Peer Reviewers:

Deborah Cory

- Sle chta, Ph.D. Panos Georgopoulos, Ph.D. Philip Goodrum, Ph.D. Andrew Hunt, Ph.D. Naila Khalil, M.B.B.S., M.P.H., Ph.D. Andy Menke, M.P.H., Ph.D. Paul Mushak, Ph.D. Rosalind Schoof, Ph.D., DABT Joel Schwartz, Ph.D., M.D. Kenneth Unice, M.S. Ian von Lindern, Ph.D., P.E. Kathleen Vork, Ph.D., M.P.H.

Contract No. EP

- C

- 12

- 045

Task Order 39 

# Post

- Meeting Peer Review Summary Report

External Peer Review of EPA’s Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

February 27 , 2015

Prepared for: U.S. Environmental Protection Agency

Of fice of Pollution Prevention and Toxics

1 200 Pennsylvania Aven ue, NW Washington, DC 20460 Prepared by: Versar, Inc.

6850 Versar Center Springfield, VA 22151Post

- Meeting Peer Review Summary Report

- External Peer Review of EPA’s Approach for Estimating Exposures and

Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

i TABLE OF CONTENTS

EXECUTIVE SUMMARY

................................ ................................ ................................ 1

I. INTRODUCTION

................................ ................................ ................................ ....... 4

Background on Draft Technical Approach Document

................................ ............... 4

Peer Review Process

................................ ................................ ................................ ... 4

Peer Review Meeting

................................ ................................ ................................ .. 6

II. CHARGE TO REVIEWERS

................................ ................................ ...................... 8

III. G ENERAL IMPRESSIONS

................................ ................................ ..................... 13

IV. RESPONSE TO CHARGE QUESTIONS

................................ ................................ 27

Issue 1. Monte Carlo and Sensitivity Analyses

................................ ........................ 27

Question 1a.

................................ ................................ ................................ ...... 27

Question 1b.

................................ ................................ ................................ ...... 45

Issue 2. Estimates of Lead Dust Loadings and Concentrations

................................ 51

Question 2a.

................................ ................................ ................................ ...... 51

Question 2b.

................................ ................................ ................................ ...... 55

Question 2c.

................................ ................................ ................................ ...... 60

Issue 3. Estimates of Transport of Exterior Lead Dust to Nearby Buildings

........... 63

Question 3.

................................ ................................ ................................ ........ 63

Issue 4. Estimates of Exposure to Children and Adults

................................ ............ 72

Question 4.

................................ ................................ ................................ ........ 72

Issue 5. Leggett Blood Lead Modeling

................................ ................................ ..... 82

Question 5a.

................................ ................................ ................................ ...... 82

Question 5b.

................................ ................................ ................................ ...... 91

Question 5c.

................................ ................................ ................................ ...... 96

Issue 6. Estimat es of IQ Change/Characterization of Changes in Children’s IQ

... 100

Question 6.

................................ ................................ ................................ ...... 100

Issue 7. Estimating Adult Health Effects

................................ ................................ 105

Question 7a.

................................ ................................ ................................ .... 105

Question 7b.

................................ ................................ ................................ .... 116

Question 7c.

................................ ................................ ................................ .... 123

Question 7d.

................................ ................................ ................................ .... 126Post

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

ii TABLE OF CONTENTS (continued)

Issue 8. Overall Draft Approach

................................ ................................ ............. 129

Question 8.

................................ ................................ ................................ ...... 129

V. SPECIFIC OBSERVATIONS

................................ ................................ ................... 137

APPENDICES

Appendix A

– List of Peer Reviewers and Bio graphical S ketches

Appendix B

– Meeting Agenda

Appendix C

– List of Meeting ObserversPost

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- External Peer Review of EPA’s Approach for Estimating Exposures and

Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

1 E XECUTIVE SUMMARY

Versar, Inc. (Versar), a contractor for the Environmental Protection Agency (EPA), coordinated an external peer review of EPA’s Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings and organized a two

- day public peer review meeting in Arlington, Virginia on January

13

- 14, 2015. The peer review of EPA’s Technical Approach Document was initiated with a pre

meeting written peer review managed by Versar and conducted by twelve independent expert peer reviewers. The role of the peer reviewers was to evaluate the scientific and technical merit of the Approach and provide their responses to eight charg e questions. Peer reviewers were responsible only for evaluating the quality of the science included in EPA’s Approach and were

not asked to make any regulatory recommendations or to reach consensus in either their

deliberations or written comments. The two

- day peer review meeting, which directly followed the written peer review period, was held to discuss the scientific basis supporting EPA’s Approach and to provide members of the public with an opportunity to present oral comments and observe the peer r eviewer deliberations.

On the first day of the meeting, Versar began by providing information on the overall pee r review process and introduced the peer reviewers. In addition, EPA provided background information on the Approach and associated model. An observer comment session followed the opening remarks by Versar and EPA, after which the peer reviewers began their discussion on the Approach. The discussion was moderated by the Chair, Dr. Deborah Cory

- Slechta, and focused on individual responses to EPA’ s charge questions. The second day of the meeting began with brief remarks from Versar followed by a second observer comment session and then continued discussion of responses to the charge questions. Important discussion points are

summarized in the list below.

 The peer reviewers congratulated EPA on what was clearly a very significant effort in terms of modeling and careful consideration of model parameters and characteristics to address a significant environmental health concern of considerable complex ity.

 In general, the peer reviewers felt that the document provided a very clear presentation of rationales and steps in the process. However, it was also felt that the document might be

easier to comprehend and more integrated for the reader if some of t he related Appendix material could get incorporated into the document itself which would obviate the need to continue to go back and forth between the document and Appendi c es . It could also be improved by specifically defining the audience for which the do cument is intended and modifying the presentation in accord with that audience

.

 Recognizing the significant efforts in modeling incorporated into the document, several concerns emerged from the peer reviewers with respect to the A pproach. One included

the fact that each component of the modeling included assumptions and the concern that these assumptions were being magnified across components of modeling i.e., the cumulative impact of all of the uncertainty propagated throughout the series of models.

This problem was not addressed directly in the Approach, and the concern was not obviated by the ‘sensitivity analysis’ as it was not considered to accomplish the intendedPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

2 goal. Additional comments included the need for additional data collection. A recurring

t heme was the need to define uncertainties across the A pproach components.

 Additional points raised by the peer reviewers included the need for systematic collection of additional field data that would be specifically relevant to situations of concern, and for further component

- by

- component as well as “in concert,” sensitivity/uncertainty analyses and model evaluation for most of the elements of the

A pproach.

 Some concerns were expressed about the inappropriately high soil and dust ingestion

rates and esti mates of soil and dust exposure and the need to include relative bioavailability adjustments reflecting the reduced bioavailability of lead in soil and dust (as compared with lead bioavailability in drinking water and food) as these may result in

overestim ates of blood lead levels in the updated Leggett model. A suggestion was to run the Leggett model with a range of assumptions for soil and dust ingestion rates and relative bioavailability to examine the impact on the fit of the model output to the

NHANES data.

 There was significant discussion that additional considerations be given to the use of the geometric standard deviation ( GSD ) , in terms of appropriate uses and interpretation, noting that the GSD is intended to represent variation among people in th e same exposure setting rather than reflecting variation due to different exposure sources and settings.

 There was also significant discussion by the peer reviewers regarding the limitations of the Monte Carlo analysis, particularly given the recognition that these were simply calculating uncertainties about the mean value.

 Albeit recognizing its utility, discussion about the Leggett model concerned the significant variability and uncertainties around the blood lead values calculated by the

model.

 Quest ions were raised about the inability of the current modeling to accommodate two particularly vulnerable groups, albeit this is recognized by the EPA and defined as future

research directions. The peer reviewers considered one population that is particularl y vulnerable is pregnant/breastfeeding women, given that blood leads are already elevated by lead mobilization from bone and that this lead source is directly transmitted to the fetus. At present, pregnant women are subsumed within a much larger age bracke t and the models do not accommodate the fetal compartment.

 Some concerns were also raised about the lumping within age categories; for example, including children from 0

- 6 may underestimate effects on the 1

- 2 year age group where blood lead values have always peaked in NHANES analyses. As previously noted, adults are lumped together in the 18

- 49 year category which may dilute effects on women of childbearing age. In addition, these lumpings included both sexes, while increasinglyPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

3 sex

- related differences in toxicity are being reported and

, of course

, apply to women of

child

- bearing age and pregnant/breastfeeding women.

 Additionally, several peer reviewers felt that a more selective focus of the health risk assessment might be more appropriate, in particular a focus on children and IQ. As the most sensitive endpoint, focusing on consequences for this endpoint should therefore incorporate all of the other health endpoints currently included in the Approach, and could provide the basis for a more directed approach given the time and resources obviously required for the current Approach.

 F or adult human health effects , bone lead data are considered to reflect cumulative/long

term exposure that is not necessarily reflected in the available blood lead measures. I n the absence of bone lead data , a focus on what is currently known as the most sensitive endpoint (children and IQ) would eliminate the concerns over the reliance on blood lead

levels

. In addition, this could eliminate the potential confounding of age

- related impacts with respect to health endpoints in adults that cannot be separated from lead effects in the current Approach. It might also be useful to consider including occupationally

- exposed adults (i.e., renovation workers) as they were considered to constitute a population that would have the highest and most p rolonged exposures during these activities.

 Other potential mechanisms to limit the analyses could be to confine assessments to building sites/types that would expose the greatest numbers of individuals.

Following the meeting, peer reviewers were given additional time to complete their individual written reviews, which were submitted to Versar upon completion. These final written comments are contained in Sections III, IV, V of this report and fall into three categories: general impressions, responses t o charge questions, and specific observations. Written peer review comments, as well as comments submitted to the docket by members of the public, will

be considere d by EPA as it revises the Approach

.Post

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

4 I. INTR ODUCTION

Background on Draft Technical Approach Document

The U.S. Environmental Protection Agency (EPA) is currently in the process of determining

whether or not lead

‐ based paint hazards are created by renovation, repair and painting (RRP)

activities in public and commercial buildings (P&CBs), as required under section 402(c)(3) of the Toxic Substances Control Act (TSCA). For those renovation activities in P&CBs that create

lead

‐ based paint hazards, TSCA directs EPA to address the hazards through regulat ion. The technical approach being peer reviewed is part of EPA’s continuing comprehensive approach to assess and manage risk from lead contamination in painted surfaces under TSCA. The focus of the technical approach is to assess the U.S. population’s expo sure and incremental health effects from renovation and repair activities in P&CBs. Estimates of incremental health effect changes associated with exposure to lead in the modeled renovation scenarios in the technical approach will be used by the Agency as they consider how to identify and evaluate hazards, and make a hazard finding as required by TSCA.

Peer Review Process

On August 6, 2014, EPA announced in the Federal Register the release of the draft document Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial a pproach d ocument) for the purposes of public review and comment ( FRL

- 9914

- 12, http://www.gpo.gov/fdsys/pkg/FR

2014

- 08

- 06/pdf/2014

- 18357.pdf ). Consistent with guidelines for the peer review of influential scientific assessments, Versar, Inc., an EPA contractor, was tasked with assem bling 10 to 12 scientific experts to evaluate the t echnical a pproach d ocument.

The peer review selection process was initiated with a three

- week public nomination period that was held from June 27, 2014 to July 18, 2014, as documented in the Federal Regi ster ( FRL

9912

- 63, http://www.gpo.gov/fdsys/pkg/FR

- 2014

- 06

- 27/pdf/2014

- 15123.pdf ) . During this period, members of the public were able to nominate scientific experts with knowledge and experience in one or more of the following areas: (1) multimedia routes of human exposure to lead; (2) modeling of multimedia human exposures; (3) lead exposure pathway assessment; (4) lead uptake/absorption; (5) internal biokinetic distribution and physiological effects of lead; (6) tissue concentrations of lead; (7) human growth and activity patterns; (8) exposure assessment modeling; (9) lead

- induced h ealth effects; and (10) risk assessment and uncertainty

characterization. A total of 18 experts were nominated by the public.

Following the nomination period, Versar conducted an independent search for qualified scientific experts to augment the list of publicly

- nominated candidates. Altogether, Versar contacted 63 experts (18 publicly

- nominated, 45 Versar

- identified). Of these, 31 were interested and available (12 publicly

- nominated, 19 Versar

- identified).

Versar considered and screened all 31 candidate s against the selection criteria described in the Federal Register dated June 27, 2014, which included: (1) having demonstrated expertise in the areas described above, based on information in their submitted resume, biographical sketch and/or current publi cations; (2) being free of any conflict of interest (COI) and the appearance ofPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

5 the lack of impartiality; and (3) being available to participate in

- person in a two

- day peer review meeting in the Washington DC area. In addition, candidates were queried on t heir availability in the December 2014 to January 2015 timeframe, with the exact date for the meeting to be

determined at a later time.

Following completion of the screening process , Versar provided to EPA the names of the 15
experts for the draft propos ed list of candidate reviewers. After consultation with the EPA Office of the Science Advisor, the names of the 15 candidates and their professional affiliations were published in the Federal Register on September 29, 2014 ( FRL

- 9916

- 70, http://www.gpo.gov/fdsys/pkg/FR

- 2014

- 09

- 29/pdf/2014

- 23114.pdf ) . The Federal Register also

announce d the availability of additional information on the candidates, including education and professiona l experience, via Versar’s peer review website ( https://www.peerreview.versar.com/epa/lead/ ). Additionally, biographical sketches for each of the candidate reviewers were posted to the Docket. The Federal Register provided a 21

- day period for the public to submit relevant information or documentation on the proposed list of candidates that Versar should consider during the evaluation process of selecting the final 12

reviewers. No public comment s were received on the proposed list of candidate reviewers.

Following the close of the public comment period on the proposed list of reviewers, Versar

selected the 1 2 experts who, collectively, provide d expertise spanning the multiple subject

matter are as covered in the t echnical a pproach d ocument and, to the extent feasible, best provide d a balance of perspectives. Biographical sketches for each of the selected reviewers are included in Appendix A.

Peer Reviewers

Deborah Cory

- Slechta, Ph .D. University of Rochester School of Medicine and Dentistry

Panos Georgopoulos, Ph.D. Rutgers University , EOHSI

Philip Goodrum, Ph.D

. Integral Consulting, Inc.

Andrew Hunt, Ph.D. University of Texas

, Arlington

Naila Khalil, M.B.B.S., M.P.H., Ph.D. Wright State University

Andy Menke, M.P.H., Ph.D

. Social & Scientific Systems , Inc. Paul Mushak, Ph.D.
PB Associates

Rosalind Schoof, Ph.D.

ENVIRON

Joel Schwartz, Ph.D. , M.D.
Harvard University

Kenneth Unice, M.S.

Cardno ChemRisk

Ian von Lindern, Ph.D., PE TerraGraphics Environmental Engineering , Inc.

Kathleen Vork, Ph.D., M.P.H. California Environmental Protection Agenc yPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

6 Peer Review Meeting

On January 13 and 14, 2015, Versar convened a public peer review meeting in Arlington, Virginia. This meeting was held to discuss the scientific basis supporting EPA’s draft Technical Approach Document and to provide members of the pub lic with an opportunity to observe the peer reviewer deliberations. The meeting followed the public comment period on the Technical Approach Document and the pre

- meeting written peer review period, during which the twelve selected peer reviewers evaluated EPA’s Technical Approach Document and provided preliminary comments in response to the eight charge questions.

Versar managed the pre

- meeting registration period, which allowed members of the public to register to attend the meeting in person or remotely via teleconference and/or webinar. Members of the public were able to register online, via Versar’s peer review website ( http://peerreview.versar.com/epa/lead/ ) as well as by telephone, email, or U.S. mail. In advance of the meeting, Versar provided all registered attendees with pre

- meeting handouts, which included the agenda (Appendix B) and logistics information. Approximately 26 public observers

attended the peer review meeting in person and 18 observers attended the meeting via

t eleconference and/or webinar. A list of meeting attendees is provided in Appendix C.

Peer Review Meeting

– Day One

On the first day of the meeting, Versar began by providing inform ation on the overall peer review process and introducing the peer reviewers. In addition, EPA provided background

information on the Technical Approach Document and model . A brief observer comment session followed opening remarks by Versar and EPA, during which three individuals presented

oral comments. The main concerns and questions presented by the observers included:

 C oncerns were presented by the U.S. Small B usiness Administration, Painting & Decorating Contractors of America, and Exponent on the reliability, accuracy, and amount of data present in the study.

 Kevin Bromberg of the U. S. Small Business Administration Office of Advocacy questioned the range of buildings this framework applies to because there is a large variance in the type of public spaces outlined in the study. Important items to consider mentioned by Mr. Bromberg included: the density and type of lead paint present in P&CBs, the renovation activities used (e.g., not a lot of heavy sanding takes place in these types of structures ), and what risk of exposure remains (e.g., pre

- 1960 s /pre

- 1930 s buildings have likely been renovated multiple times already and, therefore, the risk of exposure to lead paint is low ). From public offic e buildings to schools, Mr. Bromberg questioned the scope of the A pproach and the required expenditures to address the problem of lead exposure. He suggested that the focus of the A pproach target specific buildings (e.g., heavy intensity remodeling) and that more substantive data be collected

.

 Mark Casale of the Painting & Decorating Contractors of America questioned the tasks

outlined in the Approach on removal of lead from public buildings. He critiqued the

metho ds of lead removal selected by EPA for its model, citing that the use of certain

activities

, such as power

- sanding or needle gun torching

, are not common practice in thePost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

7 industry. He also noted a difference between normal renovations and actual removal of lead, which are used interchangeably in the draft Technical Approach Document

.

 Y vette Lowney of Exponent questioned whether accurate and representative data even

exi st to warrant the different model analyses, and questioned the high level of uncertainty and low availability of existing data associated with the process outlined in

the Technical Approach Document

.

Following the observer comment period , the peer reviewers began their discussions on the Technical App roach Document , which centered on individual responses to EPA’s first five charge questions . The discussion was moderated by the Chair, Dr. Deborah Cory

- Slechta.

Peer Review Meeting

– Day Two

The second day of the meeting began with b rief opening remar ks from Versar followed by a

second observer comment session. O ne observer presented comments on Day Two :

 Ron Yarbrough from the Society for Protective Coatings (SSPC) expressed concerns regarding the lead and paint removal activities and terminology mentioned in the Technical Approach Document . Mr. Yarbrough stated that power

- sanding, torching, and heat guns are no longer used in modern paint removal operations and that needle guns

are seldom used for industrial facilities a nd never used for commercial facilities

. He

stated that the “lead

- safe” practice of chemical removal (stripping) of lead

- based paint is the most acceptable

, economical , and common practice used in the painting industry, and

that EPA should not focus on the outdated technologies mentioned above

. Mr. Yarbrough answered several questions from the peer reviewers, including a question on availability of industrial hygiene data that support his claim that the lead abatement and cleanup projects his company works on do not pose a risk due to lead exposure. In

response, Mr. Yarbrough stated that for every lead abatement project, a risk assessor is hired to ensure that both environmental and worker exposure levels are below

established standards. He also mentioned that h is company would be willing to provide environmental ( dust wipe ) and OSHA mandated biomonitoring (blood lead) data t o EPA

, assuming the information could be released

.

After the observer comment session, the peer reviewers continued their discussion of the charge questions, beginning where they had left off on Day One (Charge Question 5).

Preparation of Post

- Meeting Comments

Following the public peer review meeting, peer reviewers were given additional time to revise

and complete their individual p re

- meeting written reviews. These final written comments are contained in Sections III, IV, and V of this report. Written peer review comments, as well as comments submitted to the EPA docket by members of the public, will be considered by EPA as

it revise s th e Technical Approach Document

.Post

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

8 I I. CHARGE TO REVIEWERS

Issue 1. Monte Carlo and Sensitivity Analyses

The Approach uses Monte Carlo modeling to explore variability within exterior and interior exposure scenarios. All combinations of scenario variables have been modeled and a distribution of results for each scenario was developed by iterating 10,000 times for exterior scenarios and 3,000 times for interior scenarios from the sampled variables.

Question 1a. Please comment on the use of the Mon te Carlo analyses to explore the variability within each exposure scenario. Please comment on the strengths and weaknesses of the Monte Carlo analyses.

The Approach further explored variability through a sensitivity analysis for one interior scenario

and one exterior scenario. The sensitivity analysis techniques are being used to examine the impact of sources of uncertainty on exposures. The sensitivity of the estimated exposures to assumptions about different scenario conditions can reflect the differenti al scales in which those conditions are measured. OPPT has also chosen to express sensitivity by an “elasticity” measure, which normalizes the inputs.

Question 1b. Please comment on the strengths and weaknesses of the sensitivity analysis. Please

comment on OPPT’s use of both elasticity and sensitivity scores to evaluate the impact of changes in assumptions on results.

Issue 2. Estimates of Lead Dust Loadings and Concentrations

The Approach estimates an “emission fraction” from exterior renovations which is then used to

estimate an emission rate for the AERMOD model. This is described in Section 4.2 and Appendix H. The Approach estimates a “fraction emitted” from interior renovations, which is then used to estimate interior lead dust loadings. This is des cribed in Section 5.2 and Appendix I. EPA’s Dust Study was the primary data source that contained all the required contextualizing information to process the information for use in the Approach. EPA supplemented information from the EPA Dust Study in a tar geted way to estimate loadings resulting from exterior and interior demolition as well as to better inform post

- renovation cleaning practices. One of the approaches considered was supplementing dust loading data within indoor air data derived through indus trial hygiene sampling. For exteriors, the dust study processing steps are described in Appendix H and important considerations include the distance of the sample from the renovation job, the size of the job, and mass of lead in the paint. For interiors, t he dust study processing steps are described in Section 4.2, and important considerations include the size of the job, the size of the work area, and mass of lead in the paint. Estimating a “fraction

- emitted” allows loadings to be estimated and scaled for a wide variety of room sizes, job sizes, and lead content present in the universe of P&CBs. Loadings are estimated for the work

- area, the workroom, and adjacent rooms. Loadings in each of these rooms were individually estimated for each type of activity an d then composite loadings were estimated for each multiple activity scenario by summing the relevant activity

- specific loadings for each type of room. Department of Energy reference buildings described in section 5.1 of the Approach were used to developPost

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9 ge neric room sizes and building layouts. Use of the Dust Study data to estimate lead dust loadings for P&CBs does introduce uncertainty as described in Section 9.1.1.

Appendix K of the Approach document describes an empirical based method for converting lea d loadings to lead concentrations. This method was recommended over a mechanistic mass

balance model approach when both approaches were presented to EPA’s Science Advisory Board as part of a peer consultation in 2011. The difference between the empirical a pproach presented in 2011 and the empirical approach presented in this document is the addition of two

data sources.

Question 2a. Please comment on the steps taken to process the EPA Dust Study data described

in detail in sections 4.2 and 5.2 as well as a ppendices H and I, and on the Approach to supplement Dust Study Data with information from other sources including the conversion of indoor air concentrations to indoor dust loadings.

Question 2b . Please comment on the availability of any other empirical data that contains lead dust loadings or indoor air concentrations, and supporting information, in buildings during and after renovation activities that disturb lead based paint. Please comment on any suggestions to reduce uncertainty in estimating dust lo adings present after a renovation, including at their upper

ranges.

Question 2c. Please comment on the data sources and method used to convert lead loadings to

lead dust concentrations.

Issue 3. Estimates of Transport of Exterior Lead Dust to Nearby Buil dings

The Approach describes how exterior renovation activities generate aerosolized lead dust which can be transported to and penetrate the outer shell of nearby buildings. The Approach also describes how exterior renovation activities generate lead particulate debris (larger particles) which are not transported through the air, but can be tracked into buildings. Both of these contribute to indoor dust loadings. This is described in detail in Section 5 and Appendices G and
J. EPA’s AERMOD model was us ed to estimate transport through the air. A Dust Model developed for this analysis was used to estimate track

- in and removal of lead dust over time. The Dust Model was also presented to EPA’s Science Advisory Board in 2011 as part of a peer

consultation.

Question 3. Please comment on the approach and models used to estimate outdoor air concentrations, outdoor soil and hard surface concentrations, and indoor dust loadings resulting

from exterior renovations.

Issue 4. Estimates of Exposure to Children and A dults

EPA considered environmental concentrations resulting from renovations (as described in Issue 2 and 3) and combined this information with age

- specific exposure factors and activity patterns to estimate exposures. Exposure factors were derived from E PA’s Exposure Factors Handbook and Activity Patterns were derived from EPA’s Consolidated Human Activity Database (CHAD). A sensitivity analysis was performed to determine how the location of an exposedPost

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10 individual (within one room or moving in multiple roo ms) combined with exposure start

- time influenced results and is described in Section 8. Activity patterns were varied by age and by building type and are summarized in Appendix L.

Question 4. Please comment on the overall approach for estimating exposures (both interior and exterior) through use of exposure factors organized by age, and by activity patterns, organized by age and building type.

Issue 5. Leggett Blood Lead Modeling

The Approach uses an updated Leggett blood lead model to estimate both chil dhood and adult changes in blood lead. The updates included scaling tissue lead concentrations for children and adolescents, evaluating and updating the bone lead transfer rates, and evaluating and calibrating the model using human data sets for both child ren and adults.

Question 5a. Please comment on the updates to the Leggett model and the choices made in

evaluation of data sources and calibration of the model for children and adults.

Question 5b. Please comment on the use of the updated Leggett model t o estimate blood lead levels for both children and adults in the Approach.

The Approach estimates blood lead level metrics using background values and for the renovation only. Concurrent and lifetime blood lead metrics are estimated as outputs from the

Le ggett model. Concurrent blood lead metrics were estimated at nine different time

- steps post renovation. Lifetime blood lead metrics were estimated based on the age of person at the start of

renovation and the time it takes for their blood lead to return to background. The lifetime will vary across exposure scenarios. Incremental bone lead change was also estimated as another metric of lead body burden.

Question 5c. Please comment on the various outputs derived from the Leggett model (concurrent blood lead, lifetime blood lead, and bone lead) and their appropriateness for use in

concentration

- response functions for children and/or adults.

Issue 6. Estimates of IQ Change / Characterization of Changes in Children’s IQ

In the Approach, EPA estimated IQ reductio n based on both concurrent and lifetime average blood lead concentrations based on the Lanphear et al. 2005 and Crump et al. 2013 studies. For concurrent blood lead, the Monte Carlo analysis estimated potential IQ loss based on a single “measurement” (simu lated blood lead concentration) for a predetermined (simulated) time after renovation, and IQ loss will be estimated based on the regression coefficient from the Lanphear/Crump log linear model for concurrent blood lead. Lifetime average blood lead was

est imated based on the time

- weighted average blood lead concentrations from birth until blood lead returns to background as generated by the Leggett model, and the lifetime average regression coefficients will be used to estimate potential IQ loss.

Question 6. Please comment on the use of the log

- linear IQ model (concurrent and lifetime) for children up to age 10.Post

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11 Issue 7. Estimating Adult Health Effects

A weight of evidence approach was used to consider which health effects in adults might be appropriate for this analysis. Based on the findings presented in the EPA ISA for Lead and the NTP Monograph on the Health Effects of Low

- Level Lead, concentration

- response functions were derived from studies representing renal, reproductive, developmental, and cardio vascular effects. However, there are several issues related to estimating adult health effects that complicate the analysis applied in the Approach.

1. There is a lack of understanding about the contribution of past versus recent lead exposures in adults to health effects, especially chronic health effects that may require years of insult to produce an effect (e.g., cardiovascular disease).

2. Adults have stores of lead in bone, which reflect cumulative (lifetime) lead exposures. However, the renovation exposure scenarios being modeled for the Approach are relatively short exposures, better reflected in blood lead

measurements.
3. Most of the available studies report on cohorts with higher blood lead levels than those currently reported for the majority of the US population (approximately 1 μg/dL). Often the data derived from the concentration

- response functions at blood lead levels below 1 are highly uncertain, given the lack of data at these

levels.

4. In the Approach document, renal, reproductive, dev elopmental, and cardiovascular effects are examined separately, although there may be feedback effects among them (e.g. cardiovascular disease is frequently associated with chronic kidney disease, and CKD appears to be a risk factor for CVD).

Because thes e studies report effects most often observed in adults with likely higher past Pb exposures, uncertainty exists as to the Pb exposure level, timing, frequency, and duration contributing to the associations observed with blood or bone Pb levels.

Question 7

a. Please comment on the appropriateness of deriving concentration

- response functions for Cardiovascular Disease Mortality (CVDM), reduced kidney function, and low birth weight , and their use for estimating incremental adult health effects due to renovatio n activities

for P&CBs.

Question 7b. Blood lead levels continue to decrease in the US. Please comment on the appropriateness of using concentration

- response functions from studies in which the overall

blood

- lead levels are relatively higher than contempor ary levels in the US adult population, particularly in light of inflection points in the concentration

- response functions for certain health effects (e.g. CVDM) below or above which the data do not demonstrate a positive relationship between lead exposure and adverse health impacts.

Question 7c. Concentration

- response functions based on blood lead are more prevalent in the health impacts literature, while studies using bone lead are not available for all health endpoints.

Please comment on the use of blood lead and bone lead as biometrics for projecting incremental

health effects due to renovation activities for P&CBs.Post

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12 Question 7d. In this Approach, EPA estimated adult health impacts based on concurrent and lifetime average blood lead concentrations. Pleas e comment on the appropriate time horizon over which to estimate adult health outcomes (Section 7.3.1) and whether this differs across health endpoints.

Issue 8. Overall Draft Approach

EPA has developed an Approach document for estimating incremental health effects resulting from exposure to lead from renovation, repair, and painting activities in public and commercial buildings (P&CB). The approach is intended to provide an overview of the data sources and

models used to estimate environmental media c oncentrations and exposures, blood lead levels,

and incremental health effects.

Question 8. Please comment on the overall Approach and its utility for estimating incremental

health effects due to renovation activities on the exteriors and interiors of P&C Bs. Please comment on the clarity an d transparency of the document.Post

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13 III. GENERAL IMPRESSIONS

Deborah Cory

- Slechta

First, the Agency is to be commended for what has clearly been an incredibly comprehensive and carefully considered and deliberate approach to the derivation of increases in lead exposure due to renovation. Overall, the document is extremely clearly written and for the most part, rationales are routinely provided to explain the Approach in a very comprehensible manner. The

additional details are provided in Appendix material. The one part of the approach that seemed less clear, and this may be because this reviewer is not a modeler, is the step from blood lead determinations in the Leggett models to the determination of health effects of those renovations

per se.

One thought that arose going through all of the materials is why not incorporate the Appendix materials into the document so that going back and forth is not necessary. While I understand that the Appendices include a significant numb er of pages, in many cases they also duplicate text that is included in the Approach document; the duplication could then be eliminated and thus shorten the overall document. The formatting used also significantly facilitates readability, as do the figure s and tables used; the formatting represents a significant (and much improved) departure from the review documents that this reviewer has seen over the years.

With respect to the Approach itself, which has obviously evolved over time, it is a very sophist icated and generally well thought out and inclusive approach that considers virtually all of the scenarios and factors that would apply, in ultimately calculating, within reasonable computational time requirements, the changes in risk associated with renov ation activities for 4 significant health endpoints associated with elevated lead exposure. It now includes health endpoints that include three vulnerable populations, children, the fetus and the elderly. Though this reviewer is not a modeler, the Approach seems a mastery of integrated modeling and statistical analyses. Current updating of parametric values, moreover, has further improved the fits to NHANES blood lead data as depicted over several years.

Some other considerations should be made. First, it is not entirely clear who the document is aimed at or is intended to accommodate, i.e., the lay public, scientists? The answer to the question is important because it should be used to define the level of readability and what material is incorporated. It would be useful to have an Executive summary and also a chapter with conclusions that would lead the reader through the impact of the analyses with respect to

health effects.

Having made those comments, one other impression that emerges after reading thro ugh the Approach is that this has obviously been a very significant investment of time and resources with an approach that has been designed to accommodate virtually every possible scenario in terms of populations and exposure considerations as opposed to more extensive efforts to potentially focus these efforts on what are more easily identifiable most vulnerable populations, which would most certainly be pregnant women and young children. Unfortunately, children are lumped within an age categorization th at is fairly broad (given that peak blood lead levels

occur between 1

- 2 years of age), and women of childbearing age within an extremely broad category that lumps them together with postmenopausal women who would also be anotherPost

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14 potentially vulnerable popu lation. Recognizing the clearly declining blood lead levels associated with various health outcomes in adults, aside from some other worse case scenarios, the risk to adults (except perhaps pregnant or planning to be pregnant women, see comments below), wh ere ostensibly the health effects reflect longer years of exposure, may not be worth the efforts except for occupationally

- exposed workers. Thus it might be a more reasonable effort to limit the document to those concerns or to put the resources and time i nto these more vulnerable populations and scenarios. While pointing this out, it is likely that there are regulatory considerations that weigh into this as well.

One other consideration suggested as one reads through this is that even with the incorporat ion

of relevant data

, where available, the Approach relies on a significant number of assumptions with error and uncertainty. Thus attempting to validate different components of the models and sensitivity analyses become more important in this context.

Pa nos Georgopoulos

The documents under review summarize a complex and ambitious effort towards implementing a framework linking together various modeling components describing the “source

- to

- dose

- to

effect” sequence processes associated with Pb exposures du e to renovation activities in public and commercial buildings. The distribution

- based (Monte Carlo) framework and modeling Approach presented in the document under review can be seen as a useful scientific “hypothesis generator,” appropriate for identifyin g data gaps and prioritizing questions for further analysis. The work accomplished towards implementing this Approach provides, at least in preliminary form, many

- but certainly not all

- of the “tools” needed in a “toolbox” intended to support design of experiments for targeted field and laboratory studies that are in fact needed in order to accurately characterize short

- term, transient Pb exposures and associated incremental health

effects due to PC&Bs renovation activities.

It should be recognized that the above mentioned effort has appropriate aims, follows (or at least tries to follow) valid principles of distributional assessment (though it focuses its Monte Carlo Approach entirely on distributional characterization of variability and addresses uncer tainty concerns only in a qualitative manner), and has produced concrete advancements in assembling the necessary “toolbox” for probabilistic “source

- to

- dose

- to

- effect” assessments of Pb

containing dust from renovations of P&CBs. However, at the same time, it should be emphasized that there is need for systematic collection of additional field data, specifically relevant to situations of concern, and for further component

- by

- component as well as “in concert,” sensitivity/uncertainty analysis and model evalu ation for most of the elements of this toolbox. Such analysis can lead to significant refinements and adjustments that will enhance the reliability and usefulness of the Approach for supporting a framework employed in regulatory applications and policy mak ing.

The Report under review contains, on various occasions, statements regarding the robustness of its Approach that can appear contradictory (as discussed in more detail in the comments on Question 8).Post

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15 For example, on one hand it is stated (Section 9.2.6) that

(a) “[B]ecause of the intricacy of the Approach and the complicated nature of the overall exposure assessment objective, how closely the modeled results are related to true population distribution is unknown

, ”

while on the other hand it i s stated (Section 9.2.7) that

(b) the “high level of correspondence between complicated multistep modeling and empirical blood lead levels in the current U.S. population is evidence that the many

uncertainties in the analysis are likely not introducing specific overall bias for mid

‐ range percentiles.”

The latter statement (above) is perhaps true, but not necessarily relevant to the specific objective of the Approach under review, the focus of which is on the increment al effects of transient exposures associated with renovations of P&CBs and not on the chronic population

- wide impact of cumulative and aggregate Pb exposures.

The utilization of extensive calculations performed such a multi

- step, multi

- component system
sh ould always be examined in the context of empirically confirmed knowledge. On page 1 of the Report under review it is stated that the Approach employed “approximately 1.2 billion total modeling iterations, which are believed [ emphasis added] to adequately describe the range and variability of scenarios considered in this analysis.” It is strongly recommended that a “tiered” approach be considered, utilizing the major elements of the present effort but taking more systematically into account the critical lim itations in currently available data and models. Certain limitations cannot be overcome by any set of assumptions and associated calculations (no matter how “massive” these calculations might be), and need to be properly identified and

characterized across the entire “source

- to

- dose

- to

- effect” sequence of physicochemical and biological processes and for all major exposure pathways and for selected major adverse outcome pathways of concern.

Philip Goodrum

My overall impression is that USEPA has undertaken a comprehensive analysis that is heavily

model

- dependent in order to assess a very wide range of scenarios. By in large, the Approach is well described, particularly the thought process used to balance realism, steps for addressing uncertainty due to data gaps, and computational demands. I focused my preliminary review on specific aspects of the conceptual site model, the Monte Carlo Analysis, and the presentation of risk metrics. I’ll continue to examine the details of the implementation of the Leggett m odel and the incorporation of concepts related to baseline, deviation from baseline given incremental exposures associated with renovation activities, and population level metrics.

USEPA has indicated that the modeling is intended to yield estimates of ex posure, blood lead concentrations (PbBs), and corresponding effects that represent the actual distribution among exposed populations. The effort is not intended to be intentionally biased high or low. Some assumptions and decisions do not support this go al.Post

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16 Some elements of the Approach deviate from conventional applications of models in lead risk assessment, but many of these are consistent with the state of the science and regulatory goals in this context. For example, the NHANES data were carefully co nsidered to support modifications to a life history of intakes that allowed for closer correspondence between predicted and observed population mean PbBs. In addition, while additional health effects were considered, consistent with positive associations noted in some epidemiological studies, interpretations of findings from the modeling were tempered by an acknowledgment that the available PbB data represent single points in time along a dynamic continuum of tissue concentrations. It is particularly uncl ear what magnitude and likelihood of adverse health outcomes might be associated with relative low level (absolute) PbB given the lack of substantial exposure

- response information with similar ranges and durations of exposure. Furthermore, one utility of the Leggett model is that it readily yields both bone and blood lead concentrations over a lifetime. USEPA appropriately focuses on PbB given the uncertainty in the exposure profiles that yield bone lead and the health effects associated with bone lead,

p articularly for exposure scenarios that are intermittent and variable in relative contributions to average daily intake rates of lead.

The use of Monte Carlo Analysis to examine combine effects of variability or uncertainty in selected input variables, to gether with a geometric standard deviation (GSD) term as an aggregate measure of variability seems, at face value, to lead to an exaggeration or double counting of variance. It is unclear exactly how to interpret the final summary statistics for the

PbB d istribution and associated health effects. In addition, in this context, given the importance of understanding the incremental change in PbB (and effects) that can be attributed to the specific renovation activities, an opportunity is missed to calculate this incremental change directly using the individual

- based modeling of Monte Carlo simulations. An important theme that underscores this analysis is the concept of relative risk

– what additional lead exposure can

be attributed to renovation activities c ompared with other sources of exposure, and what are the potential health consequences of this incremental increase in exposure? Tracking baseline and

scenario

- based exposures within the same individual may help to control for many age group

specific assu mptions, ultimately demonstrating that a lumping term for variance is not needed. In addition, USEPA has relied on measures of sensitivity analysis that are rooted in point estimate methods (i.e., elasticity and CV

- weighted sensitivity scores), presumably due to the computational demands of retaining the matrix of inputs associated with thousands of iterations. However, a closer examination of a subset of case studies using probabilistic sensitivity analysis

could be tractable and more informative. This feature of MCA can be as useful as the

distributions of risk themselves.

The decision to evaluate scenario uncertainty by distinguishing between scenario variables and random variables was helpful. Advanced programming facilitated the analysis and

inter pretation of a wide range of scenarios. Although USEPA deferred the probability weighting of the scenarios to a later date, the presentation of frequencies of exceedance and various risk curves to communicate results could lead to misinterpretations of th e findings, potential exaggerating risk or perhaps more problematic, obscuring potentially important combinations of conditions that may be important to investigate further.Post

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17 Andrew Hunt

The effort that had obviously gone into the A pproach D ocument was very impressive. It was well thought out and modularity of the various steps in the A pproach facilitated the review of the flood of information that was provided. Nevertheless there were areas that seemed quite opaque to me and shuttling back a nd forth between the Approach Document and the a ppendices did not facilitate the review. And, some of the appendices were quite daunting, the experiments

that were run as document ed in Appendix I were impossible to keep track of to judge the utility

of the results. I thought that certain modeling exercises went into exquisite detail (and the EPA team should be commended on their efforts), whereas in some areas where empirical data was lacking the modeling seemed less successful. This blend of sophistication and uncertainty makes one feel that some form of propagation of error across the modeling exercises, if it all possible (and perhaps it is not), would give the A pproach a more connected feel.

Naila Khalil

This is an impressive report that presents estim ated human health impact of lead due to renovation, repair, and painting activities in public and commercial buildings. To my knowledge this approach is the first of its kind that offers plausible health estimates due to lead exposures. Utilizing advance d statistical analysis, exposure modeling and comprehensive epidemiological review of relevant studies, these projected results could be used in public health decision making.

The information in terms of health impacts is accurate and is described very clearly. Lead is a toxic metal and has been inadvertently introduced in the environment, human and animal tissues.

Research over the last several decades has confirmed that lead adversely effects cardiovascular, reproductive, skeletal, and nervous system, which has been very nicely and succinctly summarized in this report. However it will be helpful to present the human health impact of modeling results in relatively simpler and n on

- technical terms to enhance clarity.

It will be very helpful to add a separate section detailing the conclusions after summarizing the results of the Approach. This summary can provide a one or two hypothetical health outcome scenarios based on the pre sented lead exposure modeling due to renovation, repair, and painting

activities in P&CB.

Andy Menke

The Approach attempts to estimate lead exposure, resulting changes in blood lead, and the resulting health effects from renovation, repair, and painting the exterior or interior of public and commercial buildings. In general, the Approach is written with clarity and broken down into sufficient detail to understand the complicated methodology. It uses sophisticated modeling including the Leggett blood lead model and Monte Carlo modeling to describe exposure and risk in hypothetical people and scenarios. Authors of the Approach carefully selected input variables based on the best available data, justifying the selection values. In addition, numerous sensitiv ity analyses were run to check the sensitivity of the model to variability in the input variables and other model parameters.Post

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18 Of concern with the Approach are the numerous sources of uncertainty, which is discussed in detail in the document. Many of thes e concerns were not adequately quantified or tested, and the cumulative impact of all of the uncertainty propagated throughout the series of models comprising the approach may substantially impact estimates of health effect. To the extent possible, it woul d greatly benefit from additional sensitivity analyses and validation of all of its individual pieces and the Approach as a whole. Furthermore, it would be beneficial to simplify the methodology as much as possible. My biggest specific concern pertains to how the document will be used considering uncertainty in the time period of exposure and the relevance of post

renovation blood lead to measured blood lead in previous studies of renal disease and cardiovascular mortality. For those 2 outcomes, previous pu blished studies assume that blood lead is not simply a measure of short

- term lead exposure, but that an individual’s blood lead concentration or ranking in the distribution is relatively consistent over many years. The post

renovation estimates of blood le ad at 50 months and lifetime average may be reasonable to estimate risk, but 5 months and 15 months are likely too short to be applicable to previously published studies of the general population. On the other hand, there are other limitations that

may res ult in an underestimation of health effects such as the understandably strict criteria for selecting outcomes to include in the Approach, which may result in missing important health risks due to limiting the age range too much or not including other outco mes caused by lead exposure. Despite these issues, my overall impression of the Approach is that the methods are reasonable and the document has transparency, as the limitations/uncertainties of the Approach have been acknowledged in a straightforward mann er.

Paul Mushak

EPA is tasked, in its Technical Approach Document , to answer the question, Do renovation, repair, and painting activities in, on, and around public and commercial buildings with lead

based paint (LBP) result in quantifiable health risks f or potentially impacted children and

adults?

The full task of EPA, as I understand the process, will combine results and conclusions from this Approach effort with other Agency actions to arrive at proposed rulemaking.

Questions of accuracy, clarity of presentation, and soundness of conclusions, given the content and nature of the Approach document, address both the conceptualization of the EPA effort and

its execution.

In its conceptualization of the Approach, EPA constructed two conceptual models: the Interior Analysis and the Exterior Analysis. These two models, depicted in Section 2: Scope and Overview, are generally correct as to use and placement of their components. They are generally correct in that they incorporate, by empirically determined seq uence in previous promulgated rulemaking (the 2008 Lead RRP Rule for residences and child

- occupied facilities [COFs]), the main components of a human health risk blueprint. The earlier effort benefited from results of a consultation peer review of that app roach document by CASAC's Lead Panel in 2007 (cited as EPA 2007c) in the current Approach draft.

One difficulty of conceptual modeling for the Approach is that two additional models are necessary. Reasons for this are in my responses to charge questions. The authors addressPost

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19 renovations as discrete, isolated exercises. Specifically, renovations are either of interiors or exteriors at some specific time. The authors exclude, with minimal justification, any scenario

where both interior and exterior renovatio ns would be occurring simultaneously.

The authors also exclude, in the case of exterior renovation, any scenarios where a nearby building receiving Pb from a building undergoing exterior renovation would itself be undergoing interior, exterior, or combin ed renovations. My arguments for the need to consider this more complex scenario are in my comments.

Such expansion of the conceptual field would, of course, make for more complex analyses. However, to the extent that simple either

- or renovation case stud ies understate the frequency of

simultaneous interior

- exterior activities or renovated − nearby renovated spatial Pb contaminations, they would potentially significantly underestimate the extent of Pb’s toxic harm to receptor risk populations.

The correctness of the execution of the Approach and the information presented appear acceptable, given the close general adherence to the conceptual scheme laid out in the conceptual models. I say "correctness" rather than "accuracy" as used in the charge res ponse template, since I don't believe "accuracy" is the right term as a technical definition and with reference to either the information used to design the approach or results from the approach. Technically, an accurate result is one that accords with a t rue value. A probabilistic distribution of outcomes voids the notion of the existence of only one true value. Nor does one usually speak of one distribution being more accurate than any other distribution, given the scenario

- based inputs.

The clarity of p resentation of the conceptual models and their execution, in my reading, is hobbled by ambiguity and characterization gaps as to who exactly are the potentially affected child and adult risk populations. The EPA Approach by its nature is a sequentially cou pled

multi

- model one: one models a set of environmental lead cycling, fate and transport variables; one then models, using the foregoing as inputs, an array of LBP and other lead sources such as LBP dusts; one then models or tries to model distributions of exposures via a lead exposure biomarker model using the foregoing as inputs; one then models translation of modeled biomarker distributions with incrementally derived adverse effect biomarker distributions to

arrive at concentration

- response distributio ns; one then subjects multi

- modeled results to sensitivity and uncertainty analyses.

The authors should clearly define, up front, all in one subsection, what the results represent. They represent a hypothetical or typical child or adult at risk or groups thereof at risk who may be impacted by exterior and/or interior work activities in public and commercial buildings with LBP surfaces. They do not apply to a specifically identifiable individual or individuals residing at some specific place(s) in the U.S.

The results could potentially apply to an individual if that individual's behavioral, toxicokinetic, epidemiological characteristics, etc. matched those of the modeled, hypothetical/typical child or adult. Confusion often arises among lay readers about th is matter, and clarifications and caveats have been reported. In the case of the EPA IEUBK childhood lead exposure biomarker model,Post

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20 for example, Agency staff did a good job in clearly stating what that model does and does not

do.

Hogan K, Marcus A, Smith R, White P. 1998. Integrated exposure uptake biokinetic model for lead in children: empirical comparisons with epidemiological data. Environ. Health Perspect. 106 (Suppl. 6): 1557

- 1567.

