EPA/600/R-11/067

STATUS REPORT:

Advances in Inhalation Dosimetry for Gases 

with Lower Respiratory Tract and Systemic 

Effects

September 2011

U.S. Environmental Protection Agency

Washington, DC
ii

DIS CLAIMER

This report has been reviewed in accordance with U.S. Environmental Protection Agency Policy and approved for 

publication. Mention of trade names or commercial products does not constitute endorsement or recommendation 

for use.
iii

AUTHORS , CONTRIBUTORS , AND REVIEWERS

AUTHORS 

John J. Stanek

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Eva D. McLanahan

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

REVIEWERS

This document has been reviewed by EPA scientists and has undergone a formal peer letter review 

performed by independent scientists external to the EPA. Comments from reviewers were evaluated 

carefully and considered by the Agency during preparation of this final report. A summary of significant 

comments made by the external peer reviewers, and EPA responses, is included in Appendix A.

INTERNAL EP A CONTRIBUTORS AND REVIEWERS

Robert Benson

Region 8, U.S. Environmental Protection Agency

U.S. Environmental Protection Agency

Denver, CO

Rebecca Dzubow

Office of Children’s Health Protection

U.S. Environmental Protection Agency

Washington, DC

Ernest Falke

Office of Pollution Prevention and Toxics

U.S. Environmental Protection Agency

Washington, DC

Brenda Foos

Office of Children’s Health Protection

U.S. Environmental Protection Agency

Washington, DC

Connie Meacham

National Center for Environmental Assessment

Office of Research and Development

U.S. Environmental Protection Agency

Research Triangle Park, NC 

Suril Mehta

Office of Children’s Health Protection

U.S. Environmental Protection Agency

Washington, DC

Deirdre Murphy

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC
iv

Reeder Sams

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Paul Schlosser

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

Ravi Subramanian

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Washington, DC

John Vandenberg

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Debra Walsh

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

EXTERNAL REVIEWERS

Phillip Worth Longest, Jr

Virginia Commonwealth University, Richmond, VA

Jeffry Schroeter

The Hamner Institute, Research Triangle Park, NC

James Ultman

Pennsylvania State University, University Park, PA

TECHNICAL S UP P ORT

James S. Lucy − Student Services Authority

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Ken Breito –Senior Environmental Employment Program

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Deborah Wales

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

Barbara Wright – Senior Environmental Employment Program

National Center for Environmental Assessment

U.S. Environmental Protection Agency

Research Triangle Park, NC

CONTRACT S UP P ORT

Gary L. Foureman (Preparation)

ICF International; 9300 Lee Highway; Fairfax, VA 22031 

Julie Kimball (Internal Reviewer)

ICF International; 9300 Lee Highway; Fairfax, VA 22031 
v

TABLE OF CONTENTS

AUTHORS, CONTRIBUTORS, AND REVIEWERS _____________________________________ III

GLOSSARY___________________________________________________________________ VII

ABBREVIATIONS AND ACRONYMS _______________________________________________IX

EXECUTIVE SUMMARY_________________________________________________________1-1

1 INTRODUCTION AND PURPOSE_____________________________________________1-4

2 INTERSPECIES GAS DOSIMETRY IN THE RFC - THE REGIONAL GAS DOSE RATIO 

(RGDR) AS A DOSIMETRIC ADJUSTMENT FACTOR (DAF) _______________________2-1

2.1 Current Applications of the DAFs - RGDRTB, RGDRPU, and Hb/g _________________________ 2-2

Table 2-1 Surface Areas for the Tracheobronchial (TB) and Pulmonary (PU) Regions 

of the Respiratory Tract in Various Species____________________________ 2-2

Table 2-2 Intercept (b0) and Coefficient (b1) Values Used to Calculate Default Minute 

Volumes Based on Body Weighta ___________________________________ 2-2

Table 2-3 Some Example Blood:Air Partition Coefficients (Hb/g) in Humans and Rats 

Expressed as a Ratio, Animal/Human ________________________________ 2-3

2.2 Assumptions for the Current Application of RGDR Procedures __________________________ 2-3

2.2.1 DAF for TB and PU Effects -- Uniformity of Flow and Deposition _____________________ 2-3

2.2.2 DAF for Extrarespiratory Effects -- Blood:Gas Partition Coefficients (Hb/g) ______________ 2-3

2.3 The RGDR for the Tracheobronchial (TB) Region - RGDRTB ____________________________ 2-4

2.4 The RGDR for the Pulmonary (PU) Region - RGDRPU _________________________________ 2-5

2.5 The DAF for the Extrarespiratory Region (ER) Region - Hb/g ____________________________ 2-7

2.6 Children's Dosimetry___________________________________________________________ 2-8

3 ADVANCES ______________________________________________________________3-1

3.1 Inhalation Dosimetry – Concepts, Design, and Results ________________________________ 3-1

Table 3-1 Modeled Predictions of Amount of O3 and SO2 Absorbed at Various Sites in 

the Airways of Three Species ______________________________________ 3-5

3.2 Inhalation Rates ______________________________________________________________ 3-6

Table 3-2 Distribution Percentiles of Physiological Daily Inhalation Rates for 

Free-Living Normal-Weight Males and Females Aged 2.6 Months to 96 

Years _________________________________________________________ 3-8

Table 3-3 Distribution Percentiles of Physiological Daily Inhalation Rates on a per 

Body Weight Basis for Free-Living Normal-Weight Males and Females 

Aged 2.6 Months to 96 Years _____________________________________ 3-10

Table 3-4 Probabilistic 24-hr Breathing Rate Estimates__________________________ 3-13

Table 3-5 Comparison of Mean 24-hour Inhalation Rates (m3/day) Determined Using 

Time-Activity-Ventilation (TAV), Metabolic Energy Conversion (MEC) or 

Doubly Labeled Water (DLW) Approaches ___________________________ 3-14

3.3 TB Region__________________________________________________________________ 3-14

3.3.1 Flow and Deposition ______________________________________________________ 3-14

Figure 3-1 Distributions of Deposition Enhancement Factor (DEF) for MTBE Vapor 

with Qin = 30 L/min in the Bifurcation Airway Models ___________________ 3-17

Figure 3-2 Distributions of Deposition Enhancement Factor (DEF) for Ethanol Vapor 

with Qin = 30 L/min in the Bifurcation Airway Models ___________________ 3-17

3.3.2 Advances in TB Inhalation Dosimetry Modeling _________________________________ 3-20

3.4 PU Region _________________________________________________________________ 3-21

3.4.1 Flow and Deposition ______________________________________________________ 3-21

Figure 3-3 Dynamic Ventilation 3He MRI After Inhalation of Hyperpolarized 3He Gas ___ 3-22

Figure 3-4 Simulated Flow Velocities from CFD Solutions in an Alveolar Sac Model ____ 3-23

3.4.2 Advances in Quantitation in Lung Geometry ____________________________________ 3-24

Table 3-6 Estimates of Right, Left, and Total Lung Volumes in Male Wistar Rats______ 3-25

Table 3-7 Summary Data on Human Lung Alveolar Number and Volume____________ 3-26

Table 3-8 Summary Table of Measures from Right Lungs of Human Cadavers _______ 3-27

Table 3-9 Functional and Morphological Features of the Developing Male Rat Lung ___ 3-28
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3.5 Extrarespiratory (ER) Region ___________________________________________________ 3-28

3.5.1 Methods and Advances for Estimating Blood:Air Partition Coefficients________________ 3-28

Table 3-10 Mean Ratios and Standard Deviations (Rmean ± SD) of Predicted to 

Experimentally Derived Human and Rat Blood:Air Partition Coefficients, P, 

and Mean Absolute Differences Between Predicted and Experimental 

Values of log P (E) for a Reference Set of Unreactive Volatile Organic 

Chemical Vapors _______________________________________________ 3-30

3.5.2 Quantitation using Inhalation PBPK Models ____________________________________ 3-31

Table 3-11 Compilation of Blood:Air Partition Coefficients used in Inhalation PBPK 

Models for Animal to Human Interspecies Extrapolation _________________ 3-33

3.5.3 HECER Derivation – PBPK and Hb/g (RfC Methods) Comparison_____________________ 3-34

Table 3-12 Estimations from Inhalation PBPK Models of Human Equivalent 

Concentrations (HECs) from Effect Levels and Internal Dose Measures in 

Laboratory Animals _____________________________________________ 3-35

Table 3-13 Comparison of Approaches for Calculating Human Equivalent 

Concentrations (HECs) for Several Gases with Effects in the 

Extrarespiratory Region (ER)______________________________________ 3-37

3.6 Children's Inhalation Dosimetry _________________________________________________ 3-37

3.6.1 Introduction and Focus ____________________________________________________ 3-37

3.6.2 PBPK Models ___________________________________________________________ 3-40

Table 3-14 Human kinetic adjustment factors (UFH-TK) obtained for inhalation exposure 

in each population group using a dose surrogate of 24 hour AUCpc ________ 3-42

Table 3-15 Air Concentration of Chloroform at Various Ages and Genders 

Corresponding to Threshold of Damage in Human Liver and Kidney _______ 3-43

Table 3-16 Age-Dependent and Gender-Specific Dose Metric Comparison of Inhaled 

Isopropanol ___________________________________________________ 3-45

Table 3-17 Tissue Concentrations in Various Compartments Expressed as Adult/Child 

(1 to 2 years old) Ratios for 8 Different Gases_________________________ 3-46

3.6.3 Respiratory Tract Flow Models ______________________________________________ 3-48

Table 3-18 Summary Listing of Findings on Morphometry and Gas Flow/Uptake 

Simulations for Human Nasal Cavities_______________________________ 3-49

Table 3-19 Selected Morphologic and Simulated Modeling Results of Hydrogen Sulfide 

Dosimetry in Casts of Human Nasal Cavities _________________________ 3-50

3.6.4 Respiratory Tract Growth __________________________________________________ 3-52

3.6.5 Child Data Collations______________________________________________________ 3-56

Figure 3-5 Alveoli count per lung as a function of age ___________________________ 3-57

Table 3-18 Lung Weights (Right and Left) of Males and Females from Birth to 

Adulthood ____________________________________________________ 3-58

4 SUMMARY AND CONCLUSIONS _____________________________________________4-1

Table 4-1 Respiratory Tract Surface Areas and Volumes for Various Species 

comparing the 1994 RfC Methods and this Report ______________________ 4-3

5 REFERENCES ____________________________________________________________5-1

APPENDIX A. SUMMARY AND DISPOSITION OF INDEPENDENT EXTERNAL PEER 

REVIEW COMMENTS___________________________________________ A-1

APPENDIX B. DEFINITIONS FOR VENTILATORY VOLUMES______________________ B-1

APPENDIX C. LITERATURE SEARCH STRATEGY ______________________________ C-1
vii

GLOS S ARY

Chronic Exposure - Multiple exposures occurring over an extended period of time, or a significant fraction of the 

animal's or the individual's lifetime.

Computational fluid dynamics (CFD) – (Three-dimensional) – A branch of fluid mechanics that uses numerical

methods and algorithms to solve and analyze problems of fluid flows. Flows may apply to liquid and gases, 

including inspired and expired air, and are thus applicable to solving flows within the respiratory tract. The 

fundamental bases of any CFD problem are the Navier-Stokes equations, which define any single-phase fluid flow. 

These equations can be simplified by removing terms describing viscosity to yield the Euler equations.

Diffusivity (gas) - The transport of matter from one point to another by random molecular motions to become 

equalized with respect to concentration. For gases, rates of diffusion increase with the temperature and are inversely 

proportional to the pressure. The interdiffusion coefficients of gas mixtures are almost independent of the 

composition. Kinetic theory shows that diffusion of a pure gas is inversely proportional to both the square root of the 

molecular weight and the square of the molecular diameter.

Dosimetric Adjustment Factor (DAF) - A multiplicative factor used to adjust observed experimental or 

epidemiological data to human equivalent concentration (HEC) for assumed ambient scenario. See also regional gas 

dose ratio (RGDR).

Flux - The rate of flow of energy, gas or particles across a given surface.

Gas - Term referring to a compressible fluid phase of a substance. Fixed gases are gases for which no liquid or solid 

can form at the temperature of the gas, such as air at ambient temperatures.

Henry's Law Constant - The law can be expressed in several equivalent forms, a convenient form being: Cg = HC1 

where Cg and C1 are the gas-(g) and liquid-(l) phase concentrations. The constant (H) is the ratio at equilibrium of 

the gas phase concentration to the liquid-phase concentration of the gas (i.e., moles per liter in air/moles per liter in 

solution).

Identical Path Model – (One- or two-dimensional) – An anatomical mathematical model where all paths from the 

nose or mouth entrance to the alveolar sacs are identical.

Inhalation Reference Concentration (RfC) - An estimate (with uncertainty spanning perhaps an order of 

magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is 

likely to be without an appreciable risk of deleterious noncancer health effects during a lifetime. The inhalation 

reference concentration is for continuous inhalation exposures and is appropriately expressed in units of mg/m3

.

Kg – The overall mass transfer coefficient describing movement of gas from the air phase into the liquid phase of the 

respiratory tract (see also MTC).

kg – The gas phase mass transfer coefficient describing movement of gas from the gas phase to liquid/tissue 

boundary (see also MTC).

Lowest-Observed-Adverse-Effect Level (LOAEL) - The lowest exposure level at which there are statistically 

and/or biologically significant increases in frequency or severity of adverse effects between the exposed population 

and its appropriate control group.

Mass Transfer Coefficient (MTC) - A diffusion rate constant that relates the mass transfer rate, mass transfer area, 

and concentration gradient as driving force between and through phases. These coefficients may also be viewed in 

terms of resistance to flow and movement. For purposes of this report (with phases of gas and solid) MTC requires 

units of mass, time, distance, and concentration: mol/(s·m2

), mol/m3, or m/s. Examples of MTCs used in this report 

relate to movement of gases in the respiratory tract. They include the MTC designated for the gas phase only, kg, and 

an overall MTC inclusive of both the gas and liquid phases, Kg.
viii

No-Observed-Adverse-Effect Level (NOAEL) - An exposure level at which there are no statistically and/or 

biologically significant increases in the frequency or severity of adverse effects between the exposed population and 

its appropriate control. Some effects may be produced at this level, but they are not considered as adverse, nor 

immediate precursors to specific adverse effects. In an experiment with several NOAELs, the assessment focus is

primarily on the highest one for a given critical effect, leading to the common usage of the term NOAEL as the 

highest exposure without adverse effect.

Physiologically-Based Pharmacokinetic (PBPK) Modeling – (Zero-dimensional) – A mathematical modeling

technique for predicting the absorption, distribution, metabolism and excretion of a compound in humans and other 

animal species. PBPK models strive to be mechanistic by mathematically transcribing anatomical, physiological, 

physical, and chemical descriptions of the phenomena involved in complex pharmacokinetic processes. These 

models have an extended domain of applicability compared to that of classical, empirical function based, 

compartmental pharmacokinetic models.

Portal-of-Entry Effect - A local effect produced at the tissue or organ of first contact between the biological system 

and the toxicant.

Regional Gas Dose (RGDr) - The gas dose (mg/cm2

) of respiratory tract surface area (per minute) calculated for the 

respiratory tract region of interest (r) as related to the observed toxicity (e.g., calculated for the tracheobronchial 

region for an adverse effect in the conducting airways). Regions of interest may be the extrarespiratory (ER), 

tracheobronchial (TB), or pulmonary (PU).

Regional Gas Dose Ratio (RGDRr) - The ratio of the deposited gas dose in a respiratory tract region (r) for the 

laboratory animal species of interest to that of humans. This ratio is used to adjust the observed gas exposure level 

for interspecies dosimetric differences.

Sherwood Number (Sh) – A dimensionless term for the ratio of convective to diffusive forces. The air-phase mass 

transfer coefficient can be defined in terms of the Sherwood number.

Uncertainty Factor (UF) - One of several, generally 3- to 10-fold factors, used in operationally deriving the 

inhalation reference concentration (RfC) from experimental data. UFs are intended to account for (1) the variation in 

sensitivity among the members of the human population, (2) the uncertainty in extrapolating laboratory animal data 

to humans, (3) the uncertainty in extrapolating from data obtained in a study that is of less-than-lifetime exposure, 

(4) the uncertainty in using LOAEL data rather than NOAEL data, and (5) the inability of any single study to 

adequately address all possible adverse outcomes in humans. The RfC Methods (U.S. EPA, 1994) use 3 for the UF 

for interspecies extrapolation due to the incorporation of default dosimetric adjustments.

Vapor - A term referring to a gas phase at a temperature below the critical temperature of the substance where the 

same substance can also exist in the liquid or solid state. If the gas is in contact with the liquid or solid phase, the 

two phases will be in a state of equilibrium. This report is intended to consider those agents present as gaseous 

vapors at ambient temperatures.
ix

ABBREVIATIONS AND 

ACRONYMS

1,1,1-TCE 1,1,1-trichloroethane 

1,2,4-TMB 1,2,4-trimethylbenzene

2-BE 2-butoxyethanol 

2-ME ethylene glycol monomethyl ether

2D two dimensional

3D three dimensional

A overall or summation hydrogen 

bond acidity

ADAM aerosol-derived airway 

morphometry

ADC apparent diffusion coefficient

AMET amount metabolized per 24 hour 

period

ASPM axisymmetric single path model 

AUC area under the curve

AUCpc area under the parent compound’s 

arterial blood concentration vs.

time curve

AV alveolar volumes

B overall or summation hydrogen 

bond basicity

bb bronchioles

BB tracheobronchial

BW body weight

CA arterial blood concentration

CATE carbon tetrachloride

CFD computational fluid dynamic

CFDM computational fluid dynamic 

modeling

Cmax maximum concentration

CO2 carbon dioxide

CT computed tomography

CV venous blood concentration

d day

D diffusivity

D2O deuterium oxide

DAF dosimetric adjustment factor

DEF deposition enhancement factor

DF deposition fraction

DLCO diffusion capacity of carbon 

monoxide

DLW doubly labeled water

E solute excess molar refractivity 

with units of (dm3 mol-1 )/10

EAD effective air space dimension

EBZ ethylbenzene 

ECG energy cost of growth

EPA Environmental Protection Agency

ER extrarespiratory or systemic

F flux fraction

fp fractional penetration

FQPA Food Quality Protection Act

FVC forced vital capacity

g gram

GCMS gas chromatography mass 

spectrometry

GSH glutathione

3

He hyperpolarized helium-3

Hb/g blood:air or blood:gas partition 

coefficient

(Hb/g)A animal blood:gas partition 

coefficient

(Hb/g)H human blood:gas partition 

coefficient

Ht/g tissue:gas partition coefficient

HEC human equivalent concentration

HP hyperpolarized

hr hour

K absorption parameter

kg kilogram

kg gas-phase mass-transport 

coefficient

Kg overall mass transfer coefficient

kl liquid/tissue phase mass transport 

coefficient

L log of the gas-hexadecane partition 

coefficient (unitless) at 25 °C

LFER linear free energy relationship

MeI methyl iodide 

mM millimolar

MR magnetic resonance

MRI magnetic resonance imaging

MTBE methyl tertiary butyl ether

NAS National Academy of Science

O2 oxygen

O3 ozone

PBPK physiologically-based 

pharmacokinetic

PCE perchloroethylene

PD pharmacodynamic

PDIR physiological daily inhalation rate

PGME propylene glycol methyl ether

PGMEA propylene glycol methyl ether 

acetate

PK pharmacokinetic

POD point of departure

PODadj point of departure duration adjusted

ppb parts-per-billion

ppm parts-per-million

PU pulmonary

Qalv alveolar ventilation rate

Qb regional blood flow

R radius of the airway

RfC reference concentration

RGDR regional gas dose ratio

S solute dipolarity/ polarizability

SA surface area

Sh Sherwood number

SO2 sulfur dioxide

Sp available surface area

STP standard temperature and pressure

t1/2 half-life
x

TAV time-activity-ventilation

TB tracheobronchial

TCE trichloroethylene

TDEE total daily energy expenditure

TLC total lung capacity

UBA upper bronchial airway

UFH uncertainty factor for 

interindividual human 

variability

URT upper respiratory tract

v viscosity

Vd volume of distribution

VE ventilation rate or minute volume

VLDtrans volume of gas required to reach 

transitional bronchioles into the 

lung

VQ ventilator equivalent ratio

wk week

XYL m-xylene

yr year
1-1

EXECUTIVE S UMMARY 

The purpose of this report is to evaluate new scientific developments and advancements 

in gas dosimetry focusing on tracheobronchial (TB), pulmonary (PU), and

systemic/extrarespiratory (ER) inhalation dosimetry related to the U.S. EPA’s 1994 

Methods for Derivation of Inhalation Reference Concentrations and Applications of 

Inhalation Dosimetry (U.S. EPA, 1994), hereafter RfC Methods. Particular emphasis is 

given on animal to human extrapolation and inhalation dosimetry in children. The goal of 

this project is to provide information necessary for ensuring that methods and guidance 

used and implemented by EPA in inhalation risk assessment reflects the 

state-of-the-science. Overall, the scientific advances support and, in some cases, actually 

build further upon the approaches of the current methodology for gas dosimetry in the 

tracheobronchial (TB), pulmonary (PU) and extrarespiratory (ER) regions. With regards 

to gas dosimetry, there appears to be insufficient quantitative evidence to modify the RfC 

Methods specifically for children; however, in some cases, chemical-specific information 

may warrant consideration of alternative approaches or adjustments to account for this 

lifestage. It is anticipated that information will continue to become available to further 

inform this issue.

This report summarizes the status of specific inhalation dosimetry procedures for gases as 

outlined in RfC Methods, and reviews recent scientific advances in gas dosimetry related 

to these procedures. These procedures are used predominately for interspecies 

extrapolation, typically from laboratory animal inhalation exposures to humans. The 

specific procedures addressed in this report are those used for the tracheobronchial (TB) 

and pulmonary (PU) regions of the respiratory tract and the procedure used for the 

systemic or extrarespiratory (ER) region. In addition, this report presents, reviews and 

discusses information and data on inhalation dosimetry throughout the respiratory tract of 

children pertaining to the derivation of an RfC. For the purposes of this report the 

scientific literature was searched from 1985 (about 10 years prior to the issuance of RfC 

Methods) to April 30, 2011. This report is a source document for evaluation and potential 

future revisions to and updating of the gas dosimetry procedures outlined in the RfC 

Methods, as they relate to the state-of-the-science. However, it is intended neither to 

displace the current procedures for gas dosimetry in RfC Methods nor to reflect on use of 

these procedures for assessments currently posted in IRIS.

The studies identified in this update addressing overall concepts and approaches for

portal-of-entry gas dosimetry in the TB and PU regions of the airways support the 

principles and procedures in RfC Methods. In some cases these studies suggest and 

provide examples of further refinement within the existing dosimetry modeling

framework of the RfC Methods through development and application of mass transfer 

coefficients as regional measures of gas uptake. Alternative gas dosimetry procedures 
1-2

published using simplified airway models inclusive of the TB and PU regions arrive at 

tissue metrics that support the approach of the RfC Methods. Conceptually, the methods 

for extension of state-of-science flow models to the TB and PU areas promise further 

refinement and resolution for inhalation gas dosimetry.

The default procedures of RfC Methods for TB and PU gas dosimetry employ the basic 

components of inhalation, regional surface areas of the TB and PU regions, and air flow 

to these surface areas. Recently, refined methods for measurement of inhalation rates in 

humans have been developed. The advent of the doubly labeled water (DLW) technique 

in estimation of physiological daily inhalation rates (PDIR) has provided resolutions to 

concerns regarding inhalation patterns of free-living individuals across all age groups 

including children. DLW-based PDIR values are currently included in the Child-Specific 

Exposure Factors Handbook (U.S. EPA, 2008), and are being proposed for inclusion in 

other key Agency documents, including the external review draft of the Exposure Factors 

Handbook (U.S. EPA, 2009a), for all ages including children.

Recent advances in understanding the airflow to the TB and PU regions have been made.

Human flows in the PU region generally support the assumption of uniformity as 

methodological advances and increased resolution of several in vivo imaging techniques 

indicate highly uniform and homogenous flows in the alveolar regions for humans. On 

the other hand, examination of the TB regions with human models and advanced dynamic 

fluid flow programs reveal a degree of nonuniformity of flow for this region although 

apparently not to the extent that has been documented for the upper airway. Marked 

advances in morphometry of these regions for both animals and humans are being 

achieved with the development and application of stereology. These techniques, 

described as the estimation of higher dimensional information from lower dimensional 

samples, have and continue to provide more accurate estimates of flow to regions of the 

respiratory tract. A detraction regarding these advancements, however, is that most apply 

to humans and comparable information in the critical comparative component of 

interspecies extrapolation, the laboratory animal, lags.

The principal determinative component for dosimetry of the ER region is the highly 

chemical–specific blood:air partition coefficient (Hb/g). The Hb/g is also a sensitive 

component of physiologically based pharmacokinetic (PBPK) models, models that are of 

ever increasing utility to the risk assessment community. Different techniques and 

approaches have been proposed to derive these values for both human and laboratory 

animals. A set of key reviews (Abraham et al., 2005; Payne and Kenny, 2002), compiling 

and analyzing results from several of these approaches, makes several conclusions 

relevant to dosimetry and risk assessment, including that there appears to be no difference 

between human and laboratory values for a prominent subgroup of toxic gases, the 

volatile organics. Examination and compilation of Hb/gs in published inhalation PBPK 

models configured for interspecies comparisons was also undertaken. These findings also 
1-3

provide evidence that the current default dosimetry approach of RfC Methods that uses 

Hb/gs as a basis of dosimetry for the ER region, or systemic toxicity, remains valid.

Recent research relevant to inhalation gas dosimetry in children was found to closely 

follow the recommendations and guidance of the National Academy of Sciences (NAS)

on children’s risk (NRC, 1993). These recommendations include use of PBPK models to 

explore and evaluate potential child susceptibility as well as the related effort to generate 

accurate measurements and parameters to be used in these models. A number of studies 

were reviewed that followed from these activities including development of PDIRs,

morphometry of conducting airways and lung tissue using advanced state-of-science 

techniques, as well as respiratory tract function using new highly refined in vivo analyses 

of airway function. Sophisticated flow models that use these refined measures and that 

are capable of examining uptake differences of gases in the upper airways of both adults 

and children are also presented and discussed. Several PBPK models have been 

configured and parameterized with results from these newer techniques to consider child 

versus adult dosimetry. Although few data sets and models pertaining to gas dosimetry in 

children exist, the spectrum of methods and approaches is robust. In some scenarios, the 

available methods and approaches are fairly uniform in their indications of potential 

higher inhaled doses in young children, which may be in the range of up to 2-fold more 

than adult. Individual instances exceeding this range are also found but no apparent 

pattern appears to be associated with these occurrences. This range is within that built 

into RfC Methods using the human interindividual uncertainty factor (UFH) to account for

pharmacokinetic and pharmacodynamic variability and for consideration of potential 

sensitive populations and lifestages including children. It should be noted that this finding 

is very similar to that of the NAS (1993).
1-4

1 INTRODUCTION AND P UR POSE 

The purpose of this report is to evaluate new scientific developments and advancements 

in gas dosimetry focusing on tracheobronchial (TB), pulmonary (PU), and 

systemic/extrarespiratory (ER) inhalation dosimetry related to the current methodology 

used by EPA. Particular emphasis is given on animal to human extrapolation and 

inhalation dosimetry in children. The goal of this project is to provide information 

necessary for ensuring that methods and guidance used and implemented by EPA in 

inhalation risk assessment reflects the state-of-the-science.

