UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

 WASHINGTON D.C. 20460

OFFICE OF THE ADMINISTRATOR

 SCIENCE ADVISORY BOARD

July 7, 2011

EPA-SAB-11-008

The Honorable Lisa P. Jackson

Administrator

U.S. Environmental Protection Agency

1200 Pennsylvania Avenue, N.W.

Washington, D.C. 20460

Subject: SAB Review of EPA’s Approach for Developing Lead Dust Hazard Standards 

for Residences (November 2010 Draft) and Approach for Developing Lead Dust Hazard 

Standards for Public and Commercial Buildings (November 2010 Draft)

Dear Administrator Jackson:

In 2001, EPA’s Office of Pollution Prevention and Toxics (OPPT), under the Toxic Substances 

Control Act (TSCA), established lead dust hazard standards for residential buildings. The 

standards are used to identify the presence of lead hazards and are also used as clearance 

standards for lead abatement and other lead hazard control activities. OPPT is considering 

possible revision of the residential lead dust hazard standards as well as the development of lead 

dust hazard standards for public and commercial buildings. OPPT developed two draft 

documents entitled Approach for Developing Lead Dust Hazard Standards for Residences 

(November 2010 Draft) (hereafter referred to as the “Residential Document”) and Approach for 

Developing Lead Dust Hazard Standards for Public and Commercial Buildings (November 2010 

Draft) (hereafter referred to as the “Public and Commercial Document”) that describe the 

technical approach for developing the standards. OPPT sought consultative advice from the 

SAB Lead Review Panel on early drafts of the documents and requested SAB peer review of the 

revised documents.

In these two documents, EPA developed candidate lead dust hazard standards (i.e. the amount of 

lead dust present on floors and window sills) aimed at providing various levels of protection for 

sensitive populations using blood lead concentration as a marker of adverse health effects. 

Blood lead concentrations of 1.0, 2.5, and 5.0 micrograms per deciliter were selected to protect 

children against IQ deficits in both residences and public and commercial buildings. In addition, 

for public and commercial buildings where children are not expected to visit, the targeted blood 

lead concentrations were selected to protect against hypertension in adults and against adverse 

developmental effects on the fetus of a pregnant woman. To develop these candidate standards, 

EPA used an approach that relied on available data from the National Health and Nutrition 
ii

Examination Survey (NHANES) to relate lead dust loading and blood lead concentrations in 

children in both residences and public and commercial buildings. EPA also utilized biokinetic 

models that consider all lead exposure pathways (i.e. air, water, diet, soil, dust) to estimate blood 

lead levels in children and adults.

The SAB was asked to comment on the clarity and transparency of the documents, empirical 

modeling, biokinetic modeling, analyses of variability and uncertainty, and choice of models for 

developing the lead dust hazard standards. The charge questions for the two documents are 

nearly identical. Although the Panel discussed the two documents separately, the Panel’s written 

response to the charge questions are applicable to both documents, except where noted in the 

report. For both documents, the SAB supports the overall modeling approaches, however, the 

SAB has a number of recommendations aimed at improving the modeling approaches discussed 

in the documents. The SAB is not able to provide a specific recommendation about model 

selection without the benefit of a comparative analysis of the revised empirical and biokinetic 

modeling approaches. The SAB also recommends inclusion of an incremental risk assessment 

approach as described in the recommendations below. The SAB responses to the EPA’s charge 

questions are detailed in the report. Major comments and recommendations for both documents 

are provided below. 

• The SAB supports EPA’s selection of target blood lead concentrations of 1.0 and 2.5

micrograms per deciliter for children. The SAB does not support the high target 

blood lead concentration of 5 micrograms per deciliter due to recent studies indicating 

significant adverse health effects in children with blood lead concentrations well 

below 10 micrograms per deciliter. In contrast, for the development of an “adult 

hazard standard”, the concentration-response relationship between blood lead and 

adverse health effects is not as well characterized in adults as in children and the SAB 

is not making a recommendation on the appropriate target adult blood lead level 

concentration.

• In modeling the relationship between lead dust and blood lead, EPA considered all 

routes and pathways of exposure contributing to blood lead levels. Since the focus of 

EPA’s effort is on establishing a lead dust standard, the SAB recommends examining 

blood lead concentrations as a result of only lead dust exposures by using an 

incremental risk assessment approach. An incremental risk assessment approach

assesses how incremental changes in dust lead result in incremental changes in blood 

lead concentrations. In the empirical modeling approach, this can be accomplished 

by varying lead dust and comparing the slopes of the relationship between lead dust 

and blood lead concentration. In the biokinetic modeling approach, this can be 

accomplished by either zeroing out all other exposure inputs, or by varying only lead 

dust while holding all other exposures constant. This will also reduce the 

considerable uncertainty resulting from estimation of the other exposure parameters. 

• In the empirical approach, data from the NHANES was used to develop the 

relationship between lead dust levels and childhood blood lead levels in residences. 

Lead dust loading values were first converted to lead dust concentrations and then 

related to childhood blood lead levels. Since lead dust loading is a better predictor of 
iii

blood lead concentration than lead dust concentration, and because lead dust

standards are expressed as lead dust loading, the SAB recommends assessing the lead 

dust loading to blood lead concentration relationship directly, without converting lead 

dust loading to lead dust concentration. 

• The SAB is concerned that the lower lead dust loading values from the NHANES 

data were not evaluated in establishing the candidate lead dust hazard standards. The 

SAB recommends examining the full range of NHANES data including lead dust 

loading values less than 5 micrograms per square foot. Furthermore, the SAB is 

concerned that EPA’s reanalysis of the NHANES data does not reflect the importance 

of window sill contributions to blood lead concentrations and that EPA did not 

determine whether the NHANES data were representative of high risk exposures and 

the national housing stock. The SAB recommends comparing the results to other 

published epidemiologic studies to address these concerns. 

• In conducting the biokinetic modeling, the SAB recommends that EPA use the 

default input parameters indicated in Agency guidance. 

• In the absence of data to support an empirical model relating lead dust to childhood 

blood lead levels for public and commercial buildings, the SAB supports the use of 

the NHANES residential data for this purpose. 

• In developing a hazard standard for adults only, EPA used both the Leggett model 

and the Adult Lead Methodology. The SAB supports the use of the Adult Lead 

Methodology for estimating adult blood lead concentrations from lead dust in public 

and commercial buildings. 

The SAB appreciates the opportunity to provide scientific review and advice on this important 

matter. The SAB looks forward to the Agency’s response and would be pleased to provide 

additional advice on this subject matter.

Sincerely,

/signed/ /signed/

Dr. Deborah L. Swackhamer Dr. Timothy J. Buckley

Chair Chair

EPA Science Advisory Board SAB Lead Review Panel
iv

NOTICE

This report has been written as part of the activities of the EPA Science Advisory Board, 

a public advisory Panel providing extramural scientific information and advice to the 

Administrator and other officials of the Environmental Protection Agency. The Board is 

structured to provide balanced, expert assessment of scientific matters related to problems facing 

the Agency. This report has not been reviewed for approval by the Agency and, hence, the 

contents of this report do not necessarily represent the views and policies of the Environmental 

Protection Agency, nor of other agencies in the Executive Branch of the Federal government, nor 

does mention of trade names or commercial products constitute a recommendation for use. 

Reports of the EPA Science Advisory Board are posted on the EPA Web site at: 

http://www.epa.gov/sab.
v

U.S. Environmental Protection Agency

Science Advisory Board

SAB Lead Review Panel

CHAIR

Dr. Timothy J. Buckley, Associate Professor and Chair, Division of Environmental Health 

Sciences, College of Public Health, The Ohio State University, Columbus, OH

MEMBERS

Dr. Richard Canfield, Senior Research Associate, Division of Nutritional Sciences, Cornell 

University, Ithaca, NY

Dr. Corodon Scott Clark, Professor, Environmental Health, College of Medicine, University of 

Cincinnati, Cincinnati, OH

Dr. Kim Dietrich, Professor, Department of Environmental Health, College of Medicine, 

University of Cincinnati, Cincinnati, OH

Dr. Philip E. Goodrum, Senior Project Manager, Environmental Resources Management 

(ERM), Dewitt, NY

Dr. Sean Hays, President, Summit Toxicology, Allenspark, CO

Dr. Andrew Hunt, Assistant Professor, Department of Earth and Environmental Sciences, 

University of Texas at Arlington, Arlington, TX

Dr. David E. Jacobs, Adjunct Associate Professor, School of Public Health, Division of 

Occupational and Environmental Health MC922 , University of Illinois at Chicago, Chicago, IL

Dr. Michael A. Jayjock, Senior Analyst, The LifeLine Group, Langhorne, PA

Dr. Michael Kosnett, Associate Clinical Professor, Division of Clinical Pharmacology and 

Toxicology, Department of Medicine, University of Colorado Health Sciences Center, Denver, 

CO

Dr. Bruce Lanphear, Professor, Children’s Environmental Health, Faculty of Health Sciences, 

Simon Fraser University, Vancouver, BC, Canada

Dr. Thomas Louis, Professor, Department of Biostatistics, Johns Hopkins University 

Bloomberg School of Public Health, Baltimore, MD

Dr. Howard Mielke, Research Professor, Department of Chemistry and Center for 

Bioenvironmental Research, Tulane University, New Orleans, LA
vi

Dr. Joel Pounds, Scientist, Cell Biology & Biochemistry, Biological Sciences Division, Battelle 

- Pacific Northwest National Laboratory, Richland, WA

Dr. Michael Rabinowitz, Geochemist, Clinical Instructor in Neurology, Harvard University, 

Newport, RI

Dr. Ian von Lindern, President, TerraGraphics Environmental Engineering, Inc., Moscow, ID

Dr. Michael Weitzman, Professor, Pediatrics; Psychiatry, New York University School of 

Medicine, New York, NY

SCIENCE ADVISORY BOARD STAFF

Mr. Aaron Yeow, Designated Federal Officer, U.S. Environmental Protection Agency, Science 

Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-564-

2050, Fax: 202-565-2098, (yeow.aaron@epa.gov)
vii

U.S. Environmental Protection Agency

Science Advisory Board

BOARD

CHAIR

Dr. Deborah L. Swackhamer, Professor and Charles M. Denny, Jr., Chair in Science, 

Technology and Public Policy- Hubert H. Humphrey School of Public Affairs and Co-Director 

of the Water Resources Center, University of Minnesota, St. Paul, MN

SAB MEMBERS

Dr. David T. Allen, Professor, Department of Chemical Engineering, University of Texas, 

Austin, TX

Dr. Claudia Benitez-Nelson, Full Professor and Director of the Marine Science Program, 

Department of Earth and Ocean Sciences , University of South Carolina, Columbia, SC

Dr. Timothy J. Buckley, Associate Professor and Chair, Division of Environmental Health 

Sciences, College of Public Health, The Ohio State University, Columbus, OH

Dr. Patricia Buffler, Professor of Epidemiology and Dean Emerita, Department of 

Epidemiology, School of Public Health, University of California, Berkeley, CA

Dr. Ingrid Burke, Director, Haub School and Ruckelshaus Institute of Environment and Natural 

Resources, University of Wyoming, Laramie, WY

Dr. Thomas Burke, Professor, Department of Health Policy and Management, Johns Hopkins 

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

Dr. Terry Daniel, Professor of Psychology and Natural Resources, Department of Psychology, 

School of Natural Resources, University of Arizona, Tucson, AZ

Dr. George Daston, Victor Mills Society Research Fellow, Product Safety and Regulatory 

Affairs, Procter & Gamble, Cincinnati, OH

Dr. Costel Denson, Managing Member, Costech Technologies, LLC, Newark, DE

Dr. Otto C. Doering III, Professor, Department of Agricultural Economics, Purdue University, 

W. Lafayette, IN

Dr. David A. Dzombak, Walter J. Blenko Sr. Professor of Environmental Engineering , 

Department of Civil and Environmental Engineering, College of Engineering, Carnegie Mellon 

University, Pittsburgh, PA
viii

Dr. T. Taylor Eighmy, Vice President for Research, Office of the Vice President for Research, 

