P.1 

OAR Box 1126 

Prepped by Ollie Stewart 

Document Number: 

2) 11-1-123 

Docket Number: 

A-83-22 

Printed 4/18/2008 9:58:21 AM Sheet 2 of 182 
P.2 

J ,?f* /23 

rx <vEPA 

United States 

Environmental 

Protection Agency 

Office of Air Quality EPA-450/2-89-011 

Planning gnd Standards ^ June 1989 

ResearchTnangle Park NC 27711 

Air 

REVIEW OF THE NATIONAL AMBIENT 

AIR QUALITY STANDARDS FOR LEAD: 

EXPOSURE ANALYSIS METHODOLOGY 

AND VALIDATION 

OAQPS STAFF REPORT 
£3_ 

This report has been reviewed by the Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency,, and approved for publication. 

Mention of trade names or commercial products is not intended to constitute 

endorsement or recommendation for use. 
P.4 

REVIEW OF THE NATIONAL AMBIENT AIR OUALITY STANDARDS FOR LEAD: 

EXPOSURE ANALYSIS METHODOLOGY AND VALIDATION 

OAOPS STAFF REPORT 

Air Ouality Manayement Division 

Office of Air Quality Planniny and Standards 

U.S. Environmental Protection Ayency 

Research Trianyle Park, N.C. 27711 

June 1989 
P.5 

11 

Acknowledgments 

This staff paper is the product of the Office of Air Quality 

Planning and Standards. The principal author is Jeff Cohen, with 

assistance from Allan Marcus, Donna Sledge, Dave McLamb, Gail 

Brion, and John Haines. Technical contributions were provided by 

New York University Medical Center, PEI Associates, Inc., and 

Alliance Technologies Corporation. The report incorporates 

comments from OAQPS, the Office of Research and Development, the 

Office of Drinking Water, the Office of Policy, Planning, and 

Evaluation, and the Office of General Counsel within EPA and was 

formally reviewed by a subcommittee of the Clean Air Scientific 

Advisory Committee. 

Helpful comments were also submitted by a number of 

independent scientists and by representatives of the U.S. Centers 

for Disease Control, the Lead Industries Association, the 

Canadian Department of Health and Welfare, the Canadian Ministry 

of the Environment, and the California Department of Health 

Services. _ •• 

The authors wish to thank Tricia Holland, Teresa demons, 

and Virginia Wyatt for excellence in word processing and numerous 

other tasks and Dick Atherton for graphics assistance. 

tfi 
P.6 

m 

TABLE OF CONTENTS 

Page 

v 

v 

vii 

I-l 

List of Figures 

List of Tables 

Executive Summary 

I. Introduction 

II. Lead Exposure: Multimedia Considerations II-l 

A. Airborne Lead 11-3 

B. Lead in Soil 11-5 

C. Lead in Dust II-9 

D. Lead in the Diet . 

E. Lead in Water 

F. Lead in Paint 

III. Estimating Lead Exposures and Blood Lead Levels 

A. Alternative Modeling Approaches 

11-12 

11-13 

11-14 

III-l 

III-l 

111-2 

B. Calculating Blood Lead Distributions Around Predicted 

Means 

IV. Integrated Lead Uptake/Biokinetic Model IV-1 

A. Estimates of Lead Uptake IV-3 

B. Uptake and Blood Lead Concentrations IV-6 

V. Statistical Relationships Between Blood Lead and Airborne 

Lead: Use in Disaggregate and Aggregate Models ..... . V-l 

A. Disaggregate Model V-3 

B. Aggregate Model V-14 

VI. Validation of Integrated Lead Uptake/Biokinetic Exposure 

Model VI-1 

A. Validation Using East Helena Data ... . VI-2 

B. Application of Uptake/Biokinetic Model to Other Data 

Sets VI-12 

C. Conclusions VI-17 
P.7 

IV 

TABLE OF CONTENTS (Continued) 

Paye 

VII. Conclusions and Applications of Lead Exposure 

Modeling VII-1 

Appendix A. Estimates of Lead Uptake A-l 

Appendix B. Estimation of the Relationship Between Soil, Dust, 

and Ambient Lead B-l 

Appendix C. Estimating 1990 Baseline Blood Lead Averages C-l 

Appendix D. CASAC Closure Report D-1 

References 
P.8 

LIST OF FIGURES 

Number 

2-1 

2-2 

4-1 

6-1 

6-2 

Title 

Principal Pathways of Human Exposure to Lead 

and Subsequent Physiological Distribution . . . . ooo o 

Trends in Maximum Quarterly Lead Concentrations for 

Various Monitor Types oooooooooooooo o o o o 

Summary of Relationships Between Daily Lead Uptake 

and Blood Lead for Different Aye Groups 

Pa^e 

II-2 

II-6 

IV-17 

Comparison of Distribution of Measured Blood Lead Levels 

in Children, 1-5 years of Age, Living Within 2.25 miles 

of E.Helena Smelter vs. Levels Predicted by Uptake/Biokinetic 

rlOQ G I ^KUl l D j o o o o o o o o o o o o o o o o o o o o o o o » 1" 0 

Comparison of Distribution of Measured Blood Lead Levels 

in Children, 1-5 years of Age, Living Within 2.25 miles 

of E.Helena Smelter vs. Levels Predicted by Uptake/Biokinetic 

nOU c 1 \l\Un w ] o o o o o o o o o o o o o o o o o.o o o o o o V 1" 3 

LIST OF TABLES 

Number 

2-1 

2-2 

3-1 

3-2 

4-1 

Title 

Typical Lead Concentrations in Various Exposure 

rlGvi tdoooooooo . ooooooooooooooooo o 

Frequency Distributions of Maximum Quarterly Lead 

Concentrations by Type of Site . ooooooooooo o 

Comparison of Estimated GSDs Across Several Studies o o o 

Illustrative Blood Lead Distributions Assuming Different 

Mean Blood Leads and Geometric Standard Deviations . . « 

Estimated 1990 Average Lead Intake and Uptake in 2-Year 

Children Under Average Air Lead Levels'OOOOOOOOO , O 

11-2 

11-6 

II1-6 

III-8 

IV-4 
_ _ P.9 

vi 

4-2 Predicted Equilibrated Blood Lead Levels Over Time Among 

Children with Constant Lead Uptakes . ......... . . IV-15 

5-1 Disaggregate Model of Contributions from Various Media to 1990 

Median Blood Lead Levels of U.S, Children (2 Years of Age): 

Background Levels and Incremental Contributions from Air. . . V-9 

5-2 Blood Lead/Air Lead Slopes in Children for Different Ayes 

and CXpU5UP€ 5 OOOOOOOOOOOOOOOOOOOOOO O V "I D 

6-1 Comparison of Inteyrated Lead Uptake/Biokinetic Model 

Predictions to 1983 Measurements in East Helena ...,, . VI-6 

6-2 Children's Blood Lead Levels Measured in Omaha, 1971-1977 

Vs, Integrated Uptake/Biokinetic Model Predictions. ... . VI-14 

6=3 Children's Blood Lead Levels Measured in Silver Valley, 

Idaho 1974-1975 Vs, Integrated Uptake/Biokinetic Model 

r ic U I v* t l Ull j o o o o o o o o o o o o o o o o o o o o o o o o V 1"1 D 

A-l Age-Specific Estimate of Total Dietary Lead Intake for 

i3JU"I7J O oooooooooooooooooooooooo o rt— J 

A-2 Total Dietary Lead Intake for 1978-1983 for Five Age 

Groups of Children ................... . A-5 

A-3 Summary of Environmental Lead Measurements from Various 

Point Source Locations .... . ........... . . A-8 

A-4 Long-Term Relationships Between Dust, Soil and Ambient 

H i I L-C u U ooooooooooooooooooooooooo o - r\ ~ 10 , 

A-5 Estimating Short-Term Responses in Equilibrium Soil and 

Dust Lead Levels from Changes in Ambient Air Lead A-13 

B-l Environmental Lead Concentrations in Omaha, Nebraska. , . . B-6 

B-2 Linear Model Parameter Estimates for Air: Soil and Dust 

Lead Relati UnSlllU^ o o o o O 0 o o o o o o o o o o o o o o o D~0 

B-3 Average Dust and Soil Lead vs. Air Lead Relationships 

U jr l»UV*dl#lU n o o o o o o o o o o o o o o o o o o o o o o o o D — O 

C-l Selected 1978 Blood Lead Levels from NHANES II .... . . C-2 

C°2 Predicted Changes in Average Blood Lead Levels Associated 

with Gasoline Lead.Trend, ................ . C-6 

C-3 Trends in Dietary Lead Intake: Total Intake (and 

Atmospheric Contributions). ............... . C-8 

C-4 Adjustments to 1978 Mean Blood Lead Levels for Dietary 

L 6 3 0 KGQU C U l OllSooooooooooooooooooooo o U~ l 1 t 

C-5 Derivation of 1990 "Baseline" Average Blood Lead Levels . . C-14 

C-6 Maternal:Child Blood Lead Concentrations and Ratios ... . C-16 
P.10 

• a i rf'^*' " •' 

v i i 

EXECUTIVElSUMMARY 

For its review of the National Ambient Air Quality Standards (NAAQS) for 

lead, EPA is assessing health risks associated with lead exposure, particularly 

near stationary sources of atmospheric lead emissions (e.g., lead smelters), 

under alternative regulatory scenarios. A critical element in this 

process wil l be an exposure analysis whereby blood lead levels are estimated 

among populations exposed under alternative lead regulations in the 

future. This report summarizes relevant information on lead exposure and 

presents the modeling methodologies that the staff believes should be 

considered for the lead NAAQS exposure analysis. Results of validating 

one of these methodologies are also presented. 

This report does not discuss health effects associated with lead, or 

the implications of different levels of lead exposure. That information 

is reviewed in the criteria document and wil l be integrated into a separate 

staff paper along with results of exposure analyses that employ the methodologies 

described here. The following discussion highlights the major points and 

conclusions discussed in this paper. 

Multiple Pathways of Exposure 

Human exposure to lead occurs through multiple pathways, and can be 

o 

traced primarily to lead in paint pigments, solder in canned foods and 

plumbing, and atmospheric emissions from motor vehicles and stationary lead 

sources. Although airborne lead is a principal starting point of environmental 

contamination, oral intake of deposited atmospheric lead is often the 

primary identifiable factor predicting the blood lead of young children. 

The principal pathways of human exposure can be seen in Figure 1. 
P.11 

viii 

Figure 1. PRINCIPAL PATHWAYS OF HUMAN EXPOSURE TO LEAD AND SUBSEQUENT 

PHYSIOLOGICAL DISTRIBUTION. 

Oownward Trends in Lead Exposure 

Lead contamination of the human environment has been occurring for 

thousands of years since its many uses began to be discovered. Widespread, 

global dispersion of lead was greatly accelerated beginning in the 1920's 

with its introduction as an anti-knock compound in gasoline. Up until 

recently, approximately 90% of airborne lead in the U.S. was due to 

automotive emissions. Use of lead in gasoline has declined by aoout 90% 

since 1978; this trend will continue as compliance with the 1986 

gasoline phasedown standard is completed and as the fleet of lead-burning 

cars shrinks. This trend has been paralleled by a major reduction in 

ambient air lead levels, which now average between 0.1 and 0.3 ug/m^ in 

most U.S. cities without lead point sources. 

The reduction in atmospheric lead, combined with the gradual phaseout 

of lead solder in canned food manufacturing and the ban of lead solder in 
P.12 

ix 

plumbing has resulted in a significant*decl ihe in dietary lead intake. 

For example, i t is estimated that from 1978 to 1985, average dietary lead 

intake in a 2-year child dropped from 52.9 micrograms (Mg) per day to 

10.4 pg/day. This trend is expected to continue. 

These recent changes in nationwide exposure and expected future 

trends, as well as contributions from the other air and non-air sources 

of lead exposure (e.g., drinking water, contaminated soils) are important 

components of the methodologies described in this report. The air lead 

contributions include both direct inhalation of lead particles and indirect 

exposure through ingestion of lead in dust and in soil . 

Lead-based paint, however, wil l continue to be the major source of 

high-dose lead exposure and symptomatic Tead poisoning for children in 

the U.S. for years to come. High exposures to lead in older housing with 

deteriorating or even intact lead-painted surfaces (via chalking or 

weathering) wil l not be significantly influenced by any changes in atmospheric 

lead emissions. Because children excessively exposed to lead-based paint 

cannot be expected to be protected by any lead NAAQS, no matter how stringent, 

they wil l not be included in the exposure analyses described in this 

report. Available data are presented to demonstrate how such estimates 

could be generated. Comprehensiveo plans for safe and effective removal 

or containment of lead painted surfaces from old housing need to be 

developed and implemented. 

Focus on Point Sources 

The focus of the exposure modeling approaches described in this 

report, and the overall lead NAAQS review, is on stationary lead sources 

such as primary and secondary lead smelters and battery plants. Although 

such sources have contributed littl e to the overall pollution load across 
PJ3_ 

large, regional areas, fallout from these sources can be severe on a 

local scale. Ambient lead levels around such point sources vary depending 

on distance, topography, wind speed and direction, plant operating parameters, 

emissions controls, particle sizes and deposition rates, etc. 

Data Limitations Preclude Quantitative Estimates for All Sensitive Sub-Groups 

To assess the health risks associated with alternative air lead 

standards, i t is necessary to estimate the blood lead (PbB) levels that 

would be distributed in the populations of concern under various air 

lead concentrations. Young children, pregnant women (as exposure surrogates 

for the fetus), and middle-aged men, are identified as particularly 

susceptible to lead-related risks. Quantitative estimates of blood lead 

distributions under alternative standards wil l be made only for young 

children and middle-aged men. 

Fetal exposure to lead, in most cases, can be expected to be dominated 

by maternal bone lead stores from past exposures. Therefore, accurately 

predicting future changes in fetal blood lead levels would require estimates 

of maternal lead burden and information on how bone lead stores would be 

mobilized and transferred across the placenta. Although i t is likely 

that there is extensive mobilization of lead, like calcium, during periods 

of physiological stress0such as pregnancy, there are no biokinetic data 

to quantify this dynamic process. In the absence of such data, no attempt 

wil l be made to estimate fetal lead exposures associated with maternal 

PbB levels under alternative standards. Given the sensitivity of the 

fetus and neonate, however, potential risks associated with pre- and neonatal exposures wil l be of major emphasis in the overall lead NAAQS 

assessment. 
P.M 

xi 

Older women may also be at increased risk due to release of 

bone lead after menopause due to the processes of osteoporosis. A 1ack 

of biokinetic information on post-menopausal demineralization precludes 

quantitative estimation of blood lead changes under alternative lead 

NAAQS for these individuals as well. 

Average Estimates Predicted by Exposure Models Will be Combined With 

Empirical Estimates of Variance to Calculate Blood Lead Distributions 

This paper presents three approaches that can be used to estimate 

the impact of inhaled and ingested lead aerosols and compounds on PbB 

levels distributed in populations living near lead point sources. Because 

most of the data input to these models are yenerally average estimates, 

all three-models first estimate PbB levels in terms of population mean 

levels. Given the significant behavioral and biological variability 

within populations, and the importance of highly exposed individuals within 

the population at risk, it is necessary to determine the entire blood 

lead distribution corresponding to a given mean PbB. This allows, for 

example, percentages of the population expected to exceed. PbB levels of 

concern to be ascertained.- Because PbB levels are typically distributed 

lognormally, it is possible to calculate percentiles of a blood lead 

distribution around a given mean PbB level by using the geometric standard 

deviation (GSD) of that population's blood lead distribution. 

Several epidemiological studies have indicated that the log values 

of measured individual PbB levels in a uniformly exposed population are 

normally distributed with a variation, including analytical variation, 

ranging between 1,3 and 1,4. For purposes of predicting future exposures, 

a range of GSD values of 1.30-1.53 has been documented for children 

living near lead point sources, and the midpoint of 1.42 will be assumed 

asm 
P.15 

x i i 

as a reasonable best estimate. A GSD value of 1.37 derived from the 

NHANES II survey wil l be used for adults. 

The three models are summarized below. 

Uptake/Biokinetic Model: Applicable to Children from Birth 

The firs t modeling approach presented is the uptake/biokinetic 

model which uses measured rates of absorption, or "uptake" of lead through 

different pathways (e.g., inhalation, ingestion) from experimental studies 

together with available mathematical ("biokinetic") models from lead 

balance studies to project either total body burden or the amount of lead 

in any of the presumed "physiological" kinetic compartments (e.g., blood, 

soft tissue, bone) at any time. The model is thereby capable of reflecting 

"non-equilibrium" lead exposures. I t has been developed at this time only 

for young children. 

The model is based on age-specific air, soil , dust, food, and water 

lead intakes, and on age-specific absorption factors. Blood lead and 

tissue lead distribution from absorbed lead is described by a linear 

pharmacokinetic model whose parameters change with age. The air lead 

concentrations around a point source can be estimated from historical data 

on emissions and site-specific information about meteorological dispersion 

parameters. To address situations when this information is not known, as 

in the case of assessing future NAAQS scenarios, a methodology was developed 

to estimate the associated soil and household dust lead concentrations 

using relationships derived from available data from a wide range of 

point source areas. This approach allows estimation of effects on soil 

and dust lead of historical changes or proposed changes in air lead levels. 

Dietary lead intake estimates are derived from historical and current 

data and future projections of the Multiple Source Food Model, originally 
P.16 

xii i 

developed in the 1986 criteri a document, and updated to include more 

recent information. 

Because the biokineti c model assumes linear rates of lead transfer 

between tissues, i t is limited to predicting low-moderate level exposures 

(i.e. , PbB < 25-30 ug/deciliter, or pg/dl). The model's mathematical 

assumptions and numerical parameters, however, combine plausible biological 

hypotheses, animal experimental data, and results of observational studies. 

Further,, i t allows explici t projections of future lead concentrations in 

various media and in turn can estimate the impacts of these differen t 

changes on differen t age groups of children. I t is this flexibilit y that 

makes the integrated uptake/biokinetic model adaptable for a wide ranye 

of predictive exposure assessments, and why i t was the focus of the validation 

exercises described in this paper. 

Aggregate Air Lead Model: Applicable to Young Children 

The second approach, referred to as an "aggregate" model, uses a 

directl y applied mathematical relationship between air lead and blood 

lead derived from community epidemiological studies that includes both 

direc t inhalation exposure and indirec t exposures via secondary deposition 

processes. This approach requires an implici t assumption that dust and soi l 

lead concentrations are approximately in equilibrium with air lead levels , 

and that the present air lead exposures reflec t historical levels . This 

model also requires estimated contributions to children's blood from 

non-air sources of lead exposure, which are derived for current and 

future years based on such nationwide data as gasoline usage, dietary 

patterns, dietary lead concentrations, and blood lead surveys. Because 

aggregated air lead:blood lead relationships are not available for adults, 

and since the importance of indirec t oral exposures to deposited atmospheric 
P.17 

xiv 

lead is relatively minor among adults, this approach is limited to young 

children. 

This type of model is best used on a site specific basis due 

to the large number of confounders and covariates such as industrial 

exposure, age of housing, and social conditions. A summary of key studies 

useful in deriving aggregate blood lead/air lead slopes for children is 

included, along with a plausible range for these slopes (3-5 pg lead/dl 

blood per ug lead/cubic meter of air). 

Disaggregate Air Lead Model: Applicable to Children and Adults 

The third approach, referred to as a "disaggregate" model, is a 

hybrid of the first two that combines separate empirical relationships 

from multiple regression models between blood lead and lead intake from 

air, food, water, dust, and soil lead sources. This model.was presented 

in the 1986 criteria document and is updated here to include more recent 

data. A number of experimental exposure and epidemiological studies, in 

which such data were available are discussed. • 

In this model, there is an implicit assumption that the soil and 

dust lead levels measured at some time reflect earlier exposures, so that 

the PbB levels estimated are essentially at equilibrium and not just 

reflecting some recent change or event. 