The current guidance, Methods for Derivation of Inhalation Reference Concentrations 

and Application of Inhalation Dosimetry [(U.S. EPA, 1994), hereafter RfC Methods], was 

made publicly available in 1994. RfC Methods is used by EPA in developing reference 

concentrations (RfCs) for the Agency’s IRIS (Integrated Risk Information System) public 

database. RfC Methods addresses broad areas of risk assessment but focuses especially on 

inhalation dosimetry and provides methods for converting inhalation exposures in 

laboratory animals to human equivalent exposure concentrations (HECs). Sections 

devoted to inhalation dosimetry are extensive including information on respiratory tract 

function and anatomy, physiology, and pathology in humans and typical laboratory 

animals. Other sections explore the properties of inhaled agents (e.g., particles and 

gases). In critical areas where important observations and application processes were not 

yet available, reasoned approaches based on scientific theory were given. These data, 

theories, and empirical observations were then synthesized into methods applicable to 

RfC derivation. These methods are also discussed in A Review of the Reference Dose and 

Reference Concentration Processes (U.S. EPA, 2002).

In 2009, the document Status Report: Advances in Inhalation Dosimetry of Gases and 

Vapors with Portal of Entry Effects in the Upper Respiratory Tract (U.S. EPA, 2009b)

was completed. The purpose of this first status document (hereafter Status I) was to 

evaluate scientific developments since 1994 and advancements in the area of gas 

dosimetry, focusing on the upper respiratory tract, and to determine how this information

might inform our approach to gas dosimetry. The focus of the evaluation was based on 

the results from an expert panel assembled in 2005 and tasked with reviewing the 

state-of-the-science of inhalation dosimetry in relationship to the RfC Methods. Status I

focused on portal-of-entry effects in the upper respiratory tract which, according to RfC 

Methods, comprises only the nasal tract or extrarespiratoy region (ER). While Status I did 

not consider advances in the area of gas dosimetry related to susceptible lifestages and 

populations, it is addressed in this report.

The current report (hereafter Status II) serves a purpose similar to Status I (U.S. EPA, 

2009b) but focuses on the remaining regions comprising the lower respiratory tract or 

thoracic (TH) region as designated by RfC Methods, the TB and PU regions. Also 
1-5

included in this report is the dosimetry used by RfC Methods for effects of gases in all 

sites other than the respiratory tract, referred to by RfC Methods as the extrarespiratory 

(ER) region. The ER region is considered to be other tissues that may be exposed to the 

gas once it enters systemic circulation via respiratory tract uptake.

This report, Status II, also evaluates new data and approaches for inhalation dosimetry of 

gases in children. This area was included in recognition of the Agency’s commitment to 

ensuring that EPA actions are protective of children, given the potential for sensitivity of 

early lifestages to some environmental exposures. RfC Methods currently considers 

children within the intraspecies variability uncertainty factor intended to account for 

intrahuman variability in response among sensitive populations and lifestages within the 

population but devotes no further analysis to the matter. The final portion of this report 

focuses on information and investigations that follow the recommendations of the 

National Academy of Sciences (NRC, 1993) to more thoroughly characterize 

susceptibility of children in relation to the adult population.

Status II is a source document for evaluation and potential future revisions to and 

updating of the gas dosimetry procedures outlined in the RfC Methods, as they relate to 

the state-of-the-science. However, it is intended neither to displace the current procedures 

for gas dosimetry in RfC Methods nor to reflect on use of these procedures for 

assessments currently posted in IRIS.
2-1

2 INTERS P ECIES GAS DOS IMETRY IN THE RFC -

THE REGIONAL GAS DOS E RATIO (RGDR ) AS

A DOS IMETRIC ADJ US TMENT FACTOR (D AF )

The purpose of dosimetry is to calculate an internal dose metric (e.g., target tissue dose, 

steady-state blood concentration) that results from an experimentally applied laboratory 

animal dose (or concentration) and estimate a human exposure dose (or concentration) 

that would result in an equivalent dose metric. Below, the steps currently used for the

dosimetric adjustment procedure for deposition in the TB and PU regions, as well as the 

ER adjustment, are reviewed.

Equation 2-1 is a general equation that may be applied to estimate a human equivalent 

concentration (HEC) from an animal point of departure (POD). The POD corresponds to 

an exposure concentration at which a particular health effect is observed. The subscript 

“ADJ” refers to an adjustment that converts the POD from the actual exposure 

concentration to an average daily exposure concentration for a continuous exposure 

scenario. This adjustment will not be considered further as it is not a focus of this report.

Equation 2-1

The DAFr is the dosimetric adjustment factor for a respiratory tract region, where r in this 

report refers to TB, PU, or ER. As can be seen here, the DAF is a factor used to convert 

an average exposure concentration for a particular laboratory species to a constant 

exposure concentration estimate for humans that would result in the same delivered dose, 

the HEC. When evaluating toxicity following inhalation exposure, in particular, dose 

refers to the mass of toxicant absorbed across an airway surface per unit surface area.

Also, for such portal-of-entry (e.g., TB and PU) effects, the DAF is termed the regional 

gas dose ratio (RGDR) and depends on animal to human ratios of two important 

parameters: minute volume or ventilation rate (VE), and surface area (SA) of the target 

region. When evaluating ER effects, the DAF depends on the ratio of animal and human 

blood:gas partition coefficients (Hb/g).
2-2

2.1 Current Applications of the DAFs - RGDRTB, RGDRPU, and Hb/g

RfC Methods provides species-specific values or algorithms to generate values for the 

parameters required to derive the RGDR for the TB and PU regions. The listing of SA 

values and the coefficients used to generate VE given in RfC Methods are presented below 

in Table 2-1 and Table 2-2. It is these values and algorithms that are to be part of the 

evaluation of new evidence and advances offered by this document. Also provided below 

is an example listing of animal and human Hb/g values as well as their A/H ratio 

(Table 2-3), this being the basis for the DAF applied for the ER region.

Table 2-1 Surface Areas for the Tracheobronchial (TB) and Pulmonary (PU) Regions of the 

Respiratory Tract in Various Species

Species TB (cm2) Source PU (cm2) Source

Human 3,200 Mercer et al. (1994b) 540,000 Mercer et al. (1994a)

Mouse 3.5 Mercer et al. (1994b) 500 Geelhaar and Weibel (1971); 

Mercer et al. (1994a)

Hamster 20.0 Yu and Xu (1987) 3,000 Lechner (1978)

Rat 22.5 Mercer et al. (1994b) 3,400 Mercer et al. (1994a)

Guinea pig 200.0 Schreider and Hutchens 

(1980) 9,000 Tenney and Remmers (1963)

Rabbit 300.0 Kliment (1973) 59,000 Gehr et al. (1981)

Source: (U.S. EPA, 1994)

Table 2-2 Intercept (b0) and Coefficient (b1) Values Used to Calculate Default Minute Volumes 

Based on Body Weighta

Species b0 b1

Rat -0.578 0.821

Mouse 0.326 1.050

Hamster -1.054 0.902

Guinea pig -1.191 0.516

Rabbit -0.783 0.83

a

Calculation of default minute volume based on body weight is conducted using the following algorithm: ((log VE) = b0 + b1 × log (BW))

Source: (U.S. EPA, 1994)
2-3

Table 2-3 Some Example Blood:Air Partition Coefficients (Hb/g) in Humans and Rats Expressed 

as a Ratio, Animal/Human

Chemical Human (Hb/g) Animal (rat, Hb/g) Animal/Human

Chloroform 6.85 20.8 3.0

Dichloromethane 8.94 19.4 2.2

Carbon tetrachloride 2.73 4.52 1.7

Chlorodibromomethane 52.7 116 2.2

Chloroethane 2.69 4.08 1.5

1,1-Dichloroethane 4.94 11.2 2.3

1,2-Dichloroethane 19.5 30.4 1.6

1,1,1-Trichloroethane 2.53 5.67 2.2

1,1,2-Trichloroethane 35.7 58 1.6

1,1,1,2-Tetrachloroethane 30.2 41.7 1.4

1,1,2,2-Tetrachloroethane 116 142 1.2

Hexachloroethane 52.4 62.7 1.2

Methylchloride 2.48 2.47 1.0

Source: Reprinted with permission of Elsevier©; Gargas et al. (1989)

2.2 As s umptions for the Current Application of RGDR Procedures

2.2.1 DAF for TB and PU Effects -- Uniformity of Flow and Depos ition

As outlined in the RfC Methods and described below, the dosimetric adjustment 

procedure for both the TB and PU regions are basically identical. The default procedure 

is based on the following assumptions: (1) the flow of inspired air (i.e., VE) into the 

region of interest (TB or PU) is uniform and even, (2) the surface area in the region of 

interest is uniform, and (3) as a consequence of these paired assumptions, there is 

uniform deposition of gas over the surface areas of these regions in the respiratory tract.

2.2.2 DAF for Extrares piratory Effects -- Blood:Gas Partition 

Coefficients (Hb/g)

As outlined in the RfC Methods, a number of assumptions exist for the application of this 

DAF. In addition to making the assumption that differences will exist between species for 

the basic biological component of Hb/g, blood, assumptions also are made that similarities 

will exist between species. These assumptions include that (1) chronic laboratory animal 

exposure scenarios are equivalent to human lifetime exposures, (2) the human 

time-integrated arterial blood concentration is less than or equal to that of the exposed 

laboratory animal such that (3) laboratory animal time-averaged arterial blood 
2-4

concentration is equal to the human equilibrium arterial blood concentration. It is also 

emphasized in RfC Methods that the equilibrium referred to here is that portion of the 

exposure period that is under conditions of “periodicity”, i.e., the periodic steady state 

concentration versus time profile is the same for every week. RfC Methods further states 

that conditions of periodicity should be met during "most" (elsewhere indicated as 90%) 

of the exposure duration. 

2.3 The RGDR for the Tracheobronchial (TB) Region - RGDRTB

The DAF for the TB region is the “regional gas dose, tracheobronchial” ratio (RGDRTB).

It is constructed with species–specific ventilation rates (or minute volumes) and surface 

areas for the TB region (U.S. EPA, 1994).

The equation for deriving a default RGDRTB for reactive and water soluble (e.g., 

Category 1) gases as it appears in RfC Methods (U.S. EPA, 1994) (Equation 4-22 and

Appendix I, Equation I-24) is shown in Equation 2-2, where VE is the minute ventilation 

rate (mL/min) and SATB the surface area (cm2

) of the TB region for laboratory animals 

(A) or humans (H).

Equation 2-2

The term to the right is the A/H ratio of the fractional penetration (fp) of the gas into the

extrarespiratory (ER) region. This term provides an estimate of the uptake of the inhaled 

gas through this upstream region in animals relative to humans. As detailed in the RfC 

Methods, fp values depend on the overall mass transfer coefficient, Kg (cm/min), surface 

area (SA), and minute volume (VE) according to Equation 2-3:

Equation 2-3

Chemical and species-specific Kg values in biological settings are rare. Therefore, when 

KgET is not known or cannot be reasonably approximated with data for either species, the 

RfC Methods default assumes that the actual value of this term is 1, such that the left term 

determines the RGDRTB, according to Equation 2-4:
2-5

Equation 2-4

This report addresses advancements in dosimetry to the TB region including overall

concepts and approaches to dosimetry, and in specifics for this region including minute 

ventilation (VE), surface area (SA), and mass transfer coefficients (Kg). Shown below is 

an example calculation of the DAF for the TB region using Equation 2-4 for a rat to 

human extrapolation assuming a rat VE of 0.250 L/min and SA of 22.5 cm2 and a human 

VE of 13.8 L/min and SA of 3,200 cm2

.

Equation 2-4 (example)

The calculation using these default parameters (U.S. EPA, 1994) results in a RGDRTB of 

2.6 indicating that rats receive nearly 3 times more dose in the TB region on a per SA 

unit basis than humans.

2.4 The RGDR for the Pulmonary (PU) Region - RGDRPU

The DAF for the pulmonary region is the “regional gas dose ratio, pulmonary” ratio 

(RGDRPU). It is constructed with species–specific ventilation values and surface areas for 

the PU region.

The equation for deriving a default RGDRPU for reactive and water soluble 

(e.g., Category 1) gases as it appears in RfC Methods (U.S. EPA, 1994) (Equations 4-23, 

4-25 and 4-28 and Appendix I Equations I-35, I-43 and I-46) is shown below as Equation 

2-5 where Qalv is the alveolar ventilation rate (mL/min) and SAPU the surface area of the 

pulmonary region for laboratory animals (A) or humans (H) (cm2

), and Kg for this region 

of interest (PU) (cm/min). Mass transfer coefficients are rarely available and are 

reasonably assumed to be very large for reactive and soluble gases, and Equation 2-5 

reduces to Equation 2-6.

Equation 2-5
2-6

Equation 2-6

The right two terms in Equation 2-5 and Equation 2-6 are the A/H ratios of the fractional 

penetration (fp) of the gas into the extrarespiratory (ER) and tracheobronchial (TB) 

regions. As explained above, these terms provide an estimate of the uptake of the inhaled 

gas in these two upstream regions (prior to entrance into the PU region).

If the fp to ET and TB regions are unknown due to lack of data on Kg, and the Kgs in 

animals are assumed to be equal to humans, and/or assumed to be equal to 1, the far left 

term in Equation 2-6, determines the RGDRPU. By combining these default scenarios for 

the fp terms in Equation 2-6, the RGDRPU can be computed from alveolar ventilation and 

pulmonary surface areas according to Equation 2-7:

Equation 2-7

Alveolar ventilation (Qalv) in the RGDRPU equations, is the appropriate parameter to use 

as it refers to the gas that reaches the alveoli and takes part in gas exchange and excludes 

that which does not, often referred to as alveolar dead space. In practice, however, 

alveolar ventilation and alveolar dead space are difficult to measure and values are not 

readily available even in the clinical setting (Appendix B). Minute volume, VE, is readily 

measured and typically reported in epidemiological and laboratory animal studies. Thus, 

the equation to determine the RGDRPU has been simplified through usage to the form 

presented in Equation 2-8.

Equation 2-8

As noted above, because chemical and species-specific Kg values in biological settings 

are rare, Equation 2-8 is the default utilized for gas exposure with effects in the PU 

region to obtain the DAF and used in calculation of the HEC.

Several recent reports in the literature, however, have investigated, derived, and applied 

Kgs for various compounds and for different regions of the respiratory tract. These reports 

include Kg values for the PU and TB regions for formaldehyde (Overton et al., 2001), Kg

values for ethanol, nitric oxide, and water vapor in the lung (Condorelli and George, 
2-7

1999), derivation and comparison of Kg values in contrasting geometries (Madasu, 2007), 

estimation of Kgs in terms of the Sherwood number and correlation with Reynolds and 

Schmidts numbers (Zhang and Kleinstreuer, 2011), and an approach for derivation for 

soluble and reactive vapors in lung tissues derived by combining the overall air-phase Kg

with analytical expressions for tissue-phase Kgs (Asgharian et al., 2011), several of which 

are discussed later in this report. Thus, incorporation of Kgs for both regional and fp 

determinations for more refined and precise inhalation gas dosimetry may be possible in 

the future.

This report addresses advancements in dosimetry to the PU region including overall 

concepts and approaches to dosimetry, and in specifics such as minute ventilation (VE), 

surface area (SA), and mass transfer coefficients (Kg). Shown below is an example 

calculation of the DAF for the PU region using Equaiton 2-8 for a rat to human 

extrapolation assuming a rat VE of 0.250 L/min and SA of 0.34 m2 and a human VE of 

13.8 L/min and SA of 54 m2

.

Equation 2-8 (example)

The calculation using these default parameters (U.S. EPA, 1994) results in a RGDRPU of 

2.9 indicating that rats receive nearly 3 times more dose to the PU region on a per SA 

unit basis than humans.

2.5 The DAF for the Extrares piratory Region (ER) Region - Hb/g

Gases with physicochemical properties that lessen their potential for effects in the 

respiratory tract (e.g., nonreactivity and higher lipid versus water solubility) may at the 

same time exhibit potential for significant uptake and accumulation in the blood where 

they can cause toxicity at systemic or remote (extrarespiratory, ER) sites. Based on these 

properties and other kinetic properties governing how such gases may be expected to 

distribute in the body, RfC Methods posits a fundamentally different DAF for gases that 

have little or no potential for reactivity in the respiratory tract. This DAF is based on 

assumptions of dose-response that are consistent with basic principles of kinetics and 

toxicity applied to the scenario of systemic toxicity from an inhaled toxicant: toxicity is 

directly related to the concentration of the agent at the target site; the concentration of the 

agent at the target site is related to the concentration of the agent in the arterial blood at 

equilibrium1

 1 The gas or its concentration multiplied by time (C × T)

; arterial blood concentration at equilibrium is related to its concentration in 

the inspired air. The last link in this process, the partitioning of the agent from the 
2-8

inspired air into the blood at the alveolar endothelial interface, is determined by the 

blood:air partition coefficient, Hb/g. Further, it is reasonably anticipated that as properties 

of blood differ between species so will the partition coefficient itself. Thus, the DAF for 

ER sites is based upon the species-specific (animal / human) ratio of the blood:air 

partition coefficient (Hb/g) at equilibrium shown here in Equation 2-9: 

Equation 2-9

Appendix J of the RfC Methods provides a mathematical derivation and application of 

this procedure as well as a case study employing a physiologically-based 

pharmacokinetic (PBPK) model parameterized for interspecies extrapolation.

In the RfC Methods, the DAF derivation for ER effects is based more on science policy 

than on an empirical procedure. Further, this policy is bi-level; (1) where if Hb/g values 

are unknown the default value for (Hb/g)A / (Hb/g)H = 1; (2) if (Hb/g)A is greater than (Hb/g)H

then a default value of 1 is also used. These procedures are justified by RfC Methods on 

the animal human datasets that were available at the time (Gargas et al., 1989). Gargas et 

al. (1989), reported that for an appreciable number of volatile and nonvolatile agents the 

(Hb/g)A was greater than the corresponding (Hb/g)H. These values are also shown above in 

Table 2-3. This report, Status II, adds new evidence and advances from sources 

identified.

2.6 Children's Dos imetry

Consideration of children’s dosimetry in RfC Methods is discussed as a component of the 

intraspecies uncertainty factor (UFH) that accounts for unknown pharmacokinetic and 

pharmacodynamic differences. The default value of this UFH is 10 and is applied to 

account for uncertainty and potential variations in susceptibility within the human 

population (interhuman variability) and the possibility that the available database is not 

representative of the population groups that may be most sensitive to the health hazards.

Early lifestages (including (1) embryo, fetus, and neonate and (2) young children) are 

also listed in Table 2-4 of the RfC Methods as 2 of the 5 sensitive populations and 

lifestages who, based on empirical observations or compromised physiological functions, 

are assumed susceptible to toxicity elicited by certain groups of chemicals. It is discussed 

further that certain populations and lifestages may be differentially susceptible, e.g., 

elderly individuals could be more susceptible to some chemicals and children to others.

RfC Methods acknowledged that very little is known about this important area of 

population sensitivity and that guidance should be developed concerning the prevalence 
2-9

of sensitive populations and lifestages and the range of sensitivities in the general 

population exposed to inhaled toxicants.

The Food Quality Protection Act (FQPA) of 1996 contains several requirements (directed 

primarily toward the evaluation of pesticides) related to a new standard described in the 

Act as “reasonable certainty of no harm.” One of the specific requirements identified was 

that the EPA considers the specific risk pesticides might have for infants and children. In 

general, the manner in which this was to be accomplished was through application of 

uncertainty factors based on an evaluation of information relevant to children. However, 

this requirement engendered considerable interest, including interest in inhalation 

dosimetry in children. On the whole, these evaluations, including conclusions by the 

NAS (1993), indicate that for most chemicals the very large majority of people, including 

children, respond sufficiently similarly so that the 10-fold intraspecies uncertainty factor 

is adequate to cover any variability that may exist in the human population. However, 

there are chemicals for which some humans may display a greater range of variability and 

sometimes that variability appears age-related, with children exhibiting a greater degree 

of sensitivity than adults. Further considerations of these matters are included in the 

section on children’s dosimetry (Section 3.6).
3-1

3 ADVANCES

RfC Methods was a state-of-the-art document for inhalation dosimetry of gases in 1994.

Perhaps because of RfC Methods, further investigations into virtually all aspects relating 

to inhalation dosimetry of gases have been undertaken. This section presents and 

discusses many of these studies, results, and concepts that provide information to advance 

our knowledge of gas dosimetry in the TB, PU, and ER regions.

3.1 Inhalation Dos imetry – Concepts, Des ign, and Res ults

Hanna et al. (2001) provided an update on the RfC Methods elaborating upon the 

physicochemical framework and the mass transport theory that underlies the dosimetric 

approach for gases. The authors made clear the distinction between the equilibrium 

assumption in which gas transport is dependent on regional blood flow (e.g., the 

endothelial blood flow of the lungs or PU region) or dynamic models in which gas 

transport occurs across a concentration gradient near the transport barrier which is 

essentially the membrane or boundary of another phase (e.g., as in the ET and TB 

regions) and how this distinction was considered in formulating the dosimetric models in 

RfC Methods.

For gases where transport is controlled by regional blood flow, it is the ventilation rate 

that determines the alveolar gas-phase concentration; the equilibrium partition coefficient 

of the gas then establishes the blood concentration in equilibrium with the alveolar gas 

concentration. Transport to systemic (or remote) sites is then determined by tissue 

specific blood flow. For a membrane barrier-limited transport model, equilibrium will not 

be established between the air and the tissue and the tissue and blood. Instead, 

concentration gradients exist in the air, the airway liquid lining layer, and the underlying 

tissue. Depending upon the physicochemical properties, the absorption of an inhaled gas 

may be controlled by gas-phase transport alone or the gas-phase in combination with the 

transport through the liquid tissue phase. An individual mass transfer coefficient through 

the gas phase (kg, cm/s) can be defined as the proportionality constant relating the flux of 

the toxicant through the liquid phase, Ng (mass transported per surface area per second), 

and the concentration difference between the central gas stream and that at the interface 

of the gas/liquid interface (ΔCg) is described in Equation 3-1.

Equation 3-1

To incorporate the blood phase with the gas and liquid + tissue phase into an analysis of 

transport, one defines the Kg. Kg is the proportionality constant between the absorptive 
3-2

flux at the gas/liquid interface and the overall concentration difference between the 

central gas stream and the blood. It can be written in terms of the individual mass transfer 

coefficients and the blood flow (Qb) as shown in Equation 3-2. Here, F is the flux fraction 

(to account for the fraction reacted in the previous transport phase), Sp the available

surface area (cm2

), Ht/g the tissue to gas partition coefficient (Henry’s Law value of the 

gas, unitless), Hb/g is the blood to gas partition coefficient (unitless), kl is the mass 

transfer coefficient in the surface-liquid/tissue phase (cm/s), and Qb the regional blood 

flow (cm/s).

Equation 3-2

The development and availability of these mass-transport coefficients is necessary for the 

full development of the procedures given in RfC Methods. In particular, mass transport 

coefficients are important for realistic determination of (1) the gas dose to a region and

(2) the fractional penetration (fp) of gases through the various regions of the respiratory 

tract. Also, use of an overall mass transport coefficient could retain a spatial

concentration distribution within the analysis rather than assuming well-stirred models in 

which concentrations are spatially uniform. Such an approach provides a more realistic 

basis from which to assess critical dose metrics, including peak concentration or peak 

flux. 

Other considerations regarding the development of these values exist. Whereas the kg can 

be scaled to any gas of interest simply through a scaling of the gas diffusivity; kg is 

highly dependent on the complexity of the airway morphometry and the airflow patterns 

induced by the morphometry. For example, based on major differences in the upper 

airway structures, one should expect important differences between rat and human mass 

transfer coefficients in the TB region. In other words, the overall mass-transport 

coefficient (Kg) can be highly species specific depending upon the nature of the gas.

Hanna et al. (2001) summarized and emphasized that development of robust data on 

physicochemical and toxicokinetic properties for specific gases or classes of gases is

necessary to develop appropriate DAFs. Furthermore, obtaining anatomical and 

physiological parameters for both sexes at various ages in both laboratory animal species 

and humans could further extend the understanding of the determinants of the DAF, their 

variability, and potential uncertainties when used to extrapolate across species.

Several approaches to estimate kgs have recently been reported (Asgharian et al., 2011; 

Zhang and Kleinstreuer, 2011; Schroeter et al., 2010; Longest and Kleinstreuer, 2005; 

Condorelli and George, 1999). In general, these approaches utilized the Sherwood 

number (Sh), a dimensionless term of the ratio of convective to diffusive forces. The kg
3-3

can be estimated using the relationship between the Sherwood number (Sh), diffusivity 

coefficient (D), and radius of the airway (R):

Equation 3-3

All of approaches used to estimate kgs, utilized variations of the classical Ranz and 

Marshal (1952a, b) approach, an empirical relationship between the Reynolds and 

Schmidt numbers. The use of Sherwood numbers to calculate Kgs may allow for the 

calculation of air-phase concentrations. 

Flux-based dosimetry estimates for formaldehyde gas to the TB and PU regions were 

developed by Overton and coworkers (2001). These estimates were inclusive of 

calculations for overall mass transport coefficients for the lower respiratory tract. The

anatomical model for the lower airways was that of Weibel with ventilation and 

respiratory tract dimensions (23 generations, including the upper airways) for a male of 

specified age, height, and weight at functional residual capacity. The dosimetry model 

employed a single path symmetric anatomical model versus a fully descriptive multi-path 

model. The model developed by Overton and coworkers (2001) is referred to as an 

“identical-path” model because all routes from the airway entrance to the terminal 

airspaces are modeled identically. Assumptions for the single path symmetric model 

included (1) all paths are identical, (2) for a given generation, the dimensions of one 

single airway or airspace and the number of airways or airspaces in the generation 

completely define the characteristics of that model generation, and (3) each airway or 

airspace in the same generation or model segment has the same transport characteristics, 

such as airflow rate, effective dispersion coefficient, and Kg. These assumptions made it 

feasible to use the dosimetry model to estimate the flux of formaldehyde to tissue in each 

airway passage, and airspace of the respiratory tract.

Formaldehyde transport and uptake for the generations comprising the TB and PU 

regions were all approximated by a one-dimensional (1D) convection-dispersion equation 

that accounted principally for molecular diffusion and absorption at the air-liquid surface.

