EPA/600/Z-92/001 

May 1992 

Guidelines for 

Exposure Assessment 

Published on May 29, 1992, Federal Register 57(104):22888-22938 

These guidelines replace the previously issued final Guidelines for 

Estimating Exposures (September 24, 1986), Federal Register 

51(185):34042-34054, and the Proposed Guidelines for Exposure-Related 

Measurements (December 2, 1988), Federal Register 53(232):48830-

48853. 

Risk Assessment Forum

U.S. Environmental Protection Agency

Washington, DC
DISCLAIMER 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

Note: This document represents the final guidelines. A number of editorial corrections have 

been made during conversion and subsequent proofreading to ensure the accuracy of this 

publication. 

ii 
CONTENTS

Lists of Tables and Figures .................................................... vii

Federal Register Preamble ..................................................... viii

Abbreviations and Acronyms .................................................. xii

Part A: Guidelines for Exposure Assessment ........................................1

1. Introduction ................................................................1

1.1. Intended Audience ...................................................1

1.2. Purpose and Scope of the Guidelines ....................................2

1.3. Organization of the Guidelines .........................................3

2. General Concepts in Exposure Assessment .......................................4

2.1. Concepts of Exposure, Intake, Uptake, and Dose ...........................5

2.1.1. Exposure ...................................................6

2.1.2. Applied Dose and Potential Dose ................................7

2.1.3. Internal Dose ................................................8

2.1.4. Exposure and Dose Relationships ................................9

2.1.4.1. Calculating Potential Dose for Intake Processes ...........10

2.1.4.2. Calculating Internal Dose for Uptake Processes ...........14

2.1.4.3. Calculating Internal Dose for Intake Processes (Especially via 

Respiratory and Oral Routes) ...........................17

2.1.5. Summary of Exposure and Dose Terms with Example Units .........18

2.2. Approaches to Quantification of Exposure ...............................19

2.2.1. Measurement of Exposure at the Point of Contact .................22

2.2.2. Estimates of Exposure from Scenario Evaluation ..................22

2.2.3. Exposure Estimation by Reconstruction of Internal Dose ............24

2.3. Relationships of Exposure and Dose to Risk ..............................25

2.3.1. Individual Risk .............................................25

2.3.2. Population Risk .............................................28

2.3.3. Risk Descriptors ............................................30

3. Planning an Exposure Assessment .............................................32

3.1. Purpose of the Exposure Assessment ...................................32

3.1.1. Using Exposure Assessments in Risk Assessment ..................33

3.1.2. Using Exposure Assessments for Status and Trends ................34

3.1.3. Using Exposure Assessments in Epidemiologic Studies .............35

3.2. Scope of the Assessment .............................................35

3.3. Level of Detail of the Assessment ......................................36

3.4. Determining the Approach for the Exposure Assessment ....................36

3.5. Establishing the Exposure Assessment Plan ..............................37

3.5.1. Planning an Exposure Assessment as Part of a Risk Assessment ......38

iii
CONTENTS (continued) 

3.5.2. Establishing the Sampling Strategy .............................39

3.5.2.1. Data Quality Objectives ...............................39

3.5.2.2. Sampling Plan ......................................40

3.5.2.3. Quality Assurance Samples ............................42

3.5.2.4. Background Level ...................................43

3.5.2.5. Quality Assurance and Quality Control ..................43

3.5.2.6. Quality Assurance and Quality Control for Previously Generated

Data ...............................................44

3.5.2.7. Selection and Validation of Analytical Methods ............44

3.5.3. Establishing the Modeling Strategy .............................44

3.5.3.1. Setting the Modeling Study Objectives ...................45

3.5.3.2. Characterization and Model Selection ...................45

3.5.3.3. Obtaining and Installing the Computer Code ..............46

3.5.3.4. Calibrating and Running the Model .....................46

3.5.3.5. Model Validation ....................................47

3.5.4. Planning an Exposure Assessment to Assess past Exposures .........48

4. Gathering and Developing Data for Exposure Assessments .........................49

4.1. Measurement Data for Point-of-contact Assessments .......................49

4.2. Obtaining Chemical Concentration Information ...........................50

4.2.1. Concentration Measurements in Environmental Media ..............50

4.2.2. Use of Models for Concentration Estimation ......................56

4.2.3. Selection of Models for Environmental Concentrations ..............57

4.3. Estimating Duration of Contact ........................................58

4.3.1. Observation and Survey Data ..................................59

4.3.2. Developing Other Estimates of Duration of Contact ................60

4.4. Obtaining Data on Body Burden or Biomarkers ...........................61

4.5. Obtaining Data for Pharmacokinetic Relationships .........................61

4.6. Obtaining Data on Intake and Uptake ...................................62

5. Using Data to Determine or Estimate Exposure and Dose ...........................63

5.1. Use of Data in Making Inferences for Exposure Assessments

...............................................................63

5.1.1. Relevance of Data for the Intended Exposure Assessment ...........64

5.1.2. Adequacy of Data for the Intended Assessment ....................64

5.1.2.1. Evaluation of Analytical Methods .......................65

5.1.2.2. Evaluation of Analytical Data Reports ...................65

5.1.2.2.1. Evaluation of Censored Data Sets. ...............66

5.1.2.2.2. Blanks and Recovery. .........................68

5.1.3. Combining Measurement Data Sets from Various Studies ...........68

5.1.4. Combining Measurement Data and Modeling Results ...............69

5.2. Dealing with Data Gaps ..............................................69

5.3. Calculating Exposure and Dose ........................................70

5.3.1. Short-term Versus Long-term Data for Population Exposures .........71

iv
CONTENTS (continued) 

5.3.2. Using Point-of-contact Data to Calculate Exposure and Dose .........72

5.3.3. The Role of Exposure Scenarios in Exposure Assessment ............72

5.3.3.1. Scenarios as a Means to Quantify Exposure and Dose .......73

5.3.3.2. Exposure Scenarios and Exposure Estimators as Input to Risk

Descriptors .........................................75

5.3.3.3. Exposure Scenarios as a Tool for Option Evaluation ........75

5.3.4. General Methods for Estimating Exposure and Dose ...............76

5.3.4.1. Preliminary Evaluation and Bounding Estimates ...........76

5.3.4.2. Refining the Estimates of Exposure and Dose ..............78

5.3.5. Using Estimates for Developing Descriptors ......................79

5.3.5.1. Individual Exposure, Dose, and Risk .....................79

5.3.5.2. Population Exposure, Dose, and Risk ...................86

6. Assessing Uncertainty .......................................................90

6.1. Role of Uncertainty Analysis in Exposure Assessment ......................90

6.2. Types of Uncertainty ................................................91

6.2.1. Scenario Uncertainty .........................................92

6.2.2. Parameter Uncertainty .......................................93

6.2.3. Model Uncertainty ..........................................96

6.3. Variability Within a Population Versus Uncertainty in the Estimate .........97

7. Presenting the Results of the Exposure Assessment ................................99

7.1. Communicating the Results of the Assessment ............................99

7.1.1. Exposure Characterization ....................................99

7.1.2. Risk Characterization .......................................101

7.1.2.1. Integration of Hazard Identification, Dose-response, and 

Exposure Assessments ................................101

7.1.2.2. Quality of the Assessment and Degree of Confidence .......102

7.1.2.3. Descriptors of Risk ..................................103

7.1.2.4. Communicating Results of a Risk Assessment to the Risk Manager

..................................................103

7.1.3. Establishing the Communication Strategy .......................103

7.2. Format for Exposure Assessment Reports .........................104

7.3. Reviewing Exposure Assessments .....................................104

8. Glossary of Terms .........................................................107

9. References ...............................................................115

v 
CONTENTS (continued) 

Part B: Response to Public and Science Advisory Board Comments ...................123

1. Introduction ..............................................................123

2. Response to General Comments ..............................................124

3. Response to Comments on the Specific Questions ................................125

vi 
LIST OF TABLES

Table 1. Explanation of exposure and dose terms ...................................20

Table 2. Examples of types of measurements to characterize exposure-related media and

parameters ..........................................................51

LIST OF FIGURES 

Figure 1. Schematic of dose and exposure .........................................12

Figure 2. Schematic of exposure estimators for unbounded simulated 

population distributions ................................................85

vii
GUIDELINES FOR EXPOSURE ASSESSMENT 

[FRL-4129-5] 

AGENCY: U.S. Environmental Protection Agency 

ACTION: Final Guidelines for Exposure Assessment 

SUMMARY: The U.S. Environmental Protection Agency (EPA) is today issuing final 

guidelines for exposure assessment. The Guidelines for Exposure Assessment (hereafter 

“Guidelines”) are intended for risk assessors in EPA, and those exposure and risk assessment 

consultants, contractors, or other persons who perform work under Agency contract or 

sponsorship. In addition, publication of these Guidelines makes information on the principles, 

concepts, and methods used by the Agency available to all interested members of the public. 

These Guidelines supersede and replace both the Guidelines for Estimating Exposures published 

September 24, 1986 (51 FR 34042-34054) (hereafter “1986 Guidelines”) and the Proposed 

Guidelines for Exposure-Related Measurements published for comment on December 2, 1988 

(53 FR 48830-48853) (hereafter “1988 Proposed Guidelines”). In response to recommendations 

from the Science Advisory Board (SAB) and the public, the 1986 Guidelines were updated and 

combined with the 1988 Proposed Guidelines and retitled as the current Guidelines for Exposure 

Assessment. 

These Guidelines establish a broad framework for Agency exposure assessments by 

describing the general concepts of exposure assessment including definitions and associated 

units, and by providing guidance on the planning and conducting of an exposure assessment. 

Guidance is also provided on presenting the results of the exposure assessment and 

characterizing uncertainty. Although these Guidelines focus on exposures of humans to 

chemical substances, much of the guidance contained herein also pertains to assessing wildlife 

exposure to chemicals, or human exposures to biological, noise, or radiological agents. Since 

these latter four areas present unique challenges, assessments on these topics must consider 

additional factors beyond the scope of these Guidelines. The Agency may, at a future date, issue 

additional specific guidelines in these areas. 

EFFECTIVE DATE: The Guidelines will be effective May 29, 1992. 

FOR FURTHER INFORMATION, CONTACT: Michael A. Callahan, Director, National 

Center for Environmental Assessment-Washington Division (8623D), U.S. Environmental 

Protection Agency, 401 M Street, S.W., Washington, DC 20460, TEL: 202-564-3259. 

viii 
SUPPLEMENTARY INFORMATION: In its 1983 book Risk Assessment in the Federal 

Government: Managing the Process, the National Academy of Sciences recommended that 

Federal regulatory agencies establish “inference guidelines” to promote consistency and 

technical quality in risk assessment, and to ensure that the risk assessment process is maintained 

as a scientific effort separate from risk management. A task force within EPA accepted that 

recommendation and requested that Agency scientists begin to develop such guidelines. 

In 1984, EPA scientists began work on risk assessment guidelines for carcinogenicity, 

mutagenicity, suspect developmental toxicants, chemical mixtures, and estimating exposures. 

Following extensive scientific and public review, these guidelines were issued on September 24, 

1986 (51 FR 33992-34054). Subsequent work resulted in the publishing of four additional 

proposals (one of which has recently become final): Proposed Guidelines for Assessing Female 

Reproductive Risk (53 FR 24834-24847), Proposed Guidelines for Assessing Male Reproductive 

Risk (53 FR 24850-24869), Proposed Guidelines for Exposure-Related Measurements (53 FR 

48830-48853), and Proposed Amendments to the Guidelines for the Health Assessment of 

Suspect Developmental Toxicants (54 FR 9386-9403). The final Guidelines for Developmental 

Toxicity Risk Assessment, published December 5, 1991 [56 FR 63798-63826], supersede and 

replace the proposed amendments. 

The Guidelines issued today continue the guidelines development process initiated in 

1984. Like the guidelines issued in 1986, the Guidelines issued today set forth principles and 

procedures to guide EPA scientists in the conduct of Agency risk assessments and to inform 

Agency decision makers and the public about these procedures. In particular, the Guidelines 

standardize terminology used by the Agency in exposure assessment and in many areas outline 

the limits of sound scientific practice. They emphasize that exposure assessments done as part of 

a risk assessment need to consider the hazard identification and dose-response parts of the risk 

assessment in the planning stages of the exposure assessment so that these three parts can be 

smoothly integrated into the risk characterization. The Guidelines discuss and reference a 

number of approaches and tools for exposure assessment, along with discussion of their 

appropriate use. The Guidelines also stress that exposure estimates along with supporting 

information will be fully presented in Agency risk assessment documents, and that Agency 

scientists will identify the strengths and weaknesses of each assessment by describing 

uncertainties, assumptions, and limitations, as well as the scientific basis and rationale for each 

assessment. 

Work on these Guidelines began soon after publication of the 1986 Guidelines. At that 

time, the SAB recommended that the Agency develop supplementary guidelines for conducting 

exposure studies. This supplementary guidance was developed by an Agency work group 

composed of scientists from throughout the Agency, a draft was peer reviewed by experienced 

ix 
professionals from environmental groups, industry, academia, and other governmental agencies, 

and proposed for comment on December 2, 1988 (as Proposed Guidelines for Exposure-Related 

Measurements). In the public notice, the Agency asked for comment on whether the proposed 

guidelines should be combined with the 1986 guidelines in order to have a single Agency 

guideline for exposure assessment. Comments from the public and the SAB were heavily in 

favor of combining the two guidelines. 

Since proposal, the Agency has reformatted the 1988 Proposed Guidelines to allow 

incorporation of the information in the 1986 Guidelines, and incorporated revisions resulting 

from additional public and SAB comments, to establish the current Guidelines. The current 

Guidelines were reviewed by the Risk Assessment Forum and the Risk Assessment Council, 

subjected to an external peer review, and presented to the SAB on September 12, 1991 for final 

comment (EPA-SAB-IAQC-92-015). In addition, the Guidelines were reviewed by the Working 

Party on Exposure Assessment, an interagency working group under the Subcommittee on Risk 

Assessment of the Federal Coordinating Committee on Science, Engineering and Technology. 

Comments of these groups have been considered in the revision of these Guidelines. The full 

text of the final Guidelines for Exposure Assessment is published here. 

These Guidelines were developed as part of an interoffice guidelines development 

program under the auspices of the Risk Assessment Forum and the Office of Health and 

Environmental Assessment in the Agency’s Office of Research and Development. The Agency 

is continuing to study risk assessment issues raised in these Guidelines, and will revise them in 

line with new information as appropriate. 

Following this preamble are two parts: Part A is the Guidelines and Part B is the 

Response to the Public and Science Advisory Board comments submitted in response to the 1988 

Proposed Guidelines. 

References, supporting documents, and comments received on the 1988 Proposed 

Guidelines, as well as a copy of these final Guidelines for Exposure Assessment are available for 

inspection and copying at the Public Information Reference Unit Docket (202-260-5926), EPA 

Headquarters Library, 401 M Street, S.W., Washington, DC, between the hours of 8:00 a.m. and 

4:30 p.m. 

__________________ _______________________________________ 

Dated: April 28, 1992 Signed by EPA Administrator 

William K. Reilly 

x 
xi
ABBREVIATIONS AND ACRONYMS 

ADD Average daily dose

AF Absorption fraction

AT Averaging time

BW Body weight

C Exposure concentration

C(t) Exposure concentration as a function of time

CO Carbon monoxide

CT Contact time

D Dose

Dapp Applied dose

Dint Internal dose

Dpot Potential dose

DQO Data quality objective

E Exposure

ED Exposure duration

EPA U.S. Environmental Protection Agency

Fadh Adherence factor for soil

f(t) Absorption function

IR Intake rate (also ingestion or inhalation rate)

J Flux

Kp Permeability coefficient

LADD Lifetime average daily dose

LOAEL Lowest observable adverse effect level

LOD Limit of detection

LT Lifetime

Mmedium Amount (mass) of carrier medium material applied to the skin

MDL Method detection limit

MEI Maximum exposed individual or maximally exposed individual

ND Not detected

PMN Premanufacture notice

QA Quality assurance

QAPjP Quality assurance project plan

QC Quality control

QL Quantification limit

xii 
ABBREVIATIONS AND ACRONYMS (continued) 

RfC Reference concentration

RfD Reference dose

SA Surface area

SAB Science Advisory Board

TEAM Total exposure assessment methodology

TUBE Theoretical upper bounding estimate

UCL Upper confidence limit (often used to refer to the upper confidence limit

of the mean) 

UR Uptake rate 

xiii 
PART A: GUIDELINES FOR EXPOSURE ASSESSMENT 

1. INTRODUCTION 

In 1984, the U.S. Environmental Protection Agency (EPA) initiated a program to ensure 

scientific quality and technical consistency of Agency risk assessments. One of the goals of the 

program was to develop risk assessment guidelines that would be used Agencywide. The 

guidelines development process includes a public review and comment period for all proposed 

guidelines as well as Agency Science Advisory Board review. Following the review process, the 

guidelines are revised if needed and then issued as final guidelines. The Guidelines for 

Estimating Exposures (hereafter “1986 Guidelines”) were one of five guidelines issued as final 

in 1986 (U.S. EPA, 1986a). In 1988, the Proposed Guidelines for Exposure-Related 

Measurements (hereafter “1988 Proposed Guidelines”) were published in the Federal Register 

for public review and comment (U.S. EPA, 1988a). The 1988 Proposed Guidelines were 

intended to be a companion and supplement to the 1986 Guidelines. 

When proposing the 1988 guidelines, the Agency asked both the EPA Science Advisory 

Board (SAB) and the public for comments on combining the 1986 and 1988 exposure guidelines 

into a larger, more comprehensive guideline; the majority of comments received were in favor of 

doing so. Thus, these 1992 Guidelines For Exposure Assessment (hereafter “Guidelines”) 

combine, reformat, and substantially update the earlier guidelines. These guidelines make use of 

developments in the exposure assessment field since 1988, both revising the previous work and 

adding several topics not covered in the 1986 or 1988 guidelines. Therefore, the 1992 guidelines 

are being issued by the Agency as a replacement for both the 1986 Guidelines and the 1988 

Proposed Guidelines. 

1.1. INTENDED AUDIENCE 

This document is intended for exposure and risk assessors in the Agency and those 

exposure and risk assessment consultants, contractors, or other persons who perform work under 

Agency contract or sponsorship. Risk managers in the Agency may also benefit from this 

document since it clarifies the terminology and methods used by assessors, which in some cases 

could strengthen the basis for decisions. In addition, publication of these guidelines makes 

information on the principles, concepts, and methods used by the Agency available to other 

agencies, States, industry, academia, and all interested members of the public. 

1
1.2. PURPOSE AND SCOPE OF THE GUIDELINES 

There are a number of different purposes for exposure assessments, including their use in 

risk assessments, status and trends analysis, and epidemiology. These Guidelines are intended to 

convey the general principles of exposure assessment, not to serve as a detailed instructional 

guide. The technical documents cited here provide more specific information for individual 

exposure assessment situations. As the Agency performs more exposure assessments and 

incorporates new approaches, these Guidelines will be revised. 

Agency risk assessors should use these Guidelines in conjunction with published 

guidelines for assessing health effects such as cancer (U.S. EPA, 1986b), developmental toxicity 

(U.S. EPA, 1991a), mutagenic effects (U.S. EPA, 1986c), and reproductive effects (U.S. EPA, 

1988b; U.S. EPA, 1988c). These exposure assessment guidelines focus on human exposure to 

chemical substances. Much of the guidance contained herein also applies to wildlife exposure to 

chemicals, or human exposure to biological, physical (i.e., noise), or radiological agents. Since 

these areas present unique challenges, however, assessments on these topics must consider 

additional factors beyond the scope of these Guidelines. 

For example, ecological exposure and risk assessment may deal with many species which 

are interconnected via complex food webs, while these guidelines deal with one species, humans. 

While these guidelines discuss human exposure on the individual and population levels, 

ecological exposure and risk assessments may need to address community, ecosystem, and 

landscape levels, also. Whereas chemical agents may degrade or be transformed in the 

environment, biological agents may of course grow and multiply, an area not covered in these 

guidelines. The Agency may, at a future date, issue specific guidelines in these areas. 

Persons subject to these Guidelines should use the terms associated with chemical 

exposure assessment in a manner consistent with the glossary in Section 8. Throughout the 

public comment and SAB review process, the Agency has sought definitions that have consensus 

within the scientific community, especially those definitions common to several scientific fields. 

The Agency is aware that certain well understood and widely accepted concepts and definitions 

in the area of health physics (such as the definition of exposure) differ from the definitions in 

this glossary. The definitions in this glossary are not meant to replace such basic definitions 

used in another field of science. It was not possible, however, to reconcile all the definitions 

used in various fields of science, and the ones used in the glossary are thought to be the most 

appropriate for the field of chemical exposure assessment. 

The Agency may, from time to time, issue updates of or revisions to these Guidelines. 

2
1.3. ORGANIZATION OF THE GUIDELINES 

These Guidelines are arranged in an order that assessors commonly use in preparing 

exposure assessments. Section 2 deals with general concepts, Section 3 with planning, Section 4 

with data development, Section 5 with calculating exposures, Section 6 with uncertainty 

evaluation, and Section 7 with presenting the results. In addition, these Guidelines include a 

glossary of terms (Section 8) and references to other documents (Section 9). 

3
2. GENERAL CONCEPTS IN EXPOSURE ASSESSMENT 

Exposure assessment in various forms dates back at least to the early twentieth century, 

and perhaps before, particularly in the fields of epidemiology (World Health Organization 

[WHO], 1983), industrial hygiene (Cook, 1969; Paustenbach, 1985), and health physics (Upton, 

1988). Epidemiology is the study of disease occurrence and the causes of disease, while the 

latter fields deal primarily with occupational exposure. Exposure assessment combines elements 

of all three disciplines. This has become increasingly important since the early 1970s due to 

greater public, academic, industrial, and governmental awareness of chemical pollution 

problems. 

Because there is no agreed-upon definition of the point on or in the body where exposure 

takes place, the terminology used in the current exposure assessment literature is inconsistent. 

Although there is reasonable agreement that human exposure means contact with the chemical or 

agent (Allaby, 1983; Environ Corporation, 1988; Hodgson et al., 1988; U.S. EPA, 1986a), there 

has not yet been widespread agreement as to whether this means contact with (a) the visible 

exterior of the person (skin and openings into the body such as mouth and nostrils), or (b) the socalled exchange boundaries where absorption takes place (skin, lung, gastrointestinal tract).1 

These different definitions have led to some ambiguity in the use of terms and units for 

quantifying exposure.2 

Comments on the 1986 Guidelines and the 1988 Proposed Guidelines suggested that EPA 

examine how exposure and dose were defined in Agency assessments and include guidance on 

appropriate definitions and units. After internal discussions and external peer review, it is the 

Agency’s position that defining exposure as taking place at the visible external boundary, as in 

(a) above, is less ambiguous and more consistent with nomenclature in other scientific fields. 

This is a change from the 1986 Guidelines. 

Under this definition, it is helpful to think of the human body as having a hypothetical 

outer boundary separating inside the body from outside the body. This outer boundary of the 

body is the skin and the openings into the body such as the mouth, the nostrils, and punctures 

1

A third, less common, scheme is that exposure is contact with any boundary outside or inside of the body, including 

internal boundaries around organs, etc. This scheme is alluded to, for example, in an article prepared by the National 

Research Council (NRC, 1985, p. 91). One could then speak of exposure to the whole person or exposure to certain 

internal organs. 

2

For example, the amount of food ingested would be a dose under scheme (a) and an exposure under scheme (b). 

Since the amount ingested in an animal toxicology study is usually termed administered dose, this leads to the use of 

both exposure and dose for the same quantity under scheme (b). There are several such ambiguities in any of the 

currently used schemes. Brown (1987) provides a discussion of various units used to describe exposures due to 

multiple schemes. 

4 
and lesions in the skin. As used in these Guidelines, exposure to a chemical is the contact of that 

chemical with the outer boundary. An exposure assessment is the quantitative or qualitative 

evaluation of that contact; it describes the intensity, frequency, and duration of contact, and often 

evaluates the rates at which the chemical crosses the boundary (chemical intake or uptake rates), 

the route by which it crosses the boundary (exposure route; e.g., dermal, oral, or respiratory), and 

the resulting amount of the chemical that actually crosses the boundary (a dose) and the amount 

absorbed (internal dose). 

Depending on the purpose for which an exposure assessment will be used, the numerical 

output of an exposure assessment may be an estimate of either exposure or dose. If exposure 

assessments are being done as part of a risk assessment that uses a dose-response relationship, 

the output usually includes an estimate of dose.3 Other risk assessments, for example many of 

those done as part of epidemiologic studies, use empirically derived exposure-response 

relationships, and may characterize risk without the intermediate step of estimating dose. 

2.1. CONCEPTS OF EXPOSURE, INTAKE, UPTAKE, AND DOSE 

The process of a chemical entering the body can be described in two steps: contact 

(exposure), followed by actual entry (crossing the boundary). Absorption, either upon crossing 

the boundary or subsequently, leads to the availability of an amount of the chemical to 

biologically significant sites within the body (internal dose4). Although the description of 

contact with the outer boundary is simple conceptually, the description of a chemical crossing 

this boundary is somewhat more complex. 

There are two major processes by which a chemical can cross the boundary from outside 

to inside the body. Intake involves physically moving the chemical in question through an 

opening in the outer boundary (usually the mouth or nose), typically via inhalation, eating, or 

drinking. Normally the chemical is contained in a medium such as air, food, or water; the 

estimate of how much of the chemical enters into the body focuses on how much of the carrier 

medium enters. In this process, mass transfer occurs by bulk flow, and the amount of the 

chemical itself crossing the boundary can be described as a chemical intake rate. The chemical 

3

The National Research Council’s 1983 report Risk Assessment in the Federal Government: Managing the Process 

often addresses the output of an exposure assessment as an exposure or a dose (NRC 1983, pp. 32, 35-36). 

4

These guidelines use the term internal dose to refer to the amount of a chemical absorbed across the exchange 

boundaries, such as the skin, lung, or gastrointestinal tract. The term absorbed dose is often used synonymously for 

internal dose, although the connotation for the term absorbed dose seems to be more related to a specific boundary 

(the amount absorbed across a membrane in an experiment, for example), while the term internal dose seems to 

connote a more general sense of the amount absorbed across one or more specific sites. For the purpose of these 

guidelines, the term internal dose is used for both connotations. The term internal dose as used here is also 

consistent with how it is generally applied to a discussion of biomarkers (NRC, 1989a). It is also one of the terms 

used in epidemiology (NRC, 1985). 

5 
intake rate is the amount of chemical crossing the outer boundary per unit time, and is the 

product of the exposure concentration times the ingestion or inhalation rate. Ingestion and 

inhalation rates are the amount of the carrier medium crossing the boundary per unit time, such 

as m3

 air breathed/hour, kg food ingested/day, or liters of water consumed/day. Ingestion or 

inhalation rates typically are not constant over time, but often can be observed to vary within 

known limits.5 

The second process by which a chemical can cross the boundary from outside to inside 

the body is uptake. Uptake involves absorption of the chemical through the skin or other 

exposed tissue such as the eye. Although the chemical is often contained in a carrier medium, 

the medium itself typically is not absorbed at the same rate as the chemical, so estimates of the 

amount of the chemical crossing the boundary cannot be made in the same way as for intake (see 

Section 2.1.3). Dermal absorption is an example of direct uptake across the outer boundary of 

the body.6 A chemical uptake rate is the amount of chemical absorbed per unit time. In this 

process, mass transfer occurs by diffusion, so uptake can depend on the concentration gradient 

across the boundary, permeability of the barrier, and other factors. Chemical uptake rates can be 

expressed as a function of the exposure concentration, permeability coefficient, and surface area 

exposed, or as a flux (see Section 2.1.4). 

The conceptual process of contact, then entry and absorption, can be used to derive the 

equations for exposure and dose for all routes of exposure. 

2.1.1. Exposure 

The condition of a chemical contacting the outer boundary of a human is exposure. Most 

of the time, the chemical is contained in air, water, soil, a product, or a transport or carrier 

medium; the chemical concentration at the point of contact is the exposure concentration. 

Exposure over a period of time can be represented by a time-dependent profile of the exposure 

concentration. The area under the curve of this profile is the magnitude of the exposure, in 

concentration-time units (Lioy, 1990; NRC, 1990): 

(2-1) 

5

Ingestion of food or water is an intermittent rather than continuous process, and can be expressed as (amount of 

medium per event) × (events per unit clock or calendar time) [the frequency of contact]; (e.g., 250 mL of water/glass 

of water ingested × 8 glasses of water ingested/day). 

6

Uptake through the lung, gastrointestinal tract, or other internal barriers also can occur following intake through 

ingestion or inhalation. 

6 
where E is the magnitude of exposure, C(t) is the exposure concentration as a function of time, 

and t is time, t2 - t1 being the exposure duration (ED). If ED is a continuous period of time (e.g., 

a day, week, year, etc.), then C(t) may be zero during part of this time.7 Integrated exposures are 

done typically for a single individual, a specific chemical, and a particular pathway or exposure 

route over a given time period.8 

The integrated exposures for a number of different individuals (a population or 

population segment, for example), may then be displayed in a histogram or curve (usually, with 

integrated exposure increasing along the abscissa or x-axis, and the number of individuals at that 

integrated exposure increasing along the ordinate or y-axis). This histogram or curve is a 

presentation of an exposure distribution for that population or population segment. The utility of 

both individual exposure profiles and population exposure distributions is discussed in Section 

2.3. 

2.1.2. Applied Dose and Potential Dose 

Applied dose is the amount of a chemical at the absorption barrier (skin, lung, 

gastrointestinal tract) available for absorption. It is useful to know the applied dose if a 

relationship can be established between applied dose and internal dose, a relationship that can 

sometimes be established experimentally. Usually, it is very difficult to measure the applied 

dose directly, as many of the absorption barriers are internal to the human and are not localized 

in such a way to make measurement easy. An approximation of applied dose can be made, 

however, using the concept of potential dose9

 (Lioy, 1990; NRC, 1990). 

Potential dose is simply the amount of the chemical ingested, inhaled, or in material 

applied to the skin. It is a useful term or concept for those instances in which there is exposure 

7

Contact time (CT) is that part of the exposure duration where C(t) does not equal zero; that is, the actual time 

periods (events, episodes) during which actual exposure is taking place. The exposure duration as defined here, on 

the other hand, is a time interval of interest for assessment purposes during which exposure occurs, either 

continuously or intermittently. 

8

An exposure pathway is the course a chemical takes from its source to the person being contacted. An exposure 

route is the particular means of entry into the body, e.g., inhalation, ingestion, or dermal absorption. 

9

Potential dose is the potential amount of the chemical that could be absorbed if it were 100% bioavailable. Note, 

however, that this does not imply that 100% bioavailability or 100% absorption is assumed when using potential 

dose. The equations and discussion in this chapter use potential dose as a measurable quantity that can then be 

converted to applied or absorbed dose by the use of the appropriate factors. Potential dose is a general term referring 

to any of the exposure routes. The terms respiratory dose, oral dose, or dermal dose are sometimes used to refer to 

the route-specific potential doses. 

7 
to a discrete amount of chemical or transport medium, such as eating a certain amount of food or 

applying a certain amount of material to the skin.10 

The potential dose for ingestion and inhalation is analogous to the administered dose in a 

dose-response experiment. Human exposure to environmental chemicals is generally inadvertent 

rather than administered, so in these Guidelines it is termed potential dose rather than 

administered dose. Potential dose can be used for dose-response relationships based on 

administered dose. 