Texas Tech University, Lubbock, TX

Dr. Elaine Faustman, Professor and Director, Institute for Risk Analysis and Risk 

Communication, School of Public Health, University of Washington, Seattle, WA

Dr. John P. Giesy, Professor and Canada Research Chair, Veterinary Biomedical Sciences and 

Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Dr. Jeffrey K. Griffiths, Professor, Department of Public Health and Community Medicine, 

School of Medicine, Tufts University, Boston, MA

Dr. James K. Hammitt, Professor, Center for Risk Analysis, Harvard University, Boston, MA

Dr. Bernd Kahn, Professor Emeritus and Associate Director, Environmental Radiation Center, 

Georgia Institute of Technology, Atlanta, GA

Dr. Agnes Kane, Professor and Chair, Department of Pathology and Laboratory Medicine, 

Brown University, Providence, RI

Dr. Madhu Khanna, Professor, Department of Agricultural and Consumer Economics, 

University of Illinois at Urbana-Champaign, Urbana, IL

Dr. Nancy K. Kim, Senior Executive, Health Research, Inc., Troy, NY

Dr. Catherine Kling, Professor, Department of Economics, Iowa State University, Ames, IA

Dr. Kai Lee, Program Officer, Conservation and Science Program, David & Lucile Packard 

Foundation, Los Altos, CA (affiliation listed for identification purposes only)

Dr. Cecil Lue-Hing, President, Cecil Lue-Hing & Assoc. Inc., Burr Ridge, IL

Dr. Floyd Malveaux, Executive Director, Merck Childhood Asthma Network, Inc., Washington, 

DC

Dr. Lee D. McMullen, Water Resources Practice Leader, Snyder & Associates, Inc., Ankeny, 

IA

Dr. Judith L. Meyer, Professor Emeritus, Odum School of Ecology, University of Georgia, 

Lopez Island, WA

Dr. James R. Mihelcic, Professor, Civil and Environmental Engineering, University of South 

Florida, Tampa, FL

Dr. Jana Milford, Professor, Department of Mechanical Engineering, University of Colorado, 

Boulder, CO
ix

Dr. Christine Moe, Eugene J. Gangarosa Professor, Hubert Department of Global Health, 

Rollins School of Public Health, Emory University, Atlanta, GA

Dr. Horace Moo-Young, Dean and Professor, College of Engineering, Computer Science, and 

Technology, California State University, Los Angeles, CA

Dr. Eileen Murphy, Grants Facilitator, Ernest Mario School of Pharmacy, Rutgers University, 

Piscataway, NJ

Dr. Duncan Patten, Research Professor, Hydroecology Research Program , Department of Land 

Resources and Environmental Sciences, Montana State University, Bozeman, MT

Dr. Stephen Polasky, Fesler-Lampert Professor of Ecological/Environmental Economics, 

Department of Applied Economics, University of Minnesota, St. Paul, MN

Dr. Arden Pope, Professor, Department of Economics, Brigham Young University , Provo, UT

Dr. Stephen M. Roberts, Professor, Department of Physiological Sciences, Director, Center for 

Environmental and Human Toxicology, University of Florida, Gainesville, FL

Dr. Amanda Rodewald, Professor of Wildlife Ecology, School of Environment and Natural 

Resources, The Ohio State University, Columbus, OH

Dr. Jonathan M. Samet, Professor and Flora L. Thornton Chair, Department of Preventive 

Medicine, University of Southern California, Los Angeles, CA

Dr. James Sanders, Director and Professor, Skidaway Institute of Oceanography, Savannah, 

GA

Dr. Jerald Schnoor, Allen S. Henry Chair Professor, Department of Civil and Environmental 

Engineering, Co-Director, Center for Global and Regional Environmental Research, University 

of Iowa, Iowa City, IA

Dr. Kathleen Segerson, Philip E. Austin Professor of Economics , Department of Economics, 

University of Connecticut, Storrs, CT

Dr. Herman Taylor, Director, Principal Investigator, Jackson Heart Study, University of 

Mississippi Medical Center, Jackson, MS

Dr. Barton H. (Buzz) Thompson, Jr., Robert E. Paradise Professor of Natural Resources Law 

at the Stanford Law School and Perry L. McCarty Director, Woods Institute for the Environment, 

Stanford University, Stanford, CA

Dr. Paige Tolbert, Professor and Chair, Department of Environmental Health, Rollins School of 

Public Health, Emory University, Atlanta, GA
x

Dr. John Vena, Professor and Department Head, Department of Epidemiology and Biostatistics, 

College of Public Health, University of Georgia, Athens, GA

Dr. Thomas S. Wallsten, Professor and Chair, Department of Psychology, University of 

Maryland, College Park, MD

Dr. Robert Watts, Professor of Mechanical Engineering Emeritus, Tulane University, 

Annapolis, MD

Dr. R. Thomas Zoeller, Professor, Department of Biology, University of Massachusetts, 

Amherst, MA

SCIENCE ADVISORY BOARD STAFF

Dr. Angela Nugent, Designated Federal Officer, U.S. Environmental Protection Agency, 

Science Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 

202-564-2218, Fax: 202-565-2098, (nugent.angela@epa.gov)

Mr. Aaron Yeow, Designated Federal Officer, U.S. Environmental Protection Agency, Science 

Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-564-

2050, Fax: 202-565-2098, (yeow.aaron@epa.gov)
xi

ACRONYMS

ALM Adult Lead Methodology

EPA United States Environmental Protection Agency

GM Geometric Mean

GSD Geometric Standard Deviation

HUD United States Department of Housing and Urban Development

IEUBK Integrated Exposure Uptake Biokinetic Model

NHANES National Health and Nutrition Examination Survey

OPPT EPA’s Office of Pollution Prevention and Toxics

Pb Lead

PbB Blood lead

PbD Lead dust

QL Quasi-likelihood

SAB Science Advisory Board
xii

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY .................................................................................................. 1

2. BACKGROUND ................................................................................................................... 6

3. RESPONSE TO EPA CHARGE QUESTIONS................................................................. 7

3.1. Charge Question 1 – Approach Document ............................................................................. 7

3.2. Charge Question 2 – Empirical Models................................................................................ 10

3.2. Charge Question 3 – Biokinetic Models............................................................................... 14

3.3. Charge Question 4 – Analyses of Variability and Uncertainty............................................. 17

3.4. Charge Question 5 – Choice of Model for Hazard Standards............................................... 21

4. REFERENCES.................................................................................................................... 23

APPENDIX A – CHARGE TO THE SAB FROM EPA ....................................................... A-1

APPENDIX B – EDITORIAL COMMENTS ........................................................................ B-1
1

1. EXECUTIVE SUMMARY

This report was prepared by the Science Advisory Board (SAB) Lead Review Panel (the 

“Panel”) in response to a request by EPA’s Office of Pollution Prevention and Toxics (OPPT) to 

review two documents entitled Approach for Developing Lead Dust Hazard Standards for 

Residences (November 2010 Draft) (hereafter referred to as the “Residential Document”) and 

Approach for Developing Lead Dust Hazard Standards for Public and Commercial Buildings 

(November 2010 Draft) (hereafter referred to as the “Public and Commercial Buildings 

Document”). OPPT sought consultative advice from the SAB Lead Review Panel on early drafts 

of the documents (USEPA SAB Lead Review Panel, 2010) and sought SAB peer review of these 

documents. The SAB Lead Review Panel held a public meeting on December 6-7, 2010 and

deliberated on the charge questions (see Appendix A) and held a follow-up teleconference on 

February 22, 2011. The Panel’s draft report was approved by the Chartered SAB on May 18, 

2011. There were 5 charge questions for each document that focused on: the clarity and 

transparency of the document, empirical modeling, biokinetic modeling, analysis of variability 

and uncertainty, and choice of model. The two documents utilize a very similar technical 

approach and the charge questions are nearly identical. Although the Panel discussed the two 

documents separately, the Panel’s written response to the charge questions are applicable to both 

documents, except where noted in the report. This Executive Summary highlights the Panel’s 

major findings and recommendations.

Charge Question 1 - Overall Technical Approach

EPA’s Residential Document describes the methods that EPA proposes to develop

candidate lead dust (PbD) hazard standards for floors and windowsills in residences and childoccupied facilities (including daycare facilities). Blood lead (PbB) concentrations resulting from 

candidate PbD standards are estimated using two different modeling approaches, i.e. empirical 

and biokinetic. The results are compared against a range of target PbB concentrations (1.0, 2.5, 

and 5 μg/dL) that offer differing levels of protection against IQ deficits in children.

EPA’s Public and Commercial Document describes the methods that EPA proposes to 

develop candidate PbD hazard standards for floors and windowsills in public and commercial 

buildings. The approach for estimating the impact of candidate PbD hazard standards on 

children in public and commercial buildings is identical to the approach used in the Residential 

Document. In public and commercial buildings where children are not likely to visit, EPA is 

considering development of an “adult hazard standard” using biokinetic models. The modeling 

results are compared against a target range of adult PbB concentrations (1.0, 2.5, 5, 10, and 20 

μg/dL) that offer differing levels of protection against hypertension in adults and adverse 

developmental effects on the fetus of a pregnant woman who occupies a public or commercial 

building.

The SAB supports EPA’s selection of target PbB concentrations of 1.0 μg/dL and 2.5 

μg/dL for children, but does not support the target PbB concentration of 5 μg/dL due to recent 

studies indicating significant adverse health effects in children with PbB concentrations well 

below 10 μg/dL (Canfield et al., 2003; Lanphear et al., 2005). The concentration-response 

relationship between blood lead and adverse health effects is not as well characterized in adults 
2

as in children and the SAB is not making a recommendation on the appropriate target adult blood 

lead level concentration.

Although the SAB generally supports the overall modeling approaches described in both 

documents, it recommends a variation on its application. The current application not only 

considers PbD, but also other sources and pathways of exposure (e.g. diet, drinking water) and 

their contribution to absolute levels of PbB. Since the focus of EPA’s effort is on establishing a 

PbD standard, the SAB recommends that EPA apply its empirical and biokinetic modeling 

approaches so that the PbD pathway of exposure is more effectively isolated. This can be 

achieved through an incremental modeling approach where the incremental influence of PbD 

(loading or concentration) on PbB concentration is examined. This approach is advantageous in 

isolating the PbD contribution and excluding the additional uncertainty associated with other 

exposure pathways including air, water, soil, and diet. The SAB strongly recommends inclusion 

of this incremental risk assessment approach which assesses how changes in incremental PbD 

levels result in incremental changes in PbB concentrations. 

With a few key exceptions, the SAB found both documents to be thoughtfully developed 

and well written. These documents provide important quantitative insights into the relationships 

among the variables and the value of different models for estimating PbB concentrations from 

PbD hazards. The general overall approaches discussed in the documents were clear. However, 

the overall clarity and transparency of both documents can be improved by including an 

executive summary, providing an adequate context for how the standards can be used, expanding

the discussions on the degree of improvement in PbB concentrations that differing candidate 

PbD standards will achieve, and providing an analysis of the differences between the different 

approaches.

Charge Question 2 - Empirical Models

Under the Agency’s empirical modeling approach, the PbD to childhood PbB relationship 

is derived from the National Health and Nutrition Examination Survey (NHANES) data as 

described in Dixon et al. (2009). However, EPA deviated from the analysis provided by Dixon 

et al. (2009) due to concerns over their use of log-log regression model approach and other 

criticisms. The SAB finds many of the criticisms of the Dixon model to be not well-supported, 

lacking clarity, and in some instances are inaccurate. The SAB expresses confidence in the 

Dixon model results and recommends that the Agency continue to include these results in 

comparisons between the various modeling approaches.

EPA also performed a reanalysis of the NHANES data using a quasi-likelihood 

generalized linear modeling method (hereafter, the “NHANES QL model”). EPA’s reanalysis 

using the NHANES QL model included a conversion from PbD loading to PbD concentration in 

order to compare the results with the results of the biokinetic modeling. The SAB expresses 

support for inclusion of the NHANES QL model in the analysis, but strongly recommends that 

EPA perform a direct analysis of the PbD loading to PbB relationship without converting PbD

loading to PbD concentration. For comparison between empirical modeling and biokinetic 

modeling purposes, the SAB recommends that the PbD loading to PbD concentration conversion 

be performed within the biokinetic model. The SAB further recommends that EPA include data 
3

from other studies describing the relationship of PbD loading to concentration to assess the 

validity of the modeled loading/concentration relationship. 

