Although the comment discusses issues with the use of the peak concentration for exposure estimation, the peak concentration was not used in simulations. Rather, simulations were run at the model intercept, set at the mean profile liter in the original dataset, which was well-supported by the underlying dataset. This point corresponded approximately to the fifth liter in a sampling series and is representative of a liter of water drawn at approximately 30 seconds of flushing. The peak of predicted concentrations for locations with LSL occurred slightly before this point. EPA also did not fit models to estimate highest possible concentration peaks, as doing so would have required adjustment for LSL volume and plumbing volume by site (Lytle et al., 2019), for which insufficient data were available. The WALSC was not used in this analysis for several reasons. First, not all sites in all studies included in the primary benefits analysis contained complete profiles representing the first draw and LSL-related concentration increases through decline and stabilization of lead concentrations. As such, limiting the analysis to locations with this full profile would have greatly reduced the available data. Additionally, as flushing sufficient to avoid exposure to water that has stagnated in LSL may be uncommon consumer behavior (Riblet et al., 2019), this was deemed an inappropriate measure of exposure for benefits estimation. EPA has added text in Appendix F.3 of the Final LCRR EA to clarify this estimate (USEPA, 2020a).
WLL Dataset Selection, Cleaning, and Model Performance
Summary of Comments
The commenter notes concerns regarding the data used for water concentration modeling, including data selection and assignment to LSL and CCT categories. The commenter suggests that use of splines and log-transformation on a reduced dataset may obscure model performance.
Agency Response
EPA conducted extensive review of the sampling data collected from the 15 water systems included in the profile liter dataset. Chapter 6, Section 6.2 and Appendix F have been updated in the Final LCRR EA (USEPA, 2020a) to better describe data selection, validation of LSL status using notes in the original data sets, and CCT categorization for the profile liter dataset. The full dataset meeting preliminary selection criteria that was available to EPA for this analysis is present in the docket, with a field describing whether each record was included in the analysis. In general, data were removed from the analysis if there was no data for important parameters, such as LSL status (unlike national level data on the location of LSLs these data were compiled from studies with detailed information on LSL status providing a high level of accuracy for LSL status), or information regarding cumulative volume after stagnation at which samples were collected. For locations where LSLRs had occurred and determination of timing in relationship to sampling was feasible, the last sample taken after LSLR was selected for analysis, to avoid short-term increases in lead concentration that may occur after replacement which are managed through the use of filters and flushing protocols.
Since proposal, EPA has updated CCT categories for three city water systems in response to AWWA comments, and in response to comments from a peer reviewer during submission of a manuscript related to this work (Stanek et al., 2020). Specifically, data were re-categorized from "Representative" to "Partial" to reflect input regarding city water systems for Cincinnati before 2006, Providence, and Halifax. These changes slightly decreased differences among interventions in simulated water lead concentrations and related BLLs (see Section 6.2 and Appendix F.1 of the Final LCRR EA), decreasing benefits slightly (see Section 6.4 of the Final LCRR EA).
EPA does not agree that log-transformation obscures model performance in this case. Lead concentrations at the tap show a censored, right-skewed distribution. EPA opted for log-transformation of lead concentrations rather than employing more complex methods, such as Bayesian techniques, or lower-power non-parametric methods, to manage the skewed data distribution. The geometric mean, which is a more appropriate measure of central tendency for lead concentration at the tap than the arithmetic mean, is equal to the arithmetic mean of log-transformed values after back-transformation to the original scale.
EPA disagrees with the commenter that the use of a spline transformation is inappropriate and obscures data patterns. The use of a spline transformation for cumulative sample volume (Profile Liter) was indicated by the availability of lead concentrations for samples taken at different points in the sampling series after stagnation. The effect of the cumulative volume drawn from a tap after stagnation on WLL is non-linear. While EPA could have selected just one or two points in the sampling series, such as the first liter, the fifth liter, and/or the peak lead concentration for each given location, doing so would have greatly reduced the number of samples and locations available for analysis, and would have obscured variability present in the full dataset. Appendix F.3 of the Final LCRR EA has been updated to provide additional detail on the choice of spline function and additional comparisons to support that the placement of inflection points reflected the underlying data variation in a reasonable manner.
Data Availability for Non-LSL Locations
Summary of Comments
Multiple commenters, including North Dakota Department of Environmental Quality, City of Aurora, Colorado Water Utility Council, and City of Golden, responded to EPA's request for additional information regarding the effects of CCT on lead concentrations in locations without LSL stating that they agreed the data were limited, but that they had no additional data to add. Commenters noted that LSLs are not the only source of lead in drinking water, and that other plumbing components such as lead solder and fixtures, can contribute to elevated lead levels. One commenter also noted the difficulty of fully distinguishing non-LSL locations due to historical patch jobs and other issues that could hide the presence of an LSL. One commenter requested guidance on how to best sample and track impacts of CCT in non-LSL locations. Multiple comments raised concerns regarding the development of benefits estimates and CCT optimization requirements based on the limitations of the existing data.
Agency Response
EPA agrees that LSLs are not the only source of lead that can contribute to elevated concentrations at the tap. However, where present LSLs are the largest source of lead in drinking water (Sandvig et al., 2008). EPA received additional data from the AWWA during the comment period pertaining to State of Michigan LCR 2019 compliance tap sampling. This dataset contains 12,960 samples collected from homes, businesses, and other buildings in 630 water systems from January to October of 2019.
The Michigan 2019 compliance data allowed some comparisons on non-LSL locations in systems with and without CCT, based on available drinking water inventories. The analysis of those data has been added to Appendix F, Section F.4 of the Final LCRR EA (USEPA, 2020a), and supports a conclusion that the presence of CCT at non-LSL locations can reduce lead in drinking water concentrations. Because the comparisons are necessarily across differing water systems rather than comparisons of before/after CCT optimization, EPA acknowledges that the current data still limit accurate analysis of the non-LSL household benefits of optimizing CCT and therefore could not be used with high certainty in the primary quantified assessment of benefits for the final LCRR. EPA finds that the information based on the Michigan data in Appendix F and the sensitivity analyses in Appendix G of the Final LCRR EA do provide useful information for decision making, demonstrating the potential for significant benefits resulting from the reduction of water corrosivity at households without LSLs. The sensitivity analysis in Appendix G assigned benefits to CCT in non-LSL location using the range of geometric mean WLL for non-LSL locations in the original, un-simulated, dataset. EPA estimated that the final LCRR will produce improvements in CCT at between 8 and 17 million households.
In response to the commenter asking for guidance on how to best sample and track impacts of CCT in non-LSL locations, EPA has determined that the first draw sampling methodology is an effective sampling technique to identify lead corrosion from taps, solder, pipes and fittings within the home. EPA requires this sampling method under the Final LCRR at all non-LSL sample locations. The final rule's tiering requirements for non-LSL tap sampling still prioritize those locations with the highest risk for elevated levels of lead namely, copper pipes with lead solder installed before the effective date of the state's lead ban. In addition, the Agency intends to develop implementation guidance targeting the areas of the rule that are most likely to support compliance. EPA will also provide training and other supporting materials that will help states and water systems implement the revised rule and promote greater national consistency.
EPA disagrees with commenters that have indicated that insufficient information exists for the development of benefits estimates and regulatory requirements associated with CCT. As described in Chapter 6 of the Final LCRR EA, EPA has quantified and monetized benefits associated with CCT changes at LSL households using 18,039 samples collected from 1,638 homes in 15 cities representing 14 city water systems across the United States and Canada (see Section 6.2 of the Final LCRR EA). These data were used to fit a LME model with explicit statistical controls for sampling methods (through the `Profile liter' term), city, site, and sampling event timing; that model was used to simulate concentrations for samples in cities, sites, and sampling events not in the original dataset. EPA has also characterized these through sensitivity analysis (Appendix G of the Final LCRR EA), and has determined a set of non-quantified benefits associated with the regulatory changes associated with CCT. Non-quantified benefits of improved CCT optimization and monitoring for elevated lead concentrations for occupants in homes that do not have LSL (Section 6.6 of the Final LCRR EA), and estimate that over the 35-year period of analysis, between 8 (low-cost) and 17 (high-cost) million homes may see reductions in tap water lead concentrations. This substantial assessment of the benefits associated with various levels of CCT implementation along with the estimated costs and engineering assessment of the CCT technology has provided sufficient information to the Agency to set regulatory CCT requirements.
Post-Flint Voluntary State Actions and WLL Dataset Limitations
Summary of Comments
Several commenters, expressed concerns regarding accounting of costs and benefits considering both non-compliance and voluntary state actions to reduce WLL. A commenter cited the CoSTs study, and EPA has assumed that the commenter refers to discussions in ASDWA Costs of States' Transactions Study (CoSTS) (USEPA, 2020a) regarding incorporations of voluntary post-Flint state actions in baseline benefits calculations. One of these commenters also discussed additional concerns regarding the dataset used for WLL estimation and reporting of results.
A commenter discusses data limitations in EPA's WLL profile dataset with respect to geographic coverage and the number of city water systems. They also critique excess precision in results reporting.
This same commenter discusses potential issues with double-counting of benefits: applying benefits obtained under a current rule to projected benefits under a revised rule. This commenter interprets the inclusion of WLL data from Flint Michigan, and DC during periods in which these systems were non-compliant with the Current LCR as evidence of such "double-counting."
A second commenter cites the ASDWA's CoSTS to discuss the argument that voluntary state actions post-Flint shouldn't be incorporated in baseline benefits analysis, as some states must match federal regulations exactly.
Agency Response
EPA used lead concentration data from samples collected in 14 city water systems to support the estimate of central tendency and variance in lead concentration for each intervention category. The Final LCRR EA discusses potential issues with this approach, including limited geographic coverage that could encompass diverse source water chemistry and infrastructures. EPA used all available data meeting inclusion criteria, described further in response comments regarding limited data from AWWA, Indiana Department of Environmental Management, North Dakota Department of Environmental Quality, City of Aurora, and City of Golden. EPA disagrees that precision presented in the Proposed LCRR EA (USEPA, 2019a) and Final LCRR EA (USEPA, 2020a) represent "excess precision." The presented results are intermediate to the longer modeling process for BLL, and no policy action is required based on these results in isolation. EPA reported geometric means of simulated values to several digits of precision to better represent how those intermediate outputs were incorporated in the BLL modeling. Rounding to lower precision is inappropriate for intermediate steps of an analysis. This provides detail in intermediate steps such that they could be reproduced more readily. Also included are simulated GSDs and a plot showing broad intervals around the simulated values to avoid misleading readers regarding the known variability present in the estimation. EPA also discussed the uncertainty regarding generalizability in the text. No changes were made to the Final LCRR EA based on this comment.
EPA disagrees with the comment that the economic analysis is double counting, or inflating, the benefits of the final LCRR by assuming non-compliance with the previous LCR and full compliance with the revised LCRR. In fact, to avoid doing so, EPA assumes full compliance under both the previous LCR and the final LCRR. EPA has not included any additional non-LCR required actions by states in the baseline previous LCR cost or benefit values. Therefore, when EPA calculates the incremental benefits, by subtracting the benefits of the previous LCR, from the benefits of the final LCRR, only the benefits associated with the changes in requirements under the LCRR are included in the benefit estimate. In addition, EPA is not justifying the final LCRR based on compliance issues with the previous LCR.
Comparing values across the rules would not be appropriate given the changes in benefit estimation methodology over the time period.
The use of data from cities like Flint and Washington, as well as the 13 other cites, is appropriate in determining baseline and post intervention WLLs that represent combinations of LSL and CCT status. The LSL and CCT status of individual samples in the dataset are the relevant predictors of WLL. Compliance with regulatory requirements is not considered in the subpart of the benefits analysis. Later the Safewater LCR model predicts the interventions that will result in reductions in WLLs based on data sources outlined in Chapters 4 and 5 of the Final LCRR EA and a methodology provided in Chapter 5 and Appendix B of the Final LCRR EA (USEPA, 2020a).
EPA disagrees that compliance with previous regulations is relevant to estimating the effects of LSL presence or CCT optimization on WLL. EPA acknowledges that physical factors other than LSL, CCT, or position in sampling series ("Profile Liter") may affect WLL. To address these unknown effects of location, timing and water quality, EPA used a LME model that controlled for other effects of city water system, site, and sampling event through the inclusion of random effects.
