 ADVANCE \y63

	UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

	REGION III

1650 Arch Street

	Philadelphia, Pennsylvania  19103-2029

January 15, 2015

The Honorable Dana Aunkst

Acting Secretary, Pennsylvania Department of Environmental Protection

Rachel Carson State Office Building

P.O. Box 2063

Harrisburg, PA  17105-2063

Dear Acting Secretary Aunkst:

On September 5, 2014, the U.S. Environmental Protection Agency (EPA)
received a revision to the Pennsylvania State Implementation Plan (SIP)
from the Pennsylvania Department of Environmental Protection.  The SIP
revision requests redesignation from nonattainment to attainment, and
approval of the maintenance plan of the Philadelphia-Wilmington,
PA-NJ-DE 1997 annual and the 2006 24-hour PM2.5 area.  EPA has
determined that the submittal is administratively and technically
complete, and is reviewing it to prepare Notice of Proposed Rulemaking.

If members of your staff have any questions, they may direct them to
Rose Quinto, Office of Air Program Planning, at<PHONE_NUMBER>.  She is
the principal contact for this rulemaking.

	     Sincerely,

							/s/

David L. Arnold, Acting Director

                          			            Air Protection Division

cc:  Joyce E. Epps, Director, PADEP Bureau of Air Quality

	Customer Service Hotline: 1-800-438-2474


Draft March 12, 2004 Page 1 of 7 Appendix A Protruding Fine Mesh Screen Modification Section through Sea Chest Opening to the Ocean Protruding Fine Mesh Sea Water Intake Draft March 12, 2004 Page 2 of 7 Detail Item 1: Outer Bar Screen Draft March 12, 2004 Page 3 of 7 Detail Item 2: Fine Mesh Inner Screen Detail Item 3: Fine Mesh Frame and Inner Diffuser Detail Item 4: Protruding Main Frame Draft March 12, 2004 Page 4 of 7 Appendix B Horizontal Flow Modifier Plan View of Bottom Sea Chest Horizontal Flow Modifier Note: This modification is for use on sea chests that are located on the bottom of the vessel and no more than 30 ° of the horizontal. Flow Modifier Plate Draft March 12, 2004 Page 5 of 7 Section YY through Horizontal Flow Modifier Notes: 1. The basic assembly consists of a flow modifier plate that is stiffened using 4" flat bar welded to the under side. These flat bar stiffeners also assists in funnelling the flow into the existing intake structure. 2. A coarse mesh has been included around the perimeter of the new intake structure. This is to prevent larger animals ( like turtles) getting trapped in the gap between the hull and the flow modifier plate ( looks similar to a reef ledge to some animals). 3. 8 brackets ( 4" PFC) are permanently welded to the hull as the primary attachment points. 4. 8 legs off the flow modifier plate ( 1/ 2" plate) attach to the brackets on the hull. 3 of the bracket to leg connections use hinge pins, the other 5 legs use bolts. Releasing the bolts allows the flow modifier to swing down for maintenance or cleaning of the sea chest intake. A lifting lug should be added to the hull to allow lifting equipment can be used to safely open and close this new structure. A lifting lug has been incorporated in the costs for this item. 5. All materials used for the construction of this item will be mild steel coated in anti fouling paint. Flow Modifier Plate Draft March 12, 2004 Page 6 of 7 Plan View of Side Sea Chest Horizontal Flow Modifier Note: This modification is for use on sea chests that are located on the side of the vessel at angles greater than 30 ° of the horizontal. The direction of the flow louvres should be adjusted during the design and construction of this equipment such that they are horizontal Flow Louvres Draft March 12, 2004 Page 7 of 7 Notes: 1 The basic assembly consists of a diffuser plate nested in a number of flow louvres. The diffuser ensures that the flow is evenly distributed across the louvres and the louvres ensure that the flow is horizontal at a velocity of 0.5 ft/ sec or less. 2 2 brackets ( 2" equal angles) are permanently welded to the hull as the primary attachment points. These run the entire width and at each end of the sea chest modification. 3 The horizontal flow modifier is attached to the brackets on the hull by way of a hinge on one side and bolts on the other. By releasing the bolts, the horizontal flow modifier may be swung out away from the hull for access to the existing sea chest. 4 All materials used for the construction of this item will be mild steel coated in anti fouling paint. Flow Louvres


Observed Cryptosporidium Occurrence Exhibit 4.12 summarizes the observed Cryptosporidium occurrence of the ICR and ICRSS studies. See section 4.2.1 for a description of the study, laboratory methods, and method used to count oocysts. Additional data on observed occurrence are available in the Occurrence and Exposure Assessment ( USEPA 2003c). The data shown for " total oocysts" were used to generate the modeled Cryptosporidium distributions shown below.

Exhibit 4.12 Summary of Observed Source Water Cryptosporidium Total Oocyst Occurrence   Filtered Plant Data Data Set Total Number of Plants Number of Plants with at Least One Positive Sample ( Percent) Observed Plant Mean Data ( oocysts/ L)

Mean Median 90th Percentile ICR 350 154 ( 44%) 0.066 0.000 0.190 ICRSSL 40 34 ( 85%) 0.040 0.020 0.100 ICRSSM 40 34 ( 85%) 0.080 0.020 0.110 Notes: Total oocysts include non empty and empty oocysts. Non empty oocysts include oocysts with internal structures and with amorphous structures. For each plant, all monthly observations were averaged over the sampling period ( 12 months for ICRSSM and ICRSSL and 18 months for ICR) to produce plant mean data. The mean, median, and 90th percentile shown summarize plant mean Cryptosporidium for all plants.

