3 

Estimating Workplace Air and Worker Blood 

Lead Concentration using an Updated 

Physiologically-based Pharmacokinetic 

(PBPK) Model 

October 2013 

Air, Community and Environmental Research Branch

Office of Environmental Health Hazard Assessment

California Environmental Protection Agency

Estimating Workplace Air and Worker Blood Lead 

Concentration using an Updated 

Physiologically-based Pharmacokinetic (PBPK) 

Model 

October 2013

Office of Environmental Health Hazard Assessment (OEHHA)

California Environmental Protection Agency

Authors

Kathleen Vork, Ph.D., Research Scientist

Jim Carlisle, D.V.M., M.Sc., Staff Toxicologist

Joseph P. Brown, Ph.D., Staff Toxicologist

Air, Community and Environmental Research Branch

OEHHA Scientific Reviewers

Robert Blaisdell, Ph.D., Chief (Retired), Exposure Modeling Section, 

Air Toxicology and Epidemiology Branch

Melanie Marty, Ph.D.

Assistant Deputy Director for Scientific Affairs

Andrew G. Salmon, M.A., D.Phil., Chief, Air Toxicology and Risk Assessment 

Section, Air, Community and Environmental Research Branch

David Siegel, Ph.D., Chief, 

Air, Community and Environmental Research Branch

Lauren Zeise, Ph.D.

Deputy Director for Scientific Affairs

___________________________________________________________________

George Alexeeff, Ph.D., Director

Allan Hirsch, Chief Deputy Director

i 
External Scientific Reviewers

Earlier versions of this document were reviewed by five independent peer reviewers 

selected for their expertise in the fields of toxicology, occupational medicine, industrial 

hygiene, mathematics, pharmacokinetics, and physiologically-based pharmacokinetic 

(PBPK) modeling. Their invaluable expertise contributed to the development of the final 

document. The peer reviewers are listed below.

John R. Froines, Ph.D., University of California, Los Angeles, Department of 

Environmental Health Sciences, Los Angeles, CA

Gary L. Ginsberg, Ph.D., University of Connecticut, School of Community Medicine,

 Farmington, CT

Dale B. Hattis, Ph.D., Clark University, The George Perkins Marsh Institute, 

 Worcester, MA

Michael J. Kosnett, M.D., M.P.H., University of Colorado, Department of Medicine and

Department of Environmental and Occupational Health, Denver, CO

Richard W. Leggett, Ph.D., Oak Ridge National Laboratory, Environmental Sciences 

Division, Oak Ridge, TN

ii
Table of Contents

 Summary.................................................................................................................. 1

1 Introduction............................................................................................................... 5

2 Methods and Results ................................................................................................ 6

2.1 Selection and modification of lead model........................................................... 6

2.2 Predicting blood lead from workplace air and vice versa (Task 1) ..................... 7

2.2.1 Model adjustments and assumptions .......................................................... 7

2.2.2 Inhalation transfer coefficient....................................................................... 8

2.2.3 Validation of the Leggett+ Model ................................................................. 9

2.2.4 Simulating Workers’ Blood Lead using Leggett+ ....................................... 10

2.3 Time to decline to target BLL following removal from workplace exposure

(Task 2) .............................................................................................................. 13

2.3.1 Scenario one: Constant PbA resulting in identified blood lead levels ........ 14

2.3.2 Scenario two: Declining workplace air concentrations sustaining over

 40 years a BLL reached in one year.......................................................... 18

3 Discussion .............................................................................................................. 19

3.1 Updates to approach taken by Center for Policy Alternatives to predict air 

lead/blood lead relationships .............................................................................. 20

3.2 Limitations and uncertainty............................................................................... 21

3.2.1 Population BLL variability .......................................................................... 21

3.2.2 Breathing rate ............................................................................................ 22

3.2.3 Erythrocyte saturation................................................................................ 22

3.2.4 Lead’s toxic effects could alter the kinetics of lead in the body.................. 23

3.2.5 Particle size distribution............................................................................. 23

iii
3.2.6 Gastrointestinal absorption........................................................................ 25

3.2.7 Lead in bone.............................................................................................. 25

4 Summary and Conclusions..................................................................................... 25

A Appendix: Review, Selection, Modification, and Testing of Lead Models ............... 27

A.1 Lead biokinetics ............................................................................................... 27

A.1.1 Pulmonary deposition and clearance......................................................... 27

A.1.2 Gastrointestinal absorption........................................................................ 28

A.1.3 Erythrocyte uptake and saturation ............................................................. 29

A.1.4 Uptake and elimination from blood and soft tissue .................................... 29

A.1.5 Uptake and elimination from bone ............................................................. 29

A.2 Model screening............................................................................................... 30

A.2.1 Bert model ................................................................................................. 32

A.2.2 Leggett model............................................................................................ 33

A.2.3 O’Flaherty model ....................................................................................... 37

A.3 Model selection ................................................................................................ 40

A.4 Further evaluation and adjustment of the nonlinear Leggett model.................. 42

A.4.1 Coding integrity.......................................................................................... 42

A.4.2 Assessing model performance .................................................................. 44

A.5 Conclusion ....................................................................................................... 65

B Appendix: OEHHA Modifications to the Adjusted core Model to Accommodate 

Workplace Exposure in Leggett+ Model ................................................................. 67

B.1 Description of exposure features added to the nonlinear Leggett model ......... 67

B.1.1 Breathing rate............................................................................................ 67

B.1.2 Inhalation transfer coefficient..................................................................... 68

iv
B.2 Methods for deriving a coefficient for the transfer of inhaled lead to blood in 

workers............................................................................................................... 71

B.2.1 Studies selected for analysis ..................................................................... 71

B.2.2 Lead particle dosimetry using the MPPD2 model ...................................... 72

B.3 Results ............................................................................................................. 74

B.3.1 Dosimetry results from MPPD2 ................................................................. 74

B.3.2 Dosimetry results from ICRP Human Lung Model publication 66 lookup 

tables......................................................................................................... 80

B.3.3 Default inhalation transfer coefficient (ITC)................................................ 82

B.4 Methods for assessing the performance of the Leggett+ model....................... 85

B.4.1 Study and subject selection criteria ........................................................... 86

B.4.2 Data extraction .......................................................................................... 87

B.5 Results ............................................................................................................. 90

B.5.1 Test 1: Goodness of fit .............................................................................. 92

B.5.2 Test 2: Model performance versus exposure duration............................... 94

B.6 Conclusion ....................................................................................................... 95

C Appendix: Acronyms, symbols and special terms................................................... 97

D References ............................................................................................................. 99

v
Tables 

1: Parameters employed in OEHHA’s application of the Leggett+ model................... 11

2: PbA concentrations and corresponding BLL........................................................... 12

3a: Workplace air lead concentration (PbA) (μg/m3) for different durations of 

exposure corresponding to the BLL reached ......................................................... 14

3b: Days for BLL to decline to 15 μg/dL after removal from workplace exposure

(limit BLL reached at the end of exposure period) ................................................. 15

A-1: Parameter values from Nie and Leggett and adjusted core model......................... 56

A-2: Estimate of fit of predicted to observed BLLs for 47 smelter workers..................... 57

A-3: Distribution of Lead in Various Tissues - Postmortem Data, Original Model 

Prediction, Adjusted Model Prediction.................................................................... 64

B-1: Airborne lead concentration and particle mass median aerodynamic diameter

from Park and Paik (2002) .................................................................................... 74

B-2: Lung dosimetry analysis of occupational airborne lead exposure by the MPPD2 

model: multiple activity levels................................................................................. 76

B-3: Lung dosimetry analysis of occupational airborne lead exposure by the MPPD2 

model with different speciation assumptions (data of Park and Paik 2002; 

Spear et al. 1998a)................................................................................................. 78

B-4: Lung dosimetry analysis of occupational airborne lead exposure in brass 

foundry workers by the MPPD2 model (data of Liu et al. 1996)............................. 80

B-5: Particle deposition by activity level and particle size by the ICRP Human Lung 

Model (ICRP 1994) ................................................................................................ 81

B-6a: Inhalation transfer coefficient by worker group and activity level........................... 84

B-6b: TWA inhalation transfer coefficients by occupational setting.................................. 84

B-7: Model inputs........................................................................................................... 90

B-8: Measured BLL versus BLL predicted by Leggett+.................................................. 91

vi
Figures

1: Rise in BLL over 40 years in the 95th percentile worker ....................................... 13

2: Modeled skeletal and blood lead levels for the median worker during and after 

workplace exposure (Leggett+ model) ................................................................... 17

3: Modeled skeletal and blood lead levels for the median worker during and after 

workplace exposure (Leggett+ model) ................................................................... 18

A-1: Measured versus predicted BLL (adjusted nonlinear Leggett model).................... 59

A-2: Model performance versus job tenure ................................................................... 60

A-3: Plasma lead versus whole blood lead concentration - predictions from the 

adjusted Leggett model and data from two worker cohorts.................................... 62

A-4: Urine lead versus whole blood lead concentration - predictions from the 

adjusted Leggett model and data from two worker cohorts.................................... 62

B-1: Model validation diagram....................................................................................... 86

B-2: Check for systematic bias – Griffin et al. 1975....................................................... 93

B-3: Check for systematic bias – Williams et al. 1969................................................... 94

B-4: Model performance versus exposure duration – Griffin et al. 1975........................ 95

vii
viii
Summary

This document presents the results of a modeling effort by the California Environmental 

Protection Agency’s (Cal/EPA) Office of Environmental Health Hazard Assessment 

(OEHHA) to estimate air and blood lead concentrations among workers under various 

exposure conditions. These estimates are intended to accompany proposed changes to 

workplace standards for lead exposure developed by the California Department of 

Public Health’s Occupational Lead Poisoning Prevention Program (CDPH-OLPPP). This 

work was performed under contract with the CDPH-OLPPP.

The principal tasks requested of OEHHA were to:

1. Estimate various concentrations of lead in workplace air inhaled by workers 

without respiratory protection that could result in specified lead concentrations in 

workers’ blood. 

2. Estimate the time it would take for workers’ blood lead levels to return to 15 

μg/dL (micrograms of lead per deciliter of blood) following the cessation of 

occupational lead exposure.

Because there are no chamber or field studies that include measurements of air lead 

levels and blood lead levels (BLLs) over the time span of interest (40-year working 

lifetime), some type of model must be used to predict the relationship between lead 

exposure and BLL. The model must reflect the complexity of lead absorption, 

distribution, metabolism, and excretion, and be able to address time-dependent

conditions.

OEHHA reviewed the available lead pharmacokinetic models and selected the Leggett 

model (nonlinear version) as best suited to complete the required tasks. To the extent 

possible, OEHHA calibrated the model to fit observed data and tested the validity of the 

adjusted model. Briefly, OEHHA simultaneously adjusted blood, bone, and urine 

clearance parameters in the core model to fit blood, bone, and urine data collected from 

workers chronically exposed to lead, as well as the general population environmentally 

exposed to lead. We then performed multiple tests to ensure that predictions from the 

1
adjusted model in the range of BLLs of interest to CDPH-OLPPP compared well to 

observed data. Once confident that the adjusted core Leggett model predicted valid 

BLLs, OEHHA used the model to complete the required tasks. The results are 

presented below.

Task 1: Blood Lead Levels Resulting from Lead in Workplace Air

OEHHA added exposure features and adjustments to the core nonlinear biokinetic 

model for lead published by Richard W. Leggett in 1993. We added the exposure model 

to the adjusted core model (resulting model called Leggett+) so that we could simulate 

workplace inhalation exposure that would predict a range of workers’ BLLs after 40 

years of occupational exposure and a given background ambient air and dietary 

exposure. The original Leggett model was a general model not specifically designed to 

address workplace exposure scenarios. Therefore, OEHHA needed to add exposure 

features which addressed breathing rate (BR) and the fraction of inhaled lead 

transferred to blood (“inhalation transfer coefficient” or “ITC”) under workplace exposure 

conditions. OEHHA derived an ITC based on published particle size distribution data 

from a variety of workplaces and a recently developed model for predicting upper and 

lower respiratory tract deposition based on particle size and other parameters.

The results of this modeling are expressed as 8-hr time-weighted average workplace air 

lead concentrations (PbA) (in micrograms of lead per cubic meter of air [μg/m3

]) and 

corresponding BLLs (in units of μg/dL) in a median worker. Using population BLL 

distributions based on epidemiologic data, we calculated BLLs for workers at the 90th

and 95th percentiles of the population, as shown in Table S-1.

2
Table S-1: Workplace air lead concentrations (PbA) and corresponding BLL1

8-hr TWA

PbA (μg/m3

)

Predicted BLL (μg/dL)

50th percentile 90th percentile 95th percentile

0.5 2.3 4 5

0.8 2.7 5 6

2.1 4.6 8 10

2.4 5 9 11

2.8 5.5 10 12

3.9 6.9 13 15

5.0 8.2 15 18

6.0 9.3 17 20

6.5 10 18 22

7.5 11 20 24

10.4 14 25 30

11.5 15 27 32

12.6 16 30 35

17.6 20 37 43

25.0 25 46 54

34.0 30 55 65

1 Assumptions as in Table 1; TWA, time-weighted average workplace air concentration given ambient and oral background intake 

leading to a BLL of 1.5 μg/dL; PbA, workplace air lead concentrations (in micrograms of lead per cubic meter of air [μg/m3

]); BLL, 

blood lead level; BLL values that CDPH-OLPPP asked OEHHA to model are in bold; GSD, geometric standard deviation used to 

derive 90th and 95th percentile estimates = 1.6. See Table 1. No measure of variability was given by (Leggett 1993) or (O'Flaherty 

1993), (O'Flaherty et al. 1998), O’Flaherty (2000).

Task 2: Time Taken to Restore Blood Lead to 15 μg/dL

Using the adjusted core Leggett model, OEHHA evaluated the time required for 

workers’ BLLs to decline from a much higher BLL to 15 μg/dL following cessation of 1, 

10, 25, or 40 years of workplace lead exposure. CDPH-OLPPP asked that we model the 

decline of BLLs from limits of 20, 30, 40, 50, and 60 μg/dL to some lower BLL. We used

a target of 15 μg/dL because the CDPH-OLPPP Medical Guidelines recommend that a 

worker with an elevated BLL not return to work until his or her BLL is below 15 μg/dL 

(CDPH 2009).

OEHHA considered two different scenarios for modeling time to decline. In the first 

scenario, workers are exposed to constant airborne lead concentrations during work 

hours, resulting in BLLs of 20, 30, 40, 50 or 60 μg/dL at the end of each exposure

3
period. The results for scenario one are presented in Table S-2. Note the substantial

(about five-fold) difference in the time it takes to decline to 15 μg/dL after 40 years of 

exposure reaching a BLL of 60 μg/dL compared to a BLL reaching 30 μg/dL, due to a 

greater rate of lead accumulation in the bones at the higher BLL. In the second 

scenario, workers are exposed to constant PbA during work hours, resulting in BLLs of 

20, 30, 40, 50, or 60 μg/dL within the first year. During the balance of the exposure 

period, the assumed workplace air concentrations decrease gradually so that BLLs are 

sustained at the level reached within the first year. Scenario one and two result in 

similar times to decline to 15 μg/dL for all BLLs and all exposure periods.

Table S-2: Days for BLL to decline to 15 μg/dL after removal from workplace 

exposure (limit BLL reached at the end of exposure period)1

Exposure 

duration Percentile

BLL at beginning of MRP1 (μg/dL)

20 30 40 50 60

Days to decline to 15 μg/dL

1 year

50th 21 128 280 435 615

90th 38 234 511 795 1123

95th 45 277 605 940 1329

10 years

50th 31 200 400 630 920

90th 57 365 731 1151 1681

95th 67 432 865 1362 1989

25 years

50th 32 207 416 670 1005

90th 58 378 760 1224 1836

95th 69 447 899 1448 2172

40 years

50th 32 210 425 685 1045

90th 58 384 776 1251 1909

95th 69 454 919 1481 2259

1 Medical Removal Protection – Under Cal/OSHA regulations whenever an employee’s BLL exceeds specified limits he or she must 

be removed from high lead exposure until his or her BLL returns to an acceptable level; μg/dL, micrograms per deciliter; Limit BLL 

reached exposure at the end of the exposure period. GSD, geometric standard deviation used to derive 90th and 95th percentile 

estimates = 1.6. See Table 1. No measure of variability was given by (Leggett 1993) or (O'Flaherty 1993), (O'Flaherty et al. 1998), 

O’Flaherty (2000).

4
1 Introduction

The California Department of Public Health’s Occupational Lead Poisoning Prevention 

Program (CDPH-OLPPP) is in the process of recommending changes to the Cal/OSHA

standards relating to workplace exposures to lead. CDPH-OLPPP contracted with the 

California Environmental Protection Agency’s (Cal/EPA) Office of Environmental Health 

Hazard Assessment (OEHHA) to provide physiologically-based pharmacokinetic 

(PBPK) modeling to support that effort. The support consists primarily of two tasks using

PBPK modeling to predict:

1. the ranges of concentrations of inorganic lead in workplace air in micrograms 

per cubic meter (μg/m3

) that would result in blood lead levels (BLLs) of 

interest (5, 10, 15, 20, and 30 micrograms per deciliter [μg/dL]) for the 50th, 

90th, and 95th percentile of workers exposed to lead by inhalation.

2. the rate of BLL decline to 15 μg/dL - the level proposed by CDPH-OLPPP -

following cessation of occupational lead exposure that resulted in BLLs in a 

range from 20 to 60 μg/dL with exposure histories from 1 to 40 years.

Completion of these tasks will supply important information supporting the consideration 

of a revised lead standard: Task 1 model predictions of the air concentrations resulting 

in certain BLLs will inform the choice of a health-based permissible exposure limit (PEL)

intended to ensure that BLLs in workers would stay below a level proposed by CDPHOLPPP. Task 2 supports the prediction of the length of time overexposed workers will 

have to be kept away from workplace exposure in order for their BLL to return to 15 

μg/dL.

This document begins with a description of OEHHA’s methods for selecting and 

modifying the lead model, predicting blood lead, analyzing the time for BLLs to return to 

15 μg/dL from a range of higher BLLs. It is followed by a discussion of the ways OEHHA 

has updated the model and the limitations and uncertainties that remain. The final 

Section summarizes OEHHA’s work and its concluding statements. Appendix A covers 

the review, selection, modification, and testing of the candidate model for predicting 

blood levels from workplace exposure to lead. Appendix B reports the procedures for 

5
deriving an exposure module and adding it to the adjusted core model to accommodate 

workplace exposure conditions. This appendix then (Leggett+) reports the procedures 

for checking accuracy of the combined model, Leggett+. Finally, Appendix C defines 

acronyms that appear throughout this report.

2 Methods and Results

2.1 Selection and modification of lead model

In order to complete the two above-mentioned tasks, OEHHA evaluated the available 

models to determine which model best describes what is known about the complex 

pharmacokinetics of lead and could be most easily modified to estimate worker 

exposure. The results of this review are summarized below. A detailed comparison and 

evaluation of these multi-compartmental biokinetic models, including a summary of each 

model’s conceptual structure, advantages, and limitations, is in Appendix A. OEHHA 

reviewed the following models:

• Leggett (1993) model

• O'Flaherty (1993), (1995); O'Flaherty et al. (1998); O'Flaherty (2000) model

• Bert et al. (1989) model

The U.S. Environmental Protection Agency’s Adult Lead Model (U.S. EPA 2003) is a 

steady-state model that is unable to accommodate the time-dependent requirements of 

the above tasks, and was eliminated from further consideration. The All-Ages Lead 

Model (U.S. EPA 2005), based on the Leggett model, was also considered for this 

project. However, it had not been released in final form at the time of this report and 

was therefore not considered further.

As discussed in Appendix A, OEHHA found the Leggett model to be the best suited for 

use in an occupational lead exposure scenario because it: 

• is sufficiently flexible to allow modeling of the required scenarios.

• has an optional algorithm allowing for nonlinear kinetics to account for red blood 

cell saturation at higher BLLs.

6
• provides a good fit to data from humans exposed to environmental lead and

limited data from lead workers.

Dr. Leggett coded his published model in FORTRAN (Formula Translating System), 

which is an old computer language. Our preferred platform for PBPK modeling is Matrix 

LaboratoryTM (MATLAB) (MATLAB 2012). Therefore, OEHHA coded the Leggett model 

into script language used by MATLAB. We then compared the output from the original

and nonlinear models recoded in MATLAB with the output generated by the author 

(personal communication with Dr. Leggett, 2011) to ensure that the coding was 

accurate.

2.2 Predicting blood lead from workplace air and vice versa (Task 1)

To support the development of a new PEL, OEHHA used the Leggett model (nonlinear

version) with added exposure features to predict workplace airborne lead 

concentrations that would lead to BLLs of interest to CDPH-OLPPP following various 

simulated 40-year workplace exposures.

2.2.1 Model adjustments and assumptions

In preparation for these tasks, OEHHA modified the Leggett model by: 1) adjusting 

bone, urine clearance, and blood parameters to improve the fit of the model to observed 

data; 2) assuming a time-weighted average breathing rate of 26 m3

/day, which reflects

time-weighted breathing rates based on assumed activity levels for workplace and nonworkplace exposure to airborne lead; and 3) setting a default value of 30% for transfer 

of inhaled lead to blood (“inhalation transfer coefficient”) for particles in the size range 

found in industrial settings. We named the modified version of the Leggett model 

“Leggett+”, to distinguish between the original model and the version OEHHA modified.

These new features are described in detail and tested in Appendices A and B. We 

describe the derivation of the ITC briefly here because it introduces a new approach to 

estimating the transfer of inhaled lead to blood from exposures in the workplace.

7
2.2.2 Inhalation transfer coefficient

For any given air lead concentration, the proportion of inhaled particles that deposits in 

the head, ciliated regions of the lung, and alveoli is determined by the size of the 

particles and the individual’s breathing rate. Generally, smaller particles will deposit 

deeper in the lung while coarser particles tend to be deposited in the head and ciliated 

regions where they are cleared by ciliary action or secretions and swallowed. Very small 

particles will to a large extent be exhaled.

