Peer Reviewers:

Deborah Cory-Slechta, 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

Office of Pollution Prevention and Toxics

1200 Pennsylvania Avenue, NW

Washington, DC 20460

Prepared by:

Versar, Inc.

6850 Versar Center

Springfield, VA 22151
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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. GENERAL 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. Estimates 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. .................................................................................................... 126
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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 Biographical Sketches

Appendix B – Meeting Agenda

Appendix C – List of Meeting Observers
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EXECUTIVE 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 premeeting 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 charge 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 reviewer deliberations.

On the first day of the meeting, Versar began by providing information on the overall peer 

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 complexity.

 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 the 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 Appendices. It could also be 

improved by specifically defining the audience for which the document 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 Approach. 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 intended 
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goal. Additional comments included the need for additional data collection. A recurring 

theme was the need to define uncertainties across the Approach 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 

Approach.

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

rates and estimates 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 

overestimates 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 the 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.

 Questions 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 particularly 

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 bracket 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 increasingly 
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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.

 For adult human health effects, bone lead data are considered to reflect cumulative/longterm exposure that is not necessarily reflected in the available blood lead measures. In 

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 prolonged 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 to charge questions, and specific observations. Written peer 

review comments, as well as comments submitted to the docket by members of the public, will 

be considered by EPA as it revises the Approach.
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I. INTRODUCTION

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 regulation. 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 exposure 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 approach document) for 

the purposes of public review and comment (FRL-9914-12, http://www.gpo.gov/fdsys/pkg/FR2014-08-06/pdf/2014-18357.pdf). Consistent with guidelines for the peer review of influential 

scientific assessments, Versar, Inc., an EPA contractor, was tasked with assembling 10 to 12 

scientific experts to evaluate the technical approach document. 

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 Register (FRL9912-63, 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 health 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 candidates 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 publications; (2) being free of any conflict of interest (COI) and the appearance of 
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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 their 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 proposed 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 

announced the availability of additional information on the candidates, including education and 

professional 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 comments 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 12 experts who, collectively, provided expertise spanning the multiple subject 

matter areas covered in the technical approach document and, to the extent feasible, best 

provided 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 Agency
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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 public 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

teleconference 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 information 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: 

 Concerns were presented by the U.S. Small Business 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-1960s/pre-1930s

buildings have likely been renovated multiple times already and, therefore, the risk of 

exposure to lead paint is low). From public office buildings to schools, Mr. Bromberg 

questioned the scope of the Approach and the required expenditures to address the 

problem of lead exposure. He suggested that the focus of the Approach 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 

methods 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 the 
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industry. He also noted a difference between normal renovations and actual removal of 

lead, which are used interchangeably in the draft Technical Approach Document.

 Yvette Lowney of Exponent questioned whether accurate and representative data even 

exist 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 Approach 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 brief opening remarks from Versar followed by a 

second observer comment session. One 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 and 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 his company would be willing to provide 

environmental (dust wipe) and OSHA mandated biomonitoring (blood lead) data to 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 pre-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 revises the Technical Approach Document.
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II. 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 Monte 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 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 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 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 interiors, 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 activity 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 develop 
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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 converting lead 

loadings to lead concentrations. This method was recommended over a mechanistic massbalance 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 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.

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 loadings 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 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 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 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 and hard surface concentrations, and indoor dust loadings resulting 

from exterior renovations.

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 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 exposed 
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individual (within one room or moving in multiple rooms) 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 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.

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.

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 

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.

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 estimated 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 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.
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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 

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, developmental, 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 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 birth 

weight, and their use for estimating incremental adult health effects due to renovation 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 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 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.
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Question 7d. In this Approach, EPA estimated adult health impacts based on concurrent and 

lifetime average 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.

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.
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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 number 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 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.

With respect to the Approach itself, which has obviously evolved over time, it is a very 

sophisticated 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 renovation 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 through 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 that 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 another 
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potentially vulnerable population. 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), where 

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 into 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 incorporation 

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.

Panos 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-toeffect” sequence processes associated with Pb exposures due 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 identifying 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 uncertainty 

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 Pbcontaining 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 evaluation 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 making.

