Chapter 10 Assessing Air Quality: Monitoring

Table of Contents

10.1 Introduction ............ ............ ............ ............ ............ ...... 1

10.2 Air Toxics Monitoring: Recent Advances ............ ............ ............ ...... 2

10.3 Monitoring for Air Toxics Risk Assessments: Why Monitor? ............ ............ . . 4

10.4 Planning for Air Toxics Monitoring ............ ............ ............ ........... 9

10.4.1 General Planning Approach ............ ............ ............ .......... 10

10.4.2 Specific Planning Issues ............ ............ ............ ............ 14

10.5 Implementing Air Toxics Monitoring ............ ............ ............ ......... 19

10.5.1 Locating Monitors and Selecting Sample Size ............ ............ ....... 19

10.5.1.1 Locating Monitors ............ ............ ............ .... 19

10.5.1.2 Selecting Sample Size ............ ............ ............ . 24

10.5.1.3 Setting Up a Monitoring/Sampling Program ............ ........ 25

10.5.2 Data Analysis and Reporting ............ ............ ............ ......... 28

10.5.3 The Use of Monitoring Data to Calculate Exposure Concentrations ............ . . 29

10.6 Monitoring Methods, Technologies, and Costs ............ ............ ............ . 31

10.6.1 Ambient Air Monitoring ............ ............ ............ ............ 33

10.6.2 Sampling Costs ............ ............ ............ ............ ....... 35

10.7 Archiving Air Toxics Monitoring Data ............ ............ ............ ....... 35

10.8 Using Air Monitoring Data to Evaluate Source Contribution ............ ............ . . 37

References ............ ............ ............ ............ ............ ............ 38

April 2004 Page 10-1

10.1 Introduction

In environmental investigations, the term “monitoring” describes the collection of actual samples

of environmental media and then subjecting those samples (usually) to chemical analysis to

determine the identity and concentration of the various pollutants in the sample. A distinction

may also be made between sampling (i.e., stack testing) and monitoring (i.e., for ambient

concentrations). In air toxics risk assessment, this process commonly consists of collecting air

samples and either evaluating the samples at the monitoring station itself, or sending them to a

laboratory for evaluation. 

For air toxics risk assessments, monitoring and analysis can help determine the concentration of

both those pollutants in air and those that have migrated into other media, such as soil, water,

sediments, and biota. This chapter discusses the use of monitoring to evaluate pollutants in air. 

Chapter 19 discusses the use of monitoring in media other than air.

Many aspects of a monitoring program will depend on the spatial scale of the assessment being

supported by the measurement program:

• Micro-scale – highly localized regions up to 100 meters in size; these might reflect city

blocks or individual households.

• Middle-scale – regions of several blocks with sizes of 100 to 500 meters.

• Neighborhood-scale – an extended area with uniform land use (and, hence, relatively

homogeneous receptor population), extending up to several kilometers in size.

• Urban-scale – overall city or county conditions, perhaps up to 50 km in size.

• Regional- or national-scale – a state, several states, or the entire nation.

Air toxics risk assessments often examine exposure to relatively large numbers of people over

relatively large geographic areas (e.g., a neighborhood or urban area, county, or larger). In these

instances, the risk managers and analysts must carefully use their planning and scoping activities

to develop the questions they want to answer and identifying the types of data they will need to

answer those questions. For some questions and data needs, monitoring is the preferred tool for

estimating inhalation exposure concentrations for air toxics risk assessment, either as the primary

way of determining concentrations in air or as a way to test and normalize model results (and

look for gaps in the emissions inventory).

This chapter provides an overview of monitoring, including recent advances by EPA (Section

10.2); the reasons for monitoring (Section 10.3); how to plan a monitoring program (Section

10.4); implementation (Section 10.5); available air monitoring methods (Section 10.6); archiving

monitoring data (Section 10.7); and using monitoring data to evaluate source contribution

(Section 10.8).
April 2004 Page 10-2

EPA’s Ambient Monitoring Technology

Information Center (AMTIC)

AMTIC (http://www.epa.gov/ttn/amtic/welcome.html) is

centered around the exchange of ambient monitoring

related information. Established in 1991 as an electronic

Bulletin Board System (BBS), AMTIC has evolved with

changing technology into a page on the World Wide

Web. AMTIC is operated by EPA’s Office of Air

Quality Planning and Standards (OAQPS) through the

Monitoring and Quality Assurance Group (MQAG). 

AMTIC contains information on all the Reference and

Equivalent methods for the Criteria pollutants, the toxic

organics (TO) Methods for air toxics and other

noncriteria pollutant methodologies, Federal Regulations

pertaining to ambient monitoring, ambient monitoring

quality assurance/quality control (QA/QC) related

information, information on ambient monitoring related

publications, ambient monitoring news, field and

laboratory studies of interest, and updates on any new or

developing EPA Ambient Air standards.

10.2 Air Toxics Monitoring: Recent Advances

EPA recently published a draft

National Air Toxics Monitoring

Strategy that describes the structure of

the national air toxics monitoring

program, including its history, status,

and expected products.(1) At the start

of the program, EPA’s focus was on

“nationally pervasive” priority

pollutants. In recent years, EPA has

initiated local scale monitoring studies

to address potential air toxics problem

areas.

EPA’s air toxics monitoring is

structured into four groups – national

level, local scale, persistent

bioaccumulative toxics (PBTs), and

“other” EPA-specific programs. 

• The National Air Toxics Trends

System (NATTS) program is a

network of monitoring stations at

22 urban or rural locations across the country (see Exhibit 10-1). The focus for these sites is

on seven “nationally pervasive” priority pollutants (formaldehyde, arsenic, chromium,

benzene, 1,3-butadiene, acrolein, and light absorbing carbon). All of the stations are

expected to become operational in early 2004.

• Local scale monitoring studies are designed to complement NATTS, but they are shorterterm (less than 2 years) and have more flexible study requirements to go beyond the scope of

the NATTS. Local-level studies provide information of urban/local interest that is not

achievable with a single monitoring site at a city. For example, these studies may address

specific source categories or better characterize pollutant levels associated with different

locations in a metropolitan area. EPA plans to implement 10 to 15 local scale monitoring

projects that are implemented by state, local, and tribal (S/L/T) air pollution control agencies.

• Programs that monitor atmospheric deposition of PBTs include (1) the National Atmospheric

Deposition Program – Mercury Deposition Network (NADP – MDN), a multi-agency

program with approximately 90 monitoring sites; (2) the Integrated Atmospheric Deposition

Network (IADN), a partnership between EPA and Canada, which is measuring PBTs in the

Great Lakes Region; and (3) the National Dioxin Air Monitoring Network (NDAMN), a 30-

site research program. 

• A variety of EPA Regional air toxics monitoring activities that existed prior to NATTS

continue.
April 2004 Page 10-3

Exhibit 10-1. National Air Toxics Trends Stations (NATTS) Sites

January 2003 Startup January 2004 Startup Pilot Programs 

Providence, RI

Roxbury, MA

New York, NY

Washington, DC

Decatur (Atlanta), GA

Hazard, KY*

Detroit, MI

Deer Park (Houston), TX

St. Louis, MO

Bountiful, UT

Grand Junction, CO*

San Jose, CA

Seattle, WA

Chittenden County, VT*

Rochester, NY

Tampa, FL

Chesterfield, SC*

Chicago, IL

Mayville, WI

Harrison County, TX*

Phoenix, AZ

La Grande, OR*

Barcelona/San Juan, PR

Providence, RI

Keeney Knob, WV*

Tampa, FL

Detroit, MI

Rio Rancho, NM

Cedar Rapids, IA

San Jacinto, CA

Grand Junction, CO*

Seattle, WA

* rural site

Source: EPA’s Latest Findings on National Air Quality(2)

EPA has encouraged a significant effort over the past few years to increase reporting of air toxics

sampling results to EPA’s AirData database website (http://www.epa.gov/air/data). For example,

the Lake Michigan Air Directors Consortium (LADCO), the Northeast States for Coordinated

Air Use Management (NESCAUM), and the California Air Resources Board (CARB) “mined”

existing data from approximately 300 existing monitoring sites across the U.S. to provide

information about the spatial pattern, temporal profile, and general characteristics of air toxics

compounds. EPA collected additional data for this analysis from a year long monitoring study

carried out in four urban areas and six smaller city/rural areas. A number of reports, newsletters,

and related documents describing EPA’s air toxics monitoring efforts are available at EPA’s

Ambient Monitoring Technology Information Center website.(3)
April 2004 Page 10-4

10.3 Monitoring for Air Toxics Risk Assessments: Why Monitor?

Air toxics programs have long used monitoring to evaluate the concentration of chemicals in air. 

In general, monitoring (sampling and analysis) results may help:

• Identify and estimate current exposures to ambient concentrations of air toxics (outdoor

and/or indoor) at a specific location of concern (e.g., a school or neighborhood). As an

example, EPA tracks ozone concentrations at numerous locations around the country, with

results available over the Internet (http://www.epa.gov/airnow/) for many locations, virtually

in real-time. As another example, air toxics monitoring can be used to evaluate the impacts

of a specific source on a nearby receptor (“source-oriented” monitoring).

• Develop or refine values for specific parameters needed by air dispersion models (for

example, study-specific release data, meteorological conditions). 

• Validate the predictions of a model in specified circumstances (e.g. validate that the location

of highest exposure predicted by the model is correct, which increases confidence that a

maximally exposed subpopulation has been identified – may be difficult to do without a very

dense monitoring network).

• Track trends in air quality levels (e.g. to determine whether air pollution programs have

generally been effective at reducing exposures). 

• Identify gaps in emissions inventories (e.g., monitoring identifies an airborne chemical that is

not reported in existing emissions inventories) or close gaps that might be present in existing

data (e.g., concentrations of specific air toxics in specific releases).

• Determine compliance with air toxics legal requirements (e.g., permit limits at a factory,

emissions limitations on motor vehicles). 

• Gather data in support of enforcement actions.

Ultimately, the choice of whether to monitor or model (or both) depends on the goals of the

assessment, the exposure setting, other specific project circumstances (e.g., many communities

want monitoring as part of a risk assessment), and the assessing entity. For example, to

understand the exposure an actual individual receives as they move about their daily activities,

personal monitoring is the best option because it reflects the pattern of this movement. However,

such studies are rarely done outside of research settings. As another example, compliance with a

permitted release rate may also require monitoring as the preferred method of measurement. 

Exhibit 10-2 provides a brief comparison of modeling versus monitoring.
April 2004 Page 10-5

Exhibit 10-2. Comparison of Modeling and Monitoring Approaches for

Estimating Ambient Air Exposure Concentrations (ECs)

Modeling Monitoring

Modeling is relatively fast and inexpensive compared to

monitoring. Many screening-level models can be run in

spreadsheet formats and require relatively simple input

parameters. Many dispersion models, along with technical

reference manuals and other support documents, are

available for free download from EPA’s Support Center

for Regulatory Air Models (SCRAM) website

(http://www.epa.gov/ttn/scram/). Resources normally need

to be expended to enhance the local air toxics emission

inventories to make air toxics modeling more precise.

With monitoring, it takes time to build data, and

there are methodological limits and logistical

issues. How expensive monitoring is depends on

what you are trying to do and how much you are

willing to pay. Monitoring does not always require

equipment purchase, and some states and local

areas already have equipment. Some less

expensive monitoring techniques are now available

(e.g., passive samplers). 

Modeling results can estimate concentrations over a large

spatial area (e.g., a 50-km radius from a source) and can

provide a “big picture” view of the assessment area. 

Modeling also allows for analysis of EC at multiple points

throughout the assessment area. The downside of

modeling, however, is that these are predicted

concentrations.

Monitoring results provide actual measured

concentrations. Multiple locations may be required

to characterize concentrations over an area,

although Geographic Information Systems (GIS)

methods facilitate interpolation between locations. 

The downside is that the monitoring may not be

representative of a large geographic area.

Screening-level models can provide a predicted estimate of

whether significant concentrations are likely. A simple

screening analysis may be sufficient to make a risk

management decision that no action is required.

Monitoring can be used to identify and measure

exposures for specific individuals at a specific

location of concern (e.g., a school). This data can

provide a quick screen to determine whether more

extensive monitoring is needed.

Models can be used to identify areas where maximum

concentrations are likely to occur, and thus where to focus

efforts for additional tiers of the assessment. Uncertainties

in model parameters and the discrete division of the wind

field used in models (often with only eight wind directions)

can result in incorrect identification of the locations of

maximum concentration.

Monitoring can identify areas and actual levels of

exposure occurring at the monitoring sites. 

Monitoring can also be used to indicate the point of

maximal exposure if the monitoring is designed for

that purpose. The selection of the monitoring

locations is critical; if placed in the wrong

locations, monitors can provide incorrect and

misleading information about maximal exposures. 

Models can be used to identify the subset of chemicals of

potential concern (COPCs) and exposure pathways/routes

that have the greatest contribution to risk. This can be

helpful in focusing efforts for additional tiers of the

assessment as well as determining appropriate risk

management actions.

Monitoring can be used to confirm significant

exposure pathways and routes. (Measured

concentrations can be compared to risk-based

screening levels). It also can be used to identify

compounds that may not have been suspected and,

hence, were not included in models (i.e.,

monitoring allows identification of gaps in the

emissions inventory).

Models allow “what if” scenarios to be evaluated (e.g.,

what if a permitted emission were doubled?).

Monitoring can only evaluate current conditions.

More complex modeling may allow explicit predictions

and estimates of variability in exposure.

A large number of samples generally is needed to

characterize variability; this may be prohibitively

expensive. Monitoring, however, provides a direct

and reliable means to characterize variability.

Models often use simplifying assumptions and data inputs

that may or may not be representative of the specific

assessment area. This introduces uncertainty into model

predictions.

Monitoring can be used to confirm actual exposure

levels, to investigate assumptions or calibrate

models to site-specific conditions, and to close gaps

in data, reducing uncertainties. 
April 2004 Page 10-6

Air toxics risk assessments, however, tend to examine potential exposures to hazardous air

pollutants (HAPs) and other air toxics for a relatively large number of people over relatively

large geographic areas (e.g., a neighborhood or urban area, county, or larger). In these instances,

the risk managers and analysts must carefully use their planning and scoping activities to develop

the questions they want to answer and to identify the types of data they will need to answer those

questions. For some questions and data needs, monitoring is the preferred tool. For others,

modeling is better. In general, most air toxics risk assessments will benefit from some

combination of both modeling and monitoring to provide the depth and breadth of information

that will be necessary to answer the assessment questions (see hypothetical example in Exhibit

10-3).

Exhibit 10-3. Hypothetical Example of a Combined Modeling and Monitoring Program

This figure illustrates a hypothetical set of isopleths for annual average air concentrations that a

dispersion model predicted, assuming a single source (black dot) near the center of the geographic

region. Note that the model predicts the point of maximal exposure to be somewhere within the area

bounded by grid points 2, 4, S1, and S3, based on the existing information on release rate, wind

direction, and effective release height. In this hypothetical example, a monitoring station was used to

measure ambient concentrations as a means of evaluating the model predictions. Note that the

monitoring location is not in the area of estimated highest concentration and, therefore, might not

provide a better estimate of maximum exposure.

Indeed, most air toxics risk assessments that evaluate exposures to populations receiving impacts

from one or more sources should generally consider using modeling as their primary tool to

evaluate and characterize exposures and risks. In certain instances, assessors may use monitoring

as the primary tool to evaluate exposure concentrations for potentially exposed populations. The

utility of modeling for neighborhood and larger scale analyses is that it provides a better picture

of the variation of exposure conditions over the assessment area domain (i.e., modeling provides

spatial resolution) and allows a more straightforward approach to source allocation (i.e., what

portion of the risk is caused by each of the modeled sources). 
April 2004 Page 10-7

Monitoring, on the other hand, only provides estimates of concentrations at the point at which

samples are taken, and it is often difficult to clearly define the spatial coverage that those

measured concentrations represent. In addition, it is often difficult to use monitoring data for

source allocation (especially for chemicals emitted by numerous sources). Monitoring plays a

crucial role in identifying important chemicals that the emissions inventories may not have

captured. In rarer instances, assessors can use monitoring as the primary tool to evaluate

exposures for potentially exposed populations; however, this method carries a corresponding

increase in the uncertainty of the results (see Section 10.4 on how to use ambient monitoring data

to develop estimates of exposure concentration). (Note that, in limited circumstances,

geostatistical techniques such as kriging are sometimes applied to estimate concentration

variation between a set of monitors. This topic is beyond the scope of this reference manual;

however, assessors are encouraged to carefully consider the uncertainties associated with this

type of approach and whether alternate tools, such as air dispersion modeling, would provide a

better understanding of concentration gradients across the study area. In addition, the average

concentration of atmospheric pollutants across a study area is sometimes estimated by averaging

the results of all the monitors in the area. However, since pollutant concentration can change

rapidly across space and time, combining data across monitors may “average out” very important

information about exposure at a particular monitoring location. It is for this reason that

combining data across monitors is not commonly performed and assessors are encourage to

carefully consider the pros and cons of attempting such an analysis. If monitors are combined,

the results should, nevertheless, be reported alongside the results of each of the individual

monitors.)

If assessors make the choice to implement a monitoring program, it is important to carefully

design the sampling and analysis approach to provide meaningful input into the risk management

decision. Because sampling and analysis are relatively expensive and time consuming, a welldesigned monitoring program can ensure the efficient use of resources. Well designed and

implemented monitoring programs quantify not only the concentrations but also information

related to the associated data uncertainty. The study-specific conceptual model and analysis plan

that assessors develop during the planning and scoping phase help ensure a well-designed

sampling and analysis program that will yield results suitable for decision-making purposes. 

Monitoring programs are commonly designed to:

• Use a sampling methodology that results in scientifically defensible data and that meets

regulatory criteria or other concerns – it is important to utilize methodologies that are

scientifically defensible and acceptable within a regulatory context;

• Identify and quantify air toxics (or their breakdown products) of interest with respect to

contribution to risk in all media of interest (including, in some cases, non-air media; see

Chapter 19);

• Attain quantitation requirements (e.g., quantitation limits) sufficient to compare to doseresponse values (e.g., the sensitivity should be sufficient to allow reliable measurements

below concentrations anticipated to produce adverse health effects);

• Demonstrate acceptable confidence in the data set to be used for decision-making based on

quality assurance benchmarks including benchmarks for precision, accuracy,

representativeness, completeness, and comparability; and
April 2004 Page 10-8

Quality Assurance Project Plan (QAPP) and Data Quality Objectives (DQO) Process

As Chapter 6 introduced, a QAPP is part of the overall risk assessment analysis plan that ensures the

quality of data used in decisions. Generally included in the data quality program is the DQO process,

which establishes the criteria that must be met if data are to meet the needs of a decision-maker (e.g. it

establishes the error bounds on data, which are related in turn to the uncertainties a decision-maker,

can tolerate in reaching a defensible decision). Assessors can accomplish this goal through the

following seven steps:(a)

1. State the problem.

2. Identify the decision to be made.

3. Identify inputs to the decision (i.e., which data are needed).

4. Define the study boundaries (i.e., what factors, scenarios, etc., will be included in the study to

produce these data).

5. Develop a decision rule (i.e., how the data will relate to a specific decision to be made).

6. Specify limits on decision errors (i.e., how much uncertainty can exist and still allow a defensible

decision to be made).

7. Optimize the design of the study to ensure the data quality meets the decision rule.

The QAPP specifies precisely how to collect and analyze the data to meet the goals established by the

DQO process. The QAPP establishes specific procedures that assessors follow to meet DQOs. These

DQOs include procedures for identifying reliable methods, choosing sample locations and

frequencies, handling samples, calibration of equipment, recording and archiving of data, and analysis

of the data. The DQO goal is to ensure that all members of the project team understand, and follow,

procedures that will ensure the results of the study meet the data quality needs of a decision. Once

these DQOs have been established, it is necessary to develop a plan as to how the participants will

meet them in practice while collecting the data for the study.

(a)U.S. Environmental Protection Agency (EPA). 1994. Guidance for the Data Quality Objectives

Process, EPA QA/G-4, Office of Research and Development, EPA/600/R-96/055; available at

http://ww.epa.gov/swerust1/cat/epaqag4.pdf.

• Provide for a clear and unambiguous data validation and reporting methodology so

monitoring results can be tracked, verified, and validated when they are used in decisions.

The design of a monitoring program that meets data quality objectives (DQO) and quality assurance

project plan (QAPP) requirements depends on the answers to four questions: 

1. What is the risk management decision to be made, and how will assessors use

monitoring results in that decision? Monitoring programs typically are a component of

risk assessments that support risk management decisions; these decisions normally focus on

how best to reduce risks from exposure to air toxics through reducing or otherwise limiting

emissions.
aA sensitivity analysis shows the relative effect of uncertainty in each aspect of an assessment on the overall

uncertainty in that assessment. Ideally the data quality objectives will be more stringent for those measurements that

play a larger role in the final decision, since narrowing the uncertainty in these measurements significantly reduces

uncertainty associated with the decision.

April 2004 Page 10-9

2. How accurately must the results be to be useful in these decisions? The reliability of

monitoring program results must be adequate for the needs of the risk management decision. 

For example, risk assessors need to quantify air concentrations and/or exposures within some

bounds of accuracy and/or precision. It is important to meet these criteria of accuracy and

precision, but not necessarily to exceed them. As noted in Appendix H, the data quality

objectives must provide results that allow reliable decision-making. However, resources that

participants devote to one aspect of a monitoring program, such as choosing a larger number

of sampling sites, will draw resources away from another aspect of the program, such as

sampling for a larger number of air toxics. This is why it is essential to understand fully the

decision that the given set of results will support, other results that assessors will need to

support that same decision, and how participants can balance monitoring results across these

different data needs to reduce the levels of uncertainty to acceptable levels. Assessors can

achieve this goal by conducting a sensitivity analysis(a), which determines what aspects of a

full monitoring program will require the greatest attention and resources; monitoring results

that play the most significant role in a decision may require the greatest allocation of

resources.

3. What methodologies are available to monitor at a particular level of quality? The

choice of monitoring method depends on the specific air toxic(s) to be analyzed, the objective

of the monitoring (as the DQOs specified), the time over which a result is to apply, and

available resources. It is important to note here that there do not currently exist valid

methods (either field, lab, or both) for a large number of chemicals that may be of interest;

for methods that do exist, the achievable sensitivity may not match the DQOs (this is another

reason that modeling is often used as the primary decision making tool since these issues are

irrelevant to models).

4. What resources are available for the monitoring program? The choice of a monitoring

strategy often depends primarily on available resources (e.g., time, money). These factors are

of particular concern in air toxics monitoring because most studies of chronic exposure

generally require a minimum of one full year of data to characterize chronic exposure. It is

not uncommon to have a lag time of two years or more from the beginning of a monitoring

study to a final report when one considers the time it takes to plan the monitoring study,

obtain access to land, build the monitoring structures, run the study, analyze the samples,

validate the results, and write the data report.

10.4 Planning for Air Toxics Monitoring

As noted above, planning is a critical part of any air toxics monitoring program. The discussion

of planning below first describes a recommended general approach (Section 10.4.1) and then

outlines several specific planning issues (Section 10.4.2). EPA has developed resources that

provide additional details on operating procedures, with discussions of data quality issues,

definitions, and applications to specific methodologies.(4)
April 2004 Page 10-10

Examples of Study-Specific Questions

• What is the maximum plausible value of EC

for the population in a geographic region,

taking into account spatial and temporal

variability and uncertainty?

• What is the location of this maximal value

within the geographic region?

• Which air toxics are found at the highest

concentrations with respect to their doseresponse values (e.g., which air toxics have

the greatest potential to produce a hazard

quotient above one)?

• Do monitoring results generally agree or

disagree with the value of air concentrations

identified by existing models?

10.4.1 General Planning Approach

Planning an air toxics monitoring program involves a step-wise integration of sampling protocols

with data quality criteria and data analysis processes that are consistent with the study-specific

conceptual model (CM), QAPP, and DQO processes. Although presented step-wise, the process

is actually iterative, and decisions at one step may require verification or modification of

assumptions or decisions made at previous steps.

1. Understand the problem. As noted above, assessors may design monitoring programs to

support a number of different types of management decisions. For risk assessments, the CM

can focus participants’ understanding of both the scope and the breadth of the problem that

the sampling and analysis are to address. The most important questions to answer

immediately are: whether assessors will use monitoring results to characterize exposure and

risk, whether they will use results to evaluate air quality model performance and look for

gaps in the emissions inventory, or whether they will use results for both reasons. This is a

critical question for participants to answer, because the data needs can be drastically

different, depending on how the assessors will use the monitoring data.

2. Identify existing data. Sampling and

analysis for risk assessment may not be

necessary if the information to be

developed is already available from other

sources and meets the quality

requirements for decision making. The

data sources discussed in Chapter 4 may

provide sufficient information for the risk

management decision.

3. Itemize data needs. Where existing data

are insufficient to answer the

study-specific questions, it will be

necessary to obtain new data through

monitoring. Potential data needs include: 

filling gaps in emissions inventory data;

providing input data for models and

validating modeling results; generating new data to more fully characterize exposures in

areas, populations, or pathways; establishing trends over time; or supplementing a body of

data to increase their quality for the risk management decision. The process for itemizing

data needs includes articulating critical decision criteria (which may drive data quality needs

and/or selection of specific methods), applying these criteria to determine areas where

existing data are insufficient, and identifying the manner in which new data can supplement

existing data to meet the decision criteria. In many ways, the identification and enumeration

of data needs acts a bridge between the conceptual model and the DQO process.

4. Define data quality needs. The reliability (e.g., accuracy and precision) of monitoring

results must be adequate to meet the needs of the risk management decision. However, given

finite resources, even well-designed studies may not be able to achieve all quality criteria. 

That limitation makes it important to determine which criteria are essential for addressing the
April 2004 Page 10-11

study-specific decision problem and for focusing resources on meeting (and not necessarily

exceeding) those criteria. 

The DQO process determines general data quality objectives to meet specific needs. This

process can be informed both by a well specified decision statement and by a sensitivity

analysis to determine which aspects of a full monitoring program will require the greatest

attention and resources to support that decision. Identification of data quality needs at this

level is targeted on the specific problem identified in Step 1, but is independent of the

specific methods to be applied. It is important to base data quality criteria at this step on

what is required to answer the problem identified in Step 1, not on impressions of best

available analytical methods, approaches used in the past, or consideration of questions that

might be of general scientific interest but are not of direct use in the decision problem. A

common approach is to consider all aspects of sample and data handling from collection to

data report writing, as these affect the confidence with which decisions can be made through

the introduction of random or systemic errors. A number of factors affect data quality,

including bias related to sampling error (e.g., taking only a single sample at one location,

which may or may not be representative of actual ambient concentrations) and relative

precision related to analysis methods.

5. Select monitoring methods to meet data quality needs. The choice of monitoring method

depends on the scale of the assessment, specific contaminant(s) to be analyzed, the sampling

time over which the result is derived (e.g., a sample collected over 15 minutes versus a

sample collected over 24 hours), the decision criteria or other reporting limit needs, and the

resources available (see Section 10.3). Methodologies include the sampling methods and

techniques, sampling program design (i.e, sampling frequency, coverage, and density), as

well as analytical methods. The data quality needs identified in Step 4 represent the total data

quality requirements of all aspects of the sampling and analysis process necessary to support

risk-based decision-making. Therefore, evaluation of all aspects of sampling and analysis

with respect to data quality needs is necessary for proper method selection. 

The QAPP process involves balancing decisions for method selection to meet data and

quality needs. Selection of the methods for both sampling and data analysis defines the

approach and defines what is termed the measurement quality objectives. Although there is

a natural tendency to select sampling and analysis methods based on previous data, it is

important that the benefit of consistency and likely improved comparability are not

outweighed by data gaps that Step 3 identified. For example, in a risk assessment for

chlorinated volatile solvents, the presence of fluorinated volatile solvents may cause

assessors to overestimate chlorinated concentrations due to analytical interferences. The

method selection generally takes into account the known or suspected presence of other

chemicals having similar toxic effects, symptoms, and mechanisms, and/or that which

otherwise may affect sampling and analysis results. To take this into account, the study may

require adding chemicals to the target analyte list, selecting a method where these compounds

are not potential interferents, or limiting the scope of the study with stated assumptions about

contributions from these undefined factors (e.g., stating only that the measured concentration

is the sum of a defined set of analytes and not applicable to any one analyte in the mixture).
April 2004 Page 10-12

Detection Limits and Limits of Quantitation

The detection limit is the minimum concentration that an analyst can reliably expected to find (i.e.,

detect) in a sample, if it is present. For any given method (e.g., the method to analyze for volatile

organic compounds [VOCs] in air), this limit is established in each lab for each instrument and is

called the method detection limit or MDL. An MDL of 1μg/m3, indicates that a field sample that

contains 1 μg/m3 or below of contaminant will probably not be detected by the instrument in question.

The limit of quantitation (LOQ), on the other hand, is the minimum concentration for which the

analyst can reliably say that the substance is present in the sample and at a specific concentration

within some pre-established limits of precision and accuracy. If the limit of quantitation is 2 μg/m3,

then measurement results above 2 μg/m3 may be reported as not only indicating the presence of the

substance in the sample, but as indicating the specific concentration measured (i.e., positive

identification, certain concentration). Measurements between the MDL and the LOQ , indicate the

presence of the substance in the sample, but analysts can only make an estimate of the concentration

(i.e., certain identification, uncertain concentration). NOTE: It is common (but incorrect) to refer to

the quantitation limit as the detection limit. The LOQ, practical quantitation limit (PQL), estimated

quantitation limit (EQL), and sample quantitation limit (SQL; see below) are all limits of quantitation,

not detection. Thus, when one says “benzene was not detected at a detection limit of 5 μg/m3,” this

most likely actually means “benzene was not detected; the limit of quantitation was 5 μg/m3.” 

Likewise, when a lab reports a measurement as “<5 μg/m3,” this most likely means “not detected; the

limit of quantitation was 5 μg/m3.” There is much confusion on this point and analysts must clarify

with the laboratory exactly what they mean in their lab reports (and what the analyst needs to have

reported to them for their risk assessment activities). For air toxics risk assessments, the MDL is

largely irrelevant for purposes of estimating exposure and the limit of quantitation is the critical

information that needs to be reported (see Chapter 7).

In establishing limits of detection and quantitation, it is

necessary to give the confidence level associated with the

detection limit and the limit of quantitation. In this figure, the

confidence level is 99 percent. The Limit of Detection (LOD) is

then the minimum concentration that has a 99 percent

probability of producing a result above background noise

(background is shown in the figure as a horizontal bar) using a

specific method. The LOD includes two considerations: an

instrument detection limit, accounting for variation in the

instrument when it is presented with repeated samples at the

same concentration, and additional variation caused by the need

to sample, handle the sample, etc. (which can cause variations

in the relationship between the concentration in the

environmental medium and the concentration presented to the

instrument). The LOD is the horizontal line in the bar marked A. Note that the range of variation of

results from a concentration at the LOD (shown as the bar marked A), and the lower end of this range

just barely avoids moving into the range of background variability. 
April 2004 Page 10-13

Detection Limits and Limits of Quantitation (continued)

The LOQ assumes best practice in performing the measurements. It also is of interest to ask what the

LOQ would be using more common, routine practice. The Practical Quantitation Limit (PQL) is the

minimum concentration that has a 99 percent probability of producing a result above the LOD under

routine lab conditions (shown as the bar marked C). Under these conditions, the variation will be

larger than under ideal conditions, and so the PQL is higher than the LOD. Each lab must establish

these parameters for each method on each analytical instrument. When actual environmental samples

are evaluated on an instrument, the actual PQL reported for any given sample may vary (for example,

if a sample is highly concentrated and needs dilution before analysis, the resulting PQL for that

sample will be elevated by an amount proportional to the dilution). It is for this reason that PQLs

reported for actual samples are referred to as a sample quantitation limits or SQLs. When using

analytical monitoring data for air toxics risk assessment purposes, the MDL is irrelevant. The SQL is

the key factor in developing exposure concentrations (see Chapter 7).

Having established these terms, some system then is needed to “flag” results as being either usable or

unusable for the purposes of decision-making. For example, in the Superfund program,(a) results are

flagged “R” if the data are unusable for some reason and “J” if the data fall between the SQL and the

MDL. A more thorough description of data qualifiers if presented in Appendix I.

(a)U.S. Environmental Protection Agency. 1992. Guidance for Data Usability in Risk Assessment (Part

A). Office of Emergency and Remedial Response, Washington, D.C. EPA Publication 9285.7-09A;

available at http://www.epa.gov/superfund/programs/risk/datause/parta.htm.

6. Develop systems to ensure that data meet decision requirements. Setting the objectives

and selecting sampling and methods capable of meeting the DQOs are the prelude to

determining whether and to what degree the data may support risk management decisions. 

Having collected and analyzed the data, it will be necessary to determine whether decisions

can now be made with the desired confidence. For example, the actual data collected must be

assessed for quality and compared against any decision criteria such as toxicity dose-response

values. Where the quality is insufficient to support the decision (e.g., insufficient to

determine whether the benchmark is or is not exceeded), the previous steps may need to be

re-assessed.

It is also important to evaluate the contribution to uncertainty that is related to sample

collection and sample program design as well as analytical method uncertainty. Sampling

uncertainty is decreased when sampling density increases, however resource limits often

constrain sample density. Typically, errors in the collection of field samples are much greater

than errors introduced by preparation, handling, and data analysis; yet, most sampling studies

have devoted resources to assessing and mitigating laboratory errors. Ultimately, the proper

use of a QAPP that considers the entire process (sample collection through lab data reporting)

allows for evaluation of and reduction in uncertainty across all the activities of the

monitoring program, focusing resources on those aspects contributing most significantly to

uncertainty affecting decision-making.
April 2004 Page 10-14

7. Develop documentation. The QAPP and other planning documents must record the results

of the environmental data collection design process. Information to be documented includes

the assumptions, findings, outliers, biases, data confidences, and other factors that are critical

to implementation, as well as evaluation and eventual interpretation of the data collected. 

Data collected and analyzed is often reviewed thoroughly to ensure they are adequate to

support decisions; sufficient documentation allows such a review.

10.4.2 Specific Planning Issues

The design of the monitoring program also raises some specific issues:

• Select appropriate monitoring or sampling methods for the chemical(s) to be measured. 

In general, it is important that the methods selected have the sensitivity needed to monitor at

concentrations likely to be of health and/or regulatory concern. At a minimum, the PQL or

SQL should be below any relevant health benchmarks (e.g., the human health dose-response

values discussed in Chapter 12). For some chemicals, the limit of the current technology may

not allow for a PQL or SQL that is below a health benchmark (or, that level may be reached,

but at a higher cost). In such instances, the planning and scoping team must decide how best

to balance resources to support data quality needs. 

• Select appropriate monitoring sites, sample collection frequency, and length of

sampling time for the spatial and temporal variation of the scale being assessed and for

the objective of the air toxics monitoring being conducted. The way monitoring captures

this variation depends on the particular measure(s) needed to support the risk management

decision. For example, the monitoring goal might be to estimate the average long-term

exposure to people spread over a large geographic region (e.g., the average urban exposure

for a typical resident in a town). In this case, measurements spaced on a grid throughout that

region, or selected with a spatial density proportional to population density, may be

appropriate. On the other hand, if the goal is to identify or verify the maximum modeled

exposure or to perform a screening-level assessment in a population living down-wind from

an industrial source, sampling should be performed at the location likely to represent the

highest exposure, or in several different regions to identify the site representing the highest

exposure. Again, issues such as atmospheric photochemistry and differential settling of

metals are important considerations.

Assessors often make similar decisions when considering temporal variation. For example,

samples may vary over time due to fluctuations (e.g., emission rates from a facility may

fluctuate over time) or a systematic temporal trend (e.g., a facility might change its

production methods or products over time). In the former case, it is necessary to obtain

enough samples spread over a large interval of time to estimate the mean over the

measurement interval. In the latter case, the samples must be spaced in time so as to capture

the trend (i.e., a time-trend study must be performed). In addition, the objective of a study

may be to capture high short-term spikes in chemical concentrations. In this case, samples

collected over a 24-hour period may “dilute out” these spikes, and frequent shorter term

samples (e.g., collected over 15 minutes) may be required.
April 2004 Page 10-15

A recent evaluation of many of the issues regarding variability was recently published using

data from a wide range of monitoring sites throughout the United States (see also Exhibit 

10-4).(5) These results support the conclusions that: (1) environmental variability is a more

important source of uncertainty than analytical uncertainty, emphasizing the need to carefully

select the location and timing of monitoring; (2) temporal variability dominates data

variability, emphasizing the need to not only carefully select the timing of monitoring, but to

ensure that results are properly averaged over relevant exposure periods; and, (3) analytical

uncertainty becomes a more significant contributor to overall uncertainty as ambient

concentrations approach background levels.

• Most often, the monitoring efforts address the four main sources of variability in

measurements. These four sources are:

– Analytical. The same sample analyzed repeatedly yields different concentrations.

– Sampling. Duplicate samples collected using two identical monitoring devices from the

same location and time yield different concentrations. This type of duplicate sampling is

often performed to determine the precision of the method. In general, a minimum of 10

percent of the measurements in a monitoring program should be co-located to collect

duplicate samples.

– Temporal. Repeated samples at different times at the same location yield different

concentrations.

– Spatial. Samples from different locations at the same time yield different concentrations.

Ideally, assessors allocate monitoring resources in a manner that is consistent with the

relative contribution of these four sources to uncertainty. However, uncertainty may not be

evident prior to establishing the sampling program. Some insights on the relative

contributions can be obtained from the recent study of monitoring variability,(5) but it

generally will be necessary to perform an analysis of the analytical uncertainty, the precision,

and the degree of spatial and temporal variability before a firm judgment of the relative

contributions can be made.

As noted previously, ambient air monitoring data may not provide a completely accurate

picture of exposure. There are several reasons for this limitation. First, air toxics monitors

usually are physically located to provide an estimate of air concentration at a specific

location. The assessor must then determine how representative the results are to populations

in the geographic area around the monitor. For some chemicals, monitoring results can be

reasonably representative, especially if the concentration does not show high levels of spatial

variability. For other chemicals, results may not be very representative at all, especially at

some distance from the monitor. In addition, because people move around outside, their

exposures are an average of the ambient air concentrations over the geographic regions in

which they move; this exposure may not correspond to the average at any particular

monitoring location. People also receive protection from the ambient environment, either in

vehicles or by moving indoors or through filters. Thus, ambient air concentrations measured

through monitoring and analysis can be taken as an indication of the potential for exposure at

a given location.
April 2004 Page 10-16

Exhibit 10-4. Temporal and Spatial Sources of Variability in Formaldehyde Sampling

The four graphs in this exhibit summarize the results of Bortnick and Stetzer,(4) obtained by

sequentially removing sources of variability. Note that the analytical variability is the smallest source

of variability in this case, followed by sampling variability and temporal/spatial variability. Clearly in
April 2004 Page 10-17

Exhibit 10-4 (continued)

this case, a choice of sampling focused on temporal and spatial contributions to variability is needed;

since temporal variability dominated, primary attention would focus first on that component in each

sampled geographic region.
b

 “Drift” refers to the fact that monitoring systems that are calibrated generally change their electronic and

other characteristics in time, so the calibration factor also changes in time. 

April 2004 Page 10-18

• Follow and define standard operating procedures. Risk assessors follow and define

standard operating procedures both in the field (during sample collection and transport to the

laboratory) and in the laboratory (during sample analysis). Procedures include those related

to sample collection, sample transport, sample storage (including prevention of sample

degradation), and chain of custody procedures, as well as sample analysis, validation, and

data reporting. Procedures to identify potential problems are put in place. Periodic audits

(both field and lab) are commonly performed to ensure procedures are being followed and

that measurement and analytical devices are working properly.

• Determine quantitation and compare limits. A common approach is to determine

quantitation limits and compare them against relevant decision needs, including health

benchmarks and likely environmental levels. These quantitation limits should be below the

health benchmarks and environmental levels to provide data of use in risk-based decisions. 

• Properly calibrate measurement processes. One way to ensure the accuracy of the method

is to properly calibrate measurement processes. To accomplish this, assessors perform

calibration on a time schedule shorter than the time needed for the equipment to “drift”(b)

further than is permitted under the criteria of accuracy and precision. It is for this reason that

it is essential that systems be re-calibrated periodically, on a schedule that is related to the

data quality objectives. In addition, it is desirable to cross-calibrate measurement methods by

comparing results from several individuals and labs. In an inter-laboratory comparison, split

and duplicate samples are submitted to several labs simultaneously, the results are collected,

and variation between labs are assessed. Ideally, sample analysis in a monitoring study

would be conducted at a laboratory that has participated in such an inter-laboratory

comparison and has been certified to produce results within acceptable data quality limits.

• Adequately record and archive results. The best monitoring program can fail due to

improper record-keeping. A periodic, random check of the archived records (e.g., computer

files) is commonly made against “hard copies” to ensure the integrity of the process of

recording the data. The recording of all results, including a description of the QA/QC and

Data Quality Indicators, is essential because risk managers will use the results in their

decisions.

• Match measurement intervals to the relevant modeling assumptions or health

endpoints. Different health effects require varying averaging time-periods. Cancer and

other chronic effects generally require averages over relatively long periods such as a year or

more (up to a lifetime). In this case, samples may be taken randomly or systematically

throughout the year, with the criterion of obtaining an accurate estimate of the mean. Acute

effects, however, require an understanding of the temporal variability over short periods of

time. For example, monitors need to measure benzene concentrations within shorter time

intervals (e.g., 15 minute, one-hour, 24-hour) for comparison with a health benchmark

reflective of the same time period. 
April 2004 Page 10-19

• Ensure that temporal sampling reflects diurnal (time-of-day) and seasonal variability. 