For the dermal route, potential dose is the amount of chemical applied, or the amount of 

chemical in the medium applied, for example as a small amount of particulate deposited on the 

skin. Note that as all of the chemical in the particulate is not contacting the skin, this differs 

from exposure (the concentration in the particulate times the time of contact) and applied dose 

(the amount in the layer actually touching the skin). 

The applied dose, or the amount that reaches the exchange boundaries of the skin, lung, 

or gastrointestinal tract, may often be less than the potential dose if the material is only partly 

bioavailable. Where data on bioavailability are known, adjustments to the potential dose to 

convert it to applied dose and internal dose may be made.11 

2.1.3. Internal Dose 

The amount of a chemical that has been absorbed and is available for interaction with 

biologically significant receptors is called the internal dose. Once absorbed, the chemical can 

undergo metabolism, storage, excretion, or transport within the body. The amount transported to 

an individual organ, tissue, or fluid of interest is termed the delivered dose. The delivered dose 

may be only a small part of the total internal dose. The biologically effective dose, or the 

amount that actually reaches cells, sites, or membranes where adverse effects occur (NRC, 1990, 

p. 29), may only be a part of the delivered dose, but it is obviously the crucial part. Currently, 

most risk assessments dealing with environmental chemicals (as opposed to pharmaceutical 

assessments) use dose-response relationships based on potential (administered) dose or internal 

10It is not useful to calculate potential doses in cases where there is partial or total immersion in a fluid such as air or 

water. In these cases, it is more useful to describe the situation in terms of exposure (concentration of the chemical 

in the medium times the time of contact) or absorbed dose. For cases such as contact with water in a swimming 

pool, the person is not really exposed to the entire mass of the chemical that would be described by a potential dose. 

Nor is it useful to calculate dermal applied doses because the boundary layer is being constantly renewed. The use 

of alternate ways to calculate a dose that might occur while swimming is discussed in Section 2.1.4.2, in conjunction 

with Equations 2-7 and 2-8. 

11This may be done by adding a bioavailability factor (range: 0 to 1) to the dose equation. The bioavailability factor 

would then take into account the ability of the chemical to be extracted from the matrix, absorption through the 

exchange boundary, and any other losses between ingestion and contact with the lung or gastrointestinal tract. When 

no data or information are available to indicate otherwise, the bioavailability factor is usually assumed to be 1. 

8 
dose, since the pharmacokinetics necessary to base relationships on the delivered dose or 

biologically effective doses are not available for most chemicals. This may change in the future, 

as more becomes known about the pharmacokinetics of environmental chemicals. 

Doses are often presented as dose rates, or the amount of a chemical dose (applied or 

internal) per unit time (e.g., mg/day), or as dose rates on a per-unit-body-weight basis (e.g., 

mg/kg/day). 

Distributions of individual doses within a population or population segment may be 

displayed in a histogram or curve analogous to the exposure distributions described in Section 

2.1.1. The utility of individual dose profiles, as well as the utility of population distributions of 

dose are described more fully in Section 2.3. 

2.1.4. Exposure and Dose Relationships 

Depending on the use of the exposure assessment, estimates of exposure and dose in 

various forms may be required. 

C Exposure concentrations are useful when comparing peak exposures to levels of 

concern such as short-term exposure limits (STELs). They are typically expressed in 

units such as :g/m3, mg/m3, mg/kg, :g/L, mg/L, ppb, or ppm. 

C Exposure or dose profiles describe the exposure concentration or dose as a function 

of time. Concentration and time are used to depict exposure, while amount and time 

characterize dose; graphical or tabular presentations may be used for either type of 

profile. 

Such profiles are very important for use in risk assessment where the severity of effect is 

dependent on the pattern by which the exposure occurs rather than the total (integrated) 

exposure. For example, a developmental toxin may only produce effects if exposure occurs 

during a particular stage of development. Similarly, a single acute exposure to very high 

contaminant levels may induce adverse effects even if the average exposure is much lower than 

apparent no-effect levels. Such profiles will become increasingly important as biologically 

based dose-response models become available. 

C Integrated exposures are useful when a total exposure for a particular route (i.e., the 

total for various pathways leading to exposure via the same route) is needed. Units of 

integrated exposure are concentration times time. The integrated exposure is the total 

area under the curve of the exposure profile (Equation 2-1). Note that an exposure 

profile (a picture of exposure concentration over time) contains more information 

than an integrated exposure (a number), including the duration and periodicity of 

exposure, the peak exposure, and the shape of the area under the time-concentration 

curve. 

9
C Time-weighted averages are widely used in exposure assessments, especially as part 

of a carcinogen risk assessment. e-weighted average exposure concentration 

(units of concentration) is the integrated exposure divided by the period where 

exposure occurs, and is useful in some of the equations discussed below in estimating 

dose. e-weighted average dose rate is the total dose divided by the time period 

of dosing, usually expressed in units of mass per unit time, or mass/time normalized 

to body weight (e.g., mg/kg/day). e-weighted average dose rates such as the 

lifetime average daily dose (LADD) are often used in dose-response equations to 

estimate effects or risk.12 

The discussion in the next three sections focuses on exposure via inhalation, oral intake, 

and dermal absorption. es are possible, however, including direct 

introduction into the bloodstream via injection or transfusion, contamination of exposed lesions, 

placental transfer, or use of suppositories. 

calculated in a similar manner, depending on whether an intake or uptake process is involved. 

Although equations for calculating exposure, dose, and their various averages are in 

widespread use in exposure assessment, the assessor should consider the implications of the 

assumptions used to derive the equations. plifying assumptions used in deriving the 

equations may mean that variations in exposure concentration, ingestion or inhalation rate, 

permeability coefficient, surface area exposed, and absorption fraction can introduce error into 

the estimate of dose if average values are used, and this must be considered in the evaluation of 

uncertainty (Section 6). 

2.1.4.1. Calculating Potential Dose for Intake Processes 

The general equation for potential dose for intake processes, e.g., inhalation and ingestion 

(see Figure 1 for illustration of various exposures and doses) is simply the integration of the 

chemical intake rate (concentration of the chemical in the medium times the intake rate of the 

medium, C times IR) over time: 

(2-2) 

A tim

A tim

Tim

Other exposure rout

The exposures and doses for these routes can be 

Sim

12Current carcinogen risk models, such as the linearized multistage procedure and other linear nonthreshold models, 

use lifetime exposures to develop the dose-response relationships, and therefore use lifetime time-weighted average 

exposures to estimate risks. Within the range of linearity for risk, this procedure effectively treats exposures and 

doses as a series of “units,” with each unit of dose being equal to any other unit of dose in terms of risk potential 

without respect to prior exposure or dose patterns. Current research in the field of dose-response modeling is 

focusing on biologically based dose-response models which may take into account the effects of the exposure or 

dose patterns, making use of all of the information in an exposure or dose profile. For a more indepth discussion on 

the implications of the use of time-weighted averages, see Atherley (1985). 

10 
(2-3) 

(2-4) 

where Dpot is potential dose and IR(t) is the ingestion or inhalation rate. 

The quantity t2 - t1, as before, represents the period of time over which exposure is being 

examined, or the exposure duration (ED). ay contain times where the 

chemical is in contact with the person, and also times when C(t) is zero. e represents 

the actual time period where the chemical is in contact with the person. 

ingestion, where actual contact with food or water is intermittent, and consequently the actual 

contact time may be small, the intake rate is usually expressed in terms of a frequency of events 

(e.g., 8 glasses of water consumed per day) times the intake per event (e.g., 250 mL of 

water/glass of water consumed). ittent air exposures (e.g., 8 hours exposed/day times one 

cubic meter of air inhaled/hour) can also be expressed easily using exposure duration rather than 

contact time. exposure duration will be used in the examples below to refer 

to the term t2 - t1, since it occurs frequently in exposure assessments and it is often easier to use. 

Equation 2-2 can also be expressed in discrete form as a summation of the doses received 

during various events I: 

where EDi

 is the exposure duration for event I. 

approximation if the contact time is very short), Equation 2-3 becomes: 

where ED is the sum of the exposure durations for all events, and and are the average 

values for these parameters. 

considerably. 

segments where C and IR are approximately constant. et, 

Equation 2-2 may be used. 

The exposure duration m

Contact tim

For cases such as 

Interm

Hereafter, the term

If C and IR are nearly constant (which is a good 

Equation 2-4 will not necessarily hold in cases where C and IR vary 

In those cases, Equation 2-3 can be used if the exposure can be broken out into 

If even this condition cannot be m

11
Figure 1. 

Schematic of dose and exposure.

12
(2-5) 

For risk assessment purposes, estimates of dose should be expressed in a manner that can 

be compared with available dose-response data. 

based on potential dose (called administered dose in animal studies), although dose-response 

relationships are sometimes based on internal dose. 

Doses may be expressed in several different ways. 

for example, gives a total dose accumulated over the time in question. e is 

the dose rate, which has units of mass/time (e.g., mg/day). 

dose rate is not necessarily constant. e is a useful 

number for many risk assessments. 

Exposure assessments should take into account the time scale related to the biological 

response studied unless the assessment is intended to provide data on the range of biological 

responses (NRC, 1990, p. 28). any noncancer effects, risk assessments consider the period 

of time over which the exposure occurred, and often, if there are no excursions in exposure that 

would lead to acute effects, average exposures or doses over the period of exposure are sufficient 

for the assessment. of average daily doses (ADDs). 

An ADD can be calculated from Equation 2-2 by averaging Dpot over body weight and an 

averaging time, provided the dosing pattern is known so the integral can be solved. 

to have such data for human exposure and intake over extended periods of time, so some 

simplifying assumptions are commonly used. ng Equation 2-4 instead of 2-2 or 2-3 involves 

making steady-state assumptions about C and IR, but this makes the equation for ADD easier to 

solve.13 For intake processes, then, using Equation 2-4, this becomes: 

where ADDpot is the average daily potential dose, BW is body weight, and AT is the time period 

over which the dose is averaged (converted to days). 

concentration is best expressed as an estimate of the arithmetic mean regardless of the 

distribution of the data. es that 

C and IR are approximately constant. 

For effects such as cancer, where the biological response is usually described in terms of 

lifetime probabilities, even though exposure does not occur over the entire lifetime, doses are 

often presented as lifetime average daily doses (LADDs). of 

Equation 2-5, with lifetime (LT) replacing the averaging time (AT): 

Frequently, dose-response relationships are 

Solving Equations 2-2, 2-3, or 2-4, 

The dose per unit tim

Because intake and uptake can vary, 

An average dose rate over a period of tim

For m

These averages are often in the form

It is unusual 

Usi

As with Equation 2-4, the exposure 

Again, using average values for C and IR in Equation 2-5 assum

The LADD takes the form

13The assessor should keep in mind that this steady state assumption has been made when using Equation 2-5, and 

should be able to discuss what effect using average values for C, IR, and ED has on the resulting estimate. 

13 
(2-6)

14This relationship is described by Fick’s Law, where J = Kp @ C where C represents the steady-state concentration of 

the chemical, J is the steady-state flux, and Kp is the permeability coefficient. 

15The permeability coefficient, Kp, can be experimentally calculated for a chemical and a particular barrier (e.g., skin 

type) by observing the flux rate in vitro (typical units: mg chemical crossing/sec-cm2

), and dividing it by the 

concentration of the chemical in the medium in contact with the barrier (typical units: mg chemical/cm3

). 

allows the relationship between bulk concentration and the crossing of the chemical itself to be made. p has the 

advantage of being fairly constant over a range of concentrations and can be used for concentrations other than the 

one used in the experiment. ical uptake rate, relating the crossing of the barrier of the chemical itself in 

terms of the bulk concentration, then becomes C times Kp times the surface area exposed (SA). 

14 

(2-7) 

(2-8) 

The LADD is a very common term used in carcinogen risk assessment where linear 

nonthreshold models are employed. 

2.1.4.2. Calculating Internal Dose for Uptake Processes (Especially via the Dermal Route) 

For absorption processes, there are two methods generally in use for calculating internal 

dose. monly used for dermal absorption from a liquid where at least partial 

immersion occurs, is derived from the equation for internal dose, Dint, which is analogous to 

Equation 2-2 except that the chemical uptake rate (C @ Kp @ SA) replaces the chemical intake rate 

(C @ IR). 

where Kp is the permeability coefficient, and SA is the surface area exposed. 

will vary over time, and although Kp may not vary over time, it may vary over different parts of 

the body. edium crossing the 

boundary can be observed or measured, the carrier may or may not cross the absorption barrier; 

the equations must be in terms of the chemical itself crossing. ical across 

the barrier (or flux, J) is not directly measurable, and is dependent on many factors including the 

nature of the chemical, the nature of the barrier, active transport versus passive diffusion 

processes, and the concentration of the chemical contacting the barrier. 

between the flux and the exposure concentration14 is usually expressed as a permeability 

coefficient, Kp, which is experimentally measurable.15 The internal dose that is analogous to the 

potential dose in Equation 2-4 would be: 

This 

K

The chem

The first, com

Thus, 

Both C and SA 

Unlike the intake processes, where the rate of the carrier m

The flow of the chem

The relationship 
where is the average surface area exposed and the ADDint (average daily internal dose) 

becomes: 

(2-9) 

(2-10) 

(2-11) 

(The corresponding LADDint would be obtained by substituting LT for AT.) 

method to use when calculating internal dose for a swimmer. 

assumed to be exposed to a layer of water with an average chemical concentration for a period 

of time (ED). ass of the chemical that comes in contact with the 

skin. ptions necessary in going from Equation 2-7 to Equation 2-9 are comparable to 

those made in deriving Equation 2-5. e, and Kp may 

not be constant over different parts of the body. If the assumption used to derive Equation 2-5 

(that these variables are nearly constant) does not hold, a different form of the equation having 

several terms must be used. 

The second method of calculating internal dose uses empirical observations or estimates 

of the rate that a chemical is absorbed when a dose is administered or applied. 

a small or known amount of material (such as a particulate) or a chemical (such as a pesticide) 

contacts the skin. ical to the skin, Dpot, can often be calculated from 

knowing the concentration (C) and the amount of carrier medium applied (Mmedium), either as a 

whole or on a unit surface area basis. ple, potential dose from dermal contact with soil 

can be calculated using the following equation: 

where Dpot is potential dose, Mmedium is amount of soil applied, and Fadh is the adherence factor for 

soil (the amount of soil applied to and adhering to the skin on a unit surface area per unit time). 

The relationship between potential dose and applied dose for dermal exposures is that 

potential dose includes the amount of the chemical in the total amount of medium contacting the 

skin, e.g., the amount of chemical in the soil whether or not all the chemical itself ever comes in 

direct contact, and applied dose includes only that amount of the chemical which actually 

directly touches the skin. app) and the 

internal (or absorbed) dose (Dint) can be thought of as: 

This is the 

The total body surface area (SA) is 

It is not necessary to know the m

The assum

Recall that both C and SA will vary over tim

It is useful when 

The potential dose of a chem

For exam

Theoretically, the relationship between the applied dose (D

15
(2-14) 

where f(t) is a complicated nonlinear absorption function, usually not measurable, having the 

dimensions of mass absorbed per mass applied per unit time. unction will vary 

due to a number of factors (concentration gradient of chemical, carrier medium, type of skin, 

skin moisture, skin condition, etc.). e from the start of 

exposure until time T, it would yield the absorption fraction, AF, which is the fraction of the 

applied dose that is absorbed after time T. ulative number and 

can increase with time to a possible maximum of 1 (or 100% absorption), but due to competing 

processes may reach steady state long before reaching 100% absorption. 

becomes: 

Dint = Dapp @ AF (2-12) 

where AF is the absorption fraction in units of mass absorbed/mass applied (dimensionless). 

If one assumes that all the chemical contained in the bulk material will eventually come 

in contact with the skin, then Dapp equals Dpot and using Equation 2-12, the Dint equation 

becomes: 

Dint = Dpot @ AF (2-13) 

and (using Equations 2-9 and 2-10) consequently: 

where Mmedium is the mass of the bulk material applied to the skin. 

this approximation will by no means always give credible results. 

chemical contained in the bulk medium can actually contact the skin. 

liquids or small amounts of material, the applied dose may be approximately equal to the 

potential dose, in cases where there is contact with more than a minimal amount of soil, there is 

research that indicates that using this approximation may cause serious error (Yang et al., 1989). 

When this approximation does not hold, the assessor must make assumptions about how much of 

the bulk material actually contacts the skin, or use the first method of estimating internal dose 

outlined above. 

Unfortunately, almost no data are available concerning the relationship between potential 

dose and applied dose for dermal exposures. ental data on absorption fractions derived 

for soil commonly use potential dose rather than applied dose, which may make the experimental 

data at least in part dependent on experimental conditions such as how much soil was applied. 

The absorption f

If f(t) could be integrated over tim

The absorption fraction is a cum

Equation 2-11 then 

For reasons explained below, 

The key is whether all the 

Although with certain 

Experim

If 

16
the exposure assessment conditions are similar to those in the experiment, this would not usually 

introduce much error, but if the conditions vary widely, the error introduced may be difficult to 

determine. 

As a practical matter, estimates of absorption fraction are often crude approximations and 

may be difficult to refine even if some data from experiments are available in the published 

literature. Typically, absorption experiments report results as an absorption fraction after a given 

time (e.g., 50% after 24 hours). Since absorption fraction is a function of several variables such 

as skin temperature, pH, moisture content, and exposed surface area, as well as characteristics of 

the matrix in which the chemical occurs (e.g., soil particle size distribution, organic matter 

content, and moisture content), it is often difficult to make comparisons between experimental 

data and conditions being considered for an assessment. 

With single data points, it may not be clear whether the experiment reached steady state. 

If several data points are available from different times in the experiment, a plot of absorption 

fraction vs. time may be instructive. For chemicals where data are available for steady-state 

conditions, the steady-state value will probably be a good approximation to use in assessments 

where exposure duration is at least this long, provided the conditions in the experiment are 

similar to those of the case being assessed. Assessors should be very cautious in applying 

absorption fractions for moderately absorbed chemicals (where observed experimental 

absorption fractions are not in the steady-state part of the cumulative curve), or in using 

experimental data for estimates of absorption over a much shorter duration than in the 

experiment. 

In almost all cases, the absorption fraction method of estimating internal dose from 

applied dose gives only an approximation of the internal dose. The interested reader is referred 

to U.S. EPA (1992b) for more thorough guidance on dermal exposure assessment. 

2.1.4.3. Calculating Internal Dose for Intake Processes (Especially via Respiratory and Oral 

Routes) 

Chemicals in air, food, or drinking water normally enter the body through intake 

processes, then are subsequently absorbed through internal uptake processes in the lung or 

gastrointestinal tract. Sometimes it is necessary to estimate resulting internal dose, Dint, after 

intake. In addition, if enough is known about the pharmacokinetics of the chemical to make 

addition of doses across routes a meaningful exercise, the doses must be added as internal dose, 

not applied dose, potential dose, or exposure. 

Theoretically, one could calculate Dint in these cases by using an equation similar to 

Equation 2-7; but C in that equation would become the concentration of the chemical in the lung 

or gastrointestinal tract, SA would be the internal surface area involved, and Kp would be the 

17
(2-15) 

(2-16) 

permeability coefficient of the lung or gastrointestinal tract lining. the 

pharmaceutical field may be helpful in determining, for example, internal surface areas, all of the 

data mentioned above are not known, nor are they measurable with current instrumentation. 

Because Equations 2-2 through 2-4 estimate the potential dose Dpot, which is the amount 

ingested or inhaled, and Equations 2-11 and 2-12 provide relationships between the applied dose 

(Dapp) and internal dose (Dint), all that is necessary is a relationship between potential dose and 

applied dose for intake processes. 

common assumption is that for intake processes, the potential dose equals the applied dose. 

Although arguments can be made that this assumption is likely to be more nearly accurate than 

for the case of soil contact, the validity of this assumption is unknown at this point. 

the assumption of equality means that whatever is eaten, drunk, or inhaled touches an absorption 

barrier inside the person. 

Assuming potential dose and applied dose are approximately equal, the internal dose 

after intake can be estimated by combining Equations 2-2 or 2-3 and 2-10 or 2-11. 

Equations 2-3 and 2-11, this becomes: 

The ADDint for the two-step intake/uptake process becomes: 

Using average values for and in Equations 2-15 and 2-16 involves the same assumptions 

and cautions as were discussed in deriving the ADD and LADD equations in the previous two 

sections, and of course, the same cautions apply to the use of the absorption fraction as were 

outlined in Section 2.1.4.2. 

2.1.5. 

Table 1 provides a summary of the exposure and dose terms discussed in Section 2.1, 

along with examples of units commonly used. 

Although data from

Again, data on this topic are virtually nonexistent, so a 

Essentially, 

Using 

Summary of Exposure and Dose Terms With Example Units 

18
2.2. APPROACHES TO QUANTIFICATION OF EXPOSURE 

Although exposure assessments are done for a variety of reasons (see Section 3), the 

quantitative exposure estimate can be approached from three different ways16: 

1. The exposure can be measured at the point of contact (the outer boundary of the 

body) while it is taking place, measuring both exposure concentration and time of 

contact and integrating them (point-of-contact measurement), 

2. The exposure can be estimated by separately evaluating the exposure concentration 

and the time of contact, then combining this information (scenario evaluation), 

3. The exposure can be estimated from dose, which in turn can be reconstructed through 

internal indicators (biomarkers,17 body burden, excretion levels, etc.) after the 

exposure has taken place (reconstruction). 

These three approaches to quantification of exposure (or dose) are independent, as each is 

based on different data. The independence of the three methods is a useful concept in verifying 

or validating results. Each of the three has strengths and weaknesses; using them in combination 

can considerably strengthen the credibility of an exposure or risk assessment. Sections 2.2.1 

through 2.2.3 briefly describe some of the strengths and weaknesses of each approach. 

16These three ways are approaches for arriving at a quantitative estimate of exposure. Sometimes the approaches to 

assessing exposure are described in terms of “direct measures” and “indirect measures” of exposure (e.g., NRC, 

1990). Measurements that actually involve sampling on or within a person, for example, use of personal monitors 

and biomarkers, are termed “direct measures” of exposure. Use of models, microenvironmental measurements, and 

questionnaires, where measurements do not actually involve personal measurements, are termed “indirect measures” 

of exposure. The direct/indirect nomenclature focuses on the type of measurements being made; the scenario 

evaluation/point-of-contact/reconstruction nomenclature focuses on how the data are used to develop the dose 

estimate. The three-term nomenclature is used in these guidelines to highlight the point that three independent 

estimates of dose can be developed. 

17Biomarkers can be used to study exposure, effects, or susceptibility. The discussion of biomarkers in these 

guidelines is limited to their use in indicating exposure. 

19 
Table 1. Explanation of exposure and dose terms 

Term Refers to Generic units Specific examp

Exposure Contact of chemical with outer boundary of a 

person, e.g., skin, nose, mouth. 

concentration x time Dermal: 

Respiratory: 

Oral: 

(mg chem/L wat

(mg chem/kg soi

(ppm chem in air(:g/m3 air) • (day

(mg chem/L wat(mg chem/kg foo

Potential dose 

Amount of a chemical contained in material 

ingested, air breathed, or bulk material 

applied to the skin. 

mass of the chemical: Dose rate is mass of the chemical/time; 

the dose rate is sometimes normalized to 

body weight: mass of chemical/unit body weight • time 

Dermal: Respiratory: Oral: 

(mg chem/kg soi

= mg chem in so(:g chem/m3 air) 

(min exposed) = 

(mg chem/L wat

exposed = mg ch(also dose rate: m

Applied dose 

Amount of chemical in contact with the 

primary absorption boundaries (e.g., skin, 

lungs, gastrointestinal tract) and available for 

absorption 

as above Dermal: 

Respiratory: Oral: 

(mg chem/kg so

• (% of chem in chem actually to

(:g chem/m3 air

(% of chemical achemical actuall

(mg chem/kg foo

of chemical touc

actually touchin

(also absorbed davailable to orga

20

(dose rate: mg ch
Table 1. Explanation of exposure and dose terms (continued) 

Term Refers to Generic units Specific examp

Internal (absorbed) dose 

The amount of a chemical penetrating across 

an absorption barrier or exchange boundary via either physical or biological processes. 

as above Dermal: 

Respiratory: 

Oral: 

mg chemical ab

mg chemical ab

mg chemical ab(dose rate: mg cday) 

Delivered dose 

Amount of chemical available for interaction with any particular organ or cell. 

as above mg chemical av

(dose rate: mg c

21
2.2.1. Measurement of Exposure at the Point of Contact 

Point-of-contact exposure measurement evaluates the exposure as it occurs, by measuring 

the chemical concentrations at the interface between the person and the environment as a 

function of time, resulting in an exposure profile. The best known example of the point-ofcontact measurement is the radiation dosimeter. This small badge-like device measures exposure 

to radiation as it occurs and provides an integrated estimate of exposure for the period of time 

over which the measurement has been taken. Another example is the Total Exposure 

Assessment Methodology (TEAM) studies (U.S. EPA, 1987a) conducted by the EPA. In the 

TEAM studies, a small pump with a collector and absorbent was attached to a person’s clothing 

to measure his or her exposure to airborne solvents or other pollutants as it occurred. A third 

example is the carbon monoxide (CO) point-of-contact measurement studies where subjects 

carried a small CO measuring device for several days (U.S. EPA, 1984a). Dermal patch studies 

and duplicate meal studies are also point-of-contact measurement studies. In all of these 

examples, the measurements are taken at the interface between the person and the environment 

while exposure is occurring. Use of these data for estimating exposures or doses for periods that 

differ from those for which the data are collected (e.g., for estimates of lifetime exposures) will 

require some assumptions, as discussed in Section 5.3.1. 

The strength of this method is that it measures exposure directly, and providing that the 

measurement devices are accurate, is likely to give the most accurate exposure value for the 

period of time over which the measurement was taken. It is often expensive, however, and 

measurement devices and techniques do not currently exist for all chemicals. This method may 

also require assumptions to be made concerning the relationship between short-term sampling 

and long-term exposures, if appropriate. This method is also not source-specific, a limitation 

when particular sources will need to be addressed by risk managers. 

2.2.2. Estimates of Exposure from Scenario Evaluation 

In exposure scenario evaluation, the assessor attempts to determine the concentrations of 

chemicals in a medium or location and link this information with the time that individuals or 

populations contact the chemical. The set of assumptions about how this contact takes place is 

an exposure scenario. In evaluating exposure scenarios, the assessor usually characterizes the 

chemical concentration and the time of contact separately. This may be done for a series of 

events, e.g., by using Equation 2-3, or using a steady-state approximation, e.g., using Equation 

2-4. 

The goal of chemical concentration characterization is to develop estimates of exposure 

concentration. This is typically accomplished indirectly by measuring, modeling, or using 

existing data on concentrations in the bulk media, rather than at the point of contact. Assuming 

22
the concentration in the bulk medium is the same as the exposure concentration is a clear source 

of potential error in the exposure estimate and must be discussed in the uncertainty analysis. 

Generally, the closer the medium can be measured to the point of contact (in both space and 

time), the less uncertainty there is in the characterization of exposure concentration. 

The goal of characterizing time of contact is to identify who is exposed and to develop 

estimates of the frequency and duration of exposure. Like chemical concentration 

characterization, this is usually done indirectly by use of demographic data, survey statistics, 

behavior observation, activity diaries, activity models, or, in the absence of more substantive 

information, assumptions about behavior. 

The chemical concentration and population characterizations are ultimately combined in 

an exposure scenario, and there are various ways to accomplish this. One of the major problems 

in evaluating dose equations such as Equations 2-4 through 2-6 is that the limiting assumptions 

or boundary conditions used to derive them (e.g., steady-state assumptions; see Section 2.1.4) do 

not always hold true. Two major approaches to this problem are (1) to evaluate the exposure or 

dose equation under conditions where the limiting assumptions do hold true, or (2) to deal with 

the uncertainty caused by the divergence from the boundary conditions. As an example of the 

first way, the microenvironment method, usually used for evaluating air exposures, evaluates 

segments of time and location where the assumption of constant concentration is approximately 

true, then sums over all such time segments for a total exposure for the respiratory route, 

effectively removing some of the boundary conditions by falling back to the more general 

Equation 2-3. While estimates of exposure concentration and time-of-contact are still derived 

indirectly by this method, the concentration and time-of-contact estimates can be measured for 

each microenvironment. This avoids much of the error due to using average values in cases 

where concentration varies widely along with time of contact.18 

As examples of the second approach, there are various tools used to describe uncertainty 

caused by parameter variation, such as Monte Carlo analysis (see Section 5). Section 6 discusses 

some of these techniques in more detail. 

One strength of the scenario evaluation approach is that it is usually the least expensive 

method of the three. Also, it is particularly suited to analysis of the risk consequences of 

proposed actions. It is both a strength and a weakness of scenario development that the 

18This technique still may not deal effectively with the problem of short-term “peak concentrations” exceeding some 

threshold leading to an acute effect. Even the averaging process used in a microenvironment may miss significant 

concentration spikes and average them out to lower concentrations which are apparently less toxicologically 

significant. A similar problem exists when evaluating sources; a “peak release” of a toxic chemical for a short time 

may cause serious acute effects, even though the average concentration over a longer period of time might not 

indicate serious chronic effects. 

23 
evaluation can be performed with little or no data; it is a technique that is best used when some 

knowledge exists about the soundness, validity, and uncertainty of the underlying assumptions. 

2.2.3. Exposure Estimation by Reconstruction of Internal Dose 

Exposure can also be estimated after it has taken place. If a total dose is known, or can 

be reconstructed, and information about intake and uptake rates is available, an average past 

exposure rate can be estimated. Reconstruction of dose relies on measuring internal body 

indicators after exposure and intake and uptake have already occurred, and using these 

measurements to back-calculate dose. However, the data on body burden levels or biomarkers 

cannot be used directly unless a relationship can be established between these levels or 

biomarker indications and internal dose, and interfering reactions (e.g., metabolism of unrelated 

chemicals) can be accounted for or ruled out. Biological tissue or fluid measurements that reveal 

the presence of a chemical may indicate directly that an exposure has occurred, provided the 

chemical is not a metabolite of other chemicals. 

Biological monitoring can be used to evaluate the amount of a chemical in the body by 

measuring one or more of the following items. Not all of these can be measured for every 

chemical: 

C the concentration of the chemical itself in biological tissues or sera (blood, urine, 

breath, hair, adipose tissue, etc.), 

C the concentration of the chemical’s metabolite(s), 

C the biological effect that occurs as a result of human exposure to the chemical (e.g., 

alkylated hemoglobin or changes in enzyme induction), or 

C the amount of a chemical or its metabolites bound to target molecules. 

The results of biomonitoring can be used to estimate chemical uptake during a specific 

interval if background levels do not mask the marker and the relationships between uptake and 

the marker selected are known. The time of sampling for biomarkers can be critical. 

Establishing a correlation between exposure and the measurement of the marker, including 

pharmacokinetics, can help optimize the sampling conditions. 

The strengths of this method are that it demonstrates that exposure to and absorption of 

the chemical has actually taken place, and it theoretically can give a good indication of past 

exposure. The drawbacks are that it will not work for every chemical due to interferences or the 

reactive nature of the chemical, it has not been methodologically established for very many 

chemicals, data relating internal dose to exposure are needed, and it may be expensive. 

24
2.3. RELATIONSHIPS OF EXPOSURE AND DOSE TO RISK 

Exposure and dose information are often combined with exposure-response or doseresponse relationships to estimate risk, the probability of an adverse effect occurring. There are 

a variety of risk models, with various mathematical relationships between risk and dose or (less 

frequently) exposure. A major function of the exposure assessment as part of a risk assessment 

is to provide the exposure or dose values, and their interpretations. 

The exposure and dose information available will often allow estimates of individual risk 

or population risk, or both. Presentation of risks in a risk assessment involves more than merely 

a numerical value, however. Risks can be described or characterized in a number of different 

ways. This section discusses the relationships between exposure and dose and a series of risk 

descriptors. 

