NUREG/CR-6755

ANL/EAD/TM/02-1

Technical Basis for

Calculating Radiation

Doses for the Building

Occupancy Scenario Using

the Probabilistic

RESRAD-BUILD 3.0

Code

Argonne National Laboratory

 

U.S. Nuclear Regulatory Commission

Office of Nuclear Regulatory Research

Washington, DC 20555-0001
 NUREG/CR-6755

 ANL/EAD/TM/02-1

Technical Basis for 

Calculating Radiation 

Doses for the Building 

Occupancy Scenario Using 

the Probabilistic 

RESRAD-BUILD 3.0 

Code

Manuscript Completed: January 2002

Date Published: February 2002

Prepared by:

B.M.Biwer, S.Kamboj, J.Arnish, C.Yu, S.Y.Chen

Argonne National Laboratory

9700 South Cass Avenue, Bldg. 900

Argonne, IL, 60439-4832

NRC Project Manager:

T. Mo

Prepared for

Division of Systems Analysis and Regulatory Effectiveness

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

Washington, DC 20555-0001

NRC Job Code Y6112
NUREG/CR-6755

ANL/EAD/TM/02-1

TECHNICAL BASIS FOR CALCULATING RADIATION DOSES FOR THE

BUILDING OCCUPANCY SCENARIO USING THE PROBABILISTIC

RESRAD-BUILD 3.0 CODE

Prepared by

B.M. Biwer, S. Kamboj, J. Arnish, C. Yu, and S.Y. Chen

Environmental Assessment Division

Argonne National Laboratory

Submitted to

Tin Mo

U.S. Nuclear Regulatory Commission

Office of Nuclear Regulatory Research

Radiation Protection, Environmental Risk

and Waste Management Branch

January 2002

iii

ABSTRACT

The purpose of this report is to illustrate the use

of the probabilistic RESRAD-BUILD 3.0 code to

simulate the light industrial use of a

decontaminated building as one of the building

occupancy scenarios described in NUREG/CR5512 and NUREG-1727. The report illustrates

how a data template file of updated input

parameters and their distributions may be

constructed to transition from the screening

approach for dose analysis in NUREG/CR-5512

to a site-specific approach of the RESRADBUILD code. The intent of this illustration is not

to recommend a set of “default“ parameters, but

to assist users in formulating a method for

selecting and constructing a set of 

parameters to be used with the code for

demonstrating compliance with the dose criteria

in the license termination rule. The report

describes an example of a dose analysis

performed by using the probabilistic RESRADBUILD 3.0 code and the data template file

developed for this report to provide insight into the

dose values that may be obtained by simulating

light industrial use as the building occupancy

scenario.

v

CONTENTS

Abstract ................................................................. iii

Executive Summary ........................................................ ix

Foreword ................................................................ xi

Acknowledgments ......................................................... xii

Abbreviations ............................................................. xiii

1 Introduction ........................................................... 1-1

2 Evaluation of the Technical Basis of the Building Occupancy Scenario Using 

the RESRAD-BUILD and DandD Codes .................................... 2-1

3 Updated RESRAD-BUILD Input Parameter Information ........................ 3-1

3.1 Resuspension Rate ................................................ 3-1

3.2 Air Exchange Rate for Building and Room .............................. 3-8

3.3 Deposition Velocity ................................................ 3-14

3.4 Source Erosion Rate, Volume Source ................................. 3-16

3.5 Removable Fraction ............................................... 3-20

3.6 Time for Source Removal or Source Lifetime ............................ 3-22

4 Development of a RESRAD-BUILD Building Occupancy Scenario Data

Template File ......................................................... 4-1

4.1 Methodology for Developing a RESRAD-BUILD Building Occupancy

Scenario Data Template File ......................................... 4-1

4.2 Parameter Values Selected for the Data Template File .................... 4-2

5 Results and Discussion ................................................. 5-1

6 Summary ............................................................ 6-1

7 References ........................................................... 7-1

Appendix A: Unmodified RESRAD-BUILD Input Parameter Distributions ............... A-1

Appendix B: Parametric Distribution Types ...................................... B-1

FIGURES

2.1 Exposure Pathways in the RESRAD-BUILD Code ........................... 2-2

2.2 Inhalation Pathway in DandD and RESRAD-BUILD Codes .................... 2-7
vi

FIGURES (Continued)

3.1 Indoor Resuspension Rate Probability Density Function ...................... 3-9

3.2 Building and Room Air Exchange Rate Probability Density Function ............. 3-14

3.3 Idealized Representation of Indoor Particle Deposition Velocity ................. 3-15

3.4 Trimodal Nature of Aerosol Particle Size Distribution ......................... 3-16

3.5 Indoor Deposition Velocity Probability Distribution ........................... 3-18

3.6 Source Erosion Rate Probability Density Function ........................... 3-20

3.7 Removable Fraction Probability Distribution ................................ 3-21

3.8 Time for Source Removal or Source Lifetime Probability

Density Function ..................................................... 3-25

A.1 Concrete Shielding Density Probability Density Function ...................... A-4

A.2 Indoor Fraction Cumulative Distribution Function for RESRAD-BUILD ........... A-7

A.3 Probability Density Function for Room Area ................................ A-8

A.4 Room Height Probability Density Function ................................. A-9

A.5 Shielding Thickness Probability Density Function ............................ A-10

A.6 Absolute Humidity Probability Density Function for RESRAD-BUILD ............. A-11

A.7 Alternative Absolute Humidity Probability Density Function Based on Outdoor

Humidity Levels ...................................................... A-12

A.8 Concrete Source Density Probability Distribution Function .................... A-14

A.9 Concrete Source Porosity Probability Density Function ....................... A-16

A.10 Air Release Fraction Probability Density Function ........................... A-17

A.11 Wet + Dry Zone Thickness Probability Density Function ...................... A-19

A.12 Source Thickness Probability Density Function ............................. A-20

A.13 Water Fraction Available for Evaporation .................................. A-22

A.14 Indirect Ingestion Rate Probability Density Function .......................... A-27
vii

TABLES

2.1 Evaluation of the Technical Basis for the Building Occupancy

Scenario Using the RESRAD-BUILD and DandD Codes ..................... 2-4

2.2 Technical Basis Used in RESRAD-BUILD and DandD Codes 

for the Building Occupancy Scenario ..................................... 2-6

3.1 RESRAD-BUILD Parameters Selected for Assignment of Probability

Density Functions .................................................... 3-2

3.2 Indoor Resuspension Rates ............................................ 3-3

3.3 Resuspension Factors from Previous Studies .............................. 3-6

3.4 Residential Air Exchange Rates Distribution Characteristics ................... 3-10

3.5 Outside Air Exchange Rates for Commercial Buildings ....................... 3-13

3.6 Estimated Indoor Deposition Velocities by Particle Size ....................... 3-17

3.7 Estimated Deposition Velocities by Particle Size in Residences with

and without Furniture .................................................. 3-18

3.8 Estimated Indoor Deposition Velocities for Various Radionuclides .............. 3-18

3.9 Influence of Surface and Contaminant Types on Smear Tests ................. 3-22

3.10 Percent Removal of Contamination for Different Sampling Methods ............. 3-23

3.11 Source Lifetime Variation with Air Exchange Rate and Room Height

for a Fixed Resuspension Factor of 1 × 10-6 m-1............................. 3-25

3.12 Source Lifetime and Resuspension Factor for Different Removable

Fractions with an Air Exchange Rate of 0.5 h-1 and a 2.3-m Room Height ........ 3-26

4.1 Mean, Median, and Most Probable Values of Probabilistic Parameters in the

RESRAD-BUILD Building Occupancy Scenario ............................. 4-3

4.2 RESRAD-BUILD Building Occupancy Scenario Data Template File for

Deterministic and Probabilistic Runs ..................................... 4-5

5.1 Results of Probabilistic Dose Analysis for Selected Radionuclides Using

the RESRAD-BUILD Data Template File .................................. 5-2

5.2 Pathway Doses for Selected Radionuclides Using the RESRAD-BUILD

Data Template File ................................................... 5-3

5.3 Comparison of Deterministic Dose Calculated Using Data Template File,

Mean, and Median Parameter Values with Mean Dose from Distribution .......... 5-4
viii

TABLES (Continued)

A.1 Density of Shielding Materials Allowed in RESRAD-BUILD .................... A-3

A.2 Concrete Density from Various Sources .................................. A-4

A.3 Cumulative Distribution Functions for the Indoor Fraction ..................... A-5

A.4 Relative Frequency of Hours Worked by Persons Working

35 Hours or More per Week ............................................ A-5

A.5 Statistics for Fraction of Time Spent Indoors at Work ........................ A-6

A.6 Room Height in New Conventional and Manufactured Homes, 1996 ............. A-8

A.7 Density of Shielding Materials Allowed in RESRAD-BUILD .................... A-13

A.8 Concrete Density from Various Sources .................................. A-13

A.9 Bulk Density and Porosity of Rocks Commonly Used as Building Materials ....... A-15

A.10 Inhalation Rate Distributions ............................................ A-23

A.11 Summary of EPA-Recommended Values for Inhalation ....................... A-24

A.12 Indirect Ingestion Rates ................................................ A-26

B.1 Continuous Probability Density Distribution Functions ........................ B-4
ix

EXECUTIVE SUMMARY

The U.S. Nuclear Regulatory Commission (NRC)

has developed a Standard Review Plan (SRP

[NUREG-1727]) to assist in demonstrating

compliance with the radiological criteria that

decontaminated and decommissioned licensed

nuclear facilities must meet. A graded approach

is given for dose modeling that ranges from a

conservative screening to a site-specific analysis.

The conservative approach is embodied in

NUREG-5512, where a screening approach is

used to assess potential radiation doses to

individuals for the building occupancy scenario.

While the transition from a screening approach to

a site-specific approach was discussed

conceptually in the SRP, a direct application was

not given. This report discusses the use of the

RESRAD-BUILD 3.0 computer code for

performing such a site-specific analysis. The

technical basis for the light industrial building

occupancy scenario, the screening approach,

and the site-specific approach are examined, and

a process is developed for selecting the

appropriate site-specific input parameters for use

in RESRAD-BUILD.

The probabilistic RESRAD-BUILD 3.0 code was

used to simulate the light industrial use of a

decontaminated building for the building

occupancy scenario as described in NUREG/CR5512 and NUREG-1727. Because RESRADBUILD is designed to model more detailed,

sophisticated scenarios than those implemented

in the screening approach, more detailed input

data are needed to satisfy the requirements for

the RESRAD-BUILD analysis. Both RESRADBUILD and the NRC screening method model

external exposure from surface sources,

inhalation of resuspended 

contamination, and inadvertent ingestion of

surface contamination. The screening method

relies on a generic release model using the

resuspension factor as the primary controlling

release parameter. On the other hand, RESRADBUILD uses a different approach and considers

many important factors, including the removable

fraction, source lifetime, air release fraction,

mixing and transport in air, and surface

deposition and resuspension.

Parameter distributions for RESRAD-BUILD were

updated, and appropriate input data were

selected and placed in a template file. Only six

distributions were selected for use in the

template file on the basis of applicability and sitespecificity. Those parameters for which a

distribution was used were the deposition

velocity, resuspension rate, building air exchange

rate, receptor indirect ingestion rate, air release

fraction, and the time for source removal.

Deterministic values for the other parameters

were selected according to their treatment in

NUREG/CR-5512. The template file was

developed not for the purpose of recommending a

set of “default” parameters for a site-specific

analysis, but to provide users with a reasonable

method, consistent with the technical basis for

the light industrial building occupancy scenario

as explained in this report, for selecting

appropriate input data for a site-specific analysis.

Sample probabilistic runs of RESRAD-BUILD

were conducted using the template file with six

radionuclides (Co-60, Sr-90, Cs-137, Ra-226, U238, and Pu-239) to provide the analyst with an

idea of the type of results that could be expected

when performing a site-specific analysis. 

1 Argonne National Laboratory is operated for the U.S. Department of Energy by the University of Chicago,

under contract W-31-109-Eng-38.

xi

FOREWORD

This contractor technical report, NUREG/CR-6755, was prepared by Argonne National Laboratory1 staff

under their U.S. Department of Energy (DOE) Interagency Work Order (JCN Y6112) with the Radiation

Protection, Environmental Risk and Waste Management Branch, Division of Systems Analysis and

Regulatory Effectiveness, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission. 

The purpose of this report is to illustrate the use of the probabilistic RESRAD-BUILD 3.0 code to simulate

the light industrial use of a decontaminated building as one of the building occupancy scenarios described

in NUREG/CR-5512, “Residual Radioactive Contamination From Decommissioning.” The report explains

how to transition from the screening approach for dose analysis in NUREG/CR-5512 to a site-specific

approach by using the RESRAD-BUILD code. It illustrates how a set of updated parameter values and their

distributions may be constructed to be used with the code for site-specific dose analysis. The purpose of

this illustration is not to recommend a set of “default” parameters, but to assist code users in formulating a

method for selecting and constructing a set of parameters for site-specific and probabilistic dose analysis

to demonstrate compliance with the license termination rule, 10 CFR Part 20, Subpart E, in a risk informed

manner. 

This NUREG/CR report is not a substitute for NRC regulations, and compliance is not required. The

approaches and/or methods describe in this report are provided for information only. Publication of this

report does not necessarily constitute NRC approval or agreement with the information contained herein.

Use of product or trade names is for identification purposes only and does not constitute endorsement by

the NRC or Argonne National Laboratory.

 

 

Cheryl A. Trottier, Chief

Radiation Protection, Environmental Risk

& Waste Management Branch

Division of Systems Analysis & Regulatory Effectiveness

Office of Nuclear Regulatory Research
xii

ACKNOWLEDGMENTS

We would like to recognize Tin Mo, the

U.S. Nuclear Regulatory Commission (NRC)

Project Manager, for his helpful guidance in

ensuring the high quality and timeliness of the

work performed as planned.

We also would like to thank three NRC managers

— Cheryl A. Trottier, Chief of the Radiation

Protection, Environmental Risk and Waste

Management Branch; Thomas King, Director,

Division of Risk Analysis and Applications, Office

of Nuclear Regulatory Research (RES); and John

Greeves, Director, Division of Waste

Management, Office of Nuclear Material Safety

and Safeguards (NMSS) — for their managerial

and financial support of the project.

The NRC staff members made valuable

contributions to the work performed, and their

cooperation in reviewing, critiquing, and providing

timely feedback on draft project reports, as well

as their effective participation at the numerous

project review meetings and workshops, were of

great value. We are especially thankful to Rateb

(Boby) Abu-Eid, Duane Schmidt, Richard Codell,

and Gary Purdy of NMSS. 

We appreciate the cooperation and support of

this project shown by Alexander Williams,

RESRAD project manager in the Office of

Environmental Management (EM) of the

U.S. Department of Energy (DOE); Andrew

Wallo, Director of DOE's Air, Water and

Radiation Division; and Harold Peterson in DOE's

Office of Environmental Health (EH) for their

cooperation and support of this project. We would

also like to express special thanks to

Anthony Dvorak, Director of the Environmental

Assessment Division at Argonne, for his support

and encouragement, and to Jing-Jy Cheng of

Argonne for providing technical peer review.

Finally, we are grateful to Juanita Beeson and her

staff at the NRC Publications Branch and

John DePue, Pat Hollopeter, and Marita Moniger,

technical editors at Argonne, for their thorough

review and helpful suggestions. We also thank

the staff of the Document Processing Center of

Argonne's Information and Publishing Division for

preparing the manuscript.
xiii

ABBREVIATIONS

ANS American Nuclear Society

ARF airborne release fraction

ASHRAE American Society of Heating, Refrigeration, and Air-Conditioning Engineers

ASTM American Society for Testing and Materials

BLA Bureau of Labor Statistics

BNL Brookhaven National Laboratory

EC degree(s) Centigrade

CFR Code of Federal Regulations

cm centimeter(s)

cm2 square centimeter(s)

cm3cubic centimeter(s)

d day(s)

DCF dose conversion factor

DOE U.S. Department of Energy

D&D decontamination and decommissioning

dpm disintegration(s) per minute

EPA U.S. Environmental Protection Agency

FGR Federal Guidance Report

EF degree(s) Fahrenheit

ft foot (feet)

ft2square foot (feet)

g gram(s)

h hour(s)

HT tritium gas

HTO tritiated water

HVAC heating, ventilation, and air-conditioning

ICRP International Commission on Radiological Protection

in. inch(es)

ISO International Organization for Standardization

LHS Latin Hypercube Sampling

m meter(s)

m2 square meter(s)

m3 cubic meter(s)

μg microgram(s)

μm micrometer(s)

mg milligram(s)

mm millimeter(s)

mrem millirem

NAHB National Association of Home Builders

NBS National Bureau of Standards

NCDC National Climatic Data Center

NRC U.S. Nuclear Regulatory Commission

pCi picocurie(s)

PFT perfluorocarbon tracer

RF respirable fraction

RH relative humidity

s second(s)

SD standard deviation

yr year(s)

1-1

1 INTRODUCTION

The U.S. Nuclear Regulatory Commission (NRC)

has taken steps to ensure that residual

radioactive contamination remaining after

licensed nuclear facilities are decontaminated

and decommissioned meets established

requirements and radiological criteria (Subpart E

to Title 10, Code of Federal Regulations, Part 20

[10 CFR Part 20]). To support the implementation

of the requirements, the NRC developed the

Decommissioning Standard Review Plan (NRC,

2000) in which the technical basis is provided

regarding dose modeling for demonstrating

compliance with the radiological criteria. In that

approach, the NRC prescribed a graded approach

to dose modeling that ranges from a conservative

screening to a site-specific analysis, taking into

account site conditions and the relevant

information available.

The conservative approach is exemplified in

NUREG/CR-5512 (Kennedy and Strenge, 1992;

McFadden et al., 2001), where a screening

approach is used to assess potential radiation

doses to individuals. On the other hand, the sitespecific analysis is embodied in the RESRADBUILD 3.0 computer code designed to

accommodate a more in-depth site-specific

analysis for buildings with residual contamination

(NUREG/CR-6697 [Yu et al., 2001]). 

The RESRAD-BUILD computer code (Yu et al.,

1994) is designed to provide a site-specific

approach. This code has been developed by

Argonne National Laboratory and approved by the

U.S. Department of Energy (DOE) for use in

evaluating radioactively contaminated buildings

and is used in the United States and abroad. The

RESRAD-BUILD code complements NRC's

licensing efforts in developing methods for

demonstrating compliance with decontamination

and decommissioning (D&D) rules.

Representative parameter distributions were

developed for input to the RESRAD-BUILD

computer code for use in a building occupancy

scenario (NUREG/CR-6697 [Yu et al., 2000]).

These distributions were initially derived to test

the probabilistic analysis capabilities that were

being added to the RESRAD-BUILD code. A

further benefit of these distributions and the

additional related information presented with them

(Yu et al., 2000) was to give the user a starting

point for developing site-specific distributions for a

customized analysis. RESRAD-BUILD is

designed to provide a site-specific analysis, and

the representative distribution provided for a given

input parameter, such as the room area, is often

inappropriate for the specific site being analyzed.

The representative distribution was developed to

encompass a range of potential sites and may

not be appropriate for a specific site. It is

intended that the representative distribution be

replaced with values specific to the site being

analyzed.

Although the transition from a screening

approach to a site-specific approach has been

discussed conceptually in the NRC’s Standard

Review Plan (NRC, 2000), it is desirable to

understand the technical basis leading to such a

transition. To accomplish this purpose, this report

evaluates the technical bases of the screening

approach and the site-specific approach, from

which representative parameters of importance

are identified. In particular, it focuses on the

scenario representing the light industrial use of a

building following decontamination. In this

process, the analysis also evaluates reasonable

sets of input parameters to be used for a sitespecific analysis. The intended purpose is not to

recommend a set of “default” parameters, rather it

is to assist users in formulating a method of

selecting a reasonable set of parameters for

meeting the dose criteria.

Section 2 of this report evaluates the technical

basis for the building occupancy scenario. The

scenario itself and the approaches taken in both

the RESRAD-BUILD code and the DandD code

(designed to implement the screening approach

[McFadden et al., 2001]) to address the exposure

pathways considered are discussed. This

discussion provides insight to help determine

whether a site may qualify for release using the

more site-specific analysis provided by RESRADBUILD when the DandD screening model criteria

are not met. The relative importance of the

various input parameters to a potential exposure

pathway for each model is also discussed.

The representative RESRAD-BUILD parameter

distributions from NUREG/CR-6697 are

reexamined in Section 3 of this report. Included in
1-2

that section is an updated list of data and

representative distributions for those RESRADBUILD input parameters for which more

information was available. Where appropriate,

related DandD parameters are discussed to

highlight differences in the RESRAD-BUILD and

DandD models. For completeness, those

distributions not updated from NUREG/CR-6697

are provided in Appendix A. Appendix B

describes the parametric distribution types

supported in RESRAD-BUILD.

Section 4 details the methodology used to select

a reasonable set of input parameters for

RESRAD-BUILD to evaluate a building

occupancy scenario. These parameters were

placed in a template input file for analysis. The

results of the analysis are discussed in

Section 5. A report summary is given in

Section 6.
2-1

2 EVALUATION OF THE TECHNICAL BASIS OF THE BUILDING OCCUPANCY

SCENARIO USING THE RESRAD-BUILD and DandD CODES

The building occupancy scenario as described in

NUREG/CR-5512 (Kennedy and Strenge, 1992)

calculates potential exposure of a screening

group to both fixed and removable thin-layer

surface-contamination sources. The screening

group, as defined in NUREG-1549 (NRC, 1998),

consists of adult males who work in light

industry. They occupy and work in a commercial

facility in a normal manner without deliberately

disturbing sources of residual contamination. The

occupancy is assumed to begin immediately

after decommissioning and release of the

building, before significant radioactive decay of

residual radionuclides occurs. The length of

exposure is a full work year. Behavioral

parameters represent the characteristics of the

screening group, and the parameter values

chosen for this analysis represent the average

behavior within that group. The screening group is

used as a surrogate group for assessing potential

exposures to contaminated buildings at all sites.

The exposure pathways selected are (1) external

exposure to penetrating radiation from surface

sources, (2) inhalation of resuspended surface

contamination, and (3) inadvertent ingestion of

surface contamination. Both RESRAD-BUILD (Yu

et al., 1994) and DandD (McFadden et al., 2001)

can estimate the dose to a receptor in the

building for the building occupancy scenario.

Both use the dose conversion factors from

Federal Guidance Report (FGR) 11

(Eckerman et al., 1988) for inhalation and

ingestion intake and dose coefficients from FGR

12 (Eckerman and Ryman, 1993) for external

exposure. Both codes use the radionuclide data

from International Commission on Radiological

Protection (ICRP) Publication 38 (ICRP, 1983). 

The RESRAD-BUILD code is a pathway analysis

model designed to evaluate the potential

radiological dose to an individual who works or

lives in a building contaminated with radioactive

material. It considers the releases of

radionuclides into the indoor air by diffusion,

mechanical removal, or erosion. The transport of

radioactive material inside the building from one

room or compartment to another is calculated

with an indoor air quality model. The air quality

model evaluates the transport of radioactive dust

particulates, tritium, and radon progeny due to (1)

air exchange between rooms and with outdoor

air, (2) the deposition and resuspension of

particulates, and (3) radioactive decay and

ingrowth. A single run of the RESRAD-BUILD

code can model a building with up to 3 rooms or

compartments, 10 distinct source locations, 4

source geometries, 10 receptor locations, and 8

shielding materials. A shielding material can be

specified between each source-receptor pair for

external gamma dose calculations.

Seven exposure pathways are considered in

RESRAD-BUILD: (1) external exposure directly

from the source; (2) external exposure to

materials deposited on the floor; (3) external

exposure due to air submersion; (4) inhalation of

airborne radioactive particulates; (5) inhalation of

aerosol indoor radon progeny; (6) inadvertent

ingestion of radioactive material directly from the

sources; and (7) inadvertent ingestion of

materials deposited on the surfaces of the

building rooms or compartments. Figure 2.1

illustrates the different exposure pathways in the

RESRAD-BUILD code and shows the relationship

of the release mechanism to pathways. The user

can define the source as a point, line, area, or

volume. The volume source can consist of five

layers of different materials, with each layer being

porous, homogeneous, and isotropic. Currently,

67 radionuclides are included in the RESRADBUILD database. All 67 radionuclides have halflives of 6 months or greater and are referred to as

principal radionuclides. It is assumed that the

short-lived progeny with half-lives of 6 months or

less, referred to as the associated radionuclides,

are in secular equilibrium with their parent

principal radionuclide.

The DandD code is intended to be a screening

tool for assessing potential doses from

decommissioned sites on the basis of a

philosophy of moving from simple, prudently

conservative calculations toward more realistic

simulations, as necessary. These levels of

calculations are intended to produce generic

dose estimates that are unlikely to be exceeded

at real sites. 

Various scenarios can be analyzed with the

DandD code, including the building occupancy
2-2

External Exposure

Directly from the Source

Inhalation of Indoor

Radon Progeny/Tritium

Inhalation of Airborne

Radioactive Dust

External Exposure

due to Air Submersion

External Exposure due

to Deposited Material

Ingestion of Deposited

Radioactive Material

Ingestion of Removable

Radioactivity 

Indoor Air Concentration 

of Radon Progeny/Tritium

Release Rate

of Radon/Tritium

into Indoor Air

Radon/Tritium

Diffusion

Indoor Airborne Concentration

of Radionuclides

Release 

Rate of 

Radionuclides

into Air

Mechanical

Removal of

Material from

the Source

Surface

Concentration of 

radionuclides in 

deposited dust particles

Radioactive Material Inside Building

Resuspension

Deposition

Figure 2.1 Exposure Pathways in the RESRAD-BUILD Code
2-3

I

f f Q

T

s

t T

n R s

n

R

= < R

24

, ,

scenario. The exposure pathways selected in the

building occupancy scenario include external

exposure to penetrating radiation from surface

sources, inhalation of resuspended surface

contamination, and inadvertent ingestion of

surface contamination. The DandD code

database contains 249 primary radionuclides. All

dose values generated by the DandD scenario

analysis are calculated by multiplying the parent

nuclide activity by a dose factor. This dose factor

accounts for the contributions to the dose values

from exposure to radiation from the parent and

the progeny in the following manner. The

radiations included in the dose factors for a

parent are those associated with decay of the

parent, plus radiation from progeny that are

always in secular equilibrium (half-lives less than

9 hours and less than one-tenth the listed parent

half-life). Radiation from decay chain members

that meet these criteria are included with the

radiation from their parent radionuclides as

implicit progeny; the progeny that are not implicit

are defined as explicit.

The capabilities of the RESRAD-BUILD and

DandD codes for evaluating the building

occupancy scenario are presented in Table 2.1.

Table 2.2 lists the parameters used in the

RESRAD-BUILD and DandD codes for different

pathways. Very few parameters are required in

DandD; RESRAD-BUILD, however, requires many

more site-specific parameters. Some of the

parameters in the two codes are common or

related. Figure 2.2 shows the mechanism

involved in inhalation pathway dose calculations

in the DandD and RESRAD-BUILD codes.

Physical parameters that can be changed in the

DandD analysis are the resuspension factor, area

of contamination, and the removable fraction. For

an area of contamination of 10 m2 or greater, the

calculated pathway doses are independent of the

area of contamination. For an area of

contamination of less than 10 m2, the calculated

pathway doses change linearly with the area of

contamination. The resuspension factor and the

removable fraction affect the inhalation pathway

dose. The external pathway doses in DandD do

not depend on these two physical parameters.

For calculating the external pathway dose using

an area of contamination of 10 m2 or greater, the

area source is assumed to be infinite in lateral

extent, and no correction is applied for finite

source size or receptor distance relative to the

source.

The total receptor dose in the RESRAD-BUILD

code from all pathways except direct external

exposure depends on the radionuclide air

concentration in the room. The radionuclide air

concentration in turn depends on the source

injection rate. Air deposition of particulates also

depends on the air concentration in the room.

The physical parameters required in RESRADBUILD to calculate the source injection rate for a

point, line, or area source are (1) removable

fraction, (2) source lifetime, and (3) air release

fraction. For a volume source, the parameters

include (1) source erosion rate, (2) source area,

(3) density of source material, and (4) air release

fraction. 

Equation 2.1 below gives the source injection rate

for a point, line, or area source in RESRADBUILD (Equation D.2 from Yu et al., 1994). The

source injection rate is zero for times greater

than the time required to remove the entire

source (t > TR). To calculate the air concentration

of a principal radionuclide in a one-room air

quality model, some additional physical

parameters are required, including deposition

velocity, resuspension rate, room dimensions,

and air exchange rate. Equation 2.2 (derived from

Equation A.28 in Yu et al., 1994) gives the air

concentration of principal radionuclide n under

equilibrium conditions. Since the room

dimensions are fixed in a site-specific analysis,

the physical parameters required to calculate air

concentration for an area source are

(1) removable fraction, (2) source lifetime, (3) air

release fraction, (4) deposition velocity,

(5) resuspension rate, and (6) air exchange rate. 