The SAB is concerned that the lower PbD levels from the NHANES data (<5 μg/ft2

) were 

not evaluated in establishing the candidate PbD hazard standards. The SAB recommends 

examining the full range of NHANES data including the lower PbD loading values. 

The SAB is concerned that EPA’s reanalysis of the NHANES data does not reflect the 

importance of window sill contributions to PbB and that EPA did not determine whether the 

NHANES data were representative of high risk exposures and the national housing stock. The 

SAB recommends comparing the results to other epidemiologic data to address these concerns. 

Charge Question 3 - Biokinetic Models

In both documents, EPA used two biokinetic models, the Integrated Exposure Uptake 

Biokinetic Model for Lead in Children (IEUBK) and the Leggett model, to estimate children’s 

PbB concentrations resulting from the candidate PbD standards. The SAB finds that the results 

from the IEUBK model used in this approach may not be accurate due to the selection of input 

parameters differing from the default input parameters recommended in the model guidance. 

The SAB recommends including IEUBK modeling using the default input parameters. The SAB 

also recommends providing greater transparency in the rationale for the selection of input 

parameters differing from the defaults. Since the IEUBK is clearly the preferred model over the 

Leggett model for estimating children’s PbB concentrations, the SAB recommends that the 

Leggett model results be moved to an appendix. 

In the Public and Commercial Document, in the development of the “adult hazard 

standard”, EPA used the Leggett model and the Adult Lead Methodology (ALM) to estimate 

adult PbB concentrations resulting from the candidate PbD standards. The SAB supports the use 

of the ALM because it is simple to use, is used extensively in other EPA Programs, and produces 

more plausible estimates of average population PbB concentrations than the Leggett model.

Charge Question 4 - Analyses of Variability and Uncertainty

EPA expressed the results of the biokinetic modeling as a lognormal distribution using a 

geometric standard deviation (GSD) as a way of representing variability. The SAB supports 

expressing variability for the biokinetic modeling in this manner. For the empirical modeling 

results, the SAB recommends estimating the variability in the predicted PbB distribution directly 

from the NHANES data. 

The SAB acknowledges limited exposure data for relating PbD to childhood PbB in 

public and commercial buildings. The NHANES data relate PbD to childhood PbB in residential 

settings and application of this data set in establishing the PbD to childhood PbB relationship in 

public and commercial buildings introduces uncertainty. Nevertheless, the SAB believes that the 

NHANES data are the best surrogates given that there are no data available to suggest that the 

relationship between PbD and PbB differs from public and commercial buildings to residential 

buildings.
4

The advantage of the NHANES data and the empirical approach is that the true 

variability between PbD loading and PbB is captured from a population representative sample. It 

should be noted that levels of PbD and PbB observed within this study were at relatively low 

levels. Therefore, the modeled relationship between PbD loading and PbB is largely defined by 

lower PbD loadings. Accordingly there will be greater uncertainty in the relationship of PbD and 

PbB levels above the range of data in NHANES. To address this, EPA might consider data from 

other epidemiologic studies that represent higher ranges, (e.g. Lanphear, 1996; Lanphear 1998). 

EPA has performed appropriate analyses to adjust for covariates. However, the overall model 

explains less than 50% of variance and it has a high non-zero intercept term. Clearly, there are 

many factors that contribute to the variance in PbB. Unmeasured variables effects, which are 

reflected in the intercept values of the regressions, require further consideration. Uncertainty in 

the intercept directly affects the baseline PbB concentration and also increases the variance and 

uncertainty in the predicted values. 

The input parameters used in the IEUBK modeling differ from those recommended in the 

model guidance, particularly for the geometric standard deviation (GSD) term. In the IEUBK

model, the GSD term is intended to reflect variability in PbB concentrations between children 

that are exposed to the same lead media concentrations. In the EPA documents, however, a 

range of GSD parameters are derived from NHANES survey results, which reflects variability in 

PbB concentrations from different lead media concentrations and exposures. If EPA considers 

the GSD from NHANES, it needs to be applied appropriately to reflect variation in PbB for a 

constant exposure. 

Agreement between the mean empirical estimates and the biokinetic modeling estimates 

would provide considerable comfort in using either, or both, to develop a standard. On the other 

hand, significant differences in the means or intercept values could suggest that the baseline 

PbBs are not adequately explained, or there are important input variables missing, or the 

NHANES database is not representative of the population of concern and the intercept includes 

significant unmeasured effects. To aid in this comparison between the empirical and biokinetic 

models, the SAB recommends running the biokinetic models in three ways: (1) using the 

standard Agency default parameters, (2) adjusting the baseline input parameters to those values

that best reflect the NHANES population, and (3) adjusting the baseline parameters to those 

values that best reflect the population to which the regulation will apply. 

Charge Question 5 - Choice of Model for Hazard Standards

For both documents, EPA proposes to use the NHANES QL model to estimate children’s 

PbB concentrations from the candidate PbD standards. The SAB believes it is premature to 

recommend a specific model for developing PbD hazard standards without additional analysis 

and justification. As discussed in the empirical modeling section of this report, the SAB

supports the inclusion of the NHANES QL model in the analysis; however, the documents do not 

provide an adequate justification for the Agency’s choice of the model. The SAB also would 

like to see the results of the NHANES Dixon et al. (2009) log-log model considered in EPA’s 

comparison of modeling approaches. Furthermore, the SAB expresses concern about the EPA’s 

selection of input parameters for the IEUBK model and judges it premature to reject the IEUBK 

approach. 
5

The SAB recommends comparing the results obtained from the revised NHANES QL 

and IEUBK models to existing results of the Dixon model, using methods comparable to those 

employed in both documents and also using the incremental approach. Until then, the SAB is 

unable to recommend a specific model for developing PbD hazard standards. 

For the Public and Commercial Document, in the development of an “adult hazard 

standard”, EPA proposes to use the Adult Lead Methodology to estimate adult PbB 

concentrations from the candidate PbD standards. As discussed in the biokinetic modeling 

section of the report, the SAB supports EPA’s decision to use of the ALM over the Leggett 

model for this analysis. 
6

2. BACKGROUND

Human exposure to lead may cause a variety of adverse health effects, particularly in 

children. EPA’s Office of Pollution Prevention and Toxics (OPPT) regulates toxic substances, 

such as lead, through the Toxic Substances Control Act (TSCA). Through TSCA, OPPT

established lead dust (PbD) hazard standards for residential buildings in 2001. Under these 

standards, lead is considered a hazard when equal to or exceeding 40 micrograms (μg) of lead in 

dust per square foot on floors and 250 micrograms of lead in dust per square foot on interior 

window sills. The standards are used to identify the presence of lead hazards and are also used 

as clearance standards for lead abatement activities. OPPT is considering possible revision of 

the residential PbD hazard standards as well as the development of PbD hazard standards for 

public and commercial buildings. 

EPA previously sought consultative advice from the SAB Lead Review Panel on early 

drafts of technical approach (August 2010 Consultation Report) and sought SAB peer review of

two draft documents entitled Approach for Developing Lead Dust Hazard Standards for 

Residences (November 2010 Draft) (hereafter referred to as the “Residential Document”) and 

Approach for Developing Lead Dust Hazard Standards for Public and Commercial Buildings 

(November 2010 Draft) (hereafter referred to as the “Public and Commercial Document”) which 

describe the technical approach for developing the standards. 

EPA’s charge questions on these two documents are presented in Appendix A, and focus 

on the clarity and transparency of the document, empirical modeling, biokinetic modeling, 

analysis of variability and uncertainty, and choice of model. The SAB Lead Review Panel held a 

public meeting on December 6-7, 2010 to deliberate on the charge questions and a follow-up 

teleconference on February 22, 2011. The Panel’s draft report was approved by the chartered 

SAB on May 18, 2011. The two documents utilize the same technical approach and the charge 

questions are nearly identical. Although the Panel discussed the two documents separately, the 

Panel’s written response to the charge questions are applicable to both documents, except where 

noted in the report. Editorial comments are presented in Appendix B.
7

3. RESPONSE TO EPA CHARGE QUESTIONS

3.1. Charge Question 1 for Both Documents – Approach Document

OPPT has developed an Approach document for developing the hazard standards for 

floors and windowsills in residences and public and commercial buildings. This includes a 

description of the empirical and biokinetic approaches, as well as the resultant analyses 

used to estimate candidate lead dust hazard standards for residences. Please comment on 

the clarity and transparency of the document.

This general charge question pertains to the overall approach and the clarity and 

transparency of the documents. EPA’s Residential Document describes the approach EPA has 

taken to examine candidate lead dust (PbD) hazard standards for floors and windowsills in 

residences and child-occupied facilities (including daycare facilities). Blood lead (PbB) 

concentrations resulting from candidate PbD standards are estimated using two different 

modeling approaches, empirical and biokinetic. The results are compared against a range of 

target PbB concentrations that offer differing levels of protection against IQ deficits in children.

EPA’s Public and Commercial Document describes the methods that EPA proposes to 

examine candidate PbD hazard standards for floors and windowsills in public and commercial 

buildings. The approach for estimating the impact of candidate PbD hazard standards on 

children in public and commercial buildings is identical to the approach used in the Residential 

Document. For public and commercial buildings where children are not likely to visit, EPA is 

considering development of an “adult hazard standard”. Adult PbB concentrations resulting 

from candidate PbD hazard standards are estimated using the Adult Lead Methodology, which is 

used extensively in EPA’s Superfund Program. The results are compared against a range of 

target PbB concentrations (1.0, 2.5, 5, 10, 20 μg/dL) that offer differing levels of protection

against hypertension in adults and adverse developmental effects on the fetus of a pregnant 

woman who occupies a public or commercial building.

Target Blood Lead Concentrations

The SAB generally supports the overall modeling approaches described in both 

documents and supports EPA’s selection of target total PbB concentrations of 1.0 and 2.5 μg/dL

for children. The SAB does not support a selection of a target PbB concentraion of 5 μg/dL due 

to recent studies indicating significant adverse health effects in children with PbB concentrations

well below 10 μg/dL (Canfield et al., 2003; Lanphear et al., 2005). The concentration-response 

relationship between blood lead and adverse health effects is not as well characterized in adults 

as in children and the SAB is not making a recommendation on the appropriate target adult blood 

lead level concentration. 

One advantage of using a target total PbB concentration is that the proposed PbD 

standard will take into account exposure to lead from other media sources, which can be 

significant, especially in minority and low-income communities. This provides EPA an 

opportunity to incorporate environmental justice principles into its decision-making. One 
8

disadvantage to using a target total PbB concentration is the uncertainty of estimating the lead 

exposures from all other media, as detailed below.

Incremental Risk Assessment Approach

Although the SAB supports the selection of target absolute childhood PbB concentrations 

of 1 and 2.5 μg/dL, the SAB believes the current approach of evaluating a PbD level that by itself

would achieve a given target PbB concentration is flawed, because lead is a multi-media 

pollutant. This may simply be a function of how the data are presented. In any case, the SAB 

concludes that a simpler and more scientifically valid approach is to assess how changes in 

incremental PbD levels result in incremental changes in PbB concentrations, holding important 

covariates and other exposure inputs (i.e. air, water, soil, diet) at either zero and/or at national 

averages. This dynamic approach has been adopted by the Office of Environmental Health 

Hazard Assessment of the California Environmental Protection Agency (Carlisle and Dowling, 

2007; Carlisle, 2009) and was also used in a pooled analysis of PbD/PbB studies (Lanphear et al., 

1998). This approach requires a means of determining the incremental impact on PbB resulting 

from exposure to both floor and sill PbD. This can be achieved using both the biokinetic and 

empirical models and helps to alleviate uncertainty about the assumptions made for all other 

sources of lead exposure and the uncertainty about the absolute PbB concentrations. This 

method will enable the Agency to focus on likely changes in PbB from a decrement in PbD

levels. In the current EPA documents, it is implied that little improvement is likely to occur, 

regardless of PbD level, because current population PbB concentrations are near the target PbB 

concentration, driven largely by other sources of lead. 