In preparing exposure information for use in a risk assessment, the use of several 

descriptors, including descriptors of both individual and population risk, often provides more 

useful information to the risk manager than a single descriptor or risk value. Developing several 

descriptors may require the exposure assessor to analyze and evaluate the exposure and dose 

information in several different ways. The exposure assessor should be aware of the purpose, 

scope, and level of detail of the assessment (see Sections 3.1 through 3.3) before gathering data, 

since the types and amounts of data needed may differ. The questions that need to be addressed 

as a result of the purpose of the assessment determine the type of risk descriptors used in the 

assessment. 

2.3.1. Individual Risk 

Individual risk is risk borne by individual persons within a population. Risk assessments 

almost always deal with more than a single individual. Frequently, individual risks are 

calculated for some or all of the persons in the population being studied, and are then put into the 

context of where they fall in the distribution of risks for the entire population. 

Descriptions of individual risk can take various forms, depending on the questions being 

addressed. For the risk manager, there are often key questions in mapping out a strategy for 

dealing with individual risk. For cancer (or when possible, noncancer) assessments, the risk 

manager may need answers to questions such as: 

C Are individuals at risk from exposure to the substances under study? Although for 

substances, such as carcinogens, that are assumed to have no threshold, only a zero 

dose would result in no excess risk; for noncarcinogens, this question can often be 

addressed. In the case of the use of hazard indices, where exposures or doses are 

compared to a reference dose or some other acceptable level, the risk descriptor would 

be a statement based on the ratio between the dose incurred and the reference dose. 

25
C To what risk levels are the persons at the highest risk subjected?

C Who are these people, what are they doing, where do they live, etc., and what might be

putting them at this higher risk? 

C Can people with a high degree of susceptibility be identified? 

C What is the average individual risk? 

In addressing these questions, risk descriptors may take any of several forms: 

C an estimate of the probability that an individual in the high end of the distribution may 

suffer an adverse effect, along with an explanation (to the extent known) of the 

(exposure or susceptibility) factors which result in their being in the high end; 

C an estimate of the probability that an individual at the average or median risk may 

suffer an adverse effect; or 

C an estimate of the probability that an individual will suffer an adverse effect given a 

specific set of exposure circumstances. 

Individuals at the high end of the risk distribution are often of interest to risk managers 

when considering various actions to mitigate risk. These individuals often are either more 

susceptible to the adverse health effect than others in the population or are highly exposed 

individuals, or both. 

Higher susceptibility may be the result of a clear difference in the way the chemical is 

processed by the body, or it may be the result of being in the extreme part of the normal range in 

metabolism for a population. It may not always be possible to identify persons or subgroups 

who are more susceptible than the general population. If groups of individuals who have clearly 

different susceptibility characteristics can be identified, they can be treated as a separate 

subpopulation, and the risk assessment for this subgroup may require a different dose-response 

relationship from the one used for the general population. When highly susceptible individuals 

can be identified, but when a different dose-response relationship is not appropriate or feasible to 

develop, the risks for these individuals are usually treated as part of the variability of the general 

population. 

Highly exposed individuals have been described in the literature using many different 

terms. Because of unclear definitions, terms such as maximum exposed individual,19 worst case 

19The uppermost portion of the high-end exposure range has generally been the target for terms such as “maximum 

exposed individual,” although actual usage has varied. 

26 
exposure,20 and reasonable worst case exposure21 have sometimes been applied to a variety of ad 

hoc estimates with unclear target ranges. The term maximum exposed individual has often been 

used synonymously with worst case exposure, that is, to estimate the exposure of the individual 

with the highest actual or possible exposure. An accurate estimate of the exposure of the person 

in the distribution with the highest exposure is extremely difficult to develop; uncertainty in the 

estimate usually increases greatly as the more extreme ends of the distribution are approached. 

Even using techniques such as Monte Carlo simulations can result in high uncertainty about 

whether the estimate is within, or above, the actual exposure distribution. 

For the purpose of these guidelines, a high end exposure estimate is a plausible estimate 

of the individual exposure for those persons at the upper end of an exposure distribution. The 

intent of this designation is to convey an estimate of exposures in the upper range of the 

distribution, but to avoid estimates that are beyond the true distribution. Conceptually, the high 

end of the distribution means above the 90th percentile of the population distribution, but not 

higher than the individual in the population who has the highest exposure. High-end dose 

estimates are described analogously. 

The concept of the high-end exposure, as used in this guidance, is fundamentally 

different from terms such as worst case, in that the estimate is by definition intended to fall on 

the actual (or in the case of scenarios dealing with future exposures, probable) exposure 

distribution. 

Key Point: The primary objective when developing an estimate of high-end exposure or 

dose is to arrive at an estimate that will fall within the actual distribution, rather than 

above it. (Estimates above the distribution are bounding estimates; see Section 5.3.4.1) 

Often this requires professional judgment when data are sparse, but the primary objective 

of this type of estimator is to be within this fairly wide conceptual target range. 

20The term “worst case exposure” has historically meant the maximum possible exposure, or where everything that 

can plausibly happen to maximize exposure, happens. While in actuality, this worst case exposure may fall on the 

uppermost point of the population distribution, in most cases, it will be somewhat higher than the individual in the 

population with the highest exposure. The worst case represents a hypothetical individual and an extreme set of 

conditions; this will usually not be observed in an actual population. The worst case and the so-called maximum 

exposed individual are therefore not synonymous, the former describing a statistical possibility that may or may not 

occur in the population, and the latter ostensibly describing an individual that does, or is thought to, exist in the 

population. 

21The lower part of the high-end exposure range, e.g., conceptually above the 90th percentile but below about the 

98th percentile, has generally been the target used by those employing the term “reasonable worst case exposure.” 

Above about the 98th percentile has been termed the “maximum exposure” range. Note that both these terms should 

refer to estimates of exposure on the actual distribution, not above it. 

27 
The relationship between answering the questions about high-end individual risk and 

what the exposure assessor must do to develop the descriptors is discussed in Section 3.4. 

Individual risk descriptors will generally require the assessor to make estimates of high-end 

exposure or dose, and sometimes additional estimates (e.g., estimates of central tendency such as 

average or median exposure or dose). 

Another type of individual risk descriptor results from specific sets of circumstances that 

can be hypothesized as part of a scenario, for example: 

C What if a homeowner lives at the edge of this site for his entire life? 

C What if a pesticide applicator applies this pesticide without using protective 

equipment? 

C What if a consumer uses this product every day for ten years? Once a month? Once a 

week? 

C What risk level will occur if we set the standard at 100 ppb? 

The assumptions made in answering these assessment-specific postulated questions 

should not be confused with the approximations made in developing an exposure estimate for an 

existing population or with the adjustments in parameter values made in performing a sensitivity 

analysis. The assumptions in these specific questions address a purer “if/then” relationship and, 

as such, are more helpful in answering specific hypothetical or anecdotal questions. The answers 

to these postulated questions do not give information about how likely the combination of values 

might be in the actual population or about how many (if any) persons might actually be subjected 

to the calculated risk. 

Exposure scenarios employing these types of postulated questions are encountered often 

in risk assessments, especially in those where actual exposure data are incomplete or 

nonexistent. Although the estimates of individual exposure derived from these assumptions 

provide numerical values for calculating risk, they do so more as a matter of context than a 

determination of actual exposure. They are not the same types of estimates as high-end exposure 

or risk, where some statement must be made about the likelihood of their falling within a 

specified range in the actual exposure or risk distribution. 

2.3.2. Population Risk 

Population risk refers to an estimate of the extent of harm for the population or 

population segment being addressed. Risk managers may need questions addressed such as the 

following: 

C How many cases of a particular health effect might be probabilistically estimated for a 

population of interest during a specified time period? 

28
C For noncarcinogens, what portion of the population exceeds the reference dose (RfD), 

the reference concentration (RfC), or other health concern level? 

C For carcinogens, how many persons are above a certain risk level such as 10-6 or a 

series of risk levels such as 10-5, 10-4, etc? 

C How do various subgroups fall within the distributions of exposure, dose, and risk? 

C What is the risk for a particular population segment? 

C Do any particular subgroups experience a high exposure, dose, or risk? 

The risk descriptors for population risk can take any of several forms: 

C a probabilistic projection of the estimated extent of occurrence of a particular effect 

for a population or segment (sometimes called “number of cases” of effect); 

C a description of what part of the population (or population segment) is above a certain 

risk value of interest; or 

C a description of the distribution of risk among various segments or subgroups of the 

population. 

In theory, an estimate of the extent of effects a population might incur (e.g., the number 

of individual cases that might occur during a specified time) can be calculated by summing the 

individual risks for all individuals within the population or population segment of interest. The 

ability to calculate this estimate depends on whether the individual risks are in terms of 

probabilities for each individual, rather than a hazard index or other nonprobabilistic risk. The 

calculation also requires a great deal more information than is normally available. 

For some assessments, an alternate method is used, provided certain conditions hold. An 

arithmetic mean dose is usually much easier to estimate than the individual doses of each person 

in the population or population segment, but calculating the hypothetical number of cases by 

using mean doses, slope factors, and population size must be done with considerable caution. If 

the risk varies linearly with dose, and there is no threshold below which no effect ever occurs, an 

estimate of the number of cases that might occur can be derived from the definition of arithmetic 

mean. If A = T/n, where A is the arithmetic mean of n numbers, and T is the sum of the same n 

numbers, simple rearrangement gives T = A n. If the arithmetic mean risk for the population 

(A) can be estimated, and the size of the population (n) is known, then this relationship can be 

used to calculate a probabilistic estimate of the extent of effects (T).22 Even so, several other 

cautions apply when using this method. 

22Since the geometric mean (G) is defined differently, use of the geometric mean individual risk (where G does not 

equal A, such as is often found in environmental situations) in the above relationship will obviously give an 

erroneous (usually low) estimate of the total. Geometric means have appropriate uses in exposure and risk 

assessment, but estimating population risk in this way is not one of them. 

29 
Individual risks are usually expressed on an upper bound basis, and the resulting number 

of cases estimated in this manner will normally be an upper bound estimate due to the nature of 

the risk model used. This method will not work at all for nonlinear dose-response models, such 

as many noncancer effects or for nonlinear carcinogenic dose-response models. 

In practice, it is difficult even to establish an accurate mean health effect risk for a 

population. This is due to many complications, including uncertainties in using animal data for 

human dose-response relationships, nonlinearities in the dose-response curve, projecting 

incidence data from one group to another dissimilar group, etc. Although it has been common 

practice to estimate the number of cases of disease, especially cancer, for populations exposed to 

chemicals, it should be understood that these estimates are not meant to be accurate predictions 

of real (or actuarial) cases of disease. The estimate’s value lies in framing hypothetical risk in an 

understandable way rather than in any literal interpretation of the term “cases.” 

Another population risk descriptor is a statement regarding how many people are thought 

to be above a certain risk level or other point of demarcation. For carcinogens, this might be an 

excess risk level such as 10-6 (or a series of levels, i.e., 10-5, 10-4, etc.). For noncarcinogenic risk, 

it might be the portion of the population that exceeds the RfD (a dose), the RfC (an exposure 

concentration), an effect-based level such as a lowest observed adverse effect level (LOAEL), 

etc. For the exposure assessor, this type of descriptor usually requires detailed information about 

the distribution of exposures or doses. 

Other population risk descriptors address the way the risk burden is distributed among 

various segments of the subject population. The segments (or subgroups) could be divided by 

geographic location, age, sex, ethnic background, lifestyle, economic factors, or other 

demographic variables, or they could represent groups of persons with a typical sensitivity or 

susceptibility, such as asthmatics. 

For assessors, this means that data may need to be evaluated for both highly exposed 

population segments and highly sensitive population segments. In cases involving a highly 

exposed population segment, the assessor might approach this question by having this segment 

of the population in mind when developing the descriptors of high-end exposure or dose. 

Usually, however, these segments are identified (either a priori or from inspection of the data) 

and then treated as separate, unique populations in themselves, with segment-specific risk 

descriptors (population, individual, etc.) analogous to those used for the larger population. 

2.3.3. Risk Descriptors 

In summary, exposure and dose information developed as part of an exposure assessment 

may be used in constructing risk descriptors. These are statements to convey information about 

risk to users of that information, primarily risk managers. Risk descriptors can be grouped as 

30
descriptors of individual risk or population risk, and within these broad categories, there are 

several types of descriptors. Not all descriptors are applicable to all assessments. As a matter of 

policy, the Agency or individual program offices within the Agency may require one or more of 

these descriptors to be included in specific risk assessments. Because the type of descriptor 

translates fairly directly into the type of analysis the exposure assessor must perform, the 

exposure assessor needs to be aware of these policies. Additional information on calculating and 

presenting exposure estimates and risk descriptors is found in Sections 5 and 7 of these 

Guidelines. 

31
3. PLANNING AN EXPOSURE ASSESSMENT 

Exposure assessments are done for a variety of purposes, and for that reason, cannot 

easily be regimented into a set format or protocol. Each assessment, however, uses a similar set 

of planning questions, and by addressing these questions the assessor will be better able to 

decide what is needed to perform the assessment and how to obtain and use the information 

required. To facilitate this planning, the exposure assessor should consider some basic 

questions: 

Purpose: Why is the study being conducted? What questions will the study address and 

how will the results be used? 

Scope: Where does the study area begin and end? Will inferences be made on a 

national, regional, or local scale? Who or what is to be monitored? What chemicals and 

what media will be measured, and for which individuals, populations, or population 

segments will estimates of exposure and dose be developed? 

Level of Detail: How accurate must the exposure or dose estimate be to achieve the 

purpose? How detailed must the assessment be to properly account for the biological 

link between exposure, dose, effect, and risk, if necessary? How is the depth of the 

assessment limited by resources (time and money), and what is the most effective use of 

those resources in terms of level of detail of the various parts of the assessment? 

Approach: How will exposure or dose be measured or estimated, and are these methods 

appropriate given the biological links among exposure, dose, effect, and risk? How will 

populations be characterized? How will exposure concentrations be estimated? What is 

known about the environmental and biological fate of the substance? What are the 

important exposure pathways? What is known about expected concentrations, analytical 

methods, and detection limits? Are the presently available analytical methods capable of 

detecting the chemical of interest and can they achieve the level of quality needed in the 

assessment? How many samples are needed? When will the samples be collected? How 

frequently? How will the data be handled, analyzed, and interpreted? 

By addressing each of these questions, the exposure assessor will develop a clear and 

concise definition of study objectives that will form the basis for further planning. 

3.1. PURPOSE OF THE EXPOSURE ASSESSMENT 

The particular purpose for which an exposure assessment will be used will often have 

significant implications for the scope, level of detail, and approach of the assessment. Because 

32
of the complex nature of exposure assessments, a multidisciplinary approach that encompasses 

the expertise of a variety of scientists is necessary. Exposure assessors should seek assistance 

from other scientists when they lack the expertise necessary in certain areas of the assessment. 

3.1.1. Using Exposure Assessments in Risk Assessment 

The National Research Council (NRC, 1983) described exposure assessment as one of 

the four major areas of risk assessment (the others are hazard identification, dose-response 

assessment, and risk characterization). The primary purpose of an exposure assessment in this 

application is often to estimate dose, which is combined with chemical-specific dose-response 

data (usually from animal studies) in order to estimate risk. Depending on the purpose of the 

risk assessment, the exposure assessment will need to emphasize certain areas in addition to 

quantification of exposure and dose. 

If the exposure assessment is part of a risk assessment to support regulations for specific 

chemical sources, such as point emission sources, consumer products, or pesticides, then the link 

between the source and the exposed or potentially exposed population is important. In this case, 

it is often necessary to trace chemicals from the source to the point of exposure by using source 

and fate models and exposure scenarios. By examining the individual components of a scenario, 

assessors can focus their efforts on the factors that contribute the most to exposure, and perhaps 

use the exposure assessment to select possible actions to reduce risk. For example, exposure 

assessments are often used to compare and select control or cleanup options. Most often the 

scenario evaluation is employed to estimate the residual risk associated with each of the 

alternatives under consideration. These estimates are compared to the baseline risk to determine 

the relative risk reduction of each alternative. These types of assessments can also be employed 

to make screening decisions about whether to further investigate a particular chemical. These 

assessments can also benefit from verification through the use of personal or biological 

monitoring techniques. 

If the exposure assessment is part of a risk assessment performed to set standards for 

environmental media, usually the concentration levels in the medium that pose a particular risk 

level are important. Normally, these assessments place less emphasis on the ultimate source of 

the chemical and more emphasis on linking concentration levels in the medium with exposure 

and dose levels of those exposed. A combination of media measurements and personal exposure 

monitoring could be very helpful in assessments for this purpose, since what is being sought is 

the relationship between the two. Modeling may also support or supplement these assessments. 

If the exposure assessment is part of a risk assessment used to determine the need to 

remediate a waste site or chemical spill, the emphasis is on calculating the risk to an individual 

or small group, comparing that risk to an acceptable risk level, and if necessary determining 

33
appropriate cleanup actions to reach an acceptable risk. The source of chemical contamination 

may or may not be known. Although personal exposure monitoring can give a good indication 

of the exposure or dose at the present time, often the risk manager must make a decision that will 

protect health in the future. For this reason, modeling and scenario development are the primary 

techniques used in this type of assessment. Emphasis is usually placed on linking sources with 

the exposed individuals. Biological monitoring may also be helpful (in cases where the 

methodology is established) in determining if exposure actually results in a dose, since some 

chemicals are not bioavailable even if intake occurs. 

If the exposure assessment is part of a risk assessment used as a screening device for 

setting priorities, the emphasis is more on the comparative risk levels, perhaps with the risk 

estimates falling into broad categories (e.g., semi-quantitative categories such as high, medium, 

and low). For such quick-sorting exercises, rarely are any techniques used other than modeling 

and scenario development. Decisions made in such cases rarely involve direct cleanup or 

regulatory action without further refinement of the risk assessment, so the scenario development 

approach can be a cost-effective way to set general priorities for future investigation of worst 

risk first. 

If the exposure assessment is part of a risk assessment that is wholly predictive in nature, 

such as for the premanufacture notice (PMN) program, a modeling and scenario development 

approach is recommended. In such cases, measurement of chemicals yet to be manufactured or 

in the environment is not possible. In this case again, the link between source and exposed 

individuals is emphasized. 

Not only are risk assessments done for a variety of purposes, but the toxic endpoints 

being assessed (e.g., cancer, reproductive effects, neurotoxic effects) can also vary widely. 

Endpoints and other aspects of the hazard identification and dose-response relationships can 

have a major effect on how the exposure information must be collected and analyzed for a risk 

assessment. This is discussed in more detail in Section 3.5.1. 

3.1.2. Using Exposure Assessments for Status and Trends 

Exposure assessments can also be used to determine whether exposure occurs and to 

monitor status and trends. The emphasis in these exposure assessments is on what the actual 

exposure (or dose) is at one particular time, and how the exposure changes over time. Examples 

of this type of assessment are occupational studies. Characteristics and special considerations 

for occupational studies have been discussed by the National Institute for Occupational Safety 

and Health (NIOSH, 1988). 

Exposure status is the snapshot of exposure at a given time, usually the exposure profile 

of a population or population segment (perhaps a segment or statistical sample that can be 

34
studied periodically). Exposure trends show how this profile changes with time. Normally, 

status and trends studies make use of statistical sampling strategies to assure that changes can be 

interpreted meaningfully. These data are particularly useful if actions for risk amelioration and 

demonstration of the effectiveness of these actions can be made through exposure trend 

measurements. 

Measurement is critical to such assessments. Personal monitoring can give the most 

accurate picture of exposure, but biological or media monitoring can indicate exposure levels, 

provided a strong link is established between the biological or media levels and the exposure 

levels. Usually this link is established first by correlating biological or media levels with 

personal monitoring data for the same population over the same period. 

3.1.3. Using Exposure Assessments in Epidemiologic Studies 

Exposure assessments can also be important components of epidemiologic studies, where 

the emphasis is on using the exposure assessment to establish exposure-incidence (or doseeffect) relationships. For this purpose, personal monitoring, biological monitoring, and scenario 

development have all been used. If the population under study is being currently exposed, 

personal monitoring or biological monitoring may be particularly helpful in establishing 

exposure or dose levels. If the exposure took place in the past, biological monitoring may 

provide useful data, provided the chemical is amenable to detection without interference or 

degradation, and the pharmacokinetics are known. More often, however, scenario development 

techniques are used to estimate exposure in the past, and often the accuracy of the estimate is 

limited to classifying exposure as high, medium, or low. This type of categorization is rather 

common, but sometimes it is very difficult to determine who belongs in a category, and to 

interpret the results of the study. Although epidemiologic protocols are beyond the scope of 

these Guidelines, the use of exposure assessment for epidemiology has been described by the 

World Health Organization (WHO, 1983). 

3.2. SCOPE OF THE ASSESSMENT 

The scope of an assessment refers to its comprehensiveness. For example, an important 

limitation in many exposure assessments relates to the specific chemical(s) to be evaluated. 

Although this seems obvious, where exposure to multiple chemicals or mixtures is possible, it is 

not always clear whether assessing “all” chemicals will result in a different risk value than if 

only certain significant chemicals are assessed and the others assumed to contribute only a minor 

amount to the risk. This may also be true for cases where degradation products have equal or 

greater toxicological concerns. In these cases, a preliminary investigation may be necessary to 

determine which chemicals are likely to be in high enough concentrations to cause concern, with 

35
the possible contribution of the others discussed in the uncertainty assessment. The assessor 

must also determine geographical boundaries, population exposed, environmental media to be 

considered, and exposure pathways and routes of concern. 

The purpose of the exposure assessment will usually help define the scope. There are 

characteristics that are unique to national exposure assessments as opposed to industry-wide or 

local exposure assessments. For example, exposure assessments in support of national 

regulations must be national in scope; exposure assessments to support cleanup decisions at a 

site will be local in scope. Exposure assessments to support standards for a particular medium 

will often concentrate on that medium’s concentration levels and typical exposure pathways and 

routes, although the other pathways and routes are also often estimated for perspective. 

3.3. LEVEL OF DETAIL OF THE ASSESSMENT 

The level of detail, or depth of the assessment, is measured by the amount and resolution 

of the data used, and the sophistication of the analysis employed. It is determined by the purpose 

of the exposure assessment and the resources available to perform the assessment. Although in 

theory the level of detail needed can be established by determining the accuracy of the estimate 

required, this is rarely the case in practice. To conserve resources, most assessments are done in 

an iterative fashion, with a screening done first; successive iterations add more detail and 

sophistication. After each iteration, the question is asked, is this level of detail or degree of 

confidence good enough to achieve the purpose of the assessment? If the answer is no, 

successive iterations continue until the answer is affirmative, new input data are generated, or as 

is the case for many assessments, the available data, time, or resources are depleted. Resourcelimited assessments should be evaluated in terms of what part of the original objectives have 

been accomplished, and how this affects the use of the results. 

The level of detail of an exposure assessment can also be influenced by the level of 

sophistication or uncertainty in the assessment of health effects to be used for a risk assessment. 

If only very weak health information is available, a detailed, costly, and in-depth exposure 

assessment will in most cases be wasteful, since the most detailed information will not add 

significantly to the certainty of the risk assessment. 

3.4. DETERMINING THE APPROACH FOR THE EXPOSURE ASSESSMENT 

The intended use of the exposure assessment will generally favor one approach to 

quantifying exposure over the others, or suggest that two or more approaches be combined. 

These approaches to exposure assessment can be viewed as different ways of estimating the 

same exposure or dose. Each has its own unique characteristics, strengths, and weaknesses, but 

the estimate should theoretically be the same, independent of the approach taken. 

36
The point-of-contact approach requires measurements of chemical concentrations at the 

point where they contact the exposed individuals, and a record of the length of time of contact at 

each concentration. Some integrative techniques are inexpensive and easy to use (radiation 

badges), while others are costly and may present logistical challenges (personal continuoussampling devices), and require public cooperation. 

The scenario evaluation approach requires chemical concentration and time-of-contact 

data, as well as information on the exposed persons. Chemical concentration may be determined 

by sampling and analysis or by use of fate and transport models (including simple dilution 

models). Models can be particularly helpful when some analytical data are available, but 

resources for additional sampling are limited. Information on human behavior and physical 

characteristics may be assumed or obtained by interviews or other techniques from individuals 

who represent the population of interest. 

For the reconstruction of dose approach, the exposure assessor usually uses measured 

body burden or specific biomarker data, and selects or constructs a biological model that uses 

these data to account for the chemical’s behavior in the body. If a pharmacokinetic model is 

used, additional data on metabolic processes will be required (as well as model validation 

information). Information on exposure routes and relative source strengths is also helpful. 

One of the goals in selecting the approach should include developing an estimate having 

an acceptable amount of uncertainty. In general, estimates based on quality-assured 

measurement data, gathered to directly answer the questions of the assessment, are likely to have 

less uncertainty than estimates based on indirect information. The approach selected for the 

assessment will determine which data are needed. All three approaches also require data on 

intake and uptake rates if the final product of the assessment is a calculated dose. 

Sometimes more than one approach is used to estimate exposure. For example, the 

TEAM study combines point-of-contact measurement with the microenvironment (scenario 

evaluation) approach and breath measurements for the reconstruction of dose approach (U.S. 

EPA, 1987a). If more than one approach is used, the assessor should consider how using each 

approach separately can verify or validate the others. In particular, point-of-contact 

measurements can be used as a check on assessments made by scenario evaluation. 

3.5. ESTABLISHING THE EXPOSURE ASSESSMENT PLAN 

Before starting work on an exposure assessment, the assessor should have determined the 

purpose, scope, level of detail, and approach for the assessment, and should be able to translate 

these into a set of objectives. These objectives will be the foundation for the exposure 

assessment plan. The exposure assessment plan need not be a lengthy or formal document, 

37
especially for assessments that have a narrow scope and little detail. For more complex exposure 

assessments, however, it is helpful to have a written plan. 

For exposure assessments being done as part of a risk assessment, the exposure 

assessment plan should reflect (in addition to the objectives) an understanding of how the results 

of the exposure assessment will be used in the risk assessment. For some assessments, three 

additional components may be needed: the sampling strategy (Section 3.5.2), the modeling 

strategy (Section 3.5.3), and the communications strategy (Section 7.1.3). 

3.5.1. Planning an Exposure Assessment as Part of a Risk Assessment 

For risk assessments, exposure information must be clearly linked to the hazard 

identification and dose-response relationship (or exposure-response relationship; see Section 

3.5.4). The toxic endpoints (e.g., cancer, reproductive effects, neurotoxic effects) can vary 

widely, and along with other aspects of the hazard identification and dose-response relationships, 

can have a major effect on how the exposure information must be collected and analyzed for a 

risk assessment. Some of these aspects include implications of limited versus repeated 

exposures, dose-rate considerations, reversibility of toxicological processes, and composition of 

the exposed population. 

C Limited Versus Repeated Exposures. Current carcinogen risk models often use 

lifetime time-weighted average doses in the dose-response relationships owing to their 

derivation from lifetime animal studies. This does not mean cancer cannot occur after 

single exposures (witness the A-bomb experience), merely that exposure information 

must be consonant with the source of the model. Some toxic effects, however, occur 

after a single or a limited number of exposures, including acute reactions such as 

anesthetic effects and respiratory depression or certain developmental effects 

following exposure during pregnancy. For developmental effects, for example, 

lifetime time-weighted averages have little relevance, so different types of data must 

be collected, in this case usually shorter-term exposure profile data during a particular 

time window. Consequently, the exposure assessors and scientists who conduct 

monitoring studies need to collaborate with those scientists who evaluate a chemical’s 

hazard potential to assure the development of a meaningful risk assessment. If shortterm peak exposures are related to the effect, then instruments used should be able to 

measure short-term peak concentrations. If cumulative exposure is related to the 

effect, long-term average sampling strategies will probably be more appropriate. 

C Dose-Rate Effects. The use of average daily exposure values (e.g., ADD, LADD) in a 

dose-response relationship assumes that within some limits, increments of C times T 

(exposure concentration times time) that are equal in magnitude are equivalent in their 

38
potential to cause an effect, regardless of the pattern of exposure (the so-called 

Haber’s Rule; see Atherley, 1985). In those cases where toxicity depends on the dose 

rate, one may need a more precise determination of the time people are exposed to 

various concentrations and the sequence in which these exposures occur. 

C Reversibility of Toxicological Processes. The averaging process for daily exposure 

assumes that repeated dosing continues to add to the risk potential. In some cases, 

after cessation of exposure, toxicological processes are reversible over time. In these 

cases, exposure assessments must provide enough information so that the risk assessor 

can account for the potential influence of episodic exposures. 

C Composition of the Exposed Population. For some substances, the type of health 

effect may vary as a function of age or sex. Likewise, certain behaviors (e.g., 

smoking), diseases (e.g., asthma), and genetic traits (e.g., glucose-6-phosphate 

dehydrogenase deficiency) may affect the response of a person to a chemical 

substance. Special population segments, such as children, may also call for a 

specialized approach to data collection (WHO, 1986). 

3.5.2. Establishing the Sampling Strategy 

If the objectives of the assessment are to be met using measurements, it is important to 

establish the sampling strategy before samples are actually taken. The sampling strategy 

includes setting data quality objectives, developing the sampling plan and design, using spiked 

and blank samples, assessing background levels, developing quality assurance project plans, 

validating previously generated data, and selecting and validating analytical methods. 

3.5.2.1. Data Quality Objectives 

All measurements are subject to uncertainty because of the inherent variability in the 

quantities being measured (e.g., spatial and temporal variability) and analytical measurement 

variability introduced during the measurement process through sampling and analysis. Some 

sources of variability can be expressed quantitatively, but others can only be described 

qualitatively. The larger the variability associated with individual measurements, the lower the 

data quality, and the greater the probability of errors in interpretation. Data quality objectives 

(DQOs) describe the degree of uncertainty that an exposure assessor and other scientists and 

management are willing to accept. 

Realistic DQOs are essential. Data of insufficient quality will have little value for 

problem solving, while data of quality vastly in excess of what is needed to answer the questions 

asked provide few, if any, additional advantages. DQOs should consider data needs, costeffectiveness, and the capability of the measurement process. The amount of data required 

39
depends on the level of detail necessary for the purpose of the assessment. Estimates of the 

number of samples to be taken and measurements to be made should account for expected 

sample variability. Finally, DQOs help clarify study objectives by compelling the exposure 

assessor to establish how the data will be used before they are collected. 

The exposure assessor establishes data criteria by proposing limits (based on best 

judgment or perhaps a pilot study) on the acceptable level of uncertainty for each conclusion to 

be drawn from new data, considering the resources available for the study. DQOs should 

include: 

C A clear statement of study objectives, to include an estimation of the key study 

parameters, identifying the hypotheses being tested, the specific aims of the study, and 

how the results will be used. 

C The scope of study objectives, to include the minimum size of subsamples from which 

separate results may be calculated, and the largest unit (area, time period, or group of 

people) the data will represent. 

C A description of the data to be obtained, the media to be sampled, and the capabilities 

of the analytical methodologies. 

C The acceptable probabilities and uncertainties associated with false positive and false 

negative statements. 

C A discussion of statistics used to summarize the data; any standards, reference values, 

or action levels used for comparison; and a description and rationale for any 

mathematical or statistical procedures used. 

C An estimate of the resources needed. 

3.5.2.2. Sampling Plan 

The sampling plan specifies how a sample is to be selected and handled. An inadequate 

plan will often lead to biased, unreliable, or meaningless results. Good planning, on the other 

hand, makes optimal use of limited resources and is more likely to produce valid results. 