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). 
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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 is 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 incremental 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 

should 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 limitations 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 model 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 exposure, 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 goal.
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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 considered 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 unclear 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, 

particularly 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, together 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 distribution 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 compared 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 groupspecific assumptions, 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 

interpretation 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 the findings, potential 

exaggerating risk or perhaps more problematic, obscuring potentially important combinations of 

conditions that may be important to investigate further.
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Andrew Hunt

The effort that had obviously gone into the Approach Document was very impressive. It was 

well thought out and modularity of the various steps in the Approach facilitated the review of 

the flood of information that was provided. Nevertheless there were areas that seemed quite 

opaque to me and shuttling back and forth between the Approach Document and the appendices 

did not facilitate the review. And, some of the appendices were quite daunting, the experiments 

that were run as documented 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 Approach a more connected feel.

Naila Khalil

This is an impressive report that presents estimated 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 advanced 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 non-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 presented 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 sensitivity 

analyses were run to check the sensitivity of the model to variability in the input variables and 

other model parameters.
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Of concern with the Approach are the numerous sources of uncertainty, which is discussed in 

detail in the document. Many of these 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 would 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 postrenovation blood lead to measured blood lead in previous studies of renal disease and 

cardiovascular mortality. For those 2 outcomes, previous published 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 postrenovation estimates of blood lead 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 result 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 outcomes 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 manner.

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 leadbased paint (LBP) result in quantifiable health risks for 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 sequence 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 approach 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 address 
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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 renovations 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 combined 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 studies 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 response 

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 true 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 presentation 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 coupled 

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 distributions; 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 this matter, and clarifications and caveats 

have been reported. In the case of the EPA IEUBK childhood lead exposure biomarker model, 
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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 document 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 the 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 only 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 required. 

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 for 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 applications 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 managerial 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. 
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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 simultaneous 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 many 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 “renovation 

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 assume 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 interior 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 parts 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 health effects is much 

more uncertain and less convincing. My review focused on the assumptions used to estimate soil 
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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, concerns 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 use 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 and 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 recommend 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 endpoints 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 complex, 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 policy 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), however, 

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 extracted 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 include:

 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/cm2for 

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 with 
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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 historical 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 

supplementing 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 results 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.

 Development 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 these 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 model, 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).
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 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 clear 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 be 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. Considering 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, as 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 of 

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, support 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 knowledge, 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 experience 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.
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Generally, empirical models are limited to the range of data and conditions 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 error 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, 

the 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 are 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, 

necessitating 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 limitations 

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 several 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 informative 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 

these 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 Approach 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 chain 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 sensitivity 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 health 

policy cannot be determined at this point.

Kathleen Vork

Accuracy 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 the 
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incidence of adverse health effects. An independent evaluation of Version 5 predictions 

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 Information

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 

components 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 NHANES 

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 exposure 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. 
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IV. RESPONSE TO 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 considered 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 considering 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 definitely 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 uncertainty need to be addressed in a Monte Carlo approach. Chapter 2 of 

USEPA’s Exposure Factors 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 process 
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(NRC, 1994). [...] Proper characterization of variability and uncertainty 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 USEPA document (on page 2-5) describes the four-tier 

approach of the International Programme 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 two-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 fraction 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 uncertainty 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) that 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 susceptibility-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 then “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.” 

Gender-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 genderrelated differences in behavior and activity patterns, as well as in 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. Most 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 receptor 
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buildings) to be industrial buildings. Currently it seems the Monte Carlo modeling 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 selected. 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 representative (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 United 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 maps 

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. 

References

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 uncertainty 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 and 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 represents 
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variability associated with both the biokinetic differences in individuals as well as their 

differences in environmental media concentrations and intakes.” The NHANES data for all 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 material 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 statistic 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 underscore 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 95th percentile of 2.9 

μg/dL

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

μg/dL

This one misapplication of a summary statistic equates to an overestimate of the 95th 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, respectively.

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 renovation 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 of 
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the baseline PbB distribution for the given age groups. If the GSD is underestimated, then an 

adjustment 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 baseline 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 individual). 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 expertise 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 number 

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 over- 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 Monte 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 and scenario variables that 
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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 distributional 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 commercial 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 decision 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 people'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 economics 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 sources 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.
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- What would be the scientific basis to assume that interior and exterior renovation 

activities being done simultaneously can be evaluated as discrete/independent versus 

interactive 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 renovations 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 Clean 

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.

References

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 PJ, 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. 

Partridge J, Schneider R, Gibson R, Phillips P. 2004. Using Spatial Analyst to determine the 

effects of building demolition on elevated blood lead levels in children less than 72 months of 

age in St. Louis County: A preliminary look. Epidemiol. 15: S201.
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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 data) 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 use 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.