EPA disagrees that premise plumbing is the primary source of lead in drinking water for locations with LSL and disagrees that the choice of the "fifth liter" necessarily overestimates benefits. As the commenter notes, sources of lead in municipal drinking water are not homogeneously distributed along the water mains, service lines, and premise plumbing to the consumers' taps. Therefore, the concentration of lead at the tap will continuously vary in a nonlinear fashion; this will differ by address, city water system, and time/date of water use and may include both dissolved and particulate lead. Most past studies of BLL in relationship to water have included samples, such as fully flushed or first-draw samples, that may not be representative of real world exposures, and therefore, may have underestimated potential contributions of water to BLL elevations (Stanek et al., 2020). However, due to limited data regarding water usage in relationship to common sampling protocols, EPA did not consider the effects of human usage patterns. The use of the "fifth liter" was primarily driven by support for that point along the sampling series in the underlying data, and although it is close to the modeled peak lead concentration, does not represent the actual peak of either the fitted model, or the underlying data. Appendix F.3 of the Final LCRR EA has been added to clarify this choice in simulated WLL and BLL.
90th Percentile in WLL Estimation
Summary of Comments
A commenter stated that use of the 90[th] percentile lead concentrations is inappropriate for calculating the central tendency of lead concentrations.
Agency Response
EPA agrees that the use of 90[th] percentile lead concentrations would be inappropriate for estimated central tendency lead concentration values. The Agency did not use 90[th] percentile lead concentrations to calculate central tendencies of lead concentrations in the proposed or final rule analyses. Real-world study data rather than the 90[th] percentile risk management metrics were used to fit models describing potential effects of intervention and to simulate the geometric mean of WLL for exposure simulations. Additional information regarding data source and cleaning have been added in Appendix F.1 of the Final LCRR EA (USEPA, 2020a). Lead 90[th] percentile data were used for estimation of how many exceedances would occur under the previous rule and the LCRR. These are directly relevant to the cost of implementing the rule and have been retained.
16.4.2 Assignment of Lead Concentrations to Populations in Baseline and Tracking Concentration Changes in SafeWater
Sampling Tier Design for Geographic Diversity and Lead Concentrations at Non-LSL Locations
Summary of Comments
Overall, the commenter stated that sampling and managing CCT to reduce lead concentrations at locations with LSLs may limit optimization of CCT for the entire service area. The commenter stated that LSLs serve a small portion of housing stock in most water systems, that lead solder, galvanized lines impacted by LSLs, and brass fixtures can also increase lead concentrations. To support this point, the commenter presented the results of an analysis of Michigan water sampling data, where using first draw sample data they contended that in water systems with both LSL and non-LSL locations, the highest lead concentrations were found in non-LSL locations approximately 25 percent of the time.
The commenter states that requiring geographic representativeness for other water quality parameters along with lead sampling may not be possible for some water systems due to the location and extent of LSL housing stock, and stated that compliance samples should be collected in locations that coincide with water quality parameter modeling. The commenter suggested that sampling and managing water primarily to reduce lead in locations with LSL may result in decisions that do not adequately address lead release at locations without LSLs and that do not adequately manage other metals, longevity of infrastructure, or aesthetic interest. The commenter also states that larger multi-family structures are less likely to be serviced by LSLs, and that in some areas, vulnerable populations, including low-income and non-white populations, may be clustered in newer low-income housing or larger multi-family structures that experience non-LSL lead contributions to WLL. The commenter criticizes the decision to not consider the benefit of managing lead levels in homes without LSLs.
The commenter recommended tap sample pool tiering revision to avoid weighting compliance data towards portions of water systems with LSLs, as well as more holistic guidance regarding CCT optimization that considers factors other than lead. The commenter recommended revision of tap sampling tiers to diversify samples for geographic area, water quality parameter measurement locations, and non-LSL sites.
Agency Response
EPA agrees that locations without LSL can experience significant lead release from solder, brass, and other plumbing components, and that locations without LSLs constitute the majority of housing stock in most water systems. However, where present, LSLs represent the largest contributor to lead in tap water (Sandvig et al., 2008; Kaplan, 2017). EPA used the data provided by AWWA for the state of Michigan's 2019 compliance sampling to investigate several questions, including the potential benefits of CCT improvements for non-LSL households (AWWA, 2020). This analysis is presented in Appendix F, Section F.4 and Appendix G, Section G.1 of the Final LCRR EA (USEPA, 2020a). Based on multiple sensitivity analyses, EPA estimated that the final LCRR will produce improvements in CCT at between 8 and 17 million households without LSLs. Although EPA had insufficient data to quantify national benefits to the non-LSL households in its primary analysis the Agency considered the unquantified benefits to these households in its decision making process.
EPA examined the commenter's analysis of the Michigan 2019 data, especially as it related to systems that had both LSL and non-LSL locations where non-LSL locations showed the highest lead concentrations. EPA disagrees with the commenter that the use of first draw data alone is the appropriate metric for drawing a comparison between WLLs in homes with and without LSLs. The State of Michigan's LCR (Michigan Department of Environment, Great Lakes, and Energy (2019) require system with LSL to collect a 1st and 5th liter sample from those lines therefore the presence of 5[th] liter samples indicates the presence of an LSLs at the sampled location. After data cleaning as detailed in Appendix F, Section F.4 of the Final LCRR EA (USEPA, 2020a), the Michigan 2019 sampling data contained 47 cities for which there are both homes with and without fifth liter samples. One of those cities had no lead detected in any sample. Addresses from the Michigan 2019 data without inferred LSLs represent the maximum lead concentration in 8 of 47 cities. Of those addresses, only three locations had lead concentrations exceeding 10 ug/L, and the highest concentration was 78 ug/L. In contrast, in the 38 water systems where locations with inferred LSL presence had the highest concentrations, 29 of the locations had lead concentrations exceeding 10 ug/L. The highest concentration was 590 ug/L, in a fifth liter sample. The corresponding 90[th] percentile calculations for the first and fifth liters for the Michigan 2019 dataset can be found in "Derivation of Initial P90 Categorization_Final Rule.xlsx." In general, this comparison supports the importance of prioritizing locations with LSLs in compliance sampling tiers.
EPA disagrees that tap sampling tiers focused on LSL locations are likely to result in increased risk of lead exposure from other sources. EPA's tap sample tiering requirements are designed to look first at the locations with the highest potential for lead release. LSLs have the highest potential (Sandvig et al., 2008; Kaplan, 2017). In the absence of sufficient LSL sampling locations samples must be taken from the lead solder sites which have the next highest likelihood of increased WLL. EPA's revised sampling tiers ensure priority is given to the highest-risk sources of lead while allowing for flexibility in water systems without sufficient numbers of LSLs in any given tier, or with a substantial fraction of multi-family residences in the structure inventory. See response to comment Section 9.0.
EPA requires installation of CCT when the lead 90[th] percentile value exceeds 15 ug/L and CCT re-optimization when the 90[th] percentile value exceeds 10 ug/L. EPA has demonstrated in its analysis that under the new LSL focused sampling requirements that more systems will be compelled to take corrective actions to lower lead levels through increased use of CCT and re-optimization of existing CCT. CCT is designed to reduce corrosion of lead throughout a water system and in general the chemical reactions that bind lead to the distribution system source material work as well at locations with lead solder and high lead brass as it does in LSLs. Non-LSL systems will still sample from locations that have the greatest risk for high WLLs and are still required to take corrective action by installing or re-optimized CCT to reduce corrosion.
Benefit Timing for LSLR and CCT Optimization
Summary of Comments
A commenter discussed multiple points regarding the timing of modeled benefits and expected lags in benefits under the Proposed LCRR. First, the commenter discussed assumptions that LSLR, whether partial, or full, will result in immediate and permanent reductions in WLL and concerns regarding lags in effectiveness as well as overall effectiveness of LSLR, noting that it is unclear how much of the lead in tap water comes from LSLs. The commenter characterizes the primary Proposed LCRR EA analysis as having assumed that 100 percent of lead in drinking water comes from LSLs (USEPA, 2019a).
The commenter cites the USEPA SAB's finding that studies of LSLR have been too short to fully assess reductions in WLLs, and that partial LSLRs may result in longer-lasting and more severe elevations in lead concentration (USEPA, 2011).
Additionally, the commenter notes that effects of CCT optimization will not be immediate. The commenter notes that POU use is unlikely to be perfect as assumed in the model. Finally, the commenter discusses details of the immediacy of reductions in BLL in response to both LSLR and CCT implementation, along with effects of averaging BLL over individual lifetimes to estimate the benefit of the rule over time and contends that inaccuracies here will result in an overestimate of IQ benefits.
Agency Response
EPA disagrees that it is necessary to incorporate a lag in benefits after LSLR. Short-term effects of LSLR are managed through the distribution of pitcher filters to affected residences and post-replacement testing to ensure WLL have decreased. This limits exposure to lead from short-term spikes after LSLR. The final rule contains text requiring six months of cartridges and instructions to help consumers maintain the filters and use them consistently. The Final LCRR EA accounts for the costs of these pitcher filters. Additionally, although LSLRs have been known to cause short-term increases in lead concentration, new high pressure flushing methods can help to minimize these, though this method may not be suitable for all locations (WRF, 2018). Text has been added in Section 6.3 of the Final LCRR EA to note the possibility of imperfect use of pitcher filters and other POU devices (USEPA, 2020a). Also see response to comment Section 6.
EPA does not agree that there is great uncertainty in how much LSLs contribute to WLL or in how much LSLR will lower WLL. The benefits analysis was based on real-world data from locations with LSLs. Many of the sites sampled that did not have LSLs had undergone LSLR. In some cases, both before and after samples were included for residences, for multiple types of replacement, including full and partial. The "after" LSLR values in the dataset come from 212 households and the sample time post replacement generally ranges in time after the LSLR from 1 month to multiple years, where those times are known. WLL reductions attributed to LSLR in the primary benefits analysis are, in general, directly related to LSL presence/absence rather than changes in other plumbing components. Appendix F.1 in the Final LCRR EA has been updated to better describe the data selection and cleaning process.
EPA did consider the EPA SAB's comments regarding partial LSLs in developing both the economic analysis and the proposed and final rule. Because partial replacements may be necessary for emergency maintenance, they cannot be banned, and, partial LSLRs can reduce lead concentrations at the tap (see Final LCRR EA Section 6.2). However, as the commenter notes, partial LSLRs introduce risk for increased lead in drinking water due to disturbance of the remaining LSL. Because of this risk, partial LSLRs do not count towards the LSLR goal rate or mandatory LSLR rate in the final LCRR. Therefore, only the impact of full replacements and the total removal of preexisting partial LSLs are assessed in the benefits analysis. Also see response to comment Section 6. No changes have been made to the economic analysis for the final rule in response to this comment.
EPA does not agree with the commenter's characterization that the benefits analysis assumes 100 percent of lead in drinking water comes from LSLs. Although the primary benefits analysis lacked data for characterizing reductions in lead due to CCT improvements in locations without LSLs, locations without LSLs were not modeled as having no lead in drinking water, but as having a distribution of lead concentrations, centered around 0.82 ug/L for all CCT categories. Additional sensitivity analysis presented in Appendix G of the Final LCRR EA shows the additional benefits that may be expected under improved CCT in non-LSL locations. See response to comment Section 16.4.1 regarding locations without LSLs.
EPA agrees with the comment that there will be a delay in the reduction of lead concentrations throughout the distribution system after CCT is re-optimized. Under the revised analysis for the final LCRR, EPA included a 2-year lag between the installation of CCT and the accrual of benefits associated with these reduced lead concentrations. EPA does not agree that there will be a significant lag between the implementation of POU devices and lower lead concentrations being consumed. POU devices are designed to meet the filtration standard at the time of installation. Therefore, EPA does not include a lag between POU implementation, and the benefits associated with lower lead concentrations in the water consumed. No changes have been made to the Final LCRR EA on the basis of this comment.