(2.1)

where

I

n

s = injection rate of radionuclide n into the

indoor air of the room (pCi/h),

fR = removable fraction of the source

material,

 f = fraction of removed material that

becomes indoor dust,
2-4

Table 2.1. Evaluation of the Technical Basis for the Building

Occupancy Scenario Using the RESRAD-BUILD and DandD Codes

Component RESRAD-BUILDa,b DandD Remarks

Source

description

• Up to 10 sources

• Volume, area, line, or point

source of any dimension

• Floor is contaminated 

• Infinite area source for

the direct exposure

pathway

Handling of

radionuclidesc

• 67 principal radionuclides

• Half-lives 6 months or

longer

• In secular equilibrium with

progeny of half-lives less

than six months

• 249 primary

radionuclides

• Half-lives 10 minutes or

longer

• In secular equilibrium

with progeny if half-lives

are (1) less than 9 hours

and (2) less than onetenth the listed parent

half-life

DandD has many

more short-lived

radionuclides in its

database.

Building

description

• Up to a three-room

structure

• Air exchange.

• One large structure

• Air exchange is not

explicitly modeled

Receptor

location with

respect to

source

• Up to 10 receptor locations

at any distance from the

source 

• Only one receptor at a

fixed location (specified

by FGR 12 geometry)

with respect to the

source

RESRAD-BUILD has

an external exposure

model to handle any

source-receptor

configuration.

Pathways • Direct external exposure

from surface source

• Inhalation of airborne

radioactive particulates

• Inadvertent ingestion of

source material directly

G Inadvertent ingestion of

deposited materials

G Exposure to deposited

materials 

G Exposure due to air

submersion

G Inhalation of aerosol

indoor radon progeny

• External exposure due to

surface source

• Inhalation of

resuspended surface

contamination

• Inadvertent ingestion of

surface contamination

RESRAD-BUILD is a

more sophisticated

code and can model

site-specific situations.

Time

dependence

• 10 time steps in a single

run

• Calculates average timeintegrated dose over the

exposure duration

• Radionuclide

concentration changes

with radioactive ingrowth,

decay, and mechanical

erosion

• A single time step

• Calculates average

time-integrated dose

over one-year duration

• Radionuclide

concentration changes

with radioactive ingrowth

and decay

Air concentration • Dynamic air quality model

• Different source release

mechanisms: diffusion

and particulate injection

• Simple and static linear

relationship between air

concentration and

contamination 

DandD assumes

infinite source, and air

concentration is

derived from the

resuspension factor,
2-5

whereas in RESRADBUILD there is uniform

depletion of source

over the source

lifetime.

Ingestion

pathway

• Direct ingestion of

removable material

G Ingestion of deposited

material

• Direct ingestion of

removable material

RESRAD-BUILD also

considers ingestion

from deposited

materials.
2-6

C

I

V Q

n s

n

rn d

R d

rn R

=

+ −

+

( ) +

,

l l

l l

l l

0

Table 2.1. Evaluation of the Technical Basis for the Building

Occupancy Scenario Using the RESRAD-BUILD and DandD Codes (Continued)

Component RESRAD-BUILDa,b DandD Remarks

External

exposure

pathways

• Directly from the source

G Materials deposited on the

floor

G Air submersion

• Directly from the source RESRAD-BUILD

considers two more

external exposure

pathways.

Shielding

correction

G Eight shielding materials • No shielding correction

Transport of

contamination

from one room

to another

G With an indoor air quality

model

G Air exchange between the

rooms and with outside air

G The deposition and

resuspension of

particulates

G Radioactive decay and

ingrowth

• No transport considered

H-3 (tritium) G Special H-3 model for

volume source 

• No special H-3 model

Radon G Radon diffusion and

radon flux model

• Not included Not required for NRC

compliance.

a RESRAD-BUILD includes four more pathways than DandD (as indicated by open squares).

b An open square (G) represents pathways, models, and mechanisms not included in the DandD code.

c DandD includes many more short-lived radionuclides than RESRAD-BUILD.

TR = time to remove material from the

source (source lifetime) (d),

Qn

s = total radionuclide activity in the

source (pCi), and

24

=

ti

m

e

co

nv

er

si

on

fa

ct

or

(n

u

m

be

r

of hours per day) (h/d).

(2.2)

where

C

n = air concentration of radionuclide n in

the room(pCi/m3),

?r n = radioactive decay constant of

radionuclide n (1/h),

?d = deposition rate in the room (1/h), =

,

V A

V

d ×

Vd = deposition velocity (m/h),

?R = resuspension rate (1/h),

 V = volume of the room (m3), and
2-7

Q0 = flow of air from the room to the outside

(m3/h).

A parameter’s relative importance would also

depend on the contaminants of concern. For

example, for a long-lived radionuclide or in a

situation where the radioactive decay constant is

much smaller than the resuspension rate, the air

concentration in a one-room air quality model

under steady-state condition (RESRAD-BUILD

code assumption) does not depend on

resuspension rate and deposition rate. For other

radionuclides, when all other parameters are kept

fixed in RESRAD-BUILD and the resuspension

rate value is reduced, the resultant air

concentration also decreases, in turn reducing

the inhalation and immersion pathway doses.

However, the surface contamination from

deposition may increase, in turn increasing the

dose from indirect ingestion and external

exposure from deposited material. In this case,

the total dose may increase in RESRAD-BUILD

even though the inhalation 

Table 2.2. Technical Basis Used in RESRAD-BUILD and DandD

Codes for the Building Occupancy Scenario

Pathway Common Parameters RESRAD-BUILD DandD

Direct external

exposurea

• Exposure duration (d)

• Average surface source

activity (pCi/m2) 

• DCF for surface source

[(mrem/yr)/(pCi/m2)]

• Source area (m2

)

• Shielding parameters

(material thickness in cm

and density in g/cm3)

• Source location (m,m,m)

• Receptor location (m,m,m)

• Source area

(unlimited or size

in m2)

Inhalation • Occupancy factor

• DCF (mrem/pCi)

• Removable fraction

• Receptor inhalation rate

(m3/d)

• Average surface source

activity (pCi/m2)

• Source area (m2)

• Room dimension (area in m2

and height in m)

• Air exchange rate (1/h)

• Air release fraction

• Source lifetime (d)

• Resuspension rate (1/s)

• Deposition velocity (m/s)

• Resuspension

factor (1/m)

• Source area

(unlimited or size

in m2)

Direct

ingestionb

• Exposure duration (d)

• Average surface activity

(pCi/m2)

• DCF (mrem/pCi)

• Source area (m2

)

• Direct ingestion rate (1/h)

• Effective transfer

rate for ingestion

(m2/h)

• Source area

(unlimited or size

in m2)

Ingestion of

deposited

materials

NAc• All inhalation pathway

parameters except for

inhalation rate and DCF

• Indirect ingestion rate (m2

/h)

NA
2-8

• DCF (mrem/pCi)

External

exposure to

deposited

materials

NA • All inhalation pathway

parameters except for

inhalation rate and DCF

• Shielding parameters

• Receptor location

• DCF [(mrem/yr)/(pCi/g)]

NA

External

exposure due

to air

submersion

NA • All inhalation pathway

parameters except for

inhalation rate and DCF

• Air submersion DCF

[(mrem/yr)/(pCi/m3)]

NA

Inhalation of

indoor radon

progeny

NA • All inhalation pathway

parameters

• Radon release fraction

NA

a RESRAD-BUILD uses FGR 12 volumetric dose conversion factors (DCFs) and surface source DCFs

(Kamboj et al., 1998).

b To calculate the direct ingestion pathway dose, the effective transfer rate for ingestion is required for the

DandD code and the source area and direct ingestion rate are required for the RESRAD-BUILD code. The

effective transfer rate for ingestion in the DandD code is related to the source area and the direct ingestion

rate in the RESRAD BUILD code. This relationship is:

effective transfer rate for ingestion = source area × direct ingestion rate.

c NA = not applicable.
2-9

Loose Source 

Contamination

Inhalation Dose

Loose Source

Contamination

Release to air

(removable fraction,

air release fraction,

source lifetime)

Inhalation Dose

Release to air

(resuspension 

factor)

Loose Deposited

Contamination

Mixing and

transport

among

compartments

(air exchange rate,

room area and

height [volume])

Release to air

(resuspension

rate)

Deposit onto

surfaces

(deposition velocity)

DandD RESRAD-BUILD

Figure 2.2 Inhalation Pathway in DandD and RESRAD-BUILD Codes
2-10

C

V

d C

n d

rn R

n

=

+

( ) .

l l

pathway dose is reduced with the decrease in the

resuspension rate, because of a higher indirect

ingestion dose resulting from increased

deposition. The surface contamination from

deposition (C

n

d

), assuming a steady-state

condition, can be given by Equation 2.3 (Equation

B.3 in Yu et al., 1994).

(2.3)

The physical parameters required for the direct

external exposure pathway in RESRAD-BUILD 

depend on the source type, source receptor

locations, and the shielding characteristics

between the source-receptor pair. For the area

source, the other important parameter is the

source area; for the volume source, the other

important parameters are source material, area,

thickness, and source erosion rate. If the source

receptor locations are fixed and there is no

shielding between the source-receptor pair in a

particular scenario, the important parameters in

the external exposure pathway are the source

area, thickness, and material.
3-1

3 UPDATED RESRAD-BUILD INPUT PARAMETER INFORMATION

A strategy was previously developed to rank the

input parameters as to their importance (high,

medium, and low priority) and to identify

parameters for detailed distribution analysis (Yu

et al., 2000 [NUREG/CR-6697]). Table 3.1 lists

those parameters (i.e., high and medium priority;

referred to as 1st and 2nd priority) selected for

the assignment of probability density functions.

These parameters were reexamined in light of the

discussion in Section 2, their site specificity, and

the additional data collected since

NUREG/CR-6697 was prepared. However, data

for a number of the parameters are site-specific,

relatively easy to obtain, and will not be further

evaluated. These parameters include the indoor

fraction, room area, room height, and humidity.

New information is provided in this report for the

six parameters for which additional information is

available. Supporting data have been added to the

resuspension rate, air exchange rate, deposition

velocity, source erosion rate, removable fraction,

and source lifetime parameter descriptions. The

assigned input parameter distribution for

RESRAD-BUILD has changed slightly for the

resuspension rate and removable fraction. In

addition, the sections on the resuspension rate

and source lifetime have been expanded with a

more indepth discussion concerning their

relationship with the resuspension factor as used

in DandD.

The following sections address those parameters

for which additional data have been obtained. For

completeness, Appendix A contains the

information on the remaining parameters as

provided in NUREG/CR-6697 (Yu et al., 2000).

3.1 Resuspension Rate (Indoor)

Description: The resuspension rate (indoor)

represents the rate at which material deposited

on interior surfaces is resuspended into the

indoor air. Resuspension is the result of airflow or

a mechanical disturbance, such as walking

across a surface or sweeping. 

Unit: 1/s

Probabilistic Input:

Distribution: Loguniform

Defining Values for Distribution:

Minimum: 2.5 × 10-11 Maximum: 1.3 × 10-5

Discussion: Indoor resuspension of

contamination can lead to internal exposure via

inhalation and external exposure via submersion.

The resuspension rate is the fraction of deposited

particles resuspended per unit time. Factors that

can affect resuspension include the type of

disturbance (air flow vs. mechanical), the

intensity of the disturbance, the type of surface,

particle size distribution, and physical and

chemical characteristics of the particles.

Relatively little work has been conducted in

measuring or estimating indoor resuspension

rates. The most recent work by Thatcher and

Layton (1995) monitored an SF6 tracer in a

residential setting under varying conditions. Table

3.2 gives the results based on particle size.

These results demonstrate that the larger particle

sizes are more susceptible to resuspension.

Other studies investigating the characteristics

and dynamics of indoor dust behavior have shown

that the source of larger particle sizes in indoor

air is primarily from resuspension (Wallace et al.,

1997; Vette et al., 2001). Thus, it is important to

note, as has been done previously (Jones and

Pond, 1967), that a large fraction of resuspended

material is nonrespirable.

Nonrespirable material is that material that

cannot be inhaled directly into the lungs. Larger

particulates, greater than approximately 2.5 μm,

cannot reach the deep respiratory tract where

gas exchange occurs. This material becomes 

trapped in the nasal passages or upper

respiratory tract where it can dissolve and be

absorbed into the blood or cleared by mechanical

action (swallowing) into the 
3-2

Table 3.1. RESRAD-BUILD Parameters Selected (Priority 1 and 2) for

Assignment of Probability Density Functions

Parameter Prioritya Typeb

Assigned

Distribution

Type

Report

Sectionc

Removable fraction 1 P,B Uniform 3.5

Resuspension rate (1/s) 1 P,B Loguniform 3.1

Shielding density (g/cm3) 1 P Uniform A.1.1

Source density, volume source (g/cm3) 1 P Uniform A.2.1

Air exchange rate for building and room (1/h) 2 B Lognormal 3.2

Air release fractionc 2 B Triangular A.2.4

Deposition velocity (m/s) 2 P Loguniform 3.3

Direct ingestion rate (g/h for volume source and 1/h for

all

 other sources)

2 B None

recommende

d

A.3.2

Humidity (g/m3) 2 P,B Uniform A.1.6

Indoor fraction 2 B Empirical A.1.2

Indirect ingestion rate (m2/h) 2 B Loguniform A.3.3

Receptor inhalation rate (m3/d) 2 M,B Triangular A.3.1

Room area (m2) 2 P Triangular A.1.3

Room height (m) 2 P Triangular A.1.4

Shielding thickness (cm) 2 P,B Triangular A.1.5

Source erosion rate, volume source (cm/d) 2 P,B Triangular 3.4

Source porosity 2 P Uniform A.2.2

Source thickness, volume source (cm) 2 P Triangular A.2.6

Time for source removal or source lifetime (d) 2 P,B Triangular 3.6

Volumetric water content 2 P Uniform A.2.3

Water fraction available for evaporation 2 P Triangular A.2.7

Wet + dry zone thickness (cm) 2 P Uniform A.2.5

a Priority as determined in NUREG/CR-6697 (Yu et al., 2000). For RESRAD-BUILD, excluded parameters

include radionuclide concentration and source length or area.

b P = physical, B = behavioral, M = metabolic; when more than one type is listed, the first is primary and the

next is secondary.

c Section of this report providing the distribution assigned to the parameter.

gastrointestinal tract or exhaled. Current

radiological inhalation dose conversion factors

(e.g., those presented in Eckerman et al., 1988)

are based on the assumption that the particle

diameter size follows a lognormal distribution with

an activity median aerodynamic diameter of 1

:m. However, as discussed above, such a

distribution may not be the case for resuspended

material where the median may be skewed to

larger particle sizes. On the other hand, not

enough information is available to provide

definitive resuspension rate estimates according

to particle size. Thus, the current resuspension

rate model and assigned distribution are particle

size independent; the assigned distribution

incorporates data from all studies with and

without particle size 
3-3

determination. The latest available resuspension

rate information is discussed below.

Earlier studies of indoor resuspension of

radioactive contamination reported the extent of

resuspension in terms of a resuspension factor

(Rf), that is, the ratio of airborne contamination to

the amount deposited on surfaces. The following

derivation provides an approximate conversion

between the resuspension factor and the

resuspension rate. Assuming a conservation of

mass, the total change in the amount of airborne

particulate material in a room (the left-side of

Equation 3.1) is equal to an increase due to the

amount resuspended, a decrease due to

depositing material, and a
3-4

Table 3.2. Indoor Resuspension Rates

Resuspension Rate (1/s)a

Minimum Maximum

Resuspension

Factor (m-1)

Air Exchange Rate

(1/h)

Room Height

(m) Conditions Reference Comments

7.7 × 10-7 1.1 × 10-6 1.2 × 10-4 9.0 2.59 4 – 6 people

walking

Brunskill (1967) Change room, 1 –

3% removed by

smears, 50% by

water wash.

5.1 × 10-10

1.1 × 10-10

2.5 × 10-11

1.9 × 10-9

5.1 × 10-7

1.1 × 10-7

2.5 × 10-8

1.9 × 10-6

1.9 × 10-4

3.9 × 10-5

9.4 × 10-6

7.1 × 10-4

0.0 2.44 Vigorous work,

including sweeping

(ZnS)

Vigorous walking

(ZnS)

Collecting

contaminated

samples (ZnS)

Light sweeping with

fans on for

circulation (CuO)

Fish et al. (1967) Measurements of

ZnS and CuO

tracers.

3.3 × 10-8 2.2 × 10-7 4 × 10-6

to

2 × 10-5

10 3.00 Pu Ikezawa et al.

(1980)

Cleanup following

accidental failure

of a Pu glove box.

1.9 × 10-10

9.5 × 10-8

4.8 × 10-7

1.9 × 10-10

9.5 × 10-9

4.8 × 10-8

2.5 × 10-10

1.2 × 10-7

6.1 × 10-7

2.5 × 10-10

1.2 × 10-8

6.1 × 10-8

2 × 10-8

1 × 10-5

5 × 10-5

2 × 10-8

1 × 10-6

5 × 10-6

10.9 3.15 Plutonium oxide, no

movement

14 steps/min

36 steps/min

Plutonium nitrate,

no movement

14 steps/min

36 steps/min

Jones and Pond

(1967)

Contamination

applied in solution

and allowed to

dry.

4.2 × 10-8

3.3 × 10-8

3.7 × 10-6

1.1 × 10-5

4.8 × 10-8

3.9 × 10-8

4.3 × 10-6

1.3 × 10-5

2.5 × 10-6

2.0 × 10-6

2.2 × 10-4

6.8 × 10-4

20 3.00 Alpha, no work

performed

Beta, no work

performed

Alpha, floors

scrubbed with

cotton

Beta, floors

Kvostov and

Kostyakov (1969)

Investigation of a

“hot” laboratory.
3-5

scrubbed with

cotton
3-6

Table 3.2. Indoor Resuspension Rates (Continued)

Resuspension Rate (1/s)a

Minimum Maximum

Resuspension

Factor (m-1)

Air Exchange Rate

(1/h)

Room Height

(m) Conditions Reference Comments

2.4 × 10-9 2.4 × 10-6 9.0 × 10-4 0 1.22 Ba35SO4 Shapiro (1970) Membrane with

Ba35SO4 ignited

and combustion

products

deposited on floor;

maximum value of 

subsequent 

measurements

made while

banging on floor.

1.4 × 10-6

3.8 × 10-7

3.2 × 10-6

2.1 × 10-6

2.0 × 10-6

5.4 × 10-7

4.5 × 10-6

2.9 × 10-4

2.2 × 10-4

5.9 × 10-5

4.9 × 10-4

3.2 × 10-5

9.0 2.59 Personal air

samplers

Area air samplers

Personal air

samplers

Area air samplers

Tagg (1966) 100 steps/min,

contaminated

floor;

100 steps/min,

contaminated

clothing.

2.8 × 10-10

1.2 × 10-10

5.0 × 10-9

2.3 × 10-8

1.1 × 10-7

9.4 × 10-9

NAb

NAb

NAb

NAb

NAb

NAb

1.4 × 10-6

6.0 × 10-7

9.4 × 10-6

2.0 × 10-5

6.1 × 10-5

3.2 × 10-6

0.3 2.4 0.3 – 0.5 μm

particles

0.5 – 1 μm particles

1 – 5 μm particles

5 – 10 μm particles

10 – 25 μm

particles

> 25 μm particles

Thatcher and

Layton (1995)

Estimated for

residence with

four residents

performing

“normal” activities.

Assumed air

exchange rate of

0.3 h-1.

a Estimated from the resuspension factor using Equation 3.5 and minimum and maximum values for the deposition velocity from Section 3.3.

b Not applicable. Resuspension factors were estimated from resuspension rates for Thatcher and Layton (1995).
3-7

V

dC

dt C A C A C V

A

s r A dep A a = ⋅ ⋅ l − ⋅ ⋅ n − ⋅ ⋅ l

C

H

C

H

S

r

A

dep

a

l n

= + l











.

C

H

C

H

S C

r

A

dep

A a

l n

− − ⋅ l = 0.

R

C

C H

f

A

s

r

dep a

= =

+ ⋅

l

n l

l r = Rf(vdep + la H) ⋅

decrease due to ventilation (terms 1 through 3,

respectively, on the right-side of Equation 3.1). 

(3.1)

where 

CA

= the contaminant air concentration,

CS

= the contaminant surface

concentration,

lr = the resuspension rate, 

V = A @ H, the room volume, 

A = the contaminated surface area

(assumed to be the floor where

deposition occurs), 

H = room height,

vdep = the deposition velocity, and

?a = air exchange rate, the number of air

changes per unit time.

Dividing both sides of the equation by the room

volume and assuming equilibrium conditions

(dCA/dt = 0), we obtain:

(3.2)

Separation

of the

surface

and air

terms gives

(3.3)

The resuspension factor is the ratio of the air to

surface concentration, as shown in Equation 3.4,

from which the relation between the resuspension

rate and the resuspension factor is derived (see

Equation 3.5).

(3.4)

(3.5)

Table 3.2 gives some indoor resuspension rates

and the corresponding resuspension factors as

determined using Equation 3.5. Only those

references with the additional requisite data

(room height and air exchange rate) were used to

estimate the resuspension rate from the

resuspension factor. Because the deposition

velocity was not measured in each case, the

minimum and maximum values from the

deposition velocity distribution (Section 3.3) were

used to provide a resuspension rate range for

each resuspension factor. Data for the

resuspension rates was provided in the case of

Thatcher and Layton (1995), from which

corresponding resuspension factors were derived

using deposition velocities supplied in that

reference. Healy (1971) has previously studied

the correlation of the resuspension factor with the

resuspension rate. Sansone (1987) and Beyeler

et al. (1999) have reviewed the earlier work in the

context of resuspension factors rather than rates.

Table 3.3 summarizes previous work in the area

of resuspension factors not addressed in Table

3.2. 

A number of factors (physical activity / location,

contaminated particle / floor characteristics,

contamination source, and housekeeping

practices) must be considered in selecting an

appropriate distribution for the resuspension rate

from the data discussed above. Many of the

studies from which resuspension factors were

derived or presented do not include enough

information on room volume, contaminated

surface area, and/or the ventilation rate in order to

make a rough estimate of the resuspension rate

using the relationship in Equation 3.1. The

magnitude of the resuspension factor, however,

can be roughly correlated with the conditions

under which they were obtained and can be

compared with resuspension factors that have

corresponding resuspension rates, as shown in

Table 3.2.

Physical activity is an important factor in the

resuspension of particulate matter. A dramatic

example was presented by Wallace et al. (1997)

who monitored air particle concentrations during

a study of an occupied townhouse. Coarse

particle (5 – 10 μm) air concentrations were

shown to be orders of magnitude higher during
3-8

the periods of time that the occupants were not

sleeping or away. Even working in front of a

computer had a large impact on coarse particle

(> 2.5 μm) resuspension (Wallace et al., 1997).

Thus, because of the lower air concentrations

Table 3.3. Resuspension Factors from Previous Studies

Resuspension

Factor (m-1) Conditions Reference Comments

1.8 × 10-6

4.3 × 10-5

I-131

Active work in open space

Active work in confined, 

 unventilated space

Chamberlain and

Stanbury (1951)

I-131 labeled brick and plaster dust (bulk

of dust < 1 μm), as reported in Sansone

(1987).

2.5 – 19 × 10-5 UF4 powder Bailey and Rohr

(1953)

Normal operations at a uranium

processing plant.

1 × 10-9 – 

 4.2 × 10-6

7 × 10-8 – 

 4 × 10-5

Uranium, total surface activity

using ratemeter, larger if

removable activity values used.

Ra, total surface activity using

ratemeter, larger if removable

activity values used.

Eisenbud et al.

(1954)

Estimated from surface and airborne

activity at 5 uranium processing plants.

Estimated from surface and airborne

activity at 10 radium plants

4 × 10-5 “Dusty operations” Barnes (1959) As reported in NUREG-1640.

5 × 10-4

3 × 10-5

U compounds

0.5-h samples

8-h samples

Becher (1959) As reported in Sansone (1987). Wipes

used to measure surface activity

0.2 – 5.9 × 10-5

0.5 – 14 × 10-4

U

Ore sampling plant

Uranium reduction plant

Utnage (1959) As reported in Sansone (1987).

1.5 × 10-2

5 – 12 × 10-3

7.9 × 10-3

9.3 × 10-3

2 × 10-2

Be and compounds

Loading/unloading Be blocks

Cleaning Be blocks

Be cyclotron target preparation

Be compound synthesis

Warehouse inventory

Hyatt et al. (1959) Resuspension factor as estimated by

Sansone (1987). Surface contamination

measured by wipe; maximum values for

wipe and air concentration used.

0.4 – 26 × 10-5

0.8 – 14 × 10-4

U compounds

8-h air samples

10-min air samples

Schulz and Becher

(1963)

As estimated by Sansone (1987),

measurements from operating UF6

manufacturing plant, surface

contamination measured by wipes.

1.0 × 10-4

1.3 × 10-4

1.45 × 10-4

1.0 × 10-4

1.35 × 10-3

9.7 × 10-3

U

Undisturbed

Fans on

Fans on with movement

Pu

Undisturbed

Fans on

Fans on with movement

Glauberman et al.

(1967)

As reported in Sansone(1987),

operating uranium processing plant,

abandoned precious metals. 

Recovery plant (Pu contamination),

surface contamination measured with

smears.
3-9

4.2 × 10-4

1.0 × 10-2

Be

Two men sweeping vigorously

Sweeping after vacuuming

Mitchell and Eutsler

(1967)

As reported in Sansone(1987).

Unventilated storeroom with wood floor,

smears used for surface contamination.

#1.7 × 10-7

4.7 – 7.5 × 10-6

#0.7 × 10-7

1.0 – 1.7 × 10-5

UO2

In ethanol, undisturbed

In ethanol, 60 steps/min

Powder, undisturbed

Powder, 60 steps/min

Cortissone et al.

(1968)

As reported in Sansone (1987).
3-10

Table 3.3. Resuspension Factors from Previous Studies (Continued)

Resuspension

Factor (m-1) Conditions Reference Comments

1.2 – 5.3 × 10-3

2.0 – 4.2 × 10-3

Chrysotile

Contaminated lab coat

handling contaminated materials

Carter (1970) As reported in Sansone (1987), surface

contamination measured by vacuuming.

1.2 × 10-4

3.3 × 10-4

Sr applied in solution

Co applied in solution

Gorodinsky et al.

(1972)

As reported in Sansone (1987), after 1 h

in wind tunnel; steel, painted steel,

stainless steel, vinyl plastic, and organic

glass surfaces had essentially the same

results.

0.2 – 13 × 10-6

0.01 – 1.5 × 10-6

0.3 – 10 × 10-6

0.08 – 1.5 × 10-6

Be (aqueous suspension

applied)

Fan on

Fan off

Ammonium fluoroberyllate

(aqueous solution applied)

Fan on 

Fan off

Kovygin (1974) As reported in Sansone (1987).

From polyvinyl chloride surface, 1.8 m/s

air flow with fan on.

4 × 10-5 Pu under unspecified conditions Wrixon et al. (1979) As estimated in Sansone (1987), using

data from the reference.

5.7 × 10-4 Workplace for I-125

immunoassay studies

Dunn and

Dunscombe (1981)

Surface contamination measured using

wipes with 70% isopropyl alcohol.

5.5 × 10-8 to

 1.1 × 10-7

Radioactive particulates Ruhter and Zurliene

(1988)

Activity in Three Mile Island auxiliary

building during cleanup after accident.

4.25 × 10-7

7.79 × 10-6

8.97 × 10-7

U; surface contamination

measured using wipes, three

1-year averages

Spangler (1998) U storage area at operating uranium fuel

fabrication plant.

1.7 × 10-7

4.2 × 10-8

Primarily Co-60 and Cs-137

during decommissioning

shutdown mode

Nardi (1999) No forced air ventilation, measurements

at a “pump repair” facility.

measured, studies conducted with no physical

activity may be nonrepresentative and might

underestimate the value of the resuspension

factor in a light industrial environment. Other

studies have also indicated that a major source of

larger particles (> 1.0 μm) in indoor air is from

resuspension (Vette et al., 2001). 