However, 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 sufficient 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 document 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 peerreviewed 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 accommodate 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 and 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 tabulated 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 Interior 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 the 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.
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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 

receptor, 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 likely 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, but 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 where 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 periods 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 

characterizing 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 expansion 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 imparting 

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. 
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Furthermore, this factor could be seen to affect the amount of time for 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, populations 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 overall 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 this 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 testing’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 blood 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 lockstep with remobilized Ca needed for 

fetal skeletal growth. Bone Pb is reabsorbed during other physiological stresses from childhood 
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to adulthood. Bone Pb releases from immobilization are a known phenomenon. The magnitude 

of such releases appears to be affected 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 exposure, 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 association 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 accumulated 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 of 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., individuals 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 population 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 exposures. 

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 

socioeconomic 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 typically 

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. A 
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number of these would appear to be more precise for modeling renovation 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 significant 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 Health 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 GA: 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 numbers 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-related 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 associated with more significant exposures via high 

LBP loadings and fraction of surfaces covered with LBP. How much more than the stratacombined national average is to be found within NHANES strata data? Receptor individuals 

would therefore have higher PbB backgrounds 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 

the 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 exposure 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 of 
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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. Overall, 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 illustrative 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 potentially 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 lower 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/ft2. There 

are some current studies that show a more meaningful threshold for exposure and health risk 

should be in the range of 5 μg Pb/ft2. To the extent that EPA is currently looking at downward 

revision of its floor dust Pb enforceable standard from 40 μg/ft2, I don't see much 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 in 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.
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References

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

Lead and Arsenic 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 prevalence 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’ concerns 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 elderly, 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 children 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 and 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 noted 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 finetuning. 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 Census 
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stratifies the U.S. into numerous Metropolitan Statistical Areas, and 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 focus 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 equal 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 occur 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 else 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 kids 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 reflects, 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.
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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 and 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 explore 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 merits 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 Xs, I need values for the bs. 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 bs. 

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 Xs? 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 more 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 nothing 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.
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I could assume that actual doses are normally distributed about the mean with some standard 

deviation. 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 describing the dependence of the 

mean on the Xs 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 bs are obtained by regression modeling, the issue is that when 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 90th 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 90th percentile of exposure. 

Another key point is that the Monte Carlo simulations assume that the bs 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 bs, but the distribution of 

the Xs. 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 

condition. 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 Xs are clearly correlated. Estimating the distribution 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 environmental 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 Public 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 90th percentile of the distribution. 
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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 assessment. 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 presentation 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 95th 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 

address 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.

Ian 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 central-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 supporting 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 challenges 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 variables included in the 

analysis and reduce some of the potential bias introduced by assuming variable independence.
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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 interindividual 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 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 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 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.

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 “sensitivity 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) of 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 

analysis 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 large change in the input 

variables illustrates how model predictions would vary over a broader range of input value 
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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 universally 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 calculations (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 constant. 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 for 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 OAT 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 review 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 assigned 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 allowed 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. However 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.
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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 sensitivity 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 variables 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 

multivariate 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.
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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 

difficult 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, the 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 input. 

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 not 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 nonconcurrent.

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.
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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 representative 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 effect 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 understandable, 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 indices 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 results 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 itself, 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 results – 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 Approach. 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 

measured 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, as 
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numerous other uncertainties could effect a similar change. A causal effect 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 two 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 calculations. 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 sensitivity 

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.
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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 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 interiors, 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 activity 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 develop 

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 converting lead 

loadings to lead concentrations. This method was recommended over a mechanistic massbalance 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 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 comment 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 renovations that involve multiple walls, which seems a more likely scenario?
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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 understand 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 understand 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 paint 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 likely 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 indoor 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.
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The descriptions of the variables for the interior renovations with respect to behaviors 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 data 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 renovation.

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 limited 

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 practices 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). GSA 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 existing 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.action
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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-actenergy-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 

renovation 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 

potential 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 which 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 EPA 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 

activities. 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 appendices that these terms might be more appropriately 
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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 conversion. 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 fraction 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 situation, 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 any 

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 information 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 examine 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 studies (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 specifically focus on evaluating model 

assumptions and reduce associated prediction uncertainties.
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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 reducing 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, condition 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 estimation). 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 ratio 
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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 1989 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 refers 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/cm2.” 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 the 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/cm2threshold.