The sampling design specifies the number and types of samples needed to achieve DQOs. 

Factors to be considered in developing the sampling design include study objectives, sources of 

variability (e.g., temporal and spatial heterogeneity, analytical differences) and their relative 

magnitudes, relative costs, and practical limitations of time, cost, and personnel. 

Sampling design considers the need for temporal and spatial replication, compositing 

(combining several samples prior to analysis), and multiple determinations on a single sample. 

A statistical or environmental process model may be used to allocate sampling effort in the most 

efficient manner. 

40
Data may be collected using a survey or an experimental approach. It may be desirable 

to stratify the sample if it is suspected that differences exist between segments of the statistical 

population being sampled. In such cases, the stratified sampling plan assures representative 

samples of the obviously different parts of the sample population while reducing variance in the 

sample data. The survey approach estimates population exposure based on the measured 

exposure of a statistically representative sample of the population. In some situations the study 

objectives are better served by an experimental approach; this approach involves experiments 

designed to determine the relationship between two or more factors, (e.g., between house 

construction and a particular indoor air pollutant). In the experimental approach, experimental 

units are selected to cover a range of situations (e.g., different housing types), but do not reflect 

the frequency of those units in the population of interest. An understanding of the relationship 

between factors gained from an experiment can be combined with other data (e.g., distribution of 

housing types) to estimate exposure. An advantage of the experimental approach is that it may 

provide more insight into underlying mechanisms which may be important in targeting 

regulatory action. However, as in all experimental work, one must argue that the relationships 

revealed apply beyond that particular experiment. 

A study may use a combination of survey and experimental techniques and involve a 

variety of sampling procedures. A summary of methods for measuring worker exposure is found 

in Lynch (1985). Smith et al. (1987) provide guidance for field sampling of pesticides. Relevant 

EPA reference documents include Survey Management Handbook, Volumes I and II (U.S. EPA, 

1984b); Soil Sampling Quality Assurance User’s Guide (U.S. EPA, 1990a); and A Rationale for 

the Assessment of Errors in the Sampling of Soils (U.S. EPA, 1989a). A detailed description of 

methods for enumerating and characterizing populations exposed to chemical substances is 

contained in Methods for Assessing Exposure to Chemical Substances, Volume 4 (U.S. EPA, 

1985a). 

Factors to be considered in selecting sampling locations include population density, 

historical sampling results, patterns of environmental contamination and environmental 

characteristics such as stream flow or prevailing wind direction, access to the sample site, types 

of samples, and health and safety requirements. 

The frequency and duration of sample collection will depend on whether the risk assessor 

is concerned with acute or chronic exposures, how rapidly contamination patterns are changing, 

ways in which chemicals are released into the environment, and whether and to what degree 

physical conditions are expected to vary in the future. 

There are many sources of information on methods for selecting sampling locations. 

Schweitzer and Black (1985) and Schweitzer and Santolucito (1984) give statistical methods for 

selecting sampling locations for ground water, soil, and hazardous wastes. A practical guide for 

41
ground-water sampling (U.S. EPA, 1985b) and a handbook for stream sampling (U.S. EPA, 

1986d) are also available. 

The type of sample to be taken and the physical and chemical properties of the chemical 

of concern usually dictate the sampling frequency. For example, determining the concentration 

of a volatile chemical in surface water requires a higher sampling frequency than necessary for 

ground water because the chemical concentration of the surface water changes more rapidly. 

Sampling frequency might also depend on whether the health effects of concern result from 

acute or chronic exposures. More frequent sampling may be needed to determine peak 

exposures versus average exposure. 

A preliminary survey is often used to estimate the optimum number, spacing, and 

sampling frequency. Factors to be considered include technical objectives, resources, program 

schedule, types of analyses, and the constituents to be evaluated. Shaw et al. (1984), Sanders 

and Adrian (1978), and Nelson and Ward (1981) discuss statistical techniques for determining 

the optimal number of samples. 

Sampling duration depends on the analytical method chosen, the limits of detection, the 

physical and chemical properties of the analyte, chemical concentration, and knowledge of 

transport and transformation mechanisms. Sampling duration may be extended to ensure 

adequate collection of a chemical at low concentration or curtailed to prevent the breakthrough 

of one at high concentration. Sampling duration is directly related to selection of statistical 

procedures, such as trend or cross-sectional analyses. 

Storage stability studies with periodic sample analysis should normally be run 

concurrently with the storage of treated samples. However, in certain situations where chemicals 

are prone to break down or have high volatility, it is advisable to run a storage stability study in 

advance so that proper storage and maximum time of storage can be determined prior to sample 

collection and storage. Unless storage stability has been previously documented, samples should 

be analyzed as soon as possible after collection to avoid storage stability problems. Individual 

programs may have specific time limits on storage, depending on the types of samples being 

analyzed. 

3.5.2.3. Quality Assurance Samples 

Sampling should be planned to ensure that the samples are not biased by the introduction 

of field or laboratory contaminants. If sample validity is in question, all associated analytical 

data will be suspect. Field- and laboratory-spiked samples and blank samples should be 

analyzed concurrently to validate results. The plan should provide instructions clear enough so 

that each worker can collect, prepare, preserve, and analyze samples according to established 

protocols. 

42
Any data not significantly greater than blank sample levels should be used with 

considerable caution. All values should be reported as measured by the laboratory, but with 

appropriate caveats on blank sample levels. The method for interpreting and using the results 

from blank samples depends on the analyte and should be specified in the sampling plan. The 

following guidance is recommended: 

C For volatiles and semivolatiles, no positive sample results should be reported unless 

the concentration of the compound in the sample exceeds 10 times the amount in any 

blank for the common laboratory contaminants methylene chloride, acetone, toluene, 

2-butanone, and common phthalate esters. The amount for other volatiles and 

semivolatiles should exceed 5 times the amount in the blank (U.S. EPA, 1988d). 

C For pesticides and polychlorinated biphenyls (PCBs) no positive sample results should 

be reported unless the concentration in the sample exceeds 5 times that in the blank 

(U.S. EPA, 1988d). If a pesticide or PCB is found in a blank but not in a sample, no 

action is taken. 

C For inorganics, no positive sample results should be reported if the results are less than 

5 times the amount in any blank (U.S. EPA, 1988e). 

3.5.2.4. Background Level 

Background presence may be due to natural or anthropogenic sources. At some sites, it 

is significant and must be accounted for. The exposure assessor should try to determine local 

background concentrations by gathering data from nearby locations clearly unaffected by the site 

under investigation. 

When differences between a background (control area) and a target site are to be 

determined experimentally, the control area must be sampled with the same detail and care as the 

target. 

3.5.2.5. Quality Assurance and Quality Control 

Quality assurance (QA) assures that a product meets defined standards of quality with a 

stated level of confidence. QA includes quality control. 

Quality assurance begins with the establishment of DQOs and continues throughout the 

measurement process. Each laboratory should have a QA program and, for each study, a 

detailed quality assurance project plan, with language clear enough to preclude confusion and 

misunderstanding. The plan should list the DQOs and fully describe the analytes, all materials, 

methods, and procedures used, and the responsibilities of project participants. The EPA has 

prepared a guidance document (U.S. EPA, 1980) that describes all these elements and provides 

complete guidance for plan preparation. Quality control (QC) ensures a product or service is 

43
satisfactory, dependable, and economical. A QC program should include development and strict 

adherence to principles of good laboratory practice, consistent use of standard operational 

procedures, and carefully-designed protocols for each measurement effort. The program should 

ensure that errors have been statistically characterized and reduced to acceptable levels. 

3.5.2.6. Quality Assurance and Quality Control for Previously Generated Data 

Previously generated data may be used by the exposure assessor to fulfill current needs. 

Any data developed through previous studies should be validated with respect to both quality 

and extrapolation to current use. One should consider how long ago the data were collected and 

whether they are still representative. The criteria for method selection and validation should also 

be followed when analyzing existing data. Other points considered in data evaluation include 

the collection protocol, analytical methods, detection limits, laboratory performance, and sample 

handling. 

3.5.2.7. Selection and Validation of Analytical Methods 

There are several major steps in the method selection and validation process. First, the 

assessor establishes methods requirements. Next, existing methods are reviewed for suitability 

to the current application. If a new method must be developed, it is subjected to field and 

laboratory testing to determine its performance; these tests are then repeated by other 

laboratories using a round robin test. Finally, the method is revised as indicated by laboratory 

testing. The reader is referred to Guidance for Data Useability in Risk Assessment (U.S. EPA, 

1990b) for extensive discussion of this topic. 

3.5.3. Establishing the Modeling Strategy 

Often the most critical element of the assessment is the estimation of pollutant 

concentrations at exposure points. This is usually carried out by a combination of field data and 

mathematical modeling results. In the absence of field data, this process often relies on the 

results of mathematical models (U.S. EPA, 1986e, 1987b, 1987c, 1988f, 1991b). EPA’s Science 

Advisory Board (U.S. EPA, 1989b) has concluded that, ideally, modeling should be linked with 

monitoring data in regulatory assessments, although this is not always possible (e.g., for new 

chemicals). 

A modeling strategy has several aspects, including setting objectives, model selection, 

obtaining and installing the code, calibrating and running the computer model, and validation 

and verification. Many of these aspects are analogous to the QA/QC measures applied to 

measurements. 

44
3.5.3.1. Setting the Modeling Study Objectives 

The first step in using a model to estimate concentrations and exposure is to clearly 

define the goal of the exposure assessment and how the model can help address the questions or 

hypotheses of the assessment. This includes a clear statement of what information the model 

will help estimate, and how this estimate will be used. The approach must be consistent with 

known project constraints (i.e., schedule, budget, and other resources). 

3.5.3.2. Characterization and Model Selection 

Regardless of whether models are extensively used in an assessment and a formal 

modeling strategy is documented in the exposure assessment plan, when computer simulation 

models such as fate and transport models and exposure models are used in exposure assessments, 

the assessor must be aware of the performance characteristics of the model and state how the 

exposure assessment requirements are satisfied by the model. 

If models are to be used to simulate pollutant behavior at a specific site, the site must be 

characterized. Site characterization for any modeling study includes examining all data on the 

site such as source characterization, dimensions and topography of the site, location of receptor 

populations, meteorology, soils, geohydrology, and ranges and distributions of chemical 

concentrations. For exposure models that simulate both chemical concentration and time of 

exposure (through behavior patterns) data on these two parameters must be evaluated. 

For all models, the modeler must determine if databases are available to support the site, 

chemical, or population characterization, and that all parameters required by the model can be 

obtained or reasonable default values are available. The assessment goals and the results of the 

characterization step provide the technical basis for model selection. 

Criteria are provided in U.S. EPA (1987b, 1988f) for selection of surface water models 

and ground-water models respectively; the reader is referred to these documents for details. 

Similar selection criteria exist for air dispersion models (U.S. EPA, 1986e, 1987c, 1991b). 

A primary consideration in selecting a model is whether to perform a screening study or 

to perform a detailed study. A screening study makes a preliminary evaluation of a site or a 

general comparison between several sites. It may be generic to a type of site (i.e., an industrial 

segment or a climatic region) or may pertain to a specific site for which sufficient data are not 

available to properly characterize the site. Screening studies can help direct data collection at 

the site by, for example, providing an indication of the level of detection and quantification that 

would be required and the distances and directions from a point of release where chemical 

concentrations might be expected to be highest. 

The value of the screening-level analysis is that it is simple to perform and may indicate 

that no significant contamination problem exists. Screening-level models are frequently used to 

45
get a first approximation of the concentrations that may be present. Often these models use very 

conservative assumptions; that is, they tend to overpredict concentrations or exposures. If the 

results of a conservative screening procedure indicate that predicted concentrations or exposures 

are less than some predetermined no-concern level, then a more detailed analysis is probably not 

necessary. If the screening estimates are above that level, refinement of the assumptions or a 

more sophisticated model are necessary for a more realistic estimate. 

Screening-level models also help the user conceptualize the physical system, identify 

important processes, and locate available data. The assumptions used in the preliminary analysis 

should represent conservative conditions, such that the predicted results overestimate potential 

conditions, limiting false negatives. If the limited field measurements or screening analyses 

indicate that a contamination problem may exist, then a detailed modeling study may be useful. 

A detailed study is one in which the purpose is to make a detailed evaluation of a specific 

site. The approach is to use the best data available to make the best estimate of spatial and 

temporal distributions of chemicals. Detailed studies typically require much more data of higher 

quality and models of greater sophistication. 

3.5.3.3. Obtaining and Installing the Computer Code 

It may be necessary to obtain and install the computer code for a model on a specific 

computer system. Modern computer systems and software have a variety of differences that 

require changes to the source code being installed. It is essential to verify that these 

modifications do not change the way the model works or the results it provides. If the model is 

already installed and supported on a computer system to which the user has access, this step is 

simplified greatly. 

Criteria for using a model include its demonstrated acceptability and the ease with which 

the model can be obtained. Factors include availability of specific models and their 

documentation, verification, and validation. These so-called implementation criteria relate to the 

practical considerations of model use and may be used to further narrow the selection of 

technically acceptable models. 

3.5.3.4. Calibrating and Running the Model 

Calibration is the process of adjusting selected model parameters within an expected 

range until the differences between model predictions and field observations are within selected 

criteria. Calibration is highly recommended for all operational, deterministic models. 

Calibration accounts for spatial variations not represented by the model formulation; functional 

dependencies of parameters that are either nonquantifiable, unknown, or not included in the 

model algorithms; or extrapolation of laboratory measurements to field conditions. 

46
Extrapolation of laboratory measurements to field conditions requires considerable care since 

many unknown factors may cause differences between laboratory and field. 

The final step in the modeling portion of an exposure assessment is to run the model and 

generate the data needed to answer the questions posed in the study objectives. 

Experience and familiarity with a model can also be important. This is especially true 

with regard to the more complex models. Detailed models can be quite complex with a large 

number of input variables, outputs, and computer-related requirements. It frequently takes 

months to years of experience to fully comprehend all aspects of a model. Consequently, it is 

suggested that an exposure assessor select a familiar model if it possesses all the selection 

criteria, or seek the help of experienced exposure modelers. 

3.5.3.5. Model Validation 

Model validation is a process by which the accuracy of model results is compared with 

actual data from the system being simulated. There are numerous levels of validation of an 

environmental fate model, for example, such as verifying that the transport and transformation 

concepts are appropriately represented in the mathematical equations, verifying that the 

computer code is free from error, testing the model against laboratory microcosms, running field 

tests under controlled conditions, running general field tests, and repeatedly comparing field data 

to the modeling results under a variety of conditions and chemicals. In essence, validation is an 

independent test of how well the model (with its calibrated parameters) represents the important 

processes occurring in the natural system. Although field and environmental conditions are 

often different during the validation step, parameters fixed as a result of calibration are not 

readjusted during validation.23 

The performance of models (their ability to represent measured data) is often 

dramatically influenced by site characterization and how models represent such characteristics. 

Characterizing complex, heterogenous physical systems presents major challenges; modeling 

representations of such systems must be evaluated in light of that difficulty. In many cases, the 

apparent inability to model a system is caused by incomplete physical characterization of the 

system. In other cases the uncertainties cannot be readily apportioned between the model per se 

and the model’s input data. 

In addition to comparing model results with actual data (thus illustrating accuracy, bias, 

etc.), the model validation process provides information about conditions under which a 

simulation will be acceptable and accurate, and under what conditions it should not be used at 

23In other words, a fundamental rule is that a model should not be validated using data that were already used to 

generate or calibrate the model, since doing so would not be an independent test. 

47 
all. All models have specific ranges of application and specific classes of chemicals for which 

they are appropriate. Assessors should be aware of these limitations as they develop modeling 

strategies. 

3.5.4. Planning an Exposure Assessment to Assess Past Exposures 

In addition to the considerations discussed in Sections 3.5.1 through 3.5.3, if the data are 

being collected to assess past exposures, such as in epidemiologic studies, they need to be 

representative of the past exposure conditions, which may have changed with time. The scope 

and level of detail of the assessment depends greatly on the availability and quality of past data. 

Several approaches for determining and estimating past exposure are provided in the literature 

(Waxweiler et al., 1988; Stern et al., 1986; NIOSH, 1988; Greife et al., 1988; Hornung and 

Meinhardt, 1987). 

48
4. GATHERING AND DEVELOPING DATA FOR EXPOSURE ASSESSMENTS 

The information needed to perform an exposure assessment will depend on the 

approach(es) selected in the planning stage (Section 3). For those assessments using point-ofcontact measurements, the information includes: 

C Measured exposure concentrations and duration of contact. 

For assessments using the scenario evaluation method for estimating exposures, the 

needed information includes: 

C Information on chemical concentrations in media, usually desirable in the format of a 

concentration-time-location profile. 

C Information on persons who are exposed and the duration of contact with various 

concentrations. 

For assessments estimating exposure from dose, the information includes: 

C Biomarker data. 

C Pharmacokinetic relationships, including the data to support pharmacokinetic models. 

If dose is to be calculated, data are needed on: 

C Intake and uptake, usually in the form of rates. 

Information on sources, both natural and anthropogenic is usually helpful. If the agent 

has natural sources, the contribution of these to environmental concentrations may be relevant. 

These background concentrations may be particularly important when the results of toxicity tests 

show a threshold or distinctly nonlinear dose-response relationship. In a situation where only 

relative or additional risk is considered, background levels may not be relevant. 

4.1. MEASUREMENT DATA FOR POINT-OF-CONTACT ASSESSMENTS 

This approach requires that chemical concentrations be measured at the interface between 

the person and the environment, usually through the use of personal monitors; there are currently 

no models to assist in the process of obtaining the concentration-time data itself. The chemical 

concentrations contacted in the media are measured by sampling the individual’s breathing zone, 

food, and water. These methodologies were originally developed for occupational monitoring; 

they may have to be modified for exposures outside the workplace. An example of this is the 

development of a small pump and collector used in the TEAM studies (U.S. EPA, 1987a). In 

order to conduct these studies, a monitoring device had to be developed that was sufficiently 

small and lightweight so that it could be worn by the subjects. 

The Total Human Exposure and Indoor Air Quality (U.S. EPA, 1988h) report is a useful 

bibliography covering models, field data, and emerging research methodologies, as well as new 

techniques for accurately determining exposure at nonoccupational levels. 

49
New data for a particular exposure assessment may be developed through the use of 

point-of-contact methods, or data from prior studies can sometimes be used. In determining 

whether existing point-of-contact monitoring data can be used in another assessment, the 

assessor must consider the factors that existed in the original study and that influenced the 

exposure levels measured. Some of these factors are proximity to sources, activities of the 

studied individuals, time of day, season, and weather conditions. 

Point-of-contact data are valuable in evaluating overall population exposure and checking 

the credibility of exposure estimates generated by other methods. 

4.2. OBTAINING CHEMICAL CONCENTRATION INFORMATION 

The distribution of chemical concentrations is used to estimate the concentration that 

comes in contact with the individual(s) at any given time and place. This can be done through 

personal monitoring, but for a variety of reasons, in a given assessment, personal monitoring 

may not be feasible. Alternative methods involve measuring the concentration in the media, or 

modeling the concentration distribution based on source strength, media transport, and chemical 

transformation processes. For exposure scenario evaluation, measurements and modeling of 

media concentrations are often used together. 

Many types of measurements can be used to help determine the distribution of chemical 

concentrations in media. They can be measurements of the concentrations in the media 

themselves, measurements of source strength, or measurements of environmental fate processes 

which will allow the assessor to use a model to estimate the concentration in the media at the 

point of contact. Table 2 illustrates some of the types of measurements used by exposure 

assessors, along with notes concerning what additional information is usually needed to use these 

measurements in estimating exposure or dose. For epidemiologic studies, questionnaires are 

often used when data are not measurable or are otherwise unavailable. 

4.2.1. Concentration Measurements in Environmental Media 

Measured concentration data can be generated for the exposure assessment by a new field 

study, or by evaluating concentration data from completed field study results and using them to 

estimate concentrations. Media measurements taken close to the point of contact with the 

individual(s) in space and time are preferable to measurements far removed geographically or 

temporally. As the distance from the point of contact increases, the certainty of the data at the 

50
Table 2. Examples of types of measurements to characterize exposure-related media and parametersa 

Type of measurement (sample) Usually attempts to characterize (whole) Examples 

Typical inf

A. POSURE SCENARIO EVALUATION 1. Fixed-location monitoring Environmental medium; samples used to 

establish long-term indications of media 

quality and trends. 

National Stream Quality Accounting 

Network (NASQAN),b water quality 

networks, air quality networks. 

Population lomonitoring lodistance betwexposure; tim

concentration 

2. Short-term media monitoring Environmental or ambient medium; samples 

used to establish a snapshot of quality of medium over relatively short time. 

Special studies of environmental media, indoor air. 

Population losince it must b

concentration

of pollutants b

point of expoconcentration 

3. onitoring of facilities Release rates to the environment from 

sources (facilities). s of relationships between release amounts and 

various operating parameters of the facilities. 

Stack sampling, effluent sampling, 

leachate sampling from landfills, 

incinerator ash sampling, fugitive emissions sampling, pollution control 

device sampling. 

Fate of pollut

environment tlocation and a

4. Food samples 

(also see #9 below) 

Concentrations of contaminants in food supply. 

FDA Total Diet Study Program, c 

market basket studies, shelf studies, cooked-food diet sampling. 

Dietary habits 

groups. Relat

sampled and g

demographic) concentration

5. Drinking water samples Concentrations of pollutants in drinking 

water supply. 

Ground Water Supply Survey, d 

Community Water Supply Survey, e tap 

water. 

Fate and distr

sample to poi

served by sperates. For exp

cooking and spatterns and v

FOR USE IN EX

Source m

Often given in term

51

aTo characterize dose, intake or uptake information is also needed (see Section 2).bU.S. EPA (1985c).cU.S. EPA (1986f).dU.S. EPA (1985c).eU.S. EPA (1985a).
Table 2. Examples of types of measurements to characterize exposure-related media and paramete

Type of measurement (sample) Usually attempts to characterize (whole) Examples 

Typical info

6. Consumer Products Concentration levels of various products. Shelf surveys, e.g., solvent 

concentration in household cleaners.f 

Establish use pa

particular produ

usage levels, ex

7. Breathing Zone Measurements Exposure to airborne chemicals. Industrial hygiene studies, 

occupational surveys, indoor air studies. 

Location, activimonitoring loca

measures/avoid

8. Microenvironmental Studies Ambient medium in a defined area, e.g., 

kitchen, automobile interior, office setting, parking lot. 

Special studies of indoor air, house dust, contaminated surfaces, radon 

measurements, office building studies. 

Activities of stumonitoring loca

9. ace Soil Sample Degree of contamination of soil available for 

contact. 

Soil samples at contaminated sites. Fate of pollutio

exposed popula

10. Soil Core Soil including pollution available for groundwater contamination; can be an indication of 

quality and trends over time. 

Soil sampling at hazardous waste sites. Fate of substan

bioavailability, function of acti

11. Fish Tissue Extent of contamination of edible fish tissue. National Shellfish Survey.g Relationship of 

individuals or phabits; preparat

Surf

52

fU.S. EPA (1985a). gU.S. EPA (1986f). 
Table 2. Examples of types of measurements to characterize exposure-related media and paramete

Type of measurement (sample) Usually attempts to characterize (whole) Examples 

Typical inf

B. 1. Air Pump/Particulates and Vapors Exposure of an individual or population via 

the air medium. 

TEAM study, h carbon monoxide 

study. i 

Breathing zone sampling in industrial settings. 

Direct measure

time sampled. 

population, relathe population mrelationships betimes for the sa

between samplepopulations. of the sampled populations chadetail. 

2. Passive Vapor Sampling Same as above. Same as above. Same as above. 

3. ple Food/Split Sample Drinking Water Exposures of an individual or population via 

ingestion. 

TEAM study.j Same as above. 

4. Skin Patch Samples Dermal exposure of an individual or 

population. 

Pesticide Applicator Survey.k 1) Same as abo

2) Skin penetra

C. POSURE ESTIMATION FROM RECONSTRUCTED DOSE 1. Breath Total internal dose for individuals or 

population (usually indicative of relatively 

recent exposures). 

Measurement of volatile organic chemicals (VOCs), alcohol. (Usually 

limited to volatile compounds). 

1) Relationship population; expnot) pharmacok

storage reservo

2) Relationship burden. 

FOR USE IN POINT-OF-CONTACT MEASUREMENT 

In 

Split Sam

FOR USE IN EX

53

hU.S. EPA (1987a). iU.S. EPA (1987a). jU.S. EPA (1987a). kU.S. EPA (1987d). 
Table 2. Examples of types of measurements to characterize exposure-related media and paramete

Type of measurement (sample) Usually attempts to characterize (whole) Examples 

Typical info

2. Blood Total internal dose for individuals or 

population (may be indicative of either 

relatively recent exposures to fat-soluble organics or long term body burden for 

metals). 

Lead studies, pesticides, heavy metals (usually best for soluble compounds, although blood lipid analysis may reveal lipophilic compounds). 

1) Same as abo2) Relationship burden. 

3. Adipose Total internal dose for individuals or 

population (usually indicative of long-term 

averages for fat- soluble organics). 

NHATS,l dioxin studies, PCBs (usually 

limited to lipophilic compounds). 

1) Same as abo2) Relationship burden. 

4. Total internal dose for individuals or 

population (usually indicative of past exposure in weeks to months range; can 

sometimes be used to evaluate exposure patterns). 

Heavy metal studies (usually limited to metals). 

1) Same as abo2) Relationship 

body burden. 

5. Urine Total internal dose for individuals or 

population (usually indicative of elimination rates); time from exposure to appearance in 

urine may vary, depending on chemical. 

Studies of tetrachloroethylenem and 

trichloroethylene.n 

1) Same as abo2) Relationship 

burden. 

Nails, Hair 

54

lU.S. EPA (1986g). mU.S. EPA (1986h). nU.S. EPA (1987e). 
point of contact usually decreases, and the obligation for the assessor to show relevance of the 

data to the assessment at hand becomes greater. For example, an outdoor air measurement, no 

matter how close it is taken to the point of contact, cannot by itself adequately characterize 

indoor exposure. 

Concentrations can vary considerably from place to place, seasonally, and over time due 

to changing emission and use patterns. This needs to be considered not only when designing 

studies to collect new data, but especially when evaluating the applicability of existing 

measurements as estimates of exposure concentrations in a new assessment. It is a particular 

concern when the measurement data will be used to extrapolate to long time periods such as a 

lifetime. Transport and dispersion models are frequently used to help answer these questions. 

The exposure assessor is likely to encounter several different types of measurements. 

One type of measurement used for general indications and trends of concentrations is outdoor 

fixed-location monitoring. This measurement is used by EPA and other groups to provide a 

record of pollutant concentration at one place over time. Nationwide air and water monitoring 

programs have been established so that baseline values in these environmental media can be 

documented. Although it is not practical to set up a national monitoring network to gather data 

for a particular exposure assessment, the data from existing networks can be evaluated for 

relevance to an exposure assessment. These data are usually somewhat removed, and often far 

removed, from the point of contact. Adapting data from previous studies usually presents 

challenges similar to those encountered when using network data. If new data are needed for the 

assessment, studies measuring specific chemicals at specific locations and times can be 

conducted. 

Contaminant concentrations in indoor air can vary as much or more than those in outdoor 

air. Consequently, indoor exposure is best represented by measurements taken at the point of 

contact. However, because pollutants such as carbon monoxide can exhibit substantial indoor 

penetration, indoor exposure estimates should consider potential outdoor as well as indoor 

sources of the contaminant(s) under evaluation. 

Food and drinking water measurements can also be made. General characterization of 

these media, such as market basket studies (where representative diets are characterized), shelf 

studies (where foodstuffs are taken from store shelves and analyzed), or drinking water quality 

surveys, are usually far removed from the point of contact for an individual, but may be useful in 

evaluating exposure concentrations over a large population. Closer to the point of contact would 

be measurements of tap water or foodstuffs in a home, and how they are used. In evaluating the 

relevance of data from previous studies, variations in the distribution systems must be considered 

as well as the space-time proximity. 

55
Consumer or industrial product analysis is sometimes done to characterize the 

concentrations of chemicals in products. The formulation of products can change substantially 

over time, similar products do not necessarily have similar formulations, and regional differences 

in product formulation can also occur. These should be considered when determining relevance 

of extant data and when setting up sampling plans to gather new data. 

Another type of concentration measurement is the microenvironmental measurement. 

Rather than using measurements to characterize the entire medium, this approach defines 

specific zones in which the concentration in the medium of interest is thought to be relatively 

homogenous, then characterizes the concentration in that zone. Typical microenvironments 

include the home or parts of the home, office, automobile, or other indoor settings. 

Microenvironments can also be divided into time segments (e.g., kitchen-day, kitchen-night). 

This approach can produce measurements that are closely linked with the point of contact both in 

location and time, especially when new data are generated for a particular exposure assessment. 

The more specific the microenvironment, however, the greater the burden on the exposure 

assessor to establish that the measurements are representative of the population of interest. 

Adapting existing data bases in this area to a particular exposure assessment requires the usual 

evaluation discussed throughout this section. 

The concentration measurement that provides the closest link to the actual point of 

contact uses personal monitoring, which is discussed in Section 4.3. 

4.2.2. Use of Models for Concentration Estimation 

If concentrations in the media cannot be measured, they can frequently be estimated 

indirectly by using related measurements and models. To accomplish this, source and fate 

information are usually needed. Source characterization data are used as input to transport and 

transformation models (environmental fate models). These models use a combination of general 

relationships and situation-specific information to estimate concentrations. In exposure 

assessments, mathematical models are used extensively to calculate environmental fate and 

transport, concentrations of chemicals in different environmental media, the distribution of 

concentrations over space and time, indoor air levels of chemicals, concentrations in foods, etc. 

In determining the relevance of this type of model for estimating concentrations, the same rules 

apply as for the measurements of concentrations discussed in the previous section. When 

concentrations in the media are available, models can be used to interpolate concentrations 

between measurements. Because models rely on indirect measurements and data remote from 

the point of contact, statistically valid analytical measurements take precedence when 

discrepancies arise. When it is necessary to estimate contributions of individual sources to 

overall concentrations, models are commonly used. 

56
Source characterization measurements usually determine the rate of release of chemicals 

into the environment from a point of emission such as an incinerator, landfill, industrial facility, 

or other source. Often these measurements are used to estimate emission factors, or a 

relationship between releases and facility operations. Since emission factors are usually 

averages over time, the assessor must determine whether given emission factors from previous 

work are relevant to the time specificity and source type needed for the exposure assessment. 

Generally, emission factors are more useful for long-term average emission calculations, and 

become less useful when applied to intermittent or short-term exposures. 

Environmental fate measurements can be either field measurements (field degradation 

studies, for example) or laboratory measurements (partition coefficients, hydrolysis, or 

biodegradation rates, etc.). Approximations for these rates can sometimes also be calculated 

(Lyman et al., 1982). 

Environmental fate models calculate estimated concentrations in media, that in turn are 

linked to the concentrations at the point of contact. The use of estimated properties or rates adds 

to the uncertainty in the exposure concentration estimate. When assessors use these methods to 

estimate exposures, uncertainties attributable to the model and the validation status of the model 

must be clearly discussed in the uncertainty section (see discussion in Section 6). 

4.2.3. Selection of Models for Environmental Concentrations 

Selection of an appropriate model is essential for successful simulation of chemical 

concentrations. In most cases assessors will be able to choose between several models, any of 

which could be used to estimate environmental concentrations. There is no right model; there 

may not even be a best model. There are, however, several factors that will help in selecting an 

appropriate model for the study. The assessor should consider the objectives of the study, the 

technical capabilities of the models, how readily the models can be obtained, and how difficult 

each is to use (U.S. EPA, 1987b, 1988f). 

