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 Estim ating Exposures (Septem b er 24, 1986), Federal Register 51(185):34042-34054, and the Proposed Guidelines for Exposure-Related Measurem ents (Decem ber 2, 1988), Federal Register 53(232):48830- 48853.

Risk Assessm ent Forum U.S. Environm ental Protection Agency W a shington, DCDISCLAIMER

This docum ent has been reviewed in accordance with U.S. Environm ental Protection Agency policy and approved for publication. Mention of trade nam e s or com m e rcial products does not constitute endorsem e nt or recom m e ndation for use.

Note: This docum ent represents the final guidelines. A num ber of editorial corrections have been m a de during conversion and subsequent proofreading to ensure the accuracy of this publication.

iiCONTENTS

L i s t s o f T a b l e s a n d F i g u r e s

.................................................... v i i

F e d e r a l R e g i s t e r P r e a m b l e

..................................................... v i i i Abbreviations and Acronym s

.................................................. x i i

Part A: Guidelines for Exposure Assessm ent ........................................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 Assessm ent .......................................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 ...........1 0 2.1.4.2. Calculating Internal Dose for Uptake Processes ...........1 4 2.1.4.3. Calculating Internal Dose for Intake Processes (Especially via Respiratory and Oral Routes) ...........................1 7 2.1.5. Sum m a ry of Exposure and Dose Term s with Exam ple Units .........1 8 2.2. Approaches to Quantification of Exposure ...............................1 9 2.2.1. Measurem ent of Exposure at the Point of Contact .................2 2 2.2.2. Estim ates of Exposure from Scenario Evaluation ..................2 2 2.2.3. Exposure Estim ation by Reconstruction of Internal Dose ............2 4 2.3. Relationships of Exposure and Dose to Risk ..............................2 5 2.3.1. Individual Risk .............................................2 5 2.3.2. Population Risk .............................................2 8 2.3.3. Risk Descriptors ............................................3 0

3. Planning an Exposure Assessm ent .............................................3 2 3.1. Purpose of the Exposure Assessm ent ...................................3 2 3.1.1. Using Exposure Assessm ents in Risk Assessm ent ..................3 3 3.1.2. Using Exposure Assessm ents for Status and Trends ................3 4 3.1.3. Using Exposure Assessm ents in Epidem iologic Studies .............3 5 3.2. Scope of the Assessm ent .............................................3 5 3.3. Level of Detail of the Assessm ent ......................................3 6 3.4. Determ ining the Approach for the Exposure Assessm ent ....................3 6 3.5. Establishing the Exposure Assessm ent Plan ..............................3 7 3.5.1. Planning an Exposure Assessm ent as Part of a Risk Assessm ent ......3 8
iiiCONTENTS (continued)

3.5.2. Establishing the Sam p ling Strategy .............................3 9 3.5.2.1. Data Quality Objectives ...............................3 9 3.5.2.2. Sampling Plan ......................................4 0 3.5.2.3. Quality Assurance Samples ............................4 2 3.5.2.4. Background Level ...................................4 3 3.5.2.5. Quality Assurance and Quality Control ..................4 3 3.5.2.6. Quality Assurance and Quality Control for Previously Generated Data ...............................................4 4 3.5.2.7. Selection and Validation of Analytical Methods ............4 4 3.5.3. Establishing the Modeling Strategy .............................4 4 3.5.3.1. Setting the Modeling Study Objectives ...................4 5 3.5.3.2. Characterization and Model Selection ...................4 5 3.5.3.3. Obtaining and Installing the Computer Code ..............4 6 3.5.3.4. Calibrating and Running the Model .....................4 6 3.5.3.5. Model Validation ....................................4 7 3.5.4. Planning an Exposure Assessm ent to Assess past Exposures .........4 8

4. Gathering and Developing Data for Exposure Assessm ents .........................4 9 4.1. Measurem ent Data for Point-of-contact Assessm ents .......................4 9 4.2. Obtaining Chem ical Concentration Inform ation ...........................5 0 4.2.1. Concentration Measurem ents in Environm ental Media ..............5 0 4.2.2. Use of Models for Concentration Estim ation ......................5 6 4.2.3. Selection of Models for Environm ental Concentrations ..............5 7 4.3. Estim ating Duration of Contact ........................................5 8 4.3.1. Observation and Survey Data ..................................5 9 4.3.2. Developing Other Estim ates of Duration of Contact ................6 0 4.4. Obtaining Data on Body Burden or Biom arkers ...........................6 1 4.5. Obtaining Data for Pharm acokinetic Relationships .........................6 1 4.6. Obtaining Data on Intake and Uptake ...................................6 2

5. Using Data to Determ ine or Estim ate Exposure and Dose ...........................6 3 5.1. Use of Data in Making Inferences for Exposure Assessm ents ...............................................................6 3 5.1.1. Relevance of Data for the Intended Exposure Assessm ent ...........6 4 5.1.2. Adequacy of Data for the Intended Assessm ent ....................6 4 5.1.2.1. Evaluation of Analytical Methods .......................6 5 5.1.2.2. Evaluation of Analytical Data Reports ...................6 5 5.1.2.2.1. Evaluation of Censored Data Sets

. ...............6 6 5.1.2.2.2. Blanks and Recovery

. .........................6 8 5.1.3. Com b ining Measurem ent Data Sets from Various Studies ...........6 8 5.1.4. Com b ining Measurem ent Data and Modeling Results ...............6 9 5.2. Dealing with Data Gaps ..............................................6 9 5.3. Calculating Exposure and Dose ........................................7 0 5.3.1. Short-term Versus Long-term Data for Population Exposures .........7 1

ivCONTENTS (continued)

5.3.2. Using Point-of-contact Data to Calculate Exposure and Dose .........7 2 5.3.3. The Role of Exposure Scenarios in Exposure Assessm ent ............7 2 5.3.3.1. Scenarios as a Means to Quantify Exposure and Dose .......7 3 5.3.3.2. Exposure Scenarios and Exposure Estimators as Input to Risk Descriptors .........................................7 5 5.3.3.3. Exposure Scenarios as a Tool for Option Evaluation ........7 5 5.3.4. General Methods for Estim ating Exposure and Dose ...............7 6 5.3.4.1. Preliminary Evaluation and Bounding Estimates ...........7 6 5.3.4.2. Refining the Estimates of Exposure and Dose ..............7 8 5.3.5. Using Estim ates for Developing Descriptors ......................7 9 5.3.5.1. Individual Exposure, Dose, and Risk .....................7 9 5.3.5.2. Population Exposure, Dose, and Risk ...................8 6

6

. A s s e s s i n g U n c e r t a i n t y .......................................................9 0 6.1. Role of Uncertainty Analysis in Exposure Assessm ent ......................9 0 6.2. Types of Uncertainty ................................................9 1 6.2.1. Scenario Uncertainty .........................................9 2 6.2.2. Param eter Uncertainty .......................................9 3 6.2.3. Model Uncertainty ..........................................9 6 6.3. Variability W ithin a Population Versus Uncertainty in the Estim ate .........9 7

7. Presenting the Results of the Exposure Assessm ent ................................9 9 7.1. Com m unicating the Results of the Assessm ent ............................9 9 7.1.1. Exposure Characterization ....................................9 9 7.1.2. Risk Characterization .......................................1 0 1 7.1.2.1. Integration of Hazard Identification, Dose-response, and Exposure Assessments ................................1 0 1 7.1.2.2. Quality of the Assessment and Degree of Confidence .......1 0 2 7.1.2.3. Descriptors of Risk ..................................1 0 3 7.1.2.4. Communicating Results of a Risk Assessment to the Risk Manager ..................................................1 0 3 7.1.3. Establishing the Com m unication Strategy .......................1 0 3

7.2. Form at for Exposure Assessm ent Reports .........................1 0 4 7.3. Reviewing Exposure Assessm ents .....................................1 0 4

8

. G l o s s a r y o f T e r m s .........................................................1 0 7

9

. R e f e r e n c e s ...............................................................1 1 5

vCONTENTS (continued)

Part B: Response to Public and Science Advisory Board Com m e nts ...................1 2 3

1. Introduction ..............................................................1 2 3

2. Response to General Com m e nts ..............................................1 2 4

3. Response to Com m e nts on the Specific Questions ................................1 2 5

viLIST OF TABLES

Table 1. Explanation of exposure and dose term s ...................................2 0 Table 2. Exam ples of types of m easurem ents to characterize exposure-related m e dia and p a r a m e t e r s ..........................................................5 1

LIST OF FIGURES Figure 1. Schem a tic of dose and exposure .........................................1 2 Figure 2. Schem a tic of exposure estim ators for unbounded sim u lated population distributions ................................................8 5

viiGUIDELINES FOR EXPOSURE ASSESSMENT [FRL-4129-5]

AGENCY: U.S. Environm ental Protection Agency

ACTION: Final Guidelines for Exposure Assessm ent

SUMMARY: The U.S. Environm ental Protection Agency (EPA) is today issuing final guidelines for exposure assessm ent. The Guidelines for Exposure Assessm ent (hereafter “Guidelines”) are intended for risk assessors in EPA, and those exposure and risk assessm ent consultants, contractors, or other persons who perform work under Agency contract or sponsorship. In addition, publication of these Guidelines m a kes inform ation on the principles, concepts, and m e thods used by the Agency available to all interested m e m b ers of the public. These Guidelines supersede and replace both the Guidelines for Estim ating Exposures published Septem ber 24, 1986 (51 FR 34042-34054) (hereafter “1986 Guidelines”) and the Proposed Guidelines for Exposure-Related Measurem ents published for com m e nt on Decem b er 2, 1988 (53 FR 48830-48853) (hereafter “1988 Proposed Guidelines”). In response to recom m e ndations from the Science Advisory Board (SAB) and the public, the 1986 Guidelines were updated and

com b ined with the 1988 Proposed Guidelines and retitled as the current Guidelines for Exposure Assessm ent. These Guidelines establish a broad fram ework for Agency exposure assessm ents by describing the general concepts of exposure assessm ent including definitions and associated units, and by providing guidance on the planning and conducting of an exposure assessm ent. Guidance is also provided on presenting the results of the exposure assessm ent and characterizing uncertainty. Although these Guidelines focus on exposures of hum ans to chem ical substances, m u ch of the guidance contained herein also pertains to assessing wildlife exposure to chem icals, or hum an exposures to biological, noise, or radiological agents. Since these latter four areas present unique challenges, assessm ents on these topics m u st consider additional factors beyond the scope of these Guidelines. The Agency m a y, 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 Environm ental Assessm ent-W a shi ngton Division (8623D), U.S. Environm ental Protection Agency, 401 M Street, S.W ., W a shington, DC 20460, TEL: 202-564-3259.

viiiSUPPLEMENTARY INFORMATION: In its 1983 book Risk Assessment in the Federal Government: Managing the Process , the National Academ y of Sciences recom m e nded that Federal regulatory agencies establish “inference guidelines” to prom ote consistency and technical quality in risk assessm ent, and to ensure that the risk assessm ent process is m a intained as a scientific effort separate from risk m a nagem e nt. A task force within EPA accepted that

recom m e ndation and requested that Agency scientists begin to develop such guidelines. In 1984, EPA scientists began work on risk assessm ent guidelines for carcinogenicity,

m

u tagenicity, suspect developm ental toxicants, chem ical m i xtures, and estim ating exposures. Following extensive scientific and public review, these guidelines were issued on Septem ber 24, 1986 (51 FR 33992-34054). Subsequent work resulted in the publishing of four additional proposals (one of which has recently becom e final): Proposed Guidelines for Assessing Fem a le Reproductive Risk (53 FR 24834-24847), Proposed Guidelines for Assessing Male Reproductive Risk (53 FR 24850-24869), Proposed Guidelines for Exposure-Related Measurem ents (53 FR 48830-48853), and Proposed Am endm ents to the Guidelines for the Health Assessm ent of Suspect Developm ental Toxicants (54 FR 9386-9403). The final Guidelines for Developm ental Toxicity Risk Assessm ent, published Decem ber 5, 1991 [56 FR 63798-63826], supersede and replace the proposed am endm ents. The Guidelines issued today continue the guidelines developm ent 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 assessm ents and to inform Agency decision m a kers and the public about these procedures. In particular, the Guidelines standardize term inology used by the Agency in exposure assessm ent and in m a ny areas outline the lim its of sound scientific practice. They em phasize that exposure assessm ents done as part of a risk assessm ent need to consider the hazard identification and dose-response parts of the risk

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

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

num ber of approaches and tools for exposure assessm ent, along with discussion of their appropriate use. The Guidelines also stress that exposure estim ates along with supporting inf o rm ation will be f u lly presented in Agency risk assessm ent docum ents, and that Agency scientists will identify the strengths and weaknesses of each assessm ent by describing uncertainties, assum p tions, and lim itations, as well as the scientific basis and rationale for each

assessm ent. W o rk on these Guidelines began soon after publication of the 1986 Guidelines. At that tim e, the SAB recom m e nded that the Agency develop supplem entary guidelines for conducting exposure studies. This supplem entary guidance was developed by an Agency work group

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

ixprofessionals from environm ental groups, indus try, academ ia, and other governm e ntal agencies, and proposed for com m e nt on Decem ber 2, 1988 (as Proposed Guidelines for Exposure-Related Measurem ents). In the public notice, the Agency asked for com m e nt on whether the proposed guidelines should be com b ined with the 1986 guidelines in order to have a single Agency guideline for exposure assessm ent. Com m e nts from the public and the SAB were heavily in favor of com b ining the two guidelines. Since proposal, the Agency has reform atted the 1988 Proposed Guidelines to allow incorporation of the inform ation in the 1986 Guidelines, and incorporated revisions resulting from additional public and SAB com m e nts, to establish the current Guidelines. The current Guidelines were reviewed by the Risk Assessm ent Forum and the Risk Assessm ent Council, subjected to an external peer review, and presented to the SAB on Septem ber 12, 1991 for final

com m e nt (EPA-SAB-IAQC-92-015). In addition, the Guidelines were reviewed by the W o rking Party on Exposure Assessm ent, an interagency working group under the Subcom m ittee on Risk Assessm ent of the Federal Coordinating Com m ittee on Science, Engineering and Technology. Com m e nts of these groups have been considered in the revision of these Guidelines. The full text of the final Guidelines for Exposure Assessm ent is published here. These Guidelines were developed as part of an interoffice guidelines developm ent program under the auspices of the Risk Assessm ent Forum and the Office of Health and Environm ental Assessm ent in the Agency’s Office of Research and Developm ent. The Agency is continuing to study risk assessm ent issues raised in these Guidelines, and will revise them in line with new inf o rm ation as appropriate. Following this pream ble are two parts: Part A is the Guidelines and Part B is the Response to the Public and Science Advisory Board com m e nts subm itted in response to the 1988 Proposed Guidelines. References, supporting docum ents, and com m e nts received on the 1988 Proposed Guidelines, as well as a copy of these final Guidelines for Exposure Assessm ent are available for inspection and copying at the Public Inform ation Reference Unit Docket (202-260-5926), EPA Headquarters Library, 401 M Street, S.W ., W a shi ngton, DC, between the hours of 8:00 a.m

. and 4:30 p.m

.

__________________ _______________________________________ Dated: April 28, 1992 Signed by EPA Adm i nistrator W illiam K. Reilly

xxiABBREVIATIONS AND ACRONYMS

ADD Average daily dose AF Absorption f r action AT Averaging tim e BW Body weight C E xposure concentration C(t) Exposure concentration as a function of tim e CO Carbon m onoxide CT Contact tim e D D ose D app Applied dose D int Internal dose D pot Potential dose DQO Data quality objective E E xposure ED Exposure duration EPA U .S. Environm ental Protection Agency F adh Adherence factor for soil f ( t) Absorption f unction IR Intake rate (also ingestion or inhalation rate) J F l u x K

p Perm eability coef f i cient LADD Lifetim e average daily dose LOAEL L owest observable adverse effect level LOD L im it of detection LT Lifetim e M m e dium Am ount (m ass) of carrier m e dium m a terial applied to the skin MDL M ethod detection lim it MEI M axim um exposed individual or m a xim a lly exposed individual ND Not detected PMN P rem a nufacture notice QA Quality assurance QAP j P Q uality assurance project plan QC Quality control QL Quantif ication lim it

xiiABBREVIATIONS AND ACRONYMS (continued)

RfC R eference concentration RfD R eference dose SA Surface area SAB S cience Advisory Board TEAM T otal exposure assessm ent m e thodology TUBE Theoretical upper bounding estim ate UCL U pper confidence lim it (often used to refer to the upper confidence lim it of the m ean) UR Uptake rate

xiiiPART A: GUIDELINES FOR EXPOSURE ASSESSMENT

1. INTRODUCTION

In 1984, the U.S. Environm ental Protection Agency (EPA) initiated a program to ensure scientif ic quality and technical consistency of Agency risk assessm ents. One of the goals of the program was to develop risk assessm ent guidelines that would be used Agencywide. The guidelines developm ent process includes a public review and com m e nt 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 Estim ating 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 Measurem ents (hereafter “1988 Proposed Guidelines”) were published in the Federal Register for public review and com m e nt (U.S. EPA, 1988a). The 1988 Proposed Guidelines were intended to be a com p anion and supplem ent to the 1986 Guidelines. W h en proposing the 1988 guidelines, the Agency asked both the EPA Science Advisory Board (SAB) and the public for com m e nts on com b ining the 1986 and 1988 exposure guidelines into a larger, m o re com p rehensive guideline; the m a jority of com m e nts received were in favor of doing so. Thus, these 1992 Guidelines For Exposure Assessm ent (hereafter “Guidelines”)

com b ine, reform at, and substantially update the earlier guidelines. These guidelines m a ke use of developm ents in the exposure assessm ent 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 replacem ent for both the 1986 Guidelines and the 1988 Proposed Guidelines.

1.1. INTENDED AUDIENCE This docum ent is intended for exposure and risk assessors in the Agency and those exposure and risk assessm ent consultants, contractors, or other persons who perform work under Agency contract or sponsorship. Risk m a nagers in the Agency m a y also benefit from this docum ent since it clarifies the term inology and m e thods used by assessors, which in som e cases could strengthen the basis for decisions. In addition, publication of these guidelines m a kes inform ation on the principles, concepts, and m e thods used by the Agency available to other agencies, States, industry, academ ia, and all interested m e m b ers of the public.

11.2. PURPOSE AND SCOPE OF THE GUIDELINES There are a num ber of different purposes for exposure assessm ents, including their use in risk assessm ents, status and trends analysis, and epidem iology. These Guidelines are intended to convey the general principles of exposure assessm ent, not to serve as a detailed instructional guide. The technical docum ents cited here pr ovide m o re specific inform ation for individual exposure assessm ent situations. As the Agency perform s m o re exposure assessm ents 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), developm ental toxicity (U.S. EPA, 1991a), m u tagenic effects (U.S. EP A, 1986c), and reproductive effects (U.S. EPA, 1988b; U.S. EPA, 1988c). These exposure assessm ent guidelines focus on hum an exposure to chem ical substances. Much of the guidance contained herein also applies to wildlife exposure to chem icals, or hum an exposure to biological, physical (i.e., noise), or radiological agents. Since these areas present unique challenges, however, assessm ents on these topics m u st consider additional factors beyond the scope of these Guidelines. For exam ple, ecological exposure and risk assessm ent m a y deal with m a ny species which are interconnected via com p lex food webs, while these guidelines deal with one species, hum ans. W h ile these guidelines discuss hum an exposure on the individual and population levels, ecological exposure and risk assessm ents m a y need to address com m unity, ecosystem , and landscape levels, also. W h ereas chem ical agents m a y degrade or be transform e d in the environm ent, biological agents m a y of course grow and m u ltiply, an area not covered in these guidelines. The Agency m a y, at a future date, issue specific guidelines in these areas. Persons subject to these Guidelines should use the term s associated with chem ical exposure assessm ent in a m a nner consistent with the glossary in Section 8. Throughout the public com m e nt and SAB review process, the Agency has sought definitions that have consensus within the scientif ic com m unity, especially those def i nitions com m on to several scientif ic f i elds. 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 m eant 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 m o st appropriate for the field of chem ical exposure assessm ent. The Agency m a y, from tim e to tim e, issue updates of or revisions to these Guidelines.

21.3. ORGANIZ A TION OF THE GUIDELINES These Guidelines are arranged in an order that assessors com m only use in preparing exposure assessm ents. Section 2 deals with general concepts, Section 3 with planning, Section 4 with data developm ent, 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 term s (Section 8) and references to other docum ents (Section 9).

32. GENERAL CONCEPTS IN EXPOSURE ASSESSMENT

Exposure assessm ent in various form s dates back at least to the early twentieth century, and perhaps before, particularly in the fields of epidem iology (W orld Health Organization [W HO], 1983), industrial hygiene (Cook, 1969; Paustenbach, 1985), and health physics (Upton, 1988). Epidem iology is the study of disease occurrence and the causes of disease, while the latter fields deal prim arily with occupational exposure. Exposure assessm ent com b ines elem ents of all three disciplines. This has becom e increasingly im portant since the early 1970s due to greater public, academ ic, industrial, and governm e ntal awareness of chem ical pollution problem s. Because there is no agreed-upon definition of the point on or in the body where exposure takes place, the term inology used in the current exposure assessm ent literature is inconsistent. Although there is reasonable agreem ent that hum an exposure m eans contact with the chem ical or agent (Allaby, 1983; Environ Corporation, 1988; H odgson et al., 1988; U.S. EPA, 1986a), there has not yet been widespread agreem ent as to whether this m e ans contact with (a) the visible exterior of the person (skin and openings into the body such as m outh and nostrils), or (b) the socalled exchange boundaries where absorption takes place (skin, lung, gastrointestinal tract). 1 These different definitions have led to som e am biguity in the use of term s and units for quantifying exposure. 2 Com m e nts on the 1986 Guidelines and the 1988 Proposed Guidelines suggested that EPA

exam ine how exposure and dose were defined in Agency assessm ents 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 am biguous and m o re consistent with nom enclature in other scientific fields. This is a change from the 1986 Guidelines. Under this definition, it is helpful to think of the hum an 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 m outh, the nostrils, and punctures

1 A third, less com m on, schem e is that exposure is contact with any boundary outside or inside of the body, including internal boundaries around organs, etc. This schem e is allude d to, for exam ple, 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 exam ple, the am ount of food ingested would be a dose under schem e (a) and an exposure under schem e (b). Since the am ount ingested in an anim al toxicology study is us ually term ed adm i nistered dose, this leads to the use of both exposure and dose for the sam e quantity under schem e (b ). There are several such am biguities in any of the currently used schem e s. Brown (1987) provides a discussi on of various units used to describe exposures due to
m

u ltiple schem e s.
4and lesions in the skin. As used in these Guidelines, exposure to a chem ical is the contact of that chem ical with the outer boundary. An exposure assessm ent is the quantitative or qualitative evaluation of that contact; it describes the intens ity, frequency, and duration of contact, and often evaluates the rates at which the chem ical crosses the boundary (chem i cal intake or uptake rates), the route by which it crosses the boundary (exposure route; e.g., derm al, oral, or respiratory), and the resulting am ount of the chem ical that actually crosses the boundary (a dose) and the am ount absorbed (internal dose). Depending on the purpose for which an exposure assessm ent will be used, the num erical output of an exposure assessm ent m a y be an estim ate of either exposure or dose. If exposure

assessm ents are being done as part of a risk assessm ent that uses a dose-response relationship, the output usually includes an estim ate of dose. 3 Other risk assessm ents, for exam ple m a ny of those done as part of epidem iologic studies, use em pirically derived exposure-response relationships, and m a y characterize risk without the interm ediate step of estim ating dose.

2.1. CONCEPTS OF EXPOSURE, INTAKE, UPTAKE, AND DOSE The process of a chem ical 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 am ount of the chem ical to biologically significant sites within the body (internal dose 4 ). Although the description of contact with the outer boundary is sim p le conceptually, the description of a chem ical crossing this boundary is som e what m o re com p lex. There are two m a jor processes by which a chem ical can cross the boundary from outside to inside the body. Intake involves physically m oving the chem ical in question through an opening in the outer boundary (usually the m outh or nose), typically via inhalation, eating, or drinking. Norm ally the chem ical is contained in a m e dium such as air, food, or water; the estim ate of how m u ch of the chem ical enters into the body focuses on how m u ch of the carrier

m e dium enters. In this process, m a ss transfer occurs by bulk flow, and the am ount of the chem ical itself crossing the boundary can be described as a chem ical intake rate. The chem ical

3 The National Research Council’s 1983 report Risk Assessment in the Federal Government: Managing the Process often addresses the output of an exposure assessm en t as an exposure or a dose (NRC 1983, pp. 32, 35-36).

4 These guidelines use the term internal dose to refer to the am ount of a chem ical absorbed across the exchange boundaries, such as the skin, lung, or gastrointestinal tract. The term absorbed dose is often used synonym ously for internal dose, although the connotation for the term absorb ed dose seem s to be m o re related to a specific boundary (the am ount absorbed across a m e m b rane in an experim e nt, for exam ple), while the term internal dose seem s to connote a m o re general sense of the am ount absorbed across one or m o re specific sites. For the purpose of these guidelines, the term internal dose is used for both connota tions. The term internal dose as used here is also consistent with how it is generally applied to a discussion of biom arkers (NRC, 1989a). It is also one of the term s used in epidem iology (NRC, 1985).
5intake rate is the am ount of chem ical crossing the outer boundary per unit tim e, and is the product of the exposure concentration tim es the ingestion or inhalation rate. Ingestion and inhalation rates are the am ount of the carrier m e dium crossing the boundary per unit tim e, such

as m 3 air breathed/hour, kg food ingested/day, or liters of water consum ed/day. Ingestion or inhalation rates typically are not constant over tim e, but often can be observed to vary within known lim its. 5 The second process by which a chem ical can cross the boundary from outside to inside the body is uptake. Uptake involves absorption of the chem ical through the skin or other exposed tissue such as the eye. Although the chem ical is often contained in a carrier m e dium , the m e dium itself typically is not absorbed at the sam e rate as the chem ical, so estim ates of the

am ount of the chem ical crossing the boundary cannot be m a de in the sam e way as for intake (see Section 2.1.3). Derm al absorption is an exam ple of direct uptake across the outer boundary of the body. 6 A chem ical uptake rate is the am ount of chem ical absorbed per unit tim e. In this process, m a ss transfer occurs by diffusion, so uptake can depend on the concentration gradient across the boundary, perm eability of the barrier, and other factors. Chem ical uptake rates can be expressed as a function of the exposure concentration, perm eability 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 chem ical contacting the outer boundary of a hum an is exposure. Most of the tim e, the chem ical is contained in air, water, soil, a product, or a transport or carrier

m e dium ; the chem ical concentration at the point of contact is the exposure concentration. Exposure over a period of tim e can be represented by a tim e-dependent profile of the exposure concentration. The area under the curve of this profile is the m a gnitude of the exposure, in concentration-tim e units (Lioy, 1990; NRC, 1990):

(2-1)

5 Ingestion of food or water is an interm ittent rather than continuous process, and can be expressed as (am ount of
m e dium per event) × (events per unit clock or calendar tim e) [the frequency of contact]; (e.g., 250 m L 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.
6where E is the m a gnitude of exposure, C(t) is the exposure concentration as a function of tim e, and t is tim e, t 2 - t 1 being the exposure duration (ED). If ED is a continuous period of tim e (e.g., a day, week, year, etc.), then C(t) m a y be zero during part of this tim e. 7 Integrated exposures are done typically for a single individual, a specific chem ical, and a particular pathway or exposure route over a given tim e period. 8 The integrated exposures for a num ber of different individuals (a population or population segm ent, for exam ple), m a y then be displayed in a histogram or curve (usually, with integrated exposure increasing along the abscissa or x-axis, and the num ber 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 segm ent. The utility of both individual exposure profiles and population expos ure distributions is discussed in Section 2.3.

2.1.2. Applied Dose and Potential Dose Applied dose is the am ount of a chem ical 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

som e tim es be established experim e ntally. Usually, it is very difficult to m easure the applied dose directly, as m a ny of the absorption barriers are internal to the hum an and are not localized in such a way to m a ke m easurem ent easy. An approxim a tion of applied dose can be m a de, however, using the concept of potential dose 9 (Lioy, 1990; NRC, 1990). Potential dose is sim p ly the am ount of the chem ical ingested, inhaled, or in m a terial 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 wher e C(t) does not equal zero; that is, the actual tim e periods (events, episodes) during whic h actual exposure is taking place. The exposure duration as defined here, on the other hand, is a tim e interval of interest for assessm ent purposes during which exposure occurs, either continuously or interm ittently.

8 An exposure pathway is the course a chem ical takes from its source to the person being contacted. An exposure route is the particular m eans of entry into the body, e.g., inhalation, ingestion, or derm al absorption.

9 Potential dose is the potential am ount of the chem ical that could be absorbed if it were 100% bioavailable. Note, however, that this does not im ply that 100% bioavailab ility or 100% absorption is assum e d when using potential dose. The equations and discussion in this chapter use potential dose as a m easurable quantity that can then be converted to applied or absorbed dose by the use of the appr opriate factors. Potential dos e is a general term referring to any of the exposure routes. The term s respiratory dose, oral dose, or derm al dose are som e tim es used to refer to the route-specific potential doses.
7to a discrete am ount of chem ical or transport m e dium , such as eating a certain am ount of food or applying a certain am ount of m a terial to the skin. 10 The potential dose for ingestion and inhalation is analogous to the adm i nistered dose in a dose-response experim e nt. Hum a n exposure to environm ental chem icals is generally inadvertent rather than adm i nistered, so in these Guidelines it is term ed potential dose rather than adm i nistered dose. Potential dose can be used for dose-response relationships based on adm i nistered dose. For the derm al route, potential dose is the am ount of chem ical applied, or the am ount of chem ical in the m e dium applied, for exam ple as a sm all am ount of particulate deposited on the skin. Note that as all of the chem ical in the particulate is not contacting the skin, this differs from exposure (the concentration in the particulate tim es the tim e of contact) and applied dose (the am ount in the layer actually touching the skin). The applied dose, or the am ount that reaches the exchange boundaries of the skin, lung, or gastrointestinal tract, m a y often be less than the potential dose if the m a terial is only partly bioavailable. W h ere data on bioavailability are known, adjustm e nts to the potential dose to convert it to applied dose and internal dose m a y be m a de. 11

2.1.3. Internal Dose The am ount of a chem ical that has been absorbed and is available for interaction with biologically significant receptors is called the internal dose. Once absorbed, the chem ical can undergo m e tabolism , storage, excretion, or transport within the body. The am ount transported to an individual organ, tissue, or fluid of interest is term ed the delivered dose. The delivered dose

m a y be only a sm all part of the total internal dose. The biologically effective dose, or the

am ount that actually reaches cells, sites, or m e m b ranes where adverse effects occur (NRC, 1990, p. 29), m a y only be a part of the delivered dose, but it is obviously the crucial part. Currently,

m o st risk assessm ents dealing with environm ental chem icals (as opposed to pharm aceutical

assessm ents) use dose-response relationships based on potential (adm inistered) dose or internal

10 It is not useful to calculate potential doses in cases where th ere is partial or total im m e rsion in a fluid such as air or water. In these cases, it is m o re useful to describe the situation in term s of exposure (concentration of the chem ical in the m e dium tim es the tim e of contact) or absorbed dose. For cases such as contact with water in a swim m i ng pool, the person is not really exposed to the entire m a ss of the chem ical that would be described by a potential dose. Nor is it useful to calculate derm al applied doses because the boundary layer is being constantly renewed. The use of alternate ways to calculate a dose that m i ght occur while swim m i ng is discussed in Section 2.1.4.2, in conjunction with Equations 2-7 and 2-8.