White PD, Van Leeuwen P, Davis BD, Maddaloni M, Hogan KA, Marcus AH, Elias RW. 1998. The conceptual structure of the integrated exposure uptake biokinetic model for lead in children. Environ. Health Perspect. 106 (Suppl. 6): 1513

- 1530.

The clarity of presentation of the analyses and their results in the body of the documen t varies with the complexity of the methodologies employed. Given the considerable complexity of a number of Approach sections, presentation clarity was generally OK, but use of arcane jargon should be mixed more frequently with simple English to convey th e messages. In a number of places, the text appears to whiz through the material. The document needs to be more than what seems in parts just an interoffice set of exchanges among modelers and probabilistic distribution jockeys. The readers and users will be a broader universe. For example, the document should have an early and better discussion of what the assumptions are that go into use of Probabilistic Risk Assessments (PRA) such as Monte Carlo rather than just point estimates or modelings that employ o nly default values for input and computational parameters. One requirement would be

that a minimum amount of case

- or scenario

- specific measurement data exists to permit useful PRA, to include the Agency's judgments about how much data are the minimum requ ired. Otherwise, risk assessment remains risky assessment (Mushak, 2011 p. 716).

Mushak P. Lead and Public Health. Science, Risk and Regulation (Elsevier, 2011, pp. 980).

The Conclusions appear generally sound; there just weren't enough of them to better guide the reader and end user. Conclusions were guided by the sensitivity analysis, which identified and quantified those variables we would have expected to have been the most robust in translating LBP renovating activities to overall LBP health risks fo r children and adults.

The scope of conclusions were generally sound within the input information available and quite conservative, in that Section 9, the Uncertainty Analyses portion, provided detailed discussions of limits to assumptions and application s of outcomes.

Post

- Meeting Comments:

I’ve served on numerous advisory panels over the years and have witnessed the good, the bad, and the ugly. I was quite impressed with the quality and productivity of the meeting ’ s deliberations. The technical and man agerial grasp of the issues and their calendar needs by Agency technical staff/management, the multi

- topical expertise exhibited by Panel members, and the conduct of the meeting and issue evaluations by our Chair and Versar management were

excellent. Panel scrutiny of the draft Approach was quite detailed while the task discussions were kept within the time limits.Post

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21 I also note that a number of the Panel members had similar concerns in their pre

- meeting comments over various sections of the draft Approach document indicating some common areas of concerns and questions arrived at independently.

I would like to offer some lingering and some new concerns relative to pre

- meeting comments.

Conceptual Models

I was interested in the comments of those public attendees who represented some aspect of the structural renovation and repair industry and any light that might be shed on my stated concerns in pre

- meeting statements about such topics as the frequency of s imultaneous interior and

exterior renovations. The Approach draft made exterior renovation analyses a significant fraction of the overall document and put considerable effort into dissecting the exterior renovation segment of the approach into almost as m any risk assessment categories as was done with the interior analyses.

While listening to public comments at the meeting from those in some aspect of the renovation industry, it appeared to me that there is not a single service entity called the “ renovati on industry ,” but multiple industries. I also sensed that these various services may or may not be fully cognizant of, or have a grasp of, what the rest of the relevant industries are doing in the commercial and public structure sectors.

I also have to as sume that the industry survey referred to by the Agency and that is a work in progress does not principally concern any of the services described by the public commenters. In particular, one public commenter in one of the relevant industry sectors appeared to opine and conclude that not only is there little simultaneous interior and exterior renovation being performed but very little exterior renovation activity is taking place in the U.S. public and commercial renovation sector, at least relative to interi or activities.

But is that factually the case across the renovation universe? And if so, would it not beg the question of why the Agency has made exterior renovation analyses a major part of the approach? Also, if so, how can exterior renovations escape being a key part of commercial/public building renovation, given the extended and extensive history of the use of exterior LBPs for exterior surfaces of commercial and public building masonry and iron/steel?

It would probably be useful in the earlier part s of the document for the authors to briefly enumerate the what, the why and some of the how of exterior renovations involving exterior LBP surfaces so that the general interested reader has a sense of why such renovations are necessary. By contrast, most readers would probably have an intuitive grasp of the why and how of building interior renovations.

Rosalind Schoof

I thought the overall approach used in EPA’s analysis made sense and provided a useful way to explore the potential incremental exposures associated with various kinds of renovation activities. Making the step from incremental exposures to incremental heal th effects is much more uncertain and less convincing. My review focused on the assumptions used to estimate soilPost

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22 and dust exposure and on the implications of the exposure estimates on the blood lead modeling. I did not provide comments on the Monte Carlo analysis, estimation of lead dust loadings, or estimates of the transport of exterior dust into buildings. Although outside my area of expertise, I found the descriptions of these aspects of the analysis to be quite readable and plausible. Nevertheless, co ncerns noted during our meeting regarding propagation of errors and obtaining a better understanding of the prevalence of the lead removal practices described needs to be addressed. My primary concern about the estimates of soil and dust exposure is the us e of what I consider to be inappropriately high soil and dust ingestion rates and the failure to include relative bioavailability adjustments reflecting the reduced bioavailability of lead in soil and dust (as compared with lead bioavailability in drinking water and food). I am concerned that these factors may have resulted in overestimates of blood lead levels as the Leggett model was being updated. I recommend that the source and uncertainty in key lead intake assumptions be

discussed in the main text an d that alternate values be considered in reevaluating the Leggett model. I also share concerns raised by other panel members regarding the GSD used to estimate the distribution of blood lead levels (BLLs). The GSD should represent the expected variability in BLLs in a group of children exposed to the same exposure setting. The GSD should not be based on NHANES data because that GSD reflects variation in exposure sources and settings, as well as variation in child behaviors and toxicokinetics. Finally, I rec ommend that the results of the exposure analysis be presented separately from the health risk estimates. The primary reason for this recommendation is that there is currently very high uncertainty in deriving risk estimates for the selected health endpoint s for short term exposures. Due to these uncertainties, I also recommend that only low birth weight be retained as a health end point for adults.

Joel Schwartz

No general impressions provided

.

Kenneth Unice

The conceptual model of the Approach is compl ex, but is a reasonable representation of the important exposure pathways. As such, the results of the Approach should useable for risk management in the current form subject to reviewing and better elucidating certain assumptions and risk assessment polic y decisions that were made in the exposure and health effects assessments. In some cases, it seems evident that some decisions were treated as risk assessment policy decisions (i.e. where there was not a clear choice based on scientific principles), howeve r, this may reflect a lack of a clear upfront focus on mechanisms of toxicity and lead distribution rather than a lack of science to inform the decision. An example of a risk assessment policy

decision that are the times that blood lead measurements are ex tracted from the biokinetic model and the definition of lifetime dose, which may have been better focused by a review of the mechanisms of metals toxicity generally, and lead toxicity, specifically.

Selected key recommendations regarding the Approach incl ude :

 Development of a clear statement of the scope of the assessment reflecting that the 2002 HUD survey data has been truncated at XRF measurements of >1.0 mg/cm 2 for each vintage of home. It is recommended that consideration be given to changing the title of the Approach to make clear that the scope of the Approach is P&CBs withPost

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23 identified lead paint hazards (rather than the entire universe of P&CBs). In addition, it is recommended that the Approach explain in general terms how the frequency of P&CBs with identified lead paint hazards will be determined in subsequent steps of the U.S. EPA analysis.

 Preparation of an information request requesting current and histo rical economic activity data from trade associations representing commercial painting contractors that abate lead paint hazards in P&CBs. A decline in economic activity in lead paint abatements over the last 20 to 30 years may be correlated with reductions in the fraction of P&CBs with lead paint hazards. This economic data may be further interpreted if surveys of lead paint hazards in P&CBs were to be conducted in the

future.

 Preparation of a hazard identification section of the Approach document supplem enting the hazard identification presented in the Framework document focused on mechanisms of lead toxicity and distribution to focus the risk assessment policy decisions made in the approach.

 Consideration of screening step and sensitivity analysis resul ts to reduce the number of Monte Carlo analyses necessary to meet the goals and objectives of the Approach.

 Inclusion of a review describing how U.S. EPA has assessed acute or sub

- chronic lead exposures within the Superfund and other programs.

 Developme nt of a rationale for utilizing the metric of maximum 1

- month rolling average or maximum 3

- month rolling average blood concentration building on the conclusions of the hazard identification section, as well as prior U.S. EPA

assessments

 Improvement of the focus of the risk characterization by selecting the most sensitive endpoints, likely to be IQ decrement in children and fetal developmental effects. If retained, a rational should be provided for the kidney and cardiovascular endpoints justifying why thes e endpoints are more sensitive than IQ decrement in children or fetal development.

 Preparation exemplar example calculations and point estimate inputs and outputs to provide context to the types of parameter combinations that lead to low, medium and high changes in blood lead, bone lead and health status. The absence of contextual point estimates is particularly difficult to overcome because the output distributions presented in Section 8 reflect risk assessment policy decisions about which scenarios

to m odel, rather than population frequencies.

 Presentation of both incremental and absolute blood lead concentrations in the Approach, as well as frequency of exceedance (e.g. % ≥1  g/dL, % ≥5  g/dL, % ≥10

 g/dL).Post

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24  Inclusion of a qualitative or semi

- qualitative analysis or discussion of possible correlations between variables in the Monte Carlo analysis with reference to Section

B.5.5 in RAGS Volume 3 Part A.

 Refinement of the uncertainty analysis by qualitative or semi

- quantitative consideration of the possibility of age

- cohort effects, or the effect of age on naturally occurring decrements in biological function. For example, glomerular filtration rate (GFR) decreases with age in healthy adults as part of a normal kidney aging process; the possibility that the association between lead (which increases with cross

- sectional age) and GFR (which decreases with age due to normal physiological aging processes) has not been fully explored in the Approach.

The authors of the Approach are to be commended for preparing a thoroughly documented and sophisticated assessment of incremental health effects from lead potentially associated with Renovation, Repair, and Painting Activities in Public and Commercial Buildings. It is cl ear that the technical contributors have assembled the models and approach with care, and in the interest of making available a useful set of exposure and health effects predictions to inform risk management decisions. I am confident that my comments may b e readily addressed and considered without the need for significant modifications to the approach.

Ian von Lindern

EPA indicates that the “scenario

‐ specific approach” is intended to make hazard determinations for individuals inside or nearby P&CBs resulting from renovations. C onsidering the variable lead release rates, heterogeneity in building sizes and configurations, and the short

‐ term nature of the exposures; conceptualizing the problem is challenging, and difficult to describe mathematically, a s the complexities multiply through the series of non

- linear systems

. Nevertheless, EPA has developed a well laid

- out approach supported by appropriate discussion, figures, tables and appendices. The methodology is comprehensive, incorporating a sequence o f complex multi

- disciplinary mathematic models that, in turn, estimate source characteristics, dust lead releases, transport, environmental concentrations, human intake, blood lead levels, and potential human health effects. Most of the component models, s upport for the input data, and the application originate from peer

- reviewed sources and have been successfully utilized in past scientific and regulatory activities. Overall, the presentation is balanced and provides objective

assessment of current knowled ge, available data, data gaps, and uncertainties associated with each of the major steps and analyses.

However, by design and necessity, the methodology involves different disciplines and analyses techniques, each requiring particular expertise and exper ience to implement; and each carries inherent constraints in the applicability of its respective products. The methodology is undertaken in steps with the results of one analysis providing input for the following model. Due to the relative availability of support data, degree of scientific acceptance and concurrence with established protocols, and specific objectives of the underlying investigations and studies; there are significant disparities in the levels of uncertainty and confidence in the results of different steps. The resulting mix of empirical and mechanistic models and projections manifests as a chain of analyses, with each link inheriting the uncertainty of its predecessor.Post

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25 Generally, empirical models are limited to the range of data and conditi ons of the parent studies. Similarly, mechanistic models are limited by the lack of data to inform key parameter selection, variable constructs, or validation. Both suffer this admitted deficiency in this Approach. These shortcomings can propagate both err or and uncertainty through the stepwise modeling process. As a result, errors or uncertainties introduced in one step will propagate through the remaining steps, with the cumulative uncertainty inherent in the final outcome. Unlike the proverbial chain,

th e strength of this approach is not determined by the weakest link, but by the product of the uncertainties in each component model.

The EPA appropriately acknowledges these concerns and suggests approaches to address the

weaknesses. The data discussions a re straight

- forward, often conceding that little data are available to support inputs and assumptions, or test outputs. Additionally, the studies relied on were rarely designed specific to the component model needs and required post

- processing,

necessitati ng particular assumptions about the nature of the underlying data. As a result, there will be considerable uncertainty in the outcomes, as acknowledged by EPA in Section 9

“The Approach described here is data

‐ intensive, and thus, understanding the limitat ions and uncertainties associated with the input data and assumptions is critical. In addition, any uncertainties and limitations of the data will be propagated throughout the remainder of the downstream calculations and thus can have implications for seve ral different parts of the Approach.”

Unfortunately, despite the immense effort undertaken, it is unlikely that unqualified conclusions and quantitative answers can be provided to decision

- makers by this Approach. In order for the

results to be informati ve in assessing risk and potential outcomes, the uncertainties must be conveyed in a meaningful manner that aid in making better decisions. This can only be accomplished in the context of the intended audience and use. EPA has generally indicated that

thes e hypothetical scenarios will be incorporated in later analyses; applying yet another model that will require characterizing nation

- wide distributions of variables, that are admittedly poorly characterized for even the hypothetical constructs in the Approa ch document.

As a result, it is difficult, if not impossible, to comment on the prognosis for this Approach to satisfy the intended regulatory function outlined in the Federal Register. EPA has done a competent, if not excellent job, in constructing a cha in of hypothetical models that simulate potential lead releases, fate and transport, consequent exposures, and potential health effects. EPA has appropriately discussed and transparently presented the deficiencies and potential uncertainties, and a sensiti vity scheme to reflect the potential effects of those uncertainties on key outcome variables. EPA has likely done as good a job as can be done, given these real constraints. Whether this system sufficiently reflects reality to support protective human heal th policy cannot be determined at this point.

Kathleen Vork

A ccuracy of Information

One of the most important components of USEPA’s proposed Approach is the link between intake, blood lead levels, body burden of lead over time and incremental increases in thePost

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26 incidence of adverse health effects. An independent evaluation of Version 5 pre dictions suggests that adult blood lead levels are under predicted and back

- calculated intake is too high. Testing the model with robust time series data and making additional adjustments to key parameters could remove this problem.

Clarity of Informati on

The main document and appendices could benefit from a list of project

- related terms defined in the text for the purposes of this project along with their definitions (e.g. renovation

- related, Approach, time

- spent, Version

- 5 etc.). In addition, some terms appear to refer to the same object (e.g. The Leggett Model versus Version

- 5 (meaning the most recently adjusted FORTRAN version of the Leggett model). Some key information about studies, data and benchmarks used to calibrate, test and judge
component s of the Approach needs to be included in the document so that these steps are transparent. For example, sufficient information was not available in the main document or appendices to reproduce Figure M

- 6. Also, it was not clear whether summaries of NHAN ES data for the age groups appearing in Figure M

- 4 were extracted from published reports or summarized by USEPA from the NHANES database.

Soundness of Conclusions

Although USEPA’s most recent model predicts blood lead levels very close to those measured in young children exposed to very low levels of lead, it is not evident that the model performs well at higher episodic or continuous exposures for either children or adults. In addition, adults may indeed represent a susceptible population for P&CB expo sure due to time

- spent as well as already having elevated body burdens where an incremental increase may present a hazard. Methods used to account for major areas of uncertainty such as those used to develop reference dose or reference exposure levels or permissible exposure levels in other governmental programs may be an effective screening tool or alternative approach for determining if P&CB renovation activities meet the threshold for regulation under TSCA authority.Post

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27 IV

. RESPONSE T O CHARGE QUESTIONS

Issue 1. Monte Carlo and Sensitivity Analyses

The Approach uses Monte Carlo modeling to explore variability within exterior and interior exposure scenarios. All combinations of scenario variables have been modeled and a distribution

of results for each scenario was developed by iterating 10,000 times for exterior scenarios and 3,000 times for interior scenarios from the sampled variables.

Question 1a. Please comment on the use of the Monte Carlo analyses to explore the variability within each exposure scenario. Please comment on the strengths and weaknesses of the Monte Carlo analyses.

Deborah Cory

- Slechta

While not being able to provide detailed comments on this question, the use of the Monte Carlo analyses to explore variability within each exposure scenario seems to be a valid approach to generate the distributions.

Panos Georgopoulos

The Monte Carlo analyses implemented in the work under review have assembled and coded a collection of useful modeling tools that can be expanded and iteratively refined in the future. The fact that coding has utilized an open and versatile platform (Python 2.7) and linked in a probabilistic framework diverse “legacy” models (such as AERMOD and the updated Leggett PBPKM) should be c onsidered a strength of the approach. However, there are two general weaknesses of the Approach:

 The focus of the “distributional” (Monte Carlo) treatment is exclusively on variability, while uncertainty is either addressed qualitatively or handled by con sidering sensitivity testing of “fixed” variables. The need to treat explicitly distributions describing both

uncertainties and variability’s is further discussed in these comments. The current
treatment of certain “uncertain” variables as fixed is definit ely a weakness of the Approach.

 Dependencies and correlations among variables and/or parameters that in fact can be important and affect the structure of a Monte Carlo scheme are generally ignored. This is in fact recognized in the Report: In Section 9.2 .5 it is stated that “[T]he Approach does not account for correlations between many of the input variables. ... By not allowing for correlations, however, both the means and the 95th percentiles could be biased either high or low.”

Both variability and un certainty need to be addressed in a Monte Carlo approach. Chapter 2 of USEPA’s Exposure F actor s Handbook (USEPA 2011) states (on page 2

- 1) that “[P]roperly addressing variability and uncertainty will increase the likelihood that results of an assessment

or analysis will be used in an appropriate manner. Characterizing and communicating variability and uncertainty should be done throughout all the components of the risk assessment processPost

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28 (NRC, 1994). [...] Proper characterization of variability and uncerta inty will also support effective communication of risk estimates to risk managers and the public. [...] U.S. EPA (1995), following the NRC (1994) recommendation, has advised the risk assessor to distinguish between variability and uncertainty.” The same US EPA document (on page 2

- 5) describes the four

- tier approach of the International Program me on Chemical Safety (WHO, 2006) for addressing uncertainty and variability, that is also consistent with USEPA practices and recommendations:
The four tiers include “ the use of default assumptions; a qualitative, systematic identification and characterization of uncertainty; a qualitative evaluation of uncertainty using bounding estimates, interval analysis, and sensitivity analysis; and a more sophisticated one

- or tw o

- stage probabilistic analysis.” Unfortunately, the Approach, as described in the Report, does not address uncertainty in a detailed manner. As an example, the following statement on uncertainty appears in section 9.1.1 “The Approach estimates the fractio n of lead emitted during a renovation activity and is intended to be the best possible estimate given the data available. The values were similar to the single available emission fraction in the literature. Hence, the emission fractions represent an uncert ainty in the Approach results. The effect of this uncertainty (to either lower or raise incremental blood lead changes) cannot be quantitatively assessed due to lack of data on actual emission fractions from renovation activities.” In fact, this
appears to be generally adopted in the current Approach. A systematic sensitivity/uncertainty analysis should help assess the effect of the actual emission fraction being lower or higher than the presumed “best possible estimate” (according to the quotation above) t hat was used in the

calculations.

Variable range “lumping.” Another issue that represents a potential weakness of the Monte Carlo implementation involves the “lumping” of variable ranges in a manner that can affect outcomes as well as the transparency of the Approach. For example, on page 12, Section 2.7, in the discussion of “Health Effects and Age Groups Covered in the Analysis” (also Table 2

- 3) it is stated that “[t]he Approach modeled building occupants in three age ranges: 0

– 10, 18

– 49, and

50

– 80 years .” This statement needs to be clarified in view of the fact that these age groups do not explicitly/separately consider critical age ranges, such as, e.g. ages 0

- 6 (pre

- school) and ages over 65 (retirees), while relevant activity patterns and various susce ptibility

- related factors are distinctly different for such age sub

- ranges. Of course, one must assume that calculations are actually performed at an age “resolution” appropriate for the analysis and the available data (i.e.

for each 1

- year period) and the n “lumped” together for the task of characterizing health effects. However, such “lumping” can “mask” important patterns that can provide useful insights to the analysis; it is therefore necessary to explain and justify the rationale of such “lumping.”

G ender

- related Differences: A concern related to the above is that gender differences do not explicitly appear in the Monte Carlo Approach, where, in fact it is well established that gender

related differences in behavior and activity patterns, as well as i n physiology and in toxicokinetic/toxicodynamic attributes have the potential to substantially affect not only exposure and dose but also health outcomes.

Locations of receptor buildings. More information is needed on how the location of receptor building is randomized in the Monte Carlo scheme. It should be different based on industrial zones, residential zones, or commercial zones and activity

- based zoning should ideally be

considered. M ost of the urban l areas in the US are subject to strict zoning laws. For example, a scenario in an industrial zone could have all or most of the buildings (including the receptorPost

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29 buildings) to be industrial buildings. Currently it seems the Monte Carlo mo deling scheme randomly assigns receptor buildings from the set of Agricultural, Residential, Commercial, Industrial, and Education. The zone should ideally be a scenario

- specific input and receptor buildings should be selected on the basis of the zone sele cted. The utilization of zoning is discussed further in Appendix Q (“Approach to estimate universe of P&CBs”) but its incorporation in the MC scheme needs further clarification. Specifically, how the Maryland P&CBs data are used to derive nationally repres entative (spatial) distributions needs further clarification and justification: on page Q

- 2 it is stated that “National estimates are [...] extrapolated from the Maryland estimates based on the ratios of the total number of employer

establishments in the U nited States to the total number in Maryland for the industries comprising the four building categories included in the analysis.” However, the procedure described in Appendix Q appears to be both tedious and error

- prone, relying on visual inspection of ma ps and satellite images. Furthermore it is stated that “[A]nother limitation of the analysis is the assumption that the Maryland data are representative of the number of buildings per parcel nationally.” An evaluation of this assumption is necessary.

Ref erences

NRC. 1994. Science and Judgment in Risk Assessment. National Research Council, Committee on Risk Assessment of Hazardous Air Pollutants, Commission on Life Sciences, National

Academies Press.

USEPA. 1995. Guidance for Risk Characterization.

WHO. 2006. Draft Guidance on Characterizing and Communicating Uncertainty in Exposure

Assessment.

Philip Goodrum

I was initially very encouraged by the overall framework for the probabilistic analysis

– defining a structured set of exposure scenarios, using a biokinetic model that can accommodate intermittent exposures and yield exposure profiles over time that reproduces NHANES, implementing a dust model to convert mass loadings to concentrations, and introducing MCA to propagate source of variability and un certainty across various submodels, which provides an opportunity to yield distributions of PbB that can be associated with inputs and help elucidate the key risk drivers. While the CSM figures in Section 2 include the use of GSD in the sequence of steps, it isn’t until Section 7.4 (page 90) that the premise for using GSD is introduced. From a practical view, use of GSD in this context seems like a natural choice. There’s a regulatory

track record for its use in lead risk assessment with both the IEUBK a nd ALM models, and applying MCA techniques to biokinetic models poses both a programming challenge and is hampered by a “lack of literature data to use to estimate these distributions”. The GSD approach does also have drawbacks, so it is a tradeoff. Use of MCA together with the GSD in the fashion described is confusing.

USEPA proposes a lognormal model with output from Leggett representing the geometric mean and a GSD of 1.9 based on summary statistics on PbB distributions reported for NHANES survey data since 1999 (Appendix O). USEPA correctly notes, “The NHANES GSD representsPost

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30 variability associated with both the biokinetic differences in individuals as well as their
differences in environmental media concentrations and intakes.” The NHANES data for al l age groups combined actually yields GSD values of 1.9 and 2.0 for the survey years between 1999 and 2012. USEPA indicates that the lower of the two GSDs was selected to represent the biokinetic variability; this choice between GSD values has not materia l effect on the results, but the overall conceptual approach does.

Adjustments were made to the Leggett model to calibrate the model to match the average NHANES PbB results, as detailed in Appendix M. USEPA should verify which NHANES central tendency statistic specifically was used

– the arithmetic mean or the geometric mean (equivalent to the median of a lognormal). If Leggett was calibrated to match the arithmetic means, then subsequent application of the Leggett model output as if it was the GM of the lognormal model would introduce a substantial upward bias in risk. For positively skewed distributions such as the lognormal, this change in interpretation effectively shifts the entire curve to the right, yielding PbB estimates for each summary stati stic that are biased high. USEPA could recalibrate the Leggett model to the GM summary statistics, or more simply just parameterize the lognormal with the (AM, GSD) parameter pair (or apply the mathematical conversion to yield the corresponding GM). To u nderscore the importance of this bias, consider the following (hypothetical) example:

 Assume output from the Leggett model is 1 μg/dL

 If this is actually the arithmetic mean and the GSD is 1.9, then the corresponding GM of the lognormal distribution is 0. 47 μg/dL

 Incorrect application of the output as GM = 1, GSD = 1.9, yields a 95 th percentile of 2.9 μg/dL

 Correct application of the output as GM = 0.47, GSD = 1.9, yields a 95 th percentile of 1.3 μg/dL

This one misapplication of a summary statistic equate s to an overestimate of the 95 th percentile
of more than 100%.

At the start of Appendix O, USEPA acknowledges that the MCA approach already attempts to represent many other sources of variability in exposure and uptake that can contribute to the

overall variability in PbB: “In the Approach, numerous variables are sampled in order to capture the expected range in the U.S. population.” Tables A

- 3 and B

- 3 list the variables that are represented in the MCA for the Exterior and Interior scenarios, respective ly.

Without applying a GSD term, the MCA will still yield a distribution of PbB, but does not explicitly account for variability in biokinetics. Alternatively, applying the GSD term from NHANES will account for all sources of variability, clearly double

- counting some of the factors that are accounted for in the MCA. USEPA should first examine the distribution predicted by the current MCA approach and determine what (if any) underestimate of the GSD may be present. To do this effectively, the MCA can be executed in a parallel fashion

– for each iteration (hypothetical individual), model all exposures BOTH with and without the additional/incremental exposure attributable to a given renovation scenario. If the GSD for the

set of results without the renovat ion scenario approximates the NHANES results (i.e., GSD of 1.9), then this is supporting evidence that the model as executed can reproduce the variance ofPost

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31 the baseline PbB distribution for the given age groups. If the GSD is underestimated, then an adjust ment factor can be applied to account for the difference.

Importantly, by running the parallel calculations for baseline and baseline + renovation scenario, all of the incremental changes in PbB attributable to renovation can be calculated explicitly, for each hypothetical individual (iteration) and collectively for the simulated population. By controlling for all other sources of modeled variance, this approach yields a more robust and informative analysis then separately comparing the MCA results to a b aseline distribution of

PbB.

There’s still a lack of understanding of dependencies among input variables and no truly functional approach to address this issue with MCA. It is rare to have the paired information (i.e., multiple measures in the same indiv idual). Therefore, efforts to explore different

correlation matrices would be ad hoc , at best, and overcomplicate the probabilistic analysis. So

while I do not advocate such an ad hoc approach, greater clarity and attention to this source of uncertainty needs to be fully present in the final discussion and interpretations of the modeling results. It is briefly discussed in Section 9.2.5.

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of exp ertise and I am unable to respond to the charge question.

Andy Menke

The use of Monte Carlo methods is appropriate for this analysis. The methods were able to look at many different scenarios and account for variability within scenarios. An appropriate n umber of iterations were run for each scenario to achieve model convergence. The extensive quality assurance tests provide confidence that the models were implemented correctly. One limitation of this method is that correlated input variables may lead to o ver

- or underestimates. This was mentioned in the Approach, but they were not able to convey the potential impact on the output. I wonder if this issue could be further discussed or investigated in an additional sensitivity analysis. Another concern with M onte Carlo, similar to other models, is that input distributions need to be realistic and appropriate to get useful output. I did not identify any inputs that seemed unreasonable or unrealistic. Altogether, there are many assumptions and potential sources of uncertainty in these models. The most important concern is that propagation of error throughout the Approach may substantially affect the final estimates of health effects.

Paul Mushak

My work does not typically involve crunching probabilistic distributions using such methods as Monte Carlo. Rather, my expertise centers on the toxicological and toxicokinetic scientific validity and completeness of exposure and adverse effect scenarios an d scenario variables thatPost

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32 are input for such analyses. Similarly, the validity and coherence of sensitivity analytical outcomes are evaluated, in my comments, as to what we know about the larger picture for those variables most robust for impacting distrib utional outcomes.

I offer comments on a number of the topics headlined in Chapter 2, Scope and Overview, within

the above boundaries.

Scope of the Approach

As detailed in General Impressions , I believe the Scope section needs to better emphasize what is the underlying analytical approach and to do so both up front and throughout the interior

sections.

That is, EPA's approach employs sequenced and coupled multiple model estimates of the potential impact of various types of renovation activities in comm ercial and public buildings containing lead

- based paint (LBP) on interior and exterior surfaces on hypothetical or typical risk individuals. A coupled, multi

- model approach, on its face, requires the use of probabilistic distributional analyses that would help characterize exposures of these hypothetical, typical

individuals.

The use of a Monte Carlo analysis is called for, given the above. However, the "why" of Monte Carlo analyses is not explicitly simplified for the benefit of the lay reader and decisi on or policy maker. That reader is more likely to know that Monte Carlo is a gambling casino in Monaco. EPA should at least note upfront that the approach deals with the odds of sets of outcomes due to variability and not an exercise in gambling with peopl e's health.

Conceptual Models, Computational Models, and Associated Scenario Variables

The Approach document sets forth its main elements within two conceptual models: a model

constructed for an interior renovation and one for an exterior renovation. The two models, as presented in Figures 2.1 and 2.2, suggest such models are assumed to apply just for independent activities, i.e., do not apply to concurrent and potentially interacting scenarios. The models allow for multiple renovations over time.

The ec onomics of renovation activity in the case of commercial and public buildings would, at first glance, seem to argue for interior and exterior renovations being done concurrently. This prompts several questions:

- Are there data available to EPA from such s ources as the construction and renovation industries quantifying the current frequency of combined interior and exterior work during renovations?

- If the frequency of combined interior and exterior renovations done concurrently is significant, are the two conceptual models in Ch. 2 adequate? See my comments on the Approach, Sec. 4.4.Post

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33 - What would be the scientific basis to assume that interior and exterior renovation activities being done simultaneously can be evaluated as discrete/independent versus

interac tive events?

- If these two forms of activities can be considered to be interactive when done concurrently, what does the required third conceptual model look like?

- A fourth conceptual model would concern cases where nearby buildings affected by

exterior r enovations are themselves being renovated.

Apart from renovation, repair and painting models for commercial and public buildings, where

under TSCA do total demolitions of such LBP

- containing buildings fit?

This lead

- liberating scenario would be particularly problematic where multi

- building complexes

or block

- level rehabilitation activities are being carried out and entail demolitions. Full demolition of commercial and public buildings obviously makes irrelevant any modeling of
occupant exposures for the demolished structures, but would heavily impact nearby occupied buildings. This would particularly apply in diverse urban scenarios. If not under TSCA, what else governs such activities? Stationary source emissions under §§ 108, 109 or 112 of the C lean Air Act (CAA)? Significant contamination of nearby areas from demolition of LBP

- containing buildings such as deteriorated housing comprises a growing literature. Lead emissions and transport in such cases are from both interior and exterior LBP.

Refe rences

Farfel MR, Orlova AO, Lees PSJ. 2003. A study of urban housing demolitions as sources of lead in ambient dust: Demolition practices and exterior dust fall. Environ. Health Perspect. 111:

1228

- 1234.

Farfel MR, Orlova AO, Lees PSJ , Rhode C, Ashley P J, Chisholm JJ Jr. 2005. A study of urban housing demolition as a source of lead in ambient dust on sidewalks, streets, and alleys. Environ.

Res. 99: 204

- 213.

Jacobs DE, Catalin B, Welch A , Mucha AP, Stites N, Dixon S, Evens A, MacRoy P, Erdal S, Persky V , Phoenix J, Cali S . 2009. Lead particulate deposition from housing demolition. http://www.nchh.org/Portals/0/Contents/Article0858.pdf

.

Mucha AP, Stites N, Evens A , MacRoy PM, Persky V , Jacobs DE . 2009. Lead dustfall from demolition of scattered site family housing: Developing a sampling methodology. Environ. Res. 109(2): 143

- 148.

Pa r tridge J, Schneider R, Gibson R , Phillips P. 2004. Using Spatial Analyst to determine the

effects of b uilding demolition on elevated blood lead levels in children less than 72 months of age in St. Louis County: A preliminary look. Epidemiol. 15: S201.Post

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34 Rabito FA, Igbal S, Shorter CF , Osman P, Philips PE, Langlois E, White LE. 2007. The

association between demolition activity and children's blood lead levels. Environ. Res. 103: 345

- 351.

Selection of Computational Models

The approach is based on sequential coupling of multiple models. Consequently, choices of the least discordant models (versus empirical d ata) is essential to avoid perpetuation and sequenced magnification of potentially erroneous information. Overall, the authors employ valid criteria and have exercised good judgment in approaching choices for models, with some exceptions

noted below. The u se of models that are peer

- reviewed or would be likely to pass peer review and models employed in the earlier, but conceptually similar LRRP Rule supporting analyses done in 2008 (cited in the Approach document) are appropriate, given what's available.

Ho wever, EPA should clarify and better defend its use of Monte Carlo probabilistic analyses with reference to its Criterion #2−avoid models that require more inputs than can be determined realistically using existing data. Are the existing empirical data suf ficient for such analyses in the case of dust generation and other exposure pathways, especially if one only has data from the earlier dust study for residences and child

- occupied facilities (COFs) used in support of the 2008

LRRP Rule?

The Approach docum ent also assumes that if a model has been peer

- reviewed, it has been shown to be adequately validated and calibrated with measurement data, at least to the extent required for applications in this document. EPA should spell out what meets its definition of a peer

reviewed model for purposes of this Approach. For example, what is its track record in actual use? Would the O’Flaherty model also have some use along with the Leggett exposure model for modeling fetal exposures, for which it has the potential to a ccommodate an actual fetal compartment? This addition would better define the female child

- bearing age band, would better quantify fetal Pb exposures, and better characterize Pb concentration

– low birth weight relationships.

Identified Scenario Variables a nd Range of Renovation Activities

Scenario variables selected for both the Exterior and Interior cases are presented in Table 2

- 1 and, overall, appear to represent a reasonable and plausible tabulation of the major variables. Similarly, the activities ta bulated in Table 2

- 2 appear appropriate. Table 2

- 2's reference to "demolition" presumably refers to removal of major structural areas, but without total demolition of the entire building.

The scenario variables as tabulated, again, appear to treat the Int erior and Exterior activity groupings as independent, non

- concurrent cases. We would reasonably expect, on economic grounds, simultaneous renovations of both interiors and exteriors. In particular, the LBP

- leaded dust pathways for the younger subsets of th e 1

- 10 year child age band would be expected to be not merely additive but, to some extent, synergized when concurrent Interior

- Exterior renovations take place. Dust Pb intakes would be expected to be the major factor in Pb exposures of children 6 months

- 5 years of age for lifetime body

- lead burdens, lifetime PbB, and as rank ordering of concurrent blood lead levels.Post

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35 As noted in section I. General Impressions, the authors would probably need to consider cases where buildings impacted by nearby exterior renovations are themselves being renovated. Missing from the tabulation of scenario variables is any reference to dust Pb transport by family members, etc., to the home environment of very young children, i.e., when dust Pb releases to the family members’ work areas have occurred during working hours. The underlying conceptual premise is that the main pathway for exposures of all risk groups is dust Pb contact. Deposition of leaded dusts on clothing, shoes, office paraphernalia, etc. exposes the first

recep tor

, but also becomes a mobile pathway for Pb secondary, take

- home exposure of young residents.

Similarly, little is noted about any gradients to dust lead loadings or levels in multi

- story public and commercial buildings as a function of floor level, other than a crude one of configuration. Interior renovations of upper floors make for lower floor levels likely to be contaminated with movement of upper

- floor occupants in typical work activities. Exterior activity leads to track

- in

dusts that more likel y contaminate the lowest levels. There are some data available showing dust lead gradients on floors versus distance from entry, using door mat and other measures of surface Pb loadings and concentrations (U.S. EPA, 1996; see citation below).

No reference is made to the impact of total demolitions of LBP

- containing commercial and public buildings on nearby commercial and public structures in downtown urban zones. This activity would not only be a major contributor to neighborhood or area dust Pb levels

, bu t would be a pervasive one. Leaded dusts from this source would impact receptor populations via multiple pathways. See the list of articles giving data and providing discussions on building demolitions in dense urban areas, given above.

It's not clear whe re in the Approach the authors quantitatively addressed public and commercial building densities and their effects on nearby building occupants. Estimating the effects of renovations of old, LBP

- containing public and commercial buildings on nearby receptor occupants in lower Manhattan, for example, would be expected to require more complex analyses than for, say, Pittsfield, MA, even though both are in the Northeast lead paint belt.

Time Intervals for Exposures Within Renovations

The estimates of time pe riods for active, dust

- generating and post

- renovation activity appear to be reasonable and plausible. Authors may wish to note that estimates of dust

- generating and
post

- activity time spans are important for addressing various issues having to do with

char acterizing Pb exposure metrics and subsequently the health risk metrics. I note these next.

Selected Exposure Metrics and Associated Health Effects

The authors have done a reasonable job in their discussions and selection of exposure metrics. Some expans ion of the discussion is merited for better comprehension.

One factor of importance is the minimal time interval required to impart an adverse effect on the selected health metric(s). In theory, if there is a minimally required time threshold for impartin g some adverse effect that is not achieved by intermittent or transitory lead intakes during renovation activities, then presumably these activities do not harm or pose the risk thereof.Post

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36 Furthermore, this factor could be seen to affect the amount of time f or active dust generation and
post

- renovation exposures that are permissible.

However, it is generally accepted that the sole lead exposure factor in quantification of lead

concentration/dose

- toxic response relationships for general, vs. occupational, pop ulations is the amplitude/intensity of exposure and not intensity X time (AUC: area under the curve). Once the maximum concentration is achieved, some increment of adverse effect is produced. Cumulative relationships for occupational exposures, by contrast , employ integral AUC approaches. That being the case, renovation activities that produce elevated body lead burdens (bones) or circulating exposure metrics (whole blood, plasma, spontaneous Pb

- Urine) produce adverse health risk, however short the exposure . This is the case for children and can be the case for adults regarding certain effects. While some published studies show that Pb hematotoxicity expressed as erythrocyte protoporphyrin elevation in adults requires around 4

- 6 weeks or more,

that duration of time for toxicity expression is kinetically determined by human erythrocyte turnover rates, not toxicity expression time at the cell or tissue level per se.

A second and particularly critical factor is the relative irreversibility of the toxic effects selected for the Approach, e.g., Section 6. It is generally accepted that lead's developmental neurotoxicological harm through much of early childhood is irreversibly expressed. That being so, EPA does not have the option of selecting as part of their over all approach just the most recent renovation scenario, as it does in the document. For example, a child who has been impacted by LBP dusts from six renovations over his/her early years such that he/she has irreversibly lost 1 IQ point each time, will have lost a total of 6 IQ points from six renovations, not just 1 Q point from the most recent renovation. The assumption in modeling the most recent renovation as a surrogate for cumulative toxic harm for kids presents only 17% of the full

adverse effect in th is example.

A third factor is the temporal concordance of lead in the target organ(s) and lead in the exposure biomarker. For effects on children's IQs, what is the concordance of PbB with Pb

- brain? Some early experimental data from the 1970s appeared to suggest that lead lingers in brain longer than it does in the blood compartment. However that has not generally been confirmed and is not generally accepted. See the earlier EPA criteria documents about this point. One methodological difficulty with such t esting’s is the need for scrupulous removal of occluded blood in brain tissue that may contribute significant amounts of lead. Even in cases of fatal lead poisonings of children, brain Pb on autopsy was shown to be much lower than the extremely high levels in corresponding blood, so Pb contamination of brain tissue by residual blood would be a problem.

A fourth factor, which was mentioned in passing in the Approach report, is continued declines in bone Pb burdens and thus any rank

- ordered Pb releases to bl ood in younger versus older U.S. residents, especially urban area residents. As a consequence, intermittent or sequential inputs to body Pb from multiple renovation activities become an increasingly more significant fraction of the total Pb in the body.

A fifth factor is the fact that renovation Pb intakes and uptakes going to bone do not remove toxicity risk in either the near

- or long

- term. Bone Pb releases occur in pregnancy and lactation, particularly during the third trimester of pregnancy, in lockste p with remobilized Ca needed for fetal skeletal growth. Bone Pb is reabsorbed during other physiological stresses from childhoodPost

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37 to adulthood. Bone Pb releases from immobilization are a known phenomenon. The magnitude of such releases appears to be affecte d by adequacy of dietary Ca. Also, Pb releases during osteoporotic changes in menopause occur. In essence, Pb movement from renovation

- related intakes to a bone reservoir should not be taken as an argument that harm is minimal.

Age Groups and Selected Age

- Related Health Effects

The selected age groups and the selected key toxic effects related to the age groups are both appropriate.

The childhood age band of 1

- 10, while it goes beyond the CDC high risk age range, < 72 months of age, in terms of lead e xposure, is defensible. Current thinking and supporting evidence indicates discernible developmental neurotoxic evidence beyond this CDC advisory and guidance risk band (Lanphear et al., 2005; Crump et al., 2013 reanalysis). The finding of the
strongest as sociation of PbB and IQ loss with concurrent PbB values would indicate continuing neurotoxic harm. While one interpretation of the concurrent PbB:effect association is that there

is rank

- ordering of concurrent PbB with size of Pb levels in the bones accumu lated across earlier ages, one statistical analysis suggested that the association is not merely rank

- ordering dependent (Chen et al., 2005, Hornung et al., 2009).

References

Chen A, Dietrich KN, Ware JH et al. 2005. IQ and blood lead from 2 to 7 years o f age: are the effects in older children the residual of high blood lead concentrations in 2

- year

- olds? Environ. Health Perspect. 113: 597

- 601.

Hornung RW, Lanphear BP, Dietrich KN et al. 2009. Age of greatest susceptibility to childhood lead exposure: a new statistical approach. Environ. Health Perspect. 117: 1309

- 1312.

Estimating Time

- Dependent Background Intakes

Background Pb Intakes and associated blood lead levels have varied over the population chronologies operative for this Approach, i.e., indivi duals in the U.S. population up to age 80, and higher body Pb burdens in older versus younger individuals. The authors have recognized this and have attempted to factor changing body lead burdens and circulating lead biomarkers in the U.S. general populati on into the analyses. This is done through use of the serialized National Health and Nutrition Examination Surveys (NHANES) reaching back to 1976 in order to capture the subgroups of individuals likely to have had the highest earlier non

- renovation Pb expo sures. I believe some further factoring may be useful as noted below.

NHANES are statistically constructed to be representative of nationwide snapshots of overall health measures such as blood lead or as representative of national demographic and

socioeco nomic strata within the overall snapshot. The design, a clustered, multi

- stage probability sampling, does not allow for disaggregation from the national values to geographically specific areas, e.g., Indianapolis, IN. Notwithstanding, this limitation is ty pically ignored by a number of researchers comparing local to national Pb exposure figures. However, the national snapshot represents up to 72 national demographic and socioeconomic strata. APost

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38 number of these would appear to be more precise for modeling ren ovation type and renovation activity scenarios than just use of the serial NHANES single, aggregated

- strata figures. For example, such NHANES national strata as urban density and any one of four broad geographic strata would be expected to have a significa nt impact on the Approach's treatment of interior and

exterior renovation activities.

References

National Center for Health Statistics. Annest JL, Mahaffey K. Blood lead levels for persons ages

6 months

- 74 years, United States, 1976

- 1980. Vital and Healt h Statistics. Series 11, No. 233. Washington DC: U.S. Department of Health and Human Services. Pub. No. 84

- 1683, August,

1984.

Mushak P, Crocetti AM. The Nature and Extent of Lead Poisoning in Children in the United States: A Report to Congress. Atlanta G A: Agency for Toxic Substances and Disease Registry, U.S. Department of Health and Human Services. Delivered to the Office of the Speaker of the U.S. House of Representatives, July 15, 1988.

Crocetti AF, Mushak P, Schwartz J. 1990. Determination of number s of lead

- exposed U.S. children by areas of the United States: An integrated summary of a report to the U.S. Congress on childhood lead poisoning. Environ. Health Perspect. 89: 125

- 135.

The impact would be expressed on both background and renovation

- relat ed Pb exposures. Older areas of the U.S. containing highly urbanized municipalities are two strata within all of the serialized NHANES findings to 2011

- 2012 that would arguably provide an added dimension to the renovation LBP hazard, given they are associa ted with more significant exposures via high LBP loadings and fraction of surfaces covered with LBP. How much more than the strata

combined national average is to be found within NHANES strata data ? Receptor individuals would therefore have higher PbB back grounds and would be impacted by renovations generating higher LBP

- based dust Pb loadings and levels.

Monte Carlo Methods

The toxicological and toxicokinetic rationales and general use of scenario variables for doing the analyses appear to be appropriate and justified with caveats for these as given in earlier

comments.

Implementing the Monte Carlo approach appears adequately described as to scope and attempts at comprehensiveness. However, the authors should try to reduce the ambiguity and opacity in

th e text. I can't see any lay reader or policy type being able to swim through the Section without wrestling with jargon and elusive clarity. As noted elsewhere, this is one section where it seems the writing is intended for a narrow, technical audience and not a wider consumer universe. How does the Agency deal with the above

- discussed issue of irreversible neurotoxic effects in children and the cumulative neurotoxic effects in terms of cumulative IQ loss arising from multiple renovations in children's Pb ex posure histories? Are there data from the construction and renovation industries that show the frequency of renovations nationwide or regionally? EPA can, if data exist, model impacts from both a most

- recent renovation and the average number ofPost

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39 renovations over a typical child's 1

- 10 yr. period of life, the latter better reflecting cumulative IQ point loss.

Results of the Monte Carlo Analysis

The Approach describes results of a large number of runs for both Exterior and Interior Monte

Carlo scenarios. Ove rall, the various runs do provide insight into the magnitude and distribution of results and trends across scenario variables, as was intended and explicated as to purpose. The authors correctly qualify the results by making clear they are only for illustr ative exposure scenarios for Approach purposes. One cannot extrapolate from these Monte Carlo results to any quantitative estimates of structure types with estimated media levels and what follows from there or the proportions of the U.S. populations potent ially affected.

I am, at the same time, troubled by the likely difficulty of translating what is Approach

- specific, and what needs validation with current renovation practices. As noted earlier, the authors would need to get some idea about what fraction of commercial and public building renovations in current practice involve simultaneous interior and exterior renovation activities. If this fraction is not trivial, then the conceptualization of the approach would need to be expanded to

accommodate a third , interactive model for coverage of simultaneous renovations: that is, for the case where (simultaneous) renovations would involve exposures and adverse health risks for interior activities that are affected by what's going on with exteriors and vice

- versa

.