The primary consideration in selecting a model is the objective of the exposure 

assessment. The associated schedule, budget, and other resource constraints will also affect 

model selection options. Models are available to support both screening-level and detailed, sitespecific studies. Screening models can provide quick, easy, and cost-effective estimates of 

environmental concentrations. They can support data collection efforts at the site by indicating 

the required level of detection and quantification and the locations where chemical 

concentrations are expected to be highest. They are also used to interpolate chemical 

concentrations between measurements. Where study objectives require the best estimates of 

spatial and temporal distributions of chemicals, more sophisticated models are available. These 

57
models require more and better data to characterize the site, and therefore site-specific data may 

be needed in order to use them. 

The technical capabilities of a model are expressed in its ability to simulate site-specific 

contaminant transport and transformation processes. The model must be able to simulate the 

relevant processes occurring within the specified environmental setting. It must adequately 

represent the physical setting (e.g., the geometric configuration of hydrogeological systems, river 

widths and depths, soil profiles, meteorological patterns, etc.) and the chemical transformation 

processes. Field data from the area where doses are to be estimated are necessary to define the 

input parameters required to use the models. In cases in which these data are not available, 

parameter values representative of field conditions should be used as defaults. Assumptions of 

homogeneity and simplification of site geometry may allow use of simpler models. 

In addition, it is important to thoroughly understand the performance characteristics of 

the model used. This is especially true with regard to the more complex models. Detailed 

models can be quite complex with a large number of input variables, outputs, and computerrelated requirements. 

4.3. ESTIMATING DURATION OF CONTACT 

As discussed in Section 2, the duration of contact is linked to a particular exposure 

concentration to estimate exposure. Depending on the purpose of the assessment and the 

confidence needed in the accuracy of the final estimate, several approaches for obtaining 

estimates of duration of contact can be used. 

Ideally, the time that the individual is in contact with a chemical would be observed and 

recorded, and linked to the concentrations of the chemical during those time segments. Although 

it is sometimes feasible to do this (by point-of-contact measurement, see Section 4.1), many 

times it is not. In those cases, as in concentration characterization, the duration of contact must 

be estimated by using data that may be somewhat removed from the actual point of contact, and 

assumptions must be made as to the relevance of the data. 

It is common for the estimate of duration of contact at a given concentration to be the 

single largest source of uncertainty in an exposure assessment.24 The exposure assessor, in 

developing or selecting data for making estimates of duration of contact, must often assume that 

the available data adequately represent exposure. 

24Conversely, it may be stated that the largest source of uncertainty is the concentration for a given exposure 

duration. Often, however, the concentration in the media is known with more certainty than the activities of the 

individual(s) exposed. 

58 
4.3.1. Observation and Survey Data 

Observation and recording of activities, including location-time data, are likely to be the 

types of data collection closest to the point of contact. This can be done by an observer or the 

person(s) being evaluated for exposure, and can be done for an individual, a population segment, 

or a population. The usual method for obtaining these data for population segments or 

populations is survey questionnaires. Surveys can be performed as part of the data-gathering 

efforts of the exposure assessment, or existing survey data can be used if appropriate. 

There are several approaches used in activity surveys, including diaries, respondent or 

third-party estimates, momentary sampling, videomonitoring, and behavioral meters. The diary 

approach, probably the most powerful method for developing activity patterns, provides a 

sequential record of a person’s activities during a specified time period. Typical time-diary 

studies are done across a day or a week. Diary forms are designed to have respondents report all 

their activities and locations for that period. Carefully designed forms are especially important 

for diary studies to ensure that data reported by each individual are comparable. The resulting 

time budget is a sample of activity that can be used to characterize an individual’s behavior, 

activities, or other features during the observation period. Sequential activity monitoring forms 

the basis of an activity profile. Several studies have demonstrated the reliability of the diary 

method in terms of its ability to produce similar estimates. One study (Robinson, 1977) found a 

0.85 correlation between diary estimates using the yesterday and tomorrow approaches and a 

0.86 correlation between overall estimates. However, no definitive study has established the 

validity of time-diary data. Questionnaires are used for direct questions to collect the basic data 

needed. Questionnaire design is a complex and subtle process, and should only be attempted 

with the help of professionals well-versed in survey techniques. A useful set of guidelines is 

provided in the Survey Management Handbook (U.S. EPA, 1984b). 

Respondent estimates are the least expensive and most commonly used questionnaire 

alternative. Respondents are simply asked to estimate the time they spend at a particular 

activity. Basically, the question is, how many hours did you spend doing this activity (or in this 

location or using a certain product)? In exposure studies, respondents may be asked how often 

they use a chemical or product of interest or perform a specific activity. These data are less 

precise and likely to be somewhat less accurate than a carefully conducted diary approach. 

At a less demanding level, respondents may be asked whether their homes contain items 

of interest (pesticides, etc.). Since this information is not time-of-activity data, it is more useful 

in characterizing whether the chemical of interest is present. It does, however, give the assessor 

some indication that use may or may not occur. 

59
Estimates from other respondents (third parties) use essentially the same approach, 

except that other informants respond for that individual. Here the question is how many hours 

per week does the target person spend doing this activity? 

Momentary (beeper) sampling or telephone-coincidental techniques ask respondents to 

give only brief reports for a specific moment — usually the moment the respondent’s home 

telephone or beeper sounds. This approach is limited to times when people are at home or able 

to carry beepers with them. 

Methods that use behavioral meter or monitoring devices are probably the most 

expensive approach, since they require the use or development of equipment, respondent 

agreement to use such equipment, and technical help to install or adjust the equipment. 

The Exposure Factors Handbook (U.S. EPA, 1989c) contains a summary of published 

data on activity patterns along with citations. Note that the summary data and the mean values 

cited are for the data sets included in the Handbook, and may or may not be appropriate for any 

given assessment. 

4.3.2. Developing Other Estimates of Duration of Contact 

When activity surveys cannot be used to estimate duration of contact, it may be estimated 

from more indirect data. This is the least expensive and most commonly used approach for 

generating estimates of duration of contact; it is also the least accurate. But for some situations, 

such as assessing the risk to new chemicals being introduced into the marketplace or in assessing 

future possible uses of contaminated sites, it is the only approach that can be used. 

In general, the methods used to make these estimates fall into two areas: (1) those where 

the time it takes to perform an activity is itself estimated, and (2) those where an average 

duration of contact is estimated by combining the time of a unit activity with data on the use of a 

product or commodity. 

Methods that try to estimate the time of a particular activity include general time-andmotion studies that might be adapted for use in an exposure assessment, general marketing data 

which include time of use, anecdotal information, personal experience, and assumptions about 

the amount of time it takes to perform an activity. 

Methods that estimate average times for activities from product or commodity use 

usually interpret data on product sales or marketing surveys, water use, general food sales, etc. 

Information on use can be combined with an estimate of the number of persons using the product 

to estimate the average consumption of the product. If an estimate of the duration of contact 

with one unit (product, gallon of water, etc.) can be made, this can then be multiplied by the 

average number of units consumed to arrive at an estimate of average duration of contact for 

each individual. 

60
Duration-of-contact estimates based on data collected close to the actual point of contact 

are preferable to those based on indirect measurements; both of these are preferred to estimates 

based on assumptions alone. This hierarchy is useful in both the data-gathering process and 

uncertainty analysis. 

4.4. OBTAINING DATA ON BODY BURDEN OR BIOMARKERS 

Body burden or biomarker data denote the presence of the chemical inside the body of 

exposed individuals. In a reconstructive assessment, these data, in conjunction with other 

environmental monitoring data, may provide a better estimate of exposure. 

A biomarker of exposure has been defined as an exogenous substance or its metabolite or 

the product of an interaction between a xenobiotic agent and some target molecule or cell that is 

measured in a compartment within an organism (NRC, 1989a). Examples of simple direct 

biomarkers include the chemical itself in body fluid, tissue, or breath. Measurable changes in the 

physiology of the organism can also constitute markers of exposure. Examples include changes 

in a particular enzyme synthesis and activity. The interaction of xenobiotic compounds with 

physiological receptors can produce measurable complexes which also serve as exposure 

biomarkers. Other markers of exposure include xenobiotic species adducted to protein or DNA, 

as well as a variety of genotoxicity endpoints, such as micronuclei and mutation. Some 

biomarkers are specific to a given chemical while others may result from exposure to numerous 

individual or classes of compounds. 

Biomarker data alone do not usually constitute a complete exposure assessment, since 

these data must be associated with external exposures. However, biomarker data complement 

other environmental monitoring data and modeling activities in estimating exposure. 

4.5. OBTAINING DATA FOR PHARMACOKINETIC RELATIONSHIPS 

To estimate dose from exposure, one must understand the pharmacokinetics of the 

chemical of interest. This is particularly true when comparing risks resulting from different 

exposure situations. Two widely different exposure profiles for the same chemical may have the 

same integrated exposure (area under the curve), but may not result in the same internal dose due 

to variations in disposition of the chemical under the two profiles. For example, enzymes that 

normally could metabolize low concentrations of a chemical may be saturated when the chemical 

is absorbed in high doses, resulting in a higher dose delivered to target tissues. The result of 

these two exposures may even be a different toxicological endpoint, if pharmacokinetic 

sensitivities are severe enough. 

An iterative approach, including both monitoring and modeling, is necessary for proper 

data generation and analysis. Data collection includes monitoring of environmental media, 

61
personal exposure, biomarkers, and pharmacokinetic data. It may involve monitoring for the 

chemical, metabolites, or the target biomarker. Monitoring activities must be designed to yield 

data that are useful for model formulation and validation. Modeling activities must be designed 

to simulate processes that can be monitored with available techniques. The pharmacokinetic 

data necessary for model development are usually obtained from laboratory studies with animals. 

The data are generated in experiments designed to estimate such model parameters as the time 

course of the process, absorption, distribution, metabolism, and elimination of the chemical. 

These data, and the pharmacokinetic models developed from them, are necessary to interpret 

field biomarker data. 

4.6. OBTAINING DATA ON INTAKE AND UPTAKE 

The Exposure Factors Handbook (U.S. EPA, 1989c) presents statistical data on many of 

the factors used in assessing exposure, including intake rates, and provides citations for the 

primary references. Some of these data were developed by researchers using approaches 

discussed in Section 4.2.1 (for example, Pao et al. [1982] used the diary approach in a study of 

food consumption). Intake factors included are: 

C drinking water consumption rates; 

C consumption rates for homegrown fruits, vegetables, beef, and dairy products; 

C consumption rates for recreationally caught fish and shellfish; 

C incidental soil ingestion rates; 

C pulmonary ventilation rates; and 

C surface areas of various parts of the human body. 

The Exposure Factors Handbook is being updated to encompass additional factors and to 

include new research data on the factors currently covered. It also provides default parameter 

values that can be used when site-specific data are not available. Obviously, general default 

values should not be used in place of known, valid data that are more relevant to the assessment 

being done. 

62
5. USING DATA TO DETERMINE OR ESTIMATE EXPOSURE AND DOSE 

Collecting and assembling data, as discussed in the previous section, is often an iterative 

process. Once the data are assembled, inferences can be made about exposure concentrations, 

times of contact, and exposures to persons other than those for whom data are available. During 

this process, there usually will be gaps in information that can be filled by making a series of 

assumptions. If these gaps are in areas critical to the accuracy of the assessment, further data 

collection may be necessary. 

Once an acceptable data set25 is available, the assessor can calculate exposure or dose. 

Depending on the method used to quantify exposure, there are several ways to calculate 

exposure and dose. This chapter will discuss making inferences (Section 5.1), assumptions 

(Section 5.2), and calculations (Section 5.3). 

5.1. USE OF DATA IN MAKING INFERENCES FOR EXPOSURE ASSESSMENTS 

Inferences are generalizations that go beyond the information contained in a data set. 

The credibility of an inference is often related to the method used to make it and the supporting 

data. Anecdotal information is the source of one type of inference, but the assessor has only 

limited knowledge of how well one anecdote represents the realm of possibilities, so anecdotes 

as a basis for inference should be used only with considerable caution. Professional judgment is 

usually preferred to anecdotes assuming that it is based on experience representing a variety of 

conditions. Statistical inferences also are generalizations that go beyond the data set. They may 

take any of several forms (see any statistics textbook for examples), but unlike those described 

above, a statistical inference will usually include a measure of how certain it is. For that reason, 

statistical inferences are often preferable to anecdotes or professional judgment provided the data 

are shown to be relevant and adequate. 

As discussed above, the primary use of data from exposure-related measurements is to 

infer more general information about exposure concentrations, contact times, exposures, or 

doses. For example, measured concentrations in a medium can be used to infer what the 

concentration might be at the point of contact, which may not have been measured directly. 

Point-of-contact measurement data for one group of people may be used to infer the exposures of 

a similar group, or to infer what the exposures of the same group might be at different times. 

In all cases, the exposure assessor must have a clear picture of the relationship between 

the data at hand and what is being characterized by inference. For example, surface water 

25An acceptable data set is one that is consistent with the scope, depth, and purpose of the assessment, and is both 

relevant and adequate as discussed in Section 5.1. 

63 
concentration data alone, although essential for characterizing the medium itself, are not 

necessarily useful for inferring exposures from surface water, since other information is 

necessary to complete the link between surface water and exposure. But the medium’s 

characteristics (over space and time) can be used, along with the location and activities of 

individuals or populations, to estimate exposures. Samples taken for exposure assessment may 

be designed to characterize different aspects (or components) of exposure. For example, a 

sample taken as a point-of-contact exposure measurement is qualitatively different from a 

sample of an environmental medium or body fluid. 

Different measurements taken under the general category of exposure-related 

measurements cannot necessarily all be used in the same way. The exposure assessor must 

explain the relationship between the sample data and the inferences or conclusions being drawn 

from them. In order to do this, data relevance, adequacy, and uncertainty must be evaluated. 

5.1.1. Relevance of Data for the Intended Exposure Assessment 

When making inferences from a data set, the assessor must establish a clear link between 

the data and the inference. When statistically based sampling is used to generate data, relevance 

is a function of how well the sample represents the medium or parameter being characterized. 

When planning data collection for an exposure assessment, the assessor can use information 

about the inferences that will be made to select the best measurement techniques. In many cases 

data are also available from earlier studies. The assessor must determine (and state) how 

relevant the available data are to the current assessment; this is usually easier for new data than 

for previously collected information. 

5.1.2. Adequacy of Data for the Intended Assessment 

Table 2 in the previous section illustrated how different types of measurements may be 

used to characterize a variety of concentrations, contact times, and intake or uptake parameters. 

Nevertheless, just because certain types of measurements generally can be used to make certain 

inferences, there is no guarantee that this can always be done. The adequacy of the data to make 

inferences is determined by evaluating the amount of data available and the accuracy of the data. 

Evaluation of the adequacy of data will ensure that the exposure assessment is conducted with 

data of known quality. 

In general, inadequate data should not be used, but when it can be demonstrated that the 

inadequacies do not affect results, it is sometimes possible to use such data. In these cases, an 

explanation should be given as to why the inadequacies do not invalidate conclusions drawn 

from them. In some cases, even seriously inadequate or only partially relevant data may be the 

only data available, and some information may be gained from their consideration. It may not be 

64
possible to discard these data entirely unless better data are available. If these data are used, the 

uncertainties and resulting limitations of the inferences should be clearly stated. If data are 

rejected for use in favor of better data, the rationale for rejection should be clearly stated and the 

basis for retaining the selected data should be documented. QA/QC considerations are 

paramount in considerations of which data to keep and which to discard. 

Outliers should not be eliminated from data analysis procedures unless it can be shown 

that an error has occurred in the sample collection or analysis phases of the study. Very often 

outliers provide much information to the study evaluators. Statistical tests such as the Dixon test 

exist to determine the presence of outliers (Dixon, 1950, 1951, 1953, 1960). 

5.1.2.1. Evaluation of Analytical Methods 

Analytical methods are evaluated in order to develop a data set based on validated 

analytical methods and appropriate QA/QC procedures. In a larger sense, analytical methods 

can be evaluated to determine the strength of the inferences made from them, and in turn, the 

confidence in the exposure assessment itself. Consequently, it is just as important to evaluate 

analytical methods used for data generated under another study as it is to evaluate the methods 

used to generate new data. 

The EPA has established extensive QA/QC procedures (U.S. EPA, 1980). Before 

measurement data are used in the assessment, they should be evaluated against these procedures 

and the results stated. If this is not possible, the assessor must consider what effect the unknown 

quality of the data has on the confidence placed on the inferences and conclusions of the 

assessment. 

5.1.2.2. Evaluation of Analytical Data Reports 

An assortment of qualifiers is often used in data validation. These qualifiers are used to 

indicate QA/QC problems such as uncertain chemical identity or difficulty in determining 

chemical concentration. Qualifiers usually appear on a laboratory analysis report as a letter of 

the alphabet next to the analytical result. Some examples of data qualifiers, applied by U.S. EPA 

regional reviewers for Contract Laboratory Program (CLP) data include: 

B (blank) - the analyte was found in blank samples; 

J (judgment) - the compound is present but the concentration value is estimated; 

U (undetected) - the chemical was analyzed for but not detected at the detection limit; 

R (reject) - the quality control indicates that the data are unusable. 

The exposure assessor may contact the laboratory or the person who validated the data if the 

definitions of the qualifiers are unclear. Since the exposure assessment is only as good as the 

65
data supporting it, it is essential to interpret these types of data properly to avoid misrepresenting 

the data set or biasing the results. 

5.1.2.2.1. Evaluation of censored data sets. Exposure assessors commonly encounter data sets 

containing values that are lower than limits deemed reliable enough to report as numerical values 

(i.e., quantification limits [QL]). These data points are often reported as nondetected and are 

referred to as censored. The level of censoring is based on the confidence with which the 

analytical signal can be discerned from the noise. While the concentration may be highly 

uncertain for substances below the reporting limit, it does not necessarily mean that the 

concentration is zero. As a result the exposure assessor is often faced with the problem of 

having to estimate values for the censored data. Although a variety of techniques have been 

described in the literature, no one procedure is appropriate under all exposure assessment 

circumstances; thus, the exposure assessor will need to decide on the appropriate method for a 

given situation. Techniques for analyzing censored data sets can be grouped into three classes 

(Helsel, 1990): simple substitution methods, distributional methods, and robust methods. 

Simple substitution methods, the most commonly encountered technique, involve 

substitution of a single value as a proxy for each nondetected data value. Frequently used values 

have included zero, the QL, QL/2, and QL//2.26 

In the worst-case approach, all nondetects are assigned the value of the QL, which is the 

lowest level at which a chemical may be accurately and reproducibly quantitated. This approach 

biases the mean upward. On the other hand, assigning all nondetects the value of zero biases the 

mean downward. The degree to which the results are biased will depend on the relative number 

of detects and nondetects in the data set and the difference between the reporting limit and the 

measured values above it. 

In an effort to minimize the obvious bias introduced by choosing either zero or the QL as 

the proxy, two other values have been suggested, i.e., QL/2 and QL//2. Assigning all 

nondetects as QL/2 (Nehls and Akland, 1973) assumes that all values between the QL and zero 

are equally likely; therefore, an average value would result if many samples in this range were 

measured. Hornung and Reed (1990) discuss the merits of assigning a value of QL//2 for 

nondetects rather than QL/2 if the data are not highly skewed (geometric standard deviation < 

3.0); otherwise they suggest using QL/2. 

Based on reported analyses of simulated data sets that have been censored to varying 

degrees (Gleit, 1985; Horning and Reed, 1990; Gilliom and Helsel, 1986; Helsel and Cohn, 

26Some programs, such as the U.S. Department of Energy (1991), do not recommend this procedure at all, if it can be 

avoided. 

66 
1988), it can be concluded that substitution with QL/2 or QL//2 for nondetects will be adequate 

for most exposure assessments provided that the nondetects do not exceed 10% to 15% of the 

data set or the data are not highly skewed. When such situations arise, the additional effort to 

make use of more sophisticated methods as discussed below is recommended. On the other 

hand, the exposure assessor may encounter situations in which the purpose of the assessment is 

only to serve as a screen to determine if a health concern has been triggered or if a more detailed 

study is required, then assigning the value of the QL to all nondetect values can be justified. If, 

when using this conservative approach, no concern is indicated, then no further effort is 

warranted. This method cannot be used to prove an unacceptable risk exists, and any exposure 

values calculated using this method should be caveated and clearly presented as less than 

estimates. 

Distributional methods, unlike simple substitution methods, make use of the data above 

the reporting limit to extrapolate below it. One such technique is the use of log-probit analysis. 

This approach assumes a lognormal probability distribution of the data. In the probit analysis, 

the detected values are plotted on the scale and the nondetectable values are treated as 

unknowns, but their percentages are accounted for. The geometric mean is determined from the 

50th percentile. As discussed by Travis and Land (1990), limitations of the method have been 

pointed out, but it is less biased and more accurate than the frequently used substitution methods. 

This method is useful in situations where the data set contains enough data points above the 

reporting limit to define the distribution function for the exposure values (i.e., lognormal) with 

an acceptable degree of confidence. The treatment of the nondetectable samples is then 

straightforward, assuming the nondetectable samples follow the same distribution as those above 

the reporting limit. 

Robust methods have an advantage over distributional methods in so far as they do not 

assume that the data above the reporting limit follow a defined distribution (e.g., lognormal) and 

they are not subject to transformation bias in going from logarithms back to original units. 

Gilliom and Helsel (1986) have described the application of several approaches to data sets of 

varying sample size and degree of censoring. These methods involve somewhat more data 

manipulation than the log-probit method discussed earlier in this Section, but they may be more 

appropriate to use when the observed data do not fit a lognormal distribution. Generally, these 

methods only assume a distributional form for the censored values rather than the entire data set, 

and extrapolation from the uncensored data is done by using regression techniques. 

In summary, when dealing with censored data sets, a variety of approaches can be used 

by the exposure assessor. Selecting the appropriate method requires consideration of the degree 

of censoring, the goals of the exposure assessment, and the accuracy required. Regardless of the 

method selected, the assessor should explain the choice made and how it may affect the 

67
summary statistics. Presenting only the summary statistics developed by one of these methods 

should be avoided. It is always useful to include a characterization of the data by the percentage 

of detects and nondetects in language such as “in 37% of the samples the chemical was detected 

above the quantitation limit; of these 37%, the mean concentration was 47 ppm, the standard 

deviation was 5 ppm, etc.” 

5.1.2.2.2. Blanks and recovery. Blank samples should be compared with the results from their 

corresponding samples. When comparing blank samples to the data set, the following rules 

should be followed (outlined in Section 3): 

C Sample results should be reported only if the concentrations in the sample exceed 10 

times the maximum amount detected in the blank for common laboratory 

contaminants. Common laboratory contaminants include: acetone, 2-butanone (or 

methyl ethyl ketone), methylene chloride, toluene, and phthalate esters. 

C Sample results should be reported only if the concentrations in the sample exceed 5 

times the maximum amount detected in a blank for chemicals that are not common 

laboratory contaminants. 

In general, for other types of qualifiers, the exposure assessor may include the data with 

qualifiers if they indicate that a chemical’s concentration is uncertain, but its identity is known. 

If possible, the uncertainties associated with the qualifier should be noted. 

Chemical spike samples that show abnormally high or low recoveries may result in 

qualified or rejected data. Assessors should not use rejected data; these samples should be 

treated as if the samples were not taken, since the resulting data are unreliable. Typically, 

analytical results are reported from the laboratory unadjusted for recovery, with the recovery 

percentage also reported. The assessor must determine how these data should be used to 

calculate exposures. If recovery is near 100%, concentrations are not normally adjusted 

(although the implicit assumption of 100% recovery should be mentioned in the uncertainty 

section). However, the assessor may need to adjust the data to account for consistent, but 

abnormally high or low recovery. The rationale for such adjustments should be clearly 

explained; individual program offices may develop guidance on the acceptable percent recovery 

limits before data adjustment or rejection is necessary. 

5.1.3. Combining Measurement Data Sets From Various Studies 

Combining data from several sources into a single data set must be done cautiously. The 

circumstances under which each set of data was collected (target population, sampling design, 

location, time, etc.) and quality (precision, accuracy, representativeness, completeness, etc.) must 

be evaluated. Combining summary statistics of the data sets (e.g., means) into a single set may 

68
be more appropriate than combining the original values. Statistical methods are available for 

combining results from individual statistical tests. For example, it is sometimes possible to use 

several studies with marginally significant results to justify an overall conclusion of a 

statistically significant effect. 

The best way to report data is to provide sufficient background information to explain 

what was done and why, including clear documentation of the source of the data and including 

any references. 

5.1.4. Combining Measurement Data and Modeling Results 

Combining model results with measurement data must be done with an understanding of 

how this affects the resulting inferences, conclusions, or exposure estimates. If model results are 

used in lieu of additional data points, they must be evaluated for accuracy and representativeness 

as if they were additional data, and the uncertainty associated with this data combination must be 

described fully, as discussed in Section 5.1.3. 

On the other hand, measurement data are often used within the context of the model 

itself, as calibration and verification points, or as a check on the plausibility of the model results. 

If measurements are used within the model, the uncertainty in these measurements affects the 

uncertainty of the model results, and should be discussed as part of the uncertainty of the model 

results. 

5.2. DEALING WITH DATA GAPS 

Even after supplementing existing measurement data with model results, there are likely 

to be gaps in the information base to be used for calculating exposures and doses. There are 

several ways to deal with data gaps. None are entirely satisfactory in all situations, but they can 

be useful depending on the purposes of the assessment and the resources available. The 

following options can be used singly or in combination: 

C New data can be collected. This may be beyond the reach of the assessor’s resources, 

but promises the best chance for getting an accurate answer. It is most likely to be a 

useful option if the new data are quick and easy to obtain. 

C The scope of the assessment can be narrowed. This is possible if the data gaps are in 

one pathway or exposure route, and the others have adequate data. It may be a viable 

option if the pathway or route has values below certain bounds, and those bounds are 

small relative to the other pathways being evaluated. This is unlikely to be 

satisfactory if the part of the assessment deleted is an important exposure pathway or 

route and must be evaluated. 

69
C Conservative27 assumptions can be used. This option is useful for establishing 

bounds on exposure parameters, but limits how the resulting exposures and doses can 

be expressed. For example, if one were to assume that a person stays at home 24 

hours a day as a conservative assumption, and used this value in calculations, the 

resulting contact time would have to be expressed as an upper limit rather than a best 

estimate. When making conservative assumptions, the assessor must be aware of 

(and explain) how many of these are made in the assessment, and how they influence 

the final conclusions of the assessment.28 

C Models may be used in some cases, not only to estimate values for concentrations or 

exposures, but also to check on how conservative certain assumptions are. 

C Surrogate data may also be used in some cases. For example, for pesticide 

applicators’ exposure to pesticides, the EPA Office of Pesticide Programs (U.S. EPA, 

1987d) assumes that the general parameters of application (such as the human activity 

that leads to exposure) are more important than the properties of the pesticide in 

determining the level of exposure.29 This option assumes that surrogate data are 

available and that the differences between the chemical and the surrogate are small. 

If a clear relationship can be determined between the concentration of a chemical and 

the surrogate (usually termed an indicator chemical) in a medium, this relationship 

could also be used to fill data gaps. In any case, the strength and character of the 

relationship between the chemical and the surrogate must be explained. 

C Professional judgment can be used. The utility of this option depends on the 

confidence placed in the estimate. Expert opinion based on years of observation of 

similar circumstances usually carries more weight than anecdotal information. The 

assessor must discuss the implications of these estimates in the uncertainty analysis. 

5.3. CALCULATING EXPOSURE AND DOSE 

Depending on the approach used to quantify exposure and dose, various types of data 

will have been assembled. In calculating exposures and doses from these data, the assessor 

needs to direct attention specifically to certain aspects of the data. These aspects include the use 

27“Conservative” assumptions are those which tend to maximize estimates of exposure or dose, such as choosing a 

value near the high end of the concentration or intake rate range. 

28Obviously, the mathematical product of several conservative assumptions is more conservative than any single 

assumption alone. Ultimately, this could lead to unrealistically conservative bounding estimates (see Section 5.3). 

29Note that when using a passive dosimetry monitoring method, what is measured is the amount of chemical 

impinging on the skin surface or available for inhalation, that is, exposure, not the actual dose received. Factors 

such as dermal penetration, are, of course, expected to be highly chemical dependent. 

70 
of short-term data for long-term projections, the role of personal monitoring data, and the 

particular way the data might be used to construct scenarios. Each of these aspects is covered in 

turn below. 

5.3.1. Short-Term Versus Long-Term Data for Population Exposures 

Short-term data, for the purposes of this discussion, are data representing a short period 

of time measured (or modeled) relative to the time period covered in the exposure assessment. 

For example, a 3-day sampling period would produce short-term data if the exposure assessment 

covered a period of several years to a lifetime. The same 3-day sampling period would not be 

considered short-term if the assessment covered, say, a few days to a week. 

Short-term data can provide a snapshot of concentrations or exposures during that time, 

and an inference must be made about what that means for the longer term if the exposure 

assessment covers a long period. The assessor must determine how well the short-term data 

represent the longer period. 

Even when short-term population data are statistically representative (i.e., they describe 

the shape of the distribution, the mean, and other statistics), use of these short-term data to infer 

long-term exposures and risks must be done with caution. Using short-term data to estimate 

long-term exposures has a tendency to underestimate the number of people exposed, but to 

overestimate the exposure levels to the upper end of the distribution, even though the mean will 

remain the same.

30 Both concentration variation at a single point and population mobility will 

drive the estimates of the levels of exposure for the upper tail of the distribution toward the 

mean. If short-term data are used for long-term exposure or dose estimates, the implications of 

this on the estimated exposures must be discussed in the assessment. Likewise, use of long-term 

monitoring data for specific short-term assessments can miss significant variations due to shortterm conditions or activities. Long-term data should be used cautiously when estimating shortterm exposures or doses, and the implications should be discussed in the assessment. 

30Consider, for example, a hypothetical set of 100 rooms (microenvironments) where the concentration of a 

particular pollutant is zero in 50 of them, and ranges stepwise from 1 to 50 (nominal concentration units) in the 

remainder. If one person were in each room, short-term “snapshot” monitoring would show that 50 people were 

unexposed and the others were exposed to concentrations ranging from 1 to 50. If the concentration in each room 

remained constant and people were allowed to visit any room at random, long-term monitoring would indicate that 

all 100 were exposed to a mean concentration of 12.75. The short-term data would tend to overestimate 

concentration and underestimate the number of persons exposed if applied to long-term exposures. If only average 

values were available, the long-term data would tend to underestimate concentration and overestimate the number 

exposed if applied to short-term exposures. Because populations are not randomly mobile or static, the exposure 

assessor should determine what effect this has on the exposure estimate. 

71 
5.3.2. Using Point-of-Contact Data to Calculate Exposure and Dose 

Point-of-contact exposure assessments are often done with the intent of protecting the 

individuals, often in an occupational setting. When exposures are being evaluated to determine 

whether they exceed an action level or other benchmark, point-of-contact measurements are the 

most relevant data. 

Typically, point-of-contact measurement data reflect exposures over periods of minutes 

to perhaps a week or so. For individuals whose exposures have been measured, these data may 

be used directly as an indication of their exposure during the sampling period, provided they are 

of adequate quality, measure the appropriate chemical, and actually measure exposure while it 

occurs. This is the only case in which measurement data may be used directly as exposure data. 

When using point-of-contact measurements, even with statistically based data, several 

inferences still must be made to calculate exposure or dose: 

C Inferences must be made to apply short-term measurements of exposure to long-term 

estimates of exposure; these are subject to the cautions outlined in Section 5.3.1. 