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/cm2)

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 C12 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%
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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 of 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 residential 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, particularly 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/cm2, 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 concern 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 such 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 development 

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 are significant risk co-factors in 

childhood lead poisoning. These variables are often multiplicative in effecting outcome blood 
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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 95th %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 

“worst‐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 95th %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 blood 

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 residing 

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 distributions. 

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 generating 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 coating 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 activities 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.html
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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]). 

References

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 Airflow and 

Contaminant Transport In and Around 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 samples 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 protocol 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 

...”
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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 Rasmussen 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 al. (2005) side by side wipe loadings and concentrations (using preweighed 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. Prokhorova, 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.
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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-square 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 could 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 than 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 produce 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 this 

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 concentration 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, concentration or vintage effects. A Table showing concentrations predicted 

(central tendency and distribution statistics) for typical loading rates would be helpful.
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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 proportional 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 due 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 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 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 and 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 point 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, 
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but it has not been fully validated for receptors very close to the release 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 models, 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 immediately 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 expect 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 derivation 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 this 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 inherently 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” and “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 issues 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/quic/). 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.
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Philip 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. These are line sources, or block 

sources. If AEROMOD is a buoyant plume based model, will it accurately report on the 

transport of an aerosolized dust from renovation activity? Is an accurate description of the 

particle size distribution produced by the renovation 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 data 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 soil? 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 both 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)

The 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 sources (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)
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It seems unlikely that 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 become 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 exterior 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 (FloorPb)

Cleaning 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, results 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 dust 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 reviewing 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 exist 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 of “ability to/desire to” clean-up 

associated with the practice.
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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 breathing 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 time.

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 individuals 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 the 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 in 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 Section 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 average 0.5 μg Pb/m3for 10 days of exterior 

renovation activities at older buildings with higher LBP Pb loadings, and assuming a deposition 
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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/m2/day or 800 μg/m2for 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/m2remain. 

By contrast, renovation-based deposition of air Pb to soil would be trivial, simply because of the 

masses of soil volumes taken for calculations. Using EPA's figure for soil mass of 2 g/cm3, 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 Pb/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 and 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 frequencies.

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 for 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-inisolation conceptual model underestimates 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 numbers 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.
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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 scenarios 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 estimated 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 structure. However the emission fraction appears to have been calculated 

taking into account only the exterior wall surface area.

Emission rate = (mg Pb/s) / (ft2exterior wall/s) = (mg Pb/ft2exterior wall)

Fraction aerosolized = (mg Pb released/ft2exterior wall) / (mg Pb in paint/ft2exterior wall) 

= (mg Pb released/mg Pb in paint)
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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 Pbcontaining 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/m2/day for a mat placed inside the entry way. When multiplied by 0.318 m2 * 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 the 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 difference 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 dust 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 for 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. 
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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 screening 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 indoor 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 deposited 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 important, 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 

aerosol, 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-suspension. 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 summarizing 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 accomplished 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 

emission 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.
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(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 reports 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 through 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_hill/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 activities 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 to 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 likely 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 instructed (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 represents 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 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 exposed 

individual (within one room or moving in multiple rooms) 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.
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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 adults >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 Overall 

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 childbearing/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 given 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 concern 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 occupied 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 fraction 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 

resolution with an approach that utilizes CHAD and individual-specific activity factors. Intake 
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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” “Meteorological 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, specific “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 prior 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) is 

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 inappropriate 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 variability (i.e., repeat measures with the same cohort) is unavailable.
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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 noting 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 estimating 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 spent 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 suggests 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 factors 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 Exposure Factors 

Handbook. It was noted that there are some recent data to be examined on this point. 

I would note 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; 
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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 approach 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 and 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, each 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 variability, 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 derive 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 water 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 M4), lead 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 degree 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 times the estimates from the alternate assumptions; whereas at the higher soil 
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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 were 

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.
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Estimated Lead Intakes 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

Total 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

Total 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

Total 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

Total intake 7.6 21.0 52.7

Default Intake/Reduced 

Intake 1.5 2.1 2.4

* RBA = relative bioavailability adjustment
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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 percentile 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 trace 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 distribution 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 most 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 improvements 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 rates 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), suggesting that the US EPA(2011) soil ingestion rate 

substantially overestimates soil ingestion. 