The use of preliminary screening steps for both exterior and interior activities in order to minimize potentially unnecessary iteration steps is procedurally prudent. The use of worst case scenarios is appropriate and also appears to entail conservative criteria (exterior renovations) so as not to underestimate the frequency of effects of concern. Similar testings’ for screening

exceedances were employed for interior renovations. However, in this latter case, low bounds to loadings and times of such lowe r bound loadings in tandem with worst

- case assumption for other variables resulted in no exclusions, e.g., an IQ decrement output of 0.105 or higher.

The authors note the maximum dust Pb loading for eliminated scenarios was 5 μg Pb/ft 2 . There

are some cur rent studies that show a more meaningful threshold for exposure and health risk should be in the range of 5 μg Pb/ft 2 . To the extent that EPA is currently looking at downward revision of its floor dust Pb enforceable standard from 40 μg/ft 2 , I don't see mu ch comfort in floor levels around 5 μg Pb/dl as appropriate for MC iterative exclusion.

In general, the trends observed in a number of cases are in sync with what the available evidence would predict with regard to what is already well known or expected i n terms of likely relationships among the variables. That is, they are consistent with what a "qualitative" sensitivity analysis would quickly tell you.

Older buildings had not only higher lead levels in LBP loadings (up to 50% for the residential

surface case and likely for public and commercial building interiors and exteriors)

, but had higher fractions of total surface painted with LBP. Similarly, distance was inversely proportional to size of PbB increase, which is in accord with what would be expected from aerometric modeling of leaded dusts transport. Also, containment efficiency decreases the size of PbB increase. This is also to be expected.Post

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40 References

U.S. Department of Housing and Urban Development. 2011. American Healthy Homes Survey.

Lead and A rsenic Findings. Washington, D.C: Office of Healthy Homes and Lead Hazard

Control.

U.S. Department of Housing and Urban Development. 2001. National Survey of Lead and Allergens in Housing. Washington, D.C.

Jacobs DE, Clickner R, Zhou J et al. 2002. The p revalence of lead

- based paint hazards in U.S. Housing. Environ. Health Perspect. 110: A599

- A606.

However, the value of the probabilistic analyses lies with categories that do not show simple effects. The authors note that size of renovated building has a complex effect on PbB. For example, effects of small and intermediate size renovated buildings are somewhat anomalous unless one factors in necessary aerometric considerations. Ditto for what's seen in the tall versus low building trending.

Post

- Meeting Comments (1a and 1b):

A number of criticisms and concerns were expressed by other Panel members at the meeting and in pre

- meeting comments having to do with the universe of inputs to the Monte Carlo analyses (MCA).

I agree with Dr. Georgopoulos ’ concern s about the issues of age

- range lumping and gaps in

MCA

- relevant gender distinctions that affect the analyses on, for example, physiological and toxicokinetic grounds. Some of these were touched on in my pre

- meeting comments.

Young children and the elder ly, at opposite ends of the age and developmental spectrum, arguably require tighter stratifications of age bands within the currently used boundaries, i.e., the three gross age bands. This is especially of interest for both prenatal/gestational and early postnatal populations in terms of enhanced Pb intakes/uptakes. Pb releases from maternal bone during pregnancy are trimester

- dependent. Infants and toddlers ingest more Pb per anthropometric measure and are more vulnerable developmentally than are older ch ildren within

this same 1

- 10 years old band.

Existence of gender distinctions as to Pb in

- vivo behavior and relative indices of toxicity for women per se and for the fetus in pregnancy need to be better addressed, as illustrated in my

earlier comments an d those of others. For example, there are gaps in the handling of gender differences in toxicokinetics not addressed in the updated Leggett model.

The need for more zone

- specific information as to the nature and the location of receptor buildings was not ed by Dr. Georgopoulos and is a valid issue for mention. For example, commercial and public buildings would not be randomly distributed in communities with effective zoning. But this is not a topic, as I see it, that is readily amenable to much further fin e

tuning. Much if not most of the U.S. inventory of commercial and public buildings is of some vintage, predating the current era of more rigid zoning. Also, the U.S. Bureau of the CensusPost

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41 stratifies the U.S. into numerous Metropolitan Statistical Areas, an d into categories of population, i.e., how many people potentially affected. These differences present with different fractions of commercial and public building stock.

I also believe the Agency needs to prioritize its efforts on renovation analyses to f ocus on those building types which involve the highest numbers of potentially Pb

- exposed occupants, both with regard to structures being renovated and downwind structures being impacted by upwind structures being renovated. Why would there be a seemingly e qual effort for characterizing renovation of a warehouse with 25 employees and renovations in or near to high

- rise office buildings having hundreds or thousands of occupants? This carries over into zoning.

Warehouses with low worker

- occupant counts will oc cur in “ industrial ” zones versus office buildings with high occupancy being typically in areas zoned “ office/institutional. ”

Dr. Goodrum presented some very insightful translations of mathematical modeling concerns, e.g., indexing of variability via such elements as appropriate uses and interpretations of the geometric standard deviation (GSD).

I have some additional points. The GSD is a critical population Pb exposure statistical marker and its correct use and interpretation is not merely academic. All e lse being equal, the greater the GSD accompanying geometric means, the greater the number of individuals in the upper tail of the distribution attending the typical log

- normal distribution of Pb exposure biomarkers. Where

those individuals are Pb

- exposed k ids at the higher strata of Pb exposure, they are the subpopulation who require the most interventional attention.

This can be illustrated with the current version of EPA ’ s IEUBK model (Version 1.1, bin 11). Using default values, the old value of concern , the child age band of 12

- 36 months, and the GSDs 1.4, 1.6 and 1.8 (the model ’ s upper limit), the corresponding percentages of kids exceeding this 10 unit cutoff are: 0.05, 0.997 and 3.09 %, respectively. For a simulated population of 2000 kids, the head counts of the highest exposed are 1, 20 and 60 kids, respectively. That is, 60 times more children will have the worst exposures using a GSD of 1.8

vs. 1.4.

The GSD trends with the increasing heterogeneity of population Pb exposures/sources. We see

this for site

- specific Pb contamination sources/pathways and population

- wide survey statistics, as seen in the NHANES series, 1976 to 2010

- 2012. Among the trend relationships presenting in the serial NHANES surveys is that of increasing population GSD over time and with decline in population mean exposures both overall and across the various national socioeconomic and demographic strata.

The increase of the NHANES overall GSD from 1.4 for the 1976

- 80 survey up to 1.9

- 2.0 in the recent surveys presumably reflec ts, inter alia, increasing heterogeneity or multiplicity of Pb sources. As Dr. Schwartz noted, the size of the GSD of 1.9

- 2.0 may subsume within it a bimodal distribution, and the upper component probably reflects a discretely exposed population subset,

e. g., children in old housing exposed to LBP.Post

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42 Reference

U.S. EPA. 1986. Air Quality Criteria for Lead. EPA

- 600/8

- 83/028cF. Ch. 3. Table 11

- 7.

GSDs of the size 1.9

- 2.0 for lead exposed communities are seen with waste sites, specifically Superfund sites at smelters, mills, etc., where there are heterogeneous distributions of Pb contamination, e.g., Pb hotspots.

Rosalind Schoof

The analyses and basis for the distributions used were clearly explained. I did not have

comments on the renovation scenarios a nd assumed distributions for building characteristics and exposure frequency. During our meeting substantial information was presented suggesting that the renovation scenarios modeled are unlikely to be used by contractors. This is an issue EPA should expl ore further. As described in my comments on question 4, I think the uncertainty in some of the background exposure assumptions should have been considered. Furthermore, as noted during our meeting, the potential for propagation of errors within the model m erits further

consideration.

Joel Schwartz

Comments on Dose Estimates and Monte Carlo Simulations

To understand the issue of what the Monte Carlo simulations do and do not tell us, let us consider the following thought experiment. Suppose the expected value of internal dose depends on three factors, X1

, X2 , and X3 . We then have: E(D)=b1X1 + b2X2 + b3X3

.

To predict the mean value of D given the X s, I need values for the b s. To obtain these I eschew statistics and go up to a cave on a holy mountain and pray. After many days there is the sound of thunder, but God is not in the thunder. There is a roar of wind, but God is not in the wind, and finally a still small voice tells me the true values of the b s

.

I now have no uncertainty that the mean dose in the population given exposures X1

, X2 , and X3

is b1X1 + b2X2 + b3X3 , and there is no need for the Monte Carlo simulations. What does this tell me about the dose, not to the average child, but to the child at the 90th percentile of the distribution, given the X s? Nothing. But there is a child at the 90th percentile,

and I care about his/her dose.

Now let us consider a slightly more realistic scenario, and assume that b1

, b2 , and b3 are estimates drawn from three distributions. If we have some idea what the distributions are, we can randomly sample from them (of which mor e later) and compute the mean dose with, say, 10,000 different choices of b1

, b2 , and b3 . This allows us to understand the sampling variability of the mean dose. But the 90th percentile of the possible choices for the mean dose to a child

still tells us no thing about the dose to the child at the 90th percentile of dose for the same exposure. All the Monte Carlo simulations do is sample uncertainty for the mean.Post

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43 I could assume that actual doses are normally distributed about the mean with some standard

dev iation. But that assumes that e.g. the child at the 90th percentile has 1.7 SD higher dose than the child at the mean, no matter what the values of X1

, X2 , or X3 . Put another way, it assumes that they coefficients b1

, b2 , and b3 are the same whether descri bing the dependence of the

mean on the X s or the 90th percentile. So, for example, we assume a given increase in iron stores will decrease lead dose by the same amount in children at the 10th percentile as in children at the 90th percentile of dose.

That is possible, but I think unlikely. But if we cannot make that assumption, we cannot estimate what the 90th percentile of dose is in a given exposure scenario. In the case where the values of

the b s are obtained by regression modeling, the issue is that whe n EPA analyzed their experiment on aerosolization, they fit a least squares regression, which estimates the average aerosolization given the predictors. Had they fit a quantile regression, they could have estimated coefficients for the predictors of the 90 th percentile of aerosolization. They would no longer be assuming they are the same coefficients predicting the mean, and could use them, and sample from their distribution to estimate what happens to the 90 th percentile of exposure.

Another key point is that the Monte Carlo simulations assume that the b s are multivariate normal, with zero covariance. That is, given that the coefficient for one predictor, e.g. hand to mouth activity, is at the upper end of its range tells me nothing about what part of its range the coefficient of another exposure is. This may not be true.

To estimate dose, we need to know not merely the distribution of the b s , but the distribution of

the X s. Here the assumption of no covariance is clearly false. In general, there are more layers of paint and higher lead concentrations in the paint in older buildings. Older buildings have higher air exchange rates. Older buildings are typically in areas with higher building densities, meaning the mean distance to the next building is lower. Older buildings are more likely to be in worse

con dition. Older buildings are more likely to be in neighborhoods with more children who are iron deficient and will have higher gut absorption rates. Outdoor soil and air lead are correlated. In general, the X s are clearly correlated. Estimating the distribu tion of risk needs to take into account this correlation, or clustering of factors that predict dose, or it will substantially

underestimate the risk. This is discussed in detail in :

Schwartz J, Bellinger D, Glass T. Expanding the scope of risk assessment to better include differential vulnerability and susceptibility. Am J Public Health, 2011 Dec; 101 Suppl

. 1:88

- 93

.

Schwartz J, Bellinger D, Glass T. Exploring potential sources of differential vulnerability and susceptibility in risk from envir onmental hazards to expand the scope of risk assessment. Am J Public Health, 2011 Dec; 101 Suppl

. 1:94

- 101

.

Schwartz J, Bellinger D, Glass T. Expanding the scope of risk assessment: Methods of studying

differential vulnerability and susceptibility. Am J P ublic Health, 2011 Dec; 101 Suppl

. 1:102

- 109)

.

The model needs to explicitly take into account these correlations among exposures, which results in more people in the higher tail of the distribution, and also to estimate the effects not just on the mean, but on the 90 th percentile of the distribution.Post

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44 Kenneth Unice

The Approach is exceptionally complex and requires an appreciable amount of time to comprehend. In reviewing the Approach, it seemed that there may have been several opportunities to screen out pathways or scenarios that may have helped focus the assessmen t. I appreciate that the intent was to essentially develop a matrix of scenarios where variability and uncertainty was characterized to better inform risk management. However, this flexibility is at the expense of transparency in the methodology.

The pre sentation of exemplar results in Section 8 of the Approach is not transparent. The lack of illustrative point estimates (where the inputs and associated outputs are tabulated) makes it very difficult to interpret figures such as Figure 8

- 14, which presents mean and 95 th percentile IQ decrements. There is no context to assist the reader in understanding example combinations of parameters that result in the central tendency and upper bound exposures. It is acknowledged that the trends analysis was informative , however, in the absence of traceable example scenario point estimates, it is very difficult to evaluate whether the upper bound estimates fall within the bounds of physical plausibility. From a practical standpoint it is often difficult to separately

ad dress uncertainty and variability, as has been suggested in U.S. EPA guidance. However, the Approach would have benefited from a brief discussion about how the unique contributions of parameter variability and uncertainty are addressed in the approach.

Ia n von Lindern

With sufficient iterations, appropriately constructed Monte Carlo analyses can resolve variability propagating through the respective models by appropriately defining component variances. There are deficiencies in characterizing both the ce ntral

- tendencies and variances of many of the variables in the numerous constructs. EPA has searched out the available peer

- reviewed data sources, noted the insufficiencies, and provided competent descriptions where data are available. Assumptions and supp orting logic are included for others with deficiencies duly noted.

The large number of variables and sub

- categories result in enormous potential combinations, challenging even EPA’s computational capacity. In order to reduce the iterations required, EPA has secured a reasonable and doable number for required iterations by collapsing the many of the variable combinations to common bins, based on a common pre

- supposed outcome. EPA should be commended for its ingenuity and acumen in overcoming capacity chall enges with this innovative programming efficiency. However, the Monte Carlo does not resolve component uncertainty, but abets it by requiring educated guesses for variable distributions and propagates it through serial modeling

Kathleen Vork

I agree that correlation is likely among some variables. Where this is likely based on professional judgment, removing one of two variables suspected of being highly correlated and comparing results might provide a means for reducing the number of variabl es included in the analysis and reduce some of the potential bias introduced by assuming variable independence.Post

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45 For Estimating Biokinetic Variability

Appendix O reports that the blood lead GSD is derived from GSDs obtained from NHANES

data. There is a good chance these GSDs include variation in levels of exposure. Work to derive a GSD published by Griffin et al (1999) considered this when calculating the inter

individual GSD for use in the IEUBK model. By combining blood lead levels across geographic areas, age and occupational groups and genders, it is possible that composite GSDs include variation in levels of exposure in addition to bio kinetic variability. I recommend that USEPA

reconsider a GSD of 1.6 for use here.

The full reference to Griffin et al (1999) appears in OEHHA 2013 located at:

http://www.oehha.ca.gov/air/exposure_assess/pbpkwrkshop2013.html

The Approach further explored variability through a sensitiv ity analysis for one interior scenario and one exterior scenario. The sensitivity analysis techniques are being used to examine the impact of sources of uncertainty on exposures. The sensitivity of the estimated exposures to assumptions about different sce nario conditions can reflect the differential scales in which those conditions are measured. OPPT has also chosen to express sensitivity by an “elasticity” measure, which normalizes the inputs.

Question 1b. Please comment on the strengths and weaknesses o f the sensitivity analysis. Please comment on OPPT’s use of both elasticity and sensitivity scores to evaluate the impact of changes in assumptions on results.

Deborah Cory

- Slechta

While not being able to provide detailed response to the specifics of the sensitivity analysis, the inclusion of the sensitivity analysis is important to the analysis and its outcome provides additional credibility to the analysis.

Panos Georgopoulos

The “sensitivity analysis” presented in the report under review is one of its weaker elements and requires terminology corrections as well as clarification and additional work. Quotation marks were used in the previous sentence in order to emphasize the fact that the calculations presented in the report are essentially “sensitiv ity testing” at two single points, involving two arbitrary (one “small” and one “large”) perturbations of the variables of concern, rather than the procedures traditionally constituting a full scale “sensitivity analysis.”

Indeed, in Section 8.3 (p. 21) o f the Report, a process called “Numerical Sensitivity Analysis” is described as follows: “Define a “base case” and vary each variable value by 5 percent and 50 percent to determine the percentage change in the blood lead results. The 5 percent change analy sis represents a “local” sensitivity analysis; the small change in the input variables illustrates how small changes in the current values would affect the model predictions. The 50 percent change analysis represents a “global” sensitivity analysis; the la rge change in the input variables illustrates how model predictions would vary over a broader range of input valuePost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

46 assumptions. [...] the numerical sensitivity analysis included variables that were either point

estimates or continuous distributions in the Approach. Each value was increased by both 5 percent and 50 percent to perform both a local and global sensitivity analysis. The exception is the lead dust penetration factor, which is set to 1 in the Approach; this value was decreased by 5 percent and 95 percent instead because the value cannot exceed 1.”

The above description of sensitivity calculations raises various concerns: The terms “local sensitivity analysis” and “global sensitivity analysis” have in fact very specific, well established

and univer sally accepted meanings: The former refers to sensitivity analysis involving multiple perturbations around a nominal point of the model’s response surface, while the latter refers to sensitivity analysis across the entire response surface (see, for example , page 71 of USEPA, 2009, Guidance on the Development, Evaluation, and Application of Environmental Models,

EPA/11/K

- 09/003; also, any standard reference, such as, e.g. Saltelli et al. 2004 or Saltelli et al. 2008). One

- at

- a

- time (OAT) sensitivity calculat ions (as the ones performed in the work under review) are the “most tractable” as they select a “base case set” of input values and perturb each input variable by a given percentage away from the base value while holding all other input

variables consta nt. Such sensitivity calculations yield “biased” local measures of sensitivity (no matter how “large” or “small” a perturbation they consider), that depend on the choice of base case values. One way to avoid this bias is to use the “Morris's OAT” scheme fo r screening purposes because it is a relatively simple global sensitivity analysis method: it “entails computing a number of local measures (randomly extracted across the input space) and then taking their average” (see, e.g. USEPA, 2009). So, Morris's OA T provides a measure of the importance of an input factor in generating output variation, and while it does not quantify interaction effects, it does provide an indication of the presence of interaction. It is strongly

recommended that the work under revie w considers at some point employing an actual global sensitivity analysis (such as, e.g. Morris’s OAT).

Another sensitivity

- related issue that needs to be clarified is the reason for including in the sensitivity testing sampled variables that have been as signed continuous variability distributions (e.g. exchange rates). Presumably (with proper coding), the set of Monte Carlo simulations should have provided sufficient response information for the entire sampling space of the variable, that would have allow ed the construction of global sensitivity metrics.

Another (relatively minor) point involves terminology: it appears that various truncated distributions, such as truncated normal, truncated lognormal are simply referred to as normal and lognormal. Howeve r the specific attributes of the truncation are always important and that should always be kept in mind, no matter what terminology is used.

References

Saltelli A., Tarantola S., Campolongo F., Ratto M. 2004. Sensitivity Analysis in Practice: A

Guide to Assessing Scientific Models. Hoboken, NJ: Wiley.

Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M.,Tarantola S., 2008. Global Sensitivity Analysis: The Primer. New York: Wiley.Post

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

47 USEPA. 2009. Guidance on the Development, Evaluation, and Application of Environmental Models, EPA/11/K

- 09/003

Philip Goodrum

The decision to use candidate scenarios to explore sensitivity analysis is sound. However, the elasticity approach should be used with caution. The CV weighted sensitiv ity score is better, and is more likely to approximate the actual contribution of each variable when each random variable is allowed to vary jointly. Elasticity methods will illuminate the fundamental rank order of the different modules that the input var iables are applied to. But within a given module, it is very common that the variables appear to contribute equally. See for example, how the first three variables in Table 8.7 share the same elasticity results. A full statistical analysis using

multiva riate regression models to assess predictors of variance in MCA output variables is preferred.

The sensitivity analysis could be more comprehensive in terms of questions posed. For example, it may be informative to assess if the rank order and magnitude of sensitivity of a set of variables depends on:

1. Definition of threshold for risk

– either absolute PbB value of incremental change/difference from baseline (accomplished by subgrouping the output by different candidate thresholds)

2. Summary statistic selected for the risk metric (e.g., mean, upper percentile)

3. Definition of baseline (not relevant if baseline is run in parallel

– see comments above)

4. Time horizon for summary statistics (averaging time for period after renovation)

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.

Andy Menke

Multiple sensitivity analyses were conducted to evaluate the model's sensitivity to varying inputs. These were important and useful as they addressed a key possible weakness in the methodology (incorrect inputs). The use of a single base case and varying inputs effectively showed the model sensitivity to those individual input variables. Showing elasticity and the sensitivity score were useful and effectively conveyed the message, even to someone who is not an expert in Monte Carlo methods. However, it is unfortunate that the sensitivity score was missing for so many variables, including some that had relatively high elasticity. The sensitivity analyses did not cover every input or assumption going into the model and the authors should pursue additional sensitivity analyses to further validate the model; I’m unable to give specific

recommendations since this is not one of my areas of expertise.Post

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48 Paul Mushak

Chapter 8 in my downloaded copy was editorially garbled in terms of appearing to have been put together by several authors simultaneously and then combined without editing. This made it

difficul t to follow the flow of the substance of the text. My copy had umpteen page 21s. I was able to sequence the section by text flow and Table/Figure numbering. Between July 28th and release to the reviewers, editing would have been desirable.

Nonetheless, th e construction of the sensitivity analyses was reasonable, generally plausible, and useful for the Approach's needs and within conceptual models that involve coupled multi

- model methodologies. This was the case for both the Exterior (Sec. 8.3) and Interior (Sec. 8.5) scenarios. The combined use of categorical and numerical sensitivity analyses permitted the exercise to capture trends (categorical analyses) and percentage changes in outputs (numerical analyses) with tweaking at 5 and 50% variable values. The trends for the exterior case were in the directions expected for the Exterior scenario, and likewise for the Interior scenario.

Elasticity appeared at first glance to be a very useful exercise with respect to the relative robustness of input variables in terms of having a quantitative impact on output changes. It was not clear to me what this adds to quantitative interpretations of the Approach. One can of course use elasticity in a relative way to see how much a percent change in output is related to inp ut. On the other hand, the total impact of an input is also the size of the input as well as relative robustness. A relatively small impact of a given change in input to output is magnified if the amounts of input changes are typically large in renovations . Conversely, a highly robust impact variable in relative terms may not translate to a total change that is significant if its size in typical renovation activities is very small.

The difficulty of omitting a conceptual and implementing model that does no t account for simultaneous interior and exterior renovations and potential interactive effects of one on the other carries over to both the Monte Carlo analyses and the sensitivity analyses. The issue of

simultaneous renovations would need to be addressed before one can discuss this aspect further.

In both the sensitivity analyses and the preceding Monte Carlo runs the matter of cumulative impacts of irreversibly decreased IQ points should also be addressed beyond just the use of a single, most recent renovation and renovations that are either interior or exterior and non

concurrent.

Rosalind Schoof

My primary concern with the sensitivity analysis is that it may fail to capture the cumulative impacts of multiple sources of uncertainty.

Joel Schwartz

No comments provided for this charge question

.Post

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49 Kenneth Unice

The sensitivity and elasticity scores are reasonable metrics to interpret the sensitive parameters. However, it is unclear how these sensitivity scores were taken into account in the Approach. For example, lead in interior and exterior paint is identified as a highly sensitive parameter based on both elasticity and sensitivity score. However, a discussion of the lead in paint parameter is conspicuously absent from the discussion in Section 9.

The sensitivity analysis results suggest that a nationally repr esentative survey of lead based paint concentration in P&CBs by vintage (or an analysis of changes the economic activity of lead paint hazard mitigation in P&CBs) could potentially significantly reduce uncertainty in subsequent steps of the Approach. This conclusion seems particularly salient because surrogate residential data was used to represent P&CBs. It is unknown whether more frequent renovation cycles may have reduced the lead content in P&CBs to a loading less than that of residential

structures.

Ian von Lindern

The potential relative effects of the variable uncertainty in simulation models can be assessed through sensitivity analyses by performing baseline runs, holding most variables constant, modifying the select variables, and measuring the e ffect on outcome predictions. The outcomes can be compared in various ways to assess the effect of the change. EPA has adapted this methodology in the sensitivity analyses presented. The elasticity and COV measures are adequate to the task, are understanda ble, and the standardized measure corrects for scale allowing informed judgments about relative effects among competing variables. The two examples presented demonstrate that the equations are solvable with reasonable expenditure of resources, and useful i ndices can be produced to evaluate.

However, the effect measured is specifically limited to the values of the baseline variables. Generally, these effects are measured near the means of the variables in the underlying baseline equations, where the result s are usually more reliable. Sensitivity measurements obtained from applying values from the extremes of the variable distributions can be quite different, particularly with non

- linear variable constructs. At the end of the day, as with all childhood lead health protective measures, EPA will be making key decisions based on the most

- vulnerable populations, who reside in the tails of many of the variable distributions in this approach. The sensitivity approach presented here requires the model to assess itse lf, and it is equally important to evaluate its performance outside of its baseline comfort zone. Although the technique is appropriate, the objective of the sensitivity analyses might be modified from demonstrating minor effects to justify reliance on the result s

– to purposefully identifying baseline situations where the effect is significant in terms of the intended use of the model.

These issues are compounded with serial

- modeling, fixed variable products, and the bin/lookup table aspects of the Appro ach. The sensitivity of one component model to inputs from a preceding model can be tested by varying the preceding model input. The varying input can be achieved by modifying parameter or variable values in the preceding model and the result can be

measur ed in the output of both models. However, the change in the downstream model cannot necessarily be interpreted as causal relationship with the modification in the preceding model, asPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

50 numerous other uncertainties could effect a similar change. A causal effe ct would need to be teased out by testing combinations of changes in the preceding model. Similar complications arise with any intermediate variables calculated from fixed variables values or the use of the lookup tables, as the change in the product of tw o or more fixed variables could be due to the modification of any single of combined changes. Numerous sensitivity runs will likely be required to fully resolve whether a particular effect is plausibly related to the modifications through the chain of calc ulations. Such an effort is resource intensive and should be focused on the particular determinations and decisions to be made with the output.

The sensitivity analyses in the Approach are presented for the purpose of assessing model performance, refining model parameters, identifying key information gaps, etc. It should also be pointed out that the ultimate purpose of this effort is to make future human health protectiveness decisions based on these model outputs. EPA should elaborate as to how these sen sitivity analyses will be used to inform decision

- makers, specifically identifying whether any modification tested as a potential error in resolving uncertainty would result in a Type I or Type II error (false positive or false negative) determinations by the decision maker.

Kathleen Vork

I defer to the other panel members who have more in

- depth knowledge of this method.Post

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

51 Issue 2. Estimates of Lead Dust Loadings and Concentrations

The Approach estimates an “emission fraction” from exterior renovations which is then used to

estimate an emission rate for the AERMOD model. This is described in Section 4.2 and Appendix H. The Approach estimates a “fraction emitted” from interior renovations, which is then used to estimate interior lead dust loadings. This i s described in Section 5.2 and Appendix I. EPA’s Dust Study was the primary data source that contained all the required contextualizing information to process the information for use in the Approach. EPA supplemented information from the EPA Dust Study in a targeted way to estimate loadings resulting from exterior and interior demolition as well as to better inform post

- renovation cleaning practices. One of the approaches considered was supplementing dust loading data within indoor air data derived through industrial hygiene sampling. For exteriors, the dust study processing steps are described in Appendix H and important considerations include the distance of the sample from the renovation job, the size of the job, and mass of lead in the paint. For interio rs, the dust study processing steps are described in Section 4.2, and important considerations include the size of the job, the size of the work area, and mass of lead in the paint. Estimating a “fraction

- emitted” allows loadings to be estimated and scaled for a wide variety of room sizes, job sizes, and lead content present in the universe of P&CBs. Loadings are estimated for the work

- area, the workroom, and adjacent rooms. Loadings in each of these rooms were individually estimated for each type of activi ty and then composite loadings were estimated for each multiple activity scenario by summing the relevant activity

- specific loadings for each type of room. Department of Energy reference buildings described in section 5.1 of the Approach were used to devel op generic room sizes and building layouts. Use of the Dust Study data to estimate lead dust loadings for P&CBs does introduce uncertainty as described in Section 9.1.1.

Appendix K of the Approach document describes an empirical based method for convertin g lead loadings to lead concentrations. This method was recommended over a mechanistic mass

balance model approach when both approaches were presented to EPA’s Science Advisory Board as part of a peer consultation in 2011. The difference between the empiri cal approach presented in 2011 and the empirical approach presented in this document is the addition of two

data sources.

Question 2a. Please comment on the steps taken to process the EPA Dust Study data described
in detail in sections 4.2 and 5.2 as well as appendices H and I, and on the Approach to supplement Dust Study Data with information from other sources including the conversion of indoor air concentrations to indoor dust loadings.

Deborah Cory

- Slechta

This reviewer doesn’t feel qualified to comm ent on this charge question. However, one question that does not seem to be addressed in the approach is that renovation on a single wall was used for the modeling (p. 16). Is there information on how common that is, or how values would be changed by renov ations that involve multiple walls, which seems a more likely scenario?Post

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52 Panos Georgopoulos

[Note: The Report under review does not have a section 5.2]. The steps taken to process the data from USEPA Pb Dust Study and the effort to supplement this information with data from other sources are generally reasonable; however the uncertainties associated with these steps need to be characterized in more detail. The fact that the types of buildings considered in most studies are residential poses questions regarding the direct applicability of relevant data fits to other settings.

Philip Goodrum

I have not yet reviewed this in detail, but would like to underst and how the additional of two new empirical studies factored in or changed the mathematical relationship.

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.

Andy Menke

These sections are written with exceptional detail and clarity. With the caveat that this is not my area of expertise, the methods used seemed logical and appropriate. My only concern was that I

did not understa nd whether or not the equation for lead removed takes into account the depth of the lead paint on the wall or which layers of paint contain lead. For example, if there are 5 layers of paint on the wall and only the inner most layer has lead, even if the pa int had a high lead content and a large fraction it was removed, there might be very little lead paint removed, but the equation would likely suggest a much higher amount. Conversely, a lower lead content and only one layer of paint on the wall would likel y yield a greater amount of lead removed than the equation would suggest. This issue is also complicated because the average depth of lead paint may change over time as a result of an increasing number of renovations since lead paint was

used.

Paul Mushak

My downloaded copy of the Approach document has a garbled Comment Intro to Issue 2. Sections 3 and 4 actually deal with media concentrations related to exterior and interior renovations respectively. In Issue 2 Intro comments, these sections appear to be erroneously renumbered to be Sections 4 and 5. For example, Issue Intro comments refer to interior renovation as being in Section 5.2. Section 5 is the Leggett model coverage and has no Section 5.2. Question 2a is erroneous in referring to conversion of i ndoor air concentrations to loadings instead of the need to convert dust Pb loadings to Pb concentrations. Question 2a also refers to

Secs 4.2 and 5.2 instead of 3.2 and 4.2.Post

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

53 The descriptions of the variables for the interior renovations with respect to be haviors of affected occupants makes no mention of the role of interactions of receptor occupants with renovation crews and how these interactions would affect placement of occupants in a work shift, placement in each of the work shifts, etc.

A 2009 report described the role of systems engineering in occupant

- renovation crew interactions that affect or potentially affect occupant exposures during commercial building renovations. The report describes the integration of spatial, organizational, and temporal d ata to determine how occupants typically utilize building spaces during different work shifts while there is renovation. It notes that the nature of the renovation causes the building configuration (location of occupants) to change many times during renova tion.

Reference

Yee PH. 2009. An Automated Method to Identify Occupant Interactions in Renovations of Occupied Buildings. CIFE Technical Report #TR 185, Stanford University. Ph.D. Dissertation and Center for Integrated Facilities Engineering.

The use of the EPA Dust Study is appropriate, mainly because of the relative paucity of

renovation

- related Pb exposure data for public and commercial buildings themselves. This requires consideration of the extent to which the Dust Study is still valid as even a lim ited surrogate for renovations of public and commercial buildings. Added to the differences in physical and use characteristics of housing

- based Dust Study results versus commercial and public structures is the question of further changes in renovation pra ctices used in the Dust Study and now in use for public and commercial structures.

I do not see any reference in Sections 3 and 4 to any data that would be available from the huge public building stock of the U.S. General Services Administration (GSA). GS A has an extensive protocol for the thousands of buildings it owns or leases and which are occupied by 1.1 million Federal employees. The GSA owns or leases over 354 million square feet in 9,600 buildings such as offices, post offices, courthouses. Of this tally, 1,500 are owned and 8,100 are leased ( www.gsa.gov/portal/category/21100).

The Facilities Standards for Public Building Services establishes design standards and criteria for new buildings and for the repair, renovation and modernization of existin g buildings. The GSA also appears to carry out changes in renovation protocols and practices in response to changes of an environmental or other nature. For example, it used Recovery Act funds to renovate 447 Federal buildings to enhance energy efficiency

.

References

PBS

- P100 Facilities Standards for the Public Buildings Services. 2014. Washington, D.C: U.S. General Services Administration, 295 pp.

www. gsa.gov/portal/mediaId/187607/filename/P100_Version_2014.actionPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

54 Clark CC.2014. GSA credits Recovery Act for energy

- efficient buildings. Government Executive, Nov. 6, 2014. www.govexec.com/management/2014/71/gsa

- credits

- recovery

- act

energy

- efficient

- buildings/98351/

Earlier in my comments on the document I drew attention to the absence of any approach scenario that accounted for simultaneous interior and exterior renovations. Section 4.4 basically ignores this scenario and dismisses the matter with two short paragraphs that are quite inadequate. Section 4.4 says that simultaneous interior and exterior renovations would be done at such a low frequency that one need not consider the activity as affecting the overall Approach. Where is the evidence even implying that such is the case? The economics of building

renova tion would argue for savings from simultaneous renovations.

As I also noted earlier, total demolitions of old commercial or public structures with high LBP loadings as part of projects within neighborhood or wide area renovation projects would be a
potent ial heavy and pervasive source of Pb dusts available for transport to nearby buildings with occupants. No mention of this appears in Sections 3 and 4. See earlier comments for citations quantifying this hazard.

The authors should examine the extent to whi ch adults in office and other buildings can serve as carriers of workplace Pb dusts from workplace renovations to their homes, exposing young children and other family members.

Rosalind Schoof

I have no comments on this aspect of the analysis.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The conceptual model, mass balance approach, and analysis of the interior and exterior EPA Dust Study data appears to be reasonable.

Ian von Lindern

The post

- processing of the EP A Dust Study data accomplished in Appendices H and I represents

a commendable use and re

- analysis of a unique data set that provides a foundation that quantifies and informs reviewers regarding the relative magnitude and lead

- dust releases in RRP

activitie s. The consistency of methodology across the several experiments in finding the common variable in this application and results is impressive.

The categorization of dust into Bulk and Particulate Debris ( readily identifiable and easily cleaned after the RRP activity) and Dust Debris (residual after initial pick

- up actions) is reasonable, understandable, and convenient; and will translate well in future assessment control regimes. EPA does indicate in the a ppendices that these terms might be more appropriatelyPost

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Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

55 defined as paint dust and not total dust emitted during the activity, and it’s use is intended to account for all the lead emitted from the paint, not dust from within the substrate or other

disturbed portions of the building. That point might be made in the main document as well for clarity.

The estimate of the variable DustFrac = the fraction of the total amount of removed lead that remains as dust at the end of the renovation , is key to the utility of the method, and was accomplished in a reasonable and defensible manner given the nature of the underlying data. The later necessity of assuming wall loading by vintage when applying this formula is a likely source of uncertainty resulting from this conv ersion. In Section 9 EPA indicates estimation of the fraction of lead emitted during a renovation activity is intended to be the “best possible estimate given the data available”, and the values obtained are similar to the “single available

emission fract ion in the literature”; the emission fractions represent an uncertainty, the effect of which “cannot be quantitatively assessed due to lack of data on actual emission fractions from renovation activities”. This is a fair and honest appraisal of the situati on, and one in which the

re

- analysis of existing data has added to the basis of knowledge and will inform decision

- makers accordingly.

Kathleen Vork

I defer to the other panel members who have more in

- depth knowledge of this material.

Question 2b. Please comment on the availability of any other empirical data that contains lead dust loadings or indoor air concentrations, and supporting information, in buildings during and after renovation activities that disturb lead based paint. Please comment on a ny suggestions to reduce uncertainty in estimating dust loadings present after a renovation, including at their upper ranges.

Deborah Cory

- Slechta

This reviewer is not aware of any additional empirical data that could be used to provide

additional infor mation that could be used for the modeling. The only way evident to this reviewer for reducing uncertainty would be to have carried out the types of experiments that were done initially for the study, except perhaps to now re

- do the study to be able to exa mine the validity of values predicted by the modeling.

Panos Georgopoulos

This reviewer is not aware of any currently available data that would substantially reduce uncertainties in estimating dust loadings after a renovation; though some international s tudies (in particular from China) have been conducted recently, it is doubtful that measurements from these studies would be directly relevant to US situations. There is clearly a need for carefully designed issue

- specific measurement studies that would sp ecifically focus on evaluating model assumptions and reduce associated prediction uncertainties.Post

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56 Philip Goodrum

As this area is outside of my professional experience, I am not aware of additional datasets. In general, I support the strategy of reducin g uncertainty by first identifying drivers and prioritizing those input variables. This often requires an iterative approach. It would be important to consider differences in dust control/mitigation measures implemented before aggregating study

results.

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.

Andy Menke

I am not aware of any other data sources that would improve on the estimates provided by the current sources of data. Ideally additional variables would be taken into account to better predict loadings or concentrations (e.g., depth of the lead paint, cond ition of the previous paint, etc. ), but I am not aware of any published studies that could be used to improve the equations.

Paul Mushak

I am not aware of additional data sets which contain dust Pb levels from LBP disturbed in the

course of interior and exterior renovations.

Rosalind Schoof

I have no comments on this aspect of the analysis.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The purpose of XRF surveys is typically to identify the presence of lead

- based paint above a mass per surface area threshold. As such, these surveys tend to report and focus on maximum XRF concentrations (as an indicator that the structure contained LBP), rather than estimate of mean or UCL mean lead loadings (suitable for exposure estima tion). It appears the survey relied on in the Approach presented residential structure maximum XRF lead loadings, which may create a positive bias in exposure estimates by an unknown margin. Additionally, a distinction has not been made between XRF Loading and component types. A discussion of the typical ratioPost

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57 of structure average to maximum XRF readings may be informative with respect to understanding the likelihood of elevated blood lead for the Approach scenarios.

With respect to Page 18, Page 42, Page C

- 7, and C

- 17, the XRF Loadings parameter for exterior and interior spaces does not appear to be well

- documented in the Approach. For the lead loading, Appendix C refers to the 1990 HUD National survey, which included 284 private

homes assessed between 19 89 and 1990 with XRF measures from two interior rooms and one

exterior surface. However the main text in Section 3.2.2 and Section 4.2 cite the 2002 HUD National Survey of Lead and Allergens in Housing for the XRF measurements. The text in Appendix C refer s to “Table 2” of the “HUD report”. It is unclear the specific HUD document that “Table 2” refers to, and whether/how the data presented in Table C

- 3 are weighted.

The clarification document indicated that the 2002 HUD survey was used where “XRF exceeded 1.0 mg/cm 2 .” As shown in Table 2.1a, truncation of the HUD data results in a lead paint distribution very different from a nationally representative distribution. It is recommended that the statistical analysis be more transparently described in t he Approach document, including:

 whether the statistical analysis focused on the structure maximum lead concentration;

 the number of data points as well as the percentiles of the XRF distributions before and after truncation; and

 the percentage of residential structures in each vintage that did not have a lead paint hazard as defined by the 1.0 mg/cm 2 threshold.

Table 2.1a : Frequency distribution of lead loading in NSLAH housing units (Table 4.6, Page 4

- 10 of WESTAT report dated April, 2001) versus distribution approximated from USEPA Approach Appendix C Tables C

- 3 (exterior) and C

- 12 (interior) geometric mean and geometric standard deviation (GM, GSD).

Largest Paint Lead Loading in the Housing Unit (g/cm 2 ) Exterior

[Percent of Housing Units] Interior [Percent of Housing Units]

2002 NSLAH (Year of

Construction

1960

- 1977) USEPA Approach

Appendix C (Approximated from GM, GSD in Table
C

- 3 for Year of
Construction 1960

- 1979) 2002 NSLAH

(Year of

Construction

1960

- 1977) USEPA Approach

Appendix C (Approximated from GM, GSD in Table C

12 for Year of
Construction 1960

- 1979)
≥0 100% 100% 100% 100%
≥0.0003 57% 100% 39% 99%
≥0.0006 31% 97% 21% 97%
≥0.0008 26% 94% 16% 94%
≥0.001 24% 91% 16% 90%
≥0.0013 18% 85% 12% 85%
≥0.004 10% 38% 6% 46%
≥0.010 3% 8% 2% 16%Post

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58 It is unclear the degree to which residential structures assessed in the 2002 HUD survey are representative of the distribution of lead paint in P&CBs in 2014 by vintage. The national residential surveys indicate a decrease in the fraction o f residential structures with interior and

exterior lead

- containing paint (e.g. National Survey of Lead and Allergens in Housing, April, 2001, p. 4

- 16).

The text on Page C

- 6 and Page C

- 17 (C1.6 and C2.8): states:

“The concentration of lead in the exterior paint of the building was assumed to be a function of the age of the building. This assumption is based on the fact that the concentration of lead in paint has decreased with time, particularly leading up to and following the ban on lead in reside ntial structures in 1978.”

The distributions shown on Appendix C Tables C

- 3 and C

- 12 are not consistent with this statement. For example, the geometric mean and average concentration of lead is greater for interior paint for the period 1960 to 1979 (GM = 0.00366; AM = 0.00599) as compared to 1950 to 1959 (GM = 0.00314; AM = 0.00473). It seems unusual that that content would be higher in the 1960 to 1979 time category bin.

In summary, it appears that further discussion of the XRF measurements, particularl y for more recent vintage P&CBs, may be advisable, taking into account that:

 Both the 1989

- 1999 HUD survey and the 2002 NSLAH survey appear to focus on maximum XRF measurements in the housing unit rather than average or upper

confidence limit of the mean XRF measurements;

 The Approach truncates the lower bound of the XRF distributions at 1 mg/cm 2 , which may appreciably bias lead estimates, particularly for newer P&CBs; and

 The degree to which lead content in residential units reflects P&CBs (which may be more frequently renovated than residential structures) is unknown.

Ian von Lindern

The EPA has accomplished an apparently exhaustive effort to identify all peer

- reviewed studies and support data

- sources in developing the document. The scarcity of data available to support variance definitions and point values for fixed variables is a con cern for the reliability of this Approach. There may be other sources of data from trade organization, or monitoring results collected in association with construction activities that are relevant to this approach. If EPA is aware, or is made aware, of suc h sources of information, then efforts to review and incorporate it accordingly would be advised. Personal air monitoring information is one potential example.

The ultimate use of the Approach will be to assess risk to lead hazards and support developmen t of protective measures, if required. As with any childhood lead poisoning hazard, this will involve protecting children in the upper ranges of the blood lead distributions. These children reside in the upper end ranges of several of the variables that ar e significant risk co

- factors in childhood lead poisoning. These variables are often multiplicative in effecting outcome bloodPost

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59 lead levels, resulting in log

- normal distributions. These variables are interacted through the Monte Carlo Approach, resulting in additional multiplicative effects, which are eventually stabilized in an aggregate distribution reflecting a total population response. Policy

- makers will eventually have to identify a portion of the tail of this distribution that is potentially at

- risk, and

determine the cost

- benefits of effecting protective measures.

EPA also indicated confidence in predictions beyond the 95 th %tile is a concern due the uncertainties in the variances of the underlying variables and the number of iterations required

for convergence. A traditional alternative to the proposed methodology is to define and evaluate worst case scenarios by directly defining combinations of high risk variables. EPA indicated that

“w orst

‐ case runs tended to overestimate exposures when compared to maximum or 99th percentile values from Monte Carlo iterations, as indicated during testing runs”, perhaps implying that 95 th %tiles would be more appropriate . These statements should be supported and further investigated, as determining the root causes of the model’s failure to predict worst

- case scenarios could be enlightening. This indicates that the worst

- case selected was inconsistent with the output distribution, which could be due to either the definition of worst

- case, or an inappropriate tail on the upper

- end of the distribution. These should be resolved prior to using this Approach for decision

- making. Developing the best estimations of variances in key variables is the first step. There is a potential corrective step in applying the GSD to the b lood lead distribution at the end of the Leggett model, which could be used to bring the overall variance in predicted blood lead levels in line with NHANES.

Additionally, the low end of the distribution should not be ignored because the children residin g the cleanest most advantageous environments are those who will suffer the largest IQ decrement due to the log

- linear IQ effect. It is not inconceivable, that a worst

- case might be found among

children who reside in the low

- end of many risk

- cofactor distr ibutions.

Kathleen Vork

Task

- and operation

- based personal breathing zone indoor (in containment structure) air concentrations during 15 bridge projects involving lead

- based paint removal and surface repainting were provided to Fed OSHA. A full description of the aerosol generat ing activities in addition to those captured in USEPA’s dust study are described in (Vork 2003). In addition to surface preparation and component removal, substantially elevated air concentrations were also measured during equipment handling, surface coat ing and cleanup activities. Some of these activities may also be relevant to P&CB activities. For example, high PBZ air concentrations occurred during equipment filter changes and containment handling. Air concentrations during

these and other activitie s suggest that ignoring these sources of dust generation may under

estimate hazards due to P&CB activities.

The full reference to Vork (2003) appears in OEHHA 2013 located at:

http://www.oehha.ca.gov/air/exposure_assess/pbpkwrkshop2013.htmlPost

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60 Question 2c. Please comment on the data sources and method used to convert lead loadings to
lead dust concentrations.

Deborah Cory

- Slechta

This reviewer does not feel qualified to answer this charge question.

Panos Georgopoulos

The selection of data sources and methods used to convert Pb loadings to Pb dust concentrations should be considered reasonable given the information currently available. However, it would

have been useful to “benchmark” the empirical approach via comparative evaluation, for a small set of representative situation scenarios, with a multizone model for indoor dynamics, such as, e.g. the CONTAM 3.1 model of NIST (National Institute of Standards and Technology; see http://www.bfrl.nist.gov/IAQanalysis/ ; also Wang [ 2010 ] ).

Reference s

Walton G.N., Dols, W.S. NIST (National Institute of Standards and Technology) 2013.

CONTAM User Guide and Documentation. Report NISTIR 7251

Wang, L. (2010). Using CFD Capabilities of CONTAM 3.0 for Simulating Air flow and Contaminant Transport In and A round Buildings. HVAC&R Research Journal 16(6).

Philip Goodrum

No comments provided for this charge question

.

Andrew Hunt

From Appendix K

Data Sources

HUD survey data “...the estimated wipe lead loadings were paired with the Blue Nozzle lead concentrations at each home to develop the loading

- to

- concentration statistical relationship.”

Study by Adgate at al. 1995 “...Wipe samp les were collected for lead loadings and were compared to dust concentrations collected by a vacuum cleaner from wall to wall carpet and area rugs greater than 48 square feet.”