11 This m a y 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 chem ical to be extracted from the m a trix, absorption through the exchange boundary, and any other losses between ingestion and contact with the lung or gastrointestinal tract. W h en no data or inform ation are available to indicate otherwise, the bioavailability factor is usually assum e d to be 1.
8dose, since the pharm acokinetics necessary to base relationships on the delivered dose or biologically effective doses are not available for m o st chem icals. This m a y change in the future,

as m o re becom e s known about the pharm acokinetics of environm ental chem icals. Doses are often presented as dose rates, or the am ount of a chem ical dose (applied or internal) per unit tim e (e.g., m g /day), or as dose rates on a per-unit-body-weight basis (e.g.,

m g /kg/day). Distributions of individual doses within a population or population segm ent m a y 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 m o re fully in Section 2.3.

2.1.4. Exposure and Dose Relationships Depending on the use of the exposure assessm ent, estim ates of exposure and dose in various form s m a y be required.

C Exposure concentrations are useful when com p aring peak exposures to levels of concern such as short-term exposure lim its (STELs). They are typically expressed in units such as : g/m 3 , m g /m 3 , m g /kg, : g/L, m g /L, ppb, or ppm

.

C Exposure or dose profiles describe the exposure concentration or dose as a function of tim e. Concentration and tim e are used to depict exposure, while am ount and tim e characterize dose; graphical or tabular presentations m a y be used for either type of profile. Such profiles are very im portant for use in risk assessm ent where the severity of effect is dependent on the pattern by which the exposure occurs rather than the total (integrated) exposure. For exam ple, a developm ental toxin m a y only produce effects if exposure occurs during a particular stage of developm ent. Sim ilarly, a single acute exposure to very high contam inant levels m a y induce adverse effects even if the average exposure is m u ch lower than apparent no-ef f ect levels. Such prof iles will becom e increasingly im portant as biologically based dose-response m odels becom e 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 sam e route) is needed. Units of integrated exposure are concentration tim es tim e. 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 tim e) contains m o re inform ation than an integrated exposure (a num ber), including the duration and periodicity of exposure, the peak exposure, and the shape of the area under the tim e-concentration

curve.

9C Tim e -weighted averages are widely used in exposure assessm ents, especially as part of a carcinogen risk assessm ent. e-weighted average exposure concentration (units of concentration) is the integrated exposure divided by the period where exposure occurs, and is useful in som e of the equations discussed below in estim ating dose. e-weighted average dose rate is the total dose divided by the tim e period of dosing, usually expressed in units of m a ss per unit tim e, or m a ss/tim e norm a lized to body weight (e.g., m g /kg/day). e -weighted average dose rates such as the lifetim e average daily dose (LADD) are often used in dose-response equations to estim ate effects or risk. 12 The discussion in the next three sections focuses on exposure via inhalation, oral intake, and derm al absorption. es are possible, however, including direct introduction into the bloodstream via injection or transfusion, contam ination of exposed lesions, placental transfer, or use of suppositories. calculated in a sim ilar m a nner, 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 assessm ent, the assessor should consider the im plications of the

assum p tions used to derive the equations. p lifying assum p tions used in deriving the equations m a y m ean that variations in exposure concentration, ingestion or inhalation rate, perm eability coefficient, surface area exposed, and absorption fraction can introduce error into the estim ate of dose if average values are used, and this m u st 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 sim p ly the integration of the chem ical intake rate (concentration of the chem ical in the m e dium tim es the intake rate of the

m e dium , C tim es IR) over tim e:
(2-2) A tim

A tim

Tim

Other exposure rout

The exposures and doses for these routes can be

Sim

12 Current carcinogen risk m odels, such as the linearized m u ltistage procedure and other linear nonthreshold m odels, use lifetim e exposures to develop the dose-response relationships, and therefor e use lifetim e tim e-weighted average exposures to estim ate risks. W ithin 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 term s of risk potential without respect to prior exposure or dose patterns. Cu rrent research in the field of dose-response m odeling is focusing on biologically based dose-response m odels which m a y take into account the effects of the exposure or dose patterns, m a king use of all of the inform ation in an exposure or dose profile. For a m o re indepth discussion on the im plications of the use of tim e-we ighted averages, see Atherley (1985).
10(2-3)

(2-4) where D pot is potential dose and IR(t) is the ingestion or inhalation rate. The quantity t 2 - t 1 , as before, represents the period of tim e over which exposure is being

exam ined, or the exposure duration (ED). a y contain tim es where the chem ical is in contact with the person, and also tim es when C(t) is zero. e represents the actual tim e period where the chem ical is in contact with the person. ingestion, where actual contact with food or water is interm ittent, and consequently the actual contact tim e m a y be sm all, the intake rate is usually expressed in term s of a frequency of events (e.g., 8 glasses of water consum ed per day) tim es the intake per event (e.g., 250 m L of water/glass of water consum ed). ittent air exposures (e.g., 8 hours exposed/day tim es one cubic m e ter of air inhaled/hour) can also be expressed easily using exposure duration rather than contact tim e. exposure duration will be used in the exam ples below to refer to the term t 2 - t 1 , since it occurs frequently in exposure assessm ents and it is often easier to use. Equation 2-2 can also be expressed in discrete form as a sum m a tion of the doses received during various events I:

where ED i is the exposure duration for event I. approxim a tion if the contact tim e is very short), Equation 2-3 becom e s:

where ED is the sum of the exposure durations for all events, and and are the average values for these param e ters. considerably. segm ents where C and IR are approxim a tely constant. e t, Equation 2-2 m a y 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

11Figure 1. Schematic of dose and exposure.

12(2-5) For risk assessm ent purposes, estim ates of dose should be expressed in a m a nner that can be com p ared with available dose-response data. based on potential dose (called adm i nistered dose in anim al studies), although dose-response relationships are som e tim es based on internal dose. Doses m a y be expressed in several different ways. for exam ple, gives a total dose accum u lated over the tim e in question. e is the dose rate, which has units of m a ss/tim e (e.g., m g /day). dose rate is not necessarily constant. e is a useful

num ber for m a ny risk assessm ents. Exposure assessm ents should take into account the tim e scale related to the biological response studied unless the assessm ent is intended to provide data on the range of biological responses (NRC, 1990, p. 28). a ny noncancer effects, risk assessm ents consider the period of tim e 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 assessm ent. of average daily doses (ADDs). An ADD can be calculated from Equation 2-2 by averaging D pot over body weight and an averaging tim e, provided the dosing pattern is known so the integral can be solved. to have such data for hum an exposure and intake over extended periods of tim e, so som e sim p lifying assum p tions are com m only used. ng Equation 2-4 instead of 2-2 or 2-3 involves

m a king steady-state assum p tions about C and IR, but this m a kes the equation for ADD easier to solve. 13 For intake processes, then, using Equation 2-4, this becom e s:

where ADD pot is the average daily potential dose, BW is body weight, and AT is the tim e period over which the dose is averaged (converted to days). concentration is best expressed as an estim ate of the arithm e tic m ean regardless of the distribution of the data. e s that C and IR are approxim a tely constant. For effects such as cancer, where the biological response is usually described in term s of lifetim e probabilities, even though exposure does not occur over the entire lifetim e, doses are often presented as lifetim e average daily doses (LADDs). of Equation 2-5, with lifetim e (LT) replacing the averaging tim e (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

13 The assessor should keep in m i nd that this steady state assum p tion has been m a de 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 estim ate.
13(2-6)

14 This relationship is described by Fick’s Law, where J = K p @ C where C represents the steady-state concentration of the chem ical, J is the steady-state flux, and K p is the perm eability coefficient.

15 The perm eability coefficient, K p , can be experim e ntally calculated for a ch em ical and a particular barrier (e.g., skin type) by observing the flux rate in vitro (typical units: m g chem ical crossing/sec-cm 2 ), and dividing it by the concentration of the chem ical in the m e dium in c ontact with the barrier (typical units: m g chem ical/cm 3 ). allows the relationship between bulk concentration and the crossing of the chem ical itself to be m a de. p has the advantage of being fairly constant over a range of concentr ations and can be used for concentrations other than the one used in the experim e nt. ical uptake rate, re lating the crossing of the barrier of the chem ical itself in term s of the bulk concentration, then becom e s C tim es K p tim es the surface area exposed (SA).
14 (2-7)

(2-8) The LADD is a very com m on term used in carcinogen risk assessm ent where linear nonthreshold m odels are em ployed.

2.1.4.2. Calculating Internal Dose for Uptake Processes (Especially via the Dermal Route) For absorption processes, there are two m e thods generally in use for calculating internal dose. m only used for derm al absorption from a liquid where at least partial im m e rsion occurs, is derived from the equation for internal dose, D int , which is analogous to Equation 2-2 except that the chem ical uptake rate (C @ K p @ SA) replaces the chem ical intake rate (C @ IR).

where K p is the perm eability coefficient, and SA is the surface area exposed. will vary over tim e, and although K p m a y not vary over tim e, it m a y vary over different parts of the body. e dium crossing the boundary can be observed or m easured, the carrier m a y or m a y not cross the absorption barrier; the equations m u st be in term s of the chem ical itself crossing. ical across the barrier (or flux, J) is not directly m easurab le, and is dependent on m a ny factors including the nature of the chem ical, the nature of the barrier, active transport versus passive diffusion processes, and the concentration of the chem ical contacting the barrier. between the flux and the exposure concentration 14 is usually expressed as a perm eability coefficient, K p , which is experim e ntally m easurable. 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 relationshipwhere is the average surface area exposed and the ADD int (average daily internal dose) becom e s:
(2-9)

(2-10)

(2-11) (The corresponding LADD int would be obtained by substituting LT f o r AT.)

m e thod to use when calculating internal dose for a swim m e r.
assum e d to be exposed to a layer of water with an average chem ical concentration for a period of tim e (ED). a ss of the chem ical that com e s in contact with the skin. p tions necessary in going from Equation 2-7 to Equation 2-9 are com p arable to those m a de in deriving Equation 2-5. e, and K p ma y not be constant over different parts of the body. If the assum p tion used to derive Equation 2-5 (that these variables are nearly constant) does not hold, a different form of the equation having several term s m u st be used. The second m e thod of calculating internal dose uses em pirical observations or estim ates of the rate that a chem ical is absorbed when a dose is adm i nistered or applied.

a sm all or known am ount of m a terial (such as a particulate) or a chem ical (such as a pesticide) contacts the skin. ical to the skin, D pot , can often be calculated from knowing the concentration (C) and the am ount of carrier m e dium applied (M m e dium ), either as a whole or on a unit surface area basis. ple, potential dose from derm al contact with soil can be calculated using the following equation:

where D pot is potential dose, M m e dium is am ount of soil applied, and F adh is the adherence factor for soil (the am ount of soil applied to and adhering to the skin on a unit surface area per unit tim e). The relationship between potential dose and applied dose for derm al exposures is that potential dose includes the am ount of the chem ical in the total am ount of m e dium contacting the skin, e.g., the am ount of chem ical in the soil whether or not all the chem ical itself ever com e s in direct contact, and applied dose includes only that am ount of the chem ical which actually directly touches the skin. app ) and the internal (or absorbed) dose (D int ) 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 com p licated nonlinear absorption function, usually not m easurable, having the dim e nsions of m a ss absorbed per m a ss applied per unit tim e. unction will vary due to a num ber of factors (concentration gradient of chem ical, carrier m e dium , type of skin, skin m o isture, skin condition, etc.). e from the start of exposure until tim e T, it would yield the absorption fraction, AF, which is the fraction of the applied dose that is absorbed after tim e T. u lative num ber and can increase with tim e to a possible m a xim u m of 1 (or 100% absorption), but due to com p eting processes m a y reach steady state long before reaching 100% absorption. becom e s:

D int = D app @ AF (2-12)

where AF is the absorption fraction in units of m a ss absorbed/m ass applied (dim ensionless). If

one assum e s that all the chem ical contained in the bulk m a terial will eventually com e in contact with the skin, then D app equals D pot and using Equation 2-12, the D int equation becom e s: D int = D pot @ AF (2-13)

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

where M m e dium is the m a ss of the bulk m a terial applied to the skin. this approxim a tion will by no m eans always give credible results. chem ical contained in the bulk m e dium can actually contact the skin. liquids or sm all am ounts of m a terial, the applied dose m a y be approxim a tely equal to the potential dose, in cases where there is contact with m o re than a m i nim a l am ount of soil, there is research that indicates that using this approxi m a tion m a y cause serious error (Yang et al., 1989). W h en this approxim a tion does not hold, the assessor m u st m a ke assum p tions about how m u ch of the bulk m a terial actually contacts the skin, or use the first m e thod of estim ating internal dose outlined above. Unfortunately, alm o st no data are available concerning the relationship between potential dose and applied dose for derm al exposures. e ntal data on absorption fractions derived for soil com m only use potential dose rather than applied dose, which m a y m a ke the experim e ntal data at least in part dependent on experim e ntal conditions such as how m u ch 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

16the exposure assessm ent conditions are sim ilar to those in the experim e nt, this would not usually introduce m u ch error, but if the conditions vary widely, the error introduced m a y be difficult to determ ine. As a practical m a tter, estim ates of absorption fraction are often crude approxim a tions and

m a y be difficult to refine even if som e data from experim e nts are available in the published literature. Typically, absorption experim e nts report results as an absorption f r action af ter a given tim e (e.g., 50% after 24 hours). Since absorption fraction is a function of several variables such as skin tem p erature, pH, m o isture content, and exposed surface area, as well as characteristics of the m a trix in which the chem ical occurs (e.g., soil particle size distribution, organic m a tter content, and m o isture content), it is often difficult to m a ke com p arisons between experim e ntal data and conditions being considered for an assessm ent. W ith single data points, it m a y not be clear whether the experim e nt reached steady state. If several data points are available from different tim es in the experim e nt, a plot of absorption fraction vs. tim e m a y be instructive. For chem icals where data are available for steady-state conditions, the steady-state value will probably be a good approxim a tion to use in assessm ents where exposure duration is at least this long, provided the conditions in the experim e nt are sim ilar to those of the case being assessed. Assessors should be very cautious in applying absorption fractions for m oderately absorbed chem icals (where observed experim e ntal absorption fractions are not in the steady-state part of the cum u lative curve), or in using experim e ntal data for estim ates of absorption over a m u ch shorter duration than in the experim e nt. In alm o st all cases, the absorption fraction m e thod of estim ating internal dose from applied dose gives only an approxim a tion of the internal dose. The interested reader is referred to U.S. EPA (1992b) for m o re thorough guidance on derm al exposure assessm ent.

2.1.4.3. Calculating Internal Dose for Intake Processes (Especially via Respiratory and Oral Routes) Chem icals in air, food, or drinking water norm a lly enter the body through intake processes, then are subsequently absorbed through internal uptake processes in the lung or gastrointestinal tract. Som e tim es it is necessary to estim ate resulting internal dose, D int , after intake. In addition, if enough is known about the pharm acokinetics of the chem ical to m a ke addition of doses across routes a m eaningful exercise, the doses m u st be added as internal dose, not applied dose, potential dose, or exposure. Theoretically, one could calculate D int in these cases by using an equation sim ilar to Equation 2-7; but C in that equation would becom e the concentration of the chem ical in the lung or gastrointestinal tract, SA would be the internal surface area involved, and K p would be the

17(2-15)

(2-16) perm eability coefficient of the lung or gastrointestinal tract lining. the pharm aceutical field m a y be helpful in determ ining, for exam ple, internal surface areas, all of the data m e ntioned above are not known, nor are they m easurable with current instrum e ntation. Because Equations 2-2 through 2-4 estim ate the potential dose D pot , which is the am ount ingested or inhaled, and Equations 2-11 and 2-12 provide relationships between the applied dose (D app ) and internal dose (D int ), all that is necessary is a relationship between potential dose and applied dose for intake processes.

com m on assum p tion is that for intake processes, the potential dose equals the applied dose. Although argum ents can be m a de that this assum p tion is likely to be m o re nearly accurate than for the case of soil contact, the validity of this assum p tion is unknown at this point. the assum p tion of equality m eans that whatever is eaten, drunk, or inhaled touches an absorption barrier inside the person. Assum i ng potential dose and applied dose are approxim a tely equal, the internal dose after intake can be estim ated by com b ining Equations 2-2 or 2-3 and 2-10 or 2-11. Equations 2-3 and 2-11, this becom e s:

The ADD int for the two-step intake/uptake process becom e s:

Using average values for and in Equations 2-15 and 2-16 involves the sam e assum p tions and cautions as were discussed in deriving th e ADD and LADD equations in the previous two sections, and of course, the sam e 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 sum m a ry of the exposure and dose term s discussed in Section 2.1, along with exam ples of units com m only 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

182.2. APPROACHES TO QUANTIFICATION OF EXPOSURE Although exposure assessm ents are done for a variety of reasons (see Section 3), the quantitative exposure estim ate can be approached from three different ways 16 : 1. The exposure can be m easured at the point of contact (the outer boundary of the body) while it is taking place, m easuring both exposure concentration and tim e of contact and integrating them (point-of-contact m easurem ent), 2. The exposure can be estim ated by separately evaluating the exposure concentration and the tim e of contact, then com b ining this inform ation (scenario evaluation), 3. The exposure can be estim ated from dose, which in turn can be reconstructed through internal indicators (biom a rkers, 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 m e thods is a useful concept in verifying or validating results. Each of the three has strengths and weaknesses; using them in com b ination can considerably strengthen the credibility of an exposure or risk assessm ent. Sections 2.2.1 through 2.2.3 briefly describe som e of the strengths and weaknesses of each approach.

16 These three ways are approaches for arriving at a quantita tive estim ate of exposure. Som e tim es the approaches to assessing exposure are described in term s of “direct m easures” and “indirect m easures” of exposure (e.g., NRC, 1990). Measurem ents that actually involve sam p ling on or within a person, for exam ple, use of personal m onitors and biom arkers, are term ed “direct m easures” of exposure. Use of m odels, m i croenvironm ental m easurem ents, and questionnaires, where m easurem ents do not actually involve personal m easurem ents, are term ed “indirect m easures” of exposure. The direct/indirect nom enclature focuses on the type of m easurem ents being m a de; the scenario evaluation/point-of-contact/reconstruction nom enclature focu ses on how the data are used to develop the dose estim ate. The three-term nom enclature is used in thes e guidelines to highlight the point that three independent estim ates of dose can be developed.

17 Biom arkers can be used to study exposure, effects, or susceptibility. The discussion of biom arkers in these guidelines is lim ited to their use in indicating exposure.
19Table 1.Explanation of exposure and dose termsTermRefers toGeneric unitsSpecific example unitsExposureContact of chemical with outerboundaryof aperson,e.g.,skin,nose,mouth.concentration x timeDermal:Respiratory:Oral:(mgchem/L water) • (hrs ofcontact)(mg chem/kg soil)•(hrsof contact)(ppmchemin air) • (hrs ofcontact)(:g/m3air) • (days ofcontact)(mg chem/L water) • (min of contact)(mg chem/kg food)•(min of contact)PotentialdoseAmount of a chemical contained in materialingested,airbreathed,orbulk materialapplied to the skin.mass ofthe chemical:Doserateis mass of the chemical/time;the dose rate is sometimes normalized tobodyweight: mass of chemical/unit bodyweight • timeDermal:Respiratory:Oral:(mg chem/kg soil)•(kg soil on skin)= mgchemin soil applied to skin(:g chem/m3air) • (m3airbreathed/min)•(min exposed)=:g chemical in air breathed(mgchem/L water) • (L water consumed/day) • daysexposed = mgchemical ingested in water(also dose rate: mg/day)ApplieddoseAmount of chemical in contact with theprimaryabsorption boundaries (e.g.,skin,lungs,gastrointestinal tract)and available forabsorptionas aboveDermal:Respiratory:Oral:(mg chem/kg soil)•(kg soil directlytouching skin)•(%of chemin soil actuallytouching skin)= mgchemactuallytouching skin(:g chem/m3air) • (m3airdirectlytouching lung)•(%of chemical actuallytouching lung)= mgchemical actuallytouching lung absorption barrier(mg chem/kg food)•(kg food consumed/day)•(%ofchemical touching g.i. tract) = mgchemicalactuallytouching g.i.tract absorption barrier(also absorbed dose rate: mg/day) chemicalavailable to organ orcell

20(dose rate: mgchemical available to organ/day)Table 1.Explanation of exposure and dose terms (continued)TermRefers toGeneric unitsSpecific example unitsInternal(absorbed)doseThe amount of a chemical penetrating acrossan absorption barrierorexchange boundaryvia eitherphysical orbiological processes.as aboveDermal:Respiratory:Oral:mgchemical absorbed through skinmgchemical absorbed via lungmgchemical absorbed via g.i. tract(dose rate: mgchemical absorbed/day or mg/kg •day)DelivereddoseAmount of chemical available forinteractionwith anyparticularorgan orcell.as abovemgchemical available to organ orcell(dose rate: mgchemical available to organ/day)

212.2.1. Measurement of Exposure at the Point of Contact Point-of-contact exposure m easurem ent evaluates the exposure as it occurs, by m easuring the chem ical concentrations at the interface between the person and the environm ent as a function of tim e, resulting in an exposure prof ile. The best known example of the point-ofcontact m easurem ent is the radiation dosim eter. This sm all badge-like device m easures exposure to radiation as it occurs and provides an integrated estim ate of exposure for the period of tim e over which the m easurem ent has been taken. Another exam ple is the Total Exposure Assessm ent Methodology (TEAM) studies (U.S. EPA, 1987a) conducted by the EPA. In the TEAM studies, a sm all pum p with a collector and absorbent was attached to a person’s clothing to m easure his or her exposure to airborne solvents or other pollutants as it occurred. A third

exam ple is the carbon m onoxide (CO) point-of- contact m easurem ent studies where subjects carried a sm all CO m easuring device for several days (U.S. EPA, 1984a). Derm al patch studies and duplicate m eal studies are also point-of-contact m easurem ent studies. In all of these

exam ples, the m easurem ents are taken at the interface between the person and the environm ent while exposure is occurring. Use of these data for estim ating exposures or doses for periods that differ from those for which the data are collected (e.g., for estim ates of lifetim e exposures) will require som e assum p tions, as discussed in Section 5.3.1. The strength of this m e thod is that it m easures exposure directly, and providing that the

m easurem ent devices are accurate, is likely to give the m o st accurate exposure value for the period of tim e over which the m easurem ent was taken. It is often expensive, however, and

m easurem ent devices and techniques do not currently exist for all chem icals. This m e thod m a y also require assum p tions to be m a de concerning the relationship between short-term sam p ling and long-term exposures, if appropriate. This m e thod is also not source-specific, a lim itation when particular sources will need to be addressed by risk m a nagers.

2.2.2. Estimates of Exposure from Scenario Evaluation In exposure scenario evaluation, the assessor attem p ts to determ ine the concentrations of chem icals in a m e dium or location and link this inform ation with the tim e that individuals or populations contact the chem ical. The set of assum p tions about how this contact takes place is an exposure scenario. In evaluating exposure scenarios, the assessor usually characterizes the chem ical concentration and the tim e of contact separately. This m a y be done for a series of events, e.g., by using Equation 2-3, or using a steady-state approxim a tion, e.g., using Equation 2-4. The goal of chem ical concentration characterization is to develop estim ates of exposure concentration. This is typically accom p lished indirectly by m easuring, m odeling, or using existing data on concentrations in the bulk m e dia, rather than at the point of contact. Assum i ng

22the concentration in the bulk m e dium is the sam e as the exposure concentration is a clear source of potential error in the exposure estim ate and m u st be discussed in the uncertainty analysis. Generally, the closer the m e dium can be m easured to the point of contact (in both space and tim e), the less uncertainty there is in the characterization of exposure concentration. The goal of characterizing tim e of contact is to identify who is exposed and to develop estim ates of the frequency and duration of exposure. Like chem ical concentration characterization, this is usually done indirectly by use of dem ographic data, survey statistics, behavior observation, activity diaries, activity m odels, or, in the absence of m o re substantive inform ation, assum p tions about behavior. The chem ical concentration and population characterizations are ultim ately com b ined in an exposure scenario, and there are various ways to accom p lish this. One of the m a jor problem s in evaluating dose equations such as Equations 2-4 through 2-6 is that the lim iting assum p tions or boundary conditions used to derive them (e.g., steady-state assum p tions; see Section 2.1.4) do not always hold true. Two m a jor approaches to this problem are (1) to evaluate the exposure or dose equation under conditions where the lim iting assum p tions do hold true, or (2) to deal with the uncertainty caused by the divergence from the boundary conditions. As an exam ple of the first way, the m i croenvironm ent m e thod, usually used for evaluating air exposures, evaluates segm ents of tim e and location where the assum p tion of constant concentration is approxim a tely true, then sum s over all such tim e segm ents for a total exposure for the respiratory route, effectively rem oving som e of the boundary conditions by falling back to the m o re general Equation 2-3. W h ile estim ates of exposure concentration and tim e-of-contact are still derived indirectly by this m e thod, the concentration and tim e-of-contact estim ates can be m easured for each m i croenvironm ent. This avoids m u ch of the error due to using average values in cases where concentration varies widely along with tim e of contact. 18 As exam ples of the second approach, there are various tools used to describe uncertainty caused by param e ter variation, such as Monte Carlo analysis (see Section 5). Section 6 discusses

som e of these techniques in m o re detail. One strength of the scenario evaluation approach is that it is usually the least expensive

m e thod 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 developm ent that the

18 This technique still m a y not deal effectively with the pr oblem of short-term “peak concentrations” exceeding som e threshold leading to an acute effect. Even the averag ing process used in a m i croenvironm ent m a y m i ss significant concentration spikes and average them out to lower c oncentrations which are apparently less toxicologically significant. A sim ilar problem exists when evaluating sour ces; a “ p eak release” of a toxic chem ical for a short tim e m a y cause serious acute effects, even though the aver age concentration over a longer period of tim e m i ght not indicate serious chronic effects.
23evaluation can be perf orm e d with little or no data; it is a technique that is best used when som e knowledge exists about the soundness, validity, and uncertainty of the underlying assum p tions.

2.2.3. Exposure Estimation by Reconstruction of Internal Dose Exposure can also be estim ated after it has taken place. If a total dose is known, or can be reconstructed, and inform ation about intake and uptake rates is available, an average past exposure rate can be estim ated. Reconstruction of dose relies on m easuring internal body indicators after exposure and intake and uptake have already occurred, and using these

m easurem ents to back-calculate dose. However, the data on body burden levels or biom arkers cannot be used directly unless a relationship can be established between these levels or biom arker indications and internal dose, and in terfering reactions (e.g., m e tabolism of unrelated chem icals) can be accounted for or ruled out. Bi ological tissue or fluid m easurem ents that reveal the presence of a chem ical m a y indicate directly that an exposure has occurred, provided the chem ical is not a m e tabolite of other chem icals. Biological m onitoring can be used to evaluate the am ount of a chem ical in the body by

m easuring one or m o re of the following item s

. Not all of these can be m easured for every chem ical:

C the concentration of the chem ical itself in biological tissues or sera (blood, urine, breath, hair, adipose tissue, etc.),

C the concentration of the chem ical’s m e tabolite(s),

C the biological effect that occurs as a result of hum an exposure to the chem ical (e.g., alkylated hem oglobin or changes in enzym e induction), or

C the am ount of a chem ical or its m e tabolites bound to target m o lecules. The results of biom onitoring can be used to estim ate chem ical uptake during a specific interval if background levels do not m a sk the m a rker and the relationships between uptake and the m a rker selected are known. The tim e of sam p ling for biom arkers can be critical. Establishing a correlation between exposure and the m easurem ent of the m a rker, including pharm acokinetics, can help optim ize the sam p ling conditions. The strengths of this m e thod are that it dem onstrates that exposure to and absorption of the chem ical 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 chem ical due to interferences or the reactive nature of the chem ical, it has not been m e thodologically established for very m a ny chem icals, data relating internal dose to exposure are needed, and it m a y be expensive.

242.3. RELATIONSHIPS OF EXPOSURE AND DOSE TO RISK Exposure and dose inform ation are often com b ined with exposure-response or doseresponse relationships to estim ate risk, the probability of an adverse effect occurring. There are a variety of risk m odels, with various m a them atical relationships between risk and dose or (less frequently) exposure. A m a jor function of the exposure assessm ent as part of a risk assessm ent is to provide the exposure or dose values, and their interpretations. The exposure and dose inform ation available will often allow estim ates of individual risk or population risk, or both. Presentation of risks in a risk assessm ent involves m o re than m e rely

a num erical value, however. Risks can be desc ribed or characterized in a num ber of different ways. This section discusses the relationships between exposure and dose and a series of risk descriptors. In preparing exposure inform ation for use in a risk assessm ent, the use of several descriptors, including descriptors of both i ndividual and population risk, often provides m o re useful inform ation to the risk m a nager than a singl e descriptor or risk value. Developing several descriptors m a y require the exposure assessor to analyze and evaluate the exposure and dose inform ation in several different ways. The exposure assessor should be aware of the purpose, scope, and level of detail of the assessm ent (see Sections 3.1 through 3.3) before gathering data, since the types and am ounts of data needed m a y differ. The questions that need to be addressed as a result of the purpose of the assessm ent determ ine the type of risk descriptors used in the

assessm ent.

2.3.1. Individual Risk Individual risk is risk borne by individual persons within a population. Risk assessm ents alm o st always deal with m o re than a single individual. Frequently, individual risks are calculated for som e 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 form s, depending on the questions being addressed. For the risk m a nager, there are often key questions in m a pping out a strategy for dealing with individual risk. For cancer (or when possible, noncancer) assessm ents, the risk

m a nager m a y 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 assum e d 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

com p ared to a reference dose or som e other acceptable level, the risk descriptor would be a statem ent based on the ratio between the dose incurred and the reference dose.