Source: USEPA 2003c.

Modeled Cryptosporidium Occurrence Each of the three data sets shown in Exhibit 4.12 was used to fit an occurrence model for filtered plants using the approach outlined in section 4.2.2. As explained in that section, the modeling produces a collection of plausible occurrence distributions from each of the three data sets. Exhibits 4.13 through 4.15 summarize the resulting collection of distributions from each of the three models. The solid center curves represent the mean distribution across a given collection, and the dotted lines give a 90 percent confidence bound for the true filtered occurrence distribution based on the particular data set.

As outlined in section <IP_ADDRESS>, all three of these models were used, independently, as input to the benefits modeling. Each model is also used, along with a distribution of lab method recovery rates, to simulate results of initial LT2ESWTR Cryptosporidium monitoring. The assumed recovery rate distribution is based on " spiked" sample evaluations of the lab method that will be used for initial monitoring ( EPA Economic Analysis for the LT2ESWTR Proposal June 2003 4 33 1e 005 0.0001 0.001 0.01 0.1 1 10 Plant Mean Cryptosporidium Concentration ( Total oocysts/ L) 0% 20% 40% 60% 80% 100%

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

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

5th % tile Bound = 28%

Median Estimate = 23%

95th % tile Bound = 18% This graph shows modeled variability and uncertainty in source water Cryptosporidium occurrence for filtered systems. It summarizes 1,000 plausible curves used in the Economic Analysis.

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

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

Variability Uncertainty Methods 1622/ 23). The overall result of this Monte Carlo simulation is a predicted distribution of systems assigned to each LT2ESWTR treatment bin; this distribution serves as input to the cost model.

Exhibit 4.13 Modeled Source Water Cryptosporidium Occurrence ICR Data for Filtered Systems Source: USEPA 2003c. Economic Analysis for the LT2ESWTR Proposal June 2003 4 34 1e 005 0.0001 0.001 0.01 0.1 1 10 Plant Mean Cryptosporidium Concentration ( Total oocysts/ L) 0% 20% 40% 60% 80% 100%

Cumulative Percentage of Plants 5th/ 95th % tile Median 1e 005 0.0001 0.001 0.01 0.1 1 10 Plant Mean Cryptosporidium Concentration ( Total oocysts/ L) 0% 20% 40% 60% 80% 100%

Cumulative Percentage of Plants 5th/ 95th % tile Median Exhibit 4.14 Modeled Source Water Cryptosporidium Occurrence ICRSSM Data Exhibit 4.15 Modeled Source Water Cryptosporidium Occurrence ICRSSL Data Source: USEPA 2003c. Economic Analysis for the LT2ESWTR Proposal June 2003 4 35 1e 005 0.0001 0.001 0.01 0.1 1 10 Plant Mean Cryptosporidium Concentration ( Total oocysts/ L) 0% 20% 40% 60% 80% 100%

Cumulative Percentage of Plants ICR Unfiltered ICR Filtered SSM SSL Exhibit 4.16 Comparison of Modeled Source Water Cryptosporidium Occurrence by Data Set, Median Curves Only Source: USEPA 2003c Exhibit 4.16 compares the central tendency of the cumulative distributions for the ICR, ICRSSM, and ICRSSL data sets. As indicated by the shape of the curves, the filtered plant ICR data set describes a source water occurrence pattern with a greater frequency of high oocyst concentrations than either the ICRSSM or ICRSSL system data sets. The relatively shallower " steepness" of the ICR curve and the resulting lower and higher limits on the range of oocyst concentrations represented in the distribution implies more overall variability from plant to plant than the other two data sets. In other words, in addition to suggesting a greater frequency of high concentrations, the ICR data set also suggests a greater frequency of lower values than do the other two data sets. Exhibit 4.16 also shows for reference the central tendency of the cumulative distributions for unfiltered systems using the ICR data set.

Even though the ICRSS data represent higher sample volumes and better recoveries, the ICR distribution includes data from more source waters. Each of these three distributions represent a plausible picture of the national occurrence of Cryptosporidium. It is difficult to weigh the relative importance of sample recovery versus representativeness of the sample population. Therefore, all three occurrence distributions are used in both the benefits analyses in Chapter 5 and the cost analyses in Chapter 6. The three distributions taken together encompass a range of values that help characterize the actual occurrence of Cryptosporidium in surface waters and the uncertainty surrounding it.

4.5.4 Finished Water Cryptosporidium Occurrence for Filtered Plants Pre LT2ESWTR finished water Cryptosporidium concentrations ( presented in this section) will be compared to predicted Post LT2ESWTR finished water levels ( presented in Chapter 5) to assess the 7 As a separate part of predicting Pre LT2ESWTR baselines, the baseline of plants and systems subject to LT2ESWTR provisions was reduced to account for the percentage of plants that are predicted to have installed microfiltration/ ultrafiltration ( MF/ UF) before the implementation of LT2ESWTR. Plants with MF/ UF will not be required to conduct LT2ESWTR monitoring and will be exempt from additional Cryptosporidium reduction requirements.