Study by Rasmussen et al. 2013 “...technicians followed a vacuum sampling protoc ol to collect a composite sample of active dust from all dry living areas of each house ... Fine (<80 μm) and coarse (80

– 300 μm) particle size fractions were collected and were combined to calculate loading values based on: measurements of the area sampled, dust mass, dust lead concentration

...”Post

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61 Loading is the measurement that is usually made (by wiping) but concentration is the value required for the model. The HUD survey data and the Adgate data compare loadings made by wiping with concentrations made with vacuum cleaner sampling.

1. The collection processes are quite different wiping relies on adhesion of dust to the wipe, vacuum sampling relies on lifting particles from the surface which is dependent on the

vacuum cleaner motor.

2. It has been noted previously that the blue nozzle sampler, as used in the HUD survey, has been found to have poor recovery rates. Compared to the widely used BRM and CAPS samplers (average dust recoveries for BRM, CAPS and blue nozzle were found to be 89%, 84% and 30% respectively). Lead recovery was no better (lead recoveries for BRM, CAPS and blue nozzle were found to be 81%, 72% and 26% respectively) (USEPA, 1995).

3. Recovery of dust from carpets and rugs is very different than from hard surfaces (where wipe samples are typically collected from). Comparing recovery in the Adgate study

between dust collected from non

- porous surfaces using polyethylene filters (which is not the collection media routinely used in wipe sampling such as “Lead Wipes” and “Ghost Wipes”) with dust collected by vacuum sampling from “wall

- to

- wall” carpets is problematic. If the vacuum samples are collected from “wall

- to

- wall” carpets in the Adgate study the loading vs. concentration is not even for side by side sampling.

4. The Rasmus sen study data makes no comparison between wipe loading and any concentration value, as all the loading and concentration values are based on vacuum sampling only.

More useful data sets may be available than those relied on above.

In a study Johnson et a l. (2005) side by side wipe loadings and concentrations (using pre

weighed wipes) were made “...Floor dust loading. Field teams acquired four indoor dust wipe samples

—— two followed strict ASTM sampling protocol for measurement of dust lead loading, and two utilized pre

- weighed wipe media for determination of dust mass loading.” Unpublished data from this study may be available from the author

.

Johnson, D.L., J. Hager, A. Hunt , D.A. Griffith, S. Blount, S. Ellsworth, J. Hintz, R. Lucci, A. Mittiga, D. Prokh orova, L. Tidd, M. M. Millone, and M. Vincent [2005] Initial results for urban metal distribution in house dusts of Syracuse, NY, USA. Science in China, Series C : 48 , suppl I, 92

- 99.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.Post

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62 Andy Menke

The use of multiple data sources provided larger, more stable estimates and allowed comparison to further validate the Dust Study findings in a wider range of lead values. The improved SE’s

and R

- squa re support the use of all 3 studies. The use of the equation to convert loadings to concentrations (from section 3.7.2) seems appropriate, as the error variance is relatively low and

the R

- square is relatively high. I am not aware of any variables that cou ld be added to the equation to better predict concentrations.

Paul Mushak

The conversion calculations have now been the subject of two peer review panel consultations, in 2007 and 2011. I concur in the recommendations of the 2011 panel consultation.

Rosalind Schoof

During our meeting substantial information was presented suggesting that the renovation scenarios modeled are unlikely to be used by contractors. This is an issue EPA should explore further. I have no other comments on this aspect of the analysis.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The approach used to relate concentration and loading is reasonable.

Ian von Lindern

The bio

- kinetic model requires lead concentrations, or the amount of lead per mass of ingested dust, to predict blood lead levels, necessitating conversion of lead

- loading to lead concentration. The empirical analysis and resultant equations to accomplish the conversion are appropriate and

draw on a data

- base considerably larger tha n many of the other conversions and assumptions made in the Approach. EPA combined three databases without adjustments for the pooled analyses, developing a single formula for the conversion. Conventional logic would suggest that, if the objective is to pr oduce an estimated concentration for a particular risk assessment, then the regression equation should likely be corrected for paint concentration variables (vintage or XRF loading), which the HUD database showed were predictors of lead loading. It seems t his Approach inherently assumes lead concentration in floor dust (predicted by earlier models in this Approach) is a function of paint lead concentration, which varies with the dust loading. The error term recognizes that variance in that different concent ration values can apply to the same lead loading, implying the equation produces a distribution of probable concentrations from an input loading. If this is correct, then, for the Monte Carlo Approach, it is not necessary to adjust for paint loading, conce ntration or vintage effects. A Table showing concentrations predicted (central tendency and distribution statistics) for typical loading rates would be helpful.Post

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63 However, it is important to recognize that the purpose of the conversion is to facilitate accurate estimates of lead intake for blood lead projections. That is accomplished by multiplying dust lead concentration by ingestion rates. Ingestion rates are moderated, at least for children, by

hand

- to

- mouth activity. As such, intake is directly propo rtional to concentration. This conversion method implies that intake is directly proportional to floor loading, which may not be the case. Floor dust loading only needs to be of sufficient mass to load the hands, at which point concentration is the driving variable for intake and blood lead. A highly concentrated lead dust can result in higher intakes (provided there is sufficient mass) than a lower concentration reflecting higher loading due to more “dustiness”. The calculation of loading in this exercise may be a hangover from the earlier effort that needed to respond to a regulatory loading variable. In this case, it might be useful (if possible) to calculate concentration directly from the

databases.

Kathleen Vork

It wasn’t clear to me how exposures d ue to contaminated air (FIG 2

- 1) are incorporated in the analysis.

Issue 3. Estimates of Transport of Exterior Lead Dust to Nearby Buildings

The Approach describes how exterior renovation activities generate aerosolized lead dust which

can be transported to and penetrate the outer shell of nearby buildings. The Approach also describes how exterior renovation activities generate lead particulate debris (larger particles) which are not transported through the air, but can be tracked into building s. Both of these contribute to indoor dust loadings. This is described in detail in Section 5 and Appendices G and J. EPA’s AERMOD model was used to estimate transport through the air. A Dust Model developed for this analysis was used to estimate track

- in and removal of lead dust over time. The Dust Model was also presented to EPA’s Science Advisory Board in 2011 as part of a peer

consultation.

Question 3. Please comment on the approach and models used to estimate outdoor air concentrations, outdoor soil a nd hard surface concentrations, and indoor dust loadings resulting from exterior renovations.

Deborah Cory

- Slechta

This reviewer does not feel qualified to comment on this charge question.

Panos Georgopoulos

[Note: The exterior renovation activities generating large and fine particulate matter containing lead that can, respectively, be tracked or transported through atmospheric dispersion into nearby buildings, are described in Section 3 rather than in Section 5 of the Report under review.]

A key poi nt relevant Question 3 is actually provided in section 9.2.1 of the Report under review, where it is stated that “AERMOD was selected as the most appropriate model for the Approach,Post

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64 but it has not been fully validated for receptors very close to the releas e site and might introduce significant uncertainty to air concentrations predicted at these distances.”

Clearly, AERMOD has been selected because of its simplicity relative to other options. However, AERMOD has all the limitations of Gaussian plume model s, requiring the persistent “steady” conditions that may not be applicable to all renovation (and in particular any demolition) procedures. A basic limitation of all such plume models is that they calculate zero

concentrations at all downwind locations imm ediately after the moment when source activity ceases, while in reality, dust that is already airborne will continue to travel until depleted by deposition. Nevertheless, even if steady conditions are assumed to persist sufficiently, it is very hard to exp ect that AERMOD will provide reasonable estimates at distances of only 5 or even 50 ft. As mentioned above, the report in fact recognizes that AERMOD is not appropriate for

such short distances.

One important issue that raises special concerns is the der ivation of a set of adjustment factors for obstructions using the BPIP

- PRIME algorithms: The Report or the Appendices do not clarify how the use of these algorithms can be reconciled with the need to appropriately define dust

emissions from renovations of P&CBs in terms of volume sources.

On page 21 of the Report it is stated that “[T]heoretically, a computational fluid dynamics (CFD) model could be a more appropriate selection for this application, but no CFD model is readily available that can support th is type of generalized assessment designed to represent the range of conditions in the United States.” A similar, argument also appears in section 9.2.1 of the Report, where it is stated that “[T]o fully capture the transport of mass, particularly in urban areas with more and larger buildings, a full micro

- scale, mass

‐ conserving, fluid dynamics model would be needed. The scope of the current Approach, however, did not allow for the creation or application of such a model. In addition, such a model inherentl y would be highly uncertain due

to the effects of small

‐ scale turbulence and the interaction between the windfield and the city building.” There are multiple issues with these statements: First, the authors of the Report seem
to use the terms “uncertain” a nd “uncertainty” in ways that do not conform to commonly accepted practices in the field. Indeed “such a [fluid dynamics] model” would not in fact be more “uncertain” than AERMOD (or any other model of a similar level), that would necessarily

have to deal with the same “effects of small

‐ scale turbulence and the interaction between the

wind field and the city buildings,” as the more detailed models. Second, these statements seem to imply that Computational Fluid Dynamics (CFD) modeling is the only available alternative to applying AERMOD (or similar plume models), while in fact there are various models available at “intermediate” levels of sophistication and detail (and of resources/effort required to apply them), that could reasonably address micro scale iss ues of atmospheric dispersion in an urban setting: An example is the Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System of the Los Alamos National Laboratory ( http://www.lanl.gov/projects/q uic/ ). Although it is not suggested here that QUIC or a similar model could (or should) replace a simple and fast model such as AERMOD in a large scale Monte Carlo approach, the application of such a model to a small set of appropriately selected scenarios could provide “benchmarks” for comparisons with AERMOD as well as valuable mechanistic insights on the importance of micro scale atmospheric processes, that cannot be derived by simulations, no matter how many, of an overly idealized/simplified model.Post

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65 Ph ilip Goodrum

No comments provided for this charge question

.

Andrew Hunt

Outdoor Air:

AERMOD as I understand it is a point source dispersion model. The exterior renovation activity that generates aerosolized lead dust which can be transported to and penetrate the outer shell of nearby buildings are not point source generated materials. Thes e are line sources, or block sources. If AEROMOD is a buoyant plume based model, will it accurately report on the transport of a n aerosolized dust from renovation activity? Is an accurate description of the particle size distribution produced by the renova tion activity needed for particle transport prediction in the model? “Three size classes, 2.5, 10, and 20 μm, were selected for all AERMOD runs. Choe et al. (2000) measured airborne particulate concentrations when doors with lead paint were dry scraped and dry sanded. They measured the mass fractions in sizes up to 20 μm because their preliminary experiments showed no appreciable mass above 20 μm after the renovation activity.” This data accounts for only two renovation methods (is the Choe et al.,

2000 dat a for power sanding?), is the same size distribution assumed for torching, heat gun etc. (is the temperature of the torch etc. high enough for the Pb in the paint to go into the vapor phase?).

Outdoor Soil and Hard Surface Concentrations:

“In soil, it will mix into the upper soil layer and remain in the soil, given the residence time of lead in soil is between 50 and 1000 years (Kaste et al., 2003; Klaminder et al., 2006).”

Where in the literature does it say it will mix into the upper layer of the soi l? The residence time references (Kaste et al., 2003; Klaminder et al., 2006) deal with Pb (at remote locations) not particulate bound Pb in paint near renovation sites.

The parameter values for the dust density and dust layer porosity are the same for bo th hard surfaces and soil. The layer depth (TrackInDepth) differs between hard surface and soil, with

hard surface set to a nominal value of 1 mm and soil set to 1.5 cm.

TrackInPorosity = porosity of particulate layer available for track

‐ in (unitless)

Th e soil will be a continuous layer of particulate matter (clays, sand size grains, organic matter), on which particulate debris is deposited. The particulate matter on the hard surface layer will likely be discontinuous (with windblown soil and other source s (eroded surface, degraded structures) providing a base

- line mass) on which particulate debris is deposited. It is unlikely the porosity will be the same for both.

TrackInDepth = depth of particulate layer available for track

‐ in (m)Post

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66 It seems unlikely th at 1.5 cm of soil layer is available for track

- in, the top layer of the soil (of the order of 1 mm as in the case of a hard surface) seems more reasonable (realistically the surface soil layer will adhere to footwear unless the soil is saturated and has be come a mud).

Some adhesion factor would seem appropriate, track in of hard surface dust requires the dust to adhere to the footwear and dry dust will likely resist adhesion if the particles are large. Soil

tends

Indoor dust loadings resulting from exteri or renovations:

Tracking Flux

TrackinFluxPb = PbSoilConcen x TrackingRatex (1

- MatFrac)

The ingress of outdoor dust/soil will be influenced by several factors. Pets are a frequently ignored transport agent (Wong et al. (2000) Survey of selected activities relevant to exposures to soils. Bull. Env. Contamin. Toxicol., 65:443

- 450). Ingress of dry vs. wet soil/dust affects distribution in the indoor environment (Hunt et al. (2006). Mass transfer of soil indoors by

track

‐ in on footwear. Sci. Total. Env., 370: 360

– 371).

Cleaning Flux

The cleaning flux (Cleaning Flux) is parameterized assuming a cleaning efficiency (CE) and cleaning frequency (CF) and multiplying these by the mass on the floor (FLOOR):

CleaningFluxPb = CE x CF x (Floor Pb )

Cl eaning removal of dust from the indoor dust reservoir is a stochastic process. It might be imagined that the redistribution of indoor dust by repeated tracking (pickup and drop off from footwear) and dispersion by suspension into the indoor aerosol, result s in a homogenization of the indoor dust distribution. But this process is disturbed by cleaning events. Cleaning events may be systematic, but are more likely to be random.

It seem reasonable to envisage the situation whereby the stochastic nature of du st removal (cleaning timing, frequency, intensity, and location) leads on long time scales to a buildup of outdoor dust as part of the indoor dust in a structure. Cleaning of carpeted areas is unlikely to completely removal all deposited dust.

While revi ewing dust levels in residential properties, Johnson et al. , ( Johnson et al., (2009) Geographic patterns of non

- carpeted floor dust loading in Syracuse, New York (USA) homes. Environ. Geochem. Health, 31:353

– 363) identified “...strong spatial patterns that e xist in floor dust loading, spanning an order of magnitude in value and observed at a geographic scale of 1

– 2 km, closely relate to socio

- economic/demographic aspects of a residential population...” This finding suggested that clean

- up may have a component o f “ability to/desire to” clean

- up associated with the practice.Post

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67 Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.

Andy Menke

Air lead concentration and lead deposition rates were estimated using the most robust model available, the AERMOD dispersion model. Since I was not previously familiar with the AERMOD model, I would like to see further data on the sensitivity of the model to the assumptions going into it. For example, the average brea thing height used in the models was 5.9 feet including in school models, which likely is too high considering schools will primarily consist of children. I have no comments for the other equations used to estimate track

- in and

removal of lead dust over tim e.

Paul Mushak

The overall approach for estimating eventual lead exposures of individuals within and nearby/downwind to buildings undergoing exterior renovations is plausible overall so far as it goes in terms of macro renovation activity and is described reasonably well in Section 3. The operational models described in Sec. 3 and Appendices F, G and J comport with the conceptual models that are their basis. The models described in Sec. 3 thereby capture the major relevant pathways by which indivi duals within or near buildings undergoing exterior renovation will contact lead: (1) air Pb via transport from a renovated building to a nearby structure or to the renovated structure via surface penetration mechanisms; (2) large particulate debris at the building undergoing renovation producing track

- in Pb particulate via hard surface/soil Pb to be added to interiors of the renovated buildings; and (3) aerosolized finer Pb particulate undergoing transport and then deposition to soils and hard surfaces at t he nearby outside areas of the nearby/downwind buildings.

A number of the elements in the air transport and deposition/track

- in models benefited from available data on Pb cycling modeling in the literature, and relevant study results employed were

cited i n the Appendices. The AERMOD dispersion model appears to be the best choice for air Pb transport within atmospheric lateral boundaries typifying ranges of distances appropriate for

Pb

- source

- building to receptor structure. We are not dealing with relevant points many miles apart. The dispersion model is assumed to not have to deal with dust Pb re

- entrainment, especially from hard surfaces or soil surfaces, simplifying the form of the model required.

The authors may wish to expand the discussion in the Sect ion and in Appendix F dealing with hard surfaces receiving Pb dustfall and soil surfaces. While hard surface Pb dust loadings are subject to precipitation events and runoff, as the authors note, they also accumulate Pb at very rapid and significant rates. Where Pb dustfall accumulation rates on hard surfaces effectively compete with frequency of hard surface water runoff events, that accumulation would be significant in terms of receptor exposures.

For example, for an air Pb concentration that would averag e 0.5 μg Pb/m 3 for 10 days of exterior renovation activities at older buildings with higher LBP Pb loadings, and assuming a depositionPost

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68 rate to a surface of 0.2 cm/sec and use of 86,400 sec/day, the daily hard surface loading is 80 μg

Pb/m 2 /day or 800 μg/m 2 for the 10 days of exterior renovation. Even assuming one runoff event over the 10 days that removes 50% of deposited Pb just from renovations, 400 μg Pb/m 2 remain. By contrast, renovation

- based deposition of air Pb to soil would be trivial, simply becaus e of the masses of soil volumes taken for calculations. Using EPA's figure for soil mass of 2 g/cm 3 , and assuming that deposited Pb is retained in the top 1 cm of soil depth, renovation Pb increases soil Pb by 1.5 μg Pb/ g soil/year or 1.5/36.5 = 0.04 μg P b/g soil/10 days. This is a trivial contribution to what would be typical background natural or anthropogenically enriched soils.

Reference

U.S. EPA.1996. Urban Soil Lead Abatement Project. Vol. 1: Integrated Report. Washington,

D.C: Office of Research a nd Development, Report No. EPA/600/P

- 93/001aF, p. pp.2

- 24, 2

- 25.

The models developed here relate to de

- minimis Pb exposure scenarios. That is, the authors assume the building being renovated is undergoing either an interior or an exterior renovation,

but not both concurrently. As noted earlier, I find this circumstance as a typical one to be unlikely on economic efficiency grounds. Apparently, the authors have not verified their assertion in Sec. 4.4 that simultaneous renovations would occur at low freque ncies.

The authors also assume that the nearby buildings being impacted by exterior renovations are not themselves undergoing exterior or interior renovations, or both simultaneously. This assumption would underestimate the cumulative lead toxicity risk f or receptor building occupants, especially young children if Pb transport is to child residences and COFs.

The simple mathematics of public and commercial building stock requiring repair, renovations, and modernizing within building lifetimes would argue that a high frequency of renovations would have to be occurring in many urban areas simultaneously simply to keep up with the need for economic preservation of the building stock. A simple one

- or

- other

- renovation

- type

- in

isolation conceptual model underest imates potential health risks for various receptor populations at any fixed time point.

One can illustrate this using office building counts in such densely populated urban areas as New York City. For FY 2013, The Annual Report of the NYC Property Tax by the NYC Department of Finance shows counts of all categories of commercial and other structures in NYC. Office buildings in NYC are only part of NYC

- categorized Class 4 commercial property, That is, the full category of taxable commercial space in the City is larger.

For FY2013, the full count of commercial office buildings was 6,854. This total consisted of the following number of office buildings by borough: Manhattan, 2126; Queens, 1716; Brooklyn, 1644; Staten Island, 804; Bronx, 564. These large number s argue for numerous renovations going on simultaneously in dense urban areas and with a reasonably high likelihood of

simultaneous interior and exterior renovations and with both Pb

- emitting and Pb

- receiving buildings undergoing concurrent renovations.Post

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69 Reference

City of New York. Department of Finance. Office of Tax Policy. October 2013. Annual Report of the NYC Property Tax, Fiscal Year 2013, 57 pp. Part III, p. 29. www.nyc.gov/html/dof/downloads/pdf/reports/reports_propertytax/nyc_property_tax_fy13.pdf

The models used here and in Section 4 do not address the mobility of Pb dusts via movements of building occupants, e.g., in the form of take

- home or secondary exposures of occupant families, young children especially. If dust depositions are a problem for Pb health risks of occupants, so are risks for families if dusts are deposited on occupant clothing, shoes, office items such as briefcases, etc., and then transported home.

Rosalind Schoof

I have no comments on this aspect of the analysis.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The approach appears to be reasonable, subject to a few minor comments. The methods used to determine the AERMOD parameters, impute missing data and select stations are reasonable. The relatively limited effect of obstructions shown on Figure G

- 3 suggests that the effect of obstructions might have been neglected as a simplifying step in the analysis.

Use of AERMOD for Near

- Source Exposure Point Concentrations

The applicability of Gaussian plume dispersion models such as AERMOD to near

- field

exposure scen arios on the order of feet is unclear. To increase the confidence in the results for the exposure point concentrations very close to the source, it may be advisable to compare the results of AERMOD for the exposure points closest to the building to that es timated using a simple near

- field mass

- balance box model with and without an assumed effective deposition rate, particularly for the upper bound estimates. This check would ensure that results are within plausible bounds while retaining the method used in the Approach,

Exterior Aerosol Fraction of Lead Emitted

On page D

- 7 9, Section D.1.4.3, the exterior aerosol fraction of lead emitted may be overestimated. The photographs in Farfel et al. 2003 indicate complete disturbance and

demolition of the structur e. However the emission fraction appears to have been calculated taking into account only the exterior wall surface area.

Emission rate = (mg Pb/s) / (ft 2 exterior wall/s) = (mg Pb/ft 2 exterior wall) Fraction aerosolized = (mg Pb released/ft 2 exterior w all) / (mg Pb in paint/ft 2 exterior wall)

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70 By examining the dimensional analysis above, it is apparent that accounting for the interior wall surfaces would decrease the emission fraction, because the Approach has allocated the entire mass of lead released to a relatively small fraction of the surfaces in the building with Pb

containing paint. Accounting for contribution of interior wall surfaces would decrease the apparent emission rate.

Track

- in

On Page E

- 15 (Sections E.10 and E.11), the derivation of the particle tracking rate and fraction of the dirt remaining on the floor mat is difficult to follow. It appears the particle tracking rate (E.10), was adjusted to reflect that only a fraction of the dust was retained on the mat (E.11). Table 4, number 4 from Von Lindern et al. 2003 indicates GM dust loading rates of 233 and 310 mg/m 2 /day for a mat placed inside the entry way. When multiplied by 0.318 m 2 * 0.001 mg/g, this yields a GM dust loading rate of 0.074 g/day and 0.099 g/day. This appears to have been divided by a derived factor of 13% remaining on the mat to yield a mass loading rate of approximately 0.6 g/day.

It is unclear how it was estimated that 87% of the mass on the mat is carried into the home based

on t he data in Thatcher and Layton, 1995. For example, the data in Table 2 of the paper appears to represent the contribution of historical loading on carpet in addition to the dust that accumulated in the week prior to last cleaning. It is unclear how the di fference in accumulation between tracked and untracked areas could be used to calculate the fraction of dirt remaining on the mat because the author did not present the background concentration of dust in the carpet at the beginning of the week after last cleaning. As such, it is difficult to discern whether the differences reported reflect historical dust loadings or activity that occurred during the week since last cleaning because the efficiency of the cleaning methods (which would determine

residual dus t in the materials at the start of the study in the test home) are unknown. Likewise the background concentration of dust on the mat and time of accumulation is unknown.

It is unclear the extent to which the total dust tracked in is attributable to local soil and dust in the impacted area as compared to accumulated soil and dust away from the impacted area. In principle, it is difficult to accept that 87% of the mass deposited on the mat would be carried into the home because this suggests that floor mats are essentially ineffective control measures in mitigating the impacts of dirty shoes on the surfaces of homes.

The Approach did not consider Layton and Beamer (2009) 1 . The authors used a mass balance to

estimate a track

- in rate for mid

- west residences of 0.1 g/day (as compared to the Approach geometric mean of 0.61 g/day). In addition, the authors present a literature review of track

- rates in their supplemental materials (S19, S20 and S27) where an adjustment factor was not applied to entry way mat data f

or tracked

- in soil. In view of a peer

- reviewed article where an adjustment factor has not been used, this section of the Approach would benefit from a more detailed explanation of how the fraction retained was derived in section E.11, or with comparison of the

estimated track

- in rate to other literature.Post

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71 Representative Exterior Only Soil Concentrations

On Page D

- 17 (Section D2.4), the representative exterior renovation only soil concentrations are lower than typical soil screening levels of 100 ppm (gardening soil from OSWER TRW 2013) or 400 ppm (general soil). The “dust” concentrations are subject to significant uncertainty, however these too are below or only slightly above residential floor dust screening concentrations. This suggests that a scree ning analysis might have been used to eliminate the contribution of exterior renovations to the interior space with respect to “track

- in”.

References

1 Layton, David W., and Paloma I. Beamer. "Migration of contaminated soil and airborne particulates to in door dust." Environmental science & technology 43.21 (2009): 8199

- 8205

Ian von Lindern

AERMOD is a state

- of

- the

- art dispersion model that has been appropriately applied in the Approach. Although it is not mass

- conserving, as EPA points out, the Approach should nevertheless honor the principle of mass conservation and it would be comforting if a simple mass balance were presented showing the interim and final dispositions of all the lead in the

renovation. AERMOD also does not consider re

- suspension of de posited lead. EPA makes two statements in the document alluding to early scoping runs showing that re

- suspension does not significantly influence the air concentrations and that penetration of aerosolized lead dust through the outer shells of buildings is a greater source of exposure than deposition and track

‐ in

of lead dust from exterior soils and hard surfaces. These statements should be confirmed and supported quantitatively, as transport mechanisms subsequent to deposition would be suspected to be impor tant, and should be ultimately accounted for in a mass

- balance.

The lead concentrations on hard surfaces (such as sidewalks or entryways) and in soil around the renovated building are estimated by assuming that the total emissions (minus the emitted

aero sol, reduced by the containment efficiency) is evenly distributed across the containment footprint, following removal of the containment. It is then further discounted for the hard surfaces by assuming runoff from rain events, and assumes zero re

- suspensio n. On soil surfaces the methodology assumes 100% incorporation in the surface soil, and then assumes that all of the remaining lead is tracked into the home, minus a percentage retained on the doormat. A quantitative mass balance figure or table summarizin g this process would be helpful.

Currently the model driving these estimates has only been run at the 1 g/s rate which will later be scaled to reflect estimated emission rates for activities. EPA provides no information on how this scaling will be accomp lished relative to the other assumptions being made regarding sequencing and collapsing of activities. There is a concern with how the assumptions regarding all activities being accomplished concurrently and limited to two rooms will be translated to an

em ission rate, and how accumulation in environmental reservoirs will be handled. There are also specific concerns with soils and carpets that may accumulate lead from both aerosol track

- in resulting from successive activities and the runoff or (unaddressed) re

- suspension.

The track

- in analysis in Appendix E

- 10 and E

- 11 refers uses results from the Bunker Hill Superfund Site and indicates limited information is available within the von Lindern et al

.Post

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72 ( 2003 ) study on the activity patterns of individuals living within those homes and goes on to develop estimates of particular parameters from other data sources. That study has continued and mats have been surveyed in homes from the site since 1996. Summary repo rts can be found in the Five Year Reviews required by EPA. The latest available report is from 2008 and is on line at the EPA website and is summarized in Tables 3.7 and 3.8 in the following document. The

data accumulated since 2008 has been summarized thr ough 2013 and is currently under Agency review for the 2015 Five Year Review to be published in the coming year ( http://www.epa.gov/region10/pdf/sites/bunker_hi ll/bunkerhill

- 3rd

- fyr

- 111810.pdf ).

Review of the data will suggest that, consistent with EPA conclusions in Appendices E, K and L, dust accumulation is independent of the lead concentration. Lead levels have declined substantially due to the cleanup acti vities as have blood lead levels. However, mat dust lead levels have since stabilized at rates similar to those estimated in the Approach analyses. The

stabilized levels are somewhat lower than those noted in von Lindern et al 2003 , which is

believed due t o landscape recovery from severe flooding in 1996

- 97. The mat size 0.318 m2 (as reported in Thatcher and Layton, 1995 for a similar type of analysis) is actually shown in Figure

1 of von Lindern et al 2003 .The estimated number of people in the home is like ly adequate, although it may be low as the program has always targeted homes with children as demographics permit. The characterization of the mats as a doormat is a misnomer, as the mats are placed inside the home “near the main entrance”. Residents are i nstructed (or at least requested) not to modify their behavior, including continuing the use of any door mats and home hygiene activities, and to please not clean the mat during the 2

- weeks it is the home. As such, the accumulation of dust and lead represe nts material transported into and deposited in that

dimension at that location in the home.

Kathleen Vork

I defer to the other panel members who have more in

- depth knowledge of this material.

Issue 4. Estimates of Exposure to Children and Adults

EPA considered environmental concentrations resulting from renovations (as described in Issue 2 and 3) and combined this information with age

- specific exposure factors and activity patterns to estimate exposures. Exposure factors were derived from EPA’s Exposu re Factors Handbook and Activity Patterns were derived from EPA’s Consolidated Human Activity Database (CHAD). A sensitivity analysis was performed to determine how the location of an exposed individual (within one room or moving in multiple rooms) combine d with exposure start

- time influenced results and is described in Section 8. Activity patterns were varied by age and by building type and are summarized in Appendix L.

Question 4. Please comment on the overall approach for estimating exposures (both inte rior and exterior) through use of exposure factors organized by age, and by activity patterns, organized by age and building type.Post

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73 Deborah Cory

- Slechta

In some respects, this represents a strength of the approach taken, as, fundamentally, it recognizes all of the various factors/scenarios that need to be considered in the context of

renovation effects and the health

- associated outcomes. For the choices of the ages, I believe that the split into the three age groups (children 0

- 10, adults 18

- 49 and adult s >50) is a good approach in terms of limiting the modeling requirements, but at the same time capturing the relevant ages groups. Including children 0

- 10 recognizes essentially those studies showing the importance of concurrent blood leads (as opposed to prenatal) on cognitive functions including IQ as demonstrated in longitudinal studies. Keeping the upper age group above 50 makes sense particularly as this better encompasses members of the population exposed to the higher lead levels of the past and for the focus on kidney/cardiovascular consequences. The separation of building type and age makes sense and indeed seems to have been carefully thought out and is

inclusive.

However, having said that, if considered in the context of the response to the Overa ll Impressions question above, is the question of whether collapsing across ages 1

- 10 attenuates the effects of the younger ages in particular where one would expect greater absolute blood lead levels in response to elevations. The other issue is that not separating out women of child

bearing/breastfeeding age specifically, but simply including them in an 18

- 49 year age group will underestimate the impact on this vulnerable population. In fact the age bracketing for groups of pregnant women would seemingly be of diminishing returns after e.g., age 35? This is an issue that is recognized by the authors of the Approach, and indicated as a future direction. Obviously there were some limitations encountered in attempting to model pregnancy or the fetus, but giv en the extensive time and resources afforded to the approach in general, it raises the question, as noted above, about expending that effort and resources on these vulnerable populations rather than an all

- encompassing approach. The rationale for this conc ern is that blood leads are significantly higher already during these physiological states/ages, and in pregnancy and blood lead is delivered directly to the fetus. This is a growing concern as we become increasingly cognizant of the programming effects of these fetal exposures and their ability to produce critical epigenetic types of effects.

With respect to activity patterns and building type, the paragraph on p. 12 (“In the 0

- to 10

- year age group...) is confusing as it appears to state that child occupi ed facilities are not modeled or

assessed as P&CBs? Schools and child

- occupied facilities are listed on p. 23 as one of the receptor buildings used for the modeling.

Panos Georgopoulos

On p. 89 of the Report it is stated that “[T]o convert media concentrations to intakes, exposure factors were used. In general, these exposure factors were taken from the age

‐ specific values in the EPA Exposure Factors Handbook (US EPA 2011), and they include the inhalation rate, soil/dust ingestion rate, and fracti on of soil/dust ingestion that is from soil. These factors were multiplied by the lead concentrations in the media (air, soil, and dust) to estimate the lead intakes for input in the Leggett blood lead model.” This can be inconsistent in terms of

resolutio n with an approach that utilizes CHAD and individual

- specific activity factors. IntakePost

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74 factors can be averages across activities and microenvironments. Furthermore it is not clear whether age specific calculations are lumped together in an age group.

As a more specific example, the set of sampled parameters for exterior simulations listed on

Table 7

- 2 of page 79 raises various issues. These parameters (in addition to Background Air Concentration, Background Soil Concentration, and Background Dust Loading, for which selected units and assumption of lognormal distributions are reasonable) include “Meteorological Region,” “Time Spent,” and “Age within Age Range,” all three described as unitless and assigned from a discrete distribution. The “variable” “Meteo rological Region” is the “Climate Region”. This reviewer’s experience suggests that Climate Region should be treated as a categorical (or scenario) variable and not as a sampled variable: The reason is that some of the other sampled variables in this set ( e.g. “Time Spent” in different types of buildings and other microenvironments, and “Background Concentrations”) and in the overall Approach depend on the Climate Region and region

- specific distributions should ideally be used for sampling. Furthermore, sp ecific “Ages within [an] Age Range” can be associated with drastically different activity patterns as well as physiologies, especially given the selection of age ranges for the Monte Carlo Approach; however, it is not clear whether such differentiation

of activities/physiologies is actually implemented in the calculations.

Philip Goodrum

USEPA is clear that the primary motivation for developing an intake or exposure profile for each hypothetical individual is to properly account for baseline exposures pri or to the initiation of renovation activities. The EFH guidance and CHAD database are reliable sources from which to develop reliable age group

- specific point estimates or probability distributions.

In terms of defining the start age of an individual as a random variable, the main concern is whether or it is clear how the results map back to the CSM for population(s) that are being assessed. There are two fundamental approaches to pursue, and they are not mutually exclusive: 1) “Subpopulation Scenarios”

- define a scenario (including an age range) and use modeling to support inferences for this scenario

– then discuss applicability /representativeness of this scenario to other populations (extrapolation); and 2) “Population Scenarios”

- attempt to model all scenarios jointly so that the model predictions can be explicitly compared to empirical measures of PbB among a population, for example. Both approaches are challenged by uncertainty in how to determine (i.e., quantify) the representativeness of input parameters and corresponding model predictions to the overall population.

Because some of the health effect endpoints are associated with absolute PbB estimates, an approach that accommodates a full life history (including a starting age at renovation) i s attractive. Output from such an approach can be post

- processed (i.e., data mined) to yield an efficient computational strategy. One challenge would be a scenario that allows for multiple renovation activities to occur across the timeline. It may be in appropriate to resample some of the random variables with each consecutive activity if the intra

- individual variability can be reasonably assumed to be low compared to the inter

- individual variability. Typically empirical

data on intra

- individual variabil ity (i.e., repeat measures with the same cohort) is unavailable.Post

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75 Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.

Andy Menke

Correctly modeling activity patterns seems crucial to correctly estimating exposures. Given the importance in estimating time spent in a building, I wonder if more precise categorizing of building types and age group would affect results. It is worth notin g that the sensitivity analysis showed that altering this variable had relatively little impact on blood lead estimates in adults. However, it did have a moderate effect in children. Also of concern (if I understand the document correctly), is that estimat ing time spent in buildings for 5

- 10 year old children was

based on data from 5

- 17 year old children; since activity patterns change with age, it seems differences between children ages 5

- 10 and 11

- 17 could have a meaningful impact on estimates of time spe nt in buildings for this age group.

In terms of exposure factors for renovation intake estimates, my only comment is that I'm a little surprised that ventilation rate does not vary between 18 and 80 years of age, but I'm not aware of literature that sugg ests otherwise and this issue seems unlikely to have a meaningful impact on

the results.

Paul Mushak

The overall approach for estimating exposures to Pb from interior and exterior renovations has involved appropriate use of exposure factors as available in the 2011 version of EPA's Exposure Factors Handbook and EPA's Consolidated Human Activity Database (CHAD). For purposes of this approach, CHAD involves three age range

- specific sets of activity patterns within the affected buildings while the exposure f actors address such questions as daily intakes of Pb or

other contaminant

- containing medium, ventilation rate inhalation parameters as a function of age, uptake parameters that are age

- dependent, etc.

Post

- Meeting Comments:

Dr. Schoof indicated concerns about this issue being focused on intakes and uptakes of Pb from dusts and soils that may be too high, per data in the 2011 edition of EPA ’ s E xposure F actors
H andbook . It was noted that there are some recent data to be examined on this point.

I would not e that a relatively recent comparative review by two EPA/ORD researchers, Jacqueline Moya and Linda Phillips, described in detail the pros and cons of each of three different approaches for estimating the above parameters. The methodologies described and critiqued were: mass balance studies using stable elemental tracers; behavioral pattern analysis;Post

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76 and back calculation of soil and dust intakes using the IEUBK model in combination with measured PbB levels at several Superfund sites.

The stable tracer ap proach has had the longest history, with studies using diverse tracers, diverse biokinetic assumptions, diverse study populations, etc. The authors noted one recognized shortcoming in tracer studies: they are short term. I would note they also often failed to fully capture variability, had small study cohorts, and raised questions about wide relevance of results to broader child populations. Several of the earlier tracer approaches of Calabrese and Stanek

involved children of middle

- upper income families an d were carried out in cooler Fall weather in New England. One study involved a Superfund site in a smelter community.

The behavioral pattern approach, while depicted as a mechanistic method, is relatively new in terms of validation, has multiple steps, e ach with associated variability and uncertainty, and appears to be culture

- specific. In the Canadian children ’ s study of Ross Wilson et al., cited by Dr. Schoof, the caveat was expressed that results were confined to Canadian children.

The review authors favored the back

- estimating approach using modeled blood lead levels at multiple lead contamination sites, in that soil/dust intakes were chronic in duration and levels of dust/soil could be evaluated and integrated across seasons, intraindividual variab ility, etc.

Reference

Moya J, Phillips L. 2014. A review of soil and dust ingestion studies for children. J. Expos. Sci. Environ. Epidemiol. 24: 545

- 554.

Rosalind Schoof

It was difficult to assess critical exposure assumptions because these values were presented only in appendices and no detailed justification was provided. I have significant concerns about the representativeness of some exposure assumptions used to deriv e central tendency lead intake

estimates as listed in Table L

- 10 and M

- 4. Specifically, I think the assumed soil ingestion rates are too high. It also appears that the same assumed oral absorption fraction was applied to soil and dust lead, as well as wate r and dietary lead. Abundant literature supports relative bioavailability adjustments to reflect the reduced bioavailability of lead in soil and dust. At the very low background soil and dust concentrations considered to represent background (Table M

4), l ead in soil and dust contributes more than 50% of lead intake. As soil and dust lead concentrations increase, they begin to dominate intakes. This means that if soil and dust ingestion rates and lead absorption from soil and dust are overestimated, the deg ree of overestimation of lead intakes will be magnified at higher concentrations.

In the table below, I illustrate an example for a 2

- 3 year old child assuming that soil and dust ingestion was 0.06 grams/day (an assumption supported by Wilson et al. 2013 ) instead of EPA’s assumption of 0.1 gram/day. I also assumed that soil and dust intakes were reduced by a relative bioavailability factor of 0.6 (the default value in the IEUBK model). Then I compared lead intakes using the values from EPA’s analysis vs. these alternate soil ingestion and RBA values for three dust lead concentrations, 75.7 (i.e., the background value used by EPA), 400 and 1200 mg/kg. As can be seen in the table, at the lowest soil concentration EPA’s assumptions produce

lead intakes 1.5 ti mes the estimates from the alternate assumptions; whereas at the higher soilPost

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77 concentration, lead intakes using EPA’s assumptions are 2.5 times the estimates using the alternate assumptions. Similar ratios resulted when both soil and dust concentrations wer e increased to 400 and 1200 mg/kg. If these alternate values had been considered while assessing the predictiveness of the Leggett model, EPA might have found better agreement with observed blood lead levels. Furthermore, these alternate assumptions might yield more plausible predictions for blood lead level distributions at higher lead concentrations in soil and dust. Consequently, it is critical that EPA examine these sources of uncertainty and their impacts on the exposure estimation.Post

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78 Estimated Lead I ntakes for a 2

- 3 Year Old Child

Case 1

- Showing impact of increasing dust concentrations

Default Soil/Dust Intake (i.e., 100 mg/day)

Conc

A (ug/g) Conc

B (ug/g) Conc

C (ug/g) Intake

A (ug/day) Intake

B (ug/day) Intake

C (ug/day)
diet, water, air 5.2 5.2 5.2

dust 75.7 400 1200 4.2 22.0 66.0

soil 44.6 44.6 44.6 2.0 2.0 2.0

T otal intake 11.4 29.2 73.2

Reduced Soil/Dust Intake (i.e., 60 mg/day) and RBA = 0.6

Conc

A (ug/g) Conc

B (ug/g) Conc

C (ug/g) Intake

A (ug/day) Intake

B (ug/day) Intake

C (ug/day)
diet, water, air 5.2 5.2 5.2

dust 75.7 400 1200 1.5 7.9 23.8

soil 44.6 44.6 44.6 0.9 0.9 0.9

T otal intake 7.6 14.0 29.8

Default Intake/Reduced

Intake 1.5 2.1 2.5

Case 2

- Showing impact of increasing soil and dust concentrations

Default Soil/Dust Intake (i.e., 100 mg/day)

Conc

A (ug/g) Conc

B (ug/g) Conc

C (ug/g) Intake

A (ug/day) Intake

B (ug/day) Intake

C (ug/day)
diet, water, air 5.2 5.2 5.2

dust 75.7 400 1200 4.2 22.0 66.0

soil 44.6 400 1200 2.0 18.0 54.0

T otal intake 11.4 45.2 125.2

Reduced Soil/Dust Intake (i.e., 60 mg/day) and RBA = 0.6

Conc

A (ug/g) Conc

B (ug/g) Conc

C (ug/g) Intake

A (ug/day) Intake

B (ug/day) Intake

C (ug/day)
diet, water, air 5.2 5.2 5.2

dust 75.7 400 1200 1.5 7.9 23.8

soil 44.6 400 1200 0.9 7.9 23.8

T otal intake 7.6 21.0 52.7

Default Intake/Reduced

Intake 1.5 2.1 2.4

* RBA = relative bioavailability adjustmentPost

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79 Regarding soil and dust ingestion rates, the Exposure Factors Handbook (US EPA 2011) is listed as the source of the assumed rates. The analysis that forms the basis for these ingestion rates is not well supported by estimates available for the 2011 document. Stanek and Calabrese (2000) and Stanek et al. (2001a, b) identified much lower mean and median soil ingestion estimates than the IEUBK model default values. Stanek and Calabrese (2000) estimate a mean soil ingestion rate of 31 mg/day and a one

- year 95th percenti le of 106 mg/day for 1

- 4 year old children from a study in Anaconda, Montana. Stanek et al. (2001a) indicates the median is 24 mg/day and the 95th percentile is 91 mg/day. A mass

- balance methodology was utilized in which eight naturally occurring soil tr ace elements (Al, Si, Ti, Ce, Nd, La, Y, and Zr) believed to be poorly absorbed by the gastrointestinal tract were employed to provide soil ingestion estimates. The initial analysis of study results was presented in a publication (Calabrese et al. 1997a) that has been subsequently followed by five publications further refining the original analysis. The most recent of these publications (Stanek and Calabrese 2000; Stanek et al. 2001a) provided the best characterization of the daily soil ingestion rate dis tribution available at the time of US EPA’s analyses for the 2008 Child

- Specific Exposure Factors Handbook and the 2011 Exposure Factors Handbook. The latest analyses should have been relied on by US EPA (2008, 2011) because they reflected the latest and m ost improved statistical methodology to provide estimates of uncertainty, as well as predicting cumulative long

- term (i.e., annual) soil ingestion rates. Instead US EPA (2008) inexplicably relied on the initial analyses and discounted subsequent improvemen ts in the statistical methods used to analyze the data.

Alternate soil and dust ingestion estimates are now well supported by new analyses published since the US EPA (2011) values were issued. These include analyses by Stanek et al. (2012a, b), and by US EPA (Ozkaynak et al. 2011) and Health Canada (Wilson et al. 2013) authors.

 A recent soil ingestion data meta

- analysis by Stanek et al. (2012 a, b) results in a mean soil ingestion rate of 26 mg/day. Stanek et al. (2012 a, b) assume that the ingestion rate s pertain to incidentally ingested soil. Presumably this rate then represents the total amount of soil ingested both directly outdoors, and that portion of indoor dust composed of soil. US EPA’s soil and dust ingestion rate is assumed to be apportioned 0. 45 to soil and 0.55 to dust. Thus for a 0.1 gram/day combined ingestion rate, 0.045 grams/day would be ingestion of soil outdoors and 0.055 grams/day would be ingestion of dust (including both dust generated indoors and soil tracked into the house), sugges ting that the US EPA(2011) soil ingestion rate substantially overestimates soil ingestion.

 An analysis of combined soil and dust ingestion developed using a mechanistic hand

to

- mouth activity based approach and USEPA’s Stochastic Human Exposure and

Dose Simulation (SHEDS) model predicted a mean combined soil and dust ingestion rate of 68 mg/day for children ages 3 through 6 years old (Ozkaynak et al. 2011). SHEDS predicted that approximately 60 percent of total intake is attributable to soil ingestion, wh ile 30 percent and 10 percent of the total intake is attributable to ingestion of dust from hands and objects, respectively. The average combined soil and dust ingestion rate of 68 mg/day (or 0.068 grams/day) falls well below the USEPA (2011) recommended i ntake of 0.1 gram/day.Post

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80  Similarly, Wilson et al. (2013) estimated soil ingestion rates using a probabilistic mechanistic model. Combined soil and dust ingestion rates were estimated, with a mean probabilistic rate of 61 mg/day, for toddler’s age 7 months through 4 years old.

The combined results of these efforts by Stanek et al. (2012 a, b), Ozkaynak et al. (2011) and Wilson et al. (2013) support the use of lower soil and dust ingestion rates than those currently recommended by USEPA (2011) and used in t he exposure analysis.

Regarding the relative bioavailability of lead in soil and dust, US EPA has long incorporated a 60% relative bioavailability factor in the IEUBK model to adjust lead intakes from soil and dust. A review of studies of lead absorption from soil supports the use of this adjustment as a central tendency estimate (US EPA 2007). US EPA (2007) does not review the literature on relative bioavailability of lead in dusts, but many supporting references are available (Oliver et al. 1999,

Turner and Simmonds 2006, Turner and Ip 2007, von Lindern et al. 2003, Yu et al. 2006).

Alternate assumptions for soil and dust ingestion rates and reduced bioavailability of lead in soil and dust should be considered in the exposure analysis. For the exterior scenario, I would also

like to better understand if lead concentrations in soil are assumed to affect indoor dust concentrations. In the IEUBK model, dust concentrations are assumed to be 0.7 times the soil concentrations. This assumption is too high and c ould lead to overestimating exposures; values in the range of 0.3

- 0.4 have been supported in the literature.

References

Oliver, D. P., McLaughlin, M. J., Naidu, R., Smith, L. H., Maynard, E. J. & Calder, I. C. 1999. Measuring Pb Bioavailability from Hous ehold Dusts Using and in Vitro Model. Environ Sci Technol 33, 4434

- 4439.

Ozkaynak H., J. Xue, V. Zartarian, G. Glen, and L. Smith. 2011. Modeled estimates of soil and dust ingestion rates for children. Risk Analysis 31(40):592

- 608.

Stanek, E. and E. Cal abrese. 2000. Daily soil ingestion estimates for children at a Superfund site. Risk Analysis 20(5):627

- 635.