The most plausible disaggregate relationship, or "slope" between 

inhaled air lead and children's blood lead, derived from analysis of 3 

point source studies, is 1.97 pg/dl per py/m3. The average inhalation 

slope for adults derived from the most reliable experimental and epidemiology 

studies is 1.4 pg/dl per pg/m3. A factor of 1.3 is applied to account 

for the resorption of lead from bone in adults and a slope of 1.8 pg/dl 

per pg/m3 is derived. The slope values used in the "disaggregate" model 
P.18 

•>r a-x^.r^-A; . 

XV 

are in general, consistent with.those derived from the "uptake/ biokinetic" 

model. 

Uptake/Biokinetic Model Validations Successful; Other Applications 

Should Proceed with Caution 

Results of several validation exercises utilizing the uptake/biokinetic 

model comparing predicted and observed blood lead levels are presented 

in this paper. The most detailed analysis was performed with data gathered 

around the smelter in East Helena, Montana. Two types of validation efforts 

were undertaken with this data set: 1) in the first effort, the best 

historical data regarding such model parameters as measured air, yard soil, 

and household dust lead exposure estimates were used; 2) in the second 

validation effort, predicted levels of air (using dispersion modeling that 

accounted for fugitive emissions and background contributions), and soil 

and dust (using generalized relationships derived from empirical analyses 

of a wide range of point source data) were used to estimate PbB levels. 

The latter work was undertaken to determine how well the model behaved 

when actual measurements of necessary input data are not available. This 

was necessary so that the reliability of the model could be assessed for 

policy analysis purposes when less than full information is available to 

use with the model. Results of the different East Helena validations 

indicate good concordance between observed and predicted average PbB 

levels in children living near the smelter. The differences observed 

were not statistically significant. 

Less extensive validation exercises on other published data are reported 

in this paper to test the uptake/biokinetic model at other U.S. locations 

and times including Omaha, Nebraska, from 1971 to 1977 and Silver Valley, 

Idaho, in 1974-1975. In these other validations, the model performed 
P.19 

X VI 

reasonably well in predicting average PbB levels below 25-35 pg/dl . As 

expected at higher exposure levels , the linear model underestimated actual 

exposures. 

Given the many uncertainties in the input data and the biological 

variabilit y that cannot be incorporated, the results of the validation 

exercises presented reveal that the lead uptake/biokinetic model performs 

quite well in predicting mean blood lead concentrations in children 

livin g near point sources of lead at exposure levels of current relevance. 

Using this methodology, along with the approach to estimate lognormal 

blood lead distributions using empirically-derived GSDs, exposure analyses 

of childhood populations livin g near stationary lead sources (i.e. , "case 

studies") wil l be prepared. Blood lead distributions for middle-aged men 

in these areas wil l also be estimated using the disaggregate approach. 

Results of these analyses wil l be incorporated into the staff paper to 

better inform the Administrator on the impacts of alternative lead NAAQS. 

The uptake/biokinetic model0can also be a useful tool in estimating 

PbB levels in children livin g with different lead hazards, such as heavily 

contaminated soil s from historical deposition near major urban roadways 

or closed smelters or mines. Ongoing regulatory effort s by differen t 

components of EPA to control concentrations of lead in air, water and 

soi l have created a significan t need to model blood lead concentrations 

that delineates specific routes of multi-media lead exposure. The 

"uptake/biokinetic" model adequately provides such specificit y for 

young children. Further refinements are underway to include non-linearities 

in absorption rates and biokinetics . Unti l then, the model is limited to 

estimating relativel y low to moderate exposures and should be used with 

caution in other applications. 
P.20 

REVIEW OF THE NATIONAL AMRIENT AIR QUALITY STANDARDS FOR LEAD: 

Exposure Analysis Methodology and Validation 

I.. INTRODUCTION 

EPA is assessing health risks associated with lead exposure, especially 

near stationary sources of atmospheric lead emissions (e.g., lead smelters), 

under alternative regulatory scenarios for its review of the National 

Ambient Air Quality Standards (NAAQS) for lead. The scientific and 

technical information on lead has been reviewed in EPA's "Air Quality 

Criteria for Lead" (EPA, 1986). A "staff paper", in final preparation, 

will evaluate and interpret the most relevant of that information to help 

the Administrator in selecting the averaging times, forms, and levels for 

the primary and secondary standards.* A critical element in this process 

will be an exposure analysis whereby blood lead levels are estimated 

among populations exposed under alternative lead NAAQS in the future. 

This report summarizes relevant information on lead exposure and. 

presents the modeling methodologies that the staff believes should be considered for the lead NAAQS exposure analysis. Results of validating one 

of these methodologies are also presented. Using methodologies described 

and validated in this report, exposure analyses of populations living near 

stationary lead sources (i.e., "case studies") are being prepared. Results 

of these analyses will be incorporated into the staff paper alony with the 

overall health risk assessment. The staff will use results of the case 

studies to better inform the Administrator on the impacts of alternative 

lead NAAQS. 

*The current primary standard for lead (to protect public health) is 1.5 

micrograms per cubic meter (pg/m-^), maximum arithmetic mean averaged over 

a calendar quarter. The secondary standard (to protect public welfare) 

is identical to the primary standard. 
1-2 

Normally, material of this type would be incorporated into the staff 

paper. Due to the complex nature of lead exposure analysis, we chose to 

place this material in a separate report to achieve a more thorough 

review and also to facilitate other applications of lead exposure analyses 

unrelated to the NAAQS review. This report should then be considered as 

a supplement to the OAQPS staff paper. It is intended for those readers 

familiar with the technical information contained in the criteria document 

(hereafter referred to as "CD"). This report does not discuss health 

effects associated with lead or the implications of different levels of 

lead exposure. That information is reviewed in the criteria document and 

will be integrated in the staff paper with results of the exposure analysis. 

This paper, as will be done with the staff paper, has been circulated for 

review by the Clean Air Science Advisory Committee (CASAC) and the public. 

A copy of the closure letter from the CASAC sub-committee that reviewed 

the August 1988 draft report is contained in-Appendix D„ 

The approach used in this paper is to assess and integrate exposurerelated information derived from the criteria document review that the 

staff believes should be considered in the review of the primary lead 

NAAQS. Section II presents relevant features of human exposure to 

atmospheric and non-atmospheric sources of lead through various pathways. 

Section III introduces different approaches to estimate the impact of 

alternative air lead levels on lead body burdens among different populations, 

Sections IV and V discuss the methodologies of these different approaches 

in detail. Section VI presents results of validation exercises using the 

uptake/biokinetic model. Section VII discusses some applications of the 

modeling approaches described in this report in addition to their role in 

the review of the lead NAAQS. 
P.22 

- •*? •'* • -^ i$.J'''•'- ••-'' - .'' ' 

II. LEAD EXPOSURE: MULTIMEDIA CONSIDERATIONS 

To assess the risks associated with alternative lead NAAQS, it is necessary 

to understand the influence exerted by atmospheric lead on the total lead 

exposure of the population(s) of concern through various exposure pathways. 

Relevant data on these pathways and the relationships between air lead and 

lead in other media are summarized in this section. The goal of the exposure 

assessments presented in this report is to address aggregate lead absorption 

from many pathways for populations living near stationary lead sources that 

will be affected by alternative lead NAAQS. Much of the methodoloyy presented 

can also be applied to other populations with different exposure profiles. 

Exposure situations (other than operatiny stationary lead facilities) dominated 

by single, intense, and/or intermittent sources are not included for lack of 

adequate data, and/or because any lead NAAQS would have little, if any, protective effect for those situations. They include most significantly, high paint 

lead exposure (see Subsection II.F below) and occupational exposures. (Indirect 

exposures of children to occupational dusts tracked home by working parents 

are implicitly included in the modeling). Other miscellaneous intermittent 

sources not addressed here include various consumer products (e.g., leaching 

of lead-glazed pottery), and household activities or hobbies, such as stained 

glass construction and removal of lead paint soldering. See Section 7.3.2 of 

the CD for a more complete discussion of these kinds of exposure sources. 

The sources and pathways of human lead exposure are diayrammed in 

Figure 2-1 and typical levels of lead in different media to which U.S. 

populations are exposed are presented in Table 2-1. Human exposure to lead 

can be traced primarily to lead in paint pigments, solder in canned foods and 

plumbing, as well as to atmospheric lead. Althouyh airborne lead is a 

principal starting point of environmental contamination, oral intake of 

deposited atmospheric lead is often the primary identifiable factor predicting 
H-2 

VIR 

KIONIV . 

PtCtt UNIMll 

Figure 2-1. PRINCIPAL PATHWAYS OF HUMAN EXPOSURE TO LEAD AND SUBSEQUENT 

PHYSIOLOGICAL DISTRIBUTION. ADAPTED FROM CD, FIGURE 7-1. 

Medium 

TABLE 2-1 . TYPICAL LEAO CONCENTRATIONS IN VARIOUS EXPOSURE MEDIA 

Rura] Area Urban Area Near Point Source(s)a References 

Ambient Air (ug/m-3)'> 

Indoor Air (pg/m-3) 

Soil (ppm) 

Street Oust (ppm)d 

House Oust (ppm)d 

Typical Foods (ppm) 

Water (pg/1) 

Paint* 

0.1 

0.03 • 0.08 

S - 30 

80 - 130 

(90) 

50 • SOO 

(300) 

0.002 - 0.8 

5 - 75 pg/L 

<1 - >5 mg/cm? 

0.1 - 0.3 

0.03 - 0.2 

30 - 4500 

100 - 5,000 

(1500) 

50 - 3.000 

(1000) 

Same 

Same 

Same 

0.3 - 3.0 

0.2 - 2.4 _ 

150 - 15,000 

(25,000) 

100 - 20,000 

(10,000) 

Same 

Same 

Same 

Faoro, 1988 

See Footnote c 

CO, Table 7-11 ; Mielk e 

e t al. ; 1983; LaBell e 

e t al. . 1987; See Table A-3 

Nriagu, 1978; CO, 

Table 7-26 

U.S. EPA, 1977; 

Landrigan e t al. , 1975; 

Morse e t al. , 1979; 

Anqle and Mclntire . 1979 

Fleqe l e t al. . 1988 

Briskin , 1988 

Billic k and Gray, 1978 

••"Within 2-5 km of sources including primary and secondary lead smelters, battery plants. 

^Represents quarterly averages monitored in 1986. 

cRange of indoor/outdoor ratios used (0.3 - 0.8) from CD, Table 7-6 except near pofnt sources ttfiere large particles predominate and infiltration into homes is low, ratio appears to be closer to 0.3 (Cohen and Cohen, 1980). 

^Values in parentheses represent estimates provided in CO (Tables 7-18 and 7-20) as typical averages. 

eSince there may be several layers of lead-based paint on a given surface, absolute concentration of lead is less 

useful than my/cm2. Surveys by HUD in Pittsburgh showed that more than 70* of pre-1940 dwelling units and 20* of post1960 units had at least one surface with more than 1.5 mq/cm2

 lead paint (NAS, 1980). 
P.24 

II-3 

the blood lead of young children. Jjighe.correl ations have been found between 

children's blood lead and the lead content of household dust, playground 

dust, mouthing behavior, lead on children's hands, and household cleanliness 

(Sayre et al. , 1974; Baker et al. , 1977; Charney et al. , 1980; Roels et 

al. , 1980, Yankel et al. , 1977; Angle et al. , 1979; Walter et al. , 1980; 

Brunekreef et al. , 1981; Quah et al. , 1982; Bellinger et al. , 1986; Bornschein 

et al. , 1986; Schilling and Bain, 1988). Large scale isotopic studies 

provide strong evidence that air lead has a rapid and measurable effec t on 

ingested lead, even in adult populations at low levels of air lead (Facchetti 

et al. , 1985). I t is necessary, therefore, to attempt to quantify how 

atmospheric emissions can influence human exposure, not only through direc t 

inhalation of lead-containing particles , but through ingestion of lead 

deposited onto soil , dusts, vegetation, and other environmental surfaces. 

Unti l recently, between 85-90% of airborne lead in the U.S. originated 

from gasoline combustion, with most of the remainder from stationary industrial 

processes such as primary and secondary lead smelting, battery plants, and 

combustion of oil , coal, waste oi l and municipal waste. Because of the 

accelerated reduction of the lead content in gasoline and the continuing 

phaseout of older lead-burning vehicles, concern regarding lead emissions 

into the ambient air is shiftin g almost exclusively to relativel y confined 

areas surrounding significan t stationary sources of lead and thi s paper's 

focus wil l be on estimating exposures and risks in those locales. 

A. Airborne Lead 

1 . Physical, Chemical and Spatial Characteristics 

Ambient air lead levels result from current industrial emissions, 

lead-contaminated road and wind-blown dust, automotive exhaust, and 

solid waste combustion. The industrial contribution includes point, fugitiv e 
II-4 

process, and materials handling emissions from lead mining, smelting, and 

recovery operations as well as various fabrication and manufacturing processes. 

Current contributions from roads and other fugitive dust sources vary with 

location and the influence of historically contaminated soil. 

In general, about 50% of .automotive lead emissions deposits within a 

few hundred meters of major roadways (Daines et al., 1970; Huntzicker et 

al., 1975; Ingalls and Garbe, 1982), while the remaining particles are small 

enough to remain airborne and can travel hundreds or thousands of kilometers. 

This long-distance transport likely accounts for the surface contamination 

of polar glaciers, oceans, and other remote locations around the globe 

(Settle and Patterson, 1982). 

In contrast to automobile exhaust, atmospheric lead emissions from 

industrial plants that process lead and its products have contributed little 

to the overall pollution load across large, regional areas although fallout 

from these sources can be severe on a local scale. The trajectories of 

atmospheric emissions from industrial sources vary with wind speed and 

direction. High concentrations around lead smelters and other major 

emitters can be dominated by fugitive emissions, predominately made up of 

large [>7 micrometers (pm)] lead particles resulting from materials 

handling, furnace upsets, and furnace charging and tapping operations 

(Landrigan et al., 1975; Jennett et al., 1977; Battye et al., 198b). 

Stack and vent emissions can also produce significant and frequent 

short distance impacts, especially in complex terrain or around older 

facilities (Iaccarino, 1988). Beyond the immediate area ( « 2 km) of lead 

stationary sources, process emissions from stacks, predominately as lead 

sulfates and oxides with a size range between 1 and 10 pm, become the major 

source of lead in soils and air (Dorn et al., 1976; Roels et al., 1980; 

Davidson and Osborn, 1984). 
P.26_ 

11-5 

2. Ambient Concentrations 

As indicated in the CD (Section 7.2.1) and EPA (1989), air lead 

levels in urban areas and near point sources have been markedly reduced 

since 1977 by the use of unleaded gasoline in new cars equipped with 

catalyti c converters, the gradual phaseout of older lead-burning cars, 

the lead-in-gasoline phasedown program, and steady reductions in emissions 

from stationary sources in compliance with state implementation plans 

toward attainment of the lead and particulate matter NAAQS and partl y 

because of decreased industrial production. Total atmospheric lead emissions 

dropped 94% between 1978 and 1987; gasoline lead use dropped by 90% over that 

period and industrial source emissions dropped by over one-half (EPA, 1989). 

Recent (1980-1986) air quality data for both point source-oriented sites 

(predominantly "SLAMS" sites), roadside sites ("NAMS", micro-scale) and other 

sites (middle-scale, neighborhood), are summarized in Table 2-2 (Battye, 

1988). By 1986, the only quarterly average concentrations over 1.5 pg/m3 

were recorded at monitors near stationary sources. Twelve counties in 

the U.S. reported quarterly averages above 1.5 pg/m3 in the 1986-1987 

period; 13 more were on the margin of exceeding the NAAQS but either had 

inadequate numbers of samples or were slightl y below the standard level . 

Trends in maximum quarterly averages between" 1980 and 1986 are 

o 

illustrated in Figure 2-2. Even more dramatic declines are evident in data 

confined to roadside monitoring (see Battye, 1988 and. EPA, 1989). 

B. Lead in Soil 

The natural occurrence of lead in the earth's crust averayes 

5-50 pg lead/g soil in various soils* (CD, p. 7-28), although soils near 

*0ne pg/g is equivalent to one part per million or 1 ppm. 
P.27 

II-6 

TAHLE 2-2 . FREQUENCY DISTRIBUTION 

Sit e type/tim e f-r ame 

1980 througn 1986 

A l l monitors 0 

NAMSC 

SLAMS 

Microscale roadsided 

Middle scale 

Neighborhood scale 

1986 only 

All monitors 

NAMS 

SLAMS 

Microscale roadside 

Middle scale 

Neighborhood scale 

1987 only 

All monitors 

NAMS 

SLAMS 

Percentage of 

OF MAXIMUM QUARTERLY 

(•jg/m3)d 

sites in 

ranqes (concentrations 

<0.5 

77.5 

78.9 

73.4 

24.1 

45.7 

68.7 

93.0 

97.5 

92.9 

1U0.0 

100.0 

94.4 

93.0 

97.4 

93.1 

0.5-1.0 

15.4 

17.2 

16.7 

37.9 

47.1 

24.3 

2.5 

1.3 

2.2 

0.0 

0.0 

0.0 

2.8 

0.0 

2.3 

1.0-1.5 

3.2 

4.7 

2.8 

25.9 

7.1 

7.0 

1.2 

1.3 

0.9 

U.O 

0.0 

O.U 

1.7 

1.3 

1.7 

concentrat 

in uq/m^) 

1.5-2.0 

1.5 

1.1 

1.7 

10.3 

U.U 

O.U 

1.6 

0.0 

1.3 

U.O 

0.0 

5.6 

0.4 

0.0 

0.0 

AIR LEAO CONCENTRATIONS 

ion 

>2.U 

2.3 

U.2 

3.4 

1.7-

U.O 

U.U 

1.8 

O.U 

2.7 

U.O 

O.U 

0.0 

2.0 

1.3 

2.9 

Mean 

0.39 

0.41 

U.38 

U.74 

U.46 

0.37 

U.17 

0.14 

U.21 

0.18 

U.12 

0.14 

0.20 

0.20 

0.20 

"lumber of 

siteyears 

3,279 

471 

1,158 

58 

7U 

115 

514 

79 

226 

9 

9 

18 

458 

78 

175 

d

0ata are from the SAROAO and AIRS systems and represent the numoer of site-years for 

which the maximum quarterly concentration fall within the designated ranges. To De 

included, a valid site-year must have at least three quarters of data with at least 

observationionss pe perr quarter. quarter -

^Includes sites previously classified into categories (e.g. 

meet any of the current definitions. 

"urban") that do not 

CNAMS refers to the National Ambient Monitoring Station network, while SLAMS refers 

to the State and Local Ambient Monitoring Station network. Eacn of these two networks 

includes both roadside and point source monitors; however, in the case of lead, the NAMS 

network tends to focus on roadside sites, while the SLAMS network incorporates more 

point sources and other special purpose monitors. 

^Microscale sites were within 5-lb meters from a major roadway. Middle scale sites 

had-further setbacks and define concentrations up to several city blocks. Neighborhood 

scale sites define concentrations in more extended areas of uniform land use within 

dties (0.5-4 km2). After 1986, data collection at these sites stopped. 

Source: Battye (1988). 

ro 

S 

00 

3 

Z 

o 

$ 

H 

Z 

w 

u 

z o 

u 

> • < 

• J 

CC 

H 

tC 

< 

ZS 

or 

1 -

0.9 -

o.a -

0.7 -

0.6 -

0.5 -

0.4 -

0.3 -

0.2 -

0.1 -

0 -

, 

\ 

\ ' — 

\ \ v-v^--^^ 

^~^-~^ ^tr^Z^m~~~~~J^\ 

— i— i - i i i 

a ALL 

$ NAMS 

"A SLAMS 

* 

i 1 I" ' 

79 81 v

 8 3 

Year 

85 87 

FIGURE 2-2. TRENDS IN MAXIMUM QUARTERLY AIR LEAD CONCENTRATIONS 

FOR VARIOUS MONITOR SITES, BAfTYE (1988). 
P.28 

II-7 

naturally occurring outcrops can reach?several hundred ppm. Urban soils 

are contaminated by lead from a combination of automotive and point source 

deposition as well as from lead in paint on indoor and outdoor surfaces. 