The SAB strongly recommends that EPA consider inclusion of an incremental risk 

assessment approach in its analysis. Specific advantages of the incremental risk assessment 

approach include:

1. For the empirical models, incremental PbB can be estimated directly from the partial 

regression plots and, possibly, from the standardized coefficients of the regression 

(depending on the magnitude of co-variance with other factors).

2. For the biokinetic models, exposures from non-dust ingestion pathways (diet, air, soil, 

and water) can either be set to zero or held constant at baseline levels, thereby 

eliminating many sources of uncertainty from estimating these exposures.

3. The incremental approach facilitates risk management policy decisions regarding a 

target incremental PbB by providing a simple and clear presentation of the 

relationship between delta PbD and delta PbB, as well as the key factors that 

contribute to variability and uncertainty.

The incremental risk assessment approach does require specific decisions and 

assumptions, including:

1. Percentile of the PbB distribution that is the basis for the target risk level. For 

example, a delta PbB of 1 or 2 μg/dL at the 90th percentile.

2. Whether or not the PbD standard is intended to reflect the mass contribution of lead 

to dust from all sources (including non-residential sources) or more specifically lead 
9

sources associated with the residence (i.e., paint). If the standard is intended to 

control levels from any source, then the “baseline” PbD loading should be set to zero. 

The incremental risk assessment approach does not explicitly account for other sources of 

lead, which may be significant in minority and low-income communities. However, EPA can 

still apply environmental justice principles in its decision-making by selecting a target risk level 

(both incremental PbB concentration and percentile) that would be sufficiently protective for all 

populations, particularly for populations that have greater lead exposure. 

Clarity and Transparency of Document

With a few key exceptions, both documents are well written. These analyses provide 

important quantitative insights into the relationships among the variables and the value of 

different models for predicting residential PbD hazards for US children. The general overall 

approaches discussed in the documents were clear. However, there are several critical ways in 

which the overall clarity and transparency of both documents can be improved. Comments and 

recommendations on the clarity and transparency of specific assumptions and calculations of the 

empirical and biokinetic modeling are presented in the responses to those charge questions. 

The documents would benefit from the inclusion of an executive summary. The 

summary should explain the strengths and weaknesses of both the empirical and mechanistic 

modeling approaches in a way that can be grasped by practitioners. The Executive Summary 

should conclude with recognition of the generally robust findings across different models and 

data sets, which serve to strengthen the confidence in the results. 

The documents do not currently provide an adequate description of how the standards 

will be used. The SAB recommends adding a description of the two principal uses for the 

standards. For example, the first use of the standards is as a means to identify a PbD hazard (as a 

component of a “lead-based paint hazard”). The second use of the standards is for “clearance,” 

i.e., to determine if PbD levels following repairs or remedial action and cleanup in both marketrate and low-income federally assisted housing and other covered child-occupied facilities and 

public and commercial buildings has been adequate. For example, if PbD levels remain at levels 

above the standard, then repeated cleanup and remedial action would be required until 

compliance is achieved. In addition, levels of PbD greater than the standard would be disclosed 

to residents or buyers before they are obligated under a sales or lease contract, under existing 

EPA and HUD regulations.

The documents can be made more transparent by expanding the discussions on the degree 

of improvement in PbB concentrations that differing candidate PbD standards will achieve. The 

residential document states, “The results of the analyses...confirm that, under reasonable input 

assumptions, both the empirical and biokinetic models predict that large proportions (17–99 

percent) of young children would have blood-lead levels above all three target levels, even if the 

standards were set at loading levels far less than the current values (40 μg/ft2 for floor dust and 

250 μg/ft2 for window-sill dust). This general finding is robust across reasonable ranges of 

model inputs and exposure factor assumptions” (p.45). This seems to imply that the PbD 

standard will make little difference in PbB concentrations no matter how low it is set. However, 
10

if the residential floor PbD standard were to be reduced from 40 μg/ft2 (the current standard) to 

10 μg/ft2, the percentage of children with PbB concentrations above 5 μg/dL would improve 

from 83% to 53% (using the NHANES quasi-likelihood model, holding window sill PbD to 50 

μg/ft2). The Dixon log-log model results showed that the same reduction in PbD levels would 

result in an improvement from 52% to 24% of children with PbB concentrations greater than 5 

μg/dL. These improvements are quite large, yet are not transparent in the EPA documents. 

The documents can be made more clear and transparent by providing greater specificity 

and further technical detail rather than using value-laden terms, such as “reasonable assumptions” 

or “reasonable ranges”. 

The documents can also be made more transparent by showing the magnitude of the 

differences between the approaches. For example, the geometric mean PbB concentrations at a 

floor PbD loading of 5 μg/ft2 and window sill PbD loading of 50 μg/ft2 in the Dixon log-log 

model and the EPA quasi-likelihood (central tendency) model are very close at 3.8 and 4.1 μg/dL, 

respectively. Similarly, the percent with PbB concentrations above 5 μg/dL in both models is 33% 

and 38% respectively, again, very similar.

3.2. Charge Question 2 for Both Documents – Empirical Models

The empirical approach involves the estimation of blood-lead impacts based on analyses of 

empirical data from the 1999–2004 National Health and Nutrition Examination Survey 

(NHANES). Two analyses were used. First, the regression relationships among floor and 

windowsill dust, other covariates, and blood-lead concentrations that Dixon et al. (2009) 

derived were applied to predict blood-lead levels for the various hazard standards 

(combinations of floor and windowsill dust loadings). The second was an independent 

reanalysis of the NHANES data to derive alternate models for predicting blood-lead 

impacts; the variations from the Dixon et al. (2009) approach included changes to the form 

of the dust-loading variables and application of models that are inherently linear at low 

lead exposures, a relationship that is supported by a wide range of biokinetic data, and 

regression of blood-lead values against estimated dust concentrations, rather than dust 

loading. Please comment on the EPA reanalysis. 

The SAB commends the Agency for consideration of data such as NHANES in 

developing the PbD hazard standards. The Agency examined the Dixon et al. (2009) analysis of 

the NHANES data, which used a log-log regression model and also performed a reanalysis of the 

NHANES data using a quasi-likelihood generalized linear modeling methods (hereafter, the 

“NHANES QL model”). 

Dixon et al. (2009) Analysis

The Agency states that the Dixon analysis presents obstacles to its use for evaluating 

blood-lead impacts of floor and sill PbD hazard standards. The SAB finds many of the criticisms 

of the Dixon model to be not well-supported, lacking clarity, and in some instances are 

inaccurate. 
11

One of the Agency’s main criticisms of the Dixon log-log regression model is that it 

“does not appear to be consistent with linear low-dose biokinetics (e.g., linear dependence of 

PbB on lead dose under steady-state conditions), currently theorized to occur at low levels, that 

is supported by a large body of experimental and human data (USEPA 2006)” (p. 11 of the

Residential Document; p. 22 of the Public and Commercial Document). The SAB does not 

believe that a linear relationship between low dose lead intake and PbB in certain human 

biokinetic studies must constrain the development of an empirical model relating PbD loading to 

PbB. The SAB believes that, notwithstanding linear low dose toxicokinetics pertaining to lead 

ingestion, there can be multiple reasons that might result in a nonlinear relationship between 

interior PbD and PbB in optimized empirical models of the indoor residential environment. For 

example, these include differential confounding of PbD by soil lead and nonlinear rates of 

transfer of dust to the hands and mouth. EPA should carefully consider its rejection of the Dixon 

log-log regression model. 

EPA also seems to believe that the log-log Dixon analysis shows that PbB decreases as 

floor PbD increases at the upper tail of the data distribution, which is not consistent with the idea 

that higher exposures should result in higher PbB concentrations. The Dixon analysis used log 

transformation because that was the best fit to the dataset. The SAB does not believe that it is 

correct to state that the log-log approach results in a decrease in PbB as PbD increases, because 

the Dixon model does not in fact show such a relationship. While PbB concentrations do appear 

to level out or plateau at higher floor PbD levels, none of the published (Lanphear et al., 1998;

Lanphear et al., 2002; Dixon et al., 2009), data show the former declining at higher PbD levels. 

EPA also states that the Dixon log-log approach introduces co-linearity in the method 

used to impute missing window sill PbD loadings. Yet it appears EPA used other variables that 

are also likely to introduce some co-linearity. The documents’ clarity can be improved by a 

more detailed description of the choice of methods used to impute missing window sill PbD

loadings. Because floor and window sill PbD levels are so highly correlated, it is not clear why 

using floor PbD values to impute missing window sill PbD values is less valid than the EPA 

method of imputing missing values. Different imputation methods might best be explored 

further in the sensitivity analysis sections of documents. Another approach that could be 

examined for the imputation of missing PbD loading values, developed specifically for imputing 

PbD loading values below the detection limit, is that of Succop et al. (2004).

The SAB has confidence in the Dixon model results and recommends that the Agency 

continue to include these results in comparisons between the various modeling approaches.

NHANES QL Model

The SAB expresses support for inclusion of the NHANES QL model in the analysis, but 

has several comments and recommendations to improve the modeling approach.

Statistical Software Package

The SAB has concerns regarding the status of the software ("Survey") used to implement 

the QL modeling. The document references only a faculty web page 
12

(http://faculty.washington.edu/tlumley/survey/) as the source for what needs to be a very 

sophisticated software package to produce valid and efficient analyses. Most readers will not be 

familiar with the level of maturity and field testing of this particular package (an important 

activity for all software, commercial or open source), or the specific algorithms utilized for the

present analyses, and might have concerns that it has not been vetted sufficiently to qualify for 

use in guiding important public health policy decisions. It will be important for EPA to 

document performance in other important settings so that readers have a basis for trusting the 

validity and efficiency of this package to a degree similar to that of well-known commercial 

software such as SAS. If this is an experimental package, then it would be necessary to replicate 

the analyses using other software designed to accomplish the same goals to validate results.

Conversion of Lead Dust Loading to Lead Dust Concentration

The QL model used a conversion from PbD loading to PbD concentration and a second 

conversion back to dust loading as the output. While it is clearly necessary to convert dust 

loading to dust concentration for the purposes of comparing the empirical and biokinetic 

modeling approaches, it is not clear that the PbD loading to concentration conversion is needed 

for the quasi-likelihood data analysis. If the PbD loading to concentration regression is not used, 

the “noise” in the empirical models will likely be reduced, increasing the certainty in the results. 

The SAB strongly recommends that EPA perform the analysis using the QL model without the 

PbD loading to concentration conversion. The SAB recommends that the PbD loading to 

concentration conversion should take place in the biokinetic modeling. 

Additionally, the EPA documents should include analyses of other data sets to determine 

if the estimated regression of PbD loading with PbD concentration is consistent. The estimated 

regression used by EPA uses data from a HUD National Survey from the 1990s, which used a 

blue nozzle vacuum dust collection method to compare with dust wipe sampling. There are other 

data sets, such as the Rochester Lead-In-Dust study (Lanphear et al., 1995) that can be used to 

assess the validity of the loading/concentration relationship. For example, the Lanphear et al. 

(1995) study evaluated a wipe sampling method, a cyclone vacuum method, and an open-faced 

filter cassette vacuum method in a side-by-side study design that assessed the relative predictive 

value of each method compared to children’s PbB concentration. It is possible that the different 

sampling methods capture different particle size distributions, which can in turn affect the PbD

level.

Window Sill Lead Dust Assumptions

The SAB believes that EPA should consider the possibility that window sill PbD may 

exert a stronger influence on PbB than its analysis of the NHANES data using the QL model 

appears to suggest. Window sill PbD loadings are generally far higher than floor PbD loading 

(Jacobs et al., 2002). It may be noted that in the analysis of the NHANES data by Dixon et al. 

