ppendices ppendices to the Economic Analysis for the to the Economic Analysis for the ong ong Term 2 Enhanced Surface Water Treatment Rule Term 2 Enhanced Surface Water Treatment Rule A L PREPARED FOR:

U. S. ENVIRONMENTAL PROTECTION AGENCY Office of Ground Water and Drinking Water PREPARED BY:

THE CADMUS GROUP, INC. 1901 North Fort Myer Drive Suite 900 Arlington, VA 22209 US EPA CONTRACT: 68 C 02 026 Work Assignment: 1 21 June 2003 Proposal Draft LT2ESWTR EA Appendices Economic Analysis for the LT2ESWTR Proposal June 2003 Appendices Appendix A: Pre LT2 Removal Credit Appendix B: Predicted Plant Binning Appendix C: Benefits Appendix D: National Costs for Rule Implementation and Monitoring Appendix E: Unit Costs for Technologies Considered in the LT2ESWTR Appendix F: Technology Selection Forecast Methodology Appendix G: Technology Selection Results Appendix H: Treatment Costs for Filtered Plants Appendix I: Unit Costs for Uncovered Finished Water Reservoirs Appendix J: Estimation of Household Costs Appendix K: Additional Information on the Approach For Valuing Time Losses Appendix L: Calculations Supporting The Cost of Illness ( COI) Analysis Appendix M: Small Community Surface Water and GWUDI Systems by State Appendix N: Dose Response Analysis Revised Following SAB Review Appendix O: Assigning LT2ESWTR Costs and Benefits Appendix P: Sensitivity Analyses for Cost of Illness Values Appendix Q: Treatment Costs for Filtered and Unfiltered Plants Appendix R: Sensitivity Analysis for AIDS Related Mortality Rate Appendix S: Analysis of Individual Risk by Initial Bin Appendix T: Risk Assessment Model   Program and Data Files Appendix U: Cost Models Appendix V: Total Annualized Costs for Rule Alternatives Appendix W: Screening Analysis Economic Analysis for the LT2ESWTR Proposal A 1 June 2003 Appendix A Pre LT2ESWTR Removal Credit A. 1 Introduction To appropriately assess the costs and benefits of the LT2ESWTR, it is necessary to estimate how many plants will be required to provide treatment as a result of the rule and the level of treatment they must provide. One essential factor in performing this assessment is determining the number of plants that may be able to get Cryptosporidium removal credit for treatment technologies already in place. This appendix discusses the various technologies that could earn removal credit and will estimate the percentage of plants by size category that have or will have such technologies in place prior to promulgation of the LT2ESWTR.

Specifically, this appendix addresses the following toolbox technologies:

° Combined filter performance ° Softening plants with multiple settling basins ° Conventional plants with multiple settling basins ° Multiple filters Data sources and the population size categories are discussed first, followed by an analysis of each treatment configuration. The appendix concludes with a summary of log removal credits for existing treatment.

A. 2 Data Sources A number of information sources were reviewed to determine the performance and layouts of surface water treatment plants. A summary of each source, the information it contains, and its advantages and disadvantages follow.

A. 2.1 The Information Collection Rule ( ICR), USEPA 1996 The ICR was a survey conducted by EPA from 1997 through 1998. It consists of 18 months of data collected from all large systems serving over 100,000 people. Information in the survey included water quality parameters, such as turbidity and pH, along with process units in the plant and their sequences. The ICR survey is the most comprehensive database available for large systems.

A. 2.2 Partnership for Safe Drinking Water Data, 1999 Analyses in this appendix draw from the Year 2000 annual report for the Partnership for Safe Drinking Water ( the Partnership), as well as additional turbidity data gathered and analyzed by EPA. The Partnership is a cooperative effort among EPA and several professional water associations. Plants in the Economic Analysis for the LT2ESWTR Proposal A 2 June 2003 Partnership undergo voluntary audit programs in an effort to improve their plant performance and achieve better water quality. According to the annual report, about 211 of the total Partnership plants were large plants serving more than 100,000 people, 81 were medium plants serving between 10,000 and 100,000 people, and 33 were small plants serving less than 10,000 people. All of these plants were surface water plants. Because of this representation, the survey is best suited for use with large systems. It is less appropriate to use for medium systems, although with a sample size of 81 medium plants, it is probably a fair representation of medium plants. It is probably inadequate to represent small systems as its small plants are less than half a percent of the small plants nationwide and most of the plants in the database are larger systems. Because the Partnership is a voluntary association of plants actively seeking to improve water quality, these plants are likely to perform better than plants nationwide.

A. 2.3 American WaterWorks Association ( AWWA) Water:\ Stats Database, 1996 The AWWA Water:\ Stats Database contains the results of a survey of the AWWA's members conducted in 1996. It contains information on the treatment processes in place at the plants that responded to the survey along with information on disinfection byproducts and other water quality parameters. The database includes information on 1,134 treatment plants. Of these, 377 are large plants, 722 are medium plants, and only 35 of the plants are small plants. Of these plants 540 were surface water plants. Because of the large number of medium plants, this is probably the best available source of data for medium plants. It is also a good source of data for large plants. The small number of small plants probably makes this source less adequate for characterization of small plants.

A. 2.4 Community Water Systems Survey ( CWSS), 1995 The CWSS was conducted by EPA in 1995. It surveyed 2,000 community water systems across all size categories. It includes information on the type of treatment processes the plants have in place. Although the sample size in this survey is adequate to represent plants in all size categories nationwide, there are some significant problems with using this data source to estimate in place treatment technologies. First, there were a large number of non responses to the survey, especially by small systems. There was also a great deal of confusion over the wording of many of the questions on the survey, leading to difficulty in classifying many of the process units. This resulted in a large number of processes being classified in " other" categories. As a result, this survey is probably the least reliable source used in this analysis.

A. 2.5 National Rural Water Association ( NRWA) Survey, 2000 This survey was conducted of members of the NRWA in 1999 2000. It includes data on water quality parameters, as well as treatment configurations. The survey includes information on 129 small surface water systems, the largest survey of small systems available. It should be noted, however, that the survey still only encompasses about 1 percent of small systems nationwide and the plants were not necessarily chosen to obtain a representative nationwide sampling of various types of systems. The water quality data also only includes two samples per plant   one in the Summer and one in the Winter   instead of the monthly data included in other surveys. Therefore, although this is the best data source available for small systems, it is still very limited in its usefulness. Economic Analysis for the LT2ESWTR Proposal A 3 June 2003 A. 2.6 Regulatory Impact Analyses ( RIAs) for the Interim Enhanced Surface Water Treatment Rule ( IESWTR) ( 1998) and the Proposed Long Term 1 Enhanced Surface Water Treatment Rule ( LT1ESWTR) ( 2000)

These reports analyzed the impact of these two rules on surface water systems. The reports include characterization of plants' performance prior to the promulgation of these rules and predictions concerning what types of treatments plants that would need to add to comply with these rules.

A. 2.7 Interview of Industry Experts To assess the applicability of the various data sources and the assumptions made about them, industry experts were interviewed on the topics analyzed in this appendix. Experts on the subjects of small systems, large systems, data surveys, and treatment technologies were interviewed. The experts were asked questions concerning the validity and applicability of the data sources listed above. They were also questioned regarding the reasonableness of the assumptions in this appendix.

A. 3 Comparison and Use of Data Sources Using the expert opinion and information from each of the sources, determinations were made on which data sources to use for each of the size categories and treatment categories.

A. 3.1 Large Systems The ICR is a comprehensive survey that was mandated of all large systems in the country. The large number of systems makes it the most comprehensive and least biased source of information for large systems. In addition, although the information on treatment trains was self reported, EPA performed a data quality check that should have lessened inconsistencies in reporting. Both Water:\ Stats and CWSS were voluntary surveys with non response rates much greater than 50 percent. The low response rate could lead to some bias in the data. There was also little independent review of the data, which means that there could be inconsistent labeling of process units. This was a noted problem in CWSS. Therefore, ICR data was used preferentially for large systems to obtain treatment train data. The Partnership data were used for information on turbidity. For treatment processes that could not be determined from ICR data, Water:\ Stats or the CWSS was used.

A. 3.2 Medium Systems Water:\ Stats and CWSS are the two main sources of data about treatment trains for medium water systems. They contain similar numbers of medium systems and both were voluntary surveys with high non response rates. There was some confusion noted with the listing of technologies in the CWSS survey that may have led to a higher rate of misclassification than with Water:\ Stats. There was not enough information, however, to clearly favor one data source over the other, so the two were considered equivalent, and when data existed in both databases they were averaged.

Medium and large plants have been found to be similar both in terms of water quality and treatment effectiveness. Examining the medium and large plants in Water:\ Stats revealed that source water quality as measured by source turbidity and finished water quality as measured by disinfection by Economic Analysis for the LT2ESWTR Proposal A 4 June 2003 products were nearly identical for medium and large plants ( see USEPA 2003d Appendix B). Therefore, the types and effectiveness of the treatment processes are thought to be fairly similar, and using data from large plants ( ICR data) for medium plants results in an acceptable estimate of medium plants. For this reason, ICR data were used for medium plants when specific information on medium plants was unavailable. For turbidity, data from the Partnership were used. The Partnership data are an aggregate of medium and large plants, but they are acceptable to apply to medium plants.

A. 3.3 Small Systems Water:\ Stats database contains information on only 33 small plants. Therefore, this source was not used for small plants. CWSS contains the largest number of small plants of all the data sources. The size of the sample may offset the possible misclassification and the low response rate. The NRWA survey has a smaller number of plants that are less representative of the national population. However, the survey was conducted by making site visits to the plants, so there is probably less miscategorization of data. For that reason, NRWA was used as the preferential database for small systems with CWSS being used when data were unavailable from NRWA. For turbidity data, the data collected for the proposed LT1ESWTR RIA ( USEPA 2000l) was used, as it contained the largest number of plants and had better quality data.

A. 4 Population Size Categories Evaluated Because of the limited number of plants in many of the above sources, it was decided not to split plants into nine population categories as is traditionally done in such analyses. Such a split would leave many of the small size categories with too few plants to be statistically significant. Therefore, only the small (< 10,000), medium ( 10,001 100,000), and large (> 100,000) population size categories were used for analysis.

A. 5 Combined Filter Performance The combined filter performance option requires systems to achieve turbidity of 0.15 NTU in the combined filter effluent ( CFE). Conventional plants will need to meet the requirements of 0.3 NTU 95 percent of the time in the CFE per the IESWTR and the LT1ESWTR. Plants that can optimize their process to achieve 0.15 NTU 95 percent of time in the CFE will be able to receive an additional 0.5 log Cryptosporidium removal credit for the LT2ESWTR.

A. 5.1 Medium and Large Plants   Turbidity To determine the number of medium and large plants that can achieve the Cryptosporidium reduction credit for the combined filter performance option prior to implementation of the IESWTR, 1999 data from the Partnership were examined. The data include turbidity results from 220 plants of the 325 that are members of the Partnership. As noted earlier, most plants in the Partnership are in the medium and large size categories. So this data set is assumed to be representative of medium and large plants. Economic Analysis for the LT2ESWTR Proposal A 5 June 2003 A. 5.2 Pre IESWTR Exhibit A. 1 summarizes these data, showing the percentage of plants that exceed monthly 95th percentile turbidities of 0.1, 0.2, and 0.3 for a given number of months. To interpret the exhibit, consider the following example: the value for 1 month and 0.3 NTU is 20 percent. This means that 20 percent of the plants exceeded 0.3 NTU in their CFE for at least 1 month out of the year. Conversely, 80 percent of the plants never exceeded a 95th percentile turbidity value of 0.3 NTU.

To estimate the percentage of plants in the Partnership below 0.15 for all months, linear interpolation was used. From Exhibit A. 1, estimates of plants above 0.1 and 0.2 NTU for any given month are 42 percent and 74 percent, respectively. The value for 0.15 NTU would then be:

( 42% + 74%)/ 2 = 58%.

Therefore, 42 percent ( 100 percent  58 percent) of plants did not exceed 0.15 NTU and met the requirement to obtain the 0.5 log Cryptosporidium removal credit ( less than 0.15 NTU 95 percent of the time).

To directly extrapolate Partnership estimates to all medium and large plants nationwide may be misleading. The plants in the Partnership tend to be the largest, best run plants in the country. Also, by virtue of their voluntary participation in the program, these plants are likely to be dedicated to improving plant performance. This may lead to plants in the Partnership performing better than those not in the Partnership. Therefore, other information was evaluated to determine the percent of medium and large plants that can obtain these values.

The Technical Work Group ( TWG) for the IESWTR estimated the number of conventional systems that would need to take action to achieve 95th percentile turbidity goals of 0.1, 0.2, and 0.3 NTU. The TWG estimated that there would be no difference in the number of plants that would need to make changes to achieve 0.1 and 0.2 NTU; there would only be changes in the type of treatment they would use. Examining Exhibit 5.2 of the IESWTR RIA ( USEPA 1998b) shows the TWG determined that 20 percent of medium systems and 23 percent of large systems would not have to make changes to comply with a 0.1 or 0.2 NTU 95th percentile turbidity limit. These values were used to represent the levels that all plants could achieve prior to implementation of the LT2ESWTR. The Partnership plants were not analyzed separately as the IESWTR analysis would have already taken these plants into account.

Exhibit A. 2 summarizes the estimated percentage of Partnership plants and all plants estimated to be meeting the combined filter performance requirement prior to the IESWTR. Economic Analysis for the LT2ESWTR Proposal A 6 June 2003 Exhibit A. 1 Percent of Plants that Exceeded 95th Percentile Monthly Turbidity at least N out of 12 Months 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 1 2 3 4 5 6 7 8 9 10 11 12 Number of Months ( N) Percent 0.1 NTU 0.2 NTU 0.3 NTU Source: Personal Communication, Eric Bissonnette, EPA 2/ 28/ 01. Economic Analysis for the LT2ESWTR Proposal A 7 June 2003 Exhibit A. 2 Percentage of Medium and Large Plants Estimated to Meet Combined Filter Performance Requirements ( Pre IESWTR and Pre LT1ESWTR)

Size Category ( Population Served) Total Plants in Size Category No. of Plants in Partnership [ 1] Percent of Partnership Plants Meeting < 0.15 NTU [ 2] Percent of All Plants Meeting < 0.15 NTU [ 3]

a b c d Medium (> 10k and < 100k) 1,645 81 20%

Large (> 100k) 464 211 23%

Total 2,371 325 Notes: [ 1] Number of all plants in the country in the given size category that belong to the Partnership. Estimated from Partnership for Safe Water Annual Data Summary Report  January 2000. [ 2] Estimated from EPA evaluation of Partnership data ( personal communication from Eric Bissonette, EPA 2 28 01). [ 3] Derived from Exhibit 5.2 of the RIA for the IESWTR ( USEPA 1998b).

A. 5.3 Post LT1ESWTR/ IESWTR Exhibit A. 2 summarizes data from 1999 or before, prior to implementation of the IESWTR. It is expected that many plants will improve filter performance to comply with the requirements of IESWTR. The Regulatory Impact Analysis ( RIA) for the IESWTR ( USEPA 1998b) estimates that approximately 51 percent of medium and 46 percent of large systems will modify their treatment processes to comply with the rule requirements. Plants are expected to make changes such as adding additional polymer, automating filter monitoring and control, and implementing better filter inspection and maintenance programs. These are plants that would not have been performing at these turbidity levels in 1999, but are predicted to achieve lower turbidity limits of at least 0.3 NTU before the promulgation of the LT2ESWTR. It is reasonable to assume that some plants that make changes to achieve 0.3 NTU will also be able to achieve 0.15 NTU without additional changes. To estimate the percentage of these plants that will achieve the additional removal to levels of 0.15 NTU 95 percent of the time after implementation of the LT1ESWTR and IESWTR, the following assumptions were used:

° The ratio of plants that achieve 0.15 NTU to those that achieve 0.3 NTU is the same for the universe of plants.

° Based on Partnership data ( Exhibit A. 1), about half the plants that achieve 0.3 NTU ( 80 percent) also achieve 0.15 NTU ( 42 percent).

Although the Partnership plants tend to be better run on the whole, many of the elements of the Partnership program are similar to those required by the IESWTR. For example, both the IESWTR and the Partnership require filter benchmarking and peer reviews. Therefore, using the ratio of Partnership plants that achieve 0.3 NTU to those that achieve 0.15 NTU is a reasonable proxy for how all plants might be expected to perform after the IESWTR and LT1ESWTR are implemented. Applying this 1 A package plant is a plant that treats small amounts of water and is modular, usually shipped from the factory whole. The plants need only to be hooked up to an inlet and outlet pipe to be operational.

Economic Analysis for the LT2ESWTR Proposal A 8 June 2003 percentage ( 50 percent) to the percent of medium and large plants expected to implement filter changes to comply with the IESWTR ( 51 and 46 percent respectively) gives 26 and 23 percent of medium and large plants, respectively. For example, for large systems:

% achieving 0.15 NTU = ( % achieving 0.3 NTU)*(% achieving 0.3 NTU that can achieve 0.15 NTU) = ( 46%)*( 50%) = 23%

It should be noted that these numbers may be slightly optimistic because many plants may also have to change operations to remove more total organic carbon ( TOC) to meet Stage 1 Disinfection By Products Rule requirements. Increasing TOC removal may interfere with turbidity removal in some plants. The percent of plants; to qualify for the credit above are in addition to the plants that were calculated as meeting the requirements for the 0.5 log removal credit before promulgation of IESWTR. Exhibit A. 3 summarizes the percentage of plants estimated to achieve the combined filter performance requirements at the time the LT2ESWTR takes effect.

Exhibit A. 3 Percentage of Plants meeting Combined Filter Performance Requirements ( 0.15 NTU 95% of the time)

Size ( Population Served) Pre IESWTR Conditions Additional Plants Meeting Combined Filter Performance Following IESWTR Total Percent Meeting Combined Filter Performance, Pre LT2SWTR Medium ( > 10k and < = 100k) 20% 26% 46%

Large (> 100k) 23% 23% 46%

Source: Derived from Exhibits A. 1 & A. 2 and IESWTR ( USEPA 1998b) and proposed LT1ESWTR ( USEPA 2000l) RIAs.