Elevated soil lead concentrations (as high as 2000 ppm) have been found 

within 10 feet of wood frame houses painted with lead-based paint (Ter Haar 

and Aronow, 1974). Accumulations of lead in soils (and dusts) around brick 

or stone structures have also been found, and can be partially attributed 

to washoff lead collected on roofs, ledges, and exterior walls (Wheeler and 

Rolfe, 1979), and possibly to an aerodynamic effect of the structures on 

deposition of lead from nearby.sources. Recent data from Baltimore (Mielke 

et al., 1983) and in and around Chicayo and "downstate" Illinois (LaBelle 

et al., 1987) indicate that lead accumulates in soil away from painted 

structures and known industrial lead sources, in a pattern at least partially 

related to traffic activity and distance from nearby roads. 

The upper layer* of roadside soils may contain deposited atmospheric 

lead in concentrations of 10 to 500 ppm in excess of natural levels within 

10 meters of the roadbed, beyond which concentrations decline abruptly, 

depending on traffic density and volume and vehicle speed (Page and Ganje, 

1970; Motto etal., 1970; Quarles et al, 1974; Wheeler and Rolfe, 1979; 

Pierson and Brachaczek, 1976; Mielke et al., 1983; LaBelle et al., 1987). 

Soil lead concentrations in a sample of city parks have also been reported 

to be high, ranging from 200 to 3300 ppm (Chow et al., 1975; Zimdahl and 

Hassett, 1977). 

*Because of the vertical gradient of lead in soil (i.e., lead concentration 

decreases with soil depth), the techniques used in sampling soils 

influence the lead concentrations found. In this report, soil lead data 

refer to the upper 1-5 centimeter layer, which is the most relevant for 

potential intake by children. 
P.29 

II-8 

Topsoil lead concentrations around various lead point sources generally 

decrease exponentially within a 3-10 km radius from 100-60,000 ppm down to 

background levels (Yankel et al. , 1977; Schmitt et al. , 1979; Roels et al. , 

1978; Diemel et al. , 1981; Fugas, 1977). However, elevated soi l lead 

levels have even been found at distances exceeding 20-25 km from some 

smelters (Wixson, 1978). Limited data indicate that lead in soi l near 

primary and secondary lead smelters occurs as lead oxides, sulfide , sulfate , 

and elemental lead (Olson and Skogerboe, 1975; Corrin and Natusch, 1977). 

Much of the lead in the atmosphere is deposited on terrestria l surfaces 

where i t is retained in organic complexes or adsorbed to hydrous iron 

oxides near the soi l surface (CD, p. 7-31). The abilit y of soi l to immobilize 

lead largely depends on soi l pH and organic content (i.e. , fulvi c and. humic 

substances). Many U.S. soils' appear to have a large capacity to bind lead 

with only a small fraction dissolved in soi l moisture where i t is available 

for plant uptake. The environmental impact of lead on the biota of terrestria l 

and aquatic ecosystems is discussed in Chapter 6 of the CD. 

Improvements in air quality do not necessarily involve reductions in 

soi l lead at the same rate. The decontamination of the environment is a 

very complex and slow process, particularl y for soi l lead which has been 

shown to remain at elevated.levels several years after lead manufacturing 

plants have ceased operating (Prpic-Majic et al. , 1984). While lead in 

soi l is relativel y immobile, i t stil l may enter the food chain, be ingested 

directly , be mobilized by mechanical forces such as gardening, or may be 

redispersed into the air and resuspended into water by wind scattering of 

soi l particles and dust.. Consequently, populations livin g in areas with 

historical accumulations of lead in soi l may continue to be exposed to lead 

for indefinit e periods despite effective controls of atmospheric emissions. 
P.30 

-' '•• ' ; j ! 

II-9 

C. Lead in Dust -••-.•• 

Dust is a normal component of the home (i.e., "house dust") as well as 

the outdoor environment where it can be found on sidewalks, playgrounds, 

driveways, and other hard surfaces. Anthropogenic materials deposited on 

these outdoor surfaces will be referred to as "street dusts". In addition, 

the very top layer of soils (including leaf litter) to which people, particularly 

children, come in direct contact, are considered in the criteria document 

to be "soil" dusts". 

Both house dust and street/soil dust contain lead from atmospheric 

deposition, "natural" soil, and paint chips. As with roadside soil, significant 

correlations between lead concentrations in street dust and the proximity 

and density of traffic have been found (Rolfe et al., 1977; Lau and Wony, 

1982). Concentrations of lead in dusts on playgrounds, streets, and soil 

surfaces also increase with higher air lead concentrations and with proximity 

to stationary lead.sources (Roels et al., 1980; Brunekreef et al., 1981; 

Yankel et al., 1977). As summarized by Nriagu (1978), street dusts from 

different U.S. cities contained between 300 and 18,000 ppm lead. A survey 

of street dusts in 77 midwestern cities showed an average lead content of -

1,636 ppm in residential neighborhoods and 2,413 ppm in commercial and 

industrial areas (NAS, 1980). Limited chemical analyses of roadside soils 

and dusts from U.S. cities found lead predominately as sulfate, along with 

minor amounts of oxide and halide salts (Olson and Skogerboe, 1975; Corrin 

and Natusch, 1977). Pavement dust, street dust, gutter debris, and household 

dust samples contain mostly large lead particles ranging between 40 and 

1000 pm (Pierson and Brachaczek, 1976; Sturges and Harrison, 1985; Duggan 

and Inskip, 1985). Higher lead concentrations are usually found in the 

smaller sized fractions. The concentration in a sample of street dust 
P.31 

11-10 

sieved at 100 pm, for example, is usually at least two to four times greater 

than that in the same sample sieved at 1,000 pm (Duggan and Inskip, 1985) 

and 76% of the composite weight of house dust was measured in particles 

smaller than 149 pm (Que Hee et al., 1985). This is significant given 

that the bioavailability of soil lead tends to increase with decreasing 

particle size (Barltrop and Meek, 1979). In addition, preliminary work 

indicates that smaller particles (<100-250 pm) readily adhere to fingers 

and are thus likely to be ingested (Duggan and Inskip 1985; Que Hee et al., 

1985). 

Unlike lead that is incorporated into soil, lead in surface dusts, 

both indoors and outdoors, is mobile and can be expected to respond over 

time to a much greater degree to changes in atmospheric lead emissions. 

Airborne lead deposited on streets, sidewalks and driveways is subject 

to distribution by wind and water. Windblown particles associated with 

dust are.apt to be redeposited within the urban environment by the complex 

wind currents caused by buildings and street canyons. Following the 

installation or supplementation of emission controls at lead point sources, 

and significant improvements in air lead quality, significant drops in 

concentrations of lead in playground dust, surface dust on soil, and in 

house dust have been observed within 1 to 2 years (Morse et al., 1979; 

Prpic-Majic et al., 1984). Another study found a weak correlation between 

dust lead samples collected two months apart in the same houses in Wales 

(Davies et al., 1985). The authors noted that this is consistent with the 

suggestion that external influences, e.g., soil, are important for dust 

lead and differences in weather or season could influence the amount of 

soil brought into the house and thereby cause short-term changes in dust 

composition. This illustrates again the potential responsiveness of house 

dust lead levels to changes in air quality. 
__________________^_^___ _ P.32_ 

11-11 

There is some evidence to suggest, that this same relationship applies 

in non-point source areas and that lead in surface dusts should decline 

as a result of the decline in gasoline lead emissions. A survey of 

rainwashed areas in the U.K. and New Zealand found that the normal 

acidity of rain (pH between 4 and 5) was insufficient to completely dissolve 

and transport lead particles, and that residue near streets poses a health 

hazard to children who are prone to ingest street dusts (Day et al., 1979). 

However, Laxen and Harrison (1977) found that a light rainfall (2 to 3 

mm) is sufficient to remove 90% of the lead from the road surface, mainly 

to surrounding soil and to waterways. Furthermore, based on deposition 

fluxes around a major roadway over a year and concentrations of lead in the 

road drainage water, Harrison et al. (1985) estimated that removal of lead 

in runoff waters exceeds that deposited on the roadsides. The rate of 

removal of lead from street dusts is also dependent on the frequency and 

efficiency of street cleaning operations.' It appears reasonable to expect 

that with declining input, lead concentrations in street and household ° 

dusts will also drop due to precipitation, wind, street and house cleaning. 

Surveys of a diverse set of homes indicate a wide range of house dust lead 

levels between 10 and 35,000 ppm (EPA, 1977; Harrison, 1979; Angle and 

Mclntire, 1979; Thornton et al., 1985; Clark et al., 1985) and as high as 

100,000 ppm within 2 km of a smelter (Landrigan et al., 1975). Lead levels 

in house dust can vary considerably depending on house cleaning practices, 

as demonstrated by Charney et al., (1983), as well as on the presence and 

condition of lead-based painted surfaces, the presence of cigarette smoke, 

the amount of dust and soil blown into or carried into the house on clothing 

and shoes (especially on those occupationaliy exposed to lead) and pets, 

indoor sources of lead other than paint (e.g., soldering), the permeability 
11-12 

of the home to outdoor air (which can vary with season), and outdoor concentrations of air lead. Lead concentrations in house dust are also dependent 

on other constituents in the dust and it is therefore important to consider 

the total amount of leaded dust as well as dust lead concentration. 

To assess the impact of atmospheric lead on children's total exposure, 

it is necessary to estimate the contributions of different air lead levels 

to outdoor and indoor soil/dust lead levels as well as the amount of dirt a 

child may ingest, both inadvertantly and deliberately. These issues are 

addressed in the modeling approaches presented in Sections IV and V. 

D. Lead in the Diet 

The ingestion of food is a major component of most individuals' 

total lead uptake, although the relative contribution is a function of their 

age, and the size and type of diet. The occurrence of lead in the diet may be 

a result pf a) natural sources of lead; b) deposition of airborne lead particles 

onto crops, forage, feed, soils, and water; and c) the harvesting, processing, 

transportation, packaging, preparation, and storage of food during w°nich lead 

can be introduced at every stage either by atmospheric deposition or through 

metallic contamination, particularly from plumbing or solder in cans. 

Recent studies on lead content of various foods, both before and after 

processing, packaging, and preparation, and on food consumption patterns 

in the U.S., provide information on dietary lead intakes for different 

populations (Beloian and McDowell, 1981; Wolnik et al., 1983; National Food 

Processors Associations, 1982; Pennington, 1983; U.S. FDA, 1983, 1984). 

Using these data for 1982-83, a "Multiple Source Food Model" was developed 

in the CD that apportions lead in "typical" child and adult diets to the 

following sources: natural soil lead, direct atmospheric lead added to 

food crops before harvest and during processing, indirect atmospheric lead 
P.34 

11-13 

that has been incorporated in soils , lead in solder from food cans and 

drinking water, and lead whose origin cannot be determined at present 

(CD, Section 7.3.1.2). The model was validated by using the same parameters 

along with more recent food data from 1984 and 198b to successfully predict 

continuing declines in dietary lead intake. For example, dietary lead intake 

for a 2-year old child has dropped from about 45-50 pg/day in 1978 to an 

estimated 13.1 pg/day in 1985; comparable declines (from 45-50 pg/day to 

15-20 pg/day) have been seen in different adult populations since 1982 

(Flegel et al. , 1988.) Even greater reductions had been achieved in the early 

to mid-1970's (CD, p. 7-49). These trends are attributable to the drastic 

reduction in gasoline lead emissions and the voluntary phaseout of lead-soldered 

cans by U.S. manufacturers since the 1970's. This downward shif t is expected 

to continue up unti l the early 1990's as lead from both these sources drops 

further. The methods by which estimates of dietary lead intake from the 

Multiple Source Food Model are derived and applied to modeling total lead 

exposure are discussed in Appendix A and Section V. 

E. Lead in Water 

Lead is a natural, usually wery minor, constituent of surface and yround 

waters. Atmospheric lead can enter aquatic systems through direc t fallou t or 

in surface runoff as suspended particles or adsorbed to soi l particles . Under 

most conditions (pH, temperature, alkalinity) , lead forms insoluble salts and 

precipitates to sediments, which probably accounts for the low lead content of 

U.S. surface water supplies; the average concentration ranging between 3 and 4 

micrograms Pb per lite r water (pg/L) (NAS, 1980). 

In contrast, lead levels in household, school, and offic e building 

drinking water can be much higher due to plumbing corrosion and subsequent 

leaching of lead, ranging between 10 and 30 pg/1 on average. The combination 

of corrosive (i.e. , soft or acidic) water and lead pipes or lead soldered 
P.35 

11-14 

joints in distribution systems or houses creates localized zones of high lead 

concentrations up to 380 pg/1 (Worth et al. , 1981). The combination of new solder 

and corrosive water can result in concentrations commonly over 1000 mg/L. In \ 

general, levels are highest in samples of hot and/or stagnant "firs t draw" water. 

Drinking Water is a major source of lead exposure for many infants while they 

are dependent on baby formulas during their firs t year, as well as for young 

children. The 1986 Safe Drinking Water Amendments banned the use of lead solder or 

flux and lead-bearing pipes and fittings , to be implemented and enforced by the 

States by June 1988. In August 1988, EPA's Office of Drinkiny Water proposed revising 

the existing lead standard of 50 pg/L to 5 pg/L, measured at the entry point to the 

distribution system or the treatment plant, with a "no-action level" average of 10 

pg/L, measured at the home tap. 

Estimated contributions that natural, atmospheric, and solder lead make to 

total lead exposure via drinking water and total diet are addressed in the modeling 

approaches presented in Sections IV and V. 

F. Lead in Paint 

Ingestion of lead-containing paint is considered to be the most frequent 

cause of severe lead intoxication among children (Chisolm, 1984; CDC, 1985). 

Significant correlations have been found between the quantity and condition of lead 

painted surfaces in homes and blood and fecal lead levels in inner city children i 

(Urban, 1976; CD, pp. 11-156 to 11-160). Significant differences in blood lead ! 

levels have been found in relation to housing condition among children as youny as 

nine monthsj with highest levels in children living in deteriorating pre-World 

War I I housing, intermediate levels in well-maintained and rehabilitated older 

i 

housing, and lowest levels in children in public housing and newer units free of j 

lead paint (Clark et al. , 1985). Other investigations indicate that in addition 

to peeling lead-based paints, "intact" lead-based paint contributes to elevated 

blood lead levels in children via chalking or weathering (Gilbert et a>., 
_. ._^. P.36_ 

11-15^^"'""'" ""' 

1979; Galke et al. , 1975) or can be exposed-to children who chew or gnaw upon 

the surfaces. Further risks are associated with conventional paint removal 

techniques (i.e. , sanding, burning heat guns) which can generate concentrations 

of fine lead dust up to 100-100,000 pg/m3 (NIBS, 1988; Inskip and Atterbury, 

1983). 

Under the 1971 Lead-Based Paint Poisoning Prevention Act, which has 

since been amended, the Department of Housing and Urban Development has . 

regulatory and research responsibilities for eliminating the hazards of 

lead-based paint poisoning in federally funded housing. In 1977, the 

Consumer Product Safety Commission banned household paint (including toy 

and furniture paint) containing more than 0.06% lead as hazardous. This 

was an important step but i t does not affect hazards posed by lead-based 

paints already present on old housing surfaces. Prior to 1940, some interio r 

paints contained more than 50% (500,000 ppm) lead. Use of lead pigment 

paints declined slowly between 1940 and the late 1960's after which i t 

declined at a rapid rate (NAS, 1972). Pope (1986a) estimates that in 1980, 

the interio r and/or exterior surfaces of between 21.5 and 47.3 millio n 

households in the U.S., mostly buil t before 1960, contained greater than 

0.7 mg/cm^ lead in painted surfaces, a level considered hazardous to young 

children by the Centers for Disease Control (CDC). Between 6.2 and 13.6 

million children under the age of 7 are estimated to have resided in lead-based 

painted housing in 1980 (Pope, 1986a). Even i f coated with low-level leaded 

paint, the underlying layers of lead^-based paint in older homes represent a 

large reservoir of lead exposure in children, particularl y i f there is 

peeling paint, broken or cracked plaster, or holes in the walls . The 

number of lead-based painted homes with these deteriorating conditions 

(i.e. , "unsound") is estimated to have been between 800,000 to 2.9 millio n 
n-16 

in 1980 with approximately 235,000 to 842,000 children under age 7 livin g 

in such homes (Pope, 1986a). 

Lead-based paint continues to be the major source of high-dose lead 

exposure and symptomatic lead poisoning for children in the U.S. (Chisolm, 

1971; CDC, 1985), and i t appears that exposure to lead-based paint wil l 

continue to be a problem for decades to come (Schneider and Lavenhar, 1986). 

Between 1973 and 1980, only 10% of pre-1940 housing units and.5% of 

housing units built.i n the 1940's had been removed from our nation's housing 

stock by demolition, disaster, or by other means such as conversion to 

commercial space (Bureau of the Census, 1983). Less than half of these 

removed units were located in central cit y areas where lead poisoning is 

most prevalent. In addition, poor urban families often have no choice but 

to liv e in poorly maintained older housing, given that the vast majority of 

new housing units created from 1973 to 1980 is located outside of central 

cit y areas and that acute shortages of modern and lead-free, low income 

rental units exis t in many citie s (Farfel , 1985). Many rural families 

are also afflicte d with a lead paint risk since about 41% of rural housing 

is "substandard" (ATSDR, p. VI-15). 

High exposures to children livin g in older housing with flakin g or 

intac t lead-based paint wil l not be significantl y influenced by any changes 

in atmospheric lead emissions. Children with pica for paint chips or 

others livin g in deteriorating lead-based painted homes who are excessively 

exposed to paint lead-contaminated dust through normal hand to mouth activit y 

cannot be expected to be protected by any lead NAAQS no matter how stringent . 

For thi s reason and because exposure to paint lead is so highly variable, 

no attempt wil l be made here to quantitativel y estimate exposures of children 

who are excessively exposed to lead-based paint under various air lead 
P.38 

11-17 

levels, as is done for other exposureamedia. Some of the available data, 

although sparse, are discussed at the end of Appendix A to demonstrate how 

some estimates could be generated. Despite the small amount of data, and the 

great variability in paint lead exposures, it is clear that any exposures 

and blood lead levels predicted for children under various air lead levels 

using the approaches in Section III will be significantly higher for children 

with high paint lead exposure. Comprehensive plans for safe and effective 

removal or containment of lead painted surfaces from old housing need to be 

developed and implemented, otherwise pediatric plumbism will persist until 

much of the present housing stock is completely rehabilitated or demolished 

(Chisolm, 1986). Preventing excessive exposure to existing sources of lead 

in and around housing must be addressed by an appropriate combination of 

legislation, housing code inspection and enforcement, financial incentives, 

parental education, and other programs. 
P.39 

III. ESTIMATING LEAD EXPOSURES AND BLOOD LEAD LEVELS 

A. Alternative Modeling Approaches 

To assess the health risks associated with alternative air lead 

standards, it is necessary to estimate the blood lead (PbB).levels that 

would be distributed in the population(s) of concern under various air lead 

concentrations. [The amount of lead measured in whole blood is an index of 

the rapidly diffusible fraction of the total body burden of absorbed lead 

and is generally used as the dosage, or index of exposure, in investigating 

the various human health effects associated with lead.] Lead exposure occurs 

through multiple media but unfortunately, longitudinal assessments of 

simultaneous multi-media exposures have not yet been made. Potentially 

useful longitudinal analyses are now being carried out as part of the 

Cincinnati Lead Program Project (Clark et al., 1987). Three approaches 

are examined here that can be used to estimate or predict the impact of inhaled 

and ingested lead aerosols and compounds on the body burden of lead as indexed 

by blood lead. These approaches are presented in detail in Sections IV and 

V for consideration for using one, two, or all three in assessiny the 

protection provided by alternative lead NAAQS. 