The location of the surface-contaminated area(s)

will affect the estimated resuspension factor and

resuspension rate. Deposited material on the

floor in a high-traffic area is much more likely to

be resuspended than deposited material on

equipment, walls, or seldom traversed areas. The

same is also true for “fixed” residual

contamination in remediated areas. The larger the

constantly disturbed area, the larger the

resuspension factor. Thus, this consideration is

closely allied with physical activity as previously

discussed.

The likelihood of particle resuspension is related

to its adherence to the surface. Contamination

remaining after remediation is expected to be

relatively “fixed”, that is, hard to remove because

it is tightly bound (e.g., chemically bonded) or

deep within microscopic depressions of the

surface. In the latter case, it is actually the
3-11

inability of mechanical action to contact the

contamination when a rough surface is

encountered. In contrast, deposited material is

generally loosely bound and relatively easily

resuspended. Thus, the primary source of the

contamination (“fixed” or deposited) must also be

taken into consideration when determining the

resuspension factor or resuspension rate for use

in risk assessment.

Normal housekeeping operations, such as

dusting. sweeping, mopping, and vacuuming, in a

light industrial environment will minimize potential

risks to building occupants from resuspension of

contaminated materials during normal working

hours. Risks to the cleaning staff will be elevated,

but only for short periods of time. These

operations reduce the buildup of contaminated

material from deposition and further reduce any

residual “fixed” contamination. Thus, those

buildings with more frequent cleaning schedules

are expected to have lower resuspension

factors/rates than those sporadically cleaned,

because the more loosely bound material from

deposition is maintained at lower levels.

As discussed in Section 2, the RESRAD-BUILD

input parameters, such as the source lifetime,

removable fraction, and air release fraction, are

used to control the amount of contaminated

material that becomes available within the

building from residual contamination. In turn, the

resuspension rate in RESRAD-BUILD must

account for resuspension of the resultant

deposited contamination. In DandD, however, the

resuspension factor is the only input that

accounts for airborne contamination and therefore

must be more concerned with the source

(residual contamination), which is not as easily

removed as deposited material. Thus, the input

distribution in RESRAD-BUILD for the

resuspension rate covers a wider range of

equivalent values than that for input for the

resuspension factor in DandD. In the latter,

values should be limited to “fixed” contamination

in order to avoid violating the contamination mass

balance and overestimating inhalation exposure.

A loguniform distribution is suggested to

represent the resuspension rate in RESRADBUILD because of the limited data available and

the wide range of estimated values. The wide

range in the estimated values can be attributed

primarily to differences in particle size and indoor

human activity levels. To represent an

occupational setting, the lowest value involving

any type of activity in Table 3.2 was chosen, 2.5

× 10-11 s-1. Similarly, the largest value in

Table 3.2, 1.3 × 10-5 s-1, was chosen as the

maximum value for the distribution. Figure 3.1

shows the probability density function selected

for the indoor resuspension rate.

3.2 Air Exchange Rate for Building 

 and Room

Description: The air exchange (or ventilation)

rate for a building or a room is the total volume of

air in the building or room replaced by outside air

per unit of time.

Unit: 1/h

Probabilistic Input:

Distribution: Truncated lognormal-n

Defining Values for Distribution:

Underlying mean value: 0.4187

Standard deviation: 0.88

Lower quantile value: 0.001

Upper quantile value: 0.999

Discussion: Air exchange involves three

processes: (1) infiltration — air leakage through

random cracks, interstices, and other

unintentional openings in the building; (2) natural

ventilation — airflow through open windows,

doors, and other designed openings in the

building; and (3) forced, or mechanical, ventilation

— controlled air movement driven by fans.

The average infiltration rate for a building can be

expressed as the number of air changes per hour

or air exchange rate (h-1). A single building can

have a range of air exchange rates, depending on

environmental conditions at a particular time

(e.g., seasonal/diurnal ambient wind speed and

temperature); other factors include building type,

construction, and ventilation system. A number of

studies have attempted to characterize building

air exchange rates under different environmental

conditions for buildings with different leakage

characteristics.
3-12

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.E+08

1.E+09

1.E+10

1.E-12 1.E-11 1.E-10 1.E-09 1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03

Resuspension Rate (s-1)

f(x)

Loguniform Distribution

Minimum = 2.5  ́ 10-11 s-1 

Maximum = 1.3  ́ 10-5 s-1

A comprehensive study of residential ventilation

rates was published by Pandian et al. (1993). To

evaluate the distribution of ventilation rates of a

large population of homes in the United States,

the researchers analyzed a Brookhaven National

Laboratory (BNL)

database consisting of more than 4,000

residential perfluorocarbon tracer (PFT)

measurements from approximately 100 individual

studies. Table 3.4 presents summary statistics

from that study on air exchange rates in the

United States and regionally. Pandian et al.

(1993) also analyzed the data by season and by

the number of levels of the homes. They

concluded that (1) exchange rates are higher in

the Southwest than in the Northeast and

Northwest; (2) summer ventilation rates are much

higher than winter and fall rates; and (3) multilevel

residences have higher air exchange rates than

single-level residences. The authors present both

arithmetic and geometric means and standard

deviations, as well as percentile distributions.

Murray and Burmaster (1995) also used the data

compiled by BNL and the PFT technique to

estimate univariate parametric probability

distributions for air exchange rates for residential

structures in the United States. The analysis was

characterized by four key points: (1) the use of

Figure 3.1. Indoor Resuspension Rate Probability Density Function data for 2,844 households; (2) a four-region
3-13

breakdown based on heating degree days;

(3) estimation of lognormal distributions as well

as provision of empirical (frequency) distributions;

and (4) provision of these distributions for all of

the data. The authors summarized distributions

for subsets of the data defined by climate region

and season. The coldest region (Region 1) was

defined as having 7,000 or more heating degree

days, the colder region (Region 2) as having

5,500–6,999 heating degree days, the warmer

region (Region 3) as having 2,500–5,499 heating

degree days, and the warmest region (Region 4)

as having fewer than 2,500 heating degree days.

The months of December, January, and February

were defined
3-14

Table 3.4. Residential Air Exchange Rates (h-1) Distribution Characteristics

Distribution

Type Min. Max. Mean SD Comments References

Lognormal 0.3 2.2 0.9 1.8 Charleston, S.C. (n = 20 houses) Doyle et al. (1984)

0.2 2.3 0.6 1.8 Colorado Springs, Colo. (n = 16 houses)

0.3 2.2 0.5 2.1 Fargo, N.D. (n = 11 houses)

0.7 1.4 1.0 1.3 Portland, Maine (n = 11 houses)

0.2 2.3 0.8 1.8 All cities (n = 58 houses)

Calculated infiltration rates based on post-weatherization

measurements of “effective leakage area”

Normal 0.36 0.71 0.62 0.25 Pre-retrofit in one house: Berk et al. (1981)

0.18 0.56 0.33 0.14 n = 17 measurements with fan on

n = 11 measurements with fan off

0.22 0.69 0.49 0.11 Post-retrofit in one house: 

0.10 0.33 0.20 0.08 n = 16 measurements with fan on

n = 11 measurements with fan off

Normal 0.08 0.27 0.17 0.06 n = 12 energy-efficient houses Lipschutz et al. (1981)

Lognormal 0.1 3.1 0.5 median n = 312 houses in North America Grimsrud et al. (1983), as

cited in Godish (1989)

0.1 3.6 0.9 median Subsample of low-income housing

Lognormal 0.17 1.33 0.33 median n = 8 mobile home measurements Godish and Rouch (1988)

0.18 1.45 0.36 median n = 10 UFF-insulated home measurements

Normal 0.22 0.50 0.35 0.08 n = 9 houses in upstate New York Offermann et al. (1985)

0.47 0.78 0.63 0.10 With mechanical ventilation off

With mechanical ventilation on

Normal 0.40 0.98 0.27 n = 10 houses in Washington State Lamb et al. (1985)

0.23 1.00 0.30 Pre-weatherization retrofit

Post-weatherization retrofit
3-15

Table 3.4. Residential Air Exchange Rates (h-1) Distribution Characteristics (Continued)

Distribution

Type Min Max Mean SD Comments References

Lognormal 0.89 3.44 All regions (n = 1,836) geometric mean, SD Pandian et al. (1993)

0.34 1.88 Northwest (n = 423)

0.40 2.07 Northeast (n = 423)

1.86 3.02 Southwest (n = 990)

1.99 3.28 All regions (n = 1,836) arithmetic mean, SD 

0.42 0.33 Northwest (n = 423)

0.60 2.23 Northeast (n = 423)

3.25 3.79 Southwest (n = 990)

0.76 0.88 All regions All seasons (n = 2844) Murray and Burmaster

0.55 0.47 All regions Season 1 (n = 1139) (1995)

0.65 0.57 All regions Season 2 (n = 1051) arithmetic mean, SD

1.50 1.53 All regions Season 3 (n = 529)

0.41 0.58 All regions Season 4 (n = 125)

0.40 0.30 Region 1 All seasons (n = 467)

0.55 0.48 Region 2 All seasons (n = 496)

0.55 0.42 Region 3 All seasons (n = 332)

0.98 1.09 Region 4 All seasons (n = 1,549)

0.66 0.87 West Region (arithmetic mean and SD) Koontz and Rector (1995)

0.57 0.63 North Central Region

0.71 0.60 Northeast Region

0.61 0.51 South Region

0.63 0.65 All

0.47 2.11 West Region (geometric mean and SD)

0.39 2.36 North Central Region

0.54 2.14 Northeast Region

0.46 2.28 South Region

0.46 2.25 All
3-16

as Season 1; March, April, and May as

Season 2; June, July, and August as Season 3;

and September, October, and November as

Season 4. The authors concluded that the air

exchange rate was well fit by lognormal

distributions for small sample sizes except in a

few cases. The mean and standard deviations are

listed in Table 3.4. The authors recommended

that the empirical or lognormal distribution may

be used in indoor air models or as input variables

for probabilistic health risk assessments. 

In a study sponsored by the U.S. Environmental

Protection Agency (EPA) (Koontz and Rector

1995), a similar data set as analyzed by Murray

and Burmaster (1995), was used. However, an

effort was made to compensate for the

nonrandom nature of the data by weighting

results to account for each state’s share of

occupied housing units. As shown in Table 3.4,

the results of Murray and Burmaster (1995) are

similar to those for Koontz and Rector (1995).

Air exchange rates from other representative

residential studies are also summarized in

Table 3.4. The type of distribution can vary,

depending on the type of study. For example, a

survey of various housing types by Grimsrud et

al. (1983) demonstrated that houses generally

have air exchange rates that fall in a lognormal

distribution between 0.1 and approximately 

3 h-1, with most clustered in the 0.25–0.75 range.

However, some older (“leaky”) houses, including

low-income housing, had infiltration rates

exceeding 3 h-1. In contrast, Lipschutz et al.

(1981) obtained measurements of air infiltration

into 12 energy-efficient houses in Oregon by

using a tracer gas decay analysis. A narrow

range of values was found (0.08–0.27 h-1), which

reflects the extremely “tight” building construction

and ventilation systems installed in the houses.

Doyle et al. (1984) measured air exchange rates

in 58 weatherized houses during a 4- to 5-month

period during both winter and summer sampling

periods. The houses were located in Fargo, North

Dakota; Colorado Springs, Colorado; Portland,

Maine; and Charleston, North Carolina. The

investigators determined the geometric means

and geometric standard deviations for air

exchange rates for each city and for the entire

sample. Because of the relatively small number

of measurements in each city, conclusions about

the geographic distribution of air exchange rates

are limited. However, combining the data for the

cities provides an overall lognormal distribution

with a geometric mean of 0.8h-1 and a geometric

standard deviation of 1.8 (with rates ranging from

0.2 to 2.3 h-1), which appears to encompass

most air exchange rates determined in other

studies.

Studies on the air exchange rates of large

commercial buildings have been much more

limited. Table 3.5 lists results from some studies

on commercial buildings. These values are

relatively close to those for residential

construction. Although the primary outside air

source for large buildings is the mechanical

ventilation system, infiltration is the primary

outside air source for residential homes

(American Society of Heating, Refrigeration, and

Air-Conditioning Engineers [ASHRAE], 1997). In

either case, a continuous supply of outside air is

required to dilute and eventually remove indoor

contaminants. Thus, the air exchange

requirements are expected to be similar for both

residential and commercial construction.

However, differences in local airflow and

temperature, as well as air exchange, may be

required to maintain workers’ comfort according

to their activity level.

Turk et al. (1987) examined the outdoor

exchange rates of 38 buildings in the Pacific

Northwest. The buildings included schools,

libraries, and office buildings in mild and harsh

climates measured during different seasons of

the year. The results are shown in Table 3.5. The

arithmetic mean and standard deviation are

1.52 h-1 and 0.873, respectively. Although this set

of data is limited, the mean falls between the

arithmetic means determined by Pandian et al.

(1993) and Murray and Burmaster (1995), that is,

1.99 and 0.76 h-1, respectively, for residential air

exchange rates. The air exchange data from

Persily and Grot (1985) and Silberstein and Grot

(1985), as shown in Table 3.5, fall within the

range observed by Turk et al. (1987). The studies

by Weschler et al. (1994), Dietz and Goodrich

(1995) and Fisk et al. (2000) also fall within the

same range. The study of a laboratory/office

complex by Weschler et al. (1989) has two

values outside this range, 4.0 and 8.2 h-1.

However, maximum values of 11.77 and 45.6 h-1

were used by Murray and Burmaster (1995) and

Pandian et al. (1993), respectively.
3-17

Table 3.5 Outside Air Exchange Rates for Commercial Buildings

Building Air

Exchange Rate (h-1) Building Description Reference

0.33 – 1.04 Large office buildings Persily and Grot (1985)

0.9 The National Archive Building Silberstein and Grot (1985)

0.0 – 0.5

0.5 – 1.0

1.0 – 1.5

1.5 – 2.0

2.0 – 2.5

2.5 – 3.0

3.0 – 3.5

3.5 – 4.0

4.0 – 4.5

38 commercial buildings studied in the Pacific

Northwest during all seasons of the year. Two

buildings were sampled twice at different times of

the year.

Number of buildings:

 3

 10

 9

 8

 6

 2

 0

 1

 1

Turk et al. (1987)

0.6, 4.0, and 8.2 Three buildings in an office/laboratory complex Weschler et al. (1989)

0.3 – 1.9 1

st floor of Burbank, California, office building over a

14-month period

Weschler et al. (1994)

2.5 Classroom building on a college campus Dietz and Goodrich (1995)

0.45 – 0.53 and

 0.68 – 0.74

Two different floors, each with its own air handling

unit, in the same office building. Range of air

exchanges observed over a 7-week period.

Fisk et al. (2000)

While the data on commercial building air

exchange rates are limited, the distribution of

rates is expected, in part because of human

comfort considerations, to be similar to

residential structures when averaged over the

United States for all four seasons of the year.

Thus, a generic lognormal distribution has been

assigned to the building exchange rate to

represent an average over all conditions. The

mean and standard deviation of the distribution

are those obtained by Turk et al. (1987), 1.52 h-1

and 0.88, respectively. As discussed above, the

mean falls within the average mean found by

different residential studies and is consistent 
3-18

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Air Exchange Rate (h-1)

f(x)

Lognormal Distribution

Underlying m = 0.4187

Underlying s = 0.88

Figure 3.2. Building and Room Air Exchange Rate

Probability Density Function

l

d

vdAd

V

= ,

with other commercial building studies. The

standard deviation is the same as observed by

Murray and Burmaster (1995). Because of the

limited data set and variations across different

industries, climates, and seasons, this

distribution is only an approximation to potential

building air exchange rates for light industry.

Figure 3.2 displays the probability density

function for the building air exchange rate. The

same lognormal distribution is assigned to room

exchange rates because the building air

exchange rate is an average of the rooms within.

3.3 Deposition Velocity (Indoor)

Description: This parameter represents the

indoor deposition velocity of contaminant

particles in the building air.

Unit: Meters per second (m/s)

Probabilistic Input:

Distribution: Loguniform

Defining Values for Distribution:

Minimum: 2.7 × 10-6 Maximum: 2.7 × 10-3

Discussion: The deposition velocity

characterizes the rate at which particles in the

indoor air deposit on a surface. The deposition

rate, ?d, of particles in indoor air due to deposition

is often expressed as

(3.6)
3-19

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02

Particle Diameter (mm)

Deposition Velocity (m/s) 

Figure 3.3. Idealized Representation of Indoor Particle Deposition Velocity

where

vd

= the deposition velocity,

Ad

= the surface area available for

deposition, and

V = the volume of air. 

For indoor deposition, the deposition velocity

depends on particle and room properties.

Important particle properties include diameter,

density, and shape; room properties include air

viscosity and density, turbulence, thermal

gradients, and surface geometry. 

Nazaroff and Cass (1989) have developed a

relationship for the indoor deposition velocity of

particulates as a function of particle size. Such

theoretical calculations are not likely to produce

satisfactory results because of lack of knowledge

about near-surface flow conditions (Nazaroff et al.

1993), but they can provide 

insight into the general trend of deposition

velocity as a function of particle size.

Figure 3.3 presents an idealized representation of

deposition velocity on a floor as a function of

particle size on the basis of the methodology in

Nazaroff and Cass (1989).

Because deposition velocities depend on particle

size, it is expected that the probability density

function distribution of deposition velocities is

dependent on the particle size distribution. The

particle size distribution in the atmosphere

typically exhibits three modes (Seinfeld and

Pandis, 1998). Fine particles (particles less than

2.5 :m in diameter) can be divided into two

modes — nuclei and accumulation. The nuclei

mode (particles approximately 0.005 to 0.1 :m in

diameter) contains the largest number of particles

in the atmosphere but represents only a few

percent of the total mass of airborne particles

(Seinfeld and Pandis, 1998). Nuclei mode

particles are formed from the condensation of

atmospheric gases, such as combustion

products. Depletion of nuclei mode particles

occurs primarily through coagulation with larger

particles. The accumulation mode (particles

approximately 

0.1 to 2.5 :m in diameter) accounts for a large

portion of the aerosol mass. Accumulation mode

particles are formed through coagulation of

particles in the nuclei mode and through

condensation of gases onto smaller particles.

Because removal mechanisms are not as

efficient for this size range, particles tend to

accumulate (thus the term “accumulation mode”).

Coarse particles (diameters greater than 2.5 :m)

constitute the third mode. Coarse mode particles

are formed primarily from mechanical processes.

Other sources of coarse particles include

windblown dust and plant particles. 

Each of the three particle size modes can be well

characterized by lognormal distributions (John,

1993). Using the means and standard deviations

from Whitby and Sverdrup (1980), Figure 3.4

demonstrates the trimodal nature of the particle

size distributions commonly found. Similar

distributions are expected for indoor air

concentrations, with the exception of some

indoor source contributions, because the building

shell has been shown to be an insignificant

barrier to particle sizes under 10 :m (Yu et al.,

2000).
3-20
3-21

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02

Particle Diameter (mm)

dN/d ln n 

Average background

Urban average

Background+local sources

A broad probability density function distribution is

expected for the deposition velocity when

comparing the trend in deposition velocity with

the distribution of particles by size (Figures 3.3

and 3.4, respectively) and taking into

consideration the variability of each. Experimental

estimates provide support for such an

assumption, as shown in Tables 3.6 through 3.8.

In addition, Vette et al. (2001) and Mosley et al.

(2001) both observed a comparable U-shaped

curve, as suggested by Figure 3.3, when plotting

particle size against indoor deposition rate

(decay rate, Eq. 3.6), which is directly

proportional to the deposition velocity. However,

numerical information was not presented in either

study for further evaluation here. Deposition rate

data over a broad particle size range were

presented by Wallace et al. (1997) and Abt et al.

(2000), as converted to deposition velocity in

Table 3.6, provide further supporting evidence. A

similar trend for deposition velocity as a function

of particle size, as suggested by Figure 3.3, is

also observed for deposition of particles outdoors

(Sehmel, 1980).

In conjunction with particle size and mass, a

small difference in the local air handling system

(such as changes due to climate or season) can

easily cause a shift in deposition velocity

because deposition is dependent on local airflow

patterns (Nazaroff and Cass, 1989). Because the

deposition velocity input in RESRAD-BUILD is

used for all particle sizes and species under a

potential range of airflow conditions, a loguniform

distribution is assigned, with minimum and

maximum values of 2.7 × 10-6 m/s and

2.7 × 10-3 m/s, respectively, as found in

Tables 3.6 through 3.8. This distribution is shown

in Figure 3.5.

3.4 Source Erosion Rate, Volume Source

Description: The source erosion rate parameter

represents the amount of contaminated material

(expressed as the thickness of the layer

[distance perpendicular to the contaminated

surface)] removed per unit of time.

Figure 3.4 Trimodal Nature of Aerosol Particle Size Distribution
3-22

Table 3.6. Estimated Indoor Deposition Velocities by Particle Size

Particle Size

(:m)

Deposition

Velocity (m/s) Comments Reference

0.71 1.7 × 10-5 7Be with natural air exchange Lang 

1.4 1.3 × 10-5 (1995)

2.8 6.7 × 10-5

0.71 1.33 × 10-4 7Be with forced air exchange

1.4 2.66 × 10-4

2.8 3.88 × 10-4

1 – 2 1.7 × 10-4 Data Set 1 (different sample dates using Thatcher 

2 – 3 3.7 × 10-4 SF6 tracer) and

3 – 4 5.1 × 10-4 Layton 

4 – 6 1.1 × 10-3 (1995)

1 – 2 1.9 × 10-4 Data Set 2

2 – 3 5.0 × 10-4

3 – 4 5.6 × 10-4

4 – 6 1.2 × 10-3

1 – 5 3.1 × 10-4 Data Set 3

5 – 10 9.1 × 10-4

10 – 25 1.6 × 10-3

>25 2.7 × 10-3

0.07 1.72 × 10-5 Estimates based on data in Offermann et al. Nazaroff

0.10 2.7 × 10-6 (1985) from cigarette combustion and Cass

0.12 3.8 × 10-6 (1989)

0.17 3.8 × 10-6

0.22 4.7 × 10-6

0.26 8.9 × 10-6

0.35 8.2 × 10-6

0.44 8.7 × 10-6

0.56 9.8 × 10-6

0.72 1.51 × 10-5

0.91 1.3 × 10-4

<2.5 3 × 10-5 and

3 × 10-5

Sulfate ion particulates at two locations Sinclair et al.

(1985)

2.5 – 15 1 × 10-2 and

2 × 10-3

Calcium ion particulates at two locations

0.3 1.4 × 10-4 Estimated from decay rates using Eq. 3.6,

assuming a residence with an 8-ft (2.438-m)

ceiling height and all deposition to the 

floor. Area/volume then equals 1/2.438 1/m.

Wallace et 

0.5 2.5 × 10 al. (1997) -4

1 4.0 × 10-4

2.5 5.2 × 10-4

5 1.0 × 10-3

>10 1.9 × 10-3

0.02 – 0.1 7.3 × 10-4 Particles generated during cooking activities.

Estimated from decay rates using Eq. 3.6, 

assuming a residence with an 8-ft (2.438-m) 

ceiling height and all deposition to the 

floor. Area/volume then equals 1/2.438 1/m.

Abt et al. 

0.1 – 0.2 7.5 × 10 (2000) -4

0.2 – 0.3 5.4 × 10-4

0.3 – 0.4 5.1 × 10-4

0.4 – 0.5 4.74 × 10-4

0.7 – 1 7.3 × 10-4

1 – 2 6.6 × 10-4
3-23

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E-06 1.E-05 1.E-04 1.E-03 1.E-02

Indoor Deposition Velocity (m/s)

f(x)

Loguniform Distribution

Minimum = 2.7  ́ 10-6 m/s 

Maximum = 2.7  ́ 10-3 m/s

Figure 3.5 Indoor Deposition Velocity Probability Distribution

2 – 3 8.0 × 10-4

3 – 4 1.0 × 10-3

4 – 5 1.2 × 10-3

5 – 6 1.3 × 10-3

6 – 10 2.1 × 10-3

Table 3.7. Estimated Deposition Velocities by

Particle Size in Residences with and without

Furniture

Average Deposition Velocity (m/s)

Particle

Size (:m) Without Furniture With Furniture

0.5 6.1 × 10-5 8.2 × 10-5

2.5 1.33 × 10-4 1.73 × 10-4

3.0 1.37 × 10-4 2.25 × 10-4

4.5 2.88 × 10-4 2.88 × 10-4

5.5 3.04 × 10-4 3.24 × 10-4

Source: Fogh et al. (1997).

Table 3.8. Estimated Indoor

Deposition Velocities for Various

Radionuclides

Isotope

Mean Deposition

Velocity (m/s)

Cs-137 6.4 × 10-5

Cs-134 6.2 × 10-5

I-131 (particulate) 1.1 × 10-4

Be-7 7.1 × 10-5

Ru-103 2.0 × 10-4

Ru-106 1.7 × 10-4

Ce-141 3.1 × 10-4

Ce-144 3.9 × 10-4

Zr-95 5.8 × 10-4

Nb-95 1.9 × 10-4

Source: Roed and Cannell (1987).
3-24

Unit: Centimeters per day (cm/d)

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution:

Minimum: 0.0

Maximum: 5.6 x 10-7

Most likely: 0.0

Discussion: The source erosion rate is highly

dependent on the location of the contamination.

In the building occupancy scenario,

contamination on walls could remain indefinitely if

located in little-used areas not subject to periodic

washing or cleaning. Furthermore, such residual

wall contamination could have been covered with

paint or another type of sealant during prior

remediation or general maintenance activities. In

addition, little or no wear also can be expected

for some floor areas for the same reasons. At the

other extreme are contaminated floor areas

subject to heavy foot or vehicle traffic, such as in

warehousing operations. However, such areas are

usually covered (carpet or tile), sealed, or waxed

on a periodic basis, thus reducing the potential

for erosion. 

A triangular distribution was selected to represent

the source erosion rate. A value of 0 was chosen

for both the minimum and most likely values

because contamination on both walls and floors

in little-used areas can be expected to remain in

place indefinitely. Even high-use areas may not

experience erosion if they remain protected by

paint or sealant. Under normal occupancy

conditions (not remedial activities), a maximum

value is expected as a result of traffic over floor

areas. Contaminated wood, concrete, and

(possibly) ceramic tile are expected to be the

primary flooring materials affected. Contaminated

carpet would be expected to have been removed

by remedial activities. However, aside from

studies on abrasion, little information is available

in the general literature on normal wear of

concrete or wood surfaces over extended periods

of time. 

A rough approximation for the maximum value

can be obtained by considering that any eroded

materials would become airborne for at least

short periods of time. A conservative assumption

was made that all airborne indoor particulate

matter is a result of erosion of the floor surface.

Typically, outdoor air is a significant source of

indoor air particulate concentrations (see

Yu et al., 2000); however, this contribution was

not considered. The erosion rate of a concrete

floor was estimated to maintain an average

particulate air concentration of 100 :g/m3

(Section 3.4) with a room air exchange rate of

1.52/h (Section 3.2). A floor area of 36 m2

(Section A.1.3), a room height of 3.7 m

(Section A.1.4, used to estimate the room

volume), and a concrete density of 2.4 g/cm3

(Section A.2.1) were used. The estimated erosion

rate was 5.6 × 10-7 cm/d. Figure 3.6 shows the

probability density function used for the source

erosion rate.

In the case of renovation or remedial actions, the

source erosion rate can be quite high. For

example, thin-volume sources in wood or

concrete could be removed in seconds with

power sanders or sandblasting techniques. Other

examples include the complete removal of wood,

carpet, or drywall sections within seconds to

minutes. For such a scenario, the user can input

values appropriate to the contaminated source

and the removal technique under consideration.

In the case of contaminated metal sources, the

database generated by the National Bureau of

Standards (NBS; currently the National Institute

for Standards and Technology) can provide some

information on carbon and stainless steels

(Sullivan, 1993). In a study that the NBS

conducted over a period of 17 years in 47 different

soils, carbon steel's uniform erosion rates ranged

from 2 × 10-6 to 5 × 10-5 cm/d with a mean value

of 1 × 10-5 cm/d (Romanoff, 1957). The other

study was conducted over a period of 14 years in

15 soils for 304 and 316 stainless steels. The

erosion rates for 304 stainless steel ranged from

4.7 × 10-8 to 3.0 × 10-10 cm/d, with a mean value

of 

1.4 × 10-8 cm/d; for 316 stainless steel, the range

was from 1.6 × 10-8 to 7.7 × 10-11 cm/d, with a

mean value of 3.6 × 10-9 cm/d (Gerhold 

et al., 1981). It was also observed that the

corrosion rates typically decreased over time.