EPA does not agree with the comment that benefits are overestimated due to the timing of reductions in BLLs in relationship to the rule implementation. The lag in benefits is implemented through lifetime averaging of individual BLLs at age 7, which, for individuals born before rule implementation, incorporates yearly BLL from both before and after rule implementation. It is possible that the time it takes for children to reach a new equilibrium BLL concentration after a reduction in WLL will result in a brief time lag, but we expect this to be small. The half-life of lead in blood is approximately 20 to 30 days, and changes in lead exposure result in a new quasi-steady state BLL in approximately 75 to 100 days (USEPA, 2013). This comment is further addressed under response to comment Section 16.4.6 "SafeWater Calculation of Benefits and Monetized Results." No changes have been made to the Final LCRR EA on the basis of this comment.
EPA indicates in the primary benefits analysis that uncertainty exists in the data and it may not be representative of all situations. However, this is the largest best available real-world dataset compiled to examine the effects of LSL removal and CCT optimization that EPA is aware of. Generally, EPA acknowledges that in some areas, such as in the timing of benefits relative to intervention, benefits may be overestimated. However, other commenters, including the SAB, have noted that benefits may have been greatly underestimated in other areas.
16.4.3 Estimation of Blood Lead Level (BLL) Changes in Children
Natural Resources Defense Council (NRDC) discussed concern for underestimating exposure when using water lead concentrations representative of first draw samples to estimate BLLs through SHEDS-IEUBK. The SAB also requested additional details on the role of biological variability in the relationship between lead intake and BLLs, as well as additional blood lead distributional information. Each of these topic areas are discussed below.
Selection of Water Lead Concentrations for BLL Modeling
Summary of Comments
A commenter expressed concern regarding underestimation of exposure that could occur through using concentrations representative of first draw samples to estimate BLL through SHEDS-IEUBK modeling. The commenter notes that first-draw samples are almost always lower in lead concentration than samples drawn later in a sampling series. As such, they contend that the use of first liter samples would underestimate exposure in LSL locations, and thus benefits related to both CCT optimization and LSL removal. The commenter recommended use of a more dynamic model to estimate exposure that accounts for the fluctuation in lead concentration that occurs after stagnation and notes that sampling methodologies relying on first-liter draws after stagnation will have similar underestimates of exposure.
Agency Response
EPA corrects the commenter on the mistaken impression that lead exposure estimates are based solely on collected first liter WLL. EPA used the geometric mean and GSD for the simulated lead concentration at the mean value for cumulative sample volume (Profile Liter) for input into the SHEDS-IEUBK model for BLL comparisons, and to estimate benefits. Real-world exposure studies that capture the similarity of sampling profile to typical water usage patterns are uncommon (but see Riblet et al., 2019). Therefore, the choice of cumulative volume position in the sampling series for BLL simulation was indicated largely by strong representation in the underlying data. See Chapter 6, Section 6.2 and Appendix F.3 of the Final LCRR EA (USEPA, 2020a). Appendix F.3 has been added to the Final LCRR EA to better describe the simulated WLL relationship to the sampling series after stagnation.
Further, based on additional information received during public comment, the final rule contains a requirement that at homes with LSLs, fifth liter samples will be collected after water has sat stagnant for a minimum of 6 hours. Only the first liter will be required for locations without LSLs. The inclusion of fifth liter samples in the required sampling protocol for locations with LSLs is expected to increase the number of systems with LSLs that would exceed the lead trigger level and action level (see Chapter 4, Section 4.3.5 of the Final LCRR EA).
Biological Variability
Summary of Comments
The SAB requested additional details that explain the role that biological variability plays in the relationship between lead intake and BLLs in the analysis done by EPA.
Agency Response
EPA agrees with the SAB that further explanation is needed in the economic analysis pertaining to how biological variability in the relationship between lead intake and BLLs is handled in the analyses. In its basic coupled form, SHEDS-IEUBK only represents exposure variability and does not consider biological variability associated with inter-individual differences in the relationship between lead exposure and blood lead. Uncoupled IEUBK applies a GSD of 1.6 to outputs to account for biological variability and measurement error, but because the IEUBK component of SHEDS-IEUBK has been reduced to deterministic regression equations, EPA was unable to apply this GSD to account for biological variability in our outputs. To account for this biological variability, EPA applied a biological variance correction factor of 0.185 for 1- to <2-year-olds and 0.176 for 2- to <7-year-olds to the predicted blood lead variance estimated by the SHEDS-IEUBK model. These biological variance correction factors are consistent with what was used in the Zartarian et al. (2017) analysis and adjust the variance of the blood lead distribution so that the GSDs match those of NHANES data from 2009-2014. Additional details about the calculation of these biological variance correction factors can be found in Zartarian et al. (2017). In response to these comments, EPA has added additional clarification of these biological variance correction factors to Chapter 6 of the Final LCRR EA (USEPA, 2020a).
Blood Lead Distribution
Summary of Comments
The SAB expressed interest in seeing additional information related to the BLL distribution, paying particular attention to the proportion of children estimated to have BLLs above 5 μg/dL.
Agency Response
EPA agrees that additional blood lead distributional information from the SHEDS-IEUBK model could be added to the economic analysis for the purposes of providing useful information on the scope of impacts across the modeled population. In response to these comments, EPA has added additional blood lead distributional information to Appendix G of the Final LCRR EA (USEPA, 2020a). Exhibit G.1, Exhibit G.2, Exhibit G.3, and Exhibit G.4 present 25[th], 75[th], 95[th], and 99[th] percentile coupled SHEDS-IEUBK BLL estimates for each year of life up to age 7 for each LSL and CCT scenario, as well as an average lifetime BLL assuming a child lived in the corresponding LSL/CCT scenario for their entire life. To facilitate comparison with the 5 ug/dL level of blood lead, lifetime average BLLs above 5 ug/dL are in bold lettering in each of these exhibits. Lifetime average BLLs in these exhibits start to exceed 5 ug/dL in the 95[th] percentile exhibit, and only in the LSL/no CCT and LSL/partial CCT scenarios.
16.4.4 WLL Reductions through Avoided IQ Point Loss
Summary of Comments
EPA received comments from the SAB requesting the Agency clarify choices and decisions for the functions and metrics used to quantify avoided IQ loss associated with the reduction in WLLs. Generally, the commenters provide citations from the literature that strengthens the evidence for a relationship between blood lead and IQ loss, and in particular, a steeper slope in the relationship between blood lead and IQ loss at lower BLLs. Additionally, the SAB requested more discussion on the choice of the specific function used to estimate IQ loss based on blood lead, and the choice of lifetime vs. concurrent blood lead.
Agency Response
EPA will continue to use lifetime average blood lead changes (over the period of birth to age 7) to estimate benefits of the rule, with the concentration-response function based on Crump et al. (2013). Additional language was added in Chapter 6 (see Section 6.4 of the Final LCRR EA) to discuss the implication of this choice for benefits, and a sensitivity analysis was run using concentration-response functions based on Kirrane and Patel (2014).
Generally, the commenters provide citations from the literature that strengthen the evidence for a relationship between blood lead and IQ loss, and in particular, a steeper slope in the relationship between blood lead and IQ loss at lower BLLs. EPA has added additional language in Chapter 6 of the Final LCRR EA describing how the choice of the beta estimate from Crump et al. (2013) to both the concurrent metric from that study and the lifetime and concurrent beta estimates from Lanphear et al. (2005).
The choice to use Crump et al. (2013) was made to minimize the issues with over estimating the predicted IQ loss at the lowest levels of lead exposures (< 1 ug/dL). Crump et al. (2013) added 1 to the BLLs before making the log transformation to ensure that the log-linear function held at lower concentrations of lead. The other papers did not make such a transformation, and their lower level estimates were difficult to interpret (as log of zero is undefined). However, the Lanphear results addressed this issue through the National Ambient Air Quality Standards  with a low-dose linearization. Results based on the corrected Lanphear betas are presented in Appendix G, Section G.5 (Exhibits G.14 through G.17 of the Final LCRR EA), both with and without low-dose linearization, and do result in higher benefits estimates.
As stated in Appendix G:
      Both of the alternative concentration-response functions [from Lanphear et al (2005)] result in higher benefits than those calculated using the concentration-response function from Crump et al. (2013) in Chapter 6. As can be seen below, use of the low-dose linearization slightly decreases national annual benefits as compared to use of the log-linear function with the same slope but without a low-dose linearization. Specifically, using the alternative concentration-response function increases the benefits by 50% and 54% with and without the low-dose linearization assuming a 3% discount rate, and 50% and 54% with and without the low-dose linearization assuming a 7% discount rate, compared to the high cost estimate Crump et al. (2013) based estimates presented in Chapter 6.
Table 1 displays the estimated slope coefficients, or beta estimates, and R[2] values for the Lanphear (2005), Kirrane and Patel (2014), and Crump et al. (2013) studies all of which examined data from the same cohort. Crump et al. (2013) used data from Lanphear (2005) to calculate beta values and R[2] for lifetime and concurrent exposure; all other analyses performed and the resulting R[2] estimates are quite similar. Crump et al (2013) further tested to identify which BLL measure best described the data and did not find a statistical difference in the significance of the lifetime and concurrent BLL explanatory variables. They performed sensitivity analysis where 72 "influential points" were removed from the dataset without a clear explanation. Upon exclusion of these 72 data points the researchers found that the lifetime BLL explanatory variable was no longer significant in a model that also includes the other four BLL measures. The exclusion of these 72 data points without justification appears to support the conclusion that the authors are making. However, without these exclusions, the choice between lifetime and concurrent exposure is not driven by statistics but becomes situational. Table 7 shows the coefficient for lifetime is nearly the same as in Table 6 of Crump et al., (2013) where it is the sole measure (-5.34 versus -5.32). However, the coefficient for concurrent is much smaller in Table 7 then in Table 6 (-2.18 versus -4.42). This provides evidence for lifetime as the stronger predictor. Additionally, the paper states "Taken at face value, use of concurrent BPb [blood lead level] to describe the exposure response implies that the effect of BPb upon IQ is reversible, as zero concurrent BPb would indicate zero effect of BPb upon IQ, regardless of the past history of exposure. This seems unlikely to be true" (Crump et al., 2013 p. 797)."
Table 1. Comparison of Coefficients[a] based on the Lanphear et al. (2005) Dataset
BLL Variable
 Kirrane and Patel (2014)
Lanphear (2019) erratum
Crump et al. (2013) Reanalysis ln(BLL)
Crump et al. (2013) Independent Analysis ln(BLL+1)

β (95% CI)
R2
β (95% CI)
R[2 b]
β (95% CI)
R[2]
β (95% CI)
R[2]
                                     Early
                             -2.21 (-3.38, -1.04)
                                     0.643
                             -2.21 (-3.38, -1.04)
                                      n/a
                             -2.21 (-3.38, -1.03)
                                     0.643
                             -2.46 (-3.81, -1.10)
                                     0.659
                                     Peak
                             -2.86 (-4.10, -1.61)
                                     0.640
                             -2.86 (-4.10, -1.61)
                                      n/a
                             -2.86 (-4.10, -1.61)
                                     0.640
                             -2.48 (-3.83, -1.14)
                                     0.656
                                   Lifetime
                             -3.14 (-4.39, -1.88)
                                     0.641
                             -3.25 (-4.51, -1.99)
                                      n/a
                             -3.19 (-4.45, -1.94)
                                     0.641
                             -3.25 (-4.66, -1.83)
                                     0.659
                                  Concurrent
                             -2.65 (-3.69, -1.61)
                                     0.641
                             -2.65 (-3.69, -1.61)
                              Reported as highest
                             -2.65 (-3.69, -1.61)
                                     0.641
                             -3.32 (-4.55, -2.08)
                                     0.653
a Coefficients were adjusted for site, birth weight, mother's education, mother's IQ, & HOME inventory score.
b R[2] not reported in Lanphear et al. (2019) however the paper reported concurrent was the largest R[2]
Source: Table 2 and Table 5, Crump et al. (2013), Table 4 from Lanphear et. al (2019) Erratum.