Economic Analysis for the LT2ESWTR Proposal June 2003 4 36 benefits of the regulatory alternatives. As with the treatment characterization presented above, the finished water Cryptosporidium occurrence estimates must account for finished water improvements predicted to result from implementation of other existing rules or rules under development for implementation prior to LT2ESWTR. This section describes the methodology for predicting Cryptosporidium finished water occurrence based on the treatment in place following the IESWTR, LT1ESWTR, Stage 1 DBPR, and Stage 2 DPBR.

Compliance with the IESWTR, LT1ESWTR, and the Stage 1 and 2 DBPRs requires or will require some systems to modify their treatment processes to improve performance or control formation of DBPs. Other rules are not expected to have an appreciable impact because they affect mainly ground water systems. EPA used the EAs from the IESWTR and the LT1ESWTR, along with more recent plant performance data, to predict the number of systems in each size category that have made or will need to make treatment modifications and the effectiveness of those modifications. ( These rules have not been in place long enough for all plants to have actually implemented modifications and to have gathered information on their effectiveness.) The IESWTR and the LT1ESWTR establish filtration requirements EPA believes provide finished water having a minimum 2 log ( 99 percent) reduction in Cryptosporidium concentrations relative to source water levels. Although systems are required to meet only the 2 log removal target, it is recognized that most systems will achieve greater levels of Cryptosporidium removal. For example, some systems have unit processes in addition to conventional treatment that provide higher Cryptosporidium removals. To capture the different levels of treatment, EPA estimated a range of Cryptosporidium removals for calculating finished water occurrence. 7 Exhibit 4.17 shows the range of Cryptosporidium log reduction that treatment plants are expected to achieve just prior to implementation of the LT2ESWTR. To account for systems using conventional treatment and minimally meeting the IESWTR and LT1ESWTR effluent turbidity requirements ( shown in the top half of Exhibit 4.17 as the standard estimate), the low end of the range is set at 2.0 log. This reduction is based on the requirements of the IESWTR and LT1ESWTR ( which specify 2.0 log removal) and on several studies that show plants can achieve 2 log Cryptosporidium treatment credit even under stressed conditions. These studies are described in Chapter 7 of the Occurrence and Exposure Assessment ( USEPA 2003c). As described in section 4.5.1, many plants are expected to be eligible for 0.5 log additional treatment credits for additional unit processes or for achieving effluent turbidity below 0.15 NTU. For these systems, the range of performance was shifted up by 0.5 log to reflect the improved performance, as shown in the bottom half of Exhibit 4.17.

Based on the studies cited in the Occurrence and Exposure Assessment, large systems under the standard estimate are thought to achieve a maximum Cryptosporidium log reduction of 4.5. Many factors can negatively affect the log reduction systems are able to achieve, and small systems are thought to be more significantly impacted by these factors. For example, smaller systems typically have fewer filters than large systems. One consequence is that a single filter performing poorly is more likely to cause poorer overall system performance. In addition, backwashing a filter in a small system with few filters is more likely to cause hydraulic fluctuations that could result in poorer performance of the other filters. Small systems also tend to have less automated control and monitoring equipment, which makes controlling temporary aberrations more difficult. For this reason, the maximum log reduction small Economic Analysis for the LT2ESWTR Proposal June 2003 4 37 systems are estimated to achieve is smaller than that achieved by large systems ( for the standard estimate, 3.5 log vs. 4.5 log). For systems eligible for an additional 0.5 log reduction credit, the top end of the range is increased to 4.0 log for small systems and 5.0 log for large systems.

Exhibit 4.17 Predicted Ranges of Cryptosporidium Reduction Pre LT2ESWTR System Size ( Population Served) Exhibit Range of Log Reduction Mode   Lower End Mode   Higher End Small (< 10,000) Standard Estimate 4.18a 2.0  3.5 2.25 log 2.75 log Large ( $ 10,000) Standard Estimate 4.18b 2.0  4.5 2.5 log 3.0 log Small (< 10,000) Estimate w/ 0.5 Log Removal Credit 4.18c 2.5  4.0 2.75 log 3.25 log Large ( $ 10,000) Estimate w/ 0.5 Log Removal Credit 4.18d 2.5  5.0 3.0 log 3.5 log Source: Chapter 7, LT2ESWTR Occurrence Assessment ( USEPA 2003c).

The log reduction values at the low and high ends of the ranges are thought to be the exception more than the rule. The studies cited in the Occurrence and Exposure Assessment noted median and/ or average log reductions that fall in the middle of the ranges shown in Exhibit 4.17. Although little specific information is available on how often these values occur, there is thought to be a central tendency. Therefore, modes of 2.5 to 3.0 log were chosen for the distribution of log reductions for large systems under the standard estimate ( see second row of Exhibit 4.17). Values in between 2.5 and 3.0 log are thought to have the same possibility of being modes.

As described above, small systems are more likely to have filtration performance problems. The modes at small systems are, therefore, assumed to be lower than for large systems   2.25 and 2.75 logs under the standard estimate ( first row of Exhibit 4.17). The bottom half of Exhibit 4.17 shows the estimated modes for large and small systems for systems getting a 0.5 log reduction credit, which was also applied to the modes.