C Inferences must be made about the representativeness of the individual or persons 

sampled for the individual or population segment for which the assessment is done. 

C Inferences must be made about the factors converting measured exposure to potential 

or internal dose for use in a risk assessment. 

C If the assessment requires it, inferences must be made about the relationship between 

the measured chemical exposures and the presence and relative contribution of 

various sources of the chemical. 

5.3.3. The Role of Exposure Scenarios in Exposure Assessment 

Exposure scenarios have several functions in exposure and risk assessments. First, they 

are calculational tools to help the assessor develop estimates of exposure, dose, and risk. 

Whatever combination of data and models is used, the scenario will help the assessor to picture 

how the exposure is taking place, and will help organize the data and calculations. Second, the 

estimates derived from scenarios are used to develop a series of exposure and risk descriptors, 

which were discussed in Section 2.3. Finally, exposure scenarios can often help risk managers 

make estimates of the potential impact of possible control actions. This is usually done by 

changing the assumptions in the exposure scenario to the conditions as they would exist after the 

contemplated action is implemented, and reassessing the exposure and risk. These three uses of 

exposure assessments are explained in Sections 5.3.3.1, 5.3.3.2, and 5.3.3.3, respectively. 

An exposure scenario is the set of information about how exposure takes place. An 

exposure scenario generally includes facts, data, assumptions, inferences, and sometimes 

professional judgment about the following: 

72
C The physical setting where exposure takes place (exposure setting) 

C The exposure pathway(s) from source(s) to exposed individual(s) (exposure 

pathways) 

C The characterization of the chemical, i.e., amounts, locations, time variation of 

concentrations, source strength, environmental pathways from source to exposed 

individuals, fate of the chemical in the environment, etc. (characterization of the 

chemical) 

C Identification of the individual(s) or population(s) exposed, and the profile of contact 

with the chemical based on behavior, location as a function of time, characteristics of 

the individuals, etc. (characterization of the exposed population) 

C If the dose is to be estimated, assumptions about the transfer of the chemical across 

the boundary, i.e., ingestion rates, respiration rates, absorption rates, etc. (intake and 

uptake rates) 

It usually is necessary to know whether the effect of concern is chronic, acute, or 

dependent on a particular exposure time pattern. 

The risk characterization, the link between the development of the assessment and the use 

of the assessment, is usually communicated in part to the risk manager by means of a series of 

“risk descriptors,” which are merely different ways to describe the risk. Section 2.3 outlined two 

broad types of descriptors: individual risk descriptors and population risk descriptors, with 

several variations for each. To the exposure or risk assessor, different types of risk information 

require different risk descriptors and different analyses of the data. The following paragraphs 

discuss some of the aspects of developing and using exposure scenarios in various functions for 

exposure assessment. 

5.3.3.1. Scenarios as a Means to Quantify Exposure and Dose 

When using exposure scenario evaluation as a means to quantify exposure and dose, it is 

possible to accumulate a large volume of data and estimated values, and both the amount and 

type of information can vary widely. The exposure scenario also contains the information 

needed to calculate exposure, since the last three bullets above (Section 5.3.3) are the primary 

variables in most exposure and dose equations. 

As an example, consider Equation 2-5, the equation for lifetime average daily potential 

dose (LADDpot). This equation uses the variables of exposure concentration (C), intake rate (IR), 

and exposure duration (ED) as the three primary variables. Body weight (BW) and averaging 

time (AT) (in this case, lifetime, LT) are not related to the exposure or dose per se, but are 

averaging variables used to put the resulting dose in convenient units of lifetime average 

exposure or dose per kg of body weight. 

73
In looking at the three primary variables (C, IR, and ED), the exposure assessor must 

determine what value to use for each to solve the equation. In actuality, the information 

available for a variable like C may consist of measurements of various points in an 

environmental medium, source and fate characterizations, and model results. There will be 

uncertainty in the values for C for any individual; there will also be variability among 

individuals. Each of these primary variables will be represented by a range of values, even 

though at times, the boundaries of this range will be unknown. How exposure or dose is 

calculated depends on how these ranges are treated. 

In dealing with these ranges in trying to solve the equation for LADD, the assessor has at 

least two choices. First, statistical tools, such as the Monte Carlo analysis, can be used to enter 

the values as frequency distributions, which results in a frequency distribution for the LADD. 

This is an appropriate strategy when the frequency distributions are known for C, IR, and ED (or 

for the uptake analogs, C, Kp, SA, and ED introduced in Section 2), and when these variables are 

independent. 

A second approach is to select or estimate discrete values from the ranges of each of the 

variables and use these values to solve the LADD equation. This approach usually results in a 

less certain estimate, but may be easier to do. Which values are used determines how the 

resulting estimate will be described. Several terms for describing such estimates are discussed in 

Section 5.3.3.2. 

Since exposure to chemicals occurs through a variety of different pathways, contact 

patterns, and settings, sufficient perspective must be provided to the users of the assessment 

(usually risk managers) to help them make an informed decision. Providing this perspective and 

insight would be relatively straightforward if complete and accurate information were known 

about the exposure, dose, and risk for each and every person within the population of interest. In 

this hypothetical situation, these individual data could actually be arrayed next to the name of 

each person in the population, or the data could be compiled into frequency distribution curves. 

From such distributions, the average, median, maximum, or other statistical values could easily 

be read off the curves and presented to the risk manager. In addition, accurate information could 

be provided about how many persons are above certain exposure, dose, or risk levels as well as 

information about where various subgroups fall within the subject distribution. 

Unfortunately, an assessor rarely has these kinds of data; the reality an assessor faces 

usually falls far short of this ideal. But it is precisely this kind of information about the 

distribution of exposure, dose, and risk that is needed many times by the risk assessor to 

characterize risk, and by the risk manager to deal with risk-related issues. 

In the absence of comprehensive data, or if the scenario being evaluated is a possible 

future use or post-control scenario, an assessor must make assumptions in order to estimate what 

74
the distribution would look like if better data were available, or if the possible future use 

becomes a reality. Communicating this estimated distribution to the risk manager can be 

difficult. The assessor must not only estimate exposure, dose, and risk levels, but must also 

estimate where those levels might fall on the actual distributions or estimated distributions for 

potential future situations. To help communicate where on the distribution the estimate might 

fall, loosely defined terms such as reasonable worst case, worst case, and maximally exposed 

individual have been used by assessors. Although these terms have been used to help describe 

the exposure assessor’s perceptions of where estimated exposures fall on the actual or potential 

distribution for the future use, the ad hoc nature of the historical definitions used has led to some 

inconsistency. One of the goals of these Guidelines is to promote greater consistency in the use 

of terms describing exposure and risk. 

5.3.3.2. Exposure Scenarios and Exposure Estimators as Input to Risk Descriptors 

As discussed in Section 2.3, risk descriptors convey information about risk to users of 

that information, primarily risk managers. This information usually takes the form of answers to 

a relatively short set of questions, not all of which are applicable to all assessments. Section 

5.3.5 provides more detail on how the exposure assessor’s analysis leads to construction of the 

risk descriptors. 

5.3.3.3. Exposure Scenarios as a Tool for Option Evaluation 

A third important use for exposure scenarios is as a tool for evaluating proposed options 

for action. Risk managers often have a number of choices for dealing with environmental 

problems, from taking no action on one extreme to a number of different actions, each with 

different costs, on the other. Often the exposure scenarios developed as part of the baseline risk 

assessment provide a powerful tool to evaluate the potential reduction of exposure and risk for 

these various options, and consequently are quite useful in many cost-benefit analyses. 

There are several additional related uses of exposure scenarios for risk managers. They 

may help establish a range of options for cleanup by showing the sensitivity of the risk estimates 

to the changes in assumed source or exposure levels. The exposure assessor can use the 

sensitivity analysis of the exposure scenario to help evaluate and communicate the uncertainty of 

the assumptions, and what can be done to reduce that uncertainty. Well-crafted and soundly 

based exposure scenarios may also help communicate risks and possible options to community 

groups. 

Although it is beyond the scope of these Guidelines to detail the methods used for option 

evaluation and selection, the assessor should be aware of this potential use. Discussing strategy 

75
(and specific information needs) with risk managers is usually prudent before large resource 

expenditures are made in the risk assessment area. 

5.3.4. General Methods for Estimating Exposure and Dose 

A variety of methods are used to obtain estimates of dose necessary for risk 

characterization. These range from quick screening level calculations and rules of thumb to 

more sophisticated techniques. The technique to be used in a given case is a matter of the 

amount of information available and the purpose of the assessment. Several of the methods are 

outlined in the following sections. 

Normally it is neither practicable nor advisable to immediately develop detailed 

information on all the potential pathways, since not all may contribute significantly to the 

outcome of the assessment.31 Rather, evaluation of the scenario is done in an iterative manner. 

First, screening or bounding techniques are used to ascertain which pathways are unimportant, 

then the information for the remaining pathways is refined, iteratively becoming more accurate, 

until the quantitative objectives of the assessment are met (or resources are depleted). 

In beginning the evaluation phase of any assessment, the assessor should have a 

scenario’s basic assumptions (setting, scope, etc.) well identified, one or more applicable 

exposure pathways defined, an equation for evaluating the exposure or dose for each of those 

exposure pathways, and the data and information requirements pertinent to solving the equations. 

Quality and quantity of data and information needed to substitute quantitative values or ranges 

into the parameters of the exposure equation will often vary widely, from postulated assumptions 

to actual high-quality measurements. Many times, there are several exposure pathways 

identified within the scenario, and the quality of the data and information may vary for each. 

A common approach to estimating exposure and dose is to do a preliminary evaluation, 

or screening step, during which bounding estimates are used, and then to proceed to refine the 

estimates for those pathways that cannot be eliminated as of trivial importance. 

5.3.4.1. Preliminary Evaluation and Bounding Estimates 

The first step that experienced assessors usually take in evaluating the scenario involves 

making bounding estimates for the individual exposure pathways. The purpose of this is to 

eliminate further work on refining estimates for pathways that are clearly not important. 

31There are some important exceptions to this statement. First, the public or other concerned groups may express 

particular interest in certain pathways, which will not normally be dropped entirely at this point. Second, for routine 

repetitive assessments using a certain standard scenario for many chemicals, once the general bounding has been 

done on the various possible pathways, it may become standard operating procedure to immediately begin 

developing information for particular pathways as new chemicals are assessed. 

76 
The method used for bounding estimates is to postulate a set of values for the parameters 

in the exposure or dose equation that will result in an exposure or dose higher than any exposure 

or dose expected to occur in the actual population. The estimate of exposure or dose calculated 

by this method is clearly outside of (and higher than) the distribution of actual exposures or 

doses. If the value of this bounding estimate is not significant, the pathway can be eliminated 

from further refinement.32 

The theoretical upper bounding estimate (TUBE) is a type of bounding estimate that can 

be easily calculated and is designed to estimate exposure, dose, and risk levels that are expected 

to exceed the levels experienced by all individuals in the actual distribution. The TUBE is 

calculated by assuming limits for all the variables used to calculate exposure and dose that, when 

combined, will result in the mathematically highest exposure or dose (highest concentration, 

highest intake rate, lowest body weight, etc.). The theoretical upper bound is a bounding 

estimate that should, if the limits of the parameters used are known, ensure that the estimate is 

above the actual exposures received by all individuals in the population. It is not necessary to go 

to the formality of the TUBE to assure that the exposure or dose calculated is above the actual 

distribution, however, since any combination that results in a value clearly higher than the actual 

distribution can serve as a suitable upper bound. 

The bounding estimate (a limit of individual exposure, dose or risk) is most often used 

only to eliminate pathways from further consideration. This is often done in screening-level 

assessments, where bounding estimates of exposure, dose, or risk provide a quick and relatively 

easy check on whether the levels to be assessed are trivial relative to a level that would cause 

concern. If acceptably lower than the concern level, then additional assessment work is not 

necessary. 

Bounding estimates also are used in other types of assessments. They can be used for 

deregulation of chemicals when pathways or concentrations can be shown to present 

insignificant or de minimis risk. They can be used to determine whether more information is 

needed to determine whether a pathway is significant; if the pathway’s significance cannot be 

ruled out by a bounding estimate, test data may be needed to refine the estimate. 

There are two important points about bounding estimates. First, the only thing the 

bounding estimate can establish is a level to eliminate pathways from further consideration. It 

cannot be used to make a determination that a pathway is significant (that can only be done after 

more information is obtained and a refinement of the estimate is made), and it certainly cannot 

32“Not significant” can mean either that it is so small relative to other pathways that it will not add perceptibly to the 

total exposure being evaluated or that it falls so far below a level of concern that even when added to other results 

from other pathways, it will be trivial. Note that a “level of concern” is a risk management term, and the assessor 

must discuss and establish any such levels of concern with risk managers (and in some cases, concerned groups such 

as the local community) before eliminating pathways as not significant. 

77 
be used for an estimate of actual exposure (since by definition it is clearly outside the actual 

distribution). Second, when an exposure scenario is presented in an assessment, it is likely that 

the amount of refinement of the data, information, and estimates will vary by pathway, some 

having been eliminated by bounding estimates, some eliminated after further refinement, and 

others fully developed and quantified. This is an efficient way to evaluate scenarios. In such 

cases, bounding estimates must not be considered to be equally as sophisticated as an estimate of 

a fully developed pathway, and should not be described as such. 

Experienced assessors can often eliminate some obvious pathways more or less by 

inspection as they may have evaluated these pathways many times before.33 In these cases, the 

assessor must still explain why the pathway is being eliminated. For less experienced assessors, 

developing bounding estimates for all pathways is instructive and will be easier to defend. 

5.3.4.2. Refining the Estimates of Exposure and Dose 

For those pathways not eliminated by bounding estimates or judged trivial, the assessor 

will then evaluate the resulting exposure or dose. At this point, the assessor will make estimates 

of exposure or dose that are designed to fall on the actual distribution. The important point here 

is that unlike a bounding estimate, these estimates of exposure or dose should focus on points in 

the actual distribution. Both estimates of central tendency and estimates of the upper end of the 

distribution curve are useful in crafting risk descriptors. 

Consider Equation 2-6 for the lifetime average daily potential dose (LADDpot), an 

equation often used for linear, nonthreshold carcinogen risk models. The assessor will use the 

data, ranges of data, distributions of data, and assumptions about each of the factors needed to 

solve the equation for dose. Generally, both central estimates and high-end estimates are 

performed. Each of these estimates has uncertainty (perhaps unquantifiable uncertainty), and the 

better the quality and comprehensiveness of data used as input to the equation, the less 

uncertainty. 

After solving the equation, the assessor will determine whether the uncertainty associated 

with the answer is sufficiently narrow to allow the risk descriptors to be developed (see Section 

3.4) and to answer satisfactorily the questions posed in the exposure assessment statement of 

purpose. Evaluating whether the data, uncertainty, risk descriptors, and answers to the questions 

are good enough is usually a joint responsibility of the risk assessor and the risk manager. 

Should the estimates of exposure or dose have sufficiently narrow uncertainty, the 

assessor can then proceed to develop the descriptors and finish the assessment. If not, the data or 

assumptions used usually will have to be refined, if resources allow, in an attempt to bring the 

33Experienced assessors may also be able to determine quickly that a pathway requires refined estimation. 

78 
estimated exposure or dose closer to what the assessor believes are the actual values in the 

population. Refining the estimates usually requires that new data be brought into 

consideration;34 this new information can be other studies from the literature, information 

previously developed for another, related purpose that can be adapted, or new survey, laboratory, 

or field data. The decision about which particular parts of the information base to refine should 

be based both on which data will most significantly reduce the uncertainty of the overall 

exposure or dose estimate, and on which data are in fact obtainable either technologically or 

within resource constraints. 

After refinement of the estimate, the assessor and risk manager again determine whether 

the estimates provided will be sufficient to answer the questions posed to an acceptable degree, 

given the uncertainties that may be associated with those estimates. Refinements proceed 

iteratively until the assessment provides an adequate answer within the resources available. 

5.3.5. Using Estimates for Developing Descriptors 

Risk assessors and risk managers are encouraged to explore a range of ways to describe 

exposure and risk information, depending on the purpose of the assessment and the questions for 

which the risk manager must have answers. Section 2.3 outlines a series of risk descriptors; in 

the sections below, these are discussed in the context of how an exposure assessor’s analysis of 

the data would lead to various descriptors for risk. 

5.3.5.1. Individual Exposure, Dose, and Risk 

Questions about individual risk are an important component of any assessment, 

especially an estimate of the high end of the distribution. Section 5.3.4.1 indicated that bounding 

estimates are actually a useful but limited form of individual risk estimate, a form which is by 

definition beyond the highest point on the population distribution. This section deals with 

estimates that are actually on the distribution of exposure, dose, or risk. 

There are several approaches for arriving at an individual risk estimate. Since calculation 

of risk involves using information from fields other than exposure assessment, the reader is 

advised to consult other Agency guidelines for more detailed discussions (e.g., U.S. EPA, 1986b, 

1986c, 1988b, 1988c, 1991a). The uncertainty in the risk estimate will depend heavily on the 

quality of the information used. There are several steps in the process: 

First, the question of unusual susceptibility of part of the population must be addressed. 

If equal doses result in widely different responses in two individuals, it may be necessary to 

consult with scientists familiar with the derivation of the dose-response relationship for the 

34It also can involve new methods or additional methods for analyzing the old data. 

79 
chemical in question in order to ascertain whether this is normal variability among members of a 

population. Normal variability should have been considered as part of the development of the 

dose-response relationship; unusual susceptibility may not have been. If such a highly 

susceptible subgroup can be identified, it is often useful to assess their risk separately from the 

general population. It will not be common, given the current data availability, to clearly identify 

such susceptible subgroups. If none can be identified, the default has usually been to assume the 

dose-response relationship applies to all members of the population being assessed. Where no 

information shows the contrary, this assumption may be used provided it is highlighted as a 

source of uncertainty. 

Second, after the population or population segment can be represented by a single doseresponse relationship, the appropriate dose for use in the dose-response relationship 

(absorbed/internal dose, potential dose, applied dose, effective dose) must be identified. For 

dose-response relationships based on administered dose in animal studies, potential dose will 

usually be the human analogue. If the dose-response relationship is based on internal dose, then 

that is the most appropriate human dose. If the estimates of exposure and dose from the 

exposure assessment are in an inappropriate form (say, potential dose rather than internal dose), 

they must be converted before they are used for risk calculations. This may involve analysis of 

bioavailability, absorption rates as a function of form of the chemical and route, etc. If these 

data are not available, the default has been to assume the entire potential dose becomes the 

internal dose.35 As more data become available concerning absorption for different chemicals, 

this conservative assumption may not always be the best, or even a credible, default. Whatever 

assumption is made concerning absorption (or the relationships among any of the different dose 

terms if used, for that matter), it should be highlighted in the uncertainty section. 

Once the first two steps have been done, and the dose-response relationship and type of 

dose have been identified, the exposure and dose information needs to be put in the appropriate 

form. Ideally, this would be a distribution of doses of the appropriate type across the population 

or population subgroup of interest. This may involve converting exposures into potential doses 

or converting potential doses into internal, delivered, or biologically effective doses. Once this 

is accomplished, the high-end estimate of dose will often (but not always) lead fairly directly to 

the high-end estimate of risk. The method used to develop the high-end estimate for dose 

35The unstated assumption is often made that the relationship between administered dose and absorbed dose in the 

animal is the same as that between potential dose and internal dose in humans, provided a correction is made for 

body weight/surface area. In other words, the bioavailability and absorption fractions are assumed to be the same in 

the human as in the animal experiment. If no correction is made for absorption, this leads to the assumption that the 

absorption percent is the same as in the animal experiment from which the dose-response relationship was derived. 

Note this uncorrected conversion of potential dose to internal dose does not assume “100% absorption” unless there 

was 100% absorption in the animal study. 

80 
depends on the data available. Because of the skewed nature of exposure data, there is no exact 

formula that will guarantee an estimate will fall into this range in the actual population if only 

sparse data are available. 

The high-end risk is a plausible estimate of the individual risk for those persons at the 

upper end of the risk distribution. The intent of this descriptor is to convey an estimate of risk in 

the upper range of the distribution, but to avoid estimates that are beyond the true distribution. 

Conceptually, high-end risk means risks above the 90th percentile of the population distribution, 

but not higher than the individual in the population who has the highest risk. This descriptor is 

intended to estimate the risks that are expected to occur in small but definable high-end segments 

of the subject population. The use of “above the 90th percentile” in the definition is not meant to 

precisely define the range of this descriptor, but rather to clarify what is meant conceptually by 

high end. 

The high-end segments of the exposure, dose, and risk populations may represent 

different individuals. Since the location of individuals on the exposure, dose, and risk 

distributions may vary depending on the distributions of bioavailability, absorption, intake rates, 

susceptibility, and other variables, a high exposure does not necessarily result in a high dose or 

risk, although logically one would expect a moderate to highly positive correlation among 

exposure, dose, and risk. 

When the complete data on the population distributions of exposures and doses are 

available, and the significance of the factors above (bioavailability, etc.) are known to the extent 

to allow a risk distribution to be constructed, the high-end risk estimate can be represented by 

reporting risks at selected percentiles of the distributions, such as the 90th, 95th, or 98th percentile. 

When the complete distributions are not available, the assessor should conceptually target 

something above the 90th percentile on the actual distribution. 

In developing estimates of high-end individual exposure and dose, the following 

conditions must be met: 

C The estimated exposure or dose is on the expected distribution, not above the value 

one would expect for the person with the highest estimated risk in the population. 

This means that when constructing this estimate from a series of factors 

(environmental concentrations, intake rates, individual activities, etc.), not all factors 

should be set to values that maximize exposure or dose, since this will almost always 

lead to an estimate that is much too conservative. 

C The combination of values assigned to the exposure and dose factors can be expected 

to be found in the actual population. In estimating high-end exposures or doses for 

81
future use or post-control scenarios, the criterion to be used should be that it is 

expected to be on the distribution provided the future use or control measure occurs.36 

Some of the alternative methods for determining a high-end estimate of dose are: 

C If sufficient data on the distribution of doses are available, take the value directly for 

the percentile(s) of interest within the high end. If possible, the actual percentile(s) 

should be stated, or the number of persons determined in the high end above the 

estimate, in order to give the risk manager an idea of where within the high end-range 

the estimate falls. 

C If data on the distribution of doses are not available, but data on the parameters used 

to calculate the dose are available, a simulation (such as an exposure model or Monte 

Carlo simulation) can sometimes be made of the distribution. In this case, the 

assessor may take the estimate from the simulated distribution. As in the method 

above, the risk manager should be told where in the high-end range the estimate falls 

by stating the percentile or the number of persons above this estimate. The assessor 

and risk manager should be cautioned that unless a great deal is known about 

exposures or doses at the high end of the distribution, simulated distributions may not 

be able to differentiate between bounding estimates and high-end estimates. 

Simulations often include low-probability estimates at the upper end that are higher 

than those actually experienced in a given population, due to improbability of finding 

these exposures or doses in a specific population of limited size, or due to nonobvious 

correlations among parameters at the high ends of their ranges.37 Using the highest 

estimate from a Monte Carlo simulation may therefore overestimate the exposure or 

dose for a specific population, and it is advisable to use values somewhat less than the 

highest Monte Carlo estimated value if one is to defend the estimate as being within 

the actual population distribution and not above it. 

36This means that estimates of high-end exposure or dose for future uses are limited to the same conceptual range as 

current uses. Although a “worst-case” combination of future conditions or events may result in an exposure that is 

conceivably possible, the assessor should not merely use a worst-case combination as an estimate of high-end 

exposure for possible future uses. Rather, the assessor must use judgment as to what the range of exposures or doses 

would plausibly be, given the population size and probability of certain events happening. 

37For example, although concentration breathed, frequency, duration, and breathing rate may be independent for a 

consumer painting rooms in a house under most normal circumstances, if the concentration is high enough, it may 

affect the other parameters such as duration or breathing rate. These types of high-end correlations are difficult to 

quantify, and techniques such as Monte Carlo simulations will not consider them unless relationships are known and 

taken into account in the simulation. If extreme concentration in this case resulted in lower breathing rate or 

duration, a noncorrected Monte Carlo simulation could overestimate the exposure or dose at the high end. Far less 

likely, due to self-preservation processes, would seem the case where high concentration increases duration or intake 

rate, although this theoretically might also occur. 

82 
Simulations using finite ranges for parameters will result in a simulated 

distribution with a calculable finite maximum exposure, and the maximum exposures 

calculated in repeated simulations will not exceed this theoretical maximum.

38 When 

unbounded default distributions, such as lognormal distributions, are used for input 

parameters to generate the simulated exposure distributions, there will not be a finite 

maximum exposure limit for the simulation, so the maximum value of the resulting 

simulated distribution will vary with repeated simulations. The EPA’s Science 

Advisory Board [SAB] (U.S. EPA, 1992a) has recommended that values above a 

certain percentile in these simulations be treated as if they were bounding estimates, 

not estimates of high-end exposures (see Figure 2). The SAB noted that for large 

populations, simulated exposures, doses, and risks above the 99.9th percentile may not 

be meaningful when unbounded lognormal distributions are used as a default. 

Although the Agency has not specifically set policy on this matter, exposure 

assessors should observe the following caution when using simulated distributions. 

The actual percentile cutoff above which a simulation should be considered a 

bounding estimate may be expected to vary depending on the size of the population. 

Since bounding estimates are established to develop statements that exposures, doses, 

and risks are “not greater than...,” it is prudent that the percentile cutoff bound 

expected exposures for the size of the population being evaluated. For example, if 

there are 100 persons in the population, it may be prudent to consider simulated 

exposures above the 1 in 500 level or 1 in 1000 level (i.e., above the 99.5th or 99.9th 

percentile, respectively) to be bounding estimates. Due to uncertainties in simulated 

distributions, assessors should be cautious about using estimates above the 99.9th 

percentile for estimates of high-end exposure regardless of the size of the population. 

The Agency or individual program offices may issue more direct policy for setting 

the exact cutoff value for use as high-end and bounding estimates in simulations. 

C If some information on the distribution of the variables making up the exposure or 

dose equation (e.g., concentration, exposure duration, intake or uptake rates) is 

available, the assessor may estimate a value which falls into the high end by meeting 

the defining criteria of “high end”: an estimate that will be within the distribution, 

but high enough so that less than 1 out of 10 in the distribution will be as high. The 

assessor often constructs such an estimate by using maximum or near-maximum 

values for one or more of the most sensitive variables, leaving others at their mean 

38This maximum is the theoretical upper bounding estimate (TUBE). 

83 
85

Figure 2. Schematic of exposure estimators for unbounded simulated population distributions. 
values.39 The exact method used to calculate the estimate of high-end exposure or 

dose is not critical; it is very important that the exposure assessor explain why the 

estimate, in his or her opinion, falls into the appropriate range, not above or below it. 

C If almost no data are available, it will be difficult, if not impossible, to estimate 

exposures or doses in the high end. One method that has been used, especially in 

screening-level assessments, is to start with a bounding estimate and back off the 

limits used until the combination of parameter values is, in the judgment of the 

assessor, clearly in the distribution of exposure or dose. Obviously, this method 

results in a large uncertainty. The availability of pertinent data will determine how 

easily and defensibly the high-end estimate can be developed by simply adjusting or 

backing off from the ultra conservative assumptions used in the bounding estimates. 

This estimate must still meet the defining criteria of “high end,” and the assessor 

should be ready to explain why the estimate is thought to meet the defining criteria. 

A descriptor of central tendency may be either the arithmetic mean risk (average 

estimate) or the median risk (median estimate), but should be clearly labeled as such. Where 

both the arithmetic mean and the median are available, but differ substantially, it is helpful to 

present both. 

Exposure and dose profiles often fall in a skewed distribution that many times appears to 

be approximately lognormally distributed, although statistical tests for lognormality may fail. 

The arithmetic mean and the median are the same in a normal distribution, but exposure data are 

rarely normally distributed. As the typical skewness in the distribution increases, the exposure 

or dose distribution comes to resemble a lognormal curve where the arithmetic mean will be 

higher than the median. It is not unusual for the arithmetic mean to be located at the 75th 

percentile of the distribution or higher. Thus, the arithmetic mean is not necessarily a good 

indicator of the midpoint (median, 50th percentile) of a distribution. 

The average estimate, used to describe the arithmetic mean, can be approximated by 

using average values for all the factors making up the exposure or dose equation. It does not 

necessarily represent a particular individual on the distribution, but will fall within the range of 

the actual distribution. Historically, this calculation has been referred to as the average case, but 

as with other ad hoc descriptors, definitions have varied widely in individual assessments. 

When the data are highly skewed, it is sometimes instructive to approximate the median 

exposure or dose, or median estimate. This is usually done by calculating the geometric mean of 

the exposure or dose distribution, and historically this has often been referred to as the typical 

39Maximizing all variables, as is done in bounding estimates, will result in virtually all cases in an estimate that is 

above the bounds of this range, that is, above the actual values seen in the population. 

85 
case, although again, definitions have varied widely. Both the average estimate and median 

estimate are measures of the central tendency of the exposure or dose distribution, but they must 

be clearly differentiated when presenting the results. 

It will often be useful to provide additional specific individual risk information to provide 

perspective for the risk manager. This specific information may take the form of answers to 

what if questions, such as, what if a consumer should use this product without adequate 

ventilation? For the risk manager, these questions are likely to put bounds on various aspects of 

the risk question. For the assessor, these are much less complicated problems than trying to 

estimate baseline exposure or dose in an actual population, since the answers to these questions 

involve choosing values for various parameters in the exposure or risk equations and solving 

them for the estimate. 

This type of risk descriptor is a calculation of risk to specific hypothetical or actual 

combinations of factors postulated within the exposure assessment. It is often valuable to ask 

and answer specific questions of the “what if” nature to add perspective to the risk assessment. 

Each assessment may have none, one, or several of these specific types of descriptors. 

The answers to these questions might be a point estimate or a range, but are usually fairly simple 

to calculate. The answers to these types of postulated questions, however, do not directly give 

information about how likely that combination of values might be in the actual population, so 

there are some limits to the applicability of these descriptors. 

5.3.5.2. Population Exposure, Dose, and Risk 

Questions about population exposure, dose, and risk are central to any risk assessment. 

Ideally, given the time and methods, the assessor might strive to construct a picture of exposure, 

dose, and risk in which each individual exposure, dose and risk is known. These data could then 

be displayed in a frequency distribution. 

The risk manager, perhaps considering what action might be necessary for this particular 

situation, might ask how many cases of the particular effect might be probabilistically estimated 

in a population during a specific time period, or what percentage of the population is (or how 

many people are) above a certain exposure, dose, or risk level. 

For those who do the assessments, answering these questions requires some knowledge 

of the population frequency distribution. This information can be obtained or estimated in 

several ways, leading to two descriptors of population risk. 

The first is the probabilistic number of health effect cases estimated in the population of 

interest over a specified time period. This descriptor can be obtained either by summing the 

individual risks over all the individuals in the population, or by multiplying the slope factor 

obtained from a carcinogen dose-response relationship, the arithmetic mean of the dose, and the 

86
size of the population. The latter approach may be used only if the risk model assumes a single 

linear, nonthreshold response to dose, and then only with some caution.40 If risk varies linearly 

with dose, knowing the arithmetic mean risk and the population size can lead to an estimate of 

the extent of harm for the population as a whole, excluding sensitive subgroups for which a 

different dose-response curve may need to be used. For noncarcinogens, or for nonlinear, 

nonthreshold carcinogen models, using the arithmetic mean exposure or dose, multiplying by a 

slope factor to calculate an average risk, and multiplying by the population size is not 

appropriate, and risks should be summed over individuals.41 

Obviously, the more relevant information one has, the less uncertain this descriptor, but 

in any case, the estimate used to develop the descriptor is also limited by the inherent 

uncertainties in risk assessment methodology, e.g., the risk estimates often being upper 

confidence level bounds. With the current state of the science, this descriptor should not be 

confused with an actuarial prediction of cases in the population (which is a statistical prediction 

based on a great deal of empirical data). 