It is important to recognize that source terms and meteorological conditions can vary

systematically both over a day and throughout the seasons. Monitoring programs commonly

reflect this pattern, providing proper averages throughout a day (by sampling at selected time

points in a day) and between the seasons (by sampling in the different seasons).

In general, most monitoring schemes that are designed to attenuate and validate a model will

collect samples and analyze a relatively short list of “indicator compounds.” If attenuation and

validation are the primary motivation for sample collection, it may not be necessary to measure

every compound being modeled, as long as it can be assumed that unmodeled compounds would

be expected to behave similarly. However, the amount and type of data collected in the

monitoring program designed to validate predicted model results should match the assumptions

of the modeling program. For example, if the goal of the modeling program is to estimate long

term (usually annual average) concentrations, then monitoring data must also be collected in

sufficient quantity to develop an annual average value to compare to the model results. (In

general, monitoring samples collected every six days for a year are required to develop a stable

estimate of annual average.)(2)

10.5 Implementing Air Toxics Monitoring

Implementing a monitoring program raises two issues in addition to the items above that relate to

planning for a monitoring study. These include selecting the actual location of monitors and

selecting methods for data analysis and reporting. Each is discussed in a separate subsection

below.

10.5.1 Locating Monitors and Selecting Sample Size

Determining the location of an air toxics monitor depends on a number of factors, including the

specific purpose of the monitoring (e.g., confirm modeled concentrations at a specific location,

estimate background concentrations), meteorological and terrain constraints, and the relative

magnitude and location of the source(s) of concern versus other emissions sources that might

contribute to measured air concentrations. For example, locations too close to a source may

underestimate exposure concentrations if the plume has not yet reached ground level where

people can come into contact with the contaminants. Locations too far from the source may also

underestimate exposure concentrations for large groups of people due to the dispersion that takes

place between the point of touch-down of the plume and the point of monitoring.

10.5.1.1 Locating Monitors

EPA’s Quality Assurance Handbook for Air Pollution Measurement Systems(6) provides a set of

consistent QA practices that will improve the quality of the nation’s ambient air quality

monitoring data and ensure comparability among sites across the nation. Although these

practices were developed specifically for criteria air pollutants, they provide useful guidance for

air toxics risk assessments. Exhibit 10-5 summarizes some of the Handbook’s guidance on the

relationship between topography, air flow, and the location of monitoring locations. The

following factors are usually considered when siting monitors:
April 2004 Page 10-20

• Perform measurements at locations that are representative of exposure. Determining the

location will depend on whether the goal is to quantify exposures in general, or exposures to

the maximally exposed individual. In the latter case, locations too close to a source may

underestimate exposure if the plume has not yet reached ground level where people can come

into contact with the contaminant. Locations too far from the source may also underestimate

exposure to large groups of people due to the dispersion that takes place between the point of

touch-down of the plume and the point of monitoring. Exhibit 10-3 above presented an

example of this issue. In that hypothetical example, the area of maximum concentrations

predicted by the air quality model falls somewhere within the area bounded by grid points 2,

4, S1, and S3. If the goal of monitoring is to verify these maximum concentrations, then the

ideal location for the monitor would be on the plume centerline at the exact point of touchdown of the plume. However, if the goal of monitoring is to verify maximum concentrations

at the point of actual exposures, location at the site indicated in Exhibit 10-3 may be more

appropriate (measurements at the point of plume touch-down may overestimate maximum

actual exposure if there are no individuals within that area). It is essential to determine

whether monitoring will estimate exposures to existing individuals or to hypothetical

individuals who might move into currently unoccupied areas.

Exhibit 10-5. Relationships of Topography, Air Flow, and Monitoring Site Selection

Station Category Characterization

A (ground level) Heavy pollutant concentrations, high potential for pollutant buildup. A site

3-5 m (10-16 ft) from a major traffic artery that has local terrain features

restricting ventilation. A sampler probe that is 3-6 m (10-20 ft) above ground.

B (ground level) Heavy pollutant concentrations, minimal potential for a pollutant buildup. A

site 3-14 m (15-50 ft) from a major traffic artery, with good natural

ventilation. A sampler probe that is 3-6 m (10-20 ft) above ground.

C (ground level) Moderate pollutant concentrations. A site 15-60m (5-200 ft) from a major

traffic artery. A sampler probe that is 3-6 m (10-20 ft) above ground.

D (ground level) Low pollutant concentrations. A site > 60 m (> 200 ft) from a traffic artery. 

A sampler probe that is 3-6 m (10-20 ft) above ground.

E (air mass) A sampler probe that is 6-45 m (20-150 ft) above ground. Two subclasses: (1)

good exposure from all sides (e.g., on top of a building), or (2) directionally

biased exposure (probe extended from a window).

F (source-oriented) A sampler that is adjacent to a point source. Monitoring that yields data

directly relatable to the emissions source.

Source: Table 6.5 of EPA’s Quality Assurance Handbook for Air Pollution Measurement Systems(6)

When source location is the goal of monitoring, the siting of a monitor depends on the

meteorological conditions and the spatial locations of suspected sources. Again, the

hypothetical example in Exhibit 10-3 provides some insights. If the source is suspected to be

at the center of the geographic area, and if the wind direction is predominantly towards the

east (as it is in that example), the monitor or sampler would be located to the east of the

source and operated both at times when the wind blows towards the east and when the wind

blows in the opposite (or another) direction. Support for the claim that the source is located
April 2004 Page 10-21

at the origin, and dominates exposures in the area around the monitor, would then be

strongest if the ambient concentration increases significantly when the wind blows towards

the east and drops significantly when it blows in other directions. If the data did not indicate

this effect, then the source is not at the center, or there is an additional, and perhaps more

significant, source in the area.

• Take into account shielding and concentrating effects. Buildings, hills, and trees can have

shielding and concentrating effects. These effects may cause assessors to underestimate

exposure if either measurement sites are shielded from normal air flow or if these same

structures produce high concentrations downwind due to lee effects. Unless there is a pattern

of movement of people that make sites near buildings and other structures of particular

interest, assessors should perform measurements away from the influence of these structures. 

It is particularly important to locate monitors away from such structures if the goal is to

locate sources, as the flow patterns for air are highly complex near these structures, greatly

complicating the ability to identify the source location from monitoring data.

• Be aware that sources of air toxics from mobile sources (cars, trucks, etc.) can

complicate measurements of ambient air concentrations produced by stationary

sources. For the estimates of exposures from stationary sources, it may be preferable to

make measurements at locations away from roads. Monitoring should occur at distances

ranging from 3 to 61 meters from a major traffic artery (see Exhibit 10-5). These roads

provide, in a sense, a “background” level, or noise, above which the source must rise to

create a discernible signal. Of course, if total ambient exposure from all sources is to be

estimated, and the exposed population spends a significant fraction of time near roads, this

factor may be captured by selecting a sample of sites near those roads.

• Make sure that the heights of monitoring and sampling devices are consistent with the

breathing zones of people when public exposures are being evaluated. This is generally

between 1 and 2 meters (the lower end being for children and the upper end for adults). 

While less important for highly dispersed gases (i.e., gases with high diffusion coefficients),

this consideration can be important for heavy gases and particulates, which produce

significant vertical gradients of concentration.

• Keep in mind that background concentrations can be difficult to determine. Although

background concentrations can be difficult to determine, it is important to estimate this factor

as accurately as possible at the location of measurement (see below for a discussion of

background concentrations). Unfortunately, even background levels can vary dramatically

over time and over a geographic area, and so assessors should exercise caution in using past

studies and studies from other geographic areas in establishing background for a

measurement location. Meteorological and pollutant source information must also be

carefully considered in selecting an appropriate background monitoring location. The

location must not be near major sources of the contaminant, or in the predominant downwind direction of those sources. The number of background samples should be determined

during planning/scoping/problem formulation stage, and be based on statistical testing criteria

specified in the DQOs.
April 2004 Page 10-22

The choice of monitoring or sampling locations depends on the spatial scale of the assessment

being supported by the measurement program (i.e., micro, middle, neighborhood, urban,

regional, or national). Note that samples collected (generally) at the micro-scale, middle-scale, or

neighborhood-scale for the specific purpose of determining the impact of a source or co-located

groups of sources on a specific population are called source-oriented monitoring samples.

In each case, selection of sites for the monitoring program should consider whether:

• A mean value is needed for a region (in which case, the sampling must be sufficient to allow

interpolation of a surface concentration across that region, from which a mean may be

estimated, or a mobile monitor/sampler must be used while moving throughout the region).

• A mean value is needed for an area. In this case, the monitor would be placed so as to

capture the average of all the sources in the area (i.e., it is usually not oriented towards one

source).

• A maximum value is needed (for example, for a screening assessment or an estimate of the

maximum exposure to an individual from a particular source or co-located groups of sources;

in this case, the task is to identify a location as close as possible to this point of maximal

exposure).

• A distribution of exposures across the population in the region is needed, in which case

sampling might be performed across a region. Information on the number of monitoring

stations needed to perform this analysis with an acceptable level of accuracy/precision was

recently evaluated and discussed by the Lake Michigan Air Directors Consortium

(http://www.ladco.org/toxics.html).

• A test of a model is being conducted (in which case the location is selected to provide the

most meaningful and unambiguous test of the model predictions under established source

term and meteorological conditions).

In all five cases above, it is important to determine compounds that might interfere with the

measurement of target compounds and, to the extent feasible, locate sampling devices in areas

where such interference is small (without compromising the need to cover a geographic region). 

It also is important to establish one or more “background” and/or “control” locations so the

elevation of concentrations or exposures at sampling locations due to sources not located in the

assessment area can be determined.

In each case, site selection can improve through use of release data (source terms) and dispersion

models. An accurate estimate both of average exposures and distributions of exposure (i.e.,

concentration measured across different monitors) generally will require adequate sampling in

geographic regions characterized by the highest concentrations in addition to sampling in less

impacted areas. Since such regions may represent a small fraction of the area in the overall

study region, it may be necessary to “over-sample” in the highest exposed areas to ensure the

points of maximal exposure are not missed. This process might be accomplished, for example,

by sampling on a grid, with the grid density higher in the area surrounding the suspected point of

maximal exposure; this will be particularly important if initial monitoring/sampling indicates

high spatial variability in the area around the point of maximal exposure. For example, regions
April 2004 Page 10-23

Background and Control Samples 

Background monitors are monitors that are place in the predominant upwind direction (relative to

sources) in the assessment area to measure the concentrations of the COPC in air that is moving into

the assessment area. The results of such monitoring is helpful in understanding the monitoring results

obtained in the assessment area; however, background monitoring results should not be subtracted

from assessment area monitoring results because of the uncertainties in the background monitor as a

truly representative measure of long term ambient background concentrations. Instead, EPA

recommends bar charts that compare contemporaneous concentrations of a chemical in a background

monitor to the same chemical at assessment area monitors; these charts provide a sense of the

potential influence of background concentrations on the assessment area. 

Unlike a background monitor, which is located upwind of the assessment area, a control monitor is

located within the assessment area and is sited in such a way as to determine the average

concentration of all pollutant sources, once mixing has occurred (including chemicals blowing into

the assessment area from outside sources, mobile source emissions, and stationary source emissions

within the assessment area). Control monitors should be located away from direct influence of any

one or group of sources in the assessment area. Similar to background monitoring results, control

monitor results should not be subtracted from other assessment area monitoring results (or modeling

results). Instead, a simple bar chart comparison is usually adequate to compare the general “urban

soup” to more focused monitors.

near known, large emissions sources, and downwind of the predominant wind direction, should

probably receive increased attention in sampling if a distribution of concentration is being

developed across a larger assessment area. If samples were taken only in relatively nonimpacted areas, the resulting distribution might not reflect the actual exposure of many area

residents. (Ultimately, this is one of the prime reasons for using modeling to evaluate exposure;

namely, that models can estimate exposure concentration at as many geographic points in a

assessment area as the analyst wishes and for which sufficient emissions inventory data and

computing power are available. Thus, modeling obviates these monitoring concerns.)

For the case of model testing, random sampling is not required or even desired. Instead,

sampling is performed specifically in one or more locations where the conditions of emissions

and dispersion are well established, and where there are no interfering sources or compounds. 

An ideal situation is a single, known source and a stable wind pattern during the period of

sampling. Even in such cases, however, it will be necessary to provide a sampling grid covering

the plume dimensions, since small errors in assigning wind direction can result in significant

differences between model results and measurements. By sampling at a variety of locations in

the plume, it is possible to adjust the model to determine whether a better fit might be obtained

by more accurate information on the wind field, effective stack height, and other parameters.

As part of the national-scale assessment component of the 1996 National-Scale Air Toxics

Assessment (NATA) activities, EPA compared monitoring to modeling results by using selected

locations and compounds (seven HAPs) throughout the U.S. (see

www.epa.gov/ttn/atw/nata/mtom_pre.html). The comparison goal was to assess the closeness of

modeling and monitoring results, which would expose the overall uncertainty in estimating

exposures. They found, for example, that modeled results generally underestimated results at

monitors when the modeling was performed to predict air concentrations at the precise location
April 2004 Page 10-24

(Equation 10-1)

of the monitor; however, results were more comparable when the maximum concentration that

the model predicted was compared against the maximum monitor concentration, without the

requirement that modeling and monitoring be at the same location. These results indicate that

uncertainties in the modeling produced errors that shifted the location of the point of maximal

exposure, but not necessarily the magnitude of maximal exposure. A significantly more detailed

uncertainty analysis currently is underway, with results expected in 2004 (these will be available

at the NATA website).

10.5.1.2 Selecting Sample Size

With respect to determining the quality of any estimates of mean concentration or exposure at a 

location, the coefficient of variation (CV) should be calculated to determine the number of

samples needed to meet DQOs established by the decision problem. If F is the standard

deviation of a set of N measurements performed randomly throughout a geographic region and

randomly in time, and : is the mean for that sample set, the value of CV is:

The target value of CV depends on the decision criteria establishing the needed accuracy of an

estimate of concentration or exposure, but a general target of less than 0.5 (50 percent) is

suggested and a value of 0.2 or less should be possible. (This discussion assumes that the

samples are representative of the geographic area and time period for which the average is being

calculated.)

The above calculation of CV requires knowledge of F and :, which can only be obtained after

the sampling program has been underway. It is possible, however, to estimate F from an initial

guess of the mean concentration or exposure, :, through regression functions such as those

established by Bortnick and Stetzer.(5) An example of such a regression is shown below based on

a scatter plot of data from benzene monitoring.

Note that F increases as : increases. The authors use a lognormal relationship between F and ::
April 2004 Page 10-25

(Equation 10-2)

(Equation 10-3)

They perform a weighted least-squares regression (solid line in the figure above) and obtain for

the case of benzene:

The approximate size of N needed to produce the desired value of CV may then be estimated

from the above equation if an estimate of : is available from either past monitoring data, similar

geographic regions, or models.

10.5.1.3 Setting Up a Monitoring/Sampling Program

While the design of a monitoring program will depend in many ways on the kind of monitoring

to be conducted, there are some general aspects of all monitoring programs that assessors should

consider. EPA guidance describes many of these issues in detail.(7)

The general aspects related to designing a monitoring program that supports risk assessment are

developed and written down in the planning, scoping, problem formulation phase (particularly,

much of the following information is included in the study-specific conceptual model and the

analysis plan and QAPP for monitoring activities). This activity involves three steps: (1)

identify the sources, including the contaminants, the concentrations, the timing and locations of

releases, as well as the hypotheses you want to test (e.g., whether a source exists, its relative

contribution to overall exposures, etc.); (2) determine the exposure pathways (which in the case

of air monitoring is inhalation and perhaps dermal absorption through immersion in air); and (3)

determine the receptors of interest, including any sensitive subpopulations, their locations, how

they are exposed, and relevant health benchmarks (e.g., IURs or RfCs). The conceptual model

can be used to identify where significant exposures are likely to occur to receptors of interest,

which in turn helps to guide the selection of monitoring sites. The following steps are then often

used to develop, conduct, and evaluate the results of monitoring:

1. Collect and review existing air monitoring information for the site. This information should

include data on concentrations, sources, locations of receptors, and other environmental data

(e.g., meteorological data) needed to guide decisions. The sources of these data will depend

on the location of the site, but a good start is to consider results from some of the national

monitoring networks.

2. Determine the level of sophistication needed by the monitoring program. This level is

established in the QAPP and the DQOs. The sophistication might range from simple

screening procedures (e.g., to determine whether there are any exposures of concern) to more

sophisticated methods intended to develop accurate maps of exposure across the region.

3. Develop a clear air monitoring plan, including determining the following: types of air

monitors (these depend on the compounds identified as being of interest); the number and
April 2004 Page 10-26

A typical monitoring station,

located at a site with easy

access, power, and protection

for the equipment

location of monitors; the frequency and duration of monitoring, sampling and analysis of

samples; and any QA/QC procedures that must be in place to meet DQOs.

4. Develop a detailed, written plan for day-to-day activities related to how equipment will be

maintained and calibrated, and how to document results and QA/QC procedures. The data

maintenance plan should include development of a system of logbooks for entering data,

along with procedures to ensure the data are entered correctly and the logbooks are archived. 

There should be a clear procedure for maintaining chain-of-custody for both the samples and

the logged results.

5. Evaluate the air monitoring results for their validity and reliability, including summary

indicators of data quality (e.g., the data qualifiers discussed elsewhere in this chapter), and

summarize these results so decision-makers can understand this quality and ensure the quality

meets decision needs. This evaluation should include a summary of the statistical procedures

used and the air concentration results, and an estimate of uncertainty in results deemed usable

by the analyst (including uncertainty due to monitoring equipment, handling of samples, and

sample analysis).

There are a number of specific issues that arise in Step 3 above that relate to the development of

the monitoring program. These issues are summarized here in roughly the order in which they

would be approached in developing a real program:

• Establishing sampling locations. Sampling may be

purposive, random, or systematic. Purposive sampling

refers to locating the monitor at a particular location

because that location is of special interest. While such

sampling can be useful to address specialized questions

(such as the impacts of a specific source, or the reliability

of model results), they generally are less useful for risk

assessment purposes, and care should be taken when

averaging the results along with results from the other

forms of sampling. Random sampling involves selecting

monitoring locations in a random and unbiased manner,

with no correlation between locations (other than,

perhaps, the fact that they are all in a defined region). 

Assessors could establish locations by creating a grid, and

then randomly selecting the two coordinates (x and y) in

that grid. Random sampling has the advantage of well

established and relatively easy to apply statistical methods

for evaluating results, but runs the risk of missing some

“hot spots” of exposure. Systematic sampling involves

establishing a grid and placing monitors systematically on

the grid nodes. This ensures that sampling is uniform across an area, although statistical

analysis is more complex because the samples are not truly random. Exhibit 10-6 illustrates

common types of sampling programs.

There also are practical considerations in selecting locations, regardless of which of the three

procedures above is used. Monitors and samplers will require access to land, both in terms of
April 2004 Page 10-27

permission to locate the equipment and the ability to reach the site. It must also be possible to

provide electrical power, and some protection of the equipment against theft, vandalism, and

other disturbance; therefore, a fence may be needed.

Exhibit 10-6. Common Types of Sampling

Purposive sampling focuses the sampling effort in specific locations (in this example, the area

estimated to have the highest concentration). Most air quality sampling is purposeful (i.e.,

monitoring stations are located in areas where monitoring is feasible (e.g., locations that are

accessible), areas of direct (e.g., maximum) impact, or where concerns about potential

exposures have been raised). Grid sampling consists of regularly-spaced samples in a

predetermined grid. Random sampling consists of samples in locations selected by chance.

• Determining the types of equipment and samples. The sampling/monitoring method will

depend on the compound being sampled, as well as the need for grab samples or composite

(continuous) monitoring. See Section 10.6.1 for more detail on this issue.

• Conducting field screening. Before establishing the monitoring site, it is useful to conduct

some limited screening of the region using relatively simply methods. This will help identify

locations likely to be of interest (e.g., likely locations of maximal exposure). If this isn’t

possible, modeling results might be used. Guidance on this issue can be found in EPA’s

Field Screening Methods Catalog.

(8) These results generally should not, however, be used in
April 2004 Page 10-28

Field Blanks

A field blank is a clean sample, carried to the

sampling site, exposed to sampling conditions,

returned to the laboratory, and treated as an

environmental sample. Field blanks are used to

demonstrate that:

• Equipment cleaning has adequately removed

contamination introduced by sampling at

previous sites;

• Sampling and sample processing have not

resulted in contamination; and

• Sample handling and transport, lab transport,

and lab measurement have not introduced

contamination.

the risk assessment of chronic exposures because a small number of samples taken over a

short period of time will not provide an accurate estimate of long term exposure.

• Accounting for temporal and meteorological factors. Sampling must account for the fact

that concentrations will fluctuate in time, in part because of meteorology (e.g., the wind

blows in different directions during the day, carrying the contaminant to different locations). 

Where variability is high, a larger number of samples will be needed to achieve a desired

level of accuracy. The sampling program should include a full annual cycle covering the

seasons for a chronic exposure assessment. Where this is not possible due to limits on

resources, the sampling should at least include two temporal extremes (e.g., under windy

conditions blowing from major sources to the monitor, and under calm conditions). It is

essential to include the variability of the samples in any estimates of accuracy for the

monitoring location.

• Implementing QA/QC measures. It is

essential that well-established, clear and

documented methods for assuring the

quality and reliability of data be

developed. Many of these issues are

described in the text box on the QAPP

discussed in Section 10.3. A sampling

protocol must be developed detailing (1)

conditions under which samples are

collected; (2) how training of individuals

will be conducted; (3) how the precision

and accuracy will be ensured so results are

obtained reproducibly; and (4) the

analytical strategies that will be used to

ensure quantitation limits are met. 

Measures are also put into place to ensure

that samples are handled appropriately from collection through analysis (e.g., chain-ofcustody requirements, allowable sample holding times).

Sampling devices used to collect, store, preserve, and transport samples must not alter the

sample in any way that complicates analysis. Samples should be stored in a way that keeps

the concentration as close as possible to that in the field. QC samples must be collected,

stored, transported, and analyzed in a way that is identical to the treatment of site samples. 

For example, both field and trip blanks, which are sampling devices that have not been used

for sampling in the field but otherwise are brought through all of the other procedures to

which field samples will be subjected, must be treated identically to the actual field samples. 

These field and trip blanks provide information on the extent to which samples might become

contaminated by non-site-related materials during handling in the field (field blanks) and

subsequent transport back to the lab for analysis (trip blanks).

10.5.2 Data Analysis and Reporting

As Section 10.4.1 mentions, adequate data analysis, recording, and archiving is essential to the

design and conduct of a monitoring program. It is important that assessors enter each data point
April 2004 Page 10-29

into a file with relevant qualifiers, including location of sample; date and time of sample; method

of sampling and relevant operating characteristics (e.g., flow rate); transfer process; storage time;

analysis method; and identity of people performing all stages of the measurement. The integrity

of this database should then be assessed periodically by comparing a random sample of file

information against hard copies (e.g., laboratory books) to ensure reliability of transcription. For

results using a common methodology, there should be a record of several key aspects of the

method that assure reliability:

• A description of the calibration process, including certification of any standards used in that

calibration.

• Results of any inter-laboratory comparison of uses of the method, and certification that the

laboratory performing the analysis for the sampling program falls within a reasonable range

of these inter-laboratory results.

• A record of background levels and levels in blanks, allowing a comparison of these against

sample results. 

• A summary of the frequency of “detects,” or fraction of samples with values above the MDL

or SQL (see Exhibit 10-7). If this fraction is small and the chemical is thought to be present,

it may indicate that improvements in the method are needed. Of course, if the SQLs are well

below any health benchmark, a small fraction of detects or quantifiable results need not

trigger a call for improvements.

• A policy on significant digits and how these are related to the accuracy of the method. All

results should be reported only with a number of digits consistent with this accuracy. In

addition, rounding rules should also be established and followed. 

• A description of how summary quantities such as means are calculated. This description

includes such factors as how outliers are identified and dealt with, the possible influence of

this process on sample mean and variance, and how results below the SQL are handled. For

example, some laboratories will report a chemical that they detect below the SQL as “not

detected” simply because it is below the SQL and they cannot accurately quantify it. Other

labs will report such a chemical as detected, but with an estimated concentration and qualify

the value as “J.” In general, labs should report detected chemicals, regardless of whether they

can accurately quantify their concentration. The use of J-qualified data for risk assessment

purposes is described below.

• A detailed description of the QA/QC flags that are used by the lab to report data and a clear

description of how the lab deals with samples that are associated with blanks that are

contaminated.

10.5.3 The Use of Monitoring Data to Calculate Exposure Concentrations

As the above noted, monitoring data can, under limited circumstances, be used to estimate

exposure concentrations in the vicinity of the monitor. Some general rules that apply to this

activity are as follows:
April 2004 Page 10-30

• Data from different monitors should not be combined to estimate exposure concentrations

(with the exception of co-located duplicate monitors – see below).

Exhibit 10-7. Illustration of SQL and MDL

People often refer to the SQL as the “detection limit.” When a lab reports a result as “undetected” (U) or “not

detected” (ND), this is the level to which they are usually referring. The SQL is really a limit of quantitationthat

can vary between samples (the MDL, on the other hand, is a true “detection limit”). For example, a result of 

“5U :g/m3” usually means “not detected at a sample-specific quantitation limit of 5 :g/m3.” When a chemical is

detected, but below the SQL (i.e., a “J” value), risk assessors often use the J value as is (i.e., the J value is used

with no modification). When a chemical is not detected in a sample, but there is reason to believe it may be

present, even at very small amounts (e.g., the chemical is found in some samples, but not in others), risk assessors

often use 1⁄2 the SQL as a surrogate concentration for risk assessment purposes (in the example above, half of

“5U” is 5/2 = 2.5). It is usually not appropriate to use 1⁄2 the MDL as a surrogate for concentration for exposure

assessment purposes. The process of assessing and combining monitoring data for exposure assessment purposes

is discussed in more detail in Appendices H and I.

• Monitoring data at a location are not generally used to describe variation of exposure

concentrations experienced by individuals in a population of people, although temporal

differences for the population as a whole (e.g., exposure to the population during the

winter versus exposure to the population during the spring) may be appropriate. 

Variation in exposure concentration within a population is preferably described by

looking at exposure concentrations across a set of monitors in the assessment area.

• The representativeness of the exposure concentrations, as represented by any one

monitor’s data, depends on the amount and quality of the data collected, and the

individual chemicals involved. For example, some pollutants may be “regional” in

nature, meaning that their concentration tends to be relatively homogeneous over a large

area. In that case, a given monitor may be broadly representative of ambient

concentrations throughout the region. Some compounds, on the other hand, show sharp
April 2004 Page 10-31

concentration gradients over space and the monitor may only be reflective of exposure

concentrations for people living very near to the monitoring station.

• To assess acute exposures with monitoring samples, the results from the individual

samples (not their average) should be compared to acute health benchmarks, and the

sampling time should match the averaging time of the acute health benchmark (see

Chapter 13).

• For chronic exposure assessment, all the valid samples collected and analyzed for a

monitor (taken routinely throughout the course of at least one year) are averaged (see

below) to provide an estimate of the long term exposure concentration.

Appendix I provides a general overview of how monitoring data should be evaluated, processed,

and displayed to develop estimates of exposure concentration. 

10.6 Monitoring Methods, Technologies, and Costs

EPA has developed a number of methods to measure the concentration of air toxics in ambient

air. The majority of this information is found on EPA’s Ambient Monitoring Technology

Information Center (AMTIC) website (Exhibit 10-8), and assessors involved in monitoring

should become familiar with this website and its contents. Given the breadth and scope of this

website’s contents, it is not possible here to fully review all of the information here. This section

only provides an introduction to the methods. Appendix E summarizes relevant information

from two key EPA compendia of methods, primarily for ambient air monitoring. In addition, this

chapter does not examine indoor air measurements, as EPA has provided monitoring

recommendations only for radon. 

EPA has developed a Compendium of Methods for the Determination of Toxic Organic

Compounds in Ambient Air to assist federal, state, and local regulatory personnel in developing

and maintaining necessary expertise and up-to-date monitoring technology for characterizing

organic pollutants in the ambient air (Exhibit 10-9).(9) The Compendium contains a set of 17

peer-reviewed, standardized methods for the determination of volatile, semi-volatile, and

selected toxic organic pollutants in the air. The Compendium, along with updates and addenda,

is available at EPA’s AMTIC Website at http://www.epa.gov/ttn/amtic/airtox.html.

Exhibit 10-8. EPA’s Ambient Monitoring Technology Information Center (AMTIC)

Information on ambient concentrations for a wide variety of compounds can be found through AMTIC

(http://www.epa.gov/ttn/amtic/welcome.html). This Center facilitates the exchange of ambient

monitoring-related information collected throughout the U.S., and can provide valuable insights into

the selection of monitoring methods. Established in 1991 as an electronic bulletin board system

(BBS), AMTIC has evolved with changing technology into a page on the World Wide Web. It is

operated by EPA’s OAQPS through the Monitoring and Quality Assurance Group (MQAG). The

database contains information on all the Reference and Equivalent Methods for the criteria pollutants,

the toxic organic (TO) Methods for air toxics and other noncriteria pollutant methodologies, Federal

Regulations pertaining to ambient monitoring, ambient monitoring QA/QC related information,

information on ambient monitoring related publications, ambient monitoring news, field and

laboratory studies of interest, and updates on any new or developing EPA Ambient Air standards.
April 2004 Page 10-32

Exhibit 10-9. EPA’s Toxic Organic (TO) Monitoring Methods

Method Description

TO-1 Method for the Determination of Volatile Organic Compounds (VOCs) in Ambient Air using

Tenax® Adsorption and Gas Chromatography/Mass Spectrometry (GC/MS)

TO-2 Method for the Determination of VOCs in Ambient Air by Carbon Molecular Sieve Adsorption

and Gas Chromatography/Mass Spectrometry (GC/MS)

TO-3 Method for the Determination of VOCs in Ambient Air using Cryogenic Preconcentration

Techniques and Gas Chromatography with Flame Ionization and Electron Capture Detection

TO-4A Determination of Pesticides and Polychlorinated Biphenyls in Ambient Air Using High Volume

Polyurethane Foam (PUF) Sampling Followed by Gas Chromatographic/Multi-Detector

Detection (GC/MD)

TO-5 Determination of Aldehydes and Ketones in Ambient Air Using High Performance Liquid

Chromatography (HPLC)

TO-6 Determination of Phosgene in Ambient Air Using High Performance Liquid Chromatography

(HPLC)

TO-7 Method for the Determination of nitrosodimethylamine (NDMA) in Ambient Air Using Gas

Chromatography

TO-8 Method for the Determination of Phenol and Methylphenols (Cresols) in Ambient Air Using

High Performance Liquid Chromatography

TO-9A Determination of Polychlorinated, Polybrominated, and Brominated/Chlorinated Dibenzo-pDioxins and Dibenzofurans in Ambient Air

TO-10A Determination of Pesticides and Polychlorinated Biphenyls in Ambient Air Using Low Volume

Polyurethane Foam (PUF) Sampling Followed by Gas Chromatographic/Multi-Detector

Detection (GC/MD)

TO-11A Determination of Formaldehyde in Ambient Air using Adsorbant Cartridge Followed by High

Performance Liquid Chromatography (HPLC)

TO-12 Method for the Determination of Non-methane Organic Compounds (NMOC) in Ambient Air

Using Cryogenic Preconcentration and Direct Flame Ionization Detection (PDFID)

TO-13A Determination of Polycyclic Aromatic Hydrocarbons (PAHs) in Ambient Air Using Gas

Chromatography/Mass Spectrometry (GC/MS)

TO-14A Determination of VOCs in Air Using Specially Prepared Canisters with Subsequent Analysis by

Gas Chromatography

TO-15 Determination of VOCs in Air Collected in Specially-Prepared Canisters and Analyzed by Gas

Chromatography/Mass Spectrometry (GC/MS)

TO-16 Long-Path Open-Path Fourier Transform Infrared Monitoring of Atmospheric Gases

TO-17 Determination of VOCs in Air Using Active Sampling Onto Sorbent Tubes
April 2004 Page 10-33

10.6.1 Ambient Air Monitoring Methods and Technologies

The term “monitoring method” is a comprehensive term that includes everything from the sample

collection devices to analytical laboratory methods. These methods fall into three broad

categories related to the time scale over which concentration will be averaged:

• Grab samples provide a quasi-instantaneous measurement of a concentration. They

generally are obtained in the field usually over a period of 24 hours or less and then returned

to the laboratory for analysis. The sampling may be automated, allowing a time-series of

samples to be drawn, but all samples still are generally returned to the laboratory for analysis. 

In rare instances, a mobile laboratory may be co-located with the sampling location, in which

more “real-time” data is possible.

• Continuous monitors provide a time series of measurements in the field, with a stream of

data at selected intervals (e.g., once each 24 hours). These monitors may be fully automated

versions of grab sampling, taking samples at a set interval but then analyzing the samples

internally rather than returning to the lab. An alternative is a continuous flow monitors,

which draw ambient air through a chamber and analyzes it in real time (e.g., the

semi-continuous formaldehyde monitor developed by the EPA, which runs through one

complete cycle of sampling and analysis in 10 minutes).

• Time-integrated samples are collected over an extended period of time. Only the total

pollutant collected is measured, and so only the average concentration during the sampling

period can be determined. As with grab samples, these measurements generally are obtained

in the field and returned to a laboratory for analysis.

Monitoring methods/systems can also be divided into a different set of categories based on the

method of collection:

• Integrated air sampling devices use a pump to draw air continuously into the sample

chamber, over a reactive medium, or through a filter during a prescribed period of time; the

sample is returned to the laboratory for analysis.

• Direct-read monitors draw air through a measurement system and provide a direct reading

of the concentration without returning samples to the lab.

• Automated monitoring systems collect samples, perform the analysis, and report results at

regular intervals in the field.

• Air deposition monitors rely on deposition properties of compounds (e.g., particulates), and

may consist of active and/or passive, wet and/or dry sampling methods.

• Passive monitors allow the compound to diffuse into contact with an active material; these

generally are analyzed in the lab, although some indicate the presence of a compound by a

color change.
April 2004 Page 10-34

Exhibit 10-10. 33 Urban HAPs (Nationwide Basis)

acetaldehyde

acrolein

acrylonitrile

arsenic and compounds

benzene

beryllium and compounds

1,3-butadiene

cadmium and compounds

carbon tetrachloride

chloroform

chromium and compounds

coke over emissions

1,2-dichloropropane

dioxin

ethylene dibromide

ethylene dichloride

ethylene oxide

formaldehyde

hexachlorbenzene

hydrazine

lead and compounds

manganese and compounds

mercury and compounds

methylene chloride

nickel and compounds

polychlorinated biphenyls

polycylic organic matter

propylene dichloride

quinolene

1,1,2,2-tetrachloroethane

tetrachloroethylene

trichloroethylene

vinyl chloride

Compounds monitored in the NATA National Scale Assessment pilot

sites are indicated by italics.

• Grab sampling devices use an essentially instantaneous sampling method, such as an

evacuated chamber into which ambient air is allowed to enter at a fixed rate; the sample

collected is returned to the laboratory for analysis.

In some circumstances, grab samples may be collected by volunteers (for example, when

residents near an industrial complex organize to capture samples when a strong odor is present). 

This process is commonly referred to as a “bucket brigade.” Bucket brigades may provide useful

information that a problem may exist that warrants more in-depth evaluation. They are also

helpful, in some circumstances, to help the affected community become more involved in the air

toxics evaluation process. Nevertheless, care should be taken to ensure that all of the necessary

sampling and analysis protocols and QA/QC are established, understood, and followed by the

bucket brigade team members to ensure that the grab samples are of sufficient quality to be used

for decision making purpose at hand. 

Mobile air monitoring platforms are sometimes used to evaluate air quality parameters. A

“mobile platform” can be anything from a VOC sampling apparatus on a movable trailer to a

sophisticated multi-pollutant sampling and analytical mobile trailer. The utility of mobile

platforms is that they can be moved from place to place relatively easily (e.g., for hotspots

analysis) and may only require a place to park the platform and an electrical hookup (as opposed

to the more difficult process of establishing fixed monitoring locations, which requires access to

land, often by establishing a leasing agreement, and permanent security measures, such as

fencing). 

Most existing air toxics

monitoring programs have

focused on the 188 HAPs, and

especially on the 33 urban HAPs

identified by OAQPS on a

nationwide basis (Exhibit 10-10)

as generally presenting the

greatest contribution to risk to

public health from air toxics in

urban areas. Note that the

highest-risk HAPs in a specific

region or community may differ

from this list. A significant

database exists on national

exposures to these compounds,

especially those monitored by the

National-Scale Air Toxics

Assessment (see Chapter 2 and

the website at

www.epa.gov/ttn/atw/nata). A

general starting point for most

monitoring efforts should be an

initial screening analysis to identify the COPCs. A description of the general process for

screening analyses of this type is provided in Chapter 1. 
April 2004 Page 10-35

EPA has not developed methods for many compounds, including some of the 33 urban HAPs.

Potential deficiencies in particular monitoring methods include:

• Quantitation limits are not low enough relative to environmental levels and/or health

benchmarks;

• Lack of available standards for monitoring protocols (e.g., standards developed by the

National Institute of Science and Technology);

• Methods are not practical or easy to implement;

• Compound stability is so poor that the compound degrades significantly between the time it

is collected and the time it is analyzed, resulting in poor to no recovery at the time of

analysis;

• Recover efficiencies are too low, resulting in poor precision and/or quantitation limits that

are not low enough for use relative to health benchmarks;

• Methods have not been sufficiently tested in the laboratory and field;

• Methods are not producing results that are comparable to established methods; and

• Poor reliability.

The deficiencies noted in Exhibit 10-11 are particularly important and have been identified by

EPA as needing methodology development.(10) Because they present a similar challenge, EPA

has targeted several VOCs for programs to improve monitoring capabilities (Exhibit 10-12). In

addition, both diesel exhaust (a complex mixture), acrolein, and arsenic require additional

method development to yield accurate, reliable, and field-tested monitoring methods.

10.6.2 Sampling Costs

There is no general guideline for the costs associated with monitoring programs, as they depend

on quite an array of factors. Several of the more critical include:

• Whether samples are analyzed “in house” or contracted out.

• Whether monitoring equipment is available or must be purchased or leased.

• The number of monitoring results or samples required (there is some economy of scale, but

increased numbers of results also increases cost).

• Whether personnel must be hired and/or trained.

• The potential cost of leases and insurance for monitoring sites.

• Laboratory analytical costs for special analytes. For example, dioxin samples can run as high

as $1,000 per sample, making an extensive dioxin sampling scheme generally out of reach for

most studies.

10.7 Archiving Air Toxics Monitoring Data

When appropriate, results of a monitoring program should be submitted to the relevant air toxics

database, such as EPA’s Air Quality System (AQS).(11) The AQS website

(www.epa.gov/ttn/airs/airsaqs/sysoverview.htm) provides detailed information on submitting and

retrieving such data, including instructions on the file format for the data. Archived data may be

accessed at the AQS site.(12)
April 2004 Page 10-36

Exhibit 10-11. Identified Deficiencies in Available Monitoring Methods

Compound Candidate Method Deficiency

1,3-butadiene

1,2-dibromoethane

1,2-dichloroethane

TO14A/15 sensitivity issue;

false highs

acrylonitrile TO14A/15 NIST standard needed;

recovery problems

ethylene oxide None/NIOSH 1614 poor storage stability

1,1,2,2-tetrachloroethane TO-15 NIST standard needed

arsenic and compounds IO-3 sensitivity issues;

filter contamination;

resource intensive

beryllium and compounds None resource intensive;

XRF sensitivity issue

mercury and compounds IO-5 requires special equipment

acrolein None TO-11A results in unstable derivative

poor recovery

2,3,7,8-tetrachlorodibenzo-p-dioxin TO-9A resource intensive

Exhibit 10-12. VOC Compounds Needing Improved

Monitoring Methods

vinyl chloride

1,2-dichloroethene

dichloromethane

chloroform

1,2-dichloroethane

benzene

carbon tetrachloride

1,2-dichloropropane

trichloroethene

cis- and trans-1,3-dichloropropene

1,1,2-dichoroethane

1,2-dibromoethane

tetrachloroethylene

1,1,2,2-tetrachloroethane

hexachlorobutadiene

acrylonitrile

1,3-butadiene

ethylene oxide
April 2004 Page 10-37

Use of Historical Monitoring Data

Historical monitoring data for an assessment area

may be of use in developing the analysis plan. 

They can help with a range of uses, including:

• Identifying the types of chemicals that may be

present in the air;

• Selecting locations for monitors;

• Performing preliminary screening level risk

estimates; and

• Establishing acceptable monitoring protocols.

The utility of historical data will, of course, be

based on an assessment of the quality of the data.

For example, data that were not collected with

sufficient QA/QC, may not be useful for any of

the above purposes.

10.8 Using Air Monitoring Data to Evaluate Source Contribution

Caution should be used in interpreting the

results of a measurement as being uniquely

associated with a given source. Most

measurements from monitoring data are,

depending on the chemical, a combination of

background concentrations and the same

chemical released from possibly multiple

sources. Benzene, for example, is present in

background air, is released from mobile

sources, and is used and released from

multiple types of stationary sources. This is

not to say that monitoring data cannot be used

to identify releases from a source. Under

certain circumstances, analysis of multiple

measurements at different locations may

indicate a spatial pattern consistent with the

known air dispersion pattern accompanying

that source (and inconsistent with the patterns

from other sources).