The chemical form of the inhaled lead affects its solubility and therefore influences 

absorption from the respiratory tract and gut. For purposes of developing a coefficient 

for the transfer of inhaled lead to blood, OEHHA chose to make the cautious 

assumption that lead is inhaled in a highly soluble form and that inhaled lead particles 

deposited in the alveolar region of the lung are absorbed to the blood within a day with 

essentially 100% efficiency. Particles deposited in the head and ciliated regions of the 

lung are cleared to the gut where they are absorbed with less efficiency.

Particle size distribution has been considered a significant influence on the percentage 

of inhaled lead transferred to the blood although, as will be shown later, the fraction 

ultimately transferred to the blood does not vary greatly by particle size distribution in 

the range 1 – 15 μm mass median aerodynamic diameter (MMAD). This is because the 

decrease in the fraction deposited deep in the lung when particle sizes are large is 

offset by an increase in the total head deposition fraction (larger particles are not 

exhaled but deposit in the head region) and subsequent swallowing and gut absorption.

In order to determine what default value to use for the percentage of inhaled lead 

transferred to the blood we: 1) reviewed published literature on particle size distribution

in a variety of industrial workplaces with differing lead operations that generate a range 

of particle sizes (fine to coarse) and extracted particle MMADs; 2) estimated the 

proportion of inhaled lead particles that deposits in the head, ciliated regions of the lung, 

and the alveoli, using the reported MMADs and the Multi-path Particle Dosimetry 

version 2 model (MPPD2) (ARA 2012); and 3) derived a transfer factor according to 

Equation 1:

8
Eq. (1): Inhalation transfer = (alveolar deposition x lung absorption)

 + (ciliated and head region deposition x average gut absorption).

We calculated inhalation transfer coefficients for four different industrial settings (two 

that generate finer particles and two that generate coarser particles) and five activity 

levels (resting, sitting, light work, moderate work, heavy work). We selected 30% as our 

default inhalation transfer coefficient (ITC) after analyzing the data in three different 

ways. We calculated:

1) an ITC for each occupational setting assuming an average BR of 25 L/min during 

the exposure period (range 30.1% - 30.5%).

2) ITCs for all four occupational settings and all five activity levels (range 28% -

32%, midpoint 30%).

3) a time-weighted average (TWA) transfer coefficient for each occupational setting 

using the same activity weighting factors we used to derive a 24-hr average 

breathing rate (range 29% - 31%; midpoint 30%).

The reader is referred to Appendix B for a full discussion of the rationale and

assumptions used for deriving the default value.

Validation of the Leggett+ Model

As mentioned previously, OEHHA adjusted the bone, urine clearance, and blood 

parameters in the Leggett model to improve the model fit to observed worker data. We 

performed multiple tests of the model to ensure that predictions in the range of BLLs of 

interest to CDPH-OLPPP from the adjusted model compared well to tissue lead levels 

measured in workers and the general population. We were able to verify that the 

adjusted Leggett model predicted BLLs after chronic exposures very close to measured 

BLLs; the model performed well regardless of job tenure; predicted levels of lead in 

blood, urine, and bone compared well to measured levels in chronically-exposed 

workers; and predicted levels of lead in all tissues compared well to measured levels in 

the general population.

Once OEHHA was comfortable that the core model described above was performing 

well, we added an exposure component (BR and ITC) to the core model so that we 

9
could model workplace exposures based on personal breathing zone lead 

concentration. To test its performance, we used Leggett+ to reproduce (to the extent 

possible) exposure scenarios in a published field study and a chamber study, and 

compared model predictions to measured BLLs from each study. These comparisons 

show that, in the range of BLLs of interest to CDPH-OLPPP, the Leggett+ model 

predicts BLLs similar to observed BLLs in these studies (see Appendix B). 

2.2.3 Simulating Workers’ Blood Lead using Leggett+

The simulations in this report assumed a standard background BLL of 1.5 μg/dL based

on the background levels observed in the U.S. general population (Schober et al. 2006).

This BLL represents constant exposure to an ambient air level of 0.006 μg/m3 (the 2004

annual average level in California [SCAQMD 2008]), along with 14.6 μg/day of 

background oral intake from all non-work sourcesor a combined uptake of 1.8 μg/day 

(see Table 1). There is uncertainty in estimating individual background intake, but the 

background constitutes a small fraction of total exposure for most lead workers. Inputs 

and assumptions used in simulating the specific exposure scenarios requested by 

CDPH-OLPPP are shown in Table 1.

10
Table 1: Parameters employed in OEHHA’s application of the Leggett+ model1

Parameter definition Units Value Reference

Age at start of exposure years 25 Based on retirement at age 65

Exposure duration years 40 high-end assumption

Initial blood lead concentration μg/dL 1.5 (CDC 2009; Schober 2006)

Workplace airborne lead 

concentration (PbA) μg/m3 0.5 - 210 back calculated (Tables 2 & 3a)

Transfer fraction of inhaled lead 

to blood unitless 0.30 (see Appendix B)

Breathing rate2 m3/day 26 (see Appendix B)

Background lead intake after 

absorption μg/day 1.8 Back-calculated to maintain BLL at 1.5 μg/dL 

Yearly exposure fraction days/year 250/365 (U.S. EPA 1991)

Body weight kg 73 (ICRP 2002)

BLL geometric standard deviation 

(GSD) in U.S. population. unitless 1.6 (U.S. EPA 2011: Griffin et al. 1999)

1 dL, deciliter; m3

, cubic meter; μg, microgram, kg, kilogram;

2 Breathing rates for sedentary, light, and moderate activity are weighted 

by work and non-work time in a day and by the yearly exposure fraction. A more detailed description of our assumptions appears in 

the Appendices and below under the section entitled: Limitations and Uncertainty (see text).

We used the Leggett+ model and the parameters listed in Table 1 to estimate the 

constant air concentrations that yield BLLs in the range of 2 – 30 μg/dL for the 50th

percentile worker after 40 years of workplace exposure. We calculated the 90th and 95th

percentile BLLs from the 50th percentile BLLs using Equations 2 and 3 - the standard 

statistical formulas for determining percentiles of a lognormal distribution.

Eq. (2): BLL (50th percentile) = BLL (95th percentile) / GSD 1.64

Eq. (3): BLL (90th percentile) = BLL (50th percentile) x GSD 1.282

The results are presented in Table 2.

11
Table 2: Workplace air lead concentrations (PbA) and corresponding BLL1

8-hr TWA

PbA (μg/m3

)

Predicted BLL (μg/dL)

50th percentile 90th percentile 95th percentile

0.5 2.3 4 5

0.8 2.7 5 6

2.1 4.6 8 10

2.4 5 9 11

2.8 5.5 10 12

3.9 6.9 13 15

5.0 8.2 15 18

6.0 9.3 17 20

6.5 10 18 22

7.5 11 20 24

10.4 14 25 30

11.5 15 27 32

12.6 16 30 35

17.6 20 37 43

25.0 25 46 54

34.0 30 55 65

1 BLL, blood lead level; μg/dL, microgram per cubic deciliter; assumptions as in Table 1; 8-hr TWA, eight hour time-weighted 

average workplace air concentration given ambient and oral background intake leading to a BLL of 1.5 μg/dL; workplace air lead 

concentrations (PbA) (in micrograms of lead per cubic meter of air [μg/m3

]); BLL, blood lead level; BLL values that CDPH-OLPPP 

asked OEHHA to model are in bold.

To obtain the 90th and 95th percentile BLLs, we assumed that individual variability in BLL 

in the U.S. general population is lognormally distributed with a GSD of 1.6. A GSD of 

1.6 in the U.S. general population is suggested by Griffin et al. (1999) and has been 

adopted by the U.S. EPA (2011). However, before selecting a GSD of 1.6, OEHHA 

verified that it reasonably represents the variability in BLLs in studies of children, adult 

volunteers, and workers (for a description of our analysis, see section 3.2.1 Population 

BLL variability). As described previously, we applied the GSD to the 50th percentile BLL 

to derive the 90th and 95th percentile BLLs.

Figure 1 depicts the rise in BLL in the 95th percentile worker who reaches the limit BLL 

gradually over 40 years of workplace exposure. After achieving a stable background 

BLL with two years of exposure to background (non-workplace) sources of lead alone, 

12
workplace PbA is added to background lead for 40 years. Note that BLLs climb rapidly 

during the first year of workplace exposure and continue to climb at a slower rate over 

the next two years and a very slow rate for the remaining years of exposure.

Figure 1: Rise in BLL over 40 years in the 95th percentile worker 1

1 BLL, blood lead level; corresponding 8-hr TWA air lead concentrations for BLLs of 5, 10, 15, 20, and 30 μg/dL are 0.5, 2.1, 3.9, 

6.0, and 10.4 μg/m3 respectively.

2.3 Time to decline to target BLL following removal from workplace exposure 

(Task 2)

Under the current Cal/OSHA-required medical removal protection program (MRP), 

whenever an employee's BLL exceeds specified limits he or she must be removed from 

high lead exposure work areas until his or her BLL returns to an acceptable level. Using 

the adjusted core model (Leggett+ without the exposure module), OEHHA simulated the 

time it may take to decline to a lower BLL for a range of elevated BLLs and exposure 

histories of interest to CDPH-OLPPP. OEHHA considered two different scenarios for 

0 5 10 15 20 25 30 35 40 45 0

5

10

15

20

25

30

35

Time in years

Blood lead level (μg/dL)

13
modeling time to decline. In the first scenario, workers reach the BLL limit at the end of 

the exposure period. In the second scenario, workers reach the BLL limit within the first 

year of exposure and the BLL limit is maintained for the remainder of the exposure 

period.

2.3.1 Scenario one: Constant PbA resulting in identified BLLs

OEHHA used Leggett+ to estimate the constant 8-hr TWA air concentration that would 

result in BLLs of 20, 30, 40, 50, and 60 μg/dL (referred to as “limit BLLs”) at the end of 

the exposure period (Table 3a). Daily exposure was then reduced to background level 

at the end of the exposure period, and the time needed for each BLL to decline to 15 

μg/dL was predicted (Table 3b).

Table 3a: Workplace air lead concentration (PbA) (μg/m3

) for different durations of 

exposure corresponding to the BLL reached1

BLL (μg/dL) reached

Exposure Period

1 year 10 years 25 years 40 years

20 23 18 18 18

30 44 35 34 34

40 75 60 60 59

50 125 101 100 100

60 210 169 166 166

1

PbA, workplace air lead concentrations; BLL, blood lead level predicted by the model for the 50th percentile worker; μg/dL, 

microgram per deciliter

The workplace air concentrations found in Table 3a are within the range of air 

concentrations measured in lead-related industrial and construction workplaces 

(Hodgkins et al. 1992; Liu et al. 1996; Virji et al. 2009; Vork 2003).

At the end of each exposure period, the absorbed daily dose was reduced to the 

background level that sustained a BLL of 1.5 μg/dL, and the time needed for each BLL 

to decline to 15 μg/dL was predicted. The days needed to decline to 15 μg/dL for the 

50th, 90th, and 95th percentile worker for each of five exposure periods appear in Table 

3b. For example, 166 μg/m3 of workplace air concentration together with background 

14
levels of ambient air and oral sources of lead over 40 years produced a BLL of 60 μg/dL 

(Table 3a column 5). It took an average of 1045 days for the BLL to return to 15 μg/dL

for the median worker (Table 3b column 7). We estimated the 90th and 95th percentiles 

from 50th percentile BLLs using Equations 2 and 3 - the standard statistical formulas for 

determining percentiles of a lognormal distribution.

Table 3b: Days for BLL to decline to 15 μg/dL after removal from workplace 

exposure (limit BLL reached at the end of exposure period)1

Exposure duration Percentile

BLL at beginning of Medical Removal Protection (μg/dL)

20 30 40 50 60

Days to decline to 15 μg/dL

1 year

50th 21 128 280 435 615

90th 38 234 511 795 1123

95th 45 277 605 940 1329

10 years

50th 31 200 400 630 920

90th 57 365 731 1151 1681

95th 67 432 865 1362 1989

25 years

50th 32 207 416 670 1005

90th 58 378 760 1224 1836

95th 69 447 899 1448 2172

40 years

50th 32 210 425 685 1045

90th 58 384 776 1251 1909

95th 69 454 919 1481 2259

1 Medical Removal Protection – Under Cal/OSHA regulations whenever an employee’s BLL exceeds specified limits he or she must 

be removed from high lead exposure until his or her BLL returns to an acceptable level.; μg/dL, micrograms per deciliter; GSD, 

geometric standard deviation used to derived 90th and 95th percentile estimates = 1.6. See Table 1. OEHHA applied a standard 

statistical equation for a lognormal distribution of BLLs in the worker population because no measure of variability was given by 

(Leggett 1993) or (O'Flaherty 1993), (O'Flaherty et al. 1998), (O’Flaherty et al. 2000).

Note the substantial increase in the time it takes to decline to 15 μg/dL for higher limit 

BLLs. The time needed to decline to 15 μg/dL after 40 years of exposure reaching a

BLL of 60 μg/dL (two half-lives) is about five times longer than the time needed to 

decline to 15 μg/dL after 40 years of exposure reaching a BLL of 30 μg/dL (one halflife). This striking difference is due to a greater proportion of lead accumulating in the 

15
skeleton at a BLL of 60 μg/dL compared to 30 μg/dL because, as BLLs rise, an 

increasingly larger portion of lead remains unbound.

Figures 2 and 3 illustrate the effect on buildup and elimination of lead in a median 

worker’s skeleton for two exposure histories (i.e., BLL of 30 reached after 40 years of 

exposure versus a BLL of 60 μg/dL reached after 40 years of exposure). In each 

scenario, after a year of exposure to non-workplace sources of lead alone, workplace 

air concentration is added to background for 40 years. Finally, workplace exposure 

ceases and background exposure levels become the only source of exposure for the 

remaining years as BLLs decline to the target BLL of 15 μg/dL. In Figure 2, the 

simulation is terminated when the BLL reaches 15 μg/dL after 210 days following the 

end of workplace exposure. In Figure 3, the simulation is terminated when the BLL

reaches 15 μg/dL after 1045 days following the end of workplace exposure.

Note that the skeletal lead pool is not kinetically homogeneous. It is apparent in Figure 3 

that turnover is faster in the mainly trabecular bone, which is about 20% of total bone,

than in the mainly cortical bone, which is about 80% of total bone (Skerfving et al. 

1987).

It can be seen in Figure 2 that BLL declines from a peak of 30 to 15 μg/dL before much 

lead is released from bone. For the occupational exposure scenario depicted in Figure 

2, the predicted decline in BLL during the first few years after exposure corresponds 

mostly to declining lead in trabecular bone.

16
Figure 2: Modeled skeletal and BLLs for the median worker during and after 

workplace exposure (Leggett+ model)1

1 Constant 8-hr TWA air concentration of 34 μg/m3

. BLL is 30 μg/dL at the end of 40 years; Bone mineral loss after age 35 is 

ignored. Adding bone loss rates of 10% per decade could result in a higher concentration of lead in bone than shown in this figure 

(Leggett et al. 1982; O'Flaherty 2000).

0 5 10 15 20 25 30 35 40 45

0

10

20

30

40

50

60

Time in years

Lead in μg/dL blood volume or μg/g bone 

Trabecular bone

Cortical bone

Blood

17
Figure 3: Modeled skeletal and BLLs for the median worker during and after 

workplace exposure (Leggett+ model)1

1 Constant 8-hr TWA air concentration of 166 μg/m3

. BLL is 60 μg/dL at the end of 40 years; Bone mineral loss after age 35 is 

ignored. Adding bone loss rates of 10% per decade could result in a higher concentration of lead in bone than shown in this figure 

(Leggett et al. 1982), (O'Flaherty 2000).

2.3.2 Scenario two: Declining workplace air concentrations sustaining over 40 years a 

BLL reached in one year

CDPH-OLPPP also asked OEHHA to check whether there would be different times to 

decline if someone reached the limit BLL earlier than in the first scenario and stayed at 

that level for a number of years before being removed from exposure. Thus, in scenario 

two, we assumed workers were exposed to airborne lead for 1, 10, 25, or 40 years, 

resulting in BLLs of 20, 30, 40, 50, or 60 μg/dL within the first year of exposure. Then, 

the modeled airborne lead concentration was allowed to decline linearly in order to 

0 5 10 15 20 25 30 35 40 45

0

50

100

150

200

250

Time in years

Trabecular bone

Cortical bone

Blood

Lead in μg/dL blood volume or μg/g bone

18
sustain the BLLs at the level reached at the end of the first year (±10%) for the 

remaining years of exposure.

OEHHA conducted this exercise in two stages. First, OEHHA back-calculated the daily 

absorbed lead that would result in sustained 50th percentile (average worker) BLLs of 

20, 30, 40, 50, or 60 μg/dL. Then, OEHHA back-calculated the PbA that would deliver 

those daily absorbed dosages for each of the 16 exposure scenarios using parameters 

listed in Table 1 for initial (pre-employment) blood lead concentration, background air 

lead concentration, transfer fraction of inhaled lead, daily uptake of lead (backcalculated from pre-exposure BLL) and breathing rates, and yearly exposure fraction.

Modeling sustained BLLs at a constant level over extended periods (greater than one 

year) required air concentrations to decline due to releases of accumulated skeletal 

lead. Modeled air concentrations decline linearly over the course of the exposure so that 

the BLLs peak after the first year and remain at a constant BLL for the duration of the 

exposure. Differences in air concentration at the end of the exposure period for a 

sustained BLL (scenario two) versus air concentrations for a BLL that reaches the limit 

BLL at the end of the exposure period (scenario one) are minimal (1% or less) (data not 

shown).

Scenario one and two result in the same, or almost the same, times to decline for all 

BLLs and all exposure periods (data for scenario two not shown). For example, even for 

a BLL of 60, for the 95th percentile worker, the time to decline to 15 μg/dL after removal 

from workplace exposure is less than a month longer (6.3 versus 6.2 years) for a worker 

with a sustained BLL of 60 μg/dL over 40 years compared to a worker who reaches 60 

μg/dL only at the end of the 40-year period.

3 Discussion

OEHHA updated the approach taken by the Center for Policy Alternatives (CPA) to 

predict air lead/blood lead relationships through modeling workplace exposure to lead 

and its influence on overall body burden of lead in workers. This approach is explained 

in Section 3.1. The limitations to OEHHA’s new model and remaining uncertainties in

our approach are explained in Section 3.2.

19
3.1 Updates to approach taken by Center for Policy Alternatives to predict air 

lead/blood lead relationships

In developing the 1978 PEL, Federal OSHA considered the CPA’s application of the 

pharmacokinetic Bernard model to predict air lead/blood lead relationships (Ashford et 

al. 1977).

The CPA application:

• modeled BLL following five years of exposure in a constant air environment.

• assumed a linear relationship between air lead and BLLs between 30 and 100 

μg/100 g blood.

• assumed that at air lead concentrations below 12.5 μg/m3 all particles are small 

and are absorbed with an efficiency of 37%; above 12.5 μg/m3 all particles are 

large and absorbed with an efficiency of 8% (this is referred to as “Assumption C” 

by Federal OSHA [OSHA1978]).

• used a standard deviation (SD) of 9.5 μg/dL estimated from observed 

distributions of BLLs in individual industries in the 1970s independent of the 

distribution of air lead levels.

OEHHA also used a pharmacokinetic model to predict air lead/blood lead relationships 

but our approach differs from Federal OSHA’s 1978 approach in several important 

ways. OEHHA:

• used the more recently developed Leggett+ model.

• modeled BLL at the end of a 40-year working lifetime at a constant air lead level.

OSHA’s 1978 approach is not sufficiently health protective because body burden

does not reach equilibrium at 5 years but rather continues to rise with constant 

exposure (Barry 1975).

• accounted for nonlinear kinetics between BLLs and air lead levels. Research 

conducted since the Federal OSHA’s 1978 standard shows that as BLLs rise 

above 25 μg/dL, the relationship between air concentration and BLL noticeably 

departs from linearity, likely due to red blood cell saturation (Azar 1975).

20
• developed an alternative approach for addressing particle size distribution based 

on lung dosimetry analysis and empirical data as suggested by Froines et al. 

(1995) (see Appendix B). Assumption C does not have an empirical basis and is 

not considered valid (Froines et al. 1995; Liu et al. 1996).

• applied a GSD of 1.6 with the assumption that BLLs are lognormally distributed

(Griffin 1999), whereas Ashford applied a SD which implies that BLLs are 

normally distributed.

OEHHA compared model predictions given the approach described above to 

measurements from seven worker datasets and three postmortem data sets from the 

general population. These comparisons show that the model predictions are valid within 

the range of BLLs of interest to CDPH-OLPPP. However, every model has limitations 

and remaining uncertainties. A brief discussion of some limitations and uncertainty 

surrounding the Leggett+ model follows.

3.2 Limitations and uncertainty 

3.2.1 Population BLL variability

In applying pharmacokinetic and other models to estimate changes in BLL in response 

to various lead exposure scenarios, we have employed distributions of BLL in the 

general population. The GSD we have chosen (1.6) was derived by Griffin et al. (1999) 

from several epidemiologic studies with paired environmental and BLL measurements in 

children (White et al. 1998). We cannot be certain that the BLLs in the worker 

population to which these estimates will be applied will be distributed in the same way 

as the BLLs in the children that were the basis for the GSD used in our modeling effort 

(Griffin 1999; U.S. EPA 2009). However, we checked this GSD against GSDs derived 

from data available on adults exposed to lead in controlled chamber studies and field 

studies of workers who wore personal breathing zone air monitors.

Using the method recommended by Griffin et al. (1999) for calculating GSD from sitespecific data when conducting a risk assessment, we calculated GSDs from BLL data 

on adult workers and volunteers from Kehoe (Gross 1979, 1981), Griffin et al. (1975), 

Williams et al. (1969), and Azar et al. (1975). BLLs from these studies ranged from 10 to 

21
93 μg/dL. We calculated GSDs by log transforming 8-hr TWA air and blood lead 

concentrations, grouping BLLs by levels of total lead intake, and deriving the GSD from 

the regression variance (mean squared error [MSE]). GSDs ranged from 1.4, 1.4, and 

1.97 for the low (24 to 30 μg/d), medium (33 to 66 μg/d), and high (>100 μg/d) total 

intake groups respectively. We concluded that the GSD we have chosen is in the range 

expected for worker populations.

