4.5, Column A). [ B] Number of filtered plants = Column A * Exhibit 4.3, Column K. [ C] Percentage of plants predicted to have installed MF/ UF to comply with Stage 1 DBPR, from SWAT technology results for Pre Stage 2 DBPR ( USEPA 2003d). [ F] Percentage of plants predicted to have installed MF/ UF to comply with Stage 2 DBPR, from SWAT technology results for Post Stage 2 DBPR ( USEPA 2003d). [ G] Number of filtered systems subject to treatment = linked nonpurchased and purchased systems ( Exhibit 4.3, Column J) * ( 1 Column F). This column includes nonpurchased systems that conducted monitoring, but excludes systems that monitored but that are predicted to install MF/ UF to comply with the Stage 2 DBPR. Purchased systems are included so their populations can be used to determine benefits of treatment installed under the LT2ESWTR ( customers of purchased systems will incur benefits of treatment installed at the associated nonpurchased system). [ H] Number of filtered plants = Column G * Exhibit 4.3, Column K. [ I] Population = ( Pop. served by linked nonpurchased and purchased systems ( Exhibit 4.3, Column M)  pop. served by unfiltered systems ( Exhibit 4.5, Column A)) * ( 1 F). Economic Analysis for the LT2ESWTR Proposal June 2003 4 32 4.5.3 Source Water Cryptosporidium Occurrence for Filtered Plants For filtered plants, the results of plant specific source water monitoring for Cryptosporidium will dictate the additional treatment required to meet provisions of the LT2ESWTR. This subsection summarizes observed Cryptosporidium data and discusses statistical modeling of the observed data used to estimate underlying " true" Cryptosporidium concentration distributions. The " true" distributions are then used to project bin classifications for filtered systems.