### 3



## Estimating Workplace Air and Worker Blood Lead Concentration using an Updated Physiologically

- based Pharmacokinetic (PBPK) Model

October 201 3

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 P hysiologically

- based Pharmacokinetic (P BPK) 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

A ir, Community and Environmental Research Branch

O EHHA 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 Rese arch Branch Lauren Zeise, Ph.D. Deputy Director for Scientific Affairs

___________________________________________________________________ George Alexeeff, Ph.D., Director
Allan Hirsch, Chief Deputy Director

iExternal 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
, F armington, 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

iii3.2.6 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 Workp lace 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

ivB.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

## T ables

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 c orresponding 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: M odel inputs

................................ ................................ ................................ ........... 90
B

- 8: Measured BLL versus BLL predicted by Leggett+

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

vi

## F igures

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 system atic bias

– Griffin et al. 1975

................................ ....................... 93
B

- 3: Check for systema tic bias

– Williams et al. 1969

................................ ................... 94
B

- 4: Model performance versus exposure dur ation

– Griffin et al. 1975

........................ 95

viiviii

## Summary

This document presents the results of a modeling effort by the California Environmental Protection Agency’s (Cal/EPA) Office of Env ironmental 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 wor kplace 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 bl ood) 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 (BLL s ) over the time span of interest (40

- year working lifetime), some type of mod el 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 lea d 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 perform ed multiple tests to ensure that predictions from the

1adjusted 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 t he 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 ad ded 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’ BLL s 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 dev eloped 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

- h r time

- weighted average workplace air
lead concentrations (PbA) (in microgram s of lead pe r cubic meter of air [μg/m 3 ]) and corresponding BLL s ( in units of μg/dL) in a median worker. Using population BLL distributions based on epidemiologic data, we calculated BLLs for workers at the 90 th
and 95 th percentiles of the p opulation, as sho wn in Tabl e S

- 1.

2Table S

- 1: Workplace air lead concentrations (PbA) and corresponding BLL 1

8

- hr TWA PbA (μg/m 3 ) Predicted BLL (μg/dL)

50 th percentile 90 th percentile 95 th 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
2 5

. 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/m 3 ]); BLL, blood lead level; BLL values that CDPH

- OLPPP asked OEHHA to model are in bold ; GSD, geometric standard deviation used to derive 90 th and 95 th 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
U sing the adjusted core Leggett model , OEHHA evaluated the time required for
workers’ BLL s to decline from a much higher BLL to 15 μg/d L 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, w or ker s are exposed to constant airborne lead concentrations during work hours, resulting in BLL s of 20, 30, 40, 50 or 60 μg/dL at the end of each exposure

3period. The results for scena rio 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 bone s 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 time s 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 exp osure (limit BLL reached at the end of exposure period) 1

Exposure duration Percentile BLL at beginning of MRP 1 (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 90 th and 95 th 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) model ing to support that effort. The support consists primarily of two tasks using PBPK modeling to predict:

1. the range s of concentrations of inorganic lead in workplace air in micrograms per cubic meter (μg/m 3 ) that would result in blood lead levels (BLLs) of
i nterest (5, 10, 15, 20, and 30 micrograms per deciliter [ μg/dL ] ) for the 50 th

, 90 th

, and 95 th 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 t hese tasks will supply important information supporting the consideration
of a revised lead standard : Task 1 m odel predictions of the air concentrations resulting
in certain BLL s w ill inform the choice of a health

- based permissible exposure limit (PEL)
intended to ensure that BLL s in workers would stay below a level proposed by CDPH

OLPPP

. Task 2 supports the prediction of the lengt h of time overexposed worker s will have to be kept away fr om workplace exposure in order for their BLL to return to 15 μg/dL.
This document begins with a descri ption of OEHHA’s methods for selecting and modifying the lead model, predicting blood lead , anal yzing the time for BLL s to return to 15 μ g/dL from a range of higher BLL s

. It is followed by a discuss ion 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

5deriving an exposu re 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 defin es acronyms that appear throughout this report.


## 2 Met hods and Results

2.1 Selection and modification of lead model In order to complete the t wo above

- mentioned tasks, OEHHA evaluated the available
models to determine which model best describe s 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, a nd 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 considere d for this project. However, it ha d 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 exposur e scenario because it:

• is sufficiently flexible to allow mod eling 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 expose d to environmental lead and
limited data from lead workers

. Dr. Leggett coded his published model in FORTRAN ( For mula Tran slating System )
, which is an old computer language. Our preferred platform for PBPK modeling is Matrix LaboratoryTM (M ATLAB ) (MATLAB 2012)

. Therefore, OEHHA coded t he Leggett model into script language used by MATLAB. We then compared the output from the original
and nonlinear model s recoded in MATLAB with the output generated by the author (personal communication with Dr. Legg ett, 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
simulat ed 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 m 3 /day, which reflects
time

- weighted breathing rates based on assumed activity levels for workplace and non

workplace exposure to airborne lead; and 3) setting a default value of 3 0 % for transfer of inhaled lead to blood (“inhalation transfer coefficient”) for particles in the size range
foun d in industrial settings. We named the modifi ed 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.