An upper and lower bound, combined with a selected mode, defines a triangular distribution. In Exhibit 4.18, the minimum and maximum of the range define the base of the triangle and the mode defines the top. Each graph in Exhibit 4.18 corresponds to the range of one of the rows in Exhibit 4.17. Two triangles are shown for each range, illustrating the lower and upper modes. The triangles defined by all the modes in between are not shown, but are indicated by the two way arrow on each graph. Each possible triangle, of which there is an infinite number, is considered equally likely to represent the log reduction distribution for a given system size category and treatment scenario. For example, Exhibit 4.18a shows two triangular distributions for small systems under the standard treatment estimate, which, as described in Exhibit 4.17, have a range of 2.0 to 3.5 log reduction and modes of 2.25 and 2.75 log. For the same group of systems, there are an infinite number of triangles with the same range and with modes between 2.25 and 2.75 log. Taken together, the collection of all the distributions for a given group of Economic Analysis for the LT2ESWTR Proposal June 2003 4 38 systems ( e. g., small systems without a 0.5 log reduction credit) reflects the uncertainty about the true Cryptosporidium log reduction.

These triangular distributions are used in benefit and cost modeling. For a given model iteration for a given system size, the model first decides whether systems get the 0.5 log reduction credit ( 36 percent of small systems, 55 percent of medium systems, and 58 percent of large systems are assumed to qualify). Then it randomly selects a mode from the appropriate set of triangular distributions. From the individual distribution associated with that selected mode, the model randomly picks 100 log removal values from that distribution and uses these log reduction values to predict finished water Cryptosporidium concentrations. This process is repeated for 250 modes.

Exhibit 4.19 shows an example of the finished water concentration distributions generated with a modeling process similar to that described above ( the curves in 4.19 were generated choosing only 250 points from each triangular distribution). These distributions were generated using the same process as the source water Cryptosporidium concentrations for different data sets in Exhibit 4.16 so that the differences between them would be directly comparable. Because disinfection in unfiltered systems is expected to have a negligible effect on Cryptosporidium concentrations, finished water concentrations in unfiltered plants are assumed to be identical to source water concentrations ( compare Exhibits 4.16 and 4.19). For filtered plants, however, modeled source water concentrations were higher than those for unfiltered plants ( see Exhibit 4.16), but modeled finished water concentrations were well below those same unfiltered plant concentrations. Economic Analysis for the LT2ESWTR Proposal June 2003 4 39 2 3 4 5 Log Removals Relative Probability Mode = 3.0 Mode = 2.5 Exhibit 4.18b Distribution of Cryptosporidium Reduction in Large Systems Pre LT2ESWTR, Standard Estimate 2 3 4 5 Log Removals Relative Probability Mode = 3.5 Mode = 3.0 Exhibit 4.18d Distribution of Cryptosporidium Reduction in Large Systems Pre LT2ESWTR, Estimate With 0.5 Log Reduction Credit 2 3 4 5 Log Removals Relative Probability Mode = 3.25 Mode = 2.75 Exhibit 4.18c Distribution of Cryptosporidium Reduction in Small Systems Pre LT2ESWTR, Estimate With 0.5 Log Reduction Credit 2 3 4 5 Log Removals Relative Probability Mode = 2.75 Mode = 2.25 Exhibit 4.18a Distribution of Cryptosporidium Reduction in Small Systems Pre LT2ESWTR, Standard Estimate Source: USEPA 2003c. Economic Analysis for the LT2ESWTR Proposal June 2003 4 40 0% 20% 40% 60% 80% 100%

1.00E 10 1.00E 09 1.00E 08 1.00E 07 1.00E 06 1.00E 05 1.00E 04 1.00E 03 1.00E 02 1.00E 01 1.00E+ 00 1.00E+ 01 Finished Water Cryptosporidium ( Oocysts/ L) Cumulative Probability ICR FIL  SM ICRSSL  SM ICRSSM  SM ICR UNF  SM 0% 20% 40% 60% 80% 100%

1.00E 10 1.00E 09 1.00E 08 1.00E 07 1.00E 06 1.00E 05 1.00E 04 1.00E 03 1.00E 02 1.00E 01 1.00E+ 00 1.00E+ 01 Finished Water Cryptosporidium ( Oocysts/ L) Cumulative Probability ICR FIL  LG ICRSSL  LG ICRSSM  LG ICR UNF LG Exhibit 4.19a Predicted Finished Water Cryptosporidium Occurrence Pre LT2ESWTR, Small Systems Exhibit 4.19b Predicted Finished Water Cryptosporidium Occurrence Pre LT2ESWTR, Large Systems Source: USEPA 2003c Economic Analysis for the LT2ESWTR Proposal June 2003 4 41 4.5.5 Comparison of EPA Finished Water Cryptosporidium Estimates with Aboytes et al. ( 2000)

A study by Aboytes et al. ( 2000) provides an alternative perspective on Cryptosporidium occurrence in finished drinking water and the efficacy of treatment. This study involved collecting 100 L finished water samples on a monthly basis from 80 surface water utilities. Samples were analyzed for infectious Cryptosporidium parvum with a cell culture PCR method. The objective of the study was to " assess the adequacy of treatment to protect against infectious Cryptosporidium in drinking water." All utilities in the study were enrolled in the Partnership for Safe Water, a voluntary cooperative program that seeks to optimize treatment plant performance. Most samples had turbidity below 0.1 NTU and all were below 0.3 NTU, the standard set by the IESWTR. Among 1,674 samples of 100 L each, 24 were positive for infectious C. parvum ( LeChevallier 2001). The authors determined an average CC PCR recovery efficiency of 32.3 percent. Hence, if it is assumed that one infectious oocyst accounted for each positive sample, and the oocyst count is adjusted for average recovery, these results produce a mean concentration of infectious oocysts of 4.4 × 10 4 oocysts/ L, or 0.044 oocysts/ 100 L.