3.2.2 Breathing rate

We assumed a time-weighted average daily breathing rate of 26 m3/day for workers.

This rate is based on a weighted combination of sedentary, light, and moderate activity 

(see Appendix B). However, some sources might consider this breathing rate low for 

workers in strenuous activity jobs (U.S. EPA 1997). Workers breathing more or less 

than this amount may have greater or lesser lead exposures, respectively, depending 

on breathing patterns, lung morphology, and other factors.

3.2.3 Erythrocyte saturation

Almost all of the lead in blood binds to the erythrocytes (Booker et al. 1969). There is 

experimental evidence that the relationship between the plasma lead and blood lead 

concentrations is nonlinear (Barton 1989; Chamberlain 1985; Manton and Malloy 1983; 

Manton and Cook 1984; Marcus 1985a, 1985b, 1985c). This nonlinear behavior is not 

completely understood but may result from a reduced rate of flow from plasma into red 

blood cells (RBCs) as certain lead-binding components of these cells become saturated 

(Chamberlain 1985; Leggett 1993; Marcus 1985a, 1985b, 1985c; O'Flaherty 1991; 

Raghavan et al. 1980). Leggett provided an optional function that OEHHA incorporated 

into the Leggett+ model (Equation A-1, Appendix A). Leggett suggested a threshold of 

saturation at 60 μg/dL RBCs, corresponding to about 25 μg/dL of whole blood, and a 

maximum limit of saturation between 140 μg/dL and 350 μg/dL of RBCs. OEHHA 

eliminated the threshold for saturation, as it has no biological basis, and set the 

maximum limit of saturation to 270 μg/dL in RBCs (corresponding to a whole blood 

concentration of about 115 μg/dL). After making this and other modifications, we tested 

the model with several datasets from the literature. The results of these tests appear in 

22
Appendix A of this report. However, the limit of saturation remains a source of 

uncertainty. There is also likely to be intra-individual as well as inter-individual variability 

in the saturation level (Fleming et al. 1998; Schwartz et al. 2000).

3.2.4 Lead’s toxic effects could alter the kinetics of lead in the body

Exposure to toxic levels of lead can damage organ systems, thereby altering the 

uptake, distribution, and clearance of lead. Kidney damage has been reported among 

chronically exposed workers at BLLs as low as 30 μg/dL (Kim et al. 1996). More severe 

acute exposure produces anemia, which may lower the threshold of RBC binding, and 

kidney disease, which may decrease the rate of whole body elimination (Castellino et al. 

1995; O’Flaherty et al. 1982). These factors could partly account for the wide variability 

in half-lives of BLL decline in studies of workers removed from high lead exposure.

However, we are not aware of any studies that have examined the extent of such 

influences directly. None of the models specifically account for these toxic effects, 

except to the extent that these pathological processes may have affected the kinetics of 

lead in the individuals upon which the models were calibrated.

3.2.5 Particle size distribution

As stated earlier, the proportion of inhaled lead retained in the lung and in the 

extrathoracic regions of the respiratory tract and then gradually absorbed or cleared by 

the upward propelling action of the ciliated regions of the lungs and ingested is 

influenced by particle size distribution. Coarse particles tend to be cleared by ciliary 

action and swallowed where they can be absorbed by the gut. The percent absorption 

tends to be significantly greater in the lung than in the digestive tract. Therefore, one 

might expect that exposure to the same mass of coarser particles would lead to lower 

BLLs than exposure to finer particles. However, OEHHA’s analysis of particle size 

distribution (explained in detail in Appendix B) led us to conclude that the overall 

transfer of mass to the blood is very similar.

One of the challenges in modeling air lead/blood lead relationships is what assumptions 

about particle size distribution to apply generically to the entire lead industry. There is 

some general knowledge on the types of industrial processes and construction tasks 

23
that tend to generate coarse and fine particles (Froines et al. 1986; Hinds 1982; 

Hodgkins et al. 1991a; Hodgkins et al. 1991b; Liu et al. 1996; Park and Paik 2002; Virji 

et al. 2009; Vork 2003). However, information on the distribution of particle size for 

individual workplaces is not currently collected as part of a routine industrial hygiene 

program. Even if particle size distribution were available for different industrial 

operations and construction tasks, multiple operations often occur in the same 

workplace, and exposures can be mixed. In addition, occupational regulations must 

apply to the range of industrial and construction operations involving lead exposure, and 

incorporating varying particle size distributions into the model is impractical.

As stated earlier, the existing Federal OSHA standard addressed this issue by 

assuming that at air lead concentrations up to 12.5 μg/m3 all particles are small and 

absorbed with 37% efficiency, and above this cut point all particles are large and 

absorbed with 8% efficiency. Federal OSHA applied a generic cutoff point because for 

some processes air concentration and particle size are correlated (OSHA 1978).

However, this assumption has been challenged (Froines et al. 1995; Liu et al. 1996). 

OEHHA developed an alternative approach based on lung dosimetry analysis and 

empirical data.

In summary, we evaluated published particle size distribution data from a variety of 

industrial workplaces with differing operations that generate a range of particle sizes 

(fine to coarse). Using the reported MMADs and the MPPD2, OEHHA estimated the 

deposition and clearance in the head and lung of inhaled lead for a variety of 

occupational settings.

OEHHA’s modeling of deposition and clearance is based on published particle size 

distribution data from a variety of real industrial workplaces. However, uncertainty 

remains because the actual particle size distribution for a given workplace on a given 

day may vary from the assumed particle size distribution. This may result in an over- or 

under-estimation of the amount of lead absorbed for a given air lead concentration, 

although our analysis indicates that particle size distribution (at least in the particle size 

range of 1 – 15 μm MMAD) plays a smaller role in the ultimate transfer of inhaled lead 

to blood than previously thought.

24
3.2.6 Gastrointestinal absorption

Early mass balance studies observed that the fraction of lead absorbed through the gut 

varies depending on the presence or absence of food (see section A.1 entitled “Lead 

biokinetics” in Appendix A). In addressing inhaled lead cleared from the lung by ciliary 

action and swallowed, OEHHA calculated a 24-hr TWA gut absorption assuming 10 

hours fasting, 10 hours with liquids between meals, two hours intake with solids, and 

two hours in which no lead is swallowed. If the actual proportion of time in each of these 

conditions differs from OEHHA’s assumption, then either a lesser or greater fraction of 

lead would be absorbed from the gut.

3.2.7 Lead in bone

OEHHA was able to check the adjusted core model with bone measurements in one 

smelter worker after a long history of exposure documented through measurements of

lead in blood over time. In addition, we compared the predicted ratio of lead deposited 

in trabecular/cortical bone to ratios from bone lead measured in nine retired smelter 

workers. Predicted ratios of bone lead were slightly larger than those observed in the 

nine retired workers (by a factor of about 1.8 or less depending on the length, timing 

and level of exposure history simulated).

To check predictions from the adjusted core model of lead in bone in active workers, it 

would have been ideal to have had access to measurements from studies that 

measured lead in trabecular and cortical bone over time from chronically exposed 

workers. We are aware of one such dataset (Fleming et al. 1997; Nie et al. 2005).

However, we do not currently have access to these data. Should more data on 

exposure history, blood and bone lead measurements become available, further 

refinement of the model may be possible.

4 Summary and Conclusions

CDPH-OLPPP requested that OEHHA model the time it would take for workers’ BLLs to 

decline to a lower level - recommended by CDPH-OLPPP - following cessation of 

occupational exposure that resulted in BLLs ranging from 20 – 60 μg/dL with exposure 

histories of 1 to 40 years. OEHHA selected the Leggett model because it has an 

25
optional algorithm that accounts for nonlinear kinetics as well as the flexibility we 

needed to conduct our own tests and, if needed, adjust model parameters to achieve 

optimal performance for our identified task. To achieve good performance from the 

model, OEHHA adjusted urine, bone, and blood parameters in the nonlinear Leggett 

model. These adjustments produced predictions comparable to data collected in the 

American Smelting and Refining Company (ASARCO) cohort and predictions of lead in

separate tissue groups comparable to measurements from other lead workers and the 

general population. These studies are discussed and compared with OEHHA’s 

modeling results in Appendix A. Studies that measured BLLs and PbA are discussed 

and compared with OEHHA’s modeling results in Appendix B.

Despite the limitations and uncertainties discussed above, OEHHA believes that the 

modeled BLLs and corresponding air lead concentrations represent reasonable and 

scientifically defensible estimates. Our model predictions are in good agreement with 

experimental and epidemiological data. Most of the data used to test the Leggett+ 

model came from healthy male workers. Given the limitations and uncertainties 

mentioned above, blood lead predictions generated from this model may not reflect 

those observed in workers with personal characteristics or exposure conditions 

substantially different from the study subjects used to evaluate the Leggett+ model.

In the appendices of this report, we provide more detail about our process for selecting, 

evaluating, and applying the Leggett+ model. We adjusted the core Leggett model, 

added an exposure module, and tested Leggett+ for use in predicting chronic 

environmental and occupational exposures in adults. As described above, the Leggett+ 

model performed well in predicting observed BLLs. It should be noted that no effort was 

made to evaluate model predictions of exposure in childhood or in acute or short-term 

(less than 30 days) exposure scenarios. In addition, this new model has not been 

evaluated for chronic exposures leading to BLLs over 60 μg/dL.

26
A Appendix: Review, Selection, Modification, and Testing of 

Lead Models

In Appendix A, we describe our selection criteria for models to include in our review 

process. We then explain our evaluation criteria, baseline model requirements, and 

rationale for selecting the Leggett model. Finally, we describe the process we used to 

calibrate the Leggett model to fit observed data and test the final adjusted model.

CDPH-OLPPP outlined two tasks for OEHHA, which we have described in the main 

body of this report. The second task - to estimate the time required for workers with a 

long history of sustained high BLLs to eliminate lead down to levels recommended by 

CDPH-OLPPP - requires a time-dependent biokinetic model. Therefore, we focused first 

on models that can best represent this uptake and elimination process. We began our 

evaluation by reviewing the lead biokinetics literature. We reviewed the important 

attributes of lead uptake, distribution, and clearance that PBPK and biokinetic models 

need to take into account. This review helped to develop a basis for choosing a 

biokinetic model that would be suitable for the second task identified in the main body of 

this report. A summary of our findings from that review follows.

A.1 Lead biokinetics

Lead enters the body principally through the lungs and the digestive tract where 

differing amounts from each pathway are absorbed into the bloodstream. Upon entering 

the bloodstream, it is rapidly transported into the liver, kidney, bone, spleen, lung, heart, 

and skeletal muscle. Elimination is primarily through the kidneys and feces, secondarily 

through sweat, hair, and nails.

A.1.1 Pulmonary deposition and clearance

Hursh et al. (1969) estimated that at nominal breathing rates, adult lungs transfer 35%

of inhaled lead mass suspended in ambient air to blood. However, the amount of lead 

deposited in the lungs and subsequently absorbed into the blood depends on several 

factors. The deposition rate and the region of deposition depend on the shape and the 

size distribution of particles, as well as individual breathing rate (volume of air inhaled 

27
per unit time). For example, Kehoe balance experiments published in Gross (1981), 

which included delivery of submicron (median count diameters from 0.05 to 0.11 μm) 

and micron (0.75 to 1.20 μm) particles, estimated pulmonary deposition after subjects 

engaged in a mix of sedentary and more strenuous work. Mean pulmonary deposition of 

submicron particles ranged from 20 to 71% and of micron particles ranged from 43 to 

64%.

Clearance rates depend on the region of deposition (i.e., within or outside of the ciliated 

region) and on particle solubility. The ciliated regions of the bronchial tree carry particles 

back to the pharynx where they are swallowed and potentially absorbed through the 

gastrointestinal tract. In the alveolar region, clearance occurs mostly by absorption 

directly into the blood from the pulmonary tissue (Booker et al. 1969; Castellino et al. 

1995; Dinman 1991; Monosson, 2011). However, some mechanical clearance also 

occurs in this region of the respiratory tract (ICRP 1994). For example, clearance by the 

macrophage system occurs for highly insoluble particles.

A.1.2 Gastrointestinal absorption

A large range of gastrointestinal absorption fractions (1% to 80%) has been reported in 

the literature. This wide range occurs in part because absorption of lead from the 

gastrointestinal tract depends strongly on a variety of factors, including the level of 

minerals, fat, protein, and vitamin D present in the intestines; the body’s iron or zinc 

status; the amount of lead and the physical and chemical form administered; and the 

length of fasting (Leggett 1993).

Gastrointestinal absorption of lead measured in studies on adult humans falls in the 

following categories:

• intake with solids, 3% to 20% (Chamberlain et al. 1978; Flanagan et al. 1982; 

Harrison et al. 1969; Heard and Chamberlain 1982; James et al. 1985; 

Rabinowitz et al. 1976; Rabinowitz et al. 1980)

• intake with liquid between meals, 8% to 30% (Blake 1976; Chamberlain et al. 

1978)

28
• ingestion with liquids after several hours of fasting, 30% to 70% (Chamberlain et 

al. 1978; Flanagan et al. 1982; Heard and Chamberlain 1982; Hursh and 

Suomela 1968; James et al. 1985; Rabinowitz et al. 1976; Rabinowitz et al. 

1980)

A.1.3 Erythrocyte uptake and saturation 

The ratio of lead found in red blood cells to lead in the plasma varies according to the 

dose and the time elapsed from absorption. However, 94-99% of blood lead is bound to 

the erythrocytes (Booker et al. 1969). Previous studies indicate that the relationship 

between the plasma lead and blood lead concentrations is nonlinear (Barton 1989; 

Chamberlain 1985; Manton and Malloy 1983; Manton and Cook 1984; Marcus 1985a, 

1985b, 1985c). This nonlinear behavior of lead is not completely understood but may 

result from a reduced rate of flow from plasma into RBCs as certain lead-binding 

components of these cells become saturated (Chamberlain 1985; Leggett 1993; Marcus 

1985a, 1985b, 1985c; O'Flaherty 1991; Raghavan et al. 1980).

A.1.4 Uptake and elimination from blood and soft tissue

A pulse dose of lead is distributed from the blood to other tissues with a half-life of about 

30 days. Once released from blood, the highest levels of lead in soft tissue are found in 

the kidney and liver. Less lead mass is found in the lungs, spleen, heart, skeletal 

muscles, and brain. Although there is uncertainty about uptake and release time 

constants for other tissues, most are much slower than for blood. Retention curves for 

the liver and kidney exhibit multiple half-lives ranging from a few days to one year; halflives range from two years in the brain to 3.5 months to 5 years in other soft tissue 

(Leggett 1993). This information was mainly obtained from animal studies, contributing 

to the uncertainty.

A.1.5 Uptake and elimination from bone

Constant exposure to lead leads to slow accumulation in the body because of the high 

affinity for bone and incorporation into the bone matrix. The same high affinity is true of 

hair and nails, which are considered elimination pathways. Lead substitutes for calcium,

becoming part of the hydroxyapatite crystal during bone remodeling. Lead can also be 

29
incorporated by diffusion into bone matrix without becoming part of the hydroxyapatite 

crystal. Bone thus acts as a “sink” for lead. Bone lead therefore has a long half-life of 

elimination when exposure ceases. Autopsy studies revealed that among occupationally 

exposed and non-occupationally-exposed young men, 95% of the lead body burden 

was in bone, with the other 5% in soft tissues (Barry 1975; Castellino et al. 1995).

Based on data from Christoffersson et al. (1986) and additional measurements of lead 

in vertebral bone biopsies, blood, and urine of present or former lead workers, Skerfving 

et al. (1987) concluded the following for trabecular and compact bone:

• The skeletal lead pool is not kinetically homogeneous; turnover is faster in the 

mainly trabecular vertebrae than in the mainly compact finger bone.

• The “average overall half-life is probably 5-10 years.”

For occupational exposure scenarios, the predicted decline in total bone lead 

corresponds to a half-life of 10-12 years during the first few years after exposure. By 25 

years after the end of exposure, the rate of decline slows to a half-life of 25-30 years 

(Leggett 1993).

An ideal model would incorporate each of the important factors reviewed above.

A.2 Model screening

Prior to selecting models for review, OEHHA reviewed publications that examined and 

compared earlier models, particularly those supporting the Federal OSHA standards for 

lead and issues surrounding the BLL/PbA relationship (Castellino et al. 1995; Hattis 

1981; Liu et al. 1995). Based on a review of the biokinetics of lead in the adult human 

studies that measured air and blood lead concentrations in adult subjects, OEHHA 

considered five of the most recent human PBPK and biokinetic models published in the 

peer-reviewed literature and available for use in an accessible format. Accessible 

formats include descriptions from the literature that can be turned into functional 

modeling code, usable scripts that can be read by modeling software, or executable 

graphical user interface (GUI) programs. 

30
Of the five models considered, the U.S. EPA All-Ages Lead Model had not been 

released in final form at the time of our review and therefore was not evaluated further.

Only summaries of various model runs and not the code for the model itself are 

available. The U.S. EPA Adult Lead Model is a steady-state model and therefore not 

able to accommodate the time-dependent requirements of tasks outlined in the Scope 

of Work. Liu et al. (1995) evaluated some of the hybrid models described in Hattis 

(1981) and those developed by Batschelet et al. (1979), Bernard (1977), Bert et al. 

(1989), Marcus (1985a, 1985b, 1985c), and Rabinowitz et al. (1976). These 

investigators concluded that the Hybrid P and Bert models performed similarly and 

better than the other models. We chose to evaluate the Bert model instead of the Hybrid 

P model and forego evaluating the models already evaluated by Liu et al. (1995). 

The remaining three models accommodate time-dependent changes in the lead body 

burden and were reviewed for meeting the general requirements of the above tasks:

• The Bert et al. (1989) model is available as a MATLAB script. 

• The Leggett model (Leggett 1993; Pounds and Leggett 1998) is available as a 

FORTRAN script.

• The O'Flaherty (1993, 1995) model is published in Advanced Continuous 

Simulation Language (ACSL) code (O'Flaherty 2000) and available as a GUI

program.

Our review focused on:

• important attributes of lead uptake, distribution, and clearance for evaluating 

PBPK and biokinetic models.

• model structure, key parameters, and behavior of Leggett, O’Flaherty, and Bert 

models relative to chronic exposure to lead among workers.

• lead elimination from blood, soft tissue, and bone.

• flexibility to adjust internal parameter values to fit additional occupational 

exposure data if needed.

31
Multi-compartmental models, such as the Bert, Leggett, and O’Flaherty models, can be 

used to predict BLL over time. Compartment lead concentrations are determined from 

the lead masses and compartment volumes. The models simulate lead biokinetics as 

several interconnected tissue compartments that exchange lead between tissues and 

the central compartment using one of two approaches: The O’Flaherty model simulates 

exchanges between plasma and soft tissues as flow-limited processes and exchanges 

between plasma and bone as a combination of flow-limited and diffusion-limited 

processes. The Bert and Leggett models are diffusion-limited, i.e., they assume that 

exchanges between the various compartments are governed by rates of diffusion 

across compartment boundaries, represented as first-order rate constants. 

In the next section, we describe each model in its original form and briefly summarize 

any subsequent modifications.

A.2.1 Bert model

Bert and co-workers developed a biokinetic model to predict BLLs in the general 

population (Bert et al. 1989). They built their model based in part on previous work of 

Batschelet et al. (1979), Bernard (1977), Marcus (1985a, 1985b, 1985c), and 

Rabinowitz et al. (1976). They calibrated their model using long-term changes in levels 

of lead in cortical bone in autopsy studies by Barry (1975). This adjustment allowed

them to correct some of the shortcomings of prior models developed by Rabinowitz et 

al. (1976) and Bernard (1977). 

A.2.1.1 Original Bert model

The Bert et al. (1989) model is comprised of six well-mixed compartments - two uptake 

compartments (lungs and digestive tract) and four transfer compartments (blood, tissue, 

trabecular bone, and cortical bone). The transfer of lead is represented as a set of four 

first-order differential mass balance equations with substantially different rates of 

transfer of lead for each of the four compartments. Bert et al. (1989) assumed that blood 

volume is proportionate to body weight and scaled blood volume to include soft tissue 

and bone volumes. There are four elimination pathways – exhaled air, feces, urine, and 

32
other excreta (hair, nails, and sweat). Tissue volumes are linked to blood volume, which

is a function of body weight.

Short-term changes in body burden in the model were calibrated to the data collected 

by Rabinowitz et al. (1976) on bone lead levels from cross-sectional autopsy data, and 

one subject from the experimental data collected by Griffin et al. (1975). The calibrated 

model was tested against experimental exposure data from three other study subjects in 

the Griffin et al. (1975) study. Simulations from the Bert model appear in the Meridian 

report cited in the preamble to the Federal Lead in Construction standard (Meridian 

Research Inc. 1992).

The Bert et al. (1989) model was designed to predict BLLs in the range found in the 

general population, BLLs significantly below those found in occupationally exposed 

cohorts.

A.2.1.2 Bert model modifications

Vork (2003) added an exposure module and a means for estimating the initial values of 

lead in each compartment as a function of pre-exposure BLL. Vork tested Bert's model 

on blood lead measurements taken during short-term exposures (i.e., 1 to 3 months) on 

a bridge rehabilitation project (Sussell et al. 1992). The investigator found that the 

model predicted measurements reasonably well (Vork 2003).

A.2.2 Leggett model

The Leggett (1993) model accounted for more of the known biokinetic factors than the 

model by Bert et al. (1989). The original linear form of the Leggett model describes the 

age- and time-dependent distribution and excretion of lead injected directly into blood.

Leggett also included separate models for the nonlinear kinetics of lead in blood when 

red blood cells begin to saturate, for particle deposition and clearance, and for 

gastrointestinal deposition and clearance. The conceptual basis of this model relies on 

earlier studies of alkaline earth metabolism in adult man (Leggett 1993).

33
A.2.2.1 Original Leggett model

The Leggett model includes 13 well-mixed compartments. There are three soft tissue 

compartments (rapid turnover, intermediate turnover, and tenacious retention). There 

are two liver compartments, three kidney compartments, and a brain compartment. 