72.2.2 Inhalation t ransfer c oefficient For any given air lead concentration, the proportion of inhaled par ticles 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 depo sited 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
absor ption 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 deposite d 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 siz e 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 m ass median aerodynamic diameter ( MMAD ) . This is because the decrease in the fraction deposited deep in the lun g when particle sizes are large is offset by an increase in the total head deposition fraction (larger particles are not
exha led 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 o f 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
E quation 1 :

8Eq. (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 particle s) 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 sett ing 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 fa ctors we used to derive a 24

- h r 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 Legge tt 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 predicte d 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

9could m odel 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 s imulations in this report assumed a standard background BLL of 1.5 μ g/dL based on the background levels observed in the U.S. general population (Schober et al. 2006)

. T his BLL represent s constant exposure to an ambient air level of 0.006 μg/m 3 (the 2004 annual average level in California [ SCAQMD 2008 ] ), along with 14.6 μg/day of background oral intake from all non

- work sources or a combined uptake of 1.8 μg/day (see Table 1)

. There is uncertainty in estimating individual background intake, but the backgro und 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.

10Table 1: Parameters employed in OEHHA’s application of the Leg gett+ model 1

Parameter definition Units Value Reference

A ge 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 200 9; Schober 2006 ) Workplace a irborne lead concentration (Pb A ) μg/m 3 0.5

- 210 back calculated ( Tables 2 & 3 a )
Transfer fraction of inhaled lead to blood unitless 0.3 0 (see Appendix B )

Breathing rate 2 m 3 /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 2 5 0/365 (U.S. EPA 1991)
Body weight kg 7 3 (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; m 3 , cubic meter; μg , microgram, k g, 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 appear s 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 t he 50 th percentile worker after 40 years of workplace exposure. We calculated the 90 th and 95 th percentile BLLs from the 50 th percentile BLLs using Equations 2 and 3

- the standard statistical formulas for determining percenti les 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

.

11Table 2: Workplace air lead concentrations (PbA) and corresponding BLL 1

8

- hr TWA PbA (μg/m 3 ) Predicted BLL (μg/dL)

50 th percentile 90 th percentile 95 th 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
2 5

. 0 25 46 54
34.0 30 55 65

1 BLL, blood lead level; μg/dL, microgram per cubic deciliter; a ssumptions 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/m 3 ]); BLL, blood lead level; BLL values that CDPH

- OLPPP asked OEHHA to model are in bold

. To obtain the 90 th and 95 th percentile BLL s, 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 worker s (for a description of our analysis
, see section 3 .2 .1 Population
BLL variabilit y ). As described previously, we applied the GSD to the 50 th percentile BLL
to derive the 90 th and 95 th percentile BLLs

. Figure 1 depicts the rise in BLL in the 95 th 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,

12workplace PbA is added to background lead for 40 years. Note that BLLs climb rapidly during the first year of workplace exposure and con tinue 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; c orresponding 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/m 3 respectively.
2.3 T ime 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 yearsBlood lead level (μg/dL)

13modeling 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 BLL s OEHHA used Leggett+ to estimate the constant 8

- hr TWA air concentration that would
result in BLLs of 2 0, 30, 40, 50, and 60 μg/dL (referred to as “limit BLLs ” ) at the end of the exposure period (Table 3 a). Daily exposure wa s then reduced to background level at the end of the exposure period, and the time ne eded for each BLL to decline to 15 μg/dL was predicted (Table 3 b).

Table 3a: Workplace air l ead concentration ( PbA ) (μg/m 3 ) for different duration s of exposure corresponding to the BLL reached 1

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 50 th 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 (Hodgk ins 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
50 th , 90 th , and 95 th percentile worker for each of five exposure periods appear in Table
3b. For example, 166 μ g/m 3 of workplace air concentration together with background

14levels of ambient air and oral sources of lead over 40 years produced a BLL of 60 μ g/dL (Table 3a column 5 ). It took an average of 1045 days for the BLL to return to 15 μ g/dL
for the median worker (Table 3b column 7 ). We estimated the 90 th and 95 th percentiles
from 50 th percentile BLLs using Equations 2 and 3

- the standard statistical formulas for determining percenti les 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, geome tric standard deviation used to derived 90 th and 95 th percentile estimates = 1.6. See Table 1. OEHHA applied a standard statistical equation for a lognormal distribution of BLLs in the worker population because n o measure of variability was given by (Legge tt 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 half

life). This striking difference is due to a greater proportion of lead accumulating in the

15skeleton at a BLL of 60 μg/dL compared to 30 μg/dL because, as BLLs rise
, an increasingly larger portion of lead remains unbound

. Figure s 2 and 3 illustrate the effect on buildup and elimination of lead in a median worker’s skeleton for two exposur e 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 backgro und 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 t urnover 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 correspon ds mostly to declining lead in trabecular bone

.