(2009), a model for childhood PbB that included both window sill dust and floor PbD yielded an 

R2 value of 23.0%, compared to an R2 value of 19.4% for a model that considered floor PbD

alone. 
13

In a recent study (Clark et al., 2011) from the HUD Evaluation, window sill PbD was 

found to have a significant impact on PbB through two pathways: its contribution to floor PbD

which had a direct impact on PbB and through entry PbD which had an impact on floor PbD and 

then on PbB. In another study, an increase in sill PbD loading from 50 to 700 μg/ft2

was 

associated with a doubling of the proportion of children who have a PbB concentration greater 

than 10 μg/dL, from 10% to 20% (Lanphear, 2006). The SAB recommends comparing the QL 

modeling results of their NHANES data analysis with these relevant studies in the literature. 

NHANES Data Handling

The SAB has several comments and recommendations on how EPA handled the 

NHANES data in their reanalysis using the QL model, particularly related to truncation of results, 

detection limits, and flooring type.

The documents do not display the results of the different models when PbD levels are 

below 5 μg/ft2

. This omission unnecessarily truncates the results and reduces the document’s 

transparency. Greater transparency would be achieved if lower PbD levels were also examined. 

For example, the Dixon et al. model displayed the results down to 0.25 μg/ft2 (Dixon, et al. 

2009). While there may be important analytical and feasibility constraints at such a level, the 

SAB strongly believes that the scientific relationship between PbD and PbB should be 

considered below 5 μg/ft2 to fully describe the relationship.

The documents would benefit from a more detailed explanation of how the impact of 

floor surface type was modeled in the QL model analysis of the NHANES dataset. For example, 

did the variable encompassing floors that were “smooth and cleanable” include or exclude floors 

that were carpeted (Table 3-4 in both documents, and Appendix B, Table B-2 in both documents)? 

The narrative would benefit from a brief discussion of any apparent reasons why floor condition 

exerted a greater influence on the Dixon log-log empirical model (Dixon et al, 2009) compared

to the EPA NHANES QL model (Table 6-1 in the Residential Document and Table 7-1 in the 

Public and Commercial Document).

Comparison of NHANES Data with Other Studies 

The SAB concludes that the results of NHANES data modeling should be compared to 

other epidemiologic studies, such as the Rochester Lead-In-Dust study (Lanphear et al, 1996) 

and a pooled analysis of data from 12 childhood PbB investigations (Lanphear et al, 1998). To 

the extent that there is consistency in the slope of PbD and PbB relationship observed in 

NHANES and these other epidemiologic studies, (which unlike NHANES accounted for 

potential confounding by lead in soil and water) there will be enhanced support for relying on the 

analysis of the NHANES data for PbD hazard standard development. 

The SAB believes that the documents would gain greater clarity if they were to examine 

the influence of higher PbD loadings than those in the NHANES database, because higher 

loadings are likely to be more representative of higher risk environments. The SAB believes that 

any PbD standard selected should help to ensure that populations with the highest exposures are 

adequately protected. Other high exposure data sets that EPA could examine include the 
14

Rochester Lead-In-Dust Study (Lanphear et al., 1995), the Evaluation of the HUD Lead Hazard 

Control Grant Program (NCHH and UC 2004) and the pooled dust analysis (Lanphear et al.,

1998). All of these data sets have higher PbD and PbB values than the NHANES database.

The SAB believes the documents could be improved by examining how well the 

NHANES data represent the nation’s housing stock. This evaluation could easily be 

accomplished by comparing certain demographic information in the NHANES database with the 

American Housing Survey and Current Population Survey databases. Such an exercise was 

completed for the HUD National Survey of Lead and Allergens in Housing (NSLAH), which 

found that variables such as region, race and ethnicity, housing tenure and type, poverty-toincome ratio, urbanization and others were not significantly different (Jacobs et al., 2002) when 

comparing the smaller NSLAH data set to the larger data sets. If the NHANES data are 

representative of both the population and its housing, confidence and transparency will be 

increased.

3.2. Charge Question 3 – Biokinetic Models

Charge for the Residential document:

Two biokinetic models were used to estimate children’s blood lead concentrations including 

EPA’s Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK), and 

the Leggett model. Information from the exposure scenarios is used to estimate relative 

contributions of exposures from different sources (soil, dust, air, diet, and water) and in 

different microenvironments. Please comment on the use of these models and the inputs to 

these models. 

Charge for the Public and Commercial Document:

Two biokinetic models were used to estimate children’s blood lead concentrations including 

EPA’s Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK), and 

the Leggett model. Information from the exposure scenarios is used to estimate relative 

contributions of exposures from different sources (soil, dust, air, diet, and water) and in 

different microenvironments. The Leggett model and EPA’s Adult Lead Methodology 

were used to estimate adult blood lead levels resulting from candidate floor and windowsill 

hazard standards. Please comment on the use of these models and the inputs to these 

models.

Estimation of Children’s Blood Lead Concentrations

The SAB supports the use of the Integrated Exposure Uptake Biokinetic (IEUBK) Model 

for estimating children’s PbB concentrations for both residential and public and commercial 

settings, but has specific comments and recommendations for improving the model runs. 

Additionally, the SAB believes that the Leggett model is less scientifically credible for 

estimating children’s PbB concentrations than the IEUBK model, when compared to the 

empirical results. The SAB therefore recommends moving the results from the Leggett model to 

an appendix. 
15

The SAB believes that the results from the IEUBK model used in this approach may not 

be accurate due to the selection of input parameters differing from what is recommended in the 

model guidance. The clarity and transparency of this modeling approach can be enhanced by 

providing a more complete description and justification for selection of input parameters. For 

example, the geometric standard deviation (GSD) is a key parameter in the IEUBK model that 

requires careful selection, as it exerts considerable influence on the estimated number of children 

who might have a PbB increment in excess of a targeted value. The GSD term reflects the 

collective contributions of individual variability in intake, uptake, and biokinetics for a 

population of children that are exposed to the same lead concentrations in dust and other 

exposure media. Individuals exposed to the same PbD concentration may experience different 

intake rates due to variability in soil and dust ingestion rates, for example. By specifying the 

GSD in this manner, the IEUBK model can be used to estimate the distribution of PbBs 

associated with a fixed concentration. The GSD of 1.6 recommended in the model guidance is 

considered to be a broadly applicable value, and was derived from several epidemiologic studies 

with paired environmental and PbB measurements in children (White et al., 1998). In the EPA 

documents, however, GSDs of 1.9, 2.1, and 2.3 were used, which were derived from the 

NHANES data without controlling for lead media concentrations. These GSDs do not reflect the 

variability of PbB concentrations associated with fixed lead media concentrations, and therefore 

are different from what the GSD term in the IEUBK model is intended to represent. The SAB 

recommends using a GSD of 1.6, or providing a justification for deviating from this default value.

The SAB recommends providing greater transparency in how the input values were 

selected and justified if they differ from EPA guidance. If different input values are used in the 

IEUBK modeling with sufficient justification, the SAB recommends including modeling with the 

default input parameters for comparison purposes.

The biokinetic models predict that non PbD exposure sources diet and water together 

contribute about twice as much to PbB as PbD sources (Table 6-2 in November 5, 2010 

Residential document). The SAB recommends that these observations be highlighted and 

presented in a very transparent manner. To further examine the impact of these non dust sources, 

the SAB recommends including several model runs using a range of values for diet and water as 

part of the sensitivity analysis described in the response to Charge Question 4. Because PbD

hazard standards are closely tied to predicted PbB concentrations, the SAB strongly suggests that 

the sensitivity be used to evaluate how PbBs predicted from non-dust pathways compare with 

alternative PbB thresholds. A threshold may be defined based on the relationship between total 

exposure and total PbB, or alternatively between the additional (incremental) change in PbB 

associated with exclusively dust-lead exposure.

Estimation of Adult Blood Lead Concentrations

For the development of an “adult hazard standard”, the Leggett model and EPA’s Adult 

Lead Methodology were used to estimate adult PbB concentrations resulting from candidate 

floor and windowsill hazard standards for public and commercial buildings. The SAB supports 

the use of the Adult Lead Methodology. The ALM is advantageous because it is a relatively 

simple and easily understood model and because the EPA has considerable experience using the 

approach. In addition, the ALM produced more plausible estimates of average population PbB 
16

concentrations than did the Leggett model. The SAB recommends that the results of the Leggett 

modeling be moved to an appendix.

Conversion of Lead Dust Loading to Lead Dust Concentration

As noted previously, the SAB believes that converting PbD loading to PbD

concentration in the empirical modeling is not appropriate. The SAB appreciates the need to 

make results consistent between the empirical models and biokinetic models. The SAB suggests 

that it is more scientifically sound to make this conversion within the biokinetic model for 

purposes of comparison with the empirical model. Therefore, in running the biokinetic model, 

EPA should convert PbD loading to PbD concentration to estimate PbB concentration.

Appendix E of both EPA documents presents a mechanistic model used in the PbD

loading to PbD concentration conversion. The SAB has two concerns, one conceptual and one 

computational, about the accuracy of this model for converting loadings to concentrations. First, 

recent work suggests that the model does not accurately represent the sources and composition of 

the large majority of particulate mass on indoor surfaces (please see individual comments from 

Dr. Michael Jayjock from the August 2010 Consultation Report). The second concern is a 

possible missing unit conversion factor of 10,000,000. This concern is described in further detail 

in Appendix B of this report.

Limitations of IEUBK and ALM Modeling Approaches

In support of clarity and transparency, it should be recognized that the EPA documents’ 

reliance on the IEUBK and the ALM limits the EPA to considering chronic, steady-state 

exposures, which is appropriate for setting the PbD hazard standards. This limitation is valid 

whether the models are used to simulate the contribution of PbD to absolute PbB or an increment 

change in PbB. While the SAB endorses the use of the IEUBK and ALM models, neither of 

these modeling approaches is adequate to simulate an acute or intermittent exposure to lead in 

dust. Recognition and a brief description of this limitation should be part of both the Residential 

and the Public and Commercial documents.

Similarly, entrainment of PbD to air is not considered in the biokinetic models. That is, 

in reality, lead in dust may contribute to lead in air, an input not explicitly included in the 

IEUBK or ALM models. EPA should consider this limitation and evaluate the potential impact 

of this limitation on PbB predictions, as ignoring the contribution of PbD to airborne lead may 

under-predict the contribution of dust loading on PbB predictions. Although the SAB is not 

convinced that entrainment is a significant contributor to PbB, this issue may help explain 

discrepancies in PbB predictions using the empirical vs. biokinetic models.

Other Biokinetic Modeling Issues

The impact lead in soil has on lead concentrations/loading in indoor dust was not 

adequately addressed in the assessment. The mechanistic model for indoor dust generation 

(Figure 3-5, Figure E-1, and elsewhere) designates the tracked in material as “soil”. The name of 

this term needs to be expanded to also include exterior PbD. The material that is tracked in is 
17

derived from a number of locations including the surface of the soil, sidewalks, roadways, porch 

and entryways etc. This material generally comes from the surfaces of these areas. Soil lead 

measurements are usually determined for cores of soil, most often about one inch in depth. 

Mielke et al. (2007) have developed a method (PLOPS) to obtain a sample from the surface of 

soil areas. Data using such a method would more likely represent tracked in particles than soil 

core concentrations. Terms such as “Outdoor Soil” and “outdoor soil particles” would be more 

accurately characterized by “outdoor soil and dust particles”. 

3.3. Charge Question 4 for Both Documents – Analyses of Variability and Uncertainty

Monte Carlo methodology was not used to evaluate the impacts of variability and 

uncertainty in model parameters on blood-lead estimates as insufficient data exist 

concerning the potential variability in many key model variables to support informative 

Monte Carlo modeling. Instead, point estimates of central tendency (geometric mean) 

blood-lead concentrations in children are derived utilizing statistical models based on 

empirical data and on biokinetic models of blood lead, coupled with assumptions regarding 

distributions of highly uncertain variables. The sensitivity of the deterministic 

relationships between dust lead and blood lead to changes in key variables and covariates is 

explored through sensitivity analyses. The modeling inputs and assumptions that most 

strongly affect the predicted blood-lead distributions associated with candidate lead-dust 

hazard standards have been identified, based on the measures of statistical uncertainty 

from the empirical analyses and sensitivity analyses of the biokinetic models. Please 

comment on the characterization of variability and uncertainty.