The second type of population risk descriptor is an estimate of the percentage of the 

population, or the number of persons, above a specified level of risk, RfD, RfC, LOAEL, or 

other specific level of interest. This descriptor must be obtained by measuring or simulating the 

population distribution, which can be done in several ways. 

First, if the population being studied is small enough, it may be possible to measure the 

distribution of exposure or dose. Usually, this approach can be moderately to highly costly, but 

it may be the most accurate. Possible problems with this approach are lack of measuring 

techniques for the chemical of interest, the availability of a suitable population subset to monitor, 

and the problem of extrapolating short-term measurements to long-term exposures. 

40For example, when calculating risks using doses and “slope factors,” the risk is approximately linear with dose 

until relatively high individual risks (about 10-1) are attained, after which the relationship is no longer even 

approximately linear. This results from the fact that no matter how high the dose, the individual risk cannot exceed 

1, and the dose-risk curve approaches 1 asymptotically. This can result in artifacts when calculating population risk 

from average individual doses and population size if there are individuals in the population in this nonlinear risk 

range. Consider a population of five persons, only one of whom is exposed. As an example, assume a lifetime 

average daily dose of 100 mg/kg/day corresponds to an individual risk of 4 × 10-1. Increasing the dose fivefold, to 

500 mg/kg/day, would result in a higher individual risk for that individual, but due to the nonlinearity of the doserisk curve, not yet a risk of 1. The average dose for the five persons in the population would then be 100 mg/kg/day. 

Multiplying the “average risk” of 4 × 10-1 by the population size of five results in an estimate of two cases, even 

though in actuality only one person is exposed. Although calculating average individual dose, estimating individual 

risk from it, and multiplying by the population size is a useful approximation if all members of the population are 

within the approximately linear range of the dose-risk curve, this method should not be used if some members of the 

population have calculated individual risks higher than about 10-1, since it will overestimate the number of cases. 

41In these cases, a significant problem can be the lack of a constant (or nearly constant) “slope factor” that would be 

appropriate over a wide exposure/dose range, since the dose-response curve may have thresholds, windows, or other 

discontinuities. 

87 
Second, the distribution itself may be simulated from a model such as an exposure model 

(a model that reports exposures or doses by linking concentrations with contact times for subsets 

of the population, such as those living various distances from a source) or a Monte Carlo 

simulation. Although this may be considerably less costly than measurements, it will probably 

be less accurate, especially near the high end of the distribution. Although models and statistical 

simulations can be fairly accurate if the proper input data are available, these data are often 

difficult to obtain and assumptions must be made; use of assumptions may reduce the certainty 

of the estimated results. 

Third, it may be possible to estimate how many people are above a certain exposure, 

dose, or risk level by identifying and enumerating certain population segments known to be at 

higher exposure, dose, sensitivity, or risk than the level of interest. 

For those who use the assessments, this descriptor can be used in the evaluation of 

options if a level can be identified as an exposure, dose, or risk level of concern. The options 

can then be evaluated by estimating how many persons would go from the higher category to the 

lower category after the option is implemented. 

Questions about the distribution of exposure, dose, and risk often require the use of 

additional risk descriptors. In considering the risks posed by the particular situation being 

evaluated, a risk manager might want to know how various subgroups fall within the 

distribution, and if there are any particular subgroups at disproportionately high risk. 

It is often helpful for the risk assessor to describe risk by an identification, and if 

possible, characterization and quantification of the magnitude of the risk for specific highly 

exposed subgroups within the population. This descriptor is useful when there is (or is expected 

to be) a subgroup experiencing significantly different exposures or doses from that of the larger 

population. 

It is also helpful to describe risk by an identification, and if possible, characterization and 

quantification of the magnitude of risk for specific highly sensitive or highly susceptible 

subgroups within the population. This descriptor is useful when the sensitivity or susceptibility 

to the effect for specific subgroups within the population is (or is expected to be) significantly 

different from that of the larger population. In order to calculate risk for these subgroups, it will 

sometimes be necessary to use a different dose-response relationship. 

Generally, selection of the subgroups or population segments is a matter of either a priori 

interest in the subgroup, in which case the risk manager and risk assessor can jointly agree on 

which subgroups to highlight, or a matter of discovery of a subgroup during the assessment 

process. In either case, the subgroup can be treated as a population in itself and characterized the 

same way as the larger population using the descriptors for population and individual risk. 

88
(5-1) 

Exposures and doses for highly-exposed subpopulations can be calculated by defining the 

population segment as a population, then estimating the doses as for a population. 

must make it clear exactly which population was considered. 

A special case of a subpopulation is that of children. 

during childhood, when low body weight results in a higher dose rate than would be calculated 

using the LADDpot (Equation 2-6), it is appropriate to average the dose rate (intake rate/body 

weight) rather than dose. pot equation then becomes 

where LADDpot is the lifetime average daily potential dose, EDi is the exposure duration (time 

over which the contact actually takes place), is the average exposure concentration during 

period of calendar time EDi, is the average ingestion or inhalation rate during EDi, BWi is 

body weight during exposure duration EDi, and LT is the averaging time, in this case, a lifetime 

(converted to days). of the LADDpot equation, if applied to an exposure that occurs 

primarily in childhood (for example, inadvertent soil ingestion), may result in an LADDpot 

calculation somewhat higher than that obtained by using Equation 2-6, but there is some 

evidence that it is more defensible (Kodell et al., 1987; additional discussion in memorandum 

from Hugh McKinnon, EPA, to Michael Callahan, EPA, November 9, 1990). 

The assessor 

For exposures that take place 

The LADD

This form

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6. ASSESSING UNCERTAINTY 

Assessing uncertainty may involve simple or very sophisticated techniques, depending on 

the requirements of the assessment. Uncertainty characterization and uncertainty assessment are 

two activities that lead to different degrees of sophistication in describing uncertainty. 

Uncertainty characterization generally involves a qualitative discussion of the thought processes 

that lead to the selection and rejection of specific data, estimates, scenarios, etc. For simple 

exposure assessments, where not much quantitative information is available, uncertainty 

characterization may be all that is necessary. 

The uncertainty assessment is more quantitative. The process begins with simpler 

measures (i.e., ranges) and simpler analytical techniques (i.e., sensitivity analysis), and 

progresses, to the extent needed to support the decision for which the exposure assessment is 

conducted, to more complex measures and techniques. The development and implementation of 

an appropriate uncertainty assessment strategy can be viewed as a decision process. Decisions 

are made about ways to characterize and analyze uncertainties, and whether to proceed to 

increasingly more complex levels of uncertainty assessment. 

6.1. ROLE OF UNCERTAINTY ANALYSIS IN EXPOSURE ASSESSMENT 

Exposure assessment uses a wide array of information sources and techniques. Even 

where actual exposure-related measurements exist, assumptions or inferences will still be 

required (see Section 5.2). Most likely, data will not be available for all aspects of the exposure 

assessment and those data that are available may be of questionable or unknown quality. In 

these situations, the exposure assessor will have to rely on a combination of professional 

judgment, inferences based on analogy with similar chemicals and conditions, estimation 

techniques, and the like. The net result is that the exposure assessment will be based on a 

number of assumptions with varying degrees of uncertainty. 

The decision analysis literature has focused on the importance of explicitly incorporating 

and quantifying scientific uncertainty in risk assessments (Morgan, 1983; Finkel, 1990). 

Reasons for addressing uncertainties in exposure assessments include: 

C Uncertain information from different sources of different quality must be combined. 

C A decision must be made about whether and how to expend resources to acquire 

additional information (e.g., production, use, and emissions data; environmental fate 

information; monitoring data; population data) to reduce the uncertainty. 

C There is considerable empirical evidence that biases may result in so-called best 

estimates that are not actually very accurate. Even if all that is needed is a best90
estimate answer, the quality of that answer may be improved by an analysis that 

incorporates a frank discussion of uncertainty. 

C Exposure assessment is an iterative process. The search for an adequate and robust 

methodology to handle the problem at hand may proceed more effectively, and to a 

more certain conclusion, if the associated uncertainty is explicitly included and can 

be used as a guide in the process of refinement. 

C A decision is rarely made on the basis of a single piece of analysis. Further, it is rare 

for there to be one discrete decision; a process of multiple decisions spread over time 

is the more common occurrence. Chemicals of concern may go through several 

levels of risk assessment before a final decision is made. Within this process, 

decisions may be made based on exposure considerations. An exposure analysis that 

attempts to characterize the associated uncertainty allows the user or decision-maker 

to better evaluate it in the context of the other factors being considered. 

C Exposure assessors have a responsibility to present not just numbers but also a clear 

and explicit explanation of the implications and limitations of their analyses. 

Uncertainty characterization helps carry out this responsibility. 

Essentially, the construction of scientifically sound exposure assessments and the 

analysis of uncertainty go hand in hand. The reward for analyzing uncertainties is knowing that 

the results have integrity or that significant gaps exist in available information that can make 

decision-making a tenuous process. 

6.2. TYPES OF UNCERTAINTY 

Uncertainty in exposure assessment can be classified into three broad categories: 

1. Uncertainty regarding missing or incomplete information needed to fully define the 

exposure and dose (scenario uncertainty) 

2. Uncertainty regarding some parameter (parameter uncertainty) 

3. Uncertainty regarding gaps in scientific theory required to make predictions on the 

basis of causal inferences (model uncertainty) 

Identification of the sources of uncertainty in an exposure assessment is the first step 

toward eventually determining the type of action necessary to reduce that uncertainty. The three 

types of uncertainty mentioned above can be further defined by examining some principal causes 

for each. 

Exposure assessments often are developed in a phased approach. The initial phase 

usually involves some type of broad-based screening in which the scenarios that are not expected 

to pose a risk to the receptor are eliminated from a more detailed, resource-intensive review, 

usually through developing bounding estimates. These screening-level scenarios often are 

91
constructed to represent exposures that would fall beyond the extreme upper end of the expected 

exposure distribution. Because the screening-level assessments for these nonproblem scenarios 

usually are included in the final exposure assessment document, this final document may contain 

scenarios that differ quite markedly in level of sophistication, quality of data, and amenability to 

quantitative expressions of uncertainty. These also can apply to the input parameters used to 

construct detailed exposure scenarios. 

The following sections will discuss sources, characterization, and methods for analyzing 

the different types of uncertainty. 

6.2.1. Scenario Uncertainty 

The sources of scenario uncertainty include descriptive errors, aggregation errors, errors 

in professional judgment, and incomplete analysis. 

Descriptive errors include errors in information, such as the current producers of the 

chemical and its industrial, commercial, and consumer uses. Information of this type is the 

foundation for the eventual development of exposure pathways, scenarios, exposed populations, 

and exposure estimates. 

Aggregation errors arise as a result of lumping approximations. Included among these 

are assumptions of homogeneous populations, and spatial and temporal approximations such as 

assumptions of steady-state conditions. 

Professional judgment comes into play in virtually every aspect of the exposure 

assessment process, from defining the appropriate exposure scenarios, to selecting the proper 

environmental fate models, to determining representative environmental conditions, etc. Errors 

in professional judgment also are a source of uncertainty. 

A potentially serious source of uncertainty in exposure assessments arises from 

incomplete analysis. For example, the exposure assessor may overlook an important consumer 

exposure due to lack of information regarding the use of a chemical in a particular product. 

Although this source of uncertainty is essentially unquantifiable, it should not be overlooked by 

the assessor. At a minimum, the rationale for excluding particular exposure scenarios should be 

described and the uncertainty in those decisions should be characterized as high, medium, or 

low. The exposure assessor should discuss whether these decisions were based on actual data, 

analogues, or professional judgment. For situations in which the uncertainty is high, one should 

perform a reality check where credible upper limits on the exposure are established by a “what 

if” analysis. 

Characterization of the uncertainty associated with nonnumeric assumptions (often 

relating to setting the assessment’s direction and scope) will generally involve a qualitative 

discussion of the rationale used in selecting specific scenarios. The discussion should allow the 

92
reader to make an independent judgment about the validity of the conclusions reached by the 

assessor by describing the uncertainty associated with any inferences, extrapolations, and 

analogies used and the weight of evidence that led the assessor to particular conclusions. 

6.2.2. Parameter Uncertainty 

Sources of parameter uncertainty include measurement errors, sampling errors, 

variability, and use of generic or surrogate data. 

Measurement errors can be random or systematic. Random error results from 

imprecision in the measurement process. Systematic error is a bias or tendency away from the 

true value. 

Sampling errors concern sample representativeness. The purpose of sampling is to make 

an inference about the nature of the whole from a measurement of a subset of the total 

population. If the exposure assessment uses data that were generated for another purpose, for 

example, consumer product preference surveys or compliance monitoring surveys, uncertainty 

will arise if the data do not represent the exposure scenario being analyzed. 

The inability to characterize the inherent variability in environmental and exposurerelated parameters is a major source of uncertainty. For example, meteorological and 

hydrological conditions may vary seasonally at a given location, soil conditions can have large 

spatial variability, and human activity patterns can vary substantially depending on age, sex, and 

geographical location. 

The use of generic or surrogate data is common when site-specific data are not available. 

Examples include standard emission factors for industrial processes, generalized descriptions of 

environmental settings, and data pertaining to structurally related chemicals as surrogates for the 

chemical of interest. This is an additional source of uncertainty, and should be avoided if actual 

data can be obtained. 

The approach to characterizing uncertainty in parameter values will vary. It can involve 

an order-of-magnitude bounding of the parameter range when uncertainty is high, or a 

description of the range for each of the parameters including the lower- and upper-bound and the 

best estimate values and justification for these based on available data or professional judgment. 

In some circumstances, characterization can take the form of a probabilistic description of the 

parameter range. The appropriate characterization will depend on several factors, including 

whether a sensitivity analysis indicates that the results are significantly affected by variations 

within the range. When the results are significantly affected by a particular parameter, the 

exposure assessor should attempt to reduce the uncertainty by developing a description of the 

likely occurrence of particular values within the range. If enough data are available, standard 

statistical methods can be used to obtain a meaningful representation. If available data are 

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inadequate, then expert judgments can be used to develop a subjective probabilistic 

representation. Expert judgments should be developed in a consistent, well-documented manner. 

Examples of techniques to solicit expert judgments have been described (Morgan et al., 1979; 

Morgan et al., 1984; Rish, 1988). 

Most approaches for analyzing uncertainty have focused on techniques that examine how 

uncertainty in parameter values translates into overall uncertainty in the assessment. Several 

published reports (Cox and Baybutt, 1981; U.S. EPA, 1985f; Inman and Helton, 1988; Seller, 

1987; Rish and Marnicio, 1988) have reviewed the many techniques available; the assessor 

should consult these for details. In general, these approaches can be described, in order of 

increasing complexity and data requirements, as either sensitivity analysis, analytical uncertainty 

propagation, probabilistic uncertainty analysis, or classical statistical methods. 

Sensitivity analysis is the process of changing one variable while leaving the others 

constant and determining the effect on the output. The procedure involves fixing each uncertain 

quantity, one at a time, at its credible lower-bound and then its upper-bound (holding all others 

at their medians), and then computing the outcomes for each combination of values. These 

results are useful to identify the variables that have the greatest effect on exposure and to help 

focus further information gathering. The results do not provide any information about the 

probability of a quantity’s value being at any level within the range; therefore, this approach is 

most useful at the screening level when deciding about the need and direction of further 

analyses. 

Analytical uncertainty propagation involves examining how uncertainty in individual 

parameters affects the overall uncertainty of the exposure assessment. Intuitively, it seems clear 

that uncertainty in a specific parameter may propagate very differently through a model than 

another variable having approximately the same uncertainty. Some parameters are more 

important than others, and the model structure is designed to account for the relative sensitivity. 

Thus, uncertainty propagation is a function of both the data and the model structure. 

Accordingly, both model sensitivity and input variances are evaluated in this procedure. 

Application of this approach to exposure assessment requires explicit mathematical expressions 

of exposure, estimates of the variances for each of the variables of interest, and the ability either 

analytically or numerically to obtain a mathematical derivative of the exposure equation. 

Although uncertainty propagation is a powerful tool, it should be applied with caution, 

and the assessor should consider several points. It is difficult to generate and solve the equations 

for the sensitivity coefficients. In addition, the technique is most accurate for linear equations, 

so any departure from linearity must be carefully evaluated. Assumptions, such as independence 

of variables and normality of errors in the variables, need to be checked. Finally, this approach 

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requires estimates of parameter variance, and the information to support these may not be readily 

available. 

Probabilistic uncertainty analysis is generally considered the next level of refinement. 

The most common example is the Monte Carlo technique where probability density functions are 

assigned to each parameter, then values from these distributions are randomly selected and 

inserted into the exposure equation. After this process is completed many times, a distribution of 

predicted values results that reflects the overall uncertainty in the inputs to the calculation. 

The principal advantage of the Monte Carlo method is its very general applicability. 

There is no restriction on the form of the input distributions or the nature of the relationship 

between input and output; computations are also straightforward. There are some disadvantages 

as well as inconveniences, however. The exposure assessor should only consider using this 

technique when there are credible distribution data (or ranges) for most key variables. Even if 

these distributions are known, it may not be necessary to apply this technique. For example, if 

only average exposure values are needed, these can often be computed as accurately by using 

average values for each of the input parameters. Another inconvenience is that the sensitivity of 

the results to the input distributions is somewhat cumbersome to assess. Changing the 

distribution of only one value requires rerunning the entire calculation (typically, several 

hundreds or thousands of times). Finally, Monte Carlo results do not tell the assessor which 

variables are the most important contributors to output uncertainty. This is a disadvantage since 

most analyses of uncertainty are performed to find effective ways to reduce uncertainty. 

Classical statistical methods can be used to analyze uncertainty in measured exposures. 

Given a data set of measured exposure values for a series of individuals, the population 

distribution may be estimated directly, provided that the sample design was developed properly 

to capture a representative sample. The measured exposure values also may be used to directly 

compute confidence interval estimates for percentiles of the exposure distribution (American 

Chemical Society, 1988). When the exposure distribution is estimated from measured exposures 

for a probability sample of population members, confidence interval estimates for percentiles of 

the exposure distribution are the primary uncertainty characterization. Data collection survey 

design should also be discussed, as well as accuracy and precision of the measurement 

techniques. 

Often the observed exposure distribution is skewed; many sample members have 

exposure distributions at or below the detection limit. In this situation, estimates of the exposure 

distribution may require a very large sample size. Fitting the data to a distribution type can be 

problematic in this situation because data are usually scant in the low probability areas (the tails) 

where numerical values vary widely. As a consequence, for data sets for which the sampling has 

been completed, means and standard deviations may be determined to a good approximation, but 

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characterization of the tails of the distribution will have much greater uncertainty. This 

difference should be brought out in the discussion. For data sets for which sampling is still 

practical, stratification of the statistical population to oversample the tail may give more 

precision and confidence in the information in the tail area of the distribution. 

6.2.3. Model Uncertainty 

At a minimum, the exposure assessor should describe in qualitative terms the rationale 

for selection of any conceptual and mathematical models. This discussion should address the 

status of these approaches and any plausible alternatives in terms of their acceptance by the 

scientific community, how well the model(s) represents the situation being assessed, e.g., highend estimate, and to what extent verification and validation have been done. Relationship errors 

and modeling errors are the primary sources of modeling uncertainty. 

Relationship errors include errors in correlations between chemical properties, structurereactivity correlations, and environmental fate models. In choosing to use these tools, the 

exposure assessor must decide among the many possible functional forms available. Even 

though statistics on the performance of the methodology for a given test set of chemicals may be 

available and can help guide in the selection process, the exposure assessor must decide on the 

most appropriate methodology for the chemical of interest based on the goals of the assessment. 

Modeling errors are due to models being simplified representations of reality, for 

example approximating a three-dimensional aquifer with a two-dimensional mathematical 

model. Even after the exposure assessor has selected the most appropriate model for the purpose 

at hand, one is still faced with the question of how well the model represents the real situation. 

This question is compounded by the overlap between modeling uncertainties and other 

uncertainties, e.g., natural variability in environmental inputs, representativeness of the modeling 

scenario, and aggregation errors. The dilemma facing exposure assessors is that many existing 

models (particularly the very complex ones) and the hypotheses contained within them cannot be 

fully tested (Beck, 1987), although certain components of the model may be tested. Even when a 

model has been validated under a particular set of conditions, uncertainty will exist in its 

application to situations beyond the test system. 

A variety of approaches can be used to quantitatively characterize the uncertainty 

associated with model constructs. One approach is to use different modeling formulations 

(including the preferred and plausible alternatives) and consider the range of the outputs to be 

representative of the uncertainty range. This strategy is most useful when no clear best approach 

can be identified due to the lack of supporting data or when the situations being assessed require 

extrapolation beyond the conditions for which the models were originally designed. 

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Where the data base is sufficient, the exposure assessor should characterize the 

uncertainty in the selected model by describing the validation and verification efforts. 

Validation is the process of examining the performance of the model compared to actual 

observations under situations representative of those being assessed. Approaches for model 

validation have been discussed (U.S. EPA 1985e). Verification is the process of confirming that 

the model computer code is producing the proper numerical output. In most situations, only 

partial validation is possible due to data deficiencies or model complexity. 

6.3. VARIABILITY WITHIN A POPULATION VERSUS UNCERTAINTY IN THE 

ESTIMATE 

For clarity, it should be emphasized that variability (the receipt of different levels of 

exposure by different individuals) is being distinguished from uncertainty (the lack of knowledge 

about the correct value for a specific exposure measure or estimate). Most of the exposure and 

risk descriptors discussed in this report deal with variability directly, but estimates must also be 

made of the uncertainty of these descriptors.42 This may be done qualitatively or quantitatively, 

and it is beyond the scope of this report to discuss the mechanics of uncertainty analysis in detail. 

It is an important distinction, however, since the risk assessor and risk manager need to know if 

the numbers being reported for exposures take variability, uncertainty, or both, into 

consideration. 

Not all approaches historically used to construct measures or estimates of exposure 

attempted to distinguish variability and uncertainty. In particular, in many cases in which 

estimates were termed worst case, focusing on the high end of the exposed population and also 

selection of high-end values for uncertain physical quantities resulted in values that were seen to 

be quite conservative. By using both the high-end individuals (variability) and upper confidence 

bounds43 on data or physical parameters (uncertainty), these estimates might be interpreted as 

“not exceeding an upper bound on exposures received by certain high-end individuals.” 

Note that this approach will provide an estimate that considers both variability and 

uncertainty, but by only reporting the upper confidence bound, it appears to be merely a more 

42Each measure or estimate of exposure will have its associated uncertainty which should be addressed both 

qualitatively and quantitatively. For example, if population mean exposure is being addressed by use of direct 

personal monitoring data, qualitative issues will include the representativeness of the population monitored to the 

full population, the representativeness of the period selected for monitoring, and confidence that there were not 

systematic errors in the measured data. Quantitative uncertainty could be addressed through the use of confidence 

intervals for the actual mean population exposure. 

43The confidence interval is interpreted as the range of values within which the assessor knows the true measure lies, 

with specified statistical confidence. The upper bound confidence limit is the higher of the two ends of the 

confidence interval. 

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conservative estimate of the variability. High-end estimates which include consideration of 

uncertainty should be presented with both the upper and lower uncertainty bounds on the highend estimate. This provides the necessary information to the risk manager. Without specific 

discussion of what was done, risk managers may view the results as not having dealt with 

uncertainty. It is fundamental to exposure assessment that assessors have a clear distinction 

between the variability of exposures received by individuals in a population, and the uncertainty 

of the data and physical parameters used in calculating exposure. 

The discussion of estimating exposure and dose presented in Section 5.3.4 addresses the 

rationale and approaches for constructing a range of measures or estimates of exposure, with 

emphasis on how these can be used for exposure or risk characterization. The distinction 

between these measures or estimates (e.g., average versus high end) is often a difference in 

anticipated variability in the exposures received by individuals (i.e., average exposure integrates 

exposures across all individuals, while high-end exposure focuses on the upper percentiles of the 

exposed group being assessed.) Although several measures can be used to characterize risk in 

different ways, this does not address which of these measures or characterizations is used for 

decisions. The selection of the point or measure of exposure or risk upon which regulatory 

decisions are made is a risk management decision governed by programmatic policy, and is 

therefore beyond the scope of these guidelines. 

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7. PRESENTING THE RESULTS OF THE EXPOSURE ASSESSMENT 

One of the most important aspects of the exposure assessment is presenting the results. It 

is here that the assessment ultimately succeeds or fails in meeting the objectives laid out in the 

planning as discussed in Section 3. This section discusses communication of the results, format 

considerations, and suggested tips for reviewing exposure assessments either as a final check or 

as a review of work done by others. 

7.1. COMMUNICATING THE RESULTS OF THE ASSESSMENT 

Communicating the results of an exposure assessment is more than a simple summary of 

conclusions and quantitative estimates for the various pathways and routes of exposure. The 

most important part of an exposure assessment is the overall narrative exposure characterization, 

without which the assessment is merely a collection of data, calculations, and estimates. This 

exposure characterization should consist of discussion, analysis, and conclusions that synthesize 

the results from the earlier portions of the document, present a balanced representation of the 

available data and its relevancy to the health effects of concern, and identify key assumptions 

and major areas of uncertainty. Section 7.1.1 discusses the exposure characterization, and 

Section 7.1.2 discusses how this is used in the risk characterization step of a risk assessment. 

7.1.1. Exposure Characterization 

The exposure characterization is the summary explanation of the exposure assessment. 

In this final step, the exposure characterization: 

C provides a statement of purpose, scope, level of detail, and approach used in the 

assessment, including key assumptions; 

C presents the estimates of exposure and dose by pathway and route for individuals, 

population segments, and populations in a manner appropriate for the intended risk 

characterization; 

C provides an evaluation of the overall quality of the assessment and the degree of 

confidence the authors have in the estimates of exposure and dose and the 

conclusions drawn; 

C interprets the data and results; and 

C communicates results of the exposure assessment to the risk assessor, who can then 

use the exposure characterization, along with characterizations of the other risk 

assessment elements, to develop a risk characterization. 

As part of the statement of purpose, the exposure characterization explains why the 

assessment was done and what questions were asked. It also reaches a conclusion as to whether 

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the questions posed were in fact answered, and with what degree of confidence. It should also 

note whether the exposure assessment brought to light additional or perhaps more appropriate 

questions, if these were answered, and if so, with what degree of confidence. 

The statement of scope discusses the geographical or demographic boundaries of the 

assessment. The specific populations and population segments that were the subjects of the 

assessment are clearly identified, and the reasons for their selection and any exclusions are 

discussed. Especially sensitive groups or groups that may experience unusual exposure patterns 

are highlighted. 

The characterization also discusses whether the scope and level of detail of the 

assessment were ideal for answering the questions of the assessment and whether limitations in 

scope and level of detail were made because of technical, practical, or financial reasons, and the 

implications of these limitations on the quality of the conclusions. 

The methods used to quantify exposure and dose are clearly stated in the exposure 

characterization. If models are used, the basis for their selection and validation status is 

described. If measurement data are used, the quality of the data is discussed. The strengths and 

weaknesses of the particular methods used to quantify exposure and dose are described, along 

with comparison and contrast to alternate methods, if appropriate. 

In presenting the exposure and dose estimates, the important sources, pathways, and 

routes of exposure are identified and quantified, and reasons for excluding any from the 

assessment are discussed. 

A variety of risk descriptors, and where possible, the full population distribution is 

presented. Risk managers should be given some sense of how exposure is distributed over the 

population and how variability in population activities influences this distribution. Ideally, the 

exposure characterization links the purpose of the assessment with specific risk descriptors, 

which in turn are presented in such a way as to facilitate construction of a risk characterization. 

A discussion of the quality of the exposure and dose estimates is critical to the credibility 

of the assessment. This may be based in part on a quantitative uncertainty analysis, but the 

exposure characterization must explain the results of any such analysis in terms of the degree of 

confidence to be placed in the estimates and conclusions drawn. 

Finally, a description of additional research and data needed to improve the exposure 

assessment is often helpful to risk managers in making decisions about improving the quality of 

the assessment. For this reason, the exposure characterization should identify key data gaps that 

can help focus further efforts to reduce uncertainty. 

Additional guidance on communicating the results of an exposure assessment can be 

found in the proceedings of a recent workshop on risk communication (American Industrial 

Health Council, 1989). 

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7.1.2. Risk Characterization 

Most exposure assessments will be done as part of a risk assessment, and the exposure 

characterization must be useful to the risk assessor in constructing a risk characterization. Risk 

characterization is the integration of information from hazard identification, dose-response 

assessment, and exposure assessment into a coherent picture. A risk characterization is a 

necessary part of any Agency report on risk whether the report is a preliminary one prepared to 

support allocation of resources toward further study or a comprehensive one prepared to support 

regulatory decisions. 

Risk characterization is the culmination of the risk assessment process. In this final step, 

the risk characterization: 

C integrates the individual characterizations from the hazard identification, doseresponse, and exposure assessments; 

C provides an evaluation of the overall quality of the assessment and the degree of 

confidence the authors have in the estimates of risk and conclusions drawn; 

C describes risks to individuals and populations in terms of extent and severity of 

probable harm; and 

C communicates results of the risk assessment to the risk manager. 

It provides a scientific interpretation of the assessment. The risk manager can then use the risk 

assessment, along with other risk management elements, to make public health decisions. The 

following sections describe these four aspects of the risk characterization in more detail. 

7.1.2.1. Integration of Hazard Identification, Dose-Response, and Exposure Assessments 

In developing the hazard identification, dose-response, and exposure portions of the risk 

assessment, the assessor makes many judgments concerning the relevance and appropriateness of 

data and methodology. These judgments are summarized in the individual characterizations for 

hazard identification, dose-response, and exposure. In integrating the parts of the assessment, 

the risk assessor determines if some of these judgments have implications for other parts of the 

assessment, and whether the parts of the assessment are compatible. For example, if the hazard 

identification assessment determines that a chemical is a developmental toxicant but not a 

carcinogen, the dose-response and exposure information is presented accordingly; this differs 

greatly from the way the presentation is made if the chemical is a carcinogen but not a 

developmental toxicant. 

The risk characterization not only examines these judgments, but also explains the 

constraints of available data and the state of knowledge about the phenomena studied in making 

them, including: 

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C the qualitative, weight-of-evidence conclusions about the likelihood that the chemical 

may pose a specific hazard (or hazards) to human health, the nature and severity of 

the observed effects, and by what route(s) these effects are seen to occur. These 

judgments affect both the dose-response and exposure assessments; 

C for noncancer effects, a discussion of the dose-response behavior of the critical 

effect(s), data such as the shapes and slopes of the dose-response curves for the 

various other toxic endpoints, and how this information was used to determine the 

appropriate dose-response assessment technique; and 

C the estimates of the magnitude of the exposure, the route, duration and pattern of the 

exposure, relevant pharmacokinetics, and the number and characteristics of the 

population exposed. This information must be compatible with both the hazard 

identification and dose-response assessments. 

The presentation of the integrated results of the assessment draws from and highlights 

key points of the individual characterizations of hazard, dose-response, and exposure analysis 

performed separately under these Guidelines. The summary integrates these component 

characterizations into an overall risk characterization. 

7.1.2.2. Quality of the Assessment and Degree of Confidence 

The risk characterization summarizes the data brought together in the analysis and the 

reasoning upon which the assessment is based. The description also conveys the major strengths 

and weaknesses of the assessment that arise from data availability and the current limits of 

understanding of toxicity mechanisms. 