There are four bone compartments (cortical surface and volume, and trabecular surface 

and volume). Leggett also provides separate models for the respiratory tract and the 

gastrointestinal tract (Leggett 1993).

Although six sets of parameters are included as a means of characterizing the agedependent behavior of lead in the growing body in the published Leggett (1993) model, 

we focused our review on the adult portion of the model because we were only 

interested in modeling adult worker exposure. Age dependence refers to inclusion of 

children with their pharmacokinetic differences from adults into the model. Our review 

was, therefore, limited to the parameter set that represents the adult body (i.e., ≥ 25 

years of age).

The Leggett (1993) model was originally calibrated based on lead balance studies in 

healthy adults receiving lead tracers by injection, ingestion, or inhalation; postmortem 

measurements in environmentally exposed men, women, and children; and biopsy and 

autopsy measurements on occupationally exposed subjects. These data were 

supplemented with experimental, occupational, environmental, and medical data on the 

biokinetics of elements with physicochemical properties similar to those of lead and with 

findings from lead studies in laboratory animals. 

Compartment volumes in the model are assumed to be a function of body weight. 

Tissue uptake and loss are defined in terms of transfer half-lives. A pulse dose of lead is 

assumed to enter the bloodstream and distribute rapidly to tissues and RBCs. Once a 

peak RBC content is attained, the RBC content and hence the total blood content 

begins to decline with a half-life on the order of 30 days. Exchanges between a 

diffusible plasma compartment and non-skeletal tissues are modeled as first-order. 

The gastrointestinal tract is modeled as four anatomically-based segments, with 

absorption occurring only from the small intestine. Liver and kidney each consist of two 

34
sub-compartments. Half-lives range from a few days in some parts of the liver and 

kidney to one year in other parts of these organs, two years in the brain, and 3.5 months 

to 5 years in other soft tissues (Leggett 1993).

Lead in blood distributes to a diffusible fraction, a plasma-bound fraction that is not 

diffusible, and the red cells. The relationship between plasma and red cells is nonlinear. 

This non-linearity becomes noticeable above a concentration of about 25 μg/dL of lead 

in whole blood or 60 μg/dL in RBCs. This change in kinetics may result from a reduced 

rate of flow from plasma into RBCs as certain lead-binding components of these cells 

become saturated. Leggett provided a separate function for the model to account for the 

effects of RBC saturation illustrated by Pounds and Leggett (1998) in Figure 6 of their 

publication. Equation A-1 represents this nonlinear function for the fraction of lead 

deposited in RBCs.

Eq. (A-1): RBC deposition fraction = baseline deposition fraction

 x [1.0 – (lead concentration in RBCs – threshold concentration) / 

 (saturation concentration – threshold concentration)]1.5

Leggett (1993) suggests 60 μg/dL for the RBC threshold (about 25 μg/dL whole blood) 

and a range from 140 μg/dL to 350 μg/dL for the limit of RBC saturation (62 μg/dL to 

154 μg/dL whole blood). 

There are five elimination pathways considered in the model – urine, feces, sweat, 

exhaled air, and other excreta (losses in hair, nails, and skin). Two major elimination 

pathways for absorbed lead are through urine and feces, with the other elimination 

pathways playing a minor role in overall excretion. 

For the long-term retention of skeletal deposits of lead, the Leggett (1993) model is 

based on rates of bone remodeling. The rate of elimination from the adult skeleton was 

compared in part to data from occupational studies. During the first few years after the 

end of modeled exposures, the predicted decline in total bone lead levels corresponds 

to a half-life of 10-12 years. The duration of half-life increases to 25 years by 25-30 

years post-exposure.

35
A.2.2.2 Leggett model modifications

Brito et al. (2005) derived bone transfer parameters from measurements in a cohort of 

over 300 active smelter workers in New Brunswick, Canada. As part of this investigation 

team, Nie et al. (2005) compared predictions of bone levels from the linear Leggett 

model (i.e., the Leggett (1993) model without the RBC saturation function) to bone 

levels of lead measured from the entire cohort of retired smelter workers. BLLs were 

monitored frequently following retirement of these workers during a period from 1966 to 

1999 (Fleming et al. 1999). Average BLLs among these workers declined from about 70 

μg/dL in the 1960s to 25 μg/dL in 1996 (Fleming et al. 1997).

In 1994 and 1999, measurements of lead were also taken from heel (trabecular) and 

tibia (cortical) bones on a subset of these workers. Nie et al. (2005) condensed the 

original Leggett model (without a RBC saturation function) into a three-compartment 

model (cortical bone, trabecular bone, and blood), used blood lead measurements to 

model exposure histories, and compared the trabecular and cortical bone compartment 

predictions to X-Ray Fluorescence (XRF) measurements of lead in bone mineral from 

the cohort of smelter workers. These investigators concluded that bone predictions 

using the linear version of the Leggett model consistently under-predicted levels of lead 

in cortical bone and substantially under-predicted lead in trabecular bone in these 

workers.

Nie et al. (2005) showed that lowering the transfer coefficients for both cortical and 

trabecular bone, thereby increasing the half-life in bone, improved the fit to 

measurements taken from the tibia (cortical) and calcaneus (trabecular) bone by XRF 

methods. After modifying bone transfer rates in the simplified Leggett model, Nie et al. 

(2005) checked the performance of the altered Leggett model by fitting the blood lead 

histories from each worker to blood lead predictions from the model and evaluated the 

bone lead predictions as a function of each worker’s blood lead history. Nie et al. (2005)

concluded that elimination rates were age-dependent and published five age-specific 

models, which predicted bone levels much closer to those predicted by the original 

(linear) Leggett model. In addition, the authors observed that older workers had a 

history of higher exposures and that change in transfer rates from bone to blood with 

36
age in Nie et al.’s alterations of the original Leggett model is also affected by exposure 

history (Nie et al. 2005).

Nie et al. (2005) highlighted their comparison of the predictions of BLL decline among 

retired workers using the linear model structure and parameters for adults published in 

Leggett (1993) without the RBC saturation component and the Nie et al. (2005)

simplification of Leggett’s model. Nie et al. (2005) did not do a comparison of the 

predictions of the Leggett (1993) model including the saturated RBC function to the 

retired worker data. This added nonlinear function would have produced a much higher 

level of lead in the skeleton during years when BLLs were much higher relative to the 

levels at the time each worker’s retirement. Hence, the nonlinear model would have 

predicted levels of lead in bone much closer to those observed in the retired New 

Brunswick smelter workers.

A.2.3 O’Flaherty model

O'Flaherty (1991) published her model in 1991 and then continued to update it over a 

period of ten years, adding in differences with age in bone turnover, red blood cell 

saturation, and gender differences in model parameters during adulthood (O'Flaherty 

1991, 1993, 1995; O'Flaherty et al. 1996; O'Flaherty et al. 1998; O'Flaherty 2000). Bone 

turnover in adults and differences in bone turnover between males and females were 

acknowledged but not addressed directly by Leggett in his model (Leggett 1993).

A.2.3.1 Original O’Flaherty model

The O’Flaherty PBPK model was originally structured as a rat bone and blood model 

with compartments for liver, kidney, other well perfused tissues, and poorly perfused 

tissues (O'Flaherty 1991). The human model was parameterized based on human data 

whenever possible, but in a few instances, data from experimental animals were used.

The human model is both age- and gender-dependent, and addresses multiple 

pathways of exposure and the biokinetics of lead in children as well as adults

(O'Flaherty 1991, 1993, 1995, 1996; O'Flaherty et al. 1998; O'Flaherty 2000). 

The two uptake compartments are lungs and digestive tract. Elimination is via the liver 

(30%) and kidneys (70%). Cardiac output, clearances, and organ and tissue volumes 

37
are expressed as functions of body weight and age. There are five different point 

estimates of body weight for different age ranges; two for the early childhood phase of 

growth and three for the subsequent adolescent growth spurt and stabilization at mature 

adult weight. Other anatomic and physiologic features of the model are tied to body 

weight.

In this model, the lead mass fluxes in soft tissues are limited by the rates of delivery of 

lead to the tissues, i.e., the product of the plasma lead concentration and the rate of 

plasma flow to the tissue. This model assumes lead partitions and equilibrates 

instantaneously between plasma and soft tissues. Tissue volumes, blood flow rates, 

and bone turnover rates are linked to body weight by expressions that reproduce 

physiologic measurements. The model treats glomerular filtration rate as a function of 

body weight and age, becoming nonlinear at one year of age.

Lead is incorporated into forming bone and returned to plasma as bone is resorbed. 

O’Flaherty used calcium isotope uptake studies to estimate rates of new bone accretion

(bone remodeling) in the model. The loss of lead from blood is modeled as a triphasic 

exponential function with 42% eliminated with a half-life of 57 days, 16% eliminated with 

a half-life of 1.3 years, and the remainder lost with a half-life of 24.4 years. The latter 

slopes are consistent with the input to the blood from soft tissues and the skeleton, 

respectively.

A.2.3.2 O’Flaherty model modifications

O’Flaherty published updates of the human model largely based on new information 

from studies of lead in cynomolgus monkeys and trend curves for skeletal mass in men 

and women (O’Flaherty et al. 1996; O'Flaherty et al. 1998; O’Flaherty 2000). These 

publications characterized the relationship of plasma lead concentration to blood lead 

concentration and further adjusted bone parameters by increasing the diffusion and 

permeability constants by a factor of five and accounting for bone loss in adulthood.

Further suggested alterations based on worker data also appeared around the same 

time (Fleming et al. 1999). Briefly, Fleming et al. selected a subset of workers from the 

same lead smelter population as Nie et al. (2005) described above for an initial 

38
evaluation of the 1993 version of the model. Detailed blood lead records from two hiring 

groups comprised of 10 workers with long exposure histories and 10 workers with more 

recent exposure histories were used to derive oral and inhalation exposures as model 

input. In addition, bone and blood lead measurements of lead were taken after a 10-

month strike and again three years later. These investigators simulated bone lead levels 

relative to each worker’s cumulative blood lead index and concluded that model 

predictions did not distinguish between hiring groups and the model over-estimated 

amounts of lead in cortical bone relative to the worker data. In addition, a sensitivity 

analysis indicated that a reduction in red cell lead-binding coefficients and/or a reduction 

in the parameter representing rates of bone mineral formation would bring model 

predictions closer to measurements of bone in the smelter worker cohort.

Reducing the red cell coefficients in the O’Flaherty model had the effect of making the 

plasma lead/whole blood lead ratio more extreme at high (above 60 μg/dL) blood lead 

concentrations – leading to proportionately higher estimated uptake of lead to bone. In 

addition, lowering bone mineral formation rates in the model would reduce the transfer 

of lead to trabecular and cortical bone, respectively. 

The investigators then evaluated the revised version of the O’Flaherty model for the 

smelter population as a whole. The revised model explained trends for the accumulation 

of lead in cortical bone and the release of lead from bone stores. However, the authors 

concluded that model predictions for the accumulation of lead in trabecular bone did not 

track observed levels in the calcaneus (mostly trabecular) (Fleming et al. 1999).

The modeled lead concentrations for trabecular bone were 2- to 5-fold less than those 

observed in the mostly trabecular calcaneus bone and similar to the under-predictions 

observed by Nie et al. (2005) of the Leggett (1993) model without the RBC saturation 

function. In addition, Fleming et al. mentioned that human lead concentrations 

measured for several trabecular sites have suggested higher uptake and/or lower 

turnover of lead than demonstrated by the model.

Independently, O'Flaherty (2000) altered a 1998 version of the human model by adding 

bone loss coefficients as a function of age and gender in older adults. O’Flaherty 

39
adjusted her model to account for bone changes in adulthood because peak bone mass 

is known to be reached between ages 25 and 30 and then begins to slowly decline. To 

account for the release of lead from this slow loss of bone after age 30, a first-order loss 

of bone was incorporated into the existing model. O’Flaherty calibrated bone loss 

predictions to quantitative estimates of cortical and trabecular bone mass as functions 

of age from International Commission for Radiological Protection (ICRP) trend curves 

for skeletal mass in men and women to age 60. This calibration introduced cortical bone 

losses of 3% per decade and trabecular bone losses of 7-11% per decade after age 30 

into O’Flaherty’s latest revision (O'Flaherty 2000).

We obtained a GUI version of O’Flaherty’s model, which allows the user to alter 

exposure levels, hematocrit, and body weight, and to view 21 fixed internal parameter 

settings. We were not able to check that the computer code describes the intended 

structure of the O’Flaherty model or adjust internal parameters. However, we were able 

to see that internal parameter settings are consistent with those published in O'Flaherty 

(2000).

This model handles some physiologic functions differently for males and females even 

after accounting for body weight. The model predicts a 49% decline in BLL in women 

and a 51% decline in men in the first year following removal from 50 μg/m3 lead 

exposure and predicts lower BLLs for women than for men for the same exposure 

scenario. In addition, pregnancy and lactation, both of which would influence lead 

pharmacokinetics, are not modeled explicitly. A single linear function describes 

respiration rates as a function of body weight in both sexes up to one year of age. After 

one year, the linear slopes for males and females diverge. This is consistent with the

measured results of Popovic et al. (2005).

A.3 Model selection

In deciding which of the models reviewed above was most appropriate for the tasks 

outlined in the report, OEHHA considered whether the model in question:

• incorporates nonlinear changes in how the body distributes and eliminates lead 

during environmental as well as occupational exposures.

40
• can be altered if needed to improve predictions for a broad range of exposure 

conditions inherent in lead-related work.

Bert et al. (1989) did not incorporate the influence of RBC saturation into his model, 

stating that at low levels of exposure, such as those found in the general population, 

nonlinearities involved with lead transfer or distribution are not expected to be a 

concern. However, for the purposes of modeling exposure scenarios for Tasks 1 and 2, 

significant nonlinearities would be expected, for example, in the distribution of lead due 

to RBC saturation (Leggett 1993). As BLLs rise above 25 μg/dL, the relationship 

between air concentration (exposure level) and BLL noticeably departs from linearity.

Ignoring this nonlinearity dramatically underestimates levels of lead that accumulate in 

bone. Therefore, we determined that the Bert model was not suitable because it ignored 

changes that occur when BLLs exceed about 25 μg/dL and RBC saturation begins to be 

a concern (Bert et al. 1989). OEHHA did not attempt to create a hybrid of Bert’s model 

and Leggett’s algorithm for RBC saturation since the biokinetics in the Bert model were 

less detailed than in the Leggett model. Consequently, OEHHA did not consider the 

Bert model any further for the purposes outlined in this report.

O’Flaherty modeled the growing body, initially scaling her model developed for rats to 

humans, and then revising it with data from monkeys and humans. Thus, her model was 

not designed with adult workplace exposure in mind initially. Some of the animal kinetics 

data may differ from the kinetics of lead in human bones, for example, in workers

subjected to long exposure periods in adulthood (O'Flaherty et al. 1998). As new data 

became available, modifications to the O’Flaherty model have been introduced in the 

literature to improve predictions in occupationally exposed workers.

The O’Flaherty model available to OEHHA is in the form of a GUI with only a limited 

ability to alter some parameter values. To use the O’Flaherty model for the purposes 

outlined in this report would have required that we translate the published ACSL model 

format to a newer form of ACSL or to a form executable in MATLAB. The ACSL 

published model is very lengthy and translating the model to a usable format was 

beyond the scope of this project. Because the GUI model did not meet our criterion for 

41
flexibility to adjust internal parameter values to fit additional occupational exposure data, 

we did not consider it further.

Given the evidence presented by the work of Brito et al. (2001), Fleming et al. (1997, 

1999), and Nie et al. (2005), it is apparent that both the original O’Flaherty model and 

the original linear form of the Leggett model under-predict lead levels in trabecular bone 

with the high exposures seen in workers. However, Leggett provided an optional 

algorithm that could be added to the linear model code easily which allows the user to 

correct for this under-prediction. In addition, the Leggett model gave us a great deal of 

flexibility to adjust internal parameter values to fit occupational data by publishing a 

much simpler mass-transfer structure than the volume and flow structure published by 

O’Flaherty. Therefore, we chose to work with the nonlinear Leggett model from this 

point forward for the purposes outlined in this report.

A.4 Further evaluation and adjustment of the nonlinear Leggett model

Leggett calibrated his model for lower-level environmental exposures that are quite 

different from the higher-level, chronic occupational exposures that CDPH-OLPPP

asked OEHHA to model. Therefore, it was essential to check the performance of the 

model with data providing lead levels in bone or blood after chronic exposures relevant 

to lead workers. We were able to obtain additional data of this nature which allowed us 

to compare BLL and bone predictions from the Leggett model with observations from 

studies of workers with chronic exposure.

A.4.1 Coding integrity

Before we could evaluate the nonlinear Leggett model, OEHHA translated Leggett’s 

description of his model from Leggett (1993) into code so that we could run it using 

MATLAB. This made it possible to add the nonlinear algorithm to Leggett’s original 

model. This re-coding effort was validated by comparing predictions from MATLAB to 

predictions from the FORTRAN-coded form of Leggett’s model with and without the 

algorithm depicting RBC saturation (personal communications with R. Leggett 2011). 

Dr. Leggett provided the FORTRAN output for model runs with the following exposure 

inputs:

42
1. injection: acute input of 1 μg lead to blood 

2. ingestion: acute input of 1 μg lead to stomach contents 

3. ingestion: chronic input of 1 μg/day of lead to stomach contents for 20,000 days 

Dr. Leggett provided time series model output with calculated organ or compartment 

lead contents as a function of time for three intake scenarios for a male worker, 

assuming there is no lead in any compartment before the acute intake or start of chronic 

intake. Gastrointestinal (GI) uptake is assumed to be 15% for both the initial lead input 

to stomach and lead endogenously secreted into the small intestine (SI). The following 

GI transfer coefficients were applied: 

• 24/day from stomach to SI, 

• 6/day from SI to upper lower intestine (ULI), 

• 1.8/day from ULI to lower intestine (LLI), and 

• 1/day from LLI to feces.

The transfer coefficient from urinary bladder to urine is 12/day.

Linear RBC kinetics is assumed in all three cases.

Leggett also provided model output for the nonlinear case where uptake of 100 μg/day 

of lead to blood occurs continuously for 10 years (3650 days). To simplify comparisons 

Leggett assumed that there is no lead in the body at the start of intake.

Dr. Leggett provided the output values for blood lead concentration (μg/dL) over time 

with the nonlinear case as well as the contents (μg) of 

• extra vascular fluid (EVF), 

• liver, 

• kidneys, 

• brain, and 

• bone.

43
OEHHA assessed concordance between Leggett’s output and that generated by 

MATLAB using analysis of variance at distant time points. The difference between 

values at distant time points was about four percent or less, which is considered a good 

fit (data not shown). This finding increased our confidence that we recoded the model 

correctly.

A.4.2 Assessing model performance

Having recoded the model, our next aims were to 1) determine how well the nonlinear 

Leggett model predicts the dynamics of worker exposure, removal from exposure, and 

the time required to eliminate lead in a worker’s blood following exposures in the range 

of interest to CDPH-OLPPP, and 2) refine the model, if necessary, to improve its 

predictive ability for chronically exposed workers.

OEHHA’s assessment of model performance proceeded in two phases. In phase one,

we checked how well the nonlinear Leggett model predicted BLLs in a cohort of 

chronically exposed lead workers after a lengthy strike. In phase two we made changes 

to selected model parameters to improve the fit of the model to observed worker data 

and tested the performance of the adjusted model.

A.4.2.1 Phase one assessment: Preliminary assessment of model performance

Methods for phase one

In order to test overall model performance we compared the BLLs measured in a large 

cohort of workers at the end of a long strike with: 1) BLLs predicted by the nonlinear 

Leggett model using Leggett’s suggested parameter values, and 2) BLLs predicted by 

the model after adjusting the RBC saturation value.

If the model were an accurate description of lead pharmacokinetics in chronically 

exposed workers, then there should be no systematic under- or over-prediction of BLLs 

and the average difference between BLLs observed in workers and those predicted by 

the model should be small. In addition, there should be no systematic relationship 

between model performance and job tenure, indicating that the bone and long-lived 

tissue compartments of the model are performing reasonably well.

44
The details of our assessment are presented below.

Data for phase one

Data from the open literature are available to assess the dynamics of BLLs after several 

years of workplace exposure followed by a decline of blood lead after exposure in 

workers’ assigned work area ceases (Fleming et al. 1997; Nie et al. 2005, Hattis 1981; 

Lynam and Nelson 1981; O'Flaherty 1986; Schutz et al. 1987). Some studies of workers 

removed from workplace exposure (e.g., under a medical removal protection program), 

however, are confounded by ongoing workplace exposure because the workers 

continued to work somewhere else onsite where some lead exposure might still occur 

(O'Flaherty 1986). Therefore, OEHHA did not consider these studies suitable. 

Nevertheless, we were able to locate five studies that described removal from leadrelated work either due to a strike or retirement (Fleming et al. 1997; Hattis 1981; 

Lynam and Nelson 1981; Schutz et al. 1987; Nie et al 2005). Of these, two studies 

provided time-dependent assessments (modeled results) of BLLs at the individual level 

(Hattis 1981; Schutz et al. 1987), although Schutz et al. (1987) did not report 

measurements for individual study subjects. The most robust dataset was presented in 

Hattis (1981). Therefore we selected Hattis (1981) for assessing model performance.

Both Hattis (1981) and Lynam and Nelson (1981) conducted studies using data 

collected by the ASARCO smelter. These data included BLLs before and immediately 

after a nine-month strike at the ASARCO primary lead smelter in Glover, Missouri. The 

data include pre-employment BLLs for most workers and an estimate of pre-strike BLLs 

from complete histories of BLLs leading up to the strike in 1976. It also includes BLLs 

taken before workers were re-exposed. In their study, workers were included if they had 

at least two sets of pre-strike blood lead measurements taken within six months prior to 

the strike, while working, and a measurement taken within two days upon returning to 

work after the strike. The mean length of employment was six years.