To compare results from Aboytes et al. with EPA finished water Cryptosporidium estimates based on results from the ICR and ICRSS, it is necessary to consider the fraction of oocysts that are infectious. Because oocysts lose viability in the environment, it is expected that infectious oocysts are only a fraction of the total number of oocysts in a water sample. While the CC PCR method registers only infectious oocysts, the ICR Method and EPA Methods 1622/ 23 count total oocysts without regard to whether they are viable and infectious. To estimate the fraction of oocysts that may be infectious, EPA evaluated a study by LeChevallier et al. ( 2003) that analyzed several hundred source water samples from six utilities using both the CC PCR method and Method 1623. Oocysts were detected in 60 of 593 samples ( 10.1 percent) by Method 1623 and infectious oocysts were detected in 22 of 560 samples ( 3.9 percent ( by the CC PCR procedure. Recovery efficiencies for the two methods were similar. According to the authors, these results suggest that approximately 37 percent of the oocysts detected by Method 1623 were viable and infectious. Based on these results, as well as consideration of the structure of counted oocysts, EPA assumes that the fraction of oocysts that is infectious may average from 15 to 25 percent for the ICR and 30 to 50 percent for the ICRSS ( described in Section 5.2.4).

If the estimates of mean large plant finished water oocyst concentrations from Exhibit 4.19b are multiplied by 40 percent to adjust for the fraction of oocysts that are infectious, the mean finished water concentrations of infectious oocysts are as follows:

ICR Mean = 6.1 × 10 5; ICRSSM Mean = 2.5 × 10 5; ICRSSL = 1.4 × 10 5 ( oocysts/ L)

Thus, the mean finished water infectious oocyst level reported in Aboytes et al. of 4.4 × 10 4 oocysts/ L is a factor of 7 greater than the EPA mean estimate based on the ICR and a factor of 30 greater than the ICRSSL estimate. While the reason for this significant discrepancy is unknown, it cannot be fully attributed to potential error in factors such as the fraction of oocysts that are infectious. Rather, it may indicate that the ICR and ICRSS underestimate source water Cryptosporidium occurrence and/ or that EPA has overestimated treatment efficacy, as discussed below.

Unfortunately, source water data are not available for plants in the Aboytes et al. study, so it is not possible to directly determine their average treatment efficiency. However, removal efficiency may be estimated based on other survey data. The ICRSS was conducted during a time frame similar to Aboytes et al., and the filtration and separation steps used in the CC PCR method of Aboytes et al. are similar to those in Method 1622/ 23 used in the ICRSS. Consequently, the ICRSS source water data may be somewhat comparable to the Aboytes et al. finished water data. The mean source water Economic Analysis for the LT2ESWTR Proposal June 2003 4 42 Cryptosporidium concentration of plants in the ICRSS, adjusted for average recovery of 43 percent, was 0.14 oocysts/ L. If this value is multiplied by 40 percent as an estimate of the fraction of oocysts that are infectious, the result is a mean source water concentration of 0.056 infectious oocysts/ L. If this were the mean source water concentration in the Aboytes et al. study, then the plants in that survey achieved an average removal of 1.7 log to produce the mean finished water concentration of 0.0011 oocysts/ L that was measured. For the plants in the Aboytes et al. survey to have achieved a mean oocyst removal of 2.5 log, which was the lowest mean removal assumed in EPA estimates, the mean source water oocyst concentration would have to have been over twice that measured during the ICRSS. Either hypothesis indicates that EPA may have underestimated the risk from Cryptosporidium in drinking water by underestimating finished water oocyst levels.

4.5.6 Predicted Bin Classification for Filtered Plants Under the LT2ESWTR, filtered plants will be assigned to a treatment bin based on the results of source water monitoring for Cryptosporidium. Each bin is defined by a range of oocyst concentrations and determines the amount of treatment each plant must provide. Using the modeled source water occurrence distributions described in section 4.5.3, EPA predicted the percentage of filtered plants that will fall into each bin. Appendix B presents the probability functions for the occurrence distributions used to evaluate bin classification. Note that the tables in Appendix B estimate the percentage based on a prediction of the test measurement mean, which is the estimated underlying source water concentration adjusted for sampling and laboratory analysis error. Bins are based on the predicted lab results, not estimates of the " true" concentration. Exhibit 4.20 presents the central tendency of plant binning based on a Monte Carlo evaluation of the probability distributions for the three data sets of Cryptosporidium occurrence for the Preferred Regulatory Alternative. Note that the estimate based on the ICR data set is the most conservative of the three filtered plant data sets; the predicted percentage of plants requiring treatment is largest for the ICR.

The percentages shown in Exhibit 4.20 were used to determine costs of installing treatment for each occurrence distribution. Plants were expected to incur different costs depending on the bin to which they were assigned.

Exhibit 4.20 Predicted System Binning for Preferred Alternative, Based on Central Tendency of Cryptosporidium Occurrence Data Set No Action 1 Log Bin ( 0.075   1 oocysts/ L) 2 Log Bin ( 1   3 oocysts/ L) 2.5 Log Bin (> 3.0 oocysts/ L) Total ICR 65.4% 27.2% 4.4% 3.2% 100%

ICRSSM 72.9% 25.4% 1.4% 0.4% 100%

ICRSSL 77.7% 21.8% 0.5% 0.06% 100%

Note: Numbers may not add to exactly 100 percent due to rounding. Bin assignment is based on the highest running annual average ( RAA) of concentrations of 24 influent samples taken over 2 years.