EPA also has developed “receptor models” which make use of monitoring data, together with

emissions inventories, to perform source apportionment analyses, which provide a quantitative

estimate of what percent of each pollutant comes from each identified source. EPA’s Chemical

Mass Balance Model is one such example (available on EPA’s SCRAM website at

http://www.epa.gov/scram001/tt23.htm). This model uses chemical concentrations measured in

samples from sources (emissions) and receptor locations to estimate the contributions of source

types to ambient air pollutant concentrations. The model is used primarily in the development of

State Implementation Plans for PM10. The model allows the user to select samples, chemical

species, and source types for modeling, calculate source contributions and their standard errors,

evaluate goodness-of-fit and validate the model results, prepare output documentation, and graph

results.
April 2004 Page 10-38

1. U.S. Environmental Protection Agency. 2004. National Air Toxics Monitoring Strategy,

Draft. Office of Air Quality Planning and Standards, Research Triangle Park, NC, January

2004. Available at: http://www.epa.gov/ttn/amtic/files/ambient/airtox/atstrat104.pdf. 

2. U.S. Environmental Protection Agency. 2003. Latest Findings on National Air Quality; 2002

Status and Trends. Office of Air Quality Planning and Standards, Research Triangle Park,

North Carolina. EPA 454/ K-03-001. Available at: http://www.epa.gov/air/airtrends

/2002_airtrends_final.pdf

3. U.S. Environmental Protection Agency. 2004. Technology Transfer Network Ambient

Monitoring Technology Information Center. Updated March 4, 2004. Available at:

http://www.epa.gov/ttn/amtic/airtxfil.html. (Last accessed March 2004.)

4. U.S. Environmental Protection Agency. 2003. Laboratory Operations and Quality Assurance

Manual. Science and Ecosystem Support Division, Analytical Support Branch, Region 4,

Athens, GA. January 2003. Available at: http://www.epa.gov/region4/sesd/asbsop

/asbsop.html

U.S. Environmental Protection Agency. 2003. Quality Assurance Management Plan for

Region 4. U.S. Environmental Protection Agency, Region 4, Atlanta, GA. May 2003.

Available at: www.epa.gov/region4/sesd/oqa/r4qmp598.html.

5. S. Bortnick and S. Stetzer. 2002. Monitoring Methods and Network Design. Air Toxics

Exposure Assessment Workshop. June 25-27, 2002. San Francisco, California.

6. U.S. Environmental Protection Agency. 1998. Quality Assurance Handbook for Air Pollution

Measurement Systems, Volume II, Part 1: Ambient Air Quality Monitoring Program Quality

System Development. Office of Air Quality Planning and Standards, Research Triangle Park,

NC, August 1998. EPA-454/R-98-004. Available at: http://www.epa.gov/ttn/amtic/files/

ambient/qaqc/redbook.pdf

7. U.S. Environmental Protection Agency. 1989. Risk Assessment

Guidance for Superfund, Volume I, Human Health Evaluation Manual (Part A), Interim

Final. Office of Emergency and Remedial Response, Washington, D.C., December 1989.

EPA/540/1-89/002. Available at:

http://www.epa.gov/oerrpage/superfund/programs/risk/ragsa/index.htm.

U.S. Environmental Protection Agency. 1989. Procedures for Dispersion Modeling and Air

Monitoring for Superfund Pathway Analysis (Volume IV of Procedures for Conducting Air

Pathway Analyses for Superfund Applications). Office of Air Quality Planning and

Standards, Research Triangle Park, NC. EPA/450/1-89/004.

8. U.S. Environmental Protection Agency. 1988. Field Screening Methods Catalog: User’s

Guide. Office of Emergency and Remedial Response, Washington, D.C., September 1988. 

EPA 540-2-88-005 (NTIS / PB89-134159) .

References
April 2004 Page 10-39

9. U.S. Environmental Protection Agency. 1999. The EPA Compendium of Methods for the

Determination of Toxic Organic Compounds in Ambient Air. Center for Environmental

Research Information, Washington, D.C., January 1999. EPA/625/R-96/10b. Available at: 

http://www.epa.gov/ttn/amtic/airtox.html. 

U.S. Environmental Protection Agency. 1999. The EPA Compendium of Methods for the

Determination of Inorganic Toxics Compounds in Ambient Air. Office of Research and

Development, Washington, D.C., June 1999. EPA/625/R-96/01a. Available at:

http://www.epa.gov/ttn/amtic/inorg.html.

10. Whitaker, D. 2002. New Trends in Monitoring Methods. Air Toxics Exposure Assessment

Workshop. June 25-27, 2002. San Francisco, California.

11. U.S. Environmental Protection Agency. AirData. About the AQS Database. Updated

October 3, 2003. Available at: http://www.epa.gov/air/data/aqsdb.html (Last accessed

March, 2004.)

12. U.S. Environmental Protection Agency. 2004. Technology Transfer Network Air Quality

System. Download Detailed AQS Data. Updated March 11, 2004. Available at:

http://www.epa.gov/ttn/airs/airsaqs/archived%20data/downloadaqsdata.htm. (Last accessed

March 2004.)

U.S. Environmental Protection Agency. 1999. 1997 Urban Air Toxics Monitoring Program

(UATMP). Office of Air Quality Planning and Standards, Research Triangle Park, NC,

January 1999. EPA-454/R-99-036. Available at:

www.epa.gov/ttnamti1/files/ambient/airtox/r99-036a.pdf.
Chapter 11 Estimating Inhalation Exposure

Table of Contents

11.1 Introduction ............ ............ ............ ............ ............ ...... 1

11.2 Estimating Inhalation Exposure Concentrations ............ ............ ............ . . 1

11.2.1 General Approaches for Deriving Exposure Concentrations ............ ......... 2

11.2.2 Common Ways to Estimate Exposure Concentrations ............ ............ . . 3

11.3 Exposure Modeling ............ ............ ............ ............ ............ 6

11.3.1 Inhalation Exposure Modeling ............ ............ ............ ......... 9

11.3.2 Microenvironment Concentration: How is it Developed? ............ .......... 12

11.3.3 Sources of Data for Human Activity for Inhalation (and other) Exposure Assessments

............ ............ ............ ............ ............ ........ 13

11.3.4 Examples of Inhalation Exposure Models ............ ............ ........... 16

11.3.5 Exposure Modeling Examples ............ ............ ............ ........ 19

11.4 Personal Monitoring ............ ............ ............ ............ .......... 20

11.5 Exposure to a Population: Common Descriptors ............ ............ ............ 22

11.6 Evaluating Uncertainty ............ ............ ............ ............ ........ 22

11.7 Presenting the Results of an Exposure Assessment ............ ............ .......... 23

References ............ ............ ............ ............ ............ ............ 23

April 2004 Page 11-1

11.1 Introduction

The previous three chapters discussed how to quantify exposure and release rates and estimate

chemical fate and transport. This chapter discusses the final step of estimating exposure. This

chapter will discuss inhalation exposure only. Unless persistent bioaccumulative hazardous air

pollutants (PB-HAPs) are present in source emissions, most air toxics risk assessments will only

estimate inhalation exposure concentrations. Limiting the exposure assessment this way is

possible because the dose-response values that characterize inhalation risk (e.g., reference

concentrations, inhalation cancer unit risk estimates – see Chapter 12) take into consideration the

complex physical and pharmacokinetic processes that influence how the chemical reaches the

target organ, which may be a region of the respiratory tract or a remote site (see Chapter 12 for a

more detailed discussion). Specifically, other than exposure modeling to account for things like

time in different microenvironments and microenvironment concentrations, no adjustment for

other exposure parameters (e.g., body weight and inhalation rate) are warranted. For

multipathway risk assessments, however, where ingestion intake rate is the exposure parameter,

it will be necessary to consider parameters such as body weight and contact rate (e.g., amount of

soil ingested, fish eaten) for the indirect exposure pathway metrics of exposure (see Chapter 19). 

Assessors determine human exposure to an environmental pollutant via inhalation by estimating

the concentration of that pollutant in the ambient air and the contact of an individual with that air

(along with the characteristics of the contact). Because concentrations in the air vary over space

and time, it is important to know where and how long people spend their time in relation to the

contaminated air under study. Through air quality modeling and monitoring, the ambient

concentrations of pollutants in air can be estimated geographically and temporally. Through the

use of exposure modeling, estimates of exposure via the inhalation route can be adjusted from

modeling data to take into account the demographics of people in the study area and the time they

may spend in various microenvironments.

The remainder of this chapter discusses how to estimate inhalation exposure concentrations for

the risk assessment (Section 11.2); exposure modeling (Section 11.3); personal monitoring

(Section 11.4); common descriptors (Section 11.5); evaluating uncertainty (Section 11.6); and

presenting the results of an exposure assessment (Section 11.7).

11.2 Estimating Inhalation Exposure Concentrations

The ambient air exposure concentrations (ECs) can be estimated using either (or both of) two

general methods: air quality modeling and air quality monitoring. As discussed in Chapter 9, air

quality modeling involves defining the pollutant sources and release characteristics and modeling

pollutant fate and transport (how the air toxic is transported, dispersed, and transformed over the

area of interest). As Chapter 10 discussed, monitoring involves measuring ambient

concentrations of chemicals. Because of the time/expense and other limitations associated with

monitoring (most notably, questions about representativeness), modeling is the most common

approach for estimating ambient air concentrations to be used in the air toxics risk assessment. 

Monitoring is often used, instead, as a secondary tool to provide input data to the models and

validate the model results and to look for important gaps in the emissions inventory used to run

the model.
April 2004 Page 11-2

11.2.1 General Approaches for Deriving Exposure Concentrations

There are two general ways to derive the EC for a given risk assessment (see Exhibit 11-1). Both

may incorporate the results of air quality modeling and/or monitoring efforts.

Exhibit 11-1. Two General Ways to Estimate Inhalation Exposure Concentration

The left-hand side illustrates the use of ambient air concentrations as a surrogate for the EC. In this

example, the analysis assumes that individuals spend 100 percent of their time at a given location, so

the estimate of ambient concentration thus represents the EC. The right-hand side illustrates the use of

exposure modeling. In this example, the analysis assumes that an individual spends 50 percent of

his/her time at home; 15 percent at a school; and 35 percent at an office. The EC is the weighted sum

of the product of the ambient concentrations at each location and the amount of time spent there. Both

indoor and outdoor concentrations usually are considered at each location.

• Ambient Air Concentrations as a Surrogate. For screening-level evaluations, assessors

use the concentrations of air toxics generated at each modeling node (or interpolated nodes)

or the concentrations determined by a monitor (if modeling is not performed) as surrogates of

the inhalation exposure concentrations for the populations in the study locations. The default

assumption in such a screening assessment is that the population of interest is breathing

outdoor air continuously at the modeled or monitor location. This is believed to be a

conservative assumption since indoor air concentrations of air toxics are expected to be the

same or lower than the outdoor concentrations (when the indoor concentrations are produced

solely by inflow from outside air). 

• Exposure modeling. More comprehensive inhalation exposure assessments combine

estimates of ambient pollutant concentrations (e.g., from air quality models) with information

about the population of interest, including the types of people present (e.g., ethnicity, age,

sex), time spent in different microenvironments, and microenvironment concentrations. The

assessment objective is to identify a representative estimate of the pollutant concentration in

the inhaled air in each microenvironment and combine it with an estimate of the time spent in

different microenvironments (and the activities within these microenvironments) throughout

the daily routine of different groups of people with similar attributes (called cohorts).
April 2004 Page 11-3

11.2.2 Common Ways to Estimate Exposure Concentrations

Risk assessors commonly use several different ways to estimate exposure concentrations. Some

ways are used primarily for screening-level (Tier 1) assessments; others are used primarily for

more refined assessments. Exhibit 11-2 illustrates several different ways to estimate exposure

concentrations when ambient air concentrations are used as surrogates.

• Monitoring locations. Sites where air monitors are located provide a direct measure of

ambient air concentrations at those locations. However, these locations may or may not be

representative of ambient air concentrations in other parts of the study area. If monitors are

not located where people live, the monitoring results may not be of much value for the risk

assessment other than to check the accuracy of modeling. Monitoring results may be used as

inputs to exposure modeling.

• Point of maximum modeled concentration. This is the modeling node where the maximum

modeled ambient air concentration occurs, regardless of whether there is a person there or

not. This generally provides a conservative estimate of exposure and could be used as the EC

in a screening-level evaluation (for example, using the SCREEN3 model). This point can be

used to provide an estimate of “high-end” exposure to the risk manager because, although no

one may actually be living there at the present, someone might move their in the future. This

point may be referred to as the point of the “maximum exposed individual (MEI).”

• Point of maximum modeled concentration at an actual receptor location. This is the

modeling node where the maximum ambient air concentration occurs to an actual person in

the area of impact, usually at an actual residence (or, if the residence falls between modeling

nodes, an interpolated value). To identify this point precisely, it is necessary to know

detailed information about the location of actual people in the study area. As with the point

of maximum modeled concentration above, this point can be used to provide an estimate of

“high-end” exposure to the risk manager (in this case, based on current actual exposures). 

This point may be referred to as the point of the “maximum individual risk (MIR).”

• Census tract/block internal point. The U.S. Census

Bureau provides information about populations in

geographic units called census tracts, which are subdivided

into block groups/enumeration districts and blocks. In

cases where there is only limited information about the

census tract (e.g., nothing is known other than the number

of people living within the tract), the Census Bureau’s

“internal point” (sometimes referred to as a centroid) for

the tract typically is used as the point of exposure for all the

population in the tract. The internal point is a set of

geographic coordinates that generally represents the

approximate geographic center of a geographic subdivison

(see box on next page). The Census Bureau provides an

internal point for each of its geographic subdivisions (i.e., tracts, blocks, and block groups). 

Note that the internal point is not population weighted (i.e., it is not located “in the direction

of where the people are”). 
April 2004 Page 11-4

Exhibit 11-2. Illustration of Common Ways to Estimate Exposure Using Ambient Air

Concentrations as Surrogates for Exposure Concentration

 A

100 meter modeling grid

In this hypothetical example, the risk assessors have modeled a release of a volatile organic HAP from a facility

using a computerized air quality model, and the ambient air concentration is used as a surrogate for the exposure

concentration (EC). The area of impact surrounds the facility and is generally greater in the direction of the

primary wind flow (and decreases in concentration with distance from the source). The model was set to make

estimates of annual concentration at 100-meter distances from the source in a rectangular grid pattern. The points

where the model makes estimates are called “modeling nodes” or “receptors.” Note, however, that modeling

receptors do not necessarily coincide with actual people (who are also sometimes referred to as receptors) – that

is, there may or may not be a person at any given modeling node. There also is one monitoring site. 

Knowing only the information displayed in the first version of the map (A), it is difficult to say much about

exposure since we do not know where the people are in relation to the facility or the area of impact. To remedy

this, our next step is to obtain demographic data (usually from the Census Bureau) and overlay it on the above

map. We may also have first-hand knowledge of exactly where people live in the vicinity of the of the facility

which we can also include on the map. Performing this analysis and redrawing the map gives picture B (next

page).

In the second version of the map (B), we have included the census tract boundaries (dotted lines) and we also

know from study area reconnaissance that there is an uninhabited national forest to the west of the facility, a

farmer (Mr. MacDonald) directly to the north, and a small town in the northeast. (Note that the town, Smallville,

actually can be further subdivided into smaller census blocks; however, they are not shown here to keep the

picture simple.) Now that we have a better idea of where people are in relation to the facility (and the area of

impact caused by the VOC release), we are in a better position to start making some statements about how people

are exposed. Some of the more common ways to characterize the exposures that may be occurring include:

1. Monitoring Site. The monitoring site is located in one of the higher parts of the area of impact, but it is

southwest of the facility and far from most of the area’s populations. This monitoring site would not be

appropriate for describing exposure for the people of Smallville, but it could be used for people in the

immediate vicinity of the facility and to check the accuracy of the modeling.
April 2004 Page 11-5

Exhibit 11-2 (continued)

 B

2. Point of Maximum Modeled Concentration. In this example, this point is located on the facility boundary,

where no one currently lives. This point is called the Maximum Exposed Individual or MEI which is defined

as the highest estimated risk to a hypothetical exposed individual, regardless of whether people are expected

to occupy that area.

3. Point of Maximum Concentration at a Location Occupied by People. In this example, this point occurs at

Mr. MacDonald’s farm. This point is called the Maximum Individual Risk, or MIR, which is defined as the

highest estimated risk to an exposed individual in areas that people are believed to occupy. Actually, the

concentration used to represent Mr. MacDonald could be described using either an estimate of exposure at a

point (e.g., his house) or some other estimate of exposure for the larger farm if there were a good justification

for doing so (e.g., an average of all the farm’s modeled points, since Mr. MacDonald spends much of his time

working around the farm).

4. Census Tract Internal Point. In this example, we could simply use the census tract internal point to

represent exposure for all people living in the census tract. This is sometimes used, especially when you do

not have any first-hand knowledge of the area (i.e., you only have general demographic data from the Census

Bureau). However, in this example the census track internal point would not be a very good estimate of

exposure concentration because it is higher in concentration than that experienced by most of the population

(i.e., the people of Smallville) and it is lower in concentration than that of the highest exposed person (i.e., 

Mr. MacDonald).

5. Census Block Internal Points. So far, this example has focused on characterizing an individual person’s

exposure living at defined points within the study area (either a real person like Mr. MacDonald, or a

hypothetical person like the MIR). What if we wanted to know something more about how many people in

the study area are living at different levels of exposure? One way to do this is to develop a frequency

diagram that displays the exposure concentration at each of the census block internal points and identifies the

number of people living in that block (see below). This kind of representation is very helpful to the risk

managers because it gives them a sense of the range of exposures and the numbers of people living at

different levels of exposure. (In addition, the assessor may also choose to represent the exposure with

isopleths of risk (as in the above graphic) and by listing the approximate number people living within each

isopleth.)
April 2004 Page 11-6

The internal point with the highest impact in the study area may also be referred to as the

point of maximum concentration at a receptor location, although it may not be as precise as

the example above where more local knowledge is applied to locate this point.

• Population-based approaches. Exposures may be evaluated by tracking individual

members of a population and their inhalation through time and space. Such analyses may

incorporate a user-specified number of simulated individuals or population groups (cohorts)

to represent the population in the study area. A cohort is defined here as a group of people

within a population with the same demographic variables who are assumed to have similar

exposures. In this approach, the exposure analysis process consists of relating chemical

concentrations in air (outdoor and/or indoor) and tracking the movement of a population

cohort through locations where chemical exposure can occur according to a specific activity

pattern. Population-based analysis is generally accomplished using exposure models (as

described in Section 11.3 below).

• Personal monitoring. Exposures may be estimated directly by placing monitors on

individuals, which allows collection of more detailed information specific to the exposure

pattern for that individual. Such monitors are referred to as personal monitors because they

provide information on exposure to that individual, rather than to the general area in which an

individual might be moving. Personal monitoring is discussed in Section 11.4 below.

Note that the units for the EC estimates are typically expressed in terms of micrograms (or

milligrams) of pollutant per cubic meter of air. For pollutants adsorbed to particles, inhalation

exposure estimates should be provided as the concentration of these pollutants on the particles,

not the concentration of the particles themselves.

11.3 Exposure Modeling

This section discusses exposure modeling, which uses the ambient air concentration estimates

along with information about the population of interest and information on how the pollutant

concentration can vary in different microenvironments to derive estimates of exposure

concentration over the period of exposure. Information on human exposure modeling for air

toxics can be found on EPA’s Fate, Exposure, and Risk Assessment (FERA) website at

http://www.epa.gov/ttn/fera/.

For example, suppose an analyst uses the air quality model, ISCLT3, to estimate the annual

average concentration of benzene from a petroleum refinery at each census tract internal point for

every census tract within 50 km of the source (for illustration, assume this is 25 census tracts). In

a screening level analysis, the analyst may simply use the predicted ambient air concentration as

a surrogate for the population chronic exposure concentration of benzene at each of the 25

internal points.
April 2004 Page 11-7

Internal Point or Centroid: Which is Correct?

When evaluating exposure to people in a given place, the modeled air quality at the “internal point” of

a geographic entity (such as a census tract or census block) is often used as a starting point to

represent exposure for the people in that geographic entity. According to the U.S. Census Bureau:

An internal point is a set of geographic coordinates (latitude and longitude) that is

located within a specified geographic entity. A single point is identified for each

entity; for many entities, this point represents the approximate geographic center of

that entity. If the shape of the entity causes this point to be located outside the

boundary of the entity or in a water body, it is relocated to land area within the

entity. In computer-readable products, internal points are shown to six decimal

places; the decimal point is implied. The first character of the latitude or longitude is

a plus (+) or a minus (–) sign. A plus sign in the latitude identifies the point as being

in the Northern Hemisphere, while a minus sign identifies a location in the Southern

Hemisphere. For longitude, a plus sign identifies the point as being in the Eastern

Hemisphere, while a minus sign identifies a location in the Western Hemisphere.

To illustrate how internal points are established, consider the following two examples. In census tract

A, the internal point (q) is simply the geographic center of the square. In census tract B, a river flows

along the western edge of the tract and makes a sharp bend towards the tract’s eastern edge. In this

case, the “geographic center” of census tract B is actually outside the tract itself. Since the Census

Bureau requires that the internal point be within the physical boundaries of the geographic entity, the

Bureau physically moves the point into the tract, as shown (to a point that is no longer the geographic

center).

Note that the internal point is generally set to reflect the geographic center of the entity in question,

regardless of where people actually live in that entity. In other words, the point is not “population

weighted” (the Census Bureau does not provide population weighted internal points for census tracts

or block groups). Without population weighting, an exposure concentration estimated at the internal

point might not be representative of the concentrations to which persons living in the census entity

might be exposed. Analysts routinely modify the Census Bureau internal points for census tracts and

census block groups (using census block data) to locate them to a spot more representative of where

people are actually located within the geographic entity (e.g., a “population weighted” internal point).

Source: U.S. Department of Commerce, U.S. Census Bureau. 2000. Geographic Glossary (Census

2000). Available at: http://www.census.gov/geo/www/tiger/glossry2.pdf.
a

These terms are introduced and defined in Part VI of this Reference Library.

April 2004 Page 11-8

However, a limitation of this is that each person in a census tract is not breathing air at the

ambient concentration continuously. There are a variety of reasons why this is so. For example: 

C People come and go from the census tract for work, play, or travel. They may go to another

census tract in the vicinity with either a higher or lower concentration of benzene.

C People do not spend all their time outdoors (which is what our analyst has presumed in our

hypothetical example). In fact, most people spend most of their time (with some estimates of

about 90 percent) indoors. The chemical concentration of benzene may be higher or lower

indoors than outdoors.

C The benzene concentration throughout the census tract, in our example, is probably not

always the same as that at the internal point we selected (we have just assumed it was for

computational ease).

Exposure modeling was developed to try and help move an analysis into considering these

details. Thus, air quality modeling estimates how contaminated the air is in the different

locations within a study area. Exposure modeling simulates how different types of people

interact differently with that contaminated air to derive integrated (e.g., time weighted) estimates

of their exposure for the duration of interest.

This section focuses on exposure models to evaluate inhalation exposures. Exposure models are

also available for other routes of exposure as well (e.g., a model may be employed to track

patterns of food and drinking water consumption across a population). These indirect pathway

exposure models are discussed in Chapter 18.

The estimation of population exposure is a very difficult task because it requires information on

the activity patterns of the population as well as information on the air toxics concentrations

(indoor and outdoor) to which that population is exposed. Although several databases have been

developed to characterize activity patterns (see Section 11.3.3), various sources of variability

(e.g., among individuals and geographical regions) introduce uncertainty. Three main factors

affect the overall accuracy of exposure modeling:

• Uncertainties associated with indoor air toxics concentrations (note that most people spend

the majority of their time indoors);

• How well the subgroups (or cohorts) selected for analysis provide a realistic description of

the population composition in a given area; and

• Uncertainty and variability associated with the inputs and parameters of exposure models.

Exposure models can be formulated in a deterministic framework, where the value for each

input and output variable is characterized by a point estimate (i.e., a single value assumed to

apply uniformly). Alternatively, the framework may be stochastic or probabilistic, with one or

more input variables characterized by a frequency or probability distribution(a) (see Exhibit 11-3). 
April 2004 Page 11-9

If the input distributions represent variability(a) across the population, the resulting output

distribution correspondingly represents the variability of exposures across the population. On the

other hand, if the input distributions represent uncertainty(a) about input parameters, the output

distributions will represent uncertainty about exposure levels. Some of the newer exposure

models address both variability and uncertainty separately (see Section 11.3.4).

Exhibit 11-3. Deterministic versus Stochastic/Probabilistic Approaches to Exposure Modeling

In the Deterministic Approach, the

assessment assumes that each input

to the model is a specific number

(and the answer is a number).

In the Stochastic/Probabilistic

Approach, the assessment assumes

that the inputs to the model may be

specified as a distribution (and the

answer is a distribution).

11.3.1 Inhalation Exposure Modeling

Inhalation exposure is characterized by the pollutant concentration in the air (i.e., the exposure

concentration) reaching an individual’s nostrils and/or mouth (in units of μg/m3). Estimates of

air concentrations from modeling or monitoring can be used in inhalation exposure modeling.

When derived from monitoring measurements, exposure concentrations are an aggregate of the

contributions from all emissions sources impacting the monitor. When derived from modeling

studies, the estimated exposure concentrations reflect only the sources that were included in the

modeling exercise. Models have an added benefit of allowing the analyst to determine the

contribution of a source to the estimated exposure concentration for any of the exposed

population groups. (Trying to determine “what source” contributed “how much” to a monitoring

result can be a challenging and perhaps impossible task, depending on the chemical and number

of sources in the study area).
April 2004 Page 11-10

Lead Exposure Modeling

Lead (Pb) poisoning presents potentially significant risks to the health and welfare of children all over

the world today. The Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK)

attempts to predict blood-lead concentrations (PbBs) for children exposed to lead in their

environment. The model allows the user to input relevant absorption parameters (e.g., the fraction of

lead absorbed from water) as well as intake and exposure rates. Using these inputs, the IEUBK model

rapidly calculates and recalculates a complex set of equations to estimate the potential concentration

of lead in the blood for a hypothetical child or population of children (6 months to 7 years of age).

Measured or estimated blood-lead concentration is not only an indication of exposure, but also a

widely-used index for discerning future health problems. For additional information see

http://www.epa.gov/superfund/programs/lead/ieubk.htm.

 

(Equation 11-1)

Because air pollutant concentrations vary over time and space, inhalation exposure models

combine information on human activity patterns and microenvironmental concentrations to

estimate exposure concentrations. Activity patterns are defined by an individual’s or cohort’s

allocation of time spent in different activities in various microenvironments and various

geographic locations. A microenvironment is a defined space that can be treated as a

well-characterized, relatively homogeneous location with respect to pollutant concentration for a

specified time period (e.g., rooms in homes, restaurants, schools, offices; inside vehicles;

outdoors). 

A common exposure model for inhalation that combines information on microenvironment

concentrations and activity patterns calculates a time-weighted average of all exposures from

the different microenvironments in which a person spends time during the period of interest:

where: 

ECA = the adjusted average inhalation exposure concentration (μg/m

3

), 

T = total averaging time (T = 3 tj; years),

Cj = the average concentration for microenvironment j (μg/m3), and 

t

j = time spent in the microenvironment j (years).

Note that the two critical parameters that need to be evaluated in this equation are the

concentration of a chemical in a microenvironment and the amount of time spent in that

microenvironment. Exhibit 11-4 presents a simple example. General information on how

assessors go about obtaining such data is provided below. As a practical matter, most air toxics

risk assessments will not actually gather such activity pattern data for study-specific exposure

assessments. Rather, available exposure models have already incorporated much of this

information for use by the general risk assessment community. However, every model is

different and the data input requirements vary from model to model. Usually, assessors carefully

review each model’s documentation before deciding to use it to determine if it will answer the
April 2004 Page 11-11

question that needs to be answered and what resources would be needed to develop the required

inputs.

Exhibit 11-4. Simple Example of How to Estimate Exposure Concentration (EC) 

for Exposure Modeling

EC. The following exposure profile has been developed for one year (which represents, for example,

the 30 years of “work”) for a representative individual within the population of interest:

Duration Spent in Each

Microenvironment (% year)

Average Concentration of Pollutant A

in Each Microenvironment (μg/m3)

10 = outside

50 = at work

40 = inside house

80

20

10

The EC for that individual is calculated as:

EC = (0.1 × 80) + (0.5 × 20) + (0.4 × 10) = 22 μg/m3

Lifetime EC. To derive a lifetime exposure concentration for that individual, annual estimates are

combined as follows:

Duration Exposed to Each Annual

Concentration (no. years)

Annual Average Concentration of

Pollutant A (μg/m3)

 1 = newborn

 4 = pre-school

12 = school

 4 = college

30 = work

19 = retirement

10

40

30

30

22

40

The Lifetime EC is calculated as:

Lifetime EC = (1 × 10) + (4 × 40) + (12 × 30) + (4 × 30) +(30 × 22) + (19 × 40) = 30 μg/m3

70

Screening exposure estimate. One way to perform a screening level assessment using these data is to

set the EC equal to the highest air concentration modeled (e.g., 80 μg/m3 for annual adjusted or 40

μg/m3 for lifetime adjusted – see examples above) for all microenvironments. If the hazard and risk,

respectively, prove to be below acceptable risk values, the risk manager may conclude that no further

evaluation is necessary.
April 2004 Page 11-12

 (Equation 11-2)

(Equation 11-3)

11.3.2 Microenvironment Concentration: How is it Developed?

Microenvironments can be indoors (e.g., school, office, car, bus) or outdoors (e.g., filling station,

roadway). Indoor microenvironment concentrations are comprised of contributions from a

chemical in outdoor air penetrating the indoor environment and from indoor emission sources of

that same chemical (if indoor sources are within the scope of the analysis). They may be derived

from direct measurements or estimated from modeling.

There are two common approaches to modeling indoor microenvironment concentrations. One is

the microenvironment factors method, where the outdoor contribution is estimated from the

outdoor concentration and a microenvironment factor that represents the ratio of the

microenvironment concentration to the outdoor concentration. Microenvironment factors are

typically derived from concurrent measurements of concentrations in the microenvironment

(containing no indoor emission sources) and outdoors. The indoor contribution is then added to

estimate the overall microenvironment concentration (when indoor sources are included in the

scope of the assessment). A general equation for the microenvironment factors method is:

where:

Cj = concentration in microenvironment j

Mj = microenvironment factor for microenvironment j

Co = concurrent outdoor concentration

Cs = concentration contribution to the microenvironment j concentration from an indoor

emission source

The second approach is the mass-balance method. The mass balance method typically assumes

that an enclosed microenvironment is a single well-mixed “box,” although multi-chamber

configurations are possible. The time-varying concentration of an air pollutant in such a

microenvironment is estimated from several variables (see Exhibit 11-5). A general formulation

for the change in concentration in an enclosed microenvironment over time is:

where:

V = volume of microenvironment enclosure

Cj = concentration in microenvironment j

p = penetration factor (only applies to incoming air)

Q = air flow rate

k = pollutant removal rate (includes all types of removal, including atmospheric decay,

surface reactivity, surface adsorption, wall deposition, etc.)

Co = concurrent outdoor concentration

S = indoor source emission rate
April 2004 Page 11-13

The solution to this differential equation can be used to predict a time sequence of

microenvironment j concentrations.

Exhibit 11-5. Illustration of Mass Balance Method for Modeling Indoor Microenvironments

11.3.3 Sources of Data for Human Activity for Inhalation (and other) Exposure

Assessments

Numerous EPA and related databases provide information useful for conducting exposure

assessments, including information on activity pattern and demographic information useful for

inhalation exposure modeling. Types of information included are human activity surveys,

standard values for physiological processes and consumption of food and water, measured

exposure data, health status surveys and measurements, nutrition surveys, and data on the spatial

distribution of populations. This section provides several of the more notable information

sources, some of which are important for inhalation exposure modeling, and some of which are

important for modeling exposures through pathways other than inhalation (e.g., ingestion of

contaminated fish, soil, and groundwater). Because they are so important for an understanding of

exposure, we introduce them here (even though the focus of this Chapter is on inhalation). We

will revisit many of these sources in Part III (Multipathway Exposure Assessment).
April 2004 Page 11-14

Indoor vs. Outdoor Concentrations

Indoor air concentrations may be an important consideration in an air toxics risk assessment. 

Depending on the pollutant and the sources being assessed, concentration levels may be substantially

higher outdoors, in one or more indoor microenvironments, or inside vehicles. In general, pollutants

that have important indoor emission sources will have higher concentrations indoors than outdoors.

Important indoor emission sources include combustion sources, building materials, consumer

products, and occupant activities like cigarette smoking. Similarly, pollutants that are primarily

emitted by motor vehicles would be expected to have higher in-vehicle concentrations than at outdoor

locations distant from roadways.

Information that may be useful to the various methods used to estimate microenvironment

concentrations is available from studies involving measurements of indoor and personal exposure

concentrations. These include the following EPA studies:

C The Building Assessment, Survey and Evaluation (BASE) study, which was a cross-sectional

study of 100 buildings. Information relating to BASE is currently being updated to include basic

summary results from the 100 buildings studied. The raw data collected for the 100 buildings is

scheduled for release soon.(1)

C The Longitudinal Temporal Indoor Monitoring and Evaluation (TIME) Study in federal

buildings.(1)

C The Los Angeles Total Exposure Assessment Methodology (TEAM) study,(2) which collected

concurrent indoor and outdoor samples of 18 VOCs for two consecutive 12-hour periods in 1987,

around 45 homes in February and 40 homes in July.

• EPA Consolidated Human Activity Database (CHAD). CHAD contains data obtained

from human activity studies that were performed at city, state, and national levels. CHAD is

intended to provide input data for exposure/intake dose modeling and/or statistical analysis.(3)

CHAD is a master database providing access to other human activity databases using a

consistent format. This facilitates access and retrieval of activity and questionnaire

information from those databases.

The studies contained in CHAD cover a range of geographic areas. In addition to the

National Human Activity Pattern Study (NHAPS) with information about residents from 48

states, there are studies targeting residents of Baltimore, Cincinnati, Denver, Los Angeles,

Valdez, Washington DC, and the states of California and Michigan. Because the individual

studies differed based on what information was collected, not all fields in the CHAD

database are populated for all the records.

Each CHAD diary record consists of a 24-hour sequence of activities. Specified for each

activity is a start time, end time, duration, one of 113 location codes, and one of 145 activity

codes. Each diary record is tagged with a CHAD ID, which relates it to a record in the

demographic database identifying information about the subject of the diary. Demographic

fields include personal characteristics (age, gender, ethnicity, weight), social characteristics

(education, occupation, income), residential location (state, county, zipcode) and housing

characteristics (heating fuel, cooking fuel). In addition, CHAD has the capability to estimate
April 2004 Page 11-15

the relative metabolic rate for each activity in a record using random sampling from

distributions derived from clinical studies.

• EPA Exposure Factors Handbook. The Exposure Factors Handbook provides a statistical

summary of the available data on various parameters and variables used in assessing human

exposure. This Handbook is used by risk assessors who need to obtain data on standard

factors to calculate human exposure to toxic chemicals. These factors include human activity

factors and residential characteristics. Recommended values are for the general population

and also for various segments of the population who may have characteristics different from

the general population. Included are full discussions of the issues that assessors may want to

consider in deciding how to use these data and exposure parameter recommendations. (The

Exposure Factors Handbook is in final form, but as new data become available updates will

be posted).(4)

• EPA Human Exposure Database System (HEDS). HEDS is a web-enabled data repository

for human exposure studies.(5) Its mission is to provide data sets, documents, and metadata

for human exposure studies that can be easily accessed and understood by a diverse set of

users. HEDS provides only data and accompanying documentation from research studies; it

does not provide interpretations. It allows a user to download documents for review or data

sets for analysis on their own computer system. Currently contained in HEDS are various

components of the National Human Exposure Assessment Survey (NHEXAS).

• National Human Exposure Assessment Survey (NHEXAS). The National Human

Exposure Assessment Survey was developed by US EPA’s Office of Research and

Development (ORD) in the 1990’s to provide information about multimedia and

multipathway population exposure to chemicals of various types. Phase I consists of

demonstration/scoping studies using probability-based sampling designs. Volunteer

participants were randomly selected from several areas of the U.S. These studies included

personal exposure, residential concentrations, and biomarker measurements. The Arizona

study measured metals, pesticides, and VOCs. The Maryland study measured metals,

pesticides, and polycyclic aromatic hydrocarbons (PAHs). The Region 5 study, conducted in

Ohio, Michigan, Illinois, Indiana, Wisconsin, and Minnesota, measured metals and VOCs. 

Researchers worked with the participants to measure the level of chemicals in the air they

breathed, in the foods and beverages they consumed (including drinking water), in the soil

and dust around their homes, and in their blood and urine. Participants completed

questionnaires to help identify possible sources of chemical exposure. Sample collection

occurred between 1995 and 1997. The confidentiality of participants is strictly protected. 

Information about the studies can be found in the related study entries in EIMS and in the

Journal of Exposure Analysis and Environmental Epidemiology.

(6)

• CDC National Health and Nutrition Examination Survey (NHANES). NHANES is a

survey conducted by the National Center for Health Statistics (NCHS), Centers for Disease

Control and Prevention.(7) This survey has been designed to collect information about the

health and diet of people in the United States. NHANES is unique in that it combines a home

interview with health tests that are done in a Mobile Examination Center. The current

NHANES is eighth in a series of national examination studies conducted since 1960. The

results of these surveys are compiled in databases and summarized in a variety of tables and

reports. Data from direct examination, testing, and measurement of national samples of the
April 2004 Page 11-16

civilian noninstitutionalized population provide the basis for (1) estimates of medicallydefined prevalence in the US and the distribution of the population with respect to physical,

physiological, and psychological characteristics, and (2) analysis of relationships among

various measurements without reference to an explicit finite universe of persons. Reports

also present information about dietary patterns in various segments of the US population.

• U.S. Census Data. The U.S. Census provides data on the spatial distribution of population

and population subgroups at several geographic levels: national, state, county, tract, block

group and block. (For detailed analysis, Summary File 3 is most useful.) Examples of useful

spatially-resolved data for exposure assessment include: population by age, gender, and

ethnic group; house heating fuel use; estimated travel time to work by various modes of

transportation; and levels of employment in various industries. Associated geographic data

specifying boundaries of the various geographic entities for mapping are also available in

Topologically Integrated Geographic Encoding and Referencing (TIGER) files.(8)

• LandScan USA. LandScan is a high resolution population distribution database for the

continental U.S. currently under development, following the methodology used to create a

similar global database called LandScan1998 (updated in 2000).(9) LandScan uses satellite

imagery in population distribution modeling to produce population distribution data at a

much finer resolution than previously available. LandScan 1998 and 2000 have a grid cell

size of 30 seconds (<1 kilometer) and use census data in combination with many other

geospatial data, such as land use/cover, topography, slope, roads, and nighttime lights, in

order to improve the estimation and prediction of the spatial distribution of residential

populations. Future LandScan updates will use a much smaller grid cell size of 3 seconds

(<100 meters). Currently, a pilot study in a 29 county area in southeast Texas (around

Houston and Port Neches) is being conducted. LandScan will be very useful for exposure

modeling, environmental justice studies, and other types of risk assessments.

11.3.4 Examples of Inhalation Exposure Models

Several exposure models have been or are being developed by EPA and others for a variety of

purposes. Some of the important characteristics that vary among the models include:

• Ambient concentrations

– Modeling or monitoring estimates

– Time scales (e.g., averaging time)

• Exposure concentration time scale

– Time increment for calculations (e.g., by minute, hourly, seasonally, annually)

– Averaging time for reporting (e.g., hourly, annually)

• Spatial scale

– Geographic resolution of predictions (e.g., Census tracts, Census blocks, grids)

– Potential size of modeling domain (e.g., neighborhood, county, nation)

• Population activity data

– Type (e.g., time in microenvironments, commuting locations, food and water ingestion

rates)
April 2004 Page 11-17

– Temporal resolution (e.g., by minute, hourly, seasonally, annually)

– Area specific resolution (e.g., national or regional)

– Demographic resolution (e.g., by age, gender, or ethnic group)

• Framework

– Deterministic: inputs and outputs are characterized as point estimates

– Stochastic or probabilistic: inputs and outputs are characterized as distributions

representing variability and/or uncertainty; Monte Carlo techniques are used to randomly

select input values from the distributions for repeated simulations

The remainder of this section provides brief descriptions of some of the most recently developed

inhalation exposure models. The features of each model described are summarized in Exhibit

11-6.

Exhibit 11-6. Comparison of Inhalation Exposure Model Features

Model Population

Activity Data

Source of Ambient

Concentrations Spatial Resolution Framework

HEM-3 none

(screening model)

ISCST3 census blocks

(additional points

can be specified)

deterministic

HAPEM micro-environment

time/sequence,

commuting

external model or

monitoring data

census tract stochastic

TRIM.Expo

(a.k.a. APEX)

micro-environment

time/sequence,

commuting

external model or

monitoring data

depends on

resolution of air

quality and

demographic inputs

stochastic

CPIEM micro-environment

time/sequence,

commuting

external model or

monitoring data

user-specified for

the selection of

activity patterns

(e.g., state, region)

stochastic

Human Exposure Model (HEM)

The Human Exposure Model (http://www.epa.gov/ttn/fera/human_hem.html) was designed to

screen major stationary sources of air pollutant emissions efficiently, ranking the sources

according to the potential cancer risks and noncancer hazard associated with long-term (annual)

average exposure concentrations.(10) The current version, Version 3 (HEM-3), is implemented on

a Windows platform for ease of use. HEM-3 contains a version of the Gaussian atmospheric

dispersion model ISCLT2 (with included meteorological data), and U.S. Census Bureau

population data (2000) at the Census block level. A limited amount of source data are required

as model inputs (e.g., pollutant emission rates, facility location, height of the emission release,

stack gas exit velocity, stack diameter, temperature of the off-gases, pollutant properties, and

source location). HEM-3 estimates the magnitude and distribution of ambient air concentrations

of pollutant in the vicinity of each source. The model usually estimates these concentrations
b

EPA has developed the Total Risk Integrated Methodology (TRIM) for use in the assessment of air

pollutants (both hazardous and criteria). APEX comprises the inhalation exposure component of TRIM. 