The mass transfer coefficients in the nasal cavity were estimated by matching (within 

0.2%) the percent uptake predicted by an existing three-dimensional (3D) computational 

fluid dynamic (CFD) model of transport during inspiratory flow through an anatomically 

accurate reconstruction of the nasal passages of an adult human male. The resulting 

overall identical-path nasal airway mass transfer coefficients multiplied by the nasal 

surface area, corresponding to minute volumes of 7.5, 9.0, 25, and 50 L/min (nasal 

steady-state inspired flows rates of 15, 18, 50, and 46 L/min) were 1.68, 1.78, 2.98, and 

2.83 cm/s, respectively. The Kg for the lower airways was calculated with extensive 

consideration given to the kg component. The 1D equation of mass transport was then 
3-4

applied to each generation airway and airway passage of a symmetric, bifurcating 

respiratory tract anatomical model to provide predictions of local formaldehyde surface 

fluxes (dose). The results obtained included the following: (1) more than 95% of the 

inhaled formaldehyde is predicted to be retained by the respiratory tract for all activity 

states simulated (a total of 4 different minute volumes); (2) in the lower respiratory tract, 

surface flux (dose) is predicted to increase for several generations and then decrease 

rapidly, (3) compared to first pulmonary generation fluxes, the first few tracheobronchial 

generations fluxes are over 1,000 times larger; and (4) there is essentially no flux in the 

alveolar sacs. The authors stated that the predicted fluxes based on the 1D model for 

those lower regions of the respiratory tract can be used in dose-response modeling. This 

work provided information on mass transfer coefficients for the PU and TB regions 

including their derivation, and demonstrating their use in a dosimetry model for these 

regions.

Tsujino et al. (2005) developed a simplified mathematical airway model to simulate the 

transport of gases (ozone [O3] and sulfur dioxide [SO2]) in airways of laboratory animals 

(rats and dogs) and humans. The aim of the study was to examine through model 

simulations how interspecies anatomical and physiological differences influence the 

transport of the inhaled gases throughout the airways and alveoli. This comparison could

potentially provide an interspecies comparison of gas dosimetry in airways. The authors 

acknowledge and document that nearly all input parameters used were assumed or scaled, 

albeit with reasonable assumptions and allometry. Upper airways were assumed to be 

straight tubes with length, diameter, volume, and surface area all mathematically derived 

and scaled from existing information. The total length of the cylindrical upper airways 

was obtained from the sum of the widths of multiple cross sections of the upper airways, 

including the nasal cavity, pharynx, and larynx, of rats, dogs, and a human child

(described as 3 years old and weighing 13.6 kg). The dimensions from the human child 

were subsequently converted to correspond to the values of a human adult weighing 70 

kg for the simulations. The movement and velocities of gases within the airway were all 

modeled based on convection with bulk flow simplified by assuming conservation of gas 

volume without solving incompressible Navier–Stokes equations. Gas absorption at the 

surface of the airways was determined by mathematical formulations incorporating the 

basic elements of diffusivity and absorption constants (which included the absorption rate 

at the airway surface) that were scaled to each gas. The basis for this scaling was actual 

absorption data and concentration differences for these gases obtained by direct 

measurements in dog airways. Real-time changes in gas concentrations were simulated at 

three airway sites in each species: (1) the upper airway, (2) the lower airways consisting 

of the 5th or 10th bronchial generation and (3) the alveolar region. The amount of O3 and 

SO2 absorbed (modeled assuming a 10% concentration) at the airway surface was then 

calculated. Interspecies comparison was also performed for the amount of gas absorbed 

per body weight (g/BW), and for the corrected amount of gas absorbed per unit of airway 

surface area (g/cm2

). The results obtained for O3 and SO2 are shown in Table 3-1 below.
3-5

Table 3-1 Modeled Predictions of Amount of O3 and SO2 Absorbed at Various Sites in the

Airways of Three Species

Parameter Rats Dogs Humans

Ozone

Total absorbed amount (g/kg BW) 1.1 × 10-7 1.46 × 10-7 0.847 × 10-7

Upper airways (% of total) 73.9 80.7 34.4

Lower airways (% of total) 23.4 16.3 60.7

Alveolar region (% of total) 2.7 3.0 4.9

Absorbed amount per SA/unit time

Upper airways (g/cm2/ min) 1.76 × 10-7 0.89 × 10-7 1.31 × 10-7

Lower airways (g/cm2/ min ) 3.52 × 10-8 1.29 × 10-8 7.58 × 10-8

Alveolar region (g/cm2/ min) 1.56 × 10-13 1.23 × 10-13 1.40 × 10-13

Sulfur dioxide

Total absorbed amount (g/kg BW) 1.77 × 10-7 3.24 × 10-7 1.61 × 10-7

Upper airways (% of total) 98.6 99.4 96.5

Lower airways (% of total) 1.4 0.6 3.5

Alveolar region (% of total) 0.0 0.0 0.0

Source: Reprinted with permission of Informa Healthcare©; Tsujino et al. (2005)

These simulations indicate that the amount of O3 absorbed per body weight throughout 

the airways was lowest in humans (Table 3-1). However, the amount of absorbed O3 per 

surface area in each airway were fairly equivalent in the upper airways and alveolar 

regions, and were higher in humans in the lower airways, over 2 times that of rats. This 

trend was noted also for SO2. Concentrations of SO2 in the lower airways and alveoli 

were low in all species, which reflects the predicted rapid absorption of the gas in the 

upper airway. Also, these simulations were for short periods of inhalation and relatively 

high concentrations of these agents. It should be noted that many simplifications and 

assumptions were necessary in order to accomplish the simulations. Some of these were 

application of a simple three-compartment model of the airways and alveoli, without 

specific consideration of the effects of different branching patterns on the airway surface 

areas. Human airways are considered to be the least asymmetrical among mammalian 

species especially with regard to daughter tube diameter ratio and daughter branch angle 

ratio. Advances in respiratory tract imaging techniques can provide a more accurate 

airway model. Coaxial diffusion of gas molecules was not taken into account in the 

simulations, as it is well known that gas molecules in airways are transported by both 

bulk flow and diffusion. Thus the modeled gas concentrations might not accurately 

reflect actual concentrations, particularly in the peripheral airways and in the alveoli.

Nonetheless this study is of considerable value for further hypothesis testing regarding 

the variations in the kinetics of inhaled gases among experimental animals and humans. It 

numerically demonstrated that interspecies variations in anatomy and respiratory patterns 
3-6

cause significant differences in gas transport in the airways and alveoli of rats, dogs, and 

humans.

Minard et al. (2006) proposes concepts on how the application of CFD modeling may be 

developed for the lower areas of the respiratory tract, the TB and PU regions. CFD 

models are developed through a sequence of steps, each requiring data intensive input.

Major components of this process are development of the graphical grid or mesh that 

mirrors the surface over which the fluid flow is to be modeled. Initial mesh developments

were based on histological sections or digitized tissue sections and required immense 

effort to complete. Also, such approaches have fundamental limitations in using an 

inherently 2D (tissue sections) approach for compiling 3D descriptions of airway 

architecture. Minard et al. (2006) investigated the feasibility of employing magnetic 

resonance (MR) or computed tomography (CT) for this purpose. Ideally these methods 

would allow a more detailed view of areas such as airway architecture in a digital format 

that is inherently 3D and therefore ideal for subsequent computational analysis. These 

authors reported results from visualization of airway architecture in the rat using proton 

(1H) MR image data and describe computational techniques that could reduce the time 

from image analysis to complete mesh to a few days. In addition, 3D 1H MR imaging of 

rat pulmonary casts is reported as a step toward extending techniques to the lower regions 

of the respiratory tract. The authors noted that in vivo pulmonary imaging with MR or CT 

does not yet provide sufficient detail for visualizing more than the uppermost (∼4–7 

generations) pulmonary airways. Thus imaging of pulmonary casts by 1H MR is 

proposed to provide an important alternative with the capacity to capture the actual 

geometries for the development of CFD in a 3D setting. This report also explored the 

feasibility of validating CFD predictions of velocity distributions with MR by imaging 

hyperpolarized (HP) helium (3

He) in a straight pipe with a diameter comparable to the rat 

trachea at physiological flow rates. The comparison showed significant differences with 

the features in the MR image showing considerable blurring in the axial flow pattern as 

compared to the CFD solution. Thus, these proposals require more examination before 

sound recommendations can be made. Future work should continue to explore, adapt and 

refine imaging techniques to facilitate development of the concept of applying CFD 

analysis to the lower respiratory tract airways.

3.2 Inhalation Rates

Regardless of the inhalation dosimetry approach considered, inspired and/or expired air 

can be determinative components of an airborne dose to airway tissues for both humans 

and laboratory animals. The dual subcomponents of the inspired air that relate to tissue 

exposure are volume of inspired air (liters) and rate of breathing (e.g., frequency/min) 

which are typically considered together as a factor in the parameter of minute volume,

VE, in liters/min. VE is the measure most often reported in a clinical setting under very 
3-7

brief conditions. For purposes of risk assessment, which typically consider long-term 

exposures, knowledge of this parameter and variability in nonclinical settings, e.g. 

free-living, is required. Major advancement in the area of inhalation rate measurement in 

humans has been the application of methods used in free-living conditions, which would 

allow for more accurate measures inclusive of this variability.

Two prominent approaches for inhalation rate measurement in a nonclinical setting 

include (1) activity pattern questionnaires where oxygen consumption is calculated from 

daily activity patterns/energy intake and (2) differential dilution of isotopes in water 

administered orally as a bolus, usually over a two-week period. This latter method, the

doubly labeled water (DLW) method, measures oxygen lost through carbon dioxide 

production. This is accomplished by administering oral doses of water that is radiolabeled 

with both O18 and deuterium oxide (D2O). O18 is lost in time both through carbon dioxide 

production and body water loss whereas D2O is lost only through body water. After a 

period of 7-21 days (usually 14 days in humans), urine is collected and analyzed by 

GCMS. The disappearance rate of deuterium reflects water output and the disappearance 

of O18 water reflects water output plus carbon dioxide production rates. The carbon 

dioxide production rate is then calculated by the difference between the two 

disappearance rates.

The DLW method was used to calculate the physiological daily inhalation rates (PDIR) 

for 2,210 individuals aged 3 weeks to 96 years (Brochu et al., 2006b) (Table 3-2). Total 

daily energy expenditures (TDEEs; kcal/day) were determined from CO2 production rates 

using classic respirometry formulas, in which values for the respiratory quotient 

(CO2 produced /O2 consumed) were derived from the composition of the diet during the period 

of time of each study. The DLW method also allows for measurement of the energy cost 

of growth (ECG; in kcal /day) for children. TDEE and ECG measurements were

converted into PDIRs using the relationship developed by Layton (1993).

Equation 3-4

where H is the O2 uptake factor (volume of O2 consumed at standard temperature and 

pressure (STP) to produce 1 kcal of energy expended = 0.21 L O2) and VQ is the 

ventilatory equivalent ratio of the minute volume (VE) at body temperature pressure 

saturation to the O2 uptake rate (VO2 at standard temperature and pressure, dry air) = 27 

(unitless) with the conversion factor of 10-3

. The aggregate period of monitoring and 

analysis for this study was more than 30,000 person days.
3-8

Table 3-2 Distribution Percentiles of Physiological Daily Inhalation Rates for Free-Living 

Normal-Weight Males and Females Aged 2.6 Months to 96 Years

Age Group

(years) N

Body 

Weight (kg)a

Mean ± SD

Physiological Daily Inhalation Rateb (m3/day)

Percentilec

Mean ± SD 5th 10th 25th 50th 75th 90th 95th 99th

Males

0.22 to < 0.5 32 6.7 ± 1.0 3.38 ± 0.72 2.19 2.46 2.89 3.38 3.87 4.30 4.57 5.06

0.5 to < 1 40 8.8 ± 1.1 4.22 ± 0.79 2.92 3.21 3.69 4.22 4.75 5.23 5.51 6.05

1 to < 2 35 10.6 ± 1.1 5.12 ± 0.88 3.68 3.99 4.53 5.12 5.71 6.25 6.56 7.16

2 to < 5 25 15.3 ± 3.4 7.60 ± 1.28 5.49 5.95 6.73 7.60 8.47 9.25 9.71 10.59

5 to < 7 96 19.8 ± 2.1 8.64 ± 1.23 6.61 7.06 7.81 8.64 9.47 10.21 10.66 11.50

7 to < 11 38 28.9 ± 5.6 10.59 ± 1.99 7.32 8.04 9.25 10.59 11.94 13.14 13.87 15.22

11 to < 23 30 58.6 ± 13.9 17.23 ± 3.67 11.19 12.53 14.75 17.23 19.70 21.93 23.26 25.76

23 to < 30 34 70.9 ± 6.5 17.48 ± 2.81 12.86 13.88 15.59 17.48 19.38 21.08 22.11 24.02

30 to < 40 41 71.5 ± 6.8 16.88 ± 2.50 12.77 13.68 15.20 16.88 18.57 20.09 21.00 22.70

40 to < 65 33 71.1 ± 7.2 16.24 ± 2.67 11.84 12.81 14.44 16.24 18.04 19.67 20.64 22.46

65 to ≤ 96 50 68.9 ± 6.7 12.96 ± 2.48 8.89 9.79 11.29 12.96 14.63 16.13 17.03 18.72

Females

0.22 to < 0.5 53 6.5 ± 0.9 3.26 ± 0.66 2.17 2.41 2.81 3.26 3.71 4.11 4.36 4.81

0.5 to < 1 63 8.5 ± 1.0 3.96 ± 0.72 2.78 3.05 3.48 3.96 4.45 4.88 5.14 5.63

1 to < 2 66 10.6 ± 1.3 4.78 ± 0.96 3.20 3.55 4.13 4.78 5.43 6.01 6.36 7.02

2 to < 5 36 14.4 ± 3.0 7.06 ± 1.16 5.15 5.57 6.28 7.06 7.84 8.54 8.97 9.76

5 to < 7 102 19.7 ± 2.3 8.22 ± 1.31 6.06 6.54 7.34 8.22 9.11 9.90 10.38 11.27

7 to < 11 161 28.3 ± 4.4 9.84 ± 1.69 7.07 7.68 8.70 9.84 10.98 12.00 12.61 13.76

11 to < 23 87 50.0 ± 8.9 13.28 ± 2.60 9.00 9.94 11.52 13.28 15.03 16.61 17.56 19.33

23 to < 30 68 59.2 ± 6.6 13.67 ± 2.28 9.91 10.74 12.13 13.67 15.21 16.59 17.42 18.98

30 to < 40 59 58.7 ± 5.9 13.68 ± 1.76 10.78 11.42 12.49 13.68 14.87 15.94 16.58 17.78

40 to < 65 58 58.8 ± 5.1 12.31 ± 2.07 8.91 9.66 10.92 12.31 13.70 14.96 15.71 17.12

65 to ≤ 96 45 57.2 ± 7.3 9.80 ± 2.17 6.24 7.02 8.34 9.80 11.27 12.58 13.37 14.85

a

Measured body weight. Normal-weight individuals defined according to the body mass index (BMI) cutoffs. b

Physiological daily inhalation rates were calculated using the following equation: (TDEE + ECG) × H × (VE/VO2) × 10-3, where H = 0.21 L of O2/Kcal, 

VE/VO2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored daily energy cost for growth (kcal/day). Also see 

accompanying text. c

Percentiles based on a normal distribution assumption for age groups.

N = number of individuals

SD = standard deviation

Source: Reprinted with permission of Taylor & Francis©; (Brochu et al., 2006b)

The study results were also expressed and presented in terms of m3/kg-day (Table 3-3). In 

terms of m3

/kg-day, these results may be considered to be related to intake of toxic agents 

present in air inhaled for a typical daily activity pattern and thus as a basic surrogate for

an overall or systemic dose normalized to body weight. On this basis, average PDIRs 

exhibited a decrease from birth (e.g., 0.509 m3

/kg-day for males) to adulthood (e.g., 

0.247 for males), a factor of nearly 2. As shown in Table 3-3, the average rate continued 

to decrease in an age-related manner such that the average rate in the most elderly group 
3-9

tested (65- 96 years) was 0.188 ± 0.031 m3/kg-day for males and 0.172 ± 0.037 

m3

/kg-day for females, also nearly a factor of 2, compared to the 11-23 year age group.

However, the authors’ distributional analysis of the data further extended the range of 

values that were present in the sampling. Specifically, the rates at the 99th percentile for 

males and females at birth, 0.725 and 0.721 m3

/kg-day, respectively, were approximately 

2-times the corresponding values for adult males and females (0.410 and 0.383 

m3

/kg-day, respectively).
3-10

Table 3-3 Distribution Percentiles of Physiological Daily Inhalation Rates on a per Body Weight 

Basis for Free-Living Normal-Weight Males and Females Aged 2.6 Months to 96 Years

Age Group

(years) N

Body 

Weight (kg)a

Mean ± SD

Physiological Daily Inhalation Rateb (m3/kg-day)

Percentilec

Mean ± SD 5th 10th 25th 50th 75th 90th 95th 99th

Males

0.22 to < 0.5 32 6.7 ± 1.0 0.509 ± 0.093 0.356 0.390 0.447 0.509 0.571 0.627 0.661 0.725

0.5 to < 1 40 8.8 ± 1.1 0.479 ± 0.071 0.363 0.389 0.432 0.479 0.526 0.570 0.595 0.644

1 to < 2 35 10.6 ± 1.1 0.480 ± 0.059 0.383 0.405 0.441 0.480 0.520 0.556 0.578 0.618

2 to < 5 25 15.3 ± 3.4 0.444 ± 0.042 0.375 0.391 0.416 0.444 0.472 0.497 0.512 0.541

5 to < 7 96 19.8 ± 2.1 0.415 ± 0.047 0.337 0.354 0.383 0.415 0.446 0.475 0.492 0.524

7 to < 11 38 28.9 ± 5.6 0.372 ± 0.062 0.270 0.293 0.330 0.372 0.413 0.451 0.474 0.516

11 to < 23 30 58.6 ± 13.9 0.300 ± 0.047 0.222 0.239 0.268 0.300 0.331 0.360 0.377 0.410

23 to < 30 34 70.9 ± 6.5 0.247 ± 0.039 0.183 0.198 0.221 0.247 0.273 0.297 0.311 0.338

30 to < 40 41 71.5 ± 6.8 0.237 ± 0.034 0.181 0.193 0.214 0.237 0.260 0.281 0.293 0.317

40 to < 65 33 71.1 ± 7.2 0.230 ± 0.042 0.161 0.176 0.202 0.230 0.258 0.284 0.299 0.328

65 to ≤ 96 50 68.9 ± 6.7 0.188 ± 0.031 0.137 0.149 0.168 0.188 0.209 0.228 0.239 0.260

Females

0.22 to < 0.5 53 6.5 ± 0.9 0.504 ± 0.093 0.351 0.385 0.442 0.504 0.566 0.623 0.657 0.721

0.5 to < 1 63 8.5 ± 1.0 0.463 ± 0.064 0.358 0.382 0.421 0.463 0.506 0.545 0.568 0.612

1 to < 2 66 10.6 ± 1.3 0.451 ± 0.077 0.325 0.353 0.399 0.451 0.502 0.549 0.577 0.630

2 to < 5 36 14.4 ± 3.0 0.441 ± 0.071 0.323 0.350 0.393 0.441 0.489 0.532 0.559 0.607

5 to < 7 102 19.7 ± 2.3 0.395 ± 0.048 0.315 0.333 0.362 0.395 0.427 0.457 0.474 0.507

7 to < 11 161 28.3 ± 4.4 0.352 ± 0.062 0.251 0.273 0.311 0.352 0.393 0.431 0.453 0.496

11 to < 23 87 50.0 ± 8.9 0.269 ± 0.049 0.189 0.207 0.236 0.269 0.302 0.331 0.349 0.383

23 to < 30 68 59.2 ± 6.6 0.233 ± 0.042 0.163 0.179 0.204 0.233 0.261 0.287 0.302 0.331

30 to < 40 59 58.7 ± 5.9 0.235 ± 0.035 0.178 0.191 0.212 0.235 0.258 0.279 0.292 0.316

40 to < 65 58 58.8 ± 5.1 0.211 ± 0.036 0.151 0.164 0.186 0.211 0.235 0.257 0.270 0.295

65 to ≤ 96 45 57.2 ± 7.3 0.172 ± 0.037 0.112 0.125 0.148 0.172 0.197 0.220 0.233 0.258

a

Measured body weight. Normal-weight individuals defined according to the body mass index (BMI) cutoffs. b

Physiological daily inhalation rates per body weight were calculated using the following equation: (TDEE + ECG) × H × (VE/VO2) × 10-3 divided by the age 

group-specific body weight, where H = 0.21 L of O2/Kcal, VE/VO2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored 

daily energy cost for growth (kcal/day). Also see accompanying text. c

Percentiles based on a normal distribution assumption for age groups.

N = number of individuals

Source: Reprinted with permission of Taylor & Francis©; (Brochu et al., 2006b)

The current value used by U.S. EPA in the RfC Methods is 0.286 m3/kg-day based on the 

accepted indices of 20 m3

/day (13.8 L/min) and 70 kg body weight. This value (0.286 

m3

/kg-day) appears to be near the upper end of the statistical bounds for the Brochu et al. 

(2006a) average value of the adult male age 23-30 population (0.247 ± 0.039 m3

/kg-day, 

Table 3-3). The younger aged populations (2.6 months to 11 years) consistently exceeded

this value, whereas older aged populations (greater than 65 years) were consistently 

lower (Brochu et al., 2006b) (Table 3-2). When viewed in a cross-sectional body 

weight-normalized perspective, these results indicate that (1) age groups younger than the 
3-11

11-23 age group may potentially be exposed more and (2) age groups older than the 

11-23 age group may potentially be exposed less based on daily inhalation rates. This 

applies to both genders. When viewed from a longitudinal perspective (i.e. over a lifetime 

comprising these age categories), the potential for exposure is highest at a young age and 

decreases with increasing age. With either view, younger ages would likely be exposed at 

a higher rate per body weight and, if this were the sole consideration for dosimetry, 

would be considered more vulnerable than adults. It should also be noted that the higher

inhalation rate per body weight found in younger age groups compared to adults in this 

study is consistent with related processes such as increased metabolic demands and 

oxygen consumption in growing individuals; in particular lung growth where alveoli are 

being added and gas-exchange surfaces in the pulmonary area are increasing

(Section 3.6).

Brochu and coworkers (2006c) undertook a systematic examination of the various 

methods, including the DLW method to estimate PDIRs. The first part of the study

provided a critical review of traditional approaches to systematically reveal and 

understand sources of bias in each of the several approaches identified. Consideration of 

the often used time-activity-ventilation (TAV) approach showed this approach often 

considers only those activities that can be specifically accounted for thereby ignoring all 

others. Also time-activity-patterns are not well or equally-known for all age and gender 

groups and adequate statistical data are rarely available. These liabilities cause the PDIR 

estimates from this approach to be mostly underestimated. Energy expenditure 

approaches may be evaluated either as energy expenditures or food-energy intakes.

Energy expenditures may be expressed as a mean of the energy expended for the activity 

(kcal/min) or as a function of the basal metabolic rate (BMR) using metabolic equivalent 

(METs) values. As with the TAV approach, adequate data are not available for all 

age/gender groups and do not take into account energy cost/expenditure for all groups,

such as thermogenesis growth and spontaneous gesturing. Use and research of these 

methods has also indicated that they may provide overestimates of PDIR, especially 

among younger individuals. Daily-food-energy intake approaches sought to simplify the 

array and interaction of biases by using a single input parameter (i.e., food intake), which 

is then converted into PDIRs. However, sources of bias and error are now known for this 

approach. Daily food-energy loss in feces, intestinal gases and urine is not taken into 

account. It is now widely recognized that there is substantial underreporting of food 

intake surveys with the authors suggesting as much as 10–45%. Further, this 

underreporting is not uniform in the population but more pronounced in female 

adolescents and in overweight/obese individuals. 

In the second part of this study (Brochu et al., 2006c), the magnitude of under- and 

overestimations of published inhalation rates derived from the non-DLW approaches was 

described by a comparison with new sets of PDIRs and distribution percentile values 

based on TDEEs measured by the DLW method and expressed both as m3

/day and as 
3-12

m3/kg-day. The DLW-based TDEEs were derived from an aggregate period of over 

20,000 days for unrestrained free-living normal-weight individuals aged 2.6 months to 96 

years (n = 1,252). Rates were also produced with Monte Carlo simulations on the main 

input parameters for the various other approaches. The results indicated that few of the 

Monte Carlo simulation percentiles based on traditional approaches (57 out of 253) were 

close to physiological values within a gap of ± 5% or less. Relative to DLW-based 

TDEEs, outputs from time-activity-ventilation approaches were overestimated, whereas 

most of those using the metabolic equivalent approach were underestimated. Food-intake 

approaches led to underestimated rates due to biases discussed above. The most accurate 

daily inhalation rates were those based on DLW measurements with an error of about ± 

5%, as calculated in previous studies for free-living males and females aged 1 month to 

96 years during real-life situations in their normal surroundings (Table 3-2 and 

Table 3-3). Aggregate errors in all estimates (m3

/day and m3/kg-day) for the traditional 

approaches varied from -52% to +126%.

Allan and Richardson (1998) used Monte Carlo simulations to derive probability density 

functions of daily air inhalation rates for six age groups of Canadians. The objective was 

to use these functions to describe inhalation rates in probabilistic health risk assessments.

A collection of time-activity and breathing rate studies were reviewed in order to define 

random variables describing probable durations that North Americans (Canadians and 

Americans) spend at various levels of activity and their probable inhalation rates while at 

each level of activity. The general approach to developing the VE probability density 

functions involved compiling suitable minute volume data from the literature, organizing 

it by age, gender and degree of physical activity of the subjects, and estimating 

appropriate mean values, standard deviations and distribution shapes based on the 

reported data. Only data for “normal” subjects were included; for example, data 

pertaining to people with asthma or other disorders known to affect inhalation rates were 

excluded. These random variables were combined in a Monte Carlo simulation (10,000 

trials) to empirically generate probability density functions describing 24-hour inhalation 

rates for each age group. The simulations suggested that all except one age groups’ 

24-hour inhalation rates could be represented with log-normal probability density 

functions. The distribution of 24-hour inhalation rates for infants was found to be better 

represented by a normal distribution than a log-normal distribution. Arithmetic mean 

values, standard deviations, and coefficients of variation were approximated for these 

distributions (Table 3-4).
3-13

Table 3-4 Probabilistic 24-hr Breathing Rate Estimates

Age Group Gender Distribution (m3/day) Coefficient of Variation

Infants (0 – 6 months) combined 2.1 ± 0.58 0.27

Toddler (7 months–4 yr)

male 9.7 ±. 2.5 0.28

female 8.8 ±. 2.3 0.27

combined 9.3 ±. 2.4 0.28

Children (5–11 yr)

male 15.1 ±. 3.1 0.22

female 14.0 ±. 2.8 0.21

combined 14.6 ±. 3.0 0.22

Teenagers (12–19 yr)

male 17.7 ± 3.8 0.23

female 14.0 ± 2.7 0.2

combined 15.8 ± 3.7 0.25

Adults (20–59 yr)

male 17.5 ± 4.0 0.23

female 14.9 ± 3.1 0.21

combined 16.2 ± 3.8 0.24

Seniors (60+ yr)

male 15.6 ± 3.5 0.23

female 12.8 ± 2.5 0.2

combined 14.2 ± 3.3 0.24

Source: Reprinted with permission of Taylor & Francis©; Allan and Richardson (1998)

Allan et al. (2008) discussed the methodology of the three principal methods to 

determining physiological daily inhalation; TAV, metabolic energy conversion (MEC), 

and DLW. Probability density functions (PDFs) derived from recent data using the TAV 

method were also given and compared with PDFs developed based on earlier analyzed 

TAV and published MEC and DLW approaches. The sensitivity and influence of number 

of activity categories employed in the TAV method was discussed along with the 

inherent disadvantage in subjective judgment of activity levels. Similar critical analysis is 

given to the MEC approaches, whether based on daily food intake, on average daily 

energy expenditures to BMR, or as time weighted average (TWA) of energy expenditure 

across activity levels; each of these have inherent uncertainty due to judgment and 

subjective estimates. The DLW method is well suited for direct study in free-living 

scenarios and does not require invasive sampling; however, the administration and 

analysis of DLW can be cost prohibitive. The authors did not address the fact that 

judgment and subjective issues are prominent with TAV and MEC questionnaire-based 

approaches, issues that are totally absent from the DLW method. Table 3-5 shows a 

selected portion of the age-grouped data. In discussing their results, Allan et al. (2008)

compared and interpreted both the MEC and DLW findings to those of the TAV 

approach as a benchmark. This comparison showed that the estimated mean inhalation 

rates determined by the MEC approach were 8–30% lower than TAV mean inhalation 

rates. Compared to the TAV approach, mean inhalation rates determined by the DLW 
3-14

approach were 56% higher for infants, 38% lower for toddlers and children, and 1–21% 

lower for teenagers, adults and seniors.