Because these corrosion rates were obtained

while the steel was in contact with soil, lower

rates might be expected in other less corrosive

environments. Thus, these rates (used as erosion
3-25

0.0E+00

5.0E+05

1.0E+06

1.5E+06

2.0E+06

2.5E+06

3.0E+06

3.5E+06

4.0E+06

0.0E+00 1.0E-07 2.0E-07 3.0E-07 4.0E-07 5.0E-07 6.0E-07

Source Erosion Rate, Volume Source (cm/d)

f(x)

Triangular Distribution

Minimum = 0 cm/d

Maximum = 5.6 × 10-7 cm/d

Most Likely = 0 cm/d

rates) are near the most likely value of 0 for the

source erosion rate shown in Figure 3.6.

3.5 Removable Fraction

Description: The removable fraction is the

fraction of a line or area source that can be

removed. 

Unit: Unitless

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution:

Minimum value: 0.0

Maximum value: 1.0

Most likely: 0.1

Discussion: The removable fraction can account

for various events that reduce the amount of

source activity over time. In RESRAD-BUILD

calculations, this fraction of the source will be

linearly removed between time 0 and the “time of

source removal.” Source activity may be reduced

over a period of time as a result of such events as

surface washing (chemical and mechanical

action) or foot or equipment traffic if the source is

on the floor (mechanical action). Because source

activity could remain on a wall indefinitely or be

removed entirely because of heavy traffic across

floor contamination, the default distribution for the

removable fraction ranges from 0 to 1 for use in a

triangular distribution. Figure 3.7 shows the

distribution’s probability density function.

For most radionuclides, the DOE Radiological

Control Manual (DOE, 1994) allows a maximum

removable concentration that is 20% of the

maximum allowable total surface contamination

for most radionuclides except for some

Figure 3.6 Source Erosion Rate Probability Density Function
3-26

0

0.5

1

1.5

2

2.5

0 0.2 0.4 0.6 0.8 1

Removable Fraction

f(x)

Triangular Distribution

Minimum = 0

Maximum = 1

Most Likely = 0.1

transuranics and tritium (Table 2-2 in DOE,

1994). The maximum allowed removable

transuranic or tritium contamination is 4% or

100%, respectively, of the maximum allowable

surface contamination. However, conditions may

exist under these restrictions for unrestricted use

where for all radionuclides, the removable surface

contamination constitutes 20% of the surface

contamination. For the NRC, removable

concentrations of 10% were used to estimate

radionuclide screening values (NRC, 2000), and

like the DOE regulations, the removable fraction

can be higher if overall surface concentrations are

lower. Also, 

NRC (2000) sets the default removable fraction

value to 0.1 for the DandD building occupancy

scenario. Thus, a triangular distribution, as

shown in

Figure 3.7, is

suggested for the

removable fraction, with a most likely value of 0.1

and minimum and maximum values of 0 and 1,

respectively, as discussed above.

For specific

Figure 3.7 Removable Fraction Probability Distribution situations, a
3-27

number of factors must be considered, including

location of the contamination (e.g., wall or floor

and proximity to human activity), the nature of the

contaminated surface (e.g., type of material

[chemical and physical properties]), the original

form of the contaminant (chemical and physical

properties [e.g., powder vs. liquid and chemical

reactivity]), and the removal mechanism (such as

washing or foot traffic).

Smear (wipe) tests are often used to determine

the amount of “fixed” versus “non-fixed” (or

removable) contamination (Frame and Abelquist,

1999). Although the definition of removable

contamination varies, it applies to radioactive

“contamination which is removable or

transferrable under normal working conditions”

(International Organization for Standardization

[ISO], 1988) or “radioactivity that can be

transferred from a surface to a smear test paper

by rubbing with moderate pressure” (NRC,

1979a,b) or “radioactive material that can be

removed from surfaces by nondestructive means

such as casual contact, wiping, brushing, or

washing” (DOE, 1994). However, smear tests can

vary because of the material of the smear wipes

used and the potential use of a wetting agent

(Frame and Abelquist, 1999). Also, smear tests

will vary according to the contaminant, the

surface, and the pressure and technique used by

each technician performing the test (Sansone,

1987; Jung et al., 2001). Table 3.9 lists results

from early experiments that demonstrate that the

nature of the contamination and of the surface

can influence how easily removable the

radioactive contamination can be. Thus, a

specific distribution for the removable fraction

must be made on a case-by-case basis. Other 

Table 3.9 Influence of Surface and Contaminant Types on Smear Tests

Contamination

Removed (%) Contamination Surface Comments Reference

1 – 3 Low level from

normal use

Granolithic concrete

floor

Brunskill (1967)

50 Water wash of floor
3-28

0.1 – 0.2 Plutonium nitrate Paper Plutonium nitrate or

oxide in solution

was applied to the

floor and allowed to

dry for 16 hours

Jones and Pond

(1967)

6 Waxed and polished

linoleum

20 – 30 Polyvinyl chloride

10 – 20 PuO2 Polyvinyl chloride

20 – 30 Unwaxed linoleum

50 – 60 Waxed and polished

linoleum

measurement tests in the past have included

tape and modified air sensor tests. Table 3.10

presents some results comparing these methods

with smear tests on different surfaces.

In assigning a removable fraction, a number of

considerations must be taken into account. In a

decommissioned and decontaminated building,

any residual contamination might be expected to

be predominantly fixed because decontamination

efforts should have used reasonable steps in

cleaning the building. Weber (1966)

demonstrated that up to 99.9% of deposited

(dried solution) radioactive surface contamination

could be removed in some cases by using the

proper cleaning solution. This removal efficiency

is much higher than that shown by smears or

other sampling methods (e.g., see Table 3.10).

Thus, the removable fraction is highly dependent

on the decommissioning activities used to bring

the building surfaces into compliance and future

housekeeping activities. 

No information appears to be available regarding

smear tests on freshly cleaned surfaces. Smears

may be more indicative of what contamination is

available for removal by such processes as

resuspension. Multiple smears on the same area

may also be used in determining the removal

fraction (Frame and 

Abelquist, 1999). Jung et al. (2001) studied the

effect of multiple smears on stainless steel (with

different surface finishes), aluminum, and titanium

metal samples after submersion in a spent fuel

storage pool. In each case, the tenth

consecutive smear contained approximately 5 to

10% of the total removed contamination for all 10

smears. Extrapolating their smear results for the

stainless steel samples, Jung et al. (2001)

estimated the total removable contamination to

be approximately 8 to 11%; the first smear

picked up only 2 to 4% of the removable

contamination.

3.6 Time for Source Removal 

 or Source Lifetime

Description: This parameter represents the time

over which surface contamination is removed. The

parameter is used in conjunction with the

“removable fraction of source material” parameter

(Section 3.5) and the “air release fraction”

(Section A.2.4) to obtain the emission rate of

radionuclides into the indoor air.

Unit: days (d)

Probabilistic Input (allowed only for surface

contamination):

Table 3.10 Percent Removal of Contamination

for Different Sampling Methodsa

Removal (%)
3-29

Surface

Adhesive

Paper Smear

Modified

Air

Polyethylene 70.3 56.6 10.9

Glass 75.0 64.6 27.2

Plexiglass 78.0 71.3 15.8

Fiberboard (waxed) 53.8 44.3 10.2

Fiberboard (scrubbed) 56.9 23.5 9.0

Fiberboard (untreated) 73.4 23.5 6.6

Formica 73.4 70.6 26.5

Aluminum (painted) 70.0 50.3 24.8

Asphalt floor tile (untreated) 58.6 48.5 14.6

Asphalt floor tile (waxed) 74.5 74.5 30.3

Concrete (unsealed) 55.5 39.5 22.0

Concrete (sealed [seal and wax 1]) 62.2 59.5 24.0

Concrete (sealed [seal and wax 2]) 54.8 47.7 27.2

Concrete (greased) 43.5 37.5 1.32

Stainless steel 67.7 50.5 10.5

a Modified air sampler (referred to as a “smair” sampler by the authors)

causes air intake to blow across the sample surface when the sample

head is pressed against a surface.

Source: Royster and Fish (1967); contamination was simulated by thorium

dioxide dust particles approximately 1 :m in diameter at a concentration of

about 1 × 106 particles per square centimeter.

Distribution: Triangular

Defining Values for Distribution:

Minimum: 1,000

Maximum: 100,000

Most likely: 10,000 (27.4 yr)

Discussion: The RESRAD-BUILD model

considers the potential entrainment of loose

contamination from a contaminated surface to the

indoor atmosphere. The entrainment rate of the

loose contamination is calculated by using the

removable fraction parameter, the time for source

removal or source lifetime parameter, and the

total contaminant inventory on the surface.

Information on the time for source removal or

source lifetime parameter is not directly available

from the open literature. Therefore, the potential

range of this parameter was inferred on the basis

of information on other, related parameters.

Different mechanisms can result in the

entrainment of loose surface particles to the

atmosphere. Mechanical abrasion during

renovation activities would result in the highest

entrainment rate in the shortest period of time.

However, for normal building occupancy

conditions, renovation activities were excluded

from consideration. 

According to the American Nuclear Society

(ANS), an air release rate of 4 × 10-5/h is a

conservative value for use in estimating the

potential exposure resulting from the release of

solid powders piled up on a heterogeneous

surface (e.g., concrete, stainless steel, or glass)

under the condition of normal building ventilation

flow (ANS, 1998). That rate is equivalent to a

lifetime of approximately 1,000 days (or

2.74 years). Although the loose particles on the

contaminated source are not exactly the same

as a pile of solid powders, the value for the free

solid powders can be used to derive a lower

bounding lifetime value for the loose materials.

The ANS also suggests an air release rate of

4 × 10-6/h for solid powders that are covered with

a substantial layer of debris or are constrained by

indoor static conditions (ANS, 1998). This rate is

equivalent to a lifetime of approximately

10,000 days (27.4 yr). The loose contaminants

on a contaminated surface can be considered as

being restricted by some weak physical binding
3-30

C

f fC

T H

n R surf

R b

a

=

24 l

.

R

f f

T H

F

R

R b

a

=

24 l

.

Qs C A

n

surf s

= ,

Q bV

a

0 =l ,

,

24 T Q0

f fQ C

R

n

n R s

=

force and would, therefore, behave like the

constrained solid powders. The lifetime of the

constrained solid powders can be used as the

most likely value for the loose contaminants.

Erosion of the surface layer from the

contaminated material can eventually occur over

a long period of time, if there is no constant

maintenance. Therefore, all the loose

contaminants have the opportunity of being

released to the environment. To consider this

extreme case, a lifetime of 300 years

(approximately 100,000 days) was assumed. The

probability density function is shown in

Figure 3.8.

Another factor that is frequently used in the

literature for estimating air concentrations from

surface sources is the resuspension factor. The

resuspension factor is not used in the RESRAD

code, but it is a quantity closely related to the

source lifetime for a surface source. The air

concentration in a one-room air quality model

under equilibrium conditions for a surface source

with long-lived radionuclide contamination (or for

chemicals where no decay is involved) can be

given as (derived from Eq. 2.2)

(3.7)

where

(3.8)

(3.9)

V = AH, (3.10)

?

a

b = the air exchange rate (1/h),

As = source area (m2),

A = area of the compartment (m2),

H = height of the compartment (m),

Csurf = surface concentration (pCi/m2

),

fR

= removable fraction of the source

material,

f = fraction of removed material that

becomes indoor dust,

TR

= time to remove material from the

source (source lifetime) (d).

When the whole floor is contaminated, the air

concentration reduces to

(3.11)

The resuspension factor, RF, under these

assumption can be given as

(3.12)

The air release fraction, f, in RESRAD-BUILD is

the fraction of contaminated material removed

from the source released into the air that is in the

respirable particulate range. Assuming a surface

source on the floor with a removable fraction of

0.1 (Section 3.5) and an air release fraction of

0.07 (Section A.2.4), the resuspension factor can

be estimated from the source lifetime. A floor

area of 36 m2 (Section A.1.3), a room height of

3.7 m (Section A.1.4), and a room air exchange

rate of 1.52 h-1 (Section 3.2) were used. In this

case, the source lifetime of 10,000 days is

equivalent to a resuspension factor of 5 x 10-9/m.

Table 3.11 gives the source lifetime (days) for

different air exchange rates and heights for a

fixed resuspension factor of 1 × 10-6 m-1. For

these calculations, it is assumed that the

removable fraction is 1 and that the fraction that

become airborne is also 1. Assuming an airborne

fraction of 1, an air exchange rate of 0.5 h-1, and

a room height of 2.3 m, the source lifetime (in

years) can be related to resuspension factor, as

shown in Table 3.12.
3-31

0.0E+00

5.0E-06

1.0E-05

1.5E-05

2.0E-05

2.5E-05

0 20,000 40,000 60,000 80,000 100,000 120,000

Time for Source Removal or Source Lifetime (days)

f(x)

Triangular Distribution

Minimum = 1,000 days

Maximum = 100,000 days

Most Likely = 10,000 days

Figure 3.8. Time for Source Removal or Source Lifetime

Probability Density Function

TABLE 3.11. Source Lifetime (d) Variation with

Air Exchange Rate and Room Height for a 

Fixed Resuspension Factor of 1 × 10-6 m-1

 Air exchange

 rate (h-1) 

Height (m)

0.2 0.5 0.8 1.0 1.5 2.0 2.5

2.5 8.3E+04 3.3E+04 2.1E+04 1.7E+04 1.1E+04 8.3E+03 6.7E+03

3.0 6.9E+04 2.8E+04 1.7E+04 1.4E+04 9.3E+03 6.9E+03 5.6E+03

6.0 3.5E+04 1.4E+04 8.7E+03 6.9E+03 4.6E+03 3.5E+03 2.8E+03

10.0 2.1E+04 8.3E+03 5.2E+03 4.2E+03 2.8E+03 2.1E+03 1.7E+03
3-32

TABLE 3.12. Source Lifetime (yr) and Resuspension Factor for

Different Removable Fractions with an Air Exchange Rate of 0.5 h-1

and a 2.3-m Room Height

Resuspension Factor (m-1) Removable Fraction Source Lifetime (yr)

1E-08 100% 10,000

 10% 1,000

1E-06 100% 100

 10% 10

1E-04 100% 1

 10% 0.1
4-1

4 DEVELOPMENT OF A RESRAD-BUILD BUILDING OCCUPANCY

SCENARIO DATA TEMPLATE FILE

The building occupancy scenario, as described in

NUREG/CR-5512 (Kennedy and Strenge,1992)

and NUREG-1727 (NRC, 2000), accounts for

exposure to both fixed and removable residual

radioactivity on the walls, floor, and ceiling of a

decommissioned facility. It assumes that after

decommissioning, the building will be used for

commercial or light industrial activities (e.g., as

an office building or warehouse) and that the

people working in the facility may be exposed to

residual contamination. The exposure pathways

included in the building occupancy scenario are

external exposure to penetrating radiation,

inhalation of resuspended surface contamination,

and inadvertent ingestion of surface

contamination.

Both RESRAD-BUILD (Yu et al., 1994) and

DandD (McFadden et al., 2001) can be used to

estimate the dose to a worker (receptor) in the

building for the building occupancy scenario. The

application of the two models to the building

occupancy scenario was discussed in Section 2.

A methodology was developed to help ease the

transition from conducting a screening analysis

with the DandD code to performing a site-specific

analysis with the RESRAD-BUILD code for

simulated light industrial use of the

decontaminated building, as described in

NUREG/CR-5512 and NUREG-1727. The

methodology involves utilizing a data template file

developed for use with the RESRAD-BUILD code

in evaluating potential exposure for the building

occupancy scenario in deterministic and

probabilistic analyses. 

Deterministic analysis involves use of single

values for input parameters to calculate a single

dose value. In probabilistic analysis, a probability

distribution is used to describe the uncertainty in

each input parameter, resulting in a dose

distribution that reflects the uncertainty in the

input parameters. 

The methodology used to select the deterministic

parameter values for the data template file for use

in the RESRAD-BUILD building occupancy

scenario is discussed below. The basic concept

involved is to select a single representative value

for a specific parameter that, when used in a

deterministic analysis, will result in a single dose

estimate that is close to the value of the mean

dose estimate that would result from a

probabilistic analysis. Section 4.1 discusses the

methodology. Section 4.2 discusses the

development of the data template file for the

RESRAD-BUILD building occupancy scenario.

4.1 Methodology for Developing a

 RESRAD-BUILD Building Occupancy

 Scenario Data Template File

The main purpose of developing the

representative data template file to be used in

deterministic and probabilistic RESRAD-BUILD

analyses is to compile the input parameter values

needed to simulate light industrial use of the

decontaminated building as described in

NUREG/CR-5512 (Vol. 1) and NUREG-1727.

Therefore, if the value of a parameter was directly

available from the scenario description or the

derived value of the parameter from NUREG/CR5512 (Vols. 1–3) was available, that value was

used for the data template file. For a probabilistic

run, if the value of a parameter was not available

from the scenario description or NUREG/CR-5512

(Vols. 1–3), but the probability distribution for that

parameter was available, that distribution was

used. For the deterministic run, if the value of the

parameter was not directly available from the

scenario description or NUREG/CR-5512

(Vols. 1–3) but the probability distribution was

available for the parameter, then the parameter

value for the data template file was derived from

the parameter distribution. For the remaining

parameters (i.e., those for which neither value nor

distribution was available from the scenario

description), the current RESRAD-BUILD default

values were used. The building occupancy

scenario assumes the presence of fixed and

removable residual radioactivity on the surfaces of

walls, floor, and ceiling of a decommissioned

facility. Therefore, the data template file assumes

surface sources of uniform contamination on all

six surfaces. On the basis of NUREG/CR-5512,

Volume 1, the room size is fixed at 64 m2 in area

and 3 m in height. The hypothetical receptor is

assumed to be at the center of the floor at a

height of 1 m.
4-2

The principal assumptions and criteria applied in

developing the RESRAD-BUILD data template file

for a building occupancy scenario representative

of light industrial use are itemized below.

Principal Assumptions:

1. Fixed room area (8 m x 8 m) of 64 m2

 and

3 m height. [These values are consistent with

the NUREG/CR-5512, Volume 1 (Kennedy

and Strenge, 1992).]

2. Uniform surface contamination on all six

surfaces (floor, ceiling, and four walls). The

critical group receptor occupies a room with

contaminated floor, ceiling, and walls for light

industrial use activity.

3. The receptor is at the center of the floor at a

height of 1 m.

Criteria Used in Selecting Parameters Values:

1. If the parameter is directly available from the

NUREG/CR-5512 building occupancy

scenario description, that value is used (e.g.,

exposure duration), or if the derived value of

the parameter from NUREG/CR-5512 (Vols.

1–3) is available, that value is used (e.g.,

time spent inside the building, receptor

inhalation rate, direct ingestion rate,

removable fraction, etc.). 

2. If a probability distribution has been

developed for the parameter (e.g., the air

exchange rate, the air release fraction, time

for the source removal or the source lifetime),

use the parameter distribution if performing a

probabilistic analysis or, for a deterministic

analysis, use either the mean, median, or

most likely value (in case of triangular

distribution only) from the parameter

distribution that gives the most conservative

dose.

3. If the value is not available from NUREG/CR5512 and a distribution has not been

developed for the parameter, use the current

RESRAD-BUILD default value (e.g., number

of evaluation times, time, and number of

receptors).

4.2 Parameter Values Selected for 

 the Data Template File

For a single source-receptor geometry,

51 parameters are used in the RESRAD-BUILD

code to describe the exposure pathways and the

associated exposure conditions. Distributions

have been developed for 22 of these parameters

that have been identified as the most sensitive

parameters for the building occupancy scenario

(NUREG/CR-6697 [Yu et al., 2000]). Eight of

these 22 parameters are applicable to volume

sources only and, thus, are not required for the

data template file, which deals only with surface

sources. These eight parameters are source

density, humidity, source erosion rate, source

porosity, source thickness, volumetric water

content, water fraction available for evaporation,

and wet + dry zone thickness. Values for seven

parameters can be derived from the scenario

description or are available from NUREG/CR-5512

(Vols. 1–3) and thus are considered “fixed.”

These seven fixed parameters are removable

fraction, direct ingestion rate, indoor fraction,

receptor inhalation rate, room area, room height,

and shielding thickness. The shielding density

distribution is not required because shielding

thickness is considered to be zero in the

calculations. The mean, median, and most

probable values for the remaining six parameters

for which distributions were developed are listed

in Table 4.1. These six parameters are deposition

velocity, resuspension rate, building air exchange

rate, receptor indirect ingestion rate, air release

fraction, and the time for source removal (or

source life-time).

Distributions were not developed for the remaining

29 RESRAD-BUILD parameters. Dose conversion

factors for four parameters (external, inhalation,

ingestion, and air submersion) are nuclide

dependent. The values for dose conversion

factors for these parameters are from federal

guidance documents (FGR-11 and FGR-12

[Eckerman et al., 1998; Eckerman and Ryman,

1993]). The building occupancy scenario does

not consider the radon inhalation pathway.

Therefore, the radon release fraction is set at

zero to suppress that pathway. The net flow and

outdoor inflow
4-3

Table 4.1 Mean, Median, and Most Probable Values of Probabilistic Parameters in the RESRADBUILD Building Occupancy Scenario

Parameter Valuesa

Parameter

Distribution Type 

and Parameters Mean Median Most Probableb

Deposition velocity, m/s Loguniform

 min: 2.7E-6 

 max: 2.7E-3

3.9E-4 8.55E-5 NAc

Resuspension rate, 1/s Loguniform

 min: 2.5E-11 

 max: 1.3E-5

1.3E-6 6.3E-8 NA

Building air exchange rate,

1/h

Truncated Lognormal-n

 mean: 0.4187

 SD: 0.88

 lower: 0.001

 upper: 0.999

2.239 1.52 0.7

Receptor indirect ingestion

rate, m2/h

Loguniform

 min: 2.8E-5

 max: 2.9E-4

1.12E-4 9E-5 NA

Air release fraction Triangular

 min: 1E-6

 mode: 0.07

 max: 1

0.357 0.318 0.07

Time for source removal or

source life time, d

Triangular

 min: 1,000

 mode: 10,000

 max:100,000

37,000 33,255 10,000

a Parameter values selected for the data template file are in bold type.

b This is the value with the highest probability on the distribution curve. For triangular distribution, most

probable value is the most likely (mode) value. The sampling in loguniform distribution has high probability

for low values. 

c NA = not applicable.

parameters are not required because only one

room is assumed in the scenario. Ten of the

other parameters are derived from the scenario

description or are available from NUREG/CR-5512

(Vols. 1–3). These parameters are exposure

duration, receptor time fraction, receptor room,

receptor location, number of sources, source

type, source room, source direction, source

location, and source area. Eight parameters are

applicable to volume sources only and, thus, are

not required for the data template file. These eight

parameters are number of regions in volume

source, contaminated region volume source,

source erosion rate, source porosity, radon

effective diffusion coefficient, radon emanation

coefficient, dry zone thickness, and humidity.

Three more of the parameters — (1) the number

of evaluation times, (2) time, and (3) number of

receptors — are kept at their RESRAD-BUILD

default values. Finally the radionuclide

concentration is kept fixed at 1 dpm/100 cm2.

The assumptions and criteria described in

Section 4.1 were applied to create a data

template file containing the parameter values for

the deterministic and probabilistic analysis runs
4-4

for the building occupancy scenario. For the six

parameters for which developed distributions were

utilized, this process involved selecting the

appropriate value for the deterministic run. The air

release fraction, receptor indirect ingestion rate,

and deposition velocity were kept fixed at their

mean values. The building air exchange rate and

resuspension rate were fixed at their median

value. For the time for source removal, the most

likely value yields the most conservative dose

and, thus, was used for the template file for 

deterministic analysis. Table 4.2 lists all

deterministic and probabilistic parameter values.

The remarks column in Table 4.2 provides

information regarding how the values were

obtained. Note that the table includes parameters

for six sources and could be modified to

accommodate as many sources as were present

for a particular case.
4-5

Table 4.2. RESRAD-BUILD Building Occupancy Scenario Data Template File for

Deterministic and Probabilistic Runs

Parameter Units

Parameter Values/Distributions

Deterministic Remarks a Probabilisticb

External dose conversion

factor

(mrem/yr)/(p

Ci/g)

Nuclide specific Nuclide specific Values are from FGR-12.

inhalation dose conversion

factor

mrem/pCi Nuclide specific Nuclide specific Values are from FGR-11.

Ingestion dose conversion

factor

mrem/pCi Nuclide specific Nuclide specific Values are from FGR-11.

Air submersion dose

conversion factor

(mrem/yr)/(p

Ci/m3)

Nuclide specific Nuclide specific Values are from FGR-12.

Exposure duration d 365.25 365.25 To match the occupancy period of 365.25 days in

NUREG/CR-5512 building occupancy scenario.

Indoor fraction -

c 0.267 0.267 To match the 97.4 d/y time in building in NUREG/CR5512 building occupancy scenario. This is the time the

average member of the screening group spends in the

building (Table 5.15 in Beyeler et al., 1999)

Number of evaluation times - 2 2 RESRAD-BUILD current default.

Time yr 1 1

Number of rooms - 1 1 NUREG/CR-5512 building occupancy scenario

assumes only one contaminated room.

Deposition velocity m/s 3.9E-4 Loguniform 

min: 2.7E-6 

max: 2.7E-3

Value for the deterministic run is determined from the

methodology described in Section 4.1 and is the mean

value from the distribution. To suppress two pathways

(1) ingestion of deposited material and (2) exposure

from deposited material, which are not in NUREG/CR5512 building occupancy scenario, the value for

deposition velocity can be set at zero.

Resuspension rate 1/s 6.26E-8 Loguniform 

min: 2.5E-11 

max: 1.3E-5

Value for the deterministic run is determined from the

methodology described in Section 4.1 and is the

median value from the distribution.

Room height m 3 3 NUREG/CR-5512, Volume 1.

Room area m

2 64 64 NUREG/CR-5512, Volume 1.
4-6

Table 4.2. RESRAD-BUILD Building Occupancy Scenario Data Template File for

Deterministic and Probabilistic Runs (Continued)

Parameter Units

Parameter Values/Distributions

Deterministic Remarks a Probabilisticb

Air exchange rate for building

and room

1/h 1.52 Truncated

Lognormal-n 

mean: 0.4187

SD: 0.88

lower: 0.001

upper: 0.999

Value for the deterministic run is determined from the

methodology described in Section 4.1 and is the

median value from the distribution.

Net flow m

3

/h NRd NR Only one-room model is used.

Outdoor inflow m

3

/h NR NR Outdoor inflow is calculated from room volume and air

exchange rate.

Number of receptors - 1 1 Dose is calculated for one receptor.

Receptor room - 1 1 Only one-room model is used.

Receptor location m 4,4,1 4,4,1 At 1-m height from the center of the contaminated floor

surface.

Receptor time fraction - 1 1

Receptor inhalation rate m

3

/d 33.6 33.6 To match 1.4 m3/h breathing rate in NUREG/CR-5512

building occupancy scenario. This is the breathing rate

for the average member of the screening group

(Table 5.15 in Beyeler et al., 1999).

Receptor indirect ingestion

rate

m

2

/h 1.12E-4 Loguniform 

min: 2.8E-5 

max: 2.9E-4

Value for the deterministic run is determined from the

methodology described in Section 4.1 and is the mean

value from the distribution.

Number of sources - 6 6 Floor, ceiling, and 4 walls of the room are

contaminated. 

Source1 type - Area Area Only surface sources are considered in building

occupancy scenario.

Source 1 room or primary room - 1 1 Only one room is considered.

Source 1 direction - z z The direction perpendicular to the exposed area (floor).

Source 1 location - 4,4,0 4,4,0 Source center location.

Source 1 length or area m or m2 64 64 Floor is contaminated.

Source 2 type - Area Area Only surface source is considered in building

occupancy scenario.

Source 2 room or primary room - 1 1 It is the primary room of the contaminated source.
4-7

Table 4.2. RESRAD-BUILD Building Occupancy Scenario Data Template File for

Deterministic and Probabilistic Runs (Continued)

Parameter Units

Parameter Values/Distributions

Deterministic Remarks a Probabilisticb

Source 2 direction - z z The direction perpendicular to the exposed area

(ceiling).

Source 2 location - 4,4,3 4,4,3 Source center location.

Source 2 length or area m or m2 64 64 Ceiling is contaminated.

Source 3 type - Area Area Only surface source is considered in building

occupancy scenario.

Source 3 room or primary room - 1 1 It is the primary room of the contaminated source.

Source 3 direction - x x The direction perpendicular to the exposed area (wall).

Source 3 location - 0,4,1.5 0,4,1.5 Source center location.

Source 3 length or area m or m2 24 24 Wall is contaminated.

Source 4 type - Area Area Only surface source is considered in building

occupancy scenario.

Source 4 room or primary room - 1 1 It is the primary room of the contaminated source.