Source: Appendix B, Exhibit B. 8. Economic Analysis for the LT2ESWTR Proposal June 2003 4 43 4.6 Baseline for Uncovered Finished Water Reservoirs Number of Reservoirs The LT2ESWTR baseline for uncovered finished water reservoirs is presented in Exhibit 4.20. Data on uncovered finished water reservoirs were provided by EPA regional offices based on information on States in their region and do not include reservoirs that are scheduled to be covered or taken off line. Most uncovered finished water reservoirs are located in only a few States; specifically California, New York, New Jersey, and Oregon, and Puerto Rico. The largest is in Southern California. Although there are only 138 uncovered finished water reservoirs, there is a limited amount of information on them. The information provided differed by State and region. Most systems only reported reservoir volume or surface area. Very few reported both parameters, forcing EPA to make assumptions on volume to calculate surface area ( surface area was later used to determine costs for covering or treating reservoirs). Ownership and population served by each reservoir were also often unclear.

For this analysis, the reservoirs are categorized according to usable volume ( in millions of gallons, or MG). The mean volume, shown in the last column of Exhibit 4.21, is the average usable volume of all reservoirs in a volume category and is used to estimate costs in Chapter 6.

Exhibit 4.21 Baseline Numbers of Uncovered Finished Water Reservoirs Size Category ( Usable Volume in MG) Number of Reservoirs Mean Usable Volume ( MG)

0  0.1 25 0.01 > 0.1  1 7 0.43 > 1  5 44 2.59 > 5  10 12 8.55 > 10  20 10 15.32 > 20  40 9 28.68 > 40  60 4 52.99 > 60  80 4 71.44 > 80  100 6 93.57 > 100  150 6 125.34 > 150  200 2 187.00 > 200  250 4 210.75 > 250  1,000 4 781.50 > 1,000 1 3,313.80 Total 138

Source: EPA regions. Economic Analysis for the LT2ESWTR Proposal June 2003 4 44 System Sizes for Uncovered Reservoirs In order to apportion the costs to PWSs of treating water from uncovered finished water reservoirs or of covering them, EPA estimated the size of the systems that the reservoirs serve. To do this, the average daily flow for each system size ( USEPA 2001c) was used. The average daily flow was then substituted into a regression equation from the Model Systems Report ( USEPA 2000b) that predicts flow based on population ( see Exhibit 4.4). The equation yielded the population that the reservoir would be expected to serve if the entire system flow passed through the reservoir. Many reservoirs are only one of several in a system, and in such cases, there is no basis to determine what percentage of the full flow might pass through a single reservoir. Exhibit 4.22 shows the distribution of uncovered filtered water reservoirs by system size. Once size categories are determined, the number of systems is multiplied by the average population per system for that size category and divided by 2.59, the number of people per household ( U. S. Census Bureau 2001), to obtain the number of households served by such reservoirs.

Exhibit 4.22 Baseline Number of Uncovered Finished Water Reservoirs in Systems of Each Size Category System Size ( Population Served) Number of Reservoirs < 100 25 101  500 0 501  1,000 0 1,001  3,300 0 3,301  10,000 7 10,001  50,000 66 50,001  100,000 9 100,001  1 M 30 > 1 M 1 Total 138 Source: Exhibit 4.21 and regression equation from Model Systems Report ( USEPA 2000b).

Uncovered Reservoir Surface Area, Average Daily Flow, and Design Flow The costs for covering reservoirs are based largely on the surface area of the reservoir, while costs for treating the discharge for 4 log virus inactivation are based on flow through the reservoir. As with technologies used for Cryptosporidium removal, the design flow and average daily flow are used to estimate capital and O& M costs of reservoir treatment, respectively. The purpose of this section is to derive mean surface area, average daily flow, and design flow for each size category of uncovered finished water reservoirs. Economic Analysis for the LT2ESWTR Proposal June 2003 4 45 For some cases, EPA regions provided the surface area for individual uncovered finished water reservoirs. Where this information was not available, engineering assumptions were necessary. To calculate surface area if not known, a representative reservoir depth of 25 feet was assumed based on industry consultation. The mean surface area for each size category presented in Exhibit 4.23 is the average surface area of all reservoirs in the size category, whether based on actual data or based on volume with an assumed 25 foot depth.

Hydraulic residence time, defined as the time water spends inside a reservoir, can be used in conjunction with volume to estimate average daily flow through a reservoir. Hydraulic residence times in finished water storage reservoirs vary greatly among systems and seasons. The shortest times are often in the Summer, while the longest may be during the lower demand periods in the Winter. Because water systems strive to maintain a certain volume of storage in the distribution system for emergencies such as fire, residence times can be as great as several weeks. Within the last several years water systems have focused more on water quality in the distribution system and have decreased average residence time, striving to " turn over" finished water in storage facilities on a regular basis. Considering these factors, typical hydraulic residence times of 1 to 3 days were used in conjunction with volume to estimate average daily flow for reservoirs up to 100 million gallons in size, as presented in Exhibit 4.21. For the very largest reservoirs, longer hydraulic residence times are more likely; residence times as high as 21 days are assumed.