Budtz-Jørgensen et al. (2013) reanalyzed the Lanphear et al. (2005) data using additional statistical methods and a benchmark dose (BMD) approach. The BMD is the dose that leads to a specified benchmark response (BMR). Budtz-Jørgensen et al. (2013) chose a BMR of 1 IQ point and performed benchmark analyses using log-linear, linear and piecewise linear (with cut points of 7.5 and 10 ug/dL). They found a similar BMD using both the lifetime and concurrent measures of blood lead. Consistent with the results of the original Lanphear et al. (2005) analysis, Budtz-Jørgensen et al. (2013) concluded that the log-linear model provided the best fit for the data. Budtz-Jørgensen et al. (2013) also confirmed the conclusions of Lanphear et al. (2005) regarding robustness of the model (i.e., model results did not change when each study site was excluded from the analysis in turn) and choice of concurrent BLL as the measurement showing an association to IQ loss. The authors compared logarithmic models of concurrent blood lead and lifetime blood lead and identified that the BMD was associated with a loss of 1 IQ point were 0.354 and 0.355 ug/dL, respectively. Additional language has been added to the IQ section emphasizing that Budtz-Jørgensen et al. (2013) findings support the relationship between blood lead and IQ loss.
To investigate the effects of assumptions made in their initial model, Budtz-Jørgensen et al. (2013) used additional statistical techniques to model the relationship between concurrent BLL and IQ. First, the authors examined two alternate models: an extension of the logarithmic model and the Hill model. In the logarithmic extension, Budtz-Jørgensen et al. (2013) investigated the effects of adding a number other than 1 to the log function of the dose in order to avoid an infinite response at a dose of zero. The Hill model represented an alternative specification for the shape of the dose-response curve, as compared with the original logarithmic shape. In addition, Budtz-Jørgensen et al. (2013) used a hybrid approach to estimate the BMD; in this approach, the BMR represents a specified increase in risk of an adverse effect. Budtz-Jørgensen et al. (2013) set the BMR as a 1, 2.5, or 5 percent increase in having an IQ score in the lowest 5 percent of the population. Lastly, Budtz-Jørgensen et al. (2013) investigated the effects of more fully accounting for variability between studies by considering differences in the standard deviation of IQ between studies, adding interaction terms between study site and covariates, and adding interaction terms between study and lead concentration.
The constant derived to avoid an infinite response in the logarithmic model at zero dose was 0.806, which did not produce significantly different benchmark results than adding 1. The Hill model resulted in a slightly better fit than the logarithmic model but provided a more uncertain estimate and thus a lower BMD level. The alternate benchmark analyses using a 1 percent increase in risk of an adverse response (i.e., IQ score in lowest 5 percent of the population) produced identical BMD and benchmark dose lower bound (BMDL) that is the lower 95 percent confidence limit of the BMD, as use of a 1 point loss in IQ. Benchmark results also remained similar when more fully accounting for differences between studies.
The SAB cited two studies, Jusko et al (2008) and Min et al. (2009), which use more recent BLLs than those used in the Crump and Lanphear analyses. Both manuscripts mentioned by the SAB successfully identified deficits in IQ at a lower BLL than Crump et al, (2013), who used the data published by Lanphear et al. in 2005. Min et al. (2009) only examines concurrent exposure, and the authors identify that at age 4 there is a decrease of 0.77 IQ points for every 1 ug/dL increase in BLL. In Min et al (2009), the dose-response relationship between concurrent BLL and Performance IQ at 4 years showed a steep slope at lower levels (up to 7 ug/dL) but did not reach significance. Jusko et al. (2008) concluded that lifetime average of BLLs between 5-9.9 ug/dL caused a decrease of 4.9 IQ points whereas the concurrent exposure correlated with a decrease of 3.7 points for levels between 5-9.9 ug/dL. Additionally, the authors demonstrated that the slope of the blood-lead IQ relationship was steeper at lower levels of lead exposure where IQ decreased by 1.2, 0.32, and 0.15 points per 1-μg/dL increase in peak blood lead over the range of 2.1 - 10 μg/dL, 10 - 20 μg/dL, and 20 - 30 μg/dL, respectively. In both the studies, the steeper slopes at lower BLL without log transformation showed increased deficits; this reinforces the fact that reducing lead levels in lower ranges of average BLL has a significant impact on preventing IQ loss.
The Crump et al. (2013) study includes data from nearly 1200 participants, whereas both the newer studies have about 275 participants. EPA agrees with the commenter that the two studies cited lend additional evidence in support of the measurable IQ impacts occurring with changes in BLLs below 10 ug/dL. The information provided in these two papers has been added to Appendix J of the Final LCRR EA where appropriate (USEPA, 2020a). However, EPA will continue to rely on the more robust analyses based on significantly larger numbers of data points by Crump and Lanphear to support the primary economic analysis for final LCRR.
The cross-sectional studies by Kordas et al. (2005) and Evens et al. (2015), cited by the SAB, did not test IQ directly. Kordas et al used a battery of 14 cognitive tests that examined different aspects of executive functions, which showed steeper slopes for tests on math, memory and a picture vocabulary test. The second study, Evens et al. (2015), utilized a standardized test that focused on examining critical and quantitative reasoning abilities, which were shown to be influenced by lead levels even below 5 ug/dL. Recently, a study by Mohn et al. (2014) with 250 healthy participants showed a direct relationship between cognitive domains and IQ. This study points to a direct correlation between IQ as examined by the Wechsler's Scale of Intelligence and seven cognitive domains that include speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning/problem solving. Collectively, these studies provide evidence of decreases in these cognitive domains due to increased BLL; they also show a correlation between IQ and the cognitive domains. EPA has added a summary of the supporting evidence from these studies to Appendix J of the Final LCRR EA.
To continue, lifetime BLLs take into account all exposures up until the time the BLL is quantified. This includes exposure to lead during early childhood from drinking water where the child is more susceptible to greater IQ loss. Lifetime exposure also captures the variability of exposure over the timeframe of childhood. Concurrent exposure, on the other hand, takes into account only the year before the tests are done and can miss the higher BLLs at younger ages. Overall lifetime exposure is a better metric to summarize blood lead to identify IQ loss. This reasoning is supported by studies by Jusko et al. (2008). Jusko et al. identify that lifetime average of BLLs between 5-9.9 ug/dL caused a decrease of 4.9 points whereas the concurrent exposure showed a decrease of 3.7 points for levels between 5-9.9 ug/dL.
   Table 2. Data used in the Lanphear et al (2005) analysis from other studies

   Notes:
      a: Canfield et al (2003) and Jusko et al (2008) utilized participants from the same dust-control study cohort established by Lanphear et al in 1999. The analyses and endpoints (age at IQ test) were handled differently; these two studies are therefore reported separately.
      b: Study not included in Lanphear et al (2005) analysis.
      c: Extrapolated from years of participant recruitment and age at sample collection
      d:Values taken from Lanphear et al (2005) Table 2 for all studies except Jusko et al (2008) and Min et al (2009)
      e: Abbreviations are as follows: Wechsler Intelligence Scale for Children (WISC) Revised (-R); Kaufman Test of Educational Achievement (K-TEA); Wechsler Preschool and Primary Scales of Intelligence (WPPSI); Stanford-Binet Intelligence Scale (S-B); McCarthy Scales of Children's Abilities (MSCA).
16.4.5 Valuation of IQ Point Loss
Summary of Comments
EPA received a number of comments related to how the LCRR estimated the value of an IQ point for use in the benefits assessment. These comments included suggestions of alternative methods for calculating the value of an IQ point, whether EPA should be valuing incremental changes in IQ at all, whether the IQ valuation method captures willingness-to-pay, and whether the IQ point valuation differed between the LCRR and other recent proposed rulemakings that used avoided IQ loss as the basis for benefits estimation.
EPA received a comment suggesting the use of the Gould (2009) estimate of $17,815, in 2006 dollars, as the value of an IQ point.
EPA received a comment asserting that the IQ benefit calculation in the LCRR Economic Analysis is inherently different from the calculation presented in the Lead Dust Standard (USEPA, 2019b) and the Proposed National Primary Drinking Water Regulation (NPDWR) for Perchlorate Economic Analyses (USEPA, 2019c).
Agency Response
Regarding the comment suggesting use of the Gould value of an IQ point, there are no details in the Gould (2009) paper about how this estimate of the value of an IQ point was developed. Thus, EPA is continuing to use the value of an IQ point described in Section 6.4.4 and further described in Appendix K of the Final LCRR EA (USEPA, 2020a), which is based on a peer-reviewed methodology developed by Salkever (1995), and is also consistent with the value of an IQ point used in the Lead Dust Standard (USEPA, 2019b) and the Proposed NPDWR for Perchlorate (USEPA, 2019c). It is worth noting that EPA does include a sensitivity analysis in Appendix G of the Final LCRR EA using an alternative value of an IQ point as calculated in Lin et al. (2018).
There was also a suggestion that EPA ignored the impacts of future real earnings growth in the estimation of the value of an IQ point. However, EPA disagrees with this assertion, as EPA assumed an annual growth rate of 1 percent, which is consistent with long term historical averages. This choice of real earnings growth rate for the calculation of the value of an IQ point is outlined in Section K.8.2 of Appendix K of the Final LCRR EA. EPA also disagrees with the suggestion that it should additionally include potential resulting multiplier impacts of increased future earnings on gross domestic product (GDP) in its IQ valuation estimation. The current method for estimating the value of an IQ point has been previously used as part of both the Lead Dust Standard and the Proposed NPDWR for Perchlorate. Thus, to remain consistency, EPA has opted to continue to use this method for estimating the value of an IQ point without the GDP to earnings multiplier. The current value of an IQ point does not include a willingness to pay (WTP) estimate. Ideally, EPA would use estimates of WTP to value all changes in cognitive function experienced by children in response to changes in lead exposure. However, EPA is unaware of robust, peer-reviewed estimates of WTP to avoid children's cognitive function decrements, so it relies on avoided lifetime earnings decrements as a proxy for WTP. EPA conducts a health risk reduction and cost analysis (HRRCA) when proposing any NPDWR, as required in Section 1412(b)(3)(C)(i) and (ii) of the SDWA. The HRCCA is used by EPA at proposal to determine, as required by SDWA Section 1412(b)(4)(C) "whether the benefits ... justify, or do not justify, the costs." It may also be used if EPA proposes to use its authority under SDWA Section 1412(b)(6) to establish an MCL or treatment technique that "maximizes health risk reduction benefits at a cost that is justified by the benefits." 1412(b)(3)(C)(iii) of the SDWA says "[t]he Administrator may identify valid approaches for the measurement and valuation of benefits ... including approaches to identify consumer WTP for reductions in health risks from drinking water contaminants." In this rulemaking, EPA exercised this discretion to determine which valid benefits assessment methods to use when conducting a HRRCA for this regulation. In determining not to conduct an original WTP study for this rulemaking, EPA considered the time and resources that would be required to conduct the study specifically focused on the removal of lead hazards from drinking water as compared to other valid approaches for assessment of benefits. The Agency chose to focus its limited analytical resources on the assessment of the change in lifetime earnings associated with changes in IQ accruing from reductions in drinking water lead exposure to infants and young children, being a subpopulation at significant risk, as this approach is a valid and adequate proxy for WTP for rulemaking purposes and requires fewer resources than conducting an original WTP study.
EPA disagrees with the assertion that small changes in IQ should not have an economic value when aggregated at the population level. As mentioned in Appendix K, "[b]ecause the literature finds support for a continuous relationship between IQ and earnings, it is possible to quantify the impacts of even very small IQ changes." This quantification is supported by the SAB recommendation that "the characterization of IQ point loss by the summation of fractional IQ points over the entire population of children is considered defensible and appropriate." Additionally, EPA disagrees with the assertion that the magnitude of IQ changes for the LCRR has an effect on the continuous slope of the relationship between lifetime earnings and IQ. The relationship between lifetime earnings and IQ remains consistent regardless of the magnitude of changes in IQ, as larger changes are reflected by larger movements along the same curve as smaller changes.