 An analysis of combined soil and dust ingestion developed using a mechanistic handto-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, while 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 intake of 0.1 gram/day. 
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 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 the 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 could 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 Household 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. Calabrese. 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 Assessment 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 Analysis. 32(3): 448-463.
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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 concentrations 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 Solid 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 Total 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 reasonable. 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 is 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 renovation. 

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 renovation.” This statement suggests renovation frequency has 
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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 cumulative 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 sequenced 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 genderdependent information. Animal studies quite clearly 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 pregnancy 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 the 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 questions 
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that arise from this. The use of blood leads from 18-50 years of age seems to significantly overexpand 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 

for 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 inclusion 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 section 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 Georgopoulos

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) remain 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 scaling 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 “transient” 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 

data 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 

exposures, 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 size) 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 the 
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variability of all other physiological factors affecting deposition and uptake of 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 

should 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 specified.” 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 microenvironment 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 lead 

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 

combined 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 since 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 FiedlerKelly, 2014); nevertheless, the lack of available data prohibits a more detailed and robust 

analysis of variability effects.
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 Converting particulate matter intake to uptake requires both characterization of particlesize dependent deposition across in the oral cavity and across different regions of the 

respiratory system as well of bioavailability 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 that 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.envres.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 into 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 physiologically-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.
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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 Leggett 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 used 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-based 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 decrements 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 gendervariable 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 exposures 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 reviewed 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 in 
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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 basedpharmacokinetic (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 deficiencies, 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 Pbbone and PbB serial data available, e.g., Fleming 1999, Nie et al. 2005, OEHHA 2013.

The necessity to consider erythrocyte Pb saturation tweaking in 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 for 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 worker 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 body 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 affected 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 racebased 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 ALAD 2 
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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. 514: 23-29.

Bergdahl IA, Sheveleva M, Schutz A et al. 1998. Plasma and blood lead in humans: capacitylimited 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 measures 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 workers 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 number 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 the 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 EPA 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.
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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 the 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 

throughout. 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 pertinent 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 in 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 serum 

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 because 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 multiple 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 the 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 addition, 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. 
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What are the effects of these changes 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 sample 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 measured 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 regardless 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 between 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 confidence 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 confidence. 
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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 better fits.

Panos 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:

 Figure 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 lead 

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 will 

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 are 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 quantify distributions across the input variables. Choices include:
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 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 

question.

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 adequately 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 PbB 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 

trimester 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 differently 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 deliveries 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 

toxicokinetically 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. Clin. Med. 134: 631-640.
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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 differently 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 point 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 concentration 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 opportunity 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 (which 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 most 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 states1: 
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“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 amenable 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 leading 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 scenarios, 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 

cleared, 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 for 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. 20072.

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.
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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 changes 

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 potentially 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 (1996) 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 transfer 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 

overview 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 incorporated 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).
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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 lead 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 in 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 versus 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.
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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

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 concentrationresponse functions, depending on the relevant exposure period for a given 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 Approach 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 

most 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 years 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 exposure 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 using 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 noted 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 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.
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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 three 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. Presentation 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:

 Diffusible 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. Then this same metric could be estimated for 

the short-term exposure scenarios evaluated in the Approach to provide an “apples to apples” 
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comparison of biologically available Pb. See for example, the explanation of biomonitoring 

equivalents in Hays et al. 2007. 

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

1Hays, 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 extended 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 distribution for that population. 

Most observed blood lead populations show GSDs 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, intake and absorption, individual pre-dispositions and biokinetic 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 recommendation 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, and 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.
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Kathleen Vork

Cumulative blood lead index (CBLI) or “lifetime blood lead” may be useful for an assessment of 

similar exposure histories. However, 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 histories 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 predicts 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 estimated 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 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.

Deborah 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 analyses.

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).
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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 activities) 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 lead 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 measures 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. Further 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 by 
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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 because (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 consistent 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 of 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 shape, 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 enough.

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 repeated 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: 643-649.
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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 rare 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 doseresponse 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 longterm Pb contamination has totaled 300+ million metric tons and much of this accumulation 

persists in environmental 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 Lanphear 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 sub10 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 with their rising PbB level.