16Figure 2: Modeled skeletal and BLL s for the median worker during and after workplace exposure (Leggett+ model) 1

1 Constant 8

- hr TWA air concentration of 34 μg/m 3 . 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 c ould 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 yearsLead in μg/dLblood volume or μg/g bone Trabecular bone

Cortical bone Blood

17Figure 3: Modeled skeletal and BLL s for the median worker during and after workplace exposure (Leggett+ model) 1

1 C onstant 8

- hr TWA air concentration of 166 μg/m 3 . 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 c ould result in a higher concentrati on of lead in bone than shown in this figure (Leggett et al. 1982)
, (O'Flaherty 2000)

.

2.3.2 Scenario two : D eclining 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 s cenario and stayed at
that level for a number of years before being removed from exposure. Thus, i n scenario two, we assumed w orkers were exposed to airborne lead for 1, 10, 25
, or 40 years, resulting in BLL s of 20, 30, 40, 50, or 60 μg/dL within the first year of exposure

. Then,
the modeled air borne lead concentration was allowed to decline linearly i n 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 BloodLead in μg/dL blood volume or μg/g bone

18sustain 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 50 th percentile (average worker) BLLs of 20, 30, 40, 50, or 60 μg/dL

. Th en, 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 fr action of inhaled lead, daily uptake of lead (back

calculated 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 o ne ) are minimal (1% or less) (data not shown)

. Scenario one and t wo 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 f or a BLL of 60
, for the 95 th 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 6 0 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 S ection 3 .1. The limitations to OEHHA’s new model and remaining uncertainties in our approach are explained in S ection 3 .2.

193.1 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 consta nt 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/m 3 all particles are small and are absorbed with an efficiency of 37%; above 12.5 μg/m 3 all particles are large and absorbed with an efficiency of 8% (t his 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 i ndependent 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 re cently developed Leggett+ model

.

• modeled BLL at the end of a 40

- year working lifeti m e 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 w ith constant exposure (Barry 1975)

.

• accounted for nonlinear kinetics b e tween BLLs and air lead levels. R esearch
conducted since the Federal OSHA’s 1978 standard shows that as BLLs rise above 25 μg/dL, the relationship between air concentration and BLL noti ceably 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 tha t 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 p redictions 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 u ncert ainty

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 deri ved 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 dis tributed 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 site

specific data when conducting a risk assessment, we calculated GSDs from BLL dat a 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

2193 μg/dL. We calculated GSDs by log transforming 8

- hr TWA air and blood lead

c oncentrations, grouping BLLs by levels of total lead intake, and deriving the GSD from the regressio n 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
i ntake 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 m 3 /day for workers. This rate is based on a weighted combinat ion 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)

. The re 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/d L). After making this and othe r modifications, we tested
the model with several datasets from the literature. The results of these tests appear in

22Appendix 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
up take, distribution, and clearance of lead. Kidney damage has been reported among chronically exposed workers at BLL s as low as 30 μ g/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
l ead 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 cleare d 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 an alysis 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 parti cle size distribution to apply generically to the entire lead industry. There is some general knowledge on the types of industrial processes and construction tasks

23that tend to generate coarse and fine particles (Froines et al. 1986; Hinds 1982; Hodgkins e t 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
p rogram. 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 ai r lead concentrations up to 12.5 μg/m 3 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 co ncentration 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 evalu ated 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 MMAD s and the MPPD2 , OEHHA estimated the deposition and clearance in th e 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 becau se 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

. 243.2.6 Gastrointestinal absorption Early mass balance s tudies 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 Appendi x A ). In addressing inhaled lead cleared from the lung by ciliary
action and swall owed, 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 differ s 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 sli ghtly 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 , i t
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 n ot 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 wou ld 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 bec ause it has an

25optional 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 adjust ments 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 s tudies 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 predic tions 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 mod el 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 proces s 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 BLL s. It should be noted that n o effort was made to evaluate model predictions of exposure in childhood or in acute or short

- term (less than 30 days) exposure scenarios. In additi on, this new model has not been evaluated for chronic exposures leading to BLLs over 60 μg /d L

.

26

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

In A ppendix A , we describe our selection criteria for models to include in our review
pro cess. 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 i mportant attributes of le ad uptake, distribution
, and clearance that PBPK and biokinetic m odels
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, lun g, 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) estima ted that a t nominal breathing rates, adult lungs transfer 35% of inhaled lead mass suspended in ambient air to blood

. However, t he amount of lead deposited in the lungs and subsequently absorbed into the blood depends on several
factors. The d eposition rat e and the region of deposition depend on the shape and the
size distribu tion of particles, as well as individual breathing rate (volume of air inhaled

27per 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. T he ciliate d regions of the bronchial tree carry particles
back t o the pharynx where they are swallowed and potentially absorbed throug h the gastrointestinal tract. In the alveolar region, clearance occurs mostly by absorption directly into the blo od 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 ra nge of gastrointestinal absorption fractions ( 1% to 80% ) has been reported in
the literature

. This wide range occurs in part because a bsorption 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)

• i ntake with liquid between meals
, 8 % to 30 % (Blake 1976; Chamberlain et al. 1978)

28• ingestion with liquids after several hours of fasting
, 3 0% to 7 0% (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 t he relationship between the plasma lead and blood lead concentrations is nonlinear (Barton 1989; Chamberlain 1985; Manton and Malloy 1983; Manton and Cook 1984; Marcus 1985 a, 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; half

lives 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, c ontributing to the uncertainty.