EPA disagrees with this assertion that EPA used a different calculation for LCRR than the calculation presented in the Lead Dust Standard (USEPA, 2019b) and the Proposed NPDWR for Perchlorate Economic Analyses (USEPA, 2019c). The value of an IQ point that is used in the Proposed LCRR Economic Analysis (USEPA, 2019) was calculated in a consistent manner with that of the Proposed NPDWR for Perchlorate and the Lead Dust Standard. The unit values per IQ point presented in the economic analysis appear slightly different due to differences in the age at which children were assumed to accrue benefits in the analysis, as well as the year to which the values were inflated. As mentioned in Section 6.4.4 of the Final LCRR EA, benefits for the LCRR are assumed to accrue at age seven and therefore the value of an IQ point is discounted back to age seven in the LCRR analysis. This results in a slightly higher estimate for the value of an IQ point for that age, than the values used in the economic analyses for the Perchlorate Rule (USEPA, 2019c) and the Lead Dust Standard (USEPA, 2019b), which are discounted to age zero and age three, respectively. The assumption about the age at which benefits accrue has no effect on the calculation of the present value of IQ gains resulting from the rule. Additionally, the Perchlorate Rule economic analysis presents values of an IQ point in 2017 dollars, while the LCRR uses 2016 dollars. This explains the discrepancy between the values presented in the Perchlorate HRRCA Exhibit B-7, which are inflated to 2017 dollars, and the values presented in the corresponding Exhibit K.7 for the Final LCRR EA, which are inflated to 2016 dollars.
It should also be noted, and is described in Section 6.4.5 of the Final LCRR EA, that the benefits in the LCRR are further discounted back to year one of the analysis (providing the net present value) and annualized within the SafeWater LCR model. This discounting means that the value of a prevented IQ point loss related to reductions in drinking water lead concentrations in the first year of the analysis is worth more than the same prevented IQ point loss if it would occur in the third year of the analysis, which in turn has a greater value than if the prevented IQ loss accrued in the fifth year of the analysis. The only year of the analysis that uses the full value of an IQ point as specified in Section 6.4.4 is the first year, and each subsequent year is discounted further based on the assumed annual discount rate, either 3 or 7 percent. This discounting to the first year of the analysis is also performed in the economic analysis for the Perchlorate Rule, as in both rules the costs and benefits vary from year to year and are annualized. This discounting is not performed in the Lead Dust Standard, as the costs and benefits are assumed to remain constant from year to year, and as such the Final LCRR EA only presents annual costs and benefits, as opposed to total costs and benefits over a specified time period that are subsequently annualized. The method of discounting used in the LCRR and Perchlorate analyses leads to a difference in the final value of an IQ point that is applied in the calculation of the total benefits from avoided IQ loss. This type of discounting does not affect the unit value of an IQ point presented in the Final LCRR EA.
16.4.6 SafeWater Calculation of Benefits and Monetized Results
Summary of Comments
EPA received multiple comments on the LCRR related to how EPA estimated the number of children that benefit from the proposed rule revisions. Specifically, there were questions as to how EPA accounted for the possibility that many homes that would be subject to changes in WLLs would not have any children living in them.
Agency Response
EPA disagrees with the suggestion that benefits are overstated due to the notion of the existence of homes with no children, as the number of children benefiting from the rule revisions are calculated as a proportion of the PWS population, rather than as a function of the number of homes with children served by the PWS. The following response describes in more detail the methods by which benefits are calculated in the LCRR, paying specific attention to how the number of children affected by each water lead change is calculated. This information is also found in Chapter 6 of the Final LCRR EA.
IQ benefits are estimated based on LSLRs, installation of POU, and installation and re-optimization of CCT that occur over the 35-year period of analysis. EPA captures benefits in the analysis when children turn 7 years of age; therefore, it is necessary to estimate the number of 7-year-olds that are served by each PWS. That estimated number of 7-year-olds represents the pool of potential individuals for which IQ benefits may be quantified in any single year of the 35-year period of analysis. Based on United States Census demographic data from 2010, 1.34 percent of the total United States population turns seven in any given year (US Census Bureau, 2013). EPA assumes for modeling purposes that the percentage of individuals 7 years old is constant across PWSs and constant across households with and without LSLs. The population served by each PWS is also assumed to remain constant over the analysis period; therefore, the number of 7-year-olds in each PWS for each year of the analysis remains constant and is 1.34 percent of the current population served by each PWS.
EPA does not assume that all homes with replaced LSLs have children living in the home. Rather, EPA assumes that the proportion of 7-year-olds living in the homes that are undergoing a LSLR is equal to the proportion of the United States population that is 7 years old. This assumption takes care of the need to model the movement of children in and out of homes in the community, as the proportion of the population in these age groups is assumed to remain constant. In other words, if there are 1,000 households being served by a PWS that underwent a change in lead concentration, approximately 1.34 percent of the population in those households would get a benefit annually, regardless of which specific home being served by the PWS they lived in. The accrued benefit for those children who are served by a PWS that has undergone a change is then a function of changes in the average lifetime BLL of the children due to the change in lead concentration, and the subsequent avoided IQ loss.
For each year t in the analysis, the SafeWater LCR model determines for each model-PWS if changes to CCT status, LSLR, or installation of POU devices occur based on the requirements of the LCRR and the model-PWS's 90[th] percentile water lead value. SafeWater LCR then assigns to the fraction of the 7-year-old population affected by the CCT, POU, and/or LSLR a drinking water lead concentration change. This concentration change is assigned by comparing the CCT, POU, and LSLR status in year t to the baseline lead concentration status in year t-1 for the affected fraction of the system's population (see Exhibit 6-13 or Exhibit 6-14 in Chapter 6 of the Final LCRR EA for the set of possible concentration changes based on a low-cost and high-cost scenario, respectively). This concentration change is also associated with a resulting change in lifetime average BLL (derived from annual blood leads estimated from the SHEDS-IEUBK model; see Exhibit-6-24 in the Final LCRR EA). SafeWater LCR calculates these lifetime average BLLs by summing together annual BLLs from age 0 through age 6 years and dividing by 7. The SafeWater LCR model assesses the impact of the CCT, POU, and LSLR status changes on lifetime average BLLs by estimating both a lifetime average BLL assuming the new water lead scenario in year t, and a lifetime average BLL assuming the previous water lead scenario in year t. These lifetime average BLLs can be thought of as pre- and post-rule BLLs for a representative 7-year-old (associated with the fraction of 7-year-olds experiencing the given change in CCT, POU, and/or LSLR status). SafeWater LCR calculates these pre- and post-rule BLLs in order to ultimately assess the impact of the CCT, POU, and LSLR status changes on avoided IQ loss.
The SafeWater LCR model calculates pre- and post-rule representative 7-year-old lifetime BLLs for year t (the year of the rule change in water concentration) through the end of the period of analysis. As the model moves from year t through the end of the analysis, post-rule 7-year-old lifetime average BLLs become more representative of the new water lead scenario. As an example of these calculations, in year t a single year of the BLL associated with the new WLL is included in the post-rule lifetime average BLL. In year t+1, two years of the BLL associated with the new WLL are included in the post-rule lifetime average BLL, and so on. Once the model gets to year t+6, the BLL associated with the new WLL scenario is included for all seven years of the post-rule lifetime average. This year t+6 post-rule lifetime average BLL remains constant until the end of the period of analysis, as all representative 7-year-olds would have been living with the new WLL scenario their entire lives. Although the modeling assumes BLL changes coincide with WLL changes, the use of lifetime blood lead averages in the calculation of benefits partially accounts for any unmodeled biological lag that may occur in the reduction of BLLs as they relate to reductions in WLLs. Additionally, the half-life of lead in blood is approximately 30 days (USEPA, 2013), and as the SafeWater LCR model operates on a yearly timestep, any lag in BLL change resulting from changes in WLL would likely occur within a single analysis year, precluding the need to model this lag any further. Because SafeWater LCR calculates the total discounted benefit for each change in CCT, POU, and LSLR status in the year in which the change occurs, the model applies the lead concentration change, and associated BLL change, to the same fraction of 7-year-olds for all future periods left in the 35-year period of analysis.
SafeWater LCR then uses this stream of pre- and post-rule lifetime average BLLs for each year from year t through the end of the period of analysis to estimate a resulting average change in IQ points for each year based on the Crump et al. (2013) concentration-response function. The SafeWater LCR model monetizes each year's average IQ point change using both the 3 and 7 percent rate discounted values of an IQ point, which are appropriately discounted back to Year 1 of the analysis at the 3 or 7 percent discount rate. SafeWater LCR then multiplies each year's average value of avoided IQ point loss by the number of 7-year-olds annually affected by the single regulatory change from year t. The model then sums the individual year single regulatory action (single system) estimated monetized IQ values across all the changes in CCT, POU and/or LSLR status to find the total value of all IQ points saved based on water lead changes in year t. SafeWater LCR repeats this process for each year of water lead changes that occur based on the requirements of the LCRR through the period of analysis. All CCT/POU/LSLR changes that occurred in year t are carried forward to year t+1 of the analysis as the new "baseline" water lead scenario. Thus, the number of LSL houses shrinks as the model moves through the 35-year analysis period and implements changes based on the LCRR.
The SafeWater LCR model calculates total annualized benefits over the period of analysis by discounting the yearly total monetized values back to the first period of the analysis and summing to providing a net present value for IQ impacts. This value is then annualized for the 35 years.
Appendix G presents alternative BLL estimates for children, alternative concentration-response functions for the relationship between lead and IQ, and an analysis with an alternative value of an IQ point. Benefits associated with find-and-fix are not quantified. Additional information on the Safewater LCR model including source code can be found at the following website: https://www.epa.gov/sdwa/safewater-lead-and-copper-rule-lcr-database-and-associated-files-proposed-rule.
16.4.7 Unquantified Adult Cardiovascular Disease Benefits
Summary of Comments
EPA received numerous comments encouraging modification of the EA to include monetized reduced CVD mortality benefits resulting from the LCRR. One commenter provides further details on how this benefit can be estimated and provides monetary estimates of the potential increase in benefits related to this monetization. Commenters identified additional studies that could be included in the discussion of the health effects of lead. The comments also include suggestions to reassess the current set of non-monetized benefits for the purposes of identifying opportunities for monetization of multiple adverse health effects including cardiovascular mortality as well as reproductive and developmental effects, immune effects, hypersensitivity and allergy response, resistance to bacterial infection, neurological effects (in addition to IQ from childhood exposure), and cancer. They also suggest using break even analysis to assess the non-monetized benefits. The commenters suggested EPA should address the relationship between lead in drinking water and miscarriage and stillbirth.
Agency Response
EPA disagrees with comments suggesting that additional health end points beyond IQ must be monetized for this rulemaking under the SDWA. In this rulemaking, EPA used its discretion to determine which valid benefits assessment methods to use when conducting a HRRCA. In doing so, the Agency chose to focus its limited resources and prioritize the assessment of the IQ benefits accruing from reductions in drinking water lead exposure to infants and young children, because it is a population at significant risk. EPA's Final LCRR EA indicates that there are unquantified and/or non-monetized benefits due to the rule, including those associated with reductions in adverse cardiovascular effects, renal effects, reproductive effects, immunological effects, neurological effects, and cancer. These are summarized in detail in Appendix D. EPA considered all benefits, including those that it did not monetize or otherwise quantify, in the required determination under SDWA Section 1412(b)(4)(C) as to whether the costs are justified by the benefits.
Although the EPA did not quantify or monetize changes in adult health benefits for the proposed LCRR, the Agency estimated the potential changes in adult drinking water exposures and resulting BLL to illustrate the extent of lead reduction to the adult population as a result of the proposed LCRR. Commenters indicated that the Agency should include quantification and monetization of the adult CVD benefits associated with reductions in water lead concentrations in the HRRCA for the LCRR. Some of the commenters indicated that EPA has a legal obligation to include this benefit in the HRRCA under section 1412(b)(3). EPA does not agree with these commenters that a quantified assessment of CVD benefits is necessary in this HRRCA. EPA conducts a HRRCA when proposing any NPDWR, as required in section 1412(b)(3)(C)(i) and (ii) of the SDWA. SDWA Section 1412(b)(3)(C)(i)(I) requires the inclusion of quantifiable and non-quantifiable health risk reduction benefits for which there is a factual basis in the rulemaking record to conclude such benefits are likely to occur as a result of the rule. SDWA section 1412(b)(3)(C)(iii) provides that "[t]he Administrator may identify valid approaches for the measurement and valuation of benefits" for the HRRCA.