The first approach, discussed in Section IV, is to use measured rates 

of absorption, or "uptake" of lead through different pathways (e.gT, inhalation 

and ingestion) from experimental studies together with available mathematical 

("biokinetic") models from lead balance studies to project either total, 

body burden or the amount of lead in any of the presumed "physiological" 

kinetic compartments (e.g., blood, soft tissue, bone) at any time. 

The second approach, referred to as an "aggregate" model, is to 

directly apply a mathematical relationship between air lead and blood lead 

derived from community epidemiological studies that reflects both direct 
II1-2 

inhalation exposure and indirect exposures via secondary deposition processes. The third approach, referred to as a "disaggregate" model, is a 

hybrid of the first two in which separate empirical relationships between 

blood lead and lead intake from lead in air, food, water, dust, and soil, 

available from experimental exposure and epidemiological studies of different 

populations, are applied to lead concentration estimates in different 

media. The nature of the relationships detected between lead concentrations 

in blood and various environmental media, and the merits and limitations in 

using empirical relationships to represent lead exposure through multiple 

pathways in these latter two approaches, will be discussed in Section V. 

B. Calculating Blood Lead Distributions Around Predicted Means 

Because most of the data input to these models are generally averaye 

estimates, all three models estimate PbB levels in terms of population mean 

levels. The distribution of PbB levels in U.S* children is broad because 

there is a distribution of exposures those children face, a distribution of 

behavior patterns that affect the uptake of that exposure, and a distribution 

of biological absorption and excretion rates. For purposes of setting a 

lead NAAQS, it is necessary to determine the blood lead distribution 

across a defined population group corresponding to a given mean PbB for 

that group so that, for example, percentages of individuals exceeding PbB 

levels of concern can be ascertained. Determining such distributions is an 

attempt to account for the significant behavioral and biological variability 

within populations. It is the group of individuals with the potentially 

greatest adverse response to any given lead exposure that are of greatest 

concern in establishing a primary lead NAAQS. 

Consistent with measurements of other metals in tissues of human 

populations, the distribution of PbB levels for any relatively homogeneous 
111-^3

 ; 

P.41 

population closely follows a lognormal,distribution (CD, Section 11.3.4). . 

A lognormal distributio n is completely specified by it s geometric mean and 

geometric standard deviation. I t is possible, therefore, to calculate 

percentiles of a blood lead distributio n (e.g. , median or 99 percentile) 

around a mean PbB level by using the geometric standard deviation (GSD) of 

that population's blood lead distribution . Several epidemiological studies 

have indicated that the log values of measured individual PbB levels in a 

uniformly exposed population are normally distributed with a variation , 

including analytical variation, ranging between 1.3 and 1.4, when expressed 

as a GSD (Tepper and Levin, 1975; Azar et al. , 1975; Billic k et al. , 1979). 

The NHANES I I study provides the best available data on nationwide blood 

lead levels in terms of quality control and sample size. In regression 

analyses of these data, Schwartz (1985a) estimated a GSD of 1.428 for young 

children aged 6 months to 5 years, after removing the variance in PbB 

levels attributable to air lead exposure by adjusting every individual' s 

PbB level to what i t would be at zero air lead, while allowing for variations 

in background non-air lead exposure. 

After excluding from the analysis children with PbB levels above 

40 pg/dl, who would not be expected to be substantially affected by changes 

in the lead NAAQS (for example, because of higher than average pica activit y 

or excessively high exposures to lead in paint), Schwartz (1985b) reapplied 

the same technique and calculated a GSD of 1.419. Thus, a GSD without 

attribution of any source of lead exposure except gasoline lead and industrial 

air lead emissions may be taken as a rounded-off value of approximately 

1.42 for the NHANES I I population of children (CD, p. 11-31). 

Given that the focus of this exposure analysis is on populations livin g 

near lead point sources where blood lead variabilit y may be differen t compared 

W M I 
P.42 

III-4 

to the total mix of U.S. children, a separate assessment of blood lead 

GSD's for such populations is presented here. Published statistic s of PbB 

samples near various lead point sources have been assembled and analyzed 

in Cohen (1986) and Marcus (1988a). The GSDs calculated from these pointsource/blood lead surveys are liste d in Table 3-1 , along with the NHANES I I 

GSD for children. To differen t degrees, the primary origin of non-biological 

variation in lead exposure for these point source populations was the smelter, 

especially in the Roels and Yankel studies conducted when smelter emissions 

were considerably higher. I t is possible that children with exposure 

to a single, intense source of lead may have relativel y low variabilit y 

(essentially only due to intrinsi c biologic variability ) with a high 

mean blood lead. The most recent study indicates a larger variance at 

relativel y lower exposure levels near the Montana smelter surveyed in 1983, 

suggesting that multiple origins with separate variabilit y may be present 

rather than a single predominant source of variabilit y in lead exposure 

(Marcus, 1988a). 

The selection of a GSD value to model populations livin g near various 

lead point sources in the future depends on assumptions regarding variance in 

exposure in the future . Perhaps the most relevant data are from the 1983 

E. Helena, Montana study since they reflec t the most contemporary conditions. 

I t can be expected that even for this population, however, total lead exposure 

wil l have dropped substantially by 1990, the startin g point of our exposure 

analyses. This is attributable to recent and continuing downward trends in 

lead levels in canned foods, the nationwide ban on lead solder in new construction 

and plumbing repairs, the lead-in-gaspline phasedown, and perhaps increasing ' 

public awareness regarding the dangers of lead exposure and effective avoidance 

measures. Al l of these changes should result in not only lower mean baseline 
•'!•'"*-,&•; «T * , 

III-5 

TABLE 3-1. COMPARISON OF,ESTIMATED GSDs ACROSS SEVERAL STUDIES 

P.43 

' I 

Population/Reference 

Mean PbB 

(ug/dl) 

Estimated 

GSD1 

NHANES I I - total mix of 

children 1-5 years old 

(Schwartz, 1986b) 

11-year olds livin g near Belgian 

primary lead smelter (Roels 

et al. , 1980) 

1-9 year olds livin g near Idaho 

primary lead smelter (Yankel et al. , 

1977) 

1-5 year olds, livin g near Montana 

primary lead smelter (CDC, 1983) 

1-5 year olds livin g in 3 Missouri 

smelter, towns (Baker et al. , 1977) 

16.0 

21.7 

56.5 

1-5 year olds, closest to 3 non-ferrous 15.6 

smelters in U.S. (Hartwell et al. , (median) 

1983) 

9.4 

16.2 

1.42 

1.29 

1.32 

1.39 

1.53 

1.57 

iGSD calculations described in Cohen (1986) and in Marcus (1988a). 
P.44 

III-6 

PbB levels, but perhaps more importantly, in fewer high level exposure 

situations and therefore less variance in PbB levels than existed in 1983. 

Quantifying such an effect to estimate a future GSD value is difficult. It 

is likely, however, that future reductions in baseline "non-point source" 

lead exposures will result in a reduction in exposure variance so that the 

blood lead GSD will be more reflective of intrinsic biologic variance, such 

as that found in the Roels and Yankel studies. Although these latter 

studies were conducted during times of much higher exposures, the exposures 

were dominated by high smelter emissions and soil/dust contamination, 

resulting in relatively low PbB variability. For purposes of modeling 

future exposures among children living near different lead point sources, a 

range of GSD values can be considered: a lower bound of approximately 1.30 

derived from the Yankel et al. and Roels et al. studies and an upper bound 

GSD value of 1.53 derived from the 1983 CDC study of E. Helena, Montana 

children. The midpoint of this range is 1.42, which coincidentally is 

identical to the NHANES II GSD estimate. Until additional data are available, 

a range of 1.30-1.53 will therefore be assumed for children living near 

lead point sources as a reasonable range of GSD values, and the midpoint of 

.1.42 will be assumed as a reasonable best estimate. In the case study exposure 

-analyses, the larger and more conservative GSD of 1.53 will be tested in 

sensitivity analysis. 

Since blood lead estimates will also be made for middle-aged men, GSD 

values for adult blood lead distributions are needed. There is little 

information on adult blood lead GSDs from point source studies. The 

most complete data base is clearly from NHANES II, which has been 

analyzed in the CD to yield a range of GSD values, depending on race and 

place of residence, between 1.34 and 1.39 for adult women and between 
III-7 

1.37 and 1.40 for adult men (CD^ Table ,11-9); Given the uncertainties in 

extrapolating these ranges to adult populations living near point sources 

under future exposure scenarios, a midpoint value of 1.37 will be chosen as 

a reasonable estimate for both adult men and women. 

Analytical variation which exists in any measurement has an impact 

on the bias and precision of statistical estimates such as the GSDs derived 

from the studies discussed here. For this reason, it is important to 

recognize the magnitude of analytical variation in blood lead measurements 

due to measurement variation (i.e., between measurements run at the same 

time) and variation created by analyzing blood samples at different times 

(CD, p. 11-29). Using GSDs adjusted in such a way, although providing a 

more accurate characterization of a given blood lead distribution, may not 

be appropriate for this assessment because the health effects studies that 

will be used to define dose-response relationships did not correct their 

PbB measurements for analytical variance. These studies, like NHANES II 

and the point source epidemiological surveys, generally employed the best 

avail able measurement techniques and quality control. Using "corrected" 

GSDs to predict the distribution of children's PbB levels around a mean 

population PbB, and then matching those predicted PbB levels with "uncorrected" 

PbB levels0derived from health studies in order to assess the risks associated 

with that population mean PbB, would result in a somewhat biased assessment. 

Therefore, the range of GSDs presented above that are not corrected for 

analytical variance will be used in further calculations. 

Table 3-2 presents blood lead distributions associated with sample 

population mean PbB levels calculated using the GSD values derived above. 
P.46 

III-8 

These distributions were calculated using the formula given to describe 

a lognormal distribution (Yankel et al., 1977): PbB = GM(GSD)* 

where: 

GM = geometric mean PbB 

GSD = geometric standard deviation 

z = the number of standard deviations 

PbB = value of PbB at z standard deviations 

TABLE 3-2. ILLUSTRATIVE BLOOD LEAD DISTRIBUTIONS ASSUMING 

DIFFERENT MEAN BLOOD LEADS AND GEOMETRIC STANDARD DEVIATIONS 

Geometric Percentile 

GSD 

1.37 

Mean 

PbB 

4 

5 

6 

7 

90th 

6.0 

7.5 

9.0 

10.5 

95th 

6.7 

8.4 

10.1 

11.7 

99th 

8.3 

10.4 

12.5 

14.6 

99.5th 

9.0 

11.3 

13.5 

15.8 

J.42 4 6.3 7.1 9.0 9.9 

5 7.8 8.9 11.3 12.3 

6 9.4 10.7 13.6 14.8 

7 11.0 12.5 15.8 17.3 
P.47 

IV. INTEGRATED LEAD UPTAKE/BIOKINETIC MODEL 

The "uptake/biokinetic" modeling approach attempts to account for the 

following: a) the amount of lead in the body at any one time is the product 

of dynamic interactions of partially offsetting processes of absorption, 

distribution, storage, mobilization, and excretion; b) these processes vary 

with the route and rate of exposure, a person's age, nutritional and health 

status, and baseline exposure;, c) uptake from all sources by all absorption 

routes can be separately modeled, thus providing an estimate of the relative 

importance of atmospheric lead exposure, either directly or indirectly, to 

total body burden; and d) past and future trends in environmental lead 

levels due to different control efforts and regulations are important 

determinants of projected exposures. 

There have been several studies measuring the intake, uptake, and 

metabolism of lead in adult male volunteers, from which balance schemes have 

been constructed (Kehoe, 1961; Chamberlain et al., 1978, Rabinowitz et al., 

1976, 1977). By making estimates about lead intakes and various metabolic 

factors, balance schemes can be constructed for different groups with 

different exposures to lead to assess the importance of specific exposure 

factors under variable conditions (e.g., alternative lead NAAQS, phaseout 

of lead in soldered cans and in gasoline). Pre-school age children (£ 6 

years old), pregnant women (as exposure surrogates for the fetus), and 

adult men (40-59 years of age) are specified in the CD as particularly 

sensitive to lead. Of these groups, children between 2 and 3 years old 

exhibit, in general, the highest blood lead levels (Mahaffey et al., 

1982; Billick et al., 1979), most likely due to their greater hand-to-mouth 

activity as well as to various metabolic processes (Harley and Kneip,. 

1985). To predict exposure impacts of alternative air lead standards, 

model balance schemes have been devised for children aged 0-6 years and 

are discussed below. 

aa 
_P.48 

IV-2 

A lack of adequate biokinetic data for pregnant females prevents such 

modeling for the fetus. In addition, accurate biokinetic modeling of the 

sensitive population of adult males would require at least 40 years of 

cumulative exposure inputs. Such uptake modeling would be difficult given 

the great variability in exposure histories among U.S. adults and the paucity 

of data. Exposure modeling for purposes of risk estimation for adults, 

discussed in Section V.A., will rely on combining air: blood lead "disaggregated" 

relationships with estimated baseline "non-air" blood lead levels. 

A discussion of lead's absorption, excretion, retention, and distribution 

within the body under different exposure and physiological conditions is 

necessary background to the uptake/biokinetic model, and is provided in 

Chapter 10 of the CD. As will be further discussed, application of this 

or any lead exposure model must recognize the limitations of the available 

data and the significant variability among populations in their behavioral, 

exposure, and physiological characteristics. For present purposes, the 

children to be considered in the model do not include the whole U.S. population; 

they comprise groups with contrasting exposures to lead, some of them extreme, 

in order to illustrate the variations in lead exposure and absorption 

around lead point sources under different scenarios. Section IV.A presents 

methodologies to estimate average daily lead uptake-from all exposure 

sources under alternative air lead levels. Section IV.B presents methodologies 

to relate these estimates of lead uptake to average, steady-state blood 

lead levels. Although this approach predicts hypothetical outcomes and 

the absolute numbers should not be used uncritically, the model does strive 

to use the available data, with all its limitations, to the'fullest extent 

possible to provide a useful tool in eventually distinguishing the health 

impacts of alternative lead NAAQS. 
IV-3 

A. Estimates of Lead Uptake 

The method employed to estimate the degree to which each environmental 

source of lead contributes to a child's total daily lead uptake is based 

on the distribution of ambient air lead levels that would be allowable or 

expected under each standard considered. Probable exposure conditions with 

respect to other exposure media such as food and dust, and average biological 

absorption rates for each exposure route are also specified. The method 

consists of a four-step process: 

1) definition of ambient concentrations of lead for major exposure 

sources (i.e. , air, food, water, soil , dust); 

2) determination of daily lead intake according to the relationship: 

I , - Ci • [Pb]1 

where I-j is the daily lead intake from source, i , C-j is the ingestion or 

inhalation (i.e. , consumption) per day of each lead source i and [Pb]-j is 

the concentration of lead in each source i ; 

3) calculation of the amount of lead absorbed from each exposure 

source i: 

M . 3 T . <> a • u

i

 x i M i -

where U-j is lead uptake for each exposure source i , IT is the daily lead 

intake from each source i , and A-j is the percent absorption of lead, via 

the appropriate exposure route for the particular source; and 

4) calculation of the total lead uptake from all sources, Uu: 

% = ( ^ -AT ) 

The way daily lead uptake would be calculated is illustrated in Table 4.-1 

for 2-year old children living near (within 2-5 km) a lead point source. 

Details of the model parameters and the line-by-line estimates, along with 
TABLE 4-1. ESTIMATED 1990 AVERAGE LEAD INTAKE AND UPTAKE IN 2-YEAR OLD CHILDREN 

UNDER AVERAGE AIR LEAD LEVELS1 

0.25 pg/m3 0.5 pg/m3 0.75 pg/m3 1.0 pg/m3 1.25 pg/m3 l.S pg/m3 

1. Outdoor air lead (pg/m3) 

2. Indoor air lead (pg/m3) 

3. Time spent outdoors (hours/day) 

4. Time weighted average (pg/m3) 

5. Volume of air respired (m3/day) 

6. Lead Intake from air (pg/day) 

7. % deposition/absorption in lungs 

8. Total lead uptake from lungs 

(pg/day) 

9. Dietary Lead Consumption (pg/day) 

a) from solder or other metals 

b) atmospheric lead 

c) natural lead, indirect atmospheric, undetermined sources 

10. % absorption in gut 

11. Dietary lead uptake (pg/day) 

12. Street dust/soil lead (pg/g) 

13. Indoor dust lead (pg/g) 

14. Time weighted average (pg/g) 

15. Amount of dirt ingested (g/day) 

16. Lead intake from dirt (pg/day) 

17. % dirt lead absorption in gut 

18. Lead uptake from dirt (pg/day) 

19. Total lead uptake from lung and 

gut (pg/day) 

0.25 

.08 

2-4 

.09-.11 

4-5 

0.4-0.6 

42 

0.2-0.3 

5.6 

1.2 

3.6 

42-53 

4.4-5.5 

181 

271 

241-256 

.08-.135 

19.3-34.6 

25 

4.8-8.6 

9.4-14.4 

0.5 

0.15 

2-4 

.18-.21 

4-5 

0.7-1.1 

42 

0.3-0.5 

5.6 

1.2 

3.6 

42-53 

4.4-5.5 

308 

482 

424-453 

.08-.135 

33.9-61.2 

25 

8.5-15.3 

13.2-21.3 

0.75 

0.23 

2-4 

.27-.32 

4-5 

1.1-1.6 

42 

0.5-0.7 

5.6 

1.2 

3.6 

42-53 

4.4-5.5 

436 

693 

607-650 

.08-.135 

48.6-87.8 

25 . 

12.1-21.9 

17.0-28.1 

1.0 

0.3 

2-4 

.36-.42 

4-5 

1.4-2.1 

42 

0.6-0.9 

5.6 

1.2 

3.6 

42-53 

4.4-5.5 

563 

904 

790-847 

.08-.135 

63.2-114.3 

25 

15.8-28.6 

20.8-35.0 

1.25 

0.38 

2-4 

.45-.54 

4-5 

1.8-2.7 

42 

0.8-1.1 

5.6 

1.2 

3.6 

42-53 

4.4-5.5 

691 

1115 

974-1044 

.08-.135 

77.9-140.9 

25 

19.5-35.2 

24.7-41.8 

1.5 

0.45 

2-4 

.54-.63 

4-5 

2.2-3.2 

42 

0.9-1.3 

5.6 

1.2 

3.6 

42-53 

4.4-5.5 

818 

1326 

1157-1241 

.08-.135 

92.5-167.6 

25 

23.1-41.9 

28.4-48.7 

Assumptions and calculations are described in Appendix A by row number. Refers to children living near one or more lead 

point sources and unaffected by lead paint. Children living in typical urban or rural environments remote from lead point 

sources can be modeled using the same uptake calcul at'ipns and substituting parameter values where differences are noted 

in Appendix A. 
P.51 

' IV-5 

limitations of the available data base, are discussed in Appendix A. For 

comparative purposes, children living in areas generally unaffected by 

stationary lead emissions can also be modeled using most of the same parameter 

values; exceptions (indoor/outdoor particled penetration rates, respiratory 

deposition rates) are noted in Appendix A. Dust and soil lead levels 

associated with given air lead concentrations would differ substantially 

between point and non-point source areas; this too is briefly discussed 

in Appendix A. The following points are important to consider with regard 

to the uptake calculations in the table: 1) the calculations do not represent 

population-wide exposure scenarios under alternative standards, only exposure for 

the specified air lead concentrations. Ambient lead levels around a point 

source meeting a given lead NAAQS vary depending on distance, topoyraphy, 

local meteorology related to pollutant transport and diffusion, plant operating 

parameters, emissions controls, particle sizes and deposition rates, etc. 