Lynam and Nelson (1981) estimated that the mean post-strike BLL of 35 μg/dL was 

63% of the mean pre-strike BLL of 56 μg/dL, from which a half-life of 403 days can be 

calculated. This group half-life estimate was used by the authors to extrapolate 

45
individual BLLs at the end of the strike, using the individual data on BLL prior to the 

strike, in the absence of half-life data on each individual.

Hattis (1981) was able to provide more data from this cohort of workers in a report 

written for Federal OSHA. The additional data set included individual data on preexposure, pre-strike and post-strike BLLs, and years on the job for workers removed 

from exposure due to a nine-month strike. This data set provides BLLs after a 

sufficiently long post-strike interval to test how accurately each simulation predicts 

elimination from the skeleton as well as soft tissue. Since there is no reason to suspect 

that striking workers might have systematically different blood lead dynamics than 

workers as a whole, this data set is assumed to be representative of smelter workers 

generally. The information available for 66 workers included in the analysis appears in 

Table 3.1 of the Hattis (1981) report.

Phase one simulation

OEHHA followed Hattis’ methodology to model the expected post-strike BLLs for these 

workers. The pre-employment BLLs range from 10 to 85 μg/dL. Hattis excluded three

workers with pre-employment BLL of 60 μg/dL or higher. This is so high that he believed 

the individuals came from a population of workers with previous occupational exposure

(Hattis 1981).

We excluded from the study another three subjects with post-strike BLLs lower than 

pre-employment BLLs, indicating previous occupational exposure to lead, and four 

subjects with post-strike BLLs higher than pre-strike BLLs, suggesting some ongoing 

occupational exposure during the strike. In these workers, previous and ongoing 

occupational exposure could have resulted in higher bone lead. Consequently, 

continued releases of lead from the bone could have confounded the relationship 

between the exposures experienced in the current job and their corresponding BLL. No 

information was available on the previous length of employment.

The attributes of the additional seven subjects we excluded did not significantly alter the 

average and standard errors for the attributes of the 66 subjects presented in Hattis 

(1981).

46
We set up our test of the model by back-calculating background and pre-strike total 

(background plus occupational) intakes for each worker from their unique preemployment and pre-strike BLLs as estimated in Hattis (1981). Using those intakes, we 

simulated pre-employment and employment BLLs up to the time of the strike for each 

worker. We then applied the background intake only during the simulation of BLLs 

during the nine-month strike. Finally, we recorded the predicted BLL for each worker 

and compared it to each worker’s measured blood lead at the end of the nine-month 

strike.

Results of phase one assessment

For each of the 59 subjects we derived a predicted BLL and compared it to their 

measured post-strike BLL. When we ran the simulation with Leggett’s suggested 

parameter values (Leggett 1993), the average difference between the measured and 

predicted post-strike BLL was unacceptably large and indicated significant underprediction of BLLs (data not presented).

In an attempt to improve the fit, we lowered the RBC saturation value from 350 μg/dL to 

222 μg/dL (geometric mean of the upper and lower values given in Leggett [1993]) and 

reran the simulation. The average difference between measured and predicted poststrike BLL was 4.1 μg/dL, with a standard error of 0.97 μg/dL (individual data not 

presented). This indicated that the model was predicting BLLs 10 months after the end 

of workplace exposure that were 4 μg/dL lower on average than BLLs observed in the 

ASARCO cohort.

The adjustment to the RBC saturation parameter improved the fit but was still 

inadequate. This systematic under-prediction of BLLs, even with a lower RBC saturation 

parameter, suggested to us that the modeled bone lead levels were too low for 

chronically exposed workers (The full significance of this under-prediction of bone-lead 

levels will be shown later in phase two). Recommendations based on this model could 

result in: a) an air lead limit (or PEL) insufficiently low to maintain BLLs below a level 

CDPH-OLPPP determines adequately protective of worker health and, b) an 

underestimate of the removal time necessary for a worker’s BLL to fall to a lower, 

47
acceptable level. OEHHA felt that a model that more closely tracked the BLLs observed 

in available data was required.

A.4.2.2 Phase two assessments: Model calibration to observed data

In an effort to improve the predictive ability of various pharmacokinetic models, 

researchers have modified model parameters and then tested the fit of the adjusted 

model against observations. Hattis (1981) and Nie et al. (2005) adjusted bone 

parameters in the Hybrid and Leggett models, respectively, to fit the exposure 

experience of two different cohorts of smelter workers. However, neither of these 

models included the effect of RBC saturation at higher exposure levels that has been 

noted in the literature. In a formal sensitivity analysis of the O’Flaherty model, Fleming 

et al. (1999) found that adjusting bone and RBC binding parameters was vital to 

achieving a better fit between model output and observations. However, they did not 

achieve a good fit to bone measurements, nor did they compare blood lead predictions 

to observations of blood lead after modeling each worker’s unique exposure period.

O’Flaherty (2000) found that adjusting the plasma lead clearance parameter reduced 

blood lead over-predictions observed in her model at low-level exposures, but she did 

not attempt to test her adjusted model on data from occupational exposures. Our 

approach to selecting and testing adjustments to parameters in the nonlinear Leggett 

model (referred to as adjusted core model) is based on the knowledge gained from 

these efforts. Our methods and results are presented in detail below.

Objectives of phase two

The objectives of our model calibration effort were to:

• eliminate the difference between the average observed and average 

predicted BLL by adjusting selected parameters until the model predictions 

are in alignment with the observations in the ASARCO cohort data.

• produce a model that performs well regardless of job tenure, indicating that 

bone and long-lived tissue compartments are performing well.

• ensure that adjusted model parameters remain in line with data of very longlived bone lead and lead in other tissues from chronically exposed workers.

48
• ensure that modeled distributions of Pb in bones and other tissues are in line 

with autopsy data from the general population.

Selection of parameters to be adjusted in phase two

The parameters we adjusted and tested are bone, blood, and plasma (via urine). We 

selected these parameters based on the work of Hattis, Nie et al., Fleming et al., and 

O’Flaherty described above.

Phase two model calibration

In an iterative process, OEHHA made changes to the selected parameters in line with 

chronic occupational exposures. We repeatedly compared the predicted results to the 

observed ASARCO cohort data and adjusted the parameters until the under-prediction 

was eliminated. As we made changes, we checked that the adjusted parameters 

remained in line with observations of blood, plasma, urine, and bone lead data obtained 

from both occupationally and non-occupationally exposed adults. The details of the 

adjustments and our tests of model performance are presented below.

Phase two tests of model performance

OEHHA performed a number of tests with the adjusted core model to determine overall 

model performance as well as check the impact of the adjusted model parameters on 

the distribution of lead to all tissue groups in the adult human body. These tests and 

their results are described below.

Test 1: Goodness of fit

In order to test overall model performance we again used the ASARCO cohort 

described earlier in the report, with one change - we excluded an additional 12 workers 

with BLLs above 60 μg/dL because these values are out of the range of interest of 

CDPH-OLPPP. For each of the remaining 47 subjects we derived a predicted poststrike BLL using the adjusted core model and compared it to the measured post-strike 

BLL.

49
Test 2: Model performance relative to job tenure

In his initial assessment of the Bernard model, Hattis observed that there is some 

tendency for longer job tenures to be associated with over-predictions of BLLs relative 

to the ASARCO strike data. Hattis pointed out that any linear tendency resulting in a pvalue greater than 0.05 to 0.10 tentatively suggests a systematic difference between 

observation and expectation—the model might be giving a somewhat larger weight to 

job tenure than is warranted (i.e., less lead might be stored in slow-exchanging pools 

than called for in the model, or the rates at which the slow-exchanging pools 

accumulate and release lead might be somewhat off).

Based on Hattis’ observation with the Bernard model of a potential association between 

model performance and job tenure, OEHHA decided to check the performance of the 

Leggett model in relation to job tenure using the same regression analysis technique.

Specifically, we conducted a regression analysis looking at model performance 

(measured minus predicted BLL) relative to job tenure for the 47 subjects to test for a 

statistically significant trend. This analysis was performed using Microsoft Excel (2010).

Test 3: Model performance relative to measured bone lead levels in a smelter 

worker

Leggett (1993) based transfer rates from non-exchangeable bone pool to blood on 

histomorphometric measurements on human subjects and studies of retention of certain 

bone-seeking radionuclides in human subjects. Most histomorphometric measurements 

available at the time were on ribs and iliac crest, but there were also a few 

measurements for various long bones. Leggett assumed that turnover rates differ 

between trabecular and cortical bone by about a factor of six in the mature adult. He did 

not address differences in bone turnover during adulthood. Historic estimates of a single

long-term turnover rate in the adult human skeleton have ranged from about 0.007/year 

to about 0.15/year. Leggett set the adult trabecular nonexchange bone to plasma 

transfer rate at about the midpoint of this range and the cortical nonexchange bone to 

plasma transfer rate about six times slower.

50
Leggett concluded from a survey of literature that only broad comparisons between 

model predictions and findings from measurements of bone lead could be made due to 

the paucity of information on the lead exposures reported by investigators stating that 

“...the rate of decline depends somewhat on the pattern and duration of the exposure, 

which affect the distribution of bone lead at the end of exposure” (Leggett 1993).

This limitation has been somewhat overcome by a more recent study that combined 

measurements of lead in the heel and tibia bones with a robust history of BLLs from the 

study of smelter workers reported by Nie et al. (2005) and Fleming et al. (1999). Nie et 

al. observed that more lead accumulates in trabecular bone than previously predicted 

by the Leggett model and the rate of accumulation differs by age, which is correlated 

with exposure history. This supports Leggett’s statement that bone levels of lead 

depend on the pattern and duration of exposures. Therefore, we considered the Nie et 

al. data an important check for our adjusted model.

As described earlier, Nie et al. (2005) reported bone parameter adjustments for five age 

groups based on predictions from a simplified linear version of the Leggett model fit to 

bone and blood measurements from each age group. These workers were part of a 

cohort of adult workers from a smelting facility in New Brunswick, Canada (Fleming et 

al. 1997; Nie et al. 2005). Cohort BLL data were recorded routinely from the late 1960s.

In the early 1990s, workers were enrolled in a bone lead study that collected lead levels 

from the heel (trabecular) and tibia (cortical) bones. Nie et al. (2005) reported measured 

and modeled cortical and trabecular bone lead levels for each of nine retired workers in 

a table. Blood and bone measurements plotted over time were available from a graph 

for subject #1.

Nie et al. derived new bone lead transfer rates by fitting the predicted to measured bone

lead for each worker. This was accomplished by first deriving the lead intake according 

to the blood lead history and estimates of background levels in the population before 

and after 1970 (Nie et al. 2005). The authors found that bone parameter values 

substantially lower than those suggested by Leggett fit measurements of cortical and 

trabecular bone taken from chronically exposed smelter workers.

51
We were unable to get access to the full Nie et al. dataset, therefore our check is limited 

to one worker for whom both BLL and bone measurements were available or could be 

extracted from Nie et al (2005).

For this worker (subject #1), we extracted bone measurements from Table 1 and 

average BLLs for three major time periods (early high, after an initial reduction in 

exposure, and after removal from exposure) from Figure 3 in Nie et al (2005). We then 

used a method similar to Nie et al.’s for estimating intake during each time period based 

on the extracted BLLs. Finally, using the adjusted Leggett model, we modeled BLL, 

trabecular lead, and cortical lead levels four years after retirement and compared them 

to measured levels.

Although limited, this test allows us to check whether the adjusted core model provides

reasonably accurate predictions of bone lead in chronically exposed workers. Prior 

researchers have also used data from a single worker or adult to calibrate and test 

models when additional data are not available.

Test 4: Predicted and measured plasma and urine lead concentrations relative to 

whole blood lead concentrations in three worker cohorts

In Leggett’s original model he assumed that the observed nonlinear relations between 

lead in blood and plasma, urine, or other fluids and tissues observed by several 

investigators (Marcus 1985a; Chamberlain 1985; O’Flaherty 1991; Raghaven 1980) 

result from a decrease in the transfer rate from diffusible plasma to RBCs as the 

concentration of lead in RBCs increases. Leggett set a baseline saturation (S)

concentration at 350 μg/dL RBC based on data showing where the ratios urinary 

lead:BLL and plasma lead:BLL begin to increase rapidly in persons exposed for a long 

period to levels of lead found in the workplace in the 1960s and 1970s (Figures 13 and 

14 in Leggett [1993]). In contrast, Leggett suggested that a much lower value for S, 

perhaps on the order of 140 μg/dL RBC, may better represent more rapidly increasing 

urinary lead:BLL ratios for a high, acute intake by a person with a history of low intakes 

(Figure 13 in Leggett [1993]).

52
In Leggett’s equation for RBC saturation, he included a term that effectively ignores the 

onset of decreasing capacity for lead to bind in RBCs at lower levels of lead in whole 

blood. OEHHA chose to eliminate this term and therefore needed to recheck that the 

ratios of plasma and urine to whole blood lead after we made adjustments to the model 

were similar to those found by Leggett for chronically exposed workers. We also 

identified additional data sets of lead in plasma, urine, and whole blood in chronically 

exposed workers for further comparisons. These data sets and our analysis are 

described below.

Selection of data sets for Test 4:

OEHHA identified several studies in the literature which examined the relationship 

between whole blood, plasma, and/or urine lead levels measured in workers exposed to 

lead (Manton and Cook 1984; Lee 1982; Hirata et al. 1995; deSilva 1981; Cooper et al. 

1973; Wang et al. 1985). From these studies OEHHA selected three datasets that 

provided individual data and documentation or indications of chronic exposure for 

checking model performance (Manton and Cook 1984; Lee 1982; Hirata et al., 1995).

Manton and Cook (1984)

Briefly, Manton and Cook examined serum, whole blood, and renal clearance levels of 

lead from 36 patients followed by either a medical center or a health science center in 

Dallas, Texas. For most of these patients, the source of lead exposure and/or the type 

of occupation were not reported. Twenty-five patients had other neurological disease or 

symptoms not involving heavy metal intoxication or motor neuron disease. The other 11 

subjects were patients diagnosed with motor neuron disease.

Samples were collected at the convenience of the attending physician. Authors 

estimated that the lead concentrations from their methods for collecting and analyzing 

serum lead have no more than 20% uncertainty associated with them, and for blood and 

urine the uncertainty is less than 2%.

Whole blood, plasma, and urine lead measurements were taken from 36 patients; whole 

blood lead results ranged from less than 20 μg/dL to 150 μg/dL. Authors noted that at a 

blood lead concentration of 10 μg/dL, serum (plasma without clotting factors) lead is 

53
0.25 μg/dL and then rises as a steep function of blood lead concentration. In Table 1 of 

Manton and Cook, serum lead levels ranged from 0.020 to 3.33 μg/dL. The authors did 

not report urine clearance data in relation to whole blood data. 

The serum versus whole blood values that appear in Figure 2 were abstracted using 

GetDataTM (version 2.24) given that tabular data for individual subjects were not 

published in Manton and Cook (1984). Although the full range of data was visible in the 

published graph, 23 data points were within a “hatched” area of the graph below 20 

μg/dL whole blood. Therefore, we extracted individual data points in the range of whole 

BLLs between 20 and 70 μg/dL. Within this range, about 15 data points were 

extractable.

Lee (1982)

Lee examined 234 male lead workers employed in a storage battery factory in Korea 

who were tested for lead in blood and urine. In this study, the mean age was 28.4 years 

± 6.5 SD and mean work duration was 4.4 years ± 3.8 SD. The mean area air 

concentration ranged from 70 μg/m3 to 380 μg/m3 among five workplaces.

All urine analyses were made on spot samples and all urine samples were corrected to 

a specific gravity of 1.016. A single sample was obtained from each subject. However, 

the timing of blood and urine tests was not included in the description of methods.

Average blood lead was 53.8 ± 19 μg/dL. Average urine lead was 119 μg/L ± 84. The 

values that appear in Figure 1 were abstracted using GetDataTM and represent 

summary data (i.e., the mean for a given BLL ± 1 SD). Tabular data for individual 

subjects were not published in Lee (1982).

Hirata et al. (1995)

Hirata et al. followed for 15 months four workers exposed to an average ambient air 

concentration of 286 μg/m3 in a Japanese factory that manufactures lead glass-based 

paints. These workers had at least two years of exposure in the factory prior to the start 

of the study. Data from one worker assigned for a short time (one month) to the sifting 

54
work setting with a much higher average air concentration of lead (1.05 mg/m3

) were 

not included in our analysis.

Levels of lead concentration in ambient air in the workplace as measured by personal 

sampling ranged from 0.022 to 1.331 mg/m3 (mean: 0.286; SD: 0.333) in 1989 in 

workplaces other than the sifting workplace. 

Sixty sets of blood and urine samples were obtained from the four workers not assigned 

to the sifting workplace during a 15-month period.

Whole blood, plasma, and urine lead measurements taken from four workers (60 

samples over 15 months) were 52.3 μg/dL ± 7.79, 0.52 μg/dL ± 0.20, 130 μg/L ± 62.4 

(mean ± SD), respectively. Similarly, we used GetDataTM to extract urine lead vs. whole 

blood lead and plasma vs. whole blood lead data from Figures 1 and 2 in Hirata et al. 

(1995).

Using the extracted data, we modeled the plasma lead and urine lead relationships to 

whole blood lead and then plotted the predicted versus observed relationships for the 

three worker cohorts.

Test 5: Comparison of modeled tissue lead distributions to measured lead 

distributions from autopsy data

As a final test, we compared postmortem data on the distribution of lead in various 

tissues in humans chronically exposed to a low level of lead throughout life with the 

distribution predicted by the original and adjusted Leggett models. This comparison 

allowed us to examine whether changes in bone, RBC saturation, and urinary clearance 

parameters had affected lead distribution in other tissues.

Leggett derived reference organ distributions from postmortem data collected in the 

1960s and 1970s (Gross et al. 1975; Barry 1975; Tipton and Cook 1963, 1964;

Schroeder and Tipton 1968). Leggett conducted an uncertainty analysis to derive upper 

and lower bounds on the distributions given the uncertainties inherent in the data 

(details described in Leggett [1993]).

55
Results of phase two assessment

Final adjusted parameters

Table A-1 presents Leggett’s original and our final adjusted parameters for bone 

transfer and RBC saturation. For comparison we have also presented the parameters 

used by Nie et al. (2005).

Table A-1: Parameter values from Nie, Leggett and adjusted core model1

Parameter Nie Original Leggett –

nonlinear

Adjusted core 

model

C-bone non-exchange to blood 1 to 27 x 10-5 8.22 x 10-5 1.6 x 10-5

T-bone non-exchange to blood 0.4 to 22 x 10-5 49.3 x 10-5 1.97 x 10-5

Blood to C-bone non-exchange 1.5 to 7.2 x 10-4 46.2 x 10-4 3.81 x 10-4

Blood to T-bone non-exchange 2.8 to 6.3 x 10-4 46.2 x 10-4 2.82 x 10-4

RBC Saturation NA 350 270

RBC Threshold NA 60 0

1

Nie et al. (2005), Leggett (1993), and adjusted core model (this work); C-bone, cortical bone; T-bone, trabecular bone; RBC 

saturation, level in micrograms per deciliter red blood cell when cells reach binding capacity limit; RBC threshold, level in

micrograms per deciliter red blood cell when cells noticeably start to show reduced binding capacity (Leggett 1993); NA, not 

applicable.

The final bone absorption values we used fall within the range presented by Nie et al. 

(2005). We eliminated the RBC saturation threshold value of 60 μg/dL as it has no 

biological basis. (Per personal communication with Dr. Leggett, the saturation threshold 

value was originally included for mathematical convenience.) We selected a RBC 

saturation value of 270 μg/dL RBCs (corresponding to 119 μg/dL whole blood) based on 

the value derived by O’Flaherty (1996). Finally, we adjusted urine parameters to help 

correct the BLL under-prediction and bring ratios of plasma and urine lead to whole 

blood lead into line with those observed in worker cohorts.

56
Tests of Model Performance

As described previously, OEHHA repeatedly tested model performance during the 

calibration process. Only the results of the tests performed with the final adjusted model 

are presented here.

Test 1: Goodness of fit test

Table A-2 lists the attributes among 47 smelter workers along with the estimate of fit 

between the model-predicted and observed BLLs.

Table A-2: Estimate of fit of predicted to observed BLLs for 47 smelter workers1

Subject

Pre-strike 

job 

tenure 

(days)

Measured preemployment 

BLL(μg/dL)

Estimated 

pre-strike 

BLL(μg/dL)

Measured 

post-strike 

BLL(μg/dL)

Predicted 

post-strike 

BLL(μg/dL)

Measured 

minus 

predicted

BLL

(μg/dL)

5 3084 11 30.5 17 26 7

6 2266 14 38.2 36 44 -5

8 3080 10 57 41 34 0

14 3077 20 37.3 28 21 2

15 3080 10 35.3 10 42 5

23 3087 10 37.2 10 27 4

27 3084 20 34.1 32 44 -4

33 3071 21 39.9 24 31 -9

34 3071 17 26.7 20 25 -1

36 3070 35 49.9 44 35 2

39 3071 13 39.3 20 23 3

45 3066 22 52.3 37 25 6

47 3066 24 35.8 29 25 6

54 3060 34 43.9 38 29 5

59 3052 13 46.7 23 28 -1

62 3045 17 56.1 35 23 13

63 1960 20 42.4 34 34 6

67 3043 18 42.5 26 24 4

68 3045 24 57.4 40 38 -2

73 1960 20 32.2 31 37 10

88 1959 14 37.8 26 20 13

91 742 16 42.9 33 30 9

57
Subject

Pre-strike 

job 

tenure 

(days)

Measured preemployment 

BLL(μg/dL)

Estimated 

pre-strike 

BLL(μg/dL)

Measured 

post-strike 

BLL(μg/dL)

Predicted 

post-strike 

BLL(μg/dL)

Measured 

minus 

predicted

BLL

(μg/dL)

101 1953 22 52.1 37 34 -8

106 1818 17 37.8 24 31 8

108 2979 33 43.9 40 32 6

115 2912 33 45.8 35 33 3

138 2928 10 52.4 21 39 -8

157 2667 27 54.5 31 39 -4

158 2660 26 42.4 36 30 -9

159 2653 18 49.5 38 38 2

161 1617 21 46.7 22 28 -2

177 1582 35 55.4 40 36 -1

188 2541 24 41 39 39 1

191 1499 19 39 31 28 -5

202 1288 26 60.4 47 39 -1

203 2485 16 55.2 26 29 0

218 1162 14 34.1 23 35 2

221 2415 20 43.7 39 42 2

225 2408 26 52.2 47 21 -1

226 2415 10 36.5 33 24 -4

227 1148 18 54.3 34 29 -5

237 1106 36 54.8 39 27 1

257 2346 34 41.6 36 20 -10

286 2268 12 41.8 28 34 7

288 2266 27 43.3 40 18 -1

299 2247 13 47.8 27 26 6

474 1960 17 38.5 31 21 -11

Average 2433 20.4 44.3 31.5 30.6 0.9

Standard 

error

98.3 1.1 1.2 1.3 1.0 0.9

1 The attributes of the 19 subjects we excluded from the dataset did not significantly alter the average (Standard error) estimates of 

the 66 subjects presented in (Hattis 1981) for variables other than pre-strike BLLs. (job tenure 2433 (98.3) versus 2255 (102) days, 

pre-employment BLL 20 (1.1) versus 20 (0.97) μg/dL, pre-strike BLL 44 (1.2) versus 49 (1.71) μg/dL, post-strike BLL 31 (1.3) versus 

33 (1.20) μg/dL). BLL, blood lead level; μg/dL, micrograms per deciliter 

The average difference between measured and predicted post-strike BLL is 0.9 μg/dL, 

with a standard error of 0.9 μg/dL, shown at the bottom of Table A-2. This indicates that 

the adjusted core model predicts BLLs nine months after the end of workplace exposure 

58
0.9 μg/dL lower than the average BLLs observed in the ASARCO cohort. Figure A-1 

shows the relationship between the observed and predicted BLLs.