EPA exercised its discretion to identify the validity of the approaches used to measure and value CVD benefits and determined not to quantify CVD benefits for this rulemaking because the methodology which links changes in adult BLLs to CVD health endpoints, including mortality, has not yet undergone an expert panel peer review. However, EPA has considered the substantial unquantified benefits to the rule, including those associated with reductions in adverse cardiovascular effects that are described in the HRRCA. Additional details of systematic reviews between blood lead and CVD mentioned by reviewers were added to Appendix D. These include Chowdhury et al. (2018) and Navas-Acien et al. (2007).
Some commenters asserted that if the Agency monetized the benefits of CVD, the Agency would have proposed more stringent requirements because greater quantified benefits would justify more burdensome regulation. EPA disagrees. The Agency considered information from the HRRCA at proposal to determine, as required by SDWA section 1412(b)(4)(C) "whether the benefits ... justify, or do not justify, the costs." The Agency found that the quantified and non-quantified benefits justified the cost of the proposed rule requirements. EPA considered costs and benefits in its rulemaking process, as required by SDWA. The Agency established the treatment technique requirements in the rule to "prevent known or anticipated adverse effects on the health of persons to the extent feasible" consistent with section 1412(b)(7)(A) of the SDWA, while also ensuring that "[a]ny revision of a national primary drinking water regulation shall ... maintain, or provide for greater, protection of the health of persons" as required in section 1412(b)(9) of the SDWA. The EPA is not employing the discretionary provision of SDWA section 1412(b)(6)) that allows the Agency to promulgate an NPDWR that "maximizes health risk reduction benefits at a cost that is justified by the benefits" when the Agency determines at proposal that the costs are justified by the benefits of the rule. Therefore, the Agency's decision to not monetize CVD benefits did not affect the stringency of the final rule. EPA conducted an analysis of quantifiable and non-quantifiable benefits that meets the statutory requirements and EPA considered both quantified and non-quantified benefits in the rulemaking. EPA received a number of comments that encouraged the Agency to obtain more data to better estimate the costs and benefits of the proposed rule. EPA engaged in additional data collection in response to comments that has improved upon the analysis conducted for the proposed rule.
As detailed earlier, IQ loss reduction is the only outcome that has been quantified and represents the quantifiable benefit of reducing lead contamination in drinking water. At a 3 percent discount rate, this quantifiable benefit of IQ loss reduction is greater than costs so that net monetized benefits are positive. At a 7 percent discount rate, the quantifiable benefit of IQ loss reduction is not greater than costs alone. Though EPA is not required to perform a breakeven analysis according to OMB Circular A-4 (OMB, 2003), such an analysis would show that the total value of non-monetized benefits of the rule would need to exceed $127,980,000 annually to exceed costs in the low cost scenario at the 7 percent discount rate, and $253,358,000 annually to exceed costs in the high cost scenario at the 7 percent discount rate.
EPA has used peer reviewed studies that support the use of the estimates of total lead intake used in the economic analysis. Appendices D and J in the Final LCRR EA discuss, in detail, that exposure to lead causes deviations from normal physiology in the cardiovascular system (USEPA, 2020a).
Additional commenters raise the fact that lead has other adverse health effects on other neurological end points which are not IQ related, including depression, nervous system disorder, panic disorder, dementia and Attention-Deficit/Hyperactivity Disorder. EPA discusses these effects in detail in Appendix D.
The commenters also bring up the effect of lead on other physiological end points such as immune, reproductive, and developmental effects (prenatal exposure), which have not been monetized in the economic analysis. Appendix D details the effects of lead exposure on each of these end points. While there is evidence of a relationship between lead exposure and changes in these health endpoints, EPA disagrees that it is necessary to devote resources toward the development of new methods to monetize these end-points for this rulemaking.
16.4.8 Uncertainty in Benefits Estimates
Summary of Comments
One commenter asserted that the handling of variability and uncertainty in the analysis is likely to lead to misinterpretation. The commenter notes that these are discussed, but that variability and uncertainty are not adequately propagated through to the estimate of benefit and net benefit. The commenter states that the use of fixed values rather than uncertain quantities could lead to misinterpretation. The commenter notes multiple sources of uncertainty, including:
The number of water systems covered by the proposed LCRR;
The distribution of WLLs in these water systems;
The extent to which LSL replacement results in short-term increases in WLLs;
Human exposure to these WLLs, both in the baseline and as a result of implementation of each provision in the proposed LCRR;
The concentration-response functions converting increased WLL into increased BLL, and increased BLL into IQ losses;
The concentration-response function converting reductions in WLL into reduced BLL and reduced BLL into IQ gains;
The extent to which modeled IQ changes are statistically or clinically meaningful; and
The valuation of IQ changes.
The commenter further suggests that the Proposed LCRR EA reported estimates with excess precision; in particular noting that aggregate benefits and costs were reported to +/- $500, without providing support for that level of precision.
Agency Response
EPA does not agree that the handling of variability and uncertainty is likely to cause misinterpretation. The economic analysis explores potential uncertainty for each step of the benefits analysis in detail and incorporates known variability into the modeled outputs. The high and low estimates in the economic analysis are based on the largest drivers of uncertainty and capture the likely range. For the number of systems with LSLs, EPA has updated the economic analysis with more accurate inventories provided through public comment. This change has been further discussed under response to comment Sections 16.1 "General Comments" and 16.2 "Baseline Universe of Systems: Major Data Sources and Affected Entities." Additionally, EPA provides a range in the model based on number of systems with LSLs and four other factors designed to bound that uncertainty. These are handled in more detail in "Data Limitations and Uncertainty" in response to comment Section 16.2.
Variability in the distribution of WLLs in water systems were handled through detailed analysis of WLL profile data, followed by simulation. Unknown uncertainty related to the geographic extent of the original dataset is discussed in Section 6.2 of the Final LCRR EA (USEPA, 2020a).
Short-term increases in WLL after LSLR and timing of benefits are further discussed in response to comment Section 16.4.2 "Assignment of Pb Concentrations to Populations in Baseline and Tracking Concentration Changes in SafeWater."
Variability and uncertainty in human exposure to WLL based on WLL variability, and biological factors are discussed in more detail in responses to multiple commenters regarding "Water Lead Level (WLL) Estimation Method" under response to comment Section 16.4.1, as well as EPA SAB comments regarding "Biological Variability" and "Blood Lead Distribution" under response to comment Section 16.4.3. Variability in exposure is acknowledged and incorporated in SHEDS modeling; while this may not be a perfect description of exposure, it incorporates both real-world data and modeled variability based on expert opinion. The changes in WLL represent only LSLR and CCT impacts, and benefits for other components of the rule are acknowledged as unquantifiable.
Uncertainty in concentration-response functions for modeling the effects of WLL on BLL, and BLL on IQ losses involved the comparison of three functions. This is further discussed in response to comment Sections 16.4.3 Estimation of Blood Lead Level (BLL) Changes in Children and 16.4.4 WLL Reductions through Avoided IQ Point Loss.".
EPA does not agree that costs and benefits are reported with a level of precision that may lead to misinterpretation. It is important to show the relative position of cost or benefits among options. As the SafeWater LCR is a stochastic model, results are presented as stochastic values, down to one decimal place. As noted in Appendix C of the Final LCRR EA, this reporting method is intended to avoid confusion related to rounding uncertain values (such as zero values where the estimate is a decimal) rather than to presume the level of precision. To avoid misinterpretation, the EA contains long discussions of possible uncertainty, and reporting costs and benefits to the nearest thousand allowed differentiation of the impacts of each option.
16.4.9 Benefits: Other
Thoroughness of the Benefits Analysis
Summary of Comments
A commenter suggested that the approach to estimating the benefits of reducing lead exposure in their state point to a need for a more thorough benefits analysis for EPA's LCRR based on actual BLL data.
Agency Response
EPA disagrees that the commenter's approach is evidence that EPA's approach underestimates benefits, and that a more thorough benefits assessment based on actual blood lead data is necessary.
EPA's modeling of benefits is based on requirements of the LCRR, and the relationship of those requirements with the children that are served by each water system undergoing changes due to the rule. This does not necessarily provide benefits for all children living in the United States with some level of lead in their blood, but rather only for those children that are served by a PWS that undergoes a change in their water lead due to the requirements of the LCRR. The commenter's approach appears to assume that all children with BLLs above 0 are undergoing a blood lead reduction to 0, and that 20 percent of that blood lead reduction is necessarily from changes in water. As the two analyses are quite different in their approach, EPA is unable to use the state's analysis as evidence that a deficiency exists in the Agency's approach to modeling benefits. Furthermore, the approach used by EPA in the Final LCRR EA is based on state of the science methods, both for calculating the number of children affected by the rule, as well as for estimating the IQ benefit received due to various changes in water lead concentrations.
EPA, as described in Chapter 6 of the Final LCRR EA, used the SHEDS-IEUBK coupled model to estimate BLLs based on possible water lead scenarios associated with potential LCRR interventions. SHEDS-IEUBK takes in to account dietary exposure to lead, exposure from soil and air, and lead exposure from drinking water, and models exposure magnitudes probabilistically based on actual children's activity diaries (Zartarian et al., 2017). Estimates generated using the SHEDS-IEUBK method were compared to blood lead estimates reported from NHANES (2009-2014) and from the National Human Exposure Assessment Survey (NHEXAS) in Zartarian et al. (2017) and were shown to closely approximate these BLLs. NHANES in particular is a nationally representative dataset of BLLs in children in the United States, and as the SHEDS-IEUBK coupled model has been shown to be able to closely approximate these BLLs, it would be unnecessary and prohibitively costly for EPA to implement another comprehensive survey of children's BLLs. For further information on SHEDS-IEUBK model development and evaluation, refer to Zartarian et al.'s (2017) paper "Children's Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making." This model provides a more comprehensive representation of blood lead levels which could lead to altered life outcomes. EPA believes that using the SHEDS-IEUBK coupled model provides sufficient estimates of blood lead levels for use in the Final Rule EA, which precludes the need for the actual measurement of BLLs in children.
Need for WTP Survey or Literature Search Related to Drinking Water Safety
Summary of Comments
EPA received a comment asserting that the Agency should conduct a WTP survey specifically related to the removal of lead from drinking water or conduct a literature review for published material on the WTP to remove lead contamination and/or more generally WTP for improvements in the safety of drinking water. The commenter claims that the "SDWA provides that EPA is to consider "valid approaches" for the measurement and valuation of benefits under this subparagraph, including approaches to identify consumer willingness to pay for reductions in health risks from drinking water contaminants." [42 U.S.C. § 300g-1 (A)(3)(C)(iii)]. The commenter also provides a number of supporting studies associated with expenditures on bottled water, the percent of income in developing countries spent to obtain safer drinking water and a WTP survey conducted in Florida demonstrating values for reducing non lead pollutants in the public water supply.