Stanek, E., E. Calabrese, and M. Zorn. 2001a. Soil ingestion distributions for Monte Carlo risk assessment in children. Human and Ecological Risk A ssessment 7(2):357

- 368.

Stanek, E., E. Calabrese, and M. Zorn. 2001b. Biasing factors for simple soil ingestion estimates in mass balance studies of soil ingestion. Human and Ecological Risk Assessment 7(2):329

- 355.

Stanek E.J., E.J. Calabrese, and B. Xu . 2012a. Meta

- Analysis of Mass

- Balance Studies of Soil Ingestion in Children. Risk Analysis. 32(3): 433

- 447.

Stanek E.J., B. Xu, and E.J. Calabrese. 2012b. Equation Reliability of Soil Ingestion Estimates

in Mass

- Balance Soil Ingestion Studies. Risk Analy sis. 32(3): 448

- 463.Post

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81 Turner, A. & Ip, K. H. 2007. Bioaccessibility of metals in dust from the indoor environment: application of a physiologically based extraction test. Environ Sci Technol 41(22), 7851

- 7856. Turner, A. & Simmonds, L. 2006. Elemental conc entrations and metal bioaccessibility in UK household dust. Sci Total Environ 371(1

- 3), 74

- 81.

US EPA. 2007. Estimation of Relative Bioavailability of Lead in Soil and Soil

- like Materials Using In vivo and In vitro Methods. OSWER 9285.7

- 77. Office of Soli d Waste and Emergency Response, U.S. Environmental Protection Agency, Washington, DC.

USEPA. 2008. Child

- Specific Exposure Factors Handbook. EPA/600/R

- 06/096F. National Center for Environmental Assessment, Office of Research and Development, Washington, DC. September 2008.

von Lindern, I., Spalinger, S., Petroysan, V. & von Braun, M. 2003. Assessing remedial effectiveness through the blood lead: soil/dust lead relationship at the Bunker Hill Superfund Site in the Silver Valley of Idaho. Science of the T otal Environment 303(1

- 2), 139

- 170.

Wilson R., H. Jones

- Otazo, S. Petrovic, I. Mitchell, Y. Bonvalot, D. Williams and G.M. Richardson. 2013. Revisiting Dust and Soil Ingestion Rates Based on Hand

- to

- Mouth Transfer. Human and Ecological Risk Assessment 19( 1):158

- 188.

Yu, C. H., Yiin, L.

- M. and Lioy, P. J. 2006. The Bioaccessibility of Lead (Pb) from Vacuumed House Dust on Carpets in Urban Residences. Risk Analysis 26(1), 125

- 134.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The exposure estimation approach is complex, but appears to be reasonable. The approach used to assign the blood lead geometric standard deviation (GSD) is reasonable. The methods used to estimate background concentrations and intakes appear to be reason able. For track

- in, it could be interesting to compare the rates implied by the model mass balance to the background lead dust loading shown in Table N

- 1. The approach to estimate the universe of P&CBs using the Maryland Department of Planning (MDP) data i s reasonable, however, the supplemental information about the distances between P&CBs and all other building types does not appear to be presented or summarized in this Appendix. My primary comment on the exposure assessment

relates to the time since renov ation.

I found the discussion of the time since last renovation to be somewhat confusing because the optimal amount of time to follow blood after the last renovation and the renovation frequency are not necessarily related. The exposure metric should be selected based on considerations of

dose

- response and hazard identification/critical exposure windows. The approach states:

“ Capturing all the different time periods allows analysis of the health effects over the range of assessed time periods since the re novation.” This statement suggests renovation frequency hasPost

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82 informed hazard identification and dose

- response assessment of Pb with regard to acute/intermittent exposures.

References

1 Goyer R. 2004. Issue Paper on the Human Health Effects of Metals. http://www.epa.gov/raf/publications/pdfs/HUMANHEALTHEFFECTS81904.PDF

.

Ian von Lindern

The overall approach is sound and based on relevant surveys and reasonable assumptions that have been employed in accepted risk assessment protocols. There remains some concern with the simplifying assumptions limiting renovations to 2 adjacent rooms and that all renovation activities run in parallel. It seems this might under

- predict cumul ative exposures to individuals who might have longer term exposures during protracted renovation activities; in addition to potential under

- prediction of media concentrations in environmental sinks such as carpets and soils that may accumulate lead from se quenced activities.

Kathleen Vork

The use of exposure factors as described in the Approach document seems reasonable.

Issue 5. Leggett Blood Lead Modeling

The Approach uses an updated Leggett blood lead model to estimate both childhood and adult changes in blood lead. The updates included scaling tissue lead concentrations for children and adolescents, evaluating and updating the bone lead transfer rates, and evaluating and calibrating the model using human data sets for both children and adults.

Question 5a. Please comment on the updates to the Leggett model and the choices made in evaluation of data sources and calibration of the model for children and adults.

Deborah Cory

- Slechta

As based on newer or more precise information, the updates to the Leggett model are appropriate. One limitation of course is the fact that the model is forced to rely on values obtained from males, and cannot at the present time, given insufficient data, calculate gender

dependent information. Animal studies quite cl early show sex

- dependent differences in effects of lead, and some human data also support these observations.

This deficiency also relates to the comment above given that blood lead values increase with physiological conditions in women that include pregn ancy and breastfeeding, during which time lead is being delivered directly to the fetus. As the potential programming effects of lead exposure become clearer, this dose to the fetus in particular becomes of concern. These deficiencies are clearly worth t he effort to bring to the Approach.

Related to this, changes were made in the default maternal blood lead levels used in the model. While the updating at least based on NHANES data seems reasonable, there are two questionsPost

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83 that arise from this. The use of blood leads from 18

- 50 years of age seems to significantly over

expand the range of realistic maternal inputs, i.e., how many women actually deliver after the age of 35? How does a change from e.g., 18

- 35 alter outputs? This is mentioned in Appendix L

fo r the low birth weight health effect, and it is stated that the Leggett model does not predict different blood leads between ages 20 and 50; that seems at odds with NHANES data over the years, and also raises the question of whether this is due to the incl usion of mixed genders.

Changes were made in the RBC saturation parameters, but the rationale for the changes are not actually defined (M

- 5

- M

- 6). If the empirical basis for a threshold is not well defined, why make a change at all? It seems like this sec tion needs a better rationale.

The model parameter adjustments made for bone and blood lead levels in adults clearly improve their predictive validity.

Recommendations made at the end of Appendix M for future iterations of the model are sound.

Panos Geo rgopoulos

Though the selection of data sources and the associated update and calibration of the Leggett model for children and adults is reasonable, it should be noted that the fundamental scientific structure of the model (and associated limitations) rem ain essentially the same as over 20 years ago. The updates included scaling tissue concentrations for children and adolescents, updating bone lead transfer rates, and calibrating the model using human data sets across multiple ages. It appears that scalin g was implemented in a “quasi

- deterministic” manner and it does not explicitly incorporate distributions representing intra

- individual and inter

- individual physiological and biochemical variability. USEPA has plans for refining and improving the model, as stated on pages M

- 19 to M

- 20 of Appendix M; however, these are (as per the relevant subsection’s title) “Issues to consider at future date.” At this point, however it is recognized in the report that the updated model has not been tested/evaluated for “tra nsient” exposures, such as those associated with renovations of P&CBs. Specifically, on page M

- 14 it is stated, regarding the updates applied to the Leggett model:

 “It should be noted, however, that this calibration only applies to chronic exposures; no

d ata were available to perform analysis on shorter

- term exposures such as those experienced during a renovation event.” Since the original Leggett (1993) model was in fact evaluated with the Rabinowitz et al. (1976) data, that reflect, short

- to

- intermediate exposures, it should be explained how any modified versions of the Leggett model can still reproduce the patterns in those data.

Some additional/specific points follow:

 Though the model code has the ability to accept inputs on relatively short

- term

expo sures, it does not appear that the updates have accounted for effects of activity and variability of physiology on uptake rates. These effects can be particularly significant for both the inhalation and ingestion routes of particulate matter (of variable s ize) for children. An exposure

- aware PBTK model should be able to account not only for different inhalation and blood flow rates for different activity levels but also for thePost

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84 variability of all other physiological factors affecting deposition and uptake o f particles in the respiratory system (See, e.g., Sasso et al., 2010).

 Gender

- related differences in the toxicokinetics of absorption, metabolism and excretion of Pb may account for gender

- differences in Pb toxicodynamics and in Pb neurotoxicity (see, e. g. Senut et al. 2012; Vahter et al. 2007; Power et al. 2014), but the original Leggett ( 1993) model does not appear to provide sufficient mechanistic information to assess such issues. On page M

- 20 of Appendix M it is stated that “[T]he Leggett model

shoul d be updated to consider gender.”

 The Leggett model cannot calculate in utero exposures that in fact, may be playing an important role in subsequent neurodevelopment. (See, e.g. Ray et al., 2014)

 On page 54 of the Report it is stated that “[Th]e Leggett model does not include an exposure model and thus does not accept exposure concentrations directly. Instead, the model uses total inhalation and ingestion intakes (administered doses) as inputs. Thus, the exposure concentration time series are converted to intakes using intake parameter inputs. To obtain estimates of blood lead, both the time series of the media inputs (air, indoor dust, and outdoor soil) and the age

‐ specific exposure factor values that govern intake and absorption processes must be specifi ed.” However, the problem here is that both uptake processes and the uptake/intake ratio depend on physiological processes that are activity

- dependent, such as deposition rate of inhaled particulate matter, blood flow, etc. Activities are both age and micr oenvironment dependent (among many other factors) and it is not explained how these dependencies can be accounted for in the Leggett

model.

 In Section 7.4 the report states that “[T]he distribution of modeling results does not include variability from variables that were set to point estimates in the model. One such set of variables [...] biokinetic parameters (e.g., rate of movement of lead from plasma to bone) in the blood lead model. These variables were not incorporated as distributions owing to the overall uncertainty in the parameter estimates themselves and the lack of reliable literature data to use to estimate these distributions. Thus, to account for these differences, additional variability was incorporated into the iteration

‐ specific blood le ad estimate. The Leggett model prediction for the given iteration was treated as the geometric mean of the broader distribution of exposure factors and biokinetic differences. A representative geometric standard deviation (GSD) was then selected and

combin ed with this geometric mean to estimate a series of new iteration

‐ specific blood lead values that accounts for these differences. [...] For the Approach, a value of 1.9 was selected for this GSD. This value is fairly constant across the NHANES surveys sin ce the year 2000. [...] For bone lead, no similar nationally representative survey spanning both children and adults could be found. Because these data were not available, no GSD was applied to the bone lead estimates from the Leggett model.” Clearly, this is a rather simplistic treatment of variability (compared to standard methods currently used in pharmacokinetic modeling practice, such as NONMEM; see e.g. Owen and Fiedler

Kelly, 2014) ; nevertheless, the lack of available data prohibits a more detailed a nd robust analysis of variability effects.Post

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8 5  Converting particulate matter intake to uptake requires both characterization of particle

size dependent deposition across in the oral cavity and across different regions of the respiratory system as well of bioa vailability of the lead carried by the particles. Though the AERMOD calculations allocate particle sizes in three size “bins” (2% is assumed to be of diameter less than 2.5 μm, 72 % between 2.5 and 10 μm, and 26% between 10 and 20 μm), it does not appear th at the Leggett model allows dosimetric considerations accounting for particle size.

References

Owen, J. S., Fiedler

- Kelly, J. 2014. Introduction to Population Pharmacokinetic/Pharmacodynamic Analysis with Nonlinear Mixed Effects Models. New York: Wiley

Power, M.C., Korrick, S., Tchetgen Tchetgen, E.J., Nie, L.H., Grodstein, F., Hu, H., Weuve, J., Schwartz, J., and Weisskopf, M.G. 2014. Lead exposure and rate of change in cognitive

function in older women. Environmental Research 129:69

- 75. DOI:10.1016/j.e nvres.2013.12.010

Rabinowitz, M.B., Wetherill, G.W., and Kopple, J.D. 1976. Kinetic analysis of lead metabolism in healthy humans. Journal of Clinical Investigation 58 (2):260

- 70

Ray, P.D., Yosim, A., and Fry, R.C. 2014. Incorporating epigenetic data int o the risk assessment process for the toxic metals arsenic, cadmium, chromium, lead, and mercury: strategies and challenges. Front Genet 5:201. DOI:10.3389/fgene.2014.00201

Sasso, A., Isukapalli, S., and Georgopoulos, P. 2010. A generalized physiologicall y

- based toxicokinetic modeling system for chemical mixtures containing metals. Theoretical Biology and Medical Modelling 7 (1):17. DOI:10.1186/1742

- 4682

- 7

- 17

Senut, M.C., Cingolani, P., Sen, A., Kruger, A., Shaik, A., Hirsch, H., Suhr, S.T., and Ruden, D. 2012. Epigenetics of early

- life lead exposure and effects on brain development. Epigenomics 4 (6):665

- 74. DOI:10.2217/epi.12.58

Vahter, M., Akesson, A., Liden, C., Ceccatelli, S., and Berglund, M. 2007. Gender differences in the disposition and toxicity of metals. Environmental Research 104 (1):85

- 95. DOI:DOI 10.1016/j.envres.2006.08.003

Philip Goodrum

The conceptual approach to calibrate the model default scenario (absent additional exposures) so that it matches NHANES baseline is smart. I support the concept and view it as an improvement over prior uses of Leggett. Please see prior comments regarding the central tendency summary statistic that is defined by the model output.Post

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86 Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge question.

Andy Menke

This is outside of my area of expertise.

Paul Mushak

The authors need to expand the discussion of the exposure computational model of Legge tt to include, first, a comparative discussion of the Leggett and O'Flaherty models. The authors need to say something more substantive about the Leggett model in Sec. 5 than simply a couple of peer review committees and a previous group of EPA authors use d and/or recommended the Leggett model. Pros and cons of the two models for use in intermittent, short

- term renovations would be very helpful to the lay reader and decision maker

- policy person.

The upgrades in the Leggett model do help for renovation

- base d short term Pb exposures for kids and adults, as presented in Appendix M, but there are still considerable limitations with the Leggett model. For example, fetal exposures to Pb in pregnant women defines a major risk population and is coupled to decrement s in birth weight as adverse effect, yet the Leggett model not only does not have a useful fetal compartment, there is still a need to further refine gender

variable intake and biokinetic variables, particularly bone Pb transport kinetics and fractionation of gender

- specific trabecular to cortical bone differences. Related to this is the need to deal with osteoporotic changes in menopause using Leggett.

Our 2005 SAB All Ages Lead Model panel considered these two models for use with childhood and adult expo sures along with the near

- steady state chronic exposure, childhood IEUBK model for kids up to 84 mos. of age. While an Agency All

- Ages model is still a work

- in

- progress, there are some useful discussions in the Panel's final report and those can be reviewe d for anything helpful. Similarly, the CASAC lead panel 2007 and 2011 consultations discussed the various

models

, but in the restricted context of use for intermittent, periodic, or short

- term exposures attending renovations of residences, COFs, public and commercial buildings.

One of the major limitations of the O'Flaherty model, not shared by Leggett for use in

renovation

- type short

- term Pb exposure episodes is its relative lack of flexibility to generate serially adjustable background blood lead values, on which simulated PbB increments from interior/exterior renovations can be superimposed. The desk

- top C++ version I have used computed body lead burdens from birth. Accommodations for era

- variable periods of different Pb exposures consists of either pre

- 1970 or post

- 1970 exposures. Some flexibility is accessible to a user using the original main

- frame ACSL version of O'Flaherty. Other limits are present inPost

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87 O'Flaherty for the desktop user. For example, Pb uptake rate in very young children is only assigned 50% at birth, declining to the adult figure of 8% at age 8.

The O'Flaherty model, however, is a flow/diffusion

- limited physiologically based

pharmacokinetic (PBPK) model and should be able to provide the most flexibility in predicting Pb dispositions for the greatest range of ages and the most diverse spectrum of exposure conditions (e.g., Lakind, 1998). The O'Flaherty model may be modified to simulate the toxicokinetics of Pb in sensitive subpopulations exposed to renovation activity Pb, such as
pregnant women. Overall, O'Flaherty can simulate changes in bone metabolism, tissue volumes, blood flow rates, etc. linked to pregnancy, and pregnancy related changes such as hemodilution and bone resorptions at various trimesters, individuals having nutrient defi ciencies, and changes accompanying aging or osteoporotic disease.

As to the nature of the updates and their calibration, they represent reasonable tweaks to the original 1993 version and some of the intermediate ones. The use of various selected calibration data appear to be reasonable in the case of children and the various worker populations with Pb

bone and PbB serial data available, e.g., Fleming 1999, Nie et al. 2005, OEHHA 2013.

The necessity to consider erythrocyte Pb saturation tweaking i n the Leggett or any other model is that erythrocyte saturation thresholds and maxima employed will either underestimate or approximate Pb

- bone values over time. The OEHHA document does a very good job of rationalizing its choice of computational models fo r the specific needs of that agency, i.e., chronic Pb exposure of workers exposed for up to 40 years followed by retirement and bone Pb releases to the central compartment. However, the OEHHA model dealt with empirical data that was gathered from chronic w orker exposures.

Several questions persist with the question of erythrocyte Pb saturation. For example, the child age band and the two adult age bands encounter renovation activities over very limited time periods and with quite low PbB background and bod y lead burden levels. Are young children in the Approach's 1

- 10 years age band likely to have a significant impact from tweaking or not tweaking RBC saturation parameters? Here, the background PbB will be quite low and Pb

- Bone
will likewise be low and affe cted by immature trabecular and cortical bone physiologies. Does the pregnancy age band in women used in the Approach, 18

- 49 years of age, require special attention in the matter of erythrocyte Pb binding and combined maternal

- fetal circulation, given

the changes occurring physiologically during pregnancy, e.g., hemodilution? The older subsets of the most aged band in the Approach, 50

- 80 years of age, will have acquired the largest body lead burdens and bone levels and will be of relatively more interest.

It is useful to consider the topic of RBC saturation and its modeling in the context of the race

based genetic heterogeneity of Pb binding. We not only are interested in the level of lead versus fractional binding to RBC

, but the isozyme variants of 

- ALA

- Dehydratase and extent of RBC binding. The enzyme is a critical step in the heme biosynthetic pathway and the actual site of Pb binding in whole blood, based on extensive recent evidence (for example, Bergdahl et al., 1998).

Three genotypic variants exist for this race distribution

- specific enzyme: ALA

- D 1:1, the native variant; ALA

- D 1:2, the heterozygotic carrier variant; and ALA

- D 2:2, the homozygotic variant. These variants bind Pb to significantly different extent. Studies in children show the AL AD 2Post

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88 allele (mainly the 1:2 carrier form) present with much higher PbB levels (e.g., Astrin et al., 1987; Wetmur et al., 1991) while Pb workers present a mixed picture (Fleming et al., 1999; Lee et al., 2001). Eurocentric populations show 80% of ALA

- D 1:1, 18

- 19% ALA

- D 1:2. Afrocentric populations show little of the variants, while Asian populations are intermediate.

References

Astrin KH, Bishop DF, Wetmur DJ et al. 1987. 

- aminolevulinic acid dehydratase isozymes and lead toxicity. Ann. N.Y. Acad. Sci. 5 14: 23

- 29.

Bergdahl IA, Sheveleva M, Schutz A et al. 1998. Plasma and blood lead in humans: capacity

limited binding to 

- aminolevulinic acid dehydratase and other lead

- binding components.

Toxicol. Sci. 46: 247

- 253.

Fleming DE, Chettle DR, Webber CE et al. 1999. The O'Flaherty model of lead kinetics. An evaluation using data from a lead smelter population. Toxicol. Appl. Pharmacol. 161: 100

- 109.

Lee BK, Lee GS, Stewart FS et al. 2001. Associations of blood pressure and hypertension with

lead dose measur es and polymorphism in the vitamin D receptor and 

- aminolevulinic acid dehydratase genes. Environ. Health Perspect. 109: 383

- 389.

Wetmur JG, Lehnert G, Desnick RJ. 1991. The 

- aminolevulinate dehydratase polymorphism: higher blood lead levels in lead wo rkers and environmentally exposed children with the 1

- 2 and

2

- 2 isozymes. Environ. Res. 56: 109

- 119.

Post

- Meeting Comments (All of Issue 5):

A number of the peer reviewers shared my concerns about the limitations of the Leggett model. In particular, a nu mber of Dr. Georgopoulos’ concerns echoed mine.

Rosalind Schoof

As described in my response to Question 4, I believe that alternate assumptions for soil and dust ingestion rates and relative bioavailability of lead in soil and dust should have been applied during the effort to update the Leggett model. These assumptions are some of the most critical to the exposure assessment outcome and it is difficult to understand why they were not examined early in the process. Soil and dust ingestion rates on th e order of 0.06 grams/day instead of 0.1 gram/day and a relative bioavailability factor of 0.6 are plausible and supported by available literature. If these alternate values had been considered while assessing the predictiveness of the Leggett model, US E PA might have concluded that the model did not need some of the changes that were implemented. Furthermore, these alternate assumptions might yield more plausible predictions for blood lead level distributions at higher lead concentrations in soil and dust

. Consequently, it is critical that US EPA examine these sources of uncertainty and their impacts on the exposure estimation. I recommend that EPA run the Leggett model with a range of assumptions for soil and dust ingestion and relative bioavailability to examine the impact on the fit of the model output to the NHANES data.Post

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89 Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The general approach to model calibration appears to be reasonable.

The FORTRAN code is not well documented. The code would benefit from additional commented explanation, particularly with respect to aspects of the FORTRAN implementation that are not readily apparent from the conceptual model. For example, the numerical integration technique used in t he model is difficult to discern, and it is unclear how well mass

- balance is preserved when the exposure concentrations change over time (and depart from pseudo

- steady state concentrations). Similarly, the purpose of IF/THEN switches should be documented

t hroughout. A statement regarding conservation of mass balance as well as typical mass % error should be provided in the Approach.

The discussion of RBC threshold in Appendix M is interesting and clear, however, with regard

to dose

- response, a more pertine nt question might be whether the choice of threshold meaningfully affects systemic distribution of Pb (e.g. total tissue body burden, bone lead concentration, and urinary excretion). It may be the case that knowledge about the threshold is only important i n the context of the Approach because an imprecise biomarker (whole blood concentration) has been used to assess Pb dose response for short

- term, or acute, exposure
scenarios. Whole

- blood is certainly a reasonable (and likely more stable) biomarker than se rum or speciated diffusible Pb in plasma for the purpose of evaluating chronic health effects, however, as discussed in my responses to Questions 6 and 7, it is unclear whether whole blood is

a reasonable biomarker for short

- term Pb exposure scenarios beca use the speciation of lead between bioavailable and unavailable forms is likely different for acute and chronic scenarios due to serum protein binding and RBC sequestration. These uncertainties may be reasonably addressed by selecting a metric that relies on time

- weighted average, such as the maximum 3

- month rolling average blood concentration.

Ian von Lindern

No comments with respect Leggett Model modifications at this time.

Kathleen Vork

The goal of this model is to predict blood lead levels after m ultiple doses of lead and the time course of lead released from blood until a return to background levels in children and adults exposed during and after renovation activities in public and commercial buildings. The description of Version 5 updates to th e Leggett model indicate that the RBC saturation feature of the Leggett model has been activated, the RBC saturation and threshold parameter values were reduced to 270 and zero respectively and the RBC deposition fraction was reduced
from 24% to 22%. In a ddition, the release of lead from cortical bone to plasma was reduced to follow the data provided in Rowland et al (1964) and the rate of release of lead from trabecular bone was decreased and age

- adjusted for adults.Post

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90 What are the effects of these change s on model performance?

For Adults

It looks like activating the RBC saturation feature greatly reduced predicted blood lead levels as observed in panel 1 of figure M

- 6 for this worker. Increasing the cortical bone release rate

reduced the lead levels in cortical bone as observed in panel 2. Age adjusting trabecular bone release rates slightly reduced the lead levels in trabecular bone as observed in panel 3.

The assessment of mean square error (MSE) relative to predictions from Version 4 and data

from 209 smelter workers provides an assessment of improved fit to bone and blood lead predictions from Version 5. However, this test does not determine whether Version 5 produces a systematic bias under

- or over

- predicting blood lead levels relative to the sa mple of 209 chronically exposed smelter workers. Also, it is unclear what occupational and background intake values were applied to Version 4. Were they the same as Version 5 where occupational

intake values are back

- calculated from blood lead levels meas ured in 1994, 1999 and 2008 and added to background intake values for each worker according to their age, date of hire?

Although the MSE is greatly reduced by the changes made in Version 5, additional tests would clarify that changes to the model did not alter the overall integrity of the model. For example:

 Are predictions of blood lead unbiased relative to observations from a large group of exposed workers?

 Is performance from the model (i.e. measured minus predicted blood lead levels) consistent reg ardless of exposure duration (indicating whether or not the model properly treats long term storage of lead)?

USEPA could repeat the tests performed by Hattis (1981) and OEHHA (2013) to help answer these questions.

Retesting the nonlinear relation betwee n whole blood and plasma, whole blood and urine clearance with measurements of chronically exposed workers similar to that conducted by Leggett (1993) and OEHHA (2013) would provide confidence that the model continues to function properly. In addition, it is important to retest the distribution of lead in humans chronically exposed to low level lead throughout life similar to that conduced in Table 3 of Leggett (1993) and in Table A

- 3 of OEHHA (2013) for adults. These additional tests would provide confid ence that Version 5 serves the purposes set by USEPA (predict blood lead levels

associated with intake from air and dust levels of lead and the time course of blood lead associated with incremental changes in adverse health outcomes) with reasonable confid ence.Post

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91 Question 5b. Please comment on the use of the updated Leggett model to estimate blood lead levels for both children and adults in the Approach.

Deborah Cory

- Slechta

Model parameter adjustments and calibrations are detailed in Appendix M and compare Leggett 4 version to Leggett 5. In both cases, i.e., adults and children, the parametric adjustments appear to significantly improve the model fits. Thus, within the realm of ages that are included in the models, these appear to be evolving to bett er fits.

P anos Georgopoulos

The general concerns about the (updated) Leggett model were summarized in the comments for Question 5a.

Clearly, the variabilities and uncertainties associated with the blood levels calculated by the Leggett model (given the limitations in both the model’s structure and in the available input data) are very large and raise concerns that should be taken into account when interpreting these levels. The estimates presented in the Report raise various concerns; for example:

 Figu re 8

- 7 shows equal blood lead change for all age groups for industrial buildings: This is surprising because small children (0

- 10 years) would not be likely to spend time in industrial buildings. Adults in the upper range if the age group (50

- 80 years), e. g. retirees over 65 years old would also be less likely to spend significant time inside industrial buildings. A discussion and explanation of these results is necessary.

 In Figure 8

- 18 the ceiling height is positively correlated with exposure and blood l ead concentration. However, while increase in ceiling height may be related to larger renovated/painted wall areas, it is expected to increase the volume of the room and thus

reduce concentrations of inhalable lead in a room. Actual concentration levels wi ll depend on many other factors, including air exchange rates, and the overall relationship can be quite complex. At a minimum, the results of Figure 8

- 18 point to the need for further investigation.

Philip Goodrum

Leggett has been previously reviewed by USEPA and various independent peer review panels and is a strong candidate for expressing lifetime exposures. Use of a single model is cleaner than running IEUBK and ALM separately, if the data on some key variables such as soil and dust ingestion rate a re lacking for adolescents. The modeling framework supports point estimate and probabilistic approaches, which is useful as a rigorous tool for predicting distributions and examining potential risk drivers.

The challenge for implementation of the Leggett model is that a separate module is needed to generate the input file of intake rates over various age ranges. Here, the time varying component of exposure needs to be considered carefully, especially given the disparity in data sources available to quanti fy distributions across the input variables. Choices include:Post

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92  Time step (how frequently to select a value from each RV)?

 Renovation accounting

– how many, how long per activity, and at when point in the

lifeline?

 Period of exposure (how many time steps to string together to represent ages)

– how do you handle the exit from the model domain?

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertise and I am unable to respond to the charge

qu estion.

Andy Menke

This is outside of my area of expertise, but it does seem like the updated model clearly improved

blood lead estimation in both children and adults.

Paul Mushak

The updated Leggett model is sort of it among computational models at this point, so the question boils down to whether any of the three risk age bands may be underestimated or overestimated as to Pb exposures using the updated Leggett. My specific concern here would be the extent to which the Leggett

- updated version adequat ely estimates lead exposures to the fetus during pregnant women’s contact with Pb in buildings being renovated or from nearby exterior renovations. A useful fetal compartment in the updated model remains to be developed. We cannot say, either for women's P bB predictions or particularly fetal exposures at the various trimesters of variable toxic risk that simulations will be correctly capturing actual exposure risks. Specifically, fetal Pb exposures from maternal bone Pb and bone Ca resorption in the final

t rimester are higher than in earlier gestational periods. Such resorption is stimulated by fetal skeletal growth in the third trimester. Consequently, the increased Pb release from maternal bone during the final trimester would have to be simulated differen tly than for the first and second trimesters. Further, embryotoxicity is expressed in the first several weeks of gestation, where subtle neurobehavioral teratological (as opposed to anatomic terata) toxicity is of concern.

It should be noted that Pb deliv eries to the fetus, especially in late pregnancy, tend to be underestimated using simply PbB, a relatively static metric, in terms of total Pb delivery to the fetus during gestation. Total Pb fluxes as estimated by Gulson et al. (1999) would be more

toxico kinetically meaningful, if more complex, for in utero Pb exposure assessments.

Reference

Gulson BL, Pounds JG, Mushak P et al. 1999. Estimation of cumulative lead releases (lead flux) from the maternal skeleton during pregnancy and lactation. J. Lab. Cli n. Med. 134: 631

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93 Lead toxicokinetics would operate to impart fetal and embryotoxic risk at different times versus renovation activities. A renovation occurring with exposure of women late in pregnancy will entail tweaking the exposure model different ly than for those women early in pregnancy, e.g., in the Pb embryotoxic stage. In theory, a pregnancy Pb risk population occupying renovated buildings or buildings downwind of renovated buildings can encounter Pb at any stage of their specific pregnancies.

Rosalind Schoof

For the reasons described in responses to earlier questions, I do not believe the updated model is providing accurate estimates of blood lead levels at higher exposure levels , and I recommend that EPA run the Leggett model with a range of assumptions for soil and dust ingestion and relative bioavailability to examine the impact on the fit of the model output to the NHANES

data.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The use of the model appears to be appropriate subject to the concerns expressed in Question 5c about not using the model to better understand the speciation of lead in whole blood versus apparent health effects.

A consistent theme in the literature is that whole blood is an imperfect marker for health risk associated with lead. For example, Barbosa et al. (2005) 1 write:

“Plasma

- Pb likely represents a more relevant index of exposure to, distribution of, and

health risks associated with Pb than does BPb. Indeed, from a physiologic poin t of view, we can assume that the toxic effects of Pb are primarily associated with plasma

- Pb because this fraction is the most rapidly exchangeable one in the blood compartment. In recent years increased attention has been paid to monitoring the concentra tion of Pb in plasma (or serum). However, research on associations between plasma

- Pb and toxicologic outcomes is still sparse, and a significant gap in knowledge remains.”

This assessment provides the Agency risk assessors and the peer review panel an opp ortunity to reflect on what factors should be considered when making risk management decisions for low dose acute metals exposures, taking into account there may be several mechanisms in mammals that mitigate the adverse effects of trace metals exposure (w hich are ubiquitous). As such, it is worth considering whether more attention to measures of “diffusible” or “available lead” should be qualitatively discussed or semi

- quantitatively considered in the Approach, as these factors are

taken into account in mo st lead biokinetic models.

The Approach has not explicitly evaluated how “free lead” changes at over time (although bone may be considered a biomarker of free lead AUC). The U.S. EPA/ERG (2004) RAF Issue paper

on the human health effects of metals states 1 :Post

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94 “Lead is bound within different body compartments and may be judged to “accumulate” in one or more of them, but the most toxicologically relevant systemic lead is that within the relatively labile plasma fraction. “Free lead” in blood plasma is amenabl e to either rapid excretion for transfer to soft tissues.”

It is recommended that the speciation of Pb in whole blood (diffusible/protein bound/RBC and whole blood/serum ratio) be discussed for various scenarios:

 Blood speciation for 10 days of intake le ading to a 3

- month average whole blood
concentration of 1, 5 and 10  g/dL;

 Blood speciation for 30 days of intake leading to a 3

- month average whole blood
concentration of 1, 5 and 10  g/dL;

 Blood speciation for 90 days of intake leading to a 3

- month average whole blood
concentration of 1, 5 and 10  g/dL; and

 Blood speciation for daily intake leading to an annual average blood concentration of 1,

5 and 10  g/dL

For each of the above scenari os, it is recommended that these metrics be calculated for children, adults and the elderly. In addition, it is recommended that the increase in bone lead concentration (and baseline bone lead concentration) be reported for each scenario. As free Pb is

cle ared, it is possible that Pb in whole blood is not biologically available with respect to target organ effects because it is bound to serum proteins and sequestered in RBCs. The above information is expected to be useful in selecting the best dose metric f or dose

- response. It should also be noted that the concept of “biological monitoring equivalents” may be used to further increase the weight of evidence for selecting the best dose metric. See Figures 5, 7 and 8 in Hays et al. 2007 2

.

References

1 Barbosa Jr, F., Tanus

- Santos, J. E., Gerlach, R. F., & Parsons, P. J. (2005). A critical review of biomarkers used for monitoring human exposure to lead: advantages, limitations, and future

needs. Environmental Health Perspectives , 1669

- 1674.

2 Hays, S. M., et al. "Biomonitoring equivalents: a screening approach for interpreting biomonitoring results from a public health risk perspective." Regulatory Toxicology and Pharmacology 47.1 (2007): 96

- 109.

Ian von Lindern

No comments with respect Leggett Model modifications at this time.

Kathleen Vork

Version 5 needs further adjustments before it can be used for USEPA’s purposes.Post

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95 For Adults

When tested with a subset of the smelter worker data provided in Hattis (1981) by methods described in OEHHA (2013), Version 5 substantially under

- predicted, on average, blood lead

levels after a 9

- month strike. In contrast to the worker data provided by Nie et al (2005), this set of data provides a more stable exposure profile in that no regulatory or technological cha nges occurred during the exposure period. In addition, predicting blood lead levels after a 9

- month strike removes the confounding effect of ongoing occupational exposure, tests the release of lead from all body stores including those from bone and potent ially captures effects of lead on bone mineral releases that were not characterized by a radium or calcium analog (Leggett 1993). In addition, changing the saturation parameter values to those published by OEHHA (2013) in the absence of other changes made in the OEHHA

- adjusted Leggett model may have distorted the relationship between whole blood, plasma and urine clearance. The RBC saturation value published by OEHHA was determined by reducing the average value determined experimentally by O’Flaherty (19 96) and repeatedly comparing predictions with data published by Hirata et al (1995) and others after adjusting bone, blood and urine parameters.

USEPA may want to consider scaling compartment mass and volume to body weight with adjustments for age, gender and hematocrit levels. Some data from adult females has been generated from O’Flaherty’s work on nonhuman primates (O’Flaherty 1998) could be included in Version 5 to examine the transfer of lead during pregnancy and to breast fed infants. A

lactation t ransfer coefficient for lead appears in Appendix J of the most recent document

entitled:

Air Toxics Hot Spots Program Risk Assessment Guidelines: Revised Technical Support Document for Exposure Assessment And Stochastic Analysis (OEHHA 2012) . A brief

ove rview and announcement of its release is located at:

http://www.oehha.ca.gov/air/hot_spots/tsd082712.html

The other references appear in OEHHA 2013 located at:

http://www.oehha.ca.gov/air/exposure_assess/pbpkwrkshop2013.html

For Children

I could not locate where the treatment and summary of NHANES 2008

- 2009 data provided in Figure M

- 4 is described in the text. Were these data extracted from the CDC database and analyzed for purposes of this project? Were the results peer reviewed? Were blood lead levels

from NHANES restricted to data from males in order to match tissue volumes incor porated into Leggett model Version 5?

It appears that intake levels from air, water, dietary and background environmental sources changes very little between ages 1 and 10 whereas the blood lead declines by about 50%. This seems to reflect the change in body weight rather than intake. Bone uptake and loss parameter values are those provided in the original Leggett model (Leggett 1993).Post

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96 The effect of exposure that leads to blood lead levels observed over time in children, for example, from environmental lead exposures and during home renovations in studies conducted in the 1980s and 1990s has not been tested. Repeated measures of elevated blood lead data from children with some indication of decline in exposure concurrent to decline in blood lead levels would contribute to the confidence that Version 5 predictions are valid. Data from children with blood lead levels around 10 ug/dL and estimates of lead exposure from environmental sources could test Version 5’s response to higher levels of exposure such as data from the Cincinnati prospective study.

The Approach estimates blood lead level metrics using background values and for the renovation only. Concurrent and lifetime blood le ad metrics are estimated as outputs from the Leggett model. Concurrent blood lead metrics were estimated at nine different time

- steps post renovation. Lifetime blood lead metrics were estimated based on the age of person at the start of

renovation and the time it takes for their blood lead to return to background. The lifetime will vary across exposure scenarios. Incremental bone lead change was also estimated as another metric of lead body burden.

Question 5c. Please comment on the various outputs derived from the Leggett model (concurrent blood lead, lifetime blood lead, and bone lead) and their appropriateness for use

in concentration

- response functions for children and/or adults.

Deborah Cory

- Slechta

Model parameter adjustments and calibrations are detailed in Appendix M and compare Leggett 4 version to Leggett 5. In both cases, i.e., adults and children, the parametric adjustments appear to significantly improve the model fits.

Panos Georgopoulos

Please see comments on Questions 5a and 5b. While i n principle the different outputs derived from the Leggett model (i.e. concurrent blood lead, lifetime blood lead, and bone lead) should be appropriate for use in CR functions for children and adults, the uncertainties associated with these outputs should be taken into account.

Philip Goodrum

The use of concurrent and lifetime average summary statistics is a reasonable compromise in terms of model run efficiency and utility for risk assessment. It’s unclear what the health risk is from short term spike v ersus prolonged period lower level exposure that yields PbB above some threshold. The Approach document addresses this uncertainty well.

Why can’t the results be post

- processed in real time to report additional statistics of potential

interest such as: 1 ) maximum daily PbB and age when this occurred; 2) period of time (days) that risk metric (e.g., delta PbB > 1 ug/dL) was exceeded.Post

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97 Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

This question is outside my area(s) of expertis e and I am unable to respond to the charge question.

Andy Menke

The Leggett model provided concurrent blood lead at several different time periods, lifetime average blood lead, and bone lead. These can all be useful and appropriate in concentration

response functions, depending on the relevant exposure period for a giv en outcome and the availability of studies investigating that particular biomarker and the outcome. Bone lead is relatively straightforward, as it should be used in concentration

- response functions based on studies of bone lead and the outcome. The Approac h estimated concentration

- response functions based only on papers using blood lead. For blood lead there is the added complication of determining the most relevant time period for estimating health effect, which may differ based

on the outcome. None of the Leggett model outputs are precisely equivalent to blood lead as measured in the relevant previously published studies since the intra

- person correlation over time will differ due to the transient nature of exposure in P&CB’s. Lifetime average may be the

m ost appropriate for kidney disease and CVD mortality, as those estimates are based on studies with participants in a relatively steady

- state of exposure where the measurement of blood lead is thought to reflect (at least in terms of order ranking) many yea rs or even decades of exposure. The relevant exposure period for IQ change in children is less clear. Both concurrent and lifetime average blood lead was associated with IQ change, with a slightly stronger association

for concurrent. Due to the nature of e xposure being investigated in the Approach and the likely

reduced intra

- person correlation over time compared to participants of previous published studies, it may be preferable to focus on a conservative measure, lifetime average blood lead,

instead of us ing concurrent blood lead for change in IQ among children.

Concurrent blood lead levels while pregnant and possibly a much smaller window of time during the pregnancy will likely be more important for the low birth weight outcome. However,

it should be n oted that a renovation may cause a low birth weight baby after even after the
mother’s blood lead has returned to baseline due to renovation

- related increased bone lead and subsequent pregnancy

- induced release of bone lead into the blood stream. Another wa y to think about it is that women who are exposed to renovation

- related lead will have a bimodal peak in blood lead levels if they get pregnant: one peak around the height of exposure similar to everyone else and a second peak when they are pregnant which could be decades later (possibly more peaks if a woman has multiple pregnancies after the renovation exposure). None of the Leggett outputs seems ideal and I’m not sure which would most appropriately cover both the initial high peak in blood lead and the m ilder but longer term risk due to increased bone lead.Post

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98 Paul Mushak

Use of the renovation

- produced incremental increase in PbB as the measure for concurrent PbB and the background

- estimated PbB via Pb

- Bone and lifetime PbB appears appropriate. My

concerns about use of any of the inputs from updated Leggett appear in 5a and 5b responses.

Rosalind Schoof

Due to our limited understanding of health effects associated with short term changes in blood lead levels, it is appropriate to have considered these th ree outputs. I recommend that the results of the exposure analyses be presented separately from the health risk estimates to increase transparency and to facilitate future updates as additional dose response information is compiled

in the future. Presentat ion of exposure model results is especially important considering that “concurrent” and “lifetime” may be defined differently for different health endpoints, i.e., exposure results considered should be presented or restated within each health risk analysis

.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

Figure M

- 1 in Appendix M shows a conceptual model intended to be representative of lead biokinetic models. Figure M

- 1 overlooks an important subtlety of metals dose

- response and biokinetic modeling that not all Pb in “blood” is necessarily biologically available to exert systemic effects on tissues. For example, as discussed in the ATSDR Toxicological Profile for Lead, Marcus, (1985) modeled four blood compartments:

 Diffusibl e Pb in plasma;

 “Shallow” RBC pool;

 “Deep” RBC pool; and

 Protein

- bound Pb in plasma.

Similarly, the Leggett model considers :

 extravascular fluid (presumably accounting for protein binding);

 RBCs; and

 Diffusible plasma.

As acknowledged in the Approach, there is limited data on Pb acute dose

- response. However, the weight of evidence of the Approach would be greatly improved if an attempt was made to evaluate diffusible Pb plasma or more carefully evaluate Pb in bone (which should provide a representation of AUC diffusible plasma). For example, several “lifetime” or “chronic”

scenarios with better characterized health effect levels could be run in the model to determine Pb AUC diffusible plasma or the change in bone load. Th en this same metric could be estimated for

the short

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99 comparison of biologically available Pb. See for example, the explanation of biomonitoring equivalents in Hays et al. 20 07.

In the current Approach, it is unknown how the fraction between diffusible plasma and the other blood compartments is the same or different from chronic exposure scenarios. The relatively small changes in bone Pb concentrations discussed in Section 8 .3 suggest that the AUC of Pb in diffusible plasma may not be very different from background diffusible plasma concentrations over reasonable averaging times (such as 3 months) for the vast majority of scenarios evaluated.

Reference

1 Hays, S. M., et al. " Biomonitoring equivalents: a screening approach for interpreting biomonitoring results from a public health risk perspective." Regulatory Toxicology and Pharmacology 47.1 (2007): 96

- 109.

Ian von Lindern

With regard to central tendency values, the extend ed response function allows for the calculation of number of statistical descriptions of time

- weighted or peak values. This is an advantage presented by this procedure. The current advice seems to be to use the measures that best

simulate those in the base studies used to support and develop the dose response relationships for the downstream health effects. Should future analyses or relevant data become available, developing and adopting other descriptive statistics should be relatively straight

- forward.

However, the blood lead model does yield a single central tendency, or typical measurement assumed to represent the geometric mean for a population of similarly exposed individuals. A geometric standard deviation ( GSD ) is then applied to estimate a distrib ution for that population. Most observed blood lead populations show GSD s in the range of 1.5 to 2.0 and greater. These

GSDs are empirical and reflect several sources of variance in blood lead response, ranging from differences in media concentrations, int ake and absorption, individual pre

- dispositions and bio

kinetic response, and SES factors. In the Approach, EPA is recommending a GSD of 2.0, reflecting the large national populations in NHANES. As EPA indicates in Appendix O, a value

of 1.6 is recommended to apply to an assumed geometric mean blood level derived from the IEUBK model, and is “intended for use at contaminated sites, where the exposed population may be more homogeneous”. Although this may be true, it is important to remember that the

1.6 rec ommendation pre

- supposes characterization of critical exposure values. In the case of the IEUBK recommendation, typical or central tendency exposure values are input to the model, and the distribution is predicted by applying the GSD to the predicted mean. In the Approach case, the exposures are characterized as a series of distributions combined through a Monte Carlo front end, with the resulting aggregation input to the Leggett model. It seems possible that employing the Monte Carlo for exposure input, an d then applying the full NHANES observed

2.0 GSD to the individual predictions and aggregating all the predictions could result in an

overall GSD greater than 2.0. It seems the overall Monte Carlo exercise should result in a total population blood lead GSD of 2.0. A Table showing the final aggregate blood lead distributions would aid in assessing this possibility.Post

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100 Kathleen Vork

Cumulative blood lead index (CBLI) or “lifetime blood lead” may be useful for an assessment of similar exposure histories. Howev er, for unknown exposure histories, this metric would be misleading. For example, someone exposed for 10 years with a blood lead of 30 and someone exposed for 30 years with a blood lead of 10 ug/dl could have a similar CBLI. Since these two exposure hist ories could result in different incidence or types adverse health outcomes, a hazard

assessment based on this metric could be confounded.

An independent assessment of blood lead for a given exposure in adults suggests that Version 5 of the Leggett model p redicts lower blood lead levels and estimates of cumulative blood lead times time than the updated OEHHA version of the Leggett model. This could be in part due to

the data and methods used to evaluate Version 5.

Studies of lead in bone in multiple sites suggest that lead levels vary substantially across the skeleton. Since occupational studies of bone lead generally reflect the kinetics in isolated parts

of the skeleton rather than in the total skeleton measurements from those studies could misrepresent long term body burden.

Issue 6. Estimates of IQ Change/Characterization of Changes in Children’s IQ

In the Approach, EPA estimated IQ reduction based on both concurrent and lifetime average blood lead concentrations based on the Lanphear et al. 2005 and Crump et al. 2013 studies. For concurrent blood lead, the Monte Carlo analysis estimated potential IQ loss based on a single “measurement” (simulated blood lead concentration) for a predetermined (simulated) time after renovation, and IQ loss will be estim ated based on the regression coefficient from the Lanphear/Crump log linear model for concurrent blood lead. Lifetime average blood lead was

estimated based on the time

- weighted average blood lead concentrations from birth until blood lead returns to backg round as generated by the Leggett model, and the lifetime average regression coefficients will be used to estimate potential IQ loss.

Question 6. Please comment on the use of the log

- linear IQ model (concurrent and lifetime) for children up to age 10.

De borah Cory

- Slechta

The use of the log

- linear approach seems well justified and has been replicated across studies including different analyses of the 7 different studies used in the pooled analysis of longitudinal study data and replicated in other analys es.