Source 4 direction - x x The direction perpendicular to the exposed area (wall).

Source 4 location - 8,4,1.5 8,4,1.5 Source center location.

Source 4 length or area m or m2 24 24 Wall is contaminated.

Source 5 type - Area Area Only surface source is considered in building

occupancy scenario.

Source 5 room or primary room - 1 1 It is the primary room of the contaminated source.

Source 5 direction - y y The direction perpendicular to the exposed area (wall).

Source 5 location - 4,0,1.5 4,0,1.5 Source center location.

Source 5 length or area m or m2 24 24 Wall is contaminated.

Source 6 type - Area Area Only surface source is considered in building

occupancy scenario.

Source 6 room or primary room - 1 1 It is the primary room of the contaminated source.

Source 6 direction - y y The direction perpendicular to the exposed area (wall).

Source 6 location - 4,8,1.5 4,8,1.5 Source center location.

Source 6 length or area m or m2 24 24 Wall is contaminated.

Air release fraction for all

sources

- 0.357 Triangular 

min: 1E-6 

mode: 0.07

max: 1

Value for the deterministic run is determined from the

methodology described in Section 4.1 and is the mean

value from the distribution.
4-8

Table 4.2. RESRAD-BUILD Building Occupancy Scenario Data Template File for

Deterministic and Probabilistic Runs (Continued)

Parameter Units

Parameter Values/Distributions

Deterministic Remarks a Probabilisticb

Direct ingestion rate for all

sources 

g/h (volume)

and

1/h(other) 

4.91E-7 4.91E-7 Calculated from the default ingestion rate of 1.1E-4

m

2

/h in NUREG/CR-5512 building occupancy scenario.

(See relationship in Table 2.2.) The ingestion rate of

1.1 × 10-4 m2/h represents the average member of the

screening group (Beyeler et al., 1999).

Removable fraction - 0.1 0.1 10% of the contamination is removable (NUREG/CR5512 building occupancy scenario default). The default

parameter value for the loose fraction for the building

occupancy scenario is 0.1 (Table C7.1, NUREG-1727).

Time for source removal or

source lifetime

d 10,000 Triangular 

min: 1,000 

mode: 10,000

 max: 100,000

Value for the deterministic run is determined from the

methodology described in Section 4.1 and is the most

likely value from the distribution.

Radon release fraction - 0 0 Radon inhalation pathway is suppressed.

Radionuclide concentration dpm/ m2 100 100 For all six sources surface concentration equivalent to

1 dpm/100 cm2

Number of regions in volume

source

- NR NR Only surface source is considered.

Contaminated region-volume

source

- NR NR Only surface source is considered.

Source thickness, volume

source

cm NR NR Only surface source is considered.

Source density, volume source g/cm3 NR NR Only surface source is considered.

Source erosion rate, volume

source

cm/d NR NR Only surface source is considered.

Source porosity - NR NR Only surface source is considered.

Radon effective diffusion

coefficient

m

2

/s NR NR Pathway is suppressed in the analysis.

Radon emanation coefficient - 0 0 Pathway is suppressed in the analysis.

Shielding thickness cm 0 0 No shielding is assumed between the source and

receptor.

Shielding density g/cm3 NR NR No shielding is assumed between the source and

receptor.

Shielding material - NR NR No shielding is assumed between the source and

receptor.

Dry zone thickness cm NR NR The parameter is only required for the tritium volume
4-9

source.
4-10

Table 4.2. RESRAD-BUILD Building Occupancy Scenario Data Template File for

Deterministic and Probabilistic Runs (Continued)

Parameter Units

Parameter Values/Distributions

Deterministic Remarks a Probabilisticb

Wet + dry zone thickness cm NR NR The parameter is only required for the tritium volume

source.

Volumetric water content - NR NR The parameter is only required for the tritium volume

source.

Water fraction available for

evaporation

- NR NR The parameter is only required for the tritium volume

source.

Humidity g/m3 NR NR The parameter is only required for the tritium volume

source.

a Parameter values used in the deterministic run.

b Parameter values or distributions used in the probabilistic run. Distributions were developed for many more parameters (such as indoor fraction, room

height, room area, receptor inhalation rate, and shielding thickness), but scenario-specific constant values are used in the probabilistic analysis.

c A dash indicates that the parameter is dimensionless.

d NR = parameter not required for the analysis.
4-11
5-1

5 RESULTS AND DISCUSSION

To provide insight into the performance of the

RESRAD-BUILD code when the template file is

used, probabilistic dose distributions were

calculated for six radionuclides. The

radionuclides used in these examples were

Co-60, Sr-90, Cs-137, Ra-226, U-238, and

Pu-239. These radionuclides were selected

because of their different dominant pathways in

dose calculations. The doses are calculated for

the first year after the release of the building. For

this calculation, the mean of the peak dose and

the peak of the mean dose will be the same. For

most radionuclides, in the building occupancy

scenario in RESRAD-BUILD, the maximum dose

would occur from exposure in the first year.

However, ingrowth of progency may, in some

cases, cause the dose to increase with time

(e.g., for Ra-226 and Ra-228). Table 5.1 lists the

values obtained from the probabilistic dose

analysis (with 200 sample values and 3

repetitions) at 5% cumulative distribution intervals

for these selected radionuclides. For this

analysis, the values and distributions from Table

4.2 were used. 

The results listed in Table 5.1 show that for Co-60

and Cs-137, there was very little variability in

dose values. For U-238 and Pu-239, on the other

hand, a large variability in doses was observed.

Table 5.1 also presents the mean value of the

dose distribution and the minimum and maximum

dose. The doses are presented at 5-percentile

increments.

Table 5.2 presents the dose distribution results

for the selected radionuclides for the three

dominant pathways — (1) external exposure,

(2) inhalation, and (3) ingestion. The results from

two other active pathways (air submersion and

external exposure from deposited material) 

are not provided because the doses from those

pathways were at least an order of magnitude

smaller than those for the pathways shown. This

analysis provides information about the dominant

pathway for each radionuclide and the dose

variation in the individual pathways. Practically no

difference was observed in the external exposure

pathway at different dose percentiles; therefore,

only the mean value from the distribution is given

in the table. For the inhalation and ingestion

pathways, results at 50%, 90%, 95%, and the

mean are provided. For Co-60 and Cs-137,

external exposure was the dominant pathway.

Since the external exposure pathway did not

depend significantly on any parameter for which a

distribution was used, practically no variability in

dose values was observed. For Sr-90, Ra-226,

and Pu-239, ingestion was the dominant

pathway; for U-238, inhalation was the dominant

pathway. Because the inhalation pathway had

the largest variability in this analysis, doses from

exposure to U-238 showed the greatest variability

(see Table 5.1) in the probabilistic dose analysis

results.

Table 5.3 lists the deterministic results for the

selected radionuclides on the basis of the data

template file parameter values. This table also

compares those results (proposed methodology)

with the results obtained by using the mean and

median value from the distributions. The mean

dose obtained from the probabilistic analysis

(Table 5.1) was also listed for comparison. As

expected, it made no difference which value was

used for Co-60 and Cs-137; however, for all other

radionuclides, the proposed methodology

provided the values closest to the mean value

obtained from the probabilistic analysis.
5-2

Table 5.1. Results (mrem/yr per dpm/100 cm2) of Probabilistic Dose Analysis for Selected

Radionuclides Using the RESRAD-BUILD Data Template File

Dose

Percentile Co-60 Sr-90 Cs-137 Ra-226 U-238 Pu-239

5 1.90 × 10-3 1.89 × 10-4 5.26 × 10-4 3.09 × 10-3 4.88 × 10-4 4.64 × 10-3

10 1.90 × 10-3 1.89 × 10-4 5.26 × 10-4 3.10 × 10-3 5.60 × 10-4 4.90 × 10-3

15 1.90 × 10-3 1.90 × 10-4 5.27 × 10-4 3.10 × 10-3 6.20 × 10-4 5.12 × 10-3

20 1.90 × 10-3 1.91 × 10-4 5.27 × 10-4 3.11 × 10-3 6.75 × 10-4 5.33 × 10-3

25 1.90 × 10-3 1.92 × 10-4 5.27 × 10-4 3.11 × 10-3 7.39 × 10-4 5.62 × 10-3

30 1.90 × 10-3 1.93 × 10-4 5.27 × 10-4 3.12 × 10-3 8.05 × 10-4 5.85 × 10-3

35 1.90 × 10-3 1.93 × 10-4 5.27 × 10-4 3.12 × 10-3 8.69 × 10-4 6.08 × 10-3

40 1.90 × 10-3 1.94 × 10-4 5.27 × 10-4 3.13 × 10-3 9.41 × 10-4 6.35 × 10-3

45 1.90 × 10-3 1.95 × 10-4 5.27 × 10-4 3.14 × 10-3 1.06 × 10-3 6.83 × 10-3

50 1.90 × 10-3 1.97 × 10-4 5.27 × 10-4 3.15 × 10-3 1.13 × 10-3 7.12 × 10-3

55 1.90 × 10-3 1.98 × 10-4 5.27 × 10-4 3.16 × 10-3 1.26 × 10-3 7.58 × 10-3

60 1.90 × 10-3 2.00 × 10-4 5.27 × 10-4 3.18 × 10-3 1.35 × 10-3 7.97 × 10-3

65 1.90 × 10-3 2.01 × 10-4 5.27 × 10-4 3.19 × 10-3 1.50 × 10-3 8.44 × 10-3

70 1.90 × 10-3 2.04 × 10-4 5.27 × 10-4 3.21 × 10-3 1.69 × 10-3 9.10 × 10-3

75 1.90 × 10-3 2.07 × 10-4 5.28 × 10-4 3.25 × 10-3 1.90 × 10-3 9.98 × 10-3

80 1.90 × 10-3 2.10 × 10-4 5.28 × 10-4 3.30 × 10-3 2.16 × 10-3 1.10 × 10-2

85 1.90 × 10-3 2.16 × 10-4 5.29 × 10-4 3.34 × 10-3 2.63 × 10-3 1.30 × 10-2

90 1.90 × 10-3 2.29 × 10-4 5.31 × 10-4 3.45 × 10-3 3.31 × 10-3 1.51 × 10-2

95 1.91 × 10-3 2.58 × 10-4 5.35 × 10-4 3.69 × 10-3 4.30 × 10-3 1.94 × 10-2

mean 1.9 × 10-3 2.06 × 10-4 5.29 × 10-4 3.28 × 10-3 1.65 × 10-3 9.05 × 10-3

min. 1.89 × 10-3 1.87 × 10-4 5.24 × 10-4 3.08 × 10-3 3.83 × 10-4 4.26 × 10-3

max. 1.94 × 10-3 3.94 × 10-4 6.10 × 10-4 9.62 × 10-3 2.47 × 10-2 9.47 × 10-2
5-3

Table 5.2. Pathway Doses (mrem/yr per dpm/100 cm2) for Selected Radionuclides

Using the RESRAD-BUILD Data Template File

ExternalaInhalation Ingestion

Radionuclides mean 50% 90% 95% mean 50% 90% 95% mean

Co-60 1.87 ×10-3 1.32 ×10-6 4.74 ×10-6 6.23 ×10-6 2.04 ×10-6 2.91 ×10-5 2.96 ×10-5 3.03 ×10-5 2.92 ×10-5

Sr-90 1.26 ×10-5 8.27 ×10-6 2.85 ×10-5 5.02 ×10-5 1.44 ×10-5 1.74 ×10-4 1.83 ×10-4 1.96 ×10-4 1.79 ×10-4

Cs-137 4.70 ×10-4 2.12 ×10-7 7.23 ×10-7 1.06 ×10-6 3.39 ×10-7 5.70 ×10-5 5.97 ×10-5 6.22 ×10-5 5.83 ×10-5

Ra-226 1.42 ×10-3 5.98 ×10-5 2.19 ×10-4 2.94 ×10-4 9.76 ×10-5 1.66 ×10-3 1.82 ×10-3 2.06 ×10-3 1.76 ×10-3

U-238 2.60 ×10-5 7.89 ×10-4 2.88 ×10-3 3.88 ×10-3 1.29 ×10-3 3.10 ×10-4 3.41 ×10-4 3.92 ×10-4 3.31 ×10-4

Pu-239 7.55 ×10-7 2.87 ×10-3 1.05 ×10-2 1.41 ×10-2 4.69 ×10-3 4.08 ×10-3 4.48 ×10-3 5.15 ×10-3 4.36 ×10-3

a For the external exposure pathway, practically no variability in dose values was observed; therefore, only the results at mean value are provided.
5-4

Table 5.3. Comparison of Deterministic Dose (mrem/yr per dpm/100 cm2)

Calculated Using Data Template File, Mean, and Median Parameter Values

with Mean Dose from Distribution

Radionuclide

Dose Results Based on Choosing Parameter

Values for Deterministic Analysisa

Mean from Dose

Distributionb

Data Template

File Value Mean Value Median Value

Co-60 1.90 × 10-3 1.90 × 10-3 1.90 × 10-3 1.90 × 10-3

Sr-90 2.08 × 10-4 1.91 × 10-4 1.93 × 10-4 2.05 × 10-4

Cs-137 5.27 × 10-4 5.27 × 10-4 5.27 × 10-4 5.29 × 10-4

Ra-226 3.22 × 10-3 3.11 × 10-3 3.12 × 10-3 3.28 × 10-3

U-238 2.32 × 10-3 7.07 × 10-4 8.70 × 10-4 1.65 × 10-3

Pu-239 1.13 × 10-2 5.43 × 10-3 6.02 × 10-3 9.05 × 10-3

a Parameters and their mean and median values are shown in Table 4.1.

b Mean dose values are taken from Table 5.1.
6-1

6 SUMMARY

This report examines the use of the probabilistic

RESRAD-BUILD 3.0 code to simulate the light

industrial use of a decontaminated building as

described in NUREG/CR-5512 and NUREG-1727.

Because RESRAD-BUILD is designed to model

more detailed, sophisticated scenarios than

those that the DandD code was designed to

model, more detailed input data are needed to

satisfy the requirements for the RESRAD-BUILD

analysis. Parameter distributions for RESRADBUILD were updated, and appropriate input data

were selected and placed in a template file. An

analysis was performed using the template file to

provide insight into the potential results when

using RESRAD-BUILD to simulate the building

occupancy scenario. Exposure pathways not

considered in this scenario were suppressed. Six

different radionuclides were selected for use in

the analysis to account for different dominant

exposure pathways in the calculations.

RESRAD-BUILD and DandD both model external

exposure from surface sources, inhalation of

resuspended contamination, and inadvertent

ingestion of surface contamination. However,

more detailed models in the RESRAD-BUILD

code allow it to consider more complex situations

than the DandD code. As discussed in Section 2,

DandD relies on a simple release model using

the resuspension factor as the primary controlling

release parameter. On the other hand, RESRADBUILD uses a different approach and considers

many important factors, including the removable

fraction, source lifetime, air release fraction,

mixing and transport in air, and surface

deposition and resuspension.

Previously available parameter distributions for

input to RESRAD-BUILD were reviewed and 

updated with the most recent data in Section 3.

Currently, 22 parameters, out of 51 input

parameters for a single source-receptor

geometry, have been assigned distributions

because they have been identified as the most

sensitive parameters for the building occupancy

scenario. As discussed in Section 4, only six of

these distributions were selected for use in the

template file on the basis of applicability and sitespecificity. Those parameters for which a

distribution was used in the template file were the

deposition velocity, resuspension rate, building

air exchange rate, receptor indirect ingestion

rate, air release fraction, and the time for source

removal. Deterministic values for the other

parameters were selected according to their

treatment in NUREG/CR-5512. Several

deterministic values used in the template file are

site-specific, strongly affect the estimated dose,

and should be modified to reflect actual site

conditions when applied outside of this report.

These parameters include time spent inside the

building and room area and height.

Unit contaminant concentrations of six

radionuclides (Co-60, Sr-90, Cs-137, Ra-226, U238, and Pu-239) were used in separate

RESRAD-BUILD probabilistic analyses using the

data template file. Dose distributions for the two

radionuclides that primarily pose an external

radiation hazard (Co-60 and Cs-137) were

relatively narrow, with the minimum and

maximum values of the distribution for the total

dose within a few percent of the mean value. For

all radionuclides, the distribution of doses

calculated for external exposure was similarly

narrow. The largest variability was observed in the

inhalation pathway, the dominant pathway for U238. The ingestion pathway was the dominant

exposure pathway for Sr-90, Ra-226, and Pu-239.
6-2
7-1

7 REFERENCES

Abt, E., et al. “Relative Contribution of Outdoor

and Indoor Particle Sources to Indoor

Concentrations.” Environ. Sci. Technol., 34:3579-

3587. 2000.

American Nuclear Society. “Airborne Release

Fractions at Non-Reactor Facilities, an American

National Standard.” ANSI/ANS-5.10-1998.

Prepared by the American Nuclear Society

Standards Committee Working Group ANS-5.10,

ANS: La Grange Park, Ill. 1998.

American Society of Heating, Refrigerating and

Air-Conditioning Engineers, Inc. “1997 ASHRAE

Handbook, Fundamentals.” ASHRAE: Atlanta,

Ga. 1997.

Bailey, J.C. and R.C. Rohr. “Air-Borne

Contamination Resulting from Transferable

Contamination on Surfaces.” K-1088. U.S.

Atomic Energy Commission: Washington, D.C.

1953.

Barnes, D.E. “Basic Criteria in the Control of Air

and Surface Contamination.” In Symposium on

Health Physics in Nuclear Installations. Risø,

Denmark. May 1959.

Becher, A.F. pp. 151-156 in Proceedings of the

Symposium on Occupational Health Experience

and Practices in the Uranium Industry. HASL-58.

U.S. Atomic Energy Commission: Washington,

D.C. 1959.

Berk, J.V., et al. “Impact of Energy-Conserving

Retrofits on Indoor Air Quality in Residential

Housing.” LBL-12189. Lawrence Berkeley

Laboratory: Berkeley, Calif. 1981.

Beyeler, W.E., et al. NUREG/CR-5512, Vol. 3

Draft, “Residual Radioactive Contamination from

Decommissioning: Parameter Analysis.”

U.S. Nuclear Regulatory Commission, Office of

Nuclear Regulatory Research: Washington, D.C.

1999.

Brunskill, R.T. “The Relationship Between

Surface and Airborne Contamination.” pp. 93-105

in Surface Contamination. B.R. Fish (ed.).

Pergamon Press: New York, N.Y. 1967.
7-2

Carter, R.F. “The Measurement of Asbestos Dust

Levels in a Workshop Environment.” A.W.R.E.

Report No. 028/70. United Kingdom Atomic

Energy Authority: Aldermaston, United Kingdom.

1970.

Chamberlain, A.C. and G.R. Stanbury. “The

Hazard from Inhaled Fission Products in Rescue

Operations after an Atomic Bomb Explosion.”

Report HP/R 737. Atomic Energy Research

Establishment: Harwell, United Kingdom. June

1951.

Cortissone, C., et al. “La contaminazione dell’aria

risultante da risospensione di contaminazione di

superficie [Atmospheric contamination from

resuspension of surface contamination].” Minerva

Fisiconucleare, Giornal di Fisica Sanitaria

Protezione Radiazione, 12:63-79. 1968.

Dietz, R.N. and R.W. Goodrich. “Measurement of

HVAC System Performance and Local

Ventilation Using Passive Perfluorocarbon Tracer

Technology.” BNL-61990, Brookhaven National

Laboratory, Environmental Chemistry Division:

Upton, N.Y. 1995.

Doyle, S.M., et al. “Time-Averaged Indoor Radon

Concentrations and Infiltration Rates Sampled in

Four U.S. Cities.” Health Physics, 47:579-586.

1984.

Dunn, M.J. and P.B. Dunscombe. “Levels of

Airborne 125I during Protein Labeling.” Radiation

Protection Dosimetry, 1(2):143-146. 1981.

Eckerman, K.F. and J.C. Ryman. “External

Exposure to Radionuclides in Air, Water, and

Soil, Exposure to Dose Coefficients for General

Application.” Based on the 1987 Federal

Radiation Protection Guidance, EPA

402-R-93-081, Federal Guidance Report No. 12.

Prepared by Oak Ridge National Laboratory, Oak

Ridge, Tenn., for U.S. Environmental Protection

Agency, Office of Radiation and Indoor Air:

Washington, D.C. 1993.

Eckerman, K.F., et al. “Limiting Values of

Radionuclide Intake and Air Concentration and

Dose Conversion Factors for Inhalation,

Submersion, and Ingestion.” EPA-520/1-88-020.

Federal Guidance Report No. 11. Prepared by

Oak Ridge National Laboratory, Oak Ridge,

Tenn., for U.S. Environmental Protection Agency,

Office of Radiation and Indoor Air: Washington,

D.C. 1988.

Eckerman, K.F., et al. “Cancer Risk Coefficients

for Environmental Exposure to Radionuclides.”

EPA 402-R-99-001. Federal Guidance Report No.

13. Prepared by Oak Ridge National Laboratory,

Oak Ridge, Tenn., for U.S. Environmental

Protection Agency, Office of Radiation and Indoor

Air: Washington, D.C. 1999.

Eisenbud, M., H. Blatz and E.V. Barry. “How

Important is Surface Contamination?” Nucleonics,

12:12-15. 1954.

Fish, B.R., et al. “Redispersion of Settled

Particulates.” pp. 75-81 in Surface

Contamination. B.R. Fish (ed.). Pergamon Press:

New York, N.Y. 1967.

Fisk, W.J., et al. “Particle Concentrations and

Sizes with Normal and High Efficiency Air

Filtration in a Sealed Air-Conditioned Office

Building.” Aerosol Science and Technol., 32:527-

544. 2000.

Fogh, C.L., et al. “Size Specific Indoor Aerosol

Deposition Measurements and Derived I/O

Concentrations Ratios.” Atmospheric

Environment, 31:2193-2203. 1997.

Frame, P.W. and E.W. Abelquist. “Use of

Smears for Assessing Removable

Contamination.” Health Physics, 76(5):S57-S66.

1999. 

Gerhold, W.F., E. Escalante and B.T.

Sanderson. "The Corrosion Behavior of Selected

Stainless Steels in Soil Environments."

NBS1R81-2228. National Bureau of Standards:

Gaithersburg, Md. 1981.

Glauberman, H., W.R. Bootman and A.J. Breslin.

“Studies of the Significance of Surface

Contamination.” In Surface Contamination. B.R.

Fish (ed.). Pergamon Press: New York, N.Y.,

pp. 169-178, 1967.
7-3

Godish, T. Indoor Air Pollution Control. Lewis

Publishers: Chelsea, Mich. 1989.

Godish, T. and J. Rouch. “Residential

Formaldehyde Control by Mechanical

Ventilation.” Applied Industrial Hygiene, 3:93-96.

1988.

Gorodinsky, S.M., et al. “Experimental

Determination of the Coefficient of Passage of

Radioactive Substances from Contaminated

Surfaces into the Air of Working Premises (in

Russian).” Gig. Sanit., 37:46-50, 1972.

Grimsrud, D.T., et al. “Calculating Infiltration:

Implications for a Construction Quality Standard.”

LBL-9416, Lawrence Berkeley Laboratory:

Berkeley, Calif. 1983.

Healy, J.W. “Surface Contamination: Decision

Levels.” LA-4558-MS. Los Alamos Scientific

Laboratory: Los Alamos, N.M. 1971.

Hyatt, E.C., et al. “Beryllium: Hazard Evaluation

and Control in Research and Development

Operations.” A.M.A. Arch. Indust. Health, 19:211-

220. 1959.

Ikezawa Y., T. Okamoto and A. Yabe.

“Experiences in Monitoring Airborne Radioactive

Contamination in JAERI.” p. 495 in Radiation

Protection: A Systematic Approach to Safety -

Proceedings of the 5th International Radiation

Protection Society. Pergamon Press: New York,

N.Y. 1980.

International Commission on Radiological

Protection. “Radionuclide Transformations:

Energy and Intensity of Emissions.” ICRP

Publication No. 38. Annals of the ICRP, Vols.

11-13. Pegamon Press: New York, N.Y. 1983.

International Organization for Standardization.

“Evaluation of Surface Contamination-Part 1: Beta

Emitters and Alpha Emitters.” ISO-7503-1.

Geneva, 1988.

John, W. “The Characteristics of Environmental

and Laboratory-Generated Aerosols.” pp. 54-76,

in Aerosol Measurement, Principles, Techniques,

and Applications. Willeke, K. and P.A. Baron

(eds.). John Wiley & Sons, Inc.: New York, N.Y.

1993.
7-4

Jones, I.S. and S.F. Pond. “Some Experiments

to Determine the Resuspension Factor of

Plutonium from Various Surfaces.” pp. 83-92 in

Surface Contamination. B.R. Fish (ed.).

Pergamon Press: New York, N.Y. 1967.

Jung, H., et al. “Consistency and Efficiency of

Standard Swipe Procedures Taken on Slightly

Radioactive Contaminated Metal Surfaces.”

Operational Radiation Safety (Supplement to

Health Physics), 80:S80 - S88. 2001.

Kamboj, S., et al. “External Exposure Model

Used in RESRAD Code for Various Geometries

of Contaminated Soil.” ANL/EAD/TM-84.

Prepared by Argonne National Laboratory,

Argonne, Ill., for U.S. Department of Energy:

Washington, D.C. 1998.

Kennedy, W.E. and Strenge, D.L. NUREG/CR5512, PNL 7994, Vol. 1, “Residual Radioactive

Contamination from Decommissioning; A

Technical Basis for Translating Contamination

Levels to Annual Total Effective Dose Equivalent.”

Prepared by Pacific Northwest Laboratory for

U.S. Nuclear Regulatory Commission:

Washington, D.C. 1992.

Khvostov, N.N. and M.S Kostyakov “Hygienic

Significance of Radioactive Contamination of

Working Surfaces,” Hyg. Sanit. (English ed.),

34:43-48. 1969.

Koontz, M.D. and H.E. Rector. “Estimation of

Distributions for Residential Air Exchange Rates.”

EPA Contract No. 68-D9-0166, Work Assignment

No. 3-19. U.S. Environmental Protection Agency,

Office of Pollution Prevention and Toxics:

Washington, D.C. 1995.

Kovygin, G.F. “Certain Problems of

Substantiating the Permissible Densities of

Beryllium Surface Contamination (in Russian).”

Gig. Sanit., 39:43-45. 1974.

Lamb, B., et al. “Air Infiltration Rates in Pre- and

Post-Weatherized Houses.” JAPCA, 35:545-551.

1985.

Lang, C. “Indoor Deposition and the Protective

Effect of Houses Against Airborne Pollution.”

Ph.D Thesis Riso-R-780(en), ISBN 87-550-2024-

0. 1995. [As cited in Fogh et al., 1997.]
7-5

Lipschutz, R.D., et al. “Infiltration and Indoor Air

Quality in Energy Efficient Houses in Eugene,

Oregon.” LBL-12924. Lawrence Berkeley

Laboratory: Berkeley, Calif. 1981.

McFadden, K., et al. NUREG/CR-5512, Vol. 2, 

“Residual Radioactive Contamination from

Decommissioning: User’s Manual DandD Version

2.1.” U.S. Nuclear Regulatory Commission,

Office of Nuclear Regulatory Research:

Washington, D.C. 2001.

Mitchell, R.N. and B.C. Eutsler “A Study of

Beryllium Surface Contamination and

Resuspension.” pp. 349-352 in Surface

Contamination. B.R. Fish (ed.). Pergamon Press:

New York, N.Y. 1967.

Mosley, R.B., et al. “Penetration of Ambient Fine

Particles into the Indoor Environment.” Aerosol

Sci. Technol., 34:127-136. 2001.

Murray, D.M. and D.E. Burmaster. “Residential

Air Exchange Rates in the United States:

Empirical and Estimated Parametric Distributions

by Season and Climatic Region.” Risk Analysis,

15:459-465. 1995.

Nardi A.J. “Operational Measurements and

Comments Regarding the Resuspension Factor.”

Presentation at NRC Decommissioning

Workshop and associated transcript. March 18,

1999.

Nazaroff, W.W. and G.R. Cass. “Mass-Transport

Aspects of Pollutant Removal at Indoor

Surfaces.” Environ. Int., 15:567-584. 1989.

Nazaroff, W.W., A.J. Gadgil and C.J. Weschler.

“Critique of the Use of Deposition Velocity in

Modeling Indoor Air Quality.” pp. 148-165 in

Modeling of Indoor Quality and Exposure. Nagda,

N.L. (ed.). ASTM STP 1205. American Society

for Testing and Materials: Philadelphia, Penn.

1993.

Nuclear Regulatory Commission (U.S.) (NRC).