A. 5.4 Small Systems Small systems, those serving 10,000 people or less, are evaluated separately from medium and large systems for several reasons. Small systems often have better source water quality and, therefore, require less rigorous treatment technologies. They also tend to have less advanced control systems, less sophisticated monitoring equipment, and less operator training. Small plants tend to use conventional treatment trains less often than medium and large plants. Small systems typically prefer package plants1 including such technologies as membranes, direct filtration, and cartridge filtration. Although such plants may be able to obtain credit for those alternative technologies, they would not obtain credit for combined filter performance.

A review of available data showed only two available data sources for small plants: NRWA data and turbidity data collected for the proposed LT1ESWTR RIA. The NRWA survey included data from Economic Analysis for the LT2ESWTR Proposal A 9 June 2003 129 surface water plants. There are several issues to take into account when considering the NRWA data. The first is the small sample size. There are only 129 plants in the survey of which only 51 are conventional plants; only conventional plants would be eligible for combined filter performance credit. The plants were also not selected to represent a statistically valid nationwide sampling. The second issue is that the NRWA data consist of only two grab samples taken during the year in contrast to the Partnership data, which consists of the 95th percentile values of samples taken every 15 minutes. Such grab samples would likely miss seasonal peaks, as well as system spikes. Therefore, the samples are more likely to be representative of an average or 50th percentile value than a 95th percentile value. For this reason, the data collected for the LT1ESWTR were used as the primary data source.

The RIA for the proposed LT1ESWTR examined turbidity data for 187 small surface water plants from 13 states. Two of these states reported 95th percentile turbidity data, six of these states reported multiple daily turbidity values, and five states reported maximum daily values. These regular values give a much better representation of the performance of small plants than the two annual points of the NRWA data. The data showed that 27 percent of small plants never exceeded a 95th percentile turbidity value of 0.15 NTU and 48 percent of small plants never exceeded 0.3 NTU. Therefore, 27 percent of plants were assumed to be able to perform well enough to obtain the combined filter performance Cryptosporidium removal credit.

The NRWA data were also examined to check the reasonableness of the results based on the proposed LT1ESWTR RIA. Exhibit A. 4 shows the maximum turbidity values of small conventional plants in the NRWA survey. Sixty percent of the plants had both samples below 0.15 NTU. As mentioned earlier, this is probably closer to the 50th percentile than the 95th percentile. The RIAs for the IESWTR and the proposed LT1ESWTR assumed that plants would target 0.1 NTU under regular operation to meet 0.2 NTU limits 95 percent of the time. To be consistent with these analyses, plants were estimated to target an average of 0.075 NTU in order to achieve 0.15 NTU 95 percent of the time. Examining Exhibit A. 4 shows that approximately 30 percent of small conventional plants never exceeded 0.075 NTU in the NRWA survey. This value is less than the value for medium and large plants and close to the value derived from the proposed LT1ESWTR data. Therefore, the numbers derived from the proposed LT1ESWTR data were considered supported by the NRWA data. As mentioned above, the smaller plants would not be expected to perform as well because of less operator training, less sophisticated monitoring and control equipment, and less redundancy in systems. As a result, problems in a single piece of equipment are more likely to affect the process.

In addition to evaluating performance of conventional filtration in small plants, this analysis recognizes that there is a much smaller percentage of small conventional plants than in large and medium systems. Approximately 40 percent of the NRWA plants were conventional. If only 27 percent of these can meet the 0.15 NTU performance criteria in the CFE 95 percent of the time, then only 11 percent ( 0.4* 0.27 = 0.11) of small plants nationwide would qualify for the 0.5 log Cryptosporidium removal credit. The estimate ( 11 percent) was used to represent Pre LT1ESWTR removal credit for small plants. Economic Analysis for the LT2ESWTR Proposal A 10 June 2003 Exhibit A. 4 NRWA Percent of Conventional Plants With All Samples Below Turbidity Level 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Turbidity Percent n = 50 plants Source: National Rural Water Association ( NRWA) Survey User Database ( USEPA 2001b)

Additional plants may be able to meet the lower turbidity requirements after implementation of the LT1ESWTR. The RIA for the proposed LT1ESWTR estimated that 41 percent of small surface water plants would make filtration improvements to comply with the rule. Therefore, 41 percent of small systems would achieve 0.3 NTU. To calculate the percent that will achieve 0.15 NTU, the ratio of small plants achieving 0.15 NTU to those achieving 0.3 NTU from the LT1ESWTR RIA data ( 0.56) is used, assuming the ratio is constant as in the large and medium plant analysis. Using this ratio gives 23 percent ( 0.41 x 0.56) of small plants that would meet the 0.15 NTU limit 95 percent of the time. Added to the Pre LT1ESWTR percentage of 11 percent gives a total of 34 percent of small surface water plants that could obtain the 0.5 log Cryptosporidium removal credit for combined filter performance. Economic Analysis for the LT2ESWTR Proposal A 11 June 2003 A. 6 Plants with Multiple Sedimentation Basins in Series Plants with multiple sedimentation basins in series will generally receive a 0.5 log removal credit for the LT2ESWTR. These plants could be softening or conventional plants. Examining the ICR database for softening plants, all plants listed as " two stage softening plants" or as " coagulation/ sedimentation softening plants" meet this requirement. There were 16 and 18 percent of all softening plants in these categories, respectively. Therefore, this analysis estimates that 34 percent ( 16 + 18%) of softening plants could qualify for the credit. As there was no such data in CWSS or Water:\ Stats, the ICR value was used for all medium and large plants. NRWA indicated 1.5 percent of small systems had multiple basins. This percentage was used for small softening systems. To estimate the total percent of softening plants with multiple sedimentation basins, the percentage of softening plants assumed to have multiple basins ( 34 percent) was then multiplied by the total percent of all plants that are softening based on CWSS data.

ICR data show that approximately 3 percent of large conventional plants have multiple sedimentation basins in series. Water:\ Stats and CWSS do not distinguish the number of basins, only whether they are present or not. The NRWA survey, however, did provide data on multiple basins, showing that 1.5 percent of small systems had multiple basins. This number was used for small systems. The ICR number was used for medium and large plants. The total number of plants with multiple sedimentation basins in series would be a sum of the conventional and softening plants with multiple basins in series. Exhibit A. 5 shows the resulting data.

Exhibit A. 5 Percentage of Plants with Multiple Sedimentation Basins Size ( Population Served) Softening Conventional Total Small ( # 10k) 1.5% 1.5% 3%

Medium ( 10k  100k) 2% 3% 5%

Large ( > 100k) 2% 3% 5%

Source: ICR Aux1 Database ( USEPA 2000j) and NRWA Survey User Database ( USEPA 2001b)

A. 7 Multiple Filters in Series EPA intends to grant a 0.5 log credit for plants having multiple filters in series.

ICR and CWSS did not differentiate between GAC alone and GAC as a polishing filter. The Water:\ Stats database, however, does contain more detailed information on GAC filters. Exhibit A. 6 shows the percent of medium and large plants that have a GAC filter in addition to conventional filters as listed in Water:\ Stats. The NRWA survey showed no plants that had a GAC filter following a conventional filter. As with adsorption clarifiers, it was assumed that no plants had such technologies in addition to a conventional treatment train, but are used in addition to other processes such as direct filtration. Therefore, no small plants were assumed to have this technology. Exhibit A. 6 summarizes the percent of plants with multiple filters in series. Economic Analysis for the LT2ESWTR Proposal A 12 June 2003 Exhibit A. 6 Percentage of Plants with Multiple Filters in Series Size ( Population Served) GAC Small ( # 10k) 0%

Medium ( 10k  100k) 4%

Large (> 100k) 7%

Source: AWWA Water:\ Stats Database for GAC.

A. 8 Summary Treatment configurations in place at the time of rule promulgation are considered " credits." In other words, neither the costs nor the benefits of those configurations can be ascribed to the LT2ESWTR. Plants with these configurations are placed into a bin which is lowered by either 0.5 log or 1.0 log than what their occurrence would require of them. For example, a system in a 2.0 log treatment bin that had a 1.0 log treatment credit would need only install a technology that obtained 1.0 log credit. In this way, neither the costs nor the benefits from existing technologies are ascribed to this rule.

Exhibit A. 7 summarizes the percent of plants with treatments in place that can achieve Cryptosporidium reduction credit for LT2ESWTR beyond conventional treatment. It is possible that some plants could have multiple technologies for which they could receive credit. To determine the percent of plants that might achieve multiple credits, the chance of a plant having each of the three technologies, combined filter performance, multiple sedimentation basins, and multiple filters was considered to be independent of each other. Therefore, the chance of a small plant having lower finished water turbidity to meet the combined filtration performance requirement and multiple sedimentation basins is:

( 0.34)*( 0.03) = 1.0%.

Exhibit A. 7 present the estimates of the percentages of plants that would receive 0.5 or 1.0 log of Cryptosporidium removal credit for existing technologies. Economic Analysis for the LT2ESWTR Proposal A 13 June 2003 Exhibit A. 7 Total Percentage of Plants with 0.5 and 1.0 Log Reduction Credits Size ( Population Served) Combined filter performance Multiple Settling Basins ( Conventional and Softening) Multiple Filters 0.5 log total credit 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: Derived from Exhibits A. 1 A. 6. Economic Analysis for the LT2ESWTR Proposal B 1 June 2003 Appendix B Characterizing Cryptosporidium Concentration and Methods for Predicting Plant Binning B. 1 Introduction This appendix describes the statistics used to characterize Cryptosporidium occurrence distributions. It then explains how the monitoring frequency and calculation of the average concentration for bin determination were chosen. Lastly, it presents the data used to determine Cryptosporidium occurrence and the likelihood of a plant being categorized into a given bin for each regulatory alternative.

B. 2 Appropriate Statistical Measure to Characterize Source Waters EPA considered a variety of statistical measures by which Cryptosporidium concentrations in systems could be characterized for the purpose of determining whether additional treatment should be prescribed. These measures included the arithmetic mean, median, 90th percentile, and maximum. Consistent with Microbial Disinfection Byproduct Advisory Committee ( Advisory Committee) recommendations, EPA is proposing that Cryptosporidium levels be characterized by the arithmetic mean. Use of the mean is advantageous for several reasons. The mean can be estimated more reliably than other statistical measures. For example, with a limited number of samples, the confidence interval around the mean is substantially narrower ( i. e., less uncertain) than for a 90th percentile estimate. Defining a treatment trigger based upon a maximum value would be much less reliable than basing it on a computation involving multiple values, due to the uncertainty associated with any single sample measurement.

The mean concentration also directly relates to the average risk of the exposed population and, therefore, provides a good measure for indicating relative risks from one site versus another ( e. g., doubling the source water average concentration corresponds to about a doubling of the risk, assuming the same level of treatment at both sites). In contrast, the median would not be an informative or appropriate characterization because of the large numbers of non detection measurements expected to occur, resulting in a large number of sites with median values equal to zero. The median would fail to distinguish differences between sites that had half or more of their measurements as zero with the rest being positive values and sites which truly had measurements of zero.

B. 3 Rates of System Misclassification Having identified an expected level of laboratory analytical method performance based on results with EPA Methods 1622/ 23 in the Information Collection Rule Supplemental Surveys ( ICRSS), and having established the mean as the appropriate statistical measure to classify source water Cryptosporidium levels, a critical issue for the Advisory Committee was how accurately systems could be classified within a bin structure by a monitoring program. This analysis illustrates the impact of the number of samples analyzed and the averaging technique ( e. g., maximum running average vs. simple mean) on system misclassification rates. Economic Analysis for the LT2ESWTR Proposal B 2 June 2003 With perfect information on mean source water Cryptosporidium levels, it would be possible to assign systems to bins without error. No such perfect information exists, so the Advisory Committee recommended a sampling and testing strategy relying on Method 1622 or 1623. Each source water would require a number of samples to ensure a reasonably accurate estimate of its mean Cryptosporidium concentration.

The calculated mean that systems derive from monitoring results will differ from the true mean because of sampling and measurement error. Sampling error occurs because only a finite volume of water is assayed on each occasion. Since oocysts are very disperse in the water, many 10 L samples will contain zero oocysts when the concentration is low, but greater than zero. For example, EPA would expect most 10 L samples to contain zero oocysts when the true concentration in the water body being sampled is 0.05 oocysts/ L.

Sampling error also occurs because samples are collected at relatively low frequency. Since Cryptosporidium concentrations may experience significant temporal variation, infrequent sampling is likely to miss rare peaks in concentration and, therefore, underestimate the true mean occurrence level. Conversely, if a sample event happens to coincide with a rare occurrence peak, the calculated mean will overestimate the true mean occurrence level. By prescribing a larger number of samples at equal intervals over a long period of time, this type of sampling error can be reduced to a relatively low level.

Measurement error occurs because the total oocyst count from a volume assayed does not usually equal the total number of oocysts that were present. Studies of recovery indicate that the percentage of oocysts lost during the measurement process is variable, most often falling between 15 percent and 65 percent ( based on ICRSS data). Because the Advisory Committee recommended that systems be assigned to bins based on total oocyst counts uncorrected for recovery, EPA does not treat average recovery as a source of error. However, the variability of recovery around the mean contributes to error.

To estimate how these errors would affect the assignment of systems to bins, EPA constructed a Monte Carlo model that dealt with the error components in the following manner:

° Finite volume assayed   The model defines the number of oocysts present in a 10 L volume as a Poisson random variable, whose mean is the product of measurement recovery, volume assayed, and concentration at the time of sampling.

° Finite number of samples   True concentration varies over time as a random variable. Concentration is modeled to vary in such a way that its natural logarithm is normally distributed with standard deviation 1.762. This value was selected based on Bayesian analysis of survey data ( see Chapter 4) and on expert opinion that at any given site the Cryptosporidium concentration would vary within a three order of magnitude concentration range 95 percent of the time; i. e., 2.5 percent of the time the concentration would be less than X, and 2.5 percent of the time the concentration would exceed 1000X.

° Variable recovery   Based on laboratory performance in ICRSS, EPA assumed for the model an average recovery among all laboratories of 40 percent with a relative standard deviation of 50 percent. Recovery is modeled as a Beta random variable with parameters ( a, b) = ( 2,3). Mean recovery is therefore a/( a + b) = 2/( 2 + 3) = 0.4. The standard deviation of recovery is 0.2, which is half the mean recovery. Economic Analysis for the LT2ESWTR Proposal B 3 June 2003 With this Monte Carlo model, source water monitoring using Methods 1622 and 1623 was simulated to predict error rates in assigning systems to the bins under Rule Option A3, the Preferred Alternative. Estimates were made for different monitoring frequencies and decision rules. Monitoring frequencies included 6, 12, 18, 24, 36, and 48 samples collected over a 2 year period. System bin assignments were based on comparing a sample statistic with the selected bin boundaries. Sample statistics included the mean, maximum, 2nd highest, 3rd highest, and maximum running annual average ( Max RAA). ( Results are shown only for the 48 sample mean and the 24 sample Max RAA, the sampling frequencies allowed in the proposed Long Term 2 Enhanced Surface Water Treatment Rule ( LT2ESWTR).)

Exhibits B. 1 and B. 2 show how the probabilities of assignment to Bins 1 through 4 vary with the true source water mean concentration when the Max RAA is used with 24 samples assayed over a 2 year period ( producing 13 annual averages). Table results are based on 1,000 simulated monitoring data sets. Note that because monitoring results will not be corrected for recovery, the parameter to be considered when assessing error in bin assignment is the true mean source water concentration multiplied by 40 percent ( the estimated average method recovery). For example, Exhibit B. 1 indicates that if the true mean is 0.316 oocysts/ L, and the true mean multiplied by 40 percent recovery is 0.126 ( 0.316 × 0.4), there is a 12.9 percent probability of this water being classified in the no action bin, an 85.9 percent probability of being classified in Bin 2 ( the correct bin for this water), and about a 1 percent probability of being classified in Bin 3.