Figure A-1: Measured versus predicted BLL (adjusted core model)

1

1 ASARCO data versus predictions from final adjustments to core model parameters; BLL, blood lead level; μg/dL, micrograms per 

deciliter

A linear regression of modeled versus measured BLLs using the nonlinear Leggett 

model with Leggett’s suggested values for RBC saturation estimated a slope of 1.19,

meaning that 1.19 x predicted BLL= measured BLL. This suggests a significant (>10%) 

systematic under-prediction error. In contrast, a linear regression of modeled versus 

measured BLLs once parameters were adjusted to produce a better fit to the ASARCO 

post-strike BLLs, produced a slope of 1.02 when the intercept was forced through zero.

This means that 1.02 x predicted BLL = measured BLL. This level of systematic error is 

not significant (<5%).

59
Test 2: Model performance vs. job tenure

The regression equation analyzing model performance vs. job tenure for our final 

adjustment to model parameters is:

Eq. (A-2): Model performance (measured – predicted BLL) 

 = 5.25 – 0.00187 x (days job tenure)

The intercept value of 5.25 has a p-value = 0.11 (LCL, -1.23, UCL, 11.7), and the slope 

of 0.00187 has a p-value = 0.15 (LCL, -0.004, UCL, 0.0007).

This final equation suggested that differences in measured – predicted BLLs would not 

be expected to fall outside the deviations observed within the worker cohort at any 

reasonable length of job tenure as can be seen in Figure A-2 below.

Figure A-2: Model performance versus job tenure1

1

BLL, blood lead level; μg/dL, micrograms per deciliter

This test of consistent model performance regardless of job tenure provided further 

evidence that the basic structure and exchange ranges for the relatively long-lived 

compartments in bone and some soft tissues are performing reasonably well.

-15

-10

-5

0

5

10

15

0 500 1000 1500 2000 2500 3000 3500

model performance

(measured - predicted BLL (μg/dL)

Job tenure (days)

60
Test 3: Model performance relative to measured bone lead levels in a smelter 

worker

For the smelter worker who was the subject of our test, Leggett’s linear model predicted 

cortical and trabecular bone concentrations of 44.7 and 27.7 μg/g, respectively,

compared to cortical and trabecular bone concentrations of 60 and 160 μg/g predicted 

by our adjusted model. Corresponding measurements taken from the subject’s tibia 

(cortical) and heel (trabecular) bone were 74 (±8) and 156 (±7), respectively. After 

estimating intake from the blood lead profile taken from Figure 3 in Nie et al. (2005), 

measured trabecular bone is very similar to predictions from our adjusted model for this 

subject. In addition, the ratio of lead levels in trabecular compared to cortical bone is 

about two to one for all nine retired lead smelter workers in Table 1 in Nie et al. (2005). 

Though limited, these findings lend further support that the adjusted model provides 

reasonably accurate predictions of bone lead in chronically exposed workers.

Test 4: Predicted versus measured plasma and urine lead concentrations relative 

to whole blood lead concentrations in three worker cohorts

Figures A-3 and A-4 show the model predictions for plasma and urine lead levels 

compared to measured levels in the three cohorts of workers chronically exposed to 

lead.

61
Figure A-3: Plasma lead versus whole blood lead concentration - predictions from 

the adjusted Leggett model and data from two worker cohorts1

1

The values that appear in Figure A-3 were abstracted using GetDataTM given that tabular data for individual subjects were not 

published in Manton and Cook (1984) or in Hirata et al. (1995). Although the full range of data was visible in the published graph, 

not all data points were extractable; BLL μg/dL, blood lead level in micrograms per deciliter

Figure A-4: Urine lead versus whole blood lead concentration - predictions from 

the adjusted Leggett model and data from two worker cohorts1

1

The values from Lee (1982) that appear in Figure A-4 were abstracted using GetDataTM and represent the mean for a given BLL ± 

one standard deviation. Tabular data for individual subjects were not published in Lee (1982); BLL μg/dL, blood lead level in 

micrograms per deciliter

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00

Plasma lead (μg/dL)

Whole blood lead (μg/dL)

Manton and Cook Hirata et al. Adjusted Leggett

0

50

100

150

200

250

300

350

400

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00

Urine lead (μg/L)

Whole blood lead (μg/dL)

Hirata et al. Lee Adjusted Leggett

62
Notice that the predictions from the adjusted core model for the concentration of plasma 

lead relative to whole blood lead fall within those reported by Hirata et al. (1995) and 

Manton and Cook (1984). The predictions for concentration of lead in urine relative to 

whole blood lead also fall within those reported by Hirata et al. (1995) and Lee (1982).

The consistency of predicted whole blood lead concentration relative to plasma lead 

and urine lead, to relationships observed in worker cohorts increases our confidence 

that the final values we selected for RBC saturation and urinary clearance are 

reasonable. 

Test 5: Comparison of modeled tissue lead distributions to measured 

postmortem lead distributions

Table A-3 presents predicted tissue distributions compared to the reported reference 

ranges. Note that the proportions of lead distributed to various tissues by the final 

adjusted Leggett model continue to fall within the range of proportions observed in 

autopsy studies and those predicted by original Leggett model.

This suggests that our adjustments to bone, blood, and urine parameters did not 

significantly affect the way the model predicts how lead distributes to other key tissue 

compartments (e.g., brain and liver) at lower levels of intake (20 μg/day).

63
Table A-3: Distribution of Lead in Various Tissues - Postmortem Data, Original 

Model Prediction, Adjusted Model Prediction1

Age 20s – 30s Age 40s – 50s

Bone (%)

Postmortem data 0.75 – 0.90 0.85 – 0.95

Original model prediction 0.88 0.90

Adjusted model prediction 0.86 0.88

Blood (%)

Postmortem data 0.02 – 0.04 0.008 – 0.02

Original model prediction 0.03 0.02

Adjusted model prediction 0.03 0.02

Liver (%)

Postmortem data 0.03 – 0.07 0.02 – 0.04

Original model prediction 0.04 0.03

Adjusted model prediction 0.04 0.04

Kidneys (%)

Postmortem data 0.003 – 0.007 0.002 – 0.004

Original model prediction 0.004 0.003

Adjusted model prediction 0.003 0.002

Brain (%)

Postmortem data 0.002 – 0.004 0.0008 – 0.002

Original model prediction 0.002 0.002

Adjusted model prediction 0.003 0.002

Other tissue (%)

Postmortem data 0.04 – 0.15 0.02 – 0.08

Original model prediction 0.05 0.05

Adjusted model prediction 0.07 0.05

1

Exposure scenario: 20 μg/day uptake

64
A.5 Conclusion

In this appendix, we explained our basis for selecting, adjusting, and checking the 

nonlinear Leggett model for this task.

An initial review of the literature on lead pharmacokinetics and existing models informed 

us that nonlinear kinetics would be an important part of any model we used to predict 

BLLs from chronic occupational exposures at levels of interest to CDPH-OLPPP. In

addition, we learned from efforts by others to improve the predictive ability of the two 

most recent models (Leggett, O’Flaherty) as new data on chronically exposed workers 

have become available. We reviewed and selected data from five lead worker cohorts

(including smelter workers, battery factory workers, lead glass-based paint factory 

workers, and lead workers from undisclosed settings), as well as autopsy data from the 

general population, to check how well the model predicted blood and other tissue lead 

levels resulting from chronic exposures in the range of interest to CDPH-OLPPP.

The model significantly under-predicted worker BLLs when applying Leggett’s 

suggested parameters. Therefore, we needed to adjust bone, urine clearance, and 

blood parameters in combination to improve the fit of the model to observed data.

Multiple tests were needed to ensure that predictions in the range of BLLs of interest to 

CDPH-OLPPP from the adjusted model compared well to tissue lead levels taken from 

workers and the general population. With the data available to us we were able to check 

and verify that:

• The slope of predicted/measured blood lead is near one (test of bias).

• The slope of model performance/job tenure is not significantly different from zero 

(test of association with job tenure).

• Plasma/whole blood ratios are consistent with data from worker cohorts.

• Trabecular/cortical bone ratios are reasonable when compared to worker data.

• Tissue/body burden ratios are reasonable when compared to autopsy data.

Briefly, OEHHA adjusted urine, bone, and blood parameters in the nonlinear Leggett 

model to eliminate the under-prediction of BLLs in the ASARCO cohort and achieve 

predictions comparable to data collected on separate tissue groups from lead workers 

65
and the general population. Our adjustments reduced the under-prediction of BLLs after 

a nine-month strike among chronically exposed smelter workers from 23% to 2%. The 

adjusted model maintained reasonable tissue distribution ratios (plasma/whole blood, 

urine/whole blood, trabecular/cortical bone, tissue/body burden) observed in lead

workers and the general population. Finally, the adjusted model performed reasonably 

well regardless of length of job tenure among chronically exposed workers. Although no 

model is perfect, our review, selection, adjustments, and checks of model performance

provide confidence that the adjusted Leggett model is the best available model for the 

purposes established in the Scope of Work and that our primary objectives for this part 

of the project have been met.

66
B Appendix: OEHHA Modifications to the Adjusted Core 

Model to Accommodate Workplace Exposure in Leggett+ 

Model 

In Appendix B, we first describe the exposure features (exposure module) added to the 

adjusted nonlinear form of the Leggett model (core module) and OEHHA’s approach to 

simulating worker lead intake. Then we present our derivation of a default coefficient for 

the transfer of inhaled lead to blood among workers, including our rationale and 

assumptions. Finally, we compare predictions from Leggett+ (combined core and 

exposure modules) with observations from a controlled inhalation chamber study and an 

occupational field study that reported both airborne exposure and BLLs at the individual 

level under specific exposure conditions.

B.1 Description of exposure features added to the nonlinear Leggett model

The original Leggett model was a general model not specifically designed to address 

workplace exposure scenarios. Therefore, OEHHA needed to add a workplace 

exposure component to the nonlinear form of the Leggett model that we described in 

Appendix A of this report. The new model is renamed “Leggett+”. The exposure 

component includes features that address both workplace inhalation exposure and the 

background exposure from inhalation of ambient (non-workplace) air and dietary intake.

B.1.1 Breathing rate

The exposure module includes three different breathing rates to represent activity 

during work and non-work hours of each day. Simulations involving chronic exposure to 

workplace airborne lead include work time apportioned as 8 hours/day x 250 days/year. 

Background exposure includes intake from inhaled air during the other 16 hours per day 

and time off on weekends and during vacation in each year as well as background 

dietary intake (See Table 1). 

We calculated a time-weighted average breathing rate (BR) of 26 m3

/day based on 

minute volumes for adult males (OEHHA, 2012a). This BR reflects 10 hours of 

67
moderate activity during the day (30 L/minute = 18.4 m3

/ 8-hr workday + 2 hr off the 

job), 6 hours of light activity (13.9 L/minute = 5.0 m3/ 6-hr), and 8 hours of sedentary 

(5.9 L/minute = 2.8 m3/ 8-hr) activity off the job.

B.1.2 Inhalation transfer coefficient

OEHHA’s task is to model BLL resulting from inhalation exposure to a constant 

workplace air lead level. Therefore, we need to determine how much of the lead in the 

air a worker breathes is transferred to his or her blood. The rate and amount transferred 

to blood depends on several factors including the amount, size, and solubility of 

deposited particles and their location in the upper and lower respiratory tract. In turn, the 

location where particle deposition occurs depends on both particle size and a worker’s 

breathing rate. Finally, transfer depends on the conditions in the gut for those particles 

that deposit in the upper airways and are swallowed.

The chemical form of inhaled lead affects its solubility and therefore influences its 

absorption from the respiratory tract and gut. Some lead forms (e.g., lead acetate, lead 

chloride) are soluble in water; other forms (e.g., lead sulfide) are much less soluble

(NTP 2011). For the purposes of developing a coefficient for the transfer of inhaled lead 

to blood, OEHHA chose to make the cautious assumption that lead is inhaled in a highly 

soluble form and readily absorbed in the lungs and gut, thus making deposition in the 

lungs based on particle size the critical factor in transfer to blood.

Generally, smaller particles will deposit deeper in the lung (alveolar region), while 

coarser particles tend to be deposited in the head and ciliated regions where they are 

cleared by ciliary action or secretions and swallowed (Castellino et al. 1995). Very small 

particles are more likely to be exhaled. We assume inhaled lead particles deposited in 

the alveoli are highly soluble in water and hence absorbed to the blood rapidly (within a 

day) with essentially 100% efficiency (Holgate et al. 1999; Stellman 1998), while 

particles deposited in the head and ciliated regions of the lung are cleared to the gut 

where they are absorbed with less efficiency. As the distribution of particles shifts 

towards coarser particles, more mass is retained in the upper airways, removed and 

68
swallowed, and the amount of lead transferred to the blood via the gut becomes greater 

for a given air concentration of lead.

The size distribution of airborne lead particles depends on the industrial processes that 

generate the particles in the first place. Some information is available in the published 

literature on particle size from studies of different types of industrial operations and 

construction tasks (Park and Paik 2002; Liu et al. 1996; Spear et al. 1998b; Tsai et al. 

1997; Froines et al. 1986; Hinds 1982; Hodgkins et al. 1991a; Hodgkins et al. 1991b; 

Virji et al. 2009). Hot operations such as found in brass foundries and burning during 

bridge repair generate smoke and fume (Liu et al. 1996; Spear et al. 1998b; Vork 2003).

Workers in mechanical processes, such as cutting, grinding, grid-casting, pasting, and 

cast-on-strap unloading, have lead exposure composed predominantly of coarser 

particles (larger than 10 μm) (Liu et al. 1996).

Incorporating varying particle size distributions into the model, however, is impractical 

for a number of reasons. First, only limited information is available on the wide variety of 

lead operations and processes because particle size information is not routinely 

collected in industrial hygiene monitoring surveys. Second, even if it were available, 

assuming that a particular particle size distribution would be widely applicable across an 

industry type does not appear to be valid. Different facilities in an industry group (e.g., 

battery manufacturers) could have different processes or facility layouts. Furthermore, 

often many operations in a facility use lead and generate different particle size 

distributions. In these facilities worker exposure will likely be mixed and can vary

depending on the location of the worker. Finally, occupational lead regulations must 

apply across general and construction industries rather than to particular industries, 

processes, or operations.

If it is impractical to include varying particle size distributions into the model, how should 

particle size distribution be handled?

In the 1978 lead standard, Federal OSHA addressed the issue by assuming that at air 

lead concentrations up to 12.5 μg/m3 all particles are small and 37% of the inhaled lead 

mass is absorbed to the blood; above this cut point, all particles are large and 8% of the 

69
inhaled lead mass is absorbed to the blood. This assumption, called Assumption C, has 

been challenged by Froines and others (Froines et al. 1995; Liu et al.1996). While some 

studies have shown that when lead concentrations are high in the workplace particle 

size distribution tends to be coarser (Alexander et al. 1999; Inskip and Hutton 1987; 

Jacko and Overmyer 1979; Park and Paik 2002; Spear et al. 1998b; Tsai et al. 1997),

there are processes that generate high concentrations of smaller particles in small 

spaces (e.g., hot processes like torch cutting on bridges [Vork 2003]). We find there is 

no basis for assuming a global relationship between mass measurements in the air 

(μg/m3

) and particle size distribution (μm) as Federal OSHA did in 1978. If such a 

relationship existed, then particle size could be inferred by mass measurements and 

absorption adjusted accordingly.

The original Leggett model has a default assumption that 37% of the lead inhaled is 

cleared from the respiratory system by either direct absorption or mechanically removed 

by the ciliary escalator and swallowed. Implied in this assumption is that the other 63% 

is exhaled. Of the 37%, 95% is retained in the alveoli and absorbed directly to the blood 

with 100% efficiency. The other 5% is cleared to the gut. The model assumes that 15% 

of the lead that enters the small intestine is absorbed into the blood. OEHHA agrees 

with Leggett’s assertion that this default assumption is not valid for industrial exposures.

Leggett based these assumptions on studies of motor vehicle exhaust in which particle 

size is in the submicron range where there appears to be little ciliary clearance of 

deposited lead. In contrast, in studies of particle size distribution in lead industries,

particles tend to be much larger and therefore expected to deposit in the upper regions 

of the respiratory tract where more ciliary clearance of deposited lead is expected.

Leggett acknowledged that greater ciliary clearance may be expected in industrial 

exposures where aerosol particles are often larger.

Having concluded that neither Assumption C nor Leggett’s default assumption was valid 

for our purposes, we decided that an alternative approach based on the important 

factors involved in the transfer of inhaled particles to blood mentioned above was 

needed. OEHHA’s approach is based on: 1) published particle size distribution data 

from a variety of workplaces with differing operations that generate a range of particle 

70
sizes (fine to coarse); and 2) a recently developed model for predicting head and lung 

deposition and clearance based on particle size distributions and other parameters. In 

our derivation of a default coefficient for the transfer of inhaled lead to blood in workers, 

presented below, we have made no attempt to address nose blowing as a pathway for 

clearing lead particles from the head region (Smith et al. 2011).

B.2 Methods for deriving a coefficient for the transfer of inhaled lead to blood 

in workers

Briefly, OEHHA reviewed the literature to identify studies that provided data on particle 

size distribution from actual workplaces and selected Park and Paik (2002) and Liu et 

al. (1996) for evaluation. These studies provide particle size distribution data from 14 

industrial workplaces and five different industries with a range of particle sizes. To 

evaluate the effect of chemical speciation on deposition, we also selected Spear et al.’s 

(1998a) assessment of particle size distributions in personal breathing zone samples of 

workers at a primary smelter (Spear et al. 1998b). Next, we looked at two available 

models for estimating the percent of lead inhaled in the workplace that is deposited in 

the three regions of the respiratory tract (head, upper, and lower airways). The models 

we reviewed are the ICRP Human Respiratory Tract Model for Radiological Protection 

(ICRP 1994) and the more recent MPPD2 (ARA 2012). We selected MPPD2 for our 

analysis. Finally, we calculated the percentage of inhaled lead transferred to the blood 

of an exposed worker according to Equation B-1 (see section B.3.3.1).

B.2.1 Studies selected for analysis

Park and Paik (2002) evaluated exposure to airborne lead particles for 117 workers in 

four types of lead-related industries located in Korea. The particle sizes were measured 

using personal sampling cascade impactors. Two secondary lead smelting plants, three 

radiator manufacturing plants, four lead-acid battery manufacturing plants, and three 

lead powder manufacturing plants were studied. In addition to air samples, whole blood 

samples were taken on each worker. For each type of industry, the authors reported 

MMAD, PbA, average respirable fraction, and fraction of particles less than 1 μm 

aerodynamic diameter (AD). 

71
Liu et al. (1996) reported on the size distributions of lead aerosol from personal samples 

of workers exposed in a brass foundry and a battery manufacturing plant. Workers were 

involved in one or more of eight operations during sampling periods. Ninety-four 

cascade impactor samples were collected over a one-year period. Mean respirable, 

thoracic, and inhalable fractions (as defined by ACGIH 1994-1995) along with their 

arithmetic standard deviations were reported for four work areas each in the brass 

foundry and battery plant.

Spear et al. (1998b) evaluated 46 personal inhalable dust samples taken from workers 

in a primary lead smelter located in the United States. Samples were obtained from four 

work areas: ore storage area, sinter plant, blast furnace area, and drossing area.

Results were reported as MMAD and range as well as median and mean of the 

inhalable, thoracic, and respirable fraction. In a companion paper, Spear et al. (1998a)

evaluated the chemical speciation of lead dust associated with primary lead smelting 

using X-ray diffraction analysis. This paper reported the percent total lead sequentially 

extracted from bulk dust generated by the smelter process.

B.2.2 Lead particle dosimetry using the MPPD2 model

The original MPPD model was developed by the Chemical Industry Institute of 

Toxicology (CIIT) Center for Health Research; the National Institute of Public Health and 

the Environment, The Netherlands (RIVM); and the National Institute for Occupational 

Safety and Health (NIOSH) (Anjilvel and Asgharian 1995; ARA 2012; RIVM 2002).