Regulatory Guide 8.21, “Health Physics Surveys

for By-product Materials at NRC-licensed

Processing and Manufacturing Plants.” Office of

Standards Development, NRC: Washington, D.C.

1979a.

Nuclear Regulatory Commission (U.S.) (NRC).

Regulatory Guide 8.23, “Radiation Safety Surveys

at Medical Institutions.” Washington, D.C. 1979b.

Nuclear Regulatory Commission (U.S.) (NRC).

NUREG-1549, “Decision Methods for Dose

Assessment to Comply with Radiological Criteria

for License Termination, Division of Regulatory

Applications (Draft Report for Comment).” Office

of Nuclear Regulatory Research, NRC:

Washington, D.C. 1998.

Nuclear Regulatory Commission (U.S.) (NRC).

NUREG-1727, “NMSS Decommissioning

Standard Review Plan.” Division of Waste

Management, Office of Nuclear Material Safety

and Safeguards, NRC: Washington, D.C. 2000.

Offermann, F.J., et al. “Low Infiltration Housing in

Rochester, New York: A Study of Air-Exchange

Rates and Indoor Air Quality.” Environment

International, 8:435-445. 1985.

Pandian, M.D., W.R. Ott and J.V. Behar.

“Residential Air Exchange Rates for Use in Indoor

Air and Exposure Modeling Studies.” Journal

Exposure Analysis Environmental Epidemiology,

3(4):407-416. 1993.

Persily, A.K. and R.A. Grot. “Ventilation

Measurements in Large Office Buildings.”

ASHRAE Trans., 91(2A):488-502. 1985.

Roed, J. and R.J. Cannell. “Relationship between

Indoor and Outdoor Aerosol Concentration

Following the Chernobyl Accident.” Radiat. Prot.

Dosim., 21:107-110. 1987.

Romanoff. "Underground Corrosion." Circular 579.

National Bureau of Standards: Washington, D.C.

1957.

Royster, G.W. Jr. and B.R. Fish. “Techniques for

Assessing ‘Removable’ Surface Contamination.”

pp. 201-207 in Surface Contamination. B.R. Fish

(ed.). Pergamon Press: New York, N.Y. 1967.

Ruhter, P.E. and W.G. Zurliene. “Radiological

Conditions and Experiences in the TMI-Auxiliary

Building.” CONF-881-24-9. 1988.
7-6

Sansone, E.B. “Redispersion of Indoor Surface

Contamination.” Treatise on Clean Surface

Technology. Vol. 1. K.L. Mittal (ed.). Plenum

Press: New York, N.Y. 1987.

Schultz, N.B. and A.G. Becher. “Correlation of

Uranium Alpha Surface Contamination, Airborne

Concentrations, and Urinary Excretion Rates.”

Health Phys., 9:901-909. 1963.

Sehmel, G.A. “Particle and Gas Dry Deposition:

A Review.” Atmos. Environ., 14:983-1011. 1980.

Seinfeld, J.H. and S.N. Pandis. Atmospheric

Chemistry and Physics. John Wiley & Sons,

Inc.: New York, N.Y. 1998.

Shapiro, J. “Tests for the Evaluation of Airborne

Hazards from Radioactive Surface

Contamination.” Health Phys., 19:501-510. 1970.

Silberstein, S. and R.A. Grot. “Air Exchange

Rate Measurements in the National Archive

Building.” ASHRAE Trans., 91. 1985. 

Sinclair, J.D., L.A. Psota-Kelty and C.J.

Weschler. “Indoor/Outdoor Concentrations and

Indoor Surface Accumulations of Ionic

Substances.” Atmos. Environ., 19:315-323. 1985.

[As cited in Nazaroff and Cass, 1989.]

Spangler, D.L. “Re-Suspension Factor

Determination and Comparison Using Data from

an Operating Licensed Facility.” BWX

Technologies, Inc., presentation at NRC

Decommissioning Workshop and associated

transcript. Dec. 1, 1998. 

Sullivan, T.M. NUREG/CR-6041,

BNL-NUREG-52375, “Disposal Unit Source Term

(DUST) Data Input Guide.” prepared by

Brookhaven National Laboratory, Upton, N.Y., for

the Office of Nuclear Material Safety and

Safeguards, U.S. Nuclear Regulatory

Commission: Washington, D.C. 1993.

Tagg, B. “Determination of the Resuspension of

Plutonium Arising from Contaminated Surfaces in

a Plutonium Operational Area.” In Society for

Radiological Protection, International Symposium

on the Radiological Protection of the Worker by

the Design and Control of His Environment.

Bournemouth, England. April 18-22, 1966.

Thatcher, T.L. and D.W. Layton. “Deposition,

Resuspension, and Penetration of Particles

within a Residence.” Atmos. Environ.,

29(13):1487-1497. 1995.

Turk, B.H., et al. “Indoor Air Quality and

Ventilation Measurements in 38 Pacific

Northwest Commercial Buildings.” LBL-22315.

Lawrence Berkeley Laboratory: Berkeley, Calif.

1987.

U.S. Department of Energy. “Recommended

Tritium Surface Contamination Release Guides.”

DOE Tritium Surface Contamination Limits

Committee: Washington, D.C. February 1991.

U.S. Department of Energy. “Radiological Control

Manual.” Rev. 1, DOE/EH-0256T. DOE:

Washington, D.C. 1994.

Utnage, W.L. “Is There Significant Correlation

between Alpha Surface Contamination and Air

Concentration of Radioactive Particles in a

Uranium Feed Material Plant?” pp. 147-150 in

Proceedings of the Symposium on Occupational

Health Experience and Practices in the Uranium

Industry. HASL-58. U.S. Atomic Energy

Commission: Washington, D.C. 1959.

Vette, A.F., et al. “Characterization of IndoorOutdoor Aerosol Concentration Relationships

during the Fresno PM Exposure Studies.”

Aerosol Sci. Technol., 34:118-126. 2001.

Wallace, L., et al. “Continuous Measurements of

Particles, PAH, and CO in an Occupied

Townhouse in Reston, VA.” Measurement of

Toxic and Related Air Pollutants: Proceedings of

a Specialty Conference April 29 - May 1, 1997,

Research Triangle Park, N.C. Air and Waste

Management Association: Pittsburgh, Penn.

1997.

Weber, J. In Society for Radiological Protection,

International Symposium on the Radiological

Protection of the Worker by the Design and

Control of His Environment. Bournemouth,

England. 1966.
7-7

Weschler, C.J., H.C. Shields and D.V. Naik.

“Indoor Ozone Exposures.” JAPCA, 39:1562-

1568. 1989.

Weschler, C.J., H.C. Shields and D.V. Naik.

“Indoor Chemistry Involving O3, NO, and NO2 as

Evidenced by 14 Months of Measurements at a

Site in Southern California.” Environ. Sci.

Technol., 28:2120-2132. 1994.

Whitby, K.T. and G.M. Sverdrup. “California

Aerosols: Their Physical and Chemical

Characteristics.” p. 495 in The Character and

Origins of Smog Aerosols. G.M. Hidy, et al.

(eds.). John Wiley & Sons, Inc.: New York, N.Y.

1980. [As cited in John, 1993.]

Wrixon, A.D., et al. “Derived Limits for Surface

Contamination.” NRPB-DL2. National

Radiological Protection Board Harwell: Didcot,

Oxon, United Kingdom. 1979.

Yu, C., et al. “RESRAD-BUILD: A Computer

Model for Analyzing the Radiological Doses

Resulting from the Remediation and Occupancy

of Buildings Contaminated with Radioactive

Material.” ANL/EAD/LD-3. Argonne National

Laboratory: Argonne, Ill. 1994.

Yu, C., et al. NUREG/CR-6697, ANL/EAD/TM-98,

“Development of Probabilistic RESRAD 6.0 and

RESRAD-BUILD 3.0 Computer Codes.” Prepared

by Argonne National Laboratory, Argonne, Ill., for

U.S. Nuclear Regulatory Commission, Office of

Nuclear Regulatory Research: Washington, D.C.

2000.

Yu, C., et al. “User’s Manual for RESRAD

Version 6.” ANL/EAD/4. Argonne National

Laboratory: Argonne, Ill. 2001.
7-8
A-1

APPENDIX A:

UNMODIFIED RESRAD-BUILD

INPUT PARAMETER DISTRIBUTIONS
A-2
A-3

APPENDIX A:

UNMODIFIED RESRAD-BUILD

INPUT PARAMETER DISTRIBUTIONS

A.1 BUILDING CHARACTERISTICS

PARAMETER DISTRIBUTIONS

A.1.1 Shielding Density

Description: This parameter represents the

effective density of shielding between a receptor

and a radiation source.

Unit: Grams per cubic centimeter (g/cm3)

Probabilistic Input (allowed only for concrete):

Distribution: Uniform

Defining Values for Distribution:

Minimum: 2.2 Maximum: 2.6

Discussion: The type of shielding material along

with the shielding thickness and density

determines the gamma attenuation properties of

the shield. This parameter is important for the

external exposure pathway. For situations where

only air is between the source and receptor, the

shielding thickness should be set to 0 and the

density becomes immaterial. The type of

shielding material will often determine the

density.

In the RESRAD-BUILD code, the user must input

the shielding characteristics for each sourcereceptor pair (e.g., if there are 4 sources and 6

receptors, the code would require 24 [6 × 4]

shielding characteristics). RESRAD-BUILD

accommodates eight types of shielding

materials: concrete, water, aluminum, iron, lead,

copper, tungsten, and uranium. Table A.1 gives

the density range (if appropriate) and a single

value of density for the RESRAD-BUILD shielding

materials that have a narrow range (except

concrete). The table lists ranges for cast iron and

gives a single-value density for other materials.

The values are taken from the Health Physics and

Radiological Health Handbook (Shleien, 1992)

and from the CRC Handbook of Chemistry and

Physics (Lide, 1998). Table A.2 

Table A.1. Density of Shielding

Materials (except concrete)

Allowed in RESRAD-BUILD

Material

Density

Range

(g/cm3)

Normal

Density

(g/cm3)

Aluminum –

a 2.7

Copper – 8.96

Lead – 11.35

Steel – 7.8

Cast iron 7.0-7.4

Water – 1.0

Tungsten – 19.3 

Uranium – 19.1 

Iron – 7.87

a – = data not available.

Sources: Shleien (1992); Lide (1998).

provides the concrete density from three different

sources: Health Physics and Radiological Health

Handbook (Shleien, 1992), Properties of

Concrete (Neville, 1996), and Standard Handbook

for Civil Engineers (Merritt et al., 1995). The value

used in the code is for ordinary concrete. If the

type of concrete is known, a uniform distribution

between the given range for a known concrete

type can be used. Figure A.1 shows the

probability density function for the concrete

shielding density.

A.1.2 Indoor Fraction

Description: The indoor fraction is the fraction of

time an individual spends inside the

contaminated building (RESRAD-BUILD). 

Unit: Unitless
A-4

0

0.5

1

1.5

2

2.5

3

2 2.2 2.4 2.6 2.8 3

Shielding Density (g/cm3)

f(x)

Uniform Distribution

Minimum = 2.2 g/cm3

Maximum = 2.6 g/cm3

Figure A.1. Concrete Shielding Density Probability Density Function

Probabilistic Input:

Distribution: User-defined, continuous with linear

interpolation

Table A.2. Concrete Density from Various Sources

Concrete Density (g/cm3)

Aggregate

Shleien

(1992)

Neville

(1996)

Merritt et al.

(1995)

Ordinary (siliceous) or normal weight 2.2-2.4 2.2-2.6 2.3

Heavy weight –

a – 2.4-6.15

Limonite (goethite, hyd. Fe2O3) 2.6-3.7 – –

Ilmenite (nat. FeTiO3) 2.9-3.9 – –

Magnetite (nat. Fe3O4) 2.9-4.0 – –

Limonite and magnetite – – 3.35-3.59

Iron (shot, punchings, etc.) or steel 4.0-6.0 – 4.0- 4.61

Barite 3.0-3.8 – 3.72

Lightweight – 0.3-1.85 0.55-1.85

Pumice – 0.8-1.8 1.45-1.6

Scoria – 1.0-1.85 1.45-1.75

Expanded clay and shale – 1.4-1.8 –

Vermiculite – 0.3-0.8 0.55-1.2

Perlite – 0.4-1.0 0.8-1.3

Clinker – 1.1-1.4 –

Cinders without sand – – 1.36
A-5

Cinders with sand

– – 1.75-1.85

Shale or clay – – 1.45-1.75

Cellular – 0.36-1.5. –

No-fines – 1.6-2.0 1.68-1.8

No-fines with light weight aggregate – 0.64-

higher

–

Nailing – 0.65-1.6 –

Foam – – 0.3-1.75

a – = data not available.

Defining Values for Distribution: See Table A.3

for the input values.

Discussion: In RESRAD-BUILD, the indoor

fraction is used in the exposure calculations to

calculate the amount of time spent at each

receptor location. Actual exposure times at each

location are estimated by multiplying the

exposure duration by the indoor fraction and the

fraction of time at the receptor location.

With the exposure duration given in units of days

in RESRAD-BUILD, the indoor fraction is

represented by the fraction of the day an

individual spends indoors at work in the case of

occupational exposure. Beyeler et al. (1999)

examined records from the Bureau of Labor

Statistics (BLS) concerning the hours at work for

persons employed in the agricultural and

nonagricultural industries (BLS, 1996). The

distribution given in Table A.4 was based on the

assumption that full-time nonagricultural workers

spent 35 hours or more a week at work. However,

some workers may spend some time outside.

The U.S. Environmental Protection Agency’s

(EPA’s) Exposure Factors Handbook (EPA,

1997) contains a comprehensive review of human

activity patterns, including time spent at work.

That review extracts data for time spent at work

from the most complete and current study on

activity patterns (Tsang and Klepeis, 1996).

Table A.5 summarizes a number of distributions,

including distributions for time spent indoors at

unspecified work locations in a

plant/factory/warehouse. The distribution for fulltime workers in the plant/factory/warehouse

category is expected to be the best

representation for workers in the building

occupancy scenario and is the default for

RESRAD-BUILD. For perspective, the

50th percentile value for this distribution, 0.365,

corresponds to an 8.76-hour workday. The

cumulative distribution function for the indoor

fraction is shown in Figure A.2.

A.1.3 Room Area

Description: This parameter represents the floor

area of a specific room in the building.

Unit: Square meters (m2)
A-6

Table A.3. Cumulative

Distribution Functions

for the Indoor Fraction

Cumulative

Probability

RESRADBUILD

0 0.003

0.05 0.0347

0.25 0.306

0.50 0.365

0.75 0.403

0.90 0.469

0.95 0.500

0.98 0.542

0.99 0.594

1.0 0.692

Table A.4. Relative Frequency of Hours

Worked by Persons Working 35 Hours

or More per Week

Hours

Worked 

per

Weeka

Assuming a 5-Day

Work Week

Relative

Frequencya

Hours per

Day

Fraction

of Day

35-39 9.96 × 10-2 7 – 7.8 0.325

39-41 4.81 × 10-1 7.8 – 8.2 0.342

41-48 1.59 × 10-1 8.5 – 9.6 0.400

49-59 1.53 × 10-1 9.8 – 11.8 0.492

60-65 1.08 × 10-1 12 – 13 0.542

a Source: Beyeler et al. (1999).

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution: 

Minimum: 3 Maximum: 900

Most likely: 36

Discussion: The room area is used to determine

the mixing volume of each distinct air flow volume

(room) and the equilibrium of resuspension and

deposition. Studies concerning room size

distribution are not available. An arbitrary

distribution has been selected as a default for

use in application of RESRAD-BUILD to

commercial buildings. 
A-7

Table A.5. Statistics for Fraction of Time Spent Indoors at Work

Percentiles

Category

Population

Group Na Min Max 5 25 50 75 90 95 98 99

Fraction per Day Indoors at a Plant/Factory/WarehouseAll 383 0.001 0.692 0.021 0.243 0.354 0.394 0.465 0.490 0.535 0.594Gender Male 271 0.001 0.692 0.021 0.253 0.358 0.399 0.469 0.500 0.542 0.604Gender Female 112 0.003 0.569 0.010 0.218 0.354 0.385 0.417 0.469 0.490 0.500Age (years) 18-64 353 0.003 0.692 0.021 0.267 0.361 0.396 0.465 0.490 0.535 0.594Employment Full-Time 333 0.003 0.692 0.035 0.306 0.365 0.403 0.469 0.500 0.542 0.594Fraction per Day Spent Indoors at Work (unspecified)All 137 0.003 0.680 0.010 0.125 0.306 0.385 0.460 0.563 0.653 0.667Gender Male 96 0.007 0.680 0.014 0.170 0.328 0.415 0.531 0.583 0.667 0.680Gender Female 41 0.003 0.542 0.010 0.063 0.194 0.344 0.382 0.410 0.542 0.542Age (years) 18-64 121 0.003 0.680 0.010 0.167 0.313 0.389 0.458 0.551 0.590 0.667Employment Full-Time 97 0.007 0.680 0.010 0.208 0.333 0.406 0.479 0.566 0.667 0.680a Number of subjects in the survey.Source: Derived from cumulative minutes per day spent indoors listed in EPA (1997).
A-8

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

Indoor Fraction

F(x)

Figure A.2. Indoor Fraction Cumulative Distribution Function for RESRAD-BUILD

Site-specific distributions or deterministic values

should be used if available.

A triangular distribution is used to represent the

room area. A minimum value of 3 m2

(approximate room dimensions of 1.5 x 2 m) was

chosen to represent such areas as utility rooms

or storage closets in a commercial environment.

A maximum value of 900 m2 (slightly less than

10,000 ft2) was chosen to represent larger areas

that would correspond to the area of rooms

housing such functions as light industrial

assembly lines, small to intermediate warehouse

operations, or large assembly halls. However,

office space is generally required in support of

such larger operations. Such a requirement

skews the room size distribution toward smaller

room area, suggesting that a uniform distribution

between the minimum and maximum areas is not

appropriate. The choice of a most likely value for

a triangular distribution was arbitrary and

attempted to account for this observation. A most

likely value of 36 m2 (390 ft2) was chosen. This

value lies above what might be expected for the

area for a single-occupant office room

(approximately 12 m2, 3 m x 4 m) and is in the

range of what might be expected for a

multioccupant office room. Figure A.3 shows the

probability density function suggested for the

room area.

A.1.4 Room Height

Description: The room height is the distance

between the floor and the ceiling of a specific

room in the building.

Unit: Meters (m)

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution: 

Minimum: 2.4 Maximum: 9.1

Most likely: 3.7
A-9

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0 200 400 600 800 1000

Room Area (m2)

f(x)

Triangular Distribution

Minimum = 3 m2

Maximum = 900 m2

Most Likely = 36 m2

Figure A.3. Probability Density Function for Room Area

Discussion: The room height is used in

determining the mixing volume of each distinct

air-flow volume (room) and the equilibrium of

resuspension and deposition. Over half the new

single-family homes constructed annually have

room heights of 2.4 m (8 ft), as shown in

Table A.6. The 2.4-m (8-ft) height is considered to

be typical of residential housing (EPA, 1997).

Minimum room heights of 2.1 m (7 ft) below

beams and girders are required by the Council of

American Building Officials, with a ceiling height

of not less than 2.3 m (7.5 ft) for half of the

required area (National Association of Home

Builders [NAHB], 1998). The U.S. Department of

Housing and Urban Development requires a

minimum ceiling height of not less than 2.1 m

(7 ft) for at least half of the floor area and 1.9 m

(6 ft 4 in.) under ducts and beams.

No comprehensive study of room height in

commercial buildings exists. Room height can

vary within the same occupational setting as well

as between industries. Room height may also

vary according to climate (because of energy

efficiency considerations). A typical room height

in commercial buildings is 3.7 m (12 ft) (EPA,

1997). A minimum of 2.4 m (8 ft) is found in

smaller rooms, such as those used for 
A-10

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 2 3 4 5 6 7 8 9 10 11

Room Height (m)

f(x)

Triangular Distribution

Minimum = 2.4 m

Maximum = 9.1 m

Most Likely = 3.7 m

Figure A.4. Room Height Probability Density Function

Table A.6. Room Height in New

Conventional and Manufactured Homes,

1996

Room

Height

(m) [ft]

Conventional 

Homes (First

Floor), Percent

of Total

Manufactured

Homes,

Percent of

Total

# 2.1 [# 7] 0.1 48.2

2.3 [7.5] 1.6 37.4

2.4 [8.0] 57.8 5.1

2.6 [8.5] 0.8 1.5

2.7 [9.0] 24.2 7.7

> 2.7 [> 9] 15.5 –

Source: NAHB (1998).

individual offices or conference rooms. Larger

room heights are found in warehousing

(shipping/receiving) operations, which may have

room heights of up to approximately 9.1 m (30 ft).

Thus, for the occupational scenario, a triangular

distribution is used for the room height, with a

most likely value of 3.7 m (12 ft) and minimum

and maximum values of 2.4 (8 ft) and 9.1 m

(30 ft), respectively. This distribution is a rough

generalization, and site-specific data should be

used when available. The probability density

function is shown in Figure A.4.

A.1.5 Shielding Thickness

Description: This parameter represents the

effective thickness of shielding between a source

and receptor pair.

Unit: Centimeters (cm)

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution: 

Minimum: 0 Maximum: 30 

Most likely: 0 

Discussion: The shielding thickness parameter is

used in determining the attenuation of direct

external radiation from each source to each

receptor. Shielding thickness only affects the

external exposure pathway. For situations in

which only air is present between the source 
A-11

and receptor, the shielding thickness is 0. The

RESRAD-BUILD code requires the shielding

thickness for every source and receptor pair (e.g.,

if there were 4 sources and 6 receptors, the code

would require 24 [6 × 4] shielding thickness input

values). The same shielding object might be

assigned different thicknesses for different

source-receptor pairs because of geometry

considerations. It is highly recommended that the

shielding thickness value be obtained from a

direct measurement based on the site-specific

condition. For example, to calculate the dose for

a receptor in a room other than the room in which

the source is located, a shielding thickness

equivalent to the wall thickness should be

assumed.

Floor and wall thicknesses vary, depending on

the type of building and type of construction. To

estimate the total contaminated volume of

concrete from U.S. Department of Energy (DOE)

facilities, Ayers et al. (1999) assumed an average

concrete thickness of 12 in. (30 cm) in a building.

For external exposure calculations, this

thickness approximates an infinite thickness for

alpha-emitters, beta-emitters, and X-ray or lowenergy photon emitters. A shielding thickness of

12 in. (30 cm) would reduce the 

dose significantly from the external exposure

pathway for all radionuclides, including highenergy gamma emitters.

Little information is available for the shielding

thicknesses in actual decontamination and

decommissioning (D&D) situations; therefore, a

triangular distribution is assumed. The maximum

value is assumed to be 12 in. (30 cm), the

minimum value is chosen as 0 in. (0 cm), and the

most likely value also is chosen to be 0 in.

(0 cm) (this assumption would yield the most

conservative dose results for the external

exposure pathway). The probability density

function is shown in Figure A.5.

A.1.6 Humidity

Description: In RESRAD-BUILD, this parameter

represents the average absolute humidity in the

building. The absolute humidity is an input used

only for the tritium volume source model.

Unit: Grams per cubic meter (g/m3)

Probabilistic Input:

Distribution: Uniform

Defining Values for Distribution:

Minimum: 6.5 Maximum: 13.1

Discussion: RESRAD-BUILD requires input for

the absolute humidity, that is, the actual

concentration of water vapor in air. The relevant

data available are given in terms of the relative

humidity (RH). The RH of a water vapor-air

mixture is defined as 100 times the partial

pressure of water divided by the saturation vapor

pressure of water at the same temperature. For

this section, relative humidity was converted to

absolute humidity by assuming a total pressure

of 1 atmosphere, in conjunction with a given

temperature and partial pressure of water at that

temperature. Tabulated values for the partial

pressure of water over a range of temperatures

were obtained from Dean (1999).
A-12

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 5 10 15 20 25 30 35 40

Shielding Thickness (cm)

f(x)

Triangular Distribution

Minimum = 0 cm

Maximum = 30 cm

Most Likely = 0 cm

Figure A.5. Shielding Thickness Probability Density Function

0

0.05

0.1

0.15

0.2

0.25

4 5 6 7 8 9 10 11 12 13 14 15

Absolute Humidity (g/m3)

f(x)

Uniform Distribution

Minimum = 6.5 g/m3

Maximum = 13.1 g/m3

Figure A.6. Absolute Humidity Probability Density Function for RESRAD-BUILD

For RESRAD-BUILD, the average humidity in a

building depends on the functioning of heating,

ventilation, and air-conditioning (HVAC) systems

of the building. At normal room temperatures, the

RH in occupied buildings should be maintained

between approximately 30% and 60% to help

maintain human health and comfort (Sterling

et al., 1985). With respect to health, this range in

RH minimizes allergic reactions and bacterial and

viral growth. Human discomfort is noted at low

and high humidities. Discomfort at low RH results

from the drying of skin, hair, and respiratory

membranes. 

Because HVAC systems are designed to

maintain a healthy environment for building

occupants (the 30% to 60% RH range), a uniform

distribution for the corresponding absolute

humidity range is used in RESRAD-BUILD. The

range of 30 to 60% RH corresponds to an

absolute humidity range of 6.5 to 13.1 g of water

per cubic meter at 1 atmosphere pressure and

24EC (75EF). The probability density function is

shown in Figure A.6. However, RH values lower

than 30% may occur in buildings that do not have

a humidification system, especially during the

winter in colder climates. Also, RH values higher

than 60% may occur in buildings using natural

ventilation in more temperate climates. 

In more temperate climates where natural

ventilation may be employed, the humidity inside

the building will be more representative of the

outside levels. Data from 231 weather stations

across the coterminous 48 United States, most

with more than 30 years of recorded data, were

analyzed to obtain a perspective on ambient

outdoor humidity levels. Annual average morning

and afternoon RH levels were used in conjunction

with annual average temperature readings at

these weather stations (National Climatic Data

Center [NCDC], 1999) to estimate absolute

humidity levels. The morning and afternoon RH

levels were averaged for each station to obtain

one value for the annual average relative humidity

for use in estimating the absolute humidity. 

The resulting absolute humidity probability

density function was fit reasonably well to a

lognormal distribution by using Bayesian

estimation, as shown in Figure A.7. This

alternative distribution is only indicative of what
A-13

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Absolute Humidity (g/m3)

Frequency

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

f(x)

Frequency

f(x)

Lognormal Distribution

Underlying m = 1.98

Underlying s = 0.334

Figure A.7. Alternative Absolute Humidity Probability Density Function 

Based on Outdoor Humidity Levels

might be expected, because the sampling is not

representative of a uniform grid across the United

States; it is indicative, however, of the larger

population centers. Site-specific data should be

used when available.

A.2 SOURCE CHARACTERISTICS

PARAMETER DISTRIBUTIONS

A.2.1 Source Density, Volume Source

Description: The source density parameter

represents the effective density of each

cylindrical layer (region) in an idealized volume

source. 

Unit: Grams per cubic centimeter (g/cm3)

Probabilistic Input (allowed only for concrete):

Distribution: Uniform

Defining Values for Distribution:

Minimum: 2.2 Maximum: 2.6

Discussion: The source density parameter is

used to calculate the total amount of

radionuclides in the source volume, and it affects

the external pathway doses. In the RESRADBUILD code, the volume source can be defined

with up to five distinct parallel regions (or layers)

located along the direction parallel to the

partition, each consisting of homogeneous and

isotropic materials. RESRAD-BUILD allows the

following eight materials: concrete, water,

aluminum, iron, lead, copper, tungsten, and

uranium. Each source layer is defined by its

physical properties, such as thickness, density,

porosity, radon effective diffusion coefficient,

radon emanation fraction, and erosion rate.

Table A.7 lists the density range (if appropriate)

or a single value of density for the RESRADBUILD materials that have a narrow range of
A-14

density (except concrete). The table lists a range

for cast iron and gives a single-value density for

each of the other materials. The values are taken

from the Health Physics and Radiological Health

Handbook (Shleien, 1992) and from the CRC

Handbook of Chemistry and Physics (Lide, 1998)

(for cast iron, uranium, and tungsten). Table A.8

provides the concrete density from three different

sources: Health Physics and Radiological Health

Handbook (Shleien, 1992), 

Table A.7. Density of Shielding Materials

(except concrete) Allowed in RESRADBUILD

Material

Density Range

(g/cm3)

Normal Density

(g/cm3)

Aluminum –

a 2.7 

Copper – 8.96 

Lead – 11.35 

Steel – 7.8 

Cast iron 7.0-7.4

Water – 1.0 

Tungsten – 19.3 

Uranium – 19.1 

Iron – 7.87 

a – = data not available.