Assignment errors are most likely when the true mean is close to a bin boundary. For example, Exhibit B. 1 indicates that a water with a true mean ( multiplied by 40 percent recovery) of 0.075 oocysts/ L has about a 60 percent probability of being classified in Bin 2 and a 40 percent probability for Bin 1. Notice that classification high is more likely than classification low for these cases. This is because the Max RAA tends to slightly overestimate the true mean. In contrast, if the true mean concentration is in the middle of Bin 2, then there is about a 90 percent probability of being classified in Bin 2. Economic Analysis for the LT2ESWTR Proposal B 4 June 2003 No Action 1 Log 2 Log 2.5 Log C < 0.075 0.075 < C < 1 1 < C < 3 3 < C A B C D E F 0.0010 0.0004 1.0000 0.0000 0.0000 0.0000 0.0018 0.0007 1.0000 0.0000 0.0000 0.0000 0.0032 0.0013 0.9990 0.0010 0.0000 0.0000 0.0056 0.0022 1.0000 0.0000 0.0000 0.0000 0.0100 0.0040 0.9960 0.0040 0.0000 0.0000 0.0178 0.0071 0.9900 0.0100 0.0000 0.0000 0.0316 0.0126 0.9530 0.0470 0.0000 0.0000 0.0562 0.0225 0.8970 0.1020 0.0010 0.0000 0.1000 0.0400 0.7170 0.2810 0.0020 0.0000 0.1778 0.0711 0.4110 0.5830 0.0060 0.0000 0.3162 0.1265 0.1290 0.8590 0.0090 0.0030 0.5623 0.2249 0.0310 0.9280 0.0380 0.0030 1.0000 0.4000 0.0010 0.8680 0.1150 0.0160 1.7783 0.7113 0.0010 0.6580 0.2980 0.0430 3.1623 1.2649 0.0000 0.2840 0.5500 0.1660 5.6234 2.2494 0.0000 0.0720 0.5640 0.3640 10.0000 4.0000 0.0000 0.0070 0.2480 0.7450 17.7828 7.1131 0.0000 0.0000 0.0580 0.9420 31.6228 12.6491 0.0000 0.0000 0.0000 1.0000 56.2341 22.4936 0.0000 0.0000 0.0010 0.9990 100.0000 40.0000 0.0000 0.0000 0.0000 1.0000 Action Bins Measured Mean at 40% Recovery ( Oocysts/ L) True System Mean ( Total Oocysts/ L) Exhibit B. 1 Estimated Probability of System Being Assigned to Each of the Four LT2ESWTR Bins as a Function of True Mean Source Water Cryptosporidium Concentration Based on Max RAA Exhibit B. 3 shows equivalent information for a simple mean based on 48 samples over a 2 year period. Again, assignment errors are most likely when the true mean is close to a bin boundary. Notice that the likelihood of a source water being misclassified high is lower than for the Max RAA based on 24 samples. For example, Exhibit B. 3 indicates that if the true mean is 0.075 oocysts/ L then there is about a 65 percent probability of being classified in Bin 1 and about a 35 percent probability of being classified in Bin 2. If, however, the true mean is in the middle of Bin 2, then there is a greater than 95 percent probability of being classified in Bin 2. Economic Analysis for the LT2ESWTR Proposal B 5 June 2003 Performance of 24 Month Maximum RAA 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.001 0.01 0.1 1 10 100 Source Water Mean Concentration ( multiplied by 40% recovery) Probability of Bin Classification Bin 1 = No Action Bin 2 = 1 log Bin 3 = 2 logs Bin 4 = 2.5 logs Bin Boundaries Exhibit B. 2 Likelihood of Classification in Bins 1 4 as a Function of Source Water Mean Concentration Based on a Max RAA with 24 Samples Bin assignment error based on the Max RAA or a simple mean increased substantially when sampling frequency dropped below 24 samples ( results not shown). Therefore, the Advisory Committee recommended that at least 24 samples be used for estimating mean source water concentrations and bin determination. Economic Analysis for the LT2ESWTR Proposal B 6 June 2003 Performance of 48 Month Simple Mean 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.001 0.01 0.1 1 10 100 Source Water Mean Concentration ( multiplied by 40% recovery) Probability of Bin Classification Bin 1 = No Action Bin 2 = 1 log Bin 3 = 2 logs Bin 4 = 2.5 logs Bin Boundaries Exhibit B. 3 Likelihood of Classification in Bins 1 4 as a Function of Source Water Mean Concentration Based on a Simple Mean with 48 Samples One way the Advisory Committee members evaluated the significance of system misclassification in bin assignments was to consider a water where the true mean is 0.5 log ( i. e., factor of 3.16) from a bin boundary. This case was assessed both when the true mean is a half log below a bin boundary and when the true mean is a half log above a bin boundary. Misclassifying such a water in a lower bin would suggest that the monitoring was not protective of public health, since the relatively high occurrence level would go unaddressed. Both the Max RAA based on 24 samples and the simple mean based on 48 samples, each over a 2 year monitoring period, provided approximately the same level of protection against this kind of error. For example, for a water with a true mean at 0.5 log above the boundary between Bin 1 and Bin 2 ( 0.075 oocysts/ L), the rates of misclassification into the lowest bin for the 24 sample Max RAA and the 48 sample simple mean are 1.7 percent ( 0.017) and 1.4 percent ( 0.014), respectively.

Two graphs are provided that clearly show the tendency for misclassification under the Preferred Alternative. Exhibit B. 4 shows the distribution of source waters according to actual concentration and bin classification. Exhibit B. 5 shows the measured concentration ( on the vertical axis) versus estimated " true" concentration ( on the horizontal axis) and the resulting misclassified areas. Economic Analysis for the LT2ESWTR Proposal B 7 June 2003 Exhibit B. 4 Distribution of Cryptosporidium Occurrence for Plants and Classification to Regulatory Bins Note: Plot of distribution of source waters by " true" concentration. The three lines are the bin boundaries for the Preferred Alternative. Empty circles are assigned to the no action bin. Circles with a dark + in the center are assigned to the 1.0 log removal bin. Circles with an " x" in the center are assigned to the 2.0 log removal bin. Circles with a light + in the center are assigned to the 2.5 log removal bin. Circles with symbols that lie outside their given bin boundary are systems that are misclassified. For example, any circle with a + in it to the left of the leftmost line is one assigned to the 1.0 log removal bin despite having " true" Cryptosporidium concentrations below the bin level. Economic Analysis for the LT2ESWTR Proposal B 8 June 2003 Exhibit B. 5 Measured Cryptosporidium Concentration versus " True" Concentration and Bin Misclassification Note: Measured concentration versus " true" concentration. Dark lines represent the bin boundaries for the Preferred Alternative. Light grey shading shows areas that have samples that were classified in a bin higher than their " true" concentration. Darker grey shaded areas show areas with samples that were classified in a bin lower than their " true" concentration. Based on 24 month mean for ICR 95th percentile occurrence distribution.

Misclassifying waters too high relative to the true mean would potentially result in systems taking costly and unnecessary steps to reduce exposure. As shown by Exhibits B. 2 and B. 3, the 48 sample simple mean provides greater protection against this type of error than does the Max RAA, although monitoring costs will be twice as high. Given the concerns for bias ( high or low) in assigning systems to bins, the Advisory Committee recommended that either the Max RAA or the 48 sample simple mean be used to provide sufficiently reliable estimates for bin determination under LT2ESWTR.

B. 4 Predicted Binning for Each Regulatory Alternative This section presents the probability distributions generated from the same model described above. The distributions show the likelihood of a plant with a source water of a given true value being classified in a given bin for each regulatory alternative. The following exhibits present the results:

B. 6 Binning Probability for Alternative A2 ( 0.5 log/ 1.5 log/ 2.5 log) B. 7 Graph of Binning Probability for Alternative A2 ( 0.5 log/ 1.5 log/ 2.5 log) B. 8 Binning Probability for Alternative A3 ( 1.0 log/ 2.0 log/ 2.5 log) B. 9 Graph of Binning Probability for Alternative A3 ( 1.0 log/ 2.0 log/ 2.5 log) Economic Analysis for the LT2ESWTR Proposal B 9 June 2003 B. 10 Binning Probability for Alternative A4 ( 0.5 log/ 1.0 log) B. 11 Graph of Binning Probability for Alternative A4 ( 0.5 log/ 1.0 log)

The measured amount of Cryptosporidium may be different than the actual or " true" concentration because of sampling errors and method limitations. Exhibits B. 6, B. 8, and B. 10 show for a given " true" concentration the percentage of time that a sample would be classified in each bin. ( This section differs from section B. 3, which factors in recovery in determining probability of misclassification). The binning central tendency is presented for all three occurrence data sets ( the Information Collection Rule ( ICR), ICR Supplemental Survey Large Systems ( ICRSSL), and ICR Supplemental Survey Medium Systems ( ICRSSM)). The percentage of plants classified in each bin depends on method errors such as recovery, false positives, and analyst error, as well as accounting for sample size and the fact that the concentration in a given sample may under or over represent the concentration in the larger water body. Exhibits B. 7, B. 9, and B. 11 show the probability of classification in a bin given a " true" source water concentration in graphical form. Economic Analysis for the LT2ESWTR Proposal B 10 June 2003 Bin 1 Bin 2 Bin 3 Bin 4 No Action 0.5 log 1.5 logs 2.5 logs C < 0.03 0.03 < C < 0.1 0.1 < C < 1.0 1.0 < C A B C D E F 0.0025 0.0010 0.9980 0.0020 0.0000 0.0000 0.0040 0.0016 0.9930 0.0070 0.0000 0.0000 0.0045 0.0018 0.9910 0.0090 0.0000 0.0000 0.0050 0.0020 0.9900 0.0100 0.0000 0.0000 0.0056 0.0022 0.9900 0.0100 0.0000 0.0000 0.0100 0.0040 0.9620 0.0370 0.0010 0.0000 0.0180 0.0072 0.8980 0.0930 0.0090 0.0000 0.0320 0.0128 0.7590 0.2210 0.0200 0.0000 0.0560 0.0224 0.5140 0.4270 0.0590 0.0000 0.1000 0.0400 0.2510 0.5730 0.1760 0.0000 0.1780 0.0712 0.0630 0.5190 0.4140 0.0040 0.3160 0.1264 0.0080 0.2590 0.7160 0.0170 0.5620 0.2248 0.0000 0.0640 0.8900 0.0460 1.0000 0.4000 0.0000 0.0040 0.8590 0.1370 1.7780 0.7112 0.0000 0.0000 0.6320 0.3680 3.1620 1.2648 0.0000 0.0000 0.2850 0.7150 5.6230 2.2492 0.0000 0.0000 0.0640 0.9360 10.0000 4.0000 0.0000 0.0000 0.0020 0.9980 17.7830 7.1132 0.0000 0.0000 0.0000 1.0000 31.6230 12.6492 0.0000 0.0000 0.0000 1.0000 56.2340 22.4936 0.0000 0.0000 0.0000 1.0000 100.0000 40.0000 0.0000 0.0000 0.0000 1.0000 177.8280 71.1312 0.0000 0.0000 0.0000 1.0000 Binning Central Tendency [ 3] All Plants ICR Occurrence Distribution 52.20% 17.80% 22.67% 7.35% All Plants, ICRSSL 55.30% 28.40% 15.90% 0.50% All Plants, ICRSSM 53.20% 25.20% 19.99% 1.63% [ 1] True source water Cryptosporidium concentration ( oocysts/ liter). [ 2] Probability that a plant will fall into a given treatment bin given true and measured concentration taking into account measurement errors and the Cryptosporidium occurrence. [ 3] Result of 1,000 Monte Carlo Simulations 06/ 05/ 01. System Concentration Binning Probability [ 2]

True Mean Concentration [ 1] Measured Mean Concentration ( 40% Recovery) Exhibit B. 6 Binning Probability Given True and Measured Maximum Alternative A2 ( 0.5 log/ 1.5 log/ 2.5 log) Economic Analysis for the LT2ESWTR Proposal B 11 June 2003 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.001 0.01 0.1 1 10 100 Source Water Mean Concentration, oocysts/ liter Probability of Bin Classification Bin 1 = No Action Bin 2 = 0.5 log Bin 3 = 1.5 logs Bin 4 = 2.5 logs Exhibit B. 7 Binning Probability for Alternative A2  ( Maximum Reduction) Economic Analysis for the LT2ESWTR Proposal B 12 June 2003 Bin 1 Bin 2 Bin 3 Bin 4 No Action 1 log 2 logs 2.5 logs C < 0.075 0.075 < C< 0.1 1 < C < 3 3 < C A B C D E F 0.0032 0.0013 1.0000 0.0000 0.0000 0.0000 0.0056 0.0022 0.9990 0.0010 0.0000 0.0000 0.0100 0.0040 0.9990 0.0010 0.0000 0.0000 0.0180 0.0072 0.9950 0.0050 0.0000 0.0000 0.0320 0.0128 0.9720 0.0280 0.0000 0.0000 0.0560 0.0224 0.9140 0.0860 0.0000 0.0000 0.1000 0.0400 0.7470 0.2530 0.0000 0.0000 0.1780 0.0712 0.4490 0.5510 0.0000 0.0000 0.3160 0.1264 0.1590 0.8270 0.0130 0.0010 0.5620 0.2248 0.0230 0.9470 0.0280 0.0020 1.0000 0.4000 0.0020 0.8530 0.1310 0.0140 1.7780 0.7112 0.0000 0.6290 0.3200 0.0510 3.1620 1.2648 0.0000 0.2780 0.5780 0.1440 5.6230 2.2492 0.0000 0.0660 0.5570 0.3770 10.0000 4.0000 0.0000 0.0030 0.2560 0.7410 17.7830 7.1132 0.0000 0.0000 0.0520 0.9480 31.6230 12.6492 0.0000 0.0000 0.0030 0.9970 56.2340 22.4936 0.0000 0.0000 0.0000 1.0000 100.0000 40.0000 0.0000 0.0000 0.0000 1.0000 Binning Central Tendency [ 3] All Plants ICR Occurrence Distribution 65.40% 27.20% 4.37% 3.19% All Plants, ICRSSL 77.70% 21.80% 0.50% 0.06% All Plants, ICRSSM 72.90% 25.40% 1.39% 0.36% [ 1] True source water Cryptosporidium concentration ( oocysts/ liter). [ 2] Probability that a plant will fall into a given treatment bin given true and measured concentration taking into account measurement errors and the Cryptosporidium occurrence. [ 3] Result of 1,000 Monte Carlo Simulations 06/ 05/ 01. System Concentration Binning Probability [ 2]

True Mean Concentration [ 1] Measured Mean Concentration ( 40% Recovery) Exhibit B. 8 Binning Probability Given True and Measured Maximum Alternative A3 ( Preferred Alternative) Economic Analysis for the LT2ESWTR Proposal B 13 June 2003 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.001 0.01 0.1 1 10 100 Source Water Mean Concentration, oocysts/ liter Probability of Bin Classification Bin 1 = No Action Bin 2 = 1 log Bin 3 = 2 logs Bin 4 = 2.5 logs Exhibit B. 9 Alternative A3  ( Preferred Alternative) Economic Analysis for the LT2ESWTR Proposal B 14 June 2003 Bin 1 Bin 2 Bin 3 ( No Bin 4) No Action 0.5 log 1 log C < 0.1 0.1 < C < 1 1 < C A B C D E F 0.00178 0.0007 1.0000 0.0000 0.0000 0.00316 0.0013 1.0000 0.0000 0.0000 0.00562 0.0022 1.0000 0.0000 0.0000 0.01000 0.0040 1.0000 0.0000 0.0000 0.01800 0.0072 0.9990 0.0010 0.0000 0.03200 0.0128 0.9930 0.0070 0.0000 0.05600 0.0224 0.9550 0.0450 0.0000 0.10000 0.0400 0.8410 0.1580 0.0010 0.17800 0.0712 0.6070 0.3900 0.0030 0.31600 0.1264 0.2830 0.7030 0.0140 0.56200 0.2248 0.0870 0.8740 0.0390 1.00000 0.4000 0.0050 0.8700 0.1250 1.77800 0.7112 0.0000 0.6540 0.3460 3.16200 1.2648 0.0000 0.2940 0.7060 5.62300 2.2492 0.0000 0.0690 0.9300 10.00000 4.0000 0.0000 0.0030 0.9970 17.78300 7.1132 0.0000 0.0000 1.0000 31.62300 12.6492 0.0000 0.0000 1.0000 56.23400 22.4936 0.0000 0.0000 1.0000 Binning Central Tendency [ 3] All Plants ICR Occurrence Distribution 69.60% 22.80% 7.50% N/ A All Plants, ICRSSL 84.00% 15.60% 0.40% N/ A All Plants, ICRSSM 78.50% 19.70% 1.80% N/ A System Concentration Binning Probability [ 2]

True Mean Concentration [ 1] Measured Mean Concentration ( 40% Recovery)

[ 1] True source water Cryptosporidium concentration ( oocysts/ liter). [ 2] Probability that a plant will fall into a given treatment bin given true and measured concentration taking into account measurement errors and the Cryptosporidium occurrence. [ 3] Result of 1,000 Monte Carlo Simulations 06/ 05/ 01. Exhibit B. 10 Binning Probability Given True and Measured Maximum Alternative A4 ( 0.5 log/ 1.0 log) Economic Analysis for the LT2ESWTR Proposal B 15 June 2003 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.001 0.01 0.1 1 10 100 Source Water Mean Concentration, oocysts/ liter Probability of Bin Classification Bin 1 = No Action Bin 2 = 0.5 log Bin 3 = 1 log Exhibit B. 11 Binning Probability for Alternative A4  ( Least Reduction)

In order to bound the range of Cryptosporidium occurrence, confidence limits were developed for Cryptosporidium occurrence. The 95th percentile of the ICR distribution and the 5th percentile of the ICRSSL distribution were calculated as the high and low occurrence distributions. Together the two distributions represent the 90 percent confidence bounds of Cryptosporidium occurrence. It is at least 90 percent certain that the actual Cryptosporidium occurrence lies between the two distributions. Exhibit B. 12 shows the binning for high ( ICR 95th percentile) and low ( ICRSSL 5th percentile) occurrence distributions for the Preferred Regulatory Alternative.

Exhibit B. 12 Binning for the Cryptosporidium Occurrence Distribution, Preferred Alternative Distribution No Action Bin 1.0 Log Removal Bin 2.0 Log Removal Bin 2.5 Log Removal Bin High 61.5% 30.7% 4.6% 3.2%

Low 86.5% 13.4% 1.0% 0.0% Economic Analysis for the LT2ESWTR Proposal B 16 June 2003 B. 5 Binning for Unfiltered Plants Binning for unfiltered plants was calculated the same way for filtered plants as described above. The only difference is that the unfiltered plants have only two treatment bins, one requiring 2.0 log inactivation and another requiring 3.0 log inactivation. Analysis was conducted for all unfiltered systems serving using the ICR unfiltered data set. The results of the binning analysis are included in Exhibit B. 13 for small, medium, and large systems.

Exhibit B. 13 Binning for the Cryptosporidium Occurrence Distribution for Unfiltered Plants Size Category ( Population Served) 2.0 Log Inactivation Bin 3.0 Log Inactivation Bin Small ( # 10,000) 79.2% 20.8%

Medium ( 10,001  100,000) 79.2% 20.8%

Large ( > 100,000) 81.2% 18.8% Economic Analysis for the LT2ESWTR Proposal C 1 June 2003 Appendix C Benefits C. 1 Summary This appendix presents additional data on the risk and benefit estimates for the LT2ESWTR, supplementing or providing background for the calculations in Chapter 5. The Appendix is organized as follows:

C. 1 Summary C. 2 AIDS/ Population Ratio for Milwaukee Outbreak C. 3 Derivation of Filtered and Unfiltered Population Numbers for Mortality C. 4 Model Estimated National Cases of Illness and Death Avoided With Associated Economic Values C. 5 Individual Risk Functions C. 6 Real Gross Domestic Product ( GDP) per Capita C. 7 Income Elasticity Factors The Appendix C exhibit list is below. For Exhibits C. 4 through C. 9, each exhibit has two parts   the first based on the enhanced cost of illness and the second based on the traditional cost of illness.