A.1.5 Uptake and elimination from bone Constant exposure to l ead leads to slow accumulat ion in the body because of the high affinity for bone and incorporation into the bone matrix

. The same high aff inity 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

29incorporated by diffusion into bone matrix without becoming part of the hydroxyapatite crystal. B one 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 th e 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 l ead 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 o f 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
studie s 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 inclu de 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.

30Of the five models considered, the U

. S

. EPA A ll

- A ges L ead M o del ha d 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 t herefore 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 mod el 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. (1 989) 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

.

31Multi

- compartmental models, suc h 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 compartm ents 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 fl ow
- 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 ra te constants.
In the next section, we describe each model in its original form and briefly summarize any subsequent modifications.

A.2.1 Bert m odel Bert and co

- workers developed a biokinetic model to predict BLLs in the general population (Bert et al. 1989). Th ey 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 s tudies by Barry (1975)

. This adjustment allowed them to correct some of the shortcoming s 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 compris ed of six well

- mixed compartment s
- 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 substantia lly 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

32other 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 foun d in the general population, BLLs significantly below those found in occupational ly 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 exposure s (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 depo sition 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)

.

33A.2.2.1 Original Leggett model The Leggett model include s 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 surfa ce 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 age

dependent be havior 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 p harmacokinetic 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 studie s in
hea lthy 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 wit h experimental, occupational, environmental, and medical data on the biokinetics of elements with physicochemical p roperties 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

- live s

. 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 anat omically

- based segments, with absorption occurring only from the small intestine. Liver and kidney each consist of two

34sub

- compartments. Half

- lives range from a few days in some parts of the liver and kidney to one year in other parts of these organ s, two years in the brain
, and 3.5 months to 5 years in other soft tissue s (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 cell s is non linear.
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 the se 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 fr action 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 conside red in the model

– urine
, feces, sweat, exhaled air, and other excreta (losses in hair, nails
, and skin). Two major elimination pathway s for absorbed lead are through urine and feces, with the other elimination pathways playing a minor role in overall excr etion

. For the long

- term rete ntion 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 ye ars 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 b y 25

- 30 years post

- exposure

.

35A.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 follow ing 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), us ed 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 transfe r 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 simp lified 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

36age 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 Leg gett’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 ske leton 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 (Legget t 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 h uman data whenever possible, but in a few instances, data from experimental animals were used.
The human model is both age

- and gender

- dependent, and address es multiple pathways of exposure and the biokinetics of lead in c hildren 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

37are expressed as functions of body weight and age. There are five different point
estimates of b ody weight for different age ranges; two for the early childhood phase of growth and three for the subsequent adolescent growth spurt and sta bilization 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 plasm a 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 expr essions that reproduce physiologic measurements. The model treats glomerular filtration rate a s 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 c alcium 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% eliminate d 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 upda tes 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 b ased 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

38evaluation of the 1993 version of t he 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 blo od 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 betwe en 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 representin g 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 ext reme 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. H owever, 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

39adjusted 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 f rom 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. W e were not able to check that the computer code des cribes 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 s ome 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/m 3 lead
exposure and predicts lower BLL s for women than for me n for the same exposure
scenario. In addition, p regnancy 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 o ne 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
outlin ed 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 b road 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 in volved 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 satura tion (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. Therefor e, w e determined that the Bert model was not suitable because it ignored changes that occur when BLL s exceed about 25 μ g/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 bod y , 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 ad ulthood (O'Flaherty et al. 1998)

. As new data became available, modifications to the O’Flaherty model have been introduced in the literature to improve predict ions 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 th at 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

41flexibility 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 th at 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

mu ch simpler mass

- transfer structure than the volume and flow structure published by O’Flaherty. Therefor e , 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 th e 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 depictin g RBC saturation (personal communications with R. Leggett 2011).
Dr. Leggett provided the FORTRAN output for model runs with the following exposure inputs:

421. inje ction: acute input of 1 μg lead to blood

2. i ngestion: 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 intesti ne ( 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 bladde r 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 a ssumed that there is no lead in the body at the start of intake.
Dr. Leggett provided t he output values for blood lead concentration ( μg/dL ) over time
with the nonlinear case a s well as the contents ( μg ) of

• extra vascular fluid (EVF),

• liver,

• kidneys,

• brain, and

• bone

.

43OEHHA 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 w orker 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 s elected 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, t here 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.

44The details of our assessment are presented below.

D ata 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 studie s that described removal from lead

related 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 asse ssing 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, w orkers were in cluded 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 ye ars. 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

45ind ividual 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 pre

exposure, 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 s imulation
OEHHA followed Hattis’ methodology to model the expected post

- strike BLLs for these
workers. The pre

- employment BLLs range from 10 to 85 μg/d L

. 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 confounde d 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)

.