Panos Georgopoulos

As I am not an epidemiologist I would avoid formally commenting on this question (noting however that, whereas the Lanphear/Crump log linear IQ regression model for blood lead appears scientifically sound, it is applied in the context of the uncertainties associated with

simulated short

- term exposure estimates associated with renovation P&CBs, as discussed in the comments on the previous Questions).Post

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101 Philip Goodrum

The approach seems heavily dependent on how one interprets the “return to baseline”

– how close to baseline is required to be within the margin of error that we use to define baseline itself? And what about the fact that any quantity considered to be baseline (i.e., PbB if not for the exposure from renovation activit ies) is not a fixed value, but in fact is expected to vary as a function of calendar year and an individual’s age?

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

To assess relationship between lead exposure and IQ for children up to age 10, results reported in Lanphear et al. (2005) paper were utilized in Monte Carlo simulations. The authors had investigated various models, including a linear model, a cubic spline function, a log

- linear model, and a piecewise linear model. After testing several functional forms Lanphear et al. (2005) had concluded that the concentration

- response relationship was non

- linear; the log

- linear model best fits the data, based on R2 values

.

Four measures of lead exposure were examined: concurrent blood lead (blood lead level closest in time before the IQ test), maximum blood lead level (peak blood lead measured at any time prior to the IQ test), average lifetime blood lead (mean blood lea d from 6 months to the concurrent blood lead test), and early childhood blood lead (mean blood lead from 6 to 24 months). In addition a pooled analysis of the relationship between cord blood lead levels and IQ also was conducted in a subsample. All four me asures of postnatal exposure were highly correlated with IQ. Even though the results of the regression analyses for all blood lead measures were comparable, the concurrent blood lead level, exhibited the strongest relationship with IQ, as assessed by R2. F urther validation of use of log

- linear IQ model was based on Crump et al. (2013) paper. The authors reanalyzed the Lanphear et al. (2005) data, and confirmed the earlier findings of highly significant log

- linear models reported by Lanphear et al. (2005).

Based on this information, Approach estimated IQ decrease using both concurrent and lifetime average blood lead concentrations. However, in contrast to Lanphear et al. (2005) paper where “concurrent” blood lead (blood lead level closest in time before the IQ test) the context of this Approach, “concurrent” is not used in the same context and instead refers to a point estimate of blood lead at a given time point after the renovation. Based on the Lanphear et al. (2005) and Crump et al. (2013) studies, both concurrent and lifetime average blood lead concentrations were correlated with IQ.

For concurrent blood lead, the Monte Carlo analysis estimated potential IQ lost based on a single simulated blood lead measure after renovation, and IQ loss was estimated based on the regression coefficient from the Lanphear/Crump log linear model for concurrent blood lead. Lifetime average blood lead was estimated in EPA’s modeling based on the time weighted average blood concentrations from birth until blood lead returns to background as simulated byPost

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102 the Leggett model, and the lifetime average regression coefficients were used to estimate potential IQ loss.

As described in Approach section 6.3 “Log linear models were used in preference to the piecewise linear models beca use (1) they provide better qualitative fits across the range of blood lead being investigated (e.g., the log linear model appeared to more closely follow the trend in the data than the piecewise model); and (2) incorporating uncertainty in the log

- linear models into the IQ estimate process is more straightforward than for the piecewise models. Lanphear et al. (2005) found that the log

- linear model provided excellent fits to the data relating blood lead metrics and IQ in the pooled data (see above paragraph one)

. ” Based on the skewed nature of

blood lead distribution data, log linear model is appropriate

.

Andy Menke

The use of a log

- linear model for IQ seems appropriate to use within the range of lead levels being studied in the Approach, as it is consiste nt with the previous studies and the log

- linear model fit the data better than alternative models. We cannot exclude the possibility that the age range used in the Approach for this outcome is too wide, which may be the case if early life (e.g., <2 years o f age) blood lead is actually the relevant exposure and it is correlated with concurrent blood lead in the published studies.

Paul Mushak

Given the available data, and the model to be employed, the Approach's rationale for lifetime

and concurrent PbB vs. IQ point loss appears satisfactory. I would note that a number of critics of the Lanphear pooled analysis, and by association the Kenny Crump reanalysis, fault the statistics by seemingly arguing for implausibility of the shape of the curve. This curve sh ape, a supralinear monotonic one and not a hormetic/nonmonotonic one (where bidirectionality with change of curve slope sign occurs) is explicable in terms of receptor biology, specifically heterogeneous Pb binding receptor populations in children's brains in terms of both their binding affinities and size of receptor binding populations. A high

- affinity, low receptor size population curve segment precedes a lower binding affinity, high Pb binding receptor population segment. The shape of the upper segment, if continued to very high PbB vs. IQ data points past those in the pooled analysis, remains to be fully characterized, but we know, given continued Pb exposure, kids keep sustaining more brain damage and eventually die from the Pb exposure if it’s high en ough.

I would repeat my earlier concern that the Approach authors acknowledge the irreversibility of some potential IQ point loss based on the concurrent and lifetime PbB biomarker estimates such that cumulative IQ point loss will occur in children with r epeated renovation experiences.

Reference

Calabrese EJ, Baldwin LA. 2002. Defining hormesis. Hum. Exp. Toxicol. 21: 91

- 97.

Mushak P. 2013. Limits to chemical hormesis as a dose

- response model in health risk

assessment. Sci. Total Environ. 443: 643

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103 Post

- Meeting Comments:

Several critiques of the log

- linear, supralinear dose

- response curve of the 2005 Lanphear et al. pooled analysis have appeared. These have centered on such factors as claimed implausibility of the supralinear shape of the curve in biological terms, while others have argued for the curve shape being a mathematic or statistical artifact. My pre

- meeting comments provided a biological rationale for the curve shape. At the meeting, Dr. Cory

- Slechta noted that such curves are not

that rar e in the experimental toxicology field, and indicated her findings of these in her own

work.

Most recently, Banner and Kahn (2014) argued that the curve is a statistical artifact, these authors asserting that it arises from failure to adjust for a strong, nonlinear confounder. Hornung and Lanphear (2014) rebutted this assertion by showing that Banner and Kahn employed absurdly unrealistic scenario elements such as extreme, near perfect correlations to demonstrate

this.

References

Hornung RW, Lanphear BP . 2014. The supralinear dose

- response for environmental

toxicants: A statistical artifact? Clin. Toxicol. 52: 88

- 90.

Banner W, Kahn CM. 2014. Low blood lead level effects on intelligence: Can a dose

response curve be determined from the epidemiological data? Clin. Toxicol. 52: 113 117.

Additional discussions on this issue took place. A panel member noted that, in theory, one problem with the log

- linear dose

- response is that IQ point loss approaches infinity as blood Pb approaches zero. As a practical matter, there is little likelihood that developed country populations now or in the future will ever have their body Pb burdens go to zero. Global long

term Pb contamination has totaled 300+ million metric tons and much of this accumulation persists in env ironmental compartments that interface with human activities. The actual question is, what is the best model fit for the range of low lead PbB where the lower bound to the Lanphear et al. range is still > 0 μg/dl?

Others discussed the fractions of the La nphear et al. children distributed across the supralinear

dose

- response curve. It must be kept in mind that, toxicologically, all children in the > 10 μg/dl segment of the Lanphear dose

- response curve became subjects in that segment by first being in

the < 10 μg/dl segment. That is, PbB rose from some value near but not at zero through the sub

10 μg/dl segment before achieving PbB levels at 10 or above. Therefore, child subjects in the > 10 μg/dl segment have already sustained the disproportionate IQ loss w ith their rising PbB level.

Rosalind Schoof

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104 Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

I did not review the derivation of the log

- linear model in detail (Lanphear et al. 2005 and Crump et al. 2005). The appropriate covariates appear to be considered in the dose

- response equation.

The maximum one or three

- month rolling average is recommended as the dose metric for the

dose

- response assessment.

As mentioned under specific comments, there appears to be a typographical error in the C

- R equation on pages 60 and 72, as well as an error in the example given on page 73 that is carried through due to the typographical error. The clarification document indicates that these typographical errors will be corrected, and that the correct equations were used in the

assessment.

Ian von Lindern

The use and endorsement of the log

- linear IQ model (combined w ith the direct calculation of blood lead increment due to the proposed regulated activity) is significant departure from the manner in which EPA has implemented health protective measures in the past. Under current guidance, protectiveness determinations h ave been made based on exceeding a threshold value (e.g. blood lead levels exceeding 10 ug/dl, or perhaps 5 ug/dl). Employing the log linear response curve for the IQ deficit health effect, not only implies no

- threshold of effect, but predicts greater resp onse slopes at lower dose levels. When combined with the baseline blood lead level, this means that the lower a child’s initial blood lead level, the higher the per unit blood lead IQ decrement due the activity being regulated. Conversely, the higher the b ackground blood lead, the greater the overall IQ loss. The Monte Carlo Approach has the distinct advantage of estimating how much of the population would fall in these or intermediate categories of

response.

Ultimately, if these estimates are used for ri sk assessment purposes, a determination as to which IQ deficit (resulting from the blood lead increment) is more adverse, lowering the IQ of an otherwise unaffected child, or adding to the burden of the burden of a child already suffering IQ

deficit. This question may have significant policy considerations due to the host of risk

- cofactors involved in the baseline blood lead level. Children with the highest baseline levels are often dis

advantaged either due to higher background exposures, poor nutritional status, individual or family pre

- dispositions, poverty, community or other SES considerations. Conversely, blood lead levels near zero can result from parental and community diligence in providing lead

- safe and health supportive environments. Indexing the IQ deficit results back to the baseline conditions, (as the blood lead response is also non

- linear) would better inform decision

- makers

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105 Kathleen Vork

I defer to the other panel members who have more in

- depth knowledge of these health effe cts and associated exposures.

Issue 7. Estimating Adult Health Effects

A weight of evidence approach was used to consider which health effects in adults might be appropriate for this analysis. Based on the findings presented in the EPA ISA for Lead and the NTP Monograph on the Health Effects of Low

- Level Lead, concentration

- response functions were derived from studies representing renal, reproductive, developmental, and cardiovascular

e ffects. However, there are several issues related to estimating adult health effects that complicate the analysis applied in the Approach.

1. There is a lack of understanding about the contribution of past versus recent lead exposures in adults to health effects, especially chronic health effects that may require years of insult to produce an effect (e.g., cardiovascular disease).

2. Adults have stores of lead in bone, which reflect cumula tive (lifetime) lead exposures. However, the renovation exposure scenarios being modeled for the Approach are relatively short exposures, better reflected in blood lead measurements.

3. Most of the available studies report on cohorts with higher blood lead levels than those currently reported for the majority of the US population (approximately 1 μg/dL). Often the data

derived f rom the concentration

- response functions at blood lead levels below 1 are highly uncertain, given the lack of data at these levels.

4. In the Approach document, renal, reproductive, developmental, and cardiovascular effects are examined separately, altho ugh there may be feedback effects among them (e.g. cardiovascular disease is frequently associated with chronic kidney disease, and CKD appears to be a risk factor for CVD).

Because these studies report effects most often observed in adults with likely higher past Pb exposures, uncertainty exists as to the Pb exposure level, timing, frequency, and duration contributing to the associations observed with blood or bone Pb levels.

Question 7a. Please comment on the appropriateness of deriving concentration

- response functions for Cardiovascular Disease Mortality (CVDM), reduced kidney function, and low birthweight, and their use for estimating incremental adult health effects due to renovation activities for P&CBs.

Deborah Cory

- Slechta

Obviously, IQ reducti on remains the most sensitive endpoint, and this would also be the case in the post renovation blood lead values, but certainly the inclusion of the other endpoints brings in other vulnerable populations which is particularly important in the case of the f etus (birthweight) and the aging segment of the population. Having said that, it may be that inclusion of all of thePost

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106 health endpoints in addition to IQ, particularly as blood lead levels have continued to decline, represents diminishing returns when you co nsider the active time spent in a renovation site by the majority of the adult population, with the exception of workers involved in renovation activities perhaps. The primary problem would seem to be a focus on children and particular in

child

- occupied bu ildings, including schools and day care. An additional consideration is to the fetus, covered here by inclusion of the health endpoint of low birthweight. However, given the increasing recognition that early exposures (gestational in particular) can lead to life

- long programming alterations in physiological systems, this population (women of child

- bearing age) in particular may need additional consideration.

Panos Georgopoulos

As mentioned in the comment on Question 6, I am not an epidemiologist, but any one should recognize the fact that each calculation step needed to quantify relevant environmental and biological metrics in the source

- to

- dose sequence of “events” associated with renovation activities for P&CBs, i.e. each step leading from estimating dus t emissions to calculating biologically effective doses, is associated with substantial levels of uncertainty. Deriving CR functions that link exposure concentrations with incremental adult health effects, for diverse endpoints such Cardiovascular Disease Mortality (CVDM) thus requires convolution of uncertainties on “both sides” of exposure and effect, and any interpretation of such functions should explicitly account for this fact.

Philip Goodrum

This question already presents a good summary of the unc ertainties in the use of metrics that represent a broad range of health effects that can be associated with PbB. Ideally a dose

response relationship would be developed for each effect endpoint in order to examine the magnitude and probability of adverse effects at low level exposures. Extrapolation below the empirical range used to derive the exposure

- response relationship is always uncertain, particularly for situations where the “noise” of other sources of exposure or effects is high
relative to the “s ignal” we are trying to isolate from one source. It is important to keep in mind that baseline itself is a probability distribution; if the concentration

- response function overlaps with the baseline distribution, this greatly impacts how one interprets th e toxicological significance of an incremental change in PbB due to one more exposure pathway.

It would be helpful and informative to present the baseline distribution (and associated uncertainty in the CDF) for selected age groups together with the exposure

- response distributions for selected endpoints in order to examine this issue explicitly.

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

The hypothetical concentration response (C

- R) function determining incremental adu lt health effects due to renovation activities for P&CB presented in the Approach used estimates forPost

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107 different parameters. Parameters are uncertain and variable therefore as with any prediction model; there is uncertainty and variability of the concentrati on

- response function and the health impact approximation.

As stated by the EPA ISA, “uncertainties remain regarding the timing, frequency, duration and level of lead exposures contributing to the effects observed in epidemiologic studies”. These

uncertain ties have not been addressed in any of the epidemiologic studies evaluated for the Approach. The foremost uncertainty relates to assessment based on a single biomarker (blood primarily or bone in very studies) measurement per study subject. Therefore, ther e is uncertainty in which conceptual model best captures the association between lead exposure and health

effect.

CVD Mortality

To estimate a C

- R function between blood lead and Cardiovascular Disease Mortality (CVDM) Menke et al. (2005) study was select ed. The rationale for this study included estimation of a continuous function, the largest sample size and the most representative adult population based on age, sex, multiple confounders and blood lead levels of all studies considered.

Reduced Kidney Fu nction

Published study by Navas

- Acien et al. (2009) was chosen to derive the C

- R function. Navas

Acien et al. (2009) analyzed NHANES 1999

- 2006, estimating relationships between reduced kidney function and blood lead. The geometric mean blood lead level fo r lead was 1.58 μg/dL in 14,778 participants 20 years and older. Odds ratios were calculated for reduced estimated GFR (eGFR) (<60 mL/min/1.73m2). Odds ratios were adjusted for socio

- demographic factors (sex, race/ethnicity), as well as body mass index, ed ucation, smoking status, urinary cotinine , current and former alcohol intake, hypertension, and diabetes mellitus.

Logistic regression was applied to estimate the ORs for reduced eGFR across blood lead quartiles in men, women, and in the entire cohort. In addition, regression models were estimated that included blood lead levels as continuous variables. Blood lead data were highly skewed, and were log

- transformed. For the study cohort as a whole, the trend in fully adjusted ORs for reduced eGFR was highl y significant across blood lead quartiles. The estimated OR for the fourth quartile (1.56) was significantly elevated. Navas

- Acien et al. also estimated relative risks of reduced eGFR based on the fully adjusted regression models that included (log) blood lead as continuous variables. For the entire cohort, the estimated OR associated with an increase in blood lead from 1.1 to 2.4 g/dL (the 25th

– 75th percentiles of the observed values) was 1.31 with 95 percent confidence limits of 1.16 and 1.47.

In the App roach, the marginal increase in the prevalence of reduced GFR was calculated based on the regression results presented by Navas

- Acien et al. (2009). Estimated ORs from the simulation were multiplied by gender

- and age

- specific background prevalence estimat es for category 3

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108 Low Birth Weight

Result reported from a large retrospective study (n = 43,288) conducted in New York state from

2003 to 2005 showed that increased maternal blood lead was associated with decreased birth weight in infants (Zhu et al., 2010). This study provided data on low blood lead levels (mean blood lead level was 2.1 μg/dL), adjusted for many confounders, and measured a continuous birth weight instead of a dichotomous comparison of infants at/above and below 2500 g. Adjusted odds ratios (ORs) of blood lead levels were e stimated from logistic regression with fractional polynomials. Changes in birth weight estimates varied with blood lead levels, with the greater decreases in birth weight occurring at the lower blood lead concentrations, without

evidence of a threshold eff ect. Birth weight reduction was estimated using log transformed blood lead in fractional polynomial regressions. The best fit was obtained when blood lead was expressed using a square root transformation. After exclusion of mothers with blood lead

measurem ents less than 1 μg/dL, a linear model provided the best fit to the data, with a1μg/dL increase in blood lead associated with a 7.0g decrease in birth weight.

Background(non

- renovation) and post renovation blood lead concentrations were estimated for

th e same hypothetical individuals using the exposure models described for CVDM, and the Leggett blood lead model. In this case, however, changes in blood lead were estimated for adult (18

– 49 year old) women.

Andy Menke

It is appropriate to estimate concent ration

- response functions for cardiovascular mortality, renal disease, and low birth weight, as there is sufficient evidence to link each of these 3 outcomes to lead exposure. The weight of evidence approach in determining the outcomes in adults is much

mo re likely to miss an important outcome than include a spurious association, which I think is completely appropriate in this document. However, it should be noted that this Approach is likely missing some important outcomes or using too narrow of an age ran ge for included outcomes because the previous studies have not adequately investigated it. In terms of prioritizing the outcomes, the transient nature of the exposures studied in this Approach make CVD mortality and kidney disease less of a priority since the effect is likely to be minimal; both low birth weight and IQ loss may be substantially affected by the exposure investigated in the Approach and there is clear evidence for an association between lead and both outcomes.

Paul Mushak

Pregnant Women and Low Birth Weight vs. Renovation Activities

Use of low birth weight as the adverse adult health effect in the form of reproductive and developmental deficits is quite appropriate, in terms of weight

- of

- evidence considerations and the availability of a dat a set selected for study. The Zhu et al. (2010) study presented a number of advantages for its selection as the data set for quantifying the effect biomarker, lowered birthweight. The study, while retrospective, had a large subject size − 43,288 − adjusted for many confounders, had a mean PbB of 2.1 units, representing more recent population Pb background exposures, and treated birth weight as a continuous rather than a dichotomous

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109 The best fit for the concentration

- response relationship was a lin ear one, employing the exposure term as the square root of PbB. A 1 unit increase in PbB was associated with a 7.0 g decrease in newborn weight over the range of 1

- 10 μg/dl. As the authors noted, the dose

- response curve from the best fit finding was supral inear, the greatest incremental decline per unit PbB change

occurred at the lower PbB concentrations. There was no evidence of a threshold. As seen in Table 6.2 of the Approach, a 1 μg/dl change in maternal PbB going from 0 to 1 μg/dl was

associated with a 27.4 g reduction in birth weight. A 1 unit change going from 8 to 9 μg/dl produced a 4.7 g decrement.

Zhu et al. noted the supralinear dose

- response without threshold relationship parallel with the

best

- fit dose

- response curve for the Lanphear et al. poo led analysis.

The study population in Zhu et al.'s evaluations in terms of the strength of the concentration

response effect relationship raises some interesting issues in terms of the nature of the measured PbB values. The Zhu et al. subjects represented chronic Pb exposures before and during their pregnancies. Chronic exposure would have resulted in significant increases in body Pb burden, principally indexed as bone Pb accumulations. However, the Pb stable isotope studies of Gulson et al. (see reference s below) show that during pregnancy and lactation, the principal source of Pb in the central, circulating compartment such as PbB, arises from bone Pb releases rather than from exogenous, recent Pb intakes through such media as diet. Gulson et al. also not ed that Ca supplementation or increased dietary Ca during pregnancy can attenuate bone Pb resorption.

These findings would suggest that, in pregnancy, bone Pb is released to PbB and so there is the question of which expression of systemic exposure is appr opriate, PbB from ongoing recent Pb intakes from renovation activities or PbB from bone Pb releases induced by pregnancy. For purposes of this Approach and Sec. 6, what fractions of PbB elevations in pregnant women during renovations partition to renovatio n

- related exposures versus PbB increases from simply the stage of the pregnancy?

References

Gulson BL, Mahaffey KR, Mizon KG et al. 1995. Contribution of tissue lead to bone lead in adult female subjects based on stable isotope methods. J. Lab. Clin. Med . 125: 703

- 712.

Gulson BL, Jameson CW, Mahaffey KR et al. 1997. Pregnancy increases mobilization of lead

from maternal skeleton. J. Lab. Clin. Med. 130: 51

- 62.

Gulson BL Mahaffey KR, Jameson CW et al. 1999. Impact of diet on lead in blood and urine in

fe male adults and relevance to mobilization of lead from bone stores. Environ. Health Perspect.

107: 257

- 263.

Gulson BL, Pounds JG, Mushak P. 1999. See earlier citation.

Gulson BL, Mizon KJ, Korsch MJ et al. 2004. Blood lead changes during pregnancy and postpartum with calcium supplementation. Environ. Health Perspect. 112: 1499

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110 The cumulative amounts of Pb released can be significant during pregnancy and lactation, as noted in Gulson, Pounds, Mushak et al. (1999). The bone resorption rate generall y is highest in the third trimester when the maximum fetal skeletal growth occurs, and with it, fetal body weight. The metric in the form of birthweight would then have an explainable link to skeletal resorbable Pb levels and might be more robust than smal l

- for

- gestational

- age and preterm birth

indices.

The above data in the aggregate suggest that 1) PbB values in pregnancy populations exposed to

renovation

- derived Pb will present with PbBs that are, at most, a combination of recent Pb

intake as from renov ations and from skeletal releases during pregnancy and lactation; 2) bone Pb releases from maternal skeleton will be maximal, i.e., resorption Pb will go maximally to blood, in the third trimester owing to maximal growth spurt; and may be why 3) the Zhu et al. population shows a small

, but statistically significant relationship between birthweight and PbB

, but no relationship for preterm births and small for gestational age births.

Why is the Zhu et al. concentration

- low birthweight relationship supralinea r as dictated by the

best

- fit model showing linearity using the square root of PbB? This observation is akin to one of the findings in the Port Pirie, Australia, prospective study of subclinical lead exposures and their neurotoxic correlates. That is, in a subset of the studies, mothers with the lower PbB had babies more affected in term of the perinatal and postnatal developmental neurotoxic indicators. This observation was rationalized as reflecting fetuses serving as Pb sinks to variable degree, reducing maternal PbB, increasing fetal tissue Pb, and thereby increasing fetotoxic harm.

What are the implications for the Approach? First, we would expect that pregnant women in each of the three trimesters of pregnancy would be randomly distributed among occup ant populations exposed to renovation Pb releases. Maternal bone Pb releases and resulting contributions to PbB would likewise be randomly distributed. PbB remains to reflect bone Pb

, but we can't say with precision what fraction of a given pregnant indivi dual's PbB is renovation

based versus pregnancy

- based.

On the other hand, the Zhu et al. population had a mean PbB of 2.1 μg/dl, while the population studied by Gulson et al. involved immigrants to Australia from Eastern Europe, former Soviet

bloc countri es who had acquired large bone Pb stores from significant industrial pollution

- based exposures from childhood up to emigration from Europe. The latter would be expected to present with proportionately greater inputs to circulating lead from bone Pb "washou t" than would Zhu et al.'s subjects. Consequently, one would expect that ongoing Pb exposures from renovations would more effectively compete with maternal bone demineralization induced by pregnancy in present

- day pregnant women.

Kidney Function and Cardi ovascular Disease Mortality in Older Subjects

The authors focus on two studies for the basis of their Pb concentration

- renal function relationship analyses. The Navas

- Acien et al. study was selected on the basis it represents a study group more contempora neous with renovation

- impacted populations than its companion choice, thereby attenuating somewhat the muddling of the concentration metric by the endogenous

- exogenous question. However, I agree that this was a reasonable choice based onPost

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111 that criterion. Pr oteinuria in Navas

- Acien et al. was not associated with PbB while e

- GFR was, so the latter was appropriately selected as the indicator of reduced kidney function. The design of the study and the use by the authors of the study design resulted in relative r isk of 1.0 assigned to any individual with a renovation

- based PbB of < 0.8 μg/dl. As the authors noted, there was considerable uncertainty in the low end of the relationship as to persistence of significance.

I don't quite follow why there was no use of t he Muntner et al. study. It's true that Navas

- Acien data entailed lower exposures, at least as indexed by corresponding NHANES analysis data. However, one conundrum the authors are wrestling with is that of both reduced renal function

and cardiovascular di sease being chronic and long

- term in their genesis while the authors have to use PbB, a short term biomarker, as the concentration/dose term. Presumably, differential use of both studies would give the authors a handle on what the results look like using t wo sets of reduced kidney function populations differing in their historical body lead burdens.

An operative role for bone Pb stores in the Navas

- Acien groups may be discernible in gender

specific odds ratios seen in Table 6.4, where the significant odds ratio for women was 1.44 (1.22

- 1.69 95% CI), versus men being no different from 1. Significant osteoporotic releases of Pb in postmenopausal women would call into play a significant endogenous Pb release to PbB, a release that perhaps contributes to the ge nder

- specific finding.

As the authors have recognized in the document with regard to the older subject chronic disease states selected, a significant fraction of reduced e

- GFR and CVDM outcomes would involve long

- term Pb accumulation in bone

, but the anal yses are only left with PbB measurements.

As to implications for the renovation

- based Pb exposure

- effect analyses, the PbB measure may not be the best measure compared to Pb

- Bone measurement, but it's what's available.

The authors appropriately assigned, for the CVDM series of analyses, a hazard ratio of 1.0 for individuals where PbB was < 1.0 μg/dl. There are uncertainties in the estimates the authors

derived for the concentration

- CVDM response relationships, such as full applicability of the

Menke et al . subjects and the general population within which renovation

- related elevations of PbB would be expected. However, mainly only one of the three major age band groups taken for analysis would be of concern here for the two chronic effect metrics, those 50

- 80. An 80

- year

old woman in the U.S. would have accumulated high bone Pb burdens

, but would also have had

sustained 30

- 40 years of osteoporotic releases of endogenous Pb to blood. The osteoporotic component could likely be the major fraction of PbB, so tha t while considered a "short term" measure it reflects a comparative historical exposure picture. Again, consider this in light of the

comments above re: women in the Navas

- Acien study having the significant odds ratio for

reduced e

- GFR. However, the issue is more complicated in the case of CVDM when looking at

pre

- vs. post

- menopausal women (See Table 6.6, Approach document).

Rosalind Schoof

I believe that only low birth weight should be considered as a relevant health endpoint for adults

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112  CVDM and reduced kidney function are unlikely to be the most sensitive endpoints for assessing potential risks related to short term exposures associated with P&CB renovation activities,



 Available studies do not adequately support discerning effects of current vs. historical exposures or provide results for populations with blood lead levels as

low as current levels

Other health endpoints should be discussed qualitatively.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The concentration

- response function for each end

- point is clearly explained, and the

consideration of these health effects is warranted based on numerous hazard identification assessments considering high dose animal data and/or occupational exposure data. However,

blood lead concentration measurements for concentrations less than 10  g/dL are not particularly precise, raising questions about the ability of epidemiological studies to discern

subtle increases in health effects at l ow blood lead concentrations. For example, CDC (2007) 1 notes “the ratio of imprecision to measurement value, particularly at BLLs < 10 μg/dL, is relatively high.” Another concern is that no attempt has been made to identify the most sensitive endpoint to better focus the Approach.

Kidney

Because lead was historically formulated in gasoline and lead accumulates differentially with

age , there is a birth year

- cohort effect such that older adults, who tend to have a higher incidence of disease, also coincidentally tend to have a higher blood lead concentration due to the ongoing depuration of lead from deep tissue (bone). The most salient manifestation of a likely birth year (or age)

- cohort effect is in the apparent association of reduced glomerular filtration with blood lead. Glomerular filtration rate decreases with age due to naturally occurring physiological processes. Glassock and Rul e (2012) 2 have reviewed normal aging in the kidney and notably

write:

“The alterations in renal function that accompany aging have been the focus of attention for over a half century. In the classic work of Homer Smith, a decline in standard urea

clearanc e from 100% at the age of 30 years to 55% at the age of 89 years was described. The seminal contribution of Davies and Shock in 1950 cemented the notion of an inexorable decline of GFR with aging.” (p. 272)

“Normal aging is accompanied by progressive nephrosclerosis (glomerulosclerosis, tubular atrophy, arteriosclerosis, and interstitial fibrosis).” (p. 275)Post

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113 “Creatinine based eGFR is imprecise and substantially underestimates GFR. This contributes to the overdiagnosis of CKD in the elderly.” (p. 275)

The Approach states “Lower estimated GFR is an indicator of chronic kidney disease” (p. 63). This statement fails to consider that lower estimated GFR is also an indicator of normal aging, and that on average, knowledge of a person’s sex, race, body surfa ce area (BSA) and GFR may be sufficient to estimate their age within a reasonable bounds.

Coincidentally with natural decreases in GFR with age, cross

- sectional measurements of blood lead exhibit an increase in blood lead with age. The strength of the bir th

- year cohort effect increases with older iterations of the NHANES studies. As such, on average blood lead in cross sectional studies approximately represents a surrogate for age, particularly if sex is known. The association between age (year of birth) a nd blood lead may be difficult to control statistically in

models where blood lead is assumed a priori to be a predictor of the health effect of concern.

Below, in Figure 7.1 , the 2011

- 2012 NHANES blood lead concentrations presented in the Approach are c ompared to the mean and 25 th percentile eGFRs reported in the Nijmegen Biomedical Study (Wetzels et al. 2007) 3 . A line is drawn at 60 mL/min/1.73 m 2 to indicate the reduced GFR threshold used in the Approach. The population consisted of subjects from the

e astern Netherlands and included “ 2823 male and 3274 female Caucasian persons aged 18

– 90

years ” (p. 632). The population consisted “ of apparently healthy subjects [that were] selected by excluding persons with known hypertension, diabetes, cardiovascular

- o r renal diseases. ” (p. 632).

Healthy older adults with no apparent renal impairment are likely to have both reduced eGFR (from aging) and elevated blood lead (presumably from historical leaded gasoline and accumulated background exposures) ( Figure 7.1 ). I f the hypothesis that the relationship between blood lead and reduced eGFR is coincidental, then one would expect the coincidental relationship to be more apparent in women than in men because healthy women, on average, have lower eGFR rates than healthy m en, and thus are more likely to exhibit a eGFR < 60

ml/min/1.73 m 2 at advanced age than men. Examining the odds ratios in Table 6

- 4, this outcome is observed, with the odds ratio for men not being statistically different than 1 and the odds ratio

for women being statistically different and slightly greater than 1. It is important to note that the normal physiological aging of the kidney has been observed in many historical studies, irrespective of lead exposures and the use of lead in gasoline. It is recomm ended the plausibility

of low

- dose effects of lead on the kidney be revisited prior to finalizing the Approach.Post

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114 Figure 7.1: Blood lead versus mean 25 th percentile eGFR for men and women versus age

Low Birth Weight

The key study (Zhu et al. 2010) indicated that pre

- pregnancy BMI was considered in the analysis. However, the methods of the paper do not appear to indicate that BMI was included in the model. The paper states “ various potential confounders were abstracted from the BC files ” (p. 1472) an d “[f]or each outcome, a subset of biologically plausible risk factors in addition to PbB was selected to enter the model as potential confounders; those that remained with a significance level of 0.2 were retained” (p. 1473). The confounders listed inclu de timing of lead test, gestational age, maternal age, race, Hispanic ethnicity, education, smoking, alcohol drinking, drug abuse, in wedlock, financial assistance program participation, parity and infant sex. Given the potential importance of pre

- pregnanc y BMI as a predictor of birth weight, itPost

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115 would appear that a more granular discussion about the statistical treatment of BMI will be necessary to support the outcome of this study. It seems prudent to rule out that a statistical model without lead, but wit h BMI wouldn’t have described the data equally well.

A careful analysis of BMI is important because BMI status may impact blood lead concentration, as well as incidence of low birth weight infants, thus resulting in an apparent

association that is not ca usative. Underweight pre

- pregnancy BMI may be associated with small

for

- gestational

- age infants (e.g. Goetzinger et al. 2012) 4 and increased BMI may be associated with increased absolute GFR (e.g. Wuerzner et al. 2010) 5 . Taken together, increased BMI and

i ncreased GFR may, on average, result in lower background or reference blood lead concentrations in high BMI mothers than in low BMI mothers (due to lower dose [background mg/kg

- day lead intake] and increased lead clearance in high BMI mothers). The implica tion is that the association between increased blood lead (in low BMI mothers due to increased background dose and lower clearance) at blood lead concentrations less than 5 to 10  g/dL may be entirely coincidental with the incidence of low birth weight inf ants. Maternal BMI may be independently mediating lead distribution and elimination kinetics, as well as and newborn BMI (rather than maternal lead less than 5 to 10  g/dL mediating newborn BMI through a dose

response mechanism).

Glomerular hyperfiltrat ion can occur during pregnancy and diabetes mellitus (e.g. Helal et al. 2012) 6 resulting in meaningful increases in glomerular filtration rate. It is difficult to embrace

the concentration

- response relationship (for blood concentrations < 5 to 10  g/dL) pr oposed in the Approach without a more complete understanding of the steps that were taken to understand whether there were differences in blood level that might be attributable to factors like body mass and increased glomerular filtration, resulting in a n on

- causal association with low birth weight infants. It is not beyond the bounds of plausibility that at low environmental background doses, decreased blood lead serves as a biomarker for glomerular hyperfiltration (and diabetes or pre

diabetes in pregnanc y) and increased BMI, rather than as a mediator of low birth weight. It is important to note that gestational diabetes is a risk factor for increased birth weight. A more sophisticated elaboration of the data than presented in Zhu et al. 2010 would be hel pful in confirming or denying this hypothesis. It is noted that the majority (90%) of blood lead

concentrations in Zhu et al. 2010 are 3  g/dL or less with a median of 2  g/dL. At these low concentrations, and if the population lead intake is relatively s table, subtle changes in blood lead

due to hyperfiltration may be detectable in large populations. It could be quite informative to examine the descriptive statistics of pre

- pregnancy BMI versus blood lead, or whether a model that replaces blood lead with BMI performs worse, the same or better. Another informative analysis would be an examination of blood lead as a function of measured GFR in pregnant

females.

A final comment about this end point is that it seems curious that, if the association were indeed

causative that the same concentration

- response function would be applicable irrespective of pre

pregnancy BMI. In light of this concern, as well as questions about how the key study controlled for BMI and absolute GFR, it is difficult to embrace the low

- dose concentration

- response function proposed in the Approach.Post

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116 Cardiovascular Disease Mortality

I did not perform a detailed review of the concentration

- response function for this end

- point. I

note that BMI and diabetes mellitus were included in th e analysis of confounders. My primary concern with this endpoint is that controlling for BMI may not sufficiently control for disease states that tend to affect the clearance and distribution of lead (irrespective of a causal relationship), and there may b e complex interactions between confounders not easily addressed in routine statistical treatments. As an example, the effect of diabetes mellitus on the kidney and glomerular filtration rate likely evolves over the course of the disease, and there are pote ntial relationships between diabetes mellitus, BMI and cardiovascular disease. In large populations, it is plausible that subtle changes in glomerular filtration rate and kidney function with disease

states affect lead clearance and enhanced loss of lead t hrough albumin leakage.

References

1 CDC. 2007. Interpreting and managing blood lead levels <10 μg/dL in children and reducing childhood exposures to lead. http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5608a1.htm

. 2 Glassock, R. J., & Rule, A. D. (2012). The implications of anatomical and functional changes of the aging kidney: with an emphasis on the glomeruli. Kidney international

, 82 (3), 270

- 277.

3 Wetzels, J . F. M., Kiemeney, L. A. L. M., Swinkels, D. W., Willems, H. L., & Den Heijer, M. (2007). Age

- and gender

- specific reference values of estimated GFR in Caucasians: the Nijmegen Biomedical Study. Kidney international, 72(5), 632

- 637.

4 Am J Perinatol. 2012 March; 29(3): 153

– 158. doi:10.1055/s

- 0031

- 1284224.

5 Wuerzner, G., Pruijm, M., Maillard, M., Bovet, P., Renaud, C., Burnier, M., & Bochud, M. (2010). Marked association between obesity and glomerular hyperfiltration: a cross

- sectional study in an African po pulation. American Journal of Kidney Diseases, 56(2), 303

- 312.

6 Nature Reviews Nephrology 8, 293

- 300 (May 2012) | doi:10.1038/nrneph.2012.19

Ian von Lindern

No comments with respect adult health effects at this time.

Kathleen Vork

I defer to the other panel members who have more in

- depth knowledge of these health effects and associated exposures.

Question 7b. Blood lead levels continue to decrease in the US. Please comment on the appropriateness of using concentration

- response functions from studies in which the overall
blood

- lead levels are relatively higher than contemporary levels in the US adult population, particularly in light of inflection points in the concentration

- response functions for certain health effects (e.g. CVDM) below or abo ve which the data do not demonstrate a positive relationship between lead exposure and adverse health impacts.

Deborah Cory

- Slechta

The fact that blood lead levels being used are higher than contemporary blood leads could be viewed as imparting a kind of protection against uncertainties. In support of the development ofPost

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117 the CVD model, it is accurate to state that it is not clear whether any threshold exists for these effects, data is simply not available, and if the IQ trajectory and case is analogous, s tudies at increasingly lower blood lead levels continued not only to reveal decrements in population IQ, but in fact larger estimates at lower than at higher blood lead levels. In this reviewer’s judgment, the issues raised in the CVDM study relative to th e main conceptual models for this endpoint were quite valid and argue that really the only option, given the absence of requisite data, is to use model 1. As noted in response to 7d below, the notion of a dynamic lag factor could be

useful in this context to improve the model, although there will be assumptions that would also

have to be made to add such a factor as well.

Panos Georgopoulos

It appears that Question 7b itself summarizes some of the critical concerns associated with extrapolating and interp reting information beyond the ranges of existing data sets. These

concerns should be combined with the fact that estimated blood levels from short term exposures from renovations of P&CBs are highly uncertain.

Philip Goodrum

It is extremely important to understand how the risk metric that is defined as a threshold for regulatory action can be related to each of these concentration

- response functions. Adjustments for downward trends in the US population are very important to consider.

Andrew Hunt

No co mments provided for this charge question

.

Naila Khalil

Based on the following points it may be reasonable to assume that there is no safe level at which

lead does not have an effect:

 Low levels of lead in the blood (below 10 μg/dl) have been associated with a range of adverse health effects in humans. US National Toxicology Program published a monograph in 2012 which summarized evidence. This monograph stated that “there

is sufficient evidence of an association in children, including delayed puberty, reduced postnatal growth and adverse neurological effects (including decreased IQ and hearing), and in adults, including reduced fetal growth in pregnant women and

adverse effects on the ca rdiovascular system. Even at blood lead levels of less than 5 μg/dl there is sufficient evidence of decreased cognitive performance and increased diagnosis of attention deficit hyperactivity disorder (ADHD) and problem behaviors in children, and effects on the kidneys in adults

. ”

U

. S

. National Toxicology Program. U

. S

. Department of Health and Human Services. NTP monograph on health effects of low

- level lead. Final, dated 13 June 2012. http://ntp.niehs.nih.gov/ntp/ohat/lead/final/monographhealtheffectslowlevellead_new issn_508.pdf

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118  These findings add to the body of evidence that have steered the Centers for Disease Control and Prevention (CDC) in 20 12 to advise that children with more than 5 μg/dl blood lead are to be considered at risk and that public health actions should be

initiated.

 As justified in the Approach , a significant proportion of referenced study participants had lower blood than 5 μ g/dl, hence the applicability of the C

- R function derived from those studies is valid : “The median blood lead in the Lanphear et al. (2005) pooled data set was 9.7 μg/dL, significantly higher than current background blood lead levels among U.S. children . The current background values are, according to NHANES, on the order of 1 μg/dL. Despite the relatively high median value, the Approach used equations based on the Lanphear et al. (2005) pooled analysis

because a sufficient number of children had blood l eads in the range 1−5 g/dL to support the model fit in this range”.

 Even though the current mean blood lead levels have decreased, adults and older adults who were historically exposed to higher environmental lead prior to 1980s have accumulated a higher body lead burden in skeleton. This lead is released into circulation with age associated demineralization and osteoporosis. This endogenous exposure of re

- circulating lead predisposes to adverse health effects, especially CVD, CKD, and cognitive effects. I n the most recent NHANES 2011

- 2012 mean US blood lead level was 0.973 μg/dL. However, those aged 20 or older had the highest levels of 1.09 μg/dL

Centers for Disease Control and Prevention. Fourth Report on Human Exposure to
E nvironmental Chemicals, Updat ed Tables, (August, 2014). Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and

Prevention. Page 181 http://www.cdc.gov/exposurereport/

.

 One of the limitations of epi demiological studies that reported non

- significant adverse health association with lower blood lead level included not having a large enough sample size that has these lower blood lead levels. To cite an example that given large enough sample size in lower blood lead level range , epidemiological studies can find association between current low blood lead levels and previously reported adverse health outcome at higher blood lead levels. Bone Mineral Density (BMD) decrements were observed at mean blood lead levels as low as 5.3 μg/dL in a nationally representative group of older women in 1990

- 1991(Khalil N et al., 2008). The reviewer replicated this study on older men in 2007

- 2008 and reported significant BMD decrements at mean blood lead levels of 2.2 μg/dL (Khalil N et al., 2014). This finding was significant considering that men have lower rate of osteoporosis compared to women.

Khalil N, Cauley JA, Wilson JW, Talbott EO, Morrow L, Hochberg MC, Hillier TA, Muldoon SB, Cummings SR: Relationship of blood le ad levels to incident nonspine fractures and falls in older women: the study of osteoporotic fractures. J Bone Miner

Res 2008, 23: 1417

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119 Khalil N, Faulkner KA, Greenspan SL, Cauley JA, for The Osteoporotic Fractures in Men Research Group Associations Between Bone Mineral Density, Grip Strength, and Lead Body Burden in Older Men. J. Am Geriatr Soc .2014; 62(1):141

– 146.

PMCID: PMC4055501.

 EPA risk assessment guidelines suggest that when complete data is not available for full range of human exposure, projections or extrapolations can be made based on the existing epidemiological data.

 Health effects associated with exposure to lead occur at blood levels so low as to be essentially without a threshold. Please see the following excerpt from Gilbert et a l. (2006) below:

“The definition of “low level” lead exposure has been revised progressively downward as our tools and study designs for evaluating neurodevelopment have evolved. Hints of health effects and intellectual impairment in children with BLLs

b elow 10 μg/dL had already emerged by 1991, when CDC established 10 μg/dL as a level of concern (Fulton et al., 1987; Needleman and Bellinger, 1991). Now, not only is there overwhelming evidence of effects at low levels, but it is increasingly apparent that the rate of decline in intellectual impairment is greater at BLLs below 10 μg/dL than above (Canfield et al., 2003). Overall, every 1 μg/dL increase in blood lead results in a decrease of 0.87 IQ points. For BLLs below 10 μg/dL, a 1 μg/dL

increase results in a 1.37 IQ decrease (Canfield et al., 2003). Such a fall in average IQ is consistent with several meta

- analyses and reviews of childhood lead studies (Fulton et al., 1987; Lanphear et al., 2005; Needleman, 1990; Pocock et al., 1994; Schwartz, 1994). Sev eral independent investigators also have concluded that BLLs below 10 μg/dL are harmful (Chiodo et al., 2004; Fulton et al., 1987; Landrigan, 2000; Schwartz, 1994; Schwartz and Otto, 1991; Selevan et al., 2003; Walkowiak et al., 1998; Wasserman et al., 200 0). These studies, as well as the meta

- analyses confirm that a threshold for the adverse health effects of lead exposure cannot be calculated. Appendix A lists multiple statements supporting the absence of a threshold for the health effects of lead exposur e.

Policy questions arising from this conclusion seem to have frozen Federal initiatives. The Agency for Toxic Substances and Disease Registry (ATSDR) has refused to set a minimum risk level (MRL) and the Environmental Protection Agency has refused

to est ablish a reference dose (RfD) because some of the “health effects associated with exposure to lead occur at blood levels so low as to be essentially without a threshold” (IRIS, 2004). The Centers for Disease Control and Prevention (CDC)

states: “Because no threshold for adverse health effects in young children has been demonstrated, public health interventions should focus on eliminating all lead exposures in children. Lead concentrations in drinking water should be below the EPA action level of 15 ppb” (MM WR, 2004).”

Gilbert SG, Weiss B. A rationale for lowering the blood lead action level from 10 to 2 μg/dL. Neurotoxicology. 2006; 27:693

– 701. [PMC free article] [PubMed] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2212280/

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120  The premise of Precautionary Principle ( http://www.sehn.org/wing.html ) states that it is a public health responsibility to protect the population from expo sure to harm, when scientific investigation has found a plausible risk. Based on this principle, lead is a toxic exposure and is not safe at any level therefore exposure should be prevented.

 Blood lead is considered as endocrine disruptor (ED) (UNEP/WHO 2 012, WHO 2012). ED can act at very low doses and that low

- dose effects may disappear at higher doses, giving an illusion of safety if chemicals are not tested at low

- enough doses. Hormones work in the body in the part

- per

- billion or part

- per

- trillion level (about a teaspoon in an Olympic

- sized swimming pool) (Ahearn A 2012).

UNEP/WHO (2012). State of the Science of Endocrine Disrupting Chemicals

- 2012. Geneva, Switzerland, United Nations Environment Programme / World Health Organization

.

WHO. Endocrine disrupters and child health. Possible developmental early effects of endocrine disrupters on child health, 2012.

Ahearn A. Low

- dose effects of endocrine disruptors, with Laura Vandenberg. Environ Health Perspect 120 (2012); http://dx.doi.org/10.1289/ehp.trp060112 [Online 1 June
2012].

As recent NHANES data indicate the geometric mean blood lead levels are approximately 1ug/dl which would provide a large sample size if th e referenced epidemiological studies would

re

- analyze association of adverse health effects with current lower blood lead levels. As mentioned in the Appendix P (P

- 4) “Crump et al. also experimented with different cut points (i.e., the blood lead at which the slope of the IQ blood lead relationship changes) and blood lead metrics in an attempt to identify the lowest blood lead concentrations at which slope coefficients retained significance. They found a generally consistent pattern of most slope coefficie nts remaining negative down to the lowest cutoff point analyzed (2μg/dL.) However, the

coefficients for blood lead cutoffs less than 5

- 7 μg/dL lost statistical significance, consistent with the decreasing numbers of subjects involved. They noted, however, after fitting Piece wise models to the various blood lead metrics, that the IQ loss could be “adequately modeled at PbB levels less than 10 ug/dL. In addition, they concluded that the evidence support the existence of exposure

- response relationship for IQ loss at peak PbB values as low as 7 ug/dL, and for concurrent PbB at 5 ug/dL. As noted above, however, these latter conclusions were based on linear regressions incorporating only small proportions of the data, and thus had less power to detect significant relationships.”