Air quality dispersion modeling that accounts for these factors will be used 

in the actual case study analyses to assign air lead concentrations under 

o 

alternative standards to different "receptor" points where people liv e around 

the lead point sources; 2) the estimates in the table apply to 2-year old 

children to illustrat e the period of maximum exposure. Blood lead levels 

estimated by the biokineti c model (discussed below) account for accumulating 

exposure throughout childhood, not jus t a single year. I t is necessary, 

therefore, to make age-specific exposure estimates that reflec t changes in 

behavior and metabolism as a child ages. Age-specific exposure estimates are 

also discussed in detai l in Appendix A; and 3) the general types of exposure 

environments considered here do not include older homes with lead-based paint 

hazards. Such conditions are discussed at the end of Appendix A to illustrat e 

the severity of lead-paint hazards compared to other exposures. 
IV-6 

B. Uptake and Blood Lead Concentration 

To estimate blood lead (PbB) levels under different exposure scenarios, 

several different kinds of studies can be used to derive a relationship 

between absorbed lead (or lead uptake) and blood lead. Available studies 

include population surveys in which the blood lead concentration of 

individuals or groups is correlated with measured lead concentrations in 

air, food, water, soil, or dust; experiments in which volunteers are exposed 

to controlled air lead concentrations and their PbB levels measured, and; 

lead balance studies of individuals with measured lead intakes. The most 

relevant experimental studies including, for comparative purposes, those on 

adults, are discussed here along with descriptions of the analyses used 

to derive uptake/PbB relationships. Relationship between blood lead and 

environmental lead concentrations from population surveys are discussed as 

part of the aggregate and disaggregate models in Section V. 

1. Dietary Lead Ingestion Studies in* Infants 

Studies that have measured dietary lead intake concurrently with 

PbB levels among infants and toddlers are compared in the criteria document 

(CD, Table 11-49). Although precise estimates of dietary lead consumption 

can not be made, these studies (U.K. Central Directorate, 1982; Lacey 

et al., 1985; Sherlock et al., 1982; Ryu et al., 1983) have several advantayes: 

1) careful control and analysis of dietary intake; 2) minimal lead exposure 

to sources other than the diet as the study population (infants) is relatively 

immobile; and 3) intake/blood lead relationships can be used to estimate 

the impact of total lead exposure (i.e., from all sources) on blood lead 

because tracer studies (Chamberlain et al., 1978; Rabinowitz et al., 1976) 

show no differences in the distribution of lead to tissues whether taken up 

from lung or gut. 
P.53 

IV-7 

Both the U.K. Central Directorate and the Sherlock et al . studies 

involved infants with relativel y high PbB levels and high intakes. I t is 

well documented that the relationship between PbB and lead uptake from any 

source is approximately linear at low intake levels but "flattens out" at, 

high intake levels (CD, Sections 11.2 and 11.3). Because the PbB levels 

(< 20 pg/dl) and lead intakes of the Ryu infants are more relevant, the 

criteri a document concludes that the slope from this study (0.16 pg/dl per 

pg/day lead intake) is the best available estimate (CO, p. 11-129). 

The infants in the Ryu et al . (1983) study absorbed substantial 

quantities of lead from formula or whole milk in lead-solderd cans. 

Even lead at much lower levels from formula in glass bottles achieved 

an apparent blood lead equilibrium postnatally. Reanalyses of the Ryu 

et al . data by Marcus (1989) suggested a larger slope, about 0.24 pg/dl 

per pg/day lead intake in formula and milk . 

Using non-linear and piecewise linear models to examine PbB-dietary 

lead slopes at higher intake levels, Marcus (1989) reanalyzed the Ryu et 

a l . data, as well as data on Scottish infants (Lacey et al. , 1985) and 

school children (Laxen et al. , 1987) exposed across a wide range of water 

lead levels . Because the diets among the infants in the Lacey et al . study 

were predominantly liquid , the derived water lead/blood lead relationship 

is considered a surrogate to predict the contribution of total diet to 

blood lead levels . A piecewise linear model fitte d the data about as well 

as a cube-root or square-root model, with a PbB/water Pb slope from the 

Lacey et al . study on infants of 0.254 pg/dl per pg/L for water lead levels 

below 16 pg/L, and a much lower slope of 0.0426 above that inflectio n point; 

after partial ling out the effects of house dust lead, the Laxen et al . 

school-children had a PbB/water Pb slope of 0.161 pg/dl per pg/L for water 
IV-8 

lead levels below 15 pg/L, and a 0.0318 slope above that level (Marcus, 

1989). 

I f the slopes from Lacey et al . and Laxen et al . are converted to uptake 

slopes by assuming 1 L/d water consumption, then the three absorption coefficient s 

(0.24 from Ryu et al. , 0.254 from Lacey et al. , 0.161 from Laxen et al. ) are 

reasonably consistent for low levels of dietary lead intake. Furthermore, 

given the differences among the three study populations and their exposure 

situations , the above results are strikingl y consistent in terms of the 

shape of the dose-response relationship, the location of the inflectio n 

point, and the magnitude of the differences above and below that point . 

The higher slope found for the Lacey et al . infants compared to the Laxen 

et al . school children is expected given the reduction in lead gut absorption 

rates with age. The uptake of lead from diet depends not only on the age 

of the child , but also possibly on the chemical and physical form of the lead, 

and on other components of diet that affect lead absorption. Much lower 

coefficients may be appropriate for adults and for older children who only 

ingest lead with meals; even higher coefficient s may be appropriate for 

lead absorbed by \/ery young children who consume lead between meals and who 

have nutritional deficiencies that facilitat e lead absorption. 

The lower slope at high levels of water lead or dietar y lead uptake 

probably represents other factors that reduce absorption, e.g. , much of the 

"excessive" dietary lead intake may occur during meals and i s thus much less 

bioavailable. There i s reasonable consistency among these high-uptake slopes: 

0.0426 from Lacey et al . infants ; 0.0318 from Laxen et al . school children; 

and 0.032 from the Sherlock and Cools studies for adults (see below). 

Uncertainty about the change-over between high-absorption and lowabsorption cases probably reflect s incomplte information about the food and 

water consumption patterns and other dietary factors . For present-day 
P.55 

•~*$.-?4A.~"~' • ' ' 

IV-9 

U.S. dietary lead exposure, the steeper slopes are likel y to be more 

accurate. 

The results from the Ryu et al . and Lacey et al . studies wil l be used to 

represent the infant dose-response relationship: the midpoint of the slopes 

(0.16 and 0.254, i.e. , 0.207) estimated for low exposure levels (i.e. , < 16 

pg/L), and the Lacey et al . slope of 0,026 for higher exposure levels . Assuming 

the same lead concentration in tota l diet where the slope flattens out, the 

water lead "threshold" level from the Scottish studies of approximately 16 pg/L 

(or 16 pg/Kg) can be converted to a corresponding dietary intake level by 

multiplying by the average dietary intake for an infant . The CO cites a dietary 

intake rate of 1.502 Kg/day for a 2-year old child (Table 7-15) and a rate of 

1.0 Kg/day wil l be assuned for the infant . The resulting level of 16 pg/day 

wil l be used as the inflectio n point in applying the slopes from Ryu et al . and 

Lacey et al . (0.207 and 0.026). Because lead ^£t.a;ke/PbB relationships are of 

interest , these slopes wil l be combined with the midpoint of gastrointestinal 

absorption rates (42-53%) cited for infants in the CD, to illustrat e the 

relationship between blood lead and*daily lead uptake for infants at the 

end of thi s section. A baseline (i.e. , non-air) PbB of 4 pg/dl wil l be 

assumed, based on recent studies of PbB levels and lead intake in Boston 

and Cincinnati infants (Rabinowitz et al. , 1986; Succop et al. , 1987). 

2

° littlerJJl*£k&-JStUdlfes:-JtHL^lliiLti 

The relationship between blood lead and food and/or water lead levels 

has been examined for adults in both experimental studies in Wiich controlled 

dietary supplements were administered to volunteers, and duplicate diet 

studies. Again, several studies indicate non-linear blood lead responses at 

high exposures but a linear relationship at relativel y low intake levels 

(< 100 pg/day, or < 30 pg/dl PbB). 
IV-10 

Two studies identified in Table 11-49 and 11-50 of the CD appear most 

useful in estimating a dietary lead/blood lead relationship chiefly because 

of their relatively large sample sizes and control over confounding factors: 

Sherlock et al . (1982), a duplicate diet study of 31 mothers (and their children) 

from Ayr, Scotland; and Cools et al . (1976) in which 11 subjects and 10 controls 

were studied for blood lead response to oral dosages of lead acetate. 

The latter study was an extension of the Stuik (1974) experiment, results 

of.which wil l not be used here because responses were not followed long enough 

for blood lead to equilibrate. The weighted mean of the slopes reported in the 

CD for the Sherlock and Cools studies is 0.032 pg/dl increase in blood lead per 

pg/day intake, which is a factor of 5-6 times lower than the comparable slope 

estimated for infants at low exposure levels. A gastrointestinal absorption 

factor of 0.15, cited in the CD for adults, wil l be applied to this intake 

slope to derive a blood lead/uptake relationship. A baseline PbB level of 4 

pg/dl wil l be assumed for adults based on. projections described in Appendix C. 

The same pattern of non-linear absorption seen in children wil l be assumed for 

adults and a slope of .009 pg/dl per pg/day wil l be used for levels above 

approximately 40 pg/day, which corresponds to an inflection point of 15 pg/kg 

lead in diet, multiplied by an approximate dietary intake of 2.89 kg/day for 

middle-aged men (CD, Table 7-15). 

3. Application of Chamberlain and Heard Analysis of Adult Exposure Data 

An attempt to relate blood lead and absorbed lead was made by Chamberlain 

and Heard (1981) using epidemiological and clinical data on adult men 

(Williams et al. , 1969; Kehoe, 1961; Nordman, 19.75; Zurlo and Griff ini , 

1973; Fugas and Saric, 1979). Total dietary and airborne lead uptake was 

estimated using calculations similar to those presented in the integrated 

lead uptake model in Section IV.A. The following assumptions given in 

Chamberlain et al. , (1978) were used: a) a fraction, 0.55, of the uptake 
P.57 

IV-11 

becomes attached to red blood eelIs i b) the biological half-lif e of lead in 

blood is 18 days; c) a factor, 1.3, is to be allowed for long-term resorption 

and re-entry into blood of some of the lead which is stored in bone; and 

d) the mass of blood is 5400 grams. With these assumptions, Chamberlain 

and Heard calculated the following relationship: 

A PbB = 0.55 x 18 x 1.3 = 0.34 pg/dl per pg/day 

A Uptake 54" x 0.693 

This relationship is comparable to that derived from the Cools and 

Sherlock studies after gastrointestinal absorption is accounted for. Given 

uncertainties about some of the studies analyzed by Chamberlain and Heard 

regarding exposure levels and quality control , and because of the high 

exposure levels , this slope is presented here only for comparative, purposes 

and wil l not be used further. 

4. Lead Balance Studies: Compartmental Models 

To demonstrate a causal relationship between lead in the body and a 

biological change, i t would be ideal to know the amount of lead present at 

the sit e and time of the effect . For instance, i f lead is suspected to 

induce neuropsychological changes, a measure of the lead level present in 

nerve cells when the change occurred would be desirable. Living tissues 

can rarely be sampled, and data obtained at necropsy cannot reveal the 

variations in exposure throughout the individual' s life . For analytical 

simplicity , tissues can be grouped together on the basis of lead distributio n 

characteristics and body burdens of lead represented as a limited number of 

distinct , homogenous, and well-mixed pools or physiological compartments 

with similar kineti c properties. Mathematical biokineti c models have been 

fitte d to, data obtained in long-term balance studies and to lead isotope 

tracer experiments that estimate the rates of input to , transfer between, 
P.58 

IV-12 

and excretion from the different compartments (Rabinowitz et al., 1976, 

1977; Batschelet et al., 1979; Bernard, 1977; Mallon 1983; Harley and 

Kneip, 1985; Marcus, 1985a,b,c). Differences in the predictive lead models 

have been discussed by Bernard (1977), Batschelet et al. (1979) and Hammond 

et al. (1981). Models are shown schematically in the CD (Figures 10-3 and 

10-4). 

The choice of pools or level of aggregation in a model depends on the 

available experimental data, the appropriate time scale, and the predictive uses of the analysis, and is a compromise between accuracy and 

simplicity. For example, blood can be broken down into plasma and erythrocytes 

and then further into plasma protein-bound lead, diffusible lead, extracellular 

fluid lead, and erythrocyte proteins. Soft tissues can.be broken down into 

brain (hippocampus, medulla, etc.), kidneys, liver, hair, and so on. The 

hard tissue pools can be separated into compact (cortical.) and cancellous 

(trabecular) bones, and teeth; within each of these, separate components 

may be needed to model distinct diffusion time scales (Marcus, 1985a,c). 

Because the skeletal system in young children is rapidly developing and is 

both large and kinetically active, it is especially important to model bone 

lead in children. Until such refinements are further tested, a relatively 

simple 3- 5 pool model provides an adequate framework for predicting lead 

distributions in children. 

These models are helpful in predicting total body burden or equilibrium 

levels of lead over time in any of the presumed kinetic compartments under 

different exposure conditions. The basic assumption of the models is that 

the mass of lead in each of the compartments changes according to a system 

of coupled first-order linear differential equations with constant fractional 

transfer rates. Such models predict that when the lead intake changes from 
P.59 

IV-13 

one constant level to another, there is"aI directly proportional change in 

the mass of lead in each compartment and the attainment of a new equilibrium. 

The rates and magnitudes of these changes are dependent upon the rates of 

lead flux in the tissues and can theoretically be calculated from a compartmental 

model of the appropriate parameters. Support for a first, order kinetic model 

for lead metabolism is demonstrated by calculations, using first order models 

of soft tissue and bone concentrations of lead, and other elements (calcium 

strontium, radium), that fit human measurements as well as by using more 

complex models (Johnson and Myers, 1981; Mallon, 1983). 

A four-compartment biokinetic model of lead metabolism has been developed 

from data obtained in controlled single dose and chronic lead exposures of 

infant (9 months) and juvenile (22 months) baboons (Mallon, 1983; Kneip et 

al., 1983). Dynamic blood measurements and steady state blood and organ 

lead measurements were closely fitted to predict concentrations of lead 

in blood, liver, and kidney, and bone (the four compartments in which 95% 

of total body lead is contained) (Heard and Chamberlain, 1984). Human 

metabolism and growth patterns were applied in a computer simulation of the 

model that was then successfully validated using human autopsy data. 

The model parameters were revised using measured metabolic data for 

each organ (e.g., bone turnover rates) for children and were used to 

simulate organ lead burdens and concentrations in children with constant 

lead exposures from birth (Harley and Kneip, 1985). Although complete 

model validation is not possible, the revised model is consistent with experimental data on blood lead accumulation following dietary lead uptake among 

infants (Ziegler et al., 1978), and skeletal lead accumulation following 

controlled exposures in adults (Heard and Chamberlain, 1984). Given 
E£Q_ 

IV-14 

the available data, the model appears to provide the best estimates of PbB 

levels in children with continuous lead uptake over time. 

Table 4-2 presents PbB levels predicted by the model for male children 

under different exposures, or lead uptake levels. The values in Table 4-2 

include an adjustment for the propagation of maternal lead during pregnancy 

that persists throughout childhood. This adjustment was done using another 

kinetic model fit to blood lead data collected longtudinally in young children, 

from birth to 27 months (Succop et al., 1987) and assuming a baseline maternal 

PbB level of 4 pg/dl (see Section C.6). The following equation adapted from 

Succup et al. was used: 

dPbBt = PbB0 (e"

 a *) 

where: dPbBt = added blood lead increment propagated from maternal PbB at • 

time t; 

PbB0 = baseline maternal PbB (4 pg/dl); 

and the rate constant a = 0.072. 

The rate constant a = 0.072 mo- 1 corresponds to a mean residence time 

of 1/ a =1 4 months, or a half-life of ln(2)/ a = 10 months. This almost 

certainly represents resorption of that fraction of prenatal lead burden 

sequestered in the neonatal skeletal tissues. The apparent mean residence 

time of lead in blood in infants is much.shorter (see Section V.A.5). 

o 

No differences were found by tfie biokinetic model between the sexes 

except at older ages, and predicted PbB levels were highest among 2-3 year 

olds, consistent with results of NHANES II , the New York City screening 

program (Billick et al. , 1979), and ongoing longitudinal study (McMichael et 

al. , 1986). These results wil l be applied to estimates of the integrated lead 

uptake model to estimate children's PbB levels under alternative air lead 

levels. I t is important to note that this equilibrium blood lead model is 

linear in total lead uptake at each age. This is a necessary consequence of 
P.61 _ 

i-»mIV-15 

TABLE 4-2. 

PREDICTED EQUILIBRATED BLOOD LEAD LEVELS (pg/dl) OVER TIME AMONG CHILDREN 

WITH CONSTANT LEAD UPTAKES3 

Lead Uptake (pg/day) 

Age 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

10 

3.0 

4.9 

4.6 

4.5 

4.4 

4.4 

4.2 

3.4 

2.3 

2.6 

20 

'579 

9.0 

8.2 

8.2 

7.9 

7.8 

7.4 

5.9 

4.6 

5.2 

30 

8.9 

13.0 

11.9 

11.8 

U.4 

U.3 

10.7 

8.3 

6.9 

7.8 

40 

11.9 

17.1 

15.5 

15.4 

14.9 

14.7 

13.9 

10.8 

9.3 

10.4 

50 

TT.8 

21.1 

19.2 

19.0 

18.4 

18.2 

17.2 

13.3 

U.6 

13.0 

60 

TT.8 

24.2 

22.0 

21.8 

21.0 

20.7 

19.5 

14.9 

13.9 

15.6 

70 

20.8 

28.3 

25.6 

25.4 

24.5 

24.2 

22.8 

17.4 

16.2 

18.2 

80 

23.8 

32.3 

29.3 

29.0 

28.0 

27.6 

26.0 

19.9 

18.5 

20.8 

a

From Harley and Kneip (1985) after accounting for propagation of maternal 

blood lead during pregnancy (see text). 
IV-16 

the linear pharmacokinetic model assumed by Harley and Kneip (CD, Appendix 

11-A). The uptake/biokinetic model is thus logicall y consistent with 

aggregate and disaggregate linear total-uptake models discussed in the 

followiny sections. 

Figure 4-1 compares the relationships between lead uptake and blood lead 

derived from the various studies on infants and adults. Despite the diverse 

nature of the populations, study designs, and methodologies, there is a fai r 

degree of consistency in the relationships . Each study found that a linear 

function provided as good a fit , i f not better, than other non-linear forms at 

the relativel y low exposure levels investigated. Some experimental and epidemiological evidence suggests however, that the relationship between lead 

concentrations in tissues and cumulative lead intake is only approximately 

linear at low levels of intake, and that successive increments in intake or 

exposure result in progressively smaller contributions to blood lead concentrations (Azar et al. , 1975; Moore, 1977; Gross, 1981; Sherlock et al. , 1982). 

The curves drawn in Figure 4-1 for infants and adults do in fac t include smaller 

slopes for lead uptake values above 20-40 pg/day. This curvilinear 

relationship may be due to increased renal clearance with higher blood lead 

(Gross, 1981), distributiona l non-linearities due to differences in lead 

binding sites in differen t tissues (Hammond et al. , 1981; Marcus, 1985b; 

Manton, 1985), and/or to a sizeable pool of mobile lead in bone maintained 

more or less independently of uptake (Rabinowitz et al. , 1977; Chamberlain, 

1983). I t appears however, that none of the mechanisms introduce significan t 

non-linearities at blood lead levels below 30 pg/dl (Marcus, 1984, 1985a,c; 

Chamberlain, 1983) and that a linear mathematical model is valid for relativel y 

low to moderate lead exposures (CD, p. 10-31, Appdx. UA-2). As discussed 

above, at levels above 30-40 pg/dl , blood lead may be an inadequate index 
BLOOD LEAD (ug/dl) 

30 

25 -

20 -

„.....••"•• • ,*••——•*•" 

RYU/LACEY 

SHERLOCK/COOLS 

HARLEY Sc KNEIP 

40 60 

LEAD UPTAKE (ug/day) 

80 100 

FIGURE 4-1 , Sununary of Relationships Between Daily Lead Uptake and Blood Lead for Different Age 

Groups, Derived from a) Ryu et al. (1983) and Lacey et al. (1985) Studies on Infants; 

b) Sherlock et al. (1982) and Cools et al. (1976) Studies on Adults; and c) Harley and 

Kneip (1985) Biokinetic Model with Estimates for 2-3 year old children illustrated. 