Exhibit C. 1 Unfiltered System Population Served and AIDS Population Exhibit C. 2 Derivation of Filtered Systems' Populations Exhibit C. 3 Population at Risk and Baseline Pre LT2 Cases of Illness and Death by System Size, Filtration, and Data Set Exhibit C. 4 Cases Avoided and Benefits Annualized at 3 Percent Exhibit C. 5 Cases Avoided and Benefits Annualized at 7 Percent Exhibit C. 6 Cases Avoided and Benefits Annualized at 3 Percent, Filtered Systems Only Exhibit C. 7 Cases Avoided and Benefits Annualized at 3 Percent, Unfiltered Systems Only Exhibit C. 8 Cases Avoided and Benefits Annualized at 7 Percent, Filtered Systems Only Exhibit C. 9 Cases Avoided and Benefits Annualized at 7 Percent, Unfiltered Systems Only Exhibit C. 10 Annual Individual Risk Distributions Based on ICRSSM Occurrence Data, Filtered Community Water Systems ( CWSs) Only Exhibit C. 11 Annual Individual Risk Distributions Based on ICRSSL Occurrence Data, Filtered Community Water Systems ( CWSs) Only Exhibit C. 12 Real GDP per Capita Projections Exhibit C. 13 CPI Projections Exhibit C. 14 Factors for Incorporation of Income Elasticity into Yearly Benefits Estimates C. 2 AIDS/ Population Ratio for Milwaukee Outbreak The national mortality rate for Acquired Immune Deficiency Syndrome ( AIDS) patients due to cryptosporidiosis cannot be directly derived from the data collected during the Milwaukee outbreak, but Economic Analysis for the LT2ESWTR Proposal C 2 June 2003 these data can be adjusted to more accurately estimate mortality rates in 2000. One adjustment is needed because there are considerably fewer AIDS patients in Milwaukee and the State of Wisconsin as compared to the rest of the country. As an illustration, in 1993 there were about three times the number of persons living with AIDS per capita in the United States compared to Wisconsin. United States population living with AIDS in 1993 was 132,686 ( CDC 1993) in a total population of 257,783,000 ( U. S. Census Bureau, 2001c) compared to Wisconsin's 862 persons living with AIDS ( CDC 1993) in a total population of 5,044,318 ( U. S. Census Bureau, 2001c). A second adjustment is necessary to reflect the increased incidence of AIDS in the United States between 1993 and 1999 ( which is the data used in this EA). A further adjustment was needed to reflect the different incident rates in areas served by filtered and unfiltered systems. For all these adjustments, the analysis uses data on the incidence of AIDS from the CDC and the U. S. Census Bureau. Using these two sources allows the most consistent comparisons across geographic areas and time.

There are various other sources that were not used that provide related data. For example, Frisby et al. ( 1997) mention that approximately 1,300 people were living HIV in Milwaukee in 1993, but not all HIV patients are immunocompromised to the same degree as AIDS patients and, therefore, do not have the same risk of illness and death. Dr. Neil Hoxie, of the Bureau of Public Health in Wisconsin, in personal communications estimated that 1,203 people were probably living with AIDS in the metropolitan statistical area of Milwaukee in 1993, an estimate higher than the CDC estimate for States. Unfortunately no source reported a count of AIDS patients within the area served by the Milwaukee public water system, or the area corresponding to the 1993 Cryptosporidium outbreak. Using statewide numbers for the number of people living with AIDS would be certain to capture the area of the 1993 outbreak, but would be a likely underestimate due to rural populations with few AIDS patients being added to the total population. However, an estimate without all AIDS patients in the original outbreak area would also be an overestimate. In the face of this lack of data related specifically to the population affected by the outbreak, and a preference for comparable data for multiple years in several geographic locations, the analysis uses data from the CDC and the U. S. Census.

C. 3 Derivation of Filtered and Unfiltered Population Numbers for Mortality The greater presence of sensitive subpopulations in some areas might indicate a higher rate of mortality due to cryptosporidiosis. A number of large metropolitan areas are served by water systems that are unfiltered and the populations of immunocompromised served by unfiltered systems differs from those served by filtered systems. The analysis in Chapter 5, therefore, calculates mortality rates for filtered and unfiltered systems separately to highlight possible differences in mortality rates due to cryptosporidiosis.

The analysis seeks to compare the percentage of the total U. S. population living with AIDS that live in areas served by unfiltered systems versus filtered systems. The CDC, in their semiannual " HIV/ AIDS Surveillance Report," give the number of individuals living with AIDS by metropolitan statistical area. Population data from the metropolitan statistical areas served by the unfiltered systems was used instead of the population served by unfiltered systems. The population count data from the 2000 Census were used because they were more accurate than 1999 estimates of population. These data are used to develop adjustment factors, and so it was judged more accurate to use inputs from consistent sources, rather than mix in service population data that were self reported by utilities. Data for the relevant metropolitan areas are shown in Exhibit C. 1. Economic Analysis for the LT2ESWTR Proposal C 3 June 2003 Exhibit C. 1 Unfiltered System Population Served and AIDS Population Unfiltered Systems 2000 Census Population Number of People Living with AIDS ( 1999)

Portland, ME[ 1] 243,537 155 Portland, OR 2,265,223 1,613 Tacoma, WA 700,820 353 San Francisco, CA 7,039,362 9,151 New York, NY 21,199,865 44,994 Total 31,448,807 56,266 Sources: AIDS population information from CDC except Portland, Maine [ 1] Portland, Maine AIDS information from City of Portland, Maine.

For the purposes of developing an adjustment factor, the population remaining in the U. S. is considered to be served by filtered systems. The total population within the metropolitan statistical areas of the unfiltered systems was subtracted from the total U. S. population in 2000 to give an " adjusted" population for filtered systems, and the AIDS population of unfiltered systems was also subtracted from the national estimates of people living with AIDS. From these numbers, the percentage of AIDS patients in unfiltered and filtered systems can be determined ( Exhibit C. 2). The use of overall U. S. Census data is appropriate because the incidence rate is therefore derived from the number of persons living with AIDS and the population of which they are a part. It would be inappropriate to derive a factor based on the population served by public water systems because there are no comparable estimates for the number of people living with AIDS in that population.

Exhibit C. 2 Derivation of Filtered Systems' Populations Filtered Systems 2000 Census Population Number of People Living with AIDS ( 1999)

US Population 281,421,906 317,652 Unfiltered Population 31,448,807 56,266 Adjusted Filtered Population 249,973,099 261,386 In order to get a factor to adjust the 1993 Milwaukee AIDS mortality rate for changes in time and population for filtered systems, the total adjusted number of people living with AIDS at the end of 1999 in the entire country is divided by the adjusted 2000 national population. This percentage is divided by the AIDS/ Population ratio of Wisconsin in 1993, yielding a population and time adjustment factor. The Economic Analysis for the LT2ESWTR Proposal C 4 June 2003 Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G Filtered 160,531,102 503,429 49,493 1,418,729 53 5 150 Unfiltered 11,739,777 548,440 118,006 1,031,806 91 20 172 Filtered 155,866 16,366 451,166 16 2 48 Unfiltered 168,765 36,308 317,527 28 6 53 Filtered 271,411 25,576 836,388 29 3 88 Unfiltered 293,846 63,216 552,868 49 11 92 Filtered 9,368,257 56,528 6,041 154,483 6 1 16 Unfiltered 179,117 7,601 1,631 15,042 1 0 3 Filtered 18,484 1,768 53,127 2 0 6 Unfiltered 2,494 535 4,936 0 0 1 Filtered 31,841 2,631 99,079 3 0 10 Unfiltered 4,294 921 8,498 1 0 1 Filtered 151,162,846 446,900 42,880 1,261,972 47 5 133 Unfiltered 11,560,660 540,839 115,970 1,017,520 90 19 169 Filtered 137,382 14,397 399,681 15 2 42 Unfiltered 166,271 35,653 312,818 28 6 52 Filtered 239,570 22,887 744,887 25 2 79 Unfiltered 289,552 62,087 544,744 48 10 91 ICRSSL ICR ICRSSM Large Systems ( > 10,000) ICR ICRSSM ICRSSL Data Set All System Sizes Small Systems ( < 10,000) ICRSSL ICR ICRSSM Filtration Mean 90% Confidence Bound Pre LT2 Annual Deaths Population at Risk Pre LT2 Annual Illnesses Mean 90% Confidence Bound adjustment factor for unfiltered systems uses only the populations served by unfiltered systems in 1999 ( AIDS and non AIDS), divided by the 1993 Wisconsin AIDS/ Population ratio.

C. 4 Model Estimated National Cases of Illness and Death Avoided With Associated Economic Values Exhibits C. 3 through C. 9 summarize the risk assessment modeling. Exhibit C. 3 presents the baseline populations at risk and pre LT2ESWTR cases of illness and death. Exhibit C. 4 through C. 9 present expected results under the proposed regulations.

Exhibit C. 3 Population at Risk and Baseline Pre LT2 Cases of Illness and Death by System Size, Filtration, and Data Set Source: Benefits and Risk Model Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 1,048,583 172,409 2,410,265 144 25 316 704 $ 116 $ 1,616 $ 778 $ 52 $ 2,403 $ 1,482 $ 201 $ 3,791 $ A2 1,031,716 170,643 2,367,993 142 25 311 693 $ 115 $ 1,588 $ 768 $ 51 $ 2,366 $ 1,461 $ 199 $ 3,718 $ A3  Preferred alt. 1,018,915 169,358 2,331,467 141 25 308 684 $ 114 $ 1,564 $ 761 $ 51 $ 2,340 $ 1,445 $ 198 $ 3,666 $ A4 940,570 161,366 2,084,877 133 24 282 632 $ 108 $ 1,399 $ 717 $ 49 $ 2,181 $ 1,349 $ 189 $ 3,357 $ A1 323,915 53,589 757,802 44 8 99 217 $ 36 $ 508 $ 239 $ 15 $ 749 $ 457 $ 59 $ 1,214 $ A2 275,616 47,681 619,109 39 7 84 185 $ 32 $ 415 $ 212 $ 14 $ 653 $ 397 $ 54 $ 1,032 $ A3  Preferred alt. 256,173 45,292 560,648 37 7 78 172 $ 30 $ 376 $ 202 $ 14 $ 613 $ 374 $ 52 $ 959 $ A4 218,820 41,215 457,470 33 7 67 147 $ 28 $ 307 $ 180 $ 13 $ 539 $ 328 $ 48 $ 810 $ A1 563,930 92,474 1,367,370 77 14 177 378 $ 62 $ 917 $ 418 $ 27 $ 1,305 $ 796 $ 103 $ 2,092 $ A2 520,501 87,782 1,244,961 73 13 164 349 $ 59 $ 834 $ 393 $ 26 $ 1,226 $ 742 $ 98 $ 1,931 $ A3  Preferred alt. 498,363 84,724 1,177,415 70 13 157 335 $ 57 $ 789 $ 381 $ 25 $ 1,179 $ 715 $ 96 $ 1,849 $ A4 433,520 78,386 984,238 64 12 136 291 $ 53 $ 660 $ 344 $ 24 $ 1,045 $ 635 $ 89 $ 1,601 $

A1 63,866 7,860 167,453 7 1 19 38 $ 5 $ 100 $ 34 $ 2 $ 112 $ 72 $ 8 $ 203 $ A2 62,168 7,671 163,369 7 1 18 37 $ 5 $ 97 $ 33 $ 2 $ 108 $ 70 $ 8 $ 197 $ A3  Preferred alt. 59,994 7,439 157,807 7 1 18 36 $ 4 $ 94 $ 32 $ 2 $ 105 $ 68 $ 7 $ 190 $ A4 51,723 6,787 133,232 6 1 15 31 $ 4 $ 79 $ 28 $ 2 $ 91 $ 59 $ 7 $ 161 $ A1 20,919 2,538 56,615 2 0 6 12 $ 2 $ 34 $ 11 $ 1 $ 39 $ 24 $ 2 $ 72 $ A2 16,027 1,989 43,805 2 0 5 10 $ 1 $ 26 $ 9 $ 0 $ 30 $ 18 $ 2 $ 55 $ A3  Preferred alt. 12,601 1,571 34,606 1 0 4 7 $ 1 $ 21 $ 7 $ 0 $ 24 $ 14 $ 2 $ 43 $ A4 9,786 1,233 26,424 1 0 3 6 $ 1 $ 16 $ 6 $ 0 $ 19 $ 11 $ 1 $ 33 $ A1 36,026 3,855 105,997 4 0 12 21 $ 2 $ 63 $ 19 $ 1 $ 67 $ 41 $ 4 $ 122 $ A2 31,647 3,465 93,543 4 0 10 19 $ 2 $ 56 $ 17 $ 1 $ 59 $ 36 $ 4 $ 107 $ A3  Preferred alt. 27,791 3,130 82,951 3 0 9 17 $ 2 $ 49 $ 15 $ 1 $ 52 $ 32 $ 3 $ 94 $ A4 23,006 2,730 68,162 3 0 8 14 $ 2 $ 41 $ 13 $ 1 $ 43 $ 26 $ 3 $ 78 $

A1 984,717 164,259 2,245,273 137 25 298 666 $ 111 $ 1,518 $ 744 $ 50 $ 2,295 $ 1,410 $ 192 $ 3,580 $ A2 969,548 162,521 2,204,422 135 24 293 656 $ 110 $ 1,491 $ 735 $ 50 $ 2,264 $ 1,391 $ 191 $ 3,515 $ A3  Preferred alt. 958,921 161,506 2,174,244 134 24 290 648 $ 109 $ 1,470 $ 729 $ 49 $ 2,243 $ 1,378 $ 189 $ 3,474 $ A4 888,847 154,125 1,955,210 127 23 267 601 $ 104 $ 1,322 $ 689 $ 48 $ 2,090 $ 1,290 $ 181 $ 3,206 $ A1 302,996 50,884 702,049 42 8 93 205 $ 34 $ 475 $ 228 $ 15 $ 710 $ 433 $ 56 $ 1,147 $ A2 259,589 45,633 576,406 37 7 79 176 $ 31 $ 390 $ 204 $ 14 $ 622 $ 379 $ 52 $ 979 $ A3  Preferred alt. 243,572 43,801 526,649 36 7 74 165 $ 30 $ 356 $ 195 $ 14 $ 590 $ 359 $ 50 $ 913 $ A4 209,034 39,913 431,173 32 6 64 141 $ 27 $ 292 $ 175 $ 13 $ 521 $ 316 $ 46 $ 779 $ A1 527,904 88,212 1,266,480 73 13 166 357 $ 60 $ 856 $ 398 $ 26 $ 1,241 $ 755 $ 99 $ 1,980 $ A2 488,854 83,790 1,152,243 69 13 154 331 $ 57 $ 779 $ 376 $ 25 $ 1,167 $ 707 $ 94 $ 1,831 $ A3  Preferred alt. 470,572 81,410 1,096,721 67 12 148 318 $ 55 $ 742 $ 366 $ 24 $ 1,131 $ 684 $ 92 $ 1,759 $ A4 410,514 75,314 915,940 61 12 129 278 $ 51 $ 619 $ 331 $ 23 $ 1,007 $ 609 $ 86 $ 1,525 $

Source: Risk and Benefits Model Exhibit C. 4a Cases Avoided and Benefits Annualized at 3 Percent ( Based on Enhanced Cost of Illness)

Small Systems Large Systems ICR ICRSS Medium System Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions)

ICRSS Large System 90 % Confidence Bound ICR Mean ICRSS Medium System 90 % Confidence Bound Data Set Rule Alternative Total Annual Value of Benefits ($ Millions)

Mean 90 % Confidence Bound Annual Illnesses Avoided Mean Value of Benefits for Annual Deaths Avoided ($ Millions)

Mean 90 % Confidence Bound Mean 90 % Confidence Bound Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Filtered and Unfiltered Filtered and Unfiltered Filtered and Unfiltered All System Sizes ICRSS Large System ICR ICRSS Medium System ICRSS Large System C 5 Economic Analysis for the LT2ESWTR Proposal May 2003 Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 1,048,583 172,409 2,410,265 144 25 316 212 $ 35 $ 486 $ 778 $ 52 $ 2,403 $ 989 $ 106 $ 2,784 $ A2 1,031,716 170,643 2,367,993 142 25 311 208 $ 34 $ 477 $ 768 $ 51 $ 2,366 $ 977 $ 105 $ 2,744 $ A3  Preferred alt. 1,018,915 169,358 2,331,467 141 25 308 206 $ 34 $ 470 $ 761 $ 51 $ 2,340 $ 967 $ 105 $ 2,713 $ A4 940,570 161,366 2,084,877 133 24 282 190 $ 33 $ 421 $ 717 $ 49 $ 2,181 $ 907 $ 101 $ 2,523 $ A1 323,915 53,589 757,802 44 8 99 65 $ 11 $ 153 $ 239 $ 15 $ 749 $ 305 $ 31 $ 886 $ A2 275,616 47,681 619,109 39 7 84 56 $ 10 $ 125 $ 212 $ 14 $ 653 $ 268 $ 28 $ 764 $ A3  Preferred alt. 256,173 45,292 560,648 37 7 78 52 $ 9 $ 113 $ 202 $ 14 $ 613 $ 253 $ 27 $ 713 $ A4 218,820 41,215 457,470 33 7 67 44 $ 8 $ 92 $ 180 $ 13 $ 539 $ 225 $ 25 $ 616 $ A1 563,930 92,474 1,367,370 77 14 177 114 $ 19 $ 276 $ 418 $ 27 $ 1,305 $ 531 $ 53 $ 1,542 $ A2 520,501 87,782 1,244,961 73 13 164 105 $ 18 $ 251 $ 393 $ 26 $ 1,226 $ 498 $ 51 $ 1,434 $ A3  Preferred alt. 498,363 84,724 1,177,415 70 13 157 101 $ 17 $ 237 $ 381 $ 25 $ 1,179 $ 481 $ 50 $ 1,372 $ A4 433,520 78,386 984,238 64 12 136 88 $ 16 $ 198 $ 344 $ 24 $ 1,045 $ 432 $ 46 $ 1,204 $