Andy Menke

The Approach does not seem to be using data outside of the range included in the previous studies. The concern is that the mean level is decreasing such that a higher proportion of people are in the lowest range of blood lead included in those previous studies. This is only a concern if there is a true inflection point in the dose

- response curve. There is not strong evidence to suggest a true inflection point in those curves based on the published papers. There are two points t o keep in mind. First, there were relatively few people in the extreme ends of the lead distributionsPost

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121 in those papers, and any results focusing on the extreme ends of the association were based on very small sample sizes subject to random variability. Seco nd, the cardiovascular mortality outcome paper was based on NHANES III data which lab methods that were problematic at lower levels of blood lead. For example, quality control samples had a coefficient of variation of

30

- 33% for pools with a mean lead conc entration of 1.2

- 1.5 ug/dL (see lab manual on NHANES III website). Therefore, I would be hesitant to say this was likely a true inflection point in the

lead

- CVD mortality association. I believe the concentration

- response functions used in the Approach are the best estimates of the true associations, and it is appropriate to apply them throughout the range of blood lead investigated in the corresponding source paper. For the CVD mortality association, the best way to further investigate the possibility of a nonlinear association at the lower end of the lead distribution is to update this analysis using more recent

NHANES data. The 1999

- 2004 NHANES now has mortality data available through 2011. These participants have a much lower mean blood lead level at base line and a lower peak lead earlier in their lifetime compared to NHANES III participants.

It is likely the Approach is too conservative in terms of the range of lead exposure used in estimating chronic kidney disease risk. The slope of the regression in t he Navas

- Acien paper was based on the entire range of lead ≥0.25 μg/dL. If you believe that the association was null between 0.25 and 0.8, then you would also have to believe that the regression slope in the paper when modeling lead continuously was attenu ated due to including that “null” range of data and that an equivalent analysis excluding people <0.8 μg/dL would result in a steeper slope of the

lead

- CKD association. However, the quartile analysis in the Navas

- Acien paper did not suggest an inflection p oint anywhere in the distribution of blood lead. If you assume that the true association is continuous throughout the distribution of lead, then you are underestimating the health risks by not taking into account the association of lead between 0.25 and 0. 8 μg/dL and

CKD.

Paul Musha k

I believe, on toxicokinetic and toxicological mechanistic grounds that it was appropriate to use somewhat older studies even though there is the trend to lower overall Pb exposures.

First, the Agency has to meet the court of jurisdiction's deadline for being out the door with the Approach and the rest of the rulemaking next year. The document is not on hold nor are the upper age bands of US adult populations with reduced kidney function or CVDM on hold. The upper age bands of the current US population have high body lead burdens, however well reflected in circulating PbB. With aging, both men and women begin losing bone Pb primarily through demineralization processes. We know this from a number of studies looking at age

strati fied measures of body Pb, adjusted for the factor of declining exposures. The lost Pb shows up in blood increasingly as an increased endogenous component. For the foreseeable future, studies that partake of historically higher Pb exposures than are now occ urring are still highly

relevant. A 70

- year

- old woman, born in 1944, has lived her early childhood, older childhood, young adulthood and middle age, and even onset of menopause and menopause before the phaseout of Pb in paints (February 1978), in gasoline (phasedown, begun 1986; phaseout, 1995), in canned milk (1979) and other lead

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122 Rosalind Schoof

Consistent with my response to Question 7a, only low birth weight should be considered as a relevant health endpo int for adults in this analysis. One reason is because these studies provide

data at lower blood lead levels.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

The use of concentration

- response functions with relatively higher than contemporary concentrations is not necessarily a concern

. Age affects both blood lead concentration (due to historical lead exposure), and also typically affects the likelihood of disease, with increased incidence with age. It is unclear how successfu lly statistically methods may be used to deconvolute the age

- cohort effect from the increased probability of disease given age, however, increased and explicit attention to these relationships may strengthen the reliability of low

- dose

assessments in the f

uture. These factors may be affecting the inflection points observed in the “apparent” dose response curves.

I recommend that two points be considered for inclusion in the uncertainty section:

 Discussion regarding the age

- cohort effect where age is cor related with both increased likelihood of disease (irrespective of lead exposure), as well as, increased blood lead; and

 Discussion of disease states that likely affect retention and elimination of lead ; ( irrespective of causal relationships). For example, glomerular filtration rate decreases naturally with age, coincident with blood in lead increasing with age due to the cohort

effect.

Ian von Lindern

No comments with respect adult health effects at this time.

Kathleen Vork

Methods for establ ishing a point of departure or other benchmark along with the use of uncertainty factors are available, peer reviewed and generally accepted for estimating hazard/risk in multiple government programs. There are many examples where risk estimates require t he use of concentration response functions from exposures, species, age groups and other attributes of the study population that may not represent the candidate population.

Regarding inflection points that do not demonstrate a positive relationship betwee n lead exposure and adverse health impacts, a weight of evidence assessment or meta

- analysis may help to identify of inflection points represent a biological threshold or lack of statistical power and/or due to confounding from unmeasured influences.Post

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123 Que stion 7c. Concentration

- response functions based on blood lead are more prevalent in the health impacts literature, while studies using bone lead are not available for all health endpoints. Please comment on the use of blood lead and bone lead as biometric s for projecting incremental health effects due to renovation activities for P&CBs.

Deborah Cory

- Slechta

While bone lead values would likely be preferable, particularly for health endpoints in adults, they simply are not available and unlikely to be available in any reasonable period of time. As noted in the CVDM, corrections may be coming to the Weisskopf studies, but that study will still have inherent limitations of potential sample bias as it is based on the Normative Aging Study. It is also state d that the National Center for Health Statistics is doing such a study, but there is no indication of when that would be available, and, it too may have significant

limitations.

Panos Georgopoulos

The selection of blood lead appears to be more appropriat e than bone lead for characterizing changes associated with “transient” exposures due to renovation activities of P&CBs, and should be the more relevant metric for projecting health effects.

Philip Goodrum

It is this reviewer’s opinion that, in this context, PbB is a more practical biometric than bone Pb for several reasons. For a biometric to be a useful tool for risk management, it needs to be a reliable measure of exposure and effects for the scenario under investigation. Bone lead may be

a usefu l indicator of lifetime exposures, however, without a diary or basis for reconstructing the historic data on activity patterns in actual populations, it is unclear how to verify the reliability of predicted bone lead distributions as a metric of exposure. Epidemiological studies can be used to quantify the relationship between bone Pb and health effects, however, how strong is that relationship? What sample sizes are available to control for effects of confounding

variables?

Andrew Hunt

No comments prov ided for this charge question

.

Naila Khalil

Blood lead is thought to reflect exposure received in the past few weeks to months, but it is in reality a composite index that represents the equilibrium between ongoing external exposures, excretion, and end ogenous release from the skeleton. Blood lead and bone lead are highly correlated and both measures reflect the lead body burden.

Korrick SA, Schwartz J, Tsaih SW, Hunter DJ, Aro A, Rosner B, et al. Correlates of bone and blood lead levels among middle

- aged and elderly women. Am J Epidemiol. 2002; 156(4):335

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124 In adults, bone and teeth store 90

- 95% of the total body burden of lead, whereas in children,

bone contains only 70% of body lead. The body eliminates half of the lead in blood stream (half

life) in approximately one month, while bone is a more stable repository for lead and, therefore, bone lead levels reflect long term exposure to lead assimilated ove r years to decades. The half

life of lead in bone ranges from 10 to 30 years, depending on the rate of bone turnover, type of bone, sex, and age. In young children, due to growth and bone remodeling, bone lead is exchanged with blood lead more rapidly than in adults.

Since blood lead is the most commonly used measure of exposure, research studies generally utilized blood lead levels to report association with human health outcomes. However, measurement of blood lead cross sectionally at one time does not a llow comprehensive evaluation of lead body burden or distinguish between current or cumulative lead exposure. In contrast bone lead reflects cumulative stores of lead in the body better than does blood lead. However very few epidemiological studies report bone lead measures. Measuring bone lead is expensive, requires specialized equipment and requires study participants to travel to the location of the equipment (K

- x

- ray fluorescence); thus, fewer lead data are available for bone

than for blood.

The Approa ch document has utilized blood lead for model projections using US general population blood lead data available through NHANES from 1976 onwards. Such data is not available for bone lead measures. Based on NHANES data availability, C

- R function derivation studies (that all used blood lead, except one) blood lead measure is easier to interpret biometric for projecting incremental health effects due to renovation activities for P&CBs.

National Toxicology Program. 2012. NTP Monograph: Health Effects of Low

- L evel Lead. http://ntp.niehs.nih.gov/ntp/ohat/lead/final/monographhealtheffectslowlevellead_newissn_508.p

df

.

Andy Menke

Due to a large relevant exposure window in disease development in adults, it may be useful to use of bone lead in estimating health effects, particularly renal disease and CVD mortality. However, the limited number of studies investigating the association between bone lead and

h ealth outcomes is problematic, as the estimation of health effects should follow measures used in published papers as closely as possible. Assuming the models correctly predict lifetime average blood lead, that may be just as useful or possibly more useful in estimating chronic health effects. Blood lead may better represent lead in the body capable of affecting health risk, whereas bone lead itself may not actively affect health risk (other than being correlated with or altering blood lead levels).

Paul M ushak

EPA is correct as to these exposure biomarkers and their use. However, this is not akin to looking for lost keys under the street lamp because that’s where there’s light.

PbB as the exposure biomarker for IQ decrements is the best choice because th e measure represents the best indicator of body Pb burden in young children having Pb

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125 Young children’s bone Pb is also very mobile with limited Pb accumulation and rapid blood

bone Pb exchange.

In adults, Pb

- bone accumulates with age

, but there is an equilibrium and a modest correlation with PbB. Older subjects with bone mineral loss have increased transfers of Pb from bone to blood. In pregnancy, of course, Pb movement from bone to blood is also significant.

Rosalind Schoof

I don’t bel ieve bone lead data can be applied effectively in the dose response analysis. Incremental bone lead concentration predictions can help inform assessment of potential for longer term impacts, but may not be a critical element to include in this analysis.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

As mentioned in 7a, and 7b, given the relatively low level of precision of blood lead

concentration measurements less than 10  g/g/dL, it is unclear whether blood lead in this range is a sufficiently robust metric to elucidate dose

- response relationships.

One of the main shortcomings of the approach is that a brief review of lead distribution

mechanisms and a brief lead hazard i dentification has not been presented to better inform and focus choices about the appropriate disease biomarkers. Consideration of mechanism is particularly important given the relatively limited data on low

- dose acute as opposed to chronic

health effects in humans. Perhaps most significantly, there are likely to be important and meaningful differences in the speciation of lead in blood for acute scenarios with declining exposure potential, as compared to chronic exposure scenarios with constant intake. The se differences are related to the kinetic rates of transfer of lead to and from RBC, albumin and

small

- molecular weight proteins. Therefore, two identical measurements of lead in whole blood might reflect very different “available lead”. The U.S. EPA/ERG ( 2004) RAF ‘Issue paper on
the human health effects of metals’ states 1 :

“Lead is bound within different body compartments and may be judged to “accumulate” in one or more of them, but the most toxicologically relevant systemic lead is that within

the rela tively labile plasma fraction . “Free lead” in blood plasma is amenable to either rapid excretion for transfer to soft tissues.”

Reflecting on the findings in the Approach and lead speciation in blood, as a reviewer, I cannot help but be drawn to the find ings presented in Section 8.2.3 (marked as p. 3).

“ Overall, the bone lead changes for most adults was small, less than 0.04  g/g for
cortical bone lead, and 0.5  g/g for trabecular bone at the mean and 95 th percentile.”Post

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126 T aking bone lead as a proxy for AUC labile, or free lead, it is questionable whether a meaningful dose, clinically discernible from background variation, has been delivered to tissue as a result of the vast majority of the Scenarios in the Approach. As such , more attention on the speciation of lead in blood for the scenarios considered may be warranted. It is possible that the

case could be made to screen out scenarios based on a de minimus change in bone lead (as defined by incremental increase as compared to background variation in bone lead concentrations by age).

References

1 Goyer, R. 2004. Issue Paper on the Human Health Effects of Metals.

h ttp://www.epa.gov/raf/public ations/pdfs/HUMANHEALTHEFFECTS81904.PDF

.

Ian von Lindern

No comments with respect adult health effects at this time.

Kathleen Vork

Changes in blood lead is a more sensitive measure of exposure than changes in bone lead for

timeframes of weeks to months. Measures of elevated bone lead may be useful in identifying a sensitive subpopulation when estimating hazards due to recent P&CB activities.

Question 7d. In this Approach, EPA estimated adult health impacts based on concurrent and lifetime averag e blood lead concentrations. Please comment on the appropriate time horizon over which to estimate adult health outcomes (Section 7.3.1) and whether this differs across health endpoints.

Deborah Cory

- Slechta

This seems to be a key question to the Approac h and one for which data are not available for answers, and may differ for different health outcomes. Typical thinking is that deficits in kidney function and cardiovascular disease represent the consequences of many years of exposure, i.e., tend to repres ent chronic conditions rather than acute effects. This also makes it difficult to gauge the time horizon over which to gauge the contribution of renovation and repair

- related lead release to adult health outcomes. It raises the question as to whether short er term elevations (post

- renovation) can contribute to an overall effect (cumulative as in an allostatic load type of thinking) and ultimately accelerate these health outcomes in terms of time to onset. I’m not really aware that such an approach has actual ly been used, and the CVDM study raises the same issue or potential study of incorporation of a dynamic lag. Uncertainty in the latter would likely involve the fact that other risk factors for CVDM are also increasing, and thus a smaller

increment in lead exposure/duration of exposure could lead to a greater effect than if no other risk factors were operative. The only study in this reviewer’s recollection that might provide some useful information because it incorporated a longitudinal (albeit relatively s hort) approach was for cognitive function in aging.Post

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127 Panos Georgopoulos

It does not appear that there is sufficient information for defining appropriate “time horizons” for health endpoints other than low birth weight.

Philip Goodrum

Use of the two averaging times is a reasonable compromise. I’m not aware of any systematic evaluation of toxicological study data to examine how relationships between exposure

- response may change as a function of the duration and magnitude of incremental changes in lead

exposure.

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

To calculate adult health outcomes lifetime average blood lead concentrations will provide an aggregate exposure. This measure is defined in the Approach as the average from birth until the
post

- renovation period. For adults, published literature does not provide a definitive period of susceptibility to adverse effects of lead on human health therefore lifetime average blood lead concentrations would be a good approximat ion or surrogate measure.

Andy Menke

I’m not aware of published research that can conclusively answer this question, but I believe it will likely depend on the outcome. For renal disease and CVD mortality, lifetime average blood

lead makes a lot more sen se because previously published papers were not based on short

- term exposures or short

- term increases in blood lead. They were based on blood lead measured (for most people) in a relatively steady state. We conduct studies of blood lead and health outcomes assuming the blood lead levels (due to exogenous and endogenous exposures) are stable for many years in the sense that a given person’s ranking in the distribution of blood lead in the study population is relatively consistent for many years before and (i n cohort studies) after the measurement of blood lead. One could suggest that peak blood lead, which is best quantified in concurrent lead concentrations, may be more relevant, but I’m not aware of any research supporting that idea; also occupational studi es do not support the idea that short

- term low

- to

moderate exposure (e.g., <1 year) conveys substantial risk of kidney disease or CVD mortality.

Low birth weight is an entirely different situation where the relevant exposure period for a given

woman is li kely limited to when she is pregnant

– at most 9 months and maybe even a smaller window of time. In this case, lifetime blood lead does not seem as appropriate as concurrent blood lead because it would not include the peak around the initial exposure. The effect of the

renovation

- related exposure will vary depending on where the woman is on the exposure curve when she happens to be pregnant. However, it should be noted that a renovation may cause low birth weight after even after the mother’s blood lead has returned to baseline due to renovation

related increased bone lead and subsequent pregnancy

- induced release of bone lead into thePost

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128 blood stream. Another way to think about it is that women who are exposed to renovation

related lead will have a bimodal peak in blood lead levels if they get pregnant: one peak around the height of exposure similar to everyone else and a second peak when they are pregnant which could be decades later (possibly more peaks if a woman has multiple pregnancies after the

renovation exposure). None of the Leggett outputs seems ideal and I’m not sure which would most appropriately cover both the initial high peak in blood lead and the milder but longer term

risk due to increased bone lead.

Paul Mushak

For pregnant women, the time hor izon is the time of pregnancy, as the authors state. For older adults with cardiovascular and chronic kidney disease (CKD), the time line is longer

, but how long may require further digging. The authors can examine the literature for age

- stratified

inciden ce and prevalence of CVDM and CKD and ages at onset of either disease at a significant frequency.

Rosalind Schoof

For the low birth weight outcome, only concurrent blood lead concentrations should be used. Other health endpoints should not be used for adults. For concurrent exposures I agree with the recommendation to include both the highest one month and three month averages, recognizing that no biological basis was identified to support one or the other.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

As mentioned above, the use of concurrent blood lead lacks a consideration or discussion of how speciation of lead in blood might be different for chronic as compared to acute exposures. Furthermore, the lifetime average m etric seems somewhat confusing to me because it averages variable amounts of “zero” exposure based on the age of the exposed person.

The U.S. EPA OSWER Guidance “Assessing Intermittent or Variable Exposures at Lead Sites” presents a methodology “for the assessment of lead risks when exposures are not continuous.” Exposure at a daycare is given as an example. Some of the recommendations are specific to the limitations of the IEUBK model, however, the recommendation to use a minimum averaging

time of 3 mont hs as a minimum exposure duration seems prudent. The guidance states: “ From a public health perspective, it would be reasonable to consider the possibility of adverse health effects from acutely elevated PbB concentrations that could occur over a period of a few

months. ”

A maximum 1

- to 3

- month rolling average blood concentration is recommended for this

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129 Reference

1 U.S. EPA. 2003. Assessing Intermittent or Variable Exposures at Lead Sites. OSWER #

9285.7

- 76

. http://www.epa.gov/superfund/lead/products/twa

- final

- nov2003.pdf

.

Ian von Lindern

No comments with respect adult health effects at this time.

Kathleen Vork

I defer to the other panel members who have more in

- depth knowledge of this material.

Issue 8. Overall Draft Approach

EPA has developed an Approach document for estimating incremental health effects resulting from exposure to lead from renovation, repair, and painting activities in public and commercial buildings (P&CB). The approach is intended to provide an overview of the data sources and models used to estimate environmental media concentrations and exposures, blood lead levels, and incremental health effects.

Question 8. Please comment on the overall Approach and its utility for estimating incremental health effects due to renovation activities on the exteriors and interiors of P&CBs. Please comment on the clarity and transparency of the document.

Deborah Cor

y
- Slechta

As noted above, the document is extremely clearly written and for the most part, rationales are routinely provided which explain the Approach in a very comprehensible manner. The additional details are provided in Appendix material. The formatti ng used also significantly facilitates readability, as do the figures and tables used; the formatting represents a significant (and much improved) departure from the review documents that this reviewer has seen over the

years.

The utility of the Approach for estimating incremental health effects seems quite strong, even with the multiplicity of assumptions that this integrated modeling involves, its ability to predict blood leads is improved. Its ability to predict health effects from renovations remains an empirical question of course.

Panos Georgopoulos

As mentioned in the “General/Overall Impression” comments, the modeling Approach under review follows a generally appropriate and valid probabilistic/distributional and multistep Monte

Carlo framework. The work completed can be particularly useful in providing various “tools” (customized parameterizations of physical and biological models, regression relationships fitting available data, etc.) to support design of experiments for much needed targeted fie ld and laboratory studies and for identifying data gaps and prioritizing questions for further analysis. In order to refine and properly utilize the above mentioned “tools

, ” as well as taking into accountPost

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130 the limitations in currently available data. These limitations exist, and need to be properly
identified and characterized across the entire “source

- to

- dose

- to

- effect” sequence of physicochemical and biological processes and for all major exposure pathways and eventually all major adverse outcome pathways of concern. The authors of the Report under review seem to be sufficiently aware of the limitations and challenges associated with the Approach they implemented. For example:

 In the beginning of the discussion in Section 9 it is stated that “[I]n general, quantifying the uncertainty or predicting whether the uncertainty represents a low or high bias in the simulated health effects is not possible.”

 In Section 9.1.1 it is stated that “[T]he Approach estimates the fraction of lead emitted during a renovatio n activity [.

. .]. The values were similar to the single available emission fraction in the literature. Hence, the emission fractions represent an uncertainty in the Approach results. The effect of this uncertainty (to either lower or raise incremental

bloo d lead changes) cannot be quantitatively assessed due to lack of data on actual
emission fractions from renovation.”

 In section 9.2.1 it is stated that “AERMOD was selected as the most appropriate model for the Approach, but it has not been fully validate d for receptors very close to the release site and might introduce significant uncertainty to air concentrations predicted at
these distances.”

 In Section 9.2.5 it is stated that “[T]he Approach does not account for correlations between many of the input variables. ... By not allowing for correlations, however, both the means and the 95th percentiles could be biased either high or low.”

 In Section 9.2.6 it is stated that “[B]ecause of the intricacy of the Approach and the complicated nature of the overall exposure assessment objective, how closely the modeled results are related to true population distribution is unknown.”

In other cases, statements within the documents under review appear to be, at a certain extent, contradictory. For example, in section 5 it is stated that:

 “[P]revious comparisons using the older Leggett model have shown that, under chronic exposure conditions, the blood lead estimates obtained with the Leggett model are approximately two to three times higher than those obtained with the IEUBK model [..]. These results suggested that short

‐ term exposures in the Leggett model might be overestimated, and that lead uptake levels to give selected blood lead levels might be underestimated. These issues are no longer the case with the update d Leggett model, as shown in Appendix M.”

However, in direct contrast to the above assertion, in Appendix M (page M

- 14) it is clearly

stated that:

 “Version 5 of the Leggett model agrees well with the NHANES data. It should be noted,Post

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131 however, that this ca libration only applies to chronic exposures; no data were available to perform analysis on shorter

- term exposures such as those experienced during a
renovation event.”

The fact is that many of the “process modeling” tools (from atmospheric dispersion proc esses to microenvironmental exposures and biologically

- based toxicokinetics) used for the analysis are rather “crude” but, at the same time, there are no studies available that could be used to either refine/customize these models or to support the applica tion and/or the development of more sophisticated mechanistic models. Nevertheless, “benchmarking” the models used in the approach versus more detailed models (i.e. performing comparative simulations for a small number of selected hypothetical but well

- de fined scenarios, using on one hand modeling tools from the Approach under review and on the other hand corresponding detailed/mechanistic models) could provide useful insights on “model behavior” and, probably on the conditions under which the simpler mod els would be expected to provide results comparable to those from

the detailed models.

Finally, some notes regarding transparency and clarity of presentation:

 Though it should be recognized that presenting in a coherent fashion multistep

distributional analyses and results covering the entire “source

- to

- dose

- to

- effect” sequence, is indeed a major challenge, the Report under review could be made substantially easier to follow by more thorough editing, by providing additional summary information in tables and text boxes, and by adhering to basic typographical and notation standards. Currently there are many typos, incorrect page

- numbering (the Report seems to revert to “random” page numbering after page 103), inconsistencies in

units and in mathematical no tation, etc.

o The Report allocates critical material to the Appendices. The problem is that in many cases it is impossible to understand a procedure summarized in the Report without actually consulting the corresponding Appendix. It would be really useful if all essential information was summarized in the main Report (including lists and definitions of all variables, parameters, assumptions, etc.) and references to the Appendices were pointing to the exact page, table, etc. where relevant

information could be found. ( In an “ideal world” these references should actually be active links in the PDF files, and clicking on the link should bring up the corresponding referenced item ).

o Though not strictly incorrect, many equations utilize a variety of units, often from different systems (SI and English): for example areas may appear in square feet, square meters, and square centimeters, requiring the use of unit conversion factors (and i ncreasing the probability for mistakes in calculations). The same unit may appear in different formats, even on the same page (e.g., ug and μ g
, ft 2
and ft2, etc.).

o Mathematical notation in the Report and the Appendices is quite arbitrary. Though the Repo rt and the Appendices are not

- strictly “formal” mathematical documents, they would be more readable if they followed standard practice andPost

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132 conventions (as one example, the symbol “Adj” traditionally refers to the adjoint of a mathematical operator and he re it is used to denote an adjustment factor; as another example, taking logarithms of dimensional quantities should specify appropriate normalizations, as a logarithm can only be formally defined for a dimensionless argument, etc.). It is strongly recomme nded that mathematical notation similar to that typically used in standard simulation

- based analyses (see, e.g., Fu, 2014) is employed, to the extent possible, in future revisions of the documents under review. Furthermore, ideally, t he notation/font ( e.g. roman versus italic; ser i f versus sans serif) used for variables in the text should be

consistent with that used in the equations where the same variables appear.

 In multiple cases, approximate empirical factors, calculated from very limited data sets, are presented with three or four significant digits (see, e.g. Table 4

- 4 on page 52, where control option efficiencies have values such as 0.943, 0.956, etc.). Simulated estimates (produced from very uncertain/approximate inputs) are presented with even hi gher “presumed precision”

– for example in Table N

- 7 on page N

- 13 we see that the estimated intake rate in 2014 for a person born in 1987 is 2.027378 μ g/day. This practice should be avoided, as it may create unwarranted impressions of accuracy that are not warranted by the information actually available.

 Properly annotated visualization of this multi

- component, multi

- step approach, through detailed influence diagrams and flow information diagrams, that specifically identify different types of inputs (with characterization of associated variabilities and uncertainties), types of outcomes, assumptions, decision points, etc. is necessary in order for the proposed approach to establish a transparent and useful framework appropriate for supporting both scientif ic studies and risk

- relevant regulatory analyses. It is strongly

recommended to use standardized notation for all flowcharts that document information

processing procedures, e.g. according to the ISO 5807

- 198 standard (Information Processing

- Documentatio n Symbols and Conventions for Data, Program and System Flowcharts, Program Network Charts, and System Resources Charts, ISO 5807

- 198).

 A detailed information flow (see, e.g., Foster, 2014) for the modeling Approach needs to be presented, that would includ e variable/parameter selections (fixed values and distributions), assumptions, and decision points across each of the steps necessary to implement the Monte Carlo simulations, from Pb

- dust emissions to the population

biomarker levels and associated health effects.

 Finally, it should be emphasized that, when using a multi component modeling system, as in the present where a cascade or "network" of models exchange information, and the output of one component becomes the input to other components, the importan t process of model evaluation becomes more challenging and it cannot be limited to "ground

truthing" just the "end results" of the calculations. It is not uncommon to have a good agreement of predictions with observations though compensation of different e rrors, especially when multiple models are employed. So it is essential to ensure that the modeling system predicts the "right things for the right reasons," by evaluating each of its components, in this case involving the entire source

- to

- dose

- to

- effect s equence, separately and independently. Furthermore, this evaluation process needs to bePost

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133 performed, in each case, with empirical data that have not been already utilized in the parameterization or calibration of the component being evaluated. It is strongly recommended that the next version of the documents under review include, to the extent possible, separate evaluations of the models and approaches employed for the overall analysis.

References

Foster, E.C. 2014. Software Engineering: A Methodical Approach: APress.

Fu, M.C. , editor. 2014. Handbook of Simulation Optimization. New York: Springer

.

Philip Goodrum

Incremental health effects is workable in terms of modeling, risk communication, and development of empirical data to examine model performance. As discussed in prior comments, USEPA should consider adjusting the modeling Approach in order to run baseline and baseline + renovation in parallel.

Andrew Hunt

No comments provided for this charge question

.

Naila Khalil

A comprehensive and impressive document that presents evidence based research and uses state of the art analyses for projecting incremental health effects due to renovation activities for P&CBs. This effort will be very helpful in public health decision making. Related t o my experience and expertise, sections in which epidemiological studies and related analysis were described are very clear and transparent. It would be helpful to add a conclusion section summarizing the modeled results.

Andy Menke

The Approach is writ ten with sufficient clarity and detail to understand the methodology. The authors of the Approach seem aware of potential limitations and present the document in a straightforward manner including discussion of these potential limitations, providing the

do cument with a sense of transparency. I believe this document can be useful if it is used appropriately. My biggest concern is the use of the correct lead exposure period (i.e., concurrent vs lifetime average blood lead) to estimate potential health effects , since focusing on the wrong exposure period could substantially overestimate or underestimate health effects. My other concerns regarding the approach are mostly minor. Nevertheless, I think the results of the methods described in the Approach should be interpreted with caution since there are numerous steps and equations utilized in the Approach and each one has assumptions and potential error. Although they individually may seem minor and/or unlikely, the cumulative effect of many small biases may have a substantial effect on the final health effects estimates. Due to this, the Approach would benefit from added sensitivity analyses validating the models and fromPost

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134 simplifying the Approach as much as possible. Despite the uncertainty in the methodology, my overall assessment of the Approach is that it utilizes strong methodology and it will be able to provide the best estimates possible of health effects due to renovation activities of P&CBs.

Paul Mushak

I have discussed these issues in Part I and Part II, Question 1a.

Rosalind Schoof

With the exception of the lack of consideration of uncertainty and impacts of the soil and dust ingestion and relative bioavailability assumptions, I thought the overall approach made sense and provided a useful way to e xplore the potential incremental exposures associated with

various kinds of renovation activities , with the caveat that EPA needs to further study to likelihood of the modeled renovation activities being used by contractor . Making the step from

incremental exposures to incremental health effects is much more uncertain and less convincing. With the exception of editorial issues related to page numbers and broken up sections, the document was fairly clear. The exception is the failure to discuss the source an d uncertainty in key lead intake assumptions in the main text. These factors are only presented in appendices and no supporting analyses are provided.

Joel Schwartz

No comments provided for this charge question

.

Kenneth Unice

I have extensive experience in reviewing and developing conceptual models, environmental fate models, and PBPK/biokinetic models. On one hand, the Approach is a transparent document disclosing in great detail the methodology and rationale for decisions th at were made. I absolutely commend the authors for the level of care and attention to detail evidenced in these documents. On the other hand, even with my extensive experience, I found it very difficult to develop a conceptual understanding of the Approach due to the complexity of the analysis.

Interested stakeholders and practitioners have a variety of learning styles, and one component in the Approach that I found lacking (given the complexity of what has been assembled) is the development of representat ive point estimates, where it is clear how a specific set of inputs resulted in specific model outputs and predictions of health effects. In future assessments of this type, it is recommended that point estimates (with tabulated inputs) be prepared showing examples of central tendency and upper bound interior and exterior exposure scenarios. In addition, it is recommended that the system of equations used in the Approach be presented in a Supplemental attachment. It would be helpful to the reader to number the equations in the main report and Appendices, and to refer to these equation numbers in the sample calculations.

With regard to attempting to understand which areas of the assessment to focus on, I found the lack of a screening step that would have eli minated less important pathways from the Approach to be somewhat of an obstacle. There are many examples among U.S. EPA assessments wherePost

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135 the use of a step

- wise process, including a screening step, has been extremely useful in focusing the analysis. A rece nt example of an efficient use of the step

- wise process that comes to mind is the methodology for the evaluation of encapsulated beneficial uses of coal combustion

residuals 1 , where the steps included literature review, data collection, exposure review,

sc reening assessment and finally risk assessment. It was not necessary for the evaluation to proceed to the risk assessment step. In the current assessment of renovation and repair of C&PBs, it seems plausible that there are scenarios that would likely be ca rried through the risk assessment step, however, there is a substantial set of scenarios that could have been eliminated in a screening step to better focus on those scenarios likely to result in meaningful dose.

Another obstacle to reviewing the report w as that environmental concentrations were not presented in a manner that made it straightforward to do “plausibility” checks, such as attempting to understand how soil concentration related to indoor dust loading. In this regard, it would have been helpful to have assembled in one place a discussion of the environmental concentrations predicted by the various models, and how these concentrations compare to what is expected based on background concentrations and lead

- paint impacted facility concentrations.

I found it unusual that more attention was not spent discussing the “XRF

– lead in paint” parameter, given that it was identified as one the most, if not most, sensitive parameters based on sensitivity score. I recommend that an explicit acknowledgement a nd discussion that the

fraction of C&PBs decreases in newer structures be included in the document. It is also recommended that the methods that will be used in future steps to estimate the fraction of C&PBs with lead paint hazards by vintage be discussed at a minimum conceptual, and if know, with an explanation of the methods that will be used.

Finally, I did not find the presentation of percentiles of the model output to be informative with respect to evaluating what was done. Hypothetical scenarios wer e constructed as part of a risk assessment policy decision, and these do not reflect any one specific real

- world impact. There is no context to types of variable combinations that resulted in a median or 95 th percentile prediction. This concern would be a lleviated if representative point estimates had been provided showing how various combinations of parameters resulted in the point estimate prediction.

After completing this review, was evident that a point

- estimate spreadsheet version of the model

that would allow interested parties to examine how the model responds to adjustments in input parameters would have been an invaluable resource. While it is understood that this would have required some look up tables or visual basic code, as well as the use of unit concentration factor or biokinetic model slope factors, it would have addressed many of the concerns about the difficulty in interpreting the “results” presented in Section 8 of the report.

References

1 U.S. EPA. n.d. Coal Ash Reuse. http://www2.epa.gov/coalash/coal

- ash

- reuse

.

Ian von Lindern

The overall development of the Approach is an impressive effort that synthesizes the current knowledge and understanding of the various aspects of lead poisoning. The models have been constructed and applied in a scientifically sound with justified protocols, and is presentedPost

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136 objectively in a transparent manner. The models interact appropriately and conform to good modeling principles and practices. EPA has done a thorough job in identifying relevant studies and data sources that can inform the model components, has conducted informative post

processing of many of the underlying data sources.

Pertinent potential exposure scenarios have be en identified and characterized appropriate to the available data. The Monte Carlo estimations aid significantly in helping to characterize the aggregate blood lead and health effects distributions for the several scenarios. In total, the methodology shoul d be able to develop the outputs necessary to sequentially characterize exposures, blood lead levels, and health effects. The details can be provided in formats that can inform policy and decision

- makers.

As EPA points out, the methodology invokes numerou s variables, Monte Carlo analyses are data intensive, errors and uncertainties propagate through serial models, and non

- linear models are prone to unreliable results when transformed beyond the ranges of the underlying data; particularly at the extremes of the distributions, where decision

- making occurs. These factors all point to the Approach, despite this state

- of the

- art well executed effort, yielding highly qualified results. However, this reflects the state

- of

– the

- knowledge for this problem and is lik ely the best information that can be developed.

Kathleen Vork

Overall Approach

In its current form, hazards from P&CB activities may be underestimated by omissions and under predictions as noted above.

Clarity

As mentioned in the introductory paragraphs, a list of special terms and their definitions would help to clarify both the main document and the appendices. Also, some of the data tables provide incorrect or conflicting information (see specific instances in section III below).

T ranspare ncy

Studies in which data was key to testing updates to the Leggett model, for example, lacked descriptions the study population and methods used to prepare and summarize the data. Generally more information is needed as to why a model or dataset was cho sen or rejected and what are some important limitations. This is also true for studies of lead paint renovation tasks. A diagram showing how studies were located in the literature, what criteria was used to select or reject studies and data etc.Post

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137 V. SPE CIFIC OBSERVATIONS

Deborah Cory

- Slechta

Page Paragraph Comment or Question

12 Paragraph beginning “ In the 0

- to 10

- year age group” states that

buildings defined as child occupied facilities were not assessed in this Approach which seems at odds with other places in the Approach? Am I missing something?

16 Section 3.1.2, why in demolition were jobs characterized as involving

only one wall?

29 Section 3.6, why was no pedestrian foot traffic assumed to occur in the

job site during renovation?

56 Section 6.2 refers to changes in active bone demineralization in later life

and during pregnancy; shouldn’t this also include lactation?

80 paragraph beginning “These two different” appears to be missing words

in the first line

80 paragraph beginning “This analysis’, the word type should be types

80 paragraph beginning Table7

- 3), appears to be missing words

32 Section 8.5.2, top paragraph, line 4, the word sensitivity should be

changed to sensitive

33 Section 8.5.3, paragraph beginning “ Error! Not a valid bookmark self

reference”

35 Figure 8

- 19; legend for blue bars needs to be added

Panos Georgopoulos

Page Paragraph Comment or Question

7 2 nd from

bottom “A Monte Carlo model was constructed for the Approach separately for

exteriors and interiors using the Python programming language (Python 2012).”

Comment: It is important to specify that Python 2.7 was actually used, as programs developed 2.x “branch” versions of Python are not by default compatible with 3.x versions of the platform

12 3 rd from

bottom “The Approach modeled building occupants in three age ranges: 0

– 10,

18

– 49, and 50

– 80 years.”
Comment: The fact that these age groups do not explicitly/separately consider ages 0

- 6 (pre

- school) and ages over 65 (retirees), while

relevant ac tivity patterns and various susceptibility

- related factors are distinctly different and needs to be further clarified and justified.

12 2 nd from

bottom “EPA notes that buildings defined as child occupied facilities were not

assessed in this Approach. EPA bound the modeling parameter of time spent for young children, and will continue to evaluate this over time to ensure that child occupied facilities are not modeled or assessed as

P&CBs.”
Comment: A clarification is needed regarding this statement, especia llyPost

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138 Panos Georgopoulos

Page Paragraph Comment or Question with respect to whether such buildings cannot be considered “sources” but can be considered “receptors” of Pb dust associated with renovations

of P&CBs. 15, 28,

29 Figure 3

- 1 (3

-

7, 3

- 8) Figure 3.1

Comment: The fact that any resuspension of Pb

- containi ng dust in outdoor settings is ignored (an assumption that is questionable) and thus not included in the figure, should be explained and justified.

17 Equation in

center of page

Comment: The units that UnitConv is supposed to convert do not

actually appear in the equation.

21 2 nd paragraph “The overall average for all activities, 95 percent, was used as the horizontal containment efficiency”

Comment: It would be appropriate to consider this as an uncertain

vari able and conduct an analysis.

21 3 rd paragraph “Iyiegbuniwe et al. (2006) presented data for the efficiency of containments used during bridge de

‐ leading by abrasive blasting and found an efficiency of 91 percent for lead particles. In the absence of

addi tional data, this value was used for the vertical containment efficiency for all renovation activities.”

Comment: It would be appropriate to consider this as an uncertain variable and conduct an analysis.

21 Last

paragraph “The with

‐ HEPA activities were a ssumed to have a 90 percent

efficiency”

Comment: It should be explained how this is estimated and what, if any, is the range of uncertainty associated with this value.

22 2 nd paragraph “Theoretically, a computational fluid dynamics (CFD) model could be a more appropriate selection for this application, but no CFD model is readily available that can support this type of generalized assessment designed to represent the range of conditions in the United States.”

Comment: This statement needs to be modified si nce there are more manageable alternatives to AERMOD than CFD modeling.

22 2 nd paragraph “Early scoping runs showed that resuspension does not significantly influence the air concentrations and subsequent indoor dust loadings.”

Comment: Where is the spec ific information from these scoping runs

summarized?

22 2 nd paragraph “Therefore, air concentrations were estimated using the AERMOD values during the renovation and were set to zero following the
renovation.”

Comment: It should be noted that AERMOD can produce unreasonable results if all conditions are not steady.

23 1 st paragraph “The unit emission rate was used so that the runs could be easily scaled later by multiplying by the actual renovation

‐ specific emission rate.”Post

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139 Panos Georgopoulos

Page Paragraph Comment or Question

Comment: It needs to be clarified that the actual renovation

‐ specific emission rate would need to be constant over appropriate model calculation periods (e.g. hourly) and continuous (non

- intermittent) during the operations considered.

23 2 nd paragraph “Early test runs indicated that differences in modeled air concentrations were less than 1 percent in approximately 90 percent of the simulated

scenario

‐ days”
Comment: This statement needs further clarification, especially with respect to whether this is expected to reflect reality or could be attributed to model settings and limitations.

23 Numbered

list, number 3 3. P&CB

– Schools and Child Occupied Facilities

Comment: A clarification is needed, since on p. 12 (section 2.7) it is stated that child occupied facilities are not asses sed.

24 1 st paragraph “Receptor building locations were placed so that the average distance from the nearest wall of the renovated building equaled each of 6 target distances along each radial: 5 ft, 50 ft, 150 ft, 300 ft, 650 ft, and 800 ft.”

Comment: A note should be added regarding the applicability of

calculations at distances as small as 5 ft.

24 2 nd paragraph “Early scoping results showed that track

‐ in of lead that is deposited on exterior surfaces around the receptor building location contributes m uch less to combined exposures than atmospheric transport of lead dust directly into the receptor location.”

Comment: Where is this documented? What does “much less” mean quantitatively?

25 1 st paragraph “Residences were assumed to have 8

‐ foot ceilings, c ommercial buildings were assumed to have 9

‐ foot ceilings, schools were assumed

to have 11

‐ foot ceilings.”
Comment: These assumptions should be supplemented/supported by an uncertainty analysis.

25 Next to last

paragraph “Nine climate regions within the Un ited States, as defined by the

National Oceanographic and Atmospheric Administration (NOAA), were selected (NCDC 2011).”

Comment: This approach is appropriate for meteorology but both building properties and human demographics and activity patterns depend on climate region. This does not appear to be considered.

27 3 rd paragraph “The Approach accounts for this potential obstruction effect by including an obstruction adjustment factor.”

Comment: It should be explained how these factors (calculated with

BPIP

- PRIME) are consistent with volume sources of emissions (appropriate for dust from renovation activities).

27 Last

paragraph “The obstruction adjustment was also bound by the overall minimum

and maximum found experimentally, 0.1 and 5.3, respectively. MorePost

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Page Paragraph Comment or Question

information about the derivation of this distribution is available in Appendix G.”

Comment: It is needed to clarify how these adjustments would be

consistent with a volume emission source.

28 equation

Comment: This equation needs to be checked for dimensional

correctness.
28 Last

paragraph “If the total job was assumed to be performed on only a portion of the

building (such as one wall), only a fraction of the AERMOD air concentrations was used (i.e., 1⁄4 of the full run if only one wall was disturbed).”

Comment: Why not modify emission rates instead?

29 equation

Comment: The containment efficiency of vertical plastic cannot be set to zero based on this equation. In that case, the equation needs to be

modified to include the factor (1

– Vert PlasticEff), rather than just VertPlasticEff

32 First full

paragraph “The layer depth (TrackInDepth) differs between hard surface and soil,

with hard surface set to a nominal value of 1 mm and soil set to 1.5 cm.”

Comment: Since this assumption is arbitra ry some basic uncertainty analysis would be useful.

32 Last

paragraph “To estimate the indoor floor lead dust loading resulting from exterior

renovations transported to nearby buildings, a mechanistic mass

‐ balance model was developed. The model is an exte nsion of the hybrid

mechanistic

‐ empirical model used in the lead National Ambient Air Quality Standard exposure and risk assessments (US EPA 2007b). The results of the model have not previously been used in an EPA analysis, although the model received posi tive reviews from the SAB when presented as part of the review of the proposed approach for revising the lead hazard standard (US EPA 2010).”

Comment: It would be useful to compare estimates from this approach

to those from a standard model such as CONTAM 3.1

33 2 nd paragraph “The indoor air mass balance equation accounts for penetration into the indoor environment, removal due to air exchange, deposition from the air to the floor, and resuspension from the floor to the air.”

Comment: Further justification should be provided on why resuspension is considered indoors but ignored outdoors.

33 Text under

equation “ReceptorVolume = volume of the receptor location (m 3 )”

Comment: This is confusing terminology

– a “location” should not
have a “volume”. Is this the indoor microenvironment volume?Post

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Page Paragraph Comment or Question

34 2 nd paragraph “For the Approach, track

‐ in was assumed to occur from soil 50 percent of the time and from hard surfaces the other 50 percent of the time.”

Comment: An uncertainty analysis would be more appropriate.

3 4 Last

paragraph “Early scoping results indicated that air exchange rate, cleaning

frequency, track

‐ in rate, and the percentage of the receptor

‐ building that is carpet were sensitive variables.”

Comment: More specific information is needed on this point.

35 3 rd paragraph “Biokinetic blood lead models require lead concentrations, or the amount of lead per mass of particulate.”

Comment: The models actually require mass intake where appropriate, and activity

- dependent inhalation rates need to be combined with

airborne concentration.

35 Equation

Comment: Appropriate normalizations are needed in order to render the arguments of logarithms dimensionless.

35 Text under

equation “

Adj ln(Load)) = ln(Load)

– Average (ln( Load ))”

Comment: Appropriate normalizations are needed to render the arguments of logarithms dimensionless

37 Last

paragraph “These CBECS categories were mapped to five assessment categories

for this Approach, where CBECS categories were grouped according to layouts, room sizes, and expected time spent patterns. Five assessment categories were chosen to ensure adequate coverage of the various building types and also to balance the resource constraints of the Monte Carlo analysis.”

Comment: This needs further clarification as the mapping of the

cate gories raises various consistency questions.

38 Table 4.1,

right

- most
column “2. Warehouse, food sales, religious worship, and public assembly”

Comment: How are expected time

- spent patterns similar in these

microenvironments?

38 Table 4.1,

right

- most
col umn “3. Food service, service, strip shopping mall, enclosed mall, and retail

other than mall”

Comment: Strip malls and enclosed malls have very different layouts (and operations dynamics).

39 First full

paragraph “Each building layout has three rooms.”

Comment: How was this assumption evaluated and/or justified?

39 Bulleted list,

last 2 bullets “

- Lead dust loadings decrease exponentially with distance from the

renovation activity, and

- The renovation affects only those rooms directly adjacent to a

ren ovated room and the renovated room itself. All other rooms experience no increase in lead loading.”

Comment: Data that support these assumptions need to be presented.Post

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Page Paragraph Comment or Question

40 Last

paragraph “This job area was assumed to be relatively isolated from the rest of the

room because of the inability of the lead dust to travel the entire extent
of the room.”

Comment: What data support this assumption?

41 Text under

equation “FloorLoad = the floor lead loading after the renovation (μg/ft 2 )”

Comment: Mixing from SI and engineering units should be avoided.

42 Equation LeadRemoved = XRF x FractionRemoved x JobArea x UnitConv

Comment: This is a typical example of an equation where t wo unit systems are used; two different units for mass and two different units for

area (wall area in square cm while floor area in square ft).

77 First line Invalid/missing reference 80, 81 The following statement is repeated verbatim three times on pages 80

and 81: “This analysis gave a series of nested bins that specified a unique

combi nation of dust

‐ generating phase and rest

‐ of

‐ renovation loadings and durations. The analysis was repeated for each building type and carpet status. The result was a total of 301,930 scenarios to be run in the
Monte Carlo model.”

85 Table 7

- 4 The units of “Building Floor Area” in Table 7

- 4 are specified as cubic

meters (PDF p. 146),

Section

8.5.3 Last

paragraph Invalid/missing reference

D

- 8 Table D

- 2 A precision of three or four significant digits for estimates of aerosolized emitted fraction does not app ear justifiable.

D

- 24 Table D

- 3 In the third row, third column the second wall length entry should be 28

ft and not 14 ft

D

- 24 Table D

- 3 Units in the Table appear as ft2, ft 2 , ug , μ g, etc.