< . 
P.64 

IV-18 

for tissue lead burdens in many children (Piomelli et al. , 1984) and linear 

models are likely to lose their predictive power. For this reason, tne 

relationships depicted in Figure 4-1 are truncated at 30 pg/dl. To estimate 

PbB levels above 30 pg/dl, which is now above the PbB level of health-related 

concern for children, use of non-linear models discussed in the criteri a 

document would be required (CD, Appendix 11.B). 

The compartmental biokinetic model of lead metabolism developed by Mallon 

(1983) and Kneip et al.. (1983), and revised for children by Harley and Kneip 

(1985), relied on a broad array of experimental and observational measurements 

of mammalian metabolism and growth patterns, and has been successfully 

validated using available human experimental and autopsy data. As is the 

case for any mathematical model, there are inherent limitations arid associated 

uncertainties. Because this model is based on the most comprehensive data 

available and has been developed specifically to predict organ lead concentrations over time in young children with continuous lead uptake, i t appears 

that the outputs of the Harley and Kneip (1985) biokinetic model are the 

most appropriate to predict PbB levels, in children using the integrated 

lead uptake estimates presented in Section IV.A and Appendix A. PbB levels 

calculated from the conjunction of the integrated lead uptake and biokinetic 

models wil l be presented in the staff paper described in the Introduction 

(along with estimates using other modeling approaches that are discussed 

later) in order to estimate potential health impacts under alternative air 

lead levels. Methodologies for calculating children's and adult blood lead 

levels data under alternative standards using other data and other modeling 

approaches wil l be discussed in Section V. 
P.65 

V. STATISTICAL RELATIONSHIPS BETWEEN;BLOOD LEAD AND AIRBORNE LEAD: 

USE IN DISAGGREGATE AND AGGREGATE MODELS 

This section addresses two other methods of determining the contribution 

of air lead to total exposure and the potential health impacts associated 

with alternative air lead levels. The previous section examined the relationships between blood lead in children and estimated levels of lead 

uptake from the air, housedust, outdoor soil and dust, and diet. The two 

approaches presented here develop a direct relationship between air lead 

and blood lead from various experimental and community or epidemiological 

studies: 1) a disaggregate model in which total exposure to air lead is 

assessed by separately analyzing the relationship between blood lead and 

inhaled air lead, versus the associated changes in blood lead as a result 

of exposure to lead that has deposited onto soil, dust, food, and water; 

and 2) an aggregate model in which a single variable, air lead, serves 

as an index for lead exposure through other media affected by integrated 

atmospheric lead deposition. 

o 

o 

Experimental studies include those in which adult volunteers have 

been exposed to controlled levels of laboratory generated lead aerosols. 

Because intake of air lead through the diet and dust was probably minimal 

for the adult subjects in these experiments, these- studies underestimate 

the dose-response relationships that would be seen among children with 

changes in air lead (Angle et al., 1985). Epidemiological (i.e., community) 

studies provide correlations between air lead and blood lead in different 

populations of children and adults under varying conditions of lead, from 

current or previous atmospheric fallout and other sources, and yield doseresponse relationships at more relevant exposure levels. 
P.66 

V-2 

Because of the simultaneous presence of lead in multiple media 

with different time profiles, the assessment and use of these studies 

is not always direct. As in any attempt to model lead exposure, possible 

confounding variables that influence PbB levels but which are not related 

to air lead measurements (e.g., lead in paint, canned food and plumbing, 

socioeconomic status, parental care, housing and play conditions, calcium 

intake) cannot always be disentangled. Control for confounders can be 

achieved by comparing populations that differ only in their exposure to 

airborne-derived lead, although identifying such groups has been difficult. 

Another possibility is to perform a multivariate statistical analysis in 

which adjustments are made for some or all of the confounders before calculating 

the blood lead/air lead relationship. This requires information on the 

value of each confounder for each individual, which is also difficult to 

estimate. In the case of lead, several confounders tend to work in the 

same direction as air lead", e.g., elevated exposure and unfavorable social 

conditions often are both concentrated in central cities. Consequently, 

when statistical adjustment is incomplete, the relationship between air 

lead and blood lead will likely be inflated. Adjustment for a 

confounding variable may result in all o.f the shared variance between 

confounder and exposure variable being attributed to the confounder; this can 

inevitably lead to underestimation of the "true" impact of the exposure 

variable (Rutter, 1983). In characterizing good studies of air lead/blood 

lead relationships, several reviewers (e.g., Hammond et al., 1981; Brunekreef, 

1984), as well as the CD, discuss key factors that should be considered. These 

include a well-defined study population, a good measure of individual 

exposure, measurement of blood lead with adequate quality control, a statistical 

analysis model that is biologically plausible and consistent with the data, 
:, a -V i •&}{}• -: -"•!•:'••*': ' 

V-3 : 

and control or measurement of important covariates (CD, p. 11-63). The 

studies identified in the CD, and highlighted here, address these factors 

sufficiently tp establish meaningful relationships. 

A. Disaggregate Model 

1. Air Lead/Blood Lead Relationships in Adults 

Because the relationship between blood lead and environmental exposure 

is nonlinear across the range of potential exposure, but approximately linear 

at lower levels (CD, p. 11-65), the focus here is on studies of adults without excessive occupational or personal exposure. Longitudinal studies in 

which changes in blood lead were measured in adult volunteers- exposed to 

controlled levels of laboratory generated aerosols, in some cases with 

isotopic lead tracer, are summarized in Table 11-40 of the CD. Data from 

the most relevant studies have been reanalyzed in the CD to yield blood 

lead air lead "slopes" (S), where 3 measures the change in blood lead 

expected for a unit change in air lead. 

As noted in the CD, the blood lead inhalation slope estimates vary 

appreciably from one subject to another in the experimental studies, and 

from one study to another. The weighted slope and standard error estimates 

from the Griffin study (1.75 +_ 0.35) were combined with those calculated 

similarly for the Rabinowitz study (2.14 +^0.47), and the Kehoe study 

(1.25 _+ 0.35), yielding a pooled weighted slope estimate of 1.64 +/0.22 

(CD, pp. 11-99 to 11-102). Excluding the subjects in the Kehoe study exposed 

to very high air lead levels (up to 36 pg/m-^), results in an average slope of 

approximately 1.9 pg/dl per pg/m^. 

Several deficiencies in the individual studies are discussed in the 

CD such as uncontrolled or unmeasured air lead exposures outside the 
V-4 

chambers or difficultie s in determining non-inhalation blood lead baseline 

levels (CD, p. 11-101). Furthermore, these experimental inhalation studies 

may underestimate the overall impact of airborne lead (via deposition and 

incorporation over time into soils , dusts, water, food chain). This i s 

much less of a problem, however, in predicting changes in adult lead 

exposure in the future , compared to children. For example, although slightl y 

elevated adult blood lead levels have been observed in areas of high 

lead contamination (Barltrop et al. , 1975; Gallacher et al. , 1984; Rabinowitz 

et al. , 1985), adults do not ingest significant amounts of dir t and (unless 

under fasting conditions), normally do not absorb ingested lead at the 

same rate as children. 

The cross-sectional relationship between blood lead and air lead in 

adults has been examined in several population studies. Azar et al . (1975) 

studied five groups of men (cab drivers and offic e workers) who carried 

personal air lead monitors and whose non-air lead exposures (water, smoking) 

were also measured over a 24-hour period. Several alternative geometric 

mean regressions have been calculated, including linear and non-linear 

models assuming different non-air contributions to blood lead, and estimations 

of the effect of endogenous lead stored in the skeleton using age as a 

surrogate measure of cumulative exposure. None of the fitte d models are 

significantl y different statisticall y (CD, p. 11-81) with a pooled slope 

estimate of 1.32 +0.3 8 pg/dl per pg/m3 (CD, p. 11-105). Although the 

other population studies typicall y used less accurate measures of individual 

exposures (e.g. , Tepper and Levin, 1975; Nordman, 1975; Johnson et al. , 

1975), the range of slope estimates (1.0-2.0) i s comparable to the Azar et 

a l . and experimental inhalation results (Snee, 1981; see CD, p. 11-98). 
P.69 

V-5 

Although the studies differed in co-variables measured, populations, 

exposure conditions, etc. , the most reliable and relevant studies consistently 

yiel d inhalation slope values typicall y in the range of 1.3-2.0 pg/dl per pg/m3, 

with a weighted average slope of 1.4. The above slope estimates are derived 

from studies in which exposure to air lead was assessed over days, weeks, 

or months and are based on the assumption that an equilibrium level of 

blood lead is achieved within a few months after exposure begins. This is 

only approximately true, since some of the absorbed lead that accumulates 

in the skeleton deposits in the spongy trabecular bones (e.g. , rib , vertebrae) 

where i t may be resorbed into the blood stream. An additional factor 

therefore, should be allowed for possible re-entry of lead into the blood 

transferred from bone or other long-term storage (Chamberlain, 1983). 

Based on isotopic ratio and radiolabel tracer studies On adults, Chamberlain 

et al . (1978) estimated this factor, to be 1.3. Similar experiments late r 

indicated that about 20% (i.e. , a factor of 1.2) of an adult male's blood 

lead is from bone lead resorption (Chamberlain, 1985). Re-entry into blood 

of stored lead is likel y to be lower in children because of the rapid 

growth, high rate of turnover and smaller reservoir of lead in their skeletal 

systems. While no adjustment for bone resorption wil l be made for children's 

slopes, i t appears reasonable to apply a factor of 1.3 to the blood lead/air 

lead inhalation slope of 1.4 derived above for adults, yielding a slope 

estimate of about 1.8 pg/dl per pg/m3. 

2. Estimating Future Adult Blood Lead Averages 

Two adult populations are identifie d as especially sensitive to lead: 

middle-aged males, 40-59 years of age; and pregnant women, as exposure, 

surrogates for fetuses (see CD, Sections 13.7.2 and 13.7.3). Older women 

may also be at increased risk due to mobilization of bone lead after menopause 

due to the processes of osteoporosis (Silbergeld and Schwartz, 1987). 

aBBI 
P.70 

V-6 

Approximately 95% of adult body lead burden is sequestered in bone and 

thi s accumulation can maintain elevated blood lead levels years after high 

exposures have ended (CD, p. 10-23). In most cases, fetal exposure to lead 

can be expected to be dominated by maternal bone lead stores from past 

exposures which, in the U.S., were much higher than current levels . 

Accurately predicting future changes in fetal blood lead levels would 

require estimates of maternal lead burden and information on how bone lead 

stores would be transferred across the placenta. Although i t is likel y 

that there is extensive mobilization of lead, lik e calcium, during periods 

of physiological stress such as pregnancy, there are no biokineti c data to 

quantify thi s dynamic process. In the absence of such data, no attempt 

wil l be made to estimate fetal lead exposures associated with maternal PbB 

levels under alternative standards. Given the sensitivit y of the fetus , 

however, potential risks associated with prenatal exposures wil l be of 

major emphasis in the overall lead NAAQS assessment. 

Given a background or "baseline" blood lead estimate representing non-air 

lead adult exposures, and an average disaggregated blood lead: air lead 

relationship, the following simple equation can be used to predict blood 

lead means for adult male populations in our case studies: 

7 = y 0 + 1.8A 

where 7 = mean blood lead (pg/dl) 

y 0 = non-air background blood lead (pg/dl), and 

A = average air lead (pg/m3) 

Estimates of non-air "background" blood lead level contributions for differen t 

adult populations that wil l be used in the 1990-96 case studies are derived 

in Appendix C. Other issues involved in estimating fetal PbB levels are also 

discussed there. 
P.71 

V-7 • 

3. Air Lead/Blood Lead Relationships in Children 

Three population studies of children living near lead point sources 

in which covariates were controlled (e.g., age, sex, dust exposure) were 

extensively analyzed in the CD for the most useful and relevant estimates of 

inhalation slopes: Angle and Mclntire, 1979 (3 • 1.92 £0.60) ; Roels et al., 

1980(3 = 2.46 +_0.58); and Yankel et al., 1977/Walter et al., 1980 (3 = 1.53 

£0.064) . The median slope of the three studies is 1.97 (CD, p. U-189) ; its 

application for use in the disaggregate model is discussed below. 

4. Estimating Children's Blood Lead Levels Using Disaggregate Model 

The relationship between blood lead and direct inhalation of airborne 

lead provides information useful for changes in air lead on a time scale of 

only several months (CD, p. U-189) . Over time, suspended lead is deposited 

and incorporated into soil, dust, and water, and enters the food chain. -

Since prior and current atmospheric fallout directly modify the daily 

burden of ingested lead, larger changes in blood lead would be predicted if 

the associated changes in the surface deposition of lead were accounted for, 

rather than simply inhaled air lead (Angle et al., 1984). To account for 

the simultaneous presence of lead in multiple environmental media, the CD 

has analyzed the separate mathematical relationships between blood lead and 

dietary, soil, and dust lead (described in detail in Chapter 11,4). Using 

representative values for lead concentrations in these media, these relationships were applied in a further analysis presented in the CD (CD, Table 

13-6) as away to estimate proportional inputs to total blood lead levels 

in U.S. children. A similar disaggregate model was developed by Angle and 

Mclntire (1979) and Angle et al. (1984) in forming an integrated lead exposure function from measurements of lead in air, soil, and house dust and 

relating that to PbB levels of children living in various areas of Omaha. 

S H 
V-8 

The disaggregate model developed in the CD, intended to represent 

the then "current (i.e., 1983-84) exposure picture," is adapted here in 

Table 5-1 for different air lead levels and background levels of lead in 

food, water, dust, and soil, expected in 1990. The estimates are mean 

PbB levels for 2-year old children; comparable analyses for other ages of 

children can be done using the age-specific estimates of dietary lead intake 

presented in Appendix A. Calculations and assumptions used in deriving the 

estimates are summarized in footnotes to the table and are similar to those 

described in Table 13-6 of the CD. The following changes were made for the 

present analysis: 

1) Estimated levels of dietary lead intake for children in 1990 were 

substituted for the estimates for 1983-84 used in the CD. The 1990 projections, described in Appendix A, are based on the Multiple Source Food 

Model developed in the CD and uses more recent data on the downward 

trends in food and water; 

2) Soil and housedust lead concentrations associated with different 

air lead levels are estimated based on the long-term relationships derived 

from regression analyses of data collected from concurrent measurements of 

air, soil, and/or dust lead concentrations at about 45 lead point source 

locations (see Table A-4). These same relationships are being used to 

estimate surface soil and indoor dust lead levels in the uptake biokinetic 

model. In contrast, soil and dust lead concentrations, under different air 

lead levels were estimated in Table 13-6 of the CD by interpolation of data 

from the Angle et al. (1984) study. This study is but one of many data 

sets included in the aforementioned regression analysis described in 

Appendix B. 
P.73 

V-9 

TABLE 5-1. DISAGGREGATE MODEL OF CONTRIBUTIONS FROM VARIOUS MEDIA 

TO 1990 MEAN BLOOD LEAD LEVELS (pg/dl) OF U.S. CHILDREN 

(2 YEARS OF AGE): BACKGROUND LEVELS AND INCREMENTAL 

CONTRIBUTIONS FROM AIR1 

Source 

Background - nonair 

food and water^ 

Dust3 

Subtotal 

Background-Air 

Food and Water* 

Ingested Dust (with 

Pb deposited from air) ^ 

Inhaled air 6 

Total 

0.25 

1.5 

0.1 

TT 

0.2 

1.0 

0.5 

3.3 

0.5 

1.5 

0.1 

•ra 

0.2 

1.7 

1.0 

4.5 

Air Lead 

0.75 

1.5 

0.1 

ra 

0.2 

2.5 

1.5 

5.8 

(pg/m3) 

1.0 

1.5 

0.1 

ra0.2 

3.2 

2.0 

7.2 

1.25 

1.5 

0.1 

ra 

0.2 

4.0 

2.5 

8.3 

1.5 

1.5 

0.1 

UF 

0.2 

4.7 

3.0 

9.5 

Adapted from Table 13-6 of CD. 

^Estimated dietary intake of non-air Pb in 1990 (from Cohen, 1988a,b; Appendix A) 

9.2 pg/day x 0.16 pg/dl per pg/day (from Ryu et al., 1983 and Laxen et al:., 

1987 as re-analyzed by Marcus, 1989). 

3From CD, Chapter 7, 1/10 dust not atmospheric. Using Angle et al. (1984) 

"low area" for soil and house dust levels and median regression coefficients 

from Stark et al. (1982): (1/10) x (97 pg/g x 0.0022) + (324 pg/g x 0.0018). 

*As in 2 above, but using 1.2 pg/day for dietary intake of atmospheric lead. 

Derived for component of background Pb in food from past deposition from air 

onto soil and into other media leading into human food chain. 

degression equations of Stark et al. (1982) used along with levels of 

soil dust and house dust predicted by regression analyses of collected point 

source environmental data, described in Appendix B. For example, the mean soil 

and house dust lead concentrations associated with 1.0 pg/m3 in air are 

approximately 630 ppm and 1035 ppm, respectively. 

The effect on blood lead would be: 

(630 x 0.0022) + (1035 x 0.0018) = 3.2 pg/dl per pg/m3. 

6Using the median inhalation slope of 1.97 pg/dl per pg/m3 from the CD, p. U-189. 
•V-10 

3) The soi l and dust/blood lead regression coefficients used to compute 

soi l and housedust contributions to blood lead are from the CD analysis 

of the Stark et al . (1982) study and are described in the CD as the most 

reasonable median estimates (pp. 11-151, U-191). The coefficient s used in 

Table 13-6 of the CD are from the Angle et al . (1984) study and represent 

upper bound values. 

I t must be emphasized that the blood lead estimates presented in Table 

5-1 represent population mean PbB levels and are illustrativ e only. To 

assess health risks associated with such air lead exposures, i t would 

be necessary to calculate PbB distributions around these mean levels , as 

discussed earlie r in Section III.B . 

The uncertainty in predicting mean blood lead using the disaggregate 

model can be estimated in principle since standard errors associated with 

the air Pb:PbB slopes are available (CD, p. U-105). The differences among 

the slope estimates from differen t studies can be ascribed to many factors 

including differences in study populations, lead bioavailabilty , and to 

statistica l artifacts , e.g. , the attenuation of slopes attributable to environmental measurement uncertainties (Fuller, 1987). Differences in estimated 

standard errors of the regression coefficient s reflec t sample size, range 

of values of environmental variables in the sample, and control of other 

sources of variability . A composite slope could be estimated as a weighted 

average with weights inversely proportional to the variances for each of 

the slopes. Total variance would appropriately include both within-study 

and between-study variation and therefore al l the variances and covariances 

of the parameter estimates would have to be calculated in a series of 

iterations . Although such analysis could be conducted, i t was decided that 

the information about uncertainty of predicted mean blood lead added by 

thi s complicated procedure would not be significant , especially in view 
V -l 1 / •: 

of the large uncertainties about the-form of the dose-response model, 

the judgmental selection of key studies, and the contributions from non-air 

sources of lead. 

Since completion of the 1986 CD, several findings have become available 

that supplement previous conclusions. Unpublished analysis by Johnson and 

Wijnberg (1988) of the 1983 study of young children livin g near the East 

Helena smelter (CDC, 1983) estimate slopes for both blood lead-soi l lead 

and blood lead-dust lead of approximately 1.4 pg/dl per 1000 ppm. This 

analysis adopted modeling used by EPA in the CD to fi t other similar studies 

and adjusted for numerous covariates including children's age, air lead, 

dust and/or soi l lead depending on analysis, poor quality lead painted 

housing, smoking, and secondary occupational exposure. 