The authors concluded that, with the exception of infants, mean inhalation rates derived 

by the MEC and DLW approaches were generally lower than those developed by the 

TAV approach. Rather than to reflect on the possibility of unresolved influences related 

to the known issues of judgment and subjective estimation to the accuracy of the 

approach, the authors propose that the higher estimates of TAV approach “... appear to 

offer a level of conservatism to human health risk assessments by being more 

representative of highly exposed members of the population...”. This statement cannot be 

related either to precision or to accuracy.

Table 3-5 Comparison of Mean 24-hour Inhalation Rates (m3/day) Determined Using 

Time-Activity-Ventilation (TAV), Metabolic Energy Conversion (MEC) or Doubly 

Labeled Water (DLW) Approaches

Age Group TAV MEC DLW

Infants (0–6 mo) 2.18 3.90 3.39

Toddlers (7 mo–4 yr) 8.31 7.60 5.17

Children (5–11 yr) 14.52 11.53 9.01

Teenagers (12–19 yr) 15.57 14.47 15.49

Adults (20–59 yr) 16.57 12.77 15.37

Seniors (60+ yr) 15.02 11.35 11.86

Source: Reprinted with permission of Taylor & Francis©; Allan et al. (2008)

3.3 TB Region

3.3.1 Flow and Depos ition 

Taylor et al. (2007) examined the pattern of lung injury resulting from exposure to ozone. 

The distribution of ozone uptake was studied in a single, symmetrically branched airway 

bifurcation using CFD. Separate simulations for inspiratory and expiratory flows were 

conducted at Reynolds numbers ranging from 100 to 500, corresponding to laminar flow 

conditions, to examine the effect of flow rate on uptake. The simulations demonstrated 

the total rate of ozone uptake increased with increasing flow rate during both inspiration 

and expiration and that flux progressively decreased along the parent branch. In addition, 

hotspots of ozone flux were observed at the carina of the bifurcation for all simulated 

flow rates. However, at the lowest simulated flow rate, the location of maximum flux 

shifted to the outer wall of the daughter branch. Compared to a straight tube with a 

similar surface area, the presence of branching resulted in a enhancement of overall 
3-15

uptake. The results of these simulations may be applicable to transport and uptake of any 

gas with similar properties and are useful in revealing hotspots that may lead to focal 

regions of airway tissue injury.

Padaki et al. (2009) used CFD modeling to simulate the transport and uptake of ozone for 

comparison between an idealized model of the larynx, trachea, and first bifurcation and a 

“control” model in which the larynx was replaced by an equivalent, cylindrical tube 

segment. This comparison was performed in order to examine the effect of laryngeal

geometry on flow behavior. Inlet Reynolds numbers from 140 to 4,300 were used in the 

simulations. The results revealed a strong laryngeal jet with a reattachment point in the 

proximal trachea. Jet turbulence occurred only at the high Reynolds numbers and was 

attenuated by the first bifurcation. Hotspots previously reported at the first carina were 

confirmed by the local fractional uptake data; additional hotspots at the glottis and 

proximal trachea were also observed. These laryngeal effects were dependent on 

Reynolds number, with maximal effects (~15% enhancement of uptake efficiency) 

occurring at the highest flow rate. Although the increase in regional uptake subsided by 

the end of the model (i.e. the first bifurcation), the effect of the larynx on cumulative 

uptake persisted further downstream. Together, these results suggested that with 

prolonged exposure to a reactive gas entire regions of the larynx and proximal trachea 

could show signs of tissue exposure.

Zhang et al. (2006) employed a representative human upper airway model to describe 

uptake and deposition of MTBE and ethanol vapors. This description was accomplished 

using CFD approach. Model simulations were done under varying conditions, including 3 

inspiratory flow rates (Qin = 15, 30, and 60 L/min). The airway model utilized was 

created from a human cast consisting of two parts: the oral airway, including oral cavity, 

pharynx, larynx and trachea; and a symmetric triple bifurcation representing generations 

G0 (trachea) to G3 (referred to in the report and herein as the “upper bronchial airway” or 

UBA). This study therefore isolated the extrarespiratory (ER) region from the lower TB 

region (UBA). To attain representative modeling of airflow in such a model, a 

low-Reynolds-number model was selected (to assure laminar flow and constant fluid 

motion) and adapted to the laminar-to-turbulent flow regimes that are likely to occur in

the human airway during inhalation at the flow rates employed in the simulations. The 

deposition of vapors in each airway segment was described by the deposition fraction

(DF), which was calculated with the regional mass balance and the sum of local wall 

mass flux. An uptake parameter (K) was also calculated for both ethanol and MTBE 

using available values of diffusivity of vapor in air and liquid mucus phase and 

equilibrium partition coefficients in gas and liquid interfaces. The respiratory mass 

transfer coefficient (called hm by the authors) was also estimated.

The simulations showed that flow rate had a strong effect on vapor deposition; the lower 

the flow rate, the higher the deposition fraction due to the extended vapor residence times 

at low flow rates. Results showed that as the flow rate decreased from 60 L/min to 15
3-16

L/min, DF for MTBE increased from 2.5% to 7.7% in the UBA. The simulation showed 

further that the DFs increased in a nearly linear fashion with the distance into the airway, 

indicating consistent deposition efficiency along the airway passage. Compared with 

MTBE, DF values of ethanol were approximately three to six times greater in the oral 

airways and two to five times greater in the UBA in the range of flow rates used. The 

higher deposition of ethanol vapor may be attributed not only to its higher diffusivity but, 

more importantly, to its higher solubility in the mucus layer as indicated by the value of 

K for ethanol (413) compared to MTBE (11). Vapors that pass through the upper airway 

may further penetrate into and deposit partly in the lower airway and alveolar regions.

This suggests that compared to ethanol MTBE may penetrate further and deposit in the 

lower airways.

Simulations based on the mesh were analyzed by the authors on a more refined scale.

Local vapor deposition patterns were quantified in terms of a deposition enhancement 

factor (DEF), which is defined as the ratio of local to average deposition densities, DEF 

therefore being an indication and representation of vapor deposition “hotspots” in a given 

region. Figure 3-1 and Figure 3-2 show the distributions of these DEFs in the airway 

components of the model. These deposition patterns were clearly not homogeneous and 

were nonuniform for ethanol, which is relatively highly absorbed in the UBA, and for 

MTBE, which is not highly absorbed. The maximum DEF was ∼1.5 for MTBE in the 

UBA with the value reaching 7.8 in the UBA model for ethanol. The low maximum DEF 

values for MTBE indicated that deposition of MTBE vapor was relatively uniformly 

distributed in the upper airways with relatively little absorbed by the airway walls 

whereas the opposite appears to be the case for ethanol with the greater overall absorption 

allowing for more contrasting differences and higher DEF “hotspots.”

In the bifurcation airway model, enhanced deposition occurred mainly at the carina 

ridges and the inside walls around the carina ridges, due to the complicated airflows and 

large concentration gradients in these regions. When the absorption parameter (K)

increases above the typical value, however, deposition of MTBE increases with 

deposition patterns not changing much. With increasing absorption, however, the 

locations of enhanced deposition receive even greater deposition and the maximum DEF 

values increase.
3-17

Figure 3-1 Distributions of Deposition Enhancement Factor (DEF) for 

MTBE Vapor with Qin = 30 L/min in the Bifurcation Airway 

Models

Source: Reprinted with permission of Informa Healthcare©; Zhang et al. (2006)

Figure 3-2 Distributions of Deposition Enhancement Factor (DEF) for 

Ethanol Vapor with Qin = 30 L/min in the Bifurcation Airway 

Models

Source: Reprinted with permission of Informa Healthcare©; Zhang et al. (2006)

These simulations utilized a three dimensional computational fluid dynamic simulation 

method and provided detailed local deposition patterns for both MTBE and ethanol, 

agents widely disparate in uptake, transport and deposition. These deposition patterns 

showed clearly that tissue burdens at local sites may exceed by many times the average 

dose of the airways, i.e. they are highly nonuniform. Whereas flow rates greatly affected

deposition fractions, deposition patterns were not much altered. The localized deposition 

pattern suggested that the uptake pathway may have a preferential route along which 

local tissues are subjected to heavy exposure of vapors much the same as has been 

demonstrated for formaldehyde in the ET region of rats (e.g., Kimbell et al., 1997). Thus,
3-18

this enhanced deposition at local sites in this lower region of the respiratory tract may 

also result in tissue damage or other adverse biological responses at local sites in the first 

four generations of the human tracheobronchial tree.

Typically, animal lung models are based on the geometry of the human lung especially in 

the TB region. In human lung models, termed typical path morphometric models, the 

bronchial airway geometry is represented in terms of a typical path lung model, in which 

all airways of a given airway generation have identical diameters, lengths, and branching 

angles. Such geometry does not apply to murine species, including rats and mice. Thus, 

Madl et al. (2010) undertook the task of developing a stochastic morphometric model of 

the bronchial tree of the BALB/c mouse. Morphometric data on the TB geometry of three 

in situ lung casts of the BALB/c mouse were analyzed in terms of probability density 

functions and correlations among the different airway parameters. The results of this 

statistical analysis revealed significant differences in diameters and branching angles 

between major and minor progeny branching off from the same parent airway at a given 

airway bifurcation. Significantly, the number of bronchial airways generations along a 

given path, expressed by the termination probability, branching angles, and 

daughter-to-parent diameter ratios indicated that the location of an airway with defined 

linear airway dimensions within the lung was more appropriately identified by its 

diameter (or its parent diameter) than by an assigned generation number as per typical 

path models. This conclusion of classification by airway diameter rather than by 

generation number was the same one posited for the rat (Koblinger and Hofmann, 1988).

Application of the techniques and procedures resulted in a single datum for the 

bronchial/bronchiolar volume of 0.114 cm3

, which was defended by the authors as being 

in agreement with the few other historical estimates available. These findings for particle 

deposition in both rat and mouse indicates that these data support a stochastic rather than 

typical path selection. The computed distributions of the geometric airway parameters 

and their correlations may be used for random pathway selection of inhaled particles in 

subsequent Monte Carlo-like deposition calculations. The significance of these findings 

for gas dosimetry, especially for estimates for critical parameters such as surface area and 

character of the gas flow within this anatomy relative to humans remains to be elucidated. 

Madasu (2007) compared approaches to modeling inhaled dose in the pulmonary airways

that can characterize the axial nature of dose and injury known to occur with various 

reactive agents. These authors’ comparison was based on contrasting models representing 

the lower airways of the lung. The basis for comparative measurement of flow and 

absorption characteristics in these contrasting models is a representative 3-generational 

bifurcating unit of pulmonary airway. The first model reported was composed of Weibel 

geometry of repeated symmetric bifurcating tube geometry to which a 3D computational 

fluid dynamic model (CFDM) was applied (under conditions of steady expiratory flow) 

and typical contour plots of velocity and concentration fields obtained. The second model

was a two-dimensional model with geometry consisting of a series of rigid cylindrical 
3-19

tubes of decreasing diameter representing the branches in a generation; the tubes were 

connected by “leaky” nodes of flow in conical transition regions that represent the 

bifurcations. This model was termed by the authors as an axisymmetric single path model 

(ASPM). The basis of the comparison of these two models was their mass transfer 

coefficients for formaldehyde obtained from the designated gas characteristics and the 

gas flow conditions applied. The mass transfer coefficient (Kg) was defined to represent 

an overall coefficient of uptake or absorption. Numerical results were compared for two 

different inlet flow rates, wall mass transfer coefficients, and bifurcation angles. The 

results of these model simulations indicated that the mass transfer coefficients from the 

ASPM representation compared well with CFDM qualitatively and quantitatively. In 

general, the mass transfer coefficients from both models were noted to increase with 

bifurcation angle, inlet flow, and wall mass transfer coefficient. Further, the change in 

mass transfer coefficients at each bifurcation unit was also closely predicted, and the 

average concentration variation axially was qualitatively the same in both the predictions 

from the CFDM and ASPM models with quantitative differences observed likely due to 

the differences in flow characteristics in the branches. The authors concluded that these 

results indicated that the “simplified” ASPM was very useful in predicting mass transfer 

coefficients, flux at the walls, and hence injury sites as accurately as the “complex” 

CFDM in symmetric lung systems where it was not possible to measure them. Similar 

observations were made by Madasu et al. (2008; 2007).

Many vapor absorption models (compartmental, 1D, and 3D) operate on the basis of the 

assumption of steady state mass transport fluxes across the mucus and tissue barriers.

That is, the concentration profiles in the mucus and tissue are immediately linear and a 

constant flux is assumed through the wall in the absence of reactions. The validity of this 

assumption has recently been examined in studies by Longest and coworkers that 

evaluated the mass transport of acetaldehyde and benzene, considered highly soluble and 

moderately soluble in mucus, repsectively as sample vapors through a simple multilayer 

system composed of mucus, tissue, and blood components on a transient basis.

Specifically, Tian and Longest (2010c) showed that transient wall absorption 

significantly influences uptake during cyclic inhalation over multiple breaths in the upper 

airways. For example, Tian and Longest (2010c) found that the uptake of highly and 

moderately soluble compounds modeled with transient fluxes varied from steady state 

flux estimates by a factor of approximately 30 in nasal and upper TB models. As a result, 

it appears important to consider a time-dependent (or transient) flux value when 

estimating absorption into the respiratory airway walls. The results of Tian and Longest 

(2010c) were based on analytical and numerical solutions of transient absorption into a

simple mucus-tissue-blood wall representation. However, a constant vapor concentration 

was assumed at the air-mucus interface and the air phase transport was neglected. They 

then extended this analysis to an air-mucus-tissue-blood (AMTB) system and developed 

a boundary condition to predict transient absorption and desorption fluxes in a CFD 

model (Tian and Longest, 2010b). Results of the AMTB wall model verified that 
3-20

absorption was highly time dependent over the timescale of an inhalation cycle 

(approximately 1 to 2 s). Application of this boundary condition to CFD simulations of a 

nasal-laryngeal geometry showed, as expected, that transient absorption significantly 

affected total deposition fractions in the mucus, tissue, and blood for highly and 

moderately soluble compounds. Moreover, transient absorption was also shown to 

significantly affect the local deposition patterns and maximum local DEFs. Based on 

these previous studies, it can be concluded that transient mass absorption significantly 

affects uptake in individual wall layers for moderately and highly soluble compounds.

Based on their preliminary results, Tian and Longest (2010a) evaluated the effects of both 

transient flow fields and transient mass absorption on the uptake of highly and 

moderately soluble compounds in an upper airway model. In this study, a boundary 

condition that represents transient absorption into the airway walls was applied. A new 

dosimetry program, named Transient Absorption of Chemical Species (TAOCS) 1.0, was 

developed and implemented to determine the coefficients needed for the transient 

boundary condition expression and to apply the boundary condition to the CFD model. 

Results indicated that implementation of the transient absorption boundary condition was 

critical to predict local deposition characteristics for even highly soluble compounds. Use 

of the TAOCS program simplified the implementation of the complex transient 

absorption condition making the CFD simulation process more efficient.

3.3.2 Advances in TB Inhalation Dos imetry Modeling

Recently, Morris and Hubbs (2009) characterized the inhalation dosimetry of diacetyl, a 

component of butter flavoring vapors, through development of a CFD-PBPK hybrid 

model. Upper respiratory tract (URT) uptake of diacetyl was measured experimentally 

and used to validate the model. Model simulations were then performed to estimate tissue

(anterior and posterior) and airborne concentrations of diacetyl for the URT (i.e. nasal) 

and trachea in rats and humans. At an exposure concentration of 100 ppm, tissue 

concentrations in the nose were estimated to be 1.6 and 1.4 mM in rats and 1.4 and 1.2 

mM in humans, and in the trachea were estimated to be 1.2 and 1.1 mM in rats and 1.2 

mM in humans. The air exiting the URT was estimated to be 67 ppm in rats and 82 ppm 

in humans, and in the trachea was estimated to be 61 ppm in rats and 79 ppm in humans.

When the human model was run for mouth breathing only, the tissue concentrations in 

the trachea were predicted to be 1.5 mM and the air exiting this region to be 96 ppm.

These results demonstrated that target tissue concentrations of diacetyl in the trachea 

were highly similar in rats and humans and that diacetyl may penetrate to the lower 

airways of humans to a greater degree than in rats. The authors concluded that based on 

these relationships and differences in uptake efficiencies upper airway injury in the rat 

may be predictive of lower airway injury in humans.
3-21

3.4 PU Region

3.4.1 Flow and Depos ition 

Kauczor et al. (2002) provided a thorough review of techniques that are used for 

functional imaging of lung function. Advances in magnetic resonance imaging (MRI) 

now allow for functional imaging and direct visualization of lung ventilation. Utilizing 

hyperpolarized noble gases with MRI was a recent approach developed for ventilation 

imaging. Imaging lung tissue with either hyperpolarized xenon (Xe)-129 or Helium-3 

(

3

He) has had considerable success. These agents can be inhaled in relatively large 

quantities without substantial risks as they have no known toxic side effects and are not 

absorbed by lung tissues. Compounds containing a relatively large number of fluorine 

atoms per molecule also can be used as gaseous or liquid contrast agents for ventilation; 

inert fluorinated gases, such as tetrafluoromethane, hexafluoroethane, or sulfur 

hexafluoride have been utilized. Even more recently oxygen (at high concentrations) is 

utilized for direct visualization of ventilation. Although molecular oxygen is weakly 

paramagnetic, its effect in the lung is significant due to the enormous surface area of the 

lung and the large difference in oxygen partial pressure between breathing room air (20% 

oxygen) and pure (100%) oxygen.

3

He MRI has been especially used to visualize dynamic ventilation during both 

inspiration and expiration of ventilation in normal individuals (Kauczor et al., 2002).

Application of this technique indicates that normal ventilation in healthy lungs is 

represented by a completely homogeneous distribution at the level of resolution of 3

He 

signal. Figure 3-3 illustrates the in-life rapid and homogenous filling of the airspaces 

bilaterally (the numbers correspond to the sequence imaging times). In volunteers the 

inflow of 3

He was shown to be very rapid with the discernable signal appearing almost 

simultaneously in the upper, middle and lower portions of the lung with a uniform 

wash-in and wash-out of the gas also observed. Further advances, involving echo-planar 

imaging of axial slices having rapid temporal resolution times of 122 ms, are able to 

demonstrate in supine individuals preferential ventilation of the posterior lung zones, 

again through visualization of areas of nonhomogenous flow in the lung. Further 

demonstrations of the resolution of the 3

He- imaging is the capacity to observe even small 

(2 cm) transient ventilation defects in the lungs of smokers that appear as 

nonhomogeneous flow and distribution. In clinically healthy smokers even markedly 

smaller ventilation defects leading to nonhomogenous flow, such as those thought to 

correspond to chronic inflammation and obstruction of small airways caused by smoking, 

can be detected with 3

He MRI. Thus, these techniques provide an approach to acquire 

regional information on lung morphology and pulmonary function.
3-22

Figure 3-3 Dynamic Ventilation 3He MRI After Inhalation of 

Hyperpolarized 3

He Gas

Source: Reprinted with permission of Springer Berlin/Heidelberg©; Kauczor et al. (2002)

Whole-lung dosimetry models do not account for the flow field to the level of inside the 

alveoli and therefore may not accurately describe alveolar flow or deposition. To better 

understand the fluid characteristics at this level of the lung, Harding and Robinson (2010)

employed CFD to a model of a terminal air sac much in the manner that it has been 

applied to other respiratory tract regions, notably the extrarespiratory (ER) region. An 

expanding terminal alveolar sac using truncated spheres to represent individual alveoli 

was modeled numerically, based on dimensions from human lung casts. The flow field is 

quantified for a breathing cycle derived from pulmonary function test measurements. The 

alveolar sac model was considered representative of a terminal air unit in humans that 

could be present in Weibull generations 19 and below based upon dimensions from 

literature. The wall motion of the alveolar sac model (full expansion of 15.6%) was 

obtained in vivo using a spirometer for a 21-year-old female breathing normally in the 

sitting position. Model output was obtained for detailed regional flow rates, alveolar 

mouth to depth flow rate ratio, and penetration depth of residual air. Figure 3-4

demonstrates the directionality and range of regional flow velocities as well as their 

extent of incursion into the sac, all obtained from the model (Harding and Robinson, 

2010). Examination of the flow field in the alveoli revealed no recirculation during any 

point in the breathing cycle. Other parameters addressed with the model included the 

flow rate ratios of alveolar mouth to duct flow that were noted in the range of 0.18–0.36.

Penetration depths were less than 33% into the air sac during inhalation, decreasing in 

length for air inside the sac to zero near the wall. These results indicated dominance of 

diffusive motion over convective motion and flow at the level of the alveoli.
3-23

The authors provided further comment on the modeled alveolar flow rate ratios obtained

in that they were intermediate between two other wide-ranging estimates of 0.057 

(Kumar et al., 2009) and 1.0 (Sznitman et al., 2007). They could not provide an

explanation for this wide range, and indicated that more studies are needed before 

quantification of flow fields in the alveolar region can be clearly understood as the ratios 

that are present in vivo are also unknown.

Figure 3-4 Simulated Flow Velocities from CFD Solutions in an

Alveolar Sac Model

Source: Reprinted with permission of Informa Healthcare©; Harding and Robinson (2010)

In an earlier study, Tsuda et al. (2002) observed flow patterns of different colored 

polymerizable fluids, representing tidal and residual air, injected into rat lungs in a 

manner to simulate inhalation of tidal air. These authors concluded that the swirls seen in 

the solidified cast in the large, medium, and alveolar airways were characteristic of 

chaotic flow. They observed swirl patterns in alveoli that became more intense with 

increasing number of cycles, which were not seen by Harding and Robinson (2010), who

utilized a model of terminal air sacs. Although these authors did not indicate if their 

observations were from a terminal sac or a respiratory bronchiole, it is possible that the 

patterns observed by Tsuda et al. (2002) occurred higher up in respiratory bronchioles 

where the flow rate ratio was large enough to cause irreversibility.

It is clear that more studies are needed on pulmonary fluid flow to better understand the 

nature of tidal and residual air mixing and the conditions under which mixing occur. It is 

apparent from these disparate results that more corroborating evidence is needed before 
3-24

actual flow fields in the terminal air sacs are understood. In addition, the occurrence of 

significant localized deposition cannot be excluded without additional studies.

3.4.2 Advances in Quantitation in Lung Geometry

The estimation of alveolar number in the lung has traditionally been done by assuming a 

specific geometric shape. These geometries are then applied to small sampled volumes of 

pulmonary tissue. However, the realizations that there exists a diversity of alveolar 

shapes and that statistical error from small sample size and bias may be considerable, 

have led to alternative approaches. Hyde et al. (2004) synthesized recent approaches and 

technologies that were designed to be less prone to error and bias and to therefore 

produce more reliable counts. These authors employed the following for the counting of 

alveoli in the lungs of monkeys and rats: a fractionator which allows for systematic 

random sampling from blocks of variable slab thickness (thereby minimizing the 

inaccuracy inherent in using section sampling fractions based on the average thickness of 

sections of variable thicknesses); use of the Euler characteristic of the net of alveolar 

openings to estimate alveolar number; the disector principal (usually a counting probe for 

isolated objects) as a sampling probe of the Euler characteristic. The Euler characteristic 

of structure (an integer)2

Lung tissues from four male and one female rhesus macaques (Macaca mulatta) ranging 

in age from 28 to 157 months and in body weight from 3.4 to 11.6 kg, as well as tissue

from five male Wistar rats with age not given and varying in body weight from 503 to 

625g were used for this study. Using this approach on these tissues indicated the number 

of alveoli in the two left lung lobes in the monkey ranged from 48.8 × 106 to 67.1 × 106 

with a mean of 57.7 × 106. The average number of alveoli in the rat lung ranged from 

17.3 × 106 to 24.6 × 106, with a mean of 20.1 × 106. The coefficient of error due to 

stereological sampling was 0.06 in both monkeys and rats and the biological variation 

(coefficient of variance between individuals) was 0.13 in monkey and 0.15 in rat (left 

lobe, only). Between subdivisions (left/right in rat and cranial/caudal in monkey) there 

was an increase in variation, most markedly in the rat. With age (2-13 years) the alveolar 

volume increased 3-fold (as did parenchymal volume) in monkeys, but the alveolar 

number was unchanged. The lung volumes as estimated in rats are presented in 

applies to any level of topological complexity and is not biased 

toward any specific geometry (as have other attempts to count alveoli).

Table 3-6.