Confidence in the results of a risk assessment is consequently a function of confidence in 

the results of analysis of each element: hazard, dose-response, and exposure. Each of these 

three elements has its own characterization associated with it. For example, the exposure 

assessment component includes an exposure characterization. Within each characterization, the 

important uncertainties of the analysis and interpretation of data are explained so that the risk 

manager is given a clear picture of any consensus or lack thereof about significant aspects of the 

assessment. For example, whenever more than one view of dose-response assessment is 

supported by the data and by the policies of these Guidelines, and choosing between them is 

difficult, the views are presented together. If one has been selected over another, the rationale is 

given; if not, then both are presented as plausible alternatives. 

If a quantitative uncertainty analysis is appropriate, it is summarized in the risk 

characterization; in any case a qualitative discussion of important uncertainties is appropriate. If 

other organizations, such as other Federal agencies, have published risk assessments, or prior 

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EPA assessments have been done on the substance or an analogous substance and have relevant 

similarities or differences, these too are described. 

7.1.2.3. Descriptors of Risk 

There are a number of different ways to describe risk in quantitative or qualitative terms. 

Section 2.3 explains how risk descriptors are used. It is important to explain what aspect of the 

risk is being described, and how the exposure data and estimates are used to develop the 

particular descriptor. 

7.1.2.4. Communicating Results of a Risk Assessment to the Risk Manager 

Once the risk characterization is completed, the focus turns to communicating results to 

the risk manager. The risk manager uses the results of the risk characterization, technologic 

factors, and socioeconomic considerations in reaching a regulatory decision. Because of the way 

these risk management factors may impact different cases, consistent, but not necessarily 

identical, risk management decisions must be made on a case-by-case basis. Consequently, it is 

entirely possible and appropriate that a chemical with a specific risk characterization may be 

regulated differently under different statutes. These Guidelines are not intended to give guidance 

on the nonscientific aspects of risk management decisions. 

7.1.3. Establishing the Communication Strategy 

For assessments that must be explained to the general public, a communication strategy is 

often required. Although risk communication is often considered a part of risk management, it 

involves input from the exposure and risk assessors; early planning for a communication strategy 

can be very helpful to the ultimate risk communication. 

The EPA has guidance on preparing communication strategies (U.S. EPA, 1988g). 

Additional sources of information are the New Jersey Department of Environmental Protection 

(1988a, 1988b) and the NRC (1989b). These documents, and the sources listed within them, are 

valuable resources for all who will be involved with the sensitive issues of explaining 

environmental health risks. The NRC (1989b, p. 148) states: 

It is a mistake to simply consider risk communication to be an add-on activity for 

either scientific or public affairs staffs; both elements should be involved. There 

are clear dangers if risk messages are formulated ad hoc by public relations 

personnel in isolation from available technical expertise; neither can they be 

prepared by risk analysts as a casual extension of their analytic duties. 

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7.2. FORMAT FOR EXPOSURE ASSESSMENT REPORTS 

The Agency does not require a set format for exposure assessment reports, but individual 

program offices within the Agency may have specific format requirements. Section 3 illustrates 

that exposure assessments are performed for a variety of purposes, scopes, and levels of detail, 

and use a variety of approaches. While it is impracticable for the Agency to specify an outline 

format for all types of assessments being performed within the Agency, program offices are 

encouraged to use consistent formats for similar types of assessments within their own purview. 

All exposure assessments must, at a minimum, contain a narrative exposure 

characterization section that contains the types of information discussed in Section 7.1. For the 

purpose of consistency, this section should be titled exposure characterization. Placement of this 

section within the assessment is optional, but it is strongly suggested that it be prominently 

featured in the assessment. It is not, however, an executive summary and should not be used 

interchangeably with one. 

7.3. REVIEWING EXPOSURE ASSESSMENTS 

This section provides some suggestions on how to effectively review an exposure 

assessment and highlights some of the common pitfalls. The emphasis in these Guidelines has 

been on how to properly conduct exposure assessments; this section can serve as a final checklist 

in reviewing the completed assessment. An exposure assessor also may be called upon to 

critically review and evaluate exposure assessments conducted by others; these suggestions 

should be helpful in this regard. 

Reviewers of exposure assessments are usually asked to identify inconsistences with the 

underlying science and with Agency-developed guidelines, factors, and methodologies, and to 

determine the effect these inconsistences might have on the results and conclusions of the 

exposure assessment. Often the reviewer can only describe whether these inconsistencies or 

deficiencies might underestimate or overestimate exposure. 

Some of the questions a reviewer should ask to identify the more common pitfalls that 

tend to underestimate exposure are: 

Has the pathways analysis been broad enough to avoid overlooking a significant 

pathway? For example, in evaluating exposure to soil contaminated with PCBs, the exposure 

assessment should not be limited only to evaluating the dermal contact pathway. Other 

pathways, such as inhalation of dust and vapors or the ingestion of contaminated gamefish from 

an adjacent stream receiving surface runoff containing contaminated soil, should also be 

evaluated as they could contribute higher levels of exposure from the same source. 

Have all the contaminants of concern in a mixture been evaluated? Since risks resulting 

from exposures to complex mixtures of chemicals with the same mode of toxic action are 

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generally treated as additive (by summing the risks) in a risk assessment, failure to evaluate one 

or more of the constituents would neglect its contribution to the total exposure and risk. This is 

especially critical for relatively toxic or potent chemicals that tend to drive risk estimates even 

when present in relatively low quantities. 

Have exposure levels or concentration measurements been compared with appropriate 

background levels? Contaminant concentrations or exposure levels should not be compared with 

other contaminated media or exposed populations. When comparing with background levels, the 

exposure assessor must determine whether these concentrations or exposure levels are also 

affected by contamination from anthropogenic activities. 

Were the detection limits sensitive enough to make interpretations about exposures at 

levels corresponding to health concerns? Were the data interpreted correctly? Because values 

reported as not detected (ND) mean only that the chemical of interest was not found at the 

particular detection limit used in the laboratory analysis, ND does not rule out the possibility that 

the chemical may be present in significant concentrations. Depending on the purpose and the 

degree of conservatism warranted in the exposure assessment, results reported as ND should be 

handled as discussed in Section 5. 

Has the possibility of additive pathways been considered for the population being 

studied? If the purpose of the exposure assessment is to evaluate the total exposure and risk of a 

population, then exposures from individual pathways within the same route may be summed in 

cases which concurrent exposures can realistically be expected to occur. 

Some questions a reviewer should ask to avoid the more prevalent errors that generally 

tend to overestimate exposure are: 

Have unrealistically conservative exposure parameters been used in the scenarios? The 

exposure assessor must conduct a reality check to ensure that the exposure cases used in the 

scenario(s) (except bounding estimates) could actually occur. 

Have potential exposures been presented as existing exposures? In many situations, 

especially when the scenario evaluation approach is used, the objective of the assessment is to 

estimate potential exposures. (That is, if a person were to be exposed to these chemicals under 

these conditions, then the resultant exposure would be this much.) In determining the need and 

urgency for regulatory action, risk managers often weigh actual exposures more heavily than 

higher levels of potential exposures. Therefore, the exposure assessment should clearly note 

whether the results represent actual or potential exposures. 

Have exposures derived from “not detected” levels been presented as actual exposures? 

For some exposure assessments it may be appropriate to assume that a chemical reported as not 

detected is present at either the detection limit or one-half the detection limit. The exposure 

estimates derived from these nondetects, however, should be clearly labeled as hypothetical 

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since they are based on the conservative assumption that chemicals are present at or below the 

detection limit, when, in fact, they may not be present at all. Exposures, doses, or risks 

estimated from data using substituting values of detection limits for “not detected” samples must 

be reported as “less than” the resulting exposure, dose, or risk estimate. 

Questions a reviewer should ask to identify common errors that may underestimate or 

overestimate exposure are: 

Are the results presented with an appropriate number of significant figures? The number 

of significant figures should reflect the uncertainty of the numeric estimate. If the likely range of 

the results spans several orders of magnitude, then using more than one significant figure implies 

more confidence in the results than is warranted. 

Have the calculations been checked for computational errors? Obviously, calculations 

should be checked for arithmetic errors and mistakes in converting units. This is overlooked 

more often than one might expect. 

Are the factors for intake rates, etc. used appropriately? Exposure factors should be 

checked to ensure that they correspond to the site or situation being evaluated. 

Have the uncertainties been adequately addressed? Exposure assessment is an inexact 

science, and the confidence in the results may vary tremendously. It is essential the exposure 

assessment include an uncertainty assessment that places these uncertainties in perspective. 

If Monte Carlo simulations were used, were correlations among input distributions 

known and properly accounted for? Is the maximum value simulated by this method in fact a 

bounding estimate? Was Monte Carlo simulation necessary? (A Monte Carlo simulation 

randomly selects the values from the input parameters to simulate an individual. If data already 

exist to show the relationship between variables for the actual individuals, it makes little sense to 

use Monte Carlo simulation, since one already has the answer to the question of how the 

variables are related for each individual. A simulation is unnecessary.) 

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8. GLOSSARY OF TERMS 

Absorbed dose - See internal dose. 

Absorption barrier - Any of the exchange barriers of the body that allow differential diffusion 

of various substances across a boundary. Examples of absorption barriers are the skin, lung 

tissue, and gastrointestinal tract wall. 

Accuracy - The measure of the correctness of data, as given by the difference between the 

measured value and the true or standard value. 

Administered dose - The amount of a substance given to a test subject (human or animal) in 

determining dose-response relationships, especially through ingestion or inhalation. In exposure 

assessment, since exposure to chemicals is usually inadvertent, this quantity is called potential 

dose. 

Agent - A chemical, physical, mineralogical, or biological entity that may cause deleterious 

effects in an organism after the organism is exposed to it. 

Ambient - The conditions surrounding a person, sampling location, etc. 

Ambient measurement - A measurement (usually of the concentration of a chemical or 

pollutant) taken in an ambient medium, normally with the intent of relating the measured value 

to the exposure of an organism that contacts that medium. 

Ambient medium - One of the basic categories of material surrounding or contacting an 

organism, e.g., outdoor air, indoor air, water, or soil, through which chemicals or pollutants can 

move and reach the organism. (See also biological medium, environmental medium) 

Applied dose - The amount of a substance in contact with the primary absorption boundaries of 

an organism (e.g., skin, lung, gastrointestinal tract) and available for absorption. 

Arithmetic mean - The sum of all the measurements in a data set divided by the number of 

measurements in the data set. 

Background level (environmental) - The concentration of substance in a defined control area 

during a fixed period of time before, during, or after a data-gathering operation. 

Breathing zone - A zone of air in the vicinity of an organism from which respired air is drawn. 

Personal monitors are often used to measure pollutants in the breathing zone. 

Bias - A systematic error inherent in a method or caused by some feature of the measurement 

system. 

Bioavailability - The state of being capable of being absorbed and available to interact with the 

metabolic processes of an organism. Bioavailability is typically a function of chemical 

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properties, physical state of the material to which an organism is exposed, and the ability of the 

individual organism to physiologically take up the chemical. 

Biological marker of exposure (sometimes referred to as a biomarker of exposure) - Exogenous 

chemicals, their metabolites, or products of interactions between a xenobiotic chemical and some 

target molecule or cell that is measured in a compartment within an organism. 

Biological measurement - A measurement taken in a biological medium. For the purpose of 

exposure assessment via reconstruction of dose, the measurement is usually of the concentration 

of a chemical/metabolite or the status of a biomarker, normally with the intent of relating the 

measured value to the internal dose of a chemical at some time in the past. (Biological 

measurements are also taken for purposes of monitoring health status and predicting effects of 

exposure.) (See also ambient measurement) 

Biological medium - One of the major categories of material within an organism, e.g., blood, 

adipose tissue, or breath, through which chemicals can move, be stored, or be biologically, 

physically, or chemically transformed. (See also ambient medium, environmental medium) 

Biologically effective dose - The amount of a deposited or absorbed chemical that reaches the 

cells or target site where an adverse effect occurs, or where that chemical interacts with a 

membrane surface. 

Blank (blank sample) - An unexposed sampling medium, or an aliquot of the reagents used in 

an analytical procedure, in the absence of added analyte. The measured value of a blank sample 

is the blank value. 

Body burden - The amount of a particular chemical stored in the body at a particular time, 

especially a potentially toxic chemical in the body as a result of exposure. Body burdens can be 

the result of long-term or short-term storage, for example, the amount of a metal in bone, the 

amount of a lipophilic substance such as PCB in adipose tissue, or the amount of carbon 

monoxide (as carboxyhemoglobin) in the blood. 

Bounding estimate - An estimate of exposure, dose, or risk that is higher than that incurred by 

the person in the population with the highest exposure, dose, or risk. Bounding estimates are 

useful in developing statements that exposures, doses, or risks are “not greater than” the 

estimated value. 

Comparability - The ability to describe likenesses and differences in the quality and relevance 

of two or more data sets. 

Data quality objectives (DQO) - Qualitative and quantitative statements of the overall level of 

uncertainty that a decision-maker is willing to accept in results or decisions derived from 

environmental data. DQOs provide the statistical framework for planning and managing 

environmental data operations consistent with the data user’s needs. 

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Dose - The amount of a substance available for interaction with metabolic processes or 

biologically significant receptors after crossing the outer boundary of an organism. The potential 

dose is the amount ingested, inhaled, or applied to the skin. The applied dose is the amount of a 

substance presented to an absorption barrier and available for absorption (although not 

necessarily having yet crossed the outer boundary of the organism). The absorbed dose is the 

amount crossing a specific absorption barrier (e.g., the exchange boundaries of skin, lung, and 

digestive tract) through uptake processes. Internal dose is a more general term denoting the 

amount absorbed without respect to specific absorption barriers or exchange boundaries. The 

amount of the chemical available for interaction by any particular organ or cell is termed the 

delivered dose for that organ or cell. 

Dose rate - Dose per unit time, for example in mg/day, sometimes also called dosage. Dose 

rates are often expressed on a per-unit-body-weight basis, yielding units such as mg/kg/day 

(mg/kg-day). They are also often expressed as averages over some time period, for example a 

lifetime. 

Dose-response assessment - The determination of the relationship between the magnitude of 

administered, applied, or internal dose and a specific biological response. Response can be 

expressed as measured or observed incidence, percent response in groups of subjects (or 

populations), or the probability of occurrence of a response in a population. 

Dose-response curve - A graphical representation of the quantitative relationship between 

administered, applied, or internal dose of a chemical or agent, and a specific biological response 

to that chemical or agent. 

Dose-response relationship - The resulting biological responses in an organ or organism 

expressed as a function of a series of different doses. 

Dosimeter - Instrument to measure dose; many so-called dosimeters actually measure exposure 

rather than dose. 

Dosimetry - Process of measuring or estimating dose. 

Ecological exposure - Exposure of a nonhuman receptor or organism to a chemical, or a 

radiological or biological agent. 

Effluent - Waste material being discharged into the environment, either treated or untreated. 

Effluent generally is used to describe water discharges to the environment, although it can refer 

to stack emissions or other material flowing into the environment. 

Environmental fate - The destiny of a chemical or biological pollutant after release into the 

environment. Environmental fate involves temporal and spatial considerations of transport, 

transfer, storage, and transformation. 

Environmental fate model - In the context of exposure assessment, any mathematical 

abstraction of a physical system used to predict the concentration of specific chemicals as a 

109
function of space and time subject to transport, intermedia transfer, storage, and degradation in 

the environment. 

Environmental medium - One of the major categories of material found in the physical 

environment that surrounds or contacts organisms, e.g., surface water, ground water, soil, or air, 

and through which chemicals or pollutants can move and reach the organisms. (See ambient 

medium, biological medium) 

Exposure - Contact of a chemical, physical, or biological agent with the outer boundary of an 

organism. Exposure is quantified as the concentration of the agent in the medium in contact 

integrated over the time duration of that contact. 

Exposure assessment - The determination or estimation (qualitative or quantitative) of the 

magnitude, frequency, duration, and route of exposure. 

Exposure concentration - The concentration of a chemical in its transport or carrier medium at 

the point of contact. 

Exposure pathway - The physical course a chemical or pollutant takes from the source to the 

organism exposed. 

Exposure route - The way a chemical or pollutant enters an organism after contact, e.g., by 

ingestion, inhalation, or dermal absorption. 

Exposure scenario - A set of facts, assumptions, and inferences about how exposure takes place 

that aids the exposure assessor in evaluating, estimating, or quantifying exposures. 

Fixed-location monitoring - Sampling of an environmental or ambient medium for pollutant 

concentration at one location continuously or repeatedly over some length of time. 

Geometric mean - The nth root of the product of n values. 

Guidelines - Principles and procedures to set basic requirements for general limits of 

acceptability for assessments. 

Hazard identification - A description of the potential health effects attributable to a specific 

chemical or physical agent. For carcinogen assessments, the hazard identification phase of a risk 

assessment is also used to determine whether a particular agent or chemical is, or is not, causally 

linked to cancer in humans. 

High-end exposure (dose) estimate - A plausible estimate of individual exposure or dose for 

those persons at the upper end of an exposure or dose distribution, conceptually above the 90th 

percentile, but not higher than the individual in the population who has the highest exposure or 

dose. 

110
High-end Risk Descriptor - A plausible estimate of the individual risk for those persons at the 

upper end of the risk distribution, conceptually above the 90th percentile but not higher than the 

individual in the population with the highest risk. Note that persons in the high end of the risk 

distribution have high risk due to high exposure, high susceptibility, or other reasons, and 

therefore persons in the high end of the exposure or dose distribution are not necessarily the 

same individuals as those in the high end of the risk distribution. 

Intake - The process by which a substance crosses the outer boundary of an organism without 

passing an absorption barrier, e.g., through ingestion or inhalation. (See also potential dose) 

Internal dose - The amount of a substance penetrating across the absorption barriers (the 

exchange boundaries) of an organism, via either physical or biological processes. For the 

purpose of these Guidelines, this term is synonymous with absorbed dose. 

Limit of detection (LOD) [or Method detection limit (MDL)] - The minimum concentration 

of an analyte that, in a given matrix and with a specific method, has a 99% probability of being 

identified, qualitatively or quantitatively measured, and reported to be greater than zero. 

Matrix - A specific type of medium (e.g., surface water, drinking water) in which the analyte of 

interest may be contained. 

Maximally exposed individual (MEI) - The single individual with the highest exposure in a 

given population (also, maximum exposed individual). This term has historically been defined 

various ways, including as defined here and also synonymously with worst case or bounding 

estimate. Assessors are cautioned to look for contextual definitions when encountering this term 

in the literature. 

Maximum exposure range - A semiquantitative term referring to the extreme uppermost 

portion of the distribution of exposures. For consistency, this term (and the dose or risk 

analogues) should refer to the portion of the individual exposure distribution that conceptually 

falls above about the 98th percentile of the distribution, but is not higher than the individual with 

the highest exposure. 

Median value - The value in a measurement data set such that half the measured values are 

greater and half are less. 

Microenvironment method - A method used in predictive exposure assessments to estimate 

exposures by sequentially assessing exposure for a series of areas (microenvironments) that can 

be approximated by constant or well-characterized concentrations of a chemical or other agent. 

Microenvironments - Well-defined surroundings such as the home, office, automobile, kitchen, 

store, etc. that can be treated as homogeneous (or well characterized) in the concentrations of a 

chemical or other agent. 

Mode - The value in the data set that occurs most frequently. 

111
Monte Carlo technique - A repeated random sampling from the distribution of values for each 

of the parameters in a generic (exposure or dose) equation to derive an estimate of the 

distribution of (exposures or doses in) the population. 

Nonparametric statistical methods - Methods that do not assume a functional form with 

identifiable parameters for the statistical distribution of interest (distribution-free methods). 

Pathway - The physical course a chemical or pollutant takes from the source to the organism 

exposed. 

Personal measurement - A measurement collected from an individual’s immediate environment 

using active or passive devices to collect the samples. 

Pharmacokinetics - The study of the time course of absorption, distribution, metabolism, and 

excretion of a foreign substance (e.g., a drug or pollutant) in an organism’s body. 

Point-of-contact measurement of exposure - An approach to quantifying exposure by taking 

measurements of concentration over time at or near the point of contact between the chemical 

and an organism while the exposure is taking place. 

Potential dose - The amount of a chemical contained in material ingested, air breathed, or bulk 

material applied to the skin. 

Precision - A measure of the reproducibility of a measured value under a given set of conditions. 

Probability samples - Samples selected from a statistical population such that each sample has a 

known probability of being selected. 

Quality assurance (QA) - An integrated system of activities involving planning, quality control, 

quality assessment, reporting and quality improvement to ensure that a product or service meets 

defined standards of quality with a stated level of confidence. 

Quality control (QC) - The overall system of technical activities whose purpose is to measure 

and control the quality of a product or service so that it meets the needs of the users. The aim is 

to provide quality that is satisfactory, adequate, dependable, and economical. 

Quantification limit (QL) - The concentration of analyte in a specific matrix for which the 

probability of producing analytical values above the method detection limit is 99%. 

Random samples - Samples selected from a statistical population such that each sample has an 

equal probability of being selected. 

Range - The difference between the largest and smallest values in a measurement data set. 

Reasonable worst case - A semiquantitative term referring to the lower portion of the high end 

of the exposure, dose, or risk distribution. The reasonable worst case has historically been 

112
loosely defined, including synonymously with maximum exposure or worst case, and assessors 

are cautioned to look for contextual definitions when encountering this term in the literature. As 

a semiquantitative term, it is sometimes useful to refer to individual exposures, doses, or risks 

that, while in the high end of the distribution, are not in the extreme tail. For consistency, it 

should refer to a range that can conceptually be described as above the 90th percentile in the 

distribution, but below about the 98th percentile. (Compare maximum exposure range, worst 

case). 

Reconstruction of dose - An approach to quantifying exposure from internal dose, which is in 

turn reconstructed after exposure has occurred, from evidence within an organism such as 

chemical levels in tissues or fluids or from evidence of other biomarkers of exposure. 

Representativeness - The degree to which a sample is, or samples are, characteristic of the 

whole medium, exposure, or dose for which the samples are being used to make inferences. 

Risk - The probability of deleterious health or environmental effects. 

Risk characterization - The description of the nature and often the magnitude of human or 

nonhuman risk, including attendant uncertainty. 

Route - The way a chemical or pollutant enters an organism after contact, e.g., by ingestion, 

inhalation, or dermal absorption. 

Sample - A small part of something designed to show the nature or quality of the whole. 

Exposure-related measurements are usually samples of environmental or ambient media, 

exposures of a small subset of a population for a short time, or biological samples, all for the 

purpose of inferring the nature and quality of parameters important to evaluating exposure. 

Sampling frequency - The time interval between the collection of successive samples. 

Sampling plan - A set of rules or procedures specifying how a sample is to be selected and 

handled. 

Scenario evaluation - An approach to quantifying exposure by measurement or estimation of 

both the amount of a substance contacted, and the frequency/duration of contact, and 

subsequently linking these together to estimate exposure or dose. 

Source characterization measurements - Measurements made to characterize the rate of 

release of agents into the environment from a source of emission such as an incinerator, landfill, 

industrial or municipal facility, consumer product, etc. 

Standard operating procedure (SOP) - A procedure adopted for repetitive use when 

performing a specific measurement or sampling operation. 

Statistical control - The process by which the variability of measurements or of data outputs of 

a system is controlled to the extent necessary to produce stable and reproducible results. To say 

113
that measurements are under statistical control means that there is statistical evidence that the 

critical variables in the measurement process are being controlled to such an extent that the 

system yields data that are reproducible within well-defined limits. 

Statistical significance - An inference that the probability is low that the observed difference in 

quantities being measured could be due to variability in the data rather than an actual difference 

in the quantities themselves. The inference that an observed difference is statistically significant 

is typically based on a test to reject one hypothesis and accept another. 

Surrogate data - Substitute data or measurements on one substance used to estimate analogous 

or corresponding values of another substance. 

Uptake - The process by which a substance crosses an absorption barrier and is absorbed into 

the body. 

Worst case - A semiquantitative term referring to the maximum possible exposure, dose, or risk, 

that can conceivably occur, whether or not this exposure, dose, or risk actually occurs or is 

observed in a specific population. Historically, this term has been loosely defined in an ad hoc 

way in the literature, so assessors are cautioned to look for contextual definitions when 

encountering this term. It should refer to a hypothetical situation in which everything that can 

plausibly happen to maximize exposure, dose, or risk does in fact happen. This worst case may 

occur (or even be observed) in a given population, but since it is usually a very unlikely set of 

circumstances, in most cases, a worst-case estimate will be somewhat higher than occurs in a 

specific population. As in other fields, the worst-case scenario is a useful device when low 

probability events may result in a catastrophe that must be avoided even at great cost, but in most 

health risk assessments, a worst-case scenario is essentially a type of bounding estimate. 

114
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PART B: RESPONSE TO PUBLIC AND SCIENCE ADVISORY BOARD COMMENTS 

1. INTRODUCTION 

This section summarizes the major issues raised in public comments on the Proposed 

Guidelines for Exposure-Related Measurements (hereafter “1988 Proposed Guidelines”) 

published December 2, 1988 (53 FR 48830-48853). In addition to general comments, reviewers 

were requested to comment specifically on the guidance for interpreting contaminated blanks 

versus field data, the interpretation of data at or near the limit of detection, approaches to 

assessing uncertainty, and the Glossary of Terms. Comment was also invited on the following 

questions: Should the 1988 Proposed Guidelines be combined with the 1986 Guidelines for 

Estimating Exposures (hereafter “1986 Guidelines”)? Is the current state-of-the-art in making 

measurements of population activities for the purpose of exposure assessment advanced to the 

point where the Agency can construct guidelines in this area? Given that EPA Guidelines are 

not protocols or detailed literature reviews, is the level of detail useful and appropriate, 

especially in the area of statistics? 

The Science Advisory Board (SAB) met on December 2, 1988, and provided written 

comments in a May 1989 letter to the EPA Administrator (EPA-SAB-EETFC-89-020). The 

public comment period extended until March 2, 1989. Comments were received from 17 

individuals or organizations. 

After the SAB and public comment, Agency staff prepared summaries of the comments 

and analyses of major issues presented by the commentors. These were considered in the 

development of these final Guidelines. In response to the comments, the Agency has modified 

or clarified most of the sections of the Guidelines. For the purposes of this discussion, only the 

most significant issues reflected by the public and SAB comments are discussed. Several minor 

recommendations, which do not warrant discussion here, were considered and adopted by the 

Agency in the revision of these Guidelines. 

The EPA revised the 1988 Proposed Guidelines in accordance with the public and SAB 

comments, retitling them Guidelines for Exposure Assessment (hereafter “Guidelines”). The 

Agency presented the draft final Guidelines to the SAB at a public meeting on September 12, 

1991, at which time the SAB invited public comment for a period of 30 days on the draft. The 

SAB discussed the final draft in a January 13, 1992 letter to the Administrator of the EPA (EPASAB-IAQC-92-015). There were no additional public comments received. 

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2. RESPONSE TO GENERAL COMMENTS 

In general, the reviewers were complementary regarding the overall quality of the 1988 

Proposed Guidelines. Several reviewers requested that the Agency better define the focus and 

intended audiences and refine the Guidelines with regard to treatment of nonhuman exposure. 

The Agency has refined its approach and coverage in these Guidelines. Although these 

Guidelines deal specifically with human exposures to chemicals, additional supplemental 

guidance may be developed for ecological exposures, and exposures to biological or radiological 

entities. The Agency is currently developing separate guidelines for ecological risk assessment. 

Concerns were expressed about the Agency’s use of the terms exposure and dose. 

Consequently, the Agency reviewed its definitions and uses of these terms and evaluated their 

use elsewhere in the scientific community. The Agency has changed its definitions and uses of 

these terms from that in both the 1986 Guidelines and the 1988 Proposed Guidelines. It is 

believed that the definitions contained in the current Guidelines are now in concert with the 

definitions suggested by the National Academy of Sciences and others in the scientific field. 

Many reviewers urged the Agency to be more explicit in its recommendations regarding 

uncertainty in statistics, limits of detection, censored data sets, and the use of models. Some 

reviewers felt the level of detail was appropriate for statistical uncertainty while others wanted 

additional methods for dealing with censored data. Several commended the Agency for its 

acknowledgement of uncertainty in exposure assessments and the call for its explicit description 

in all exposure assessments, while others expressed concern for lack of acknowledgement of 

model uncertainty. Accordingly, these areas have been revisited and an entire section has been 

devoted to uncertainty. We agree with the reviewers that much more work remains to be done in 

this area, particularly with evaluating overall exposure assessment uncertainty, not only with 

models but also with the distributions of exposure parameters. The Agency may issue additional 

guidance in this area in the future. 

Some reviewers submitted extensive documentation regarding detection limits and 

statistical representations. Several submitted comments arguing against data reporting 

conventions that result in censored data sets and recommended that the Agency issue a guidance 

document for establishing total system detection limits. The Agency found the documentation to 

be helpful and has revised the sections of the Guidelines accordingly. Unfortunately, several of 

the other suggestions go beyond the scope of this document. 

The reviewers generally commented that the glossary was useful, presenting many 

technical terms and defining them in an appropriate manner. The glossary has been expanded to 

include the key terms used in the Guidelines, while at the same time correcting some definitions 

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that were inconsistent or unclear. In particular, the definitions for exposure and dose have been 

revised. 

3. RESPONSE TO COMMENTS ON THE SPECIFIC QUESTIONS 

Should the 1988 Proposed Guidelines be combined with the 1986 Guidelines? 

The SAB and several other commentors recommended that the 1986 Guidelines and the 

1988 Proposed Guidelines be combined into an integrated document. The Agency agrees with 

this recommendation and has made an effort to produce a single guideline that progresses 

logically from start to finish. This was accomplished through an extensive reformatting of the 

two sets of guidelines as an integrated document, rather than a simple joining together of the 

previous versions. 

In integrating the two previous guidelines, the Agency has revised and updated the 

section in the 1986 Guidelines that suggests an outline for an exposure assessment. A more 

complete section (Section 7 of the current Guidelines) now discusses how assessments should be 

presented and suggests a series of points to consider in reviewing assessments. 

The Agency has also expanded the section in the 1986 Guidelines that discussed 

exposure scenarios, partly by incorporating material from the 1988 Proposed Guidelines, and 

partly as a result of comments requesting clarification of the appropriate use of certain types of 

scenario (e.g., “worst case”). Section 5.3 of the current Guidelines extensively discusses the 

appropriateness of using various scenarios, estimates, and risk descriptors, and defines certain 

scenario-related terms for use in exposure assessments. 

Is the current state-of-the-art in making measurements of population activities for the 

purpose of exposure assessment advanced to the point where the Agency can construct 

guidelines in this area? 

Both the SAB and public comments recommended the inclusion of demographics, 

population dynamics, and population activity patterns in the exposure assessment process. In 

response, the Agency has included additional discussion on use of activity patterns in the current 

Guidelines, while recognizing that more research has to be done in this area. 

Is the level of detail of the Guidelines useful and appropriate, especially in the area of 

statistics? 

As might be expected, there was no clear consensus of opinion on what constitutes 

appropriate coverage. Regarding quality assurance (QA) and quality control (QC), it was felt 

that a strong statement on the need for QA/QC followed by reference to appropriate EPA 

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documents was a suitable level of detail. Statistical analyses, sampling issues, limit of detection, 

and other analytical issues all elicited many thoughtful comments. Where the recommendations 

did not exceed the scope of the document or the role of EPA, the Agency has attempted to blend 

the various recommendations into the current Guidelines. In all these areas, therefore, the 

previous sections have been revised in accordance with comments. 

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