The SAB has several comments and recommendations regarding EPA’s characterization 

of variability and uncertainty in both the empirical modeling and biokinetic modeling (i.e., both 

the IEUBK model and the ALM [slope factor] models). In general, the SAB agrees with the 

decision to move away from the use of Monte Carlo analysis (MCA) as a means of propagating 

variability and uncertainty in the biokinetic model for purposes of estimating a probability 

distribution of PbB concentrations. MCA is generally viewed as a very useful tool for exploring 

questions that require probabilistic expressions for inputs and outputs, as well as for conducting 

sensitivity analyses. However, insufficient information is available to include biokinetic 

parameters in a probabilistic evaluation. MCA limited to just the exposure and bioavailability 

variables would likely underestimate the overall variability and uncertainty in the PbB

distribution. Instead, a two-parameter lognormal distribution is used whereby the central 

tendency parameter is quantified and the variance (represented by the geometric standard 

deviation [GSD]) is specified. This approach is consistent with historical applications of the 

IEUBK and ALM models and is a reasonable simplification given the uncertainties in defining 

input distributions and biokinetic modeling needed to support MCA. 

The lognormal model is also applied to the empirical modeling approach as a means of 

specifying a probability distribution of PbBs so that threshold exceedance probabilities can be 

estimated. It is intuitively appealing to use the same expressions of variability in the empirical 

and biokinetic models as this simplifies the model specification and reduces the burden of 

comparing and contrasting alternative modeling approaches. The GSD parameter becomes the 
18

single lumping term for all sources of variability, and the choice of a lognormal model has a long 

history of use in environmental data analysis and lead risk assessment. However, the SAB

concludes that the use of the lognormal model in the empirical approach misses an opportunity to 

capitalize on a strength of the empirical approach – namely, the fact that a statistical analysis of 

the NHANES data set presumably allows for a direct measure of the extent to which variance in 

PbB can be associated with changes in PbD loading (or concentration).

The SAB recommends that EPA adopt a weight of evidence framework that allows for a 

more direct comparison of estimates of variability and uncertainty in the empirical and biokinetic 

models. For the empirical models, variability in the predicted PbB distribution can be estimated 

directly from the data rather than by imposing the lognormal distribution model with an assumed 

GSD. EPA should explore the use of 100 x (1 – α)% prediction intervals on the regression as 

well as partial regression plots that relate PbD loading to changes in PbB as a means of 

estimating the slope (i.e., delta PbB associated with the delta PbD) within the range of the 

anticipated candidate standard levels. Results from the NHANES data analysis should be 

presented both graphically and in tables. Intervals for the original Dixon et al. estimates would 

be interesting, if obtainable, as well. Note that the prediction interval is preferred over the 

confidence interval because the prediction interval is analogous to percentiles of the PbB 

distribution at a given PbD, whereas confidence intervals would provide a measure of the 

uncertainty in the mean PbB at a given PbD. To the extent that the prediction interval from the 

empirical model overlaps with the distribution obtained by the biokinetic model, this provides 

greater certainty in using either approach to establish a relationship between a PbD standard and 

a corresponding reduction in exposure and risk. 

Empirical Modeling

The SAB acknowledges that there are very limited exposure data for relating PbD to 

childhood PbB in public and commercial buildings. The NHANES data relates PbD to 

childhood PbB in residential settings and application of this data set in establishing the PbD to 

PbB relationship in public and commercial buildings introduces considerable uncertainty. The 

SAB agrees with EPA in its use of residential NHANES data for establishing the relationship 

between PbD and childhood PbB within commercial and public buildings. This approach is 

necessary since there are very limited data available from commercial and public buildings upon 

which to otherwise estimate this relationship. The SAB believes that the NHANES data are the 

best surrogates given that there are no data available to suggest that the relationship between 

PbD and PbB differs from public and commercial buildings to residential buildings.

The advantage of the NHANES data and the empirical approach is that the true 

variability between PbD loading and PbB is captured from a population representative sample. It 

should be noted that levels of PbD and PbB observed within this study were below 10 μg/ft2

(92%) and 10 μg/dL (98%), respectively. Therefore, the relationship between dust Pb loading 

and blood Pb is largely defined by PbD loadings <10 μg/ft2. Accordingly, from these data there 

will be greater uncertainty in the relationship of dust and PbB concentrations above this range. 

To address this, EPA might consider other epidemiologic studies with data at higher ranges, (e.g. 

Lanphear, 1996; Lanphear 1998).
19

EPA has performed appropriate analyses to correct for the measured variables. However, 

the model has a high non-zero intercept term and the model fit explains less than 50% of 

variance. Clearly, there are many factors that contribute to the variance in PbB. The effects of 

unmeasured variables, which are reflected in the intercept values of the regressions, require 

further consideration. Uncertainty in the intercept directly affects the baseline PbB concentration

and also increases the variance and uncertainty in the predicted values. These effects could 

combine to inflate the estimated percentage of children to exceed target PbB concentrations due 

to factors unrelated to dust loading. 

A key issue addressed in the documents is the conversion of dust concentrations to dust 

loadings. Biokinetic models require concentration terms and the hazard standards are defined in 

loading terms, so a conversion is required. EPA used a regression relationship between PbD

loading and PbD concentration measurement from HUD data. Uncertainty in the regression 

equation (p. 16 of the Residential Document and p. 27 of the Public and Commercial Document) 

should be presented by way of confidence intervals on the regression line to better understand 

the statistical uncertainty attributed to the model fitting.

NHANES QL model predictions are expected values (arithmetic mean PbB), and yet 

EPA elected to interpret these as geometric mean (GM) values. The rationale for this 

interpretation is unclear, and the consequence is to overestimate the true GM values. EPA

should consider converting model predictions to true GM values based on (weighted) estimates 

of variance.

The empirical models use regression techniques to associate PbD loading for floors and 

sills with PbB. The biokinetic models assume that sill loadings are a minor contribution to the 

total dose. The apparent insensitivity of PbBs to sill lead raises a question as to the utility of 

various sill Pb standards as a tool for reducing lead risks. This point is inferred by the summary 

tables and discussion in the report, but should be more fully developed.

Biokinetic Modeling

The input parameters used in the IEUBK model runs vary significantly from those 

recommended in other Agency regulatory programs. For example, a range of GSD parameters is 

evaluated with values based on NHANES survey results. Variability in measured PbBs from 

NHANES reflects variability from multiple sources of exposure, including differences in PbD

loadings (and concentrations). This approach represents a departure from the concept underlying 

the use of the IEUBK model in which the distribution is intended to reflect variability in the 

population of children that may be exposed to the same media concentration. The SAB 

recommends that EPA not use NHANES to derive the GSD for use in the IEUBK model. 

The SAB recommends using the default GSD of 1.6 for which the IEUBK model was 

verified. The GSD should be adjusted upward from the guidance recommendation, only if EPA 

has justification to assume that the variance in the input exposure parameters is larger than that 

anticipated in the guidance recommendations. To some extent, this selection can be informed by 

the variance noted in QL analyses. These adjustments and attendant results should then be 

discussed in terms of exposure and biological plausibility. A direct comparison of the models 
20

can then made in terms of the predicted dust loading values necessary to protect 95% of the 

childhood population. 

As noted previously, it will be instructive to compare the PbB values predicted by the 

IEUBK model to those derived from the analysis of the NHANES data. Coherence in the 

outputs between the two modeling approaches may serve to enhance certainty in the findings. 

To accomplish this comparison, the output of several iterations of the IEUBK model should be 

examined. 

1. The first run should use the default parameters currently recommended in the IEUBK 

model guidance documents and EPA advisories. The default soil/dust concentration 

should be varied by substituting the dust concentration from the loading conversion 

equations into the dust portion of the soil/dust partition, and determining a weighted 

average for the soil/dust input concentration. The soil portion of the weighted

average should remain constant at the default value. The results can be plotted 

against dust loading to show change in estimated mean PbB concentrations and 

percent to exceed criteria.

2. A second run should adjust the baseline input parameters to those values, in EPA’s 

judgment, that best reflect the NHANES population that was addressed in the QL and 

Dixon analyses. The dust concentration should be varied, the soil concentration held 

constant. The results should be plotted in the same manner. Particular care should be 

taken in selecting the soil concentration value. The soil value used in the current 

document, taken from the National Survey of Lead and Allergens in Housing 

(NSLAH), may not be reflective of the NHANES database, or the population to be 

regulated. This run should be compared with the Dixon and QL models. Particular 

attention should be paid to the intercept and slope comparisons. Water and diet input 

parameters should also be varied. 

3. A third run should be examined that sets all of the IEUBK input parameters other 

than interior dust concentration (i.e. the variables corresponding to diet, air, and 

water) to zero or held constant at baseline levels (a variety of baseline values can be 

employed with different input values such as for water and diet to examine whether it 

would have an impact on the incremental increase of PbB concentrations from the 

incremental increase in PbD levels). The incremental impact of increasing interior 

PbD on PbB should be compared to the PbB increments observed in the empirical

models from partial regression plots (or the standardized regression coefficients) 

pertaining to interior PbD.

Comparison of Empirical and Biokinetic Modeling Approaches

The decision to establish a risk metric based upon either an absolute PbB distribution or

an incremental PbB concentration may be made after addressing some of the SAB’s concerns 

noted above. Agreement in means between estimates from the empirical and biokinetic models 

would provide considerable comfort in using either, or both, to develop a standard. On the other 

hand, significant differences in the means or intercept values could suggest that the baseline 
21

PbBs are not adequately explained, or there are important input variables missing, or the 

NHANES database is not representative of the population of concern and the intercept includes 

significant unmeasured effects. If the baseline PbB exceeds the target PbB because of the 

combined contribution of exposure from other sources, then even the lowest possible PbD

standard would be ineffective at reducing exposure sufficiently to achieve a target PbB. 

Consequently, the SAB would urge EPA to revisit the definition of the risk metric and how the 

link between changes in PbD exposure to expected changes in PbB is established. Focusing on 

the delta PbB may prove to be a more viable option.

A decision to use an incremental risk assessment approach may also be informed by 

comparing the slopes (and confidence intervals on the slopes). Note that estimates of the slopes 

will be more informative if differences in the intercepts can be reconciled. Differences in the 

slopes should be explored through sensitivity analyses, and attempt to quantify each of the key 

sources of uncertainty, including dust loading to concentration conversions, baseline soil 

concentrations, the soil to dust partition coefficients, and the floor to sill ratios.

3.4. Charge Question 5 – Choice of Model for Hazard Standards

Charge for Residential Document:

The document presents two empirical models and two biokinetics models. OPPT proposes 

to use the NHANES Quasi-Likelihood, Empirical Model for the estimation of the 

residential hazard standards. Please comment on this proposed choice.

Charge for Public and Commercial Document:

The document presents empirical and biokinetic models. OPPT proposes to use the 

NHANES QL, Empirical Model and the ALM model for the estimation of the hazard 

standards for floors and windowsills for children and adults, respectively. Please comment 

on these proposed choices.

The SAB did not find that the documents provided adequate justification for the 

Agency’s choice in models to use for the development of the PbD hazard standards. The SAB

recommends greater clarity and transparency in the justification of the Agency’s choice of

models. 

Choice of Model for Children

As discussed in further detail in the empirical modeling section of this report, the SAB

supports the inclusion of the NHANES QL model in the analysis, but concludes that the 

documents did not provide adequate justification for EPA’s choice. The SAB expresses

confidence in the results of the NHANES Dixon et al. (2009) log-log model and is concerned 

that EPA’s presentation and critique of that model lacks clarity and, on certain key points, is 

likely inaccurate. The SAB would like to see the Dixon et al. (2009) model considered in EPA’s 
22

comparison of modeling approaches. Furthermore, the SAB expresses concern about the EPA’s 

implementation of the IEUBK model and judges it premature to reject the IEUBK approach. 

In this report the SAB has made specific recommendations for revising the NHANES QL 

and IEUBK models so that their products can be more meaningfully compared to the Dixon et al. 