Rosalind Schoof 

I have no comments on this model at present.
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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 with 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 have 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 response 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 background 

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 risk 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 disadvantaged 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 

and reviewers. 
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Kathleen Vork

I defer to the other panel members who have more in-depth knowledge of these health effects 

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

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, developmental, 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 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 reduction 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 fetus (birthweight) 

and the aging segment of the population. Having said that, it may be that inclusion of all of the 
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health endpoints in addition to IQ, particularly as blood lead levels have continued to decline, 

represents diminishing returns when you consider 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 buildings, 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 anyone 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 dust 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 uncertainties in the use of metrics that 

represent a broad range of health effects that can be associated with PbB. Ideally a doseresponse 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 “signal” 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 the 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 adult health 

effects due to renovation activities for P&CB presented in the Approach used estimates for 
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different parameters. Parameters are uncertain and variable therefore as with any prediction 

model; there is uncertainty and variability of the concentration-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 

uncertainties 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, there 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 selected. 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 Function

Published study by Navas-Acien et al. (2009) was chosen to derive the C-R function. NavasAcien et al. (2009) analyzed NHANES 1999-2006, estimating relationships between reduced 

kidney function and blood lead. The geometric mean blood lead level for 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, education, 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 highly 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 Approach, 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 estimates for 

category 3-5 kidney disease for the U.S. population. 
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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 estimated 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 effect. 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 

measurements 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 

the 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 concentration-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 

more 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 range 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 data 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 

variable.
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The best fit for the concentration-response relationship was a linear 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 supralinear, 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. pooled analysis.

The study population in Zhu et al.'s evaluations in terms of the strength of the concentrationresponse 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 references 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 noted 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 appropriate, 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 renovation-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 

female 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: 1499-1507.
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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 generally 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 small-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 renovations 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 supralinear 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 occupant 

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 individual's PbB is renovationbased 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 Sovietbloc countries 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 "washout" 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 Cardiovascular 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 contemporaneous 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 on 
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that criterion. Proteinuria 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 risk 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 the 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 disease 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 two 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 genderspecific 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 gender-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 analyses 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-yearold 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 that 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 

in this analysis. My reasons are twofold:
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 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 low 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 Rule (2012)2have 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 

clearance 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)
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“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 surface 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 birth-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) and 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 compared to the mean and 25th percentile eGFRs reported in the Nijmegen 

Biomedical Study (Wetzels et al. 2007)3. A line is drawn at 60 mL/min/1.73 m2to indicate the 

reduced GFR threshold used in the Approach. The population consisted of subjects from the 

eastern 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- or 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). If 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 men, and thus are more likely to exhibit a eGFR < 60 

ml/min/1.73 m2at 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 recommended the plausibility 

of low-dose effects of lead on the kidney be revisited prior to finalizing the Approach.
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Figure 7.1: Blood lead versus mean 25th 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) and “[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 include 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-pregnancy BMI as a predictor of birth weight, it 

0

0.5

1

1.5

2

2.5

20 40 60 80

Blood Lead (mcg/dL)

Age

Blood lead

2011-2012 NHANES

Blood Lead

0

2 0

4 0

6 0

8 0

100

18-24 40-44 60-64 80-84

eGFR (ml/min/1.73 m^2)

Age

eGFR

Non-diseased caucasian females of 

the Nijmegen Biomedical Study

Mean

0

2 0

4 0

6 0

8 0

100

120

18-24 40-44 60-64 80-84

eGFR (ml/min/1.73 m^2)

Age

eGFR 

Non-diseased caucasian males of 

the Nijmegen Biomedical Study

Mean

0

2 0

4 0

6 0

8 0

100

18-24 40-44 60-64 80-84

eGFR (ml/min/1.73 m^2)

Age

eGFR

Non-diseased caucasian females of 

the Nijmegen Biomedical Study

P25

0

2 0

4 0

6 0

8 0

100

18-24 40-44 60-64 80-84

eGFR (ml/min/1.73 m^2)

Age

eGFR 

Non-diseased caucasian males of 

the Nijmegen Biomedical Study

P25
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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 with 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 causative. Underweight pre-pregnancy BMI may be associated with smallfor-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 

increased 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 implication 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 infants. 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 doseresponse mechanism). 

Glomerular hyperfiltration can occur during pregnancy and diabetes mellitus (e.g. Helal et al. 

2012)6resulting in meaningful increases in glomerular filtration rate. It is difficult to embrace 

the concentration-response relationship (for blood concentrations < 5 to 10 g/dL) proposed 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 non-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 prediabetes in pregnancy) 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 helpful 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 stable, 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 prepregnancy 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.
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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 the 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 be 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 potential 

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 through 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.