46We 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 p re
employment 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
dur ing 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 B LL 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 signif icant under

prediction 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 [ 19 93] ) and
reran the simulation. The aver age difference between measured and predicte d post

strike 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 th at the modeled bone lead levels were too low for chronically exposed worker s ( The full significance of this under

- prediction of bone

- lead
levels wi ll be shown later in phase two). Recommendations based on this model could
result in: a) an air lead limit (o r 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,

47acceptable level. OEHHA felt that a model th at more closely tracked the BLLs observed in available data was required.

A.4.2.2 Phase two assessment s : M odel c alibration to o bserved d ata 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 w orker s

. 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 RB C 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 wor ker’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 occup ational 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 belo w. 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 tenur e, indicating that bone and long

- lived tissue compartments are performing well

.

• e nsure that adjusted model parameters remain in line with data of very long

lived bone lead and lead in other tissues from chronically exposed workers

.

48• e nsure 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 m odel calibration
In an iterative process, OEHHA made changes to the selected parameters in line with
chronic occupational exposures. We repeatedl y 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, urin e, 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 t ests of model performance
OEHHA performed a number of tests wit h 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 inte rest of
CDPH

- OLPPP

. For each of the remaining 47 subjects we derived a predicted post

strike BLL using the adjusted core model and compared it to the measured post

- strike
BLL.

49Test 2: Model performance relative to job tenure

In his initial assessment of th e Bernard model, Hattis observed that there is some tendency for longer job tenures to be associated with over

- predictions of BLL s relative
to the ASARCO strike data. Hattis pointed out that a ny linear tendency resulting in a p

value 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 perform ance 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 tes t 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 b lood 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

mea surements 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 pl asma transfer rate about six times slower.

50Leggett concluded from a survey of literature that o nly 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 m ore recent study that combined measurements of lead in the heel and tibia bones with a robust history of BLL s 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 trab ecular 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 patt ern and duration of exposures. T herefore, we considered the Nie et
al. data an important check for our adjusted model.
As described earlier, Nie et al. (2005) reported b one 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 la te 1960s. In the early 1990s, workers were enrolled in a bone lead study that collected lead levels from the heel (trabecula r) and tibia (cortical) bones. Nie et al. (2005) reported m easured
and modeled cortical and trabecular bone lead levels for each of nine retired workers in
a table. B lood 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 Le ggett fit measurements of cortical and
trabecular bone taken from chronically exposed smelter workers.

51We w ere 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 av ailable 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 exposur e) 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, u sing the adjusted Leggett model, we modeled BLL, trabecular lead, and cortical lead lev els four years after retirement and compared them
to measured levels.
Although limited, this test allows us to check whether the adjusted core model provid es 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 thre e 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
concentrat ion of lead in RBCs increases. Leggett set a baseline saturation (S)
concentration at 350 μ g/d L RBC based on data showing where the ratios urinary
lead: BLL an d 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 μ g/d L 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 (Fig ure 13 in Leggett [1993] )

.

52In 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 re check 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 i n the literature which examined the relationship
between whole blood, plasma, and/or urine lead levels measured in workers exposed to lead (Manton a nd 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 performa nce (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
Da llas
, 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 note d that at a blood lead concentration of 10 μg/dL, serum (plasma without clotting factors) lead is

530.25 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. T he 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
BLL s betw een 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 μ g/m 3 to 380 μ g/m 3 among five workplaces.
All urine analyses were made on spot samples and all urine samples w ere 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. followe d for 15 months four workers exposed to an av erage ambient air
concentration of 286 μg/m 3 in a Japanese factory that manufactures lead glass

- based
p aints

. 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

54work setting with a much higher average air concentration of lead (1.05 mg/m 3 ) 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/m 3 (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 Hirat a 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 chron ically 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 a ffected 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 ] ).

55Results of phase two assessment
Final adjusted parameters

Table A

- 1 presents Leggett’s original and our fina l 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 model 1

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 RB C s (corresponding to 119 μg/dL whole blood) based on the value derived by O’Flaherty (1996). Finally, we adjusted urine para meters 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.

56Tests 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 a ttributes among 47 smelter workers along with the estimate of fit
between the model

- predicted and observed BLL s

. Table A

- 2: Estimate of fit of predicted to observed BLLs for 47 smelter workers 1

Subject Pre

- strike job tenure (days) Measured pre

employment 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

57Subject Pre

- strike job tenure (days) Measured pre

employment 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) estim ates 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 T able A

- 2. This indicates that the adjusted core model predicts BLLs nine months after the end of workplace exposure

580.9 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 BLL s 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 suggest s a significant (>10%) systematic under

- prediction error. In contrast, a linear regression of modeled versus
measured BLL s once parameters were adjusted to produce a better fit to the ASARCO post

- strike BLL s, 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 significa nt (<5%).

59Test 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

– predict ed 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 tenure 1

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 reaso nably well. -15 -10

-5 0 5 10 15

0 500 1000 1500 2000 2500 3000 3500model performance(measured-predicted BLL (μg/dL)

Job tenure (days)

60Test 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 μ g/g
, resp ectively
, compared to cortical and trabecular bone concentrations of 60 and 160 μ g/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. A fter 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 c ortical bone is
about two to one for all nine retired l ead smelter workers in Table 1 in Nie et al. (2005). Though limited, th ese finding s lend further support that the adjusted model provides reasonably accurate predictions of bone lead in chronically exposed workers.