As indicated in many of the studies, the dust lead slope is similar to 

the soi l lead slope, suggesting that soi l may contribute both directl y and 

indirectl y to blood. Such hypotheses regarding cause-effect responses of 

blood lead to earlier exposures have been explored in recent longitudinal 

studies on blood lead and developmental indicators in Cincinnati (Bornschein 

et al. , 1985, 1986) and Boston (Rabinowitz et al. , 1985; Bellinger et al. , 

1987) children. The Cincinnati data set was analyzed by structural equation 

methods (Bentler, 1980) for relationships between exterior surface scrapings, 

dust lead, and blood lead. Unfortunately, the results are expressed as a. 

linear equation in logarithms of the environmental variables, and the 

standardized regression coefficients are not directl y interpretable as 

overall slopes for soi l or dust lead given the published information (Bornschein et al. , 1986). 

The analyses of the Boston data used a somewhat differen t technique— 

random effects models for longitudinal data (Ware, 1985). Unfortunately, 
V-12 

soil lead data were not sampled longitudinally. There were repeat 

measurements for blood, dust, air and water lead. The 18-month blood lead 

regression model reported by Rabinowitz et al . (1985) used the logarithm 

of the yard soil lead measurement as a predictor, so that the reported slope 

is not directly comparable to those cited in the CD. However, the slopes at 

several concentrations are similar to those cited in the CD: 8 pg/dl per 

1000 ppm soil lead when soil lead = 100 ppm, 1.6 pg/dl per 1000 ppn soil lead 

when soil lead = 500 ppm, and 0.8 pg/dl per 1000 ppm when soil lead = 1000 

ppm. Subsequent analyses of this population using composite blood lead 

averages between 6 and 24 months (and a mean soil lead level of 700 ppm) 

yielded a slope of 0.9 pg/dl per 1000 ppm soil lead, varying from 0.6 to 

1.6 according to the reported amount of mouthing by the children (Rabinowitz 

and Bellinger, 1988). Because the Boston study group did not generally live 

in crowded conditions, in homes with deteriorating leaded paint, or have 

nutritional deficiencies, their results may not be directly applicable to 

children at higher risk. 

^fokfnet.fc J^odef 

The disaggregated air, soil , and dust lead "slopes" used here are 

plausibly consistent with the uptake/biokinetic model parameters. To show 

this , we use the well-known relationship (e.g., Rabinowitz et al.", 1976) between 

blood lead increments, dPbB, and lead intake increments, dPbl; 

dPbB = dPbl * (mean residence time in blood pool)/ (volume of 

distribution in blood pool) 

For a two-year old child, the Harley-Kneip model predicts a mean residence 

time in the blood pool of about 8 days, i.e. , a blood lead half lif e of 8 

* 0.693 = 5.6 days, consistent with Duggan's (1983) estimate of 4-6 
P.77 

•• a •!; • Aiyapjiff*--"' "' ': ' ' > i 

V-13 

days. The blood lead pool is much Jarger than the blood lead volume, 

including accessible parts of the soft tissues, bone and bone marrow, and 

extracellular fluid. Rabinowitz et al. (1976) estimates that the volume 

of this pool in adult volunteers was 7.5 - 10.8 Kg (or approximately 

70-100 dl), almost twice the adult blood volume. Thus, a factor of 2 is 

used to calculate the total volume of available blood lead distribution in 

the equation below. Since the blood volume in infants (about 12% of body 

weight) is proportionately greater than in adults (about 8% of body weight), 

we will assume the blood volume in 1-5 year old children is about 10% of 

body weight, adjusted for whole blood density of 1.06 kg/1. Thus, the 

volume of distribution in a 10 kg child (about 2 years old) is taken as 

about: 

2 * 0.10 * 10 k.y/(1.06 kg/1) = 2 1 = 20 dl. 

Assuming that the total amount of dir t consumed, C, is about 

0.1 g/day, of which a fraction a = 0.25, approximately, is absorbed (see 

Appendix A.17), the leaded dir t is partitioned as some fraction p of soi l 

and 1 - p of interio r house dust.* Thus: 

dPbl = c * a * (p * PbS + (1 - p) * PbD) 

Assuming p = 0.5 i.e . equal soi l and dust access, 

dPbB = (0.1 g/d) * (0.25) * (0.5 PbS + 0.5 * PbD) * 8d/20 dl 

= 0.005 PbS + 0.005 PbD 

Thus the soi l lead vs. blood lead and the dust lead vs. blood lead slopes 

are both predicted to be about 0.005 pg/dl per ppm. 

A variety of factors may be responsible for the somewhat smaller 

empirical slope estimates, typicall y about 0.002 (see above). Even 

though the individual household PbS and PbD levels are available in 

some studies, these are not necessarily the exact exposure covariates, 

but only surrogate measures of dir t lead exposure. I t is well 
.RJ&-

V-14 

known that exposure measurement "error" tends to flatten or attenuate 

regression slopes in dose-response studies (e.g., Pickles, 1982; Fuller, 

1987; Brunekreef et al., 1987). Further, specification of the constants 

(e.g., a < 0.25 or C < 100 mg/d) could also reduce the predicted slopes. 

The agreement is nonetheless impressive. 

The air lead slope is similarly calculated. From Table 4.1, the point 

source uptake at 1.0 pg/m3 is 0.6 - 0.9 pg/d, thus 

PbB = (0.6 - 0.9) * PbA * 8d/20 dl 

= (0.24 - 0.36) PbA 

The estimated direct inhalation slope of 0.24 - 0.36 is much smaller 

than has been cited for empirical disaggregate studies. These discrepancies 

have been previously noted in Marcus (1988b) in an analysis of East Helena 

data. The differences between the uptake/biokinetic model apparent 

PbD slope and the disaggregate regression slope (0.00341 vs. 0.00151), 

apparent PbS slopes (0.00199 vs. 0.00102), and apparent PbA slopes (0.63 

vs. 1.08) were in the same direction. It is possible that the empirical 

multiple regression studies have misallocated the variance in log(PbB), 

attributing too little effect to soil and dust ingestion. We cannot determine 

whether these are artifacts of the multiple regression model or of the 

uptake/biokinetic simulation model. Nevertheless, the slope values used in 

the disaggregate model are in general consistent with the uptake/biokinetic 

model. 

B. Aggregate Model 

1. Air Lead/Blood Lead Relationship 

In the disaggregate modeling approach, "inhalation" slopes are derived 

from epidemiological studies by making adjustments for measured indirect 
P.79 

V-15 

air exposure variables (e.g., house dlis't lead). These inhalation slopes are 

then combined with separate slopes for other exposure media (e.g., dust, 

food, water) to arrive at an integrated lead exposure function. With the 

exception of Angle and Mclntire (1979), the community studies from which 

inhalation slopes have been derived have not simultaneously measured lead 

in more than two or three media. Consequently, the integrated lead exposure 

function is necessarily based on data from multiple studies involving 

different populations, exposure conditions, measurement techniques, etc. 

An alternative method of calculating the effect of changes in air lead on 

children's blood lead is to analyze individual community studies in which 

reliable "unadjusted" blood lead/air lead relationships can be derived such 

that the impact of both direct (inhaled) and indirect (via dust, soil, 

etc.) contributions of air lead are combined, or aggregated, in one variable 

(i.e., air lead). This approach has the advantage over disaggregate modeling 

(using the adjusted "inhalation" slope) in that the total impact of atmospheric 

lead on children's exposure is better captured since since air lead levels, 

in general, are causally related to other important exposure variables such 

as hand and household dust lead (Brunekreef, 1984). 

The population studies with identifiable air monitoring methods and 

reliable blood lead data are. summarized in Table 11-36 of the CD. None of 

the studies included adult populations. Since adults do not typically 

ingest much deposited lead anyway, the aggregate modeling approach will be 

limited to young children. In Table 5-2, the calculated 3 values for each 

study are presented according to the blood lead levels, ages, and type of 

location of the investigated children. Different statistical analyses of 

these studies have resulted in a range of possible values for the blood 

aa 
Table 5-2. BLOOD LEAD/AIR LEAD SLOPES1 IN CHILDREN FOR DIFFERENT AGES AND EXPOSURES 

Type of Location (Study) 

Blood Lead (yg/dl)/ 

Age (years) Urban Near Point Source(s) 

<25/l-7 8.5a»b (Brunekreef et al., 1983) 3.6-4.0* (Zielhuis et al., 1979; Brunekreef 

et al., 1981) 

<25/l-18 0.6 (Angle and 0.66* (Brunekr0eef, 1984 

1.92 Mclntire; 1979; 2.10* from Angle and 

4.40 CD) Mclntire, 1979) 

<25/10-15 - 2.46 (CD from Roels et al . 1976) 

5.3a (Roels et al . 1980) 

« 5.9-9.8a»

D

 (Brunekreef, 1984 from Roels 

et al. , 1976, 1978, 1980) 

>25/0-9 2.9 (Billic k et al. , 1979, 1980; 1.07-1.52 (Yankel et al. , 1977; Walter et al. , ,L 

Nathanson and Nudleman, 1980) 1980; Snee, 1982b; CD) °* 

2.4-3.3a (Brunekreef, 1984 from Yankel et al. , 

1977) 

>25/l-18 - • 2.6-3.7^ (Landrigan et al . 1975; Morse et al. , 

1977) 

4.6 a (Brunekreef, 1984 from Roels et al. , 1976, 

1978, 1980) 

^Slopes are in units of pg lead/dl blood per pg lead/m3 air. 

a

"Aggregate" slopes derived from group comparisons or multiple regression analyses that were unadjusted for soi l or 

dust lead, thereby attempting to account for influence of deposited atmospheric lead through these exposure routes. 

Remaining slopes represent relationships between blood lead and air lead due solely to direc t inhalation which have 

been calculated by adjusting for the influence of soi l and/or dust lead. 

^Use of low-volume particulate samplers likel y underestimated air lead exposures, and overestimated the 

3 value, especially near point source where large particles predominate. 
P.81 

V-l 7 

lead/air lead relationship (3). Unadjusted (i.e., aggregate) relationships 

are presented in addition to adjusted (i.e., disaggregate) relationships 

derived from regression analyses, the latter which refer to the blood 

lead/air lead relationships due to direct inhalation exposure. 

Relatively wide ranges of 3 values are observed for industrial and 

urban areas at low and high PbB levels and for all ages of children. The 

apparent trend towards smaller slopes with increasing PbB levels is consistent 

with findings within some studies (Angle and Mclntire, 1979; Roels et al., 

1980) and other observations of curvilinear blood lead/exposure relationships 

(Azar et al., 1975; Moore et al., 1977; Gross, 1981; Sherlock et al., 1982; 

Hammond et al., 1981). 

As discussed, the CD determined that the disaggregate inhalation 

slopes derived from the studies by Angle and Mclntire, Roels et al., and 

Yankel et al. are the most reliable given their overall quality. Aggregate 

analyses of these data, either by Brunekeef (1984) or the original authors 

(Roels et al., 1980) illustrate the significant decline in air:blood lead 

slope when adjustments are made for factors that covary with air lead (e.g., 

soil and house dust lead), and the.dependence of these slopes on exposure 

levels. For example, Brunekreef (1984) segregated the Roels et al. data 

and compared groups with large differences in exposure (i.e., <1 km from 

smelter vs. urban/rural; 3B 5.9) groups with small differences at high 

exposure levels (i.e., <1 km vs. 2.5 km from smelter; 3s3 4.6) and groups 

with small differences in exposures at low levels (i.e., 2.5 km from 

smelter/urban vs. urban/rural; 3a 9.8). As in the Brunekreef et al. (1983) 

study, the use of low-volume samplers may have underestimated air lead 

exposures, and overestimated the 3 value, especially for those children 

near the smelter where large particles predominate. The slope may be 
P.82 

V-18 

underestimated for young children, however, since this study only sampled 

children older than 10 years of age. 

It is important to note several additional studies and analyses that 

may provide equally relevant and useful information for purposes of estimating 

an aggregate blood lead/air lead slope. These studies (Brunekreef et al., 

1983; Zielhuis et al. 1979; Brunekreef et al., 1981) used well-defined 

study populations (children younger than 10 yrs. old; PbB levels below 25 

pg/dl). employed available quality control procedures for blood lead analysis, 

and measured or controlled for important covariates (see CD, Table 11-36). 

Several specific comments should be made regarding these studies: 

1. The series of studies reported by Zielhuis et al. (1979) and 

Brunekreef et al. (1981) included many environmental measurements (lead in 

ambient and indoor .air, lead in dustfall indoors and outdoors, soil, 

streetdust, floordust, tapwater, and dustiness of homes) and regression 

analyses to determine the impact of different variables on PbB levels 

(i.e., the above environmental indices as well as distance from the smelter, 

parental education, age of the child, mouthing activity, and cleanliness of the 

child). Venous blood lead samples were analyzed by standard techniques and 

although information on interlaboratory comparisons is not given in the 

original study, quality control participation is reported by one of the 

investigators in a subsequent review (Brunekreef, 1984). Air lead was 

measured at two sites 0.2 and 0.4 km from the smelter in 1976 and in 1977. 

The levels in Table 5-2 were taken from Brunekreef (1984) and represent 

those measured at 0.4 km from the smelter. Air lead was measured at 6 

sites continuously for 2 months in 1978. Brunekreef (1984) estimates 

3 = 4.0 for 1976 by assuming a difference of 2.0 pg/m3 in average 

air lead exposure levels between the 2-3 year old children with the highest 
P.83 

V-19 

and lowest PbB levels. After adjustment for parental education, the blood 

lead difference of about 8 pg/dl decreased to 7.2 ug/dl, resulting in 

a slightly lower 3 estimate (3.6). For 1977, a difference of 1.0 yg/m3 

was assumed for air lead exposure levels between smelter and control children 

aged 2-3 years who had PbB levels of 18.2 and 14.6 pg/dl, respectively, again 

resulting in a 3 of 3.6. In 1978, only children living between 0.4 and 1.0 

km of the smelter were sampled and air levels did not correlate with blood 

lead within this population. Thus, no direct estimate of 3 can be derived, 

although soil, lead and indoor and outdoor dust lead, and therefore, accumulated 

lead deposition, accounted for a significant fraction of the variance in 

PbB levels (Brunekreef et al., 1981). 

2. The more recent study by Brunekreef et al. (1983) on Dutch city and 

suburban children measured venous blood (which was analyzed as p-art of 

the European Community laboratory quality control program) and many 

environmental and social variables and potential confounders (lead in 

drinking water, soil, street and playground dust, hand dust, indoor dust, 

mouthing behavior, dietary intakes, parental education and occupation, age 

of home, etc). The. very high 3 value (8.5) derived by Brunekreef (1984) 

even after adjustment in a multiple regression analysis for six of the 

confounders, may be related to an underestimation of ambient air lead 

levels due to the fact that low volume British Smoke air monitors were 

employed, in contrast to the hi-vol samplers used in most other studies. 

The extent of inflation in the estimated slope as a result of any underestimated 

air lead level is uncertain. Brunekreef (1984) notes however that even if 

this bias is accounted for, the difference in urban and suburban air lead 

levels was probably not larger than 0.2 yg/m3. The contrast in lead 

deposition between the areas was significant (i.e., 643 vs. 220 mg/m2/day), 
V-20 

indicating that ongoing lead pollution accounted for a good deal of the 

differences in PbB levels, although variations in historical emissions can 

not be entirely ruled out. The relatively low PbB levels in this study 

(a 8-13 ug/dl) could be another partial explanation of the high 3 estimates, 

given that other studies used to derive 3 included children with PbB levels 

on average closer to, and above, 30 ug/dl. At this point, the blood 

lead/lead intake relationship is estimated to level off; below 30 ug/dl, • 

this relationship appears to be approximately linear (CD, p. 11-104). 

The remaining slopes listed in Table 5-2 are based on data from children 

whose PbB levels, as some in Yankel et al. (1977), exceeded 25 pg/dl. As 

discussed in the CD, the relationship between lead uptake and PbB levels 

above about 30 pg/dl appears to be non-linear (CD, p. 10-31). Because PbB 

levels above 25-30 pg/dl exceed those where health-related concerns are 

triggered (see CD, Chapter 13), these slopes are less relevant to the 

present review. 

In summary, the analyses using the aggregate approach assume the 

same source for most lead in air, soil, and housedust, and that adjustment of 

PbB levels for soil or dust lead to yield an inhalation slope, underestimates the "true" impact of atmospheric lead on PbB levels. A range of 

3 values can be estimated from a) additional, and apparently relevant, studies 

not used to derive inhalation slopes in the CD (i.e., Zielhuis et al., 

1979; Brunekreef et al., 1981, 1983) and b) aggregate analyses that include 

both direct (inhalation) and indirect (via soil, dust, etc.) air lead 

contributions in the key "inhalation slope" studies cited in the criteria 

document. Although far from conclusive, these studies and analyses suggest 

a range of possible blood lead/air lead aggregate slopes in the range of 2 
P.85 

V-21 

to 10 for young, moderately exposed children with the most reliable slopes 

fallin g between 3 and 5 (CD, p. 11-104). 

2. Predicting Children's Blood Lead Levels Using Aggregate Model 

To predict PbB levels associated with alternative air lead levels using 

these aggregate slopes, i t is necessary to estimate the contribution to 

total lead exposure from sources not affected by a proposed air qualit y 

standard, for example, lead-soldered food cans, lead in plumbing, and 

lead-based paint. I t should be noted that by using measured lead concentrations in dust from a wide range of locations and conditions, the integrated 

uptake model discussed in the previous section implicitl y includes averaye 

contributions to total exposure from lead-based paint, but excludes from 

the analysis high level exposures associated with deteriorated lead-based 

painted housing. 

Several difficultie s arise in attempting to explicitl y estimate 

exposures from non-air sources of lead: 

1) With the exception of non-air lead in diet , few studies provide 

detailed information on the relative contribution of various sources to 

children's PbB levels in the U.S. Estimates must be made by inference 

from earlie r survey data; and 

2) Because non-air contributions to PbB levels probably vary widely 

in space and time among children, a single estimate for the average, case 

may result in a lead NAAQS that is not protective for al l children. [This 

can be expected, for example, for children who regularly ingest lead-based 

paint. ] Conversely, compliance with the air standard wil l provide extra 

protection in areas where lead from non-air sources is below the average. 
P.86 

V-22 

In setting the 1978 lead NAAQS, the Agency estimated that non-air 

sources of lead contributed 12 pg/dl on average to children's PbB levels . 

Since then, significan t reductions in PbB levels have occurred, attributable 

not only to declines in atmospheric lead emissions but to the gradual 

conversion by manufacturers to non-lead soldered cans, in some cases reduction 

in the number of old , lead-painted homes, cleaner working conditions, and 

by parents and public health agencies to minimize children's lead 

exposure. Further declines in lead exposure can be expected as these 

trends continue and lead exposure from drinking water drops, due to 

increased awareness as a result of widespread, mandated public notifications , 

the 1986 Safe Drinking Water Act banning the use of lead solder and 

pipes in new construction and plumbing repairs, and to reductions in water 

corrosivit y by public water suppliers as compliance with the revised national 

water regulations for lead begins. The most recent, and well-conducted, 

nationwide survey of children's PbB levels was the Second National Health and 

Nutrition Evaluation Survey (NHANES II ) of 1976 to 1980. Appendix C describes 

analyses that a) start s with the average PbB level for children from 1978, 

the midpoint of the NHANES I I survey, and b) estimates a 1990 PbB average by 

adjusting the 1978 value to account for the important changes, summarized 

below, in lead exposure that have recently occurred and that "can be expected 

to continue: 

1 . Use of lead in gasoline has declined by about 90 percent since 

1978; this trend wil l continue up through 1990-92 as compliance with the 

lead in gasoline standard is completed and as the flee t of lead-burning 

cars shrinks. As observed during the NHANES I I survey period (see CD, 

Section 11.3.6), the continued dramatic decline in gasoline lead emissions 

is predicted to parallel a major shif t in PbB levels . 
P.87 

•.<•V-23 

2. A significan t reduction in dietary lead intake has occurred since 

the late 1970's, and this trend wil l continue as atmospheric lead emissions 

and deposition and the use of lead-soldered cans, continue to decline. 