5Wuerzner, 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 population. 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 above 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 of 
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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, studies 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 the 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 interpreting 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 comments 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 cardiovascular 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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 These findings add to the body of evidence that have steered the Centers for Disease 

Control and Prevention (CDC) in 2012 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 μg/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 leads 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. In 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 

Environmental Chemicals, Updated 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 epidemiological 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 lead levels to incident nonspine 

fractures and falls in older women: the study of osteoporotic fractures. J Bone Miner 

Res 2008, 23:1417-1425.
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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 al.

(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 

below 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). Several 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., 2000). 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 exposure.

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 establish 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” (MMWR, 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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 The premise of Precautionary Principle (http://www.sehn.org/wing.html) states that it is 

a public health responsibility to protect the population from exposure 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 2012, 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 the 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 coefficients 

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 to 

keep in mind. First, there were relatively few people in the extreme ends of the lead distributions 
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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. Second, 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 concentration 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 baseline 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 the 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 attenuated 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 point 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 Mushak

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 agestratified 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 occurring 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-seamed canned goods (largely done by 1990).
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Rosalind Schoof 

Consistent with my response to Question 7a, only low birth weight should be considered as a 

relevant health endpoint 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 successfully 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 future. 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 correlated 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 establishing 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 the 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 between 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. 
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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.

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 stated 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 appropriate 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 useful 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 provided 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 endogenous 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–

343. [PubMed]
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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 (halflife) 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 over years to decades. The halflife 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 allow 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 Approach 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-Level 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 

health 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 Mushak

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 the measure 

represents the best indicator of body Pb burden in young children having Pb-related IQ loss. 
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Young children’s bone Pb is also very mobile with limited Pb accumulation and rapid bloodbone 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 believe 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 identification 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. These 

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’ states1: 

“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 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 findings 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 95th percentile.”
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Taking 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

1Goyer, R. 2004. Issue Paper on the Human Health Effects of Metals. 

http://www.epa.gov/raf/publications/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 average 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 Approach 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 represent 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 shorter 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 actually 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 short) approach 

was for cognitive function in aging.
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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 approximation 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 sense 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 (in 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 studies do not support the idea that short-term low-tomoderate 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 likely 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 renovationrelated increased bone lead and subsequent pregnancy-induced release of bone lead into the 
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blood stream. Another way to think about it is that women who are exposed to renovationrelated 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 horizon 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 

incidence 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 metric 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 months 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 

assessment.
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Reference

1U.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 Cory-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 formatting 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 field 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 account 
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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 renovation 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 

blood 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 validated 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 updated 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, 
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however, that this calibration 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 processes 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 application 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-defined 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 models 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 notation, 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 increasing the probability for mistakes in calculations). The same 

unit may appear in different formats, even on the same page (e.g., ug and μg, ft2

and ft2, etc.). 

o Mathematical notation in the Report and the Appendices is quite arbitrary. 

Though the Report and the Appendices are not - strictly “formal” mathematical 

documents, they would be more readable if they followed standard practice and 
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conventions (as one example, the symbol “Adj” traditionally refers to the adjoint 

of a mathematical operator and here 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 recommended 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, the notation/font (e.g. roman 

versus italic; serif 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 higher 

“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 μg/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 scientific studies and risk-relevant regulatory analyses. It is strongly 

recommended to use standardized notation for all flowcharts that document informationprocessing procedures, e.g. according to the ISO 5807-198 standard (Information 

Processing - Documentation 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 include 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 multicomponent 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 important process 

of model evaluation becomes more challenging and it cannot be limited to "groundtruthing" just the "end results" of the calculations. It is not uncommon to have a good 

agreement of predictions with observations though compensation of different errors, 

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 sequence, 

separately and independently. Furthermore, this evaluation process needs to be 
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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 to 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 written 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 

document 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 from 
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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 explore 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 and 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 that 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 representative 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 eliminated less important pathways from the Approach 

to be somewhat of an obstacle. There are many examples among U.S. EPA assessments where 
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the use of a step-wise process, including a screening step, has been extremely useful in focusing 

the analysis. A recent 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 

residuals1, where the steps included literature review, data collection, exposure review, 

screening 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 carried 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 was 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 and 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 were 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 95th percentile 

prediction. This concern would be alleviated 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

1U.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 presented 
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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 postprocessing of many of the underlying data sources. 

Pertinent potential exposure scenarios have been 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 should 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 numerous 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 likely 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).