April 2004 Page 11-18

within a radial distance of 50 kilometers (30.8 miles) from the source. Exposure concentrations

for the residents of each Census block are assumed to be the outdoor concentration at the Census

block “internal point.” This actually represents a surrogate for exposure, as important exposure

variables (e.g., indoor-outdoor concentration differences, human mobility patterns, residential

occupancy period, breathing rates) are not explicitly addressed. Multiple facilities (including

clusters of facilities, each having multiple emission points) can be addressed by HEM-3. 

Variability and uncertainty in input data and parameters are not considered.

The Hazardous Air Pollutant Exposure Model (HAPEM5)

The latest version of EPA’s Hazardous Air Pollutant Exposure Model (HAPEM5) is a stochastic

screening-level inhalation exposure model appropriate for assessing average long-term (annual)

exposures of the general population, or a specific sub-population, over spatial scales ranging

from urban to national (http://www.epa.gov/ttn/fera/human_hapem.html). This application

requires a moderate level of computer modeling skills.

HAPEM5 uses the general approach of tracking representatives of specified demographic groups

as they move among 37 indoor, in-vehicle, and outdoor microenvironments and among

geographic locations. The estimated pollutant concentrations in each microenvironment visited

are combined into a time-weighted average concentration, which is assigned to members of the

demographic group (the cohorts). Microenvironment concentrations are estimated from outdoor

concentrations with the factors method. HAPEM5 uses five primary sources of information:

population data from the U.S. Census; population activity data from CHAD commuting data

developed by the Bureau of the Census; user supplied air quality data either from measurements

or an air dispersion model; and microenvironmental factors data. 

The previous version of HAPEM5, namely HAPEM4, was used in the NATA national scale

assessment of the 1996 NEI to develop estimates of risk, by census tract, for each of the 33 HAPs

(http://www.epa.gov/ttn/amtic/netamap.html). Specifically, HAPEM4 was used to predict

population exposure for each of 10 demographic groups in each tract.

Total Risk Integrated Methodology Exposure Event Model (TRIM.ExpoInhalation), also

known as Air Pollutants Exposure Model (APEX)

The Air Pollutants Exposure Model (APEX) comprises the inhalation portion of the TRIM

exposure module, TRIM.Expo (http://www.epa.gov/ttn/fera/human_apex.html).(b)

TRIM.Expo (a.k.a. APEX) uses a personal profile approach rather than a cohort simulation

approach. That is, individuals are selected for simulation by selecting combinations of

demographic characteristics and finding an activity pattern to match it, rather than directly

selecting an activity pattern. If the selection probabilities for the demographic characteristics are

the same as within the population to be simulated, this approach will provide a representative

sample of that population’s activity patterns without the need for post-simulation weighting of

results.
April 2004 Page 11-19

The current version (APEX3, available on the web) includes a number of useful features

including automatic site selection from large (e.g., national) databases, a series of new output

tables providing summary statistics, and a thoroughly reorganized method of describing

microenvironments and their parameters. The model has the capability to estimate

microenvironment concentration from the mass-balance method, but also provides the option of

using the factors method. Most of the spatial and temporal constraints were removed or relaxed

in APEX3. The model’s spatial resolution is flexible enough to allow for the use of finely

resolved modeled air quality values, as well as sparser measured values. Averaging times for

exposure concentrations are equally flexible. Like HAPEM5, the user must supply the air quality

data (from modeling or monitoring) to the model.

California Population Indoor Exposure Model (CPIEM)

The CPIEM(11) is a stochastic inhalation exposure model developed for the California Air

Resources Board’s (ARB’s) Indoor Program to evaluate indoor exposures for the general

California population as well as certain sub-populations. CPIEM combines indoor air

concentration distributions with Californians’ location and activity information to produce

exposure and dose distributions for different types of indoor environments.

The temporal resolution and averaging time are user-selected from the options of 1-hour, 8-hour,

12-hour, and 24-hour. The spatial resolution and modeling domain similarly are specified by the

user according to county, state region, or the entire state. Although outdoor concentrations may

be included in the application, the focus is on indoor exposures and indoor emission sources. 

The model is implemented on a Windows-based platform for ease of use. 

The model uses location/activity profiles that were collected in ARB studies. Microenvironment

concentrations are derived from measurement studies for up to nine microenvironments.

Concentration distributions from measurement studies for many pollutants and

microenvironments are included in the CPIEM database. However, for pollutants and

microenvironments not included in the database, the CPIEM presents two alternatives. The first

is to estimate indoor air concentration distributions based on distributional information for mass

balance parameters with a mass-balance module. The second is for the user to directly specify

concentration distributions.

11.3.5 Exposure Modeling Examples

The following applications of air quality modeling and exposure modeling at real-world sites

provide useful insights into air toxics modeling. The TRIM.Expo(a.k.a. APEX) inhalation

exposure model has also been used with the ISCST3 air quality model to predict human

inhalation exposures. A report documenting this aspect of the case study will be available at:

http://www.epa.gov/ttn/fera/human_apex.html.

National-scale Air Toxics Assessment (NATA). EPA’s NATA is designed to provide a

comprehensive evaluation of air toxics exposure and risk across the U.S. Activities include

expansion of air toxics monitoring, improving and periodically updating emission inventories,

improving national- and local-scale modeling, continued research on health effects and exposures

to both ambient and indoor air, and improvement of assessment tools. As noted previously, one

component of NATA is a National Scale Assessment conducted with the ASPEN and the
April 2004 Page 11-20

HAPEM4 to estimate annual average exposure concentrations of the 33 urban air toxic pollutants

in every US Census tract. Specific examples of the results of the National Scale Assessment and

additional information on NATA activities can be found on-line.(12)

Houston Case Study. This study was carried out by EPA’s Office of Air Quality Planning &

Standards (OAQPS) and the Office of Transportation and Air Quality (OTAQ) as a component of

the Integrated Urban Strategy.(13) For the Houston metropolitan area, ISCST3 modeling was

applied, using emissions data for point, non-point, and mobile sources from EPA’s 1996

National Toxics Inventory. Ambient air concentrations for numerous air toxics were predicted at

the census tract level with ISCST3 and HAPEM, which were then employed to obtain estimates

of population exposures. Modeling results were compared to the results obtained through studies

of this area carried out as part of the NATA National Scale Assessment. The study demonstrated

that modeling using ISCST3 and an improved emissions inventory provides more realistic

patterns and better agreement with monitoring data. In addition, elevated concentrations (hot

spots) were found that were not detected in the national scale analysis.

11.4 Personal Monitoring

Thus far, we have focused on monitoring devices that generally are located in a secure compound

(and sometimes on roof tops) that measure air quality that is representative of some specific

geographic scale. An alternative to such an approach is to place monitors directly on individuals,

which allows collection of more detailed information specific to the exposure pattern for that

individual. Such monitors are referred to as personal monitors because they provide

information on exposure to that individual, rather than to the general area in which an individual

might be moving. An advantage is that personal monitors reflect the time-varying concentrations

(unless they are integrating monitors) an individual experiences as he or she moves about through

various activities. Personal monitors have seen increasing use in recent years due to two factors:

they are more readily available, reliable, and cheaper than in the past, and there is growing

evidence that personal exposures may at times be correlated poorly with average values derived

for larger geographic areas (see Exhibit 11-7).

Two modes of personal monitoring have been developed. One relies on direct measurements of

air concentration for toxics in the breathing zone or otherwise on/near the body of an individual

(these are called direct measurement methods). The other relies on changes in biological

properties such as blood level of an air toxic (or metabolite). The latter is not considered here

because it does not strictly measure ambient air concentrations or estimate exposure. Personal

monitors, as with area or fixed monitors described previously in this chapter, are available in two

types:

• Active monitors use a small air pump to draw air through a filter, packed tube, or similar

device. They can be both continuous and integrated. Such a personal exposure monitor is

available to measure PM10 and PM2.5 in air using a 37 mm Teflon filter and a 4 L/min flow

rate. The pump and battery pack are worn in a bag, while the filter can be located essentially

anywhere on the body. In addition, cyclone personal samplers are available for measuring

particulates in air (the term “cyclone” refers to the fact that the sampler measures the

particulates by “spinning” the particles in an air stream, which then collect on the sides of the

device for collection and analysis). Combinations of impactor and denuder filter packs are
April 2004 Page 11-21

available to sample both aerosols and gases such as SO2, NH3, and HNO3. Different coating

materials on the diffuser tube can be used to collect different gases.

Exhibit 11-7. Examples of the Use of Personal Monitoring

• Relationship of Indoor, Outdoor and Personal Air (RIOPA) study.

(14) Indoor and outdoor

concentrations of 30 polycyclic aromatic hydrocarbons (PAHs) were measured in 55 homes in Los

Angeles, CA, Houston, TX, and Elizabeth, NJ. The study focused on areas in each city

characterized by worst-case conditions in the outdoor air, generally located close to major sources.

Integrating MSP samplers, polyurethane foam cartridges, and quartz fiber filters were used for the

field sampling, and the samples were analyzed subsequently in the lab. Among many results, the

study showed that indoor air was dominated by outdoor sources for these compounds, with

reasonably strong correlations between the indoor and outdoor air concentrations.

• National Human Exposure Assessment Survey (NHEXAS).

(15) The NHEXAS program was

designed to “describe the distribution of human exposure to multiple chemicals from multiple

routes on a community and regional scale, and its association with environmental concentrations

and personal activities.” It is being conducted in three stages: (1) design, field evaluation and

demonstration projects; (2) exposure field studies; and (3) special studies to examine issues such

as highly exposed populations and long-term exposures. Extensive statistical analyses of the data

have been performed, including characterizations of background levels of exposure to selected

chemicals, as well as correlations among environmental concentrations, individual exposures,

biomarkers, and survey data on personal activities.

• EPA’s Total Exposure Assessment Methodology (TEAM) studies(16) estimated exposures of

about 800 persons to 25 VOCs; about 300 persons to 32 pesticides; and 1,200 persons to carbon

monoxide. The general approach in all four of the main TEAM studies was the same: a

probability-based selection of respondents, so that they would represent a much larger population

(e.g., the 800 persons in the TEAM VOC studies actually represented about 800,000 persons in 8

cities); the use of personal monitors as well as outdoor monitors to estimate actual personal

exposure; and the use of an Office of Management and Budget (OMB)-approved questionnaire

and activity diary to try to pinpoint local sources. In two of the TEAM Studies for VOCs and

carbon monoxide, an effort was made to measure body burden, by collecting a breath sample from

each of the 2,000 persons involved. This was important in identifying active smoking as the main

source of exposure to benzene and styrene, for example. Also, the breath measurements identified

a “dirty dozen” pollutants that were prevalent in almost every person. The Centers for Disease

Control later collected blood samples from 800 different persons and found essentially the same

dozen pollutants prevalent in blood.

• Passive monitors rely on sorption, entrapment, etc., driven largely by diffusion. They are

primarily integrated sampling devices, giving a estimate of average exposure over the

sampling period. Examples include diffusion tubes, badges, and detector tubes. Diffusion

badges currently are available for measurement of NO2, O3, SO2, CO and formaldehyde. 

Organic vapors can be measured in passive devices using activated charcoal badges, although

the range of compounds, aside from organics, that can be sampled in this way is small.

Reviews of such methods of personal sampling can be found in Bower et al. (1997).(17) However,

many of the same limitations as ambient methods exist, and in some cases additional quantitation

limit and precision problems are present.
April 2004 Page 11-22

In general, air toxics risk assessments that rely on monitoring to characterize exposure will

generally not rely on personal monitoring because of the highly complex and resource intensive

nature of this technique, and because personal monitoring and its findings are currently more

geared toward basic research.

11.5 Exposure to a Population: Common Descriptors

There are a wide variety of ways to describe exposure to a population, some of which may be

legally required, others which may be chosen based on the requirements of the risk manager. No

matter what specific measure is chosen, the risk assessment needs a clear and scientifically

supportable rationale for the approach taken; risk assessors generally describe that approach

clearly and thoroughly in the exposure assessment portion of the risk assessment documentation. 

Risk assessors aim for there to be no ambiguity about what was done in the exposure assessment.

EPA policy and guidance recommend that exposure to a population be described using several

different ways to give the risk manager a sense of the range and magnitude of the exposures. For

example, a “high end” exposure estimate might describe the exposure experienced by actual

people in the most highly concentrated part of the area of impact, while a “central tendency”

exposure estimate might describe the exposure experienced by people in the study area who

experience more modest concentrations.

A variety of statistical values are used to describe high-end and central tendency exposures, 

including 95th percentile exposures (for high-end) and 50th percentile values for central tendency. 

Risk assessors will want to obtain and become familiar with EPA’s Risk Characterization

Handbook to better understand various ways exposure and risk can be adequately

characterized.(18) EPA’s Guidelines for Exposure Assessment(19) is also invaluable in this regard. 

Some of the alternative approaches for characterizing air toxics exposures are illustrated in

Exhibit 11-2 above.

11.6 Evaluating Uncertainty

Uncertainty includes the assumptions and unknown factors inherent in the exposure assessment.

Discussing uncertainty places the risk estimates in proper perspective. Specific uncertainties

associated with the chemical monitoring data, fate and transport models, and the input data

(especially emissions inventory data) that assessors use to estimate exposure concentrations

usually account for the bulk of uncertainty within the assessment. Exposure models also

contribute to the overall uncertainty in exposure assessment. The assessor needs to understand

the extent to which variability and uncertainty are considered in all the fate and transport and

exposure models that are used. HAPEM and other exposure models can accept input data on the

distributions of time spent in different micro-environments and produce time-average exposure

estimates for defined populations. 

The assessor should be familiar with the extent to which the various components of the exposure

assessment can and do accommodate uncertainty and variability analyses. In addition, it is

important to consider the compatibility of models in the various steps in the exposure assessment

(emissions, transport, etc.) with regard to addressing important sources of uncertainty. Once the

capabilities and data requirements of the various models are known, the assessor should consider
April 2004 Page 11-23

1. U.S. Environmental Protection Agency. 2003. Building Assessment, Survey and Evaluation

Study (BASE) Study, updated June 17, 2003, and Longitudinal Temporal Indoor Monitoring

and Evaluation (TIME) study in federal buildings. Available at:

http://www.epa.gov/iaq/largebldgs/base_page.htm. (Last accessed March 2004).

2. Wallace L., W. Nelson, R. Ziengenfus, E. Pellizzari, L. Michael, R. Whitmore, H. Zelon, T.

Hartwell, R. Perritt and D. Westerdahl. 1991. The Los Angeles TEAM Study: Personal

Exposures, Indoor-Outdoor Air Concentrations, and Breath Concentrations of 25 Volatile

Organic Compounds. J. Expos. Anal. Environ. Epidem., 1(2):157-192.

3. U.S. Environmental Protection Agency. 2003. Consolidated Human Activity Database

(CHAD). Updated November 20, 2003. Available at: http://www.epa.gov/chadnet1/. (Last

accessed March 2004).

4. U.S. Environmental Protection Agency. 2002. The Exposure Factors Handbook. Updated

December 30, 2002. Available at:

http://cfpub.epa.gov/ncea/cfm/exposfac.cfm?ActType=default. (Last accessed March 2004).

5. U.S. Environmental Protection Agency. 2003. Human Exposure Database System (HEDS).

Updated February 11, 2003. Available at: http://www.epa.gov/heds/. (Last accessed March

2004).

6. Journal of Exposure Analysis and Environmental Epidemiology, Vol. 5, No. 3, 1995.

7. Centers for Disease Control. 2003. National Health and Nutrition Examination Survey.

Updated October 30, 2003. Available at http://www.cdc.gov/nchs/nhanes.htm. (Last accessed

March 2004).

8. U.S. Census Bureau. 2004. Index of /census_2000/datasets/Summary_File_3. Available at:

http://www2.census.gov/census_2000/datasets/Summary_File_3/ (Last accessed March

2004).

the appropriate level of detail for addressing uncertainty in specific variables, and approaches for

integrating uncertainty analyses across the models.

11.7 Presenting the Results of an Exposure Assessment

The summary of exposure assessment for air toxics consists of presenting the ECs for each

chemical of potential concern (COPC) with the duration of exposure for the populations of

interest, as well as characterizing salient features of the study population(s), particularly those

that may be influencing their exposure and resultant risk (e.g., size and proximity to sources and

/or locations of highest ambient concentrations). The assumptions used to develop these

estimates should also be presented and discussed. In addition to the summary tables, it is useful

to show sample calculations for each pathway to aid in the review of the calculations. (If

exposure modeling is used, a thorough discussion with sample calculations is usually also

provided.)

References
April 2004 Page 11-24

U.S. Census Bureau. Topologically Integrated Geographic Encoding and Referencing system

(TIGER). Updated March 5, 2004. Available at:

http://www.census.gov/geo/www/tiger/index.html. (Last accessed March 2004).

9. U.S. Environmental Protection Agency. 2003. Port Neches Environmental Justice Study and

Demonstration Project for the Development of Landscan USA. Updated December 30, 2003.

Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=22406. (Last accessed

March 2004).

10. U.S. Environmental Protection Agency. 2003. Human Exposure Modeling - Human

Exposure Model (HEM). Updated April 23, 2003. Available at:

http://www.epa.gov/ttn/fera/human_hem.html. (Last accessed March 2004).

11. ICF Consulting. 2002. California Population Indoor Exposure Model, Version 2: User’s

Guide. Prepared for the California Environmental Protection Agency, California Air

Resources Board.

12. U.S. Environmental Protection Agency. 2002. The National-Scale Air Toxics Assessment.

Updated September 18, 2002. Available at: http://www.epa.gov/ttn/atw/nata/. (Last accessed

March 2004).

13. U.S. Environmental Protection Agency. 2004. Technology Transfer Network. Air Toxics

Strategy: Overview. Updated February 10, 2004. Available at:

http://www.epa.gov/ttn/atw/urban/urbanpg.html. (Last accessed March 2004).

14. Naumova, Y., Eisenreich, S., Turpin, B., Weisel, C., Morandi, M., Colome, S., Totten, L.,

Stock, T., Winer, A., Alimokhtari, S., Kwon, J., Shendell, D., Jones, J., Maberti, S. and Wall,

S. 2002. Polycyclic Aromatic Hydrocarbons in the Indoor and Outdoor Air of Three Cities in

the U.S. Environmental Science and Technology, 36: 2552-2559.

15. U.S. Environmental Protection Agency. 2003. Human Exposure Measurements: National

Human Exposure Assessment Survey (NHEXAS). Updated July 21, 2003. Available at:

http://www.epa.gov/heasd/edrb/nhexas.htm. (Last accessed March 2004).

16. Ozkaynak H., J. Xue, R. Weker, D. Butler and J. Spengler. 1995. The Particle TEAM

(PTEAM) Study: Analysis of the Data, Volume III, Draft Final Report. U.S. Environmental

Protection Agency, Research Triangle Park, NC.

Ozkaynak H., J. Xue, J. Spengler, L. Wallace, E. Pellizzari, and P. Jenkins. 1996. Personal 

exposure to airborne particles and metals: results from the Particle TEAM Study in

Riverside, California. Journal of Exposure Analysis and Environmental Epidemiology,

6(1):57-78.

Pellizzari, E. D., K. W. Thomas, C. A. Clayton, et al. 1992. Particle Total Exposure

Assessment Methodology (PTEAM): Riverside, CA Pilot Study Vol. I. U.S. Environmental

Protection Agency, Research Triangle Park, NC. (EPA 600/R-93-050).
April 2004 Page 11-25

Sheldon, L., A. Clayton, J. Keever, et al. 1992. PTEAM: Monitoring of Phthalates and

PAHs in Indoor and Outdoor Air Samples in Riverside, California. Final Report. California

Air Resources Board. Sacramento, California.

Wallace, L. 1987. The Total Exposure Assessment Methodology (TEAM) Study: Summary

and Analysis: Volume 1. U.S. Environmental Protection Agency, Office of Research and

Development, Washington, D.C. EPA/600/6-87/002a.

Wallace, L. 1989. Major sources of benzene exposure. Environ. Health. Perspect.,

82:165-169.

Wallace L., W. Nelson, R. Ziengenfus, E. Pellizzari, L. Michael, R.Whitmore, H. Zelon, T.

Hartwell, R. Perritt and D. Westerdahl. 1991. The Los Angeles TEAM Study: Personal

Exposures, Indoor-Outdoor Air Concentrations, and Breath Concentrations of 25 Volatile

Organic Compounds. J. Expos. Anal. Environ. Epidem., 1(2):157-19.

17. Bower et al. 1997. A Practical Guide to Air Quality Monitoring. National Environmental

Technology Centre, England. 

18. U.S. Environmental Protection Agency. 1992. Guidance on Risk Characterization for Risk

Managers and Risk Assessors. Risk Assessment Council, Washington, D.C., February 26,

1992.

19. U.S. Environmental Protection Agency. 1992. Guidelines for Exposure Assessment. Federal

Register 57:22888-22938, May 29, 1992. Available at 

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=15263
Chapter 12 Inhalation Toxicity Assessment

Table of Contents

12.1 Introduction ............ ............ ............ ............ ............ ...... 1

12.1.1 Hazard Identification and Dose-Response Information ............ ............ . . 1

12.1.2 Dose-Response Assessment Methods ............ ............ ............ ... 5

12.2 Hazard Identification ............ ............ ............ ............ .......... 8

12.2.1 Weight of Evidence – Human Carcinogenicity ............ ............ ........ 9

12.2.2 Identification of Critical Effect(s) – Non-Cancer Endpoints ............ ......... 11

12.3 Dose-Response Assessment for Cancer Effects ............ ............ ............ . 12

12.3.1 Determination of the Point of Departure (POD) ............ ............ ...... 13

12.3.2 Derivation of the Human Equivalent Concentration ............ ............ . . 14

12.3.3 Extrapolation from POD to Derive Carcinogenic Potency Estimates ............ . . 17

12.4 Dose-Response Assessment for Derivation of a Reference Concentration ............ .... 19

12.4.1 Determination of the Point of Departure and Human Equivalent Concentration ..... 20

12.4.2 Application of Uncertainty Factors ............ ............ ............ .... 22

12.5 Sources of Chronic Dose-Response Values ............ ............ ............ .... 24

12.6 Acute Exposure Reference Values ............ ............ ............ ........... 26

12.7 Evaluating Chemicals Lacking Health Reference Values ............ ............ ..... 31

12.7.1 Use of Available Data Sources ............ ............ ............ ....... 31

12.7.2 Route-to-Route Extrapolation ............ ............ ............ ........ 31

12.8 Dose-Response Assessment for Mixtures ............ ............ ............ ...... 32

References ............ ............ ............ ............ ............ ............ 35

April 2004 Page 12-1

Risk = ƒ (metric of exposure, metric of toxicity)

Toxicity Assessment is a 2-Step Process:

1. Hazard Identification – What types of effects does the chemical cause? Under what

circumstances?

2. Dose-response Assessment – How potent is the chemical as a carcinogen and/or for noncancer

effects?

12.1 Introduction

The purpose of the toxicity assessment is to weigh available evidence regarding the potential for

toxicity in exposed individuals (hazard identification) and to quantify the toxicity by deriving

an appropriate dose-response value (dose-response assessment). Toxicity assessment is the

second part of the general risk equation. Although the toxicity assessment is an integral and

important part of the overall air toxics risk assessment, it is usually accomplished prior to the risk

assessment. EPA has completed this toxicity assessment for many HAPs and has made available

the resulting toxicity information and dose-response values, which have undergone extensive

peer review (see Appendix C).1

In most air toxics risk assessments, little new toxicological evaluation of primary data will be

required. However, it is important to understand how the available data were analyzed to

produce the dose-responses values used in a risk assessment. In the risk characterization step, the

risk assessor will need to describe the nature of the available toxicological evidence and the

uncertainties inherent in the development of the dose-response values used in the inhalation risk

assessment (see Chapter 13).

Additionally, in the event that there are significant data analysis and interpretation issues, or if a

dose-response value does not exist and needs to be developed for a particular air toxic of interest,

this chapter provides information about how to locate toxicity assessments, accompanying doseresponse values, and relevant guidance documents. However, development and interpretation of

toxicity information and dose-response values requires toxicological expertise and should not be

undertaken by those without appropriate training and experience.

12.1.1 Hazard Identification and Dose-Response Information

As part of the hazard identification step, evidence is gathered from a variety of sources regarding

the potential for an air toxic to cause adverse health effects in humans. These sources may

include human data, experimental animal studies, and supporting information such as in vitro

laboratory tests. The source of data affects the overall uncertainties in the resulting human doseresponse values, as discussed below.
aA PBPK model estimates the dose to a target tissue or organ by taking into account the rate of absorption

into the body, distribution among target organs and tissues, metabolism, and excretion.

April 2004 Page 12-2

Epidemiology is the study of the distribution and

determinants of disease or health status in a population.

• Human data. Human toxicity data associated with exposures to air toxics may be located in

epidemiological studies, controlled exposure studies, or studies of accidental exposures. 

Well-conducted epidemiological studies that show a positive association between exposure to

a chemical and adverse health effects often provide evidence about human health effects

associated with chronic exposures. 

Such data, however, are available

only for a limited number of air

toxics. Epidemiological data also are

very difficult to interpret, because the

number of exposed individuals may be small, the incidence of effects may be low, doses are

usually not well-characterized, and there may be complicating factors such as simultaneous

exposure to multiple chemicals and heterogeneity among the exposed group in terms of age,

sex, diet, and other factors. Controlled exposure studies provide stronger evidence, since

both the exposure duration and exposure concentrations are more accurately known. 

However, such studies with humans are generally limited to acute exposure durations. 

Studies reporting health effects associated with accidental exposures may be helpful,

although exposure concentrations to air toxics may be high, and effects may be acute rather

than chronic. Also note that small sample size is often a significant limitation to interpreting

controlled and accidental exposure studies.

• Animal data. The toxicity database for most air toxics is drawn from experiments conducted

on non-human mammals such as rats, mice, rabbits, guinea pigs, hamsters, dogs, or monkeys. 

The underlying assumption is that the susceptibility of humans and these animals to the

effects of the chemicals is broadly similar because we share many common biological

attributes (e.g., similar organs, similar and, in some cases, identical metabolic processes). 

However, some observations in animals may be of uncertain relevance to humans (e.g., if

tumors are observed in an animal experiment, but the organ in which the tumor is formed

does not exist in humans). Also, it is necessary to adjust the results from animal studies to

humans due to differences in body mass, anatomy, metabolic rate, and other species-specific

factors (see, for example, Section 12.3.3). This is why derivation of dose-response values

from animal studies requires considerable expertise.

• Supporting data. Metabolic, pharmacokinetic, and genotoxicity studies are sometimes used

to infer the likelihood of adverse effects in humans. Metabolic studies on absorption,

distribution, metabolism, and elimination can provide information about the mechanisms of

toxicity associated with a particular chemical in humans. In physiologically based

pharmacokinetic (PBPK) models,(a) the body is subdivided into a series of anatomical or

physiological “compartments” that represent specific organs or lumped tissue and organ

groups, and the behavior of the chemical is modeled in each compartment. Data on a

chemical’s pharmacokinetics, genotoxicity, and possible mode of action can be used to refine

a toxicity assessment. In some cases, computer models using structure-activity relationships

(i.e., predictions of toxicological activity based on analysis of chemical structure) also may be

used as supporting evidence. EPA considers these types of data to be supportive, not

definitive, evidence of a chemical’s toxicity.
bWhile the majority of RfCs are derived for effects other than cancer, RfCs may be derived for all effects,

including cancer, when a non-linear mode of action has been demonstrated for carcinogenicity.

April 2004 Page 12-3

Information from these sources is considered in the hazard and dose-response assessment steps in

characterizing a chemical with regard to the type(s) of effect a chemical produces (the hazard)

and the circumstances in which this occurs, as well as the level of exposure required to produce

that effect. The output of the dose-response assessment is the relationship between dose (the

level of exposure) and the resulting response (the increased incidence and/or severity of adverse

effects). A dose-response assessment is the process of quantitatively evaluating toxicity

information, characterizing the relationship between the dose of the contaminant received (or the

inhalation exposure concentration, for inhalation assessments) and the incidence of adverse

health effects in the exposed subjects (which may be animal or human) and then, as appropriate,

extrapolating these results to human populations. Depending on the type of effect and the

chemical, there are two types of dose-response values that traditionally may be derived: 

predictive cancer risk estimates, such as the inhalation unit risk estimate (IUR), and predictive

non-cancer estimates, such as the reference concentration (RfC).

(b) Both types of doseresponse values may be developed for the same chemical, as appropriate.

The relationship of dose to response can be illustrated as a graph called a dose-response curve. 

There are two general types of response data that may be considered and graphed. One is termed

“continuous” and refers to responses such as the severity in changes to a physiological parameter

in a given individual as dose increases (see Exhibit 12-1, A) . The second describes the

incidence of a particular response in a population (see Exhibit 12-1, B). By convention, dose or

exposure is represented on the x-axis; response on the y-axis (Exhibit 12-1).

Inhalation Dose-Response Values(a)

Inhalation Unit Risk (IUR): The upper-bound excess lifetime cancer risk estimated to result from

continuous exposure to an agent via inhalation per μg/m3 over a lifetime. The interpretation of the

IUR would be as follows: if IUR = 2 × 10-6 μg/m3, not more than 2 excess tumors are expected to

develop per 1,000,000 people if exposed continuously for a lifetime to 1 μg of the chemical per cubic

meter of inhaled air. The number of expected tumors is likely to be less; it may even be none.

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

magnitude) of a continuous inhalation exposure to the human population (including sensitive subpopulations) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 

Generally used in EPA's noncancer health assessments.

(a)The phrase “dose-response” is used generally here and elsewhere in the document. EPA’s values for inhalation, however,

are derived for exposure concentration, although with consideration of dose. Consideration of the relationship between

exposure concentration, dose, and dosimetry (how the body handles a chemical once it is inhaled) is inherent in the derivation

of these exposure concentration-response values.
April 2004 Page 12-4

Exhibit 12-1. Examples of Dose-Response Curves

A. Continuous Response Data

Simple example of a dose-response curve for

graded responses of a specific physiological

parameter to increasing exposure.

B. Different Responses in a Population

Simple example of the incidence of three

different effects in an exposed population in

response to different exposure concentrations

(over the same duration).

While the primary focus of this chapter is on description of dose-response values relevant to

chronic (long-term) exposures, the information reviewed for developing those values may

include effects associated with acute (short-term) exposures. Additionally, information on acute

exposures is essential to the development of acute exposure reference values (see Section 12.6).

• Acute exposures are usually relatively short in duration, but relatively high in concentration

and may result in immediate respiratory and sensory irritation, chemical burns, narcosis, eye

damage, and various other effects. Acute exposures also may result in longer-term health

effects.

• Chronic exposures are usually relatively long in duration, but relatively low in concentration

and may result in health effects that do not show up immediately and that persist over the

long term, such as cardiovascular disease, respiratory disease, liver and kidney disease,

reproductive effects, neurological damage, and cancer.

Generally, chronic reference values are derived for exposure periods between seven years and a

lifetime. Acute reference values (see section 12.6) are generally developed for very short

exposures (e.g., hours to days; Exhibit 12-2). For intermediate exposures, subchronic reference

values are available from some sources (e.g., ATSDR). Most air toxics risk assessments will

focus on chronic and acute evaluations; however, under more limited circumstances, subchronic

evaluations may be performed.
April 2004 Page 12-5

Exhibit 12-2. Reference Values of Different Durations

In the Agency’s Review of the Reference Dose and Reference Concentration Processes,

2

 it was

recommended that in addition to the traditional chronic reference value (i.e., RfC or RfD) included in

the IRIS database, values of several shorter durations also be developed, where possible. As a first

step in this direction, the Review proposed the following definitions. EPA currently is considering

these and other recommendations made in the Review. These definitions are based on exposure

durations for humans, and were not intended to be rigid specifications, but simply general descriptions

of the relevant exposure time period.

• Acute: Exposure by the oral, dermal, or inhalation route for 24 hours or less.

• Short-term: Repeated exposure(a) by the oral, dermal, or inhalation route for more than 24 hours,

up to 30 days.

• Longer-term: Repeated exposure by the oral, dermal, or inhalation route for more than 30 days,

up to approximately 10 percent of the life span in humans(b) (more than 30 days up to 90 days in

typically used laboratory animal species(c)).

• Chronic: Repeated exposure by the oral, dermal, or inhalation route for more than approximately

10 percent of the life span in humans (more than approximately 90 days to 2 years in typically

used laboratory animal species).

(a)A repeated exposure may be either continuous, periodic, or intermittent. A continuous exposure is a daily

exposure for the total duration of interest. A periodic exposure is one occurring at regular intervals (e.g.,

inhalation exposure 6 hours/day, 5 days/week; or oral exposure 5 days/week). An intermittent exposure is one in

which there is no effect of one exposure on the effect of the next; this definition implies sufficient time for the

chemical and its metabolites to clear the biological system before the subsequent (i.e., noncumulative

pharmacokinetics). A periodic exposure may or may not be intermittent.

(b)An average of 70 years is typical default used for chronic exposures.

(c)Examples of typically used laboratory species include rats, mice, and rabbits.

12.1.2 Dose-Response Assessment Methods

Depending on whether a substance causes cancer and whether its dose-response curve is thought

to have a threshold, EPA may use either of two approaches in a dose-response assessment. One

approach produces a predictive estimate (e.g., inhalation cancer risk estimate), and the other

produces a reference value (e.g., RfC). Historically, the use of a predictive estimate has been

limited to cancer assessment. That is, dose-response assessments for cancer have been expressed

as predictive cancer risk estimates based on an assumption that any amount of exposure poses

some risk. Assessments of effects other than cancer usually have been expressed as reference

values at or below which no harm is expected. Many substances have been assessed both ways:

the first for cancer and the second for adverse effects other than cancer. While this use of

predictive estimates for cancer and reference values for other effects is still the practice for the

vast majority of chemicals, EPA now recognizes that there are chemicals for which the data

support an alternate approach.
April 2004 Page 12-6

All substances are poisons: there is none

which is not a poison. The right dose

differentiates a poison and a remedy.

– Paracelsus

Different Responses Exhibit Different Dose-Response Curves

Line A – A sharp increase in response with increasing dose

Line B – A more gradual increase in response with increasing dose

An important aspect of dose-response relationships is whether the available evidence suggests

the existence of a threshold. For many types of toxic responses, there is a threshold dose or

dose rate below which there are thought to be no adverse effects from exposure to the chemical. 

The human body has defenses against many toxic agents. Cells in human organs, especially in

the liver and kidneys, break down many chemicals into less toxic substances that can be

eliminated from the body in urine and feces. In this way, the human body can withstand some

chemical exposure (at doses below the threshold) and still remain healthy. For example, many

air toxics are naturally occurring substances to which people routinely receive trace exposures at

non-toxic levels.

Identification of a threshold dose depends on the

type of response and the way in which the toxic

chemical produces it. EPA has developed

guidelines 3 for assessing the dose-response for

various types of adverse effects, which provide

more information about evaluating evidence to

determine if a threshold exists.

Both the point at which the

dose-response curve begins

to ascend (its threshold,

which may be zero) and

the slope of the curve (its

steepness) provide

information about the

toxicity of a chemical

(Exhibit 12-3). The

potency of a chemical is a

measure of its strength as a

toxicant compared with

other chemicals. 

Therefore, the lower the

threshold dose, the more

potent (or toxic) the

chemical. The slope of the curve is a measure of the range of doses from the threshold dose (at

which the adverse effect is first measured) to the dose at which the effect is complete (i.e., higher

doses produce no additional incidence of that effect, although other adverse effects may begin to

appear). The steeper the dose-response curve, the smaller the range between the first appearance

of an effect and a substantial response.
April 2004 Page 12-7

Exhibit 12-3. Dose-Response Relationships for Carcinogens and Noncarcinogens

A. Example Linear Carcinogen

In the absence of clear evidence to the contrary, EPA assumes as a matter of science policy that even a very low

exposure to a cancer-causing pollutant can increase the risk of cancer (albeit a small amount). Experimental data

are used to construct a dose-response relationship and identify the point of departure – the dose that can be

considered to be near or in the range of observed responses and, thus, no significant extrapolation is needed. To

estimate the dose-response relationship at doses below the point of departure, the dose-response relationship

between the point of departure and zero is assumed to be linear. Thus, at doses below the point of departure, with

each unit of increase in exposure (dose), there is an increase in cancer response. Where evidence supports the

acceptance of a non-linear mode of action, a reference concentration approach may be employed, as shown in “B”

below. LEC50= lethal effective concentration for 50 percent of the population; EC10= effective concentration that

causes an observable adverse effect in 10 percent of the population.

B. Example Non-linear Approach

A dose may exist below the minimum health effect level for which no adverse effects occur. EPA typically

assumes that at low doses the body's natural protective mechanisms prevent or repair any damage caused by the

pollutant, so there is no ill effect at low doses. Even long-term (chronic) exposures below the threshold are not

expected to have adverse effects. The dose-response relationship (the response occurring with increasing dose)

varies with pollutant, individual sensitivity, and type of health effect. NOEL = no-observed-effect-level; NOAEL

= no-observed-adverse-effect-level; LOAEL = lowest-observed-adverse-effect-level.
April 2004 Page 12-8

Items to Include in the Hazard Identification

of an Air Toxics Risk Assessment

• List of chemicals detected 

• Summaries of toxic effects and quality of the

toxicological evidence

• Discussion that focuses the risk assessment on

chemicals most likely to cause adverse effects

Epidemiologic and toxicologic data on air toxics typically result from exposure levels that are

high relative to environmental levels. Therefore, low-dose extrapolation (prediction) is

necessary to derive an appropriate dose-response value. For a few air toxics (e.g., the criteria air

pollutants ozone and carbon monoxide), data are sufficient to characterize dose-response

relationships at environmental levels. In such cases, there is no need for extrapolation of toxicity

data to lower doses. Such is not the case for most air toxics. Low-dose extrapolation requires

either information or assumptions about the type of dose-response curve likely under low dose

situations. EPA risk assessment guidelines provide more detailed information on how EPA

performs low-dose extrapolation for chemicals with various toxic effects, such as developmental

effects or neurotoxic effects.(3)

12.2 Hazard Identification

The hazard identification, which is usually

part of an existing dose-response assessment

for each chemical, provides a summary of the

available toxicity information for the air

toxics being studied, and includes the weight

of evidence determination and identification

of critical effects. This step should answer

the following questions:

• Can exposure to a chemical be linked

causally to particular health effects?

• Could these effects occur at environmentally relevant concentrations?

• What is the nature and strength of the evidence of causation?

By definition, all HAPs and many other air toxics have the

potential to cause adverse effects in the exposed population. 

Exhibit 12-4 provides examples of cancer and non-cancer

effects, and Appendix C identifies which HAPs have been

associated with carcinogenic (cancer) effects or non-cancer

effects, along with the strength and ratings of the toxicity

evidence that has been evaluated by EPA or other international

environmental agencies.

Exhibit 12-4. Examples of

Adverse Health Effects

• Birth defects

• Tremors

• Infertility

• Skin rash

• Melanoma

An air toxics risk assessment should include in its hazard identification a summary of the quality

of the toxicological evidence (i.e., the nature and strength of the evidence of causation) for the

chemicals of concern. Study factors such as the route of exposure used, the type and quality of

health effects, the biological plausibility of findings, and the consistency of findings across

studies all contribute to the strength of the hazard identification statement.
April 2004 Page 12-9

12.2.1 Weight of Evidence – Human Carcinogenicity

A major determination made during the hazard identification step concerns the potential of a

chemical to cause cancer in humans. This determination, which involves considering (or

weighing) all the available evidence, is called the weight of evidence determination. This

determination is complicated by possible inadequacies of the published studies, as well as

differences in body processes between people and laboratory animals. EPA’s Guidelines for

Carcinogen Risk Assessment guide scientists in interpreting available studies to assess the

potential human carcinogenicity of environmental pollutants. (EPA’s carcinogen risk assessment

guidelines were first published in 1986. Revisions were proposed in 1996 and 2001 and the July

1999 draft of the revisions was adopted as interim guidance. A subsequent 2003 draft of the

Guidelines has been released for public and scientific review prior to adoption as final. The

guidelines are available on the web.)4 When compared with EPA’s original 1986 guidelines, the

1999 interim Guidelines recommend a more comprehensive evaluation of the evidence with

regard to a chemical’s potential mode of action, and a more complete description of the context

of a chemical’s carcinogenic potential (e.g., “likely carcinogenic by inhalation and not likely

carcinogenic by oral exposure”). The weight of evidence determination now includes one of five

descriptors, and is accompanied by additional text that more completely summarizes EPA’s

interpretation of the evidence. The narrative statements consider the quality and adequacy of

data and the consistency of responses induced by the agent in question (see Exhibit 12-5).

Exhibit 12-5. Information Regularly Included in a Narrative Statement Describing the

Characterization of Weight of Evidence for Carcinogenicity (1999 Interim Guidelines)

• Name of agent and Chemical Abstracts Services number, if available

• Conclusions (by route of exposure) about human carcinogenicity, using one of five standard

descriptors: “Carcinogenic to Humans” “Likely to be Carcinogenic to Humans” “Suggestive

Evidence of Carcinogenicity, but Not Sufficient to Assess Human Carcinogenic Potential” “Data

are Inadequate for An Assessment of Human Carcinogenic Potential” “Non Likely to be

Carcinogenic to Humans”.