The MPPD model can be used to predict the deposition of particles between 0.01 and 

20 μm in diameter in humans and rats. The model calculates deposition in the lung by 

the mechanisms of impaction, sedimentation, and diffusion. Despite interspecies 

differences in lung geometries, the same mathematical formulations are used for both 

species. The extra-thoracic particle deposition efficiencies used in the MPPD model 

were adopted from the ICRP (1994) Human Lung Model. Model input parameters 

include airway morphology, particle properties (size distribution, density, and 

concentration), and breathing conditions (tidal volume, breathing frequency, and mode,

i.e., oral, nasal, or both). In addition, the human model provides parameters for age72
specific modeling of infants and children. The model uses average exposure 

concentrations and breathing rates to estimate particle depositions over discrete time 

periods, i.e., temporal variations are not considered. In addition to deposition it also 

calculates retention of deposited particles as a function of time since particles are 

removed by mechanical (ciliary) action. The MPPD model has been extensively 

reviewed by U.S.EPA National Center for Environmental Assessment staff and found 

useful for rat to human extrapolation in risk assessment (Brown et al. 2005).

For comparison, the previous primary model for estimating particle deposition in 

humans is the ICRP Human Respiratory Tract Model for Radiological Protection (ICRP 

1994). This model provides tabular deposition estimates for 19 activity median 

aerodynamic diameters (AMADs) (μm), seven lung regions, three modes of breathing, 

four breathing rates, five age groups, and both sexes. No provision is made for particle 

size distribution, density, or concentration. The ICRP model has also been formulated 

as a Mathematica package by Guillermo Sanchez (Humorap 1.1) with functions to solve

compartmental models with constant fractional rates

(http://web.usal.es/~guillermo/publications/Proceedings/IRPA11Biokmod.pdf). It 

calculates the retention of particles as a function of time in the respiratory and 

gastrointestinal tracts of individuals resulting from intake of airborne particles 

(http://web.usal.es/~guillermo/biokmod/mathjournal.pdf). This model package is quite 

complex and computationally intensive. 

In short, there is no currently extant model comparable to the Multi-Path Particle 

Dosimetry Model (MPPD and subsequent versions) in terms of public availability, 

complexity, flexibility, and utility for assessing airway particle deposition and retention in 

the context of human risk assessment. OEHHA used the MPPD2 model extensively in 

the recently completed “Nickel Reference Exposure Levels” (OEHHA 2012b). In our 

estimates of lead particle depositions with the MPPD2 model we used the adult Yeh and 

Schum symmetric lung morphology with normal oronasal augmentation breathing mode 

(Yeh and Schum 1980).

73
B.3 Results

Below we present the results of the dosimetry analysis using the MPPD2 model as well 

as our analyses to check the reliability of the MPPD2 outputs. Finally, we demonstrate 

that the MPPD2 model outputs compare well to the results from the ICRP Lung Model 

found in publication 66 (ICRP 1994).

B.3.1 Dosimetry results from MPPD2

We extracted the data from Park and Paik (2002) for air lead concentration and particle 

size in smelting, radiator manufacturing, battery manufacturing, and lead powder 

manufacturing settings (Table B-1). Air concentrations ranged from means of 26 to 1084 

μg/m3

, with a grand arithmetic mean of 641 μg/m3. Particle sizes ranged from a MMAD 

of 1.3 μm to 15.1 μm, with a MMAD of 5.8 μm across all 117 workers. GSDs ranged 

from 1.5 to 9.6, with a combined GSD of 6.3. Since Park and Paik did not analyze the 

chemical form of particles, we assumed that the particles in this study were composed 

of inorganic lead, density 11.34 g/cm3

.

Table B-1: Airborne lead concentration and particle mass median aerodynamic 

diameter from Park and Paik (2002)1

Parameter/Occupational 

setting

Secondary 

Smelting,

N = 6

Radiator 

Mfg.

N = 42

Battery 

Mfg.

N = 44

Lead 

Powder 

Mfg. N = 

25

Combined

N = 117

AM ± SD Concentration μg/m3 653 ± 356 26 ± 27 1084 ± 

1828 895 ± 1501 641 ± 1391

MMAD μm 4.9 1.3 14.1 15.1 5.8

GSD 5.0 9.6 1.5 1.7 6.3

Density, g/cm3 11.34 11.34 11.34 11.34 11.34

1

N, number of samples; AM, arithmetic mean; SD, standard deviation; MMAD, mass median aerodynamic diameter; GSD, geometric 

standard deviation; Weighted averages of columns 2-5; μm, micrometer; g/cm3

, grams per cubic centimeter 

Using the MPPD2 model and data from Park and Paik above, we predicted head and 

lung deposition fractions for all four occupational settings and five different activity levels 

74
(resting, sitting, light work, moderate and heavy work) (Table B-2). The predicted 

alveolar depositions ranged from 0.21 to 14.7% for the different occupational 

settings/activity levels with an arithmetic mean of 9.4% for low physical activity (resting)

and an arithmetic mean of 7.8% for high physical activity (heavy work).

75
Table B-2: Lung dosimetry analysis of occupational airborne lead exposure by 

the MPPD2 model: multiple activity levels (data of Park and Paik 2002)1

Parameter/Occupational 

setting

Secondary 

Smelting

N = 6

Radiator 

workers

N = 42

Battery 

workers

N = 44

Pb Powder 

workers

N = 25

Combined 

N = 117

Resting

Breathing cycle, (cycles/min) 12 12 12 12 12

Tidal volume, mL 625 625 625 625 625

NP dead space, mL 50 50 50 50 50

Output (%)

Total deposition 0.756 0.743 0.997 0.996 0.760

Total head deposition 0.612 0.520 0.971 0.996 0.617

Airway deposition (TB + Alv) 0.143 0.223 0.026 0.030 0.143

Alveolar deposition (Alv) 0.093 0.117 0.006 0.009 0.094

Sitting

Breathing cycle, cycles/min 12 12 12 12 12

Tidal volume, mL 750 750 750 750 750

Output (%)

Total deposition 0.776 0.755 0.998 0.997 0.780

Total head deposition 0.627 0.527 0.975 0.971 0.631

Airway deposition (TB + Alv) 0.149 0.229 0.022 0.026 0.150

Alveolar deposition (Alv) 0.103 0.130 0.006 0.009 0.103

Light work

Breathing cycle, cycles/min 20 20 20 20 20

Tidal volume, mL 1250 1250 1250 1250 1250

Output (%)

Total deposition 0.846 0.745 0.999 0.998 0.851

Total head deposition 0.744 0.559 0.992 0.990 0.746

Airway deposition (TB + Alv) 0.102 0.186 0.007 0.008 0.105

Alveolar deposition (Alv) 0.072 0.116 0.002 0.003 0.075

Moderate work

Breathing cycle, cycles/min 23 23 23 23 23

Tidal volume, mL 1586 1586 1586 1586 1586

Output (%)

Total deposition 0.750 0.717 0.993 0.991 0.756

Total head deposition 0.605 0.426 0.890 0.891 0.623

Airway deposition (TB + Alv) 0.145 0.291 0.103 0.101 0.133

Alveolar deposition (Alv) 0.082 0.147 0.010 0.013 0.085

Heavy work

Breathing cycle, cycles/min 26 26 26 26 26

Tidal volume, mL 1920 1920 1920 1920 1920

Output (%)

Total deposition 0.744 0.711 0.994 0.992 0.750

Total head deposition 0.609 0.421 0.897 0.897 0.625

Airway deposition (TB + Alv) 0.135 0.290 0.097 0.095 0.125

Alveolar deposition (Alv) 0.075 0.125 0.006 0.009 0.078

1 Number of samples; breathing cycles and tidal volumes for all activity levels from ICRP 1994; cycles/min; normal oronasal 

augmentor; NP dead space remains the same for all activity levels; NP, naso-pharynx; TB, tracheobronchial deposition fraction; Alv, 

Alveolar deposition; 5

Deposition and clearance modes in the MPPD2 model refer to fraction deposited on lung surfaces and 

subsequently cleared through absorption and removal by ciliary action (see text); mL, mililiter

Using the Park and Paik data for heavy work activity (26 cycles/min x 1920 mL tidal 

volume), for example, the total deposition for the combined group was 75%; the total 

76
head deposition, 62.5%; total airway deposition (tracheobronchial plus alveolar), 12.5%;

and alveolar deposition, 7.8%. However, for battery and lead powder workers, nearly 

100% of the particles inhaled (14.1 μm and 15.1 μm MMAD, respectively) are deposited 

compared to nearly 75% for secondary smelting and radiator workers (4.9 μm and 1.3 

μm MMAD, respectively). Battery workers have a much higher total deposition because 

larger particles tend to deposit on respiratory mucosa, while very small particles can 

behave somewhat like a gas and therefore are partially exhaled. So even though battery 

workers have a very small fraction getting to the alveoli, almost nothing is exhaled.

In order to check that the MPPD2 model with Park and Paik data was producing reliable 

results, we conducted several additional analyses: 1) we ran the model in deposition 

and clearance mode to verify that it produced expected results; 2) we conducted an 

analysis using different chemical species of lead (Spear et al. 1998a); 3) we conducted 

analyses using two additional data sets (Liu et al. 1996; Spear et al. 1998b); and 4) we 

compared the results of the MPPD2 model to particle deposition predictions from the 

ICRP Human Lung Model, which has been the key reference work in this area for nearly 

20 years. These analyses are described below.

Running the model in deposition and mechanical clearance mode showed that a fiveday tracheobronchial deposition of lead in radiator workers (26 μg/m3

, MMAD 1.3 μm, 

GSD 9.6) was rapidly cleared by the seventh day. By contrast, alveolar deposition was 

very slowly mechanically cleared (< 3% in nine days) (data not shown).

To evaluate whether differences in lead chemical species, and therefore density, would 

have a significant influence on deposition, we conducted an analysis using the chemical 

speciation data from Spear et al. (1998a) for PbO (9.6 g/cm3), PbS (7.6 g/cm3), and 

PbSO4 (6.3 g/cm3) at a low breathing cycles/minute and constant concentration. The 

differences were not dramatic (Table B-3). For example, there were no significant 

differences in alveolar deposition fraction between species of lead among battery or 

lead powder workers.

77
Table B-3: Lung dosimetry analysis of occupational airborne lead exposure by 

the MPPD2 model with different speciation assumptions1

Parameter/Occupational setting

Secondary 

Smelting

N = 6

Radiator 

workers

N = 42

Battery 

workers

N = 44

Lead Powder 

workers

N = 25

AM ± SD concentration μg/m3 653 ± 356 26 ± 27 1084 ± 1828 895 ± 1501

MMAD μm 4.9 1.3 14.1 15.1

GSD 5.0 9.6 1.5 1.7

Pb

Pb, density, g/cm3 11.34 11.34 11.34 11.34

Total deposition fraction 0.756 0.743 0.997 0.996

Total head deposition fraction 0.612 0.520 0.971 0.996

Airway deposition fraction, TB + Alv 0.143 0.223 0.026 0.030

Alveolar deposition fraction, Alv 0.093 0.117 0.006 0.009

Mass deposition/alveolus, μg 7.83E-11 3.92E-12 8.10E-12 9.77E-12

Mass deposition/macrophage, μg 6.47E-12 3.24E-13 6.69E-13 8.08E-13

PbS

PbS, density, g/cm3 7.6 7.6 7.6 7.6

Total deposition fraction 0.748 0.720 0.997 0.996

Total head deposition fraction 0.612 0.512 0.971 0.996

Airway deposition fraction, TB + Alv 0.136 0.208 0.026 0.030

Alveolar deposition fraction, Alv 0.089 0.106 0.006 0.009

Mass deposition/alveolus, μg 7.44E-11 3.56E-12 7.95E-12 9.86E-12

Mass deposition/macrophage, μg 6.15E-12 2.94E-13 6.57E-13 8.15E-13

PbO

PbO, density g/cm3 9.64 9.64 9.64 9.64

Total deposition fraction 0.752 0.734 0.997 0.996

Total head deposition fraction 0.612 0.517 0.971 0.996

Airway Deposition fraction, TB + Alv 0.140 0.217 0.026 0.030

Alveolar deposition fraction, Alv 0.091 0.113 0.006 0.009

Mass deposition/alveolus, μg 7.66E-11 3.77E-12 7.891E-12 9.81E-12

Mass deposition/macrophage, μg 6.34E-12 3.12E-13 6.52E-12 8.11E-13

PbSO4

PbSO4, density g/cm3 6.29 6.29 6.29 6.29

Total deposition fraction 0.740 0.710 1.0 1.0

Total head deposition fraction 0.610 0.510 0.970 1.0

Airway deposition fraction, TB + Alv 0.130 0.200 0.026 0.030

Alveolar deposition fraction, Alv 0.087 0.100 0.006 0.009

Mass deposition, alveolus, μg 7.3E-11 3.4E-12 8.0E-12 9.9E-12

Mass deposition, macrophage, μg 6.0E-12 2.8E-13 6.6E-13 8.2E-13

1 MPPD2, Multipath particle deposition model v2; (Park and Paik 2002); (Spear et al. 1998a); TB, tracheobronchial; Alv, alveolar; 

AM, arithmetic mean; SD, standard deviation; μg/m3

, microgram per cubic meter; μm, micrometer; g/cm3, gram per cubic centimeter; 

Pb, inorganic lead; PbS, lead sulfide; PbO, lead oxide; PbSO4, lead sulfate; MMAD, mass median aerodynamic diameter.

In addition to the Park and Paik (2002) data, we analyzed the data of Liu et al. (1996). 

In this case, we derived truncated distributions (< 12 μm MMAD) based on weighted 

78
means of binned particle size data for airborne lead exposure of brass foundry workers. 

For four occupational activities of cutting, furnace, grinding, and pouring with adjusted 

concentrations of 22 to 122 μg/m3, MMAD of 2.06 to 4.63 μm, SD of 2.08 to 6.51 μm, 

and a breathing rate of 12 cycles/minute, 625 mL/cycle (resting), we obtained predicted 

airway depositions (TB + Alv) of 15.3 to 21.0% and alveolar depositions of 7.9 to 12.6%

(Table B-4). Despite a more limited analysis, these results are in broad agreement with 

the analysis of the Park and Paik (2002) data above (Table B-2). The airway and 

alveolar deposition fractions are 14.3 – 22.3% and 9.3 – 11.7%, respectively, in the 

Park and Paik analysis for resting activity level in secondary smelting and radiator 

manufacturing, which have similar MMADs to those in the brass foundry reported in Liu 

et al. (1996).

79
Table B-4: Lung dosimetry analysis of occupational airborne lead exposure in 

brass foundry workers by the MPPD2 model (data of Liu et al. 1996)1

Parameter/Occupational setting Cutting, 

N = 14

Furnace, 

N = 13

Grinding, 

N = 10

Pouring,

N = 13

GM, GSD concentration μg/m3 621 ± 3.2 158 ± 2.4 509 ±3.2 32 ±1.4

Adjusted concentration μg/m3 122 112 100 22

MMAD μm 4.63 2.06 4.17 2.44

SD μm 2.08 6.51 2.7 6.38

Density, g/cm3 11.34 11.34 11.34 11.34

Breathing cycle, cycles/min 12.0 12.0 12.0 12.0

Tidal volume, mL 625 625 625 625

NP dead space, mL 50 50 50 50

Total deposition fraction 0.868 0.685 0.882 0.683

Total head deposition fraction 0.688 0.516 0.672 0.530

Airway deposition fraction, TB + Alv 0.180 0.170 0.210 0.153

Alveolar deposition fraction, Alv 0.108 0.085 0.126 0.079

Mass deposition/alveolus, μg 1.70E-11 1.23E-11 1.63E-11 2.24E-12

Mass deposition/macrophage, μg 1.40E-12 1.02E-12 1.35E-12 1.85E-13

1 MPPD2, Multipath particle deposition model v2; (Liu et al. 1996); data reduced by truncated amount in largest particle size bin (> 

10 μm); cycles/min normal oronasal augmenter; GM, geometric mean; GSD, geometric standard deviation

Finally, we examined the sinter plant and blast furnace data from Table 1 in Spear et al. 

(1998b), for finer particles. Overall, the values obtained by these data (data not shown) 

were similar to the values from Liu et al. (1996) and Park and Paik (2002).

B.3.2 Dosimetry results from ICRP Human Lung Model publication 66 lookup tables

For comparison, in Table B-5 we have given the particle deposition predictions of the 

ICRP Human Lung Model (ICRP 1994) for different particle sizes and physical activity 

levels.

80
Table B-5: Particle deposition by activity level and particle size by the ICRP 

Human Lung Model (ICRP 1994)1

size (AMAD)

Region 1 μm 3 μm 5 μm 7 μm 10 μm 15 μm

0.45 m3/hr (resting)

Bronchi 0.016 0.019 0.018 0.016 0.012 0.009

Bronchioles 0.015 0.013 0.010 0.0078 0.005 0.003

Alveoli 0.14 0.14 0.11 0.082 0.056 0.032

Total deposition 0.39 0.67 0.74 0.75 0.74 0.70

0.54 m3/hr (light activity)

Bronchi 0.014 0.016 0.015 0.013 0.011 0.007

Bronchioles 0.014 0.011 0.009 0.007 0.004 0.002

Alveoli 0.15 0.14 0.10 0.078 0.052 0.029

Total deposition 0.42 0.69 0.76 0.77 0.75 0.70

1.5 m3/hr (moderate activity)

Bronchi 0.008 0.006 0.005 0.004 0.003 0.002

Bronchioles 0.007 0.005 0.004 0.003 0.002 0.001

Alveoli 0.099 0.067 0.045 0.031 0.019 0.010

Total deposition 0.53 0.79 0.83 0.82 0.78 0.72

3.0 m3/hr (heavy activity)

Bronchi 0.010 0.016 0.015 0.013 0.010 0.006

Bronchioles 0.009 0.011 0.009 0.007 0.005 0.003

Alveoli 0.12 0.10 0.073 0.053 0.034 0.018

Total deposition 0.42 0.70 0.77 0.77 0.75 0.71

1

ICRP, International Commission for Radiologic Protection; 2AMAD, Activity Median Aerodynamic Diameter; μm, microgram; m3/hr, 

cubic meters per hour; There is an error in the ICRP Publication 66 deposition tables: AMTD is correct for aerosol sizes 0.0006-0.2 

μm, but aerosol sizes 0.5-20 μm actually refer to AMAD (activity median aerodynamic diameter). For a radionuclide, AMAD is equal 

to MMAD if the radioactivity per unit mass is constant among all particle sizes in the distribution of particle sizes (Leggett 2012 

personal communication); Total deposition, including upper airway and extra-thoracic, i.e. the fraction that is not exhaled

The values derived from the ICRP lung model are similar to those derived from MPPD2 

regardless of activity level. MMAD ranges from 2 μm to 4.6 μm in the Liu data (Table B4). Using MPPD2, the total deposition ranges from 68.3% to 88.2% and deposition in 

81
the alveoli 7.9% to 12.6% (Table B-4). This is very similar to the values predicted by the 

ICRP model for particles in the 3 – 5 μm size range (67% to 74% for total deposition 

and 11% to 14% for deposition in the alveoli) in Table B-5.

B.3.3 Default inhalation transfer coefficient (ITC) 

B.3.3.1 Derivation of ITCs

We determined the percentage of inhaled lead that transfers to the blood according to 

Equation B-1.

Eq. B-1: Inhalation transfer = (alveolar deposition x lung absorption) 

+ (ciliated and head region deposition x average gut absorption)

where:

• Alveolar, ciliated, and head region deposition fractions are based on MPPD2 lung 

dosimetry analysis (Table B-2).

• Lung absorption in the alveolar region is assumed to be 100%.

• Average gut absorption of lead mechanically removed from the ciliated and head 

region and swallowed is assumed to be 30%.

We assume a higher gut absorption factor than Leggett’s default of 15% because ciliary 

clearance occurs over days in which three conditions exist when lead enters the gut -

after hours of fasting, with liquid between meals, or during meals. The range of gut 

absorption of lead in mass balance studies is 30 - 70% after several hours of fasting, 8 -

30% with liquid between meals, and 3 - 20% for intake with solids. We estimated mean 

absorption fractions (AF) for the three conditions as 50%, 19%, and 12%, respectively, 

by taking the mid-points of the ranges in published studies. (See Appendix A for a brief 

review of the balance studies that examined the range of absorption fractions under 

each condition.) We calculated a 24-hr TWA absorption of 30% assuming 10 hours 

fasting (50% AF), 10 hours with liquids between meals (19% AF), two hours intake with 

solids (12% AF),and two hours in which no lead is swallowed.

82
B.3.3.2 Calculation of inhalation transfer coefficients

Below we present a sample inhalation transfer coefficient calculation for secondary 

smelting, resting activity level.

We assume particles behave as follows based on lung dosimetry analysis using Park 

and Paik data presented in Table B-2.

• 75.6% of the lead inhaled is deposited in the respiratory tract and 24.4% is 

exhaled.

• 9.3% of the inhaled mass is deposited in the alveolar region and absorbed to 

blood.

• 66.3% of the inhaled mass is removed by ciliary action or secretions, swallowed, 

and deposited in the GI tract (total deposition fraction minus alveolar deposition 

fraction).

Then, we calculate a grand transfer factor of 29.2% by adding the amount of inhaled 

mass that is absorbed through the alveolar region of the lung to the mass swallowed 

and absorbed through the GI tract (9.3% x 100% + 66.3% x 30% = 29.2%).

B.3.3.3 Selection of a default inhalation transfer coefficient

We selected 30% as our default inhalation transfer coefficient after analyzing the data in 

several different ways. First, using the Park and Paik data and assuming an average BR 

of 25 L/min during the exposure period, we calculated an ITC for each occupational 

setting (range 30.1% - 30.5%). Note the ICRP associates a breathing rate of 25 L/min 

with light activity and U.S. EPA associates breathing rates of 14 and 30 L/min with light 

and moderate activity, respectively, for adult men. We also calculated transfer 

coefficients using the Park and Paik data for all four occupational settings and all five 

ICRP activity levels (Table B-6a). The transfer coefficients ranged from 28% to 32%

with a midpoint of 30%. Finally, we calculated a TWA transfer coefficient for each 

occupational setting using the same activity weighting factors for the 8-hr exposure 

period as used to derive a 24-hr average breathing rate (33% sedentary, 25% light, 42% 

moderate) (Table B-6b). The TWAs ranged from 29% - 31%; midpoint 30%.