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

C W ho are these people, what are they doing, where do they live, etc., and what m i ght be putting them at this higher risk?

C Can people with a high degree of susceptibility be identif ied?

C W h at is the average individual risk?

In addressing these questions, risk descriptors m a y take any of several form s:

C an estim ate of the probability that an individual in the high end of the distribution m a y 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 estim ate of the probability that an individual at the average or m e dian risk m a y suffer an adverse effect; or

C an estim ate of the probability that an individual will suf f e r an adverse ef f e ct given a specific set of exposure circum stances. Individuals at the high end of the risk distri bution are often of interest to risk m a nagers when considering various actions to m itigate risk. These individuals of ten are either m o re susceptible to the adverse health effect than others in the population or are highly exposed individuals, or both. Higher susceptibility m a y be the result of a clear dif f e rence in the way the chem ical is processed by the body, or it m a y be the result of being in the extrem e part of the norm a l range in

m e tabolism for a population. It m a y not always be possible to identify persons or subgroups who are m o re susceptible than the general population. If groups of individuals who have clearly dif f e rent susceptibility characteristics can be identif ied, they can be treated as a separate subpopulation, and the risk assessm ent for this subgroup m a y require a different dose-response relationship from the one used for the general population. W h en 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 m a ny different term s. Because of unclear definitions, term s such as m a xim u m exposed individual, 19 worst case

19 The upperm ost portion of the high-end exposure range has ge nerally been the target for term s such as “m axim um exposed individual,” although actual usage has varied.
26exposure, 20 and reasonable worst case exposure 21 have som e tim es been applied to a variety of ad hoc estim ates with unclear target ranges. The term m a xim u m exposed individual has often been used synonym ously with worst case exposure, that is, to estim ate the exposure of the individual with the highest actual or possible exposure. An accurate estim ate of the exposure of the person in the distribution with the highest exposure is extrem ely difficult to develop; uncertainty in the estim ate usually increases greatly as the m o re extrem e ends of the distribution are approached. Even using techniques such as Monte Carlo sim u lations can result in high uncertainty about whether the estim ate is within, or above, the actual exposure distribution. For the purpose of these guidelines, a high end exposure estim ate is a plausible estim ate of the individual exposure for those persons at the upper end of an exposure distribution. The intent of this designation is to convey an estim ate of exposures in the upper range of the distribution, but to avoid estim ates that are be yond the true distribution. Conceptually, the high end of the distribution m eans 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 estim ates are described analogously. The concept of the high-end exposure, as used in this guidance, is fundam e ntally different from term s such as worst case, in that the estim ate 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 prim ary objective when developing an estim ate of high-end exposure or dose is to arrive at an estim ate that will f a ll within the actual distribution, rather than above it. (Estim ates above the distribution are bounding estim ates; see Section 5.3.4.1) Often this requires professional judgm ent when data are sparse, but the prim ary objective of this type of estim ator is to be within this fairly wide conceptual target range.

20 The term “worst case exposure” has historically m eant the m a xim u m possible exposure, or where everything that can plausibly happen to m a xim i ze exposure, happens. W h ile in actuality, this worst case exposure m a y fall on the upperm ost point of the population distribution, in m o st cases , it will be som e what higher than the individual in the population with the highest exposure. The worst case re presents a hypothetical individual and an extrem e set of conditions; this will usually not be observed in an actua l population. The worst case and the so-called m a xim u m exposed individual are therefore not synonym ous, the form er describing a statistical possibility that m a y or m a y not occur in the population, and the latter ostensibly describing an individual that does, or is thought to, exist in the population.

21 The lower part of the high-end exposure range, e.g., con ceptually above the 90th percentile but below about the 98th percentile, has generally been the target used by those em ploying the term “reasonable worst case exposure.” Above about the 98th percentile has been term ed the “m axim um exposure” range. Note that both these term s should refer to estim ates of exposure on the actual distribution, not above it.
27The relationship between answering the questions about high-end individual risk and what the exposure assessor m u st do to develop the descriptors is discussed in Section 3.4. Individual risk descriptors will generally require the assessor to m a ke estim ates of high-end exposure or dose, and som e tim es additional estim ates (e.g., estim ates of central tendency such as average or m e dian exposure or dose). Another type of individual risk descriptor re sults from specific sets of circum stances that can be hypothesized as part of a scenario, for exam ple:

C W h at if a hom eowner lives at the edge of this site for his entire life?

C W h at if a pesticide applicator applies this pesticide without using protective equipm ent?

C W h at if a consum er uses this product every day for ten years? Once a m onth? Once a week?

C W h at risk level will occur if we set the standard at 100 ppb? The assum p tions m a de in answering these assessm ent-specific postulated questions should not be confused with the approxim a tions m a de in developing an exposure estim ate for an existing population or with the adjustm e nts in param e ter values m a de in perform i ng a sensitivity analysis. The assum p tions in these specific questions address a purer “if/then” relationship and, as such, are m o re helpful in answering specific hypothetical or anecdotal questions. The answers to these postulated questions do not give inform ation about how likely the com b ination of values

m i ght be in the actual population or about how m a ny (if any) persons m i ght actually be subjected to the calculated risk. Exposure scenarios em ploying these types of postulated questions are encountered often in risk assessm ents, especially in those where actual exposure data are incom p lete or nonexistent. Although the estim ates of individual exposure derived from these assum p tions provide num erical values for calculating risk, they do so m o re as a m a tter of context than a determ ination of actual exposure. They are not the sam e types of estim ates as high-end exposure or risk, where som e statem ent m u st be m a de 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 estim ate of the extent of harm for the population or population segm ent being addressed. Risk m a nagers m a y need questions addressed such as the f o llowing:

C How m a ny cases of a particular health ef f ect m i ght be probabilistically estim ated f o r a population of interest during a specified tim e period?

28C 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 m a ny 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 W h at is the risk for a particular population segm ent?

C Do any particular subgroups experience a high exposure, dose, or risk?

The risk descriptors for population risk can take any of several form s:

C a probabilistic projection of the estim ated extent of occurrence of a particular ef f ect for a population or segm ent (som etim es called “num ber of cases” of effect);

C a description of what part of the population (or population segm ent) is above a certain risk value of interest; or

C a description of the distribution of risk am ong various segm ents or subgroups of the population. In theory, an estim ate of the extent of effects a population m i ght incur (e.g., the num ber of individual cases that m i ght occur during a specified tim e) can be calculated by sum m i ng the individual risks for all individuals within the population or population segm ent of interest. The ability to calculate this estim ate depends on whether the individual risks are in term s of probabilities for each individual, rather than a hazard index or other nonprobabilistic risk. The calculation also requires a great deal m o re inform ation than is norm a lly available. For som e assessm ents, an alternate m e thod is used, provided certain conditions hold. An arithm e tic m ean dose is usually m u ch easier to estim ate than the individual doses of each person in the population or population segm ent, but calculating the hypothetical num ber of cases by using m ean doses, slope factors, and population size m u st be done with considerable caution. If the risk varies linearly with dose, and there is no threshold below which no effect ever occurs, an estim ate of the num ber of cases that m i ght occur can be derived from the definition of arithm e tic

m ean. If A = T/n, where A is the arithm e tic m ean of n num bers, and T is the sum of the sam e n
num bers, sim p le rearrangem e nt gives T = A n. If the arithm e tic m ean risk for the population (A) can be estim ated, and the size of the population (n) is known, then this relationship can be used to calculate a probabilistic estim ate of the extent of ef f ects (T). 22 Even so, several other cautions apply when using this m e thod.

22 Since the geom etric m ean (G) is defined differently, use of the geom etric m ean individual risk (where G does not equal A, such as is often found in environm ental situ ations) in the above relationship will obviously give an erroneous (usually low) estim ate of the total. Geom etric m eans have appropriate uses in exposure and risk
assessm ent, but estim ating population risk in this way is not one of them

. 29Individual risks are usually expressed on an upper bound basis, and the resulting num ber of cases estim ated in this m a nner will norm a lly be an upper bound estim ate due to the nature of the risk m odel used. This m e thod will not work at all for nonlinear dose-response m odels, such

as m a ny noncancer effects or for nonlinear carcinogenic dose-response m odels. In practice, it is difficult even to establish an accurate m ean health effect risk for a population. This is due to m a ny com p lications, including uncertainties in using anim al data for hum an dose-response relationships, nonlinearities in the dose-response curve, projecting incidence data from one group to another dissim ilar group, etc. Although it has been com m o n practice to estim ate the num ber of cases of disease, especially cancer, for populations exposed to chem icals, it should be understood that these estim ates are not m eant to be accurate predictions of real (or actuarial) cases of disease. The estim ate’s value lies in fram ing hypothetical risk in an understandable way rather than in any literal interpretation of the term “cases.” Another population risk descriptor is a st atem ent regarding how m a ny people are thought to be above a certain risk level or other point of dem a rcation. For carcinogens, this m i ght 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 m i ght 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 inform ation about the distribution of exposures or doses. Other population risk descriptors address th e way the risk burden is distributed am ong various segm ents of the subject population. The segm ents (or subgroups) could be divided by geographic location, age, sex, ethnic background, lifestyle, econom ic factors, or other dem ographic variables, or they could represent groups of persons with a typical sensitivity or susceptibility, such as asthm a tics. For assessors, this m eans that data m a y need to be evaluated for both highly exposed population segm ents and highly sensitive population segm ents. In cases involving a highly exposed population segm ent, the assessor m i ght approach this question by having this segm ent of the population in m i nd when developing the descriptors of high-end exposure or dose. Usually, however, these segm ents are identified (either a priori or from inspection of the data) and then treated as separate, unique populations in them selves, with segm ent-specific risk descriptors (population, individual, etc.) anal ogous to those used for the larger population.

2.3.3. Risk Descriptors In sum m a ry, exposure and dose inform ation developed as part of an exposure assessm ent

m a y be used in constructing risk descriptors. These are statem ents to convey inform ation about risk to users of that inform ation, prim arily risk m a nagers. Risk descriptors can be grouped as

30descriptors of individual risk or population ris k, and within these broad categories, there are several types of descriptors. Not all descriptors are applicable to all assessm ents. As a m a tter of policy, the Agency or individual program offices within the Agency m a y require one or m o re of these descriptors to be included in specific risk assessm ents. Because the type of descriptor translates fairly directly into the type of analysis the exposure assessor m u st perform , the exposure assessor needs to be aware of these policies. Additional inform ation on calculating and presenting exposure estim ates and risk descriptors is found in Sections 5 and 7 of these Guidelines.

313. PLANNING AN EXPOSURE ASSESSMENT

Exposure assessm ents are done for a variety of purposes, and for that reason, cannot easily be regim e nted into a set form at or protocol. Each assessm ent, however, uses a sim ilar set of

planning questions, and by addressing these questions the assessor will be better able to decide what is needed to perform the assessm ent and how to obtain and use the inform ation required. To facilitate this planning, the exposure assessor should consider som e basic questions:

Purpose : W hy is the study being conducted? W h at questions will the study address and how will the results be used? Scope : W h ere does the study area begin and end? W ill inferences be m a de on a national, regional, or local scale? W ho or what is to be m onitored? W h at chem icals and what m e dia will be m easured, and for which individuals, populations, or population segm ents will estim ates of exposure and dose be developed? Level of Detail : How accurate m u st the exposure or dose estim ate be to achieve the purpose? How detailed m u st the assessm ent be to properly account for the biological link between exposure, dose, effect, and risk, if necessary? How is the depth of the

assessm ent lim ited by resources (tim e and m oney), and what is the m o st effective use of those resources in term s of level of detail of the various parts of the assessm ent? Approach : How will exposure or dose be m easured or estim ated, and are these m e thods appropriate given the biological links am ong e xposure, dose, effect, and risk? How will populations be characterized? How will exposure concentrations be estim ated? W h at is known about the environm ental and biological fate of the substance? W h at are the im portant exposure pathways? W h at is known about expected concentrations, analytical

m e thods, and detection lim its? Are the presently available analytical m e thods capable of detecting the chem ical of interest and can they achieve the level of quality needed in the

assessm ent? How m a ny sam p les are needed? W h en will the sam p les be collected? How f r equently? 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 assessm ent will be used will often have significant im plications for the scope, level of detail, and approach of the assessm ent. Because

32of the com p lex nature of exposure assessm ents, a m u ltidisciplinary approach that encom p asses 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 assessm ent.

3.1.1. Using Exposure Assessments in Risk Assessment The National Research Council (NRC, 1983) described exposure assessm ent as one of the four m a jor areas of risk assessm ent (the others are hazard identification, dose-response

assessm ent, and risk characterization). The prim ary purpose of an exposure assessm ent in this application is often to estim ate dose, which is com b ined with chem ical-specific dose-response data (usually from anim al studies) in order to estim ate risk. Depending on the purpose of the risk assessm ent, the exposure assessm ent will need to em phasize certain areas in addition to quantification of exposure and dose. If the exposure assessm ent is part of a risk assessm ent to support regulations for specific chem ical sources, such as point em ission sources, consum er products, or pesticides, then the link between the source and the exposed or potentially exposed population is im portant. In this case, it is often necessary to trace chem icals from the source to the point of exposure by using source and fate m odels and exposure scenarios. By exam ining the individual com ponents of a scenario, assessors can focus their efforts on the factors that contribute the m o st to exposure, and perhaps use the exposure assessm ent to select possible actions to reduce risk. For exam ple, exposure

assessm ents are often used to com p are and select control or cleanup options. Most often the scenario evaluation is em ployed to estim ate the residual risk associated with each of the alternatives under consideration. These estim ates are com p ared to the baseline risk to determ ine the relative risk reduction of each alternative. These types of assessm ents can also be em ployed to m a ke screening decisions about whether to further investigate a particular chem ical. These

assessm ents can also benefit from verification through the use of personal or biological

m onitoring techniques. If the exposure assessm ent is part of a risk assessm ent perform e d to set standards for environm ental m e dia, usually the concentration levels in the m e dium that pose a particular risk level are im portant. Norm ally, these assessm ents place less em phasis on the ultim ate source of the chem ical and m o re em phasis on linking concentration levels in the m e dium with exposure and dose levels of those exposed. A com b ination of m e dia m easurem ents and personal exposure

m onitoring could be very helpful in assessm ents for this purpose, since what is being sought is the relationship between the two. Modeling m a y also support or supplem ent these assessm ents. If the exposure assessm ent is part of a risk assessm ent used to determ ine the need to

rem e diate a waste site or chem ical spill, the em phasis is on calculating the risk to an individual

or sm all group, com p aring that risk to an acceptable risk level, and if necessary determ ining

33appropriate cleanup actions to reach an acceptable risk. The source of chem ical contam ination

m a y or m a y not be known. Although personal exposure m onitoring can give a good indication of the exposure or dose at the present tim e, often the risk m a nager m u st m a ke a decision that will protect health in the future. For this reason, m odeling and scenario developm ent are the prim ary techniques used in this type of assessm ent. Em phasis is usually placed on linking sources with the exposed individuals. Biological m onitoring m a y also be helpful (in cases where the

m e thodology is established) in determ ining if exposure actually results in a dose, since som e chem icals are not bioavailable even if intake occurs. If the exposure assessm ent is part of a risk assessm ent used as a screening device for setting priorities, the em phasis is m o re on the com p arative risk levels, perhaps with the risk estim ates falling into broad categories (e.g., sem i -quantitative categories such as high, m e dium , and low). For such quick-sorting exercises, rarely are any techniques used other than m odeling and scenario developm ent. Decisions m a de in such cases rarely involve direct cleanup or regulatory action without further refinem e nt of th e risk assessm ent, so the scenario developm ent approach can be a cost-ef f ective way to set general priorities f o r f u ture investigation of worst risk first. If the exposure assessm ent is part of a risk a ssessm ent that is wholly predictive in nature, such as for the prem anufacture notice (PMN) program , a m odeling and scenario developm ent approach is recom m e nded. In such cases, m easurem ent of chem icals yet to be m a nufactured or in the environm ent is not possible. In this case again, the link between source and exposed individuals is em phasized. Not only are risk assessm ents 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 m a jor effect on how the exposure inform ation m u st be collected and analyzed for a risk

assessm ent. This is discussed in m o re detail in Section 3.5.1.

3.1.2. Using Exposure Assessments for Status and Trends Exposure assessm ents can also be used to determ ine whether exposure occurs and to

m onitor status and trends. The em phasis in these exposure assessm ents is on what the actual exposure (or dose) is at one particular tim e, and how the exposure changes over tim e. Exam ples of this type of assessm ent are occupational studies. Characteristics and special considerations f o r occupational studies have been discussed by the National Institute f o r Occupational Saf e ty and Health (NIOSH, 1988). Exposure status is the snapshot of exposure at a given tim e, usually the exposure profile of a population or population segm ent (perhaps a segm ent or statistical sam p le that can be

34studied periodically). Exposure trends show how this profile changes with tim e. Norm ally, status and trends studies m a ke use of statistical sam p ling strategies to assure that changes can be interpreted m eaningfully. These data are particularly useful if actions for risk am elioration and dem onstration of the effectiveness of these actions can be m a de through exposure trend

m easurem ents. Measurem ent is critical to such assessm ents. Personal m onitoring can give the m o st accurate picture of exposure, but biological or m e dia m onitoring can indicate exposure levels, provided a strong link is established between the biological or m e dia levels and the exposure levels. Usually this link is established first by correlating biological or m e dia levels with personal m onitoring data for the sam e population over the sam e period.

3.1.3. Using Exposure Assessments in Epidemiologic Studies Exposure assessm ents can also be im portant com ponents of epidem iologic studies, where the em phasis is on using the exposure assessm ent to establish exposure-incidence (or doseeffect) relationships. For this purpose, personal m onitoring, biological m onitoring, and scenario developm ent have all been used. If the population under study is being currently exposed, personal m onitoring or biological m onitoring m a y be particularly helpful in establishing exposure or dose levels. If the exposure took place in the past, biological m onitoring m a y provide useful data, provided the chem ical is am enable to detection without interference or degradation, and the pharm acokinetics are known. More often, however, scenario developm ent techniques are used to estim ate exposure in the past, and often the accuracy of the estim ate is lim ited to classifying exposure as high, m e dium , or low. This type of categorization is rather

com m on, but som e tim es it is very difficult to determ ine who belongs in a category, and to interpret the results of the study. Although epidem iologic protocols are beyond the scope of these Guidelines, the use of exposure assessm ent for epidem iology has been described by the W o rld Health Organization (W HO, 1983).

3.2. SCOPE OF THE ASSESSMENT The scope of an assessm ent refers to its com p rehensiveness. For exam ple, an im portant lim itation in m a ny exposure assessm ents relates to the specific chem ical(s) to be evaluated. Although this seem s obvious, where exposure to m u ltiple chem icals or m i xtures is possible, it is not always clear whether assessing “all” chem icals will result in a dif f e rent risk value than if only certain significant chem icals are assessed and the others assum e d to contribute only a m i nor
am ount to the risk. This m a y also be true for cases where degradation products have equal or greater toxicological concerns. In these cases, a prelim inary investigation m a y be necessary to determ ine which chem icals are likely to be in high enough concentrations to cause concern, with

35the possible contribution of the others discussed in the uncertainty assessm ent. The assessor

m

u st also determ ine geographical boundaries, population exposed, environm ental m e dia to be considered, and exposure pathways and routes of concern. The purpose of the exposure assessm ent will usually help define the scope. There are characteristics that are unique to national exposure assessm ents as opposed to industry-wide or local exposure assessm ents. For exam ple, exposure assessm ents in support of national regulations m u st be national in scope; exposure assessm ents to support cleanup decisions at a site will be local in scope. Exposure assessm ents to support standards for a particular m e dium will often concentrate on that m e dium ’s concentration levels and typical exposure pathways and routes, although the other pathways and routes are also often estim ated for perspective.

3.3. LEVEL OF DETAIL OF THE ASSESSMENT The level of detail, or depth of the assessm ent, is m easured by the am ount and resolution of the data used, and the sophistication of the analysis em ployed. It is determ ined by the purpose of the exposure assessm ent and the resources available to perform the assessm ent. Although in theory the level of detail needed can be estab lished by determ ining the accuracy of the estim ate required, this is rarely the case in practice. To conserve resources, m o st assessm ents are done in an iterative fashion, with a screening done first; successive iterations add m o re 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 assessm ent? If the answer is no, successive iterations continue until the answer is affirm ative, new input data are generated, or as is the case for m a ny assessm ents, the available data, tim e, or resources are depleted. Resourcelim ited assessm ents should be evaluated in term s of what part of the original objectives have been accom p lished, and how this affects the use of the results. The level of detail of an exposure assessm ent can also be influenced by the level of sophistication or uncertainty in the assessm ent of health effects to be used for a risk assessm ent. If only very weak health inform ation is available, a detailed, costly, and in-depth exposure

assessm ent will in m o st cases be wastef ul, since the m o st detailed inf o rm ation will not add signif i cantly to the certainty of the risk assessm ent.

3.4. DETERMINING THE APPROACH FOR THE EXPOSURE ASSESSMENT The intended use of the exposure assessm ent will generally favor one approach to quantifying exposure over the others, or suggest that two or m o re approaches be com b ined. These approaches to exposure assessm ent can be viewed as different ways of estim ating the

sam e exposure or dose. Each has its own unique characteristics, strengths, and weaknesses, but the estim ate should theoretically be the sam e , independent of the approach taken.

36The point-of-contact approach requires m easurem ents of chem ical concentrations at the point where they contact the exposed individuals, and a record of the length of tim e of contact at each concentration. Som e integrative techniques are inexpensive and easy to use (radiation badges), while others are costly and m a y present logistical challenges (personal continuoussam p ling devices), and require public cooperation. The scenario evaluation approach requires chem ical concentration and tim e-of-contact data, as well as inform ation on the exposed persons. Chem ical concentration m a y be determ ined by sam p ling and analysis or by use of fate and transport m odels (including sim p le dilution

m odels). Models can be particularly helpful when som e analytical data are available, but resources for additional sam p ling are lim ited. Inform ation on hum an behavior and physical characteristics m a y be assum e d or obtained by in terviews or other techniques from individuals who represent the population of interest. For the reconstruction of dose approach, the exposure assessor usually uses m e asured body burden or specific biom arker data, and select s or constructs a biological m odel that uses these data to account for the chem ical’s behavior in the body. If a pharm acokinetic m odel is used, additional data on m e tabolic processes will be required (as well as m odel validation inform ation). Inform ation on exposure routes and relative source strengths is also helpful. One of the goals in selecting the approach should include developing an estim ate having an acceptable am ount of uncertainty. In general, estim ates based on quality-assured

m easurem ent data, gathered to directly answer the questions of the assessm ent, are likely to have less uncertainty than estim ates based on indirect inform ation. The approach selected for the

assessm ent will determ ine which data are needed. All three approaches also require data on intake and uptake rates if the final product of the assessm ent is a calculated dose. Som e tim es m o re than one approach is used to estim ate exposure. For exam ple, the TEAM study com b ines point-of-contact m easurem ent with the m i croenvironm ent (scenario evaluation) approach and breath m easurem ents for the reconstruction of dose approach (U.S. EPA, 1987a). If m o re 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

m easurem ents can be used as a check on assessm ents m a de by scenario evaluation.

3.5. ESTABLISHING THE EXPOSURE ASSESSMENT PLAN Before starting work on an exposure assessm ent, the assessor should have determ ined the purpose, scope, level of detail, and approach for the assessm ent, and should be able to translate these into a set of objectives. These objectives will be the foundation for the exposure

assessm ent plan. The exposure assessm ent plan need not be a lengthy or form al docum ent,

37especially for assessm ents that have a narrow scope and little detail. For m o re com p lex exposure

assessm ents, however, it is helpf u l to have a written plan. For exposure assessm ents being done as part of a risk assessm ent, the exposure

assessm ent plan should reflect (in addition to the objectives) an understanding of how the results of the exposure assessm ent will be used in the risk assessm ent. For som e assessm ents, three additional com ponents m a y be needed: the sam p ling strategy (Section 3.5.2), the m odeling strategy (Section 3.5.3), and the com m unications strategy (Section 7.1.3).

3.5.1. Planning an Exposure Assessment as Part of a Risk Assessment For risk assessm ents, exposure inform ation m u st 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, reproductiv e effects, neurotoxic effects) can vary widely, and along with other aspects of the hazard identification and dose-response relationships, can have a m a jor effect on how the exposure inform ation m u st be collected and analyzed for a risk assessm ent. Som e of these aspects include im plications of lim ited versus repeated exposures, dose-rate considerations, reversibility of toxicological processes, and com position of the exposed population.

C Limited Versus Repeated Exposures. Current carcinogen risk m odels often use lifetim e tim e-weighted average doses in the dose-response relationships owing to their derivation from lifetim e anim al studies. This does not m ean cancer cannot occur after single exposures (witness the A-bom b experience), m e rely that exposure inform ation

m

u st be consonant with the source of the m odel. Som e toxic effects, however, occur after a single or a lim ited num ber of exposures, including acute reactions such as anesthetic effects and respiratory depression or certain developm ental effects following exposure during pregnancy. For developm ental effects, for exam ple, lif etim e tim e-weighted averages have little relevance, so dif f e rent types of data m u st be collected, in this case usually shorter-term exposure profile data during a particular tim e window. Consequently, the exposure assessors and scientists who conduct

m onitoring studies need to collaborate with those scientists who evaluate a chem ical’s hazard potential to assure the developm ent of a m eaningful risk assessm ent. If shortterm peak exposures are related to the effect, then instrum e nts used should be able to

m easure short-term peak concentrations. If cum u lative exposure is related to the effect, long-term average sam p ling strategies will probably be m o re appropriate.

C Dose-Rate Effects. The use of average daily exposure values (e.g., ADD, LADD) in a dose-response relationship assum e s that within som e lim its, increm ents of C tim es T (exposure concentration tim es tim e) that are equal in m a gnitude are equivalent in their

38potential 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 m a y need a m o re precise determ ination of the tim e 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

assum e s that repeated dosing continues to add to the risk potential. In som e cases, after cessation of exposure, toxicological processes are reversible over tim e. In these cases, exposure assessm ents m u st provide enough inform ation so that the risk assessor can account for the potential influence of episodic exposures.

C Composition of the Exposed Population. For som e substances, the type of health effect m a y vary as a function of age or sex. Likewise, certain behaviors (e.g.,

sm oking), diseases (e.g., asthm a ), and genetic traits (e.g., glucose-6-phosphate dehydrogenase deficiency) m a y affect the response of a person to a chem ical substance. Special population segm ents, such as children, m a y also call for a specialized approach to data collection (W HO, 1986).

3.5.2. Establishing the Sampling Strategy If the objectives of the assessm ent are to be m e t using m easurem ents, it is im portant to establish the sam p ling strategy before sam p les are actually taken. The sam p ling strategy includes setting data quality objectives, developing the sam p ling plan and design, using spiked and blank sam p les, assessing background levels, developing quality assurance project plans, validating previously generated data, and selecting and validating analytical m e thods.

3.5.2.1. Data Quality Objectives All m easurem ents are subject to uncertainty because of the inherent variability in the quantities being m easured (e.g., spatial and tem poral variability) and analytical m easurem ent variability introduced during the m easurem ent process through sam p ling and analysis. Som e sources of variability can be expressed quantitatively, but others can only be described qualitatively. The larger the variability associated with individual m easurem ents, 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

m a nagem e nt 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 advantag es. DQOs should consider data needs, costeffectiveness, and the capability of the m easurem ent process. The am ount of data required

39depends on the level of detail necessary for the purpose of the assessm ent. Estim ates of the

num ber of sam p les to be taken and m easurem ents to be m a de should account for expected

sam p le variability. Finally, DQOs help clarify study objectives by com p elling the exposure assessor to establish how the data will be used bef o re they are collected. The exposure assessor establishes data criteria by proposing lim its (based on best judgm ent 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 statem ent of study objectives, to include an estim ation of the key study param e ters, identifying the hypotheses being tested, the specific aim s of the study, and how the results will be used.

C The scope of study objectives, to include the m i nim u m size of subsam ples from which separate results m a y be calculated, and the largest unit (area, tim e period, or group of people) the data will represent.

C A description of the data to be obtained, the m e dia to be sam p led, and the capabilities of the analytical m e thodologies.

C The acceptable probabilities and uncertainties associated with false positive and false negative statem ents.

C A discussion of statistics used to sum m a rize the data; any standards, reference values, or action levels used for com p arison; and a description and rationale for any

m a them atical or statistical procedures used.

C An estim ate of the resources needed.

3.5.2.2. Sampling Plan The sam p ling plan specifies how a sam p le is to be selected and handled. An inadequate plan will often lead to biased, unreliable, or m eaningless results. Good planning, on the other hand, m a kes optim al use of lim ited resources and is m o re likely to produce valid results. The sam p ling design specifies the num ber and types of sam p les needed to achieve DQOs. Factors to be considered in developing the sam p ling design include study objectives, sources of variability (e.g., tem poral and spatial heterogeneity, analytical differences) and their relative

m a gnitudes, relative costs, and practical lim itations of tim e, cost, and personnel. Sam p ling design considers the need for tem poral and spatial replication, com positing (com bining several sam p les prior to analysis), and m u ltiple determ inations on a single sam p le. A statistical or environm ental process m odel m a y be used to allocate sam p ling effort in the m o st efficient m a nner.

40Data m a y be collected using a survey or an experim e ntal approach. It m a y be desirable to stratif y the sam p le if it is suspected that dif f e rences exist between segm ents of the statistical population being sam p led. In such cases, the stratified sam p ling plan assures representative

sam p les of the obviously different parts of the sam p le population while reducing variance in the

sam p le data. The survey approach estim ates population exposure based on the m easured exposure of a statistically representative sam p le of the population. In som e situations the study objectives are better served by an experim e ntal approach; this approach involves experim e nts designed to determ ine the relationship between two or m o re factors, (e.g., between house construction and a particular indoor air pollutant). In the experim e ntal approach, experim e ntal units are selected to cover a range of situati ons (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 experim e nt can be com b ined with other data (e.g., distribution of housing types) to estim ate exposure. An advantage of the experim e ntal approach is that it m a y provide m o re insight into underlying m echanism s which m a y be im portant in targeting regulatory action. However, as in all experim e nt al work, one m u st argue that the relationships revealed apply beyond that particular experim e nt. A study m a y use a com b ination of survey and experim e ntal techniques and involve a variety of sam p ling procedures. A sum m a ry of m e thods for m easuring worker exposure is found in Lynch (1985). Sm ith et al. (1987) provide guida nce for field sam p ling of pesticides. Relevant EPA reference docum ents include Survey Managem e nt Handbook, Volum e s I and II (U.S. EPA, 1984b); Soil Sam p ling Quality Assurance User’s Guide (U.S. EPA, 1990a); and A Rationale for the Assessm ent of Errors in the Sam p ling of So ils (U.S. EPA, 1989a). A detailed description of

m e thods for enum erating and characterizing populations exposed to chem ical substances is contained in Methods for Assessing Exposure to Chem ical Substances, Volum e 4 (U.S. EPA, 1985a). Factors to be considered in selecting sam p ling locations include population density, historical sam p ling results, patterns of environm ental contam ination and environm ental characteristics such as stream flow or prevailing wind direction, access to the sam p le site, types of sam p les, and health and safety requirem e nts.