(2009) results. Most notably, the SAB recommends (1) that results for all models be presented 

using an incremental approach that describes how changes in PbD affect changes in children’s 

PbB concentrations, while holding constant all other sources of Pb exposure and relevant 

covariates; (2) that a more transparent comparison be made between the NHANES QL and the 

Dixon log-log model by revising the NHANES QL model to use PbD loadings directly, rather 

than convert loadings to concentrations; (3) that results be presented for the 0.25 μg/ft2 – 40 

μg/ft2 range of PbD loadings, with attention to the need for clarity in describing and displaying 

results in the range below 5-10 μg/ft2; and (4) that the current implementation of the IEUBK 

model be reviewed to ensure that appropriate default values have been used and that their 

primary data sources have been fully documented. 

The SAB urges EPA to compare the results obtained from the revised NHANES QL and 

IEUBK models to existing results of the Dixon et al. model, using methods comparable to those 

employed in the EPA documents and using an incremental approach. Until then, the SAB is 

unable to recommend a specific model for developing PbD hazard standards.

Choice of Model for Adults

The SAB acknowledges the lack of an empirical data base for estimating the PbB impacts 

of adult exposure to floor and window sill dust in public and commercial buildings, necessitating 

the use of a mathematical model. In agreement with EPA, the SAB supports the use of the Adult 

Lead Methodology (ALM) adapted to accept PbD exposures. The advantages of using the ALM 

include it being a relatively simple and easily understood model and considerable use and 

application of the ALM in EPA’s Superfund Program. In addition, the adapted ALM produced 

more plausible estimates of average population PbB concentrations than the Leggett model

produced. 

Consistent with its recommendations for all other models, the SAB urges the EPA to use 

an incremental risk assessment approach when implementing and presenting the results of the 

adapted ALM. In addition, because the model also requires a conversion of PbD concentration 

to PbD loading it is important to implement any changes made to that conversion algorithm 

based on the SAB’s comments in previous sections of this report.
23

4. REFERENCES

Canfield RL, Henderson CR Jr, Cory-Slechta DA, Cox C, Jusko TA, Lanphear BP. 2003. 

Intellectual impairment in children with blood lead concentrations below 10 μg per deciliter. N. 

Engl. J. Med. 348: 1517-1526.

Carlisle J, Dowling K 2007. Child-specific benchmark change in blood lead concentration for 

school site risk assessment. California Environmental Protection Agency: Sacramento, CA. April.

Carlisle J 2009. Revised California human health screening levels for lead. California 

Environmental Protection Agency: Sacramento, CA. September.

Clark S, Galke W, Succop P, Grote J, McLaine P, Wilson J, Dixon S, Menrath W, Roda S, Chen 

M, Bornschein R, Jacobs D 2011. Effects of HUD-supported lead hazard control interventions in 

housing on children's blood lead. Envrionmental Research 111(2):301-11.

Dixon SL, Gaitens JM, Jacobs DE, Strauss W, Nagaraja J, Pivetz T, Wilson JW, Ashley PJ 2009. 

U.S. Children’s Exposure to Residential Dust Lead, 1999-2004: II. The Contribution of Leadcontaminated Dust to Children's Blood Lead Levels, Env Health Perspect 117:468-474.

Gaitens JM, Dixon SL, Jacobs DE, Nagaraja J, Strauss W, Wilson JW, Ashley PJ 2009. U.S. 

Children’s Exposure to Residential Dust Lead, 1999-2004: I. Housing and Demographic Factors 

Associated with Lead-contaminated Dust, Env Health Perspect 117:461-467.

Jacobs DE, Clickner RL, Zhou JL, Viet SM, Marker DA, Rogers JW, et al. 2002. The prevalence

of lead-based paint hazards in U.S. housing Environ Health Perspect 110: A599–A606.

Lanphear BP, Emond E, Weitzman M, Jacobs DE, Tanner M, Winter N, Yakir B, Eberly S 1995. 

A Side-By-Side Comparison of Dust Collection Methods for Sampling Lead-Contaminated 

House Dust, Environ Res 68, 114-123.

Lanphear BP, Weitzman M, Winter NL, Eberly S, Yakir B, Tanner M, Emond E, Matte TD 1996. 

Lead-contaminated house dust and urban children's blood lead levels, Am J Public Health, Oct 

86: 1416 - 1421.

Lanphear BP, Matte TD, Rogers J et al. 1998. The contribution of lead-contaminated house dust 

and residential soil to children’s blood lead levels. A pooled analysis of 12 epidemiological 

studies. Environ Res, Section A 79:51-68.

Lanphear BP, Hornung R, Ho M, Howard CR, Eberley S, and Knauf K 2002. Environmental 

lead exposure during early childhood. Journal of Pediatrics 140: 40-47.

Lanphear BP, Hornung R, Khoury J, Yolton K, Baghurst P, Bellinger DC, Canfield RL, Dietrich 

KN, Bornschein R, Greene T, Rothenberg SJ, Needleman HL, Schnaas L, Wasserman G, 

Graziano J, Roberts R. 2005. Low-level environmental lead exposure and children’s intellectual 

function: An international pooled analysis. Environmental Health Perspectives, 113, 894-899.
24

Mielke HW, Powell ET, Gonzales CR, Mielke PW Jr. 2007. Potential lead on play surfaces: 

Evaluation of the “PLOPS” sampler as a new tool for primary lead prevention, Environ Res; 103: 

154–9.

National Center for Healthy Housing (NCHH) and University of Cincinnati (UC) Department of 

Environmental Health, 2004. Evaluation of the HUD Lead-based Paint Hazard Control Grant 

Program. Final Report, Washington, DC.

Succop P, Clark CS, Chen M, Galke W 2004. Imputation of Data Values that are Less Than a 

Detection Limit, Journal Occupational and Environmental Hygiene 1:436-441.

United States Envrionmental Protection Agency Science Advisory Board Lead Review Panel 

2010. Consultation on EPA’s Proposed Approach for Developing Lead Dust Hazard Standards 

for Residential Buildings and Commercial and Public Buildings, EPA-SAB-10-011, Washington, 

DC. August. Available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/F8DA254881FEC6898525778F004C789A/$File/EP

A-SAB-10-011-unsigned.pdf

White PD, Van Leeuwen P, Davis BD, Maddaloni M, Hogan KA, Marcus AH, and 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.
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APPENDIX A – CHARGE TO THE SAB FROM EPA

EPA Charge Questions for the Approach for Developing Lead Dust 

Hazard Standards for Residences

Background

TSCA section 403 directs EPA to promulgate regulations that identify, for the purposes of Title 

X and Title IV of TSCA, dangerous levels of lead in paint, dust, and soil. EPA promulgated

regulations pursuant to TSCA section 403 on January 5, 2001, and codified them at 40 CFR part 

745, subpart D (USEPA, 2001a). These hazard standards identify dangerous levels of lead in 

paint, dust, and soil and provide benchmarks on which to base remedial actions taken to 

safeguard children and the public from the dangers of lead. Lead-based paint hazards in target

housing and child-occupied facilities are defined in these standards as paint-lead, dust-lead, and 

soil-lead hazards. A paint-lead hazard is defined as any damaged or deteriorated lead-based paint, 

any chewable lead-based painted surface with evidence of teeth marks, or any lead-based paint 

on a friction surface if lead dust levels underneath the friction surface exceed the dust-lead

hazard standards. A dust-lead hazard is surface dust that contains a mass-per-area concentration 

of lead equal to or exceeding 40 micrograms per square foot (μg/ft2

) on floors or 250 μg/ft2on

interior windowsills based on wipe samples. A soil-lead hazard is bare soil that contains total 

lead equal to or exceeding 400 parts per million (ppm) in a play area or average of 1,200 ppm of 

bare soil in the rest of the yard based on soil samples.

On August 10, 2009, EPA received a petition from several environmental and public health 

advocacy groups requesting that the EPA amend regulations issued under Title IV of TSCA 

(Sierra Club et al., 2009). Specifically, the petitioners requested that EPA lower the Agency’s 

dust-lead hazard standards issued pursuant to section 403 of TSCA from 40 μg/ft2 to 10 μg/ft2or 

less for floors and from 250 μg/ft2 to 100 μg/ft2 or less for window sills. On October 22, 2009, 

EPA granted this petition under section 553(e) of the Administrative Procedures Act, 5 U.S.C. 

553(e) (USEPA, 2009a). In granting this petition, EPA agreed to commence the appropriate 

proceeding, but did not commit to a particular schedule or to a particular outcome.

In June 2010, EPA issued a Proposed Approach for Developing Lead Dust Hazard Standards for 

Residences and submitted the document to the Science Advisory Board (SAB) Lead Review 

Panel for a consultation. The SAB Panel met July 6–7, 2010 and provided comments on the 

Proposed Approach to EPA on August 20, 2010.

The current document entitled “Approach for Developing Lead Dust Hazard Standards for 

Residences” describes the methods that EPA proposes to examine candidate hazard standards for 

floors and windowsills in residences. This document takes the SAB comments from the July, 

2010 consultation into consideration in developing several candidate standards for residences.

Charge Question 1 - Approach Document

OPPT has developed an Approach document for developing the hazard standards for floors and 

windowsills in residences. This includes a description of the empirical and biokinetic 
A-2

approaches, as well as the resultant analyses used to estimate candidate lead dust hazard 

standards for residences.

1. Please comment on the clarity and transparency of the document.

Charge Question 2 - Empirical Models

The empirical approach involves the estimation of blood-lead impacts based on analyses of 

empirical data from the 1999–2004 National Health and Nutrition Examination Survey

(NHANES). Two analyses were used. First, the regression relationships among floor and 

windowsill dust, other covariates, and blood-lead concentrations that Dixon et al. (2009) derived 

were applied to predict blood-lead levels for the various hazard standards (combinations of floor 

and windowsill dust loadings). The second was an independent reanalysis of the NHANES data 

to derive alternate models for predicting blood-lead impacts; the variations from the Dixon et al.

(2009) approach included changes to the form of the dust-loading variables and application of 

models that are inherently linear at low lead exposures, a relationship that is supported by a wide 

range of biokinetic data, and regression of blood-lead values against estimated dust 

concentrations, rather than dust loading.

2. Please comment on the EPA reanalysis.

Charge Question 3 - Biokinetic Models

Two biokinetic models were used to estimate children’s blood lead concentrations including 

EPA’s Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK), and the 

Leggett model. Information from the exposure scenarios is used to estimate relative 

contributions of exposures from different sources (soil, dust, air, diet, and water) and in different 

microenvironments.

3. Please comment on the use of the biokinetic models and the inputs to the models.

Charge Question 4 - Analyses of Variability and Uncertainty

Monte Carlo methodology was not used to evaluate the impacts of variability and uncertainty in 

model parameters on blood-lead estimates as insufficient data exist concerning the potential 

variability in many key model variables to support informative Monte Carlo modeling. Instead, 

point estimates of central tendency (geometric mean) blood-lead concentrations in children are 

derived utilizing statistical models based on empirical data and on biokinetic models of blood 

lead, coupled with assumptions regarding distributions of highly uncertain variables. The 

sensitivity of the deterministic relationships between dust lead and blood lead to changes in key 

variables and covariates is explored through sensitivity analyses. As presented in Section 6, the 

modeling inputs and assumptions that most strongly affect the predicted blood-lead distributions 

associated with candidate lead-dust hazard standards have been identified, based on the measures 

of statistical uncertainty from the empirical analyses and sensitivity analyses of the biokinetic 

models. 
A-3

4. Please comment on the characterization of variability and uncertainty. 

Charge Question 5 - Choice of Model for Residential Hazard Standards 

The document presents two empirical models and two biokinetics models. OPPT proposes to 

use the NHANES Quasi-Likelihood, Empirical Model for the estimation of the residential hazard 

standards. 