D

- 24 Table D

- 4 A precision of four significant digits for estimates of small dust fraction does not appear justifiable.

D

- 25,

D

- 26 Figure D

- 4 The caption of Figure D

- 4 is actually on page D

- 26

E

- 24 Reference to

USEPA 1997 The 1997 Exposure Factors Handbook has been superseded by the 2011

version (referenced on page E

- 25)

F

- 1 USEPA “Rain

Zones” The discussion of “15 climate regions” that correspond to USEPA “rain

zones” needs to provide clarification and a comparison (via maps) with the 9 climate regions used in the Monte Carlo approach.

G

- 12 particle size

dist ribution and particle

densities It would be useful to present quantitative outcomes of the scoping

results that support these parameterizations,Post

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Page Paragraph Comment or Question

L

- 4 First

paragraph Invalid/missing reference

L

- 9 Table L

- 10 It should be explained why the 11

- 17 years age group is excluded

N

- 1 Table N

- 1 It should be noted in the table that the P&CBs are Child Care Centers

N

- 13 Table N

- 7 A precision of five or six significant digits for estimates of daily intake ( μ g/day ) does not appear justifiable. Q

- 1 Appendix Q Some visual information (maps etc.) would be needed in order to understand the procedures described in Appendix Q

Philip Goodrum

Page Paragraph Comment or Question

80 Mid

- page Copy/paste error

– paragraphs are juxtaposed

Andrew Hunt

No specific observations provided.

Naila Khalil

Page Paragraph Comment or Question

11 Figure 2

- 4 The line graph shows that the “background lead exposure” decreases during renovation (a dip in the blood lead when renovation starts). It would be helpful to provide some additional information explaining this.

61 Second

paragraph

line 2 The sentence “Adjusted odds ratios (ORs) of blood lead levels were

estimated from logistic regression with fractional polynomials.” Can be

reworded to state :

“Adjusted odds ratios (ORs) of preterm birth, low birth weight and SGA were estimated from logistic regression with fractional polynomials.”

99 Table 8

- 1 Column 1

- Containment Options

- Line 2

– edit “Changes at all”

103 Page numbers are different after # 103.

36 after

103 Section

9,line, paragraph 2,

line 4 Word “Low” can be edited

Andy Menke

Page Paragraph Comment or Question

40 The terms “percent of room” and “fraction of room” seem to be used interchangeably. I would prefer consistent phrasing.

53 Table 4

- 5 It seems like the categories used for the amount of lead in soil were tooPost

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Page Paragraph Comment or Question broad, especially considering the difference between them. If they were

based the 25 th and 75 th percentile, then I wonder how much higher the

soil concentration would be for the 90 th and 95 th percentile.

58 Table 6

- 1 Does this table show estimates for a 1

- log increase in blood lead? Results from a log

- linear model can also be displayed for a 2

- fold increase in blood lead, which is what the original paper did.

63 3 rd paragraph The Navas

- Acien study also had better lab methods allowing a more

accurate estimation of the effects at lower levels.

63 4 th paragraph

2 nd sentence The word “lead” is used twice: “blood lead level for lead”

64 3 rd paragraph

1 st sentence “Relative risks” should be “odds ratios”.

65 I do not think stratifying CKD odds ratios by sex is justified since the

interaction in Navas

- Acien’s paper was barely significant and likely due to chance; they looked at a lot of interactions without control ling for multiple testing so it was probably inevitable that they found one. There is no biological mechanism suggesting we should expect to see interaction. Also, the Muntner paper in NHANES III found the interaction to be in the opposite direction (NS), which suggests there is

no true sex

- lead interaction.

68 4 th paragraph I do not understand why use sex

- stratified estimates for CVD mortality. There was no significant interaction in the original study and no reason to assume this difference is anything o ther than chance findings.

72 Table 6

- 7 This tables says kidney disease health effects were estimated for people with post

- renovation blood lead >1 μg/dL, but page 66 says it was estimated for people with blood lead >0.8 μg/dL.

74 Table 5

- 8 It might be ideal to present the 4 outcomes in the same order throughout the entire document. For example, this table has CVD mortality and CKD flipped compared to the order they were presented earlier in the

document.

77 1 st paragraph

1 st sentence There is an error message inserted at the end of this sentence. The same

error message is actually found occasionally throughout the document and in the appendix.

80 This whole page seems like a series of cut and paste mistakes with repeated paragraphs and missing table r eferences.

85 Last

paragraph The CVD mortality paper was not a cross

- sectional paper. It was a

prospective cohort paper. I believe the relevant point is that blood lead was only measured one time at baseline, which is an important point but not a defining characteristic of the study design. Same mistake on page 40 (2 nd page 40).

100 2 nd paragraph The third word of first sentence “is” should be “it”.

103 After this page, the page numbers start over and strangely get stuck on 21 for quite a while.Post

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Page Paragraph Comment or Question

1 The font size is inconsistent and it looks like more cut and paste errors. The errors seem to go into page 2.

L

- 3 Table L

- 3 Age 49 is missing from the table.

Paul Mushak

No specific observations provided.

R osalind Schoof

No specific observations provided.

Joel Schwartz

Page Paragraph Comment or Question

2 BLLs return to background levels

- BLLs do not necessarily go back to background. Kids are calcium and iron deficient and will continue to

absorb some.

3 Kids BBLs highest at 30 days post renovation. I disagree. They should be highest at 10 to 15 days post renovation. Don’t confuse a 30 day

half

- life under equilibrium conditions (constant exposure) to the effects of episodic exposure.

3 Results don't mimic exposures across US population. But using central tendency values for e.g. Building size, housing condition, household composition, etc. doesn't help to focus risk estimates. The risks are not central, but localized.

General. Renovati on activities have the potential to create new long

term lead exposure reservoirs, because soil lead does not disappear.

Why aren't figures 2 1 and 2 2 the same, e.g. Job characteristics. Beige

and blue boxes should be the same.

9 Renovation activities : Why are no window removals included in

interior renovations

10 Media concentrations return to background.

Not unless cleaned.

12 Why no CVD morbidity? CVD mortality is the tip of the iceberg. It is unreasonable to think that an exposure can cause e.g. fatal heart attacks, but no non

- fatal ones. This undoubtedly comes from assessing the evidence for fatal and non

fatal CVD events separately, rather than a s a group. That makes no

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Page Paragraph Comment or Question

17 Should include deteriorated conditions as realistic.

23 .0625 average distance isn't protective within congested urban areas

The modeling, and the Monte Carlo simulations ignore the correlation

between risk factors such as denser housing, deteriorated conditions, closer, calcium deficient, more LBP, etc. This underestimates risk

24 Separate risk receptor estimates or combined?

24 What about people's exposures while outside, e.g. Kids playing, people on porches, shopping centers, parks, etc. ?

27 What is an obstruction? (All risk estimated for occupants of receptor buildings.)

34 Cleaning efficiencies: ordinary vacuuming doesn't remove lead dust.

35 Importance of track in may be higher in some circumstances, e.g. areas with higher proportion of kids, denser housing, bare soil, etc.

15 Deteriorated condition of building being renovated increases

exposures.

37 Deteriorated condition of building bein g renovated increases

exposures.

General: certain heightened risks are likely to occur together: deteriorated commercial buildings with lots of LBP close to deteriorated and dense housing, with kids playing outside in bare soil, adults on porch, etc.

39 Dust loadings in rooms adjacent to interior renovations may be high if no containment. Also, poor quality work will contaminate more.

40 Condition of room/building also important.

40 Optimistic assumption that renovations in a large room won't

contaminate the entire room.

41 What is the assumed post renovation baseline cleaning? Older poorer condition buildings are likely to have more LBP, too. Also likely to have poorer quality work?

48 Assumption that "Routine cleaning continues until hitt ing background levels" is too optimistic, especially without containment or

verification.

48 No exposures assumed for people outside.

52 The estimated efficiencies of the cleanup options seem optimistic: Dry sweeping. 0.943 Horizontal plastic & sweep. 0.956

55 What data re average BLLs in 1970's were 40 ug/dL and maybe 60 ug/dL in 1960? NHANES II averages in 1976 were about 18 ug/dL,Post

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Page Paragraph Comment or Question and extrapolation of the regression model of Schwartz and Pitcher to the peak gasoline lead year of 1971 predicts about 21 ug/dL

55 Why use CVD mortality exclusively and not include morbidity too?

The time period for exterior lead to return to background is too short. Where is the soil lead going? And we still see a lead gradient in PM2.5 is sampling with distance to ro ads, despite decades since removal of lead from gasoline.

There is mobilization of lead from bones, resulting in subsequent exposure. For example the TLC chelation study showed rebounds in

blood lead in children after chelation.

EPA should at least do a sensitivity analysis for exposure to more renovations. Older building that are more likely to require renovation are geographically clustered, meaning children in some areas are likely to be expose to more than one. And workers are clearly likely to be

ex posed to more than one renovation.

Exterior renovation is more likely to occur in the warm season when children are more likely to be exposed.

Kenneth Unice

Page Paragraph Comment or Question 60, 72,

73 85 Section 6.3,

6.7 On page 60 of Section 6.2 and page 72 of Section 6.7, there is an

equation for delta IQ. On page 73 of Section 6.7 there is an example application of this equation. In the example on page 73, decreasing the renovation related blood lead to a concentration less than 9 micrograms

pe r deciliter would increase the IQ decrement, likely reflecting a typographical error in the text.

In Table 6

- 8 and Figure 1, it appears that the correct equation has been used, for example, for a renovation

- related blood lead change of 9 micrograms per de ciliter (10 micrograms per deciliter total), I can reproduce the delta IQ using:

DeltaIQ =

- 2.65 * ln ([9+1]/[1]) =

- 6.1 points

In both the example on page 73 and the equations on page 60/72, I would like to clarify whether there is a typographical error such that “+” beta should be replaced with “*” beta.

The clarification document indicated that the typographical errors willPost

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Page Paragraph Comment or Question

be corrected.

19 Section 3.2.3 Check the unit on UnitConv

B

- 3 Table B

- 3 The upper and lower bound of the wall loading variables in the table are

reversed.

C

- 1 Section C.1.1 A statement could be provided noting that crew size was determined by consultation with a professional engineer, with reference to Appendix D.

C

- 2 Sec tion C.1.1 The text refers to “Appendix C” for the RS Means job rates. It is unclear which section is being referred to, and a specific section should be provided.

C

- 3 Section

C.1.2.2 It appears that a lognormal distribution was fit to the CBECs data for

small, medium and large buildings for three building heights yielding a total of 9 mean building renovation sizes. The mean appears to be estimated by averaging 1000 simulated values fr om a best fit lognormal distribution for each tertile. The document should explain why it was necessary to use this lognormal approximation in lieu of directly using the survey data for individual buildings with weights. For example, could the “public use microdata” ( http://www.eia.gov/consumption/commercial/data/2003/index.cfm?vie

w=microdata ) have been used to directly calculated the mean building

sizes based on t he equation for the expected value of the probability mass function for each tertile? Section C1.1.3 indicates that the percent glass was estimated from the microdata with survey weights with nationally representative weights, indicating that the necessary data

should be available.

C

- 6 Section C.1.7 Surface areas were estimated for exterior painted trim, windows and doors. For clarity, a footnote to Table C

- 2 could be including describing how the surface area of a single block was defined (e.g. referring to RS Means data analysis in Appendix D).

C

- 7 Section C.1.7 NDHHS, 2011 is cited but not provided in the Appendix C references.

C

- 17 Section C.2.8 Where the text states: “The concentration of lead in the exterior paint of the building”

The text on Page C

- 17 should be changed from “exterior” to “in terior”.

C

- 15 Section 2.5 A footnote should be included to describe ADJWEIGHT (presumably an adjusted weight from the survey.

C

- 15 Section 2.6 It seems reasonable to expect appreciable variability in the amount of shelving, and the mapping presented in Table C

- 11 is plausible . I agree with this assumption.

C

- 17 Section C.3 The reference for U.S. EPA 1998a refers to Appendix G of the document. Appendix G describes briefly the 1989

- 1990 survey. It is unclear why other relevant references have not been inc luded, such as Appendix C of USEPA 1998a, which presents the raw survey data inPost

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Page Paragraph Comment or Question

U.S. EPA 1998a Table C1

- 7, and U.S. EPA Chapter 3, which presents the base year 1997 distribution. In particular, the specific reference used

derive Tables C

- 3 and C

- 12 in Appe ndix C of the Approach does not appear to be identified. The clarification document indicates that the 2002 HUD survey was

used.

D

- 1 Section D.1.1 The rationale for selection of the removal rate and crew size appears to

be reasonable. The text indicates that additional information would help refine this variable, however it is unclear whether the model is sensitive to deviations in crew size (e.g. 2, 4 or 6 person crews). Information about model sensitivity to this parameter would better clarify the impor tance of eliciting more data on crew size.

D

- 3 Section D.1.2 A unit (area/time) is not provided for RenoRate in the equation in this section. “Section D.1.2” in the last sentence is a typo. The correct
reference is “Section D.1.1”.

D

- 7 Section

D.1.4.3 Wh at does “Appendix 2.1.1.4” refer to?

D

- 12 Section D2.3 The minimum valid Room Area for the equation shown is 200 square feet (because W < job width when LW < 200). This could be noted in

Table D

- 6.

D

- 24 Section

D2.7.3 In the source column for wall area, it appears the second 14 feet should

be 28 feet. The wall width cancels out of this equation, therefore the critical parameters are the job width (6 feet) and ceiling height (8 feet). Given the vintage of the structure, an 8 foot ceiling height is likely conservative (increased wall area decreases the fraction emitted). This could be more directly presented in the unlabeled table on Page D

- 24.

E

- 6 Section E.8 The intent and purpose of this sentence is unclear: “ The resuspension rate of 1.4 x 10

- 4 s - 1 was taken from values... ” In addition, the unit of “s” appears to be incorrect.

E

- 16 Section E.12 A citation to a publically available version of the “Formaldehyde in Indoor Air Model” should be provided, or the text revi sed to cite an alternative publically available source.

F

- 2 Section F.3

and F.5 It appears the Approach may be confusing particle density with bulk soil

density. The approach states “soil density”, however, the value given is the “soil particle density”. The USEPA soil screening guidance assumes a dry soil bulk density of approximately 1500 kg/m 3 , and total porosity

is 1

- 1500/2600 ~= 0.42. It is unclear what scenario the porosity of 0.2 in the Approach represents, however, it may represent an effective a quifer porosity, rather than total porosity. The value of 2600 kg/m 3 appears to better represent concrete bulk density than soil bulk density. Default bulk densities and porosities for soil and concrete could be used in place

of the values assumed.

H

- 1,

-- There is a typo at “Sections 0 to H.14”. Should it be Section “H.6 toPost

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Page Paragraph Comment or Question

H

- 4 H.14”?

H

- 11 H.5 Should “Table D

- 2” be “Table H

- 3”?

Ian von Lindern

Page Paragraph Comment or Question App C

- 1.7Ap Appendix Section C

- 1.7: All of the equation variables above were assumed to be fixed, aside from the XRF measurements. How does this apply to the variable “frac” and its use in subsequent use in the Approach, which is a function XRF ? App. C 1.7 Appendix C 1.7 and 2.8 develop, respectively, exterior and inter ior paint lead levels by vintage to be used in subsequent analyses as follows:

The geometric mean, geometric standard deviation, and average lead content in paint (all vintages) for exteriors are shown in Table C

- 3.

Table C

- 3. Lead XRF measurements by v intage for exterior analysis.

Pre

- 1930 0.00607 2.90 0.01068

1930

- 1949 0.00377 2.38 0.00549

1950

- 1959 0.00253 2.30 0.00358

1960

- 19791 0.00331 2.34 0.0048

The distributions for the amount of lead in the paint for interior components were estimated as described in Section C.1.8, but the interior analysis used the interior component estimates for the HUD data source (US EPA, 1998a), Table 2, as shown in Table C

- 12.

Table C

- 12. Lead XRF measurements by vintage for interior analysis.

Pre

- 1930 0.00630 2.59 0.00992

1930

- 1949 0.00334 2.63 0.00534

1950

- 1959 0.00314 2.47 0.00473

1960

- 19791 0.00366 2.70 0.00599

These Tables show lower mean exterior paint concentrations than interiors, which seems contrary to experience and numerous other studies. Additionally t he explanation preceding Table C

- 12 is confusing

and there is no Section C 1.8 as referenced. App C 2.3 A simplifying assumption is made to limit exposure calculations to no more than 2 adjacent rooms under renovation. Doesn’t this limit the

definition of interior renovation to no more than 2 rooms at any time? This seems to assume away major renovation projects that might be common when older public buildings are remodeled, bringing numerous code requirements and outdated infrastructure up to date.Post

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Page Paragraph Comment or Question App C 2.6 How does shelving relate to horizontal surface area, deposition areas and cleaning efficiency in the area

- based calculations? App D 1.1 (pD

- 2) What does the term “modeled building panel” refer to? App D 1.1 / 1.3 The summary of rates of renovation shown in Table D

- 1indicate large standard deviations, in part due to small number of experiments performed. This was resolved by adopting a single value of 360 ft2/day for nearly all renovation activities removal rates, with no variance specified to accoun t for differences that are known to vary significantly within and across activities. How does this simplification of a key variable known to have significant variation play

- out in the subsequent model analyses?

Similar questions apply to Appendix D 1.3 i n fixing the Fraction Removed at 15%, Appendix D 1.4.2 use of a single lower limit for HEPA effectiveness on various equipment, Appendix Table D 2 use of a single value for aerosol lead emissions from different methods and controls, and Appendix D 1 assumi ng that 6% of paint released from all

activities is <50um and is considered available for track

- in; where each of these variables are recognized and acknowledged to exhibit considerable variance in reality.

Kathleen Vork

Page Paragraph Comment or Question

M

- 7 Table M

- 2 Values for column two reflect transfer rates from RBC rather than what is stated in the column heading and vice versa for values in column three

M

- 2 Bullets after

line 2 Remove the information appearing in parentheses and reference where

in the document these changes can be found (e.g. Appendix M, M

- 22 and following) Figure

M

- 3 Figure M

- 3 is misleading. For example, the OEHHA model starts at age

25 not zero and the Nie study found a range of bone transfer rates when stratified by age. Please correct this figure.

Table

M

- 1 Clarify what body weight is associated with the volumes and masses presented in the table.

M

- 36 Bottom of

page Are the terms CRTCONBM=CRTCON*1.8 and

TRBCONBM=TRBCON*3.*1.8 the conversion factors to correct for bone mineral as measured by XRF versus bone mineral as defined in Leggett (1993)? If so, include this conversion in the appendix text (e.g.

as another colum n or a footnote to Table M

- 1). The values for skeletal

mass in Table M

- 1 page M

- 5, for example, appear to be the uncorrected values (i.e. 73kg x 0.143 = 1050 grams). The marrow free bone mineral mass (i.e. 73 kg x 0.071 = 5182 grams which is further reduc ed to approximately 3400 grams) to coincide with XRF bone measurementsPost

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Page Paragraph Comment or Question (Fleming et al 1999).

Fleming, D.E., et al. (1999), The O’Flaherty model of lead kinetics: an evaluation using data from a lead smelter population, Toxicol Appl Pharmacol, 161 (1), 10 0

- 9.

If another conversion source was used, please cite this source.Post

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Appendix A
List of Peer Reviewers and Bio graphical S ketchesPost

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A

- 1 

### External Peer Review Meeting for

EPA’s Approach for Estimating Exposures and Incremental Health Effects from

Lead due to Renovation, Repair, and Painting Activities in

Public and Commercial Buildings

Crystal City Marriott at Reagan National Airport

1999 Jefferson Davis Highway Arlington, VA, 22202

January 13 and 14 , 201 5

LIST OF PEER REVIEWERS

Deborah Cory

- Slechta , Ph.D. (Chair) University of Rochester School of Medicine and Dentistry

Panos Georgopoulos, Ph.D. Rutgers University, EOHSI

Philip Goodrum, Ph.D

. Integral Consulting, Inc.

Andrew Hunt, Ph.D. University of Texas, Arlington

Naila Khalil, M.B.B.S., M.P.H., Ph.D. Wright State University

Andy Menke, M.P.H., Ph.D

. Social & Scientific Systems , Inc. Paul Mushak, Ph.D.

PB Assoc iates

Rosalind Schoof, Ph.D.

ENVIRON

Joel Schwartz, Ph.D. , M.D.
Harvard School of Public Health

Kenneth Unice, M.S.

Cardno ChemRisk

Ian von Lindern, Ph.D., PE TerraGraphics Environmental Engineering,

Inc.

Kathleen Vork, Ph.D., M.P.H. California Environmental Protection Agency

BIOGRAPHICAL SKETCHES OF PEER REVIEWERS

Deborah Cory

- Slechta, Ph.D. University of Rochester School of Medicine and Dentistry

Dr. Cory

- Slechta is a Professor in the Department of Environmental Medicine and the Department of Pediatrics at the University of Rochester School of Medicine and Dentistry, where she also serves as co

director of the Behavioral Sciences Facility Core and director of the Animal Behavior Core. Dr. Cory

Slechta received her Ph.D. from the University of Minnesota in 1977 and worked as a junior staff fellow of the National Center for Toxicological Research beginning in 1979. She was appointed to the faculty of the University of Rochester Medical School in 1982 and was appointed Chair of the D epartment of

Environmental Medicine and Director of the NIEHS Environmental Health Sciences Center at the University of Rochester in 1998. From 2000 to 2002, she was the Dean for Research and Director of the AAB Institute for Biomedical Sciences. Following her appointment as Dean, she concurrently served until 2007 as the Chair of the Department of Environmental and Occupational Medicine at Robert WoodPost

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- 2 Johnson Medical School and as Director of the Environmental and Occupational Health Sciences Institute, a joint Institute of the Robert Wood Johnson Medical School and Rutgers. Dr. Cory

- Slechta’s research has focused largely on environmental neurotoxicants as risk factors for behavioral disorders and neurodegenerative disease. Specifically, this has included more than 30 years of work on the impact of lead on learning and attention and associated neurochemical mechanisms, and the participation in numerous national and international committees/review panel/workshops on lead. These research efforts

have resulted in over 170 papers and book chapters to date. Dr. Cory

- Slechta has served on and chaired numerous national research review and advisory panels, including committees of the National Institutes of Health, the National Institute of Environmental Health Scien ces, the Food and Drug Administration, the National Center for Toxicological Research, the Environmental Protection Agency Science Advisory Board, the National Academy of Sciences, the Institute of Medicine, and the Agency for Toxic

Substances and Disease Registry, Centers for Disease Control and Prevention. In addition, Dr. Cory

Slechta has served on the editorial boards of several journals including Environmental Health Perspectives

, Neurotoxicology

, Toxicology

, Toxicological Sciences

, Fundamental and App lied Toxicology

, Neurotoxicology and Teratology , and American Journal of Mental Retardation . She has held the elected positions of President of the Neurotoxicology Specialty Section of the Society of Toxicology, President of the Behavioral Toxicology Society, and been named a Fellow of the American Psychological

Association.

Panos Georgopoulos , Ph.D. Rutgers University , EOHSI

Dr. Georgopoulos is Professor of Environmental and Occupational Medicine at Rutgers University

– Robert Wood Johnson Medical School. He is also a member of the Graduate Faculties of Chemical and Biochemical Engineering, Biomedical Engineering, and of Environmental Sciences at Rutgers University, and a member of the Rutgers Environmental and Occupational Health Sciences Institute (EOHSI). Dr. Georgopoulos received his Ph.D. in Chemical Engineering from the California Insti tute of Technology. At EOHSI, he established and directs the Computational Chemodynamics Laboratory (CCL), a state

- of

the

- art facility for informatics and modeling of complex environmental and biological systems. Furthermore, he co

- directs the Center for E xposure and Risk Modeling (CERM) at EOHSI. Dr. Georgopoulos’ research areas involve the development and application of mathematical and computational methods for diagnostic and mechanistic studies of multipathway physicochemical processes taking place in i nteracting environmental and biological systems. The aim of this research is to improve the understanding and quantification of human exposure and biological mechanism

- based dosimetry and risk for environmental toxics; this is being accomplished through th e ongoing development of a consistent mechanistic multiscale modular computational framework for source

- to

- dose

- to

- effect modeling of toxicant dynamics. Outcomes of this research include integrative computational models of toxicokinetic and toxicodynamic p rocesses at the cellular, tissue and whole body levels. Dr. Georgopoulos has published more than 125 peer

- reviewed articles and chapters in scientific journals, books and proceedings, and has authored or co

- authored several state and Federal government doc uments and numerous technical reports. He has served on editorial boards of scientific journals and currently

serves as member of various national and international scientific and technical committees on

environmental issues.

Philip Goodrum, Ph.D. Integra l Consulting

, Inc.

Dr. Goodrum is a Senior Managing Scientist at Integral Consulting Inc. and a member of the adjunct faculty for Syracuse University where he teaches a course in Environmental Toxicology. He received his Ph.D. in Environmental Engineering from SUNY College o f Environmental Science and Forestry in 1999, focusing his research on applications of probabilistic modeling to lead risk assessment. Prior toPost

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- 3 joining Integral Consulting Inc. in 2013, Dr. Goodrum was employed at Cardno Entrix, Environmental

Resources Man agement (ERM), ARCADIS

- US, Inc., and SRC, Inc. Dr. Goodrum has 23 years of experience in quantitative risk assessment and environmental modeling, specializing in applications to human health and ecological risk assessment, sediment remediation, groundwater compliance monitoring, and natural resource damage assessment. He is a recognized national expert in probabilistic modeling, quantitative uncertainty analysis, lead (Pb) risk assessment, and statistical methods. Dr. Goodrum served for approximately 10 yea rs as a consultant for EPA’s Technical Review Workgroup for Lead, managed EPA’s “Lead Hotline,” which assisted the public with applications of both the IEUBK model and the interim Adult Lead models, co

- authored numerous platform presentations, technical wh ite papers and guidance documents, and actively participated in the research and development of EPA’s All Ages Model for Lead. Dr. Goodrum served on EPA’s Science Advisory Board for lead and on EPA’s Clean Air Scientific Advisory Committee (CASAC)

- Lead R eview Panel. He is a member of the Interstate Technology and Regulatory Council (ITRC) workgroup on Incremental Sampling Methodology (ISM)

and serves as an instructor for ITRC for an on

- line seminar series on ISM.

Andrew Hunt, Ph.D. University of Texas

, A rlington

Dr. Hunt is an Associate Professor in the Department of Earth And Environmental Sciences at the University of Texas at Arlington where he teaches graduate courses in Human Health Risk Assessment/Probabilistic Risk Assessment, Issues in Environmen tal Health, and Toxicology. He previously was on the faculty of the Pathology Department of the State University of New York Health Science Center in Syracuse, NY (14 years), and held a position in environmental consulting for four years. For the past 25 y ears, Dr. Hunt’s research has addressed health and environmental hazards in urban and indoor environments. He has conducted a number of studies concerned with characterizing and apportioning the sources of lead in indoor dust, and with mapping the distribu tion of lead in dust across urban environments (funded by the U.K. Department of the Environment, EPA, HUD, and NSF). His work has also repeatedly focused on lead contamination of urban soil. Dr. Hunt has also published in the areas of lung disease associa ted with inhaled particles from environmental and occupational sources, and pediatric asthma in relation to environmental exposures. Dr. Hunt has served on various committees and panels, including panels for NSF on S

- STEM applications, and for EPA on the S cience Advisory Board peer review of lead dust technical analyses. He has published more than 50 peer

- reviewed articles and chapters in scientific journals, books and proceedings. In addition, Dr. Hunt has served on the editorial boards of several journals including Air & Water Borne Diseases and Journal of Environmental Geochemistry and Health

.

Naila Khalil, M.B.B.S., M

. P

. H., Ph.D. Wright State University

Dr. Khalil is an Assistant Professor in Community Health at Wright State University, Boonshoft Schoo l of Medicine, Center for Global Health. She received her Ph.D. in Environmental Epidemiology from the University of Pittsburgh in 2007. She is a physician and also holds an MPH from Pakistan. In Pakistan, Dr. Khalil was the national program manager for En vironmental Health, a collaborative effort with the World Health Organization. Before joining the Center for Global Health as Assistant Professor, Dr. Khalil
was a post

- doctoral researcher at Wright State University Boonshoft School of Medicine Lifespan He alth Research Center where her research focused on cardiovascular disease epidemiology. Dr. Khalil’s doctoral dissertation focused on multiple health outcomes associated with environmental lead exposure in older people. Dr. Khalil has published widely on l ead related health outcomes. Her research has shown that elevated blood lead concentration is linked with diminishing bone density, and elevated risk of fractures and mortality in older women. In older men she has reported a negative association between

oc cupational lead exposure and neurocognitive function and bone density. Dr. Khalil is on the editorialPost

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- 4 board of the Journal of Family Medicine & Community Health and serves as a reviewer for journals such

as Environmental Health Perspectives and British Med ical Journal . She is a member of the American Society of Bone and Mineral Research.

Andy Menke, M

. P

. H

. , Ph.D. Social & Scientific Systems, Inc.

Dr. Menke is a Senior Research Analyst at Social & Scientific Systems, Inc. He received his Ph.D. in Epidemiol ogy from Johns Hopkins University, Bloomberg School of Public Health in 2010 and his MPH from Tulane University, School of Public Health and Tropical Medicine in 2005. Prior to his position at Social & Scientific Systems, Inc., Dr. Menke was a research ass istant in the Department of Epidemiology and Welch Center for Prevention, Epidemiology and Clinical Research at the Johns Hopkins Bloomberg School of Public Health. Dr. Menke’s research experience includes the conduct and analysis of epidemiologic studies, focusing on the epidemiology of diabetes, sex steroid hormones, iron metabolism, and environmental exposure to heavy metals. Specific studies on lead and associated blood lead levels in U.S. adults and cardiovascular mortality, stroke, and end

- stage renal disease. Dr. Menke has published more than 35 peer

- reviewed journal articles and has made presentations at more than 15 scientific meetings. He serves as a reviewer for journals such as American Journal of Kidney Diseases; American Journal of Preventive M

edicine; Annals of Epidemiology; Cancer Epidemiology, Biomarkers & Prevention; Environmental Research; and Environmental Science and Technology. Dr. Menke is a member of the American Heart Association, the Society for Epidemiologic Research, and the Americ an Diabetes Association.

Paul Mushak , Ph.D.
PB Associates

Dr. Mushak is a Senior Partner at PB Associates where he is a toxicologist and human health risk assessment specialist, principally working in the area of environmental lead and other metals, me talloids, and organometals. He received his Ph.D. in Organometal and Biological Chemistry from the University of Florida in 1970. Dr. Mushak also had extensive training and research experience in metal and analytical toxicology in the Department of Pathol ogy, University of Florida School of Medicine. His post

doctoral training was at the Yale University Department of Molecular Biophysics and Biochemistry in the area of biochemical toxicology and enzymology of certain toxic and essential metals. Dr. Mushak was a visiting professor at the Albert Einstein College of Medicine (1995

- 2010) and a faculty member from 1971 to 1993 at the University of North Carolina

- Chapel Hill School of Medicine, Pathology Department. Dr. Mushak has more than 45 years of educati on, training, and experience in the areas of exposures and their determinants, analytical pediatric toxicology, toxicokinetics, modeling and health risk assessments. He was the author of the comprehensive lead book, Lead and Public Health: Science, Risk

an d Regulation (2011), 180 research articles, conference abstracts, and book chapters, and has served on approximately 50 expert advisory national and international committees including EPA’s Science Advisory Board Panel for the All Ages Lead Model, EPA’s Cl ean Air Scientific Advisory Committee (CASAC)

– Lead Review Panel, and a number of National Academy of Sciences committees on

environmental metals. Dr. Mushak has co

- authored more than two dozen risk assessment documents for

various Federal and internation al agencies on lead, arsenic, cadmium, mercury, etc. He has testified before Congressional committees on two occasions about childhood lead poisoning. Dr. Mushak has been qualified as an expert on lead and other metals risk assessment before six Federal an d six state courts. He is a member of the Society of Occupational and Environmental Health, the Society for Risk Analysis, and the Society of Toxicology, among others.Post

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- 5 Rosalind Schoof, Ph.D., DABT

ENVIRON

Dr. Schoof is a Principal at ENVIRON where she is a board certified toxicologist (Diplomate of the American Board of Toxicology (DABT)). Prior to joining ENVIRON in 2010, she was a Principal and Senior Toxicologist at Integral Consulting, Inc., Gradient Corporation, and Exponent. Dr. Schoof received

h er Ph.D. in Toxicology from the University of Cincinnati in 1982 and has more than 25 years’ experience assessing human health effects and exposures from metals and other chemical substances for a variety of projects, such as contaminated site remediation; commercial, industrial, agricultural and residential exposure assessments; product uses; and dietary exposures. Her projects have included numerous health risk assessments conducted under various U.S. and international regulatory settings, as well as regu latory, research and litigation projects. Dr. Schoof has directed evaluations of chemical toxicity, derivation of risk

- based exposure levels, health risk assessments for cancer and noncancer end points and multimedia exposure assessments. Dr. Schoof is an internationally recognized expert on evaluation of arsenic and other metals (including lead) in the environment, as well as in the human diet, and on the bioavailability of metals from soil. Her experience with lead has included numerous exposure

studies a nd risk assessments for mine and smelter sites; risk assessments for manufacturing, landfill, and waste combustion sites; regulatory and research projects for/cited by Health Canada, EPA, State of Montana; and litigation projects (e.g., former shooting ran ge; mining and smelting site). Dr. Schoof has published more than 40 peer

- reviewed articles and chapters in scientific journals, books and proceedings. She has served on numerous peer review panels for U.S. agencies (e.g., EPA, ATSDR) and Canadian

ministri es, and on several National Research Council committees. She has also served as a member of the British Columbia Contaminated Sites Science Advisory Board, the Expert Advisory Panel for the

Canadian Metals in the Human Environment

- Research Network, and the Washington Model Toxics Control Act Science Panel. Dr. Schoof was certified as a DABT starting in 1986 and is a Fellow of the Academy of Toxicological Science. She is a member of the Society of Toxicology, Society for Risk Analysis and the International S ociety for Exposure Science.

Joel Schwartz, Ph.D. , M.D.
Harvard School of Public Health

Dr. Schwartz is Professor of Environmental Epidemiology at the Harvard School of Public Health and Director of the Harvard Center for Risk Analysis. He received his Ph.D. in Theoretical Physics from Brandeis University in 1980 and his M.D. from the Univers ity of Basel, Switzerland, in 2010. His research interests are in epidemiology and the health consequences of exposure to pollutants, focusing on the health effects of lead and air pollutants. Prior to his academic appointment at the Harvard School of

Publ ic Health, Dr. Schwartz was a Senior Scientist at EPA until 1994. Dr. Schwartz has studied the cardiovascular effects of lead exposure in adults, neurological/cognitive effects in children, auditory effects in children, and effects of lead on children’s g rowth. He has also done work on sources of lead exposure, including establishing gasoline lead as the major source of lead exposure in the U.S. Dr. Schwartz has studied both acute and chronic effects of air pollution exposure and recently established that the exposure to fine combustion particles in air at concentrations well below current standards are associated with a range of adverse health effects, leading to a tightening of the U.S. air quality standards. He is also experienced in the modeling of cont inuous covariates in epidemiologic studies, both for better covariate control and to more accurately assess the relationship between exposure and response. Dr. Schwartz has published more than 600 peer

- reviewed articles and chapters in scientific journals, books and proceedings. In addition, Dr. Schwartz has served on the editorial boards of several journals including

the International Journal of Biometeorology and the American Journal of Respiratory and Critical Care

Medicine . He has served on EPA’s Scien ce Advisory Board Ad Hoc All Ages Lead Committee, EPA’s Lead Clean Air Science Advisory Committee, and the National Academy of Sciences’ Committee on Assessing Lead Exposure in Critical Populations.Post

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- 6 Kenneth Unice, M

. S

. Cardno ChemRisk

Mr. Unice is a Scien ce Advisor at Cardno ChemRisk, He received his MS in Environmental Engineering from the University of Arizona in 1998 and has 15 years of quantitative human health risk, exposure assessment, and database experience. Prior to joining Cardno ChemRisk in 2004 , Mr. Unice was employed at AMEC as an Environmental Engineer and Health Scientist. Mr. Unice’s expertise is in the areas of environmental modeling, biokinetic modeling, indoor air modeling, occupational exposure assessment, human health risk assessment, e nvironmental fate and transport analysis, computer programming and database design. He has been involved in a number of projects involving exposure and

risk/dose

- response assessments for metals including an analysis of exposures to lead

- based paint,

refine ment of a biokinetic model for cobalt which was based on a framework originally developed for the International Commission on Radiological Protection (ICRP) age specific biokinetic model for lead, and

an assessment of the adverse health effects associated with cobalt exposure. Mr. Unice has co

- authored more than 35 journal articles on the topics of biokinetic modeling, biomonitoring, environmental fate and transport models, metals exposure, pathogen risk assessment, and exposure reconstruction. Mr. Unice ha s served as an external peer reviewer for EPA and as a peer reviewer on behalf of the Pennsylvania Department of Environmental Protection. He is a member of the International Society of Exposure Science (ISES) and the Society for Risk Analysis (SRA).

Ian von Lindern, Ph.D., PE TerraGraphics Environmental Engineering, Inc.

Dr. von Lindern is the Executive Director of TerraGraphics International Foundation (TIFO) a non

- profit Humanitarian environmental response organization. He served as Chief Executive Of ficer and Principal Scientist of TerraGraphics Environmental Engineering from 1984

- 2014. He has nearly 40 years of national and international environmental engineering/science experience that includes a variety of environmental assessments; studies in air , water, and soil pollution; toxic and hazardous materials investigations; remedial and cleanup plans; human health risk assessments; and application of statistical analysis techniques to multi

- disciplinary environmental problems. He has directed over 40 m ajor health/environmental investigations involving primary and secondary lead smelters and battery processors, landfills, and uranium mill tailings at several major mining/smelting sites in the U.S., as well as internationally. Dr. von Lindern worked for t he State of Idaho on various projects involving the Bunker Hill/Coeur d’Alene Basin Hill Superfund Site for more than 35 years as the head Risk Assessor and Project Manager. In that capacity he had extensive experience in applying exposure and biokinetic l ead modeling in assessing human health risk, developing cleanup criteria and remedial design. TerraGraphics

conducted site

- specific quantitative analysis of the relationship between observed blood lead levels and environmental variables, and one of the mos t extensive evaluations of lead paint as an additional source of exposure at a CERCLA site. Blood lead data relative to yard and house dust lead levels were analyzed to evaluate the effectiveness of the yard remediation program. Dr. von Lindern has a numbe r of peer

reviewed publications on the reduction of childhood blood lead levels and remedial activities at this site. He has led five international cleanup projects in China, Russia, Dominican Republic, Senegal, and Zamfara, Nigeria where severe mortality and morbidity effects have occurred in recent years. Dr. von Lindern has served as an EPA Science Advisory Board Member on five occasions: (1) Review Subcommittee for Urban Soil Lead Abatement Demonstration Project, 1993; (2) Subcommittee Assessing

the Con sistency of Lead Health Regulations in U.S. EPA Programs, 1992; (3) Review Subcommittee Assessing the Use of the Biokinetic Model for Lead Absorption in Children at RCRA/CERCLA Sites, 1988; (4) the Ad Hoc All

- Ages Lead Model (AALM) Review; and (5) National Ambient Air Quality Standard for lead Panel (2006

- 2008). He also served on the EPA Clean Air Scientific Advisory Committee (CASAC) Subcommittee on Exposure Assessment Methodology, 1988, and was a member of

the EPA Criteria Assessment Committee for Lead in the Ambient Air from 1975

- 1986 and 2006

- 2008.Post

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- 7 Kathleen Vork, M

. P

. H

. , Ph.D. California Environmental Protection Agency

Dr. Vork is a Staff Toxicologist for the Office of Environmental Health Hazard Assessment (OEHHA) at

the California Environmental Protec tion Agency. She received her Ph.D. in Environmental Health Sciences from the University of California at Berkeley in 2003 and her MPH degree in Occupational and Environmental Health from the University of Minnesota, School of Public Health in 1988. Prior to her position at OEHHA, Dr. Vork worked for the California Childhood Lead Poisoning Prevention Program. Dr. Vork has extensive experience and expertise relating to exposure pathways and the pharmacokinetics of lead in worker and general adult populations . Dr. Vork has implemented various statistical and mathematical modeling methods to estimate, adjust and check the accuracy and consistency of predictions from models combining exposure pathways with physiologically

- based pharmacokinetic (PBPK) and

biokin etic models. She is the primary author of the report entitled “Estimating Workplace Air and Worker Blood Lead Concentration using an Updated Physiologically

- based Pharmacokinetic (PBPK) Model” (2013). She has conducted work involving the derivation of hum an lactation transfer coefficients for various chemicals including lead for the “Risk Assessment Guidelines Technical Support Documents for Exposure Assessment and Stochastic Analysis” (2012) and contributed work published in “The

Derivation of Non

- cancer Reference Exposure Levels” (2007) for the California Air Toxics Hot Spots program. Dr. Vork has worked collaboratively with multiple agencies and the public. She has served on California’s Advisory Committee for Training Regulations for Lead Paint Abatemen t while working for the California Lead Poisoning Prevention Program, and also Chaired the Lead Training Course Planning Committee while working for the Alameda County Lead Poisoning Prevention Program. Dr. Vork is currently a member of the Genetic and Env ironmental Toxicology Association of Northern California.Post

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Appendix B Meeting AgendaPost

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B

- 1 AGENDA External Peer Review Meeting for

EPA’s Approach for Estimating Exposures and Incremental Health Effects from

Lead due to Renovation, Repair, and Painting Activities in

Public and Commercial Buildings

January 13

- 14, 2015 9:00 AM to 5:00 PM (EST)

Conference #: 877

- 821

- 4158 Access Code: 3355197#

Day 1 Webinar Link: https://cc.callinfo.com/r/1ghy4cimbwqyi&eom

Day 2 Webinar Link: https://cc.callinfo.com/r/1n27z3ov64e8j&eom

DAY 1

8:30 AM EST Meeting Registration & Sign

- in

9:00 AM EST Welcome, Goals of Meeting , and Introductions

David Bottimore | Versar, Inc.

9:15 AM EST EPA Overview of Peer Review Process and Approach/Model

Stan Barone | U.S. EPA Office of Pollution Prevention & Toxics Cathy Fehrenbacher | U.S. EPA Office of Pollution Prevention & Toxics

9:50 AM EST Chair’s Introduction and Review of Charge

Deborah Cory

- Slechta, Chair | University of Rochester

10:15 AM EST Observer Comment Session

10:45 AM EST Break*

11:00 AM EST Discussion

– General Overview Comments

11:30 AM EST Discussion

- Response to Charge Questions

Issue 1: Monte Carlo and Sensitivity Analyses

12:15 PM EST Lunch*

1:15 PM EST Discussion

– Response to Charge Questions

Issue 1: Monte Carlo and Sensitivity Analyses, continued Issue 2: Estimates of Lead Dust Loadings and Concentr ations

2:45 PM EST Break*

3:00 PM EST Discussion

– Response to Charge Questions

Issue 3: Estimates of Transport of Exterior Lead Dust to Nearby Buildings Issue 4: Estimates of Exposure to Children and Adults

4:45 PM EST Wrap Up

5:00 PM EST Adjourn

*Time for breaks and lunch are approximate and at the Chair’s discretion.Post

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- External Peer Review of EPA’s Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

B

- 2 AGENDA External Peer Review Meeting for

EPA’s Approach for Estimating Exposures and Incremental Health Effects from

Lead due to Renovation, Repair, and Painting Activities in

Public and Commercial Buildings

January 13

- 14, 2015 9:00 AM to 5:00 PM (EST)

Conference #: 877

- 821

- 4158 Access Code: 3355197#

Day 1 Webinar Link: https://cc.callinfo.com/r/1ghy4cimbwqyi&eom

Day 2 Webinar Link: https://cc.callinfo.com/r/1n27z3ov64e8j&eom

DAY 2

9:00 AM EST Recap of Day 1 and Agenda for Day 2

David Bottimore | Versar, Inc.

9:15 AM EST Chair’s Review of Charge for Day 2

Deborah Cory

- Slechta, Chair

9:30 AM EST Observer Comment Session

9:45 AM EST Discussion

– Response to Charge Questions

Issue 5: Leggett Blood Lead Modeling

10:45 AM EST Break*

11:00 AM EST Discussion

– Response to Charge Questions

Issue 6: Estimates o f IQ Change/Characterization of Changes in Children’s IQ

12: 15 AM EST Lunch*

1:15 PM EST Discussion

– Response to Charge Questions

Issue 7: Estimating Adult Health Effects

2 : 45 PM EST Break*

3:00 PM EST Discussion

– Response to Charge Questions

Issue 8: Overall Draft Approach

4:00 PM EST Wrap

- up/Next Steps

David Bottimore | Versar, Inc.

4:30 PM EST Adjourn

*Time for breaks and lunch are approximate and at the Chair’s discretion.Post

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Appendix C

Li st of ObserversPost

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C

- 1 LIST OF OBSERVERS ( In

- person )

External Peer Review Meeting on EPA’s Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

January 13

- 14, 2014

Name Affiliation

Stan Barone U.S. EPA

Charles Bevington U.S. EPA

Kevin Bromberg U.S. S mall B usiness A dministration

Judith Brown U.S. EPA

Michele Burgess U.S. EPA

Doreen Cantor Paster U.S. EPA

Mark Casale Painting & Decorating Contractors of America

Mark Drozdov CES Environmental and Safety Center

Rebecca Edelstein U.S. EPA

Ernest Falke U.S. EPA

Cathy Fehrenbacher U.S. EPA

Toiya Goodlow U.S. EPA

Maria Hegstad Inside EPA

Cara Henning ICF International

Ruth Hummel U.S. EPA

Eileen Lee National Multifamily Housing

Yvette Lowney Exponent

Suril Mehta U.S. EPA

Tanya Mottley U.S. EPA
Bridget O’Brien U.S. EPA

Robert Overton ICF International

Austin Perez National Association of Realtors

Andrea Pfahles

- Hutchens U.S. EPA

Michelle Price U.S. EPA

Hans Scheifele U.S. EPA

Ron Yarbrough SSPCPost

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C

- 2 LIST OF OBSERVERS ( Webinar )

External Peer Review Meeting on EPA’s Approach for Estimating Exposures and Incremental Health Effects from Lead due to Renovation, Repair, and Painting Activities in Public and Commercial Buildings

January 13

- 14, 2014

Name Affiliation

Steve Anderson U.S. EPA

H. Kay Austin U.S. EPA

James Brown U.S. EPA

Lauren Brown Abt Associates

Walter Cybulski U.S. EPA

Alva Daniels U.S. EPA

Chris Dockins U.S. EPA

Scott Everett Utah Department of Environmental Quality

Tala Henry U.S. EPA

Ann Johnson U.S. EPA

Heather Klemick U.S. EPA

Anjali Lamba U.S. EPA

Meghan Lynch Abt Associates

William Mendez ICF International

Alison Pierce U.S. EPA

Joel Pounds Pacific Northwest National Laboratory

Bill Thayer SRC Inc.

Christina Wadlington U.S. EPA