 2 The Euler characteristic is a number that describes a shape or structure regardless of its orientation or the manner 

in which it may be bent. For simple structures it may be determined from the formula , where χ is 

the Euler characteristic, V the vertices, E the edges, and F the faces of a polyhedron shape. For a tetrahedron, for 

example, the Euler characteristic from this formula is 4 – 6 + 4 = 2.
3-25

Table 3-6 Estimates of Right, Left, and Total Lung Volumes in Male Wistar Rats

Animal # Body Weight (g) Lung Volumes (cm3

)

Right Lung Left Lung Total Lung

R5 503 10.6 8.8 19.4

R3 528 8.0 4.2 12.2

R4 573 11.4 5.5 16.9

R1 595 10.2 5.3 15.5

R2 625 12.3 6.1 18.4

Mean 565, (CV 0.09) 10.5, (CV 0.15) 6.0, (CV 0.29) 16.5, (CV 0.17)

CV = coefficient of variation

Source: Reprinted with permission of John Wiley and Sons©; Hyde et al. (2004)

Ochs et al. (2004) performed advanced stereologic analysis of human lungs for the 

purpose of evaluating the number of alveoli present in the total lung (Table 3-7). The 

stereologic method for the estimation of alveoli utilized the Euler number as the basis for 

quantification, eliminating assumptions and the resultant bias about the shape, the size, or 

the spatial orientation or distribution of alveoli. Alveolar number was estimated using 

light microscopic sections and concentrating on alveolar lumens, using their appearance 

or disappearance in a physical disector as counting events. Lungs for analysis were 

obtained from six cases of single lung transplantation, four females and two males. After 

fixation the whole lung was cut into horizontal slices of 2 cm thickness, starting with a 

random apical position between 0 and 2 cm. Alveoli were defined as alveolar lumen and 

septae, excluding respiratory bronchioles and alveolar ducts. Because of the design used, 

alveolar number estimation became completely independent of the orientation 

distribution of the lung sections sampled, making it effortless to combine the method with 

all other stereologic estimators of interest for lung quantitation. In six adult human lungs, 

the mean alveolar number determined by these procedures was 480 million (240 million 

× 2 to account for both right and left lungs), with a range of 274-790 million and the 

coefficient of variation 37% (Table 3-7). Alveolar number was observed to be closely 

related to total lung volume, with larger lungs having considerably more alveoli. The 

mean size of a single alveolus was rather constant with 4.2 × 106 μm3 (range: 3.3 × 106 to 

4.8 × 106 μm3

; coefficient of variation 10%), irrespective of the lung size. One cubic 

millimeter lung parenchyma was calculated to contain around 170 alveoli. No further 

attempts were made by the authors to obtain estimates for other parameters including 

surface areas, although such calculations were feasible.
3-26

Table 3-7 Summary Data on Human Lung Alveolar Number and Volume

Parameter Lung 1 Lung 2 Lung 3 Lung 4 Lung 5 Lung 6 Mean Value

Gender (age) Female 

(31)

Female 

(41)

Female 

(18)

Female 

(37)

Male 

(24)

Male 

(20)

Lung analyzed Left Right Right Left Right Left

N (alv), 106 137 226 220 185 275 395 240 ± 89

V(lung), cm3 1,031 1,273 1,509 1,103 1,917 2,317 1,534 ± 521

Source: Reprinted with permission of American Thoracic Society©; Ochs et al. (2004)

Wiebe and Laursen (1995) compared a stereological morphometric method with a 

standard fluid displacement method for determination of volume of right human lungs 

obtained from 4 cadavers. Comparison showed that the two methods were in very close 

agreement (Table 3-8). These authors then completed a stereological estimation of 

alveolar surface area of these same lungs. Specifically sampled sections of lung tissue 

(vertical and isotropic uniform random, IUR) were evaluated by specific counting 

techniques related to a test line in a reference space whereas the volume of the section 

was evaluated with the Cavalieri principle3

Table 3-8

. The capillary length and length density was 

also estimated by similar stereological procedures. Point counting also showed 87.5% of 

the lung is parenchyma, 5.4% is vessel volume, and 7.1% is bronchial volume. The 

authors also estimated that of the total variation encountered in the processes only, 

approximately 2%, was due to the stereological variation whereas approximately 98% 

was due to the biological variation on the sections themselves. In evaluating their 

estimates of lung surface areas by these techniques, the authors compared their results 

with other known determinations of lung surface area ( ).

 3

For a 3-dimensional case, the Cavalieri principle is: Suppose two regions (3-dimensional solids) are included 

between two parallel planes. If every plane parallel to these two planes intersects both regions in cross-sections of 

equal area, then the two regions have equal volumes. This provides an unbiased and efficient estimate of the volume 

of a solid object of arbitrary shape using systematic stereologic sectioning.
3-27

Table 3-8 Summary Table of Measures from Right Lungs of Human Cadavers

Lung Measure

Case #

Mean ± SD Reference

1 2 3 4

Volume (L)

Fluid 1.9 1.7 1.9 2.0 1.9 ± 0.13 Wiebe and Laursen (1995)

Cavalieri 2.2 1.7 2.2 2.2 2.1 ± 0.25 Wiebe and Laursen (1995)

Capillary length (m × 105)

Vertical slices 12.3 5.6 7.5 6.3 7.9 ± 3.0 Wiebe and Laursen (1995)

IUR 11.6 6.1 9.6 6.6 8.5 ± 2.6 Wiebe and Laursen (1995)

SA (m2)

Vertical section 50.3 35.0 49.4 38.5 43.3 ± 7.7 Wiebe and Laursen (1995)

IUR section 49.9 32.0 49.1 35.3 41.6 ± 9.3 Wiebe and Laursen (1995)

Total SA (m2)

40–97a Thurlbeck (1967)

78.4–81.6b Wiebe and Laursen (1995)

a

Internal surface area range for 25 pairs of lungs, free from acute or chronic disease, from patients ranging from 25 to 70 years of age. b

Calculated by authors using right lung SA mean measurements of Vertical section 43.3/0.53 = 81.6 m2 and of IUR section 41.6/0.53 = 78.4 m2.

Source: Reprinted with permission of John Wiley and Sons©; Wiebe and Laursen (1995)

Knust et al. (2009) employed advanced stereological morphometric techniques in 

measuring lung parameters in adult female CL57B6 mice (20.6 g average weight; no N 

given). Capillary length was measured using the harmonic mean of the surface weighted 

diameter. The Euler characteristic was applied in the physical fractionator with varying 

but known sampling fractions and enabled the estimation of alveolar number. The 

estimation of volume fractions of different lung compartments was carried out by point 

counting. All values were corrected for tissue shrinkage. The following measures were 

obtained for adult mice lungs (mean, CV): total values for alveolar number of 2.31 × 106

(0.23); alveolar surface area of 82.2 cm2 (0.17), alveolar air spaces of 138 mm3 (0.29); 

capillary surface area of 124 cm2 (0.13), and capillary length of 1.13 km (0.13).

Bolle et al. (2008) examined functional and morphological characteristics in the 

developing rat lung. Groups of specific pathogen-free Wistar-Kyoto (WKY) rats were 

used for the examinations. Measures recorded included lung volume, respiratory 

mechanics (intrapulmonary gas mixing, and gas exchange) and structural (alveolar 

surface area, mean linear intercept length, and alveolar septal thickness) at 7-90 days.

Four males were sacrificed at each age for analysis. A selected set of measurements are 

presented from this report in Table 3-9. 
3-28

Table 3-9 Functional and Morphological Features of the Developing Male Rat Lung

Parameter (n = 4) 7 Days 14 Days 21 Days 35 Days 90 Days

Body weight (g) 22 ± 1.4 34 ± 6.5 76 ± 8.5 165 ± 13.3 417 ± 22.6

Surface area (cm2) 744 ± 20 1,175 ± 114 1,648 ± 188 3,571 ± 490 6,536 ± 488

Total lung capacity (mL) 1.54 ± 0.07 1.9 ± 0.46 4.6 ± 2.6 7.8 ± 0.83 16.7 ± 2.46

Alveolar wall thickness (μm) 13.4 ±1.8 8.1 ± 0.6 5.4 ± 0.4 5.5 ± 0.8 6.4 ± 1.0

Source: Reprinted with permission of The American Physiology Society©; Bolle et al. (2008)

3.5 Extrares piratory (ER) Region

3.5.1 Methods and Advanc es for Es timating Blood:Air Partition 

Coefficients

The importance of blood:air partition coefficients (Hb/g) for PBPK models, and lack 

thereof, prompted several approaches and strategies to enhance their development and 

availability. Payne and Kenny (2002) reviewed, evaluated, and conducted a comparative 

analysis of several predictive methods and models utilized to calculate Hb/g. As a first step 

in their analysis, these authors gathered principal resources and approaches to derive Hb/g 

(Meulenberg and Vijverberg, 2000; DeJongh et al., 1997; Poulin and Krishnan, 1995; 

Abraham and Weathersby, 1994; Gargas et al., 1989; Abraham et al., 1985).

The basis of the comparative analysis was a test set of 12 well characterized and 

documented experimentally determined reference human Hb/g values for chemicals with a 

wide range of lipophilicity (Payne and Kenny, 2002). In order of increasing octanol-water 

partition coefficient the 12 were: acetone, isopropanol, diethyl ether, dichloromethane, 

benzene, trichloroethylene, 1,1,1-trichloroethane, toluene, cyclohexane, n-pentane, 

n-hexane, and n-heptane. The corresponding rat Hb/g values were also examined for all of 

these compounds except for n-pentane, for which data were not available. The Hb/g value 

for each compound was calculated from the various predictive models and ratios for 

predicted:experimental values were determined. The mean (Rmean) and standard deviation 

of the predicted:experimental ratio was calculated for each model using data for the 12 

chemicals. The variability was characterized through calculation of the average error (E),

which was defined as the arithmetic mean of the absolute values of the logarithm of the 

ratio (R) of predicted to experimental values where E = Mean (│log10 R│) (for example, 

a value of E of 0.301 corresponds to an average error of the predicted Hb/g equivalent to a 

factor of 100.301 = 2 higher or 2 lower than the experimental reference Hb/g value). 

Together this information was used to assess the accuracy of the various predictive 

models.
3-29

For humans, the most accurate methods for estimating Hb/g (within a factor of 2 of 

experimental values; value of E ≤ 0.301) were the empirical equations of Meulenberg and 

Vijverberg (2000) and salvation equation of Abraham and Weathersby (1994). The 

Paterson and Mackay (1989) approach also performed moderately well (Table 3-10). For 

rats, the statistical estimates obtained indicated that use of solubilities in vegetable oil 

rather than octanol gave better agreement with experimental values, which may reflect a 

greater chemical similarity of vegetable oil to rat (or human) lipid. Low ratios observed 

for rats suggest that protein binding makes a predominant contribution to partitioning 

especially for chemicals of moderate or high lipophilicity. Overall, the simple linear

equation of Meulenberg and Vijverberg (2000) was the most accurate in estimating Hb/g

in rats. The human and rat equations of Gargas et al. (1989) gave reasonable accuracy for 

moderately lipophilic chemicals but only over a restricted range of lipophilicity (range 

not specified in the text). The approach and equations of Abraham and Weathersby 

(1994), which contain consideration for protein binding and lipophilicity, fared well in 

the analysis. However, none of the approaches were considered adequate to satisfactorily 

cover the full range of lipophilicity. Therefore, the choice of method for the potential use 

in PBPK models needs to take into account the species, tissue, and chemical lipophilicity.

Payne and Kenny (2002) generally concluded that partitioning into blood was determined 

by solubility in water and lipid components, and by protein binding. For humans, the 

accuracy of predictions in general was greater than for rat. It is speculated that this is 

likely due to a considerably lesser influence of protein than with rats. The effects of 

protein binding appeared extensive in rat blood such that they can result in as much as a 

10-fold increase in Hb/g. For human blood such effects are smaller but still were estimated 

to be in the range of 2 or more over that expected from lipid and water content alone 

(data were not available to confirm these estimates). For humans, approximate estimates 

of human Hb/g (within a factor of 2 of reference values) can be made over restricted 

ranges of lipophilicity in the case of Meulenberg and Vijverberg (2000), Abraham and 

Weathersby (1994), and Gargas et al. (1989). These authors did not conclude that rat 

values were higher or lower than humans. Table 3-10 shows a summary of the results 

from the comparative analysis of Hb/g in humans and rats. 
3-30

Table 3-10 Mean Ratios and Standard Deviations (Rmean ± SD) of Predicted to Experimentally

Derived Human and Rat Blood:Air Partition Coefficients, P, and Mean Absolute 

Differences Between Predicted and Experimental Values of log P (E) for a Reference 

Set of Unreactive Volatile Organic Chemical Vapors

Measure

Study

Poulin & 

Krishnan (1995)

(oil)

Poulin & 

Krishnan (1995)

(octanol)

Paterson & 

Mackay (1989) Gargas et al. (1989) Meulenberg & Vijverberg (2000)

Abraham et al. 

(1985)

Abraham & 

Weathersby (1994)

Humana

Rmean ± SD 0.83 ± 0.38 0.76 ± 0.39 0.86 ± 0.52 1.11 ± 0.69 1.10 ± 0.46 0.76 ± 0.42 0.93 ± 0.38

E (log P) 0.175 0.218 0.224 0.325 0.156 0.276 0.166

Ratb

Rmean ± SD 0.40 ± 0.33 0.38 ± 0.34 No data 1.13 ± 1.00 0.79 ± 0.50 No data No data

E (log P) 0.537 0.590 No data 0.337, 0.209c 0.236 No data No data

Note: Actual values from which ratios were developed were not available. a

Test set included 12 chemicals. b

Test set included 11 chemicals. Data on n-pentane were not available for experimental values. c

Excluding acetone and isopropanol since they are more hydrophilic compounds with a log Pow < 0.5.

Source: Reprinted with permission of Taylor & Francis©; Payne and Kenny (2002)

In another study by Abraham et al. (2005), 155 human Hb/g values and 127 rat Hb/g values

for volatile organic compounds were collected to conduct a correlative analysis. One goal 

of the analysis was to derive an equation that could be used to predict log Hb/g for rats, 

humans, or both. The general method used for the correlation and prediction of log Hb/g

values used in this study (Abraham et al., 2005) is the solvation equation for linear free 

energy relationship (LFER) where SP is log Hb/g.

Equation 3-5

The compound descriptors following all relate to unique chemical properties of the VOC:

E, solute excess molar refractivity with units of (dm3 mol-1 )/10; S, solute dipolarity/

polarizability; A and B, the overall or summation hydrogen bond acidity and basicity; L, 

the log of the gas-hexadecane partition coefficient (unitless) at 25°C. Multiple linear 

regression analysis is used to evaluate the coefficients (lowercase italicized letters) in this 

equation.

The initial step in the analysis was to determine the level of random and measurement 

error between available rat and human Hb/g. For the random error evaluation, 86 

compounds for which both rat and human log Hb/g were available were compared

(Abraham et al., 2005). The average error from this comparison was 0.124 log units 

indicating that the ratio between Hb/g (rat) and Hb/g (human) was about 1.3 with N=86.

This value is slightly less than that reported by Gargas et al. (1989) who concluded that 

rat Hb/g was generally larger than human Hb/g by a factor of 1.5-2 (N=36). Further, this 
3-31

magnitude of error was found to be lower than estimates of interlaboratory variation 

albeit only 3 agents were available for the comparison. Nonetheless, the relationship 

identified by Abraham et al. (2005) indicated that the data on Hb/g (human) and Hb/g (rat) 

can be combined in a correlative analysis.

Correlative analyses with LFER first solved separately for rat and humans, indicated that 

the rat and human equations were comparable. This finding allowed for averaging the 

data available for both rat and human to yield values for 196 compounds. This dataset 

was then divided into two equal sets of 98 each with one set being used as a training set 

to obtain one LFER equation applicable to either rat or human Hb/g. This test set equation 

was then be applied to the second dataset (as an independent test set) for predictive and 

error assessment. These sets were then reversed with predictive and error assessment 

repeated. From this error assessment, the authors determined there was no bias in the 

predictions (Abraham et al., 2005). This finding led the authors to combine the entirety of 

the data and provide an LFER equation for prediction of Hb/g values that could be applied 

to either rat or human4

The authors concluded that this evaluation would allow for the prediction of Hb/g values 

applicable to either rat or human, accurate to 0.33 log units (

. This equation was robust with 282 data points for 196 compounds 

and a correlation coefficient (R2

) of 0.927. 

Abraham et al., 2005). They

also point out that the descriptors required for the LFER method are available for some 

3,000 volatile organic compounds, far more available than information for partition 

coefficients based on, for example, air to oil and air to saline. Lastly, it is noted that this 

was the first predictive assessment of calculations for (log) Hb/g made using the method of 

training and test sets.

3.5.2 Quantitation us ing Inhalation PBPK Models

Physiologically-based pharmacokinetic (PBPK) models are biological, integrated 

functioning systems of flow, volumes, and partitioning processes, with the purpose to 

predict the time course distribution of a chemical in the body. The robustness of such 

models is demonstrated by their ability to predict empirical observations.

When model simulations successfully predict empirical results, typically obtained 

independent of the model, it is an indication that both the model and the sensitive critical

parameters within the model have predictive validity. For example, when models that are 

parameterized and configured to predict interspecies dose extrapolation (e.g., between 

rats and humans) are successful in their predictions, the model and its parameters are both 

considered adequate. As referred to above, partition coefficients and in particular 

air:blood partition coefficients (Hb/g), are among these critical determinative parameters.

 4 The final equation with coefficients (per Equation 3-5) is: 

log Hb/g (human or rat) = -1.062 + 0.460 E +1.067 S + 3.777 A + 2.556 B + 0.375 L
3-32

It then follows that inhalation PBPK models that (1) are parameterized and configured for 

interspecies extrapolation and (2) are successful in predicting empirical results in animals 

and humans would be a source of representative Hb/g for both humans and animals. It is 

the ratio of Hb/g between animals and humans that is the basis for RfC Methods inhalation 

gas dosimetry for effects in the extrarespiratory or ER region (see Section 2).

Consequently, validated inhalation PBPK models were obtained and examined for these 

critical parameters which were extracted and constructed as a ratio in accordance with the 

RfC Methods. The results of this investigation are presented in Table 3-11. This table 

includes the PBPK model reference, chemical modeled, animal gender, species, and 

strain when available, the method used to determine the Hb/g employed in the model, and 

the A/H Hb/g ratio. Based on this analysis, in 3 instances the A/H ratios were less than 1

(e.g., 0.7, 0.6, and 0.6). For 2-BE and 2-ME, the rat values were assumed to be equal to

human Hb/g values; and for napthalene and n-butanol, the human values were assumed to 

be equal to the rat Hb/g values.
3-33

Table 3-11 Compilation of Blood:Air Partition Coefficients used in Inhalation PBPK Models for 

Animal to Human Interspecies Extrapolation 

Chemicala

(Reference)

Animal Human

A/H 

H Ratio b/g

Species/

Strain Method Hb/g Method

PCE

(Dallas et al., 1995) 18.9 ♂SD rat In vivo tissue conc –time course 10.3 Sealed vial 1.8

TCE

(Cronin et al., 1995)

14.3 ♀ Mouse Not statedi

9.2 Not statedi 1.6

13.2 ♂ Mouse Not statedi 1.4

Toluene

(Tardif et al., 1997) 18 Rat Sealed vial 15.6 Sealed vial 1.1

Xylene

(Tardif et al., 1997) 46 Rat Sealed vial 26.4 Sealed vial 1.7

EBZ

(Tardif et al., 1997) 42.7 Rat Sealed vial 28.0 Sealed vial 1.5

Ethanol

(Pastino et al., 1997)

2,140 Rat Sealed vialf 1,265 Sealed vialg 1.7

1,244 Mouse Sealed viale 1,265 Sealed vialg 1.0

Toluene

(Benignus et al., 1998) 18 Rat In vivo 15.0 - 1.2

2-BE

(Lee et al., 1998)

7,965 Rat Not statedb 7,965 Sealed vial skin: air 1b

7,965 Mouse Not statedb 1b

2-ME

(Gargas et al., 2000) 32,800 Pregnant SD rat Sealed vialb 32,800 Sealed vial 1b

Naphthalene

(Willems et al., 2001) 571 Rat Calculated 571c Calculated 1c

Ethylene glycol

(Corley et al., 2005) 17,901 ♀ SD & Wistar rat Sealed vial 17,542 Sealed vial 1.0

n-Butanol

(Teeguarden et al., 2005) 1,160 Rat Sealed vial 1,160c - 1c

PGME

(Corley et al., 2005) 4,866 Rat Sealed vial 7,107 Sealed vial (0.7)

PGMEA

(Corley et al., 2005) 1,251 Rat Sealed vial 609 Sealed vial 2.0

n-Decane

(Hissink et al., 2007) 21 Rat Sealed vial 37 Sealed vial (0.6)

1,2,4-TMB

(Hissink et al., 2007) 148 Rat Sealed vial 85 Sealed vial 1.7

Chloroform

(Liao et al., 2007)

20.8 Rat Sealed vialh 7.43 Not Statedj 2.8

21.3 Mouse Sealed vialh 7.43 Not Statedj 2.9
3-34

Chemicala

(Reference)

Animal Human

A/H 

Ratio

Hb/g

Species/

Strain Method Hb/g Method

1,1,1-TCE

(Lu et al., 2008) 5.76 Rat Sealed viald 2.53 Sealed viald 2.3

MeI

(Sweeney et al., 2009)

39.3 Rat In vivo, sealed vial 18

(male) Sealed vial 2.2

16 Rabbit (adult) In vivo, sealed vial 17.1

(female) Sealed vial 1.0

12 Rabbit (fetal) In vivo, sealed vial 17.6

(fetal) Sealed vial (0.6)

a

Chemical abbreviations: ethylene glycol monomethyl ether (2-ME); 2-butoxyethanol (2-BE); propylene glycol methyl ether (PGME); 

propylene glycol methyl ether acetate (PGMEA); trichloroethylene (TCE); perchloroethylene (PCE); 1,2,4-trimethylbenzene 

(1,2,4-TMB); ethylbenzene (EBZ); methyl iodide (MeI), 1,1,1-trichloroethane (1,1,1-TCE). b

Rat values were assumed to be equal to human Hb/g values in this model. c

Human values were assumed to be equal to the rat Hb/g values in this model. d

Experiments and values first reported by Reitz et al. (1988). e

Experiments and values first reported by Pastino et al. (1996). f

Experiments and values first reported by Kaneko et al. (1994). g

Experiments and values first reported for whole blood by Fiserova-Bergerova and Diaz (1986). h

Experiments and values first reported by Gargas et al. (1989). i

Values first reported by Fisher and Allen (1993). j

Values first reported by Steward et al. (1973)

3.5.3 HECER Derivation – PBPK and Hb/g (RfC Methods ) Comparis on 

Inhalation PBPK models use air and blood flows, predicted or measured absorption rates, 

various biological rate processes (e.g., metabolism) and partitioning over time, and a 

range of external exposure air concentrations to a given toxicant to predict dose metrics.

As explained above, the Hb/g, is a key, and often determinative, parameter.

A dose metric is the internal tissue concentration of a toxicant, or a form of that toxicant 

such as a metabolite, associated with the external exposure to a toxicant. For a tissue that 

is a focus of toxicity (i.e., a target tissue), the concentration of a toxicant in the tissue is 

considered to be the ultimate determinant of risk. The dose metric may be a concentration 

over time (e.g., area under the curve, AUC), a maximum concentration achieved (Cmax), 

or a steady-state concentration. Examples of dose metrics are Cmax of parent compound 

in the liver, AUC of a metabolite in the brain, or circulating blood concentration of parent 

compound at steady state. The concentration in the blood is often used instead of the 

concentration in a target tissue because blood concentrations are more readily measured, 

allowing for model calibration and validation, and average or steady-state tissue 

concentrations are expected to vary in proportion to blood levels.

PBPK models may be developed for a variety of purposes, one of which is interspecies 

extrapolation, the general subject of this report. The manner in which this is performed is 

to first use the animal model to estimate a dose metric (internal dose) associated with a 

given level of toxicity or response and then use the human model to estimate the external 

concentration for humans that yields the same internal tissue dose metric. The human 
3-35

estimate of the external concentration that produces that same internal dose metric is the 

human equivalent concentration or HEC. 

Several of the studies listed in Table 3-11 developed inhalation models for purposes of 

interspecies extrapolation. Table 3-12 below presents specific descriptions of the dose 

metric and the modeling estimates of the human equivalent concentration that 

corresponds to the same internal dose metric calculated for the laboratory animal based 

on the animal exposure scenario.

Table 3-12 Estimations from Inhalation PBPK Models of Human Equivalent Concentrations 

(HECs) from Effect Levels and Internal Dose Measures in Laboratory Animals

Chemicala

(Reference)

Level and

Effect Dose Metric Comments

PBPK 

Derived 

HEC

Isopropanol 

(Gentry et al., 

2002)

NOAEL

2,500 ppm

renal tissue of 

female rats Arterial blood 

concentrations, AUC

HEC derived from Table 4 (in (Gentry et al., 2002)) by applying uncertainty 

factor of 30: (159.8 × 30 = 4,767 ppm); 189.8 ppm × 30 = 5,700 ppm. 

Animals exposed for 6 hr/day, 5 days/wk. Continuous exposure modeled in 

humans.

4,767 ppm

LOAEL

3,500 ppm 

developmental

5,700 ppm

n-Butanol 

(Teeguarden et 

al., 2005)

NOAEL

500 ppm

weight gain

Arterial blood 

concentrations, AUC Weekly average blood conc. estimated for rats at 6 hr/day, 5 days/wk and 

continuous for humans. Model estimates compared against human blood 

levels from 30 min inhalation exposure. Tables and equations are provided 

for HEC calculation over wide range of butanol concentrations.

169 ppm

NOAEL

3,000 ppm

neurotoxicity

Arterial blood 

concentrations, AUC 1,066 ppm

PGME

(Kirman et al., 

2005b)

NOAEL

3,000 ppm

presence of 

sedation

Cmax, richly perfused 

tissues

Model simulations estimated NOAEL internal dose metric values in rodents 

ranging from 2,300-5,000 mg/L for exposures from 3,000 ppm for 1-78 wks 

of exposure (6 hr/day, 5 days/wk). The arithmetic mean of the NOAEL was 

4,036 mg/L. This value was used to estimate an HEC for a continuous 24 hr 

exposure.

560 ppm

White spirits 

(Hissink et al., 

2007)

NOEL

600 mg/m3

neurotoxicity

Brain concentration of 

1,2,4-TMB or decane 

determined in rats exposed 

for 6 hr/day

Model and 4-hr HEC based on main components of WS, 1,2,4-TMB and 

decane. Estimates are for acute exposure CNS effects. Human model 

validated with blood and alveolar air kinetics.

344–721c

mg/m3

LOEL

2,400 mg/m3

neurotoxicity

1,669 – 4,431c

mg/m3

2-ME

(Gargas et al., 

2000)

NOEL

10 ppm

developmental

Blood Cmax or average 

daily

AUC for 2-MAA (acetic 

acid; metabolite of 2-ME) 

in rats exposed for 

6 hr/day, 5 days/wk

The model was used to calculate an HEC for pregnant women exposed for 

8 hr/day, 5 days/wk for 270 days at various 2-ME. Human validation 

information from urinary excretion rates of 2-MAA from volunteers exposed 

to 5 ppm 2-ME

12 ppm

LOEL

50 ppm

developmental 60 ppm

Ethylene glycol

(Corley et al., 

2005)

11 ppm 

(28 mg/m3

)

b

developmental

Cmax for

glycolic acid (GA) in blood

Model was used to generate a dose-response comparison of internal dose 

surrogates (Cmax for GA in blood) in female Sprague-Dawley rats and in 

humans (Figure 10B in (Corley et al., 2005)). Several controlled rat and 

human metabolism studies were used to validate the PBPK model.

~79 ppm 

(~200 

mg/m3

)

1,1,1-TCE

(Lu et al., 2008)

NOAEL

1,500 ppm

liver effects

Average daily venous 

blood, AUC. Calculated in 

rats exposed for 6 hr/day, 

5 days/wk

Table 5 (in (Lu et al., 2008)) shows HEC calculations over a wide range of 

exposures concentrations for continuous human exposure. Four human 

data sets were used in evaluating model selection. 