Test 4: Predicted versus measured plasm a 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.

61Figure A

- 3: Plasma lead versus whole blood lead concentration

- predictions from the adjusted Leggett model and data from two worker cohorts 1

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 wer e extractable; BLL μg/dL, blood lead leve l 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 cohorts 1

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 w ere not published in Lee (1982); BLL μg/dL , blood lead leve l 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 70.00Plasma 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 70.00Urine lead (μg/L)

Whole blood lead (μg/dL) Hirata et al. Lee Adjusted Leggett

62Notice 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 relati ve 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 me asured 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 othe r key tissue compartments (e.g., brain and liver) at lower levels of intake (20 μg/day).

63Table A

- 3: Distribution of Lead in Various Tissues

- Postmortem Data, Original Model Prediction, Adjusted Model Prediction 1

Age 20 s – 30 s Age 40 s – 50 s

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 μ g/d ay uptake

64A.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 lea d 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 i n 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 gene ral 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.

Brief ly, 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

65and the gene ral population. Our adjustments reduced the under

- prediction of BLL s 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 bl ood, 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 S cope of W ork and that our primary objectives for this part of the proj ect 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 predict ions 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 BLL s at the individual level under specific exposure conditions.

B.1 D escription 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 nonl inear 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 amb ient (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 inc lude w ork time apportioned as 8 hours/day x 2 5 0 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 m 3 /day based on
minute volumes for adult males (OEHHA, 2012a)

. This BR reflects 10 hours of

67moderate activity during the day ( 30 L/minute = 18.4 m 3 / 8

- h r workday + 2 h r off the job), 6 hours of light activity (13.9 L/minute = 5.0 m 3 / 6

- h r ), and 8 hours of sedentary (5.9 L/minute = 2.8 m 3 / 8

- h r ) 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 partic les 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 f o rm 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 wat er ; 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 rea dily 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 depo sited 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 i nhaled 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 a re absorbed with less efficiency. As the distribution of particles shifts towards coarser particles, more mass is retained in the upper airways, removed and

68swallowed , 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 diffe rent 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 e xposure 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 th e 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,

o ften many operations in a facility use lead and genera te 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 tha n to particular industries, processes, or operations.

If it is im practical 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/m 3 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

69inhaled lead mass is absorbed to the blood. This assump tion, 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 tend s 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 brid ges [ Vork 2003 ] )

. We find t here is

no basis for assuming a global relationship between mass measurements in the air (μg/m 3 ) and particle size distribution (μm) as Federal OSHA did in 1978. If such a relationship existed, then particle size could be inferre d 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

o f 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 Assumpti on 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) pub lished particle size distribution data from a variety of workplaces with differing operations that generate a range of particle

70sizes (fine to coarse) ; and 2) a recently developed model for predicting head and lung deposition and clearance based on particl e 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 h ead 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 s elected 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 speciati on 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 Pa ik (2002) evaluated exposure to airborne lead particles for 117 workers in four types of lead

- relate d industries located in Korea. The particle sizes were measured using person al sampling cascade impactors. Two secondary lead smelting plants, three
radiato r manufacturing plants, four lead

- acid battery manufacturing plants, and three lead powder manufacturing plants were studied. In addition to air samples, whole blood
samp les were taken on each worker. For each type of industry, the authors reported MMAD, P bA, average respirable fraction, and fraction of particles less than 1 μm aerodynamic dia meter (AD).

71Liu et al. (1996) reported on the size distributions of lead aerosol from personal samples of workers exposed in a brass foundry and a battery manufacturi ng 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 S tates. 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 m edian 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 ; t he National Institute of Public Health and the Environment, The Ne therlands (RIVM) ; and the National Institute for Occupational Safety and Health (NIOSH) (Anjilvel and Asgharian 1995; ARA 2012; RIVM 2002)

. The M P PD model can be used to predict the deposition of particles between 0.01 and 20 μm in diameter in humans and r ats. T he 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 de position 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 age

72specific 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 m odel 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 depo sition in humans is the ICRP Human Respiratory Tract Model for Radiological Protection (ICRP 1994). This model provides tabular deposition estimates for 19 activity me dian aerodynamic diameters (AM A Ds) (μm), seven lung regions, three modes of breathing,
fo ur 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 particl es 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

- P ath Particle Dosimetry M odel (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 partic le 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).

73B.3 Results Below we present the results of the dosimetry analysis using the MPPD2 model as wel l 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 d ata 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/m 3 , with a gran d arithmetic mean of 641 μg/m 3

. 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/cm 3

. Table B

- 1: Airborne lead concentration and particle mass median aerodynamic diameter from Park and Paik (2002) 1 Parameter/Occupational setting Seco ndary Smelting, N = 6 Radiator Mfg. N = 42 Battery Mfg. N = 44 Lead Powder Mfg. N = 25 Combined N = 117

AM ± SD Concentration μg/m 3 653 ± 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/cm 3 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 ; W eighted averages of columns 2

- 5 ; μm, micrometer; g/cm 3 , grams per cubic centimet er
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)

.