Sources: Shleien (1992); Lide (1998).

Properties of Concrete (Neville, 1996), and

Standard Handbook for Civil Engineers (Merritt

et al., 1995). The value used in the code is for

ordinary concrete. If the type of concrete is

known, a uniform distribution between the given

range for a known concrete type can be used.

Figure A.8 shows the probability density function

for the concrete source density.

A.2.2 Source Porosity

Description: The source porosity is the ratio of

the pore volume to the total volume of a

representative sample of the source material.

Unit: Unitless

Table A.8. Concrete Density from Various Sources

Concrete Density (g/cm3)

Aggregate

Shleien

(1992)

Neville

(1996)

Merritt et al.

(1995)

Ordinary (siliceous) or normal weight 2.2-2.4 2.2-2.6 2.3

Heavy weight – 

a – 2.4-6.15

Limonite (goethite, hyd. Fe2O3) 2.6-3.7 – –

Ilmenite (nat. FeTiO3) 2.9-3.9 – –

Magnetite (nat. Fe3O4) 2.9-4.0 – –
A-15

0

0.5

1

1.5

2

2.5

3

2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3

Source Density (g/cm3)

f(x)

Uniform Distribution

Minimum = 2.2 g/cm3

Maximum = 2.6 g/cm3

Figure A.8. Concrete Source Density Probability Distribution Function

Limonite and magnetite – – 3.35-3.59

Iron (shot, punchings, etc.) or steel 4.0-6.0 – 4.0- 4.61

Barite 3.0-3.8 – 3.72

Lightweight – 0.3-1.85 0.55-1.85

Pumice – 0.8-1.8 1.45-1.6

Scoria – 1.0-1.85 1.45-1.75

Expanded clay and shale – 1.4-1.8 –

Vermiculite – 0.3-0.8 0.55-1.2

Perlite – 0.4-1.0 0.8-1.3

Clinker – 1.1-1.4 –

Cinders without sand – – 1.36

Cinders with sand – – 1.75-1.85

Shale or clay – – 1.45-1.75

Cellular – 0.36-1.55 –

No-fines – 1.6-2.0 1.68-1.8

No-fines with light weight aggregate – 0.64-higher –

Nailing – 0.65-1.6 –

Foam – – 0.3-1.75

a – = data not available.

Probabilistic Input (allowed only for concrete):

Distribution: Uniform

Defining Values for Distribution: 

Minimum: 0.04 Maximum: 0.25

Discussion: The source porosity parameter is

used in RESRAD-BUILD to calculate the diffusion

of radon and tritium from a volume source and is

applicable to the tritium inhalation and the radon

inhalation pathways. This parameter is only

required as input if a tritium volume source is

selected or if radon (radon-220 and radon-222)
A-16

precursors are entered as part of the volume

source.

Porosity may range from 0 to 1 and may be

reported as a decimal fraction or as a

percentage. Input to the RESRAD-BUILD code is

as a decimal fraction. A value of 0 represents a

material that is completely solid, without any void

spaces. On the other extreme, a porosity

approaching 1 represents a material that is made

up mostly of void spaces. Building materials such

as concrete, brick, or rock typically have

porosities ranging from 0 to 0.3. 

Widespread variations in concrete porosity have

been observed because of the differences in the

aggregates used, water/cement ratios in the

cement paste, and curing conditions. Cement

paste in concrete occupies from 23 to 36% of the

total volume (Culot et al., 1976), sand 25 to 30%,

and aggregates the remainder. Overall porosity of

concrete depends on the porosity of the cement

paste as well as of the aggregates. The porosity

of concrete was found to range from 0.05 to 0.25

(Culot et al., 1976). 

The porosity estimated for a concrete structure

made of Portland cement was found to vary from

0.04 to 0.20 (Frankowski et al., 1997). Table A.9

gives the bulk density and porosity of the rocks

commonly used as building materials (Bever,

1986). Materials used for thermal insulation tend

to have a very high air content, with porosities

approaching 1. Material porosity tends to be

inversely correlated with material density; low

porosity materials tend to have higher densities

than any porous materials.

On the basis of the definition of porosity, the

porosity of a material could be evaluated by

directly measuring the pore volume and the total

volume. The American Society for Testing and 

Table A.9. Bulk Density and Porosity

of Rocks Commonly Used as Building

Materials

Rock

Bulk Density

(g/cm3)

Porosity

(%)

Granite 2.6-2.7 0.5 – 1.5

Basalt 2.8-2.9 0.1 – 1.0

Sandstone 2.0-2.6 0.5 – 25.0

Limestone 2.2-2.6 0.5 – 20.0

Gneiss 2.9-3.0 0.5 – 1.5

Marble 2.6-2.7 0.5 – 2.0

Source: Bever (1986).

Materials (ASTM) has established a standard

procedure (B 276) for cemented carbide to rate

three types of porosities, depending on the pore

diameters (Type A, pore diameters < 10 :m;

Type B, pore diameters between 10 and 25 :m;

and Type C, covering porosity developed by the

presence of free carbon). Similarly, the ASTM

has developed standard test methods for porosity

of metal structure parts, and porosity tests for

electrodeposits and related metallic coatings

(http://www.astm.org/sitemap.html).

For generic applications, a uniform distribution

from 0.04 to 0.25 is suggested for the source

porosity for concrete. The minimum and

maximum values were those reported by

Frankowskiet al. (1997) and Culot et al. (1976),

respectively. The probability density function is

shown in Figure A.9.

A.2.3 Volumetric Water Content

Description: The volumetric water content is the

volume of water per unit volume of the porous

material.

Unit: Unitless

Probabilistic Input (allowed only for concrete):

Distribution: Uniform

Defining Values for Distribution:

Minimum value: 0.0 Maximum value: 0.25

Discussion: The volumetric water content is

used in RESRAD-BUILD when evaluating the

radiological risks from a volume source

contaminated with tritium. The assumption is

made that any tritium is present as tritiated

water. Because the contamination is assumed to

result from a recent spill, the amount of water in

the volume source is expected to be within the

range of the concrete’s total porosity. Thus, the

distribution for the volumetric water content is

expected to be the same as the source porosity
A-17

0

1

2

3

4

5

6

7

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Source Porosity

f(x)

Uniform Distribution

Minimum = 0.04

Maximum = 0.25

Figure A.9. Concrete Source Porosity Probability Density Function

(Section A.2.2). In any case, the maximum value

assigned to the volumetric water content should

not be greater than the maximum of the source

porosity.

A.2.4 Air Release Fraction

Description: The air release fraction is the

amount of the contaminated material removed

from the source that is released into the air and

in the respirable particulate range.

Unit: Unitless

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution: 

Minimum: 1 x 10-6 Maximum: 1

Most likely: 0.07

Discussion: The fraction released to the air is

the amount of the contaminated material removed

from the source that is actually suspended in air;

the balance of the material is assumed to be

instantaneously removed from the room. It is a

dimensionless parameter that can range from 0

(all eroded material is removed instantaneously

from the room) to 1 (all eroded material is

suspended instantaneously in the respirable

room air). This parameter depends strongly on

the erosion process. Dusting would result in low

erosion rates, but a relatively high fraction of

removed material may become suspended in air.

Vacuuming may result in higher erosion rates

than dusting, but a smaller fraction would

become airborne; a significant fraction would be

trapped in the vacuum. Mechanical disturbances,

such as sanding, scraping, or chipping, result in

a high contaminant removal rate but usually

generate a relatively small fraction of particulates

released to air. Most of the eroded material tends

to fall to the floor and is removed from the room

by housekeeping activities.

The RESRAD-BUILD code requires an air release

fraction input for each source. Entering 0 means

that none of the removable material will be

released to the air that is respirable. The dose

contributions from deposition, immersion, dust

inhalation, and indirect ingestion are effectively

suppressed. Entering 1 is very conservative
A-18

0

0.5

1

1.5

2

2.5

0 0.2 0.4 0.6 0.8 1

Air Release Fraction

f(x)

Triangular Distribution

Minimum = 1 × 10-6

Maximum = 1

Most Likely = 0.07

Figure A.10. Air Release Fraction Probability Density Function

because it will maximize the dose contributions

from these pathways. If either the removable

fraction or the erosion rate is 0, the contributions

from these pathways will be suppressed, no

matter what value is given to the air release

fraction.

The U.S. Department of Energy (DOE) handbook

on airborne release and respirable fractions (RFs)

(DOE, 1994) provides a compendium and

analysis of experimental data from which airborne

release fractions1 (ARFs) and RFs2 may be

derived. The data are given by the physical form

of the material affected (e.g., gas, liquid, solid,

surface contamination) and different suspension

stresses (e.g., spill, thermal stress, shock wave,

blast stress). The American Nuclear Society

(ANS) has published a American National

Standard for airborne release fractions at

nonreactor nuclear facilities (ANS, 1998). 

For materials in gaseous form, such as H-3, the

recommended airborne release fraction is 1.0. All

materials in the gaseous state can be

transported and inhaled; therefore, the respirable

fraction is also 1.0 (DOE, 1994).

The DOE handbook provides release fractions for

three categories of solid materials: metals,

nonmetallic or composite solids, and powders.

The bounding ARF for plutonium metal formed by

oxidation at elevated temperature was found to be

3 × 10-5, with an RF value of 0.04. ARF and RF

values of 1 × 10-3 and 1.0 were assessed to be

bounding during complete oxidation of metal

mass (DOE, 1994). The bounding values for

contaminated, noncombustible solids were found

to be 0.1 and 0.7 for ARF and RF, respectively

(these release values are for loose surface

contamination on the solid, not the solid as a

whole).

Little information is available for the building

occupancy scenario air release fraction;

therefore, a triangular distribution based on the

above data is used to generate distribution. The

maximum value is assumed to be 1 (for gaseous

forms), the minimum value chosen is that for

plutonium metal (3 × 10-5 ×0.04 = 1.2 × 10-6), and

the mode (most likely value) is the bounding

value for contaminated noncombustible solids

1 The airborne release fraction is the amount of

radioactive material that can be suspended in

air and made available for airborne transport.

2 The respirable fraction is the fraction of

airborne radionuclides as particulates that can

be transported through air and inhaled into the

human respiratory system. This fraction is

commonly assumed to include particles of 10-

:m aerodynamic equivalent diameter and less.
A-19

(0.1 × 0.7 = 0.07). Figure A.10 shows the

probability density function.

A.2.5 Wet + Dry Zone Thickness

Description: This parameter represents the

depth from the surface of the contaminated

material to the deepest point of the contaminated

zone.

Unit: Centimeters (cm)

Probabilistic Input (allowed only for volume

contamination with tritium):

Distribution: Uniform

Defining Values for Distribution:

Minimum: 5 Maximum: 30 

Discussion: The wet + dry zone thickness

parameter is used in RESRAD-BUILD in

modeling the emission rate of tritiated water

(HTO) vapor from the contamination source to the

indoor atmosphere. In a tritium handling facility,

tritium contamination of the construction

material and the equipment is recognized as an

important source in defining the requirements for

atmospheric cleanup and personnel protection.

Tritium released during the handling process can

quickly sorb to surfaces of the surrounding

materials (e.g., concrete walls and floors) and

can diffuse through many of them, resulting in

contamination of the bulk as well as of the

surface. The tritium that is absorbed/adsorbed to

the surrounding materials can then be desorbed

and released to the indoor air. This

sorption/desorption process is generally referred

to as the “tritium soaking effect” in tritium

handling facilities. 

Tritium released from the tritium handling facilities

can be in different chemical forms; the most

common ones are tritium gas (HT) and tritium

oxide, or HTO. In general, sorption and

desorption of HT occurs faster than that of HTO;

however, the total amount sorbed and desorbed

is greater for HTO than for HT (Wong et al., 1991,

Dickson and Miller, 1992). In contrast, HT can

easily be converted to HTO in the environment.

Experimental data concerning the tritium soaking

effect on construction metals also showed that

about 90% of the tritium desorbed from metal

samples was in the form of HTO, although the

samples were exposed to an atmosphere of HT

(Dickson and Miller, 1992). Because of the

conversion from HT to HTO and the potentially

longer time required for degassing of HTO

(desorption and subsequent release from the

contaminated material to the indoor air), the

tritium model incorporated into the RESRADBUILD code considers only the potential

degassing of HTO after the tritium handling

operation has stopped. 

Among all the materials that can become

contaminated, concrete is of special concern

because of its porous nature. The high porosity of

concrete materials makes them more vulnerable

to the permeation of tritiated water, which can

spread out inside the concrete matrix after the

initial surface absorption/adsorption. In RESRADBUILD, the degassing (i.e., the release) of the

HTO vapor is assumed to be controlled by

diffusion of the free HTO molecules from inside of

the concrete matrix to the concrete-atmosphere

interface (the “free” molecules are the HTO

molecules that are not bound to the concrete
A-20

matrix and are available for diffusion, see

discussion for the water fraction available for

evaporation parameter, Section A.2.7).

The diffusion of HTO is assumed to proceed like

a peeling process in which the HTO molecules

closer to the concrete-atmosphere interface are

released earlier than those farther from the

interface. As the release process continues, a

region free of free HTO molecules (i.e., the dry

zone) is formed, and its thickness increases over

time. The dry zone thickness then represents the

path length for the subsequent diffusion. The

region inside the concrete where the free HTO

molecules are distributed is called the wet zone.

As the dry zone becomes thicker, the thickness

of the wet zone decreases accordingly. In fact,

the sum of the dry zone thickness and the wet

zone thickness is assumed to remain the same

throughout the diffusion process.

Although diffusion of the HTO vapor to the bulk of

concrete materials in a tritium handling facility is

recognized (Wong et al., 1991), direct detection

of the extent of spreading into the bulk (i.e., dry +

wet zone thickness) is not possible because of

the short range of the beta radiation (DOE, 1991).

However, judging by the high porosity of concrete

materials, spreading of the HTO vapor throughout

the entire thickness is possible if the exposure is

of sufficient duration. Therefore, the thickness of

the concrete wall is assumed for the dry + wet

zone thickness parameter, which, on the basis of

engineering judgments, can be as much as 12 in.

(30 cm). A low bound of 2 in. (5 cm) is selected

because bulk contamination will not be extensive

for a short exposure period. Figure A.11 shows

the probability density function.

A.2.6 Source Thickness, Volume Source

Description: This parameter represents the

thickness of each layer in an idealized volume

source. This parameter does not apply to area,

line, or point sources.

Unit: Centimeters (cm)

Probabilistic Input: 

Distribution: Triangular

Defining Values for Distribution: 

Minimum: 2.5 Maximum: 30

Most likely: 15
A-21

0

0.01

0.02

0.03

0.04

0.05

0.06

0 5 10 15 20 25 30 35 40 45

Wet + Dry Zone Thickness (cm)

f(x)

Uniform Distribution

Minimum = 5 cm

Maximum = 30 cm

Figure A.11. Wet + Dry Zone Thickness Probability Density Function

Discussion: RESRAD-BUILD allows

consideration of a total of five distinct regions

(layers) in a volume source. The contamination is

within these regions, and the total thickness of

the volume source is the sum of the thicknesses

of these regions. The code requires a source

thickness (in centimeters) for every layer of each

volume source. The source thickness depends on

the detail of modeling desired. For example, a

wall could be modeled as a single layer or

multiple layers (e.g., a sequence of paint,

drywall, framing gap, drywall, and paint), with up

to five layers per source. It is highly

recommended that the source thickness be

obtained from direct measurement or be

estimated on the basis of the applicable building

codes. The contaminated layer thickness and

position should be based on site-specific

measurement.

With the exception of sources resulting from

neutron activation, most volume activity in

buildings will be limited to small areas (hot spots)

or rather shallow sources. For the case of

neutron activation, volume sources could extend

deep into the volume of a building structure. The

thickness of building structure materials will

place a limit on the potential thickness for volume

sources. Ayers et al. (1999) noted that the

contamination of concrete usually results from

spills, contaminated dust, or other surficial

deposition. In some instances, the contaminants

may migrate into the concrete matrix, particularly

over time and under environmental stresses.

Cracks and crevices may also provide routes for

contaminants to spread deeper into the concrete

matrix. To estimate the total contaminated

volume of concrete from DOE facilities, Ayers et

al. (1999) assumed contamination to a 2.5-cm (1-

in.) depth and an average concrete thickness of

30 cm (12 in.) in a building. For external

exposure calculations, this thickness will

approximate an infinite thickness for alphaemitters, beta-emitters, and X-ray or low-energy

photon emitters. The DandD and RESRAD-BUILD

codes use 15 cm (6 in.) as the default source

thickness for a volume source.

Little information is available for the source

thicknesses in real D&D situations; therefore, on

the basis of the above data, a triangular

distribution is assumed for source thickness. The
A-22

maximum value is assumed to be 30 cm (12 in.),

the minimum value is chosen as 2.5 cm (1 in.),

and the most likely value is the 15-cm (6-in.)

default used in the DandD and RESRAD-BUILD

codes for volume sources. Figure A.12 shows the

probability density function for the source

thickness.

A.2.7 Water Fraction Available for

Evaporation

Description: This parameter is used in

estimating the potential release rate of HTO vapor

from a volume contamination source. It is the

fraction of the total amount of HTO that will be

released to the indoor air through the diffusion

mechanism under room temperature.

Unit: Unitless

Probabilistic Input (allowed only for volume

contamination with tritium)

Distribution: Triangular

Defining Values for Distribution:

Minimum: 0.5 Maximum: 1.0

Most likely: 0.75

Discussion: In a tritium handling facility, tritium

contamination of the construction material and

the equipment is recognized as an important

radiation source in defining the requirements for

atmospheric cleanup and personnel protection.

Tritium released during the handling process can

quickly sorb to surfaces of the surrounding

materials and can diffuse through many of them,

resulting in both bulk (volumetric) and surface

contamination. The tritium that is absorbed or

adsorbed to the surrounding materials can then

be desorbed from the materials and released to

the indoor air. This sorption/desorption process is

generally referred to as the “tritium soaking effect”

in tritium handling facilities. 

Tritium released from the tritium handling facilities

can be in different chemical forms; the most

common ones are HT and tritium oxide, or HTO.

In general, sorption and desorption of HT occurs

faster than that of HTO; however, the total

amount sorbed and desorbed is greater for HTO

than for HT (Wong et al., 1991; Dickson and

Miller, 1992). In contrast, HT can easily be

converted to HTO in the environment.

Experimental data concerning the tritium soaking

effect on construction metals also 
A-23

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 5 10 15 20 25 30 35 40

Source Thickness, Volume Source (cm)

f(x)

Triangular Distribution

Minimum = 2.5 cm

Maximum = 30 cm

Most Likely = 15 cm

Figure A.12. Source Thickness Probability Density Function
A-24

showed that about 90% of the tritium desorbed

from the metal samples was in the form of HTO,

although the samples were exposed to an

atmosphere of HT (Dickson and Miller 1992).

Because of the conversion from HT to HTO and

the potentially longer time required for degassing

of HTO (desorption and subsequent release from

the contaminated material to the indoor air), the

tritium model incorporated into the RESRADBUILD code considers only the potential

degassing of HTO after the tritium handling

operation has stopped.

Among all the materials that can become

contaminated, concrete is of special concern

because of its porous nature. The high porosity of

concrete materials makes them more vulnerable

to the permeation of HTO, which can spread out

inside the concrete matrix after the initial surface

absorption/adsorption. In RESRAD-BUILD, the

degassing (i.e., the release) of the HTO vapor is

assumed to be controlled by diffusion of the HTO

molecules from inside of the concrete matrix to

the concrete-atmosphere interface. 

The diffusion rate is estimated on the basis of

information on the extent of the contamination

(thickness of dry zone, thickness of dry zone +

wet zone, and area of contamination),

characteristics of the source material (porosity

and moisture content), tritium inventory (tritium

concentration), and indoor humidity. Because not

all the tritium in the source material is available

for diffusion under ordinary building occupancy

conditions, estimation of the release rate has to

take into account the fraction of HTO available for

evaporation and diffusion.

According to the experimental observations of

Numata and Amano (1988), water exists in

concrete in two states: free water and bound

water. Free water is the liquid water that fills the

pore space and capillaries in the concrete. Bound

water is the water that combines with constituent

compounds in concrete or the constituent itself.

The fraction of free water was determined by

Numata and Amano (1988) in their thermal

desorption experiments as the fraction that was

desorbed from concrete samples when the

heating temperature was less than 200oC. The

existence of free water versus bound water was

verified in the investigation by Ono et al. (1992),

who studied sorption and desorption of tritiated

water on paints. That study found that recovery of

tritium sorbed to various paint materials was not

complete by gas sweeping under 30EC (86EF).

Residual tritium sorbed was recovered by heating

up the samples up to 800EC (1,472EF). Although

the samples used by Ono et al. (1992) were

different from the concrete samples used by

Numata and Amano (1988), it is quite conclusive

that some HTO can form strong bounding with

the source materials. In the RESRAD-BUILD

tritium model, it is assumed that under ordinary

building occupancy conditions, only the water

that fills the pore space and capillaries of the

concrete materials will evaporate and diffuse to

the indoor atmosphere. 

Numata and Amano (1988) reported that the

fraction of free HTO in concrete samples

depended on the duration of the previous

exposure of the samples to tritiated water vapor.

A shorter exposure duration resulted in a larger

fraction of free tritiated water. However, as the

exposure duration was increased to more than

60 days, equilibrium values were observed. The

fraction of free tritiated water at equilibrium was

0.72 for hardened cement paste and 0.74 for

mortar. The fraction of free ordinary water was

lower than that for tritiated water because the

ordinary water originally exists in the samples

and was the residual water left during

crystallization of the cement samples. The free

fraction was about 0.58 for both hardened cement

paste and mortar samples.

The free fractions of ordinary water reported by

Numata and Amano (1988) are consistent with

the suggestion in DOE (1994) regarding the air

release fraction of HTO from concrete materials

under accidental conditions that can cause the

temperature to reach as high as 200oC (392EF).

In the DOE report (1994), it was assumed that

tritiated water was used in concrete formation,

which is the same role as ordinary water in

Numata and Amano’s experiments.

On the basis of the above discussion, it can be

concluded that (1) the free fraction of tritiated

water in concrete materials used in tritium

handling facilities is greater than the free fraction

of ordinary water in the same materials, and (2)

the free fraction of tritiated water in the concrete

materials can be very high if the exposure

duration of the concrete materials to tritiated
A-25

water was very short. Therefore, a triangular

distribution with a minimum of 0.5, a maximum of

1.0, and a most likely value of 0.75 was assumed

for the free water fraction available for evaporation

parameter. The probability density function is

shown in Figure A.13.

A.3 HUMAN INTAKE PARAMETER

DISTRIBUTIONS

A.3.1 Receptor Inhalation Rate

Description: This parameter reflects the rate at

which a human receptor inhales air contaminated

with resuspended airborne material. 

Unit: cubic meters per day (m3/d) (RESRADBUILD)

Probabilistic Input:

Distribution: Triangular

Defining Values for Distribution:

Minimum: 12 Maximum: 46

Most likely: 33.6

Discussion: The range of estimates of inhalation

rate (Table A.10) reflects the differences in

patterns of time and activity levels, as well as

age, sex, and weight of the individual. Until

recently, inhalation rates for the “reference man

and woman,” as described by the International

Commission on Radiological Protection (ICRP,

1975), were often used as default values. The

ICRP best estimates, which are based on

16 hours of light activity and 8 hours of rest, are

as follows: 23 m3/d (range of 23–31 m3/d) for adult

males; 21 m3/d (range of 18–21 m3/d) for adult

females; and 15 m3/d for a 10-year-old child. By

using different patterns for the time and activity

levels, the EPA has proposed a wider range of

adult inhalation rates but recommends

essentially the same point estimates as the

ICRP for “average” adults (EPA, 1985, 1989,

1991, 1997).

The distribution varies widely because of

differences in time-use activity patterns that are

developed for outdoor/indoor and

occupational/residential exposures. Because

activity levels of various individuals and groups

can vary to such a significant extent, it is

preferable to derive a range of inhalation rates by

using activity data specific to the population 
A-26

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Water Fraction Available for Evaporation

f(x)

Triangular Distribution

Minimum = 0.5

Maximum = 1.0

Most Likely = 0.75

Figure A.13. Water Fraction Available for Evaporation
A-27

Table A.10. Inhalation Rate Distributions

Inhalation Rate (m3/d)

Basis

Distribution

Type Min. Max. Mean

Most

Likely Reference

Based on time-weighted average food-energy

intakes adjusted for reporting bias

Triangular Layton (1993)

 Males (lifetime average)

 Females (lifetime average)

13

9.6

17

13

14 

10 

Based on average age-adjusted daily energy

expenditure rates

Triangular Layton (1993)

 Males (18-60+ yr)

 Females (18-60+ yr)

13

9.9

17

11

15 

11 

Based on age-adjusted activity patterns and

metabolic rates for an “average” day

Triangular Layton (1993)

 Males (20-74 yr)

 Females (20-74 yr)

13

11

17

15

16 

13 

“Reference man” - Based on light activity

(16 hours) and resting (8 hours)

Triangular ICRP (1975)

 Adult male

 Adult female

 Child

23

18

-

31

21

 -

23 

21 

15 

Based on “typical” outdoor activity levels a Triangular EPA (1985, 1989, 1991)

 Adult female

 Adult male

 Average adult

17

13

-

70

79

 -

25

40

34

20 

20 

20 

Based on “typical” indoor activity levelsb Triangular EPA (1985, 1989, 1991)

 Adult female

 Adult male

 Average adult

7

4

-

34

38

-

11

21

15

15 

15 

15 

Study of age-dependent breathing rates at

realistic activity levels

- Roy and Courtay (1991)

 0-0.5 yr 1.62 

 0.5-2 yr 5.14 

 2-7 yr 8.71 

 7-12 yr 15.3 

 12-17 yr 17.7 

a Resting: 28%, light activity: 28%, moderate activity: 37%, heavy activity: 7%.

b Resting: 48%, light activity: 48%, moderate activity: 3%, heavy activity: 1%.

under study. The hourly average inhalation rate in

RESRAD-BUILD is intended to represent workers

in an occupational setting. For assessments

involving other specific activities, inhalation rates

can be selected that are thought to be

representative of these particular activities.

Similarly, if receptors of a certain age group are

being evaluated, breathing rate values should be

selected specifically for that age group.

Layton (1993) proposed three alternative

approaches for deriving inhalation rates that are

based on oxygen uptake associated with energy

expenditures: (1) average daily intakes of food

energy from dietary surveys, (2) average daily
A-28

energy expenditure calculated from ratios of total

daily expenditure to basal metabolism, and

(3) daily energy expenditures determined from a

time-activity survey. These approaches

consistently yield inhalation rate estimates that

are lower than the EPA's best “reasonable 

worst-case” estimates and ICRP (1975) reference

values. Layton's inhalation rate estimates fall in

the recommended range and may be more

accurate values for point estimates. However, the

approach needs to be further reviewed and

validated in the open literature before these lower,

less conservative inhalation rate estimates are

used.

The available studies on inhalation rates have

been summarized by the EPA (1997). Inhalation

rates are reported for adults and children

(including infants) performing various activities

and for outdoor workers and athletes. The activity

levels have been categorized as resting,

sedentary, light, moderate, and heavy.

Table A.11 summarizes inhalation rate values

recommended by the EPA both for long-term and

short-term exposure. The daily average inhalation

rates for long-term exposure for adults are

11.3 m3/d for women and 15.2 m3/d for men.

For the building occupancy scenario, a triangular

distribution is also used for input to RESRADBUILD. The most likely inhalation rate value was

taken to be 33.6 m3/d (1.4 m3/h) as 

recommended in Beyeler et al. (1999). The

minimum value of 12 m3/d (0.5 m3/h) was

selected on the basis of recommendations for

sedentary adult activities. A maximum value of

46 m3/d (1.9 m3/h) was selected because it

represented the highest average value reported in

Beyeler et al. (1999) for workers in light industry

and falls within the range of moderate to heavy

activities for both adults and outdoor workers

(Table A.11).

A.3.2 Direct Ingestion Rate

Description: “Direct ingestion” refers to the

incidental ingestion of contaminated material

directly from the source.