The derived range of mean PbB levels for children expected in 1990 

(4.2 - 5.2 pg/dl) reflect s changes that have occurred since 1978, and that 

are expected to continue, in gasoline lead emissions and deposition and canned 

food technology. Any other changes that are more difficul t to quantify are 

also probably of lesser importance for the bulk of the population. For 

example, changes for children livin g in lead-painted housing has not been 

significant on a broad scale. This range of 1990 "baseline" average estimates 

represents the mean PbB levels that would be expected in 1990 in U.S. children 

not exposed to atmospheric lead from lead point sources directl y or indirectly , 

based on the data and assumptions presented above. 

Another method to estimate.children's mean "non-air" PbB is to use 

available data on typical background levels of lead in food, water, dust, 

and soi l ingested by U.S, children and the.relationship between lead 

taken up through these media and children's PbB levels . Table 13-6 of 

the criteri a document uses such data in calculating a mean PbB of 4.42 

pg/dl to be expected at an air lead level of zero. Although this value was 

calculated based on 1983-84 FDA data on lead in food, i t is interesting that 

i t is within the range described above. This range can be used in the 

aggregate model approach to estimate ranges of mean blood lead levels under 

alternative air lead exposures in the following equation: 

mean PbB = (4.2 - 5.2 pg/dl) + (3 to 5 pg/dl per pg/m3) Air Pb 

I t must be emphasized that these estimates for non-air contributions to 

average PbB levels represent average values. Many children may be at risk 

for significantl y higher lead exposures that cannot be prevented by atmospheric 
P.88 

V-24 

emission controls. In particular, these include children who live in 

deteriorated or recently resurfaced lead-painted housing who deliberately or 

inadvertently ingest paint dust through normal mouthing activities , children 

exposed to high drinking water lead levels from eroding lead pipes or 

solders in distribution systems, children of parents who work in lead-related 

industries and are exposed to lead dust that is subsequently carried home 

on clothing, or children living near lead smelters and other point sources 

where historical accumulations of lead are excessive. I t is important that 

other regulatory agencies and public health programs, including other EPA 

components, responsible for minimizing children's lead exposures from nonair sources, or from historical accumulations of atmospheric lead deposition, 

maintain or increase, where necessary, their efforts. 
P.89 

VI. VALIDATION OF INTEGRATED LEAD UPTAKE/BIOKINETIC EXPOSURE MODEL 

While all three modeling approaches identified in Section III are 

useful in assessing the relative protection afforded by different 

lead NAAQS, the aggregate and disaggregate blood lead-air lead models are 

intended to be "equilibrium" models. However, we know that there is a great 

deal of variation in past and present exposures to lead from various 

sources and pathways. Of the three, the uptake/biokinetic model can best 

estimate the changes over time of blood lead to changes in environmental 

lead in rapidly developing young children. 

A general description of the model is provided in Section IV and 

Appendix A. Average daily uptake for young children is calculated under 

conditions specified in terms of ambient air lead levels, soil and dust 

lead levels that correspond to both historical and current atmospheric lead 

emissions, and dietary lead levels from both water and food. Additional 

exposures to paint lead can be added but are not in the present exercise 

given the high degree of variability, and the inadequate data base (see 

section II.F). Blood lead (PbB) levels in populations of young children, who 

are the most exposed and highly susceptible to lead, are estimated over time 

based on total daily lead uptake using a biokinetic compartmental model. 

Parameters such as indoor air lead exposure, time spent indoors 

vs. outdoors, absorption rates through the lung or gastrointestinal tract, 

and amount of dirt that children typically ingest through hand-to-mouth 

activity, are estimated from available data in the literature, as summarized 

in Appendix A. 

In this chapter, results of several validation exercises where predicted 

and observed blood leads were compared are presented. The most detailed 

analysis was performed with data gathered around a smelter in East Helena, 
VI-2 

Montana. Two types of validation efforts were undertaken with this data 

set: 1) in the first effort, the best data regarding such model parameters 

as observed air, soil, and dust lead exposure estimates were used; 2) in the 

second validation effort, predicted levels of air, soil and dust levels were 

used to estimate PbB levels. The latter work was undertaken to determine 

how well the model behaved when actual measurements of necessary input data 

are not available. This was necessary so that the reliability of the model 

could be assessed for policy analysis purposes when less than full information 

is available to use with the model. Also, presented in this section are 

validation exercises using observed blood lead estimates in locations other 

than East Helena. While less effort was undertaken in these exercises, 

they nonetheless provide additional information on the reliability of the 

model and provide insight regarding the sensitivity of the model results 

to uncertainties in the input data. Given the many uncertainties in the 

input data and the biological variability that cannot be incorporated, the 

results of the validation exercises presented below reveal that the lead 

uptake/biokinetic model performs quite well in predicting mean blood lead 

concentrations in children living near point sources of lead. 

A. Validation Using 1983 East Helena Data 

The data set considered first is based on a 1983 study in which the 

Montana Department of Health and Environmental Sciences (MDHES) and the 

Centers for Disease Control (CDC), in cooperation with EPA, measured 

blood lead levels in approximately 400 children ages 1-5 living around 

the ASARCO lead smelter in East Helena. The lead content of soil and 

dusts around and in individual homes was measured. Airborne lead was 

measured before and during the survey at 8 sites. Three study areas were 

designated according to their distances from the smelter: Area 1, within 
P.91 

VI-3 

1 mile of the smelter; Area 2, 1-2.25 miles from the smelter; Area 3, 

more than 5 miles from the smelter. The validation exercises included 

children from Areas 1 and 2 only. 

In order to estimate 1983 PbB levels in the East Helena children so they 

could be matched against the measured PbB levels , exposure profiles were 

generated extending as far back as 1978, depending on the children's age in 

1983 and their residential location. A five-year description of monthly 

ambient air lead concentrations throughout the study area was generated using 

the Industrial Source Complex-Long-Term (ISC-LT) dispersion model based on 

a ir lead concentrations, source sampling data of smelter emissions, and local 

meteorological data, and accounting for dry atmospheric deposition. Area and 

year-specific estimates of background lead contributions (i.e. , mobile sources, 

re-entrained soil , local minor point sources) were also included to account 

for total ambient exposure. Only one site , about 1/4 mile from the smelter, 

provided sufficientl y complete air qualit y data over the period 1978-1983 

that could be considered representative of population exposure in the vicinit y 

of the monitor although air lead data were available from monitors in ten 

other locations around East Helena. A tota l of 158 monthly average lead readings 

were available from these monitors between 1980 and 1983. These averages were 

compared with predicted air lead concentrations from the ISC-LT dispersion 

model. As would be expected with a dispersion model using limited meteorological 

data, the model predicted no more than 37% of the month-to-month variabilit y 

in monitor readings. However, simple linear regression predicting monitored 

readings as a function of a constant and ISC predictions, indicated that 

reasonably accurate estimates of air qualit y were made with the ISC model: the 

constant was within the range of the estimated background concentrations and 

was statisticall y significant , and; the coefficient for the ISC prediction 

variable was not significantl y different from 1.0. 
P.92 

VI-4 

Estimates of soi l and dust lead concentrations were also obtained using 

two methodologies. In the firs t case, home-specific observed values for 

soi l and dust lead were used. In the second case, soi l and dust lead estimates 

were made using models developed by McLamb (1988) and Marcus (1988c) 

that describe soi l and dust lead as a function of ambient lead. (For a 

description of these models, see Appendix B). The latte r approach was 

deemed necessary to determine how well the model predicts when ful l 

information on model inputs i s not available. (This i s indeed the usual 

situation when the model is used to assess the impacts of alternative 

regulatory programs on mean blood leads of a population of children.) 

Age-specific exposure parameters used as input to the uptake model 

are described in Appendix A. Estimates of dietar y lead intake for different 

age groups between 1978 and 1983, liste d in Table A-2, were calculated 

through application of the Multiple Source Food Model developed in Chapter 7 

of the CD. The latte r model uses year-specific FDA data on food lead content 

(i.e. , market basket surveys) and age-specific data on dietary patterns 

throughout childhood and has since been validated using recent 1984-85 

data (Flegel et al. , 1988). The contribution of drinking water to dietary 

lead exposure was estimated for an average tap water lead concentration 

of 12 pg/1 , which was the level reported for the State of Montana in 

1983. 

The criteri a for validation need to be carefull y defined. There is a great 

deal of individual variabilit y in PbB levels , even with available estimates 

of lead concentrations in exposure pathways such as air, dust, and soi l . 

The variabilit y i s attributable to individual biological variation , to 

unknown factors mediating exposure such as frequency of hand-mouth contact, 

and to other unattributed sources of lead. Even with good environmental data, 

the fraction of variance in the logarithm of blood lead that is accounted 
P.93 

• f '.{j-S?- ''•!•'• ' ': ' - ' ' ! 

VI-5 

for by an optimal total uptake regression model (multiple R^) rarel y exceeds 

30%. For the East Helena data, R^ = 0.28 in the best fittin g models (Johnson 

and Wijnberg, 1988). Thus, i t does not seem fruitfu l to use the individual 

predicted PbB levels as a criterio n for model validation. We used the more 

realisti c goal of matching the geometric mean PbB for children in a large 

neighborhood that was moderately homogeneous with respect to exposure. 

A more global validation involves comparing the cumulative distributio n 

function of observed PbB with that of the individual PbR estimates from the 

uptake/biokinetic model. These comparisons from the different validation 

runs are summarized in Table 6-1 . 

The simulations were al l run at two levels , one with al l parameters 

set to the lower bounds for uptake parameters in Appendix A, and the second 

with al l parameters set-to the upper bound levels in Appendix A. The 

midpoint of the all-lower and all-upper bound PbR estimates, i.e. , the 

simple mean was found to be the best predictor. 

In model run A, only ob-sjerved_ values for air , soil and dust input 

data were used. This data set was limited to the 28 observations near the 

smelter where an ambient monitor was within a few blocks of the homes. 

Model run B includes observations on 299 children. This expanded 

data set was obtained by using dispersion modeling techniques to estimate 

block specific ambient exposure estimates. 1 Comparisons between runs A.and R 

illustrat e that accurate PbB reductions result from using either observed 

1

 A similar validation exercise was conducted previously where average 

PbB levels predicted by the uptake/biokinetic model for different census 

tracts were compared to measured PbB levels in 1-5 year olds, livin g near 

two secondary lead smelters and in a reference area, in Dallas. Results 

were useful in indicating that greater refinement in the spatial scale 

would be necessary in accurately modeling high exposure situations . 

For the East Helena analysis, and in subsequent case-study modeling, 

the highest degree of spatial refinement possible (i->e., cit y blocks or 

block groups) is used. 
TABLE 6-1. COMPARISON OF INTEGRATED LEAD UPTAKE/BIOKINETIC MODEL PREDICTIONS 

TO 1983 MEASUREMENTS IN EAST HELENA 

Model 

Run Population3 Pb Air Quality Dust/Soil Lead 

Predicted vs. Measured 

Average Blood Lead0 and GSDC 

28 children livin g in 

area 1 around single 

a ir monitor with 

valid data 

89 Area 1 and 210 

Area 2 Children 

89 Area 1 and 210 

Area 2 Children 

Measured at monitor 

approx. 1/4 mile from 

smelter 

Contribution from 

smelter anission 

estimated by dis -

persion model (local 

meteorology data) 

local background Pb 

levels (e.g. , auto 

anissions, fugitiv e 

dusts) estimated 

from 1982 source 

apportionment study 

Same as for Run B 

except that dis -

persion modeling 

used meteoroloyical 

data from a nearby 

airport 

Measured i n and 

outside individual 

homes 

Measured i n and 

outside individual 

homes 

Estimated from 

generalized air: 

soil/dus t Pb 

relationships from 

regression analyses 

of available data i n 

literatur e 

16.3 vs. 16.5 pg/dl* 

1.42 vs. 1.49 

9.3 vs. 9.3 pg/dl* 

1.61 vs. 1.59 

i 

ON 

9.5 vs. 9.3 pg/dl* 

1.67 vs . 1.59 

Note: Dietary Pb exposure estimated for al l model runs based on year-specific analyses of 1980's FDA food 

concentration data and food consumption data from Pennington (1983, 1986), and U.S.D.A. Nationwide Food 

Consumption Survey, 1977-78. 

aAreas as defined i n the text 

bGeometric means 

cGeometric Standard Deviation 

*A11 differences statisticall y insignificant ; t-test s of differences of means assuming lognormal blood lead 

distributio n and non-equal variances. 

lead 

XI 

- co 
P.95 

VI-7 

or estimated ambient air data.: Run C, like run B, has 299 observations. 

However, in the latter model run, observed measures of household-specific 

soil and dust lead are replaced with estimates obtained from equations 

that explain soil and dust lead levels as functions of ambient lead (see 

Appendix B) . Also, while model run C uses dispersion model estimates 

of ambient concentrations, as does model run B, the source of meteorological 

data is different: Run B uses local meteorological data, and run C uses 

airport data. This difference allows runs B and C results to be compared 

to assess how sensitive blood lead estimates are to less location-specific 

meteorological data as well as to estimated soil and dust Pb concentrations. 

Figures 6-1 and 6-2 compare the blood lead distributions predicted by 

Runs B and C, respectively, with the actual 1983 measurements of East Helena 

children. It is important to emphasize that the model, because of the available 

input data, is designed to predict mean population responses and not individual 

PbB levels. Individuals with greater than average responses in the upper 

tail of the distribution comprise the population of concern, however. As 

discussed in Section III.B., the response of the most affected individuals 

will be estimated in case-study analyses by calculating the lognormal distribution 

around a given mean PbB level using empirically-derived estimates of PbB 

variance. The degree of protection under a given exposure scenario wiil ke 

characterized by the fraction of children with PbB below some level of concern. 

The results of the East Helena validation are shown for individual children 

in Figures 6-1 and 6-2, without the application of a GSD. The fraction, or 

percentage, protected is on the vertical axis and the potential level of 

concern is on the horizontal axis. As can be seen by these figures, and in 

scatter plots of individual predicted vs. observed PbB levels (not shown), 

there is significant individual variation at higher levels due to extra 

variability not included in the model. These figures reinforce the presumption 
P.96 

VI-8 

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BLOOD LEAD CONCENTRATION, ug/dl 

SO 60 

FIGURE 6-1. COMPARISON OF DISTRIBUTION OF MEASURED BLOOD LEAD LEVELS 

IN CHILDREN, 1-5 YEARS OF AGE, LIVING WITHIN 2.25 MILES OF E. HELENA 

LEAD SMELTER VS. LEVELS PREDICTED BY UPTAKE/BIOKINETIC MODEL. MEASURED 

SOIL AND DUST LEAD LEVELS WERE INCLUDED IN ESTIMATING UPTAKE LEVELS. 

(RUN B) 
P.97 

VI-9"" 

100 r 

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80 

70 

60 

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o 

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40 

30 

20 

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. tTT^^^^^T^^UT 

O = MODEL ESTIMATE 

O - OBSERVED 

10 20 30 40 

BLOOD LEAD CONCENTRATION, ^g/dl 

50 

FIGURE 6=2. COMPARISON OF DISTRIBUTION OF MEASURED BLOOD LEAD LEVELS 

IN CHILDREN, 1=5 YEARS OF AGE, LIVING WITHIN 2.25 MILES OF E, HELENA 

LEAD SMELTER VS, LEVELS PREDICTED BY UPTAKE/RWKINETIC MODEL, ESTIMATED 

SOIL AND DUST LEAD LEVELS FROM GENERALIZED RELATIONSHIPS WERE INCLUDED 

IN ESTIMATING UPTAKE LEVELS, (RUN C) 
P.98 

VI-10 

that the "importation" of empirically-derived PbB variance is necessary to 

fully capture variability. These figures also show that the model, as 

designed, gives a useful estimate of the mean response. Using the approach 

discussed previously of a) first estimating population mean PbB using the 

uptake/biokinetic model and b) then calculating the PbB distribution using 

an empirically-derived GSD (in this case 1.53; see Table 3-2), the modeled 

PbB distribution would be nearly identical to the observed distribution since 

the predicted mean is so close to the observed. Such comparisons are not 

illustrated here simply because the differences would be virtually indistinguishable. 

In conclusion, generally satisfactory results were obtained for 

predicting geometric mean PbB levels. Model run A can be considered a 

"pure" validation run that uses the best input data available to predict 

mean blood leads which can be compared to mean observed blood leads. 

Model runs B and C were necessary validations since exact population 

locations, local meteorology, and soil and dust lead data will r'arely be 

available for point source areas where the model will be used for policy 

analysis. Therefore, Run C can be considered a "generalized methodology" 

for "export" or application to other study areas. 

Although the differences between observed and predicted mean PbB levels 

are not statistically significant, post hoc calibration of the model to remove 

any differences was considered (Marcus and Holtzman, 1988). Both linear 

and nonlinear models were examined and it was found that calibrated 

models (including age-dependent ones) improved predictive ability of the 

model only marginally. One alternative is to simply subtract the difference 

between observed and predicted PbB levels in the generalized methodology 

Run C ( =0.2 pg/dl) or use a multiplicative factor (the latter probably 

more appropriate given the lognormality of the PbB distribution). Such a 
P.99 

VI-11 

calibration based on the results of a single, albeit large and detailed, 

validation may be inappropriate, especially considering the uncertainties 

inherent in such modeling exercises (e.g., a "positive" calibration may 

be needed in a different location). Given these uncertainties, no p_os_t 

hoc_ calibration of case study exposure estimates is currently being 

proposed although such an adjustment could easily be accommodated if 

decided upon. 

Several sensitivity runs were made in order to "fine-tune" various 

parameters for which data are incomplete such as age-specific dirt 

consumption rates and gut absorption rates for ingested lead. For example, 

in estimating the rate by which ingested lead is absorbed, the criteria 

document identifies many factors that have to be considered, among them 

person's age, physiological status, medium for the lead (e.g., food vs. 

paint or "dirt"), coincident ingestion of other elements in food (e.g., 

calcium) which compete with lead for intestinal absorption sites,.and the 

non-linear relationship between olead intake and blood lead (due for example 

to a saturable gut absorption pathway). Differences may also be attributable 

to physical or chemical differences in bioavailability of deposition from 

smelter emissions, or to the higher rate of intake in these areas compared 

to typical situations. — 

In fact, there are reasons to believe that such differences may exist 

in East Helena. The lead-bearing particles that are deposited near the 

smelter have a higher proportion of larger particles from fugitive emissions 

(e.g., erosion from tailings piles, loading operations) whereas the more 

distant deposits are predominantly smaller particles from stack emissions. 

Another factor is that parents of children who lived closer to the smelter 

(e.g., Area 1) may have been more sensitized to lead exposure and thus 

tended to control access to leaded soil and dust more than parents in 
P.100 

VI-12 

Area 2. There may also have been differences due to nutritional factors.) To 

test this, we ran the model with 20% gut absorption of dirt lead in Area 1 

and 30% absorption in Area 2. 

Two additional runs were made in which the same absorption factor, 

either 25% or 30%, was used for both areas. Area-specific results of the 

20% (Area 1) / 30% (Area 2) model were not significantly different from the 

observed geometric means. Likewise, using a 25% absorption factor for both 

areas yielded no significant difference between predicted and observed 

geometric means for the overall study area. Results using the 30% rate 

cited in the CD based on indirect chemical and animal experiments were 

not as concordant. Because of the programming complexities that would be 

introduced if dual absorption rates were used in all other applications 

of the model, a 25% absorption rate is chosen for general use since it 

yielded adequate overall results. 

As indicated in Section III, the model should be regarded as valid for 

estimating mean responses and it does appear that with the correct range of 

soil and dust lead inputs, the model adequately predicts the geometric mean 

of the observed distribution. Estimation of the higher percentiles of the 

PbB distribution requires application, to the geometric mean, of a GSD from, 

for example, NHANES II or exposure surveys around different point sources. 

B. Application of Uptake/Biokinetic Model to Other Data Sets 

There are other published data to test the uptake/biokinetic model 

at other U.S. locations and times. One data set was collected in Omaha, 

Nebraska, from 1971 to 1977 (Angle and Mclntire, 1979; Angle et al., 

1984). Another data set was collected throughout Silver Valley, Idaho, in 

1974-1975 and is described in Yankel et al. (1977), and Walter et al. (1980) 

with additional information given in the CD.