Design flow, used to estimate capital costs, represents the maximum flow exiting the reservoir. Finished water storage facilities respond to daily water demand fluctuations in the distribution system in order to maintain a more constant flow at the treatment plant. Therefore, maximum or design flow from a reservoir may be higher than the average flow at the treatment plant. Peak hourly flows in a distribution system have been estimated as three times the average flow for hydraulic modeling purposes ( Lindeburg, 1997). Therefore, the design flow presented in Exhibit 4.23 is estimated to be three times the average daily flow. Economic Analysis for the LT2ESWTR Proposal June 2003 4 46 Exhibit 4.23 Surface Area and Flows for Uncovered Finished Water Reservoirs Size Category ( Usable Volume, MG) Number of Reservoirs Mean Volume ( MG) Surface Area ( sq. ft.) Estimated Hydraulic Residence Time ( days) Average Daily Flow ( mgd) Design Flow ( mgd)

A B C D E F 0  0.1 25 0.01 176 1 0.01 0.04 > 0.1  1 7 0.43 2,952 1 0.43 1.30 > 1  5 44 2.59 14,127 1 2.59 7.78 > 5  10 12 8.55 45,703 2 4.27 12.82 > 10  20 10 15.32 84,165 3 5.11 15.32 > 20  40 9 28.68 213,890 3 9.56 28.68 > 40  60 4 52.99 283,353 3 17.66 52.99 > 60  80 4 71.44 365,472 3 23.81 71.44 > 80  100 6 93.57 517,699 3 31.19 93.57 > 100  150 6 125.34 670,246 4 31.34 94.01 > 150  200 2 187.00 999,934 4 46.75 140.25 > 200  250 4 210.75 1,126,931 4 52.69 158.06 > 250  1,000 4 781.50 3,482,390 14 55.82 167.46 > 1,000 1 3,313.80 7,666,876 21 157.80 473.40 Note: Data shown are rounded.

Sources: [ A] EPA regions. [ B] EPA regions. [ C] EPA regions for some reservoirs. Surface area, if not provided for an individual reservoir, is estimated based on data on volume ( from EPA regions) and an assumed depth of 25 feet. [ D] EPA regions. [ E] Average daily flow is equal to reservoir volume ( Column B) divided by hydraulic residence time ( Column C). [ F] Design flow is three times the average daily flow.

4.7 Households Incurring Costs Due to the LT2ESWTR Estimating the number of households in systems affected by the LT2ESWTR is necessary to derive a national distribution of the rule's annual costs. Exhibit 4.24 shows the various sets of households and their relationship to one another. Economic Analysis for the LT2ESWTR Proposal June 2003 4 47 Implementation Costs All Households Monitoring Costs Uncovered Finished Water Reservoirs Treatment Costs Unfiltered Treatment + Monitoring + Implementation Filtered Treatment + Monitoring + Implementation Unfiltered Treatment + Uncovered Reservoir + Monitoring + Implementation Filtered Treatment + Uncovered Reservoir + Monitoring + Implementation Uncovered Reservoir + Monitoring + Implementation Monitoring + Implementation Implementation Exhibit 4.24 Universe of Households Affected by Rule Provisions Exhibit 4.25 presents estimates of the number of households served by surface and GWUDI systems, further subdivided by those subject to rule provisions. The estimates were derived by dividing the total population served by CWSs subject to each rule provision per size category by an average number of people per household of 2.59 ( U. S. Census Bureau 2001). Only CWS population is converted to household estimates because it is assumed that only CWSs serve residential customers.

Exhibit 4.27 presents the estimated mean water usage rates ( in gallons per year) per household for each system size category. These estimates are based on total residential consumption and number of residential connections from the Baseline Handbook. The consumption in the two smallest size categories was adjusted based on analyses of CWSS data. Economic Analysis for the LT2ESWTR Proposal June 2003 4 48 System Size ( Population Served) Households Paying Implementation Costs Households Paying Monitoring Costs Households Paying Treatment Costs for Unfiltered Systems Households Paying Treatment Costs for Filtered Systems A B C D < 100 9,115 8,787 29 9,086 101 500 78,220 75,404 519 77,702 501 1,000 123,007 118,578 2,092 120,915 1,001 3,300 788,354 759,974 8,603 779,752 3,301 10,000 2,319,316 2,235,821 57,915 2,261,401 10,001 50,000 9,752,041 9,716,319 136,507 9,615,534 50,001 100,000 6,436,207 6,412,631 80,083 6,356,124 100,001 1 Million 26,841,790 26,743,469 323,995 26,517,795 > 1Million 19,468,929 19,397,614 3,922,990 15,545,939 Total 65,816,980 65,468,597 4,532,732 61,284,247 System Size ( Population Served) Households Paying Uncovered Finished Reservoir Costs < 100 574 101 500 0 501 1,000 0 1,001 3,300 0 3,301 10,000 13,469 10,001 50,000 541,715 50,001 100,000 133,227 100,001 1 Million 2,002,187 > 1Million 320,762 Total 3,011,934 Exhibit 4.25 Baseline Numbers of Households Incurring Costs Notes: All unfiltered systems will incur treatment costs. Not all filtered systems shown above will incur treatment costs   the households shown in Column D represent all systems that conducted monitoring less systems that are predicted to have installed MF/ UF to comply the the Stage 2 DBPR. Some of these systems will be assigned to the " no action" bin and will not incur treatment costs.