A1 63,866 7,860 167,453 7 1 19 11 $ 1 $ 30 $ 34 $ 2 $ 112 $ 45 $ 4 $ 137 $ A2 62,168 7,671 163,369 7 1 18 11 $ 1 $ 29 $ 33 $ 2 $ 108 $ 44 $ 4 $ 133 $ A3  Preferred alt. 59,994 7,439 157,807 7 1 18 11 $ 1 $ 28 $ 32 $ 2 $ 105 $ 43 $ 4 $ 128 $ A4 51,723 6,787 133,232 6 1 15 9 $ 1 $ 24 $ 28 $ 2 $ 91 $ 37 $ 4 $ 110 $ A1 20,919 2,538 56,615 2 0 6 4 $ 0 $ 10 $ 11 $ 1 $ 39 $ 15 $ 1 $ 48 $ A2 16,027 1,989 43,805 2 0 5 3 $ 0 $ 8 $ 9 $ 0 $ 30 $ 12 $ 1 $ 37 $ A3  Preferred alt. 12,601 1,571 34,606 1 0 4 2 $ 0 $ 6 $ 7 $ 0 $ 24 $ 9 $ 1 $ 30 $ A4 9,786 1,233 26,424 1 0 3 2 $ 0 $ 5 $ 6 $ 0 $ 19 $ 7 $ 1 $ 23 $ A1 36,026 3,855 105,997 4 0 12 6 $ 1 $ 19 $ 19 $ 1 $ 67 $ 26 $ 2 $ 82 $ A2 31,647 3,465 93,543 4 0 10 6 $ 1 $ 17 $ 17 $ 1 $ 59 $ 23 $ 2 $ 73 $ A3  Preferred alt. 27,791 3,130 82,951 3 0 9 5 $ 1 $ 15 $ 15 $ 1 $ 52 $ 20 $ 2 $ 64 $ A4 23,006 2,730 68,162 3 0 8 4 $ 0 $ 12 $ 13 $ 1 $ 43 $ 17 $ 1 $ 53 $

A1 984,717 164,259 2,245,273 137 25 298 200 $ 33 $ 457 $ 744 $ 50 $ 2,295 $ 944 $ 102 $ 2,652 $ A2 969,548 162,521 2,204,422 135 24 293 197 $ 33 $ 448 $ 735 $ 50 $ 2,264 $ 932 $ 102 $ 2,613 $ A3  Preferred alt. 958,921 161,506 2,174,244 134 24 290 195 $ 33 $ 442 $ 729 $ 49 $ 2,243 $ 924 $ 101 $ 2,580 $ A4 888,847 154,125 1,955,210 127 23 267 181 $ 31 $ 398 $ 689 $ 48 $ 2,090 $ 870 $ 97 $ 2,420 $ A1 302,996 50,884 702,049 42 8 93 62 $ 10 $ 143 $ 228 $ 15 $ 710 $ 290 $ 29 $ 841 $ A2 259,589 45,633 576,406 37 7 79 53 $ 9 $ 117 $ 204 $ 14 $ 622 $ 256 $ 27 $ 726 $ A3  Preferred alt. 243,572 43,801 526,649 36 7 74 50 $ 9 $ 107 $ 195 $ 14 $ 590 $ 244 $ 26 $ 686 $ A4 209,034 39,913 431,173 32 6 64 43 $ 8 $ 88 $ 175 $ 13 $ 521 $ 217 $ 25 $ 594 $ A1 527,904 88,212 1,266,480 73 13 166 107 $ 18 $ 258 $ 398 $ 26 $ 1,241 $ 506 $ 51 $ 1,460 $ A2 488,854 83,790 1,152,243 69 13 154 99 $ 17 $ 234 $ 376 $ 25 $ 1,167 $ 475 $ 49 $ 1,361 $ A3  Preferred alt. 470,572 81,410 1,096,721 67 12 148 96 $ 17 $ 223 $ 366 $ 24 $ 1,131 $ 461 $ 48 $ 1,306 $ A4 410,514 75,314 915,940 61 12 129 83 $ 15 $ 186 $ 331 $ 23 $ 1,007 $ 415 $ 45 $ 1,157 $

Source: Risk and Benefits Model ICRSS Large System Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System Total Annual Value of Benefits ($ Millions)

Mean Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System All System Sizes Filtered and Unfiltered ICR ICRSS Medium System 90 % Confidence Bound Value of Benefits for Annual Illnesses Avoided ($ Millions)

Large Systems Small Systems Data Set Rule Alternative Annual Illnesses Avoided Annual Deaths Avoided Value of Benefits for Annual Deaths Avoided ($ Millions)

Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Mean 90 % Confidence Bound Exhibit C. 4b Cases Avoided and Benefits Annualized at 3 Percent ( Based on Traditional Cost of Illness)

Mean 90 % Confidence Bound Mean 90 % Confidence Bound 90 % Confidence Bound Mean C 6 Economic Analysis for the LT2ESWTR Proposal May 2003 Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 1,048,583 172,409 2,410,265 144 25 316 596 $ 98 $ 1,368 $ 665 $ 44 $ 2,054 $ 1,260 $ 171 $ 3,229 $ A2 1,031,716 170,643 2,367,993 142 25 311 586 $ 97 $ 1,343 $ 657 $ 44 $ 2,022 $ 1,243 $ 170 $ 3,165 $ A3  Preferred alt. 1,018,915 169,358 2,331,467 141 25 308 579 $ 96 $ 1,323 $ 651 $ 44 $ 2,001 $ 1,230 $ 168 $ 3,120 $ A4 940,570 161,366 2,084,877 133 24 282 535 $ 92 $ 1,184 $ 613 $ 42 $ 1,864 $ 1,148 $ 161 $ 2,859 $ A1 323,915 53,589 757,802 44 8 99 184 $ 30 $ 430 $ 205 $ 13 $ 639 $ 389 $ 50 $ 1,035 $ A2 275,616 47,681 619,109 39 7 84 157 $ 27 $ 351 $ 182 $ 12 $ 558 $ 338 $ 46 $ 879 $ A3  Preferred alt. 256,173 45,292 560,648 37 7 78 146 $ 26 $ 319 $ 172 $ 12 $ 524 $ 318 $ 44 $ 816 $ A4 218,820 41,215 457,470 33 7 67 125 $ 24 $ 260 $ 154 $ 11 $ 461 $ 279 $ 41 $ 690 $ A1 563,930 92,474 1,367,370 77 14 177 320 $ 53 $ 775 $ 357 $ 23 $ 1,116 $ 677 $ 87 $ 1,783 $ A2 520,501 87,782 1,244,961 73 13 164 296 $ 50 $ 705 $ 336 $ 22 $ 1,048 $ 632 $ 83 $ 1,646 $ A3  Preferred alt. 498,363 84,724 1,177,415 70 13 157 283 $ 48 $ 668 $ 325 $ 22 $ 1,010 $ 609 $ 81 $ 1,577 $ A4 433,520 78,386 984,238 64 12 136 247 $ 45 $ 558 $ 294 $ 20 $ 895 $ 541 $ 75 $ 1,363 $

A1 63,866 7,860 167,453 7 1 19 31 $ 4 $ 81 $ 28 $ 2 $ 91 $ 59 $ 6 $ 166 $ A2 62,168 7,671 163,369 7 1 18 30 $ 4 $ 79 $ 27 $ 1 $ 89 $ 57 $ 6 $ 160 $ A3  Preferred alt. 59,994 7,439 157,807 7 1 18 29 $ 4 $ 76 $ 26 $ 1 $ 86 $ 55 $ 6 $ 155 $ A4 51,723 6,787 133,232 6 1 15 25 $ 3 $ 64 $ 23 $ 1 $ 74 $ 48 $ 6 $ 131 $ A1 20,919 2,538 56,615 2 0 6 10 $ 1 $ 27 $ 9 $ 0 $ 32 $ 19 $ 2 $ 59 $ A2 16,027 1,989 43,805 2 0 5 8 $ 1 $ 21 $ 7 $ 0 $ 25 $ 15 $ 2 $ 45 $ A3  Preferred alt. 12,601 1,571 34,606 1 0 4 6 $ 1 $ 17 $ 6 $ 0 $ 20 $ 12 $ 1 $ 35 $ A4 9,786 1,233 26,424 1 0 3 5 $ 1 $ 13 $ 5 $ 0 $ 16 $ 9 $ 1 $ 27 $ A1 36,026 3,855 105,997 4 0 12 17 $ 2 $ 51 $ 16 $ 1 $ 55 $ 33 $ 3 $ 99 $ A2 31,647 3,465 93,543 4 0 10 15 $ 2 $ 45 $ 14 $ 1 $ 48 $ 29 $ 3 $ 88 $ A3  Preferred alt. 27,791 3,130 82,951 3 0 9 13 $ 2 $ 40 $ 12 $ 1 $ 42 $ 26 $ 3 $ 77 $ A4 23,006 2,730 68,162 3 0 8 11 $ 1 $ 33 $ 10 $ 1 $ 35 $ 21 $ 2 $ 63 $

A1 984,717 164,259 2,245,273 137 25 298 565 $ 94 $ 1,288 $ 637 $ 43 $ 1,961 $ 1,202 $ 163 $ 3,054 $ A2 969,548 162,521 2,204,422 135 24 293 556 $ 93 $ 1,264 $ 630 $ 42 $ 1,933 $ 1,186 $ 162 $ 3,003 $ A3  Preferred alt. 958,921 161,506 2,174,244 134 24 290 550 $ 93 $ 1,247 $ 624 $ 42 $ 1,916 $ 1,174 $ 161 $ 2,965 $ A4 888,847 154,125 1,955,210 127 23 267 510 $ 88 $ 1,121 $ 590 $ 41 $ 1,791 $ 1,100 $ 154 $ 2,739 $ A1 302,996 50,884 702,049 42 8 93 174 $ 29 $ 403 $ 196 $ 13 $ 608 $ 369 $ 48 $ 979 $ A2 259,589 45,633 576,406 37 7 79 149 $ 26 $ 331 $ 174 $ 12 $ 533 $ 323 $ 44 $ 836 $ A3  Preferred alt. 243,572 43,801 526,649 36 7 74 140 $ 25 $ 302 $ 167 $ 12 $ 505 $ 306 $ 43 $ 779 $ A4 209,034 39,913 431,173 32 6 64 120 $ 23 $ 247 $ 150 $ 11 $ 447 $ 270 $ 40 $ 665 $ A1 527,904 88,212 1,266,480 73 13 166 303 $ 51 $ 726 $ 341 $ 22 $ 1,064 $ 644 $ 84 $ 1,688 $ A2 488,854 83,790 1,152,243 69 13 154 280 $ 48 $ 661 $ 322 $ 21 $ 1,000 $ 602 $ 80 $ 1,564 $ A3  Preferred alt. 470,572 81,410 1,096,721 67 12 148 270 $ 47 $ 629 $ 313 $ 21 $ 970 $ 583 $ 78 $ 1,501 $ A4 410,514 75,314 915,940 61 12 129 235 $ 43 $ 525 $ 284 $ 20 $ 862 $ 519 $ 73 $ 1,300 $

Source: Risk and Benefits Model Large Systems Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System Small Systems Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System All System Sizes Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System Value of Benefits for Annual Deaths Avoided ($ Millions) Total Annual Value of Benefits ($ Millions)

Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Exhibit C. 5a Cases Avoided and Benefits Annualized at 7 Percent ( Based on Enhanced Cost of Illness)

Data Set Rule Alternative Annual Illnesses Avoided Mean 90 % Confidence Bound Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions)

C 7 Economic Analysis for the LT2ESWTR Proposal May 2003 Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 1,048,583 172,409 2,410,265 144 25 316 180 $ 30 $ 413 $ 665 $ 44 $ 2,054 $ 845 $ 91 $ 2,374 $ A2 1,031,716 170,643 2,367,993 142 25 311 177 $ 29 $ 406 $ 657 $ 44 $ 2,022 $ 834 $ 90 $ 2,339 $ A3  Preferred alt. 1,018,915 169,358 2,331,467 141 25 308 175 $ 29 $ 400 $ 651 $ 44 $ 2,001 $ 826 $ 89 $ 2,315 $ A4 940,570 161,366 2,084,877 133 24 282 162 $ 28 $ 358 $ 613 $ 42 $ 1,864 $ 775 $ 86 $ 2,155 $ A1 323,915 53,589 757,802 44 8 99 56 $ 9 $ 130 $ 205 $ 13 $ 639 $ 260 $ 26 $ 757 $ A2 275,616 47,681 619,109 39 7 84 47 $ 8 $ 106 $ 182 $ 12 $ 558 $ 229 $ 24 $ 652 $ A3  Preferred alt. 256,173 45,292 560,648 37 7 78 44 $ 8 $ 96 $ 172 $ 12 $ 524 $ 216 $ 23 $ 610 $ A4 218,820 41,215 457,470 33 7 67 38 $ 7 $ 79 $ 154 $ 11 $ 461 $ 192 $ 22 $ 527 $ A1 563,930 92,474 1,367,370 77 14 177 97 $ 16 $ 234 $ 357 $ 23 $ 1,116 $ 454 $ 45 $ 1,317 $ A2 520,501 87,782 1,244,961 73 13 164 89 $ 15 $ 213 $ 336 $ 22 $ 1,048 $ 425 $ 44 $ 1,225 $ A3  Preferred alt. 498,363 84,724 1,177,415 70 13 157 86 $ 15 $ 202 $ 325 $ 22 $ 1,010 $ 411 $ 43 $ 1,172 $ A4 433,520 78,386 984,238 64 12 136 74 $ 13 $ 169 $ 294 $ 20 $ 895 $ 369 $ 40 $ 1,029 $

A1 63,866 7,860 167,453 7 1 19 9 $ 1 $ 24 $ 28 $ 2 $ 91 $ 37 $ 3 $ 112 $ A2 62,168 7,671 163,369 7 1 18 9 $ 1 $ 24 $ 27 $ 1 $ 89 $ 36 $ 3 $ 109 $ A3  Preferred alt. 59,994 7,439 157,807 7 1 18 9 $ 1 $ 23 $ 26 $ 1 $ 86 $ 35 $ 3 $ 105 $ A4 51,723 6,787 133,232 6 1 15 7 $ 1 $ 19 $ 23 $ 1 $ 74 $ 30 $ 3 $ 90 $ A1 20,919 2,538 56,615 2 0 6 3 $ 0 $ 8 $ 9 $ 0 $ 32 $ 12 $ 1 $ 39 $ A2 16,027 1,989 43,805 2 0 5 2 $ 0 $ 6 $ 7 $ 0 $ 25 $ 9 $ 1 $ 30 $ A3  Preferred alt. 12,601 1,571 34,606 1 0 4 2 $ 0 $ 5 $ 6 $ 0 $ 20 $ 8 $ 1 $ 24 $ A4 9,786 1,233 26,424 1 0 3 1 $ 0 $ 4 $ 5 $ 0 $ 16 $ 6 $ 1 $ 19 $ A1 36,026 3,855 105,997 4 0 12 5 $ 1 $ 15 $ 16 $ 1 $ 55 $ 21 $ 2 $ 67 $ A2 31,647 3,465 93,543 4 0 10 5 $ 1 $ 14 $ 14 $ 1 $ 48 $ 18 $ 1 $ 59 $ A3  Preferred alt. 27,791 3,130 82,951 3 0 9 4 $ 0 $ 12 $ 12 $ 1 $ 42 $ 16 $ 1 $ 52 $ A4 23,006 2,730 68,162 3 0 8 3 $ 0 $ 10 $ 10 $ 1 $ 35 $ 14 $ 1 $ 44 $

A1 984,717 164,259 2,245,273 137 25 298 171 $ 28 $ 389 $ 637 $ 43 $ 1,961 $ 808 $ 88 $ 2,267 $ A2 969,548 162,521 2,204,422 135 24 293 168 $ 28 $ 382 $ 630 $ 42 $ 1,933 $ 798 $ 87 $ 2,237 $ A3  Preferred alt. 958,921 161,506 2,174,244 134 24 290 166 $ 28 $ 377 $ 624 $ 42 $ 1,916 $ 791 $ 86 $ 2,212 $ A4 888,847 154,125 1,955,210 127 23 267 154 $ 27 $ 339 $ 590 $ 41 $ 1,791 $ 744 $ 83 $ 2,071 $ A1 302,996 50,884 702,049 42 8 93 53 $ 9 $ 122 $ 196 $ 13 $ 608 $ 248 $ 25 $ 720 $ A2 259,589 45,633 576,406 37 7 79 45 $ 8 $ 100 $ 174 $ 12 $ 533 $ 219 $ 23 $ 621 $ A3  Preferred alt. 243,572 43,801 526,649 36 7 74 42 $ 8 $ 91 $ 167 $ 12 $ 505 $ 209 $ 23 $ 587 $ A4 209,034 39,913 431,173 32 6 64 36 $ 7 $ 75 $ 150 $ 11 $ 447 $ 186 $ 21 $ 509 $ A1 527,904 88,212 1,266,480 73 13 166 91 $ 15 $ 220 $ 341 $ 22 $ 1,064 $ 433 $ 44 $ 1,250 $ A2 488,854 83,790 1,152,243 69 13 154 85 $ 15 $ 200 $ 322 $ 21 $ 1,000 $ 407 $ 42 $ 1,163 $ A3  Preferred alt. 470,572 81,410 1,096,721 67 12 148 82 $ 14 $ 190 $ 313 $ 21 $ 970 $ 395 $ 41 $ 1,119 $ A4 410,514 75,314 915,940 61 12 129 71 $ 13 $ 159 $ 284 $ 20 $ 862 $ 355 $ 39 $ 991 $

Source: Risk and Benefits Model Data Set Rule Alternative Annual Illnesses Avoided Mean 90 % Confidence Bound Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions) Value of Benefits for Annual Deaths Avoided ($ Millions) Total Annual Value of Benefits ($ Millions)

Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound ICRSS Medium System ICRSS Large System All System Sizes Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Exhibit C. 5b Cases Avoided and Benefits Annualized at 7 Percent ( Based on Traditional Cost of Illness)