83
Table B-6a: Inhalation transfer coefficient (ITC) by worker group and activity level1

Activity Level

ITC

Secondary 

smelting

Radiator 

manufacturing

Battery 

manufacturing

Lead powder 

manufacturing

Resting 29% 31% 30% 31%

Sitting 31% 32% 30% 30%

Light work 30% 31% 30% 30%

Moderate work 28% 32% 31% 30%

Heavy work 28% 30% 30% 30%

1 ITC, inhalation transfer coefficient

Table B-6b: TWA inhalation transfer coefficients (ITC) by occupational setting1

Occupational setting TWA ITC

Secondary smelting 29%

Radiator manufacturing 31%

Battery manufacturing 29%

Lead powder manufacturing 31%

1

TWA, time weighted average; ITC, inhalation transfer coefficient

Our data indicate that while particle size distribution has a significant impact on the total 

fraction of inhaled lead deposited in the head and airways and on the fraction deposited 

in the alveoli, the fraction ultimately transferred to the blood does not vary greatly by 

particle size distribution. Battery manufacturing and lead powder manufacturing, which 

tend to have much larger particle sizes (MMAD 14.1μm; 15.1μm) had similar ITCs to 

smelting and radiator manufacturing, which have much smaller particle sizes (MMAD 

4.9 μm; 1.3μm). The decrease in the fraction deposited deep in the lung when particle 

84
sizes are large, is offset by an increase in the total head deposition fraction (larger 

particles are not exhaled but deposit in the head region) and subsequent swallowing 

and gut absorption.

In summary, based on actual data on particle size distributions measured in 

occupational settings, we derived a default coefficient of 30% for the absorption of lead 

from inhaled particles for use in the exposure portion of the model.

In the next section, we checked to see that simulations from the Leggett+ model

adequately predict workplace measurements by comparing model predictions with BLL 

measurements reported in our selected studies (Griffin et al. 1975; Williams et al. 1969; 

Snee 1982).

B.4 Methods for assessing the performance of the Leggett+ model 

The purpose of the Leggett+ model is to inform changes to the California General 

Industry and Construction PEL for lead (Cal/OSHA 2007a, 2007b). Therefore, the 

primary criterion for assessing the Leggett+ model is its predictive validity for exposure 

scenarios relevant to industrial exposures and therefore of interest to CDPH-OLPPP. An 

ideal model would predict BLLs in workers exposed during relatively short or long 

periods to air concentrations in the workplace ranging from < 10 μg/m3 to > 200 μg/m3 in 

workplace air.

Because there were no measurements of workplace air lead concentration in the 

ASARCO dataset, we needed to find additional studies with exposure and blood lead 

measurements taken at the individual level to evaluate Leggett+ for task one. We found

several studies that examined the relationship between personal breathing zone air 

concentration and BLLs among lead industry workers and others exposed to lead 

(Griffin et al. 1975; Azar et al. 1975; Chavalitnitikul et al. 1984; Gross 1979, 1981; 

Hammond et al. 1981; Hodgkins et al. 1991a; Hodgkins et al. 1991b; Hodgkins et al. 

1992; Kononen et al. 1989; Rodrigues et al. 2010; Snee 1981, 1982; Williams et al. 

1969). From the studies that provided enough individual-level data to help us compare 

measured BLLs to predicted BLLs from Leggett+, we selected for further analysis two 

85
studies which cover the range of BLLs, workplace air concentrations, and exposure 

durations of interest to CDPH-OLPPP (Griffin et al. 1975; Williams et al. 1969).

From these two studies we extracted data for each study subject on beginning BLL, 

ending BLL, total daily lead intake, and duration of exposure. We input the data into the 

Leggett+ model to get a predicted BLL for each subject. We then compared the 

predicted BLL to the subject’s measured BLL (see Figure B-1). In some cases not all 

the desired information was available and we were forced to make assumptions.

However, at a minimum, studies had to provide individual-level data on continuous 

personal breathing zone airborne lead concentration (for chamber study) or on-duty 

personal breathing zone PbA (for workplace study), and ending BLL. Our assumptions 

are identified and discussed below under each study. See Table B-7 for a summary of 

model inputs for each study.

Figure B-1: Model validation diagram1

1

BLL, blood lead level; Pb, lead; m3/day, cubic meters per day

B.4.1 Study and subject selection criteria 

In an initial review of the occupational literature, it became clear that many factors, if not 

adequately controlled in the design of studies measuring air lead levels and worker BLL 

or during the analysis, could confound the relationship between workplace air 

concentrations and BLLs among exposed workers (Hodgkins et al. 1991b; Hodgkins et 

• Beginning BLL (known or 

assumed)

• Duration of exposure (known or 

assumed)

• Total daily Pb intake

Leggett +

Includes default:

• Breathing rate 

(26 m3

/day)

• Inhalation transfer 

coefficient (30%)

Input Model

Predicted

Output

Measured

Comparison

86
al. 1992; Kononen et al. 1989). Therefore, we restricted our selection of studies and 

subjects to those meeting the following criteria:

1) The study needed to report personal air concentration measurements coupled 

with blood lead measurements.

2) Only studies that reported relatively constant air concentrations over time were 

selected (i.e., no substantial changes in workplace or controlled chamber 

conditions).

3) Only studies that collected some information about the level of background 

exposure, or had information that could be used to make a reasonable 

assumption about background exposure, were selected.

4) Only subjects exposed at least six hours per day, five days per week, were 

included.

A chamber study conducted by Griffin et al. (1975) and an occupational study 

conducted by Williams et al. (Williams et al. 1969; Snee 1982) met our selection criteria.

B.4.2 Data extraction

B.4.2.1 Griffin et al. (1975)

Griffin and co-workers (Griffin et al. 1975) determined changes in BLL with time in 31 

healthy adult male volunteers who were exposed to elevated levels of airborne lead 

concentration for 23 hours/day for about 16 weeks in an environmentally controlled 

exposure chamber. There were 12 non-exposed control subjects. Two sets of 

experiments were reported. One experiment exposed subjects to an average airborne 

lead concentration of 10.9 μg/m3 in the exposure chamber over the entire exposure 

period. Another experiment exposed subjects to an average airborne lead concentration

of 3.2 μg/m3 in the exposure chamber over the entire exposure period. BLLs were 

obtained from each subject in each experiment prior to, during, and after exposure 

ended.

We removed five subjects with very short-term exposure that is more likely to be 

influenced by exposure prior to the start of the experiment (i.e., less than 30 days) and 

87
two additional subjects with suspect back-calculated intake during the exposure period 

(i.e., less than zero relative to pre-exposure intake). The remaining 24 subjects were 

included in our analysis.

Exposure periods were reported for each participant and varied by subject. The shortest 

exposure period for those included in our analysis was 42 days. For most subjects the 

exposure period was about 16 weeks. For modeling purposes we used each individual’s 

reported exposure period. 

We extracted the authors’ reported baseline BLLs, taken prior to entering the exposure 

chamber, and end-of-exposure BLLs for each subject.

For modeling purposes we used the reported mean of all daily air lead measurements 

for the entire exposure period (experiment 1 - 10.9 μg/m3; experiment 2 - 3.2 μg/m3).

Lead content in the diets of each subject was analyzed. However, the authors reported 

individual averages over the entire exposure period without providing subject numbers.

Therefore, for modeling purposes we back-calculated the daily uptake of lead 

(representing the ambient air and dietary lead intake) for each subject from the 

preexposure BLL 

B.4.2.2 Williams et al. (1969)

Williams et al. (1969) collected personal breathing zone air and blood lead 

concentrations from British battery plant workers exposed during a period of time when 

older process technology was in use (Hodgkins et al. 1992). Thirty-nine workers were 

followed. However, Williams et al. believed that 10 of 39 BLL test results were

contaminated and threw them out, leaving 29 workers with complete information.

Nineteen workers were exposed during three processes with high air concentrations (79 

– 298 μg/m3

). Ten workers, comprising the two control groups, were exposed to air 

concentrations of 8 – 13 μg/m3. Williams et al. reports that the jobs of the men selected 

for the study “...did not entail wearing respirators.” BLLs ranged from 22.5 – 33.0 μg/dL

in the control group and from 44.6 – 93.0 μg/dL in the exposed group (Snee 1982; 

Williams et al. 1969).

88
Of the 29 subjects for whom individual data were available on exposure and BLL, as 

presented in Snee (1982), we selected 16 for model validation. We excluded 13 of the 

29 subjects because they had BLLs over 61 μg/dL. BLLs above 60 are less likely to 

represent current chronic exposure levels and are outside the range of BLLs CDPHOLPPP asked us to model.

Job tenure was not available for individual subjects. For validation purposes we 

assumed job tenure of 20 years for all subjects. We based this assumption on data in 

the Hodgkins et al. (1991) study of battery workers. As reported by Hodgkins et al., the 

mean seniority of workers in both plants studied was about 20 years.

Pre-exposure BLLs were not available. We assumed that all subjects had a baseline 

BLL of 20 μg/dL at the start of their employment. The Williams et al. simulation starts in 

the late 1940s/early 1950s, under the assumption that workers in the study had worked

in the plant for 20 years. BLLs among the controls in the Kehoe inhalation studies, 

started in the 1950s, were around 20 μg/dL. For end-of-exposure BLLs we used the 

individual’s reported BLL in the study.

Time-weighted average lead exposure was available for each subject for on-duty 

exposures. The air concentrations ranged from 8 – 166 μg/m3

. We assumed that the air 

lead concentration reported in the study reasonably represented the subjects’ exposure 

over their job tenure. 

Off-duty inhalation and dietary lead exposure were assumed to be represented by the 

background BLL typical of the 1960’s and set at 20 μg/dL (Gross 1979). We backcalculated the uptake of lead from the assumed background BLL of 20 μg/dL.

89
Table B-7: Model inputs1

Study Griffin et al. (1975) Williams et al. (1969)

Study type • Chamber

• Prison volunteers

• Occupational

• Battery workers in England

Subjects • Excluded 7 of 31 subjects.

o 5 with very short-term 

exposure

o 2 with suspect intake during 

exposure period

• No smoking data reported.

• N = 24

• Excluded 23 of 39 workers. 

o10 with missing BLL data

o13 with BLLs above 60 μg/dL

• No smoking data available.

• N = 16

Breathing rate (BR) • Assumed default BR of 26 m3

/d. • Assumed default BR of 26 m3/d.

Exposure duration • No assumptions made.

• Data available for every subject.

• Range 4 – 16 w; most 16 w

• Assumed 20 y based on mean 

seniority of battery workers in 

another study.

BLL • No assumptions made.

• Beginning and ending BLL 

available for each subject.

• Assumed beginning BLL of 20 

μg/dL based on Kehoe data.

• Ending BLL is BLL at time of 

study.

Inhalation exposure • No assumptions made.

• Monitored 23 h/d.

• air lead concentration available 

for each subject.

• Experiment 1: 10.9 μg/m3

• Experiment 2: 3.2 μg/m3

• On-duty PbA available for each 

subject.

• Range 8 – 166 μg/m3

• Assumed the on duty PbA was 

constant for each subject over job 

tenure.

• Off-duty inhalation and dietary 

intake back-calculated from 

assumed background BLL of 20 

μg/dL.

Dietary exposure • No assumptions.

• back-calculated as daily uptake 

• Off-duty inhalation and dietary 

intake back-calculated from 

assumed background BLL of 20 

μg/dL.

1

BLL, blood lead level; μg, microgram; dL, deciliter; N, number; BR, breathing rate; m3/d, cubic meters per day; w, week; h/d, hour 

per day; PbA, air concentration of lead

B.5 Results

For each study, OEHHA examined whether the Leggett+ model predicted an accurate 

BLL from estimates of air concentration and dietary intake for each day during each 

90
subject’s exposure period. Table B-8 lists the attributes of subjects from each study 

along with the BLL predicted from the Leggett+ model.

Table B-8: Measured BLL versus BLL predicted by Leggett+1

Subject

ID

PbA

(μg/m3)

Exposure

Period

(days)

Total

intake

(μg/d)

Measured

BLL

(μg/dL)

Predicted

BLL

(μg/dL)

Measured

less

Predicted

Griffin et al. 1975

32 3.2 123 67.1 30 27.3 2.7

33 3.2 123 61.3 25 25.6 -0.6

34 3.2 123 73.4 28 29.1 -1.1

35 3.2 102 67.1 32 27.1 4.9

37 3.2 53 48.9 18 20 -2

38 3.2 102 46.7 23 20.3 2.7

39 3.2 98 42.6 18 18.6 -0.6

310 3.2 123 61.3 24 25.6 -1.6

311 3.2 120 58.6 27 24.8 2.2

312 3.2 123 64.1 30 26.5 3.5

313 3.2 102 56 26 23.6 2.4

314 3.2 78 67.1 25 26.7 -1.7

317 10.9 123 120.3 32 35 -3

318 10.9 123 123.1 43 35.6 7.4

320 10.9 123 115 36 33.9 2.1

321 10.9 123 135.7 37 38 -1

322 10.9 113 123.1 41 35.3 5.7

323 10.9 123 105.7 30 31.7 -1.7

326 10.9 42 150.7 39 38 1

327 10.9 123 126 39 36.2 2.8

328 10.9 123 123.1 36 35.6 0.4

329 10.9 77 120.3 31 33.6 -2.6

330 10.9 77 115 32 32.4 -0.4

331 10.9 77 103.6 28 29.5 -1.5

Average measured less predicted BLL: 0.83 μg/dL

Williams et al. 1969

41 10 7300 41 25.8 26.9 -1.1

42 12 7300 44 27.6 27.8 -0.2

43 9 7300 39 28 26.4 1.6

91
Subject

ID

PbA

(μg/m3)

Exposure

Period

(days)

Total

intake

(μg/d)

Measured

BLL

(μg/dL)

Predicted

BLL

(μg/dL)

Measured

less

Predicted

44 9 7300 39 28.8 26.4 2.4

45 13 7300 46 29 28.3 0.7

46 13 7300 46 30 28.3 1.7

47 8 7300 38 32.4 25.9 6.5

48 8 7300 38 33 25.9 7.1

49 79 7300 155 44.6 47.3 -2.7

410 166 7300 300 45.6 59.2 -13.6

411 159 7300 288 51.2 58.5 -7.3

412 129 7300 238 56.8 55.1 1.7

413 159 7300 288 59.4 58.5 0.9

414 121 7300 225 61 54 7

428 13 7300 46 22.5 28.3 -5.8

429 8 7300 38 24.6 25.9 -1.3

Average measured less predicted BLL: -0.15 μg/dL

1 BLL, blood lead level; ID, indentification number; μg/m3, microgram per cubic meter; μg/d, microgram per day; μg/dL, microgram 

per deciliter; PbA 24-hr time weighted average (TWA) air concentration of lead in chamber air; for Griffin et al. study. For Williams et 

al., PbA, on-duty 8-hr TWA. As described in the text, we back-calculated off-duty intake from an assumed background BLL of 20 

μg/dL

The difference between measured and predicted BLLs for each study subject is shown 

in the last column of Table B-8. The average difference between measured and 

predicted BLL for the Griffin et al. data set is 0.83 μg/dL; the average difference for the 

Williams et al. data set is - 0.15 μg/dL.

B.5.1 Test 1: Goodness of fit

We checked for systematic bias by conducting a regression analysis of measured 

versus predicted BLL for both the Griffin et al. and the Williams et al. datasets.

B.5.1.1 Model prediction of Griffin et al. (1975) data

A linear regression of predicted versus measured BLLs from the Griffin study using the 

Leggett+ model estimated a slope of 0.96 (i.e., 0.96 x measured BLL= predicted BLL),

suggesting that there is no evidence of systematic bias (< 10%). The average difference 

of linear (measured – predicted) intake for this cohort is 0.83 μg/dL. See Figure B-2 

below.

92
Figure B-2: Check for systematic bias – Griffin et al. 19751

1 Breathing rate (BR) = 23.5 m3

/day and ITC = 34% to reflect continuous exposure conditions in chamber study; BLL μg/dL, blood 

lead level in micrograms per deciliter

B.5.1.2 Model prediction of Williams et al. (1969) data

Using Leggett+ model, we performed a linear regression of predicted versus measured 

BLLs from the Williams study. Our analysis estimated a slope of 1.004, meaning that 1 x 

measured BLL= predicted BLL, suggesting that there is no evidence of systematic bias 

(< 10%). The average difference of linear (measured – predicted) intake for this cohort 

is -0.15 μg/dL. See Figure B-3 below.

y = 0.9646x

15

20

25

30

35

40

45

15 20 25 30 35 40 45

Predicted BLL μg/dL

Measured BLL μg/dL

Griffin data

93
Figure B-3: Check for systematic bias – Williams et al. 19691

1 Breathing rate (BR) = 26 m3

/day and ITC = 30% to reflect occupational scenario; BLL μg/dL, blood lead level in micrograms per 

deciliter

B.5.2 Test 2: Model performance versus exposure duration

Analysis of model performance versus exposure duration was limited to the Griffin data 

set. As described earlier, job tenure was not available for workers in the Williams study,

and we assumed uniform job tenure for this cohort.

The regression equation analyzing the performance of the Leggett+ model versus job 

tenure is:

• Model performance (measured – predicted BLL) = -2.5 + 0.032 x (days job 

tenure), p-value = 0.18

• The intercept value of -2.5 has a p-value = 0.32 (LCL, -7.67, UCL, 2.65), and the 

slope of 0.032 has a p-value = 0.18 (LCL, -0.02, UCL, 0.08).

y = 1.004x

20

25

30

35

40

45

50

55

60

65

20 30 40 50 60 70

Predicted BLL (μg/dL)

Measured BLL (μg/dL)

Williams data 

94
This equation suggested that differences in measured – predicted BLLs would not be 

expected to fall outside the deviations observed within the worker cohort, although the 

analysis is limited as the longest exposure period was 128 days. See Figure B-4 below.

Figure B-4: Model performance versus exposure duration – Griffin et al. 19751

1

BLL μg/dL, blood lead level in micrograms per deciliter

B.6 Conclusion

In Appendix A, we tested the adjusted core model and determined that the model 

predicted valid BLLs after a period of chronic exposure to lead at work followed by an 

extended period without workplace exposure. In Appendix B, we added an exposure 

module to the adjusted core model and re-named it the Leggett+ model. Our objectives 

for this appendix were to check the front-end exposure module and its default settings 

for breathing rate and inhalation transfercoefficient. We tested the Leggett+ model with 

data from one chamber and one occupational study. The data used to check model 

performance include relatively short or long exposure periods and personal breathing 

zone concentrations in the workplace ranging from < 10 μg/m3 to > 160 μg/m3

. These 

studies represented both steady-state exposures from study subjects who either served 

y = 0.032x - 2.51

-4

-2

0

2

4

6

8

0 20 40 60 80 100 120 140

Model performance (measured - predicted BLL 

(μg/dL)

Job tenure (days)

Griffin

95
many years in the location performing the job where they were monitored, or much 

shorter-term exposure periods in which the investigator measured blood lead multiple 

times during the exposure period. These are the conditions expected during workplace 

exposure and therefore of interest to CDPH-OLPPP.

Our analysis of model performance as described above indicates that our default 

breathing rate and coefficient for the transfer of inhaled lead to blood are reasonable.

Furthermore, this gives us confidence that Leggett+ is ready for modeling workplace air 

lead concentrations that result in BLLs of interest of CDPH-OLPPP, which in turn will 

inform CDPH-OLPPP’s recommendation for a health-based permissible exposure limit 

for lead.

96
C Appendix: Acronyms, symbols and special terms

Acronym/symbol Definition

< Less than

> Greater than

μg Microgram

μm Micrometer

ACSL Advanced Continuous Simulation Language

AD Aerodynamic diameter

Adjusted core model The adjusted version of the original nonlinear Leggett model. This part of the 

model plus a new exposure module is called Leggett+ in this report

AF Absorption fraction

Alv Alveolar region of the lung

AM Arithmetic mean

ASARCO American Smelting and Refining Company

Biokinetic Another type of computer model characterizing the transfer, distribution, and 

elimination of lead in the body

BLL Blood lead level (whole blood)

BR Breathing rate

Cal/EPA California Environmental Protection Agency

Cal/OSHA Division of Occupational Safety and Health

CDPH-OLPPP California Department of Public Health, Occupational Lead Poisoning Prevention 

Program

CIIT Chemical Industry Institute of Toxicology

cm2 Centimeter squared

cm3 Centimeter cubed

dL Deciliter

EVF Extra vascular fluid

FORTRAN Formula Translating System

g Gram

GI Gastro-intestinal

GM Geometric mean

GSD Geometric standard deviation

GUI Graphical user interface 

Half-life

The half-life or elimination t1⁄2 is the time required for the plasma concentration of 

a chemical to decrease by one-half

ICRP International Commission for Radiological Protection 

97
Acronym/symbol Definition

ITC Inhalation transfer coefficient

LCL Lower confidence limit

Leggett+ OEHHA enhanced version of the Leggett model for lead

LLI Lower-lower intestine

ln Natural log

m3 Meter cubed

MATLAB Matrix Laboratory – a proprietary software program for model building and 

execution

mL Milliliter

MMAD Mass median aerodynamic diameter

MPPD Multipath Particle Dosimetry model 

MPPD2 Multipath Particle Dosimetry model version 2

MSE Mean squared error

NIOSH National Institute for Occupational Safety and Health

NP Naso-pharynx region of the respiratory system

OEHHA Office of Environmental Health Hazard Assessment

Pb Symbol for lead

PbA Workplace air concentration of lead if not specified otherwise

PBPK

Physiologically-based Pharmaco-kinetic – refers here to the way a computer 

model characterizes how lead is taken up, distributed, metabolized, and 

eliminated in the adult human body

PEL Permissible Exposure Limit

p-value Test of statistical significance

R2 Regression coefficient indicating level of explained variability in the outcome 

variable

RBC Red blood cells (erythrocytes)

RIVM National Institute of Public Health and the Environment, The Netherlands

SD Standard deviation

SI Small intestine

t1/2 Half-life of lead in the body, body tissue or body compartment

TB Tracheobronchial

TWA Time-weighted average 

UCL Upper confidence limit

ULI Upper-lower intestine

XRF X-ray fluorescence

98
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