75Table 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 P owder 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 deposit ion 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. 00 6 0. 00 9 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 he ad deposition 0.744 0.559 0.992 0.990 0.746 Airway deposition (TB + Alv) 0. 10 2 0.186 0.007 0.008 0.105 Alveolar deposition (Alv) 0.072 0.116 0.002 0.003 0. 07 5 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, A lveolar 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 7 5 % ; the total

76head deposition
, 6 2.5 % ; total airway deposition ( trache o bronchial plus alveolar ), 12.5 % ; and alveolar deposition
, 7.8 %. However, for battery and lead powder workers, nearly 100% of the particles inhaled (14.1 μ m and 15.1 μ m MMAD
, respectively) are deposited compared to nearly 75% for s econdary s melting and r adiator workers (4.9 μ m and 1.3
μ m 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 frac tion 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 t he 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 show ed that a five

day tracheobronchial deposition of lead in radiator workers (26 μg/m 3 , 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/cm 3 ) , PbS (7.6 g/cm 3 )
, a nd
Pb SO 4 (6. 3 g/cm 3 ) at a low breathing cycles/minute and constant concentration

. T he
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
lea d powder workers.

77Table B

- 3: Lung dosimetry analysis of occupational airborne lead exposure by the MPPD2 model with different speciation assumptions 1

Parameter/Occupational setting Secondary Smelting

N = 6 Radiator workers

N = 42 Battery workers

N = 44 Lead Powder workers

N = 25 AM ± SD concentration μg/m 3 653 ± 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/cm 3 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/cm 3 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/cm 3 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 PbSO 4 PbSO 4 , density g/cm 3 6.29 6.29 6.29 6.29 Total deposition fraction 0.74 0 0.71 0 1.0 1.0 Total head deposition fraction 0.61 0 0.51 0 0.97 0 1.0 Airway deposition fraction, TB + Alv 0.13 0 0.20 0 0.026 0.03 0 Alveolar deposition fraction, Alv 0.087 0.10 0 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/m 3 , microgram per cubic meter; μm, micrometer; g/cm 3 , gram per cubic centimeter ; Pb, inorganic lead; PbS, lead sulfide ; PbO, lead o xide ; PbSO 4 , 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

78means 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
concentra tions of 22 to 122 μg/m 3 , MMAD of 2.06 to 4.63 μm , SD of 2.08 to 6.51 μ m , 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).

79Table 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/m 3 621 ± 3.2 158 ± 2.4 509 ±3.2 32 ±1.4
Adjusted concentration μg/m 3 122 112 100 22
MMAD μm 4.63 2.06 4.17 2.44

SD μm 2.08 6.51 2.7 6.38
Density, g/cm 3 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.

80Table 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 m 3 /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 m 3 /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 m 3 /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 m 3 /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; 2 AMAD, Activ ity Median Aerodynamic Diameter; μm, microgram; m 3 /hr, cubic meters per hour; T here 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 (Legge tt 201 2 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 fro m 2 μ m to 4.6 μ m in the Liu data (Table B

4). Using MPPD2, the total deposition ranges from 68.3% to 88.2% and deposition in

81the 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 o f 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)

w here:

• A lveolar, ciliated, and head region deposition fractions are based on MPPD2 lung dosimetry analysis (Table B

- 2).

• L ung absorption in the alveolar region is assumed to be 100%

.

• A verage gut absorption of lead mechanically removed from the ciliated and head
reg ion 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 thre e 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

- h r 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.

82B.3.3.2 Calculation of inhalation transfer coefficients Below we pres ent 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.

• 7 5.6 % of the lead inhaled is de posited 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

.

• 6 6.3 % of the inhaled mass is removed by ciliary action or secretions, swallowed, and deposited in the GI tract (tota l 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 t ract (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 ass uming 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 an d 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

- 6 a )

. The transfer coefficients range d 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

- h r exposure
p eriod as used to derive a 24

- h r average breathing rate (33% sede ntary, 25% light, 42% moderate) (Table B

- 6b ). The TWAs ranged from 29%

- 31%; midpoint 30%.