• Summary of human and animal tumor data on the agent or its structural analogues, their relevance,

and biological plausibility

• Other key data (e.g., structure-activity data, toxicokinetics and metabolism, short-term studies,

other relevant toxicity or clinical data)

• Discussion of possible mode(s) of action and appropriate dose-response approach(es)

• Conditions of expression of carcinogenicity, including route, duration, and magnitude of exposure

Source: EPA (1999) Guidelines for Carcinogen Risk Assessment. Review Draft(4)

Many existing carcinogen assessments were developed pursuant to EPA’s 1986 Guidelines for

Carcinogen Risk Assessment, which used a simpler but less informative weight of evidence

system (see Exhibit 12-6).

Information bearing on the qualitative assessment of carcinogenic potential may be gained from

human epidemiological data, animal studies, comparative pharmacokinetic and metabolism

studies, genetic toxicity studies, structure-activity relationship (SAR) analysis, and other studies

of an agent’s properties. Information from these studies helps to elucidate potential modes of

action and biological fate and disposition.
April 2004 Page 12-10

Exhibit 12-6. EPA’s Weight of Evidence Classification for Carcinogens (1986 Guidelines)

Group A: Human Carcinogen (sufficient evidence of carcinogenicity in humans)

Group B: Probable Human Carcinogen (B1 - limited evidence of carcinogenicity in humans; B2 -

sufficient evidence of carcinogenicity in animals with inadequate or lack of evidence in

humans)

Group C: Possible Human Carcinogen (limited evidence of carcinogenicity in animals with

inadequate or lack of human data)

Group D: Not Classifiable as to Human Carcinogenicity (inadequate or no evidence)

Group E: Evidence of Noncarcinogenicity for Humans (no evidence of carcinogenicity in adequate

studies)

Source: EPA (1986). Guidelines for Carcinogen Risk Assessment(4)

Upon such consideration, both EPA systems assign a consensus interpretation to the weight of

evidence, evaluating the likelihood that the agent is a human carcinogen. Toxicological evidence

is characterized separately for human studies and animal studies as: sufficient, limited,

inadequate, no data, or evidence of no effect. The characterizations of these two types of data are

combined, and based on the extent to which the agent has been shown to be a carcinogen in

experimental animals or humans, or both, the chemical is given a weight of evidence

classification.

Generally, no single factor is determinative. For example, strength of association is one of the

criteria for causality. A strong association between exposure and cancer in animals is more likely

to indicate causality than a weak association. However, finding of a large cancer incidence in a

single study must be balanced against the lack of consistency as reflected by null results from

other equally well-designed and well-conducted studies. In this situation, the positive association

of a single study may either suggest the presence of chance, bias, confounding factors, or

different exposure conditions. On the other hand, evidence of weak but consistent associations

across several studies suggests either causality or that the same confounder may be operating in

all of these studies.

If information is available to consider the mode of action for carcinogenicity, the carcinogenicity

assessment will evaluate that information and draw conclusions that influence the dose-response

method for the substance. If the evidence is sufficient to support a conclusion of nonlinear doseresponse, then the information on carcinogenicity may be considered in combination with the

information on other effects in deriving a reference value such as an RfC (see section 12.4). 

Otherwise, a linear dose-response approach leading to a predictive risk estimate, such as an IUR,

will usually be pursued. If the information supports it, the guidelines also accommodate the

development of a non-linear predictive risk estimate.
cA similar, more recent term, “key event,” is defined as “an empirically observed precursor” to an adverse

effect (e.g., liver cancer or other liver toxicity) consistent with a particular mode of action. The phrase “mode of

action” refers to the way a given chemical may act in the body to initiate one or more adverse effects.

April 2004 Page 12-11

Biological Effects of Carcinogens

Carcinogens are chemicals that induce cancers. Examples include:

• 4-Aminobyphenol, which targets the bladder;

• Benzene, which targets the tissue that make white blood cells;

• Asbestos, which targets the lung’s tissue;

• Benzidene, which targets the bladder;

• Beryllium, which targets the lungs;

• Chromium, which targets the respiratory tract;

• Radionucleotides, which targets bone marrow and the lungs; and

• Vinyl chloride, which targets the liver.

There are various types of carcinogens, including:

• Primary Carcinogens: A primary carcinogen is a substance that is carcinogenic as it occurs in the

environment.

• Procarcinogen: A procarcinogen is a substance that becomes carcinogenic only after conversion

from some benign form. Most environmental carcinogens are of this type.

• Cocarcinogen: A cocarcinogen is a substance that is not carcinogenic by itself, but potentiates the

carcinogenic effect of other chemicals.

Chemicals also can serve as mutagens, causing changes in genetic material that can disrupt cell

function and lead to cancer or other health problems.

12.2.2 Identification of Critical Effect(s) – Non-Cancer Endpoints

As part of the characterization of the available information on non-cancer health effects (or

including cancer, if a threshold mode of action has been established), the targets of chemical

toxicity within the body are identified, along with what have been termed “critical effects”

associated with the toxicity. A critical effect is described as “either the adverse effect that first

appears in the dose scale as dose is increased, or as a known precursor to the first adverse effect.” 

Underlying this designation is the assumption that if the critical effects are prevented, then all

other adverse effects observed at higher exposure concentrations or doses are also prevented.(c)

Note that not all observed effects in toxicity studies are considered adverse effects. The

identification of the critical effect(s) depends on a comprehensive review of the available data

with careful consideration of the exposure conditions associated with each observed effect, so

that comparisons of effect levels or potential reference values are made on a common basis (see

Section 12.4). A more comprehensive discussion of hazard identification and the evaluation of

the underlying database for non-cancer effects is included in the EPA documents Methods for

Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry

(1994) and A Review of the Reference Dose and Reference Concentration Process (2002).5
April 2004 Page 12-12

12.3 Dose-Response Assessment for Cancer Effects

The process for deriving a quantitative dose-response estimate for cancer (e.g., a cancer slope

factor) involves the following three steps:

1. Determination of the concentration associated with the point of departure or POD (Section

12.3.1);

2. Derivation of the human equivalent concentration corresponding to the POD (Section

12.3.2); and

3. Extrapolation from the POD (expressed as human equivalent concentration) to derive

carcinogenic potency estimates (Section 12.3.3).

The first two steps are also performed in the derivation of reference values such as the RfC

(Exhibit 12-7); in that case, these steps are followed by the application of uncertainty factors (see

Section 12.4).

Exhibit 12-7. Steps involved in deriving an RfC or IUR From an Animal Study
dNote that the corresponding value for ingestion exposures is the benchmark dose (BMD). This

often is used as the general term for the BMC/BMD process.

April 2004 Page 12-13

Example POD for Benzene

EPA’s characterization of the carcinogenic effects of benzene was updated in 1998. The IUR for

benzene is based on epidemiologic studies showing clear evidence of a causal association between

exposure to benzene and leukemia. The specific mechanisms by which benzene and its metabolites

lead to cancer remain uncertain.

EPA selected the Rinsky et al. 1981 epidemiologic study of 1,165 Pliofilm rubber male workers at

three facilities in Ohio as the data set for the dose-response relationship for determining the IUR. The

workers had been employed between 1940 and 1965 and were followed through 1981. Rinsky et. al.

expanded the study to include additional workers and published it in 1987. The Rinsky data suffers -

as many epidemiologic studies do - from uncertainties about exposure levels in the early years. There

are no measurements of benzene in the facilities’ air prior to 1946, so exposures for these years must

be estimated.

Using one set of exposure estimates with the Rinsky et al. study, EPA concluded that exposure to

benzene increases the risk of leukemia at a level of 40 ppm-years of occupational exposure (8

hours/day, 5 days/week, 50 weeks/year). Below this number, the shape of the dose-response curve

cannot be determined. Converting the occupational exposure of 40 ppm-years to an equivalent

lifetime of environmental exposure yields 120 ppb, as a POD, below which the shape of the doseresponse curve is uncertain.

EPA decided there is not sufficient evidence to demonstrate that the dose-response relationship below

the POD is non-linear. As a science policy default, EPA assumed low-dose linearity for extrapolation

from the POD to zero. Given a range of plausible exposure estimates for the Rinsky et al. study, the

Agency determined that the benzene inhalation unit risk at 1 μg/m

3

 ranges from 7.1 × 10-3 to 2.5 × 10-2

depending on the exposure estimates and modeling approach used to derive the POD.

Source: U.S. EPA. 1998. Carcinogenic Effects of Benzene: An Update. Office of Research and

Development, National Center for Environmental Assessment, Washington, D.C. EPA/600/P97/001F.; Rinsky, R.A., Young, R.J., and Smith, A.B. 1981. Leukemia in benzene workers. American

Journal of Industrial Medicine. 2(3) 217:245. 

12.3.1 Determination of the Point of Departure (POD)

Dose-response assessment for cancer and other effects begins with identification of the point of

departure (an exposure concentration or intake) from the experimental data. This point (in terms

of its human equivalent), while within the range of observation, is the point from which

extrapolation begins, either for the purposes of deriving a cancer risk estimate (the IUR) or a RfC

for non-cancer health effects.

The POD may be the traditional no observed adverse effect level (NOAEL), lowest observed

adverse effect level (LOAEL), or benchmark concentration (BMC).

(d) EPA has recommended

the use of the BMC approach, where possible, because the traditional use of the LOAEL or

NOAEL in determining the POD has long been recognized as having several limitations (and
April 2004 Page 12-14

generally is not used in dose-response for cancer effects. In particular, the LOAEL-NOAEL

approach:

• Is limited to one of the doses in the study and thus is dependent on study design;

• Does not account for variability and uncertainty in the estimate of the dose-response

relationship;

• Does not account for the slope of the dose-response curve; and

• Cannot be applied, where there is no NOAEL, except through the application of an

uncertainty factor.

If the dose-response data are of high quality, a mathematical dose-response model may be fitted

to the data to determine a more precise POD than the NOAEL or LOAEL. When a model is

used, the POD is calculated as the statistical lower confidence limit of the dose at which there is

a low toxic response (usually 5 or 10 percent incidence in populations with an effect or a change

in a physiological measurement indicating adversity).(6) The selection of the response percentage

is intended to coincide with the sensitivity limit of the experimental design or professional

judgment. This calculated POD is called the BMC.

The BMC approach is an alternate way of determining the point of departure for low-dose

extrapolation. It can be used in cancer and noncancer risk assessment as the starting point for

linear low-dose extrapolation, calculation of a margin of exposure, or application of uncertainty

factors for calculating RfCs or other dose-response values. BMC methods involve fitting various

mathematical models for dose-response to reported data and using the different results to select a

BMC that is associated with a predetermined benchmark response, such as a 10 percent increase

in the incidence of a particular lesion or a 10 percent decrease in body weight gain (Exhibit 

12-8). EPA has developed the Benchmark Dose Software (BMDS) to facilitate these operations. 

BMDS currently offers 16 different mathematical models that can be fit to the laboratory data. 

EPA plans to continually improve and expand the BMDS system.6

It is likely that there will continue to be situations that are not amenable to BMC modeling and

for which a NOAEL or LOAEL approach should be used. In some cases, there may be a

combination of benchmark doses and NOAELs to be considered in the assessment of a particular

agent. 

12.3.2 Derivation of the Human Equivalent Concentration 

Because inhalation toxicity studies typically involve discontinuous exposures (e.g., animal

studies routinely involve inhalation exposures of 6 hours per day, 5 days per week), the POD will

usually need to be extrapolated to a continuous exposure scenario (as appropriate for the RfC and

IUR). This duration adjustment step is essential in interpreting inhalation studies, but is not

routinely necessary for the interpretation of oral exposures. Operationally, this is accomplished
e

“C × t” is a component of Haber’s Law that refers to the default assumption (in lieu of information to the contrary)

that effects observed are related to the cumulative exposure or “area under the curve” (quantified by concentration, C, multiplied

by duration, t). It is noted that when going from a discontinuous inhalation exposure regiment to a continuous exposure, the

result will always be a lower value for concentration, thus providing an automatic margin of protectiveness for chemicals for

which C alone (vs. C × t) may be appropriate, while providing the appropriate conversion for substances for which cumulative

exposure is the appropriate measure.(4)

April 2004 Page 12-15

(Equation 12-1)

by applying a concentration-duration product, or C × t product(e) for both the number of hours

ina daily exposure period and the number of days per week that the exposures are performed. 

For example, for a POD of 100 mg/m3 derived from an animal study in which animals are

exposed by inhalation for 6 hours per day, 5 days per week, the adjustment to a continuous

exposure concentration would consider both hours per day and days per week: 

Thus, 18 mg/m3 is the POD concentration adjusted for continuous exposure versus 100 mg/m3

unadjusted. This approach assumes there is no dose-rate effect (i.e., that the same total inhaled

material produces the same effect regardless of the time over which this material was inhaled).

Exhibit 12-8. Example Derivation of Benchmark Dose Level

Illustration of the computation of a benchmark dose (BMD) and BMDL (a lower one-sided confidence

limit on the BMD) for an extra risk of 0.10 (as suggested by the BMDS guidance document), using a

one-sided 95 percent confidence interval. A number of models were fit to the data, and the log-logistic

model illustrated provides the best fit to the example data. The predicted curve comes well within the

confidence limits for each data point. Other data and models are illustrated in examples provided in

the BMDS guidance document.(6)
April 2004 Page 12-16

Choice of a Default DAF for Extrapolation from Animal Data

Depends on the Physical and Chemical Properties of the Pollutant

Gases

• Category 1 (effect in respiratory system) – default DAF based on inhalation rate, and surface area

of target portion of respiratory tract

• Category 2 (some characteristics intermediate or common to category 1&3) – default DAF is the

more restrictive of the defaults for category 1 & 3

• Category 3 (systemic effect[s]) – default DAF based on blood:air partition coefficient 

Particles

• Respiratory toxicant – default DAF based on fractional deposition, inhalation rate, and surface area

of target portion of respiratory tract

• Systemic toxicant – default DAF based on inhalation rate, body weight, and fractional deposition

Source: U.S. EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and

Application of Inhalation Dosimetry.

(5)

Exposures documented from human occupational epidemiological studies are most often

reported as 8-hr time-weighted averages (TWAs) and therefore, also are discontinuous. 

Adjustment of these exposures is usually done as part of the dosimetric adjustment to derive a

human equivalent concentration (HEC), rather than as a discrete step, and is explained below in

Section 12.3.3. The duration adjustment step also is explicitly incorporated into physiologically

based pharmacokinetic (PBPK) models used to extrapolate an animal or occupational

study-derived POD into an HEC.

After duration adjustment, the POD is converted into a human equivalent concentration

(HEC) from the experimental animal dose. This conversion may be done using default methods

specific to the particular chemical class of concern or more refined methods such as PBPK

modeling. 

The Agency’s inhalation dosimetry methodology(5) provides a recommended hierarchy, as well as

default generalized procedures for deriving dosimetric adjustment factors (DAFs) for this

extrapolation. Application of DAFs to an animal exposure value yields an estimate of the

corresponding concentration relevant to humans (i.e., the HEC) given differences in physiology

and in the form of the pollutant that influence how the chemical exerts its effect. The DAF

depends on the chemical category (i.e., gas or particle) and whether the adverse effect occurs in

the respiratory tract or outside of the respiratory tract. HECs are derived using DAFs for both

RfC development (noncancer effects) and IUR development (cancer).

When data are adequate to support it, the preferred EPA approach for calculating a HEC is to use

a chemical-specific PBPK model parameterized for the animal species and regions (e.g., of the

respiratory tract) involved in the toxicity (Exhibit 12-9).
April 2004 Page 12-17

In PBPK models, the body is subdivided into a series of anatomical or physiological

“compartments” that represent specific organs or tissue and organ groups. The transfer of

chemicals between compartments is described by a set of differential equations. The parameters

of the model are of three types: physiological parameters (such as tissue perfusions or tissue

volumes), physicochemical parameters (such as partition coefficients that describe the degree of

partitioning of a given chemical to a given tissue), and biochemical parameters describing

metabolic processes. The structure of a PBPK model is determined by the intended use of the

model, the biochemical properties of the chemical studied, and the effect site of concern.

Exhibit 12-9. Extrapolation of Inhalation Exposure to Calculate the HEC

EPA employs a hierarchy of approaches for deriving the human equivalent concentration. Preference

is given to the use of a physiologically-based pharmacokinetic model, followed by intrermediate, less

detailed approaches, which are followed by the default approach, which utilizes a DAF specific to the

type of chemical and how it exerts its effect.

With sufficient data, a PBPK model is capable of calculating internal doses to a target organ in

an animal from any exposure scenario and then estimating what human exposure would result in

this same internal dose (i.e., the HEC). A formal DAF is not calculated in this process; rather,

the model itself serves as a DAF in estimating HECs. However, constructing a PBPK model is

an information-intensive process, requiring much chemical-specific data. Consequently, these

models are usually available for only a subset of chemicals. For example, EPA’s IRIS toxicity

assessment for vinyl chloride relies on a PBPK model.

12.3.3 Extrapolation from POD to Derive Carcinogenic Potency Estimates

Observable cancer rates in laboratory or human occupational epidemiologic studies tend to be

several orders of magnitude higher than cancer risk levels that society is willing to tolerate from

involuntary chemical exposures. To obtain observable results, laboratory studies need to be
April 2004 Page 12-18

Risk = EC × IUR, where

EC = lifetime estimate of continuous inhalation

exposure to an individual air toxic

IUR = the corresponding inhalation unit risk

estimate for that air toxic

conducted at exposures usually well above environmentally relevant concentrations. Thus,

extrapolation from the POD-HEC to lower doses is usually necessary. This extrapolation is

performed consistent with the mode of action, if adequately supported. Where the mode of

action supports a biologically-based model and the data set is not rich enough to support a

biologically based model, a non-linear reference concentration approach is employed (see Section

12.4.2). When the data are insufficient to support a mode of action decision, or where the data

support a linear mode of action, a linear extrapolation is employed.

For linear extrapolation, a straight line is drawn from the point of departure expressed as a

human equivalent dose to the origin (i.e., zero incremental dose, zero incremental response) to

give an incremental probability dose unit. That is, the slope of the line expresses extra risk per

dose unit (e.g., the IUR, expressed as extra risk per μg/m3 of lifetime exposure). EPA’s 1999

proposed guidelines(4) for carcinogen risk assessment recommend the use of the lowest effective

dose using a 10 percent response level (LED10) (as estimated by the lower one-sided confidence

limit on the benchmark concentration [or BMCL10]) as the POD for linear extrapolation. This

approach is to draw a straight line between the estimated point of departure, generally, as a

default, the LED10. The LED10 is the lower 95 percent limit on a dose that is estimated to cause a

10 percent response. The linear extrapolation approach to assessing risk is considered generally

conservative of public health, including sensitive subpopulations, in the absence of specific

information about the extent of human variability in sensitivity to effects.

The inhalation cancer dose-response value derived by linear extrapolation is the IUR. It is

presented as an upper-bound estimate of the excess cancer risk resulting from a lifetime

(assumed 70-year) of continuous exposure to an agent at a concentration of 1 μg/m3 in air. As

illustrated previously in Exhibit 12-2(A), risk is the product of the slope and the estimated

exposure. The IUR is a plausible upper-bound estimate of the risk (i.e., the risk is not likely to be

higher but may be lower and may be zero). When adequate human epidemiology data are

available, maximum likelihood estimates may be used instead of upper bounds to generate the

IUR. When only animal data are available and linear extrapolation is used, the IUR is derived

from the largest linear slope that is consistent with the data (within the upper 95 percent

confidence limit). In other words, the true risk to humans, while not identifiable, is not likely to

exceed the upper-bound estimate (the IUR), and is likely to be lower. This means that any

estimate of risk for air toxics using an IUR is likely to be protective of all potentially exposed

populations. In addition, this means that air toxics risk estimates are likely to be conservative,

that is, protective of public health.

The evidence for the carcinogenic mode of

action may lead to a conclusion that the

dose-response relationship is nonlinear,

with response falling much more quickly

than linearly with dose, or may be most

influenced by individual differences in

sensitivity. In some cases this may be due

to the mode of carcinogenic action being a

secondary effect of toxicity or of an induced physiological change that is itself a threshold

phenomenon. EPA does not generally try to distinguish between modes of action that might

imply a “true threshold” from those with a nonlinear dose-response relationship. Except in

unusual cases where extensive information is available, it is not possible to distinguish between
April 2004 Page 12-19

these empirically. Therefore, as a matter of science policy, nonlinear probability functions are

only fitted to the response data to extrapolate quantitative low-dose risk estimates when the

carcinogenic mechanism of the toxicant is very well-understood. When the evidence indicates a

non-linear dose response function containing a significant change in slope, and alternate

nonlinear approach may be considered. For example, when carcinogenesis can be shown to be a

secondary effect of threshold toxicity, the EPA draft carcinogen guidelines recommend

derivation of a reference concentration.

12.4 Dose-Response Assessment for Derivation of a Reference Concentration

The reference concentration is defined as an estimate (with uncertainty spanning perhaps an order

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

sub-populations) that is likely to be without an appreciable risk of deleterious effects during a

lifetime. The RfC is expressed as a chronic exposure level to the chemical in ambient air (in

units of milligrams of the substance per cubic meter of air, or mg/m3). This value is usually

derived for use with effects other than cancer. But when a chemical’s carcinogenicity has been

shown to be associated with a nonlinear mode of action (see Agency’s Cancer Guidelines),(4) a

reference concentration may be derived for use with all effects, including cancer.

Inherent in the derivation of a reference concentration is the recognition of an exposure level

likely to be without an appreciable risk of adverse effects (e.g., a sub-threshold level for adverse

effects). The objective of this type of dose-response assessment, then, is to estimate that

exposure level for humans. The RfC is derived after a thorough review of the health effects

database for an individual chemical and identification of the most sensitive and relevant endpoint

(the “critical effect”) along with the principal study(ies) demonstrating that endpoint. In addition

to an analysis of the study data available for the chemical, risk assessors also use uncertainty

factors to account for differences in sensitivity between humans and laboratory animals, the

possibility of heightened sensitivity of some population groups (e.g., people with respiratory

disease, very young children, the aged), and any limitations of the database. The methodology

for derivation of an inhalation reference concentration is described in detail in EPA’s Methods

for Derivation of Inhalation Reference Concentrations and Application of Inhalation

Dosimetry.

(5)

The first part of this type of assessment, which involves a careful qualitative and quantitative

analysis of the study data, parallels that performed for linear cancer dose-response assessment 

(i.e., derivation of the point of departure in terms of a human equivalent concentration

[PODHEC]). The qualitative analysis is described in Section 12.2.2, while the quantitative

analysis is described in Sections 12.3.1 and 12.3.2. The latter part of this type of assessment

involves the application of uncertainty factors to address limitations of the data used (e.g., the

factors raised above).

In IRIS, EPA includes with each RfC a statement of high, medium, or low confidence based on

the completeness of the database for that substance. High confidence RfCs are considered less

likely to change substantially with the collection of additional information, while low confidence

RfCs may be especially vulnerable to change.(5)
April 2004 Page 12-20

12.4.1 Determination of the Point of Departure and Human Equivalent Concentration

In earlier sections (Section 12.2.2, 12.3.1 and 12.3.2) the analysis of the database and

identification of the critical effect, as well as the derivation of the POD in terms of human

equivalent concentrations are discussed.

In developing a dose-response assessment, toxicologists evaluate the available data for a

substance. Studies of high quality are selected, and the assessment is focused on the most

appropriate studies. As the RfC is a chronic value, preference is given to long-term studies over

short-term ones, to studies using animals that exhibit effects similar to those experienced by

humans, to studies using an appropriate exposure route (e.g., inhalation exposure for developing

an RfC), and to studies showing a clear pattern of increasing frequency or severity of response

with increasing dose. Toxicologists use the information to identify the critical effect (i.e., the

adverse effect that appears at the lowest dose). Afterwards, appropriate human data are chosen

as the basis for the RfC or, if human data are not adequate, data from the most appropriate

species are identified. If this is not known, the data from the most sensitive species is usually

chosen. This analysis is described in Section 12.2.2. The objective in identifying the critical

effect or effects is to identify the effect(s) - among all those associated with exposure to the

chemical of interest - that occur at the lowest exposure and would lead to derivation of the lowest

RfC (Exhibit 12-10).

Exhibit 12-10. Overview to Develop a Reference Concentration

The LOAEL (HEC) and NOAEL (HEC) are illustrated with low-dose extrapolation with the

application of uncertainty or modifying factors to derive the human health-protective RfC. Note that

this figure represents data from appropriate animal species.

Using the dose-response relationship for the critical effect, toxicologists identify the POD from

the experimental data. This exposure concentration (in terms of its human equivalent) which

marks the boundary between the range of observation and that of extrapolation, is the point from
April 2004 Page 12-21

Derivation of RfC Using BMC Methodology – 1,3-dichloropropene

A review of the available animal studies indicated changes to the surface cells of the nasal portion of

the respiratory tract as the critical effect for 1,3-dichloropropene. Benchmark modeling was

performed on the data demonstrating this effect. The seven statistical models for dichotomous data

from the Agency’s benchmark dose modeling software (BMDS Version.1b) were applied to the

incidence data for the adjusted administered doses. The best model fit was determined by eliminating

all models that did not have a statistically significant goodness-of-fit (p<0.05). The remaining models

were then ranked by best visual fit of the data, especially for the lower doses, as observed in the

graphical output of the Benchmark Dose Software. The model with statistically significant goodnessof-fit and best visual and statistical fit was used to estimate the BMC at 10 percent risk and the 95

percent lower confidence limit of the BMC (the BMCL). The gamma, logistic, multistage, Weibull,

and quantal-quadratic models provided statistically significant fits. The gamma model was the best fit

overall because it provided the best visual fit. This model yielded a BMC10 of 5.9 mg/m3 and a

BMCL10 of 3.7 mg/m3.

The BMCL10 was identified as the POD and was adjusted from experimental conditions to a

continuous inhalation exposure value (PODadj). Because the critical target was the nasal mucosa,

algorithms for extrathoracic effects for Category 1 gases were used to adjust continuous animal

exposure concentration to HEC. The PODHEC for a Category 1 gas was derived by multiplying the

animal BMCL10 by an interspecies dosimetric adjustment for gas:respiratory effects in the

extrathoracic area of the respiratory tract. Using default values, the adjustment factor was equal to

0.2. For example, for 1,3-dichloropropene:

PODHEC = BMCL10(HEC) = BMCL10 (adj) × 0.2 = 3.7 × 0.2 = 0.7 mg/m3

The PODHEC was divided by uncertainty factors for interspecies extrapolation (UF of 3) and

intraspecies variation (UF of 10) and rounded to one significant figure to yield the RfC for 1,3-

dichloropropene:

RfC = PODHEC / 30 = 0.02 mg/m3

which extrapolation begins for derivation of a RfC. The POD may be derived from benchmark

modeling (see Section 12.3.1 regarding the derivation of a BMCL). If the data do not meet

requirements for benchmark modeling, the POD is derived by the use of a statistical analysis to

identify the no-observed-adverse-effect-level, or NOAEL, defined as the highest dose level

administered to laboratory animals that did not cause statistically or biologically significant

observable adverse effects after chronic (usually lifetime) exposure in the studied population. In

some cases, a LOAEL is used in the absence of a NOAEL. In either case, the POD is

transformed into a continuous inhalation exposure (e.g., from an intermittent animal exposure, 6

hours/day, 5 days/week) and then into a human equivalent concentration (as described in Section

12.3.2). In order for the appropriate critical effect to be identified, a comparison of PODs across

different endpoints is done in terms of human equivalent concentrations (or potential RfC values,

which incorporate the application of UFs, need to be compared).(5)
April 2004 Page 12-22

(Equation 12-2)

12.4.2 Application of Uncertainty Factors

The RfC is an estimate derived from the PODHEC for the critical effect (based on either a

BMCLHEC, NOAELHEC or LOAELHEC) by consistent application of UFs. The UFs are applied to

account for recognized uncertainties in the use of the available data to estimate an exposure

concentration appropriate to the assumed human scenario. The general formula for deriving an

RfC from a PODHEC is:

A UF of 10, 3, or 1 is applied for each of the following extrapolations used to derive the RfC (see

Exhibit 12-11):

• Animal to human;

• Human to sensitive human populations;

• Subchronic to chronic;

• LOAEL to NOAEL; and

• Incomplete to complete database.

The UFs are generally an order of magnitude (10), although incorporation of dosimetry

adjustments or other information may result in the use of reduced UFs for RfCs (3 or 1). The

composite UF applied to an RfC will vary in magnitude depending on the number of

uncertainties involved; however, an RfC will not be derived when use of the data involves more

than four areas of extrapolation. The composite UF when four factors are used generally is

reduced from 10,000 to 3,000 in recognition of the lack of independence and the conservatism of

these factors.

The 2002 Agency review of the reference dose (RfD)/reference concentration process(2)

encouraged the development of guidance in the area of chemical-specific adjustment factors

(CSAFs). These factors utilize specific data to replace the default UFs for interspecies or interindividual variation. The review panel noted, however, that the CSAF approach for any single

substance is determined principally by the availability of relevant data. For many substances

there are relatively few data available to serve as an adequate basis to replace defaults for

interspecies differences and human variability with more informative CSAFs.

Because of this procedure to address the lack of information on the translation from experimental

data to a human scenario, the resulting RfC for many HAPs is on the order of 100 to 300 times

lower than the NOAEL actually observed in the animal testing (see Exhibit 12-12). This reflects

the lowering of the RfC to address the uncertainties in the extrapolations mentioned above. For

those HAPs that have had their effects well documented in human studies, the RfC may be much

closer to the highest concentration at which an adverse effect was not observed (e.g., within a

factor of 3 to 10).
April 2004 Page 12-23

Exhibit 12-11. Uncertainty Factors Used in the Derivation of an Inhalation RfC

Standard Uncertainty Factors Processes Considered in UF Purview

A = Animal to human

Extrapolation from valid results of long-term studies

on laboratory animals when results of studies of

human exposure are not available or are inadequate. 

Intended to account for the uncertainty in

extrapolating laboratory animal data to the case of

average healthy humans.

• Pharmacokinetics/Pharmacodynamics

• Relevance of laboratory animal model

• Species sensitivity

H = Human to sensitive human

Extrapolation of valid experimental results for

studies using prolonged exposure to average healthy

humans. Intended to account for the variation in

sensitivity among the members of the human

population.

• Pharmacokinetics/Pharmacodynamics

• Sensitivity

• Differences in mass (children, obese)

• Concomitant exposures

• Activity Pattern

• Does not account for idiosyncrasies

S = Subchronic to chronic

Extrapolation from less than chronic exposure results

on laboratory animals or humans when there are no

useful long-term human data. Intended to account

for the uncertainty in extrapolating from less than

chronic NOAELs to chronic NOAELs.

• Accumulation/Cumulative damage

• Pharmacokinetics/Pharmacodynamics

• Severity of effect

• Recovery

• Duration of study

• Consistency of effect with duration

L = LOAEL to NOAEL

Derivation from a LOAEL instead of a NOAEL.

Intended to account for the uncertainty in

extrapolating from LOAELs to NOAELs.

• Severity 

• Pharmacokinetics/Pharmacodynamics

• Slope of dose-response curve

• Trend, consistency of effect

• Relationship of endpoints

• Functional vs histopathological evidence

• Exposure uncertainties

D = Incomplete to complete data

Extrapolation from valid results in laboratory animals

when the data are “incomplete”. Intended to account

for the inability of any single laboratory animal study

to adequately address all possible adverse outcomes

in humans.

• Quality of critical study

• Data gaps

• Power of critical study/supporting studies

• Exposure uncertainties

Source: U.S. EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and

Application of Inhalation Dosimetry.

(5)
fAs noted earlier, see http://www.epa.gov/ttn/atw/toxsource/summary.html for a current listing of this

information.

April 2004 Page 12-24

Exhibit 12-12. Examples of the Use of Uncertainty Factors in Deriving RfCs

RfC from NOAEL

Example: Diesel Engine Emissions

RfC from LOAEL

Example: Toluene

Toxicity data:

144 μg chemical/m3 air (NOAELHEC from chronic

rodent study)

Uncertainty factors: 3 x 10 = 30

 3 = animal-to-human extrapolation

10 = human to sensitive human subpopulations

RfC = 144/30 = 4.8 μg/m3 = 0.005 mg/m3

Toxicity data:

119 mg chemical/m3 air (LOAELHEC from chronic

occupational study)

Uncertainty factors: 10 x 10 x 3 = 300

10 = human to sensitive human subpopulations

10 = LOAEL-to-NOAEL extrapolation

 3 = database deficiencies

RfC = 119/300 mg/m3 = 0.4 mg/m3

NOAELHEC = No-Observed-Adverse-Effect Level (Human Equivalent Concentration)

LOAELHEC = Lowest-Observed-Adverse-Effect Level (Human Equivalent Concentration)

Source: EPA’s IRIS database http://www.epa.gov/IRIS/.

In some of the older IRIS assessments a “modifying factor” may have been applied in addition to

the traditional uncertainty factors. It had been used with professional judgement when it was

determined that another uncertainty factor was needed; its magnitude depended upon the

professional assessment of scientific uncertainties of the study and database not explicitly treated

via the other uncertainty factors.(5) The 2002 Agency review of the RfD/RfC process, however,

recommended against continued use of the modifying factor. It was felt that the traditional

factors could account for any remaining uncertainties.(2)

12.5 Sources of Chronic Dose-Response Values

Appendix C provides a current listing of appropriate chronic dose-response values (i.e., RfCs or

comparable values and IURs) for HAPs.(f) References for acute exposure levels are provided

below in Exhibit 12-13. Hazard identification and dose-response assessment information for

chronic exposure, presented in Appendix C, was obtained from various sources and prioritized

according to (1) conceptual consistency with EPA risk assessment guidelines, and (2) level of

review received. The prioritization process was aimed at incorporating into our assessments the

best available science with respect to dose-response information. The sources listed below were

used, and provide this information for chemicals beyond the 188 Clean Air Act hazardous air

pollutants listed in Appendix C.

• U.S. Environmental Protection Agency (EPA). EPA has developed dose-response

assessments for chronic exposure to many pollutants. These assessments typically specify an

RfC (to protect against effects other than cancer) and/or IUR (to estimate the probability of
April 2004 Page 12-25

contracting cancer). Background documents, particularly for the more recent files, also

contain information on physical and chemical properties, toxicokinetics, and hazard

characterization. EPA disseminates dose-response assessment information in several forms,

based on the level of review. Dose-response assessments that have achieved full intra-agency

consensus are incorporated in the Integrated Risk Information System (IRIS), which is

regularly updated and available on-line (www.epa.gov/iris). All IRIS assessments since 1996

also have undergone independent external peer review. In the past, dose-response

assessments for some substances were prepared by the EPA Office of Research and

Development, but were never submitted for EPA consensus. EPA has assembled the results

of many such assessments in the Health Effects Assessment Summary Tables (HEAST). 

Although the values in HEAST have undergone some review and have the concurrence of

individual Agency program offices, they have not had enough review to be recognized as

Agency-wide consensus information. In addition, since HEAST has not been updated since

1997, other sources described here are, for many chemicals, more reliable.

• Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part of

the US Department of Health and Human Services, develops and publishes Minimum Risk

Levels (MRLs) for many toxic substances. The MRL is defined as an estimate of daily

human exposure to a substance that is likely to be without an appreciable risk of adverse

effects (other than cancer) over a specified duration of exposure. MRLs are derived for acute

(1-14 days), intermediate (>14-364 days), and chronic (365 days and longer) exposures by

inhalation and oral routes. ATSDR describes MRLs as substance-specific estimates to be

used by health assessors to select environmental contaminants for further evaluation. MRLs

are presented with only one significant figure and are considered to be levels below which

contaminants are unlikely to pose a health threat. Exposures above an MRL do not

necessarily represent a threat, and MRLs are therefore not intended for use as predictors of

adverse health effects or for setting cleanup levels. The MRL data undergo a rigorous review

process, including internal ATSDR review, peer reviews, and public comment periods. 

Appendix C shows the ATSDR chronic MRL where no IRIS value is available, because the

MRL's concept, definition, and derivation are philosophically consistent (though not

identical) with EPA's guidelines for assessing noncancer effects. ATSDR publishes MRLs as

part of pollutant-specific toxicological profile documents, and also in regularly-updated

on-line tables.7

• California Environmental Protection Agency (CalEPA). The CalEPA Office of

Environmental Health Hazard Assessment (OEHHA) has developed dose-response

assessments for many substances, based both on carcinogenicity and health effects other than

cancer. The process for developing these assessments is similar to that used by EPA to

develop IRIS values and includes significant external scientific peer review. The non-cancer

information includes inhalation health risk guidance values expressed as chronic inhalation

reference exposure levels (RELs). CalEPA defines the REL as a concentration level at (or

below) which no health effects are anticipated, a concept that is substantially similar to

EPA’s approach to non-cancer dose-response assessment. Appendix C shows the chronic

REL (including both final and proposed values) where no IRIS RfC/RfD or ATSDR MRL

exists. CalEPA’s quantitative dose-response information on carcinogenicity by inhalation

exposure is expressed in terms of the IUR, defined similarly to EPA’s IUR. Appendix C

shows specific CalEPA UREs where no IRIS values exist. CalEPA’s dose response

assessments for carcinogens and noncarcinogens are available on-line.8
April 2004 Page 12-26

• International Agency for Research on Cancer (IARC). The IARC, a branch of the World

Health Organization, coordinates and conducts research on the causes of human cancer and

develops scientific strategies for cancer control. The IARC sponsors both epidemiological

and laboratory research, and disseminates scientific information through meetings,

publications, courses and fellowships. As part of its mission, the IARC assembles evidence

that substances cause cancer in humans and issues judgments on the strength of evidence.

IARC’s categories are Group 1 (carcinogenic in humans), Group 2A (probably carcinogenic),

Group 2B (possibly carcinogenic), Group 3 (not classifiable), and Group 4 (probably not

carcinogenic). The categorization scheme may be applied to either single chemicals or

mixtures; however, IARC does not develop quantitative dose-response metrics such as

UREs,. IARC’s categories for substances are included in Appendix C to support or augment

EPA’s weight-of evidence (WOE) determinations, which do not cover all substances and in

some cases may be out-of-date. The list of IARC evaluations to date is available on-line

(http://193.51.164.11/monoeval/grlist.html).

Additionally, the EPA has compiled fact sheets for the 188 CAA hazardous air pollutants and

makes them available on the Air Toxics website (http://www.epa.gov/ttn/atw/hapindex.html). 

This collection is called the Health Effects Notebook for Hazardous Air Pollutants, and

provides for each HAP a summary of available information in the following categories: hazard

summary, physical properties, uses, sources and potential exposure, and health hazard

information. These fact sheets are useful for describing hazards associated with the 188 HAPs.

12.6 Acute Exposure Reference Values

Many air pollutants can cause adverse health effects after acute or short-term exposures lasting

from a few minutes to several days. For some pollutants, acute exposures may be of greater

concern than chronic exposures. The severity of effects from acute exposures may vary widely. 

Agency-wide guidance on how to assess toxic effects from short-term exposures is currently

being developed. This guidance for Acute Reference Exposure (ARE) levels is intended to assist

acute risk assessment activities. A variety of other short-term, acute exposure limits are also

described in Exhibit 12-12.9 Appendix C provides a current listing of acute dose-response values

for HAPs.

Methods for dose-response assessment of acute exposures are usually similar to the approach for

chronic exposure, with their derivation involving the identification of a “critical effect,”

determination of a NOAEL or comparable value for that effect, and application of uncertainty

factors (e.g., animal to human population). However, the process by which most acute inhalation

dose-response assessment values are derived differs from the chronic RfC methodology in two

important ways. First, “acute” may connote exposure times varying from a few minutes to two

weeks. The time frame for the value is critical, because the safe dose (or the dose that produces

some defined effect) may vary substantially with the length of exposure. Second, some acute

dose-response assessments include more than one level of severity. A typical assessment may

have values for level 1 (at which only mild, transient effects may occur), level 2 (above which

irreversible or other serious effects may occur), and level 3 (above which life-threatening effects

may occur). Therefore, many acute assessments present dose-response assessment values as a

matrix, with one dimension being length of exposure and the other a severity-of-effect category.
April 2004 Page 12-27

Exhibit 12-13. Examples of Available Short-Term, Acute Exposure Levels

Acronym Full Name Group or

Agency

Purpose/Definition Source/Website

AEGL Acute

Exposure

Guideline

Level

National Research

Council (NRC)

National

Advisory

Committee

(NAC)

The AEGLs represent short-term threshold or ceiling exposure values

intended for the protection of the general public, including susceptible or

sensitive individuals, but not hypersusceptible or hypersensitive individuals.

The AEGLs represent biological reference values for this defined human

population and consist of three biological endpoints for four different single

emergency (accidental) exposure periods (30 minutes, l hour, 4 hours, and 8

hours). In some instances, AEGLs also are developed for 5 or 10 minutes. 

The biological endpoints are defined as follows:

• AEGL-1 is the airborne concentration (expressed as parts per millions

[ppm] or milligrams [mg]/meters [m]3) of a substance at or above which it

is predicted that the general population, including “susceptible” but

excluding “hypersusceptible” individuals, could experience notable

discomfort. Airborne concentrations below AEGL-1 represent exposure

levels that could produce mild odor, taste, or other sensory irritations.

• AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a

substance at or above which it is predicted that the general population,

including “susceptible” but excluding “hypersusceptible” individuals,

could experience irreversible or other serious, long-lasting effects or

impaired ability to escape. Airborne concentrations below the AEGL-2

but at or above AEGL-1 represent exposure levels that may cause notable

discomfort.

• AEGL-3 is the airborne concentration (expressed as ppm or mg/m3) of a

substance at or above which it is predicted that the general population,

including “susceptible” but excluding “hypersusceptible” individuals,

could experience life-threatening effects or death. Airborne

concentrations below AEGL-3 but at or above AEGL-2 represent

exposure levels that may cause irreversible or other serious, long-lasting

effects or impaired ability to escape.

http://search.nap.edu/books/

0309072948/html/

ARE Acute

Reference

Exposure

U.S.