The f r equency and duration of sam p le collection will depend on whether the risk assessor is concerned with acute or chronic exposures, how rapidly contam ination patterns are changing, ways in which chem icals are released into the environm ent, and whether and to what degree physical conditions are expected to vary in the future. There are m a ny sources of inform ation on m e thods for selecting sam p ling locations. Schweitzer and Black (1985) and Schweitzer and Santolucito (1984) give statistical m e thods for selecting sam p ling locations for ground water, soil, and hazardous wastes. A practical guide for

41ground-water sam p ling (U.S. EPA, 1985b) and a handbook for stream sam p ling (U.S. EPA, 1986d) are also available. The type of sam p le to be taken and the physical and chem ical properties of the chem ical of concern usually dictate the sam p ling frequency. For exam ple, determ ining the concentration of a volatile chem ical in surface water requires a higher sam p ling frequency than necessary for ground water because the chem ical concentration of the surface water changes m o re rapidly. Sam p ling frequency m i ght also depend on whether the health effects of concern result from acute or chronic exposures. More frequent sam p ling m a y be needed to determ ine peak exposures versus average exposure. A prelim inary survey is often used to estim ate the optim um num ber, spacing, and

sam p ling 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 W a rd (1981) discuss statistical techniques for determ ining the optim al num ber of sam p les. Sam p ling duration depends on the analytical m e thod chosen, the lim its of detection, the physical and chem ical properties of the analyte, chem ical concentration, and knowledge of transport and transform a tion m echanism s

. Sam p ling duration m a y be extended to ensure adequate collection of a chem ical at low concentration or curtailed to prevent the breakthrough of one at high concentration. Sam p ling duration is directly related to selection of statistical procedures, such as trend or cross-sectional analyses. Storage stability studies with periodic sam p le analysis should norm a lly be run concurrently with the storage of treated sam p les. However, in certain situations where chem icals 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 m a xim u m tim e of storage can be determ ined prior to sam p le collection and storage. Unless storage stability has been previously docum ented, sam p les should be analyzed as soon as possible after collection to avoid storage stability problem s. Individual program s m a y have specific tim e lim its on storage, depending on the types of sam p les being analyzed.

3.5.2.3. Quality Assurance Samples Sam p ling should be planned to ensure that the sam p les are not biased by the introduction of field or laboratory contam inants. If sam p le validity is in question, all associated analytical data will be suspect. Field- and laboratory-spiked sam p les and blank sam p les should be analyzed concurrently to validate results. The plan should provide instructions clear enough so that each worker can collect, prepare, preserve, and analyze sam p les according to established protocols.

42Any data not significantly greater than blank sam p le levels should be used with considerable caution. All values should be reported as m easured by the laboratory, but with appropriate caveats on blank sam p le levels. The m e thod for interpreting and using the results from blank sam p les depends on the analyte and should be specified in the sam p ling plan. The following guidance is recom m e nded:

C For volatiles and sem i volatiles, no positive sam p le results should be reported unless the concentration of the com pound in the sam p le exceeds 10 tim es the am ount in any blank for the com m on laboratory contam inants m e thylene chloride, acetone, toluene, 2-butanone, and com m on phthalate esters. The am ount for other volatiles and

sem i volatiles should exceed 5 tim es the am ount in the blank (U.S. EPA, 1988d).

C For pesticides and polychlorinated biphenyls (PCBs) no positive sam p le results should be reported unless the concentration in the sam p le exceeds 5 tim es that in the blank (U.S. EPA, 1988d). If a pesticide or PCB is found in a blank but not in a sam p le, no action is taken.

C For inorganics, no positive sam p le results should be reported if the results are less than 5 tim es the am ount in any blank (U.S. EPA, 1988e).

3.5.2.4. Background Level Background presence m a y be due to natural or anthropogenic sources. At som e sites, it is significant and m u st be accounted for. The exposure assessor should try to determ ine local background concentrations by gathering data from nearby locations clearly unaffected by the site under investigation. W h en differences between a background (control area) and a target site are to be determ ined experim e ntally, the control area m u st be sam p led with the sam e detail and care as the target.

3.5.2.5. Quality Assurance and Quality Control Quality assurance (QA) assures that a product m eets defined standards of quality with a stated level of conf idence. QA includes quality control. Quality assurance begins with the establishm ent of DQOs and continues throughout the

m easurem ent 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

m i sunderstanding. The plan should list the DQOs and fully describe the analytes, all m a terials,

m e thods, and procedures used, and the responsibilities of project participants. The EPA has prepared a guidance docum ent (U.S. EPA, 1980) that describes all these elem ents and provides

com p lete guidance for plan preparation. Quality control (QC) ensures a product or service is

43satisfactory, dependable, and econom ical. A QC program should include developm ent and strict adherence to principles of good laboratory practice, consistent use of standard operational procedures, and carefully-designed protocols for each m easurem ent 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 m a y 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 m e thod selection and validation should also be followed when analyzing existing data. Other points considered in data evaluation include the collection protocol, analytical m e thods, det ection lim its, laboratory perform a nce, and sam p le handling.

3.5.2.7. Selection and Validation of Analytical Methods There are several m a jor steps in the m e thod selection and validation process. First, the assessor establishes m e thods requirem e nts. Next, existing m e thods are reviewed for suitability to the current application. If a new m e thod m u st be developed, it is subjected to field and laboratory testing to determ ine its perform a nce; these tests are then repeated by other laboratories using a round robin test. Finally, th e m e thod is revised as indicated by laboratory testing. The reader is referred to Guidance for Data Useability in Risk Assessm ent (U.S. EPA, 1990b) for extensive discussion of this topic.

3.5.3. Establishing the Modeling Strategy Of ten the m o st critical elem ent of the assessm ent is the estim ation of pollutant concentrations at exposure points. This is usua lly carried out by a com b ination of field data and

m a them atical m odeling results. In the absence of field data, this process often relies on the results of m a them atical m odels (U.S. EPA, 1986e, 1987b, 1987c, 1988f, 1991b). EPA’s Science Advisory Board (U.S. EPA, 1989b) has conclude d that, ideally, m odeling should be linked with

m onitoring data in regulatory assessm ents, although this is not always possible (e.g., for new chem icals). A m odeling strategy has several aspects, including setting objectives, m odel selection, obtaining and installing the code, calibrating and running the com puter m odel, and validation and verification. Many of these aspects are analogous to the QA/QC m easures applied to

m easurem ents.

443.5.3.1. Setting the Modeling Study Objectives The first step in using a m odel to estim ate concentrations and exposure is to clearly define the goal of the exposure assessm ent and how the m odel can help address the questions or hypotheses of the assessm ent. This includes a clear statem ent of what inform ation the m odel will help estim ate, and how this estim ate will be used. The approach m u st be consistent with known project constraints (i.e., schedule, budget, and other resources).

3.5.3.2. Characterization and Model Selection Regardless of whether m odels are extensively used in an assessm ent and a form al

m odeling strategy is docum ented in the exposure assessm ent plan, when com puter sim u lation

m odels such as fate and transport m odels and exposure m odels are used in exposure assessm ents, the assessor m u st be aware of the perform a nce characteristics of the m odel and state how the exposure assessm ent requirem e nts are satisfied by the m odel. If m odels are to be used to sim u late pollutant behavior at a specific site, the site m u st be characterized. Site characterization for any m odeling study includes exam ining all data on the site such as source characterization, dim e nsions and topography of the site, location of receptor populations, m e teorology, soils, geohydrology, and ranges and distributions of chem ical concentrations. For exposure m odels that sim u late both chem ical concentration and tim e of exposure (through behavior patterns) data on these two param e ters m u st be evaluated. For all m odels, the m odeler m u st determ ine if databases are available to support the site, chem ical, or population characterization, and that all param e ters required by the m odel can be obtained or reasonable default values are available. The assessm ent goals and the results of the characterization step provide the technical basis for m odel selection. Criteria are provided in U.S. EPA (1987b, 1988f) for selection of surface water m odels and ground-water m odels respectively; the reader is referred to these docum ents for details. Sim ilar selection criteria exist for air dispersion m odels (U.S. EPA, 1986e, 1987c, 1991b). A prim ary consideration in selecting a m odel is whether to perform a screening study or to perform a detailed study. A screening study m a kes a prelim inary evaluation of a site or a general com p arison between several sites. It m a y be generic to a type of site (i.e., an industrial segm ent or a clim atic region) or m a y 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 exam ple, providing an indication of the level of detection and quantification that would be required and the distances and direc tions from a point of release where chem ical concentrations m i ght be expected to be highest. The value of the screening-level analysis is that it is sim p le to perform and m a y indicate that no significant contam ination problem exists. Screening-level m odels are frequently used to

45get a first approxim a tion of the concentrations that m a y be present. Often these m odels use very conservative assum p tions; 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 som e predeterm i ned no-concern level, then a m o re detailed analysis is probably not necessary. If the screening estim ates are above that level, refinem e nt of the assum p tions or a

m o re sophisticated m odel are necessary for a m o re realistic estim ate. Screening-level m odels also help the user conceptualize the physical system , identify im portant processes, and locate available data. The assum p tions used in the prelim inary analysis should represent conservative conditions, such that the predicted results overestim ate potential conditions, lim iting false negatives. If the lim ited field m easurem ents or screening analyses indicate that a contam ination problem m a y exist, then a detailed m odeling study m a y be useful. A detailed study is one in which the purpose is to m a ke a detailed evaluation of a specific site. The approach is to use the best data available to m a ke the best estim ate of spatial and tem poral distributions of chem icals. Detailed st udies typically require m u ch m o re data of higher quality and m odels of greater sophistication.

3.5.3.3. Obtaining and Installing the Computer Code It m a y be necessary to obtain and install the com puter code for a m odel on a specific

com puter system

. Modern com puter system s a nd software have a variety of differences that require changes to the source code being installed. It is essential to verify that these

m odifications do not change the way the m odel works or the results it provides. If the m odel is already installed and supported on a com puter system to which the user has access, this step is sim p lif ied greatly. Criteria for using a m odel include its dem onstrated acceptability and the ease with which the m odel can be obtained. Factors include availability of specif ic m odels and their docum entation, verification, and validation. These so-called im plem entation criteria relate to the practical considerations of m odel use and m a y be used to further narrow the selection of technically acceptable m odels.

3.5.3.4. Calibrating and Running the Model Calibration is the process of adjusting selected m odel param e ters within an expected range until the dif f e rences between m odel predictions and f i eld observations are within selected criteria. Calibration is highly recom m e nded for all operational, determ inistic m odels. Calibration accounts for spatial variations not represented by the m odel form ulation; functional dependencies of param e ters that are either nonquantifiable, unknown, or not included in the

m odel algorithm s ; or extrapolation of laboratory m easurem ents to field conditions.

46Extrapolation of laboratory m easurem ents to field conditions requires considerable care since

m a ny unknown factors m a y cause differences between laboratory and field. The final step in the m odeling portion of an exposure assessm ent is to run the m odel and generate the data needed to answer the questions posed in the study objectives. Experience and f a m iliarity with a m odel can also be im portant. This is especially true with regard to the m o re com p lex m odels. Detailed m odels can be quite com p lex with a large

num ber of input variables, outputs, and com puter-related requirem e nts. It frequently takes

m onths to years of experience to fully com p rehend all aspects of a m odel. Consequently, it is suggested that an exposure assessor select a fam iliar m odel if it possesses all the selection criteria, or seek the help of experienced exposure m odelers.

3.5.3.5. Model Validation Model validation is a process by which the accuracy of m odel results is com p ared with actual data from the system being sim u lated. There are num erous levels of validation of an environm ental fate m odel, for exam ple, such as verifying that the transport and transform a tion concepts are appropriately represented in the m a them atical equations, verifying that the

com puter code is free from error, testing the m odel against laboratory m i crocosm s , running field tests under controlled conditions, running general fiel d tests, and repeatedly com p aring field data to the m odeling results under a variety of conditions and chem icals. In essence, validation is an independent test of how well the m odel (with its calibrated param e ters) represents the im portant processes occurring in the natural system

. Although field and environm ental conditions are often different during the validation step, param e ters fixed as a result of calibration are not readjusted during validation. 23 The perf orm a nce of m odels (their ability to represent m easured data) is of ten dram atically influenced by site characterization and how m odels represent such characteristics. Characterizing com p lex, heterogenous physical system s presents m a jor challenges; m odeling representations of such system s m u st be evaluated in light of that difficulty. In m a ny cases, the apparent inability to m odel a system is caused by incom p lete physical characterization of the system

. In other cases the uncertainties cannot be readily apportioned between the m odel per se and the m odel’s input data. In addition to com p aring m odel results with actual data (thus illustrating accuracy, bias, etc.), the m odel validation process provides inform ation about conditions under which a sim u lation will be acceptable and accurate, and under what conditions it should not be used at

23 In other words, a fundam e ntal rule is that a m odel shoul d not be validated using data that were already used to generate or calibrate the m odel, since doing so would not be an independent test.
47all. All m odels have specific ranges of application and specific classes of chem icals for which they are appropriate. Assessors should be aware of these lim itations as they develop m odeling 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 epidem iologic studies, they need to be representative of the past exposure conditions, which m a y have changed with tim e. The scope and level of detail of the assessm ent depends greatly on the availability and quality of past data. Several approaches for determ ining and estim ating past exposure are provided in the literature (W axweiler et al., 1988; Stern et al., 1986; NI OSH, 1988; Greife et al., 1988; Hornung and Meinhardt, 1987).

484. GATHERING AND DEVELOPING DATA FOR EXPOSURE ASSESSMENTS

The inform ation needed to perform an exposure assessm ent will depend on the approach(es) selected in the planning stage (S ection 3). For those assessm ents using point-ofcontact m easurem ents, the inform ation includes:

C Measured exposure concentrations and duration of contact. For assessm ents using the scenario evaluation m e thod for estim ating exposures, the needed inform ation includes:

C Inform ation on chem ical concentrations in m e dia, usually desirable in the form at of a concentration-tim e-location prof ile.

C Inform ation on persons who are exposed and the duration of contact with various concentrations. For assessm ents estim ating exposure from dose, the inform ation includes:

C Biom arker data.

C Pharm acokinetic relationships, including the data to support pharm acokinetic m odels. If dose is to be calculated, data are needed on:

C Intake and uptake, usually in the form of rates. Inform ation on sources, both natural and anthropogenic is usually helpful. If the agent has natural sources, the contribution of these to environm ental concentrations m a y be relevant. These background concentrations m a y be particularly im portant 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 m a y not be relevant.

4.1. MEASUREMENT DATA FOR POINT-OF-CONTACT ASSESSMENTS This approach requires that chem ical concentrations be m easured at the interface between the person and the environm ent, usually through the use of personal m onitors; there are currently

no m odels to assist in the process of obtaining the concentration-tim e data itself. The chem ical concentrations contacted in the m e dia are m easured by sam p ling the individual’s breathing zone, food, and water. These m e thodologies were originally developed for occupational m onitoring; they m a y have to be m odified for exposures outside the workplace. An exam ple of this is the developm ent of a sm all pum p and collector used in the TEAM studies (U.S. EPA, 1987a). In order to conduct these studies, a m onitoring device had to be developed that was sufficiently

sm all and lightweight so that it could be worn by the subjects. The Total Hum a n Exposure and Indoor Air Quality (U.S. EPA, 1988h) report is a useful bibliography covering m odels, field data, and em erging research m e thodologies, as well as new techniques for accurately determ ining exposure at nonoccupational levels.

49New data for a particular exposure assessm ent m a y be developed through the use of point-of-contact m e thods, or data from prior studies can som e tim es be used. In determ ining whether existing point-of-contact m onitoring data can be used in another assessm ent, the

assessor m u st consider the factors that existed in the original study and that influenced the exposure levels m easured. Som e of these factors are proxim ity to sources, activities of the studied individuals, tim e of day, season, and weather conditions. Point-of-contact data are valuable in evaluating overall population exposure and checking the credibility of exposure estim ates generated by other m e thods.

4.2. OBTAINING CHEMICAL CONCENTRATION INFORMATION The distribution of chem ical concentrations is used to estim ate the concentration that

com e s in contact with the individual(s) at any given tim e and place. This can be done through personal m onitoring, but for a variety of reasons, in a given assessm ent, personal m onitoring

m a y not be feasible. Alternative m e thods involve m easuring the concentration in the m e dia, or

m odeling the concentration distribution based on source strength, m e dia transport, and chem ical transform a tion processes. For exposure scenario evaluation, m easurem ents and m odeling of

m e dia concentrations are often used together. Many types of m easurem ents can be used to help determ ine the distribution of chem ical concentrations in m e dia. They can be m easurem ents of the concentrations in the m e dia them selves, m easurem ents of source strength, or m easurem ents of environm ental fate processes which will allow the assessor to use a m odel to estim ate the concentration in the m e dia at the point of contact. Table 2 illustrates som e of the types of m easurem ents used by exposure assessors, along with notes concerning what additional inform ation is usually needed to use these

m easurem ents in estim ating exposure or dose. For epidem iologic studies, questionnaires are often used when data are not m easurable or are otherwise unavailable.

4.2.1. Concentration Measurements in Environmental Media Measured concentration data can be generated for the exposure assessm ent by a new field study, or by evaluating concentration data from com p leted field study results and using them to estim ate concentrations. Media m easurem ents taken close to the point of contact with the individual(s) in space and tim e are preferable to m easurem ents far rem oved geographically or tem porally. As the distance from the point of contact increases, the certainty of the data at the

50Table 2.Examples of types of measurements tocharacterizeexposure-related media and parametersaType ofmeasurement (sample)Usuallyattempts tocharacterize(whole)ExamplesTypical information needed to characterizeexposureA.POSURE SCENARIO EVALUATION1.Fixed-location monitoringEnvironmental medium; samples used toestablish long-termindications of mediaquality and trends.National StreamQualityAccountingNetwork (NASQAN),bwater qualitynetworks, air quality networks.Population location and activities relative tomonitoring locations; fate of pollutants overdistance between monitoring and point ofexposure; time variation of pollutantconcentration at point of exposure.2.Short-termmedia monitoringEnvironmental orambient medium; samplesused to establish a snapshot of quality ofmediumoverrelativelyshort time.Special studies of environmental media,indoorair.Population location and activities (this is criticalsince it must be closelymatched to variations inconcentrations due to short period of study);fateof pollutants between measurement point andpoint of exposure; time variation of pollutantconcentration at point of exposure.3.onitoring of facilitiesRelease rates to the environment fromsources (facilities).s ofrelationships between release amounts andvarious operating parameters of the facilities.Stack sampling,effluent sampling,leachate sampling fromlandfills,incinerator ash sampling, fugitiveemissions sampling,pollution controldevice sampling.Fate of pollutants frompoint of entryinto theenvironment to point of exposure; populationlocation and activities; time variation of release.4.Food samples(also see #9 below)Concentrations of contaminants in foodsupply.FDA Total Diet StudyProgram,cmarket basket studies, shelfstudies,cooked-food diet sampling.Dietaryhabits of various age,sex,orculturalgroups.Relationship between food itemssampled and groups (geographic,ethnic,demographic)studied.Relationships betweenconcentrations in uncooked versus prepared food.5.Drinking watersamplesConcentrations of pollutants in drinkingwatersupply.Ground WaterSupplySurvey,dCommunityWaterSupplySurvey,etapwater.Fate and distribution ofpollutants frompoint ofsample to point of consumption.Populationserved by specific facilities and consumptionrates.Forexposure due to otheruses (e.g.,cooking and showering),need to know activitypatterns and volatilization rates.FOR USE IN EXSource mOften given in term

51aTo 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 characterizeexposure-related media and parameters (continued)Type ofmeasurement (sample)Usuallyattempts tocharacterize(whole)ExamplesTypical information needed to characterizeexposure6.ConsumerProductsConcentration levels of various products.Shelf surveys,e.g.,solventconcentration in household cleaners.fEstablish use patterns and/ormarket share ofparticularproducts,individual exposure at varioususage levels,extent of passive exposure.7.Breathing Zone MeasurementsExposure to airborne chemicals.Industrial hygiene studies,occupational surveys,indoorairstudies.Location, activities, and time spent relative tomonitoring locations.Protectivemeasures/avoidance.8.Microenvironmental StudiesAmbient mediumin a defined area,e.g.,kitchen, automobile interior, office setting,parking lot.Special studies of indoorair,housedust, contaminated surfaces, radonmeasurements, office building studies.Activities of studypopulations relative tomonitoring locations and time exposed.9.ace Soil SampleDegree ofcontamination ofsoil available forcontact.Soil samples at contaminated sites.Fateof pollution on/in soil;activities of potentiallyexposed populations.10.Soil CoreSoil including pollution available forgroundwatercontamination; can be an indication ofquality and trends over time.Soil sampling at hazardous waste sites.Fateof substance in soil; speciation andbioavailability, contact and ingestion rates as afunction of activitypatterns and age.11.Fish TissueExtent of contamination ofedible fish tissue.National Shellfish Survey.gRelationship of samples to food supplyforindividuals orpopulation ofinterest; consumptionhabits; preparation habits.Surf

52fU.S. EPA (1985a).gU.S. EPA (1986f).Table 2.Examples of types of measurements to characterizeexposure-related media and parameters (continued)Type ofmeasurement (sample)Usuallyattempts tocharacterize(whole)ExamplesTypical information needed to characterizeexposureB.1.AirPump/Particulates and VaporsExposure of an individual orpopulation viathe air medium.TEAM study,hcarbon monoxidestudy.iBreathing zone sampling in industrialsettings.Direct measurement of individual exposure duringtime sampled.Inorderto characterize exposure topopulation,relationshipsbetween individuals andthe population must be established as well asrelationships between times sampled and othertimes forthe sameindividuals,and relationshipsbetween sampled individuals and otherpopulations.ake these links, activitiesof the sampled individuals compared topopulations characterized are needed in somedetail.2.Passive VaporSamplingSame as above.Same as above.Same as above.3.ple Food/Split Sample Drinking WaterExposures of an individual or population viaingestion.TEAM study.jSameas above.4.Skin Patch SamplesDermal exposure of an individual orpopulation.Pesticide Applicator Survey.k1)Sameas above.2)Skin penetration.C.POSURE ESTIMATION FROM RECONSTRUCTED DOSE1.BreathTotal internal dose forindividuals orpopulation (usuallyindicative of relativelyrecent exposures).Measurement of volatile organicchemicals (VOCs),alcohol.(Usuallylimited to volatile compounds).1)Relationship between individuals andpopulation; exposure history(i.e.,steady-state ornot)pharmacokinetics (chemical half-life),possiblestorage reservoirswithin the body.2)Relationship betweenbreath content and bodyburden.FOR USE IN POINT-OF-CONTACT MEASUREMENTIn order to mSplit SamFOR USE IN EX

53hU.S. EPA (1987a).iU.S. EPA (1987a).jU.S. EPA (1987a).kU.S. EPA (1987d).Table 2.Examples of types of measurements to characterizeexposure-related media and parameters (continued)Type ofmeasurement (sample)Usuallyattempts tocharacterize(whole)ExamplesTypical information needed to characterizeexposure2.BloodTotal internal dose forindividuals orpopulation (maybe indicative of eitherrelativelyrecent exposures to fat-solubleorganics orlong termbodyburden formetals).Lead studies, pesticides, heavy metals(usuallybest forsoluble compounds,although blood lipid analysis mayreveal lipophilic compounds).1)Sameas above.2)Relationship betweenblood content and bodyburden.3.AdiposeTotal internal dose forindividuals orpopulation (usuallyindicative of long-termaverages for fat- soluble organics).NHATS,ldioxin studies,PCBs (usuallylimited to lipophilic compounds).1)Sameas above.2)Relationship betweenadipose content and bodyburden.4.Total internal dose for individuals orpopulation (usuallyindicative of pastexposure in weeks to months range; cansometimes be used to evaluate exposurepatterns).Heavy metal studies (usually limited tometals).1)Sameas above.2)Relationship between nails,haircontent andbodyburden.5.UrineTotal internal dose forindividuals orpopulation (usuallyindicative of eliminationrates); time fromexposure to appearance inurine mayvary,depending on chemical.Studies of tetrachloroethylenemandtrichloroethylene.n1)Sameas above.2)Relationship between urine content and bodyburden.Nails, Hair

54lU.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 assessm ent at hand becom e s greater. For exam ple, an outdoor air m easurem ent, no

m a tter 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 tim e due to changing em ission 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

m easurem ents as estim ates of exposure concentrations in a new assessm ent. It is a particular concern when the m easurem ent data will be used to extrapolate to long tim e periods such as a lifetim e. Transport and dispersion m odels are frequently used to help answer these questions. The exposure assessor is likely to encounter several different types of m e asurem ents. One type of m easurem ent used for general indications and trends of concentrations is outdoor fixed-location m onitoring. This m easurem ent is used by EPA and other groups to provide a record of pollutant concentration at one place over tim e. Nationwide air and water m onitoring program s have been established so that baseline values in these environm ental m e dia can be docum ented. Although it is not practical to set up a national m onitoring network to gather data for a particular exposure assessm ent, the data from existing networks can be evaluated for relevance to an exposure assessm ent. These data are usually som e what rem oved, and often far

rem oved, from the point of contact. Adapting data from previous studies usually presents challenges sim ilar to those encountered when using network data. If new data are needed for the

assessm ent, studies m easuring specific chem icals at specific locations and tim es can be conducted. Contam inant concentrations in indoor air can vary as m u ch or m o re than those in outdoor air. Consequently, indoor exposure is best represented by m easurem ents taken at the point of contact. However, because pollutants such as carbon m onoxide can exhibit substantial indoor penetration, indoor exposure estim ates should consider potential outdoor as well as indoor sources of the contam inant(s) under evaluation. Food and drinking water m easurem ents can also be m a de. General characterization of these m e dia, such as m a rket 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 rem oved from the point of contact for an individual, but m a y be useful in evaluating exposure concentrations over a large popul ation. Closer to the point of contact would be m easurem ents of tap water or foodstuffs in a hom e, and how they are used. In evaluating the relevance of data from previous studies, variati ons in the distribution system s m u st be considered as well as the space-tim e proxim ity.

55Consum er or industrial product analysis is som e tim es done to characterize the concentrations of chem icals in products. The form ulation of products can change substantially over tim e, sim ilar products do not necessarily have sim ilar form ulations, and regional differences in product form ulation can also occur. These should be considered when determ ining relevance of extant data and when setting up sam p ling plans to gather new data. Another type of concentration m easurem ent is the m i croenvironm ental m e asurem ent. Rather than using m easurem ents to characterize the entire m e dium , this approach defines specific zones in which the concentration in the m e dium of interest is thought to be relatively hom ogenous, then characterizes the concentration in that zone. Typical m i croenvironm ents include the hom e or parts of the hom e, office, autom obile, or other indoor settings. Microenvironm ents can also be divided into tim e segm ents (e.g., kitchen-day, kitchen-night). This approach can produce m easurem ents that are closely linked with the point of contact both in location and tim e, especially when new data are generated for a particular exposure assessm ent. The m o re specific the m i croenvironm ent, however, the greater the burden on the exposure assessor to establish that the m easurem ents are representative of the population of interest. Adapting existing data bases in this area to a particular exposure assessm ent requires the usual evaluation discussed throughout this section. The concentration m easurem ent that provides the closest link to the actual point of contact uses personal m onitoring, which is discussed in Section 4.3.

4.2.2. Use of Models for Concentration Estimation If concentrations in the m e dia cannot be m easured, they can frequently be estim ated indirectly by using related m easurem ents and m odels. To accom p lish this, source and fate inform ation are usually needed. Source characterization data are used as input to transport and transform a tion m odels (environm ental fate m odels). These m odels use a com b ination of general relationships and situation-specific inform ation to estim ate concentrations. In exposure

assessm ents, m a them atical m odels are used extensively to calculate environm ental fate and transport, concentrations of chem icals in di fferent environm ental m e dia, the distribution of concentrations over space and tim e, indoor air levels of chem icals, concentrations in foods, etc. In determ ining the relevance of this type of m odel for estim ating concentrations, the sam e rules apply as for the m easurem ents of concentrations discussed in the previous section. W h en concentrations in the m e dia are available, m odels can be used to interpolate concentrations between m easurem ents. Because m odels rely on indirect m easurem ents and data rem o te from the point of contact, statistically valid analytical m easurem ents take precedence when discrepancies arise. W h en it is necessary to estim ate contributions of individual sources to overall concentrations, m odels are com m only used.