Sources: [ A] SDWIS population from unlinked inventory ( USEPA 2000f) / 2.59 people per household ( U. S. Census Bureau 2001). [ B] Implementation baseline ( Column A) * percentage of systems subject to monitoring requirements ( 1  Exhibit 4.11, Column C). [ C] Treatment baseline population for unfiltered systems ( Exhibit 4.5, Column D) / 2.59 people per household. [ D] Treatment baseline population for filtered systems ( Exhibit 4.11, Column I) / 2.59 people per household.

Exhibit 4.26 Households Paying Treatment Costs for Uncovered Reservoirs Source: Appendix I. Economic Analysis for the LT2ESWTR Proposal June 2003 4 49 Exhibit 4.27 Mean Household Water Usage Rates by System Size System Size ( Population Served) Mean Household Water Usage Rate ( gal/ yr)

< 100 83,000 101 500 83,000 501 1,000 104,000 1,001 3,300 87,000 3,301 10,000 97,000 10,001 50,000 109,000 50,001 100,000 119,000 100,001 1 Mil 125,000 > 1 Mil 125,000 Source: USEPA 2001c, modified for systems serving 500 or fewer people based on CWSS data.

4.8 Summary of Uncertainties in Development of LT2ESWTR Baselines Uncertainties in this baseline analysis could result in either an overestimate or underestimate of the costs or benefits presented in Chapters 5 and 6. Exhibit 4.28 below presents a summary of these issues and an estimate of the effects that each source of uncertainty may have on subsequent analyses. Note that, in many cases, assumptions made in this baseline will overestimate both costs and benefits; however, costs are overestimated in more cases than are benefits. Economic Analysis for the LT2ESWTR Proposal June 2003 4 50 Exhibit 4.28 Summary of Uncertainties Affecting LT2ESWTR Baseline Estimates Assumption Section with Discussion of Uncertainty Effect on Benefit Estimate Effect on Cost Estimates Underestimate Overestimate Underor Over Estimate Underestimate Overestimate Underor Over Estimate Uncertainty in baseline data inputs ( SDWIS, ICR, and ICRSS data) 4.3.2 4.4.2 X X Point estimates instead of distributions for population and flow 4.3.3 X X CWS flow equations for NTNCWSs 4.3.3 X X No unfiltered plants have advanced disinfection 4.4.1 X X Filtered plants do not get credit for ozone and ClO2 from Stage 2 4.5.1 X Predicted Pre LT2ESWTR Cryptosporidium removal using triangular distributions ( with uncertain modes) and log reduction achieved 4.5.1 X X Uncertainty in baseline surface area and flows for uncovered finished water reservoirs 4.6 X X Note: The uncertainties associated with some assumptions are discussed in more detail in Chapters 5 and 6, and so the summaries of those assumptions are reserved until the ends of those chapters. Those key assumptions include the occurrence in source water and modeling of occurrence in finished water; risk parameters, such as infectivity and the percent of viable oocysts; and binning assignments.


[Federal Register: August 16, 2006 (Volume 71, Number 158)]
[Notices]
[Page 47200]
[DOCID:fr16au06-70]

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DEPARTMENT OF ENERGY

Federal Energy Regulatory Commission

[Project No. 405-068]

Susquehanna Power Company and PECO Energy Power Company; Notice
of Availability of Environmental Assessment

August 9, 2006.
    In accordance with the National Environmental Policy Act of 1969
and the Federal Energy Regulatory Commission's regulations, 18 CFR part
380 (Order No. 486, 52 FR 47879), the Office of Energy Projects has
reviewed an application for non-project use of project lands and waters
and has prepared an environmental assessment (EA) for public review.
The licensees seek Commission approval to permit use of the Conowingo
Hydroelectric Project, FERC No. 405, reservoir and lands to withdraw
and discharge water. The EA analyzes the environmental impacts of the
proposed water withdrawal and discharge. The project is located on the
Susquehanna River in Hartford and Cecil Counties in Maryland, and York
and Lancaster Counties in Pennsylvania.
    The EA was written by staff in the Office of Energy Projects,
Federal Energy Regulatory Commission (Commission). A copy of the EA is
attached to a Commission order titled ``Order Modifying and Approving
Non-Project Use of Project Lands and Waters'' issued August 8, 2006 and
is available for review at the Commission or may be viewed on the
Commission's Web site at http://www.ferc.gov using the ``eLibrary''

link. Enter the docket number excluding the last three digits in the
docket number field to access documents. For assistance, contact FERC
Online Support at<EMAIL_ADDRESS>or toll-free at 1-866-208-
3676, or for TTY,<PHONE_NUMBER>.
    Any comments on the EA should be filed within 30 days from the
issuance date of this notice, and should be addressed to the Secretary,
Federal Energy Regulatory Commission, 888 First Street NE., Room 1-A,
Washington, DC 20426. Please affix ``Conowingo Project No. 405-068'' to
all comments. Comments may be filed electronically via Internet in lieu
of paper filings. The Commission strongly encourages electronic
filings. See 18 CFR 385.20011(a)(1) (iii) and the instructions on the
Commission's Web site under the ``eFiling'' link.

Magalie R. Salas,
Secretary.
[FR Doc. E6-13370 Filed 8-15-06; 8:45 am]

BILLING CODE 6717-01-P