Large Systems Filtered and Unfiltered ICR ICRSS Medium System ICRSS Large System Small Systems Filtered and Unfiltered ICR C 8 Economic Analysis for the LT2ESWTR Proposal May 2003 Exhibit C. 6a Cases Avoided and Benefits Annualized at 3 Percent ( Based on Enhanced Cost of Illness) Filtered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 501,841 49,378 1,414,989 53 5 149 101 $ 10 $ 284 $ 283 $ 13 $ 976 $ 384 $ 29 $ 1,235 $ A2 484,974 47,418 1,365,967 51 5 144 97 $ 9 $ 274 $ 274 $ 12 $ 946 $ 371 $ 28 $ 1,189 $ A3  Preferred alt. 472,173 46,061 1,329,429 50 5 140 95 $ 9 $ 267 $ 267 $ 12 $ 923 $ 362 $ 27 $ 1,160 $ A4 393,828 38,895 1,078,615 42 4 114 79 $ 8 $ 216 $ 223 $ 10 $ 764 $ 302 $ 23 $ 958 $ A1 155,522 16,328 450,241 16 2 48 31 $ 3 $ 90 $ 87 $ 3 $ 321 $ 118 $ 8 $ 404 $ A2 107,224 10,682 317,383 11 1 34 21 $ 2 $ 64 $ 60 $ 2 $ 223 $ 82 $ 5 $ 281 $ A3  Preferred alt. 87,780 8,065 263,496 9 1 28 18 $ 2 $ 53 $ 49 $ 2 $ 183 $ 67 $ 4 $ 233 $ A4 50,427 4,277 162,677 5 0 17 10 $ 1 $ 32 $ 28 $ 1 $ 106 $ 38 $ 2 $ 132 $ A1 270,773 25,508 833,472 29 3 88 54 $ 5 $ 167 $ 152 $ 6 $ 561 $ 207 $ 13 $ 701 $ A2 227,344 21,047 710,687 24 2 75 46 $ 4 $ 142 $ 128 $ 5 $ 472 $ 174 $ 11 $ 592 $ A3  Preferred alt. 205,206 18,565 647,598 22 2 68 41 $ 4 $ 130 $ 116 $ 4 $ 427 $ 157 $ 10 $ 535 $ A4 140,363 12,065 466,456 15 1 49 28 $ 2 $ 93 $ 79 $ 3 $ 291 $ 107 $ 7 $ 366 $

A1 56,307 6,025 153,825 6 1 16 10 $ 1 $ 27 $ 28 $ 1 $ 96 $ 38 $ 3 $ 121 $ A2 54,609 5,813 149,439 6 1 16 10 $ 1 $ 27 $ 27 $ 1 $ 93 $ 37 $ 3 $ 116 $ A3  Preferred alt. 52,434 5,569 143,724 6 1 15 9 $ 1 $ 26 $ 26 $ 1 $ 89 $ 35 $ 3 $ 111 $ A4 44,163 4,843 119,399 5 1 13 8 $ 1 $ 21 $ 22 $ 1 $ 74 $ 30 $ 2 $ 92 $ A1 18,435 1,763 52,992 2 0 6 3 $ 0 $ 9 $ 9 $ 0 $ 34 $ 12 $ 1 $ 43 $ A2 13,544 1,231 39,815 1 0 4 2 $ 0 $ 7 $ 7 $ 0 $ 25 $ 9 $ 1 $ 32 $ A3  Preferred alt. 10,117 806 30,616 1 0 3 2 $ 0 $ 5 $ 5 $ 0 $ 19 $ 7 $ 0 $ 24 $ A4 7,302 530 22,471 1 0 2 1 $ 0 $ 4 $ 4 $ 0 $ 14 $ 5 $ 0 $ 17 $ A1 31,751 2,623 98,834 3 0 10 6 $ 0 $ 18 $ 16 $ 1 $ 58 $ 21 $ 1 $ 72 $ A2 27,371 2,278 85,939 3 0 9 5 $ 0 $ 15 $ 14 $ 1 $ 50 $ 18 $ 1 $ 63 $ A3  Preferred alt. 23,516 1,957 75,748 2 0 8 4 $ 0 $ 13 $ 12 $ 0 $ 43 $ 16 $ 1 $ 54 $ A4 18,731 1,586 60,257 2 0 6 3 $ 0 $ 11 $ 9 $ 0 $ 34 $ 13 $ 1 $ 43 $

A1 445,535 42,764 1,258,301 47 5 133 91 $ 9 $ 256 $ 255 $ 11 $ 881 $ 346 $ 26 $ 1,119 $ A2 430,365 41,219 1,213,406 45 4 128 88 $ 8 $ 247 $ 247 $ 11 $ 855 $ 334 $ 25 $ 1,079 $ A3  Preferred alt. 419,739 40,334 1,179,745 44 4 125 85 $ 8 $ 240 $ 241 $ 11 $ 835 $ 326 $ 24 $ 1,051 $ A4 349,664 34,557 958,615 37 4 101 71 $ 7 $ 195 $ 201 $ 9 $ 693 $ 272 $ 21 $ 865 $ A1 137,087 14,363 398,904 14 2 42 28 $ 3 $ 81 $ 78 $ 3 $ 288 $ 106 $ 7 $ 363 $ A2 93,680 9,246 279,141 10 1 29 19 $ 2 $ 57 $ 53 $ 2 $ 199 $ 72 $ 5 $ 249 $ A3  Preferred alt. 77,663 7,256 234,041 8 1 25 16 $ 1 $ 48 $ 44 $ 2 $ 165 $ 60 $ 4 $ 209 $ A4 43,125 3,743 140,489 5 0 15 9 $ 1 $ 29 $ 25 $ 1 $ 92 $ 33 $ 2 $ 115 $ A1 239,023 22,835 742,325 25 2 78 49 $ 5 $ 151 $ 137 $ 5 $ 503 $ 185 $ 12 $ 629 $ A2 199,973 18,720 626,235 21 2 66 41 $ 4 $ 127 $ 114 $ 4 $ 422 $ 155 $ 10 $ 530 $ A3  Preferred alt. 181,691 16,573 576,654 19 2 61 37 $ 3 $ 117 $ 104 $ 4 $ 386 $ 141 $ 9 $ 480 $ A4 121,633 10,385 404,874 13 1 43 25 $ 2 $ 82 $ 70 $ 3 $ 257 $ 94 $ 6 $ 323 $

Source: Risk and Benefits Model Annual Illnesses Avoided Mean Small Systems Large Systems ICR ICRSS Large System Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions) Total Annual Value of Benefits ($ Millions) 90 % Confidence Bound 90 % Confidence Bound Mean ICRSS Medium System ICR ICRSS Large System ICRSS Medium System Mean 90 % Confidence Bound 90 % Confidence Bound Mean Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Value of Benefits for Annual Deaths Avoided ($ Millions)

Rule Alternative Data Set ICRSS Medium System ICR ICRSS Large System All System Sizes 90 % Confidence Bound Mean Economic Analysis for the LT2ESWTR Proposal C 9 May 2003 Exhibit C. 6b Cases Avoided and Benefits Annualized at 3 Percent ( Based on Traditional Cost of Illness) Filtered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 501,841 49,378 1,414,989 53 5 149 335 $ 33 $ 943 $ 283 $ 13 $ 976 $ 618 $ 57 $ 1,835 $ A2 484,974 47,418 1,365,967 51 5 144 323 $ 32 $ 912 $ 274 $ 12 $ 946 $ 597 $ 55 $ 1,774 $ A3  Preferred alt. 472,173 46,061 1,329,429 50 5 140 315 $ 31 $ 887 $ 267 $ 12 $ 923 $ 582 $ 54 $ 1,722 $ A4 393,828 38,895 1,078,615 42 4 114 263 $ 26 $ 720 $ 223 $ 10 $ 764 $ 485 $ 46 $ 1,427 $ A1 155,522 16,328 450,241 16 2 48 104 $ 11 $ 300 $ 87 $ 3 $ 321 $ 191 $ 16 $ 610 $ A2 107,224 10,682 317,383 11 1 34 71 $ 7 $ 211 $ 60 $ 2 $ 223 $ 131 $ 10 $ 432 $ A3  Preferred alt. 87,780 8,065 263,496 9 1 28 59 $ 5 $ 176 $ 49 $ 2 $ 183 $ 108 $ 8 $ 353 $ A4 50,427 4,277 162,677 5 0 17 34 $ 3 $ 108 $ 28 $ 1 $ 106 $ 62 $ 5 $ 203 $ A1 270,773 25,508 833,472 29 3 88 181 $ 17 $ 557 $ 152 $ 6 $ 561 $ 333 $ 27 $ 1,060 $ A2 227,344 21,047 710,687 24 2 75 151 $ 14 $ 474 $ 128 $ 5 $ 472 $ 279 $ 23 $ 890 $ A3  Preferred alt. 205,206 18,565 647,598 22 2 68 137 $ 12 $ 432 $ 116 $ 4 $ 427 $ 253 $ 20 $ 803 $ A4 140,363 12,065 466,456 15 1 49 93 $ 8 $ 310 $ 79 $ 3 $ 291 $ 172 $ 13 $ 553 $

A1 56,307 6,025 153,825 6 1 16 33 $ 4 $ 91 $ 28 $ 1 $ 96 $ 62 $ 6 $ 180 $ A2 54,609 5,813 149,439 6 1 16 32 $ 3 $ 89 $ 27 $ 1 $ 93 $ 60 $ 6 $ 175 $ A3  Preferred alt. 52,434 5,569 143,724 6 1 15 31 $ 3 $ 85 $ 26 $ 1 $ 89 $ 57 $ 5 $ 167 $ A4 44,163 4,843 119,399 5 1 13 26 $ 3 $ 71 $ 22 $ 1 $ 74 $ 48 $ 5 $ 139 $ A1 18,435 1,763 52,992 2 0 6 11 $ 1 $ 31 $ 9 $ 0 $ 34 $ 20 $ 2 $ 65 $ A2 13,544 1,231 39,815 1 0 4 8 $ 1 $ 24 $ 7 $ 0 $ 25 $ 15 $ 1 $ 48 $ A3  Preferred alt. 10,117 806 30,616 1 0 3 6 $ 0 $ 18 $ 5 $ 0 $ 19 $ 11 $ 1 $ 36 $ A4 7,302 530 22,471 1 0 2 4 $ 0 $ 13 $ 4 $ 0 $ 14 $ 8 $ 1 $ 26 $ A1 31,751 2,623 98,834 3 0 10 19 $ 2 $ 59 $ 16 $ 1 $ 58 $ 35 $ 3 $ 109 $ A2 27,371 2,278 85,939 3 0 9 16 $ 1 $ 51 $ 14 $ 1 $ 50 $ 30 $ 2 $ 95 $ A3  Preferred alt. 23,516 1,957 75,748 2 0 8 14 $ 1 $ 45 $ 12 $ 0 $ 43 $ 26 $ 2 $ 82 $ A4 18,731 1,586 60,257 2 0 6 11 $ 1 $ 36 $ 9 $ 0 $ 34 $ 20 $ 1 $ 66 $

A1 445,535 42,764 1,258,301 47 5 133 301 $ 29 $ 851 $ 255 $ 11 $ 881 $ 557 $ 51 $ 1,663 $ A2 430,365 41,219 1,213,406 45 4 128 291 $ 28 $ 821 $ 247 $ 11 $ 855 $ 538 $ 50 $ 1,605 $ A3  Preferred alt. 419,739 40,334 1,179,745 44 4 125 284 $ 27 $ 798 $ 241 $ 11 $ 835 $ 525 $ 48 $ 1,563 $ A4 349,664 34,557 958,615 37 4 101 236 $ 23 $ 648 $ 201 $ 9 $ 693 $ 437 $ 41 $ 1,288 $ A1 137,087 14,363 398,904 14 2 42 93 $ 10 $ 270 $ 78 $ 3 $ 288 $ 171 $ 14 $ 545 $ A2 93,680 9,246 279,141 10 1 29 63 $ 6 $ 189 $ 53 $ 2 $ 199 $ 117 $ 9 $ 385 $ A3  Preferred alt. 77,663 7,256 234,041 8 1 25 53 $ 5 $ 158 $ 44 $ 2 $ 165 $ 97 $ 8 $ 317 $ A4 43,125 3,743 140,489 5 0 15 29 $ 3 $ 95 $ 25 $ 1 $ 92 $ 54 $ 4 $ 177 $ A1 239,023 22,835 742,325 25 2 78 162 $ 15 $ 502 $ 137 $ 5 $ 503 $ 298 $ 25 $ 951 $ A2 199,973 18,720 626,235 21 2 66 135 $ 13 $ 423 $ 114 $ 4 $ 422 $ 250 $ 20 $ 796 $ A3  Preferred alt. 181,691 16,573 576,654 19 2 61 123 $ 11 $ 390 $ 104 $ 4 $ 386 $ 227 $ 18 $ 724 $ A4 121,633 10,385 404,874 13 1 43 82 $ 7 $ 274 $ 70 $ 3 $ 257 $ 152 $ 12 $ 489 $

Source: Risk and Benefits Model ICR ICRSS Large System ICRSS Medium System ICR ICRSS Large System ICRSS Medium System Large Systems ICR ICRSS Large System ICRSS Medium System Small Systems All System Sizes Data Set Rule Alternative Annual Illnesses Avoided Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions) Value of Benefits for Annual Deaths Avoided ($ Millions) Total Annual Value of Benefits ($ Millions)

Mean Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Economic Analysis for the LT2ESWTR Proposal C 10 May 2003 Exhibit C. 7a Cases Avoided and Benefits Annualized at 3 Percent ( Based on Enhanced Cost of Illness) Unfiltered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O ICR A3  Preferred alt. 546,742 117,826 1,027,377 91 20 171 369 $ 80 $ 694 $ 494 $ 38 $ 1,451 $ 863 $ 138 $ 2,037 $ ICR SSL A3  Preferred alt. 168,393 36,286 316,421 28 6 53 114 $ 24 $ 214 $ 152 $ 12 $ 447 $ 266 $ 42 $ 627 $ ICR SSM A3  Preferred alt. 293,157 63,169 550,857 49 11 92 198 $ 43 $ 372 $ 265 $ 20 $ 778 $ 463 $ 74 $ 1,092 $

ICR A3  Preferred alt. 7,560 1,628 14,951 1 0 2 4 $ 1 $ 9 $ 6 $ 1 $ 17 $ 10 $ 2 $ 25 $ ICR SSL A3  Preferred alt. 2,483 535 4,912 0 0 1 1 $ 0 $ 3 $ 2 $ 0 $ 6 $ 3 $ 1 $ 8 $ ICR SSM A3  Preferred alt. 4,275 921 8,455 1 0 1 3 $ 1 $ 5 $ 3 $ 0 $ 10 $ 6 $ 1 $ 14 $

ICR A3  Preferred alt. 539,182 115,799 1,012,526 90 19 168 365 $ 78 $ 685 $ 488 $ 38 $ 1,433 $ 853 $ 135 $ 2,012 $ ICR SSL A3  Preferred alt. 165,909 35,632 311,559 28 6 52 112 $ 24 $ 211 $ 150 $ 12 $ 441 $ 262 $ 42 $ 619 $ ICR SSM A3  Preferred alt. 288,882 62,042 542,493 48 10 90 195 $ 42 $ 367 $ 262 $ 20 $ 768 $ 457 $ 73 $ 1,078 $

Source: Risk and Benefits Model Large Systems Small Systems All System Sizes Mean 90 % Confidence Bound Mean 90 % Confidence Bound 90 % Confidence Bound Value of Benefits for Annual Deaths Avoided ($ Millions)

Mean 90 % Confidence Bound Value of Benefits for Annual Illnesses Avoided ($ Millions) 90 % Confidence Bound Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Data Set Mean Annual Illnesses Avoided Rule Alternative Annual Deaths Avoided Total Annual Value of Benefits ($ Millions)

Mean Economic Analysis for the LT2ESWTR Proposal C 11 May 2003 Exhibit C. 7b Cases Avoided and Benefits Annualized at 3 Percent ( Based on Traditional Cost of Illness) Unfiltered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O ICR A3  Preferred alt. 546,742 117,826 1,027,377 91 20 171 111 $ 24 $ 209 $ 494 $ 38 $ 1,451 $ 605 $ 73 $ 1,622 $ ICR SSL A3  Preferred alt. 168,393 36,286 316,421 28 6 53 34 $ 7 $ 64 $ 152 $ 12 $ 447 $ 186 $ 22 $ 500 $ ICR SSM A3  Preferred alt. 293,157 63,169 550,857 49 11 92 60 $ 13 $ 112 $ 265 $ 20 $ 778 $ 325 $ 39 $ 870 $

ICR A3  Preferred alt. 7,560 1,628 14,951 1 0 2 1 $ 0 $ 3 $ 6 $ 1 $ 17 $ 7 $ 1 $ 19 $ ICR SSL A3  Preferred alt. 2,483 535 4,912 0 0 1 0 $ 0 $ 1 $ 2 $ 0 $ 6 $ 2 $ 0 $ 6 $ ICR SSM A3  Preferred alt. 4,275 921 8,455 1 0 1 1 $ 0 $ 2 $ 3 $ 0 $ 10 $ 4 $ 1 $ 11 $

ICR A3  Preferred alt. 539,182 115,799 1,012,526 90 19 168 110 $ 24 $ 206 $ 488 $ 38 $ 1,433 $ 598 $ 72 $ 1,601 $ ICR SSL A3  Preferred alt. 165,909 35,632 311,559 28 6 52 34 $ 7 $ 63 $ 150 $ 12 $ 441 $ 184 $ 22 $ 493 $ ICR SSM A3  Preferred alt. 288,882 62,042 542,493 48 10 90 59 $ 13 $ 110 $ 262 $ 20 $ 768 $ 320 $ 38 $ 858 $

Source: Risk and Benefits Model Mean Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Value of Benefits for Annual Illnesses Avoided ($ Millions) Value of Benefits for Annual Deaths Avoided ($ Millions) Total Annual Value of Benefits ($ Millions)