5. Please comment on this proposed choice.
A-4

EPA Charge Questions for the Approach for Developing Lead Dust Hazard Standards 

for Public and Commercial Buildings

Background

Section 402(c)(3) of TSCA directs EPA to revise the regulations promulgated under TSCA

section 402(a), i.e., the Lead-based Paint Activities Regulations, to apply to renovation or 

remodeling activities in target housing, public buildings constructed before 1978, and

commercial buildings that create lead-based paint hazards. In April 2008, EPA issued the final

Renovation, Repair and Painting Rule (RRP Rule) under the authority of section 402(c)(3) of 

TSCA to address lead-based paint hazards created by renovation, repair, and painting activities 

that disturb lead-based paint in target housing and child-occupied facilities (USEPA, 2008a). The 

term ‘‘target housing’’ is defined in TSCA section 401 as any housing constructed before 1978,

except housing for the elderly or persons with disabilities (unless any child under age 6 resides or 

is expected to reside in such housing) or any 0- bedroom dwelling. Under the RRP Rule, a childoccupied facility is a building, or a portion of a building, constructed prior to 1978, visited 

regularly by the same child, under 6 years of age, on at least two different days within any week

(Sunday through Saturday period), provided that each day’s visit lasts at least 3 hours and the 

combined weekly visits last at least 6 hours, and the combined annual visits last at least 60 hours. 

The RRP Rule establishes requirements for training renovators, other renovation workers, and 

dust sampling technicians; for certifying renovators, dust sampling technicians, and renovation 

firms; for accrediting providers of renovation and dust sampling technician training; for

renovation work practices; and for recordkeeping. Interested States, Territories, and Indian 

Tribes may apply for and receive authorization to administer and enforce all of the elements of 

the RRP Rule.

Shortly after the RRP Rule was published, several petitions were filed challenging the rule. 

These petitions were consolidated in the Circuit Court of Appeals for the District of Columbia

Circuit. On August 24, 2009, EPA entered into an agreement with the environmental and 

children’s health advocacy groups in settlement of their petitions (USEPA, 2009a). In this 

agreement, EPA committed to propose several changes to the RRP Rule. EPA also agreed to 

commence rulemaking to address renovations in public and commercial buildings, other than 

child-occupied facilities, to the extent those renovations create lead-based paint hazards. For 

these buildings, EPA agreed, at a minimum, to do the following:

• Issue a proposal to regulate renovations on the exteriors of public and commercial 

buildings other than child-occupied facilities by December 15, 2011 and to take final 

action on that proposal by July 15, 2013.

• Consult with EPA’s Science Advisory Board by September 30, 2011, on a methodology 

for evaluating the risk posed by renovations in the interiors of public and commercial 

buildings other than child-occupied facilities.

• Eighteen months after receipt of the Science Advisory Board’s report, either issue a 

proposal to regulate renovations on the interiors of public and commercial buildings other 

than child-occupied facilities or conclude that such renovations do not create lead-based

paint hazards.

In order to evaluate the potential risks associated with lead exposure due to renovations in public 

and commercial buildings, and the potential need for regulations on these activities, it is first 
A-5

necessary to develop the hazard standards for lead dust on window sills and floors in public and 

commercial buildings; these become the standards to help inform the impact of renovation 

activities. These standards will identify dangerous levels of lead in paint and dust, and provide 

benchmarks on which to base remedial actions taken to safeguard children and the public from 

the dangers of lead. 

In June 2010, EPA issued a document entitled “Proposed Approach for Developing Lead Dust 

Hazard Standards for Public and Commercial Buildings” and submitted the document to the 

Science Advisory Board (SAB) Lead Review Panel for a consultation. The SAB Panel met July 

6–7, 2010 and provided comments on the Proposed Approach to EPA on August 20, 2010.

The current document entitled “Approach for Developing Lead Dust Hazard Standards for Public 

and Commercial Buildings” describes the methods that EPA proposes to examine candidate 

hazard standards for floors and windowsills in public and commercial buildings. This document 

takes the SAB comments from the July, 2010 consultation into consideration in developing 

several candidate standards for public and commercial buildings.

Charge Question 1 - Approach Document

OPPT has developed an Approach document for developing the hazard standards for floors and 

windowsills in public and commercial buildings. This includes a description of the empirical 

and biokinetic approaches, as well as the resultant analyses used to estimate candidate lead dust 

hazard standards for public and commercial buildings.

1. Please comment on the clarity and transparency of the document.

Charge Question 2 - Empirical Models

The empirical approach involves the estimation of blood-lead impacts based on analyses of 

empirical data from the 1999–2004 National Health and Nutrition Examination Survey

(NHANES). Two analyses were used. First, the regression relationships among floor and 

windowsill dust, other covariates, and blood-lead concentrations that Dixon et al. (2009) derived 

were applied to predict blood-lead levels for the various hazard standards (combinations of floor 

and windowsill dust loadings). The second was an independent reanalysis of the NHANES data 

to derive alternate models for predicting blood-lead impacts; the variations from the Dixon et al.

(2009) approach included changes to the form of the dust-loading variables and application of 

models that are inherently linear at low lead exposures, a relationship that is supported by a wide 

range of biokinetic data, and regression of blood-lead values against estimated dust 

concentrations, rather than dust loading.

2. Please comment on the EPA reanalysis.

Charge Question 3 - Biokinetic Models

Two biokinetic models were used to estimate children’s blood lead concentrations including 

EPA’s Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK), and the 
A-6

Leggett model. Information from the exposure scenarios is used to estimate relative 

contributions of exposures from different sources (soil, dust, air, diet, and water) and in different 

microenvironments.

The Leggett model and EPA’s Adult Lead Methodology were used to estimate adult blood lead 

levels resulting from candidate floor and windowsill hazard standards.

3. Please comment on the use of these models and the inputs to these models. 

Charge Question 4 - Analyses of Variability and Uncertainty

Monte Carlo methodology was not used to evaluate the impacts of variability and uncertainty in 

model parameters on blood-lead estimates as insufficient data exist concerning the potential 

variability in many key model variables to support informative Monte Carlo modeling. Instead, 

point estimates of central tendency (geometric mean) blood-lead concentrations in children are 

derived utilizing statistical models based on empirical data and on biokinetic models of blood 

lead, coupled with assumptions regarding distributions of highly uncertain variables. The 

sensitivity of the deterministic relationships between dust lead and blood lead to changes in key 

variables and covariates is explored through sensitivity analyses. The modeling inputs and 

assumptions that most strongly affect the predicted blood-lead distributions associated with 

candidate lead-dust hazard standards have been identified, based on the measures of statistical 

uncertainty from the empirical analyses and sensitivity analyses of the biokinetic models. 

4. Please comment on the characterization of variability and uncertainty. 

Charge Question 5 - Choice of Model for Public and Commercial Building Hazard 

Standards 

The document presents empirical and biokinetic models. OPPT proposes to use the NHANES 

QL, Empirical Model and the ALM model for the estimation of the hazard standards for floors 

and windowsills for children and adults, respectively. 

5. Please comment on these proposed choices.
B-1

APPENDIX B – EDITORIAL COMMENTS

General Comment Pertaining to Both Documents

A summary table of variables in the NHANES database should be presented to improve 

clarity. In EPA's documents, the same term “ventilation rate” is used when describing 

physiological ventilation of the lungs and also used when describing air exchange for rooms or 

buildings. The use of the same term for two different scenarios can be confusing to the reader 

and the Panel recommends that EPA use distinct terms when referring to each of these scenarios

Editorial Comments on the Residential Document (also pertains to the corresponding portions in 

the Public and Commercial Document)

• Pages 21 and 31, Figures 3-8 and 4-2: Both of these scatter plots show the raw data, 

being the unadjusted raw NHANES data as the dots and the model predictions as the 

several curves. The figure key of 3-8 says "raw data" which is clear enough, but 4-2 did 

not. Also, using the word "predicted" in the vertical axis is unclear, since it was also for 

raw data.

• Page 32, Figure 5-1 - This figure has 9 curves. The clarity of the document would be 

improved if the figure presented only central tendencies (6 curves), which would make 

the figure less cluttered. Figure 5-2 shows only those curves, which is clearer. The upper 

and lower bounds can be presented with error bars about a few points on the central 

tendency data points. 

• Page 6, footnote a, insert “and for blood lead” after “...measurements,”

• Page 23, 2nd paragraph, 3rd line from bottom, change “data that is collected” to “data 

that are collected”

• Page 27, section 4.1.5, 1st and 2nd lines, “soil” does not appear to be needed in both lines.

• Page 28, Table 4-3 and elsewhere: The units of air concentration and blood lead are 

typically expressed as “μg”, not “mg”. Please check that the units and values are correct.

• Page 29, second line, change “current proposed hazard standards” to “current hazard 

standards” 

• Page 40, section 6.1, 2nd line, change “dust-lead levels” to “blood lead levels”

• Page 41, 3rd line from bottom, change “flood condition” to “floor condition”

• Page 45, section 7.1, second to last sentence - The meaning of the phrase "support for a 

key input" is not clear
B-2

Specific Comments on the Public and Commercial Document

• Page 44, line 4 : change “76 percent” to “24 percent”.

• Page 56, Figure 6-3: in the caption, change “Greater than 5.0” to “Greater than 2.5”

• Page 72, Table 7-2: Many of the units in the first column that are labeled “mg” 

(milligram) should be changed to “μg” (microgram). Also, the last column should be 

labeled as applying only to the Leggett model. 

• Page 74, Table 7-4: in the second column, third row, change 0.011 to 0.11; in the fifth 

row, the proportion of time that a child spends at home is listed as 0.76, in contrast to the 

information on page 35, which indicates a value of 0.83; in the last row of the table, the 

upper bound and lower bound estimate entries appear to be reversed.

• Page 74, Table 7-5: The narrative indicates this table is intended to apply to adults, but 

the caption refers to children. The contents and caption should be checked. For example, 

the dust lead absorption fraction of 0.5 applies to children, but the soil lead absorption 

fraction applies to adults. 

Edits to Appendix E of Both Documents

Page E-7

• Change “dINAIRPb/dt = change in time of the indoor air lead mass” to “dINAIRPb/dt = 

change in time of the indoor airborne lead mass in or as particulate (μg/hr)”

• Change “Indoor Sources = generation of mass due to indoor sources such as cooking or 

smoking” to “Indoor Sources = generation of mass to the indoor air due to indoor sources 

(e.g., cooking or smoking) (g/hr)”

• Change “Dander Sources = generation of mass due to human and pet dander” to 

“Dander Sources = generation of mass due to human and pet dander to the indoor air 

(g/hr)”

Page E-8

• Change “Resuspension FluxPb = resuspension of lead out of the air (μg/h)” to 

“Resuspension FluxPb = resuspension rate from floor to the air (μg/h)”

• Change “Resuspension FluxPart = deposition of particulate out of the air (g/h)” to 

“Resuspension FluxPart = resuspension rate of particulate from floor to the air (g/h)”

• Change “R = deposition rate (h-1)” to “R = resuspension rate or proportion of the mass on 

the floor going to the air per hour (h-1

)”
B-3

Page E-12

Equation 2A seems to be correct conceptually but the unit/conversions appear to be 

inconsistent. All units of the expressions within the equation should have the units of 

micrograms/h.

The third expression within this algorithm is reproduced below:

PbPaintConcen x ChipFraction x V x WallLoading x UnitConv

Units for these variables listed on page E-11 are:

mg/cm2 x 1/yr x m3x m

2

/m3 x 1 yr/8760 hr

In order for this expression to have the units of micrograms/hr one need to convert mg/cm2 to 

μg/m2:

(1 mg/cm2)(1000 μg/mg) = 1000 μg/cm2 = 1000 μg/cm2)(10,000 cm2/m2) = 10,000,000 μg/m2

As such, a conversion constant of 10,000,000 needs to be included in this algorithm to convert 

from mg/cm2 to μg/m2. Assuming this was done in the computer code would mean that the 

outputs are correct while this documentation is not. Of course, if it were coded incorrectly then 

the model output is incorrect. 

Page E-12

• In Equation 2B, the variable “PbCoverageDens” does not exist and should be 

“CoverageDens”. 

• Change “INAIPRPb = indoor mass of lead in air (μg)” to “INAIRPb = indoor mass of lead 

in air (μg)”.

• Change “INAIPRPart = indoor mass of particulate in air (μg)” to “INAIRPart = indoor mass 

of particulate in air (g)”

• Page E-20 - The title for Table E-9 appears to have been inadvertently used for Table E10 also.