640 ppm

a

Chemical abbreviations: ethylene glycol monomethyl ether (2-ME); propylene glycol methyl ether (PGME); 1,1,1-trichloroethane (1,1,1-TCE), 

1,2,4-trimethylbenzene (1,2,4-TMB), 2-methoxyacetic acid (2-MAA), glycolic acid (GA), white spirit (WS). b

The threshold blood concentration for developmental effects of 2 mM is not attainable in humans based on the modeling and maximum tolerated inhalation 

exposures reported in this paper (Corley et al., 2005). The maximum vapor concentration for EG is only 79 ppm (~200 mg/m3

) due to low volatility 

(0.06 mm Hg at 20oC) (Corley et al., 2005). Therefore, for this comparison, the human Cmax at the maximum vapor concentration (200 mg/m3) was 

estimated by the model to be ~6.5 μM. The exposure concentration predicted by the model that would yield the same Cmax in the rat is ~28 mg/m3

. c

Range of values is presented because exposure concentrations were estimated that yielded brain concentrations equivalent to observed values for 

1,2,4-TMB or decane. Values at the lower end of the range correspond to WS estimates based on 1,2,4-TMB brain concentrations, while the higher 

values are based on decane brain concentrations.
3-36

Table 3-13 combines data from Table 3-11 and Table 3-12 to present examples 

comparing approaches in estimating HEC from laboratory animal data for systemic 

effects. For example, with n-butanol an extrarespiratory effect level of 500 ppm in the 

laboratory animal study is duration and dosimetrically adjusted to an HEC using the RfC

Methods default approach (a DAF of 1; see Section 2) to yield 90 ppm. The neighboring 

column to the right shows the HEC derived using the PBPK model at 169 ppm. The ratio 

of these HECs are then compared to indicate the extent and direction of difference, such 

that the n-butanol default HEC is two-times less than estimated by the PBPK model. For 

further comparison, the actual A/H Hb/g ratio is also given, here shown for n-butanol 

which in this case is the same as the RfC Methods default.

As can be seen, the extent of difference encountered between the default and PBPK HEC 

values is quite wide, spanning nearly 10 times (e.g., isopropanol default method gives an 

HEC of 446 ppm and PBPK method gives 4,767 ppm) even for this small set of example

chemicals. In all cases, the default RfC Method provides a lower HEC than those derived

using PBPK modeling, except for PGME which is nearly equal. No general trend can be 

discerned to explain this range of differences, either between the default and PBPK HEC

or between the actual Hb/g and the PBPK HEC. It may be that other covariates, such as 

concentration–dependent metabolism may need to be further explored and evaluated. In 

application of PBPK models, it may also be necessary to thoroughly evaluate the 

origination of model parameters, including the Hb/g.
3-37

Table 3-13 Comparison of Approaches for Calculating Human Equivalent Concentrations (HECs) 

for Several Gases with Effects in the Extrarespiratory Region (ER)

Chemical 

(Reference)

Rat POD

(Table 3-12)

RfC Method HEC - PBPK 

Method 

(Table 3-12)

PBPK/RfC HEC 

POD Ratio adj

(Hb/g)A / (Hb/g)H

(Table 3-11) DAF HECa

n- Butanol 

(Teeguarden et al. 

(2005)

500 ppm 90 ppm 1.0 1 90 ppm 169 ppm 1.88

1,1,1-TCE (Lu et al. 

(2008) 1,500 ppm 270 ppm 2.3 1 270 ppm 640 ppm 2.4

PGME Kirman et al. 

(2005a) 3,000 ppm 540 ppm 0.7 1 540 ppm 560 ppm 1.04

2-ME (Gargas et al. 

(2000) 10 ppm 1.8 ppm 1 1 1.8 ppm 2.9 ppmb 1.61

Isopropanol (Gentry 

et al. (2002)

2,500 ppmc 446 ppm

1.5 1

446 ppm 4,767 ppm 10.7

3,500 ppmd 907 ppm 907 ppm 5,700 ppm 6.28

Ethylene glycol 

(Corley et al. (2005) 11 ppm -- 1 1 11 ppm 79 ppm 7.18

a

HEC derived by default RfC Methods: PODadj × DAF = HEC where the PODadj is the POD adjusted for duration of exposure in the animal study and a 

default DAF of 1 is applied for (Hb/g)A / (Hb/g)H. (e.g., for n-butanol, the PODadj = 500 ppm × 6 hr/24 hr × 5 days/7days = 90 ppm.) b

In the PBPK model for 2-ME, the HEC was calculated for a discontinuous exposure and was therefore adjusted for duration (8hr/24 hr × 5 days/7 days). c

Based on renal effects.

d

Based on developmental effects.

3.6 Children's Inhalation Dos imetry

3.6.1 Introduction and Focus

This section is focused on identification and preliminary evaluation of data, evidence, and

information relating directly to gas dosimetry in children.

Although not as explicitly as with the fetus (developmental studies) or sexual maturity 

and function (reproductive studies), the 1994 RfC Methods considers lifestages and

children in the intraspecies uncertainty factor that is designed to incorporate the range of 

response variability in human populations. This uncertainty factor is typically considered 

to have two components, pharmacodynamics and pharmacokinetics, with the latter 

component being the basis of dosimetry. It is within the kinetic portion of this uncertainty 

factor that susceptible lifestages, including children, are considered. 

The 1996 Food Quality Protection Act (FQPA), refocused interest in matters of child 

risk. Title III of this act specifically tasked the Agency in their assessments under the 

FQPA to “...ensure that there is a reasonable certainty that no harm will result to infants 

and children ...”.

Although this Act was directed at oral ingestion of pesticides, specifically those used on 

foodstuffs, the Agency considered its implications both with regard to pesticide risk 

assessments and more broadly to EPA methodology. For example, EPA developed 
3-38

approaches for interpretation and implementation of the requirements specifically to 

FQPA-required pesticide assessments (e.g., see 

http://www.epa.gov/oppfead1/trac/science/), and additionally implemented a full review 

of the Agency’s RfC/RfD processes to insure they appropriately considered the potential 

for increased childhood susceptibility (U.S. EPA, 2002). 

This Act eventually affected many organizations and resulted in a spectrum of 

implementation actions and strategies. One of the most prominent was completed by the 

state of California in implementing their Children’s Environmental Health Protection Act 

(Senate Bill 25) of 1999. The state’s Technical Support Document for the Derivation of 

Noncancer Reference Exposure Levels (OEHHA, 2008) provided extensive information 

on children as a population of concern and on pharmacodynamic and pharmacokinetic 

differences between children and adults. Appendix E of that document included an 

extensive analysis of children related data and models, including PBPK models, that 

provided insight into the range of interindividual variability in general, but focus 

extensively on the differences among infants, children and adults. This report does not 

intend to reflect on these activities or these reports, but only note them as examples of the 

movement of the risk assessment community towards further consideration of children in 

dose-response toxicity and, in this case, of children’s dosimetry.

In 1993, the NAS published its findings regarding chemical toxicity in children compared 

to adults (NRC, 1993). The report addressed both specific findings and recommendations.

Conclusions of the committee included that infants and children may be more, or less, 

susceptible than adults depending upon the chemical and the age of the subject. It was 

acknowledged that substantial changes occur in organ size, structure, and function from 

infancy through puberty; such changes could substantially affect the pharmacokinetics 

and pharmacodynamics of chemicals. Accordingly, there may be periods, or lifestages of 

vulnerability, when developing tissues are much more sensitive to toxicants than later in 

life. The NAS report (1993) also stresses the importance of recognizing that the younger 

the individual, the more pronounced his/her structural and functional anomalies and thus 

the greatest differences from adults in susceptibility to chemical toxicity can be 

anticipated, with continuous diminishment of those differences thereafter. The report also 

stated the need for scientifically defensible means to deal with toxic agents that cannot be 

directly studied in children. A specific recommendation in the report following from this 

realized the need to use PBPK models. PBPK models can be used both to simulate the 

time course of parent compounds and bioactive metabolites in blood and tissues of adult 

animals and humans and to predict target organ doses of toxic chemicals/metabolites for 

different exposure scenarios in children of different ages. 

A recommendation following from the potential use of PBPK models was that they be 

reliably developed by obtaining accurate measurements of respiratory parameters, 

circulation, metabolism, tissue and fat volumes, and partition coefficients. These 

parameters can be measured in primates or in children of different ages by noninvasive 
3-39

procedures. The parameters would be used in PBPK models, which could then be utilized 

to better estimate the concentration time course of chemicals/metabolites in potential 

target organs. It is the recommendations and statements from the NAS report (1993) that 

guides the structure and content of this section of this report. There is an additional body 

of literature on risk to children and children’s’ dosimetry that reviewed the need, 

feasibility, adequacy (or inadequacy) and data gaps of existing methods and procedures 

(Ginsberg et al., 2010; Firestone et al., 2008; Foos et al., 2008; Foos and Sonawane, 

2008; Ginsberg et al., 2008). The details of these reports are not considered here since 

they do not directly inform the state-of-the-science related to gas dosimetry in children.

Recognizing that young children have a greater ventilation rate per body weight or per 

surface area in the respiratory tract compared with adults, Ginsberg et al. (2005) analyzed 

the outcomes of gas (and particle) dosimetry approaches of RfC Methods utilizing child

(3 mo) and adult male values available from various sources for the principal 

determinants of VE, SAET,PU, and BW. The TB region was characterized differently from 

RfC Methods as comprising two separate regions termed tracheobronchial (BB) and 

bronchioles (bb) by the authors. Dosimetry was estimated for 3-month-old children and 

adults for reactive and nonreactive gases. Estimations of comparative dosimetry were 

made using a reasonable range of assumed values for Kgs thereby allowing for direct 

calculation of regional dosimetry as well as adjustment for fractional penetration through 

the various regions (see Section 2). The authors use the same Kg values for both children 

and adults indicating that no basis exists for assuming a difference. The modeling results 

suggested similar dosimetry of gases for children and adults for the ET and BB regions.

Dosimetry for the bb region generally showed a higher dose of gases in adults than in 

children. It was also noted that, based on the value of the Kg, dosimetry for adults versus

children in the PU region could be slightly different, either higher or lower but not greater 

than 2-fold different. There were no cases in which gas dose was substantially greater in

the respiratory regions of 3-month-old children compared to adults. Estimates of systemic 

doses of nonreactive gases were greater in 3-month-old children than in adults, especially 

for liver doses (up to 2-fold) of metabolites for rapidly metabolized gases. Overall, these 

results suggest the potential for a 2-fold greater inhalation dose in children (based on data 

from 3-month-old children) than in adults, although there are cases in which this 

differential could be greater or less.

As PBPK models configured for elucidating dose to children and infants were 

recommended in the NAS report and are prominent in the current literature, they will be 

featured in this section. Studies that provided insight and data for parameters needed for 

these models and/or for general knowledge about development in children related to 

aspects of dosimetry are also presented. These include reports on stages of alveoli 

growth, lung parenchyma development, age-related airway diameters and surface areas

and volumes of upper airways. Information on inhalation rates in children have been 
3-40

presented earlier (Table 3-5). In addition to the TB and PU regions, information on the 

ET regions is also included in this section.

3.6.2 PBPK Models

Firestone et al. (2008) reported results based on analyses conducted by the Office of 

Environmental Health Hazard Assessment (OEHHA) that investigated the potential 

differences between adult and child (0-18 yr) internal doses resulting from inhalation 

exposure to a toxicant. Modified PBPK models for 24 compounds were used to assess 

child/adult ratios for at least three dose metrics. Detailed methods, equations, and model 

parameters were not included in the manuscript; however, the chemicals were classified 

into one of three categories pertaining to the intrahuman uncertainty factor for 

toxicokinetic variability (UFH-TK, default value = 3.16): UFH-TK ≤ 3.16; UFH-TK > 3.16 to 

9.9; and UFH-TK ≥ 10.0. Twelve of the compounds examined had child/adult ratios ≤3.16, 

eight had ratios between 3.16 and 9.9, while four had ratios greater than 10. The authors 

found that majority of the higher ratios were in infants (< 1 yr) and child vs. adult 

metabolic differences likely account for this observation.

In addition, as reported in Firestone et al. (2008), OEHHA applied modeling to evaluate 

alternative methods for interspecies extrapolation of gas dosimetry in a limited number of 

test chemicals. Limited information on the model structures and parameters employed 

were provided; however, detailed methods, equations, and model parameters were not 

described. Blood Cmax and AUC for parent and metabolite and amount metabolized 

were the dose metrics modeled for a 24 hr simulation. Chemical-specific principal effects 

(i.e. POE vs. systemic) and thus potential target-tissue doses were not modeled. In 

general, the DAFs calculated for this set of chemicals were lower in adults (Gmean = 

1.85) and higher in children (Gmean = 1.94) compared to the current default methods.

With the exception of one case (amount of ethylbenzene metabolized), the child/adult 

DAF ratios were within a 2-fold range.

Ginsberg et al. (2008) analyzed ozone gas dosimetry in the TB region using a 

mathematical model for uptake. The TB model consisted of 15 generations of 

symmetrically-branched airway bifurcations. Air was modeled starting at the entrance of 

the trachea and thus did not simulate reactions possible in the ET region. The numerical 

simulations of reactive gas uptake utilized airway and ventilatory parameters specific to 

children of different ages (0-18 yr). The model was exercised to examine the uptake 

distribution of ozone along the gas-mucus and mucus tissue interfaces of these children at 

a constant inhalation concentration of 0.1 ppm. The results demonstrated that for all ages 

and all airway generations, the controlling resistance to uptake was the mucus layer and 

the overall Kg was not significantly different across ages. In addition, there were no 

significant differences in the predicted flux of ozone to the mucus and tissue for children 
3-41

of different ages. The authors concluded that although the analysis was conducted for 

ozone, other gases would qualitatively behave in a similar way. However, more highly 

soluble gases, such as chlorine and formaldehyde, would likely be absorbed more in the 

URT and more proximally in the lungs than less soluble gases such as ozone.

More recently, Valcke and Krishnan (2011) examined the impact of exposure route on 

the UFH-TK. A multiroute, steady-state, PBPK model was modified from the literature and 

used to compute the internal dose metrics of the area under the parent compound’s 

arterial blood concentration vs. time curve (AUCpc) and amount metabolized per 24 hours 

(AMET). Dose metrics were computed for adults (18-64 yr), neonates (10-30 d), children

(1-3 yr), elderly (65-90 yr) and pregnant women (15-44 yr) for a 24 hour inhalation 

exposure scenario to chloroform, bromoform, tri- or per-chloroethylene (TCE or PERC).

The inhalation exposure scenarios were performed at a concentration of 5 μg/m3

representative of a low, environmental level. Monte Carlo simulations were performed 

and the UFH-TK was calculated as the ratio of the 95th percentile value of internal dose 

metrics in the various population groups to 50th percentile value in adults. On the basis of 

AUCpc, the highest UFH-TK values were demonstrated in neonates for each scenario 

compound. The highest UFH-TK computed was 3.6 for bromoform, but in all other cases 

the UFH-TK values ranged from 1.2 to 2.2. A synthesis of the results from this study are 

presented in Table 3-14. These results are in agreement with those presented by Firestone 

et al. (2008) for PERC and chloroform; however, TCE was categorized as having a 

UFH-TK ≥ 10 by Firestone et al. (2008) and ≤ 3.16 by Valcke and Krishnan (2011). The 

reason for this difference cannot be determined from the limited information provided in 

the Firestone et al. (2008) report.
3-42

Table 3-14 Human kinetic adjustment factors (UFH-TK) obtained for inhalation exposure in each 

population group using a dose surrogate of 24 hour AUCpc

Substance Chloroform Bromoform Trichloroethylene Perchloroethylene

Adults (41, 18-64 yr)

b

median 15.8 25.7 21.8 37.3

95th percentile 20.2 37.5 28.8 47.2

UFH-TK 1.3 1.5 1.3 1.3

Neonates (14, 0-30 d)

95th percentile 33.4 93.1 48.4 66.6

UFH-TK 2.1 3.6 2.2 1.8

Children (2, 1-3 yr)

95th percentile 25.2 51.7 35.1 58.8

UFH-TK 1.6 2.1 1.6 1.6

Elderly (78, 65-90 yr)

95th percentile 20.4 37.6 28.8 45.8

UFH-TK 1.3 1.5 1.3 1.2

Pregnant women (29, 15-44 yr)

95th percentile 22.9 44.4 30.6 46.4

UFH-TK 1.5 1.7 1.4 1.3

Note: AUCpc, area under the arterial blood concentration vs. time curve (μg 24 hr/L) b

Shown in parentheses are the median age, range for each population group.

c

Bolded values indicate the population group with the greater UFH-TK for corresponding internal dose surrogate for each compound.

Source:Reprinted with permission of Elsevier©; Valcke and Krishnan (2011)

Both pharmacokinetics and pharmacodynamics were considered by Liao et al. (2007) in a 

study of chloroform toxicity and carcinogenicity. A PBPK and a pharmacodynamic (PD) 

model were developed and then linked to produce a hybrid PBPK/PD model to 

investigate chloroform toxicity and carcinogenicity. The PBPK model was configured for 

rats, mice, and humans with the human configuration expanded to consider different age 

groups (1 month, 3 month, 6 month, 1 year, 5 year, and 25 year old) with the age-specific

physiological values being obtained from documented literature sources. The PD model 

was used to quantitatively estimate rates for mode-of-action processes known to be 

prominently involved in the toxicity of chloroform (metabolism, reparable cell damage, 

cell death, and regenerative cellular proliferation). A PD model with chloroform 

parameters previously developed for female mice was modified to simulate both hepatic 

and renal data in male and female mice and rats. This study used a Bayesian approach 

(Markov Chain Monte Carlo algorithm) to analyze and address parameter uncertainty in 

both the PD and PBPK models. The human PBPK/PD model was developed using the 

rodent PD parameters from Bayesian analysis together with human physiological, 

partitioning, and metabolism PK parameters all of which are reasonable and are 
3-43

documented within the study. The critical PD parameters for metabolism in children were 

based on the adult values with an age-dependent adjustment for the maximum rate of 

metabolism (available and documented from literature).

The hypothesized mode-of-action used as the basis of the PD model by the authors was 

that carcinogenicity was related to regenerative hyperplasia that, in turn, occurs in 

response to cytolethality which occurs when the rate of generation of toxic metabolites 

exceeds the capacity of cellular protective and repair mechanisms (Liao et al., 2007).

Thus the absence of regenerative hyperplasia (as measured by cellular labeling indices,

LI) was a key point of control of a tumorigenic response. The human model was used to 

estimate internal doses at steady state over a range of inhalation (and oral) concentrations 

for different age groups to identify the threshold for LI below which no cytolethality 

would be expected in each age group. The simulations presented in Table 3-15 indicated

that for liver effects, a young child (≤ 5 years) was more sensitive than adults by a factor 

of about 2. For renal effects, however, the results indicated age-related increases in 

sensitivity to the toxicity of chloroform with 1-month-old infants nearly 7- to 8-fold less 

sensitive than adults, 1-year-olds about 3-fold less sensitive than adults, and no difference 

in concentration corresponding to kidney effects between adults and 5-year-old children. 

Table 3-15 Air Concentration of Chloroform at Various Ages and Genders Corresponding to 

Threshold of Damage in Human Liver and Kidney

Age Gender Air Concentration (ppm)

Liver Kidney

1 Month

Male 5.16a 7.56

Female 4.86 8.08

3 Month

Male 4.80 2.60

Female 4.79 2.85

6 Month

Male 5.13 2.19

Female 4.90 2.29

1 Year

Male 6.07 3.17

Female 5.66 3.00

5 Year

Male 6.61 1.18

Female 6.81 1.35

Adult

Male 9.24 0.887

Female 12.7 1.06

Note: Values generated from model simulations of a PBPK-PD model. a

Results given as point values only, as estimates of variability were problematic in the absence of data on cell proliferation in human liver and kidneys.

Source: Reprinted with permission of John Wiley and Sons©; Liao et al. (2007)

Sarangapani et al. (2003) used a PBPK model to evaluate the effect of age- and 

gender-specific lung morphology and ventilation rate on the inhalation dosimetry of 

several gases. The gases were selected on the basis of their potential range of reactivity 

within the respiratory tract, from reactive and soluble (ozone and isopropanol) to
3-44

relatively insoluble and nonreactive (styrene, vinyl chloride, and perchloroethylene). Ten 

age-specific PBPK models were run for males and females from 1 month of age to 75 

years. Model structure was typical of PBPK models but simplified to three main axial 

compartments of the respiratory tract: the ET, TB, and PU, with the ET and TB each

divided into three lateral subcompartments from airway lumen to circulating blood. The 

parameter sources were varied but well documented. Age-dependent changes in 

physiological parameters were developed based on sources available in the literature.

Information on BW and VE for various ages for both males and females 1 month to 75 

years were based on data reported in U.S. EPA’s Exposure Factors Handbook (1997) and 

NHANES data (CDC, 1995). Age-specific data collected in the literature also expressed 

lung volume and SA on BW allometry across age groups as a relatively constant fraction 

of body weight across lifestages. Richly perfused tissue was modeled as 84% of the total 

BW minus the volume of the other tissues with the rest of the body (16%) assumed to be 

nonperfused tissue. Alveolar ventilation (Qalv) was assumed to be 67% of VE. 

Age-dependent changes in ET airway dimensions were estimated based on in vivo

measurements of growth patterns in children and adults using CT scans where the ET 

dimensions in infants and children were computed by scaling from adult values on a 

proportional basis. Similar scaling from other literature sources was also done for 

children’s values of both TB and PU measures. The same Hb/g was used for all age 

groups. Biochemical parameters were varied with age (e.g., relative activity of CYP2E1 

26.1% at 1 month to 90% at 15 years; and alcohol dehydrogenase (ADH) 24.9% at 1 

month to 83.6% at 25 years). Dose metrics evaluated included parent and metabolite 

concentrations in blood, liver, and lung. Results for the dose metrics were expressed 

relative to the young adult (25-year-old) model which were all set at unity.

The results from the Sarangapani et al. (2003) model indicated that tissue dose metrics at 

any age generally fell within a factor of 2 of the young adult values for parent ozone, 

vinyl chloride, styrene, isopropanol, and perchloroethylene. Little variability due to 

gender was apparent at any age for any of the gases or metrics examined. The only 

exceptions were those observed in early childhood (either gender), where dose metrics 

(especially for metabolites) were as much as 12 times higher for a 1-month-old child than 

young adult values, declining to 2 times by age 5-10 years, for these same compounds.

This is shown for the parent isopropanol and its water soluble metabolites (Table 3-16).
3-45

Table 3-16 Age-Dependent and Gender-Specific Dose Metric Comparison of Inhaled Isopropanol

Age

Parent Chemical Concentration Metabolite Concentration

Male Female Male Female

1 Month 1.75 1.74 8.02 11.44

3 Month 1.77 1.78 6.68 9.14

6 Month 1.77 1.75 5.70 8.01

1 Year 1.54 1.54 4.12 5.96

5 Year 1.25 1.18 1.98 2.55

10 Year 1.05 1.03 1.53 2.04

15 Year 1.09 1.14 1.46 1.70

25 Year 1 1 1 1

50 Year 0.94 1.00 0.80 0.82

75 Year 1.04 1.03 0.89 0.93

Note: Comparisons presented as % ratio of metric at a specific age to the 25-yr-old adult set at 100%.

Source: Reprinted with permission of Informa Healthcare©; Sarangapani et al. (2003)

Pelekis et al. (2001) developed a PBPK model for adults of low (50 kg) and high (90 kg)

body weights and for a 10 kg child (1 or 2 years old). The model was applied to 

inhalation exposures of dichloromethane, tetrachloroethylene, toluene, m-xylene, styrene, 

carbon tetrachloride, chloroform, and trichloroethylene. The parent compound 

concentrations in arterial blood (CA) and venous blood (CV), and tissues (Ctissue) (but no 

metabolites) were evaluated. The values (and ranges) of the physiological and 

mechanistic parameters were obtained from literature cited in the study for all of the 

gases studied, including ranges applied for the blood:air partition coefficient and 

ventilation. Metabolism was described and limited to the liver only. The intent of the 

study was to characterize various concentration metrics in model simulations in which the 

mechanistic parameters varied between low and high adult values. Data were unavailable 

for children to determine high and low estimates for these parameters, thus average 

values for the child (childaverage) were used for comparison. The ratios of the metrics from 

these different runs characterize the pharmacokinetic behavior of the child relative to the 

adult (e.g., adulthigh/childaverage). The exposure scenario simulated was 1 ppm continuous 

for 720 hrs (30 days).

The simulation results (i.e., the concentration of the parent compound in various tissues 

and compartments) for all of the gases were expressed as the adulthigh /childaverage ratio.

These ratios indicated that the estimation of concentrations in children’s blood were 

about the same as for the adult. With other tissues metrics, however, values were 

considerably higher in a few instances. For example, the adulthigh /childaverage ratio for the 

concentration in the liver (which was dependent on metabolism) was predicted as 0.033 

for styrene, 0.037 for m-xylene, 0.061 for trichloroethylene, 0.092 for dichloromethane, 

and 0.11 for chloroform. These predictions indicate up to 30-fold higher concentrations

of the VOC chemicals in child liver than in adult liver. The average adulthigh /childaverage
3-46

ratios for the various dose metrics estimated for the composite runs by Pelekis et al. 

(2001) are shown in Table 3-17. 

Table 3-17 Tissue Concentrations in Various Compartments Expressed as Adult/Child (1 to 2 

years old) Ratios for 8 Different Gases

Gas

Adulthigh/Childaverage Ratios of Concentrationsa

Venous Blood Arterial Blood Fat Liver

Dichloromethane 0.70 0.91 0.25 0.092

Tetrachloroethylene 1.61 1.74 0.47 0.75

Toluene 0.86 0.98 0.27 0.34

m-Xylene 0.50 0.63 0.17 0.037

Styrene 0.34 0.45 0.12 0.033

Carbon tetrachloride 1.81 2.20 0.60 0.57

Chloroform 0.78 1.02 0.28 0.11

Trichloroethylene 0.77 0.97 0.27 0.061

Average ± SD 0.92 ± 0.52 1.11± 0.58 0.30 ± 0.16 0.25 ± 0.28

a

Steady-state concentration ratios for 1 ppm continuous exposures.

Note: Initial values are all from PBPK simulations. 

Source: Reprinted with permission of Elsevier©; Pelekis et al. (2001)

In an effort to evaluate the potential effects in the nasal cavity of inhaled methyl iodide 

(MeI) exposure, a PBPK model was developed, complete with parameters for sensitive 

populations and lifestages, including children (Sweeney et al., 2009). For the human child

(3 months to 15 yr), the basic measures of the total nasal surface area and the total nasal 

volume were obtained from literature. Further subdivision of these measures into nasal 

tissue types (olfactory and respiratory epithelium) in the child age categories was based 

on literature sources. Other requisite parameters, such as the thickness of the nasal tissues 

to the capillary beds were also obtained from the literature (Inagi, 1992). Breathing rates 

were based on ICRP data. The modeled point-of-departure for the effect of MeI in the 

nasal tract was a decrease (either 25% or 50% decrement from untreated levels) in 

glutathione (GSH) concentrations in the olfactory epithelium. The modeled turnover rates 

of GSH determined for the adult rat were used for all species and lifestages. Fetal human 

tissue GSH concentrations were identified from literature sources given in the study. This 

adult human model indicated that depletion of GSH in the dorsal olfactory epithelium to 

50% of control would be achieved after 24 hours of exposure to 72 ppm MeI. For 

workers exposed for 8 hrs, 50% GSH depletion would be achieved by the end of the shift 

at an exposure concentration of 110 ppm. At a target POD of 25% GSH depletion at 24 

hrs, the 24-hr adult value was 36 ppm and the 8-hr (worker) value was 50 ppm. When 

configured for the 3-month-old child the corresponding 24-hr concentration for 25% 

depletion of olfactory GSH was 8.2 ppm under these conditions. No other age-related 

results were given in the study. This concentration differential for the POD, 36 ppm for 

the adult and 8.2 ppm for the 3-month-child, indicates differential sensitivity of 3-4 fold 
3-47

resulting from a combination of biochemical (e.g., GSH turnover) and physiological (e.g., 

respiration rate) factors. However, it should be noted that the equivalent rat exposure 

concentration (associated with a 50% depletion of GSH) upon which the adult and child 

modeled HECs were based was 3.8 ppm (21 ppm for 6 hr/day, 5 d/wk), indicating that

humans including children would be less sensitive than rats to this effect.