Mean 90 % Confidence Bound Mean Large Systems 90 % Confidence Bound Mean 90 % Confidence Bound Small Systems 90 % Confidence Bound Annual Illnesses Avoided 90 % Confidence Bound All System Sizes Data Set Rule Alternative Mean Annual Deaths Avoided Economic Analysis for the LT2ESWTR Proposal C 12 May 2003 Exhibit C. 8a Cases Avoided and Benefits Annualized at 7 Percent ( Based on Enhanced Cost of Illness) Filtered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 501,841 49,378 1,414,989 53 5 149 85 $ 8 $ 241 $ 242 $ 11 $ 831 $ 327 $ 25 $ 1,054 $ A2 484,974 47,418 1,365,967 51 5 144 83 $ 8 $ 233 $ 234 $ 10 $ 806 $ 316 $ 24 $ 1,013 $ A3  Preferred alt. 472,173 46,061 1,329,429 50 5 140 80 $ 8 $ 226 $ 228 $ 10 $ 788 $ 308 $ 23 $ 986 $ A4 393,828 38,895 1,078,615 42 4 114 67 $ 7 $ 184 $ 190 $ 9 $ 652 $ 257 $ 20 $ 818 $ A1 155,522 16,328 450,241 16 2 48 26 $ 3 $ 77 $ 74 $ 3 $ 275 $ 101 $ 7 $ 344 $ A2 107,224 10,682 317,383 11 1 34 18 $ 2 $ 54 $ 51 $ 2 $ 191 $ 69 $ 4 $ 239 $ A3  Preferred alt. 87,780 8,065 263,496 9 1 28 15 $ 1 $ 45 $ 42 $ 2 $ 156 $ 57 $ 4 $ 198 $ A4 50,427 4,277 162,677 5 0 17 9 $ 1 $ 28 $ 24 $ 1 $ 90 $ 33 $ 2 $ 112 $ A1 270,773 25,508 833,472 29 3 88 46 $ 4 $ 142 $ 130 $ 5 $ 477 $ 176 $ 11 $ 598 $ A2 227,344 21,047 710,687 24 2 75 39 $ 4 $ 121 $ 109 $ 4 $ 403 $ 148 $ 9 $ 503 $ A3  Preferred alt. 205,206 18,565 647,598 22 2 68 35 $ 3 $ 110 $ 99 $ 4 $ 365 $ 134 $ 8 $ 455 $ A4 140,363 12,065 466,456 15 1 49 24 $ 2 $ 79 $ 67 $ 3 $ 248 $ 91 $ 6 $ 312 $

A1 56,307 6,025 153,825 6 1 16 8 $ 1 $ 22 $ 23 $ 1 $ 78 $ 31 $ 2 $ 99 $ A2 54,609 5,813 149,439 6 1 16 8 $ 1 $ 22 $ 22 $ 1 $ 76 $ 30 $ 2 $ 95 $ A3  Preferred alt. 52,434 5,569 143,724 6 1 15 8 $ 1 $ 21 $ 21 $ 1 $ 73 $ 29 $ 2 $ 91 $ A4 44,163 4,843 119,399 5 1 13 6 $ 1 $ 17 $ 18 $ 1 $ 61 $ 24 $ 2 $ 75 $ A1 18,435 1,763 52,992 2 0 6 3 $ 0 $ 8 $ 7 $ 0 $ 28 $ 10 $ 1 $ 35 $ A2 13,544 1,231 39,815 1 0 4 2 $ 0 $ 6 $ 5 $ 0 $ 20 $ 7 $ 0 $ 26 $ A3  Preferred alt. 10,117 806 30,616 1 0 3 1 $ 0 $ 4 $ 4 $ 0 $ 15 $ 6 $ 0 $ 19 $ A4 7,302 530 22,471 1 0 2 1 $ 0 $ 3 $ 3 $ 0 $ 11 $ 4 $ 0 $ 14 $ A1 31,751 2,623 98,834 3 0 10 5 $ 0 $ 14 $ 13 $ 0 $ 47 $ 18 $ 1 $ 59 $ A2 27,371 2,278 85,939 3 0 9 4 $ 0 $ 12 $ 11 $ 0 $ 41 $ 15 $ 1 $ 51 $ A3  Preferred alt. 23,516 1,957 75,748 2 0 8 3 $ 0 $ 11 $ 10 $ 0 $ 35 $ 13 $ 1 $ 44 $ A4 18,731 1,586 60,257 2 0 6 3 $ 0 $ 9 $ 8 $ 0 $ 28 $ 10 $ 1 $ 35 $

A1 445,535 42,764 1,258,301 47 5 133 77 $ 7 $ 218 $ 219 $ 10 $ 756 $ 296 $ 22 $ 959 $ A2 430,365 41,219 1,213,406 45 4 128 75 $ 7 $ 210 $ 211 $ 9 $ 733 $ 286 $ 21 $ 923 $ A3  Preferred alt. 419,739 40,334 1,179,745 44 4 125 73 $ 7 $ 204 $ 206 $ 9 $ 717 $ 279 $ 21 $ 899 $ A4 349,664 34,557 958,615 37 4 101 61 $ 6 $ 166 $ 172 $ 8 $ 593 $ 232 $ 18 $ 741 $ A1 137,087 14,363 398,904 14 2 42 24 $ 2 $ 69 $ 67 $ 3 $ 247 $ 91 $ 6 $ 310 $ A2 93,680 9,246 279,141 10 1 29 16 $ 2 $ 48 $ 46 $ 2 $ 170 $ 62 $ 4 $ 213 $ A3  Preferred alt. 77,663 7,256 234,041 8 1 25 13 $ 1 $ 41 $ 38 $ 1 $ 141 $ 51 $ 3 $ 178 $ A4 43,125 3,743 140,489 5 0 15 7 $ 1 $ 24 $ 21 $ 1 $ 78 $ 29 $ 2 $ 98 $ A1 239,023 22,835 742,325 25 2 78 41 $ 4 $ 129 $ 117 $ 5 $ 430 $ 158 $ 10 $ 539 $ A2 199,973 18,720 626,235 21 2 66 35 $ 3 $ 109 $ 98 $ 4 $ 362 $ 133 $ 8 $ 454 $ A3  Preferred alt. 181,691 16,573 576,654 19 2 61 31 $ 3 $ 100 $ 89 $ 3 $ 330 $ 121 $ 7 $ 411 $ A4 121,633 10,385 404,874 13 1 43 21 $ 2 $ 70 $ 60 $ 2 $ 220 $ 81 $ 5 $ 276 $

Source: Risk and Benefits Model ICRSS Medium System 90 % Confidence Bound ICR Data Set Rule Alternative ICRSS Large System 90 % Confidence Bound All System Sizes Value of Benefits for Annual Deaths Avoided ($ Millions)

Mean Small Systems Large Systems ICR ICRSS Large System Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Total Annual Value of Benefits ($ Millions)

ICRSS Medium System ICR ICRSS Large System ICRSS Medium System Mean 90 % Confidence Bound 90 % Confidence Bound Mean Annual Illnesses Avoided Mean Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions) 90 % Confidence Bound Mean Economic Analysis for the LT2ESWTR Proposal C 13 May 2003 Exhibit C. 8b Cases Avoided and Benefits Annualized at 7 Percent ( Based on Traditional Cost of Illness) Filtered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O A1 501,841 49,378 1,414,989 53 5 149 283 $ 28 $ 797 $ 242 $ 11 $ 831 $ 524 $ 48 $ 1,559 $ A2 484,974 47,418 1,365,967 51 5 144 273 $ 27 $ 770 $ 234 $ 10 $ 806 $ 507 $ 47 $ 1,507 $ A3  Preferred alt. 472,173 46,061 1,329,429 50 5 140 266 $ 26 $ 749 $ 228 $ 10 $ 788 $ 494 $ 46 $ 1,460 $ A4 393,828 38,895 1,078,615 42 4 114 222 $ 22 $ 608 $ 190 $ 9 $ 652 $ 412 $ 39 $ 1,210 $ A1 155,522 16,328 450,241 16 2 48 88 $ 9 $ 254 $ 74 $ 3 $ 275 $ 162 $ 13 $ 516 $ A2 107,224 10,682 317,383 11 1 34 60 $ 6 $ 178 $ 51 $ 2 $ 191 $ 111 $ 9 $ 366 $ A3  Preferred alt. 87,780 8,065 263,496 9 1 28 49 $ 5 $ 148 $ 42 $ 2 $ 156 $ 91 $ 7 $ 300 $ A4 50,427 4,277 162,677 5 0 17 28 $ 2 $ 91 $ 24 $ 1 $ 90 $ 52 $ 4 $ 172 $ A1 270,773 25,508 833,472 29 3 88 152 $ 14 $ 470 $ 130 $ 5 $ 477 $ 282 $ 23 $ 899 $ A2 227,344 21,047 710,687 24 2 75 128 $ 12 $ 400 $ 109 $ 4 $ 403 $ 237 $ 19 $ 755 $ A3  Preferred alt. 205,206 18,565 647,598 22 2 68 116 $ 10 $ 365 $ 99 $ 4 $ 365 $ 214 $ 17 $ 682 $ A4 140,363 12,065 466,456 15 1 49 79 $ 7 $ 262 $ 67 $ 3 $ 248 $ 146 $ 11 $ 469 $

A1 56,307 6,025 153,825 6 1 16 27 $ 3 $ 74 $ 23 $ 1 $ 78 $ 50 $ 5 $ 147 $ A2 54,609 5,813 149,439 6 1 16 26 $ 3 $ 72 $ 22 $ 1 $ 76 $ 49 $ 4 $ 142 $ A3  Preferred alt. 52,434 5,569 143,724 6 1 15 25 $ 3 $ 69 $ 21 $ 1 $ 73 $ 47 $ 4 $ 136 $ A4 44,163 4,843 119,399 5 1 13 21 $ 2 $ 58 $ 18 $ 1 $ 61 $ 39 $ 4 $ 113 $ A1 18,435 1,763 52,992 2 0 6 9 $ 1 $ 26 $ 7 $ 0 $ 28 $ 16 $ 1 $ 53 $ A2 13,544 1,231 39,815 1 0 4 7 $ 1 $ 19 $ 5 $ 0 $ 20 $ 12 $ 1 $ 39 $ A3  Preferred alt. 10,117 806 30,616 1 0 3 5 $ 0 $ 15 $ 4 $ 0 $ 15 $ 9 $ 1 $ 29 $ A4 7,302 530 22,471 1 0 2 4 $ 0 $ 11 $ 3 $ 0 $ 11 $ 6 $ 0 $ 21 $ A1 31,751 2,623 98,834 3 0 10 15 $ 1 $ 48 $ 13 $ 0 $ 47 $ 28 $ 2 $ 89 $ A2 27,371 2,278 85,939 3 0 9 13 $ 1 $ 41 $ 11 $ 0 $ 41 $ 24 $ 2 $ 77 $ A3  Preferred alt. 23,516 1,957 75,748 2 0 8 11 $ 1 $ 37 $ 10 $ 0 $ 35 $ 21 $ 2 $ 67 $ A4 18,731 1,586 60,257 2 0 6 9 $ 1 $ 29 $ 8 $ 0 $ 28 $ 17 $ 1 $ 54 $

A1 445,535 42,764 1,258,301 47 5 133 256 $ 25 $ 722 $ 219 $ 10 $ 756 $ 474 $ 44 $ 1,417 $ A2 430,365 41,219 1,213,406 45 4 128 247 $ 24 $ 696 $ 211 $ 9 $ 733 $ 458 $ 42 $ 1,366 $ A3  Preferred alt. 419,739 40,334 1,179,745 44 4 125 241 $ 23 $ 677 $ 206 $ 9 $ 717 $ 447 $ 41 $ 1,329 $ A4 349,664 34,557 958,615 37 4 101 201 $ 20 $ 550 $ 172 $ 8 $ 593 $ 372 $ 35 $ 1,097 $ A1 137,087 14,363 398,904 14 2 42 79 $ 8 $ 229 $ 67 $ 3 $ 247 $ 146 $ 12 $ 465 $ A2 93,680 9,246 279,141 10 1 29 54 $ 5 $ 160 $ 46 $ 2 $ 170 $ 99 $ 8 $ 328 $ A3  Preferred alt. 77,663 7,256 234,041 8 1 25 45 $ 4 $ 134 $ 38 $ 1 $ 141 $ 82 $ 6 $ 270 $ A4 43,125 3,743 140,489 5 0 15 25 $ 2 $ 81 $ 21 $ 1 $ 78 $ 46 $ 3 $ 151 $ A1 239,023 22,835 742,325 25 2 78 137 $ 13 $ 426 $ 117 $ 5 $ 430 $ 254 $ 21 $ 809 $ A2 199,973 18,720 626,235 21 2 66 115 $ 11 $ 359 $ 98 $ 4 $ 362 $ 213 $ 17 $ 679 $ A3  Preferred alt. 181,691 16,573 576,654 19 2 61 104 $ 10 $ 331 $ 89 $ 3 $ 330 $ 193 $ 15 $ 618 $ A4 121,633 10,385 404,874 13 1 43 70 $ 6 $ 232 $ 60 $ 2 $ 220 $ 129 $ 10 $ 417 $

Source: Risk and Benefits Model ICR ICRSS Large System ICRSS Medium System ICR ICRSS Large System ICRSS Medium System Large Systems ICR ICRSS Large System ICRSS Medium System Small Systems All System Sizes Data Set Rule Alternative Annual Illnesses Avoided Annual Deaths Avoided Value of Benefits for Annual Illnesses Avoided ($ Millions) Value of Benefits for Annual Deaths Avoided ($ Millions) Total Annual Value of Benefits ($ Millions)

Mean Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Mean 90 % Confidence Bound Economic Analysis for the LT2ESWTR Proposal C 14 May 2003 Exhibit C. 9a Cases Avoided and Benefits Annualized at 7 Percent ( Based on Enhanced Cost of Illness) Unfiltered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O ICR A3  Preferred alt. 546,742 117,826 1,027,377 91 20 171 313 $ 67 $ 588 $ 423 $ 33 $ 1,240 $ 736 $ 117 $ 1,739 $ ICR SSL A3  Preferred alt. 168,393 36,286 316,421 28 6 53 96 $ 21 $ 181 $ 130 $ 10 $ 382 $ 227 $ 36 $ 535 $ ICR SSM A3  Preferred alt. 293,157 63,169 550,857 49 11 92 168 $ 36 $ 315 $ 227 $ 18 $ 665 $ 395 $ 63 $ 932 $

ICR A3  Preferred alt. 7,560 1,628 14,951 1 0 2 4 $ 1 $ 7 $ 5 $ 0 $ 14 $ 9 $ 2 $ 20 $ ICR SSL A3  Preferred alt. 2,483 535 4,912 0 0 1 1 $ 0 $ 2 $ 2 $ 0 $ 5 $ 3 $ 1 $ 7 $ ICR SSM A3  Preferred alt. 4,275 921 8,455 1 0 1 2 $ 0 $ 4 $ 3 $ 0 $ 8 $ 5 $ 1 $ 11 $

ICR A3  Preferred alt. 539,182 115,799 1,012,526 90 19 168 309 $ 66 $ 581 $ 418 $ 32 $ 1,227 $ 728 $ 115 $ 1,718 $ ICR SSL A3  Preferred alt. 165,909 35,632 311,559 28 6 52 95 $ 20 $ 179 $ 129 $ 10 $ 378 $ 224 $ 35 $ 529 $ ICR SSM A3  Preferred alt. 288,882 62,042 542,493 48 10 90 166 $ 36 $ 311 $ 224 $ 17 $ 657 $ 390 $ 62 $ 921 $

Source: Risk and Benefits Model Annual Deaths Avoided Total Annual Value of Benefits ($ Millions)

Mean 90 % Confidence Bound Value of Benefits for Annual Illnesses Avoided ($ Millions)

Large Systems 90 % Confidence Bound Small Systems 90 % Confidence Bound Mean 90 % Confidence Bound Annual Illnesses Avoided Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. All System Sizes Mean Mean 90 % Confidence Bound Data Set Rule Alternative Value of Benefits for Annual Deaths Avoided ($ Millions)

Mean Economic Analysis for the LT2ESWTR Proposal C 15 May 2003 Exhibit C. 9b Cases Avoided and Benefits Annualized at 7 Percent ( Based on Traditional Cost of Illness) Unfiltered Systems Only Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile) Lower ( 5th % ile) Upper ( 95th % ile)

A B C D E F G H I J K L M N O ICR A3  Preferred alt. 546,742 117,826 1,027,377 91 20 171 95 $ 20 $ 178 $ 423 $ 33 $ 1,240 $ 518 $ 62 $ 1,386 $ ICR SSL A3  Preferred alt. 168,393 36,286 316,421 28 6 53 29 $ 6 $ 55 $ 130 $ 10 $ 382 $ 159 $ 19 $ 427 $ ICR SSM A3  Preferred alt. 293,157 63,169 550,857 49 11 92 51 $ 11 $ 95 $ 227 $ 18 $ 665 $ 278 $ 33 $ 743 $

ICR A3  Preferred alt. 7,560 1,628 14,951 1 0 2 1 $ 0 $ 2 $ 5 $ 0 $ 14 $ 6 $ 1 $ 16 $ ICR SSL A3  Preferred alt. 2,483 535 4,912 0 0 1 0 $ 0 $ 1 $ 2 $ 0 $ 5 $ 2 $ 0 $ 5 $ ICR SSM A3  Preferred alt. 4,275 921 8,455 1 0 1 1 $ 0 $ 1 $ 3 $ 0 $ 8 $ 3 $ 0 $ 9 $

ICR A3  Preferred alt. 539,182 115,799 1,012,526 90 19 168 93 $ 20 $ 175 $ 418 $ 32 $ 1,227 $ 512 $ 61 $ 1,370 $ ICR SSL A3  Preferred alt. 165,909 35,632 311,559 28 6 52 29 $ 6 $ 54 $ 129 $ 10 $ 378 $ 157 $ 19 $ 422 $ ICR SSM A3  Preferred alt. 288,882 62,042 542,493 48 10 90 50 $ 11 $ 94 $ 224 $ 17 $ 657 $ 274 $ 33 $ 734 $

Source: Risk and Benefits Model Mean Note: Illnesses and deaths avoided are for each year after the rule is fully implemented in 2014. Actual illnesses and deaths avoided in earlier years depends on the implementation schedule. The values for annualized benefits already account for the phased implementation of the alternatives. Value of Benefits for Annual Illnesses Avoided ($ Millions) Value of Benefits for Annual Deaths Avoided ($ Millions) Total Annual Value of Benefits ($ Millions)

Mean 90 % Confidence Bound Mean Large Systems 90 % Confidence Bound Mean 90 % Confidence Bound Small Systems 90 % Confidence Bound Annual Illnesses Avoided 90 % Confidence Bound All System Sizes Data Set Rule Alternative Mean Annual Deaths Avoided Economic Analysis for the LT2ESWTR Proposal C 16 May 2003 Economic Analysis for the LT2ESWTR Proposal C 18 June 2003 C. 5 Individual Risk Functions Exhibits C. 10 and C. 11 display individual risk functions for filtered systems based on the ICR Supplemental Survey occurrence distribution ( ICR based charts for filtered and unfiltered systems are included in Chapter 5). The individual risk functions show the percent of a population exceeding specific risk levels given the predicted outcome of a particular regulatory alternative. Economic Analysis for the LT2ESWTR Proposal C 18 May 2003 Exhibit C. 10 Annual Individual Risk Distributions Based on ICRSSM Occurrence Data, Filtered Community Water Systems ( CWSs) Only 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1.00E 06 1.00E 05 1.00E 04 1.00E 03 1.00E 02 1.00E 01 1.00E+ 00 Individual Risk Level ( Illness Rate) Percent of Population Exceeding Risk Level Pre LT2 Opt A1 Opt A2 Opt A3 Opt A4 Example: Under Pre LT2 conditions, about 55% of the population served by Filtered CWSs have annual individual risk greater than 0.0001 ( one in ten thousand), based upon ICRSSM occurrence data.