56Source characterization m easurem ents usually determ ine the rate of release of chem icals into the environm ent from a point of em ission such as an incinerator, landfill, industrial facility, or other source. Often these m easurem ents are used to estim ate em ission factors, or a relationship between releases and f acility operations. Since em ission f actors are usually averages over tim e, the assessor m u st determ ine whether given em ission factors from previous work are relevant to the tim e specificity and source type needed for the exposure assessm ent. Generally, em ission factors are m o re useful for long-term average em ission calculations, and becom e less useful when applied to interm ittent or short-term exposures. Environm ental fate m easurem ents can be either field m easurem ents (field degradation studies, for exam ple) or laboratory m easur em ents (partition coefficients, hydrolysis, or biodegradation rates, etc.). Approxim a tions for these rates can som e tim es also be calculated (Lym an et al., 1982). Environm ental f a te m odels calculate estim ated concentrations in m e dia, that in turn are linked to the concentrations at the point of contact. The use of estim ated properties or rates adds to the uncertainty in the exposure concentration estim ate. W h en assessors use these m e thods to estim ate exposures, uncertainties attributable to the m odel and the validation status of the m odel

m

u st 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 m odel is essential for successful sim u lation of chem ical concentrations. In m o st cases assessors will be able to choose between several m odels, any of which could be used to estim ate environm ental concentrations. There is no right m odel; there

m a y not even be a best m odel. There are, however, several f actors that will help in selecting an appropriate m odel for the study. The assessor should consider the objectives of the study, the technical capabilities of the m odels, how readily the m odels can be obtained, and how dif f i cult each is to use (U.S. EPA, 1987b, 1988f). The prim ary consideration in selecting a m odel is the objective of the exposure

assessm ent. The associated schedule, budget, and other resource constraints will also affect

m odel selection options. Models are available to support both screening-level and detailed, sitespecific studies. Screening m odels can provi de quick, easy, and cost-effective estim ates of environm ental concentrations. They can support data collection efforts at the site by indicating the required level of detection and quantification and the locations where chem ical concentrations are expected to be highest. They are also used to interpolate chem ical concentrations between m easurem ents. W h ere study objectives require the best estim ates of spatial and tem poral distributions of chem icals, m o re sophisticated m odels are available. These

57m odels require m o re and better data to characterize the site, and therefore site-specific data m a y be needed in order to use them

. The technical capabilities of a m odel are expressed in its ability to sim u late site-specif i c contam inant transport and transform a tion processes. The m odel m u st be able to sim u late the relevant processes occurring within the specified environm ental setting. It m u st adequately represent the physical setting (e.g., the geom etric configuration of hydrogeological system s, river widths and depths, soil profiles, m e teorological patterns, etc.) and the chem ical transform a tion processes. Field data from the area where doses are to be estim ated are necessary to define the input param e ters required to use the m odels. In cases in which these data are not available, param e ter values representative of field conditions should be used as defaults. Assum p tions of hom ogeneity and sim p lification of site geom etry m a y allow use of sim p ler m odels. In addition, it is im portant to thoroughly understand the perform a nce characteristics of the m odel used. This is especially true with regard to the m o re com p lex m odels. Detailed

m odels can be quite com p lex with a large num ber of input variables, outputs, and computerrelated requirem e nts.

4.3. ESTIMATING DURATION OF CONTACT As discussed in Section 2, the duration of contact is linked to a particular exposure concentration to estim ate exposure. Depending on the purpose of the assessm ent and the confidence needed in the accuracy of the final estim ate, several approaches for obtaining estim ates of duration of contact can be used. Ideally, the tim e that the individual is in contact with a chem ical would be observed and recorded, and linked to the concentrations of the chem ical during those tim e segm ents. Although it is som e tim es feasible to do this (by point-of-contact m easurem ent, see Section 4.1), m a ny tim es it is not. In those cases, as in concentration characterization, the duration of contact m u st be estim ated by using data that m a y be som e what rem oved from the actual point of contact, and

assum p tions m u st be m a de as to the relevance of the data. It is com m on for the estim ate of duration of contact at a given concentration to be the single largest source of uncertainty in an exposure assessm ent. 24 The exposure assessor, in developing or selecting data for m a king estim ates of duration of contact, m u st often assum e that the available data adequately represent exposure.

24 Conversely, it m a y be stated that the largest source of uncertainty is the concentration for a given exposure duration. Often, however, the concentration in the m e di a is known with m o re certainty than the activities of the individual(s) exposed.
584.3.1. Observation and Survey Data Observation and recording of activities, including location-tim e 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 segm ent, or a population. The usual m e thod for obtaining these data for population segm ents or populations is survey questionnaires. Surveys can be perform e d as part of the data-gathering efforts of the exposure assessm ent, or existing survey data can be used if appropriate. There are several approaches used in activity surveys, including diaries, respondent or third-party estim ates, m o m e ntary sam p ling, videom onitoring, and behavioral m e ters. The diary approach, probably the m o st powerful m e thod for developing activity patterns, provides a sequential record of a person’s activities during a specified tim e period. Typical tim e-diary studies are done across a day or a week. Diary form s are designed to have respondents report all their activities and locations for that period. Carefully designed form s are especially im portant for diary studies to ensure that data reported by each individual are com p arable. The resulting tim e budget is a sam p le of activity that can be used to characterize an individual’s behavior, activities, or other features during the observation period. Sequential activity m onitoring form s the basis of an activity prof ile. Several studies have dem onstrated the reliability of the diary

m e thod in term s of its ability to produce sim ilar estim ates. One study (Robinson, 1977) found a 0.85 correlation between diary estim ates using the yesterday and tom o rrow approaches and a 0.86 correlation between overall estim ates. However, no definitive study has established the validity of tim e-diary data. Questionnaires are used for direct questions to collect the basic data needed. Questionnaire design is a com p lex and subtle process, and should only be attem p ted with the help of professionals well-versed in su rvey techniques. A useful set of guidelines is provided in the Survey Managem e nt Handbook (U.S. EPA, 1984b). Respondent estim ates are the least expensive and m o st com m only used questionnaire alternative. Respondents are sim p ly asked to estim ate the tim e they spend at a particular activity. Basically, the question is, how m a ny hours did you spend doing this activity (or in this location or using a certain product)? In exposure studies, respondents m a y be asked how often they use a chem ical or product of interest or perform a specific activity. These data are less precise and likely to be som e what less accurate than a carefully conducted diary approach. At a less dem a nding level, respondents m a y be asked whether their hom es contain item s of

interest (pesticides, etc.). Since this inf o rm ation is not tim e-of -activity data, it is m o re usef ul in characterizing whether the chem ical of interest is present. It does, however, give the assessor

som e indication that use m a y or m a y not occur.

59Estim ates from other respondents (third parties) use essentially the sam e approach, except that other inform ants respond for that individual. Here the question is how m a ny hours per week does the target person spend doing this activity? Mom e ntary (beeper) sam p ling or telephone-coincidental techniques ask respondents to give only brief reports for a specific m o m e nt — usually the m o m e nt the respondent’s hom e telephone or beeper sounds. This approach is lim ited to tim es when people are at hom e or able to carry beepers with them

. Methods that use behavioral m e ter or m onitoring devices are probably the m o st expensive approach, since they require the use or developm ent of equipm ent, respondent agreem ent to use such equipm ent, and technical help to install or adjust the equipm ent. The Exposure Factors Handbook (U.S. EPA, 1989c) contains a sum m a ry of published data on activity patterns along with citations. Note that the sum m a ry data and the m e an values cited are for the data sets included in the Handbook, and m a y or m a y not be appropriate for any given assessm ent.

4.3.2. Developing Other Estimates of Duration of Contact W h en activity surveys cannot be used to estim ate duration of contact, it m a y be estim ated from m o re indirect data. This is the least expensive and m o st com m only used approach for generating estim ates of duration of contact; it is also the least accurate. But for som e situations, such as assessing the risk to new chem icals being introduced into the m a rketplace or in assessing future possible uses of contam inated sites, it is the only approach that can be used. In general, the m e thods used to m a ke these estim ates fall into two areas: (1) those where the tim e it takes to perform an activity is itself estim ated, and (2) those where an average duration of contact is estim ated by com b ining the tim e of a unit activity with data on the use of a product or com m odity. Methods that try to estim ate the tim e of a particular activity include general tim e-andm o tion studies that m i ght be adapted for use in an exposure assessm ent, general m a rketing data which include tim e of use, anecdotal inform ation, personal experience, and assum p tions about the am ount of tim e it takes to perform an activity. Methods that estim ate average tim es for activities from product or com m odity use usually interpret data on product sales or m a rketing surveys, water use, general food sales, etc. Inform ation on use can be com b ined with an estim ate of the num ber of persons using the product to estim ate the average consum ption of the product. If an estim ate of the duration of contact with one unit (product, gallon of water, etc.) can be m a de, this can then be m u ltiplied by the average num ber of units consum ed to arrive at an estim ate of average duration of contact for each individual.

60Duration-of-contact estim ates based on data collected close to the actual point of contact are preferable to those based on indirect m easurem ents; both of these are preferred to estim ates based on assum p tions 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 biom arker data denote the presence of the chem ical inside the body of exposed individuals. In a reconstructive assessm ent, these data, in conjunction with other environm ental m onitoring data, m a y provide a better estim ate of exposure. A biom arker of exposure has been defined as an exogenous substance or its m e tabolite or the product of an interaction between a xenobiotic agent and som e target m o lecule or cell that is

m easured in a com p artm ent within an organism (NRC, 1989a). Exam ples of sim p le direct biom arkers include the chem ical itself in body flui d, tissue, or breath. Measurable changes in the physiology of the organism can also constitute m a rkers of exposure. Exam ples include changes in a particular enzym e synthesis and activity. The interaction of xenobiotic com pounds with physiological receptors can produce m easurable com p lexes which also serve as exposure biom arkers. Other m a rkers of exposure include xenobiotic species adducted to protein or DNA, as well as a variety of genotoxicity endpoints, such as m i cronuclei and m u tation. Som e biom arkers are specific to a given chem ical while others m a y result from exposure to num erous individual or classes of com pounds. Biom arker data alone do not usually constitute a com p lete exposure assessm ent, since these data m u st be associated with external exposures. However, biom arker data com p lem e nt other environm ental m onitoring data and m odeling activities in estim ating exposure.

4.5. OBTAINING DATA FOR PHARMACOKINETIC RELATIONSHIPS To estim ate dose from exposure, one m u st understand the pharm acokinetics of the chem ical of interest. This is particularly true when com p aring risks resulting from different exposure situations. Two widely different exposure profiles for the sam e chem ical m a y have the

sam e integrated exposure (area under the curve), but m a y not result in the sam e internal dose due to variations in disposition of the chem ical unde r the two profiles. For exam ple, enzym e s that

norm a lly could m e tabolize low concentrations of a chem ical m a y be saturated when the chem ical is absorbed in high doses, resulting in a higher dose delivered to target tissues. The result of these two exposures m a y even be a differe nt toxicological endpoint, if pharm acokinetic sensitivities are severe enough. An iterative approach, including both m onitoring and m odeling, is necessary for proper data generation and analysis. Data collection includes m onitoring of environm ental m e dia,

61personal exposure, biom arkers, and pharm acokinetic data. It m a y involve m onitoring for the chem ical, m e tabolites, or the target biom arker. Monitoring activities m u st be designed to yield data that are useful for m odel form ulation and validation. Modeling activities m u st be designed to sim u late processes that can be m onitored with available techniques. The pharm acokinetic data necessary for m odel developm ent are usually obtained from laboratory studies with anim als. The data are generated in experim e nts designed to estim ate such m odel param e ters as the tim e course of the process, absorption, distribution, m e tabolism , and elim ination of the chem ical. These data, and the pharm acokinetic m odels developed from them , are necessary to interpret field biom arker data.

4.6. OBTAINING DATA ON INTAKE AND UPTAKE The Exposure Factors Handbook (U.S. EPA, 1989c) presents statistical data on m a ny of the factors used in assessing exposure, including intake rates, and provides citations for the prim ary references. Som e of these data were developed by researchers using approaches discussed in Section 4.2.1 (for exam ple, Pao et al. [1982] used the diary approach in a study of food consum ption). Intake factors included are:

C drinking water consum ption rates;

C consum ption rates for hom egrown fruits, vegetables, beef, and dairy products;

C consum ption rates for recreationally caught fish and shellfish;

C incidental soil ingestion rates;

C pulm onary ventilation rates; and

C surface areas of various parts of the hum an body. The Exposure Factors Handbook is being updated to encom p ass additional factors and to include new research data on the factors currently covered. It also provides default param e ter 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 m o re relevant to the assessm ent being done.

625. USING DATA TO DETERMINE OR ESTIMATE EXPOSURE AND DOSE

Collecting and assem b ling data, as discussed in the previous section, is often an iterative process. Once the data are assem b led, inferences can be m a de about exposure concentrations, tim es of contact, and exposures to persons other than those for whom data are available. During this process, there usually will be gaps in inf o rm ation that can be f illed by m a king a series of

assum p tions. If these gaps are in areas critical to the accuracy of the assessm ent, further data collection m a y be necessary. Once an acceptable data set 25 is available, the assessor can calculate exposure or dose. Depending on the m e thod used to quantify exposure, there are several ways to calculate exposure and dose. This chapter will discuss m a king inferences (Section 5.1), assum p tions (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 inform ation contained in a data set. The credibility of an inference is often related to the m e thod used to m a ke it and the supporting data. Anecdotal inform ation is the source of one type of inference, but the assessor has only lim ited 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 judgm ent is usually preferred to anecdotes assum i ng that it is based on experience representing a variety of conditions. Statistical inferences also are generalizations that go beyond the data set. They m a y take any of several form s (see any statistics textbook for exam ples), but unlike those described above, a statistical inference will usually include a m easure of how certain it is. For that reason, statistical inferences are often preferable to anecdotes or professional judgm ent provided the data are shown to be relevant and adequate. As discussed above, the prim ary use of data from exposure-related m e asurem ents is to infer m o re general inform ation about exposure concentrations, contact tim es, exposures, or doses. For exam ple, m easured concentrations in a m e dium can be used to infer what the concentration m i ght be at the point of contact , which m a y not have been m easured directly. Point-of-contact m easurem ent data for one group of people m a y be used to infer the exposures of a sim ilar group, or to infer what the exposures of the sam e group m i ght be at different tim es. In all cases, the exposure assessor m u st have a clear picture of the relationship between the data at hand and what is being characterized by inference. For exam ple, surface water

25 An acceptable data set is one that is consistent with th e scope, depth, and purpose of the assessm ent, and is both relevant and adequate as discussed in Section 5.1.
63concentration data alone, although essential for characterizing the m e dium itself, are not necessarily useful for inferring exposures from surface water, since other inform ation is necessary to com p lete the link between surface water and exposure. But the m e dium ’s characteristics (over space and tim e) can be used, along with the location and activities of individuals or populations, to estim ate exposures. Sam p les taken for exposure assessm ent m a y be designed to characterize different aspects (o r com ponents) of exposure. For exam ple, a

sam p le taken as a point-of-contact exposure m easurem ent is qualitatively different from a
sam p le of an environm ental m e dium or body fluid. Different m easurem ents taken under the general category of exposure-related

m easurem ents cannot necessarily all be used in the sam e way. The exposure assessor m u st explain the relationship between the sam p le data and the inferences or conclusions being drawn from them

. In order to do this, data relevance, adequacy, and uncertainty m u st be evaluated.

5.1.1. Relevance of Data for the Intended Exposure Assessment W h en m a king inferences from a data set, the assessor m u st establish a clear link between the data and the inference. W h en statistically based sam p ling is used to generate data, relevance is a function of how well the sam p le represents the m e dium or param e ter being characterized. W h en planning data collection for an exposure assessm ent, the assessor can use inform ation about the inferences that will be m a de to select the best m easurem ent techniques. In m a ny cases data are also available from earlier studies. The assessor m u st determ ine (and state) how relevant the available data are to the current assessm ent; this is usually easier f o r new data than for previously collected inform ation.

5.1.2. Adequacy of Data for the Intended Assessment Table 2 in the previous section illustrated how dif f e rent types of m easurem ents m a y be used to characterize a variety of concentrations, contact tim es, and intake or uptake param e ters. Nevertheless, just because certain types of m easurem ents generally can be used to m a ke certain inferences, there is no guarantee that this can always be done. The adequacy of the data to m a ke inferences is determ ined by evaluating the am ount of data available and the accuracy of the data. Evaluation of the adequacy of data will ensure that the exposure assessm ent is conducted with data of known quality. In general, inadequate data should not be used, but when it can be dem onstrated that the inadequacies do not affect results, it is som e tim es 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 som e cases, even seriously inadequate or only partially relevant data m a y be the only data available, and som e inform ation m a y be gained from their consideration. It m a y not be

64possible to discard these data entirely unless better data are available. If these data are used, the uncertainties and resulting lim itations of the inferences should be clearly stated. If data are rejected for use in favor of better data, the rationa le for rejection should be clearly stated and the basis for retaining the selected data should be docum ented. QA/QC considerations are param ount in considerations of which data to keep and which to discard. Outliers should not be elim inated from data analysis procedures unless it can be shown that an error has occurred in the sam p le collection or analysis phases of the study. Very often outliers provide m u ch inform ation to the study evaluators. Statistical tests such as the Dixon test exist to determ ine the presence of outliers (Dixon, 1950, 1951, 1953, 1960).

5.1.2.1. Evaluation of Analytical Methods Analytical m e thods are evaluated in order to develop a data set based on validated analytical m e thods and appropriate QA/QC proce dures. In a larger sense, analytical m e thods can be evaluated to determ ine the strength of the inferences m a de from them , and in turn, the confidence in the exposure assessm ent itself. Consequently, it is just as im portant to evaluate analytical m e thods used for data generated under another study as it is to evaluate the m e thods used to generate new data. The EPA has established extensive QA/QC procedures (U.S. EPA, 1980). Before

m easurem ent data are used in the assessm ent, they should be evaluated against these procedures and the results stated. If this is not possible, the assessor m u st consider what effect the unknown quality of the data has on the confidence placed on the inferences and conclusions of the

assessm ent.

5.1.2.2. Evaluation of Analytical Data Reports An assortm e nt of qualifiers is often used in data validation. These qualifiers are used to indicate QA/QC problem s such as uncertain chem ical identity or dif f i culty in determ ining chem ical concentration. Qualifiers usually appear on a laboratory analysis report as a letter of the alphabet next to the analytical result. Som e exam ples 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 sam p les; J (judgm ent) - the com pound is present but the concentration value is estim ated; U (undetected) - the chem ical was analyzed for but not detected at the detection lim it; R (reject) - the quality control indicates that the data are unusable. The exposure assessor m a y contact the laboratory or the person who validated the data if the definitions of the qualifiers are unclear. Since the exposure assessm ent is only as good as the

65data supporting it, it is essential to interpret these types of data properly to avoid m i srepresenting the data set or biasing the results.

5.1.2.2.1. Evaluation of censored data sets. Exposure assessors com m only encounter data sets containing values that are lower than lim its deem ed reliable enough to report as num erical values (i.e., quantification lim its [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. W h ile the concentration m a y be highly uncertain for substances below the reporting lim it, it does not necessarily m ean that the concentration is zero. As a result the exposure assessor is often faced with the problem of having to estim ate values for the censored data. Although a variety of techniques have been described in the literature, no one procedure is appropriate under all exposure assessm ent circum stances; thus, the exposure assessor will need to decide on the appropriate m e thod for a given situation. Techniques for analyzing censored data sets can be grouped into three classes (Helsel, 1990): sim p le substitution m e thods, distributional m e thods, and robust m e thods. Sim p le substitution m e thods, the m o st com m only 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 chem ical m a y be accurately and reproducibly quantitated. This approach biases the m ean upward. On the other hand, assigning all nondetects the value of zero biases the

m ean downward. The degree to which the results are biased will depend on the relative num ber of detects and nondetects in the data set and the difference between the reporting lim it and the

m easured values above it. In an effort to m i nim i ze 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) assum e s that all values between the QL and zero are equally likely; therefore, an average value would result if m a ny sam p les in this range were

m easured. Hornung and Reed (1990) discuss the m e rits of assigning a value of QL/ 

### / 2 for nondetects rather than QL/2 if the data are not highly skewed (geom e tric standard deviation < 3.0); otherwise they suggest using QL/2. Based on reported analyses of sim u lated data sets that have been censored to varying degrees (Gleit, 1985; Horning and Reed, 1990; Gilliom and Helsel, 1986; Helsel and Cohn,

26 Som e program s, such as the U.S. Departm e nt of Energy (1991), do not recom m e nd this procedure at all, if it can be avoided.
661988), it can be concluded that substitution with QL/2 or QL/ 

### / 2 for nondetects will be adequate for m o st exposure assessm ents provided that the nondetects do not exceed 10% to 15% of the data set or the data are not highly skewed. W h en such situations arise, the additional effort to

m a ke use of m o re sophisticated m e thods as discussed below is recom m e nded. On the other hand, the exposure assessor m a y encounter situations in which the purpose of the assessm ent is only to serve as a screen to determ ine if a health concern has been triggered or if a m o re 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 m e thod cannot be used to prove an unacceptable risk exists, and any exposure values calculated using this m e thod should be caveated and clearly presented as less than estim ates. Distributional m e thods, unlike sim p le substitution m e thods, m a ke use of the data above the reporting lim it to extrapolate below it. One such technique is the use of log-probit analysis. This approach assum e s a lognorm a l 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 geom etric m ean is determ ined from the 50 th percentile. As discussed by Travis and Land (1990), lim itations of the m e thod have been pointed out, but it is less biased and m o re accurate than the frequently used substitution m e thods. This m e thod is useful in situations where the data set contains enough data points above the reporting lim it to define the distribution function for the exposure values (i.e., lognorm a l) with an acceptable degree of confidence. The treatm e nt of the nondetectable sam p les is then straightforward, assum i ng the nondetectable sam p le s follow the sam e distribution as those above the reporting lim it. Robust m e thods have an advantage over distributional m e thods in so far as they do not

assum e that the data above the reporting lim it follow a defined distribution (e.g., lognorm a l) and they are not subject to transform a tion bias in going from logarithm s back to original units. Gilliom and Helsel (1986) have described the application of several approaches to data sets of varying sam p le size and degree of censoring. These m e thods involve som e what m o re data

m a nipulation than the log-probit m e thod discussed earlier in this Section, but they m a y be m o re appropriate to use when the observed data do not fit a lognorm a l distribution. Generally, these

m e thods only assum e 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 sum m a ry, when dealing with censored data sets, a variety of approaches can be used by the exposure assessor. Selecting the appropriate m e thod requires consideration of the degree of censoring, the goals of the exposure assessm ent, and the accuracy required. Regardless of the

m e thod selected, the assessor should explain the choice m a de and how it m a y affect the

67sum m a ry statistics. Presenting only the sum m a ry statistics developed by one of these m e thods should be avoided. It is always useful to incl ude a characterization of the data by the percentage of detects and nondetects in language such as “in 37% of the sam p les the chem ical was detected above the quantitation lim it; of these 37%, the m ean concentration was 47 ppm , the standard deviation was 5 ppm , etc.”

5.1.2.2.2. Blanks and recovery. Blank sam p les should be com p ared with the results from their corresponding sam p les. W h en com p aring blank sam p les to the data set, the following rules should be followed (outlined in Section 3):

C Sam p le results should be reported only if the concentrations in the sam p le exceed 10 tim es the m a xim u m am ount detected in the blank for com m on laboratory contam inants. Com m on laboratory contam inants include: acetone, 2-butanone (or

m e thyl ethyl ketone), m e thylene chloride, toluene, and phthalate esters.

C Sam p le results should be reported only if the concentrations in the sam p le exceed 5 tim es the m a xim u m am ount detected in a blank for chem icals that are not com m o n laboratory contam inants. In general, for other types of qualifiers, the exposure assessor m a y include the data with qualifiers if they indicate that a chem ical’s c oncentration is uncertain, but its identity is known. If possible, the uncertainties associated with the qualifier should be noted. Chem ical spike sam p les that show abnorm a lly high or low recoveries m a y result in qualified or rejected data. Assessors should not use rejected data; these sam p les should be treated as if the sam p les 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 m u st determ ine how these data should be used to calculate exposures. If recovery is near 100%, concentrations are not norm a lly adjusted (although the im plicit assum p tion of 100% recovery should be m e ntioned in the uncertainty section). However, the assessor m a y need to adjust the data to account for consistent, but abnorm a lly high or low recovery. The rationale for such adjustm e nts should be clearly explained; individual program offices m a y deve lop guidance on the acceptable percent recovery lim its before data adjustm e nt or rejection is necessary.

5.1.3. Combining Measurement Data Sets From Various Studies Com b ining data from several sources into a single data set m u st be done cautiously. The circum stances under which each set of data was collected (target population, sam p ling design, location, tim e, etc.) and quality (precision, accuracy, representativeness, com p leteness, etc.) m u st be evaluated. Com b ining sum m a ry statistics of the data sets (e.g., m eans) into a single set m a y

68be m o re appropriate than com b ining the original values. Statistical m e thods are available for

com b ining results from individual statistical tests. For exam ple, it is som e tim es possible to use several studies with m a rginally signif i cant re sults to justify an overall conclusion of a statistically signif i cant ef f ect. The best way to report data is to provide sufficient background inform ation to explain what was done and why, including clear docum entation of the source of the data and including any references.

5.1.4. Combining Measurement Data and Modeling Results Com b ining m odel results with m easurem ent data m u st be done with an understanding of how this affects the resulting inferences, conclusions, or exposure estim ates. If m odel results are used in lieu of additional data points, they m u st be evaluated for accuracy and representativeness as if they were additional data, and the uncertainty associated with this data com b ination m u st be described fully, as discussed in Section 5.1.3. On the other hand, m easurem ent data are often used within the context of the m odel itself , as calibration and verif i cation points, or as a check on the plausibility of the m odel results. If m easurem ents are used within the m odel, th e uncertainty in these m easurem ents affects the uncertainty of the m odel results, and should be discussed as part of the uncertainty of the m odel results.

5.2. DEALING WITH DATA GAPS Even after supplem enting existing m easurem ent data with m odel results, there are likely to be gaps in the inform ation 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 assessm ent and the resources available. The following options can be used singly or in com b ination:

C New data can be collected. This m a y be beyond the reach of the assessor’s resources, but prom ises the best chance for getting an accurate answer. It is m o st likely to be a useful option if the new data are quick and easy to obtain.

C The scope of the assessm ent can be narrowed. This is possible if the data gaps are in one pathway or exposure route, and the others have adequate data. It m a y be a viable option if the pathway or route has values below certain bounds, and those bounds are

sm all relative to the other pathways being evaluated. This is unlikely to be satisfactory if the part of the assessm ent deleted is an im portant exposure pathway or route and m u st be evaluated.

69C Conservative 27 assum p tions can be used. This option is useful for establishing bounds on exposure param e ters, but lim its how the resulting exposures and doses can be expressed. For exam ple, if one were to assum e that a person stays at hom e 24 hours a day as a conservative assum p tion, and used this value in calculations, the resulting contact tim e would have to be expressed as an upper lim it rather than a best estim ate. W h en m a king conservative assum p tions, the assessor m u st be aware of (and explain) how m a ny of these are m a de in the assessm ent, and how they influence the final conclusions of the assessm ent. 28

C Models m a y be used in som e cases, not only to estim ate values for concentrations or exposures, but also to check on how conservative certain assum p tions are.

C Surrogate data m a y also be used in som e cases. For exam ple, for pesticide applicators’ exposure to pesticides, the EPA Office of Pesticide Program s (U.S. EPA, 1987d) assum e s that the general param e ters of application (such as the hum an activity that leads to exposure) are m o re im portant than the properties of the pesticide in determ ining the level of exposure. 29 This option assum e s that surrogate data are available and that the differences between the chem ical and the surrogate are sm all. If a clear relationship can be determ ined between the concentration of a chem ical and the surrogate (usually term ed an indicator chem ical) in a m e dium , this relationship could also be used to f ill data gaps. In any case, the strength and character of the relationship between the chem ical and the surrogate m u st be explained.

C Professional judgm ent can be used. The utility of this option depends on the confidence placed in the estim ate. Expert opinion based on years of observation of sim ilar circum stances usually carries m o re weight than anecdotal inform ation. The

assessor m u st discuss the im plications of these estim ates 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 assem b led. 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” assum p tions are those which tend to m a xim i ze estim ates of exposure or dose, such as choosing a value near the high end of the concentration or intake rate range.

28 Obviously, the m a them atical product of several conserva tive assum p tions is m o re conservative than any single
assum p tion alone. Ultim ately, this could lead to unrea listically conservative bounding estim ates (see Section 5.3).

29 Note that when using a passive dosim etry m onitoring m e thod, what is m easured is the am ount of chem ical im pinging on the skin surface or available for inhalation, th at is, exposure, not the act ual dose received. Factors such as derm al penetration, are, of course , expected to be highly chem ical dependent.
70of short-term data for long-term projections, the role of personal m onitoring data, and the particular way the data m i ght 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 tim e m easured (or m odeled) relative to the tim e period covered in the exposure assessm ent. For exam ple, a 3-day sam p ling period would produce short-term data if the exposure assessm ent covered a period of several years to a lifetim e. The sam e 3-day sam p ling period would not be considered short-term if the assessm ent covered, say, a few days to a week. Short-term data can provide a snapshot of concentrations or exposures during that tim e, and an inference m u st be m a de about what that m eans for the longer term if the exposure

assessm ent covers a long period. The assessor m u st determ ine 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 m ean, and other statistics), use of these short-term data to infer long-term exposures and risks m u st be done with caution. Using short-term data to estim ate long-term exposures has a tendency to underestim ate the num ber of people exposed, but to overestim ate the exposure levels to the upper end of the distribution, even though the m ean will

rem a in the sam e

. 30 Both concentration variation at a single point and population m obility will drive the estim ates of the levels of exposure for the upper tail of the distribution toward the

m ean. If short-term data are used for long-term exposure or dose estim ates, the im plications of this on the estim ated exposures m u st be discussed in the assessm ent. Likewise, use of long-term

m onitoring data for specific short-term assessm ents can m i ss significant variations due to shortterm conditions or activities. Long-term data should be used cautiously when estim ating shortterm exposures or doses, and the im plications should be discussed in the assessm ent.

30 Consider, for exam ple, a hypothetical set of 100 room s (m icroenvironm ents) where the concentration of a particular pollutant is zero in 50 of them , and ranges st epwise from 1 to 50 (nom inal concentration units) in the
rem a inder. If one person were in each room , short-te rm “ s napshot” m onitoring 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
rem a ined constant and people were allowed to visit a ny room at random , long-term m onitoring would indicate that all 100 were exposed to a m ean concentration of 12.75. The short-term data would tend to overestim ate concentration and underestim ate the num ber of persons expos ed if applied to long-term exposures. If only average values were available, the long-term data would tend to underestim ate concentration and overestim ate the num ber exposed if applied to short-term exposures. Because popul ations are not random ly m obile or static, the exposure assessor should determ ine what effect this has on the exposure estim ate.
715.3.2. Using Point-of-Contact Data to Calculate Exposure and Dose Point-of-contact exposure assessm ents are often done with the intent of protecting the individuals, often in an occupational setting. W h en exposures are being evaluated to determ ine whether they exceed an action level or other benchm ark, point-of-contact m easurem ents are the

m o st relevant data. Typically, point-of-contact m easurem ent data reflect exposures over periods of m i nutes to perhaps a week or so. For individuals whose exposures have been m easured, these data m a y be used directly as an indication of their e xposure during the sam p ling period, provided they are of adequate quality, m easure the appropriate chem ical, and actually m easure exposure while it

occurs. This is the only case in which m easurem ent data m a y be used directly as exposure data. W h en using point-of-contact m easurem ents, even with statistically based data, several inferences still m u st be m a de to calculate exposure or dose:

C Inferences m u st be m a de to apply short-term m easurem ents of exposure to long-term estim ates of exposure; these are subject to the cautions outlined in Section 5.3.1.

C Inferences m u st be m a de about the representativeness of the individual or persons

sam p led for the individual or population segm ent for which the assessm ent is done.