Clewell et al. (2004) constructed a PBPK lifestage model specifically to evaluate ageand gender-specific differences in tissue dosimetry for oral, dermal, and inhalation 

exposures to a range of chemicals with various physical and toxic properties. The model 

was mostly parameterized using equations that described various age-dependent 

alterations derived from U.S. EPA (1997), which was also the source for ventilation rates 

(m3

/day); pulmonary ventilation for various ages were converted to alveolar ventilation 

based on the assumption that alveolar ventilation is approximately two-thirds of 

pulmonary ventilation. The results for the isopropanol inhalation model are the only ones 

discussed here; however, the predictions of this age-dependent model were only able to 

be validated against human kinetic data for the adult. The arterial blood concentrations of 

isopropanol and acetone (the principal metabolite of isopropanol), were estimated for a 1 

ppb continuous inhalation exposure and summarized in age-group ranges of birth to 6 

months, 6 months to 5 years, 5 to 25 years, and 25 to 75 years. In general, the model 

estimations for the average internal concentration of inhaled isopropanol and its 

metabolite acetone varied 2 to 4-fold across the range of lifestages. The highest dose ratio 

(constructed from the lifestage/average daily inhalation dose for a 25-year-old adult)

among the lifestages was 2.0 for isopropanol (birth–6 months) and 3.9 (birth–6 months) 

for acetone.

Ginsberg et al. (2002) investigated child/adult pharmacokinetic differences through 

analysis of pharmacokinetic (PK) data from 45 different chemicals, nearly all therapeutic 

drugs and all administered by routes other than inhalation. In an initial metabolic 

evaluation, the drugs were classified as to their excretion: unchanged in urine, CYP 

(various) metabolism, glucuronidation, sulfation, GSH conjugation or unclassified. The 

infants/children were classified in age as premature neonates ( ≤1 week), full-term 

neonates (≤ 1 week), newborns (1 week-2 months), early infants (2-6 months), toddlers (6 

months-2 years), preadolescents (2-12 years), adolescents (12-18 years) and adults. There 

were data from 118 adults and 248 infants/children. The kinetic parameters evaluated 

included AUC, clearance, Cmax, half-life (t1/2), and volume of distribution (Vd).

Relationships between age groups and the kinetic parameters were evaluated by 

regression analysis.

The combined results showed that, for those chemicals with clearance data (27 

substrates), premature to 2-6 months of age infants showed significantly lower clearance 

(P<0.01) whereas 6-month-old to 12-year-old children had significantly higher clearance 

(P<0.0001) than adults. The combined results (40 substrates) indicated also that the 

youngest age groups (premature neonates, full-term neonates, and newborn infants up to 
3-48

2 months) tended to have longer half lives (average 2-to-4-fold) than adults whose levels 

were attained by infants 2-6 months of age. Other results included those for the chemicals 

identified as CYP1A2 substrates (caffeine and theophylline) in which neonates to infants 

2 months of age showed about 4 to 9-fold longer half-lives than adults while older age 

groups 6-months to 12 years had significantly shorter half-lives than adults. A similar 

pattern was observed with those chemicals thought to be metabolized primarily through 

CYP3A. 

These data are for drugs orally administered, rather than from toxics being inhaled, but 

nonetheless are relevant to situations involving dosimetry in the extrarespiratory (ER) 

region of children versus adults, and thus indicate a potential for greater susceptibility in 

children. These data also demonstrate empirically the prominent feature and likely 

mechanism of children’s susceptibility, decreased clearance functions.

3.6.3 Res piratory Tract Flow Models

Garcia et al. (2009) obtained the MRI or CT head scans of seven individuals including 

those of two children, a male (7 years) and a female (8 years) and five adults in a 

vanguard study to examine inter-individual variability of nasal air flows in human 

subjects. Several prior studies had shown that actual airflow patterns in the nasal tract of 

both animals and (adult) humans is highly non-uniform with highly localized areas of 

flow that have been correlated with (at least in laboratory animals) areas of focal 

pathology in air exposures to reactive gases. Thus, these scans allowed for an initial 

evaluation of the extent of variability of these flows at the level of the individual. (All 

subjects or their representatives signed a consent form agreeing with the use of their 

scans.) These scans were successfully utilized to conduct anatomical measurements and 

to construct mesh configurations necessary for conducting CFD of the main nasal 

chamber (i.e., anterior to the nasopharyngeal region) for each individual. Breathing rates 

for the flow simulations were set at 5.5 L/min for the 7-year-old boy and 5.8 L/min for 

the 8-year-old girl with flows for the adults each allometrically adjusted with a final 

range of between 6.8 and 9.0 L/min. Simulations of steady-state inspiratory airflow were 

conducted using commercially-available CFD software. Simulations of nasal uptake of 

inhaled gas (concentration in ambient air defined to be 1 ppm by volume) were conducted 

under one of two boundary conditions, one to simulate a maximum gas uptake and a 

second boundary condition to simulate moderate uptake (approximately 80% of 

maximum) at the nasal tract walls. Results of the study are along several lines. The 

simulations predicted that, under both boundary conditions, gas was rapidly absorbed by 

the nasal mucosa once it entered the nostrils. At the end of the nasal septum, gas 

concentration in the inspired air had dropped to ~13% and ~29% of the inlet 

concentration for the maximum and moderate uptake scenarios, respectively. The spatial 

distribution of wall fluxes, especially under the maximum uptake boundary condition, 
3-49

were shown to be highly non-uniform for all scans including those of the two children.

Further analysis of the subjects showed that the extent of the non-uniform flows (where 

areas of non-uniformity were divided into categories of increasing mass flux) was not 

appreciably different among the subjects, including between adults and the two children 

(the minimal number of subjects precluded any statistical analysis). Additional analysis 

also showed that the overall rate of uptake in the nasal region, although highly 

non-uniform under localized internal conditions as shown by this study, was very similar 

from one individual to the next with no apparent differences between adults and the two 

children. Importantly, delivered dose estimated in terms of maximum (99th percentile) or 

average flux was not different between adults and children. These principal results from 

the maximum uptake condition, including some of the first available ET surface areas for 

children, are shown in Table 3-18.

Table 3-18 Summary Listing of Findings on Morphometry and Gas Flow/Uptake Simulations for 

Human Nasal Cavities

Parameter (units) Subjects

Adults Children

Gender Malea Male Female Female Femalea Male Female

Age (years) 53 NA NA NA 37 7 8

ET area (cm2) 20,085 23,219 16,683 20,688 17,752 12,093 13,027

ET volume (mL) 18.0 26.5 15.4 23.8 18.7 10.7 13.7

Total gas uptake, maximum conditions (%) 93.5 93.1 92.4 89.2 91.5 92.0 88.2

Average flux, left cavity (10-8 kg /sm2)

b 1.8 1.6 1.5 1.2 1.4 1.9 1.6

Maximum fluxc, left cavity (10-8 kg/sm2)

b 10.8 11.0 10.8 10.6 10.8 11.8 12.3

a

Data obtained from repaired casts.

b

Gas absorption rate

c

The 99th percentile flux (i.e., the flux value below which 99% of flux values fall)

NA = data not available

Source: Reprinted with permission of Informa Healthcare©; Garcia et al. (2009)

In a follow-on study from Garcia et al. (2009), Schroeter et al. (2010) utilized the reactive 

gas hydrogen sulfide (H2S) to expand the investigation of interhuman variability of nasal 

dosimetry and anatomically accurate CFD models of the nasal passages of five adults and 

two children generated from MRI or CT scan data. Preceding studies showed that 

inter-individual differences in nasal anatomy affect the distribution of airflow and 

(simulated) uptake from the airflow inside the nose, such that highly localized areas with 

potential for widely varying deposition were seen among individuals (e.g., Garcia et al., 

2009). It has also been established that H2S is a potent toxicant of the respiratory tract 

with particular and specific effects in olfactory tissue. The aim of this study was to 

characterize the variability of H2S dose to the olfactory region of humans arising from 

inter-individual differences in nasal anatomy, airflow, and inspiratory uptake patterns.
3-50

This study used essentially the same conditions of modeling with the same computational 

meshes used by Garcia et al. (2009). Several of the meshes were, however, repaired and 

further refined to provide more complete olfactory regions representative of normal or 

decongested nasal anatomy. Olfactory regions were mapped into the nasal models of all 

subjects as consistently as possible based on the prior descriptions of the extent of 

olfactory epithelial in humans. Steady–state inspiratory airflow rates were applied based 

on body weight allometry. The H2S specific kinetic parameters used were previously 

estimated by the authors by fitting in vivo uptake data in rats, then allometrically scaled 

to humans based on nasal surface areas. The air phase mass transfer was solved 

numerically under an assumed equilibrium condition across the air-tissue interface. Flows

were simulated at three different concentrations, 1, 5 and 10 ppm. Comparisons among 

individuals were made for the 99th percentile flux (i.e., the flux value below which 99% 

of flux values fall) and average flux in the olfactory regions at an exposure concentration 

of 1 ppm. The olfactory surface area in each model was partitioned into bins based on 

levels of H2S tissue flux to examine the distribution of surface area by wall flux levels.

Results included morphological measurements in human adults and children of nasal 

cavity surface areas and estimates of olfactory epithelia and airflow apportionment. The 

modeling results in terms of average flux, maximum flux, and distribution of flux ranges 

within the target area of olfactory epithelium showed uniform responses despite the 

morphological ranges characterized. Differences in nasal anatomy and ventilation among 

adults and children were not predicted to have a significant effect on H2S dosimetry in 

the olfactory region (Table 3-19). The 99th percentile flux ranged from 153.1 to 170.1 in 

adults compared to 149.2 and 159 in children, while the average flux ranged from 12.2 to 

13.6 in adults compared to 11.8 and 12.1 in children.

Table 3-19 Selected Morphologic and Simulated Modeling Results of Hydrogen Sulfide 

Dosimetry in Casts of Human Nasal Cavities

Parameter (units) Subject

Adults Children

Gender Malea Male Female Female Femalea Male Female

Age (years) 53 NA NA NA 37 7 8

Surface area of main nasal cavity (cm2) 198.7 231.5 167.3 207.9 177.0 118.9 135.1

Surface area of olfactory region (cm2) 14.4 11.5 10.5 9.9 11.2 9.1 9.6

Olfactory airflow allocation (%) 4.8 5.5 7.9 2.6 4.9 16.2 1.6

99th percentile flux (pg/cm2-s) @ 1ppm 167.7 170.1 158.9 161.3 153.1 149.2 159.0

Average flux (pg/cm2-s) @ 1ppm H2S 13.6 13.5 12.7 12.8 12.2 12.1 11.8

a

Data obtained from repaired casts.

NA = data not available

Source: Reprinted with permission of Informa Healthcare©; Schroeter et al. (2010)
3-51

Allen et al. (2004) studied the mechanisms of gas and aerosol transport in the human 

respiratory system in the upper airways of a pediatric subject (male, age 5 years).

(Approval was granted by the appropriate Institutional Review Board to utilize blinded 

MRI images.) An in vitro reconstruction of the subject’s anatomy was produced from 

MRI images with axial MRI slices every 3 mm. The model included the oral cavity, 

oropharynx, larynx, trachea, and carinal bifurcation. The computational model was 

directly imported into a CFD software program; an unstructured tetrahedral mesh was 

fitted to the topology (26,852 nodes total). Airflow was then simulated using this 

computational mesh in the CFD software. A computational model of an adult upper 

airway was derived from the cadaver of a female, aged 84 and constructed in an identical 

fashion (15,096 nodes total). The CFD airflow simulations in the adult model were 

validated through comparisons made between numerical simulations and experimental 

measurements (determined indirectly from the centerline velocity) using Phase Doppler 

Interferometry. Flow fields were solved under steady inhalation at 6.4 and 8 liter/min, 

and the resulting computational data were compared to experimental results obtained 

with this adult model to determine CFD solution validity. The numerical simulations 

provided an accurate representation of axial velocities and turbulence intensity.

Simulation results on flow resistance, axial velocities, secondary velocity vectors, and 

turbulent kinetic energy and intensity are presented for the pediatric case. 

Particularly detailed information was gathered on the nature of the laryngeal jet, which 

was expected to have the dominant effect on flow features in the upper airways. The 

development of the laryngeal jet could be clearly seen in the anticipated immediate 

post-epiglottal area at both flow rates studied. Contours of axial velocity, secondary 

velocity vectors, and turbulent kinetic energy and intensity of the modeled airway were 

all characterized in the study. The highest turbulent intensity was noted to occur 

immediately downstream of the glottic restriction followed by areas at the carinal 

bifurcation. Other key features of the modeled flow included skewed velocity profiles 

due to bends in the airways and recirculation zones due to abrupt changes in cross 

sectional area. The authors stated that these results all qualitatively agreed with the 

computational outcomes of the adult model. An unanticipated outcome between the 

pediatric and adult models was observed when the laryngeal jet from an adult model was 

compared to the laryngeal jet in the pediatric model based on the same tracheal Reynolds 

number. This comparison showed higher axial velocities, recirculation and turbulent 

kinetic energy in the adult than in the pediatric case. Although the intensity of turbulence 

in the laryngeal jet was comparable in the adults and in the child, it should be recognized 

that adults have much higher tracheal Reynolds numbers than children. A similar trend 

was noted for axial velocities which were higher in the pediatric model than would be 

expected from measurements in adults at similar tracheal Reynolds numbers. The authors 

speculated that the critical range for the dependence of turbulence on tracheal Reynolds

numbers appears to differ between adults and children for reasons that are not clear.

However, that the authors did not discuss the fact that the laryngeal area is flexible and 
3-52

that the larynxes of the models used may not be in comparable states of openness. The 

authors concluded that there was reasonable agreement between the location of flow 

structures between adults and children, suggesting that an unknown length scale 

correlation factor could exist that would produce acceptable predictions of pediatric 

velocimetry based off of adult datasets. 

3.6.4 Res piratory Tract Growth

It has been well established that the human respiratory system passes through several

distinct stages of maturation and growth that involve branching morphogenesis and 

cellular differentiation during the first several years of life and into adolescence 

(Pinkerton and Joad, 2000). The proportion of surface area to ventilation volume may be 

markedly different during these developmental stages. The significance of these 

disproportions with regard to toxicant exposure overall or to the sites of active cellular 

differentiation have yet to be elucidated.

The major proposed processes in human lung growth and development are (1) an increase 

in numbers of alveoli via septation of elementary saccules, followed by (2) increases in 

dimensions of all of the lung structures, including alveolar size and, most prominently, 

the diameter of airways, followed by (3) distension of lung due to changes in the 

mechanical properties of the chest wall. These changes are postulated to result in a 

relative under-distension of the lung followed by a relative over-distension (Zeltner et al., 

1987 for general review). De Jong et al. (2003) postulated that indications of these 

processes could be determined through in situ scanning and visualization techniques.

Therefore an institutionally sanctioned study was conducted where the CT scans of 35 

children (age range from 15 days to 17.6 years of age; 17 males, 18 females) were 

obtained and examined for these indications of growth and development. CT analysis 

allowed for accurate calculation of lung volume and estimation of lung density and 

weight (lung density × volume). Lung expansion may also be obtained by subtracting the 

inverse of the density of tissue from the inverse of the CT-measured lung density. All 

data were then plotted with respect to age (or body length) and analyzed. The data on 

volume showed a decline from birth to 2 years of age and an increase thereafter. This 

finding would be anticipated as added alveoli are of uniform size and divisions of 

existing airspace into smaller units via septation would cause the gas volume to fall. The 

subsequent increase in gas volume of tissue from age 2–8 years would be consistent with 

expansion in the size of alveoli in combination with a gradual increase in functional 

residual capacity (FRC) due to changes in the mechanical properties of the lung and chest 

wall.

In a companion study de Jong et al. (2006) used CT scans from a group of 50 young 

individuals (age range 0 – 17.2 years) to obtain estimates of various lung dimensions also 
3-53

through the period of growth. Clinical CT scans were performed and analyzed as above 

for lung weight, gas volume, lung expansion, lung surface/volume ratio, airway wall area, 

airway lumen area, airway lumen perimeter, arterial area and airway surface length/area 

ratio. The authors discussed the nature of these ratios in relation to length and growth of 

the individual but did not give specifically determined estimates of measures such as 

surface areas. For example, lung alveolar surface area to total lung volume ratio (S/V) 

was calculated using the lung expansion values at total lung capacity (TLC) per the 

following equation:

Equation 3-6

The regression of these ratios against other growth parameters, such as body length,

suggested that the relationship between these various measures was closely linked. 

Collectively these results provide functional indications of lung growth processes using 

noninvasive methods and demonstrate that CT scans can be used to provide valuable 

information about normal lung growth in addition to the more typical application of 

diagnosis of lung disease.

Rao and coworkers (2010) evaluated lung growth and development in vivo in infants and 

toddlers using multi-slice CT. The developmental process is thought to be sequential in 

terms of the alveoli, with new alveoli being added until about 24 months of age followed 

by alveolar expansion with no new alveoli added after 24 months. The high resolution 

capability of CT was applied to a group of 38 subjects (14 male, 24 female) of ages in 

this range (17 to 142 weeks; 4 to ~ 36 months). Subjects were excluded if they were born 

at <37 weeks gestation, had congenital cardiorespiratory abnormalities or histories of 

wheezing, were hospitalized for respiratory illness, or used asthma medications. The 

study was approved by an institutional review board and signed consent was obtained 

from the parents. These high-resolution scans provided a basis for significant measures.

Lung volume was calculated by summing the voxels (i.e. a volume element in 3D space)

within the lung, while lung density was calculated from the X-ray attenuation values, and 

lung tissue weight was calculated by multiplying lung volume by lung density. Tissue 

volume was calculated as lung or tissue weight divided by tissue density, which was 

assumed to be 1.065 g/mL; air volume was obtained as lung volume minus tissue volume.

The measures were regressed against body length and showed that total parenchymal 

lung volume, parenchymal air volume, and parenchymal tissue volume increased 

significantly with increasing body length. This in vivo assessment suggests that the 

growth of the lung parenchyma in infants and toddlers occurs with a constant relationship 

between air volume and lung tissue, which is consistent with lung growth occurring 

primarily by the addition of alveoli rather than the expansion of alveoli. In addition, the 

central conducting airways grow proportionately in infants and toddlers.
3-54

The pulmonary growth sequence in early life of alveolar septation followed by alveolar 

expansion was examined by Balinotti et al. (2009) with pulmonary function testing. The 

basis of the hypothesis relates to the ratio between pulmonary diffusion capacity of 

carbon monoxide (DLCO) and alveolar volume (VA). During the process of 

alveolarization, usually considered to be in the first two years of life, this ratio would 

remain constant whereas during alveolar expansion, i.e., in children older than 2 years, it 

would decrease. The authors measured DLCO and VA using single breath-hold 

maneuvers at elevated lung volumes in 50 sleeping infants and toddlers less than two 

years, between the ages of 3 and 23 months. There were an equal number of males and 

females. No subjects had cardio-respiratory malformations, and their respiratory history 

was negative for wheezing, asthma, treatment with asthma medications, or hospitalization 

for a respiratory illness. The study was approved by appropriate institutional review 

boards. Both alveolar volume and pulmonary diffusing capacity increased with increasing 

age in both male and female children. Significantly, ratio of pulmonary diffusing capacity 

to alveolar volume remained constant in this age group. The constant ratio for DLCO/VA 

in infants and toddlers is consistent with lung growth in this age occurring primarily by 

the addition of alveoli rather than the expansion of volume.

Zeman and Bennett (2006) employed in vivo methodology, aerosol-derived airway 

morphometry (ADAM), to measure the age-related changes in air space caliber of the 

small airways and alveolar dimensions. The principal of ADAM related to predictable 

gravitational settling of small inhaled particles to infer the vertical distance or effective 

air space dimension, (EAD), that the particles must have settled to become lost to the 

airway wall. ADAM involves individuals inhaling to TLC a particle aerosol of known 

size characteristics followed by breath-holds for 0-10 seconds and (non-deposited) 

particle recovery upon exhalation. EADs would then be associated with their volumetric 

depth into the lung by the exhaled volume through the principle of first in, last out. Those 

EADs calculated from the aerosol at the beginning of the exhalation would be assumed to 

be representative of the proximal airways at that depth, and those calculated from the 

aerosol toward the end of the exhalation would be representative of distal airways.

Certain EADs are closely associated with morphological correlates: EADmin is related to 

alveolar diameter, EADtrans is related to transitional bronchiole caliber, VLDtrans (volume 

of gas required to reach transitional bronchioles into the lung) is related to anatomical 

dead space.

The subjects recruited from the general local population included 53 children (6–22 

years) and 59 adults (23–80 years). Subjects had no smoking history, no history of lung 

disease, and no recent history of acute respiratory infection or viral illness within the 

previous 4 weeks. Informed consent was obtained from each volunteer. This technique 

was employed to derive EADs that was then used to estimate alveolar diameters, 

transitional bronchiolar caliber, and the volume of conducting airways anatomical dead 

space. Data were collected, then regressed according to age. Alveolar diameters were 
3-55

found to increase with age, from 184 μm at age 6 to 231 μm at age 22 based on the 

regression equations derived. This observation would account for the increase in TLC 

observed over this age range. The caliber of transitional bronchioles did not increase with 

TLC (average 572 μm), but did increase with subject age and height when the entire age 

range of 6–80 years was included (Zeman and Bennett, 2006). The anatomical dead space 

scaled linearly with lung volume, but relative to TLC did not change with age, averaging 

7.04 ± 1.55% of TLC. The authors concluded that from childhood (6 years) to adulthood 

a constant number of respiratory units is maintained; however, both the smallest 

bronchioles and alveoli expand in size to produce the increased lung volume with 

increased age and height.

It is believed that humans grow new alveoli from a few weeks before term birth until 

approximately 8 years of age, after which the alveoli are thought to enlarge as the lungs 

increase in volume or size with no new alveoli formed. To this end, Altes and coworkers 

(2004a) examined the apparent diffusion coefficient (ADC) with a gaseous contrast agent 

for MRI, hyperpolarized helium-3 (3

He), in a cohort of twelve individuals. An increase in 

ADC is a measure of volume maturation. It was expected that in the pediatric age group, 

the increase in alveolar size with increasing age will be reflected in an increase in 3

He

ADC with age. The age range of the 12-member cohort was 7 to 29 years (mean 15.6, 

standard deviation 6.9 years). All 12 of the subjects had homogenous appearing ADC 

maps. Comparing the mean ADC with other measures of maturation or lung volume gave 

correlation coefficients of 0.74 with height, 0.64 with weight, 0.76 with forced vital 

capacity (FVC) in liters, 0.81 with the predicted FVC based on the subject’s age and 

height, and 0.34 with the percent predicted FVC. In summary, it was found that the mean 

ADC increased with age in the pediatric population and that the mean ADC was lower in 

the pediatric age group than in young adults. These observations suggest that the pediatric 

subjects had smaller airspaces than the young adults. Further, the variability of the 

airspace structure, as measured by the standard deviation of the ADC values, did not 

change with age, as expected. Thus 3

He diffusion MRI of lung appears to be able to 

detect this normal maturation process of increased lung volume via increases in the size 

of the functioning alveoli.

Altes et al. (2004b) used advanced imaging techniques to detect age-related development 

in lung microstructure that relate to both lung volume and surface area. 3

He diffusion 

magnetic resonance scanning produces in vivo images of tissues weighted as to water 

diffusion through local microstructure. MRIs were acquired for each of 29 individuals (2 

separate trials for each), aged four to 30 years, and used to determine the mean ADC and 

lung volume for each subject. The mean ADC was reported to increase with increasing 

subject age (r = 0.8; P < 0.001), with a 55% increase in mean ADC from the youngest (4 

years) to oldest (30 years) subject. The lung volumes measured on MRI were highly 

repeatable for the two acquisitions (r = 0.980) and also reflected increased volumes 
3-56

concordant with the ADC. These advanced imaging results gave functional indications 

that alveoli increase in size rather than number during childhood.

3.6.5 Child Data Collations

Ménache et al. (2008) generated quantitative whole-lung models from silica casts of the 

lungs from 11 subjects between 3 months and 21 years of age. The models were based on 

a combination of cast data and published information on distal airway dimensions and 

were inclusive of the conducting airways (trachea through terminal bronchioles), the 

respiratory bronchioles, and the alveolar airways, which include alveolar ducts and sacs.

Parameters evaluated from the data included airway generation number count, length and 

diameter of terminal bronchioles and alveolar ducts, acinar length and alveolar 

dimensions (assumed spherical), and total alveolar number. Further estimates from these 

parameters and reasonable assumptions were made for alveolar volumes and the 

physiological volumes of TLC and FRC. Model dimensions for the conducting airways, 

as well as the estimated dead space, for all children fell within the range of the limited 

published information. The assumptions and estimates used produced results that were 

reasonably consistent with available physiological data for children 8 years and older.

The predicted TLC for the older individuals (aged 8 to 21 yr) fell within or near the range 

arising from published scaling equations. However, the models for children 3 years of 

age and younger resulted in predicted TLCs well below those predicted using these same 

equations by as much as an order of magnitude (data not shown). Another unexpected 

result was the total number of model calculated alveoli compared to the published 

number of alveoli as a function of age. As shown in Figure 3-5, the calculated number of 

alveoli increased linearly as a function of age in contrast to the data of Dunnill (1962) and 

Thurlbeck (1982). This suggested that the fixed relationship between respiratory airway 

volumes and alveolar volumes assumed for all ages was incorrect and that the 

relationship must be different in the younger children. These differences might be 

explained by growth in early childhood when the alveolar region is growing more than 

the airways. The airways show symmetric growth since they are complete, while the 

alveoli are increasing in both number and size. These results suggest that the geometry 

model airway dimensions for all ages are appropriate for use with dosimetry models; 

however, they also point out a need for a greater understanding of lung development for 

children 3 years of age and under.
3-57

Figure 3-5 Alveoli count per lung as a function of age

Source: Reprinted with permission of Informa Healthcare©; Ménache et al. (2008); using data from Dunnill 

(1962) and Thurlbeck (1982)

Ogiu et al. (1997) presented detailed physical mass measurements of various organs in 

4,667 Japanese subjects, aged 0-95 years, including 3,023 males and 1,644 females.