Environmental

Protection

Agency

The ARE is an informed estimate of the highest inhalation exposure

(concentration and duration) that is not likely to cause adverse effects in a

human population, including sensitive subgroups, exposed to that scenario,

even on an intermittent basis.10 For these purposes, acute exposures are

single continuous exposures lasting 24 hours or less; AREs may be derived

for any duration of interest within that period. “Intermittent” implies

sufficient time between exposures such that one exposure has no effect on the

health outcome produced by the next exposure. EPA is in the process of

finalizing the methodology for development of AREs.
Exhibit 12-13. Examples of Available Short-Term, Acute Exposure Levels

Acronym Full Name Group or

Agency

Purpose/Definition Source/Website

April 2004 Page 12-28

BEI Biological

Exposure

Indices

American

Conference of

Governmental

Industrial

Hygenists

BEIs®

 are health-based values for use by industrial hygienists in making

decisions regarding safe levels of exposure to various chemical and physical

agents found in the workplace.

http://www.acgih.org/TLV/

CEEL Community

Emergency

Exposure

Level

National Research

Council (NRC)

National

Advisory

Committee

(NAC)

CEELs are ceiling exposure values for the public applicable to emergency

exposures of foreseeable magnitude and duration, usually not exceeding 1

hour. Three CEELs were established:

• CEEL-1: Concentration above which discomfort, for example eye and

nose irritation or headaches, becomes increasingly common;

• CEEL-2: Concentration above which disability, for example, severe eye

or respiratory irritation, becomes increasingly common;

• CEEL-3: Concentration above which death or life-threatening effects, for

example, pulmonary edema, cardiac failure, or cancer, become

increasingly common.

Guidelines for Developing

Community Emergency

Exposure Levels for

Hazardous Substances

(NRC, 1993) 

EEGL Emergency

Exposure

Guidance

Level

NAS Committee

on Toxicology

Exposure levels judged to be acceptable for military personnel performing

tasks during emergency situations. Not considered safe exposure level for

routine or normal operations.

ERPG Emergency

Response

Planning

Guideline

American

Industrial

Hygiene

Association’s

(AIHA)

Emergency

Response

Planning

Committee

These guidelines are intended for application by persons trained in

emergency response planning.

 ERG-1: The maximum concentration in air below which it is believed nearly

all individuals could be exposed for up to one hour without experiencing

other than mild transient adverse health effects or perceiving a clearly defined

objectionable odor.

ERG-2: The maximum concentration in air below which it is believed nearly

all individuals could be exposed for up to one hour without experiencing or

developing irreversible or other serious health effects or symptoms that could

impair their abilities to take protective action.

ERG-3: The maximum concentration in air below which it is believed nearly

all individuals could be exposed for up to one hour without experiencing or

developing life-threatening health effects. 

http://www.bnl.gov/scapa/er

pgpref.htm

http://www.bnl.gov/scapa/sc

apawl.htm

IDLH Immediately

Dangerous to

Life or Health

Concentration

National Institute

for Occupational

Safety and Health

(NIOSH)

An immediately dangerous to life or health condition is one “that poses a

threat of exposure to airborne contaminants when that exposure is likely to

cause death or immediate or delayed permanent adverse health effects or

prevent escape from such an environment. The purpose of establishing an

IDLH is to ensure that the worker can escape from a given contaminated

environment in the event of failure of the respiratory protection equipment.

NIOSH Respirator Decision

Logic [NIOSH 1987], 

http://www.cdc.gov/niosh/id

lh/intridl4.html
Exhibit 12-13. Examples of Available Short-Term, Acute Exposure Levels

Acronym Full Name Group or

Agency

Purpose/Definition Source/Website

April 2004 Page 12-29

LOC Level of

Concern

U.S.

Environmental

Protection

Agency, Federal

Emergency

Management

Agency, U.S.

Department of

Transportation

Defined by the Technical Guidance for Hazards Analysis (a guide developed

to assist in planning for accidental chemical releases). As the concentration

of an extremely hazardous substances in air above which there may be

serious irreversible health effects or death as a result of a single exposure for

a relatively short period of time. In the 1987 Technical Guidance for Hazards

Analysis document, an LOC was estimated by using one-tenth of the IDLH

level published by the National Institute for Occupational Safety and Health. 

For the purposes of offsite consequence analysis performed as part of

accidental release requirements under Section 112(r) of the CAA, this value is

superceded by ERPG-2 values as available, and the Agency intends to

supercede those values with AEGL-2 values as they are developed and

adopted.

Technical Guidance for

Hazards Analysis. 

Emergency Planning for

Extremely Hazardous

Substances. (USEPA,

FEMA, USDOT, 1987). 

61 FR 31672; June 20, 1996

MRL Acute

Minimum

Risk Levels

U.S. Agency for

Toxic Substances

and Disease

Registry

(ATSDR)

The MRL is an estimate of human exposure to a substance that is likely to be

without an appreciable risk of adverse effects (other than cancer) over a

specified duration of exposure, and can be derived for acute exposures by the

inhalation and oral routes. Unlike the one-hour focus of most of the other

values listed here, acute MRLs are derived for exposures of 1 to 14 days

duration.

http://www.atsdr.cdc.gov/mr

ls.html

REL Reference

Exposure

Level

California EPA

Office of

Environmental

Health Hazard

Assessment

(OEHHA)

The acute REL is an exposure that is not likely to cause adverse effects in a

human population, including sensitive sub-populations, exposed to that

concentration for one hour on an intermittent basis. RELs are based on the

most sensitive, relevant, adverse health effect reported in the medical and

toxicological literature. RELs are designed to protect the most sensitive

individuals in the population by the inclusion of margins of safety. Since

margins of safety are incorporated to address data gaps and uncertainties,

exceeding the REL does not automatically indicate an adverse health impact

OEHHA has defined the lowest available acute severity level as the REL.

http://www.oehha.ca.gov/air/

pdf/acuterel.pdf

SPEGL Short-term

Public

Emergency

Exposure

Guidance

Level

National

Academy of

Sciences (NAS)

Committee on

Toxicology

The NAS develops short-term public emergency exposure guidance levels

(SPEGLs) to apply to the exposures of the general public to contaminants

during airborne chemical releases; SPEGLs are generally set at a level of 0.1

to 0.5 times the EEGL and are measured as 60 minute or 8 hour exposure

time frames.

Criteria and Methods for

Preparing Emergency

Exposure Guidance Level

(EEGL), Short-Term Public

Emergency Guidance Level

(SPEGL), and Continuous

Exposure Guidance Level

(CEGL) Documents. 1986.

National Academy Press,

National Academy of

Sciences ,Washington, D.C.
Exhibit 12-13. Examples of Available Short-Term, Acute Exposure Levels

Acronym Full Name Group or

Agency

Purpose/Definition Source/Website

April 2004 Page 12-30

STEL Short-Term

Exposure

Limit

American

Conference of

Governmental

Industrial

Hygenists

(ACGIH)

STELs are time weighted average (TWA) guidelines for the control of short

term exposure in the workplace. These are important supplements to the

eight-hour TWA exposure standards which are more concerned with the total

intake over long periods of time. Generally, STELs are established to

minimize the risk of the occurrence in nearly all workers of: intolerable

irritation; chronic or irreversible tissue change; and narcosis to an extent that

could precipitate industrial accidents, provided the eight hour TWA exposure

standards are not exceeded. STELs are recommended for those substances

only when there is evidence either from human or animal studies that adverse

health effects can be caused by high short term exposure. STELs are

expressed as airborne concentrations of substances, averaged over a period of

15 minutes.
April 2004 Page 12-31

Route to route extrapolations should only

be done by qualified toxicologists.

12.7 Evaluating Chemicals Lacking Health Reference Values

12.7.1 Use of Available Data Sources

If EPA-derived IRIS assessments are available for the chemicals being examined, these values

should generally be used in the risk assessment. Use of IRIS or other EPA-derived doseresponse values prevents duplication of effort in toxicity assessment and ensures consistency in

the dose-response values among risk assessments. If EPA-derived dose-response values are not

available, the other sources described in Section 12.6 should be given next priority. Use of these

sources in a hierarchical manner has been implemented in tables developed for the 188 hazardous

air pollutants (see Appendix C and http://www.epa.gov/ttn/atw/toxsource/table1.pdf). The

Toxicology Excellence for Risk Assessment (TERA) maintains a database of international doseresponse values (see www.TERA.org/iter).

If those sources also lack inhalation dose-response values, then route-to-route extrapolation

(discussed below) may be considered. This approach, however, may be quite detailed, and

requires assistance from a professional toxicologist. If all sources and approaches have been

researched, and no dose-response value is available, the assessor should describe the effects of

the chemical qualitatively and discuss the implications of the absence of the chemical from the

risk estimate in the uncertainty section of the risk assessment.

12.7.2 Route-to-Route Extrapolation

For cases in which appropriate dose-response values are not available for the route of exposure

being considered, but are available for another route, it may be possible to use route-to-route

extrapolation. Route-to-route extrapolation is recommended only from oral to inhaled exposure

and only for carcinogens. The ability to perform quantitative route-to-route extrapolation is

critically dependent on the amount and type of data available. Regardless of the toxic endpoint

being considered, a minimum of information is required to construct plausible dosimetry for the

routes of interest. This information includes both the nature of the toxic effect and a description

of the relationship between exposure and the toxic effect.

Data from other routes of exposure may be useful

to derive an RfC (for carcinogens only; discussed

below) only when respiratory tract effects and/or

“first pass” effects can be ruled out. First pass

effects are cases where metabolism takes place in the portal-of-entry tissues, prior to entry into

the systemic circulation. The respiratory tract can exhibit a first-pass effect after inhalation. 

Unless the first-pass effect and dosimetry are adequately understood, there can be substantial

error introduced in route-to-route extrapolation that does not account for these considerations.

Oral toxicity data should not be used for route-to-route extrapolation in the following cases

(unless these effects can be accounted for in a PBPK model):

• When groups of chemicals have different toxicity by the two different routes (e.g., metals,

irritants, and sensitizers);

• When a first-pass effect by the respiratory tract is expected;
g

CalEPA has developed IURs based on RPFs for several additional PAHs that have been classified as

probably or possibly human carcinogens (e.g., IARC).

April 2004 Page 12-32

• When a first-pass effect by the liver is expected;

• When a respiratory tract effect is established, but dosimetry comparison cannot be clearly

established between the two routes;

• When the respiratory tract is not adequately studied in the oral studies; and

• When short-term inhalation studies, dermal irritation, in vitro studies, or characteristics of the

chemical indicate potential for portal-of-entry effects at the respiratory tract, but studies

themselves are not adequate for an RfC development.

The actual impact of exposure by different routes can only be estimated by taking account of

factors that influence absorption at the portal of entry, such as (1) physicochemical characteristics

of the chemical; (2) exposure factors; and (3) physiologic parameters. The preferred method for

performing route-to-route extrapolation involves the development of a PBPK model that

describes the disposition of the chemical for the routes of interest. As previously discussed,

PBPK models account for fundamental physiologic and biochemical parameters and processes

such as blood flow, ventilatory parameters, metabolic capacities, and renal clearance, tailored by

the physicochemical and biochemical properties.

If appropriate toxicity information is not available, a qualitative rather than quantitative

evaluation of the chemical is recommended. The implications of the absence of the chemical

from the risk estimate should be discussed in the uncertainty section.

12.8 Dose-Response Assessment for Mixtures

The recommended approach for assessing risks from exposure to a mixture of pollutants (e.g.,

coke oven emissions, diesel exhaust, etc.) is to utilize a dose-response assessment developed for

that mixture or a mixture judged similar.11 12 Where such an assessment is not available, a

component-by-component approach may be employed. There are several commonly used

approaches. Selection among the approaches involves consideration of the similarity of the

mixture components with regard to their toxicological activity. There are a few groups of

toxicologically similar chemicals for which the Agency recommends the use of relative potency

factors (RPFs) or toxicity equivalence factors (TEFs). These factors have been developed by

EPA and other organizations for two classes of compounds: PAHs and dioxins/furans. The

World Health Organization (WHO) has developed TEFs for polychlorinated biphenyls (PCBs) as

an extension of the factors for dioxins/furans (see Exhibit 12-14).

• Polycyclic Aromatic Hydrocarbons (PAHs). EPA has not developed IURs or CSFs for

carcinogenic PAHs other than benzo(a)pyrene. EPA recommends use of a RPF based on the

potency of each compound relative to that of benzo(a)pyrene.13 Although several references

may be found in the literature with proposed RPFs for PAHs, EPA recommends the

following RPF values for seven PAHs, which are classified as B2, probable human

carcinogens:(g)
April 2004 Page 12-33

PAH RPF

Benzo(a)pyrene

Benzo(a)anthracene

Benzo(b)fluoranthene

Benzo(k)fluoranthene

Chrysene

Dibenz(a,h)anthracene

Indeno(1,2,3-c,d)pyrene

1.0

0.1

0.1

0.01

0.001

1.0

0.1

Thus, for these seven PAHs, the IUR for benzo(a)pyrene is multiplied by the applicable RPF

to derive the IUR.

• Dioxins, Furans, and PCBs. For carcinogenic dioxins and furans, the TEF approach has an

underlying assumption of additivity across mixture components. EPA currently recommends

TEFs for specific congeners, rather than isomeric groups (see Exhibit 12-13). TEFs were

determined by inspection of the available congener-specific data and an assignment of an

“order of magnitude” estimate of relative toxicity when compared to 2,3,7,8-TCDD. The

cancer potency of certain dioxin and furan congeners is estimated relative to 2,3,7,8-TCDD

based on other toxicity information that is available for the congeners. Scientific judgment

and expert opinion formed the basis for these TEF values. External review of the toxicity

and pharmacokinetic data utilized in setting these TEF values supported the basic approach

as a “reasonable estimate” of the relative toxicity of polychlorinated dibenzo-dioxins

(PCDDs) and polychlorinated dibenzo-furans (PCDFs).14 TEF values developed by scientific

groups over the past 15 years are provided in Exhibit 12-13. The most recent consensus of

the scientific community (including representation by EPA scientists) is represented by the

WHO 1997 values.

TEFs based on the relative cancer potencies are used to adjust the exposure concentrations of

mixture components, which are subsequently summed into a single exposure concentration

for the mixture. That exposure concentration based on TEFs is then used, along with the

2,3,7,8-TCDD IUR or noncancer reference value, to estimate cancer risks or other health

hazards for the mixture.
April 2004 Page 12-34

Exhibit 12-14. Toxicity Equivalence Factors for Dioxins, Furans and PCBs

Congener EPA

(1987)15

NATO

(1989)16

WHO

(1994)17

WHO

(1997)18

TCDDs

2,3,7,8-TCDD

1,2,3,7,8-PeCDD

1,2,3,4,5,8-HxCDD

1,2,3,7,8,9-HxCDD

1,2,3,6,7,8-HxCDD

1,2,3,4,6,7,8-HpCDD

1,2,3,4,6,7,8,9-OCDD

1

0.5

0.04

0.04

0.04

0.001

0

1

0.5

0.1

0.1

0.1

0.1

0.001

1

1

0.1

0.1

0.1

0.01

0.0001

TCDFs

2,3,7,8-TCDF

1,2,3,7,8-PeCDF

2,3,4,7,8-HxCDF

1,2,3,4,7,8-HxCDF

1,2,3,7,8,9-HxCDF

1,2,3,6,7,8-HxCDF

2,3,4,6,7,8-HxCDF

1,2,3,4,6,7,8-HpCDF

1,2,3,4,7,8,9-HpCDF

1,2,3,4,6,7,8,9-OCDF

0.1

0.1

0.1

0.01

0.01

0.01

0.01

0.001

0.001

0

0.1

0.05

0.5

0.1

0.1

0.1

0.1

0.01

0.01

0.001

0.1

0.05

0.5

0.1

0.1

0.1

0.1

0.01

0.01

0.0001

PCBs

IUPAC # Structure

77 3,3',4,4'-TCB

81 3,4,4',5-TCB

105 2,3,3',4,4'-PeCB

114 2,3,4,4',5-PeCB

118 2,3',4,4',5-PeCB

123 2',3,4,4',5-PeCB

126 3,3',4,4',5-PeCB

156 2,3,3'4,4',5-HxCB

157 2,3,3',4,4',5'-HxCB

167 2,3',4,4',5,5'-HxCB

169 3,3'4,4'5,5'-HxCB

170 2,2',3,3',4,4',5-HpCB

180 2,2',3,4,4',5,5'-HpCB

189 2,3,3',4,4',5,5'-HpCB

0.0005

– 

0.0001

0.0005

0.0001

0.0001

0.1

0.0005

0.0005

0.00001

0.01

0.0001

0.00001

0.0001

0.0001

0.0001

0.0001

0.0005

0.0001

0.0001

0.1

0.0005

0.0005

0.00001

0.01

– 

– 

0.0001

Source: EPA’s dioxin reassessment activities19
April 2004 Page 12-35

1. U.S. Environmental Protection Agency. Air Toxics Website: Summary. Updated November

28, 2003. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html. (Last accessed

March 2004).

2. U.S. Environmental Protection Agency. 2002. A Review of the Reference Dose and

Reference Concentration Process. Risk Assessment Forum, Washington, D.C., 2002.

EPA/630/P-02/002F. Available at:

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=55365

3. U.S. Environmental Protection Agency. 2002. Office of Research and Development,

National Center for Environmental Assessment. Risk Assessment Guidelines. Updated June

30, 2002. Available at: http://cfpub.epa.gov/ncea/raf/rafguid.htm. (Last accessed March

2004).

4. U.S. Environmental Protection Agency. 1999. Guidelines for Carcinogen Risk Assessment. 

 Review Draft. Risk Assessment Forum, Washington, D.C., July 1999. NCEA-F-0644. 

 Available at: http://cfpub.epa.gov/ncea/raf/rafguid.htm

U.S. Environmental Protection Agency. 1986. Guidelines for Carcinogen Risk Assessment. 

Federal Register 51(185):33992-43003, September 24, 1986. Available at:

http://cfpub.epa.gov/ncea/raf/rafguid.htm

U.S. Environmental Protection Agency. 2003. Draft Final Guidelines for Carcinogen Risk

Assessment (External Review Draft), Risk Assessment Forum, Washington, D.C.

NCEA-F-0644A. Available at http://cfpub.epa.gov/ncea/raf/rafguid.htm

5. U.S. Environmental Protection Agency. 1994. Methods for Derivation of Inhalation

Reference Concentrations and Application of Inhalation Dosimetry. Washington, D.C.

U.S. Environmental Protection Agency. 2002. A Review of the Reference Dose and

Reference Concentration Process. Risk Assessment Forum, Washington, D.C. December

2002. EPA/630/P-02/002F. Available at:

http://www.epa.gov/iris/RFD_FINAL%5B1%5D.pdf (Last accessed April 2004).

6. U.S. Environmental Protection Agency. 2002. Benchmark Dose Software (BMDS). Office

of Research and Development. Updated June 30, 2002. Available at

http://cfpub.epa.gov/ncea/cfm/bmds.cfm. (Last accessed March 2004). 

U.S. Environmental Protection Agency. 1996. Benchmark Dose Technical Guidance

Document. Risk Assessment Forum, Washington, D.C., August 1996. EPA/600/P-96/002A.

Available at:

http://www.epa.gov/cgi-bin/claritgw?op-Display&document=clserv:ORD:0603;&rank=4&te

mplate=epa.

7. Agency for Toxic Substances and Disease Registry (ATSDR). 2004. Toxicological Profile

Information Sheet. Updated March 19, 2004. Available at:

http://www.atsdr.cdc.gov/toxpro2.html. (Last accessed March 2004). 

References
April 2004 Page 12-36

Agency for Toxic Substances and Disease Registry (ATSDR). 2004. Minimal Risk Levels

(MRLs) for Hazardous Substances. Updated March 22, 2004. Available at:

http://www.atsdr.cdc.gov/mrls.html. (Last accessed March 2004).

8. State of California Office of Environmental Health Hazard Assessment. 2003. Air - Hot

Spots Guidelines. Available at: http://www.oehha.ca.gov/air/hot_spots/index.html. (Last

accessed March 2004). 

9. Office of Response and Restoration, National Ocean Service, National Oceanic and

Atmospheric Administration. 2002. Occupational Exposure Limits. Updated March 26,

2002. Available at: http://response.restoration.noaa.gov/cameo/locs/worklims.html. (Last

accessed March 2004).

10. The ARE is described in http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=60983.

U.S. Environmental Protection Agency. 1998. An SAB Report: Development of the Acute

Reference Exposure. Science Advisory Board, Washington, D.C., EPA-SAB-EHC-99-005.

November 1998. Available at: http://www.epa.gov/sab/pdf/ehc9905.pdf.

11. U.S. Environmental Protection Agency. 1986. Guidelines for the Health Risk Assessment of

Chemical Mixtures. Risk Assessment Forum, Washington, D.C. EPA/630/R-98/002. 

Federal Register 51(185):34014-34025, September 24, 1986.

12. U.S. Environmental Protection Agency. 2000. Supplementary Guidance for Conducting

Health Risk Assessment of Chemical Mixtures. Risk Assessment Forum, Washington, DC,

EPA/630/R-00/002. Available at:

http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

13. U.S. Environmental Protection Agency. 1993. Provisional Guidance for Quantitative Risk

Assessment of Polycyclic Aromatic Hydrocarbons. EPA/600/R-93/089.

14. Olson, J.R., Bellin, J.S., Barnes, D.G., et al. 1989. Reexamination of data used for

establishing toxicity equivalency factors (TEFs) for chlorinated dibenzo-p-dioxins and

dibenzofurans (CDDs 45 and CDFs). Chemosphere 18(1-6):371-381.

15. U.S. Environmental Protection Agency. 1987. Interim Procedures for Estimating Risks

Associated with Exposures to Mixtures of Chlorinated dibenzo-p-dioxins and -dibenzofurans

(CDDs and CDFs). EPA/625/3-87/012.

16. NATO/CCMS. 1989. Scientific Basis for the Development of International Toxicity

Equivalent Factor (I-TEF) Method of Risk Assessment for Complex Mixtures of Dioxins and

Related Compounds. Report No. 178, December, 1998.

17. Ahlborg, U., Becking, G.C., Birnbaum, L.S., et al. 1994. Toxicity equivalence factors for

dioxin-like PCBs: Report on a WHO-ECEH and IPCS consultation, December 1993. 

Chemosphere 28(6):1049-1067.

18. van Leeuwen, F.X.R. 1997. Derivation of toxic equivalency factors (TEFs) for dioxin-like

compounds in humans and wildlife. Organohalogen Compounds 34:237.
April 2004 Page 12-37

19. For more information on EPA’s dioxin reassessment activities, see: http://cfpub.epa.gov/

ncea/cfm/recordisplay.cfm?deid=55264&CFID=12120688&CFTOKEN=95507561,
Chapter 13 Inhalation Risk Characterization

Table of Contents

13.1 Introduction ............ ............ ............ ............ ............ ...... 1

13.2 Quantification of Cancer Risk and Noncancer Hazard ............ ............ ......... 4

13.2.1 Cancer Risk Estimates ............ ............ ............ ............ ... 5

13.2.1.1 Characterization of Individual Pollutant Risk ............ ........ 5

13.2.1.2 Characterization of Cancer Risk from Exposure to Multiple Pollutants

............ ............ ............ ............ ........ 6

13.2.2 Noncancer Hazard Estimates ............ ............ ............ .......... 8

13.2.2.1 Characterizing Individual Pollutant Hazard for Chronic Exposures ... 8

13.2.2.2 Characterizing Multiple Pollutant Hazard for Chronic Exposures .... 9

13.2.2.3 Characterizing Hazard for Acute Exposures ............ ........ 11

13.2.3 Quantifying Risk From Background Sources ............ ............ ........ 12

13.3 Interpretation and Presentation of Inhalation Cancer Risks and Noncancer Hazards ........ 13

13.3.1 Presenting Risk and Hazard Estimates ............ ............ ............ . 17

13.3.2 Exposure Estimates and Assumptions ............ ............ ............ . . 17

13.3.3 Toxicity Estimates and Assumptions ............ ............ ............ ... 18

13.3.4 Assessment and Presentation of Uncertainty in Risk Characterization ............ . 19

13.3.4.1 Practical Approaches to Uncertainty Assessment ............ .... 20

13.3.4.2 Presentation of Uncertainty Assessment ............ ........... 23

13.3.5 Additional Information ............ ............ ............ ............ . 23

References ............ ............ ............ ............ ............ ............ 25

April 2004 Page 13-1

Risk = ƒ (metric of exposure, metric of toxicity)

Risk characterization combines the information from the

exposure assessment and the toxicity assessment to

provide a quantitative estimate of potential cancer risk

and/or hazard for other adverse effects, along with a

statement of confidence about the data and methods used

Information should be presented on the range of

exposures derived from exposure scenarios and

on the use of multiple risk descriptors (e.g.,

central tendency, high end of individual risk,

population risk, important sub-populations, if

known) consistent with terminology in the

Guidance on Risk Characterization, Agency risk

assessment guidelines, and program-specific

guidance.

EPA Policy for Risk Characterization(2)

13.1 Introduction

The last component of risk assessment, risk characterization, integrates the information from the

exposure assessment (Chapter 11) and toxicity assessment (Chapter 12), using a combination of

qualitative and quantitative information and including a discussion of uncertainty and

variability.(1) The risk characterization and its components should be presented so that the details

of the analysis are transparent, clear, consistent with EPA guidance and policy, and will generally

support the conclusion that the analysis is reasonable for its intended purpose. Risk assessors

aim for the risk summary and risk conclusions to be complete, informative, and useful for

decision-makers. One way of accomplishing this is to make sure that major uncertainties

associated with determining the nature and extent of the risk are identified and discussed. 

EPA has developed several key

policies about how to characterize

and present risk assessment

information. EPA’s Policy for Risk

Characterization(2) specifies that a

risk characterization “be prepared in a

manner that is clear, transparent,

reasonable, and consistent with other

risk characterizations of similar scope

prepared across programs in the Agency.” The purpose of the memorandum was to ensure that

risk management decisions are well-supported and well-understood, both inside the EPA and

outside the Agency. The confidence in the data, science policy judgments, and the uncertainties

in the database should be clearly communicated. The 1995 Guidance for Risk Characterization

has been updated by the Handbook for Risk Characterization, which provides more background

and approaches to presenting the risk characterization results.(3) Risk assessors may want to

become familiar with the information provided in both the policy and handbook before beginning

a risk assessment.

A 1992 memorandum from EPA’s Office of the Administrator provides guidance on describing

risk assessment results.(4) This memorandum focuses on communicating the full range of

information used in developing the assessment, rather than providing only point estimates of risk

to the public. The risk characterization guidance and handbook(3) recommends presenting a full

and complete picture of risk that includes: a statement of confidence about data and methods

used to develop the assessment; greater consistency and comparability in risk assessment across

EPA programs; and statement of the level of

scientific judgment inherent in risk

management decisions. Information should

be presented on the range of exposures

derived from exposure scenarios using

multiple risk descriptors (e.g., centraltendency, high-end of individual risk,

population risk, important sub-populations, if

known). For risk management decisions, the

risk estimates are compared to legally

mandated or other risk objectives (see Part V

of this Reference Manual).
April 2004 Page 13-2

Incidence is defined by the National Cancer Institute

as “The number of new cases of a disease diagnosed

each year.” For example, a State’s cancer registry

might report that the statewide 5-year average

incidence of lung cancer (i.e., the average number of

actual people that were diagnosed by a doctor over

the 5 year period) is 700 new cases per 100,000

people (5-year averages are often used to provide an

estimate that is more stable over time). In

comparison, air toxics risk assessments provide only

a theoretical estimate of the likelihood that an

individual in the exposed population will contract

cancer as a result of exposure over a period of time

(e.g., 50 or 100 years of a facility lifetime).

Steps in an Inhalation Risk Characterization

1. Organize outputs of inhalation exposure and toxicity assessments.

2. Derive inhalation cancer risk estimates and noncancer hazard quotients for each pollutant in each

pathway for each type of receptor being studied.

3. Derive cumulative inhalation cancer risk estimates and noncancer hazards for each receptor for all

chemicals in a pathway and then across pathways.

4. Identify key features and assumptions of exposure and toxicity assessments.

5. Assess and characterize key uncertainties and variability associated with the assessment.

6. Consider additional relevant information (e.g., related studies).

Risk characterization should include a risk summary and risk conclusions that are complete,

informative, and useful for decision-makers, and which clearly identify and discuss the major

uncertainties associated with determining the nature and extent of risk. See references 2 and 3 at the

end of this chapter for more information.

Risks are often evaluated initially for

individuals within the potentially

exposed population. Population risks for

the exposed population may also be

estimated, which may be useful in

estimating potential economic costs and

benefits from risk reduction. Sensitive

subpopulations should also be considered,

when possible. Estimates of incidence

also are possible (see Exhibit 13-1).

The potential risks calculated for specific

inhalation exposures are typically

incremental risks; that is, they are

potential risks that are in addition to those

risks already faced by the population under study for reasons other than exposure to air toxics

(e.g., hereditary, lifestyle risks such as smoking). The risk estimates are used to answer questions

concerning the general risks posed to the exposed population, the risk levels of various groups

within the population, and the potential range of risks across the population (e.g., centraltendency (e.g., average) or high-end (e.g., maximum) risk for individuals within the populations

of interest).

Estimated cancer risks and noncancer hazards are generally developed for each chemical to

which people are exposed in the study area and each exposure pathway through which exposure

can occur. The results are then summed in a specific way to provide total estimates of risk and

hazard. The general steps involved in risk characterization are:

• Quantify risks and hazards for each chemical through each pathway for each receptor;

• Review exposure estimates and assumptions;

• Review toxicity estimates and assumptions;

• Assess uncertainties and variability; and

• Consider additional relevant information (e.g., related studies).
April 2004 Page 13-3

Exhibit 13-1. Estimates of Risk

Individual risk. Estimates of cancer risk are usually expressed as a statistical probability represented

in scientific notation as a negative exponent of 10. For example, an additional risk of contracting

cancer of one chance in 10,000 (or one additional person in 10,000) is written as 1×10-4 (or 1E-04).

This means that for every 10,000 people that are exposed, in the way that we have presumed, one of

those people may develop cancer over their lifetime. Likewise, a risk of one person in one million is

written 1×10-6 (or 1E-06) and a risk or one in one hundred thousand is written 1×10-5 (or 1E-05).

Population Risk. Estimates of cancer risk can be expressed as the number of people in the population

who may have the same risk level (e.g., 1,000,000 people in the exposed population under study may

have a risk of 1×10-6, 2,495 may have a risk of 1×10-5, and 300 may have a risk of 1×10-4).

Incidence. Estimates of cancer risk can be expressed as the incidence of cancer cases in a population. 

For example, the estimated incidence of cancer in a population of 500,000 individuals where the

individual risk is 1×10-5 (based on a 100 year exposure scenario) is simply:

Note that since the individual cancer risk value is a lifetime value, it is divided by 70 years (average

lifetime length) prior to multiplying by the exposure period duration (100 years). It is also important

to note the assumptions in this example calculation (e.g., average population size of 500,000

individuals and individual lifetime risk value of 1×10-5 for the 100 year period). Given these

assumptions, these possible seven new cases are the expected number of cases over the total exposure

duration of 100 years. If one wanted to estimate the number of new cases per year, simply use an

exposure duration of one year. In our example,

This points out two problems with using risk estimates to derive incidence estimates. First, a fraction

of a cancer case (which often results from this exercise) is not a very helpful statistic when assessing a

potential air toxics problem. Second, people living in different areas with the same individual risks,

but with very different exposed population sizes can end up with very different incidence rates. For

example, if our population above only had 10,000 people, the incidence rate would have been

predicted to be no more than 0.1 (versus seven). While the first situation indicates a higher potential

population impact, the second situation nevertheless indicates identical individual risk predictions for

members of the population. Both metrics are informative to the risk manager, and reflect different

considerations which may have different weights in different decisions. Other ways of describing risk

to an exposed population are also possible. 
April 2004 Page 13-4

Risk versus Hazard...What’s the Difference?

Risk assessors purposefully use the term risk to mean

the statistical probability of developing cancer over a

lifetime (even if exposure only occurs over a portion

of that lifetime). Noncancer “risks,” on the other

hand, are not expressed as a statistical probability of

developing a disease. Rather they are expressed as a

simple comparison of the exposure concentration to a

reference concentration associated with the

observable adverse health effects. To help make this

distinction, the potential harm from exposure to

carcinogens is called “risk” and the potential harm

from noncarcinogens is called “hazard.”

Risk estimates in screening-level (Tier 1) analyses typically are deterministic estimates based on

point estimates of exposure and toxicity. Deterministic estimates are useful screening tools in a

tiered analysis, but need to be qualified by transparent discussions of the nature and extent of

uncertainties in the input variables and the subsequent likely impact on the ultimate risk

characterization. Deterministic analyses with appropriate uncertainty characterization can be

used to identify situations of low incremental risk and to focus on areas where additional analysis

might improve the basis for selection of a risk management action. At higher tiers of analyses,

risk assessors commonly describe exposure (and less frequently, toxicity) by probability

distributions rather than by point values and propagate these distributions through the exposure

assessment and risk characterization process. This type of probabilistic analysis, which may

address uncertainty and variability as distinct issues, will result in an estimate of risk that is a

probability distribution rather than a point value. A more detailed discussion of the assessment

and presentation of uncertainty in the risk characterization process is provided in Section 13.3.4. 

Probabilistic uncertainty analysis is discussed in Chapter 31.

13.2 Quantification of Cancer Risk and Noncancer Hazard

Quantification of risk and hazard is the

step where exposure concentrations in air

are combined with applicable inhalation

dose-response values. Predictive cancer

risk estimates are presented separately

from noncancer hazard quotients. Risks

are quantified for the pathways, receptors,

and exposure scenarios outlined in the

conceptual site model.

Information about the distribution of

exposure and risk for the population is an

important component of risk

characterization. Distributions are often

more useful than point estimates. 

However, since developing fully distributional estimates of risk is usually out of the scope of

most risk assessments, assessors can provide a sense of the range of risks by developing both

central tendency and high-end estimates.(5)

• Central tendency estimates are intended to give a characterization of risk for the typical

individual in the population. This is usually either based on the arithmetic mean risk

(average estimate) or the median risk (median estimate).

• High-end estimates are intended to estimate the risk that is expected to occur in the upper

range of the distribution (e.g., risk above about the 90th percentile of the population

distribution). For example, the maximum exposed individual (MEI) risk or maximum

individual risk (MIR) might be used to estimate high-end risks.

An evaluation of the uncertainty in the risk descriptors is an important component of the

uncertainty discussion in the assessment. Both quantitative and qualitative evaluations of

uncertainty can be useful to users of the assessment (see Section 13.3.4 and Chapter 31).
a

EPA is currently reviewing methods for assessing cancer risk for less than lifetime exposures occurring in

childhood. EPA’s Draft Document Supplemental Guidance for Assessing Cancer Susceptibility from Early-Life

Exposure to Carcinogens (http://www.epa.gov/sab/panels/sgacsrp.html) recommends a change to the current method

for strong mutagens. This document is undergoing public and Science Advisory Board review and will be completed

sometime in the future with consideration of that review. EPA’s methods for air toxics assessments will be

consistent with the final document.

April 2004 Page 13-5

13.2.1 Cancer Risk Estimates

Estimated individual cancer risk is expressed as the upper bound probability that a person may

develop cancer over the course of their lifetime as a result of the exposures under study. This

predicted risk is the incremental risk of cancer from the exposure being analyzed that is above

the risk that the individuals in the population have already (i.e., due to non-air toxics related

issues). Due to the nature of the assumptions in their derivation, inhalation unit risks (IURs) are

generally considered to be “plausible upper-bound” estimates of potency. As such, the calculated

risks are usually a conservative estimate (i.e., the true risk may be lower).

As described above, risks may be estimated for both the central tendency (average exposure) case

and for the high-end (exposure that is expected to occur in the upper range of the distribution)

case. However, for both types of estimates, the same estimate of toxicity (i.e., an IUR or

reference concentration [RfC]) is generally used to calculate the risk. In other words, while the

estimate of exposure may be allowed to vary to derive a sense of the range of exposures in a

population, the same estimate of toxicity is used to calculate risk for both average and high-end

risks. With few exceptions, toxicity values are not currently presented as a range.

Cancer risk characterization typically is performed first for individual air toxics, then is summed

over all of the air toxics to which a person may be exposed at the same time. These steps are

described in separate subsections below.

13.2.1.1 Characterization of Individual Pollutant Risk

For inhalation exposures, chronic cancer risks for individual air toxics are typically estimated by

multiplying the estimate of long-term exposure concentration (EC) by the corresponding IUR for

each pollutant to estimate the potential incremental cancer risk for an individual:

Risk = ECL × IUR (Equation 13-1)

where:

Risk = Cancer risk to an individual (expressed as an upper-bound risk of contracting

cancer over a lifetime);

ECL = Estimate of long-term inhalation exposure concentration for a specific air toxic;

and

IUR = the corresponding inhalation unit risk estimate for that air toxic.

Performing the estimate in this way provides an estimate of the probability of developing cancer

over a lifetime due to the exposure in question. Because of the way this equation is written, the

underlying presumption is that a person is exposed continuously to the ECL for their full lifetime

(usually assumed to be 70 years).(a) The ECL is an estimate of this long-term exposure even
April 2004 Page 13-6

though it is probably based on only one year’s worth of monitoring data or a modeling run that

covers only one year’s worth of time. (As noted in Chapter 11, exposure modeling can be used,

in some cases, to derive a better estimate of the amount of time people interact with

contaminated air. Nevertheless, the probability of developing cancer is still averaged out over

the full lifetime of the individual.)

Estimates of cancer risk are usually expressed as a statistical probability represented in scientific

notation as a negative exponent of 10. For example, an additional risk of contracting cancer of

one chance in 10,000 (or one additional person in 10,000) is written as 1×10-4 (or 1E-04). This

means that for every 10,000 people that are exposed, in the way that we have presumed, one of

those people may develop cancer over their lifetime. Likewise, a risk of one person in one

million is written 1×10-6 (or 1E-06) and a risk or one in one hundred thousand is written 1×10-5

(or 1E-05). 

Because IURs are typically upper-bound estimates, actual risks may be lower than predicted (see

Chapter 12), and the true value of the risk is unknown and may be as low as zero.(5) These

statistical projections of hypothetical risk are intended as screening tools for risk managers and

cannot make realistic predictions of biological effects. Such risk estimates also cannot be used to

determine whether someone who already has cancer is ill because of a past exposure. Part VI of

this volume provides an overview of the Public Health Assessment process used to evaluate

whether past exposures resulted in current illness.

Risks for cancer are generally expressed as individual risks (i.e., the risk borne by an individual

in a larger exposed population). The number of people in the population who have the same risk

level may also be provided (e.g., 1,000,000 people in the exposed population under study have a

risk of 1×10-6, 2,495 have a risk of 1×10-5, and 300 have a risk of 1×10-4). It is also possible to

calculate the number of expected cases of cancer expected over a 70-year period by multiplying

the cancer risk to an individual by the number of individuals; however, even though the

calculation might yield an estimate of incidence, low predicted cancer incidence rates (even

vanishingly small) do not mean that individuals within the population will not get cancer because

of air toxics exposures.

13.2.1.2 Characterization of Cancer Risk from Exposure to Multiple Pollutants

People may receive exposure to multiple chemicals, rather than a single chemical, at the same

time. The concurrent exposure to multiple carcinogens may occur through the same pathway or

across several pathways. With a few exceptions (e.g., coke oven emissions), cancer doseresponse values (e.g., IURs) are usually available only for individual compounds within a

mixture. 

The following equation estimates the predicted cumulative incremental individual cancer risk

from multiple substances, and assumes an additive effect from simultaneous exposures to several

carcinogens:

RiskT = Risk1 + Risk2 + .... + Riski(Equation 13-2)

where:
April 2004 Page 13-7

Example Calculation to Estimate Cancer Risk (Hypothetical)

A Tier 1 modeling analysis was performed to estimate risk to the maximum exposed individual,

assumed to reside at the point of maximum concentration for ABC Factory. Four HAPs were

potentially of concern: benzene, dichloroethyl ether, formaldehyde, and cadmium compounds. 

Cancer risk estimates were obtained for each HAP by multiplying the estimated annual average EC by

the IUR for each HAP. The resulting upper bound cancer risk estimates ranged from 2×10-6 (benzene,

formaldehyde) to 8×10-4 (dichloroethyl ether). The cancer risk estimates for each HAP were summed

to obtain an estimate of total inhalation cancer risk (9×10-4). Note that 97 percent of the estimated

total risk results from dichloroethyl ether, and that more than 99 percent results from dichloroethyl

ether and cadmium compounds. In this hypothetical example, the risk assessor would need to decide

which HAPs to carry to higher tiers by weighing the small proportion of risk posed by benzene and

formaldehyde against the fact that these risks nevertheless exceeded one in one million.

HAP EC

μg/m3

IUR

1/(μg/m3)

Cancer Risk

Estimate(a)

Percent of

Total Risk

Benzene 0.3 7.8 × 10-6 2 × 10-6 < 1%

Dichloroethyl ether 2.5 3.3 × 10-4 8 × 10-4 97 %

Formaldehyde 0.2 1.3 × 10-4 2 × 10-6 < 1 %

Cadmium compounds 0.01 1.8 × 10-3 1 × 10-5 2 %

Total 9 × 10-4

(a) Standard rules for rounding apply which will commonly lead to an answer of one significant figure

in both risk and hazard estimates. For presentation purposes, hazard quotients (and hazard indices)

and cancer risk estimates are usually reported as one significant figure.

RiskT = total cumulative individual pathway-specific cancer risk (expressed as an upperbound risk of contracting cancer over a lifetime); and

Riski = individual risk estimate for the ith substance in the inhalation pathway.