C Inferences m u st be m a de about the factors converting m easured exposure to potential or internal dose for use in a risk assessm ent.

C If the assessm ent requires it, inferences m u st be m a de about the relationship between the m easured chem ical exposures and the presence and relative contribution of various sources of the chem ical.

5.3.3. The Role of Exposure Scenarios in Exposure Assessment Exposure scenarios have several functions in exposure and risk assessm ents. First, they are calculational tools to help the assessor develop estim ates of exposure, dose, and risk. W h atever com b ination of data and m odels 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 estim ates 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 m a nagers

m a ke estim ates of the potential im pact of possible control actions. This is usually done by changing the assum p tions in the exposure scenario to the conditions as they would exist after the contem plated action is im plem ented, and reassessing the exposure and risk. These three uses of exposure assessm ents 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 inform ation about how exposure takes place. An exposure scenario generally includes facts, data, assum p tions, inferences, and som e tim es professional judgm ent about the following:

72C 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 chem ical, i.e., am ounts, locations, tim e variation of concentrations, source strength, environm ental pathways from source to exposed individuals, fate of the chem ical in the environm ent, etc. (characterization of the chem ical)

C Identification of the individual(s) or popula tion(s) exposed, and the profile of contact with the chem ical based on behavior, location as a function of tim e, characteristics of the individuals, etc. (characterization of the exposed population)

C If the dose is to be estim ated, assum p tions about the transfer of the chem ical 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 tim e pattern. The risk characterization, the link between the developm ent of the assessm ent and the use of the assessm ent, is usually com m unicated in part to the risk m a nager by m e ans of a series of “risk descriptors,” which are m e rely 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 inform ation require different risk descriptors and different analyses of the data. The following paragraphs discuss som e of the aspects of developing and using exposure scenarios in various functions for exposure assessm ent.

5.3.3.1. Scenarios as a Means to Quantify Exposure and Dose W h en using exposure scenario evaluation as a m eans to quantify exposure and dose, it is possible to accum u late a large volum e of data and estim ated values, and both the am ount and type of inform ation can vary widely. The exposure scenario also contains the inform ation needed to calculate exposure, since the last three bullets above (Section 5.3.3) are the prim ary variables in m o st exposure and dose equations. As an exam ple, consider Equation 2-5, the equation for lifetim e average daily potential dose (LADD pot ). This equation uses the variables of exposure concentration (C), intake rate (IR), and exposure duration (ED) as the three prim ary variables. Body weight (BW ) and averaging tim e (AT) (in this case, lifetim e, 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 lifetim e average exposure or dose per kg of body weight.

73In looking at the three prim ary variables (C, IR, and ED), the exposure assessor m u st determ ine what value to use for each to solve the equation. In actuality, the inform ation available for a variable like C m a y consist of m easurem ents of various points in an environm ental m e dium , source and f a te characterizations, and m odel results. There will be uncertainty in the values f o r C f o r any individual; there will also be variability am ong individuals. Each of these prim ary variables will be represented by a range of values, even though at tim es, 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 re sults in a frequency distribution for the LADD. This is an appropriate strategy when the freque ncy distributions are known for C, IR, and ED (or for the uptake analogs, C, K p , SA, and ED introduced in Section 2), and when these variables are independent. A second approach is to select or estim ate 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 estim ate, but m a y be easier to do. W h ich values are used determ ines how the resulting estim ate will be described. Several term s f o r describing such estim ates are discussed in Section 5.3.3.2. Since exposure to chem icals occurs through a variety of different pathways, contact patterns, and settings, sufficient perspective m u st be provided to the users of the assessm ent (usually risk m a nagers) to help them m a ke an inform ed decision. Providing this perspective and insight would be relatively straightforward if com p lete and accurate inform ation 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 nam e of each person in the population, or the data could be com p iled into frequency distribution curves. From such distributions, the average, m e dian, m a xim u m , or other statistical values could easily be read off the curves and presented to the risk m a nager. In addition, accurate inform ation could be provided about how m a ny persons are above certain exposure, dose, or risk levels as well as inform ation about where various subgroups fall within the subject distribution. Unf o rtunately, an assessor rarely has these kinds of data; the reality an assessor f a ces usually falls far short of this ideal. But it is precisely this kind of inform ation about the distribution of exposure, dose, and risk that is needed m a ny tim es by the risk assessor to characterize risk, and by the risk m a nager to deal with risk-related issues. In the absence of com p rehensive data, or if the scenario being evaluated is a possible future use or post-control scenario, an assessor m u st m a ke assum p tions in order to estim ate what

74the distribution would look like if better data were available, or if the possible future use becom e s a reality. Com m unicating this estim ated distribution to the risk m a nager can be difficult. The assessor m u st not only estim ate exposure, dose, and risk levels, but m u st also estim ate where those levels m i ght fall on the actual distributions or estim ated distributions for potential future situations. To help com m unicate where on the distribution the estim ate m i ght fall, loosely defined term s such as reasonable worst case, worst case, and m a xim a lly exposed individual have been used by assessors. Although these term s have been used to help describe the exposure assessor’s perceptions of where estim ated exposures fall on the actual or potential distribution for the future use, the ad hoc nature of the historical definitions used has led to som e inconsistency. One of the goals of these Guidelines is to prom ote greater consistency in the use of term s 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 inform ation about risk to users of that inform ation, prim arily risk m a nagers. This inform ation usually takes the form of answers to a relatively short set of questions, not all of which are applicable to all assessm ents. Section 5.3.5 provides m o re 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 im portant use for exposure scenarios is as a tool for evaluating proposed options for action. Risk m a nagers often have a num ber of choices for dealing with environm ental problem s, from taking no action on one extrem e to a num ber of different actions, each with different costs, on the other. Often the exposure scenarios developed as part of the baseline risk

assessm ent provide a powerful tool to evaluate the potential reduction of exposure and risk for these various options, and consequently are quite useful in m a ny cost-benefit analyses. There are several additional related uses of exposure scenarios for risk m a nagers. They

m a y help establish a range of options for cleanup by showing the sensitivity of the risk estim ates to the changes in assum e d source or exposure levels. The exposure assessor can use the sensitivity analysis of the exposure scenario to help evaluate and com m unicate the uncertainty of the assum p tions, and what can be done to reduce that uncertainty. W e ll-crafted and soundly based exposure scenarios m a y also help com m unicate risks and possible options to com m unity groups. Although it is beyond the scope of these Guidelines to detail the m e thods used for option evaluation and selection, the assessor should be aware of this potential use. Discussing strategy

75(and specific inform ation needs) with risk m a nagers is usually prudent before large resource expenditures are m a de in the risk assessm ent area.

5.3.4. General Methods for Estimating Exposure and Dose A variety of m e thods are used to obtain estim ates of dose necessary for risk characterization. These range from quick screening level calculations and rules of thum b to

m o re sophisticated techniques. The technique to be used in a given case is a m a tter of the

am ount of inform ation available and the purpose of the assessm ent. Several of the m e thods are outlined in the f o llowing sections. Norm ally it is neither practicable nor advisable to im m e diately develop detailed inform ation on all the potential pathways, since not all m a y contribute significantly to the outcom e of the assessm ent. 31 Rather, evaluation of the scenario is done in an iterative m a nner. First, screening or bounding techniques are used to ascertain which pathways are unim portant, then the inform ation for the rem a ining pathways is refined, iteratively becom i ng m o re accurate, until the quantitative objectives of the assessm ent are m e t (or resources are depleted). In beginning the evaluation phase of any assessm ent, the assessor should have a scenario’s basic assum p tions (setting, scope, etc.) well identified, one or m o re applicable exposure pathways defined, an equation for evaluating the exposure or dose for each of those exposure pathways, and the data and inform ation requirem e nts pertinent to solving the equations. Quality and quantity of data and inf o rm ation needed to substitute quantitative values or ranges into the param e ters of the exposure equation will often vary widely, from postulated assum p tions to actual high-quality m easurem ents. Many tim es, there are several exposure pathways identified within the scenario, and the quality of the data and inform ation m a y vary for each. A com m on approach to estim ating exposure and dose is to do a prelim inary evaluation, or screening step, during which bounding estim ates are used, and then to proceed to refine the estim ates for those pathways that cannot be elim inated as of trivial im portance.

5.3.4.1. Preliminary Evaluation and Bounding Estimates The first step that experienced assessors usually take in evaluating the scenario involves

m a king bounding estim ates for the individual exposure pathways. The purpose of this is to elim inate further work on refining estim ates for pathways that are clearly not im portant.

31 There are som e im portant exceptions to this statem ent. First, the public or other concerned groups m a y express particular interest in certain pathways, which will not norm a lly be dropped entirely at this point. Second, for routine repetitive assessm ents using a certain standard scenar io for m a ny chem icals, once the general bounding has been done on the various possible pathways, it m a y becom e standard operating procedure to im m e diately begin developing inform ation for particular pathways as new chem icals are assessed.
76The m e thod used for bounding estim ates is to postulate a set of values for the param e ters 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 estim ate of exposure or dose calculated by this m e thod is clearly outside of (and higher than) the distribution of actual exposures or doses. If the value of this bounding estim ate is not significant, the pathway can be elim inated from further refinem e nt. 32 The theoretical upper bounding estim ate (TUBE) is a type of bounding estim ate that can be easily calculated and is designed to estim ate 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 assum i ng lim its for all the variables used to calculate exposure and dose that, when

com b ined, will result in the m a them atically highest exposure or dose (highest concentration, highest intake rate, lowest body weight, etc.). The theoretical upper bound is a bounding estim ate that should, if the lim its of the param e ters used are known, ensure that the estim ate is above the actual exposures received by all individuals in the population. It is not necessary to go to the form ality of the TUBE to assure that the exposure or dose calculated is above the actual distribution, however, since any com b ination that results in a value clearly higher than the actual distribution can serve as a suitable upper bound. The bounding estim ate (a lim it of individual exposure, dose or risk) is m o st often used only to elim inate pathways from further consideration. This is often done in screening-level

assessm ents, where bounding estim ates 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 assessm ent work is not necessary. Bounding estim ates also are used in other types of assessm ents. They can be used for deregulation of chem icals when pathways or concentrations can be shown to present insignificant or de minimis risk. They can be used to determ ine whether m o re inform ation is needed to determ ine whether a pathway is significant; if the pathway’s significance cannot be ruled out by a bounding estim ate, test data m a y be needed to refine the estim ate. There are two im portant points about bounding estim ates. First, the only thing the bounding estim ate can establish is a level to elim inate pathways from further consideration. It cannot be used to m a ke a determ ination that a pathway is significant (that can only be done after

m o re inform ation is obtained and a refinem e nt of the estim ate is m a de), and it certainly cannot

32 “Not significant” can m ean either that it is so sm all relativ e 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 m a nagem e nt term , and the assessor
m

u st discuss and establish any such levels of concern w ith risk m a nagers (and in som e cases, concerned groups such as the local com m unity) before elim inating pathways as not significant.
77be used for an estim ate of actual exposure (since by definition it is clearly outside the actual distribution). Second, when an exposure scenario is presented in an assessm ent, it is likely that the am ount of refinem e nt of the data, inform ation, and estim ates will vary by pathway, som e having been elim inated by bounding estim ates, som e elim inated after further refinem e nt, and others fully developed and quantified. This is an efficient way to evaluate scenarios. In such cases, bounding estim ates m u st not be considered to be equally as sophisticated as an estim ate of a fully developed pathway, and should not be described as such. Experienced assessors can often elim inate som e obvious pathways m o re or less by inspection as they m a y have evaluated these pathways m a ny tim es before. 33 In these cases, the

assessor m u st still explain why the pathway is being elim inated. For less experienced assessors, developing bounding estim ates 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 elim inated by bounding estim ates or judged trivial, the assessor will then evaluate the resulting exposure or dose. At this point, the assessor will m a ke estim ates of exposure or dose that are designed to fall on the actual distribution. The im portant point here is that unlike a bounding estim ate, these estim ates of exposure or dose should focus on points in the actual distribution. Both estim ates of central tendency and estim ates of the upper end of the distribution curve are useful in crafting risk descriptors. Consider Equation 2-6 for the lifetim e average daily potential dose (LADD pot ), an equation often used for linear, nonthreshold carcinogen risk m odels. The assessor will use the data, ranges of data, distributions of data, and assum p tions about each of the factors needed to solve the equation for dose. Generally, both central estim ates and high-end estim ates are perform e d. Each of these estim ates has uncertain ty (perhaps unquantifiable uncertainty), and the better the quality and com p rehensiveness of data used as input to the equation, the less uncertainty. After solving the equation, the assessor will determ ine 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 assessm ent statem ent 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 m a nager. Should the estim ates of exposure or dose have sufficiently narrow uncertainty, the assessor can then proceed to develop the descriptors and finish the assessm ent. If not, the data or

assum p tions used usually will have to be ref i ned, if resources allow, in an attem p t to bring the

33 Experienced assessors m a y also be able to determ in e quickly that a pathway requires refined estim ation.
78estim ated exposure or dose closer to what the assessor believes are the actual values in the population. Refining the estim ates usually requires that new data be brought into consideration; 34 this new inf o rm ation can be other studies f r om the literature, inf o rm ation 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 inform ation base to refine should be based both on which data will m o st signif i cantly reduce the uncertainty of the overall exposure or dose estim ate, and on which data are in fact obtainable either technologically or within resource constraints. After refinem e nt of the estim ate, the assessor and risk m a nager again determ ine whether the estim ates provided will be sufficient to answer the questions posed to an acceptable degree, given the uncertainties that m a y be associated with those estim ates. Refinem e nts proceed iteratively until the assessm ent provides an adequate answer within the resources available.

5.3.5. Using Estimates for Developing Descriptors Risk assessors and risk m a nagers are encouraged to explore a range of ways to describe exposure and risk inform ation, depending on the purpose of the assessm ent and the questions for which the risk m a nager m u st 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 im portant com ponent of any assessm ent, especially an estim ate of the high end of th e distribution. Section 5.3.4.1 indicated that bounding estim ates are actually a useful but lim ited form of individual risk estim ate, a form which is by definition beyond the highest point on the popula tion distribution. This section deals with estim ates that are actually on the distribution of exposure, dose, or risk. There are several approaches for arriving at an individual risk estim ate. Since calculation of risk involves using inform ation from fields other than exposure assessm ent, the reader is advised to consult other Agency guidelines for m o re detailed discussions (e.g., U.S. EPA, 1986b, 1986c, 1988b, 1988c, 1991a). The uncertainty in the risk estim ate will depend heavily on the quality of the inf o rm ation used. There are several steps in the process: First, the question of unusual susceptibility of part of the population m u st be addressed. If equal doses result in widely different responses in two individuals, it m a y be necessary to consult with scientists fam iliar with the derivation of the dose-response relationship for the

34 It also can involve new m e thods or additional m e thods for analyzing the old data.
79chem ical in question in order to ascertain whether this is norm a l variability am ong m e m b ers of a population. Norm al variability should have been considered as part of the developm ent of the dose-response relationship; unusual susceptibility m a y not have been. If such a highly susceptible subgroup can be identified, it is often us eful to assess their risk separately from the general population. It will not be com m on, given the current data availability, to clearly identify such susceptible subgroups. If none can be identified, the default has usually been to assum e the dose-response relationship applies to all m e m b ers of the population being assessed. W h ere no inform ation shows the contrary, this assum p tion m a y be used provided it is highlighted as a source of uncertainty. Second, after the population or population segm ent 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) m u st be identified. For dose-response relationships based on adm i nistered dose in anim al studies, potential dose will usually be the hum an analogue. If the dose-response relationship is based on internal dose, then that is the m o st appropriate hum an dose. If the estim ates of exposure and dose from the exposure assessm ent are in an inappropriate form (say, potential dose rather than internal dose), they m u st be converted before they are used for risk calculations. This m a y involve analysis of bioavailability, absorption rates as a f unction of f

o rm of the chem ical and route, etc. If these data are not available, the default has been to assum e the entire potential dose becom e s the internal dose. 35 As m o re data becom e available concerning absorption for different chem icals, this conservative assum p tion m a y not always be the best, or even a credible, default. W h atever

assum p tion is m a de concerning absorption (or the relationships am ong any of the different dose term s if used, for that m a tter), 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 inform ation 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 m a y i nvolve converting exposures into potential doses or converting potential doses into internal, delivered, or biologically effective doses. Once this is accom p lished, the high-end estim ate of dose will often (but not always) lead fairly directly to the high-end estim ate of risk. The m e thod used to develop the high-end estim ate for dose

35 The unstated assum p tion is often m a de that the relations hip between adm i nistered dose and absorbed dose in the anim al is the sam e as that between potential dose and in ternal dose in hum ans, provided a correction is m a de for body weight/surface area. In other words, the bioavailability and absorption fractions are assum e d to be the sam e in the hum an as in the anim al experim e nt. If no correction is m a de for absorption, this leads to the assum p tion 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 inte rnal dose does not assum e “100% absorption” unless there was 100% absorption in the anim al study.
80depends on the data available. Because of the skewed nature of exposure data, there is no exact form ula that will guarantee an estim ate will fall into this range in the actual population if only sparse data are available. The high-end risk is a plausible estim ate of the individual risk for those persons at the upper end of the risk distribution. The intent of this descriptor is to convey an estim ate of risk in the upper range of the distribution, but to avoid estim ates that are beyond the true distribution. Conceptually, high-end risk m eans risks above the 90 th percentile of the population distribution, but not higher than the individual in the population who has the highest risk. This descriptor is intended to estim ate the risks that are expected to occur in sm all but definable high-end segm ents of the subject population. The use of “above the 90 th percentile” in the def i nition is not m eant to precisely define the range of this descriptor, but rather to clarify what is m eant conceptually by high end. The high-end segm ents of the exposure, dose, and risk populations m a y represent different individuals. Since the location of individuals on the exposure, dose, and risk distributions m a y vary depending on the distributi ons 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 m oderate to highly positive correlation am ong exposure, dose, and risk. W h en the com p lete data on the population distributions of exposures and doses are available, and the significance of the factors a bove (bioavailability, etc.) are known to the extent to allow a risk distribution to be constructed, the high-end risk estim ate can be represented by reporting risks at selected percentiles of the distributions, such as the 90 th , 95 th , or 98 th percentile. W h en the com p lete distributions are not available, the assessor should conceptually target

som e thing above the 90 th percentile on the actual distribution. In developing estim ates of high-end individual exposure and dose, the following conditions m u st be m e t:

C The estim ated exposure or dose is on the expected distribution, not above the value one would expect for the person with the highest estim ated risk in the population. This m eans that when constructing this estim ate from a series of factors (environm ental concentrations, intake rates, individual activities, etc.), not all f actors should be set to values that m a xim i ze exposure or dose, since this will alm o st always lead to an estim ate that is m u ch too conservative.

C The com b ination of values assigned to the exposure and dose factors can be expected to be found in the actual population. In estim ating high-end exposures or doses for

81future 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 m easure occurs. 36 Som e of the alternative m e thods for determ ining a high-end estim ate 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 num ber of persons determ ined in the high end above the estim ate, in order to give the risk m a nager an idea of where within the high end-range the estim ate falls.

C If data on the distribution of doses are not available, but data on the param e ters used to calculate the dose are available, a sim u lation (such as an exposure m odel or Monte Carlo sim u lation) can som e tim es be m a de of the distribution. In this case, the

assessor m a y take the estim ate from the sim u lated distribution. As in the m e thod above, the risk m a nager should be told where in the high-end range the estim ate falls by stating the percentile or the num ber of persons above this estim ate. The assessor and risk m a nager should be cautioned that unless a great deal is known about exposures or doses at the high end of the distribution, sim u lated distributions m a y not be able to differentiate between bounding estim ates and high-end estim ates. Sim u lations often include low-probability estim ates at the upper end that are higher than those actually experienced in a given population, due to im probability of finding these exposures or doses in a specific population of lim ited size, or due to nonobvious correlations am ong param e ters at the high ends of their ranges. 37 Using the highest estim ate from a Monte Carlo sim u lation m a y therefore overestim ate the exposure or dose for a specific population, and it is advisable to use values som e what less than the highest Monte Carlo estim ated value if one is to defend the estim ate as being within the actual population distribution and not above it.

36 This m eans that estim ates of high-end exposure or dose for future uses are lim ited to the sam e conceptual range as current uses. Although a “worst-case” com b ination of future conditions or events m a y result in an exposure that is conceivably possible, the assessor should not m e rely us e a worst-case com b ination as an estim ate of high-end exposure for possible future uses. Rather, the assessor m u st use judgm ent as to what the range of exposures or doses would plausibly be, given the population size and probability of certain events happening.

37 For exam ple, although concentration breathed, frequency, duration, and breathing rate m a y be independent for a
consum er painting room s in a house under m o st norm a l circ um stances, if the concentration is high enough, it m a y affect the other param e ters such as duration or breathing rate. These types of high-end correlations are difficult to quantify, and techniques such as Monte Carlo sim u lations will not consider them unless relationships are known and taken into account in the sim u lation. If extrem e concen tration in this case resulted in lower breathing rate or duration, a noncorrected Monte Carlo sim u lation could overe stim ate the exposure or dose at the high end. Far less likely, due to self-preservation processes, would seem th e case where high concentration increases duration or intake rate, although this theoretically m i ght also occur.
82Sim u lations using f i nite ranges f o r param e ters will result in a sim u lated distribution with a calculable finite m a xim u m exposure, and the m a xim u m exposures calculated in repeated sim u lations will not exceed this theoretical m a xim u m

. 38 W h en unbounded default distributions, such as lognorm a l distributions, are used for input param e ters to generate the sim u lated exposure distributions, there will not be a finite

m a xim u m exposure lim it for the sim u lation, so the m a xim u m value of the resulting sim u lated distribution will vary with repeated sim u lations. The EPA’s Science Advisory Board [SAB] (U.S. EPA, 1992a) has recom m e nded that values above a certain percentile in these sim u lations be treated as if they were bounding estim ates, not estim ates of high-end exposures (see Figure 2). The SAB noted that for large populations, sim u lated exposures, doses, and risks above the 99.9 th percentile m a y not be m eaningful when unbounded lognorm a l distributions are used as a default. Although the Agency has not specifically set policy on this m a tter, exposure assessors should observe the following caution when using sim u lated distributions. The actual percentile cutoff above which a sim u lation should be considered a bounding estimate m a y be expected to vary depending on the size of the population. Since bounding estim ates are established to develop statem ents 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 exam ple, if there are 100 persons in the population, it m a y be prudent to consider sim u lated exposures above the 1 in 500 level or 1 in 1000 level (i.e., above the 99.5 th or 99.9 th percentile, respectively) to be bounding estim ates. Due to uncertainties in sim u lated distributions, assessors should be cautious about using estim ates above the 99.9 th percentile f o r estim ates of high-end exposure regardless of the size of the population. The Agency or individual program of f i ces m a y issue m o re direct policy f o r setting the exact cutoff value for use as high-end and bounding estim ates in sim u lations.

C If som e inform ation on the distribution of the variables m a king up the exposure or dose equation (e.g., concentration, exposure duration, intake or uptake rates) is available, the assessor m a y estim ate a value which f a lls into the high end by m eeting the def i ning criteria of “high end”: an estim ate 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 estim ate by using m a xim u m or near-m axim um values f o r one or m o re of the m o st sensitive variables, leaving others at their m ean

38 This m a xim u m is the theoretical upper bounding estim ate (TUBE).
8385Figure 2.Schematic of exposure estimatorsfor unbounded simulated population distributions.values. 39 The exact m e thod used to calculate the estim ate of high-end exposure or dose is not critical; it is very im portant that the exposure assessor explain why the estim ate, in his or her opinion, falls into the appropriate range, not above or below it.

C If alm o st no data are available, it will be dif f i cult, if not im possible, to estim ate exposures or doses in the high end. One m e thod that has been used, especially in screening-level assessm ents, is to start with a bounding estim ate and back off the lim its used until the com b ination of param e ter values is, in the judgm ent of the assessor, clearly in the distribution of exposure or dose. Obviously, this m e thod results in a large uncertainty. The availability of pertinent data will determ ine how easily and defensibly the high-end estim ate can be developed by sim p ly adjusting or backing off from the ultra conservative assum p tions used in the bounding estim ates. This estim ate m u st still m eet the defining criteria of “high end,” and the assessor should be ready to explain why the estim ate is thought to m eet the defining criteria. A descriptor of central tendency m a y be either the arithm e tic m ean risk (average estim ate) or the m e dian risk (m edian estim ate), but should be clearly labeled as such. W h ere both the arithm e tic m ean and the m e dian are available, but differ substantially, it is helpful to present both. Exposure and dose profiles often fall in a skewed distribution that m a ny tim es appears to be approxim a tely lognorm a lly distributed, alt hough statistical tests for lognorm a lity m a y fail. The arithm e tic m ean and the m e dian are the sam e in a norm a l distribution, but exposure data are rarely norm a lly distributed. As the typical skewness in the distribution increases, the exposure or dose distribution com e s to resem b le a lognorm a l curve where the arithm e tic m e an will be higher than the m e dian. It is not unusual for the arithm e tic m ean to be located at the 75 th percentile of the distribution or higher. Thus, the arithm e tic m e an is not necessarily a good indicator of the m i dpoint (m edian, 50 th percentile) of a distribution. The average estim ate, used to describe the arithm e tic m e an, can be approxim a ted by using average values for all the factors m a king 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 assessm ents. W h en the data are highly skewed, it is som e tim es instructive to approxim a te the m e dian exposure or dose, or m e dian estim ate. This is usually done by calculating the geom etric m ean of the exposure or dose distribution, and historically this has often been referred to as the typical

39 Maxim i zing all variables, as is done in bounding estim ates, w ill result in virtually all cases in an estim ate that is above the bounds of this range, that is, above the actual values seen in the population.
85case, although again, definitions have varied widely. Both the average estim ate and m e dian estim ate are m easures of the central tendency of the exposure or dose distribution, but they m u st be clearly differentiated when presenting the results. It will of ten be usef ul to provide additional specif i c individual risk inf o rm ation to provide perspective for the risk m a nager. This specific inform ation m a y take the form of answers to what if questions, such as, what if a consum er should use this product without adequate ventilation? For the risk m a nager, these questions are likely to put bounds on various aspects of the risk question. For the assessor, these are m u ch less com p licated problem s than trying to estim ate baseline exposure or dose in an actual population, since the answers to these questions involve choosing values for various param e ters in the exposure or risk equations and solving them for the estim ate. This type of risk descriptor is a calcula tion of risk to specific hypothetical or actual

com b inations of factors postulated within the expos ure assessm ent. It is often valuable to ask and answer specific questions of the “what if” na ture to add perspective to the risk assessm ent. Each assessm ent m a y have none, one, or several of these specific types of descriptors. The answers to these questions m i ght be a point estim ate or a range, but are usually fairly sim p le to calculate. The answers to these types of postulated questions, however, do not directly give inform ation about how likely that com b ination of values m i ght be in the actual population, so there are som e lim its 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 assessm ent. Ideally, given the tim e and m e thods, the assessor m i ght 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 m a nager, perhaps considering what action m i ght be necessary for this particular situation, m i ght ask how m a ny cases of the particular effect m i ght be probabilistically estim ated in a population during a specific tim e period, or what percentage of the population is (or how

m a ny people are) above a certain exposure, dose, or risk level. For those who do the assessm ents, answering these questions requires som e knowledge of the population frequency distribution. This inform ation can be obtained or estim ated in several ways, leading to two descriptors of population risk. The first is the probabilistic num ber of health effect cases estim ated in the population of interest over a specified tim e period. This descriptor can be obtained either by sum m i ng the individual risks over all the individuals in the population, or by m u ltiplying the slope factor obtained from a carcinogen dose-response relationship, the arithm e tic m ean of the dose, and the

86size of the population. The latter approach m a y be used only if the risk m odel assum e s a single linear, nonthreshold response to dose, and then only with som e caution. 40 If risk varies linearly with dose, knowing the arithm e tic m ean risk and the population size can lead to an estim ate of the extent of harm for the population as a whole, excluding sensitive subgroups for which a different dose-response curve m a y need to be used. For noncarcinogens, or for nonlinear, nonthreshold carcinogen m odels, using the arithm e tic m ean exposure or dose, m u ltiplying by a slope factor to calculate an average risk, and m u ltiplying by the population size is not appropriate, and risks should be sum m e d over individuals. 41 Obviously, the m o re relevant inform ation one has, the less uncertain this descriptor, but in any case, the estim ate used to develop the descriptor is also lim ited by the inherent uncertainties in risk assessm ent m e thodology, e.g., the risk estim ates often being upper confidence level bounds. W ith 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 em pirical data). The second type of population risk descriptor is an estim ate of the percentage of the population, or the num ber of persons, above a speci fied level of risk, RfD, RfC, LOAEL, or other specific level of interest. This descriptor m u st be obtained by m easuring or sim u lating the population distribution, which can be done in several ways. First, if the population being studied is sm all enough, it m a y be possible to m easure the distribution of exposure or dose. Usually, this approach can be m oderately to highly costly, but it m a y be the m o st accurate. Possible problem s with this approach are lack of m easuring techniques for the chem ical of interest, the availability of a suitable population subset to m onitor, and the problem of extrapolating short-term m easurem ents to long-term exposures.

40 For exam ple, when calculating risks using doses and “slope factors,” the risk is approxim a tely linear with dose until relatively high individual risks (about 10 -1 ) are attained, after which the relationship is no longer even approxim a tely linear. This results from the fact that no m a tter how high the dose, the individual risk cannot exceed 1, and the dose-risk curve approaches 1 asym ptotically. 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 exam ple, assum e a lifetim e average daily dose of 100 m g /kg/day corresponds to an individual risk of 4 × 10 -1

. Increasing the dose fivefold, to 500 m g /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 th e five persons in the population would then be 100 m g /kg/day. Multiplying the “average risk” of 4 × 10 -1 by the population size of five results in an estim ate of two cases, even though in actuality only one person is exposed. Although cal culating average individual dose, estim ating individual risk from it, and m u ltiplying by the population size is a us eful approxim a tion if all m e m b ers of the population are within the approxim a tely linear range of the dose-risk curve, this m e thod should not be used if som e m e m b ers of the population have calculated individual risks higher than about 10 -1 , since it will overestim ate the num ber of cases.