83Table B

- 6 a : Inhalation transfer coefficient (ITC) by worker group and activity level 1

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 setting 1

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 var y 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 partic le size s (MMAD 4.9 μm; 1.3μm). The decrease in the fraction deposited deep in the lung when particle

84sizes 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 s wallowing 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 th e model.
In the next section, w e 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 BLL s in workers exposed during relatively short or long periods to ai r concentrations in the workplace ranging from < 10 μg/m 3 to > 200 μg/m 3 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

measu rements 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 BLL s among lead industry workers and other s 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 t o help us compare measured BLLs to predicted BLLs from Leggett + , we selected for further analysis two

85studies which cover the range of BLLs, workplace air concentrations, and exposure
durations of intere st 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 (s ee 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 p rovide 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 s tudy. See Table B

- 7 for a summary of model inputs for each study.
Figure B

- 1: Model validation diagram 1

1 BLL, blood lead level; Pb, lead; m 3 /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 in take Leggett +

Includes default:

• Breathing rate ( 26 m 3 /day)

• Inhalation transfer coefficient (3 0 %) Model Input

Predicted Output

Measured Comparison

86al. 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 studi es 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 e t 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 h ou r s /day for about 1 6 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 μ g/m 3 in the exposure chamber over the entire exposure period. Another experiment exposed subjects to an aver age airborne lead concentration
of 3.2 μ g/m 3 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

87two additional subjects with suspect back

- calculated intake during the exposure perio d (i.e., less than zero rel ative to pre

- exposure intake). The remaining 24 subjects were included in our analysis.
Exposure periods were reported for each part icipant and varied by subject. The shortest exposure period for those include d in our analysis wa s 42 days. For most subjects the
expos ure 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

- expos ure 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/m 3 ; experiment 2

- 3.2 μg/m 3 ).

Lead content in the diets of each subject was analyzed. Howe ver, 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 su bject 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 thr ew them out , leaving 29 workers with complete information. Nineteen workers were exposed during three proces ses with high air concentrations (79

– 298 μg/m 3 ). Ten workers, comprising the two control groups
, were exposed to air
concentrations of 8

– 13 μ g/m 3

. Williams et al. reports that the jobs of the men selected for the study “...did not entail wearing respirators.” B LL s ranged from 22.5

– 33.0 μ g/d L in the control group and from 44.6

– 93.0 μ g/d L in the exposed group (Snee 1982; Williams et al. 1969)

.

88Of the 29 subjects for whom individual data were available on exposure and BLL, as presented in Snee (1 982), 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 CDPH

OLPPP asked us to model.

Job tenure was not avai lable 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. (1 99 1 ) 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/ear ly 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 th e 1950s, were around 20 μg/dL. For end

- of

- exposure BLLs we used the individual’s reported B LL 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/m 3

. We assumed that the air lead concentration reported in the study reasonably represented the subject s’ 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 back

calculated the uptake of lead from the assumed background BLL of 20 μg/dL

.

89Table B

- 7: M odel inputs 1

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.

o 10 with missing BLL data

o 13 with BLLs above 60 μg/dL

• No smoking data available.

• N = 16

Breathing rate (BR) • Assumed default BR of 26 m 3 /d. • Assumed default BR of 26 m 3 /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/m 3

• Experiment 2: 3.2 μg/m 3 • On

- duty PbA available for each subject.

• Range 8

– 166 μg/m 3

• 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 up take • 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; m 3 /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 ea ch

90subject’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
Av erage measured less predicted BLL: 0.8 3 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

91Subject 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
Av erage 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

- h r time we ighted average (TWA) air concentration of lead in chamber air ; for Griffin et al. study. For Williams et al., PbA, on

- duty 8

- h r TWA. As described in the text, we back

- calculated off

- duty intake from an assumed back ground 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 /d L; the average difference for the
Williams et al. data set is

- 0.15 μg /d L.

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 sys tematic bias (< 10%). The average difference of linear (measured

– predicted) intake for this cohort is 0.83 μg /d L. See Figure B

- 2
below.

92Figure B

- 2: Check for systematic bias

– Griffin et al. 1975 1

1 Breathing rate ( BR ) = 23.5 m 3 /day and ITC = 34% to reflect continuous exposure conditions in chamber study ; BLL μg/dL , blood lead leve l 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
BLL s 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 /d L

. See Figure B

- 3 below. y = 0.9646x

15 20 25 30 35 40 45

15 20 25 30 35 40 45Predicted BLL μg/dL

Measured BLL μg/dL 

## Griffin data

93Figure B

- 3: Check for systematic bias

– Williams et al. 1969 1

1 Breathing rate ( BR ) = 26 m 3 /day and ITC = 30% to reflect occupational scenario ; BLL μg/dL , blood lead leve l 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 thi s cohort.
The regression equation analyzing the performance of the Leggett+ model v ersus 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 70Predicted BLL (μg/dL)

Measured BLL (μg/dL) 

## Williams data

94This equation suggested that differences in measured

– predicted BLLs would not be expected to fall outside the deviations observed within the worker cohort
, although th e 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. 1975 1

1 BLL μg/dL , blood lead leve l 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 transfer coefficient

. We tested the Leggett+ model with
data from one cham ber 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/m 3 to > 160 μg/m 3

. These studies represented both st eady

- 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 140Model performance (measured-predicted BLL(μg/dL)

Job tenure (days) 

## Griffin

95many 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 perio d. 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 permis sible exposure limit
for lead.

96

## C Appendix: A cronyms , 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

cm 2 Centimeter squared

cm 3 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

- lif e 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

97Acronym/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

m 3 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

R 2 Regression coefficient indicating level of explained variability in the outcome variable RBC Red blood cells (erythrocytes)
RIVM National Institute of Public Health and the Env ironment, 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

## D References

ACGIH (1994

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