Transparency

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 chosen 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.
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V. SPECIFIC 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 selfreference”

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 activity 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, especially 
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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-containing 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 

variable 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 

additional 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 assumed 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 since 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 specific 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.”
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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 assessed.

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 much 

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, commercial 

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 United 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. More 
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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 – VertPlasticEff), 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 arbitrary 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 extension 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 positive 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 (m3)”

Comment: This is confusing terminology – a “location” should not 

have a “volume”. Is this the indoor microenvironment volume?
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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.

34 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 

categories 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 

column

“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 

renovated 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.
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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/ft2)”

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 two 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 

combination 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 appear 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, ft2, ug , μg, 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 

distribution 

and particle 

densities

It would be useful to present quantitative outcomes of the scoping 

results that support these parameterizations,
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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

(μg/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 

paragraphline 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 too 
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Page Paragraph Comment or Question

broad, especially considering the difference between them. If they were 

based the 25th and 75th percentile, then I wonder how much higher the 

soil concentration would be for the 90th and 95th 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 controlling 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 other 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 references.

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 (2nd 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.
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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.

Rosalind 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. Renovation activities have the potential to create new longterm 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 nonfatal CVD events separately, rather than as a group. That makes no 

sense. 
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Joel Schwartz 

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 being 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 hitting 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, 
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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 roads, 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 

exposed 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, 

7385

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 

per 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 deciliter (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 will 
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Kenneth Unice

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 Section 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 from 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 the 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 “interior”.

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 included, such as 

Appendix C of USEPA 1998a, which presents the raw survey data in 
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Kenneth Unice

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 Appendix 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 

importance 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 What 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 10-4s

-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 revised 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/m3, 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 aquifer 

porosity, rather than total porosity. The value of 2600 kg/m3appears 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 to 
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Kenneth Unice

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 interior 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 vintage 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 the 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. 
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Ian von Lindern 

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 account 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 in 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 assuming 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 column 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 reduced to 

approximately 3400 grams) to coincide with XRF bone measurements 
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Kathleen Vork

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), 100-9.

If another conversion source was used, please cite this source.
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Appendix A

List of Peer Reviewers and Biographical Sketches
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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, 2015

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 Associates

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 codirector of the Behavioral Sciences Facility Core and director of the Animal Behavior Core. Dr. CorySlechta 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 Department 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 Wood 
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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 Sciences, 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. CorySlechta has served on the editorial boards of several journals including Environmental Health 

Perspectives, Neurotoxicology, Toxicology, Toxicological Sciences, Fundamental and Applied 

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 Institute of Technology. 

At EOHSI, he established and directs the Computational Chemodynamics Laboratory (CCL), a state-ofthe-art facility for informatics and modeling of complex environmental and biological systems. 

Furthermore, he co-directs the Center for Exposure 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 interacting 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 the 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 processes 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 documents 

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.

Integral 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 of Environmental Science and Forestry in 

1999, focusing his research on applications of probabilistic modeling to lead risk assessment. Prior to 
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joining Integral Consulting Inc. in 2013, Dr. Goodrum was employed at Cardno Entrix, Environmental 

Resources Management (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 years 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 white 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 Review 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, Arlington

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 Environmental 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 years, 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 distribution 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 associated 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 Science 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 School 

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 Environmental 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 Health 

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 lead 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 

occupational lead exposure and neurocognitive function and bone density. Dr. Khalil is on the editorial 
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board of the Journal of Family Medicine & Community Health and serves as a reviewer for journals such 

as Environmental Health Perspectives and British Medical 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 

Epidemiology 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 assistant 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 Medicine; 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 American 

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, metalloids, 

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 Pathology, University of Florida School of Medicine. His postdoctoral 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 education, 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 

and 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 Clean 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 international 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 and 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.
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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 

her 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 regulatory, 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 and 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 range; 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 

ministries, 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 Society 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 University 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 

Public 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 growth. 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 continuous 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 Science 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-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

A-6

Kenneth Unice, M.S.

Cardno ChemRisk

Mr. Unice is a Science 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, environmental 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, 

refinement 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 has 

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 Officer 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 major 

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 the 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 lead 

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 most 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 number of peerreviewed 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 Consistency 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-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

A-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 Protection 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 

biokinetic 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 human 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 Abatement 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 Environmental Toxicology Association of Northern California.
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

Appendix B

Meeting Agenda
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

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 Concentrations

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-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

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 of 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-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

Appendix C

List of Observers
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

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. Small Business Administration

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 SSPC
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

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