41 In these cases, a significant problem can be the lack of a c onstant (or nearly constant) “slope factor” that would be appropriate over a wide exposure/dose range, since the dose -response curve m a y have thresholds, windows, or other discontinuities.
87Second, the distribution itself m a y be sim u lated from a m odel such as an exposure m odel (a m odel that reports exposures or doses by linking concentrations with contact tim es for subsets of the population, such as those living various distances from a source) or a Monte Carlo sim u lation. Although this m a y be considerably less costly than m easurem ents, it will probably be less accurate, especially near the high end of the distribution. Although m odels and statistical sim u lations can be fairly accurate if the proper input data are available, these data are often difficult to obtain and assum p tions m u st be m a de; use of assum p tions m a y reduce the certainty of the estim ated results. Third, it m a y be possible to estim ate how m a ny people are above a certain exposure, dose, or risk level by identifying and enum erating certain population segm ents known to be at higher exposure, dose, sensitivity, or risk than the level of interest. For those who use the assessm ents, 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 estim ating how m a ny persons would go from the higher category to the lower category after the option is im plem ented. 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 m a nager m i ght 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 m a gnitude 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 quantif ication of the m a gnitude of risk f o r specif i c 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

som e tim es be necessary to use a different dose-response relationship. Generally, selection of the subgroups or population segm ents is a m a tter of either a priori interest in the subgroup, in which case the risk m a nager and risk assessor can jointly agree on which subgroups to highlight, or a m a tter of discovery of a subgroup during the assessm ent process. In either case, the subgroup can be treated as a population in itself and characterized the

sam e 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 segm ent as a population, then estim ating the doses as for a population.

m

u st m a ke 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 LADD pot (Equation 2-6), it is appropriate to average the dose rate (intake rate/body weight) rather than dose. pot equation then becom e s

where LADD pot is the lif etim e average daily potential dose, ED i is the exposure duration (tim e over which the contact actually takes place), is the average exposure concentration during period of calendar tim e ED i , i s the average ingestion or inhalation rate during ED i , BW i is body weight during exposure duration ED i , and LT is the averaging tim e, in this case, a lif etim e (converted to days). of the LADD pot equation, if applied to an exposure that occurs prim arily in childhood (for exam ple, inadvertent soil ingestion), m a y result in an LADD pot calculation som e what higher than that obtained by using Equation 2-6, but there is som e evidence that it is m o re defensible (Kodell et al., 1987; additional discussion in m e m o randum from Hugh McKinnon, EPA, to Michael Callahan, EPA, Novem b er 9, 1990). The assessor

For exposures that take place

The LADD

This form

896. ASSESSING UNCERTAINTY

Assessing uncertainty m a y involve sim p le or very sophisticated techniques, depending on the requirem e nts of the assessm ent. 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, estim ates, scenarios, etc. For sim p le exposure assessm ents, where not m u ch quantitative inform ation is available, uncertainty characterization m a y be all that is necessary. The uncertainty assessm ent is m o re quantitative. The process begins with sim p ler

m easures (i.e., ranges) and sim p ler analytical techniques (i.e., sensitivity analysis), and progresses, to the extent needed to support the decision for which the exposure assessm ent is conducted, to m o re com p lex m easures and techniques. The developm ent and im plem entation of an appropriate uncertainty assessm ent strategy can be viewed as a decision process. Decisions

are m a de about ways to characterize and analyze uncertainties, and whether to proceed to increasingly m o re com p lex levels of uncertainty assessm ent.

6.1. ROLE OF UNCERTAINTY ANALYSIS IN EXPOSURE ASSESSMENT Exposure assessm ent uses a wide array of inform ation sources and techniques. Even where actual exposure-related m easurem ents exist, assum p tions or inferences will still be required (see Section 5.2). Most likely, data will not be available for all aspects of the exposure

assessm ent and those data that are available m a y be of questionable or unknown quality. In these situations, the exposure assessor will have to rely on a com b ination of professional judgm ent, inferences based on analogy with sim ilar chem icals and conditions, estim ation techniques, and the like. The net result is that the exposure assessm ent will be based on a

num ber of assum p tions with varying degrees of uncertainty. The decision analysis literature has f o cused on the im portance of explicitly incorporating and quantifying scientific uncertainty in risk assessm ents (Morgan, 1983; Finkel, 1990). Reasons for addressing uncertainties in exposure assessm ents include:

C Uncertain inf o rm ation f r om dif f e rent sources of dif f e rent quality m u st be com b ined.

C A decision m u st be m a de about whether and how to expend resources to acquire additional inform ation (e.g., production, use, and em issions data; environm ental fate inform ation; m onitoring data; population data) to reduce the uncertainty.

C There is considerable em pirical evidence that biases m a y result in so-called best estim ates that are not actually very accurate. Even if all that is needed is a best90estim ate answer, the quality of that answer m a y be im proved by an analysis that incorporates a frank discussion of uncertainty.

C Exposure assessm ent is an iterative process. The search for an adequate and robust

m e thodology to handle the problem at hand m a y proceed m o re effectively, and to a

m o re certain conclusion, if the associated uncertainty is explicitly included and can be used as a guide in the process of refinem e nt.

C A decision is rarely m a de on the basis of a single piece of analysis. Further, it is rare f o r there to be one discrete decision; a process of m u ltiple decisions spread over tim e is the m o re com m on occurrence. Chem icals of concern m a y go through several levels of risk assessm ent before a final decision is m a de. W ithin this process, decisions m a y be m a de based on exposure considerations. An exposure analysis that attem p ts to characterize the associated uncertainty allows the user or decision-m a ker to better evaluate it in the context of the other factors being considered.

C Exposure assessors have a responsibility to present not just num bers but also a clear and explicit explanation of the im plications and lim itations of their analyses. Uncertainty characterization helps carry out this responsibility. Essentially, the construction of scientifically sound exposure assessm ents and the analysis of uncertainty go hand in hand. The reward for analyzing uncertainties is knowing that the results have integrity or that signif i cant gaps exist in available inf o rm ation that can m a ke decision-m a king a tenuous process.

6.2. TYPES OF UNCERTAINTY Uncertainty in exposure assessm ent can be classified into three broad categories: 1. Uncertainty regarding m i ssing or incom p lete inform ation needed to fully define the exposure and dose (scenario uncertainty) 2. Uncertainty regarding som e param e ter (param eter uncertainty) 3. Uncertainty regarding gaps in scientific theory required to m a ke predictions on the basis of causal inferences (m odel uncertainty) Identification of the sources of uncertainty in an exposure assessm ent is the first step toward eventually determ ining the type of action necessary to reduce that uncertainty. The three types of uncertainty m e ntioned above can be further defined by exam ining som e principal causes for each. Exposure assessm ents often are developed in a phased approach. The initial phase usually involves som e type of broad-based screening in which the scenarios that are not expected to pose a risk to the receptor are elim inated from a m o re detailed, resource-intensive review, usually through developing bounding estim ates. These screening-level scenarios often are

91constructed to represent exposures that would fall beyond the extrem e upper end of the expected exposure distribution. Because the screening-level assessm ents for these nonproblem scenarios usually are included in the final exposure assessm ent docum ent, this final docum ent m a y contain scenarios that differ quite m a rkedly in level of sophistication, quality of data, and am enability to quantitative expressions of uncertainty. These also can apply to the input param e ters used to construct detailed exposure scenarios. The following sections will discuss sources, characterization, and m e thods 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 judgm ent, and incom p lete analysis. Descriptive errors include errors in inform ation, such as the current producers of the chem ical and its industrial, com m e rcial, and consum er uses. Inform ation of this type is the foundation for the eventual developm ent of exposure pathways, scenarios, exposed populations, and exposure estim ates. Aggregation errors arise as a result of lum p ing approxim a tions. Included am ong these

are assum p tions of hom ogeneous populations, and spatial and tem poral approxim a tions such as

assum p tions of steady-state conditions.

Professional judgm ent com e s into play in virtually every aspect of the exposure

assessm ent process, from defining the appropriate exposure scenarios, to selecting the proper environm ental fate m odels, to determ ining representative environm ental conditions, etc. Errors in professional judgm ent also are a source of uncertainty. A potentially serious source of uncertainty in exposure assessm ents arises from incom p lete analysis. For exam ple, the exposure assessor m a y overlook an im portant consum er exposure due to lack of inform ation regarding the use of a chem ical in a particular product. Although this source of uncertainty is essentially unquantifiable, it should not be overlooked by the assessor. At a m i nim u m , the rationale for excluding particular exposure scenarios should be described and the uncertainty in those decisions should be characterized as high, m e dium , or low. The exposure assessor should discuss whether these decisions were based on actual data, analogues, or professional judgm ent. For situations in which the uncertainty is high, one should perform a reality check where credible upper lim its on the exposure are established by a “what if” analysis. Characterization of the uncertainty associated with nonnum eric assum p tions (often relating to setting the assessm ent’s direction and scope) will generally involve a qualitative discussion of the rationale used in selecting sp ecific scenarios. The discussion should allow the

92reader to m a ke an independent judgm ent 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 param e ter uncertainty include m easurem ent errors, sam p ling errors, variability, and use of generic or surrogate data. Measurem ent errors can be random or system atic. Random error results from im precision in the m easurem ent process. System atic error is a bias or tendency away from the true value. Sam p ling errors concern sam p le representativeness. The purpose of sam p ling is to m a ke an inference about the nature of the whole from a m easurem ent of a subset of the total population. If the exposure assessm ent uses data that were generated for another purpose, for

exam ple, consum er product preference surveys or com p liance m onitoring surveys, uncertainty will arise if the data do not represent the exposure scenario being analyzed. The inability to characterize the inherent variability in environm ental and exposurerelated param e ters is a m a jor source of uncertainty. For exam ple, m e teorological and hydrological conditions m a y vary seasonally at a given location, soil conditions can have large spatial variability, and hum an activity patterns can vary substantially depending on age, sex, and geographical location. The use of generic or surrogate data is com m on when site-specific data are not available. Exam ples include standard em ission factors for industrial processes, generalized descriptions of environm ental settings, and data pertaining to structurally related chem icals as surrogates for the chem ical 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 param e ter values will vary. It can involve an order-of-m a gnitude bounding of the param e ter range when uncertainty is high, or a description of the range for each of the param e ters including the lower- and upper-bound and the best estim ate values and justification for these based on available data or professional judgm ent. In som e circum stances, characterization can take the f o rm of a probabilistic description of the param e ter range. The appropriate characterization will depend on several f actors, including whether a sensitivity analysis indicates that the results are signif i cantly af f ected by variations within the range. W h en the results are significantly affected by a particular param e ter, the exposure assessor should attem p t 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 m e thods can be used to obtain a m eaningful representation. If available data are

93inadequate, then expert judgm ents can be used to develop a subjective probabilistic representation. Expert judgm ents should be developed in a consistent, well-docum ented m a nner. Exam ples of techniques to solicit expert judgm ents have been described (Morgan et al., 1979; Morgan et al., 1984; Rish, 1988). Most approaches for analyzing uncertainty have focused on techniques that exam ine how uncertainty in param e ter values translates into overall uncertainty in the assessm ent. Several published reports (Cox and Baybutt, 1981; U.S. EPA, 1985f; Inm a n and Helton, 1988; Seller, 1987; Rish and Marnicio, 1988) have reviewed the m a ny techniques available; the assessor should consult these for details. In general, these approaches can be described, in order of increasing com p lexity and data requirem e nts, as either sensitivity analysis, analytical uncertainty propagation, probabilistic uncertainty analysis, or classical statistical m e thods. Sensitivity analysis is the process of changing one variable while leaving the others constant and determ ining the effect on the output

. The procedure involves fixing each uncertain quantity, one at a tim e, at its credible lower-bound and then its upper-bound (holding all others at their m e dians), and then com puting the outcom e s for each com b ination of values. These results are useful to identify the variables that have the greatest effect on exposure and to help focus further inform ation gathering. The results do not provide any inform ation about the probability of a quantity’s value being at any level within the range; theref ore, this approach is

m o st useful at the screening level when deci ding about the need and direction of further analyses. Analytical uncertainty propagation involves exam ining how uncertainty in individual param e ters affects the overall uncertainty of the exposure assessm ent. Intuitively, it seem s clear that uncertainty in a specific param e ter m a y propagate very differently through a m odel than another variable having approxim a tely the sam e uncertainty. Som e param e ters are m o re im portant than others, and the m odel structure is designed to account for the relative sensitivity. Thus, uncertainty propagation is a function of both the data and the m odel structure. Accordingly, both m odel sensitivity and input variances are evaluated in this procedure. Application of this approach to exposure assessm ent requires explicit m a them atical expressions of exposure, estim ates of the variances for each of the variables of interest, and the ability either analytically or num erically to obtain a m a them atical 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 m o st accurate for linear equations, so any departure from linearity m u st be carefully evaluated. Assum p tions, such as independence of

variables and norm a lity of errors in the variables, need to be checked. Finally, this approach

94requires estim ates of param e ter variance, and the inform ation to support these m a y not be readily available. Probabilistic uncertainty analysis is generally considered the next level of refinem e nt. The m o st com m on exam ple is the Monte Carlo technique where probability density f unctions are assigned to each param e ter, then values from these distributions are random ly selected and inserted into the exposure equation. After this process is com p leted m a ny tim es, a distribution of predicted values results that reflects the overall uncertainty in the inputs to the calculation. The principal advantage of the Monte Carlo m e thod 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; com putations are also straightforward. There are som e disadvantages as well as inconveniences, however. The exposure assessor should only consider using this technique when there are credible distribution data (or ranges) for m o st key variables. Even if these distributions are known, it m a y not be necessary to apply this technique. For exam ple, if only average exposure values are needed, these can often be com puted as accurately by using average values for each of the input param e ters. Another inconvenience is that the sensitivity of the results to the input distributions is som e what cum b ersom e to assess. Changing the distribution of only one value requires rer unning the entire calculation (typically, several hundreds or thousands of tim es). Finally, Monte Carlo results do not tell the assessor which variables are the m o st im portant contributors to output uncertainty. This is a disadvantage since

m o st analyses of uncertainty are perform e d to find effective ways to reduce uncertainty. Classical statistical methods can be used to analyze uncertainty in m easured exposures. Given a data set of m easured exposure values for a series of individuals, the population distribution m a y be estim ated directly, provided that the sam p le design was developed properly to capture a representative sam p le. The m easured exposure values also m a y be used to directly

com pute confidence interval estim ates for percentiles of the exposure distribution (Am e rican Chem ical Society, 1988). W h en the exposure distribution is estim ated from m easured exposures for a probability sam p le of population m e m b ers, confidence interval estim ates for percentiles of the exposure distribution are the prim ary uncertainty characterization. Data collection survey design should also be discussed, as well as accuracy and precision of the m e asurem ent techniques. Often the observed exposure distribution is skewed; m a ny sam p le m e m b ers have exposure distributions at or below the detection lim it. In this situation, estim ates of the exposure distribution m a y require a very large sam p le size. Fitting the data to a distribution type can be problem atic in this situation because data are usually scant in the low probability areas (the tails) where num erical values vary widely. As a consequence, for data sets for which the sam p ling has been com p leted, m eans and standard deviations m a y be determ ined to a good approxim a tion, but

95characterization of the tails of the distribution will have m u ch greater uncertainty. This difference should be brought out in the discussion. For data sets for which sam p ling is still practical, stratification of the statistical population to oversam ple the tail m a y give m o re precision and confidence in the inform ation in the tail area of the distribution.

6.2.3. Model Uncertainty At a m i nim u m , the exposure assessor should describe in qualitative term s the rationale for selection of any conceptual and m a them atical m odels. This discussion should address the status of these approaches and any plausible alternatives in term s of their acceptance by the scientific com m unity, how well the m odel(s) represents the situation being assessed, e.g., highend estim ate, and to what extent verification and validation have been done. Relationship errors and m odeling errors are the prim ary sources of m odeling uncertainty. Relationship errors include errors in correlations between chem ical properties, structurereactivity correlations, and environm ental fate m odels. In choosing to use these tools, the exposure assessor m u st decide am ong the m a ny possible functional form s available. Even though statistics on the perform a nce of the m e thodology for a given test set of chem icals m a y be available and can help guide in the selection process, the exposure assessor m u st decide on the

m o st appropriate m e thodology for the chem ical of interest based on the goals of the assessm ent. Modeling errors are due to m odels being sim p lified representations of reality, for

exam ple approxim a ting a three-dim e nsional aquifer with a two-dim e nsional m a them atical

m odel. Even after the exposure assessor has selected the m o st appropriate m odel for the purpose at hand, one is still faced with the question of how well the m odel represents the real situation. This question is com pounded by the overlap between m odeling uncertainties and other uncertainties, e.g., natural variability in environm ental inputs, representativeness of the m odeling scenario, and aggregation errors. The dilem m a facing exposure assessors is that m a ny existing

m odels (particularly the very com p lex ones) and the hypotheses contained within them cannot be fully tested (Beck, 1987), although certain com ponents of the m odel m a y be tested. Even when a

m odel 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 m odel constructs. One approach is to use different m odeling form ulations (including the preferred and plausible alternatives) and consider the range of the outputs to be representative of the uncertainty range. This strategy is m o st 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 m odels were originally designed.

96W h ere the data base is sufficient, the exposure assessor should characterize the uncertainty in the selected m odel by describing the validation and verification efforts. Validation is the process of exam ining the perform a nce of the m odel com p ared to actual observations under situations representative of those being assessed. Approaches for m odel validation have been discussed (U.S. EPA 1985e). Verification is the process of confirm i ng that the m odel com puter code is producing the proper num erical output. In m o st situations, only partial validation is possible due to data deficiencies or m odel com p lexity.

6.3. VARIABILITY WITHIN A POPULATION VERSUS UNCERTAINTY IN THE ESTIMATE

For clarity, it should be em phasized that variability (the receipt of different levels of exposure by different individuals) is being disti nguished from uncertainty (the lack of knowledge about the correct value for a specific exposure m easure or estim ate). Most of the exposure and risk descriptors discussed in this report deal with variability directly, but estim ates m u st also be

m a de of the uncertainty of these descriptors. 42 This m a y be done qualitatively or quantitatively, and it is beyond the scope of this report to discuss the m echanics of uncertainty analysis in detail. It is an im portant distinction, however, since the risk assessor and risk m a nager need to know if the num bers being reported for exposures take variability, uncertainty, or both, into consideration. Not all approaches historically used to construct m easures or estim ates of exposure attem p ted to distinguish variability and uncertainty. In particular, in m a ny cases in which estim ates were term ed 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 bounds 43 on data or physical param e ters (uncertainty), these estim ates m i ght be interpreted as “not exceeding an upper bound on exposures received by certain high-end individuals.” Note that this approach will provide an estim ate that considers both variability and uncertainty, but by only reporting the upper confidence bound, it appears to be m e rely a m o re

42 Each m easure or estim ate of exposure will have its a ssociated uncertainty which should be addressed both qualitatively and quantitatively. For exam ple, if populati on m ean exposure is being addressed by use of direct personal m onitoring data, qualitative issues will include th e representativeness of the population m onitored to the full population, the representativeness of the period select ed for m onitoring, and confidence that there were not system atic errors in the m easured data. Quantitative un certainty could be addressed through the use of confidence intervals for the actual m ean population exposure.

43 The confidence interval is interpreted as the range of va lues within which the assessor knows the true m easure lies, with specified statistical confidence. The upper bound c onfidence lim it is the higher of the two ends of the confidence interval.
97conservative estim ate of the variability. High-end estim ates which include consideration ofuncertainty should be presented with both the upper and lower uncertainty bounds on the highend estim ate. This provides the necessary inform ation to the risk m a nager. W ithout specific discussion of what was done, risk m a nagers m a y view the results as not having dealt with uncertainty. It is fundam e ntal to exposure assessm ent 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 param e ters used in calculating exposure. The discussion of estim ating exposure and dose presented in Section 5.3.4 addresses the rationale and approaches for constructing a range of m easures or estim ates of exposure, with

em phasis on how these can be used for exposure or risk characterization. The distinction between these m easures or estim ates (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 m easures can be used to characterize risk in different ways, this does not address which of th ese m easures or characterizations is used for decisions. The selection of the point or m easure of exposure or risk upon which regulatory decisions are m a de is a risk m a nagem e nt decision governed by program m a tic policy, and is therefore beyond the scope of these guidelines.

987. PRESENTING THE RESULTS OF THE EXPOSURE ASSESSMENT

One of the m o st im portant aspects of the exposure assessm ent is presenting the results. It is here that the assessm ent ultim ately succeeds or fails in m eeting the objectives laid out in the planning as discussed in Section 3. This section discusses com m unication of the results, form at considerations, and suggested tips for reviewing exposure assessm ents either as a final check or as a review of work done by others.

7.1. COMMUNICATING THE RESULTS OF THE ASSESSMENT Com m unicating the results of an exposure assessm ent is m o re than a sim p le sum m a ry of conclusions and quantitative estim ates for the various pathways and routes of exposure. The

m o st im portant part of an exposure assessm ent is the overall narrative exposure characterization, without which the assessm ent is m e rely a collection of data, calculations, and estim ates. This exposure characterization should consist of discu ssion, analysis, and conclusions that synthesize the results from the earlier portions of the docum ent, present a balanced representation of the available data and its relevancy to the health effects of concern, and identify key assum p tions and m a jor 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 assessm ent.

7.1.1. Exposure Characteriz a tion The exposure characterization is the sum m a ry explanation of the exposure assessm ent. In this final step, the exposure characterization:

C provides a statem ent of purpose, scope, level of detail, and approach used in the

assessm ent, including key assum p tions;

C presents the estim ates of exposure and dose by pathway and route for individuals, population segm ents, and populations in a m a nner appropriate for the intended risk characterization;

C provides an evaluation of the overall quality of the assessm ent and the degree of confidence the authors have in the estim ates of exposure and dose and the conclusions drawn;

C interprets the data and results; and

C com m unicates results of the exposure assessm ent to the risk assessor, who can then use the exposure characterization, along with characterizations of the other risk

assessm ent elem ents, to develop a risk characterization. As part of the statem ent of purpose, the exposure characterization explains why the

assessm ent was done and what questions were asked. It also reaches a conclusion as to whether

99the questions posed were in fact answered, and with what degree of confidence. It should also note whether the exposure assessm ent brought to light additional or perhaps m o re appropriate questions, if these were answered, and if so, with what degree of confidence. The statem ent of scope discusses the geographical or dem ographic boundaries of the

assessm ent. The specific populations and population segm ents that were the subjects of the

assessm ent are clearly identified, and the reasons for their selection and any exclusions are discussed. Especially sensitive groups or groups that m a y experience unusual exposure patterns are highlighted. The characterization also discusses whether the scope and level of detail of the

assessm ent were ideal for answering the questions of the assessm ent and whether lim itations in scope and level of detail were m a de because of technical, practical, or financial reasons, and the im plications of these lim itations on the quality of the conclusions. The m e thods used to quantify exposure and dose are clearly stated in the exposure characterization. If m odels are used, the basis for their selection and validation status is described. If m easurem ent data are used, the quality of the data is discussed. The strengths and weaknesses of the particular m e thods used to quantify exposure and dose are described, along with com p arison and contrast to alternate m e thods, if appropriate. In presenting the exposure and dose estim ates, the im portant sources, pathways, and routes of exposure are identified and quantified, and reasons for excluding any from the

assessm ent are discussed. A variety of risk descriptors, and wher e possible, the full population distribution is presented. Risk m a nagers should be given som e 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 assessm ent with specific risk descriptors, which in turn are presented in such a way as to f acilitate construction of a risk characterization. A discussion of the quality of the exposure and dose estim ates is critical to the credibility of

the assessm ent. This m a y be based in part on a quantitative uncertainty analysis, but the exposure characterization m u st explain the results of any such analysis in term s of the degree of confidence to be placed in the estim ates and conclusions drawn. Finally, a description of additional research and data needed to im prove the exposure

assessm ent is often helpful to risk m a nagers in m a king decisions about im proving the quality of the assessm ent. For this reason, the exposure characterization should identify key data gaps that can help focus further efforts to reduce uncertainty. Additional guidance on com m unicating the results of an exposure assessm ent can be found in the proceedings of a recent workshop on risk com m unication (Am e rican Industrial Health Council, 1989).

1007.1.2. Risk Characteriz a tion Most exposure assessm ents will be done as part of a risk assessm ent, and the exposure characterization m u st be useful to the risk asse ssor in constructing a risk characterization. Risk characterization is the integration of inform ation from hazard identification, dose-response

assessm ent, and exposure assessm ent into a coherent picture. A risk characterization is a necessary part of any Agency report on risk whether the report is a prelim inary one prepared to support allocation of resources toward further study or a com p rehensive one prepared to support regulatory decisions. Risk characterization is the culm ination of the risk assessm ent process. In this final step, the risk characterization:

C integrates the individual characterizations fromthe hazard identification, doseresponse, and exposure assessm ents;

C provides an evaluation of the overall quality of the assessm ent and the degree of confidence the authors have in the estim ates of risk and conclusions drawn;

C describes risks to individuals and populations in term s of extent and severity of probable harm ; and

C com m unicates results of the risk assessm ent to the risk m a nager. It provides a scientific interpretation of the assessm ent. The risk m a nager can then use the risk

assessm ent, along with other risk m a nagem e nt elem ents, to m a ke public health decisions. The following sections describe these four aspects of the risk characterization in m o re 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

assessm ent, the assessor m a kes m a ny judgm ents concerning the relevance and appropriateness of data and m e thodology. These judgm ents are sum m a rized in the individual characterizations for hazard identification, dose-response, and exposure. In integrating the parts of the assessm ent, the risk assessor determ ines if som e of these judgm ents have im plications for other parts of the

assessm ent, and whether the parts of the assessm ent are com p atible. For exam ple, if the hazard identification assessm ent determ ines that a chem ical is a developm ental toxicant but not a carcinogen, the dose-response and exposure inform ation is presented accordingly; this differs greatly from the way the presentation is m a de if the chem ical is a carcinogen but not a developm ental toxicant. The risk characterization not only exam ines these judgm ents, but also explains the constraints of available data and the state of knowledge about the phenom ena studied in m a king them , including:

101C the qualitative, weight-of-evidence conclusions about the likelihood that the chem ical

m a y pose a specific hazard (or hazards) to hum an health, the nature and severity of the observed effects, and by what route(s) these effects are seen to occur. These judgm ents affect both the dose-response and exposure assessm ents;

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 inform ation was used to determ ine the appropriate dose-response assessm ent technique; and

C the estim ates of the m a gnitude of the exposure, the route, duration and pattern of the exposure, relevant pharm acokinetics, and the num ber and characteristics of the population exposed. This inform ation m u st be com p atible with both the hazard identification and dose-response assessm ents. The presentation of the integrated results of the assessm ent draws from and highlights key points of the individual characterizations of hazard, dose-response, and exposure analysis perform e d separately under these Guidelines. The sum m a ry integrates these com ponent characterizations into an overall risk characterization.

7.1.2.2. Quality of the Assessment and Degree of Confidence The risk characterization sum m a rizes the data brought together in the analysis and the reasoning upon which the assessm ent is based. The description also conveys the m a jor strengths and weaknesses of the assessm ent that arise f r om data availability and the current lim its of understanding of toxicity m echanism s

. Confidence in the results of a risk assessm en t is consequently a function of confidence in the results of analysis of each elem ent: hazard, dose-response, and exposure. Each of these three elem ents has its own characterization associated with it. For exam ple, the exposure

assessm ent com ponent includes an exposure characterization. W ithin each characterization, the im portant uncertainties of the analysis and interpretation of data are explained so that the risk

m a nager is given a clear picture of any consensus or lack thereof about significant aspects of the

assessm ent. For exam ple, whenever m o re than one view of dose-response assessm ent 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 sum m a rized in the risk characterization; in any case a qualitative discussion of im portant uncertainties is appropriate. If other organizations, such as other Federal agencies, have published risk assessm ents, or prior

102EPA assessm ents have been done on the substance or an analogous substance and have relevant sim ilarities or dif f e rences, these too are described.

7.1.2.3. Descriptors of Risk There are a num ber of dif f e rent ways to describe risk in quantitative or qualitative term s. Section 2.3 explains how risk descriptors are used. It is im portant to explain what aspect of the risk is being described, and how the exposure data and estim ates 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 com p leted, the focus turns to com m unicating results to the risk m a nager. The risk m a nager uses the results of the risk characterization, technologic factors, and socioeconom ic considerations in r eaching a regulatory decision. Because of the way these risk m a nagem e nt factors m a y im pact different cases, consistent, but not necessarily identical, risk m a nagem e nt decisions m u st be m a de on a case-by-case basis. Consequently, it is entirely possible and appropriate that a chem ical with a specific risk characterization m a y be regulated differently under different statutes. Th ese Guidelines are not intended to give guidance on the nonscientific aspects of risk m a nagem e nt decisions.

7.1.3. Establishing the Communication Strategy For assessm ents that m u st be explained to the general public, a com m unication strategy is often required. Although risk com m unication is often considered a part of risk m a nagem e nt, it involves input from the exposure and risk asse ssors; early planning for a com m unication strategy can be very helpful to the ultim ate risk com m unication. The EPA has guidance on preparing com m unication strategies (U.S. EPA, 1988g). Additional sources of inform ation are the New Jersey Departm e nt of Environm ental Protection (1988a, 1988b) and the NRC (1989b). These docum ents, and the sources listed within them , are valuable resources for all who will be involved with the sensitive issues of explaining environm ental health risks. The NRC (1989b, p. 148) states: It is a m i stake to sim p ly consider risk com m unication to be an add-on activity for either scientific or public affairs staffs; both elem ents should be involved. There are clear dangers if risk m e ssages are form ulated 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.

1037.2. FORMAT FOR EXPOSURE ASSESSMENT REPORTS The Agency does not require a set form at for exposure assessm ent reports, but individual program of f i ces within the Agency m a y have specif i c f o rm at requirem e nts. Section 3 illustrates that exposure assessm ents are perform e d for a variety of purposes, scopes, and levels of detail, and use a variety of approaches. W h ile it is im practicable f o r the Agency to specif y an outline form at for all types of assessm ents being perform e d within the Agency, program offices are encouraged to use consistent form ats for sim ila r types of assessm ents within their own purview. All exposure assessm ents m u st, at a m i nim u m , contain a narrative exposure characterization section that contains the types of inform ation discussed in Section 7.1. For the purpose of consistency, this section should be titled exposure characterization. Placem ent of this section within the assessm ent is optional, but it is strongly suggested that it be prom inently featured in the assessm ent. It is not, however, an executive sum m a ry and should not be used interchangeably with one.