Units: g/h for volume sources

1/h for point, line, and area sources

Probabilistic Input:

Distribution: None recommended

Discussion: The direct ingestion rate is included

in the RESRAD-BUILD code to cover 
A-29

Table A.11. Summary of EPA-Recommended Values for Inhalation

Population Mean Population Mean

Long-Term Exposures Short-Term Exposures

Infants (<1 year) 4.5 m3/d Adults

Rest 0.4 m3

/h

Children Sedentary activities 0.5 m3/h

1-2 years 6.8 m3

/d Light activities 1.0 m3/h

3-5 years 8.3 m3/d Moderate activities 1.6 m3/h

6-8 years 10 m3/d Heavy activities 3.2 m3/h

9-11 years

Males 14 m3/d Children

Females 13 m3

/d Rest 0.3 m3/h 

12-14 years Sedentary activities 0.4 m3/h

Males 15 m3

/d Light activities 1.0 m3/h

Females 12 m3/d Moderate activities 1.2 m3/h

15-18 years Heavy activities 1.9 m3

/h

Males 17 m3/d

Females 12 m3

/d Outdoor workers

Hourly averagea 1.3 m3/h

Adults (19-65+yrs) Slow activities 1.1 m3

/h

Females 11.3 m3/d Moderate activities 1.5 m3/h

Males 15.2 m3

/d Heavy activities 2.5 m3/h

a Upper percentile = 3.3 m3

/h.

Source: EPA (1997).
A-30

the unlikely event that a receptor directly ingests

source material. Such a receptor could be

conducting a maintenance or renovation activity

that involves physical contact with the source.

The direct ingestion rate is normally set to 0 for

most calculations.

The magnitude of the direct ingestion rate is

highly correlated with other input parameters. For

volume sources, the total amount of material

ingested may range from 0 to a maximum

specified by the mass of the source (area ×

thickness [Section A.2.6] × density

[Section A.2.1]). In addition, the direct ingestion

rate cannot exceed the amount removed per unit

time as determined by the source erosion rate

(Section 3.4). The soil ingestion rate could be

used as a guide for this parameter. Indirect

ingestion (Section A.3.3) must also be taken into

account, as must time spent in the room with the

source. Also, the direct ingestion rate should not

cause the total physical mass of the source to be

depleted over the time of exposure and must take

into account the mass balance because of

erosion of the source resulting from other

mechanisms (Section 3.4).

For the other source types (point, line, and area),

the direct ingestion rate is expressed as a

fraction of the source ingested per hour. This rate

may range from 0, to a value less than or equal to

the removal rate that is determined by the

removable fraction (Section 3.5) and the source

lifetime (Section 3.6) input parameters. If the

direct ingestion rate is large enough to match the

removal rate, then the air release fraction

(Section A.2.4) input must be set to 0 to maintain

mass balance.

A.3.3 Indirect Ingestion Rate

Description: This parameter represents the

ingestion rate of deposited material for a receptor

at a specified location inside the building. This

rate represents the transfer of deposited

contamination from building surfaces to the

mouth via contact with hands, food, or other

objects. The indirect ingestion rate is expressed

as the surface area contacted per unit time. 

Unit: Square meters per hour (m2/h)

Probabilistic Input:

Distribution: Loguniform

Defining Values for Distribution:

Minimum: 2.8 × 10-5 Maximum: 2.9 × 10-4

Discussion: Only limited information is available

on the values for this parameter. As reported in

Beyeler et al. (1999), only eight data references

are available (Dunster, 1962; Gibson and Wrixon,

1979; Healy, 1971; Kennedy et al., 1981; Sayre

et al., 1974; Lepow et al., 1975; Walter et al.,

1980; Gallacher et al., 1984). However, half of

these studies concerned intake by children, not

by adults in an occupational setting. A larger,

secondary set of data from soil ingestion studies

is available (Yu et al., 2000), but again, the

primary emphasis has been soil ingestion rates

of children because of concern over elevated

exposures from intensive mouthing behavior in

this age group. Only two studies (Calabrese

et al., 1990; Stanek et al., 1997) have provided

empirical data for soil ingestion in adults.

Comprehensive reviews of soil ingestion by

humans can be found in EPA (1997) and Simon

(1998).

Because the indirect ingestion rate is specified

as the surface area contacted per unit time,

estimates of daily ingested amount were

converted to the proper units by using estimates

for deposited contamination (soil) concentrations

on surfaces and soil loadings on the hand

(Beyeler et al., 1999). Thus, a large uncertainty

for the indirect ingestion rate is expected; in fact,

the uncertainty is larger than the anticipated

variability across sites (Beyeler et al., 1999). For

this reason, Beyeler et al. (1999) have proposed

two alternative distributions. However, Beyeler’s

suggested procedure produces an effective

ingestion rate. It incorporates the number of

hand-to-mouth events per day and transfer

efficiencies between surface-to-hand and hand-tomouth, because these factors were not explicitly

accounted for in the calculation.

The two alternative distributions were proposed

on the basis of mean ingestion rates of 0.5 and

50 mg/d. These rates fall within the 0 to 70 mg/d

range for mean ingestion rates thought to be 
A-31

consistent with the empirical data (Calabrese

et al., 1990; Calabrese and Stanek, 1995;

Stanek et al., 1997). The minimum and maximum

ingestion rates were taken to be 0 and 200 mg/d,

respectively. In the most comprehensive study,

10 subjects were followed for 28 days, yielding an

average ingestion rate of 10 mg soil/d, with an

upper 95% value of 331 mg soil/d (Stanek et al.,

1997). Dust loadings were assumed to range

from 10 mg/m2, taken to be the lower limit in a

residential setting, to 5,000 mg/m2, taken to

correspond to heavily soiled hands.

The resulting loguniform distributions (Table A.12)

for the indirect ingestion rate parameter ranged

from 4.4 × 10-4 to 4.6 × 10-3 m2/d, with a mean of

1.8 × 10-3 m2/d; and from 5.1 × 10-2 to

4.3 × 10-1 m2/d, with a mean of 1.8 × 10-1 m2/d.

For use in RESRAD-BUILD, a 16-hour day was

assumed, resulting in distributions with means of

1.1 × 10-4 and 1.1 × 10-2 for the low and high

average ingestion rate distributions presented in

Yu et al. (2000). As discussed in Beyeler et al.

(1999), an ingestion rate corresponding to

1 × 10-2 m2/h implies mouthing an area equivalent

to the inner surface of the hand once each hour.

Such an ingestion rate appears to be an upper

bound for a commercial environment. Because

adult ingestion rates can often approach zero (the

lower bound), the lower ingestion rate distribution

has been selected as a default for use in

RESRAD-BUILD. Figure A.14 shows the

probability density function.

A.4 REFERENCES

American Nuclear Society. “Airborne Release

Fractions at Non-Reactor Facilities, an American

National Standard.” ANSI/ANS-5.10-1998.

Prepared by the American Nuclear Society

Standards Committee Working Group ANS-5.10,

ANS: La Grange Park, Ill. 1998.

Ayers, K.W., et al. “Reuse of Concrete from

Contaminated Structures.” Prepared for the

U.S. Department of Energy, Office of Science

and Technology, by the Department of Civil and

Environmental Engineering, Vanderbilt University:

Nashville, Tenn. 1999. 

Bever, M.B. (ed.-in-chief). Encyclopedia of

Materials Science and Engineering. Vol. 5 O-Q.,

The MIT Press: Cambridge, Mass. 1986.

Beyeler, W.E., et al. NUREG/CR-5512, Vol. 3

Draft. “Residual Radioactive Contamination from

Decommissioning: Parameter Analysis.” Office of

Nuclear Regulatory Research, NRC: Washington,

D.C. 1999.

Bureau of Labor Statistics. “Annual Household

Data.” Employment and Earnings, Vol. 43, No. 1.

Jan. 1996.

Calabrese, E.J. and E.J. Stanek. “Resolving

Intertracer Inconsistencies in Soil Ingestion

Estimation.” Environmental Health Perspectives,

103(5):454-57. 1995.

Table A.12. Indirect Ingestion Rates

Parameter Mean Lower Limit Upper Limit

Dust loading (mg/m2)

a 320 10 5000

Low ingestion rate input (mg/d)a 0.50 0 200

High ingestion rate input (mg/d)a 50 0 200

Low ingestion rate estimate (m2/d)a 1.8 × 10-3 4.4 × 10-4 4.6 × 10-3

High ingestion rate estimate (m2/d)a 1.8 × 10-1 5.1 × 10-2 4.3 × 10-1

RESRAD-BUILD inputb

Low ingestion rate estimate (m2/h) 1.1 × 10-4 2.8 × 10-5 2.9 × 10-4

High ingestion rate estimate (m2/h) 1.1 × 10-2 3.2 × 10-3 2.7 × 10-2

a Source: Beyeler et al. (1999).

b Assumes a 16-hour day using the results from Beyeler et al. (1999).
A-32

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0.00001 0.0001 0.001

Indirect Ingestion Rate (m2/h)

f(x)

Loguniform Distribution

Minimum = 2.8  ́ 10-5 m2/d 

Maximum = 2.9  ́ 10-4 m2/d

Figure A.14. Indirect Ingestion Rate Probability Density Function

Calabrese, E.J., et al. “Preliminary Adult Soil

Ingestion Estimates: Results of a Pilot-Study.”

Regulatory Toxicology and Pharmacology,

12(1):88-95. 1990.

Culot, M.J.V., H. G. Olson and K. J. Schiager.

“Effective Diffusion Coefficient of Radon in

Concrete, Theory and Method for Field

Measurements.” Health Physics, 30:263-270.

1976.

Dean, J.A. Lange’s Handbook of Chemistry. 15th

Edition. McGraw-Hill, Inc.: New York, N.Y. 1999.

Dickson, R.S. and J.M. Miller. “Sorption of

Tritium and Tritiated Water on Construction

Materials.” Fusion Technology, 21:850-855. 1992.

Dunster, H.J. “Maximum Permissible Levels of

Skin Contamination.” AHSB(RP)R28. United

Kingdom Atomic Energy Authority: London,

England. 1962.

Frankowski, Z., et al. “Properties of the Concrete

from 80-Year-Old Structures of Facilities in the

Radioactive Waste Repository.” Low-Level

Radioactive Waste Management 18th Conference

Proceedings, May 20-22, Salt Lake City, Utah.

U.S. Department of Energy. 1997.

Gallacher, J.E.J., et al. “Relation between Pica

and Blood Lead in Areas of Differing Lead

Exposure.” Archives of Disease in Childhood,

59:40-44. 1984.

Gibson, J.A.B. and A.D. Wrixon. “Methods for

the Calculation of Derived Working Limits for

Surface Contamination by Low-Toxicity

Radionuclides.” Health Physics (36): 3:311-321.

1979.

Healy, J.W. “Surface Contamination: Decision

Levels.” LA-4558-MS. Los Alamos Scientific

Laboratory: Los Alamos, N.M. 1971.

International Commission on Radiological

Protection. “Report of the Task Group on

Reference Man.” ICRP Publication No. 23.

Pergamon Press: Oxford, England. 1975.
A-33

Kennedy, W.E., Jr., et al. NUREG/CR-1859,

PNL-3666, “A Review of Removable Surface

Contamination on Radioactive Materials

Transportation Containers.” U.S. Nuclear

Regulatory Commission: Washington, D.C. 1981.

Layton, D.W. “Metabolically Consistent Breathing

Rates for Use in Dose Assessments.” Health

Physics, 64(1):23-36. 1993.

Lepow, M.L., et al. “Investigations into Sources of

Lead in the Environment of Urban Children.”

Environ. Res., 10:414-426. 1975.

Lide, D.R. (ed.-in-chief). CRC Handbook of

Chemistry and Physics. 79th Ed. CRC Press:

Boca Raton, Fla. 1998.

Merritt, F.S., et al. (eds.). “Construction

Materials.” Section 5 in Standard Handbook for

Civil Engineers.” McGraw-Hill, Inc.: New York,

N.Y. 1995.

National Association of Home Builders. “Factory

and Site-Built Housing, a Comparison for the 21st

Century.” NAHB Research Center, Inc., Upper

Marlboro, Md., prepared for the U.S. Department

of Housing and Urban Development, Office of

Policy Development and Research: Washington,

D.C. October 1998.

National Climatic Data Center. “Comparative

Climatic Data for the United States through

1998.” National Oceanic and Atmospheric

Administration, U.S. Department of Commerce:

Washington, D.C. 1999.

Neville, A.M. Properties of Concrete. 4th Ed.

John Wiley & Sons, Ltd.: Brisbane, Australia.

1996.

Numata S. and H. Amano. “Tritium Permeation

into Concrete.” pp. 1260-1264 in Fusion

Technology, Proceedings of the 15th Symposium

on Fusion Technology: Utrecht. 1988. 

Ono, F., et al. “Sorption and Desorption of

Tritiated Water on Paints.” Fusion Technology,

21:827-832. 1992.

Roy, M. and C. Courtay. “Daily Activities and

Breathing Parameters for Use in Respiratory

Tract Dosimetry.” Radiation Protection

Dosimetry, 35(3):179-186. 1991.

Sayre, J., et al. “House and Hand Dust as a

Potential Source of Childhood Lead Exposure.”

Am. J. of Dis. Chil., 127:167-170. 1974.

Shleien, B. (ed.). The Health Physics and

Radiological Health Handbook . Rev. Ed. Scinta

Inc.: Silver Spring, Md. 1992.

Simon, S.L. “Soil Ingestion by Humans: A Review

of History, Data, and Etiology with Application to

Risk Assessment of Radioactively Contaminated

Soil.” Health Physics, 74(6):647-672. 1998.

Stanek, E.J., et al. “Soil Ingestion in Adults —

Results of a Second Pilot-Study.” Ecotoxicology

and Environmental Safety, 36:249-257. 1997.

Sterling, E.M., et al. “Criteria for Human

Exposure to Humidity in Occupied Buildings.”

ASHRAE Transactions, 91(1B):611-622. 1985.

[As cited in 1996 ASHRAE Handbook: Heating,

Ventilating, and Air-Conditioning Systems and

Equipment, SI Edition. American Society of

Heating, Refrigerating, and Air-Conditioning

Engineers, Inc.: Atlanta, Ga. 1996.]

Tsang, A.M. and N.E. Klepeis. “Results Tables

from a Detailed Analysis of the National Human

Activity Pattern Survey (NHAPS) Response.”

Draft report prepared for the U.S. Environmental

Protection Agency by Lockheed Martin. 1996.

U.S. Department of Energy. “DOE Handbook,

Airborne Release Fractions/Rates and Respirable

Fractions for Nonreactor Nuclear Facilities,

Volume 1 – Analysis of Experimental Data.”

DOE-HDBK-3010-94. DOE: Washington, D.C.

1994. 

U.S. Environmental Protection Agency.

“Development of Statistical Distributions or

Ranges of Standard Factors Used in Exposure

Assessments.” EPA/600/8-85/010. Office of

Health and Environmental Assessment, EPA:

Washington, D.C., 1985.

U.S. Environmental Protection Agency.

“Exposure Factors Handbook.” EPA/600/8-

89/043. Office of Health and Environmental

Assessment, EPA: Washington, D.C. 1989.

U.S. Environmental Protection Agency. “Risk

Assessment Guidance for Superfund, Volume I,

Human Health Evaluation Manual, Supplemental
A-34

Guidance, Standard Default Exposure Factors.”

Interim Final, OSWER Directive 9285.6-03. Office

of Emergency and Remedial Response, EPA:

Washington, D.C. 1991.

U.S. Environmental Protection Agency.

“Exposure Factors Handbook, Update to

Exposure Factors Handbook. EPA/600/8-89/043

— May 1989.” EPA/600/P-95/002Fa,b&c.

National Center for Environmental Assessment,

Office of Research and Development, EPA:

Washington, D.C. August 1997.

Walter, S.D., A.J. Yankel and I.H. Von Lindern.

“Age-Specific Risk Factors for Lead Absorption in

Children.” Archives of Environ. Health,

53(1):53-58. 1980.

Wong, K.Y., et al. “Tritium Decontamination of

Machine Components and Walls.” Fusion

Engineering and Design, 16(1991):159-172. 1991.

Yu, C., et al. NUREG/CR-6697, ANL/EAD/TM-98,

“Development of Probabilistic RESRAD 6.0 and

RESRAD-BUILD 3.0 Computer Codes.” Prepared

by Argonne National Laboratory: Argonne, Ill., for

U.S. Nuclear Regulatory Commission, Office of

Nuclear Regulatory Research: Washington, D.C.

2000.
A-35
B-1

APPENDIX B:

PARAMETRIC DISTRIBUTION TYPES
B-2
B-3

APPENDIX B:

PARAMETRIC DISTRIBUTION TYPES

This appendix discusses the form and

characteristics of each of the parametric

distributions available in the Latin Hypercube

Sampling (LHS) module that may be used to

represent input parameters in the RESRADBUILD code. Table B.1 summarizes the

continuous probability density distribution

functions and the required input for the LHS

module.

B.1 BETA DISTRIBUTION

The LHS code incorporates a four-parameter beta

distribution that has the probability density

function:

, (B.1) f x

B p q

x A

p

B x

q

B A

p q

( )

( , )

( ) ( )

( )

=

−

−

−

−

−

+ −

1

1 1

1

where B(p,q) is the beta function,

B p q t

, (B.2) p

t

q

( , ) = ( ) dt −−

−

∫

1

1

1

0

1

p and q are shape parameters, and A and B are

the endpoints of the distribution. This distribution

is very flexible and is often used to fit empirical

data. The shape of the distribution can vary

widely depending on the relationship of p and q to

one another. This flexibility also makes the beta

distribution useful for approximating distributions

when there are insufficient data. 

As discussed below in the section on the

maximum entropy distribution (Section B.7), the

beta distribution may be used in cases where

estimates for the minimum (A), maximum (B),

mean (μ), and standard deviation (s) are

available, but little else is known. In such a case,

the shape parameters can be estimated

according to Lee and Wright (1994):

p A , (B.3)

A B

B A

= −

− −

−

−





















( )

( )( )

m

m m

s

2

1

and

q . (B.4)

A B

= p

− −

− −

(m )( m)

s

2

1

B.2 EXPONENTIAL DISTRIBUTION

The probability density function for the

exponential distribution is:

f x e for x $ 0, (B.5)

x

( ) =

−

l

l

with the mean given by 1/ l. The variablel

represents the average rate of occurrence of

successive, independent, random events. Purely

random Poisson processes exhibit such

behavior. Examples include radioactive decay,

accidents, and storm events. 

B.3 GAMMA DISTRIBUTION

The gamma distribution represents the sum of a

series of exponentially distributed random

variables. The probability density function for the

two-parameter form of the gamma distribution

(sometimes referred to as “the incomplete

gamma function”) is:

f x with x > 0, a > 0, ß > 0, and 

x e

x

( )

( )

( )

=

− − β

α

α α 1 β

Γ

(B.6)

Γ( ) or ( ) α

α = ∫

∞ − −

y e dy 1 y

0

Γ(a) = (a − 1)! for integers,

where G(a) is the gamma function. The a

parameter determines the shape of the function,
B-4

and the ß parameter controls the scale. If the

shape parameter is set to 1, the gamma

distribution becomes a scalable exponential

distribution. The mean for the gamma distribution

is a/ß. The gamma distribution is appropriate for

representing the time required for a independent

events to take place for 

Table B.1. Continuous Probability Density Distribution Functions

Distribution Input Variables

Beta A (minimum) B (maximum) p (shape factor) q (shape factor)

Exponential Types

Exponential ?

Bounded exponential ? A (minimum) B (maximum)

Truncated exponential ? lower quantile value upper quantile value

Gamma a (shape factor) ß (scale factor)

Inverse Gaussian μ ?

Lognormal Types

Lognormal μ (mean) error factor

Lognormal-b value at 0.001 quantile value at 0.999 quantile

Lognormal-n mean of underlying

normal distribution

standard dev. of

underlying normal

distribution

Bounded lognormal μ (mean) error factor A (minimum) B (maximum)

Bounded lognormal-n mean of underlying

normal distribution

standard dev. of

underlying normal

distribution

A (minimum) B (maximum)

Truncated lognormal μ (mean) error factor lower quantile value upper quantile

value

Truncated lognormal-n mean of underlying

normal distribution

standard dev. of

underlying normal

distribution

lower quantile value upper quantile

value

Loguniform Types

Loguniform A (minimum) B (maximum)

Piecewise loguniform number of intervals # observations per

interval 1...

# observations per

interval n

first point, end point

sequence

Maximum Entropy A (minimum) B (maximum) μ (mean)

Normal Types

Normal μ (mean) s (standard deviation)

Normal-b value at 0.001 quantile value at 0.999 quantile

Bounded normal μ (mean) s (standard deviation) A (minimum) B (maximum)

Truncated normal μ (mean) s (standard deviation) lower quantile value upper quantile

value

Pareto a ß

Triangular a (minimum) b (most likely) c (maximum)

Uniform Types
B-5

Uniform A (minimum) B (maximum)

Piecewise uniform number of intervals # observations per

interval 1...

# observations per

interval n

first point, end point

sequence

User Defined Types

With linear interpolation

(CDF input)

n (number of

ordered pairs)

ordered pair 1 ordered pair

2 ...

ordered pair n

With logarithmic

interpolation (CDF input)

n (number of

ordered pairs)

ordered pair 1 ordered pair

2 ...

ordered pair n

With density function input n (number of

ordered pairs)

ordered pair 1 ordered pair

2 ...

ordered pair n

Weibull a ß
B-6

f x

x

e

x

x

( )

( )

=

−

−



















λ 

π

λ m

m

2

3

2

2 2

f x e

x

( )

( )

=

−

−



















1 

2

2

2

2

σ π

m

σ

nonrandom events that occur at a constant arrival

rate ß. This distribution is often used to describe

system reliability (the length of life of industrial

equipment). 

B.4 INVERSE GAUSSIAN

The probability density function for the inverse

Gaussian distribution is given by:

. (B.7)

The distribution was originally derived as a

limiting form of distribution of sample size in

certain sequential probability ratio tests. More

information can be found in Johnson et al. (1994).

B.5 Lognormal Distribution

The lognormal distribution is defined by the

logarithm of a normal distribution and is given by

the following probability density function:

 with x > 0, (B.8) f x

x

e

x

( )

(ln )

=

−

−



















1 

2

2

2 2

s p

m

s

where μ and s are the mean and standard

deviation of the underlying normal distribution.

One advantage of this two-parameter form is that

it can take on only positive values. Whereas the

normal distribution may be thought of as

describing a random variable that is the sum of

independent effects, the lognormal distribution

may be thought of as describing a random

variable that is the result of multiplicative

processes. The lognormal distribution has the

functional form that is often used for describing

dilution of matter in water or air. Environmental

concentrations of contaminants in air and water

generally follow a lognormal distribution (Ott,

1995).

B.6 LOGUNIFORM DISTRIBUTION

The loguniform distribution is a variation on the

uniform distribution. Similar to the uniform

distribution, the loguniform distribution is useful

when little is known about the distribution

between the minimum and maximum values, but

may be more appropriate when a large range

exists between these values. The probability

density function for the loguniform distribution is:

f x for a < x < b , (B.9)

x b a

( )

(ln ln )

=

−

1

with the mean given by 

. (B.10)

b a

b a

−

ln − ln

B.7 MAXIMUM ENTROPY DISTRIBUTION

The maximum entropy distribution implemented

in the LHS code is a truncated exponential

distribution where the user specifies the mean

and the lower and upper bounds of the

distribution. In general, the inference of maximum

entropy produces broad distributions because it

ensures that no mathematical possibility is

ignored while using limited data. With knowledge

of up to four properties of a distribution (lower and

upper bounds, mean, standard deviation), a

suitable maximum entropy distribution may be

assigned (see Cullen and Frey [1999] for more

information). A uniform distribution may be

assigned using only estimates of the upper and

lower bounds; a normal distribution may be

assigned using only estimates of the mean and

standard deviation; an exponential distribution

may be assigned using only estimates of the

lower (and upper) bound(s) in conjunction with the

mean; and a beta distribution may be assigned

using estimates of the lower and upper bounds,

the mean, and the standard deviation. 

B.8 NORMAL DISTRIBUTION

The normal distribution is defined by the following

probability density function:

with — 4 < x < 4 , (B.11)
B-7

where μ is the mean (– 4 < μ < 4) and s is the

standard deviation (s > 0) of the random variable

x. The normal distribution is also known as the

Gaussian distribution and has the well-known

bell-shaped curve, being symmetric about the

mean with points of inflection at X = x ± μ. Thus,

it is completely defined by the mean and

standard deviation.

The theoretical basis for the application of the

normal distribution lies in the central limit

theorem. For a random variable x with mean μ

and standard deviation s, this theorem states

that the random variable Z has a distribution that

approaches the standard normal distribution as

n → ∞ where n is the sample size and

. (B.12)

( )

Z

x n

=

− m

s

The distribution of means of independent sample

sets of a distribution or combination of

distributions tends toward the normal distribution

as the number of sample sets becomes large.

The original distribution itself need not be a

normal distribution. In summary, the central limit

theorem suggests that any random variable

representing the sum of a large number of

independent processes or effects would tend to

be normally distributed.

Because the normal distribution has infinite tails,

the LHS module incorporated in RESRAD and

RESRAD-BUILD provides three normal

distribution options. Available are the normal

distribution itself and two restricted versions,

truncated normal (sampled between lower and

upper quantile values input by the user) and

bounded normal (sampled between lower and

upper distribution values input by the user). 

B.9 PARETO DISTRIBUTION

The Pareto distribution was originally developed

to account for the distribution of income over a

population. The probability density function for the

Pareto distribution can be given as:

f x for x $ ß. (B.13)

x

( ) =

+

aba

a 1

The mean for the Pareto distribution is given by:

m for a > 1. (B.14)

ab

a

=

− 1

B.10 TRIANGULAR DISTRIBUTION

The triangular distribution is used to model

situations where there is an absence of data. The

probability density function for the triangular

distribution is:

,

f x (B.15)

x a

c a b a

a x b

c x

c a c b

b x c

( )

( )

( )( )

( )

( )( )

=

−

− −

≤ ≤

−

− −

≤ ≤















2

2

with the mean given by:

, (B.16)

a + b + c

3

where the minimum and maximum occur at a and

c, respectively, and the most likely value at b

(the apex of the triangle). The value of b must

satisfy a # b # c.

B.11 UNIFORM DISTRIBUTION

All points within an interval having a uniform

distribution, also known as the rectangular

distribution, are equally likely. The probability

density function for the uniform distribution is:

f x for a # x # b , (B.17)

b a

( ) =

−

1

where a and b are the minimum and maximum

values of the range of the random variable

considered. The mean and variance of a uniform

distribution are (a + b)/2 and (b !a)

2

/12,

respectively. If the only available data for a

random variable are the minimum and maximum

values, the maximum entropy distribution for

such a case would be a uniform distribution. See

the section above on maximum entropy

distribution if the mean of the distribution is also

known. 
B-8

B.12 WEIBULL DISTRIBUTION

The Weibull distribution is often used as a timeto-failure model as an alternative to the

exponential distribution. The Weibull distribution

is also sometimes known as the Frechet

distribution. The probability density function for

the Weibull distribution is given as:

for a > 0 (B.18) f x e

x

x

( )

( )

=

− −











































a 

b b

a

b

a

1

and ß > 0 ,

where a is the shape parameter and ß is the

scale parameter. When a = 1, the Weibull

distribution reduces to the exponential

distribution. When a = 2, the Weibull distribution

has the form of the Rayleigh distribution.

B.13 REFERENCES AND BIBLIOGRAPHY FOR

APPENDIX B

Abramowitz, M. and I.A. Stegun. Handbook of

Mathematical Functions. Dover Publications Inc.:

New York, N.Y. 1972.

Cullen, A.C. and H.C. Frey. Probabilistic

Techniques in Exposure Assessment. Plenum

Press: New York, N.Y. 1999.

Dougherty, E.R. Probability and Statistics for the

Engineering, Computing, and Physical Sciences.

Prentice Hall: Englewood Cliffs, N.J. 1990.

Hahn, G.J. and S.S. Shapiro. Statistical Models

in Engineering. John Wiley & Sons, Inc.: New

York, N.Y. 1994.

Hoel, P.G. Introduction to Mathematical

Statistics. John Wiley & Sons, Inc.: New York,

N.Y. 1971.

Johnson, N.L. and S. Kotz. Continuous

Univariate Distributions. Vol. 2. John Wiley &

Sons, Inc.: New York, N.Y. 1970.

Johnson, N.L., S. Kotz, and N. Balakrishnan.

Continuous Univariate Distributions: Vol. 1. John

Wiley & Sons, Inc.: New York, N.Y. 1994.

Lee, R.C. and W.E. Wright. “Development of

Human Exposure-Factor Distributions Using

Maximum-Entropy Inference.” J. Exp. Anal. Env.

Epid., 4:329-341. 1994.

Ott, W.R. Environmental Statistics and Data

Analysis. CRC Press: Boca Raton, Fla. 1995.

Wyss, G.D. and K.H. Jorgensen. “A User’s

Guide to LHS: Sandia’s Latin Hypercube

Sampling Software.” SAND98-0210. Sandia

National Laboratories: Albuquerque, N.M. 1998.
B-9