In screening-level assessments of carcinogens for which there is an assumption of a linear doseresponse, the cancer risks predicted for individual chemicals may be added to estimate

cumulative cancer risk. This approach assumes that the risks associated with individual

chemicals in the mixture are additive. In more refined assessments, the chemicals under

assessment may be evaluated to determine whether effects from multiple chemicals are

synergistic (greater than additive) or antagonistic (less than additive), although sufficient data for

this evaluation are usually lacking. In those cases where IURs are available for a chemical

mixture of concern, risk characterization can be conducted on the mixture using the same

procedures used for a single compound. When more than one pathway is involved, the pathway

specific risks are generally summed first, and then summed across pathways. This process is

described in Part III of this reference manual. Note that for carcinogens being assessed based on

the assumption of nonlinear dose-response, for which an RfC considering cancer as well as other

effects has been derived, the hazard quotient approach will be appropriate (see Section 13.2.2).
April 2004 Page 13-8

13.2.2 Noncancer Hazard Estimates

For noncancer effects (as well as carcinogens being assessed based on the assumption of

nonlinear dose-response), exposure concentrations are compared to RfCs, which are estimates

(with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to

the human population (including sensitive sub-populations) that is likely to be without an

appreciable risk of deleterious noncancer effects during a lifetime (see Chapter 12).

13.2.2.1 Characterizing Individual Pollutant Hazard for Chronic Exposures

For inhalation exposures, noncancer hazards are estimated by dividing the estimate of the chronic

inhalation EC by the RfC to yield a hazard quotient (HQ) for individual chemicals:

HQ = ECC ÷ RfC (Equation 13-3)

where:

HQ = the hazard quotient for an individual air toxic;

ECC = estimate of chronic inhalation exposure to that air toxic; and

RfC = the corresponding reference concentration for that air toxic.

In screening inhalation risk assessments, which are routinely built around a particular year’s

estimate of emissions, the exposure estimate is usually based on an assumption of continuous

long-term exposure using an annual average as the estimate of exposure concentration. A more

refined assessment (e.g., by use of an exposure model) may generate an estimate of a more

realistic exposure (e.g., by the application of an exposure model or refined emissions estimates

over the longer time period).

Based on the definition of the RfC, an HQ less than or equal to one indicates that adverse

noncancer effects are not likely to occur, and thus can be considered to have negligible hazard. 

Unlike cancer risks, however, HQs greater than one are not statistical probabilities of harm

occurring. Instead, they are a simple statement of whether (and by how much) an exposure

concentration exceeds the RfC. Moreover, the level of concern does not increase linearly or to

the same extent as HQs increase above one for different chemicals because RfCs do not generally

have equal accuracy or precision and are generally not based on the same severity of effect. 

Thus, we can only say that with exposures increasingly greater than the RfC, (i.e., HQs

increasingly greater than 1), the potential for adverse effects increases, but we do not know by

how much. An HQ of 100 does not mean that the hazard is 10 times greater than an HQ of 10. 

Also an HQ of 10 for one substance may not have the same meaning (in terms of hazard) as

another substance resulting in the same HQ.
April 2004 Page 13-9

Example Calculation to Estimate Chronic Noncancer Hazard (Hypothetical)

A Tier 1 modeling analysis was performed to estimate chronic noncancer hazard to the maximum

exposed individual, assumed to reside at the point of maximum concentration for ABC Factory. Four

HAPs were potentially of concern: benzene, dichloroethyl ether, formaldehyde, and cadmium

compounds. Noncancer hazard estimates were obtained for each HAP by dividing the estimated

Exposure Concentration (EC) by the Inhalation Reference Concentration (RfC) for each HAP (note

that the EC is expressed in units of mg/m3 for this analysis). The resulting Hazard quotient (HQ)

estimates ranged from 1×10-3 (formaldehyde) to 1 (cadmium compounds). Note that no RfC was

available for dichloroethyl ether. The HQs for each HAP were summed to obtain an estimate of the

Hazard Index (HI) of 1. Note that cadmium compounds account for 95 percent of the HI, suggesting

that the other HAPs may not need further consideration (although this determination should be made

in consideration of all relevant information, including uncertainties such as confidence in the exposure

concentration and uncertainty factors used to derive each RfC). 

HAP EC

mg/m3

RfC

(mg/m3)

HQ(b) Percent of

HI

Benzene 6 × 10-4 6 × 10-2 1 × 10-2 1 %

Dichloroethyl ether(a) 5 × 10-3--- --- ---

Formaldehyde 4 × 10-4 1 × 10-2 1 × 10-3 4 %

Cadmium compounds 2 × 10-5 2 × 10-5 1 95 %

Hazard Index (HI) 1

(a) note that the absence of an RfC value means that we cannot quantitatively assess a HAP.

(b) Standard rules for rounding apply which will commonly lead to an answer of one significant figure

in both risk and hazard estimates. For presentation purposes, hazard quotients (and hazard indices)

and cancer risk estimates are usually reported as one significant figure.

13.2.2.2 Characterizing Multiple Pollutant Hazard for Chronic Exposures

Noncancer health effects data are usually available only for individual compounds within a

mixture. In these cases, the individual HQs can be summed together to calculate a multiplepollutant hazard index (HI):

HI = HQ1 + HQ2 + ...+ HQi(Equation 13-4)

where

HI = hazard index; and

HQ = hazard quotient for the ith air toxic.

For screening-level assessments, a simple HI may first be calculated for all chemicals of concern

within the inhalation pathway (adding hazards across pathways is discussed in Part III). If the HI

is less than your decision criterion, a more refined analysis is usually not performed. Adding

HQs in this fashion is based on the assumption that even when individual pollutant levels are
April 2004 Page 13-10

Procedure for Segregation of HIs by Effect

Segregation of HIs requires identification of the

major effects of each chemical, including those seen

at higher doses than the critical effect (e.g., the

chemical may cause liver damage at an EC of 20

μg/m3 and neurotoxicity at an EC of 50 μg/m3). 

Major effect categories include neurotoxicity,

developmental toxicity, reproductive toxicity,

immunotoxicity, and adverse effects by target organ

(i.e., hepatic, renal, respiratory, cardiovascular,

gastrointestinal, hematological, musculoskeletal, and

dermal/ocular effects). 

lower than the corresponding reference levels, some pollutants may work together such that their

potential for harm is additive and the combined exposure to the group of chemicals poses greater

likelihood of harm. Some groups of chemicals can also behave antagonistically, such that

combined exposure poses less likelihood of harm, or synergistically, such that combined

exposure poses harm in greater than additive manner. Where this type of HI exceeds the

criterion of interest, a more refined analysis is warranted. 

Although the HI approach encompassing all chemicals in a mixture is commonly used for a

screening-level study, it is important to note that application of the HI equation to compounds

that may produce different effects, or that act by different toxicological mechanisms, could

overestimate the potential for effects. Consequently, it is more appropriate to calculate a separate

HI for each endpoint of concern for which mechanisms of action are known to be similar. 

Because the assumption of dose additivity is most appropriate for compounds that induce the

same effect by similar modes of action, EPA’s Guidance for Conducting Health Risk

Assessment of Chemical Mixtures and Supplementary Guidance(6) suggest subgrouping pollutantspecific HQs by toxicological similarity of the pollutants for subsequent calculations; that is, to

calculate a target-organ-specific-hazard index (TOSHI) for each subgrouping of pollutants. 

This calculation allows for a more appropriate estimate of overall hazard.

Segregation of hazard indices by effect

and mechanism of action can be complex

and time-consuming because it is

necessary to identify all the major effects

and target organism for each chemical and

then to classify the chemicals according to

target organ(s) or mechanism of action. 

This analysis is not simple and a

toxicologist with familiarity in developing

TOSHIs is best suited to perform this

function. If the segregation is not

carefully done, an underestimate of true

hazard could result.

Acute HQs are developed in the same manner as chronic HQs, with the caveat that the exposure

duration associated with the exposure concentration should match the exposure duration

embodied in the acute toxicity value. Whereas summing chronic HQs to a total hazard index is a

relatively straightforward exercise, the issues related to developing acute HI are more subtle and

complex. A toxicologist familiar with acute exposure and risk analysis should be consulted to

perform this process.
April 2004 Page 13-11

13.2.2.3 Characterizing Hazard for Acute Exposures

Risk assessors can derive estimates of acute noncancer hazard for each HAP by combining the

applicable short-term exposure concentration (EC) and acute dose-response value (AV) for the

HAP to obtain the acute Hazard Quotient (HQ) for the HAP using the following equation:

HQA = ECST ÷ AV

where:

HQA = the acute hazard quotient for an individual HAP;

ECST = estimate of short-term inhalation exposure to that HAP; and

AV = the corresponding acute dose-response value for that HAP.

Note that ambient air concentrations are calculated for an exposure duration compatible with the

acute dose-response value used.

Available acute dose-response values are more diverse than chronic values, because they were

developed for different purposes and considering different exposure durations. The most

effective characterization of acute risk often is to compare the maximum estimated hourly

concentrations with a range of acute dose-response values from sources described in Chapter 12. 

If the ambient concentration is lower than all the acute benchmarks, it is generally reasonable to

conclude that the potential for significant acute hazard is negligible. If the concentration exceeds

some benchmarks but not others, the assessment should include a discussion of the implications

for the chemical of interest, with attention to the details of both the exposure scenario and the

benchmarks included in the analysis.

Acute noncancer health effects data are usually available only for individual HAPs within a

mixture. In these cases, it may be possible to combine the individual acute HQs to calculate a

multi-pollutant acute hazard index (HI) using the following formula:

HIA = HQA1 + HQA2 + ...+ HQAi

where

HIA = acute hazard index; and

HQAi = acute hazard quotient for the ith HAP.

Although this appears similar to the process for combining chronic HQs, the summing of acute

HQs is complicated by several issues that do not pertain to chronic HQs. First, acute doseresponse values have been developed for purposes that vary more widely than chronic values. 

Some sources of acute values define exposures at which adverse effects actually occur, while

other sources develop only no-effect acute values. Second, some acute values are expressed as

concentration-time matrices, while others are expressed as single concentrations for a set

exposure duration. Third, some acute values may specifically consider multiple exposures,

whereas others consider exposure as a one-time event. Fourth, some sources of acute values are

intended to regulate workplace exposures, assuming a population of healthy workers (i.e.,

without children, seniors, or other sensitive individuals). Such occupational values may also

consider cost and feasibility, factors that EPA considers the province of the risk manager rather

than the risk assessor.
April 2004 Page 13-12

Given these differences among acute values with regard to their purposes, and the different types

of acute exposure characterization that may be performed, the acute HI analysis is most

informative when limited to acute values from the same source, the same level of effects, and the

same duration. Analyses that mix sources, effects levels, and durations are likely to be

misleading.

Risk assessors commonly evaluate acute noncancer hazard using a variety of different acute

values from different sources, and discuss the resulting hazard estimates considering the purpose

for which each of value was developed. This kind of evaluation should only be done by an

experienced toxicologist. The significance of these HQs and HIs would need to be

considered in the context of the purpose of the risk assessment and the characteristics of

the dose-response values, such as their purpose, averaging time, and health endpoints. EPA

is working to provide more comprehensive guidance on what benchmarks to rely upon and plans

to develop a relevant acute benchmark methodology.

13.2.3 Quantifying Risk From Background Sources

In some cases, it may be appropriate to quantify background concentrations of the air toxics of

concern. For example, background concentrations may be a critical element in determining the

need for further reductions of emissions from a particular source. Background concentrations are

the levels of contaminants that would be present in the absence of contaminant releases from the

source(s) under evaluation. Background concentrations may occur naturally in the environment

or originate from other human sources (e.g., an industrial area upwind from the sources of

concern). 

The general approach in risk assessments and risk management decisions has often been to

assess the incremental risk posed by emissions from a particular source or group of sources. 

Various EPA programs, however, have taken specific approaches to considering background

risks, some of which are summarized in EPA’s Residual Risk Report to Congress.

(7)

A detailed analysis of background concentrations typically would require extensive data

gathering and modeling beyond that required for the incremental risk analysis. For example,

numerous nearby (and possibly distant) air toxics sources of varying types would need to be

characterized in sufficient detail to support release and exposure modeling. The data needs for

assessment of background concentrations may differ depending on what will be done with the

data. For example, if the question is simply “what is the risk to the population in a specific

place,” then an assessment of background may be unnecessary (monitoring data in the study area

may be all that is required). On the other hand, if the question is “what is the risk and what can

we do about it,” then a knowledge of how much risk is contributed from both local and

background sources may be necessary. If the risk is unacceptably high, but most of the risk is

background in nature, there may be no appropriate risk reduction strategy (especially in regard to

local sources).

Interpreting background concentrations may be difficult for anthropogenic chemicals and for

chemicals formed through chemical reactions. For example, when trying to estimate background

formaldehyde concentrations, it is difficult to screen out the reactive precursors which change in

the study area from those that change before entering the study area. Also, if a source of nitrogen

oxides (NOx) is not present, secondary formation of formaldehyde may be slowed.
April 2004 Page 13-13

The presence of high background concentrations of anthropogenic chemicals could increase

public concerns in some situations (see Part V of this reference manual for discussion of risk

communication). On the other hand, knowledge of background risks could help place the air

risks from a particular source or source area in better perspective.

In general, the most appropriate way to evaluate the contribution of background concentrations to

the risk estimate is to simply compare the risk attributable to known or estimated (e.g., through

monitoring) background concentrations in a bar chart against the risk attributable to the source(s)

being evaluated (see Exhibit 13-3). Note that the study-specific risk estimate will be based on a

metric of total exposure (when monitoring data are available) or incremental exposure (when

modeling data are available. It generally is not appropriate to subtract background concentrations

from monitored values.

Exhibit 13-3. Example Comparison of Risk Estimates from Study-specific 

and Background Sources

In this example, the estimated risk from the specific sources being evaluated (2.8×10-5) and the

estimated risk from background sources (1.0×10-5) are compared side-by-side. This places the risk

estimates from the sources of concern in an appropriate regional context.

13.3 Interpretation and Presentation of Inhalation Cancer Risks and Noncancer Hazards

In the final part of the risk characterization, risk assessors commonly present estimates of health

risk in the context of uncertainties and limitations in the data and methodology. Exposure

estimates and assumptions, toxicity estimates and assumptions, and the assessment of uncertainty

are usually discussed. Additionally, information relevant to the public health context of the

estimated risks is presented. 

EPA’s Policy for Risk Characterization(2) describes a philosophy of transparency, clarity,

consistency, and reasonableness (TCCR), and provides detailed approaches to achieving TCCR. 

Exhibit 13-4 provides an overview of EPA’s TCCR principles.
April 2004 Page 13-14

Exhibit 13-4. Transparency, Clarity, Consistency, and Reasonableness Principles

Principle Definition Criteria for a Good Risk Characterization

Transparency Explicitness in the risk

assessment process

• Describe assessment approach, assumptions,

extrapolations, and use of models

• Describe plausible alternative assumptions

• Identify data gaps

• Distinguish science from policy

• Describe uncertainty

• Describe relative strength of assessment

Clarity The assessment itself is free

from obscure language and is

easy to understand

• Employ brevity

• Use plain English

• Avoid technical terms

• Use simple tables, graphics, and equations

Consistency The conclusions of the risk

assessment are characterized

in harmony with EPA actions

• Follow statutes

• Follow Agency guidance

• Use Agency information systems

• Place assessment in context with similar risks

• Define level of effort

• Use review by peers

Reasonableness The risk assessment is based

on sound judgment

• Use review by peers

• Use best available scientific information

• Use good judgment

• Use plausible alternatives

Source: EPA Risk Characterization Guidance(3)

The risk characterization document should allow the risk manager, and the public, to know why

risk was assessed the way it was, by clearly summarizing the available data and its analysis,

uncertainties, alternative analyses, and the choices made. A good risk characterization will state

the scope of the assessment, express results clearly, articulate major assumptions and

uncertainties, identify reasonable alternative interpretations, and separate scientific conclusions

from science policy judgments. The Policy for Risk Characterization calls for the explanation of

the choices made to be highly visible.

The goal of risk characterization is to clearly communicate the key findings and their strengths

and limitations so that decision-makers can put the risk results into context with other

information critical to evaluating risk management options (e.g., economics, social values, public

perception, policies). The risk characterization will provide a means of placing the numerical

estimates of risk and hazard in the context of what is known and what is not about the potential

exposures and should include the elements listed in Exhibit 13-5. Exhibit 13-6 provides

examples of graphical presentations of risk estimates.
April 2004 Page 13-15

Exhibit 13-5. Elements Commonly Included in the Risk Characterization Discussion

• Agreement that the key contaminants were identified

• A discussion of modeled or measured air concentrations relative to background

• The magnitude of the estimated cancer risks and noncancer hazard indices, and a description of the

types of health risks potentially present, distinguishing between known effects in humans and those

found only in experimental animals

• The level of confidence in the toxicity data used to estimate risks

• A presentation of qualitative information about the toxicity of substances not included in the

quantitative risk assessment

• Level of confidence in the exposure estimates for key exposure pathways and related exposure

parameter assumptions

• The major factors driving the risks (e.g., substances, pathways)

• The major factors reducing the certainty in the results and the significance of these uncertainties

(e.g., a change in the assumption for a certain parameter could increase/decrease the risk estimate).

• The exposed population characteristics

• A comparison with location-specific health studies, if available
April 2004 Page 13-16

Exhibit 13-6. Example Comparison of Risk Results for a Hypothetical Risk Assessment

The risk of developing cancer is plotted as shown. A risk of 1×10-4 (1 E-04) indicates a probability of

one chance or less in 10,000 of an individual developing cancer. Risks of 1×10-5 (1 E-05) and 1×10-6

(1 E-06) correspond to probabilities of one chance or less in 100,000 and one million, respectively.

Values in parentheses represent EPA’s Weight-of-Evidence classification of the agent as a potential

human carcinogen: A = human carcinogen; B2 = probable human carcinogen (with sufficient evidence

in animals and inadequate or no evidence in humans). 

The hazard index is equal to the sum of the hazard quotients (i.e., exposure concentration/RfC) for

each chemical. It is not a probability. A hazard index < 1 indicates that it is unlikely for even

sensitive populations to experience adverse health effects. Thus, hazard is negligible.
April 2004 Page 13-17

13.3.1 Presenting Risk and Hazard Estimates

Risk and hazard estimates will usually be presented both to risk managers and to the public. 

Depending on the audience, risk characterizations can present information with different amounts

of technical detail as required, although avoiding the use of technical terms generally improves

clarity. Presentations may include the assumptions the risk assessment used, as well as the

distribution of risks estimated for the assessment. Multiple point estimates and risk ranges could

be discussed in both narrative and tabular forms. The discussion of results may include items

such as:

• The range of risks estimated within specified distances from the source(s) of concern;

• An estimate of population size associated with different risk levels; and/or

• A comparison of the magnitude of the risk estimate to background risks.

Key issues and conclusions should be clearly highlighted in any summary. Exhibit 13-7

identifies several summary products that can facilitate risk communication. (See also Part V of

this Reference Manual for a description of various techniques for communicating risk.)

Exhibit 13-7. Summary Products to Facilitate Risk Communication

• Executive summary – a summary with some technical detail, for audiences with some technical

knowledge (e.g., first line managers). This executive summary may sometimes be the executive

summary of the technical risk characterization itself depending on the audience.

• Bulleted list – a list highlighting the key issues and conclusions culled from the technical risk

characterization with little or no technical detail; for audiences with little or no technical

knowledge (e.g., higher-level managers, decision makers).

• Briefing packages – written products that describe key issues and conclusions for managers,

decision makers, and other public officials.

• Fact sheets, press releases, and public relations notices – written products that describe key

issues and conclusions for non-technical audiences (e.g., affected or interested public).

• Slide shows, speeches, and talks – visual presentations (perhaps accompanied by audio

presentations) and transcripts of oral presentations of key issues and their context; for mostly

non-technical audiences.

13.3.2 Exposure Estimates and Assumptions

For each exposure pathway evaluated in the risk assessment, check that all information needed to

characterize exposure is available. For each exposure pathway evaluated, exposure estimates and

assumptions should be reviewed to assure the consistency and validity of key assumptions. 

These assumptions may include, for example, the period of exposure and the modeling

assumptions. 
April 2004 Page 13-18

The risk characterization section on exposure may summarize the following exposure

information:

• Estimated exposures (chronic, subchronic, and shorter-term, as appropriate); and

• Important exposure modeling assumptions, including:

– Chemical concentration at the exposure points; and

– Frequency and duration of exposure.

Other items that could be addressed in the risk characterization summary of the exposure

assessment include:

• The most significant sources of environmental exposure:

– Data on sources of exposure from different media (when multimedia analyses are

performed);

– Estimates of the relative contribution of different sources of exposure; and

– Identification of the most significant environmental pathways for exposure (when

multimedia analyses are performed);

• Descriptions of the populations that were assessed, including the general population, highly

exposed groups, and highly susceptible groups;

• Description of the basis for the exposure assessment, including any monitoring, modeling, or

other analyses of exposure distributions (e.g., probabilistic techniques – see Part VII of this

Reference Manual); and

• Key descriptors of exposure:

– Description and illustration of the (range of) exposures to: “average” individuals, “highend” individuals, the general population, and special subpopulations such as children and

the elderly;

– Description of how the central tendency estimate was developed, including the factors

and/or methods used in developing this estimate;

– Description of how the high-end estimate was developed;

– Description of how population estimates of risk were developed; and

– Description of how any incidence calculations were performed.

13.3.3 Toxicity Estimates and Assumptions

During the risk characterization step, the risk assessor usually reviews whether all toxicity

information needed to characterize risk is available. The risk characterization section on toxicity

often summarizes the following information:

• IURs for all carcinogenic chemicals;

• Discussion of weight of evidence and classifications for all carcinogenic chemicals;

• Type of human cancer for Class A carcinogens;

• Chronic and subchronic dose-response values and shorter-term (acute) dose-response values

(if appropriate) for all chemicals (including carcinogens and developmental toxicants);

• Critical effect associated with each dose-response value;
April 2004 Page 13-19

• Discussion of uncertainties, uncertainty factors, and modifying factors used in deriving each

dose-response value and degree of confidence in dose-response values;

• Whether the dose-response values are expressed as absorbed or administered doses (applies

primarily to ingestion exposures - See Chapter 22);

• Pharmacokinetic data that may affect the extrapolation from animals to humans for doseresponse values; and

• Uncertainties in any route-to-route extrapolation.

13.3.4 Assessment and Presentation of Uncertainty in Risk Characterization

The risk estimates used in air toxics risk assessments usually are not fully probabilistic estimates

of risk but conditional estimates given a considerable number of assumptions about exposure and

toxicity. Air toxics risk assessments make use of many different kinds of scientific concepts and

data (e.g., exposure, toxicity, epidemiology), all of which are used to characterize the expected

risk in a particular environmental context. Informed use of reliable scientific information from

many different sources is a central feature of the risk assessment process. Reliable information

may or may not be available for many aspects of a risk assessment. Scientific uncertainty is

inherent in the risk assessment process, and risk managers almost always must make decisions

using assessments that are not as definitive in all important areas as would be desirable. Risk

assessments also incorporate a variety of professional and science policy judgements (e.g., which

models to use, where to locate monitors, which toxicity studies to use as the basis of developing

dose-response values). Risk managers therefore need to understand the strengths and the

limitations of each assessment, and to communicate this information to all participants and the

public.(2) A critical part of the risk characterization process, therefore, is an evaluation of the

assumptions and uncertainties inherent in the risk assessment in order to place the risk estimates

in proper perspective.

One of the key purposes of uncertainty analysis is to provide an understanding of where the

estimate of exposure, dose, or risk is likely to fall within the range of possible values. Often this

is expressed as a subjective confidence interval within which there is a high probability that the

estimate will fall. A related analysis, termed “sensitivity analysis” or “analysis of uncertainty

importance,” is often performed to identify the relative contribution of the uncertainty in a given

parameter value (e.g., emission rate, ingestion rate) or model component to the total uncertainty

in the exposure or risk estimate.(8) Often this is used either to identify which parameter values

should be varied to provide high-end vs. central-tendency risk estimates, or to identify parameter

values where additional data collection (or modeling effort) can increase the confidence in the

resulting risk estimate.

The Presidential/Congressional Commission on Risk Assessment and Risk Management

(CRARM) recommends that risk assessors respect the objective scientific basis of risks and

procedures for making inferences in the absence of adequate data.(9) Risk assessors should

provide risk managers and other stakeholders with plausible conclusions about risk that can be

made on the basis of the available information, along with evaluations of the scientific weight of

evidence supporting those conclusions and descriptions of major sources of uncertainty and

alternative views.
April 2004 Page 13-20

Sources of Uncertainty

• Scenario uncertainty. Information to fully

define exposure or risk is missing or incomplete

• Model uncertainty. Algorithms or assumptions

used in models may not adequately represent

reality

• Parameter uncertainty. Values for model

parameters cannot be estimated precisely

• Decision-rule uncertainty. Policy and other

choices made during the risk assessment may

influence risk estimates

The risk characterization typically should address the following:

• Considering the hazard and the exposure, what is the nature and likelihood of the health risk?

• Which individuals or groups are at risk? Are some people more likely to be at risk than

others? 

• How severe are the anticipated adverse impacts or effects? 

• Are the effects reversible? 

• What scientific evidence supports the conclusions about risk? How strong is the evidence? 

• What is uncertain about the nature or magnitude of the risk? 

• What is the range of informed views about the nature and probability of the risk? 

• How confident are the risk analysts about their predictions of risk? 

• What other sources cause the same type of effects or risks?

• What contribution does the particular source make to the overall risk of this kind of effect in

the affected community? To the overall health of the community? 

• How is the risk distributed in relation to other risks to the community? 

• Does the risk have impacts besides those on health or the environment, such as social or

cultural consequences? 

• The level of detail considered in a risk assessment and included in a risk characterization

should be commensurate with the problem’s importance, expected health or environmental

impact, expected economic or social impact, urgency, and level of controversy, as well as

with the expected impact and cost of protective measures.

Risk characterizations should include sufficient information to enable:

• Risk managers to make a useful risk management decision, and

• Stakeholders to understand the importance and context of that decision.

13.3.4.1 Practical Approaches to Uncertainty Assessment

There are numerous sources of

uncertainties in air toxics risk

assessments, and each merits

consideration. The degree to which these

sources of uncertainty need to be

quantified, and the amount of uncertainty

that is acceptable, varies considerably on

a study-specific basis. For a screeninglevel (Tier 1) analysis, a high degree of

uncertainty is often acceptable, provided

that conservative assumptions are used to

bias potential error toward protecting

human health. The use of conservative

assumptions is intended to result in a situation where the risk assessor is confident that the risk

estimate is unlikely to be greater than the point estimate of risk. In other words, the point

estimate of risk is expected to be at the high-end of the range of possible values. The uncertainty

characterization for a Tier 1 analysis commonly is limited to a qualitative discussion of the major

sources of uncertainty and their potential impact on the risk estimate. At higher tiers of analysis,

sensitivity analysis to quantify the impact of varying input parameter values (or model
April 2004 Page 13-21

Characterize Model Uncertainties

• List/summarize key model assumptions

• Indicate the potential impact of each

assumption on the exposure and risk estimate

– Direction

– Magnitude

algorithms) on the risk estimate, or more complete quantitative uncertainty analysis, commonly

are performed to more fully describe the range of possible or plausible values.

Practical approaches to the assessment and presentation of the principal sources of uncertainty in

risk assessments are summarized below.(10)

Characterize Scenario Uncertainty. There are uncertainties associated with the estimate of the

magnitude and extent of chemical exposure or toxicity, the spatial and temporal aggregation of

chemical concentrations to calculate the exposure concentration used in the risk characterization,

the completeness of the analysis (e.g., important exposure pathways may not have been

evaluated), and the manner in which the exposed population and/or exposure scenario were

specified for the analysis. Ideally, the key scenario uncertainties have been discussed during

planning, scoping, and problem formulation, and the analysis plan has been developed to address

these uncertainties. A limited sensitivity analysis (e.g., on key assumptions associated with

exposure) may indicate the magnitude of uncertainty associated with specific aspects of the

scenario. At a minimum, the analysis of uncertainty should identify the key scenario

uncertainties and indicate the potential impact of each on the direction and magnitude of the risk

estimate.

Characterize Model Uncertainty. There are uncertainties associated with the selection of

scientific models; these include dose-response models, models of environmental fate and

transport, and exposure models. There is always some doubt as to how well an exposure model

or its mathematical expression approximates

the true relationships between site-specific

environmental conditions. Ideally one would

like to use a fully validated model that

accounts for all the known complexities in the

parameter interrelationships for each

assessment. Often, however, only partially

validated models are available. As a

consequence, it is important to identify key

model assumptions (e.g., linearity,

homogeneity, steady-state conditions, equilibrium) and their potential impact on the risk

estimates. In the absence of field data for model validation, the risk assessor could perform a

limited sensitivity analysis (i.e., vary assumptions about functional relationships) to indicate the

magnitude of uncertainty that might be associated with model form. At a minimum, the analysis

of uncertainty should list key model assumptions and indicate the potential impact of each on the

direction and magnitude of the risk estimate.

Characterize Parameter Uncertainty. During the course of a risk assessment, numerous

parameter values are included in the calculations of chemical fate and transport and human

intake. Significant data gaps might have required that certain parameter values be assumed for

the risk assessment. For example, no information on the time spent outdoors may be available

for a specific population, and a national average may be used instead. Even if data on the

parameter of interest are available, they will be uncertain because the parameter estimates are

derived from a sample of the potentially exposed population. A first step in characterizing

parameter value uncertainty is to identify the key parameters influencing the risk estimate. This

usually can be accomplished by expert opinion or by an explicit sensitivity analysis. In a
April 2004 Page 13-22

sensitivity analysis, the values of parameters suspected of driving the risk estimates are varied,

and the degree to which changes in the input variables result in changes in the risk estimates are

summarized and compared. It may be possible to reduce parameter uncertainty in the most

sensitive parameters by additional, selective data gathering.

Characterize Decision-Rule Uncertainty. There are uncertainties associated with policy and

other choices made during the risk assessment. For example, the exposure assessment might

have evaluated an exposure duration (e.g., a subchronic exposure) for which no appropriate doseresponse value was available. Uncertainty would be associated with the choice of value to use in

the hazard characterization (e.g., an acute versus chronic value). In this situation, it might be

possible to assess hazard twice, once with the acute value, and once with the chronic value, to

may indicate the magnitude of uncertainty associated with this decision. At a minimum, the

analysis of uncertainty should identify the key decision-rule uncertainties and indicate the

potential impact of each on the direction and magnitude of the risk estimate.

Tracking Uncertainty. Ideally, one would like to quantitatively carry through the risk

assessment the uncertainty associated with each parameter in order to characterize the uncertainty

associated with the final risk estimates. However, this process can be highly complex and

resource intensive and the more practical approach for air toxics risk assessments may be to

describe qualitatively how the uncertainties might be propagated through the risk analysis. Three

different approaches to tracking uncertainty are described below: 

• Qualitative Approach. This approach involves developing a quantitative or qualitative

description of the uncertainty for each parameter and indicating the possible influence of

these uncertainties on the final risk estimates given knowledge of the models used.

• Semi-Quantitative Approach. This approach involves: (1) using available data to describe

the potential range of values that the parameters might assume; (2) performing sensitivity

analysis to identify the parameters with the most impact on the risk estimate; and (3)

performing sensitivity analysis to compute the range of exposure or risk estimates that result

from combinations of minimum and maximum values for some parameters and mid-range

values for others.

• Quantitative Approach. Probabilistic techniques such as Monte Carlo simulation analysis

can explicitly characterize the extent of uncertainty and variability in risk assessment,

especially in the exposure assessment step. Using these techniques, important variables in

the exposure assessment, as well as in the other parts of the risk assessment, are specified as

distributions (rather than as single values) according to what can be expressed about their

underlying variability and/or uncertainty. Values are sampled repeatedly from these

distributions and combined in the analysis to provide a range of possible outcomes. While

this technique can offer a useful summary of complex information, it must be noted that the

analysis is only as certain as the underlying data (and assumed forms of the distribution of

data values in the population). It is important that the risk assessor clearly expresses

individual modeled variables in a way that is consistent with the best information available.

Highly quantitative statistical uncertainty analysis is usually not practical or necessary for

most air toxics risk assessments. The general quantitative approach to propagating or

tracking uncertainty through probabilistic modeling is described in Chapter 31.
April 2004 Page 13-23

13.3.4.2 Presentation of Uncertainty Assessment

The final discussion of the risk characterization results must place the numerical estimates of risk

in the context of the uncertainties inherent in the analysis.(2) The discussion should include:

• Level of confidence in the quantitative toxicity information used to estimate risks;

• Presentation of qualitative information on the toxicity of substances not included in the

quantitative assessment;

• Level of confidence in the exposure estimates for key exposure pathways and related

exposure parameter assumptions;

• Major factors reducing certainty in the results and the significance of these uncertainties (e.g.,

adding individual risk estimates for several substances or across multiple exposure

pathways); and

• Possible graphical presentation of key parameter and risk uncertainties.

13.3.5 Additional Information 

Other studies relevant to the risk assessment being performed may be available, such as

community health studies or previous risk assessments. For example, the Agency for Toxic

Substances and Diseases Registry (ATSDR) may conduct public health assessments, health

consultations, and other activities resulting in evaluations, assessments, and recommendations on

specific public health issues related to actual or potential human exposure to hazardous materials

(see Chapter 30). ATSDR’s recommendations may include additional hazard characterization or

risk reduction activities. In addition, these activities can initiate other activities within ATSDR

such as exposure investigations, health studies, and health education.

If health or exposure studies have been identified and evaluated as adequate, the study findings

may be incorporated into the risk characterization to strengthen the conclusions of the risk

assessment. In general, a qualitative comparison of the results of available studies will usually be

sufficient.
April 2004 Page 13-24

Additional References Related to Uncertainty Analysis

Hattis. D.B. and D.E. Burmaster. 1994. Assessment of variability and uncertainty distributions for

practical risk analyses. Risk Analysis 14(5):713-730.

U.S. Environmental Protection Agency. 1997. Guiding Principles for Monte Carlo Analysis. Risk

Assessment Forum, Washington, DC. March 1997. EPA-630/R-97-001.

U.S. Environmental Protection Agency. 1988. National Emission Standards for Hazardous Air

Pollutants. Federal Register 53(145):28496-28056, Proposed Rule and Notice of Public Hearing. July

28, 1988.

Finkel, A.M. 1990. Confronting Uncertainty in Risk Management: A Guide for Decision-Makers.

Center for Risk Management, Resources for the Future. Washington, DC.

U.S. Environmental Protection Agency. 1997. Policy for Use of Probabilistic Analysis in Risk

Assessment. Office of the Administrator, Washington, DC. May 15, 1997.

National Council on Radiation Protection and Measurements. 1996. A Guide for Uncertainty Analysis

in Dose and Risk Assessments Related to Environmental Contamination. NCRP Commentary No. 14;

available at http://www.ncrp.com/comm14.html.
April 2004 Page 13-25

1. U.S. Environmental Protection Agency. 2003. Framework for Cumulative Risk Assessment

(External Review Draft). Office of Research and Development, National Center for

Environmental Assessment, Washington, D.C., April 23, 2002. EPA/630/P-02/001F.

Available at: http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=54944.

U.S. Environmental Protection Agency. 1997. Guidance on Cumulative Risk Assessment,

Part 1, Planning and Scoping. Science Policy Council, Washington, D.C.

U.S. Environmental Protection Agency. 1984. Risk Assessment and Management:

Framework for Decision Making, Washington, D.C. EPA 600/9-85-002.

Information Quality Guidelines

The U.S. Office of Management and Budget (OMB) has directed all federal agencies to develop

information quality guidelines for risk-related and other information; EPA has developed draft

guidelines pursuant to the OMB directive. While these guidelines do not apply to S/L/T governments,

they provide useful principles for developing and communicating the information developed for the

risk characterization.

The OMB guidelines denote four substantive qualifiers for information disseminated by federal

agencies. Quality is defined as the encompassing term, of which utility, objectivity, and integrity are

the constituents. Utility refers to the usefulness of the information to the intended users. Objectivity

focuses on whether the disseminated information is being presented in an accurate, clear, complete,

and unbiased manner, and as a matter of substance, is accurate, reliable, and unbiased. Integrity

refers to security – the protection of information from unauthorized access or revision, to ensure that

the information is not compromised through corruption or falsification.

The guidelines provide some basic principles for agencies to consider when developing their own

guidelines, including:

• Guidelines should be flexible enough to address all communication media and variety of scope and

importance of information products.

• Some agency information may need to meet higher or more specific expectations for objectivity,

utility, and integrity.

• Ensuring and maximizing quality, objectivity, utility, and integrity comes at a cost, so agencies

should consider using a cost-benefit approach.

• Agencies should adopt a common-sense approach that builds on existing processes and procedures.

It is important that agency guidelines do not impose unnecessary administrative burdens.

EPA developed draft information quality guidelines in response to the OMB directive

(www.epa.gov/oei/qualityguidelines). EPA’s guidelines include two components of particular

relevance to air toxics risk management: (1) guidelines to ensure and maximize the quality of

“influential” information; and (2) guidelines to ensure and maximize the quality of “influential”

scientific risk assessment information.

Source: Office of Management and Budget. 2002. Guidelines for Ensuring and Maximizing the

Quality, Objectivity, Utility, and Integrity of Information Disseminated by Federal Agencies. 67

Federal Register 36:8451. February 22, 2002 (www.whitehouse.gov/omb/fedreg/reproducible.html).

References
April 2004 Page 13-26

2. U.S. Environmental Protection Agency. 1995. Policy for Risk Characterization (“Browner

Memorandum”). Science Policy Council, Washington, D.C., March 1995. Available at:

http://64.2.134.196/committees/aqph/rcpolicy.pdf.

3. U.S. Environmental Protection Agency. 1995. Guidance for Risk Characterization. Science

Policy Council, Washington, D.C., February 1995. Available at:

http://www.epa.gov/osa/spc/htm/rcguide.htm.

4. U.S. Environmental Protection Agency. 1992. Guidance on Risk Characterization for Risk

Managers and Risk Assessment. Memorandum. Office of the Administrator, Washington,

D.C.

5. U.S. Environmental Protection Agency. 1986. Guidance Mutagenicity Risk Assessment of

Chemical Mixtures. Risk Assessment Forum, Washington, D.C., September 1986.

EPA/630/R-98/003. (Published in the Federal Register 51: 33997-8, Sept 24, 1986).

Available at: http://cfpub.epa.gov/ncea/raf/pdfs/mutagen2.pdf.

6. U.S. Environmental Protection Agency. 1986. Guidance for Conducting Health Risk

Assessment of Chemical Mixtures. Risk Assessment Forum, Washington, D.C.

EPA/630/R-98/002. Available at:

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=20533.

U.S. Environmental Protection Agency. 2000. Supplementary Guidance for Conducting

Health Risk Assessment of Chemical Mixtures. Risk Assessment Forum, Washington, D.C.

EPA/630/R-00/002. Available at:

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=20533.

7. U.S. Environmental Protection Agency. 1999. Residual Risk Report to Congress. Office of

Air Quality Planning and Standards, Research Triangle Park, NC, March 1999. EPA-45/R99/001. Available at: http://www.epa.gov/ttn/oarpg/t3/reports/risk_rep.pdf.

8. Morgan, G. and Henrrion. M. 1990. Uncertainty: A Guide to Dealing with Uncertainty in

Quantitative Risk and Policy Analysis. Cambridge University Press, Cambridge, U.K.

9. Commission on Risk Assessment and Risk Management (CRARM). 1996. Risk Assessment

and Risk Management in Regulatory Decision-Making (The “White Book”). Draft Report,

Washington, D.C.

CRARM. 1997. Framework for Environmental Health Risk Management. Final Report,

Volume 1. Washington, D.C.

CRARM. 1997. Risk Assessment and Risk Management in Regulatory Decision-Making. 

Final Report, Volume 2. Washington, D.C.

10. U.S. Environmental Protection Agency. 1989. Risk Assessment Guidance for Superfund: 

Volume I. Human Health Evaluation Manual (Part A). Office of Emergency and Remedial

Response. Washington, D.C., December 1989. EPA/541/1-89/002. Available at:

http://www.epa.gov/superfund/programs/risk/ragsa/index.htm.
PART III

HUMAN HEALTH RISK ASSESSMENT: 

MULTIPATHWAY

Chapter 14 Overview and Getting Started: Planning

and Scoping the Multipathway Risk

Assessment

Table of Contents

14.1 Introduction ............ ............ ............ ............ ............ ...... 1

14.2 Overview of Multipathway Air Toxics Risk Assessment ............ ............ ...... 3

14.2.1 Planning, Scoping, and Problem Formulation ............ ............ ......... 3

14.2.2 Analysis ............ ............ ............ ............ ............ . . 4

14.2.3 Risk Characterization ............ ............ ............ ............ .... 5

14.3 Overview of Multipathway Exposure Assessment ............ ............ ............ 5

14.4 Planning and Scoping ............ ............ ............ ............ .......... 9

14.4.1 Identifying the Concern ............ ............ ............ ............ . . 9

14.4.2 Identifying The Participants ............ ............ ............ .......... 9

14.4.3 Determining the Scope of the Risk Assessment ............ ............ ....... 9

14.4.4 Describing the Problem ............ ............ ............ ............ . 10

14.4.5 Determining How Risk Managers Will Evaluate the Concern ............ ....... 10

14.5 Tiered Multipathway Risk Assessments ............ ............ ............ ....... 10

References ............ ............ ............ ............ ............ ............ 11

April 2004 Page 14-1

Bioconcentration is the net accumulation of a substance by an organism as a result of uptake directly

from an environmental medium (e.g., net accumulation by an aquatic organism as a result of uptake

directly from ambient water, through gill membranes or other external body surfaces).