7.3. REVIEWING EXPOSURE ASSESSMENTS This section provides som e suggestions on how to effectively review an exposure

assessm ent and highlights som e of the com m on pitfalls. The em phasis in these Guidelines has been on how to properly conduct exposure assessm ents; this section can serve as a final checklist in reviewing the com p leted assessm ent. An exposure assessor also m a y be called upon to critically review and evaluate exposure assessm ents conducted by others; these suggestions should be helpful in this regard. Reviewers of exposure assessm ents are usually asked to identify inconsistences with the underlying science and with Agency-developed guidelines, factors, and m e thodologies, and to determ ine the effect these inconsistences m i ght have on the results and conclusions of the exposure assessm ent. Often the reviewer can only describe whether these inconsistencies or deficiencies m i ght underestim ate or overestim ate exposure. Som e of the questions a reviewer should ask to identify the m o re com m on pitfalls that tend to underestim ate exposure are: Has the pathways analysis been broad enough to avoid overlooking a significant pathway? For exam ple, in evaluating exposure to soil contam inated with PCBs, the exposure

assessm ent should not be lim ited only to evaluating the derm al contact pathway. Other pathways, such as inhalation of dust and vapors or the ingestion of contam inated gam e fish from an adjacent stream receiving surface runoff c ontaining contam inated soil, should also be evaluated as they could contribute higher levels of exposure from the sam e source. Have all the contaminants of concern in a mixture been evaluated? Since risks resulting from exposures to com p lex m i xtures of chem icals with the sam e m ode of toxic action are

104generally treated as additive (by sum m i ng the risks) in a risk assessm ent, f a ilure to evaluate one

or m o re of the constituents would neglect its contribution to the total exposure and risk. This is especially critical for relatively toxic or potent chem icals that tend to drive risk estim ates even when present in relatively low quantities. Have exposure levels or concentration measurements been compared with appropriate background levels? Contam inant concentrations or exposure levels should not be com p ared with other contam inated m e dia or exposed populations. W h en com p aring with background levels, the exposure assessor m u st determ ine whether these concentrations or exposure levels are also affected by contam ination 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) m ean only that the chem ical of interest was not found at the particular detection lim it used in the laboratory analysis, ND does not rule out the possibility that the chem ical m a y be present in significant concentrations. Depending on the purpose and the degree of conservatism warranted in the exposure assessm ent, 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 assessm ent is to evaluate the total exposure and risk of a population, then exposures from individual pathways within the sam e route m a y be sum m e d in cases which concurrent exposures can realistically be expected to occur. Som e questions a reviewer should ask to avoid the m o re prevalent errors that generally tend to overestim ate exposure are: Have unrealistically conservative exposure parameters been used in the scenarios? The exposure assessor m u st conduct a reality check to ensure that the exposure cases used in the scenario(s) (except bounding estim ates) could actually occur. Have potential exposures been presented as existing exposures? In m a ny situations, especially when the scenario evaluation approach is used, the objective of the assessm ent is to estim ate potential exposures. (That is, if a person were to be exposed to these chem icals under these conditions, then the resultant exposure would be this m u ch.) In determ ining the need and urgency for regulatory action, risk m a nagers often weigh actual exposures m o re heavily than higher levels of potential exposures. Therefore, the exposure assessm ent should clearly note whether the results represent actual or potential exposures. Have exposures derived from “not detected” levels been presented as actual exposures? For som e exposure assessm ents it m a y be appropriate to assum e that a chem ical reported as not detected is present at either the detection lim it or one-half the detection lim it. The exposure estim ates derived from these nondetects, however, should be clearly labeled as hypothetical

105since they are based on the conservative assum p tion that chem icals are present at or below the detection lim it, when, in fact, they m a y not be present at all. Exposures, doses, or risks estim ated f r om data using substituting values of detection lim its f o r “not detected” sam p les m u st be reported as “less than” the resulting exposure, dose, or risk estim ate. Questions a reviewer should ask to identify com m on errors that m a y underestim ate or overestim ate exposure are: Are the results presented with an appropriate number of significant figures? The num ber of significant figures should reflect the uncertainty of the num eric estim ate. If the likely range of the results spans several orders of m a gnitude, then using m o re than one significant figure im plies

m o re confidence in the results than is warranted. Have the calculations been checked for computational errors? Obviously, calculations should be checked for arithm e tic errors and m i stakes in converting units. This is overlooked

m o re often than one m i ght 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 assessm ent is an inexact science, and the confidence in the results m a y vary trem endously. It is essential the exposure

assessm ent include an uncertainty assessm ent 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 sim u lation random ly selects the values from the input param e ters to sim u late an individual. If data already exist to show the relationship between variables f o r the actual individuals, it m a kes little sense to use Monte Carlo sim u lation, since one already has the answer to the question of how the variables are related for each individual. A sim u lation is unnecessary.)

1068. 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. Exam ples of absorption barriers are the skin, lung tissue, and gastrointestinal tract wall. Accuracy - The m easure of the correctness of data, as given by the difference between the
m easured value and the true or standard value.

Administered dose - The am ount of a substance given to a test subject (hum an or anim al) in determ ining dose-response relationships, especially through ingestion or inhalation. In exposure
assessm ent, since exposure to chem icals is usually inadvertent, this quantity is called potential dose. Agent - A chem ical, physical, m i neralogical, or biological entity that m a y cause deleterious effects in an organism after the organism is exposed to it.

Ambient - The conditions surrounding a person, sam p ling location, etc.

Ambient measurement - A m easurem ent (usually of the concentration of a chem ical or pollutant) taken in an am bient m e dium , norm a lly with the intent of relating the m e asured value to the exposure of an organism that contacts that m e dium

. Ambient medium - One of the basic categories of m a terial surrounding or contacting an organism , e.g., outdoor air, indoor air, water, or soil, through which chem icals or pollutants can
m ove and reach the organism

. (See also biological m e dium , environm ental m e dium ) Applied dose - The am ount of a substance in contact with the prim ary absorption boundaries of an organism (e.g., skin, lung, gastrointestinal tract) and available for absorption.

Arithmetic mean - The sum of all the m easurem ents in a data set divided by the num ber of
m easurem ents in the data set. Background level (environmental) - The concentration of substance in a defined control area during a fixed period of tim e before, during, or after a data-gathering operation. Breathing z o ne - A zone of air in the vicinity of an organism from which respired air is drawn. Personal m onitors are often used to m easure pollutants in the breathing zone.

Bias - A system atic error inherent in a m e thod or caused by som e feature of the m easurem ent system

. Bioavailability - The state of being capable of being absorbed and available to interact with the
m e tabolic processes of an organism

. Bioavailability is typically a f unction of chem ical

107properties, physical state of the m a terial to which an organism is exposed, and the ability of the individual organism to physiologically take up the chem ical. Biological marker of exposure (som etim es referred to as a biom arker of exposure) - Exogenous chem icals, their m e tabolites, or products of interactions between a xenobiotic chem ical and som e target m o lecule or cell that is m easured in a com p artm ent within an organism

. Biological measurement - A m easurem ent taken in a biological m e dium

. For the purpose of exposure assessm ent via reconstruction of dose, the m easurem ent is usually of the concentration of

a chem ical/m etabolite or the status of a biom arker, norm a lly with the intent of relating the
m easured value to the internal dose of a chem ical at som e tim e in the past. (Biological
m easurem ents are also taken for purposes of m onitoring health status and predicting effects of exposure.) (See also am bient m easurem ent) Biological medium - One of the m a jor categories of m a terial within an organism , e.g., blood, adipose tissue, or breath, through which chem ical s can m ove, be stored, or be biologically, physically, or chem ically transform e d. (See also am bient m e dium , environm ental m e dium ) Biologically effective dose - The am ount of a deposited or absorbed chem ical that reaches the cells or target site where an adverse effect occurs, or where that chem ical interacts with a
m e m b rane surface. Blank (blank sample) - An unexposed sam p ling m e dium , or an aliquot of the reagents used in an analytical procedure, in the absence of added analyte. The m easured value of a blank sam p le is the blank value.
Body burden - The am ount of a particular chem ical stored in the body at a particular tim e, especially a potentially toxic chem ical in the body as a result of exposure. Body burdens can be the result of long-term or short-term storage, for exam ple, the am ount of a m e tal in bone, the
am ount of a lipophilic substance such as PCB in adipose tissue, or the am ount of carbon
m onoxide (as carboxyhem oglobin) in the blood. Bounding estimate - An estim ate 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 estim ates are useful in developing statem ents that exposures , doses, or risks are “not greater than” the estim ated value. Comparability - The ability to describe likenesses and dif f e rences in the quality and relevance of two or m o re data sets. Data quality objectives (DQO) - Qualitative and quantitative statem ents of the overall level of uncertainty that a decision-m a ker is willing to accept in results or decisions derived from environm ental data. DQOs provide the sta tistical fram ework for planning and m a naging environm ental data operations consistent with the data user’s needs.

108Dose - The am ount of a substance available for interaction with m e tabolic processes or biologically significant receptors after crossing th e outer boundary of an organism

. The potential dose is the am ount ingested, inhaled, or applied to the skin. The applied dose is the am ount 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
am ount crossing a specific absorption barrier (e.g., the exchange boundaries of skin, lung, and digestive tract) through uptake processes. Internal dose is a m o re general term denoting the
am ount absorbed without respect to specific absorption barriers or exchange boundaries. The
am ount of the chem ical available for interaction by any particular organ or cell is term ed the delivered dose for that organ or cell.

Dose rate - Dose per unit tim e, for exam ple in m g /day, som e tim es also called dosage. Dose rates are often expressed on a per-unit-body-weight basis, yielding units such as m g /kg/day (m g/kg-day). They are also often expressed as averages over som e tim e period, for exam ple a lif etim e. Dose-response assessment - The determ ination of the relationship between the m a gnitude of adm i nistered, applied, or internal dose and a specific biological response. Response can be expressed as m easured 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 adm i nistered, applied, or internal dose of a chem ical or agent, and a specific biological response to that chem ical 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 - Instrum e nt to m easure dose; m a ny so-called dosim eters actually m easure exposure rather than dose. Dosimetry - Process of m easuring or estim ating dose. Ecological exposure - Exposure of a nonhum an receptor or organism to a chem ical, or a radiological or biological agent.

Effluent - W a ste m a terial being discharged into the environm ent, either treated or untreated. Effluent generally is used to describe water discharges to the environm ent, although it can refer to stack em issions or other m a terial flowing into the environm ent.

Environmental fate - The destiny of a chem ical or biological pollutant after release into the environm ent. Environm ental fate involves tem poral and spatial considerations of transport, transfer, storage, and transform a tion.

Environmental fate model - In the context of exposure assessm ent, any m a them atical abstraction of a physical system used to predict the concentration of specific chem icals as a

109function of space and tim e subject to transport, interm edia transfer, storage, and degradation in the environm ent.

Environmental medium - One of the m a jor categories of m a terial found in the physical environm ent that surrounds or contacts organism s, e.g., surface water, ground water, soil, or air, and through which chem icals or pollutants can m ove and reach the organism s. (See am bient
m e dium , biological m e dium )
Exposure - Contact of a chem ical, physical, or biological agent with the outer boundary of an organism

. Exposure is quantified as the concentration of the agent in the m e dium in contact integrated over the tim e duration of that contact.
Exposure assessment - The determ ination or estim ation (qualitative or quantitative) of the
m a gnitude, frequency, duration, and route of exposure. Exposure concentration - The concentration of a chem ical in its transport or carrier m e dium at the point of contact.
Exposure pathw a y - The physical course a chem ical or pollutant takes from the source to the organism exposed.
Exposure route - The way a chem ical or pollutant enters an organism after contact, e.g., by ingestion, inhalation, or derm al absorption. Exposure scenario - A set of facts, assum p tions, and inferences about how exposure takes place that aids the exposure assessor in evaluating, estim ating, or quantifying exposures. Fixed-location monitoring - Sam p ling of an environm ental or am bient m e dium for pollutant concentration at one location continuously or repeatedly over som e length of tim e.
Geometric mean - The n th root of the product of n values.

Guidelines - Principles and procedures to set basic requirem e nts for general lim its of acceptability for assessm ents.

Haz a rd identification - A description of the potential health effects attributable to a specific chem ical or physical agent. For carcinogen assessm ents, the hazard identification phase of a risk
assessm ent is also used to determ ine whether a particular agent or chem ical is, or is not, causally linked to cancer in hum ans. High-end exposure (dose) estimate - A plausible estim ate of individual exposure or dose for those persons at the upper end of an exposure or dose distribution, conceptually above the 90 th percentile, but not higher than the individual in the population who has the highest exposure or dose.

110High-end Risk Descriptor - A plausible estim ate of the individual risk for those persons at the upper end of the risk distribution, conceptually above the 90 th 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
sam e 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 am ount 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 synonym ous with absorbed dose. Limit of detection (LOD) [or Method detection limit (MDL)] - The m i nim u m concentration of an analyte that, in a given m a trix and with a specific m e thod, has a 99% probability of being identif ied, qualitatively or quantitatively m easured, and reported to be greater than zero.

Matrix - A specific type of m e dium (e.g., surface water, drinking water) in which the analyte of interest m a y be contained. Maximally exposed individual (MEI) - The single individual with the highest exposure in a given population (also, m a xim u m exposed individual). This term has historically been defined various ways, including as defined here and also synonym ously with worst case or bounding estim ate. Assessors are cautioned to look for contextual definitions when encountering this term in the literature. Maximum exposure range - A sem i quantitative term referring to the extrem e upperm ost 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 98 th percentile of the distribution, but is not higher than the individual with the highest exposure.

Median value - The value in a m easurem ent data set such that half the m easured values are greater and half are less.

Microenvironment method - A m e thod used in predictive exposure assessm ents to estim ate exposures by sequentially assessing exposure for a series of areas (m icroenvironm ents) that can be approxim a ted by constant or well-characterized concentrations of a chem ical or other agent.

Microenvironments - W e ll-defined surroundings such as the hom e, office, autom obile, kitchen, store, etc. that can be treated as hom ogeneous (or well characterized) in the concentrations of a chem ical or other agent.

Mode - The value in the data set that occurs m o st frequently.

111Monte Carlo technique - A repeated random sam p ling from the distribution of values for each of the param e ters in a generic (exposure or dose) equation to derive an estim ate of the distribution of (exposures or doses in) the population. Nonparametric statistical methods - Methods that do not assum e a functional form with identifiable param e ters for the statistical distribution of interest (distribution-free m e thods).

Pathw a y - The physical course a chem ical or pollutant takes from the source to the organism exposed.

Personal measurement - A m easurem ent collected from an individual’s im m e diate environm ent using active or passive devices to collect the sam p les.

Pharmacokinetics - The study of the tim e course of absorption, distribution, m e tabolism , 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
m easurem ents of concentration over tim e at or near the point of contact between the chem ical and an organism while the exposure is taking place.

Potential dose - The am ount of a chem ical contained in m a terial ingested, air breathed, or bulk
m a terial applied to the skin.

Precision - A m easure of the reproducibility of a m easured value under a given set of conditions. Probability samples - Sam p les selected from a statistical population such that each sam p le has a known probability of being selected. Quality assurance (QA) - An integrated system of activities involving planning, quality control, quality assessm ent, reporting and quality im provem e nt to ensure that a product or service m eets def i ned standards of quality with a stated level of conf idence. Quality control (QC) - The overall system of technical activities whose purpose is to m easure and control the quality of a product or service so that it m eets the needs of the users. The aim is to provide quality that is satisfactory, adequate, dependable, and econom ical. Quantification limit (QL) - The concentration of analyte in a specif ic m a trix f o r which the probability of producing analytical values above the m e thod detection lim it is 99%.
Random samples - Sam p les selected from a statistical population such that each sam p le has an equal probability of being selected.
Range - The difference between the largest and sm allest values in a m easurem ent data set.

Reasonable w o rst case - A sem i quantitative term ref e rring to the lower portion of the high end of the exposure, dose, or risk distribution. The reasonable worst case has historically been

112loosely defined, including synonym ously with m a xim u m exposure or worst case, and assessors are cautioned to look for contextual definitions when encountering this term in the literature. As
a sem i quantitative term , it is som e tim es useful to refer to individual exposures, doses, or risks that, while in the high end of the distribution, are not in the extrem e tail. For consistency, it should refer to a range that can conceptually be described as above the 90 th percentile in the distribution, but below about the 98 th percentile. (Com pare m a xim u m 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 chem ical levels in tissues or fluids or from evidence of other biom arkers of exposure. Representativeness - The degree to which a sam p le is, or sam p les are, characteristic of the whole m e dium , exposure, or dose for which the sam p les are being used to m a ke inferences.

Risk - The probability of deleterious health or environm ental ef f e cts.

Risk characteriz a tion - The description of the nature and often the m a gnitude of hum an or nonhum an risk, including attendant uncertainty.

Route - The way a chem ical or pollutant enters an organism after contact, e.g., by ingestion, inhalation, or derm al absorption. Sample - A sm all part of som e thing designed to show the nature or quality of the whole. Exposure-related m easurem ents are usually sam p les of environm ental or am bient m e dia, exposures of a sm all subset of a population for a short tim e, or biological sam p les, all for the purpose of inferring the nature and quality of param e ters im portant to evaluating exposure. Sampling frequency - The tim e interval between the collection of successive sam p les. Sampling plan - A set of rules or procedures specifying how a sam p le is to be selected and handled.

Scenario evaluation - An approach to quantifying exposure by m easurem ent or estim ation of both the am ount of a substance contacted, a nd the frequency/duration of contact, and subsequently linking these together to estim ate exposure or dose.

Source characteriz a tion measurements - Measurem ents m a de to characterize the rate of release of agents into the environm ent f r om a source of em ission such as an incinerator, landf ill, industrial or m unicipal facility, consum er product, etc. Standard operating procedure (SOP) - A procedure adopted for repetitive use when perform i ng a specific m easurem ent or sam p ling operation.

Statistical control - The process by which the variability of m easurem ents or of data outputs of a system is controlled to the extent necessary to produce stable and reproducible results. To say

113that m easurem ents are under statistical control m eans that there is statistical evidence that the critical variables in the m easurem ent process are being controlled to such an extent that the system yields data that are reproducible within well-defined lim its. Statistical significance - An inf e rence that the probability is low that the observed dif f e rence in quantities being m easured could be due to variability in the data rather than an actual dif f e rence in the quantities them selves. The inf e rence that an observed dif f e rence is statistically signif i cant is typically based on a test to reject one hypothesis and accept another. Surrogate data - Substitute data or m easurem ents on one substance used to estim ate analogous or corresponding values of another substance.
Uptak e - The process by which a substance crosses an absorption barrier and is absorbed into the body.

Worst case - A sem i quantitative term referring to the m a xim u m 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 m a xim i ze exposure, dose, or risk does in fact happen. This worst case m a y occur (or even be observed) in a given population, but since it is usually a very unlikely set of circum stances, in m o st cases, a worst-case estim ate will be som e what higher than occurs in a specific population. As in other fields, the worst-case scenario is a useful device when low probability events m a y result in a catastrophe that m u st be avoided even at great cost, but in m o st health risk assessm ents, a worst-case scenario is essentially a type of bounding estim ate.

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U.S. Environm ental Protection Agency. (1987a) The total exposure assessm ent m e thodology (TEAM) study. Volum e I: Sum m a ry and analysis. Office of Acid Deposition, Environm ental Monitoring and Quality Assurance, Office of Research and Developm ent, W a shington, DC. EPA/600/6-87/002a.

U.S. Environm ental Protection Agency. (1987b) Selection criteria for m a them atical m odels used in exposure assessm ents: Surface water m odels. Office of Health and Environm ental Assessm ent, Office of Research and Deve lopm ent, W a shington, DC. EPA/600/8-87/042, NTIS PB88-139928/AS.

U.S. Environm ental Protection Agency. (1987c) Supplem ent A to the guideline on air quality
m odels (Revised). Of f i ce of Air Quality Pla nning and Standards, Research Triangle Park, NC. EPA/450/2-78/027R.

U.S. Environm ental Protection Agency. (1987d) Pesticide assessm ent guidelines for applicator exposure m onitoring - subdivision U. Office of Pesticide Program s, Office of Pesticides and Toxic Substances, W a shington, DC. EPA/540/9-87/127.

U.S. Environm ental Protection Agency. (1987e) Addendum to the health assessm ent docum ent for trichloroethylene: Updated carcinogenicity asse ssm ent for trichloroethylene. Review Draft. Office of Health and Environm ental Assessm ent, Office of Research and Developm ent, W a shington, DC. EPA/600/8-82/006FA, NTIS PB87-228045/AS.

U.S. Environm ental Protection Agency. (1988a) Proposed guidelines for exposure-related
m easurem ents. Federal Register 53(232):48830-48853.

U.S. Environm ental Protection Agency. (1988b) Proposed guidelines for assessing fem a le reproductive risk. Federal Register 53(126):24834-24847.

U.S. Environm ental Protection Agency. (1988c) Proposed guidelines for assessing m a le reproductive risk. Federal Register 53(126):24850-24869.

U.S. Environm ental Protection Agency. (1988d) Laboratory data validation functional guidelines for evaluating organic analyses. US EPA Data Review W o rk Group, prepared for the Hazardous Site Evaluation Division, Office of Solid W a ste and Em ergency Response, W a shington, DC. February 1, 1988.

120U.S. Environm ental Protection Agency. (1988e ) Laboratory data validation functional guidelines for evaluating inorganic analyses. USEPA Data Review W o rk Group, prepared for the Hazardous Site Evaluation Division, Office of Solid W a ste and Em ergency Response, W a shington, DC. July 1, 1988.

U.S. Environm ental Protection Agency. (1988f) Selection criteria for m a them atical m odels used in exposure assessm ents: Ground-water m odels. Office of Health and Environm ental Assessm ent, Office of Research and Devel opm ent, W a shington, DC. EPA-600/8-88/075, NTIS PB88-248752/AS.

U.S. Environm ental Protection Agency. (1988g) Seven cardinal rules of risk com m unication. Office of Policy Analysis, Office of Policy, Planning, and Evaluation, W a shington, DC. OPA87-020.
U.S. Environm ental Protection Agency. (1988h) Total hum an exposure and indoor air quality: An autom a ted bibliography (BLIS) with sum m a ry abstracts. Office of Acid Deposition, Monitoring, and Quality Assurance, Office of Research and Developm ent, W a shington, DC. EPA/600/9-88/011.

U.S. Environm ental Protection Agency. (1989a) A rationale for the assessm ent of errors in the
sam p ling of soils. Office of Research a nd Developm ent, W a shington, DC. EPA/600/4-90/013.

U.S. Environm ental Protection Agency. (1989b) Resolution on use of m a them atical m odels by EPA for regulatory assessm ent and decision-m a king. Environm ental Engineering Com m ittee, Science Advisory Board, W a shington, DC. EPA/SAB-EEC-89-012.

U.S. Environm ental Protection Agency. (1989c ) Exposure factors handbook. Office of Health and Environm ental Assessm ent, Office of Research and Developm ent, W a shington, DC. EPA/600/8-89/043, NTIS PB90-106774/AS.

U.S. Environm ental Protection Agency. (1990a) Soil sam p ling quality assurance user’s guide. Environm ental Monitoring System s Laboratory, Office of Research and Developm ent, W a shington, DC. EPA/600/8-89/046.

U.S. Environm ental Protection Agency. (1990b) Guidance for data useability in risk assessm ent. Interim Final. Office of Em ergency and Re m e dial Response, W a shington, DC. EPA/540/G90/008.

U.S. Environm ental Protection Agency. (1991a) Guidelines for developm ental toxicity risk
assessm ent. Federal Register 56(234):63798-63826.

U.S. Environm ental Protection Agency. (1991b) Selection criteria for m a them atical m odels used in exposure assessm ent: Atm o spheric dispersion m odels. Office of Health and Environm ental Assessm ent, Office of Research and De velopm ent, W a shington, DC. EPA/600/8-91/038.

121U.S. Environm ental Protection Agency. (1992a) Sc ience Advisory Board’s review of the draft final exposure assessm ent guidelines (SAB Final Review Draft dated August 8, 1991). EPA Science Advisory Board, W a shington, DC. EPA/SAB-IAQC-92-015, Dated January 13, 1992.

U.S. Environm ental Protection Agency. (1992b) Derm al exposure assessm ent: Principles and applications. Office of Health and Envir onm ental Assessm ent, Office of Research and Developm ent, W a shington, DC. EPA/600/8-91/011F.

W a xweiler, R.J.; Zum w alde, R.D.; Ness, G.O.; et al. (1988) A retrospective cohort m o rtality study of m a les m i ning and m illing attapulgite clay. Am . J. Ind. Med. 13:305-315.

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W o rld Health Organization (W HO). (1986) Pr inciples for evaluating health risks from chem icals during infancy and early childhood: The need for a special approach. Geneva, Switzerland: W HO, Environm ental Health Criteria 59, pp. 26-33. Yang, J.J.; Roy, T.A.; Krueger, A.J.; et al. (1989) In vitro and in vivo percutaneous absorption of benzo[a]pyrene from petroleum crude-fortifie d soil in the rat. Bull. Environ. Contam

. Toxicol. 43:207-214.

122PART B: RESPONSE TO PUBLI C AND SCIENCE ADVISORY BOARD COMMENTS

1. INTRODUCTION

This section sum m a rizes the m a jor issues raised in public com m e nts on the Proposed Guidelines for Exposure-Related Measurem ents (hereafter “1988 Proposed Guidelines”) published Decem ber 2, 1988 (53 FR 48830-48853). In addition to general com m e nts, reviewers were requested to com m e nt specifically on the guidance for interpreting contam inated blanks versus field data, the interpretation of data at or near the lim it of detection, approaches to assessing uncertainty, and the Glossary of Term s. Com m e nt was also invited on the following questions: Should the 1988 Proposed Guidelines be com b ined with the 1986 Guidelines for Estim ating Exposures (hereafter “1986 Guidelines”)? Is the current state-of-the-art in m a king

m easurem ents of population activities for the purpose of exposure assessm ent 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 usef ul and appropriate, especially in the area of statistics? The Science Advisory Board (SAB) m e t on Decem ber 2, 1988, and provided written

com m e nts in a May 1989 letter to the EPA Adm i nistrator (EPA-SAB-EETFC-89-020). The public com m e nt period extended until March 2, 1989. Com m e nts were received from 17 individuals or organizations. After the SAB and public com m e nt, Agency staff prepared sum m a ries of the com m e nts and analyses of m a jor issues presented by the com m e ntors. These were considered in the developm ent of these final Guidelines. In response to the com m e nts, the Agency has m odified or clarified m o st of the sections of the Guide lines. For the purposes of this discussion, only the

m o st significant issues reflected by the public and SAB com m e nts are discussed. Several m i nor
recom m e ndations, 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

com m e nts, retitling them Guidelines for Exposur e Assessm ent (hereafter “Guidelines”). The Agency presented the draft final Guidelines to the SAB at a public m eeting on Septem ber 12, 1991, at which tim e the SAB invited public com m e nt for a period of 30 days on the draft. The SAB discussed the final draft in a January 13, 1992 letter to the Adm i nistrator of the EPA (EPASAB-IAQC-92-015). There were no additional public com m e nts received.

1232. RESPONSE TO GENERAL COMMENTS

In general, the reviewers were com p lem e ntary 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 treatm e nt of nonhum an exposure. The Agency has refined its approach and coverage in these Guidelines. Although these Guidelines deal specifically with hum an exposures to chem icals, additional supplem ental guidance m a y be developed for ecological exposures , and exposures to biological or radiological entities. The Agency is currently developing separate guidelines f o r ecological risk assessm ent. Concerns were expressed about the Agency’s use of the term s exposure and dose. Consequently, the Agency reviewed its definitions and uses of these term s and evaluated their use elsewhere in the scientific com m unity. The Agency has changed its definitions and uses of these term s 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 Academ y of Sciences and others in the scientific field. Many reviewers urged the Agency to be m o re explicit in its recom m e ndations regarding uncertainty in statistics, lim its of detection, censored data sets, and the use of m odels. Som e reviewers f e lt the level of detail was appropriate f o r statistical uncertainty while others wanted additional m e thods for dealing with censored data. Several com m e nded the Agency for its acknowledgem e nt of uncertainty in exposure assessm ents and the call for its explicit description in all exposure assessm ents, while others expressed concern for lack of acknowledgem e nt of

m odel uncertainty. Accordingly, these areas have been revisited and an entire section has been devoted to uncertainty. W e agree with the reviewers that m u ch m o re work rem a ins to be done in this area, particularly with evaluating overall exposure assessm ent uncertainty, not only with

m odels but also with the distributions of exposure param e ters. The Agency m a y issue additional guidance in this area in the f u ture. Som e reviewers subm itted extensive docum entation regarding detection lim its and statistical representations. Several subm itted com m e nts arguing against data reporting conventions that result in censored data sets and recom m e nded that the Agency issue a guidance docum ent for establishing total system detection lim its. The Agency found the docum entation to be helpful and has revised the sections of the Guidelines accordingly. Unfortunately, several of the other suggestions go beyond the scope of this docum ent. The reviewers generally com m e nted that the glossary was useful, presenting m a ny technical term s and defining them in an appropriate m a nner. The glossary has been expanded to include the key term s used in the Guidelines, while at the sam e tim e correcting som e def i nitions

124that 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 w i th the 1986 Guidelines? The SAB and several other com m e ntors recom m e nded that the 1986 Guidelines and the 1988 Proposed Guidelines be com b ined into an integrated docum ent. The Agency agrees with this recom m e ndation and has m a de an effort to produce a single guideline that progresses logically from start to finish. This was accom p lished through an extensive reform atting of the two sets of guidelines as an integrated docum ent, rather than a sim p le 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 assessm ent. A m o re
com p lete section (Section 7 of the current Guidelines) now discusses how assessm ents should be presented and suggests a series of points to consider in reviewing assessm ents. The Agency has also expanded the section in the 1986 Guidelines that discussed exposure scenarios, partly by incorporating m a terial from the 1988 Proposed Guidelines, and partly as a result of com m e nts 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, estim at es, and risk descriptors, and defines certain scenario-related term s for use in exposure assessm ents.

Is the current state-of-the-art in making meas urements of population activities for the purpose of exposure assessment advanced to the point w h ere the Agency can construct guidelines in this area? Both the SAB and public com m e nts recom m e nded the inclusion of dem ographics, population dynam i cs, and population activity patterns in the exposure assessm ent process. In response, the Agency has included additional discussion on use of activity patterns in the current Guidelines, while recognizing that m o re 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 m i ght be expected, there was no clear consensus of opinion on what constitutes appropriate coverage. Regarding quality assurance (QA) and quality control (QC), it was f e lt that a strong statem ent on the need for QA/QC followed by reference to appropriate EPA

125docum ents was a suitable level of detail. Statistical analyses, sam p ling issues, lim it of detection, and other analytical issues all elicited m a ny thoughtful com m e nts. W h ere the recom m e ndations did not exceed the scope of the docum ent or the role of EPA, the Agency has attem p ted to blend the various recom m e ndations into the current Guidelines. In all these areas, therefore, the previous sections have been revised in accordance with com m e nts.

126