Agency Response
EPA does not agree with the commenter that willingness-to pay studies are necessary or required under the SDWA. EPA conducts a HRRCA when proposing any NPDWR, as required in Section 1412(b)(3)(C)(i) and (ii) of the SDWA. The HRCCA is used by EPA at proposal to determine, as required by SDWA Section 1412(b)(4)(C) "whether the benefits ... justify, or do not justify, the costs." It may also be used if EPA proposes to use its authority under SDWA Section 1412(b)(6) to establish an MCL or treatment technique that "maximizes health risk reduction benefits at a cost that is justified by the benefits" where the Administrator has determined that the benefits do not justify the costs. The commenter cites in part 1412(b)(3)(C)(iii) of the SDWA, but omits critical text indicating the discretionary nature of the provision: "The Administrator may identify valid approaches for the measurement and valuation of benefits ... including approaches to identify consumer willingness to pay for reductions in health risks from drinking water contaminants." In this rulemaking, EPA exercised this discretion to determine which valid benefits assessment methods to use when conducting a HRRCA for this regulation. In determining not to conduct a WTP study for this rulemaking, EPA considered the time and resources that would be required to conduct the study specifically focused on the removal of lead hazards from drinking water as compared to other valid approaches for assessment of benefits. The Agency chose to focus its limited analytical resources on the assessment of the IQ benefits accruing from reductions in drinking water lead exposure to infants and young children, being a sub population at significant risk. EPA's Final LCRR EA indicates that there are substantial unquantified and/or non-monetized benefits to the rule, including those associated with reductions in adverse cardiovascular effects, renal effects, reproductive effects, immunological effects, neurological effects, and cancer. In the proposed rule preamble, the EPA Administrator found that both a combination of the monetized and non-quantified and/or non-monetized benefits justified the cost of the proposed rule requirements under section 1412(b)(3)(C)(ii) of the 1996 Amendments to the SDWA. For the final rule EPA did not utilize SDWA Section 1412(b)(6) in the selection of the final rule regulatory requirements because EPA had determined that the benefits justified the cost at proposal. Although EPA considered benefits in its rulemaking process, as required by the SDWA, EPA relied on the feasibility standard in SDWA Section 1412(b)(4)(D)) in establishing the treatment technique requirements in the rule to "prevent known or anticipated adverse effects on the health of persons to the extent feasible" consistent with section 1412(b)(7)(A) of the SDWA, while also ensuring compliance with the requirement in Section 1412(b)(9) that "[a]ny revision of a national primary drinking water regulation shall ... maintain, or provide for greater, protection of the health of persons." Given this statutory framework, the Agency's decision to not monetize all possible benefits did not affect the outcome of the final rule. EPA's assessment of those IQ benefits that have been monetized along with the non-quantified/non-monetized benefits that have been discussed in the Final LCRR EA were sufficient to meet the requirements for the HRRCA and make a determination at proposal as to "whether the benefits ... justify, or do not justify, the costs" as required by SDWA Section 1412(b)(4)(C).
At the time of the proposed rule analysis EPA conducted a literature review to support the development of benefits associated with requiring an LSL inventory. This research sought to find studies which found a quantifiable causal relationship between property values and the presence of LSLs. This review found only two directly relevant studies: Blackhurst (2018) an unpublished and not peer reviewed hedonic study conducted in Pittsburg looking at the impact to property value of having a known LSL (referenced by the commenter), and Lu et al. (2019) that conducted a choice experiment to determine the likelihood of renters and buyers asking for the replacement of an LSL or looking for an alternative property. The study found renters were most willing to ask for the replacement of the LSL followed by looking for an alternative property. Of home buyers the study results were less conclusive with the buyer likelihood of asking for corrective action dependent on the way the buyer found out about the LSL's existence. A third study Billings and Schnepel (2017) found that purchasers were willing to pay a significant premium, above the cost of lead paint remediation, to purchase a home with the lead paint hazard already removed. These studies were used by EPA to reinforce the concept that the public disclosure of LSL addresses can create an incentive, through increased property values, to replace LSLs. But these studies were for either non-drinking water lead sources or had significant methodological issue that prevented the Agency from utilizing them in the quantification of monetization of benefits for the LCRR.
In addition, a search of the literature was conducted for the final rule. EPA identified a study by Theising (2019) on the impact of LSLR on property values in Madison, Wisconsin. This study used a variety of techniques to isolate the causal impact of LSLR on property values, including spatial and temporal fixed effects, propensity-score matching and comparisons of repeat sales of the same property. Theising found that property values increased 3 to 4 percent on average in response to LSLR. This property value appreciation exceeded the capital cost of replacement, with an implied return on investment of more than 75 percent. Because the LCRR is expected to generate substantial benefits from CCT and POU filtration, not only LSLR, and because the heterogeneity across water systems means that results from the single water system included in the Theising study may not be representative of all water systems affected by the LCRR, the results from this study cannot be extrapolated to estimate the benefits of the LCRR. However, the finding that the benefits to homeowners of LSLR exceed capital costs is broadly consistent with EPA's quantitative and qualitative analysis of the benefits and costs of the LCRR.
EPA reviewed the specific peer-reviewed studies cited in the comment and concluded that none of the evidence is sufficient to serve as the basis for the estimation of monetized benefits associated with the removal of lead from drinking water for the LCRR. EPA's analysis of each study is discussed below.
EPA has added language to the Final LCRR EA stating that there is a lack of adequate WTP literature that is applicable to reducing lead in drinking water through different approaches at heterogenous water systems across the United States and noted that the cost-of-illness approach, monetizing the impact of reducing IQ loss in children, used in the economic analysis is the best available proxy for a WTP that captures the benefits of the major provisions of the LCRR across a wide variety of water systems.
The Hu et al. (2011) study explores the relationship between bottled water use and perceptions of quality of the local water supply but does not provide a basis for estimating a WTP.
The Vasquez et al. (2009) study estimates a WTP for safe drinking water from a mid-sized urban area in Mexico, however there are many aspects of the study that deem it unfit for use in the LCRR economic analysis. First, it is unclear how the estimates of WTP in this mid-sized city in Mexico translate to WTP in the United States given that there is widespread usage of water storage and treatment facilities at homes in the city. Thus, participants in this study are already spending time and money tending to their water at home to ensure it is potable. This could affect their WTP for clean water in a way that does not generalize broadly to the United States. Additionally, this city also has issues with the reliability of the availability of running water, which also factors into the study's WTP estimates and are not relevant to the estimation of benefits for the removal of lead. The WTP question posed to participants specifies "reduce[d] water contamination (microbes, bacterium, and heavy metal)" as the outcome for which they were paying. This question captures additional drinking water issues that may be of more concern to residents than the presence of lead, and thus it is not possible to tease out the proportion of the WTP for a reduction in water contamination that is specific to the removal of lead.
The Dey at al. (2018) study was a WTP study conducted in Bangladesh to determine WTP for safe drinking water in a coastal district of approximately 300,000 people. This study made no mention of lead in drinking water, and specifically mentioned the contamination of arsenic, salinity, and iron instead. Additionally, many respondents did not own their drinking water sources, and many others shared their drinking water sources with many other people, which could affect their WTP. It is also unclear how a study conducted in Bangladesh would translate to the United States, especially because a large portion of respondents were designated as "ultra-poor", and the water sources were not water systems but rather "tubewells" that were collecting water from aquifers. It is unclear whether the respondents were paying money for their current access to drinking water.
The Chatterjee et al. (2017) study was the only peer-reviewed study mentioned that was conducted in the United States. The study estimated the monthly WTP to "improve the quality of your water" by undertaking a phone survey in Jacksonville, FL. WTP from this study was found to be $6.22 per month. The response rate for the survey was 8.3 percent, and the study sample was deemed to be a "fair" representation of the population of Duval County, the county that encompasses Jacksonville. However, it is a poor representation of the United States population as a whole as only 38 percent of respondents were male compared to 49 percent of the United States population, in addition to other differences in the racial makeup of the study population as compared with the United States population. Additionally, the water in Jacksonville is sourced from groundwater aquifers, which can sometimes produce a noticeable smell of sulfur (rotten egg), even after being aerated and disinfected with chorine at city water treatment plants. This sulfur smell has elicited concerns about tap water quality that may not be relevant to other areas of the country. The study also made no mention of lead in their telephone assessment, and thus the results are not applicable for the purposes of estimating the benefit of reducing lead from drinking water.
 The other studies mentioned in the comment about "boil water advisories" in Washington DC resulting in bottled water shortages in stores are anecdotal and are not sufficient for providing an estimate for WTP for the removal of lead from drinking water.
16.4.10 Application of Discount Rates to Benefits Analysis
Summary of Comments
EPA received comments on LCRR related to how EPA applies discount rates to the benefits analysis for the proposed rule revisions. Specifically, suggestions were made that EPA should be presenting analyses using discount rates lower than 3 percent, or even using no discount rate due to a portion of the accrued benefits being intergenerational in nature. Also, a suggestion was made to incorporate an additional discount rate that reflects whether a child was in a home at the time of LSLR or moved in later.
Agency Response
EPA disagrees with the assertion that an additional discount rate should be incorporated to account for children that were "not in the home" at the time of an LSLR, as this discounting is already captured by discounting future benefits back to year 1 of the analysis. EPA assumes for modeling purposes that the percentage of individuals who are 7-years-old is constant across PWSs and constant across households with and without LSLs. The population served by each PWS is also assumed to remain constant over the analysis period; therefore, the number of 7-year-olds in each PWS for each year of the analysis remains constant and is 1.34 percent of the current population served by each PWS. As mentioned in response to comment Section 16.4.6 SafeWater Calculation of Benefits and Monetized Results, EPA assumes that the proportion of 7-year-olds living in the homes that are undergoing an LSLR is equal to the proportion of the United States population that is 7 years old. This assumption takes care of the need to model the movement of children in and out of homes in the community as the proportion of the population in these age groups is assumed to remain constant. Any future benefits accrued based on LSL changes made during the analysis period are discounted back to year 1 of the analysis, which accounts for the need to discount benefits to the year the LSL change was made. Thus, there is no need for any additional discounting beyond what is already in the modeling to specifically account for children moving between homes at the time of an LSLR.
EPA disagrees with the suggestion that it should be presenting benefits at discount rates other than 3 and 7 percent. OMB Circular A-4 (OMB, 2003) remains the current guidance on the use of discount rates in regulatory analysis. OMB Circular A-4 recommends presenting results using discount rates of 3 and 7 percent, which is what was done in Proposed LCRR EA and Final LCRR EA. One commenter mentioned the use of OMB Circular No. A-94 (Dec. 17, 2019) as evidence for using lower discount rates than 3 percent, however this OMB Circular explicitly states that the lower rates presented in the Circular, "...do not apply to regulatory analysis or benefit-cost analysis of public investment. They are to be used for lease-purchase and cost-effectiveness analysis, as specified in the Circular." Several commenters raised the issue of intergenerational benefits being a part of the LCRR economic analysis, as the analysis period is 35 years, and the lifetime earnings calculation for the value of an IQ point assumes earnings through age 65. EPA acknowledges that the benefits analysis includes some benefits that could be viewed as intergenerational benefits however, OMB Circular A-4 does not require additional analysis at a lower discount rate, but suggests that the EPA consider doing so. EPA is not performing additional analysis at a lower discount rate, but instead describes qualitatively the results of performing such an analysis, as the quantified benefits exceed costs in both the low cost and high cost scenario at the 3 percent discount rate.
16.5 Comparison of Costs and Benefits
Summary of Comments
EPA received a number of comments on the comparison of costs and benefits. One commenter questioned if the health benefits of the rule justified the rule's costs given the overall reduction in BLLs already seen in children in the United States. One commenter suggested that the EPA is inconsistent in how it accounts for on-going Primacy Agency actions by including some of the on-going actions in the cost assumptions but excluding benefits from those actions.
Several commenters questioned whether EPA's determination that the benefits of the rule justified the costs was correct. This included the fact that under some of the assumptions and discount rates, the monetized costs of the rule exceed the monetized benefits. These comments included the assertion that EPA gave more weight to the results under the 3 percent discount rate, and less weight to the results under the 7 percent discount rate. Another commenter recommended that "EPA provide a cost-benefit analysis that demonstrates the consideration of economic reasonableness for the proposed revisions."
One commenter noted that the EPA does not quantify the benefits associated with a number of the requirements, including monitoring, public education, and LSL inventory requirements, and they suggest that the benefits should be quantified so that the net benefits of each regulatory requirement could be evaluated. The same commenter expressed concern with the use of an incremental cost analysis and argued that it is generally appropriate when multiple alternatives are considered, and that EPA should have analyzed "sunsetting" the existing LCR as an option.
EPA received a comment that suggested that the rule changes could result in fewer LSLRs each year and that this would expose children to water that exceeds the action level for lead longer than the previous LCR. EPA received a comment that suggested that EPA review whether the cost to implement any requirement for copper are justified by the associated benefits.
One comment questioned whether the estimated benefits justify the costs of the proposed LCRR given that the range of child BLLs, and water contributions are lower than they were in 1991.
A commenter asserted that because EPA did not include certain costs in the economic analysis because some Primacy Agencies already required certain activities, such as LSL inventories or school sampling, but included the benefits of these actions in the economic analysis the total benefits of the rule would be overestimated.
Agency Response
EPA agrees that due to regulatory and non-regulatory programs, average BLLs in children have declined over the past two decades in the United States. Therefore, in conducting the benefit analysis for the LCRR, EPA took these lower baseline BLLs, and lower water contributions, into account. Even with lower baseline BLLs in the United States, the Administrator has determined that the quantified and non-quantified benefits of the proposed LCRR justify the costs.