Example: Under the preferred regulatory alternative ( A3), an estimated 38% of the population served by Filtered CWSs have annual individual risk greater than 0.0001 ( one in ten thousand), based upon ICRSSM occurrence data. Economic Analysis for the LT2ESWTR Proposal C 19 May 2003 Exhibit C. 11 Annual Individual Risk Distributions Based on ICRSSL Occurrence Data, Filtered Community Water Systems ( CWSs) Only 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1.00E 06 1.00E 05 1.00E 04 1.00E 03 1.00E 02 1.00E 01 1.00E+ 00 Individual Risk Level ( Illness Rate) Percent of Population Exceeding Risk Level Pre LT2 Opt A1 Opt A2 Opt A3 Opt A4 Example: Under Pre LT2 conditions, about 55% of the population served by Filtered CWSs have annual individual risk greater than 0.0001 ( one in ten thousand), based upon ICRSSL occurrence data.

Example: Under the preferred regulatory alternative ( A3), an estimated 41% of the population served by Filtered CWSs have annual individual risk greater than 0.0001 ( one in ten thousand), based upon ICRSSL occurrence data. Economic Analysis for the LT2ESWTR Proposal C 21 June 2003 C. 6 Real Gross Domestic Product ( GDP) per Capita The real GDP per capita projections, as shown in Exhibit C. 12, are applied to the Economic Analysis in two places. First, these values are a key input to the equation that determines the income elasticity factors applied to potentially fatal health effects. The results of these calculations are presented in section C. 7 and in Exhibit 5.22. Second, the data in Exhibit C. 12 are used to compute the income growth in the value of lost time benefits, and those results are shown in section L. 9 and Exhibit L. 11. See Section <IP_ADDRESS> for a more detailed description of both types of adjustment factors.

C. 7 Income Elasticity Factors Exhibit C. 13 shows the Consumer Price Index ( CPI) adjustment factors used to estimate data to 2000. Exhibit C. 14 uses the data from Exhibits C. 12 and C. 13 to derive the income elasticity factors used in computing the value of statistical life. The individual values by year are shown in Exhibit 5.22. Economic Analysis for the LT2ESWTR Proposal C 22 June 2003 Estimates/ Projections ( Thousands) Percent Change Projection ( Billions Chained 1996$) Percent Change Projection ( 1996$) Percent Change 1990 249,439  6,707.9  26,892  1991 252,127 1.1% 6,676.4  0.5% 26,480  1.5% 1992 254,995 1.1% 6,880.0 3.0% 26,981 1.9% 1993 257,746 1.1% 7,062.6 2.7% 27,401 1.6% 1994 260,289 1.0% 7,347.7 4.0% 28,229 3.0% 1995 262,765 1.0% 7,543.8 2.7% 28,709 1.7% 1996 265,190 0.9% 7,813.2 3.6% 29,463 2.6% 1997 267,744 1.0% 8,159.5 4.4% 30,475 3.4% 1998 270,299 1.0% 8,508.9 4.3% 31,480 3.3% 1999 272,820 0.9% 8,856.5 4.1% 32,463 3.1% 2000 275,306 0.9% 9,224.0 4.1% 33,505 3.2% 2001 277,803 0.9% 9,316.2 1.0% 33,535 0.1% 2002 280,306 0.9% 9,390.8 0.8% 33,502  0.1% 2003 282,798 0.9% 9,775.8 4.1% 34,568 3.2% 2004 285,266 0.9% 10,098.4 3.3% 35,400 2.4% 2005 287,716 0.9% 10,431.6 3.3% 36,257 2.4% 2006 290,153 0.8% 10,775.9 3.3% 37,139 2.4% 2007 292,583 0.8% 11,131.5 3.3% 38,046 2.4% 2008 295,009 0.8% 11,476.6 3.1% 38,902 2.3% 2009 297,436 0.8% 11,832.3 3.1% 39,781 2.3% 2010 299,862 0.8% 12,199.1 3.1% 40,683 2.3% 2011 302,300 0.8% 12,577.3 3.1% 41,605 2.3% 2012 304,764 0.8% 12,967.2 3.1% 42,548 2.3% 2013 307,250 0.8% 13,369.2 3.1% 43,512 2.3% 2014 309,753 0.8% 13,783.6 3.1% 44,499 2.3% 2015 312,268 0.8% 14,210.9 3.1% 45,509 2.3% 2016 314,793 0.8% 14,651.5 3.1% 46,543 2.3% 2017 317,325 0.8% 15,105.7 3.1% 47,603 2.3% 2018 319,860 0.8% 15,573.9 3.1% 48,690 2.3% 2019 322,395 0.8% 16,056.7 3.1% 49,805 2.3% 2020 324,927 0.8% 16,554.5 3.1% 50,948 2.3% 2021 327,468 0.8% 17,067.7 3.1% 52,120 2.3% 2022 330,028 0.8% 17,596.8 3.1% 53,319 2.3% 2023 332,607 0.8% 18,142.3 3.1% 54,546 2.3% 2024 335,202 0.8% 18,704.7 3.1% 55,801 2.3% 2025 337,815 0.8% 19,284.5 3.1% 57,086 2.3% 2026 340,441 0.8% 19,882.4 3.1% 58,402 2.3% 2027 343,078 0.8% 20,498.7 3.1% 59,749 2.3% Year Population Real GDP Income ( Real GDP per Capita) Exhibit C. 12 Real GDP per Capita Projections Note: 1990 2000 real GDP from Bureau of Economic Analysis, all other years calculated based on percent change projections from Congressional Budget Office ( January 23, 2002). Projections for years beyond 2012 based on percent change reported for 2012 due to lack of other data. Income ( Real GDP per Capita)= Real GDP/ Population Source: Population projections from US Census Bureau ( NP T1: Middle Series). Economic Analysis for the LT2ESWTR Proposal C 23 June 2003 CPI ( Annual Average) Percent Change Adjustment Factor ( 1990 base)

1990 130.7  1.00 1991 136.2 4.2% 1.04 1992 140.3 3.0% 1.07 1993 144.5 3.0% 1.11 1994 148.2 2.6% 1.13 1995 152.4 2.8% 1.17 1996 156.9 3.0% 1.20 1997 160.5 2.3% 1.23 1998 163.0 1.6% 1.25 1999 166.6 2.2% 1.27 2000 172.2 3.4% 1.32 2001 177.1 2.9% 1.36 Year CPI  All Items Exhibit C. 13 CPI Projections Note: 1990 base factors ( all items) used to update VSL values. Source: 1990 2001 CPI values from Bureau of Labor Statistics. Economic Analysis for the LT2ESWTR Proposal C 24 June 2003 Lower ( 5th % tile) Upper ( 95th % tile) 2004 1.131 1.131 1.131 1.131 1.031 2005 1.144 1.144 1.144 1.144 1.056 2006 1.158 1.158 1.158 1.158 1.081 2007 1.171 1.171 1.171 1.171 1.107 2008 1.185 1.185 1.185 1.185 1.134 2009 1.198 1.198 1.198 1.198 1.160 2010 1.211 1.211 1.211 1.211 1.186 2011 1.224 1.224 1.224 1.224 1.213 2012 1.237 1.237 1.237 1.237 1.241 2013 1.250 1.250 1.250 1.250 1.269 2014 1.264 1.264 1.264 1.264 1.298 2015 1.277 1.277 1.277 1.277 1.327 2016 1.291 1.291 1.291 1.291 1.357 2017 1.304 1.304 1.304 1.304 1.388 2018 1.318 1.318 1.318 1.318 1.419 2019 1.332 1.332 1.332 1.332 1.452 2020 1.346 1.346 1.346 1.346 1.485 2021 1.360 1.360 1.360 1.360 1.519 2022 1.374 1.374 1.374 1.374 1.554 2023 1.388 1.388 1.388 1.388 1.590 2024 1.402 1.402 1.402 1.402 1.627 2025 1.417 1.417 1.417 1.417 1.664 2026 1.431 1.431 1.431 1.431 1.702 2027 1.446 1.446 1.446 1.446 1.741 2028 1.460 1.460 1.460 1.460 1.782 Real Income Adjustment Factors for Indirect Medical Costs ( Point Estimates) Year Factors for Fatal Illnesses Mean Value Median Value 90 % Confidence Bound Exhibit C. 14 Factors for Incorporation of Income Elasticity into Yearly Benefits Estimates Note: Income elasticity factors calculated as [( eI1  eI2  I2  I1) / ( eI2  eI1  I2  I1)]; where e= income elasticity of WTP estimate, and I= income. Source: Derived using elasticity distributions and per capita GDP projections Economic Analysis for the LT2ESWTR Proposal June 2003 D 1 Appendix D National Costs for Rule Implementation and Monitoring D. 1 Introduction This appendix presents detailed calculations and cost tables for activities associated with LT2ESWTR implementation and monitoring for bin classification for three of the four regulatory alternatives ( Alternative A1 is not discussed because it requires all plants to implement 2 additional log of Cryptosporidium treatment and thus has no bin classification monitoring requirements). Costs for all activities are estimated as one time costs. This appendix supports the discussion of the rule activities in Chapter 6. Each set of activities is detailed separately in subsequent sections.

EPA evaluated the ICR, ICRSSM, and the ICRSSL modeled Cryptosporidium occurrence distributions to estimate the percentage of plants that would fall into any treatment bin and assumed that this percentage of small plants would be triggered to conduct Cryptosporidium monitoring. The three modeled occurrence distributions are used in this appendix to establish a range of possible costs for Cryptosporidium monitoring.

D. 2 Baseline Number of Systems and Plants Implementation Implementation costs are based on the number of PWSs that have to read and understand the rule and include all nonpurchased systems, including filtered and unfiltered systems. Purchased systems, because they are assumed to not directly treat any source water, are not expected to have any implementation costs, although these and other costs will be passed on to them in the form of higher water rates.

Source Water Monitoring for Bin Classification Source water monitoring costs are structured on a per plant basis. Also, as with implementation activities, purchased plants are assumed not to treat source water and will not have any monitoring costs. There are three types of monitoring that plants may be required to conduct   turbidity, E. coli, and Cryptosporidium. Source water turbidity is a common water quality parameter used for plant operational control. Also, to meet SWTR, LT1ESWTR, and IESWTR requirements, most water systems have turbidity analytical equipment in house and operators experienced with turbidity measurement. Thus, EPA assumes that the incremental turbidity monitoring burden associated with the LT2ESWTR is negligible ( turbidity is not evaluated in this appendix). Economic Analysis for the LT2ESWTR Proposal June 2003 D 2 All nonpurchased plants in large and medium systems are required to conduct E. coli and Cryptosporidium monitoring for bin classification with two exceptions:

° Plants that achieve 5.5 log of Cryptosporidium reduction are exempt from all monitoring requirements ( estimates of plants that meet this criteria are presented in the baseline for filtered and unfiltered plants in Chapter 4).

° Plants that have 2 years of historical Cryptosporidium data that are equivalent in sample number, frequency, and data quality ( e. g., volume analyzed, percent recovery) to data that would be collected under the LT2ESWTR with EPA Method 1622/ 23 are exempt from all monitoring requirements ( for costing purposes, EPA assumes that the number of plants able to provide historical data to meet the requirements above is negligible).

Plants in small systems will be required to conduct 1 year of semi monthly E. coli source water monitoring for bin classification, with the exception of plants achieving 5.5 log of Cryptosporidium reduction ( as presented in Chapter 4). These small plants will have to monitor for Cryptosporidium only if E. coli monitoring results exceed the following levels:

° annual mean > 10 E. coli/ 100 ml for lakes and reservoirs ° annual mean > 50 E. coli/ 100 ml for flowing streams Cryptosporidium monitoring may be comprised of 1 year of semi monthly samples or 2 years of monthly samples.

Exhibits D. 1 through D. 3 present the baseline number of systems that must conduct implementation and monitoring for Alternative A2 for CWSs, NTNCWSs, and TNCWSs, based on the ICR, ICRSSM, and ICRSSL occurrence distributions, respectively.

Exhibits D. 4 through D. 6 present the baseline number of systems that must conduct implementation and monitoring for Alternative A3 for CWSs, NTNCWSs, and TNCWSs, based on the ICR, ICRSSM, and ICRSSL occurrence distributions, respectively.

Exhibits D. 7 through D. 9 present the baseline number of systems that must conduct implementation and monitoring for Alternative A4 for CWSs, NTNCWSs, and TNCWSs, based on the ICR, ICRSSM, and ICRSSL occurrence distributions, respectively. Exhibit D. 1 Baseline Implementation and Monitoring Activities for All System Types, Based on ICR Occurrence Distribution, Alternative A2 System Size ( Population Served) Implementation Monitoring for Initial Bin Classification Future Monitoring for Re Binning Baseline # PWSs Percent of Plants with > 5.5 Log Treatment Prior to Rule Promulgation Plants Per System Baseline # of Plants Conducting E. coli Monitoring Percent of Plants Triggered to Monitor for Cryptosporidium Baseline # of Plants Conducting Cryptosporidium Monitoring Percent of Plants with > 5.5 Log Treatment for LT2 Compliance Percent of Plants with > 5.5 Log Treatment for Stage 2 Compliance Baseline # of Plants Conducting E. coli Monitoring Baseline # of Plants Conducting Cryptosporidium Monitoring A B C D = A* C*( 1 B) E F = D* E G H I = D*( 1 G H) J = E* I CWSs £ 100 101 500 501 1000 1001 3,300 3,301 10,000 10,001 50,000 50,001 100,000 100,001 1 Million > 1 Million 530 757 456 1,087 1,017 959 193 205 13 3.6% 3.6% 3.6% 3.6% 3.6% 0.4% 0.4% 0.4% 0.4% 1.0 1.1 1.0 1.0 1.2 1.1 1.8 1.6 2.8 511 803 440 1,048 1,176 1,051 346 327 36 100% 100% 100% 100% 100%     511 803 440 1,048 1,176 1,051 346 327 36 8.3% 8.3% 21.4% 21.4% 21.4% 29.8% 29.8% 29.3% 29.3% 15.0% 6.3% 6.3% 1.8% 1.8% 1.5% 1.5% 1.5% 1.5% 392 685 318 804 903 723 238 226 25 392 685 318 804 903 723 238 226 25 National Totals 5,217 5,738 5,738 4,313 4,313 NTNCWSs £ 100 101 500 501 1000 1001 3,300 3,301 10,000 10,001 50,000 50,001 100,000 100,001 1 Million > 1 Million 202 236 84 55 13 3    3.6% 3.6% 3.6% 3.6% 3.6% 0.4% 0.4% 0.4% 0.4% 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 195 228 81 53 13 3    100% 100% 100% 100% 100%     195 228 81 53 13 3 0 0 0 8.3% 8.3% 21.4% 21.4% 21.4% 30.8% 30.8% 30.4% 0.0% 15.0% 6.3% 6.3% 1.8% 1.8% 1.5% 1.5% 1.5% 1.5% 149 194 59 41 10 2    149 194 59 41 10 2    National Totals 593 572 572 454 454 TNCWSs £ 100 101 500 501 1000 1001 3,300 3,301 10,000 10,001 50,000 50,001 100,000 100,001 1 Million > 1 Million 731 380 57 53 25 8 3   0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 731 380 57 53 25 8 3   100% 100% 100% 100% 100%     731 380 57 53 25 8 3 0 0 8.3% 8.3% 21.4% 21.4% 21.4% 30.8% 30.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 670 348 45 41 19 5 2   670 348 45 41 19 5 2   National Totals 1,256 1,256 1,256 1,131 1,131 Grand Totals 7,066 7,565 7,565 5,899 5,899 Notes: Detail may not add exactly to totals due to independent rounding. Sources: ( A) Number of unlinked, non purchased SW & GWUDI systems from the Third Edition Baseline Handbook, which is based on data from EPA's Safe Drinking Water Information System ( USEPA 2000h). ( B) EPA assumes only membrane plants will have > 5.5 log Cryptosporidium treatment prior to rule promulgation. Percentage estimates are from the Economic Analysis for the Stage 2 DBPR. ( C) Estimate of the number of plants or entry points per system. Derived from 1995 Community Water System Survey data.

( E) Percent of plants triggered into Cryptosporidium monitoring is estimated from the modeled Occurrence Distributions. ( G) Derived from Appendix F. This number is calculated by dividing the number of plants estimated to be achieving 5.5 log treatment by the total number of plants for the size category. ( H) EPA assumes only membrane plants will have > 5.5 log Cryptosporidium treatment as a result of the Stage 2 DBPR. Percent estimates are from the Economic Analysis for the Stage 2 DBPR.