Analyses of age-dependent changes in weights of the brain, heart, lung, kidney, spleen, 

pancreas, thymus, thyroid gland, and adrenal gland and also of correlations between 

organ weights and body height, weight, or surface area were carried out. It was concluded 

that organ weights, including lung, in the growing generation (under 19 years) generally 

increased with a coefficient expressed as (body height) × body weight0.5. Specific 

coefficients were derived for both right and left lungs and for both males and females. It 

was also noted that adult males had heavier lungs than adult females, and that the 

male:female lung weight ratios were nearly the same, 1.27 for the right lung and 1.28 for 

the left lung. The age-specific weights presented in this study for lungs only, 0-15 years 

of age, are shown in Table 3-18.
3-58

Table 3-18 Lung Weights (Right and Left) of Males and Females from Birth to Adulthood

Age

Males Females

Left Lung Right Lung Left Lung Right Lung

N Average wt

(g ± SD) N Average wt

(g ± SD) N Average wt

(g ± SD) N Average wt

(g± SD)

0 39 22.3 ± 5.7 40 28.4 ± 8.0 54 23.1 ± 7.1 52 29.1 ± 8.3

1 mo 5 42.1 ± 12.7 6 49.3 ± 16.1 4 38.7 ± 7.7 4 43.8 ± 8.1

2 11 48.4 ± 6.6 12 56.6 ± 11.0 7 45.7 ± 9.9 7 52.2 ± 8.7

3 3 46.3 ± 6.4 3 62.7 ± 11.7 6 50.2 ± 8.1 6 66.3 ± 15.6

4 11 51.1 ± 9.7 11 62.7 ± 11.5 5 51.5 ± 12.8 4 61.6 ± 14.5

5 4 51.8 ± 16.1 4 58.0 ± 18.6 7 48.3 ± 10.1 8 58.9 ± 9.6

6 8 55.5 ± 12.1 8 68.3 ± 12.4 6 62.1 ± 6.9 6 70.2 ± 6.8

7 6 72.2 ± 8.5 6 86.7 ± 12.1 1 55.0 1 68.0

8 7 66.5± 8.9 8 82.2 ± 19.0 5 62.0 ±10.2 5 74.8 ± 16.8

9 1 66.0 2 108.0 ± 31.1 8 67.6 ± 12.9 8 81.3 ± 16.1

10 4 71.9 ± 24.9 4 77.7 ± 27.6 3 53.3 ± 10.4 3 67.3 ± 9.3

11 1 50.0 1 62.0 - - - -

1 yr 15 83.9 ± 20.2 15 93.9 ± 21.9 22 76.8 ± 23.7 23 87.4 ± 30.4

2 7 100.5 ± 28.4 7 101.4 ± 21.2 14 94.8 ± 26.7 13 107.7 ± 32.0

3 17 108.4 ± 28.3 16 129.4 ± 36.0 11 112.5 ± 21.0 11 117.9 ± 25.8

4 11 118.5 ± 38.5 10 122.8 ± 32.2 4 117.3 ± 23.6 5 158.4 ± 43.2

5 8 138.1 ± 38.2 9 159.7 ± 34.3 4 113.0 ± 50.6 5 128.6 ± 43.2

6 4 194.8 ±16.0 4 230.3 ± 18.4 3 143.3 ± 12.6 4 197.5 ± 62.9

7 13 170.8 ± 61.6 14 186.9 ± 63.7 8 163.8 ± 44.2 8 200.0 ± 47.8

8 10 169.8 ± 54.7 10 204.4 ± 63.7 8 215.0 ± 50.1 8 242.5 ± 70.3

9 10 232.5 ± 75.5 9 243.3 ± 60.0 9 208.3 ± 62.8 9 247.9 ± 81.2

10 12 245.6 ± 71.9 13 255.2 ± 96.9 5 314.2 ± 42.0 4 368.0 ± 85.4

11 10 254.8 ± 77.1 10 298.5 ± 78.1 10 287.5 ± 77.4 9 300.6 ± 90.1

12 12 370.8 ± 130.1 12 398.8 ± 129.6 6 289.2 ± 89.4 6 280.0 ± 107.9

13 8 248.5 ± 131.9 8 383.4 ± 131.8 4 269.5 ± 93.2 4 303.5 ± 62.9

14 10 402.5 ± 146.1 10 467.0 ± 203.0 6 339.3 ± 54.4 6 389.2 ± 72.3

15 14 442.0 ± 155.6 13 500.3 ± 127.7 8 297.6 ± 191.9 7 344.0 ± 224.8

20-24 68 363.8 ± 129.1 61 444.9 ± 164.6 37 343.9 ± 118.1 41 363.6 ± 122.8

Source: Reprinted with permission of Lippincott Williams & Wilkins©; Ogiu et al. (1997)

In a translated Japanese study, Inagi (1992) described the collection and measurement of 

the heights of the mucous membrane in the human nasal septum from 74 cadavers, 

including 5 males and 4 females classified as “fetal/infant,” and 5 males and 3 females

aged 1 to 19 years referred to as the “infant/adolescent” group, as well as older aged 

groups. The purpose of the study was to examine histological changes in mucosal tissues 

although measurements were made in relation to age including heights of the mucous 

membrane, including both the epithelium and the underlying lamina propria. The average 

height for the epithelium of the “fetal/infant” group was estimated to be ~0.4 μm with a 

range of ~0.35 - 0.5 μm. For the remainder of the groups, the average and range of height 
3-59

was estimated to be ~0.7 μm with a range of ~0.4 - 0.9 μm. Estimation of the lamina 

propria heights (described and given as being from the convex and concave sides of the 

nasal septum) yielded: average height for fetal/infant group ~500 μm with a range of 

~300 - 700 μm; for the remainder of the groups the average and range of height was 

estimated to be ~900 μm with a range of ~400 – 1,500 μm. Such data and results may 

have utility in gas dosimetry as they give a basis for diffusion distance in mass transport 

processes, in this case across age groups including the very young. 
4-1

4 S UMMARY AND CONCLUS IONS

The new studies dealing with overall gas dosimetry in the airways support many of the 

principles and approaches of dosimetry in RfC Methods. Hanna et al. (2001) discussed

advancements within the existing dosimetry modeling framework through development 

and application of mass transfer coefficients. This direction is evidenced by the recent 

publication of several studies utilizing mass transfer coefficients to calculate dose to 

respiratory tract tissues from inhaled vapors (Asgharian et al., 2011; Madasu, 2007; 

Overton et al., 2001); however, still data gaps exist with regards to fractional penetration 

of gases and localized deposition, from a predictive and biological standpoint, in the TB 

and PU regions. Although the use of a simplified geometric model of the airways limited 

the breadth of their conclusions, the tissue metric for the alveolar area arrived at by 

Tsujino et al. (2005), g/cm2

/min, is similar to that used in the RfC Methods. The 

proposals and methods for extension of computational flow evaluation to the lower 

airways of Minard et al. (2006) should provide refinement and further resolution to flow 

and dose in the lower airways as has been done extensively for the upper airways.

Additional studies need to encompass CFD simulations in the rat and human lower 

respiratory tracts to be able to compare gas uptake rates between species, similar to what 

has been done for the URT.

The advent of the doubly labeled water (DLW) technique in estimation of physiological 

daily inhalation rates (PDIR) have provided resolutions to concerns not only regarding 

inhalation patterns of free-living individuals that are ideal for applying to long-term risk 

assessments involving inhalation exposures but also addresses lingering concerns of 

subjectiveness and bias to which virtually all time-activity-ventilation (TAV) methods are 

especially susceptible. Through a systematic consideration of aggregate errors, the 

method comparison studies available show that the DLW technique is by far the most 

accurate and reliable technique examined which included a number of TAV approaches. 

The capabilities and accuracy of DLW can reasonably be inferred over the entire range of 

its use which includes the very young (infants) and the very old. DLW-based PDIR 

values are currently included in the Child-Specific Exposure Factors Handbook (U.S. 

EPA, 2008), and being proposed for inclusion in other key Agency documents, including 

the external review draft of the Exposure Factors Handbook (U.S. EPA, 2009a), for all 

ages including children.

The studies and information relating directly to dosimetry of the tracheobronchial 

(TB) and pulmonary (PU) regions generally support the dosimetric approaches and 

assumptions of RfC Methods. Methodological advances and increased resolution of 

several in vivo imaging techniques indicate highly homogenous and uniform flows in the 

alveolar regions. On the other hand, examination of the tracheobronchial (TB) region 

with human models and advanced dynamic fluid flow programs reveal a degree of 
4-2

non-uniformity of flow for this region although apparently not to the extent that has been 

documented for the upper airway. As discussed in Status I (U.S. EPA, 2009b), these 

assumptions and thus, the default dosimetric procedure for the ET region were not 

supported as studies consistently demonstrated highly non-uniform airflow and 

deposition to airway surfaces, and advance kinetic models clearly demonstrated the 

animal/human dose to be ≥ 1.

Marked advances in morphometry of these regions are being achieved with the 

development and application of stereology. These techniques, described as the estimation 

of higher dimensional information from lower dimensional samples, have and continue to 

provide more accurate estimates of measures and vital parameters such as alveoli number 

and size characteristics, volumes and surface areas in both humans (e.g., Ochs et al., 

2004) and laboratory animals (Knust et al., 2009), all of which may influence and refine 

inhalation dosimetry of gases. Table 4-1 summarizes the various respiratory tract surface 

areas and volumes that have become available and that are documented in this report.

The significance of the blood:air partition coefficient (Hb/g) to the advanced PBPK 

models have apparently been responsible for the generation of a number of direct and 

surrogate approaches for providing these values, both animal and human. The critical and 

comprehensive analyses of Payne and Kenny (2002) and Abraham et al. (2005) of human 

and animal (rat) Hb/g for a large number of volatile organics from several sources and 

approaches made several conclusions. A major indirect conclusion affecting interspecies 

dosimetry was that for VOCs there was no difference between rat and human Hb/g. The 

other strategy to evaluate the Hb/g for purposes of interspecies dosimetry involved 

inspection of published inhalation PBPK models that were configured for interspecies 

extrapolation, and therefore had Hb/gs that were validated with simulations compared to 

relevant human empirical data. These results give indications the current dosimetry 

approach of RfC Methods that uses ratios of animal to human Hb/g as a basis of 

dosimetry for the extrarespiratory (ER) region may result in human equivalent 

concentrations that are less than estimated by PBPK models.
4-3

Table 4-1 Respiratory Tract Surface Areas and Volumes for Various Species comparing the 

1994 RfC Methods and this Report

Pulmonary (PU) Tracheobronchial (TB)

Mousea,g Ratb,g Humanc,g Moused,g Rat Human

Surface Area

This Reportf

Total alveolar 

airspace 0.082 m2 NA

78.4 – 81.6 m2

; 43.3 

m2 SD 7.7 

(Vertical section); 

41.6 m2 SD 9.3 

(IUR section)

3.5 cm2 NAe NAe

Surface Area RfC Methodsg 0.05 m2 0.34 m2 54 m2 3.5 cm2 22.5 cm2 3200 cm2

Volumef 138 mm3 (CV,0.29) 16.5 cm3

, CV 

0.17

1,534 ± 521 cm3 (n = 

6) 0.114 cm3 NAe NAe

a

Values for a C57B6 mouse weighing 20.6g from Knust et al. (2009). b

Values from Hyde et al. (2004). c

Surface area data from Wiebe and Laursen (1995); volume data from Ochs et al. (2004). d

Value for a BALB/c mouse weighing approximately 25 g from Madl et al. (2010). e

NA=data not available.

f

New information provided in this report.

g

Default values presented in the RfC Methods (U.S. EPA, 1994)

An overview of the literature available on children’s dosimetry closely follows the 

recommendations and guidance of the NAS on children’s risk (NRC, 1993). These 

recommendations include the proposal to use PBPK models to explore and evaluate 

potential child susceptibility. A recommendation linked to the development and

utilization of models is the need to generate accurate measurements and parameters to be 

used in these models. Accordingly there exist a number of studies examining various 

parameters essential to inhalation modeling including physiological daily inhalation rates, 

lung tissue and lower airway measures and function. A compelling dataset (orally 

administered therapeutics) documents the generally slower clearance rate in children

(Ginsberg et al., 2002). Flow models are available that examine uptake differences of 

gases in the upper airways of both adults and children. Also, several PBPK models that 

are configured to specifically consider child versus adult dosimetry have been developed.

Although the actual number of datasets and models relating to gas dosimetry in children 

is not yet plentiful, a number of methods and approaches are available. These methods 

and approaches indicate child vulnerability related to inhalation dosimetry is

typically in the range of 1 to 2-fold more than adult, but can be more or less. This 

range is within that built into RfC Methods using the human interindividual uncertainty 

factor (UFH) to accommodate pharmacokinetic and pharmacodynamic variability and for 

consideration of potential sensitive population and lifestages including children. It may 

also be noted that this finding is very similar to that of the NAS (1993). Consequently,

with regards to gas dosimetry, there appears to be insufficient quantitative evidence 

to modify the RfC Methods specifically for children; however, in some cases, 

chemical-specific information may warrant consideration of alternative approaches 

or adjustments to account for this lifestage. It is anticipated that information will 

continue to become available to further inform this issue.
5-1

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Willems, B; Melnick, R; Kohn, M; Portier, C. (2001). A physiologically based pharmacokinetic model for inhalation 

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0.

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A-1

AP P ENDIX A. S UMMARY AND DIS P OS ITION OF 

INDEP ENDENT EXTERNAL P EER REVIEW 

COMMENTS

The “Status Report: Advances in Inhalation Dosimetry for Gases with Effects on the 

Lower Respiratory Tract and the Body” (Status II) has undergone a formal external peer 

letter review performed by scientists in accordance with EPA guidance on peer review

(U.S. EPA, 2006). The reviewers were tasked with providing written answers to charge 

questions on both general and specific scientific aspects of the report. A summary of 

significant comments made by the external reviewers to these charge questions and 

EPA’s responses to these comments arranged by charge question follow. Editorial 

comments were considered and incorporated directly into the document as appropriate. 

A.1 External Peer Reviewer Comments - Comments and Res pons e 

to Charge:

A.1.1 Charge Ques tion 1

The primary focus of this report relates to the pharmacokinetic component of interspecies 

gas dosimetry for portal of entry effects in the lower respiratory tract. Issues related to 

pharmacodynamics, including variability in response, are specifically excluded from this 

report. Is the scope and primary focus of this report clear?

Comments:

All three reviewers commented that the scope and primary focus of the report are clear. 

However, one reviewer thought the organization was somewhat choppy and provided 

editorial suggestions to help improve the organization and flow of the text. One reviewer 

commented further that is was made very clear that this report focuses on 

pharmacokinetics and does not include any pharmacodynamic issues.

Response:

The majority of the editorial changes suggested by the reviewer were incorporated in the 

revised document.
A-2

A.1.2 Charge Ques tion 2

Have the principal studies examining interspecies gas dosimetry for effects in the TB, 

PU, and ER regions that have been reported since the issuance of the 1994 RfC Methods 

been identified in this report? Please identify and provide a rationale for any other key 

studies that should be considered for inclusion.

Comments:

One reviewer provided suggested text for four additional references (Tian and Longest, 

2010a, b, c; Zhang et al., 2006) pertaining to the assumption of steady-state mass 

transport fluxes across mucus and tissue barriers. One reviewer provided three additional 

references (Zhang and Kleinstreuer, 2011; Longest and Kleinstreuer, 2005; Ranz and 

Marshall, 1952a, b) that provide more information on mass transfer coefficients and the 

Sherwood number. One reviewer suggested including a reference regarding air-phase 

mass transfer coefficients (Condorelli and George, 1999) and a few papers related to the 

uptake of ozone (Taylor et al., 2007).

Response:

The text and references suggested by the reviewers were evaluated, and EPA agrees that

the text summarizing these additional references should be included in this report. The 

work by Tian and Longest (Tian and Longest, 2010a, b, c) and Zhang, Kleinstreuer, and 

Kim (Zhang et al., 2006) was included in Section 3.3.1. Additional citations (Zhang and 

Kleinstreuer, 2011; Condorelli and George, 1999) were provided in Section 2.3 on 

gas-phase mass transfer coefficients, and information on the use of the Sherwood number 

to estimate gas-phase mass transfer coefficients (Asgharian et al., 2011; Zhang and 

Kleinstreuer, 2011; Longest and Kleinstreuer, 2005; Condorelli and George, 1999; Ranz 

and Marshall, 1952a, b) was added to Section 3.1. The suggested references (Padaki et 

al., 2009; Taylor et al., 2007) regarding ozone modeling and uptake provided useful 

information on flow and deposition and thus were added to Section 3.3.1.

A.1.3 Charge Ques tion 3

The state-of-the-science pertaining to the focus of this report is primarily presented in 

Section 3. Is the description of those studies in this report concerned with gas dosimetry 

appropriate and accurate? Are the analyses and evaluations of the scientific evidence 

supported by the studies cited?

Comments:

One reviewer questioned whether the current DEFs are inside the range of the adjustment 

factors currently used, and whether these adjustment factors account for local deposition.

This reviewer also suggested two references on particle DEFs that discuss the impact of 
A-3

surface area size considered on the calculation of the DEF (Sweeney et al., 2009; Phalen 

et al., 2006; Balashazy et al., 1999). Two reviewers agreed that the studies included in the 

report adequately describe what is known about alveolar gas transport; however, one 

reviewer noted that future studies are needed before the occurrence of significant 

localized deposition can be ruled out. Two reviewers suggested correcting the description 

of the gas-phase mass transport coefficient (kg) to indicate that it does not depend on the 

reactivity of the gas. One reviewer suggested including more general descriptions of the 

mathematical models that may be used for inhalation dosimetry (e.g., PBPK, identical 

path, and CFD) and their advantages and limitations.

Response:

No changes were made to the document in response to one reviewers comment with 

regards to whether the range of adjustment factors accounts for the possibility of the 

various DEFs. It was inferred that this comment pertained more to the uncertainty factor 

application to derivation of RfCs, which is not a focus of this report. The Balashazy et al. 

(1999) and Phalen et al. (2006) papers were evaluated for inclusion in this report; 

however, these papers described particle deposition that was not directly related to 

inhalation gas dosimetry in the lower respiratory tract and lacked information needed for 

comparative inter- and intra-species extrapolations, thus these papers were not included.

Text was added to the end of Section 3.4.1. to clarify that additional studies are needed to 

rule out the possibility of localized deposition (“hotspots”). The description of the 

gas-phase mass transport coefficient was updated as suggested by the reviewers. General 

descriptions of the mathematical models commonly used in inhalation dosimetry were 

included in the glossary for this report; however, significant discussion of the advantages 

and limitations of these models for application in inhalation dosimetry was not included 

as this is outside the scope of this current work.

A.1.4 Charge Ques tion 4

The state-of-the-science pertaining to children’s inhalation dosimetry is presented in 

Section 3.6. Is the description of those studies in this report, as they pertain to inhalation

gas dosimetry, appropriate and accurate? Are the analyses and evaluations of the scientific 

evidence supported by the studies cited? Are there additional evidence-based studies and 

information specific to children’s inhalation dosimetry that should be considered for 

inclusion that contribute to the science and understanding of inhalation gas dosimetry in 

children?

Comments:

One reviewer commented that the available studies are included and appropriately 

presented. Another reviewer commented that description of the studies pertaining to 
A-4

children’s dosimetry is appropriate and is not aware of any additional studies that could 

be useful for the RfC methodology in children. This reviewer also commented that the 

two most extensive studies that directly applicable to RfC derivation appear to be the 

Ginsberg and Sarangapani studies, and that these studies were appropriately evaluated. 

The conclusion was important since this variability is included in the uncertainty factor 

for interindividual variability. The third reviewer thought that this section clearly 

indicates new information that can impact on future refinements of dosimetry modeling 

and default, but suggested that the subsections could be reordered.

Response:

Subsection titles were revised to more clearly indicate the content in each section. Some 

text was moved as suggested to improve the flow of the document; however, the general 

structure of the document was retained such that the overall document was arranged by 

anatomy (e.g., TB, PU, or ET region) and within each section the specific advances and 

modeling/quantitation approaches were described. A separate section pertaining to 

inhalation dosimetry in children was retained.

A.1.5 Charge Ques tion 5

This report provides new information on the pharmacokinetic component of interspecies 

gas dosimetry for effects in the TB, PU, and ER regions. Is this report successful in 

presenting and evaluating the state-of-the-science relating to this focus?

Comments:

All three reviewers were in agreement that this report adequately presented and evaluated 

the state-of-the-science related to this area. One reviewer further commented that this 

report nicely summarizes some new key studies related to inhalation gas dosimetry, but 

these studies do not seem to have much of a direct application to the refinement of the 

RfC Methods.

Response:

No response necessary.
A-5

A.1.6 Charge Ques tion 6

Please comment on the effectiveness of the Summary and Commentary in describing 

advances in the state of the science since publication of the RfC Methods document (U.S. 

EPA, 1994) (http://cfpub.epa.gov/ ncea/cfm/recordisplay.cfm?deid=71993. Please 

identify any additions, deletions or changes that would improve the effectiveness of the 

draft review document in summarizing the state of the science.

Comments:

One reviewer commented that the report does an excellent job in describing the RfC 

Methods related to the purpose of the report. However, the Summary should add points 

concerning future studies that could address some the data gaps present. This reviewer 

provided some additional studies that could be referenced in this regard. One reviewer 

commented that this section provides the necessary concluding remarks of the studies 

presented to reach the conclusion presented in this report. One reviewer thought that this 

section was generally well-written and was in agreement with the conclusions reached.

Response:

As the reviewer suggested, additional text was added to the Summary and Conclusions 

Section (Section 4) regarding studies that could be conducted to help fill known data gaps 

in inhalation dosimetry methodologies.
B-1

AP P ENDIX B. DEFINITIONS FOR VENTILATORY 

VOLUMES 

B.1 Ventilation

Ventilation is a general term for the movement of air into and out of the lungs; without a 

preceding adjective, such as alveolar or minute, the term does not have any more specific 

meaning. The symbol for ventilation is ; V stands for volume and the dot for "per unit 

time".

B.2 Minute Ventilation

Minute or total ventilation the amount of air moved in or out of the lungs per minute. 

Quantitatively, the amount of air breathed in per minute (VI) is slightly greater than the 

amount expired per minute (VE). Clinically this difference is not important, and by 

convention minute ventilation is always measured on an expired sample and symbolized 

VE. It is useful to remember that VE is the breathing frequency (f) per minute times the 

tidal volume (VT, volume of tidal breath):

Equation B-1

VE is also the sum of two other ventilations, alveolar ventilation and dead space 

ventilation. 

B.3 Alveolar Ventilation

Alveolar ventilation (VA) is the volume of air breathed in per minute that (1) reaches the 

alveoli and (2) takes part in gas exchange. Alveolar ventilation is often misunderstood as 

representing only the volume of air that reaches the alveoli. Physiologically, VA is the 

volume of alveolar air/minute that takes part in gas exchange (transfer of oxygen and 

carbon dioxide) with the pulmonary capillaries. Air that reaches the alveoli, but for one 

reason or other does not take part in gas exchange, is not considered part of VA (for 

example, air that goes to an unperfused alveolus). Such alveolar regions lacking gas 

exchange constitute alveolar dead space. Clinically, the terms hyperventilation and 

hypoventilation apply to alveolar ventilation only.
B-2

B.4 Dead Space Ventilation

Dead space ventilation is that part of minute ventilation that does not take part in gas 

exchange; it is also referred to as "wasted ventilation." Dead space ventilation (VD) 

includes (1) air that enters only conducting airways (referred to as anatomic dead 

space) and (2) air that reaches alveoli but does not exchange carbon dioxide or oxygen 

with the capillary blood. The combined volume of these two areas is often referred to 

as physiologic dead space.

Based on these definitions:

Equation B-2

or

Equation B-3

In actual practice, VE is relatively easy to measure with a spirometer (or any device that 

can measure tidal volume). However, neither VA nor VD is measured in the clinical 

setting; they are difficult to measure, and knowing their absolute value is not considered 

all that helpful.

Source: Martin (1987)
C-1

AP P ENDIX C. LITERATURE S EARCH S TR ATEGY

Literature searches were conducted on sources published from 1985 through March 30 

2011, for studies relevant to inhalation dosimetry utilizing the search terms listed below.

Searches were conducted using U.S. EPA’s Health and Environmental Research Online 

(HERO) evergreen database of scientific literature. HERO searches the following 

databases: AGRICOLA; American Chemical Society; BioOne; Cochrane Library; DOE: 

Energy Information Administration, Information Bridge, and Energy Citations Database; 

EBSCO: Academic Search Complete; GeoRef Preview; GPO: Government Printing 

Office; Informaworld; IngentaConnect; J-STAGE: Japan Science & Technology; JSTOR: 

Mathematics & Statistics and Life Sciences; NSCEP/NEPIS (EPA publications available 

through the National Service Center for Environmental Publications (NSCEP) and 

National Environmental Publications Internet Site (NEPIS) database); PubMed: 

MEDLINE and CANCERLIT databases; SAGE; Science Direct; Scirus; Scitopia; 

SpringerLink; TOXNET (Toxicology Data Network): ANEUPL, CCRIS, ChemIDplus, 

CIS, CRISP, DART, EMIC, EPIDEM, ETICBACK, FEDRIP, GENE-TOX, HAPAB, 

HEEP, HMTC, HSDB, IRIS, ITER, LactMed, Multi-Database Search, NIOSH, NTIS, 

PESTAB, PPBIB, RISKLINE, TRI, and TSCATS; Virtual Health Library; Web of 

Science (searches Current Content database among others); World Health Organization; 

and Worldwide Science. The following databases outside of HERO were searched for 

risk assessment values: ACGIH, ATSDR, CalEPA, U.S. EPA IRIS, U.S. EPA HEAST, 

U.S. EPA HEEP, U.S. EPA OW, U.S. EPA TSCATS/TSCATS2, NIOSH, NTP, OSHA, 

and RTECS. Additionally cited reference searches were conducted utilizing the 

references listed below.

Search Terms (Proposed)

Blood partitioning AND gas AND/OR equilibrium

Comparative dosimetry

Dosimetry AND children (AND gas OR particle OR aerosol)

Inhalation AND model AND pulmonary

Inhalation AND model AND pulmonary AND gas

Inhalation AND model AND tracheobronchial

Inhalation AND dosimetry AND tracheobronchial AND gas

Inhalation AND model AND tracheobronchial AND gas 

Inhalation AND dosimetry AND pulmonary AND gas

Inhalation AND model AND systemic AND gas

Inhalation AND modeling AND systemic AND gas

Inhalation AND PBPK 

Inhalation AND CFD AND pulmonary AND gas
C-2

Inhalation AND CFD AND pulmonary AND gas

Inhalation AND CFD AND tracheobronchial AND gas 

Inhalation rates AND children

Neonatal AND dosimetry

Lung AND morphometry

Lung AND lesions AND regional AND pattern

Trachea AND morphometry

Trachea AND lesions AND pattern

Pulmonary AND lesions AND pattern

Respiratory tract morphometry

(Some) Citation Search References

Gargas ML, Burgess RJ, Voisard DE, Cason GH, Andersen ME. (1989). Partition 

coefficients of low-molecular-weight volatile chemicals in various liquids and tissues. 

Toxicol Appl Pharmacol. Mar 15; 98(1):87-99

Kimbell JS, Gross EA, Richardson RB, Conolly RB, Morgan KT. (1997). Correlation of 

regional formaldehyde flux predictions with the distribution of formaldehyde-induced 

squamous metaplasia in F344 rat nasal passages. Mutat Res. Oct 31;380(1-2):143-54.

Phalen, R. F.; Oldham, M. J. (1983). Tracheobronchial airway structure as revealed by 

casting techniques. Am. Rev. Respir. Dis. 128: S1-S4