However, since EPA only quantified the health benefits associated with LSLR, CCT installation or re-optimization, and POU installation, and did not quantify for any benefits associated with these ongoing activities (LSL inventories or school sampling), EPA disagrees that it overestimated the benefits of the proposed or final LCRR.
EPA disagrees with commenters that asserted EPA's conclusion that the benefits of the proposed rule justify the costs is flawed. SDWA directs EPA to analyze the cost and benefits of any proposed rule and directs the Administrator to make a determination as to whether the benefits of the proposed rule justify the costs. SDWA makes it clear that EPA should consider, both quantified and unquantified, costs and benefits in making its determination. In addition to estimating the costs and benefits of the proposed LCRR that EPA was able to quantify, EPA also discussed in some detail a number of health benefits, such as adult CVD, and costs, such as the ecological impacts of phosphorous, that the Agency was unable to monetize or otherwise quantify. The Administrator's assessment that the benefits of the proposed LCRR justified its costs was based on the totality of the evidence, specifically the quantified and unquantified benefits and costs.
EPA also disagrees with a commenter's assertion that the Administrator's determination that the quantified and non-quantified benefits of the proposed LCRR justify the costs is a result of EPA giving more weight to the results of the economic analysis under a 3 percent discount rate, and less weight to the results under a 7 percent discount rate. This assertion is incorrect because as stated above EPA considered both quantified and unquantified benefits and costs when making this determination. Another commenter suggested that it was unclear if the determination was based on a preference for the 3% discount rate or the presence of non-monetized benefits. As stated above, the Administrator's determination that the quantified and non-quantified benefits of the proposed LCRR justify the costs was based on the totality of the evidence, not only the quantified/monetized costs and benefits.
EPA disagrees that it should provide quantified benefit estimates for each of the LCRR's regulatory requirements, including new LSL inventory and monitoring requirements, so that the net benefits of each requirement can be evaluated on its own merits. While these requirements may not directly lead to a reduction in health impacts and therefore benefits, as part of an overall regulatory schema, they allow for the benefits associated with LSLRs, CCT installation and re-optimization, and POU installation, which EPA does evaluate. Because the various components of the regulatory requirements work in concert to provide the lead health risk reduction, it would be inappropriate to try and pick and choose regulatory requirements based on each requirement's net benefits.
EPA disagrees with the comment that "sunsetting" the previous LCR should have been considered as a regulatory option. Under the SDWA, EPA must review its standards for drinking water every six years and adhere to an anti-backsliding provision: any revision to a drinking water standard must "maintain, or provide for greater, protection of the health of persons" (Section 1412(b)(9). Sunsetting the previous LCR would not meet that standard and therefore is not an appropriate alternative to include in the analysis. In addition, EPA disagrees with the assertion that under the "low-cost scenario using a 3% discount rate...the Current LCR produces annualized net costs of $364 million." EPA's incremental benefit analysis did not count the benefits associated with existing CCT under the previous LCR. Instead, only benefits associated with changes in CCT, or additional LSLR, under the previous LCR, were included in the benefits. If EPA removed the cost associated with existing CCT from the analysis ($327M), then the previous LCR, under the low cost scenario at a 3% discount rate, would have costs of $67M and benefits of $6M. Therefore, the net cost of the previous LCR is $61M, not $364M. And, this does not include the non-quantified health benefits, such as reductions in adult CVD, generated by the previous LCR. Therefore, EPA also disagrees with the statement that "USEPA should give serious consideration to stanching the endless welfare losses that the Current LCR imposes and refrain from adding to the suffering by not promulgating the Proposed LCRR."
The commenter also stated that "the use of incremental cost analysis is generally appropriate when multiple alternatives are considered, and they can be ordinally ranked in order of decreasing net benefits. But a key alternative is missing from this RIA, and without it, incremental net benefit calculations are misleading. That alternative is sun setting the Current LCR." EPA agrees that considering incremental costs and benefits is appropriate when more than one option is considered and that is why EPA conducted an incremental cost and benefit analysis between the previous LCR and the LCRR.
EPA disagrees with the comment that the LCRR would result in fewer LSLRs. As shown in Appendix C, Exhibit C-1 of the Final LCRR EA, under the LCRR, EPA estimates that between 330,228 and 429,174 full LSLRs will occur over the 35-year period of analysis. Under the previous LCR, EPA estimates that the number of LSLRs will be 8,770 to 126,292. The increase in LSLRs under the final LCRR is the result of the addition of a pro-active goal based program in addition to mandatory removal and a new requirement that PWSs replace the utility side of the LSL when households remove their segment. In addition, under the final LCRR, partial replacements (when only public side is replaced) are no longer allowed except in limited circumstances and are not counted towards the replacement rate, while under the previous LCR partial replacements counted towards the PWS's LSLR.
EPA did not modify the current requirements for copper under this rulemaking effort. SDWA includes an anti-backsliding provision that requires any revision to a drinking water standard to "maintain, or provide for greater, protection of the health of persons."
In response to the comment suggesting that the Agency "provide a cost-benefit analysis that demonstrates the consideration of economic reasonableness for the proposed revisions" EPA notes that "economic reasonableness" is not a term used in SDWA. In determining the requirements under the LCRR, EPA utilizes standards laid out in the SDWA. EPA established the treatment technique requirements in the rule based upon SDWA section 1412(b)(7)(A) which requires EPA to identify treatment techniques "which, in the Administrators judgment would "prevent known or anticipated adverse effects on the health of persons to the extent feasible" and the requirement in SDWA section 1412(b)(9) that "[a]ny revision of a national primary drinking water regulation shall ... maintain, or provide for greater, protection of the health of persons." The SDWA defines feasible as "feasible with the use of the best technology, treatment techniques, and other means which the Administrator finds, after examination for efficacy under field conditions and not solely under laboratory conditions, are available (taking cost into consideration)." EPA conducted a HRRCA when proposing the LCRR just as it would any NPDWR, as required in section 1412(b)(3)(C)(i) and (ii) of the SDWA, and EPA has updated the HRRCA for the final rule. SDWA Section 1412(b)(3)(C)(i)(I) requires the inclusion of quantifiable and nonquantifiable costs and health risk reduction benefits for which there is a factual basis in the rulemaking record. The Agency considered information from the HRRCA at proposal to determine, as required by SDWA section 1412(b)(4)(C) "whether the benefits ... justify, or do not justify, the costs." The Agency found that the quantified and non-quantified benefits justified the quantified and non-quantified cost of the proposed rule requirements. EPA did not propose to use, and is not employing the discretionary provision of SDWA section 1412(b)(6)) that allows the Agency to "promulgate an NPDWR that "maximizes health risk reduction benefits at a cost that is justified by the benefits." if the Administrator determines, based on the HRRCA, that the benefits would not justify the costs.
16.6 Estimated Cost and Benefit for Alternative Options
EPA received comment under the larger category of estimating costs and benefit for alternative options that covered topics including: clarification of units associated with reported incremental costs; the impact of the fifth liter tap sampling requirement considered in an alternative option; and, the overall insufficient number of alternative options considered in the economic analysis of the proposed rule.
Incremental Costs
Summary of Comments
One commenter asked for additional clarification regarding the units used for the incremental costs presented in Exhibits 10 and 11 of the economic analysis. Specifically, the commenter asked whether the incremental costs are expressed per year, per customer, per month, or per 1,000 gallons of water.
Agency Response
EPA thinks the commenter is referring to Exhibits 5-1 and 5-2 of the Proposed LCRR EA, which are equivalent to the same exhibits in the Final LCRR EA. Exhibits 5-1 and 5-2 provide the national annualized costs, discounted at 3 and 7 percent, that PWSs, households, and primacy agencies will incur in complying with the previous LCR and the final LCRR, respectively. The values are reported in 2016 dollars. The national cost of the final LCRR, or incremental cost, is the difference between the cost of compliance with the final LCRR and the cost of compliance with the previous LCR that these entities are projected to incur each year over the 35-year period of analysis. The incremental costs in the table are annualized values discounted at 3 and 7 percent and reported in 2016 dollars. EPA estimated costs of the final LCRR under both low cost and high cost scenarios to reflect uncertainty in the cost estimates. The low cost scenario and high cost scenario differ in their assumptions made about: (1) the existing number of LSLs in PWSs; (2) the number of PWS above the lead trigger level or action level under the previous and final monitoring requirements; (3) the cost of installing and optimizing CCT (4) the effectiveness of CCT in mitigating lead concentrations; and (5) the cost of LSLR. See Chapter 5, Section 5.2 for additional detail on the assumptions for the low and high cost scenarios.
Compliance Monitoring
Summary of Comments
Citing data from Philadelphia and Michigan, AWWA in comments argued that the impact of the fifth liter sampling requirement on compliance would be highly variable but could substantially increase the number of systems triggered to meet additional regulatory requirements under the proposed revisions.
Agency Response
EPA agrees with the commenter that the new fifth liter sampling protocol for LSL systems will likely result in higher lead levels. The Final LCRR EA (USEPA, 2020a) provides EPA's revised approach for estimating a low and high likelihood that a system's lead 90[th] percentile levels will be at or below the lead trigger level of 10 ug/L, above the trigger level but below the action level, or above the action level. Chapter 4, Section <IP_ADDRESS>.2 provides these estimates for the final rule. Chapter 9, Section 9.3.1 presents these estimates based on a first liter sample. Exhibit 9-6 includes a comparison of these likelihoods under the previous rule (referred to as the "first liter option") and the final LCRR.
Rule Options Considered
Summary of Comments
The Environmental Defense Fund (EDF) stated that the rule options explicitly considered by EPA were relatively minor variations, and that they did not include (1) reducing the lead action level or (2) requiring water systems to fully replace all LSLs. EDF endorses the comment made by the Institute for Policy Studies, which stated that "the lack of alternatives for which EPA provides details and analysis is inconsistent both with statutory requirements to consider alternatives, . . . as well as with best practices for regulatory analysis," and also criticized EPA for not exploring different degrees of stringency for the action level or the trigger level or re-examining the basis for the 15 ug/L action level. EDF further noted that the option of requiring water systems to proactively replace all LSLs, which EPA did not evaluate, was specifically recommended by the National Drinking Water Advisory Council and also endorsed by ASDWA in public comments. The Environmental Protection Network  also requested that EPA include an analysis of the costs and benefits associated with lowering the current action level to 10 ug/L (versus adding a trigger level of 10 ug/L and retaining the current action level of 15 ug/L) and retaining a 7 percent LSLR rate versus the 3 percent rate in the proposed rule.
Agency Response
In response to the comment indicating that EPA analyzed an insufficient number of alternative options. SDWA Section 1412(b)(3)(C)(i) does not mandate that EPA consider a minimum number of alternatives when proposing a treatment technique rule or treatment technique rule revision; it just requires a HRRCA for any alternatives under consideration. In addition, OMB Circular A4 (OMB, 2003), which provides guidance and best practices to federal agencies on conducting cost/benefit analyses indicates that, "The number and choice of alternatives selected for detailed analysis is a matter of judgment." OMB Circular A4 goes on to suggest criteria that should be considered in the selection of alternative options to be analyzed. The options analyzed for the LCRR rulemaking are designed to capture three of these suggested criteria. EPA considered alternative regulatory schemes associated with first or fifth liter sampling and sampling at all schools and child cares or only on request. These alternatives capture OMB Circular A4's concept of accessing option with "Different Degrees of Stringency." EPA also assessed the public provision of specific addressed versus less specific location identifiers for known LSL locations exploring OMB Circular A4's "Informational Measures Rather than Regulation" concept for alternative options. The Agency also analyzed small system compliance alternative thresholds for systems serving 3300 or fewer persons and 10,000 or fewer persons as suggested by the OMB Circular A4's criterion "Different Requirements for Different Size Firms."
For a discussion of EPA's rationale for not lowering the action level in general or specifically to 10 ug/L, see response to comment Section 19.1. For information on the Agency's rational for requiring a 3 percent LSLR mandatory replacement rate versus retaining the 7 percent rate from the previous rule and a discussion why EPA did not require proactive LSLR, see response to comment Section 6.8.
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