Bioaccumulation is the net accumulation (storage in tissue and/or organs) of a substance by an

organism as a result of uptake from all environmental sources – the medium in which they live, the

water they drink, and the diet they consume – over a period of time.

Biomagnification or Biological Magnification is the process whereby certain substances, such as

pesticides or heavy metals, transfer up the food chain and increase in concentration. A biomagnifying

chemical deposited in rivers or lakes absorbs to algae, which are ingested by aquatic organisms, such

as small fish, which are in turn eaten by larger fish, fish-eating birds, terrestrial wildlife, or humans.

The chemical tends to accumulates to higher concentration levels with each successive food chain

level. Biomagnification is illustrated in Chapter 23.

14.1 Introduction

Part II of this Reference Manual discussed how to plan for and conduct a human health risk

assessment via the direct inhalation pathway. Part III provides the same general discussion of the

various aspects of the risk assessment process; however, the discussion is focused specifically on

multipathway human health risk assessment. As noted earlier, all air toxics risk assessments

evaluate the direct inhalation pathway. In addition, multipathway risk assessment may be

appropriate generally when air toxics that persist and which also may bioaccumulate and/or

biomagnify are present in releases. These generally will focus on the persistent

bioaccumulative hazardous air pollutant (PB-HAP) compounds (Exhibit 14-1), but specific

risk assessments may need to consider additional chemicals that persist and which also may

bioaccumulate and/or biomagnify. For these compounds, the risk assessment generally will

need to consider exposure pathways other than inhalation – in particular, pathways that involve

deposition of air toxics onto soil and plants and into water, subsequent uptake by biota, and

potential human exposures via consumption of contaminated soils, surface waters, and foods. 

Substances that persist and bioaccumulate readily transfer between the air, water, and land. 

Some may travel great distances, and linger for long periods of time in the environment.

The discussion of multipathway risk assessment follows the same general framework presented

in Part II. This chapter presents an overview of multipathway risk assessment and discusses the

initial planning, scoping and problem formulation activities. The remaining chapters of this Part

focus on Exposure Assessment (Chapters 14 to 20), Toxicity Assessment (Chapter 21), and Risk

Characterization (Chapter 22). The discussions presented here supplement the information

provided earlier – readers are encouraged to refer back to the corresponding Chapters in Part II

for additional background materials.
April 2004 Page 14-2

Exhibit 14-1. PB-HAP Compounds

PB-HAP Compound

Pollution

Prevention

Priority PBTs

Great Waters

Pollutants of

Concern

TRI PBT

Chemicals

Cadmium compounds X

Chlordane X X X

Chlorinated dibenzodioxins and furans X

(a) X X(b)

DDE X X

Heptachlor X

Hexachlorobenzene X X X

Hexachlorocyclohexane (all isomers) X

Lead compounds X

©) X X

Mercury compounds X X X

Methoxychlor X

Polychlorinated biphenyls X X X

Polycyclic organic matter X

(d) X X(e)

Toxaphene X X X

Trifluralin X

(a) “Dioxins and furans” (“” denotes the phraseology of the source list)

(b) “Dioxin and dioxin-like compounds”

(c) Alkyl lead

(d) Benzo[a]pyrene

(e) “Polycyclic aromatic compounds” and benzo[g,h,i]perylene 

See Appendix D for a discussion of the derivation of this list of PB-HAPs.
April 2004 Page 14-3

For purposes of this Reference Manual, we discuss planning, scoping, and problem formulation

for multipathway human health risk assessment separately from the corresponding phase for

inhalation risk assessment. In reality, the planning, scoping, and problem formulation phase for

the multipathway assessment would be integrated with the inhalation analysis as early as feasible.

14.2 Overview of Multipathway Air Toxics Risk Assessment

The multipathway risk assessment is organized in the same way as the direct inhalation risk

assessment into three general phases:

1. Planning, scoping, and problem formulation;

2. Analysis, consisting of exposure assessment and toxicity assessment; and

3. Risk characterization.

14.2.1 Planning, Scoping, and Problem Formulation

The planning, scoping, and problem formulation phase of multimedia risk assessment focuses on

developing a common understanding of what needs to be added to the risk assessment (beyond

the direct inhalation assessment) to assess risks associated with pathways involving deposition

(i.e., transfer of the compounds to soil, water, sediment, and biota) and subsequent ingestion

exposure. The scope of the multimedia risk assessment generally is more extensive than that for

inhalation assessment, and therefore significant additional effort is likely.

It may be necessary to include on the planning and scoping team experts in multimedia

modeling, bioaccumulation, human exposure factors, and ingestion toxicology. The focus on

additional exposure pathways may influence many aspects of the risk assessment, including the

size of the study area; emission sources to be considered; the temporal and spatial resolution

required; the appropriate level of detail and documentation; trade-offs between depth and breadth

in the analysis; QA/QC requirements; analytical approaches to be used; and the staff and

monetary resources to commit. The study-specific conceptual model would also reflect the

specific concerns of air toxics that persist and which also may bioaccumulate. As with the

inhalation risk assessment, the planning, scoping, and problem formulation process is an iterative

process that reflects changing information and concerns as the multimedia risk assessment

unfolds.

The reader should become familiar with Part II of this manual before reading this Part, since Part

III focuses primarily on those aspects of the risk assessment that are unique to multipathway

analyses, including:

• How the study area is defined;

• Potentially exposed populations;

• Exposure pathways and exposure routes;

• How exposure is assessed;

• Dose-response values for non-inhalation pathways; and

• How risks are characterized.
April 2004 Page 14-4

14.2.2 Analysis

The analysis phase of the multipathway assessment is divided into two components: exposure

assessment and toxicity assessment. Exposure assessment is likely to be considerably more

complicated than the corresponding inhalation exposure assessment for several reasons:

• People can be exposed to air toxics in many more ways, including in the food they eat, the

milk they drink, and the soils on which they play.

• Time is a critical variable. Air toxics that persist and which also may bioaccumulate can

slowly build up in soils, sediments, and biota over time. With sufficient time, even relatively

small releases have the potential to result in high exposures.

• The spatial distribution of the air toxics can be complex. Chemicals can move away from

deposition points due to runoff, erosion, and the movement of contaminated animals.

Chemicals deposited over a wide area (e.g., a watershed) can concentrate in smaller areas

(e.g., a pond).

• Multimedia models often use more extensive input variables.

• Sampling and analysis may involve a wider range of media (e.g., soil, sediment) and different

types of biota (e.g., fish, shellfish, plants). Each type of sampling and analysis has its own

methods, protocols, and QA/QC procedures.

• Whereas the exposure concentration in air is the quantitative metric of exposure for

inhalation, intake is the quantitative metric of ingestion exposure in multipathway analyses. 

To quantify intake, it is necessary to (1) estimate the concentrations of chemicals of potential

concern (COPC) in water, soil, sediment, and/or food items; (2) determine how much water,

soil, sediment, and food are ingested; (3) determine the duration and temporal patterns over

which ingestion occurs; and (4) adjust for body weight, to account for the different types of

people in the population who interact with the contaminated media. Multimedia exposure

assessment uses a number of different exposure factors that provide quantitative estimates of

the physical and behavioral attributes of potentially exposed populations (e.g., how much fish

a person eats per day). Exposure factors can be treated as either constants or variables in the

exposure assessment, depending on whether a deterministic or probabilistic analysis is being

performed.

The multipathway toxicity assessment is similar to the toxicity assessment for inhalation. It

considers the same general information: (1) the types of potential adverse health effects

associated with chemical exposures; (2) dose-response relationships; and (3) related uncertainties

such as the weight of evidence for carcinogenic effects. There are two primary differences:

• A chemical’s toxicity is influenced by the route of exposure. That is, the same chemical can

result in different toxic effects (and have different dose-response values) depending on

whether the chemical is inhaled or ingested. There are a number of reasons why this may

occur. For example, when a chemical is inhaled into the respiratory tract, the primary toxic

effect may occur in the respiratory tract as a result of the inhaled chemical (a portal of entry

effect). When swallowed, on the other hand, many chemicals are absorbed into the
April 2004 Page 14-5

bloodstream through the gastrointestinal tract where they are carried directly to the liver. 

Chemicals in the liver are often metabolized extensively (either to more or less toxic

substances) before being transported by the bloodstream to other parts of the body. 

• The specific dose-response values used for the ingestion pathway – reference doses (RfDs)

for non-cancer effects and oral cancer slope factors (CSFs) – differ in form and derivation

from those used for inhalation assessments. Specifically, RfDs and CSFs are developed to

match the metric of exposure for ingestion and are expressed (usually) in terms of amount of

chemical ingested per unit of body weight per day (i.e., mg/kg-d for RfDs) and risk per

amount of chemical ingested per unit body weight per day (i.e., (mg/kg-d)-1 for CSFs).

14.2.3 Risk Characterization

The risk characterization for multipathway assessments also may be more complicated than that

for the inhalation risk assessment.

• Ingestion risk estimates are first added across all ingestion pathways and then added to

inhalation risk estimates to calculate total (i.e., cumulative) risk. Although the summation

process is relatively simple for screening-level analyses, it can become complex for more

advanced tiers of risk assessment.

• The uncertainty analysis for multipathway risk assessments may be considerably more

complex if multiple pathways are important because many more exposure factors and

variables will be involved in the quantification of risk. As noted earlier, many more specific

exposure factors can be treated as variables for probabilistic multipathway risk assessments.

• The uncertainty analysis for multipathway analysis is also much more complex due to the

larger number of pathways assessed and the larger number of measurement and modeling

inputs that are needed.

14.3 Overview of Multipathway Exposure Assessment

As with inhalation risk assessments, the exposure assessment for multipathway risk assessments

includes identifying sources, characterizing releases to the air, estimating concentrations of air

toxics in the environment, characterizing potentially exposed populations, and developing

metrics of exposure. This section provides an overview of exposure assessment for

multipathway risk assessments. Familiarity with EPA’s Guidelines for Exposure Assessment(1)

prior to beginning the multipathway exposure assessment would be helpful.

The multipathway exposure assessment covers a broader scope and may be more complex than

direct inhalation exposure assessment.

• Exposure pathways to be evaluated include multiple media (soil, water, sediment, biota) and

exposure routes in addition to inhalation (e.g., ingestion). Therefore, the exposure setting

may need additional characterization (e.g., the location and nature of water bodies and/or

agricultural crops).
April 2004 Page 14-6

Overview of Multipathway Exposure Pathways/Routes

• The evaluation of chemical fate and transport accounts for the transfer of contaminants from

air to soil and water and subsequent transport and transfer to other media. For example, air

toxics that persist and which also may bioaccumulate are deposited onto soils and can enter

surface waters via runoff; some of the compounds that deposit into water predominantly

partition into sediments. Bioaccumulation – a concentration of contaminants in biological

tissues – and subsequent transfer to humans via ingestion often play a major role in the

exposure assessment. Multimedia models can be used to describe contaminant fate and

transport through the use of partition coefficients and mass-balance techniques (see Chapter

6). Different monitoring methods (e.g., sediment or fish tissue sampling and analysis) may

be included to augment or assist in the evaluation of modeling outputs.

• In contrast to the direct inhalation assessment, in which the quantitative metric of exposure is

the ambient air concentration at the exposure point, ingestion exposures are quantified using

the chemical intake rate – the amount of chemical ingested per unit time – generally

expressed in units of milligrams of chemical per kilogram of body weight per day. 

Calculation of chemical intake rate requires information on COPC concentrations in items

ingested as well as information about the type and amount of different items eaten each day,

body weight, and exposure durations for the sub-populations of interest. Intake rate is

simply the amount of food (or other media), containing the contaminant of interest, that an

individual ingests during some specific time period (units of mass/time). Intake rate can be

expressed as a total amount (e.g., mg); as a dose rate (e.g., mg/day); or as a rate normalized to

body mass (e.g., mg/kg-day). For most chemicals, the dose-response value (e.g., reference

dose, or RfD) is based on the potential dose (i.e., the amount of chemical taken in), with no
April 2004 Page 14-7

explicit correction for the fraction absorbed. For some chemicals, it may be necessary to

adjust for such differences using physiologically based pharmacokinetic (PB-PK) models,

mathematical dosimetry models, and/or adjustment factors (see Chapter 8).

Because exposure is quantified using chemical intake rate, different types of people within a

population (e.g., childhood exposures) may need to be considered explicitly. Consumption rates,

dietary preferences, and body weight vary with age and would be accounted for in the risk

assessment. (Note that not only age, but sex, ethnicity, cultural and religious practices may also

strongly influence the exposure patterns of people within a potentially exposed population.)

Although it is possible to evaluate acute exposures for the ingestion pathway, EPA does not

generally perform acute exposure assessments, because it is unlikely that PB-HAP compounds

would concentrate to acutely toxic levels under any typical release scenario that did not pose a

much more substantial chronic risk. However, each assessment would consider the available

evidence in making this judgement. At a minimum, the risk characterization would state the

reasons why an analysis of acute health effects for non-inhalation pathways was not performed.

The multipathway exposure assessment focuses on two general categories of ingestion pathways:

incidental ingestion and food chain (Exhibit 14-2). Incidental ingestion pathways consider

exposures that may occur from ingestion of soils or surface water while an individual is engaged

in other activities (e.g., ingestion of soil while gardening or playing outside; ingestion of surface

water while swimming). Food chain pathways consider exposures that may occur if PB-HAP

compounds accumulate in the food and water people consume.

Exhibit 14-2. Human Exposure Pathways Considered for Multipathway

Air Toxics Assessments
April 2004 Page 14-8

Analysis of Groundwater Pathways

EPA’s Office of Solid Waste has considerable experience in modeling and monitoring the movement

of contaminants in groundwater. Much of that experience is based on exposure assessments

associated with land-based disposal units (i.e., where the source of contamination is in the

subsurface). For example, EPA’s Center for Exposure Assessment Modeling (CEAM) distributes

multimedia models designed to quantify the movement and concentration of contaminants (from

land-based releases at hazardous waste sites) traveling through groundwater, surface water, and food

chain media (available at http://www.epa.gov/ceampubl/). In these models, releases to the atmosphere

from the subsurface may be considered, but transfer from the air through the subsurface are not.

EPA does not have sufficent experience with air toxics multipathway analysis to identify situations in

which the groundwater may be contaminated. EPA’s Methodology for Assessing Health Risks

Associated with Multiple Pathways of Exposure(3) identifies three site-specific conditions that might

lead to greater groundwater impacts:

• Deposition rates that are several times greater than the average;

• The existence of more soluble HAPs in emissions; and

• Higher recharge rates such as would occur in areas with very permeable soil and bedrock near the

surface.

As Exhibit 14-2 suggests, the focus of the multipathway assessment is on ingestion pathways. 

Other exposure pathways may be important for particular risk assessments, including dermal

exposures (i.e., direct contact with contaminated soils, surface waters, or surface water sediments

during outside activities such as gardening or swimming); resuspension of dust (e.g., from wind

blowing across contaminated soils, or agricultural activities such as tilling) and subsequent

inhalation of the dust particles; and ingestion of contaminated groundwater. However, EPA does

not have sufficient experience with multipathway air toxics risk assessments to identify the

circumstances for which exposures via these additional pathways may represent a potential

concern.

• If site-specific circumstances suggest that dermal pathways may be of concern, EPA’s Risk

Assessment Guidance for Superfund (RAGS), Part D, Standardized Planning, Reporting and

Review of Superfund Risk Assessments,

(2) includes a relatively straightforward methodology

for dermal exposure and risk assessment, starting with soil concentrations. The Planning

Tables in the document are simple to use and incorporate into the multipathway analysis.

• Relative to the direct inhalation pathway, inhalation of soil resulting from dust resuspension

by wind erosion generally is not thought to be a significant pathway of concern for air toxics

risk assessments. If site-specific circumstances suggest that resuspension of dust may

represent a potential concern, EPA’s Methodology for Assessing Health Risks Associated

with Multiple Pathways of Exposure to Combuster Emissions (MPE) (Chapter 5 Dust

Resuspension) discusses the methods for evaluating this pathway.(3)

• If site-specific circumstances suggest that groundwater may represent a potential concern

(e.g., the presence of extremely shallow aquifers used for drinking water purposes or a karst

environment in which the local surface water significantly affects the quality of ground water

used as a drinking water source), Total Risk Integrated Methodology - Fate, Transport, and
April 2004 Page 14-9

Ecological Exposure Module (TRIM.FaTE) has the ability to assess chemicals moving into

the groundwater pathway. EPA’s Human Health Risk Assessment Protocol for Hazardous

Waste Combustion Facilities(3) and EPA’s Draft Technical Background Document for Soil

Screening Guidance discusses methods for evaluating the groundwater pathway.

14.4 Planning and Scoping

As with inhalation analyses, the key steps in the planning and scoping process include (1)

identifying the concern; (2) identifying who will be involved; (3) determining the scope of the

risk assessment; (4) describing why there may be a problem; and (5) determining how the

concern will be evaluated. The planning and scoping process for multipathway risk assessment

focuses on developing a common understanding of what needs to be evaluated to assess risks via

deposition and transfer of the air toxics to soil, water, and biota, and subsequent ingestion. More

detailed discussions of the planning and scoping process can be found in Part II of this Volume

and in guidance documents developed by EPA.(4)

14.4.1 Identifying the Concern

The driving concern for the multipathway risk assessment generally would be the same as that for

the inhalation risk assessment (e.g., regulatory requirement, community need, health concern). 

However, a number of additional specific concerns may arise. For example, the potential for

bioaccumulation in food and subsequent ingestion may raise specific concerns about areas where

people farm, economic issues such as recreational fishing, or additional exposure pathways of

potential concern (e.g., infants ingesting mother’s milk).

14.4.2 Identifying the Participants

The participants for the multipathway risk assessment generally would be the same as those for

the inhalation risk assessment. However,

• A broader range of risk managers would be involved. For example, if there is a potential for

a fishery or farm crops to become contaminated with air toxics, different persons or groups

may have the authority to make the risk management decisions – the state, local, or tribal

(S/L/T) fish and game department or the agriculture department may become involved.

• The risk assessment technical team would include additional experts (e.g., in the areas of

multimedia modeling, bioaccumulation, soil chemistry).

• The specific set of interested or affected parties may change or expand (e.g., farmers and

fishermen may be more concerned/involved).

14.4.3 Determining the Scope of the Risk Assessment

At a minimum, the scope of the risk assessment will include additional exposure pathways,

exposure routes, and potentially exposed populations or sub-populations. The details of scope

are developed during the problem formulation step (see Chapter 15).
April 2004 Page 14-10

Example Multipathway Problem Statement

Air toxics emissions may be causing increased long-term health risk to people who eat fish in Puffer

Pond that may be contaminated with mercury compound releases from the Big Air Manufacturing

Company. A multipathway risk assessment will be performed to evaluate potential long-term human

health impacts associated with consumption of contaminated fish. Ingestion risks will be assessed for

recreational fishers who eat fish caught in Puffer Pond. In addition, a modeling risk assessment using

air dispersion modeling will be conducted to estimate inhalation risks for populations within 50 km of

the Acme property boundary using residential exposure conditions.

14.4.4 Describing the Problem

As with inhalation, participants would develop a problem statement that clearly articulates the

perceived problem to be evaluated. The problem statement may also provide statements of what

is and is not included in the multipathway risk assessment and why. (Note that, in general, only

one problem statement is necessary to describe all exposure pathways, including inhalation. A

separate problem statement for each exposure pathway is not usually necessary.)

14.4.5 Determining How Risk Managers Will Evaluate the Concern

As with inhalation, the multipathway risk assessment would be designed to provide input to risk

managers to help inform the decisions they must make. Part of the planning and scoping process

is developing an understanding of the types of information needed by the risk managers and the

level of uncertainty in that information that can be tolerated.

14.5 Tiered Multipathway Risk Assessments

EPA guidance generally recommends that a tiered approach to risk assessments be taken to

identify the key chemicals, sources, and pathways that contribute most to the risk being

evaluated.(5) A tiered approach can be particularly valuable for multipathway risk assessments

because of the potential complexity commonly associated with such analyses. Often, screeninglevel analyses assume relatively high exposure factors (e.g., all of the fish a person eats comes

from a potentially contaminated pond) to determine whether risk associated with a specific

pathway appears to be significant enough to warrant more robust analysis. Subsequent tiers of

analysis, using more realistic exposure factors and perhaps involving more complex modeling

and perhaps sampling and analysis, are generally undertaken only if lower-tier analyses continue

to indicate the potential for risk. As with inhalation risk assessments, an iterative process of

evaluation, deliberation, data collection, work planning and communication is used to decide:

• Whether or not the risk assessment, in its current state, is sufficient to support the risk

management decision(s); and

• If the assessment is determined to be insufficient, whether or not progression to a higher tier

of complexity (or refinement of the current tier) would provide a sufficient benefit to warrant

the additional effort.
April 2004 Page 14-11

1. U.S. Environmental Protection Agency. 1992. Guidelines for Exposure Assessment. Federal

Register 57:22888-22938, May 29, 1992. Available at 

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=15263

2. U.S. Environmental Protection Agency. 2001. Risk Assessment Guidance for Superfund

(RAGS): Volume I - Human Health Evaluation Manual (Part D, Standardized Planning,

Reporting and Review of Superfund Risk Assessments), Final. Office of Emergency and

Remedial Response, Washington, D.C., December 2001. EPA 9285.7-47.

3. U.S. Environmental Protection Agency. 1998. Human Health Risk Assessment Protocol for

Hazardous Waste Combustion Facilities, Peer Review Draft. Office of Solid Waste and

Emergency Response, July 1998. EPA/530/D-98/001 A, B, and C and subsequent Errata

(EPA Memo, July 1999). Available at:

http://www.epa.gov/earth1r6/6pd/rcra_c/pd-o/comb_risk.htm

4. U.S. Environmental Protection Agency. 1997. Guidance on Cumulative Risk Assessment.

Part 1. Planning and Scoping. Science Policy Council, Washington, D.C., July 3, 1997.

Available at: http://www.epa.gov/osp/spc/cumrisk2.htm

U.S. Environmental Protection Agency. 2003. Framework for Cumulative Risk Assessment. 

Office of Research and Development, National Center for Environmental Assessment,

Washington, D.C., 2003. EPA/600/P-02/001F. Available at:

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=54944

U.S. Environmental Protection Agency. 2001. Risk Assessment Guidance for Superfund

(RAGS): Volume I - Human Health Evaluation Manual (Part D, Standardized Planning,

Reporting and Review of Superfund Risk Assessments) Final. Office of Emergency and

Remedial Response, Washington, D.C., December 2001. Publication 9285.7-47. Available at:

http://www.epa.gov/superfund/programs/risk/ragsd/index.htm

5. U.S. Environmental Protection Agency. 2001. Risk Assessment Guidance for Superfund

(RAGS). Volume III - Part A. Process for Conducting Probabilistic Risk Assessment. Office

of Emergency and Remedial Response, Washington, D.C., December 2001.

EPA/540/R-02/002. Available at:

http://www.epa.gov/superfund/programs/risk/rags3a/index.htm

References
Chapter 15 Problem Formulation: Multipathway

Risk Assessment

Table of Contents

15.1 Introduction ............ ............ ............ ............ ............ ...... 1

15.2 Developing the Multipathway Conceptual Model ............ ............ ............ 1

15.3 Developing the Multipathway Analysis Plan ............ ............ ............ .... 2

15.3.1 Identification of the Sources ............ ............ ............ .......... 3

15.3.2 Identification of the Chemicals of Potential Concern ............ ............ ... 3

15.3.3 Identification of the Exposure Pathways/Routes ............ ............ ....... 3

15.3.3.1 Characteristics of the Assessment Area ............ ............ . 4

15.3.3.2 Scale of the Assessment Area ............ ............ ........ 5

15.3.3.3 Use of Modeling vs. Monitoring ............ ............ ...... 5

15.3.3.4 Quantitation of Exposure ............ ............ ............ 6

15.3.3.5 Evaluation of Uncertainty ............ ............ ........... 7

15.3.3.6 Preparation of the Documentation ............ ............ ..... 7

15.3.4 Identification of the Exposed Population ............ ............ ............ 7

15.3.5 Identification of Endpoints and Metrics ............ ............ ............ . 7

15.4 Exposure Assessment Approach ............ ............ ............ ............ .... 7

15.4.1 Scenario Approach ............ ............ ............ ............ ...... 8

15.4.2 Population-Based Approach ............ ............ ............ ......... 11

References ............ ............ ............ ............ ............ ............ 12

April 2004 Page 15-1

15.1 Introduction

This chapter discusses the problem formulation step of the multipathway risk assessment, which

takes the results of the planning and scoping process and translates them into two critical

products: the conceptual model, and the analysis plan.

15.2 Developing the Multipathway Conceptual Model

As with inhalation analyses, the conceptual model (Exhibit 15-1) focuses the multipathway risk

assessment on several key elements, including sources, chemicals released, fate and transport

mechanisms, potentially exposed populations, potential exposure pathways and routes of

exposure, and potential adverse effects. Although discussed separately here, as noted in Chapter

6, the elements of the conceptual model that are unique to the multipathway human health

risk assessment should be integrated with those for the inhalation assessment as early as

feasible.

Exhibit 15-1. Generalized Conceptual Model for Multipathway Analyses

This figure highlights the multipathway components of the general air toxics risk assessment

conceptual model introduced in Chapter 6. The conceptual model for a specific multipathway risk

assessment may consider only part of this general model, or may focus more closely on specific subpopulations of concern.
April 2004 Page 15-2

Elements of the conceptual model that may be unique to the multipathway assessment include:

• Sources. The specific sources included in the analysis may be focused on the subset of all

sources that release most or all of the identified air toxics that persist and which also may

bioaccumulate.

• Chemicals of potential concern. The specific COPC will focus on those air toxics that

persist and which also may bioaccumulate (i.e., persistent bioaccumulative hazardous air

pollutants (PB-HAPs) and other non-HAP chemicals that may be of concern for persistence

and bioaccumulation).

• How the COPC move through the environment. The conceptual model will need to

consider the mechanisms by which PB-HAPs move through the environment, which include

dispersion in the air; deposition (including vapor phase transfer) to soils, surface waters, and

plant surfaces; erosion and other runoff phenomena; and uptake and bioconcentration by

biota. The physical boundaries of the study area may need to include geographic areas where

COPC may be transported after deposition (e.g., PB-HAPs may have the potential to be

deposited in a watershed and be carried out of the geographic area defined for the inhalation

pathway modeling).

• The exposure pathways/media of concern. The potential exposure pathways will include a

number of different ingestion pathways and, in some cases, dermal absorption pathways.

• The human populations potentially receiving exposure. The potentially exposed

populations may need to include persons who do not live within the study area but consume

food products that have the potential to become contaminated (e.g., recreational fisher). 

Additionally, different sensitive sub-populations may be identified (e.g., people who consume

large amounts of locally-caught fish because of cultural reasons).

• The potential adverse health effects (endpoints) that may result from exposure. The

general types of chronic health risks (cancer, non-cancer) may or may not change, depending

on the specific COPC being evaluated. However, acute exposures generally are not a concern

for multipathway analyses because it would be unlikely for air toxics to accumulate in soil,

sediment, or food items to concentrations that would pose, in the absence of a chronic hazard,

an acute hazard through the ingestion or dermal pathway.

• Metrics. The metrics used to characterize exposure and estimate risk may or may not be

different from those used in the inhalation risk assessment. For example, the inhalation

assessment may stop at a Tier 1 analysis, while the multipathway assessment may go all the

way to a Tier 3 analysis.

15.3 Developing the Multipathway Analysis Plan

As noted in Chapter 6, the analysis plan matches each element of the conceptual model with the

analytical approach that the assessor will use to develop data about that element. This section

describes the elements of the analysis plan that are unique to the multipathway assessment,

including (1) identification of sources; (2) identification of COPC; (3); identification of exposure
April 2004 Page 15-3

pathways/routes; (4) identification of exposed populations; and (5) identification of endpoints

and metrics.

15.3.1 Identification of the Sources

This part of the analysis plan identifies the sources to be included in the risk assessment. As

noted earlier, the focus of multipathway analysis is on sources of the air toxics that persist and

which also may bioaccumulate. Within that subset, certain sources may be most important for a

specific risk assessment. A tiered approach is recommended for focusing the risk assessment

from the initial set of sources to the sources that will drive risk management decisions. The

initial tier of analysis generally includes all sources of PB-HAPs. In subsequent tiers of analysis,

it may be possible to remove specific sources from the analysis that contribute a very small

fraction to the total risk estimate.

15.3.2 Identification of the Chemicals of Potential Concern

This part of the analysis plan identifies the chemicals that will be evaluated in the risk

assessment. As noted earlier, the focus of multipathway analysis is on the subset of air toxics

that persist and which may also bioaccumulate. Within that subset, certain chemicals may be

most important for a specific risk assessment. A tiered approach is recommended for focusing

the risk assessment from the initial list of COPC to the set of contaminants that will drive risk

management decisions. The initial tier of analysis generally includes all of the air toxics released

from the identified important sources. In subsequent tiers of analysis, it may be possible to

remove specific chemicals from the analysis if they contribute a very small fraction to the total

risk estimate.

15.3.3 Identification of the Exposure Pathways/Routes

This part of the analysis plan identifies the exposure pathways/routes to be evaluated. As noted

in Chapter 6, an exposure pathway consists of four elements:

• A source and mechanism of chemical release;

• One or more environmental media (i.e., air, water, soil) in which the chemical is transported

from the source;

• A point of potential human contact with the contaminated medium (referred to as the

exposure point); and

• An exposure route (e.g., inhalation, ingestion) at the contact or exposure point. The route

may be actual or potential, depending on the purpose of the assessment.

The exposure pathway is complete if all four elements can be identified; otherwise the exposure

pathway is incomplete and not considered further (see Exhibit 14-2, which presents the potential

exposure pathways considered for multipathway assessments).

The exposure points selected for the multipathway risk assessment also will depend on the choice

of multipathway assessment approach and may or may not be identical to those used in the

inhalation risk assessment. In the example presented in Chapter 11, Mr. McDonald’s house was

selected as the point of maximum inhalation concentration at a receptor location. The

multipathway assessment would likely also evaluate potential exposures via crops, meat, milk,
aMaps, aerial photos, and tools such as Geographic Information Systems (GIS) can be very helpful tools for

characterizing the exposure setting (see Part VI of this Reference Manual).

b

Sources of this information may include any existing site descriptions, preliminary risk assessments, county

soil surveys, wetlands maps, aerial photographs, U.S. Geological Survey topographic maps, U.S. Department of

Agriculture Soil Conservation Service reports, and information from state natural resources agencies.

April 2004 Page 15-4

and other foods. However, the focus would be on other exposure points or areas within the farm

(e.g., the area where forage fed to dairy cows is grown or where vegetable crops are planted), not

on the farmer’s house.

15.3.3.1 Characteristics of the Assessment Area

The physical setting is important both in developing the study-specific conceptual model and

selecting and providing input parameters for the appropriate multimedia models. As described

earlier, the physical setting includes information such as urban vs. rural setting, simple vs.

complex terrain, climate and meteorology, and other important geographic features (see Chapter

6). The most important additional information required for multipathway analyses is information

on land use, soils, and surface water bodies within the assessment area.(a) Many of the general

physical characteristics of the setting will influence the scope of multimedia modeling required. 

For example, if the sources being evaluated are located in heavily industrialized area, there may

be few, if any, agricultural areas or water bodies close enough to receive significant deposition. 

In this example, deposition to soils in nearby residential areas and subsequent exposure pathways

may be the most significant exposure pathways to examine.

• Land use. Information on land use is an important part of the physical characteristics of the

assessment area discussed in Chapter 6. For multipathway analyses, it is important to

identify specific types of land uses that may lead to exposures via ingestion pathways,

especially agriculture, fishing, recreation, and residential (indoor and outdoor, including

gardening), as well as the location of particular areas where exposures via soil may be of

concern (e.g., playgrounds, schools, day care centers). Sources for land use data are

discussed in Chapter 6.

• Soils. The type and characteristics of soils (e.g., sandy, organic, acidic, alkaline) in the

assessment area affects physical phenomena such as soil erosion rates, the types and density

of plants supported by the soils, and the physical and chemical characteristics that govern

contaminant fate and transport. For example, the bioavailability of a compound may depend

partially on soil pH. The specific information needed will depend in part on the input

requirements of the multimedia fate and transport models selected for the analysis (see

Chapter 19).(b)

• Water bodies and their associated watersheds. Water bodies and their associated

watersheds are important factors in evaluating some of the major exposure pathways/routes

considered in multipathway analyses. For example, the identification of surface water bodies

at locations in the assessment area receiving deposition from emission sources indicates the

potential for exposures to contaminants from ingestion of fish, and possibly drinking water

(drinking water is usually evaluated only if the local population obtains drinking water from
cUse, area, and location of water bodies and their associated watersheds can typically be identified by

reviewing the same land-use land classification maps, topographic maps, and aerial photographs used in

identification of land use discussed in Part II of this Reference Manual. Additional information on water body use

can also be obtained through discussions with local authorities (e.g., state environmental agencies, fish and wildlife

agencies, or local water control districts) about viability to support fish populations and drinking water sources, or

current postings of fish advisories.

April 2004 Page 15-5

Multimedia Assessments: Modeling vs. Monitoring

Most multimedia air toxics risk assessments will

develop estimates of exposure concentration for noninhalation pathways primarily through modeling. In

some instances, analysts may use monitoring to evaluate

the model. In more rare instances, however, analysts

will use monitoring to develop exposure concentrations.

surface water sources).(c) Information on fishing activity will also be useful in characterizing

the potentially exposed population.

Land use and human activities should be characterized in much the same way as Chapter 6

described, except that a broader range of activities/uses needs to be considered. It is important to

identify all activities within the assessment area that could result in exposure to contaminants via

non-inhalation pathways. These would include hunting, fishing, growing crops (e.g.,

commercially, as animal feed, or for private consumption), and incidental ingestion of soils. As

noted earlier, the multipathway assessment may need to specifically address special populations

that are located in impacted areas because of unique characteristics of the exposure setting or to

address particular community concerns. For example, a day care center or traditional Tribal

fishing/hunting area may be located in an area that is impacted by releases from a facility or

source area. Consequently, due to the site-specific exposure characteristics, exposure to children

at the day care center or tribal members may need to be addressed, because they may be

especially sensitive to the adverse effects and/or the exposure setting may be particularly

conducive to exposure. EPA has developed a policy focused on consistently and explicitly

evaluating environmental health risks to infants and children in all risk assessments.(1)

15.3.3.2 Scale of the Assessment Area

For inhalation assessments, the study area generally is limited to a 50-km radius from the

emissions sources (based on the dispersion models being used). The study area for the

multipathway risk assessment generally will be limited similarly to the area in which deposition

is modeled. However, certain potential exposure scenarios may require expansion of the study

area beyond the modeled deposition area. Examples include:

• The watershed for a lake or pond is within the modeled deposition area, but the lake or pond

(where contaminants may accumulate) is outside the deposition area.

• A commercial farm is within the deposition area, and a portion of the crops are consumed by

persons living outside the deposition area.

• A popular fishing area is located within the

deposition area, and people from outside

the deposition area come there to fish.

15.3.3.3 Use of Modeling vs. Monitoring

As this document has previously noted, risk

assessors can base estimates of current

exposure concentrations on either actual
April 2004 Page 15-6

(Equation 15-1)

measurements (i.e., monitoring data) or modeling (in this case, multimedia models). In many

cases, monitoring can be helpful in reducing uncertainties in the exposure assessment, because

multimedia modeling is more complex and involves more uncertainties. Note, however, that the

scope of potential monitoring for multipathway analysis is considerably greater than that for

inhalation analyses. A wide range of types of sampling and analysis could be conducted,

including sampling of soils, surface waters, sediments, and biota (human food items). Each type

of sample has its own methods, protocols, and QA/QC requirements (see Chapter 19). 

Multimedia sampling and analysis may require additional expertise and effort. The analysis plan,

including the quality assurance protection plan (QAPP), will need to be modified accordingly.

15.3.3.4 Quantitation of Exposure

In contrast to the inhalation assessment, in which the quantitative metric of exposure is the

ambient air concentration at the exposure point, ingestion exposures are quantified using the 

chemical intake rate – the amount of chemical ingested per unit time – generally expressed in

units of milligrams of chemical per kilogram of body weight per day. The fundamental equation

for dietary intake and ingestion pathways in general is given as:

where

I = Chemical intake rate, expressed in units of mg/kg-day. For evaluating exposure to

non-carcinogens, the intake is referred to as average daily dose (ADD); for evaluating

exposure to carcinogenic compounds, the intake is referred to as lifetime average

daily dose (LADD).

EC = Exposure concentration of the chemical in the medium of concern for the time period

being analyzed, expressed in units of mg/kg for soil and food or mg/L for surface

water or beverages (including milk).

CR = Consumption rate, the amount of contaminated medium consumed per unit of time,

event, or other measure. (e.g., kg/day for soil and food; L/day for water).

EF = Exposure frequency (number of days exposed per year).

ED = Exposure duration (number of years exposed ).

BW = Average body weight of the receptor over the exposure period (kg).

AT = Averaging time, the period over which exposure is averaged (days). For carcinogens,

the averaging time is usually 25,550 days, based on an assumed lifetime exposure of

70 years; for non-carcinogens, averaging time equals ED (years) multiplied by 365

days per year.

As noted above, modeling and/or monitoring (sampling and analysis) can be used to determine

the exposure concentration (EC) at specified exposure points. However, a variety of approaches

and assumptions can be used to determine the remaining variables in the equation, as will be

discussed in subsequent chapters. For example, calculation of the intake rate requires

assumptions about diet (i.e., how much the exposed individual eats and drinks each day) and

body weight (how much the individual weighs). Dietary assumptions need to be specific to the

type of food consumed (e.g., fish, milk, beef).
April 2004 Page 15-7

As noted in Section 6.3.3.4, the exposure duration (ED) used to calculate chemical intake rate (I)

will have an impact on the choice of toxicity values (e.g., acute vs. chronic) used to characterize

risk and hazard. As a general rule, the ED values should match the exposure assumptions used in

developing the dose-response values.

15.3.3.5 Evaluation of Uncertainty

As with the inhalation assessment, the evaluation of uncertainty includes both a summary of the

values used to estimate exposure, including their range, midpoint,and other values; and a

qualitative or quantitative discussion that evaluates which variables or assumptions have the

greatest potential to affect the overall uncertainty in the exposure assessment.

15.3.3.6 Preparation of the Documentation

The analysis plan needs to specify the approach used to document the multipathway exposure

assessment, as discussed in Chapter 20.

15.3.4 Identification of the Exposed Population

This part of the analysis plan identifies the exposed population that will be evaluated in the risk

assessment. The procedure for characterizing the potentially exposed population generally will

be similar to that described for the inhalation pathway (Chapter 6). As noted previously, it may

be necessary to include individuals who live outside the modeled deposition area. The manner in

which potentially exposed populations are characterized depends on the general approach used

for the multipathway assessment (see Section 15.4 below).

15.3.5 Identification of Endpoints and Metrics

This part of the analysis plan identifies the specific human health endpoints that will be evaluated

in the risk assessment and the metrics used to quantify exposure and risk. The multimedia

assessment uses the same general endpoints (i.e., cancer and non-cancer) and presents the central

tendency and high-end tendency descriptors required as the range of risk estimates of the

distribution. Risk characterization is discussed in more detail in Chapter 22.

15.4 Exposure Assessment Approach

A variety of approaches are available for multipathway exposure assessments. This section

describes two representative approaches that range from a relatively simple approach based on

scenarios to a very complex and data-driven approach based on mass-balance models. This

discussion is intended to illustrate some of the potential approaches available for multipathway

exposure assessment. A given risk assessment might incorporate features of either of the two

approaches outlined below, or might feature a different approach.

Regardless of the specific approach taken, EPA recommends a tiered approach to multipathway

exposure assessment, in which the exposure assessment moves from relatively simple to more

complex as warranted by the quality of available information and its ability to be used to support

the risk management decision(s). Chapter 3 provides an overview of tiered approaches to risk

assessment.
April 2004 Page 15-8

Childhood exposures need to be considered explicitly in any non-inhalation scenario. This can

be done with a separate scenario (e.g., a “resident child”) or by incorporating changes in

consumption rates, dietary preferences, and body weight with age in the exposure factors

incorporated into the scenario. EPA’s Risk Assessment Forum recently published guidance on

selecting appropriate age groups for assessing childhood exposures.(2)

15.4.1 Scenario Approach

Multipathway exposures may be evaluated by developing a number of scenarios that describe the

potential human exposures that might occur via each of the potential exposure pathways

identified in the conceptual model. An exposure scenario is a combination of exposure

pathways by which a single defined human receptor might be exposed to air toxics that persist

and which also may bioaccumulate. The specific exposure scenarios defined for a given risk

assessment would be based on the characteristics of the exposure setting, potential exposure

pathways, potential exposure points or areas, and predominant land uses and activities associated

with the potentially exposed population. For example, if the study area included a small lake

where fishing might occur, the assessment might include a “fisher” scenario that included

ingestion of fish caught in the lake.

The scenario approach generally involves relatively simple modeling and fewer data

requirements as modeling inputs. This can be performed by using “linked modeling systems”

which can either be relatively simple or incorporate highly sophisticated single-medium models

into a single multimedia system. However, these types of models do not assure conservation of

mass and therefore may under- or over-estimate exposure concentrations for particular scenarios. 

The general scenario approach involves:

• Identifying the potential exposure pathways that may be important, including the areas where

contaminants have the potential to accumulate in soils, surface waters, sediments, and biota;

and specific activities that may result in ingestion of these contaminants (either via incidental

ingestion of soil while in the contaminated areas or by consuming the contaminated plants,

animals, or surface water).

• Developing a set of scenarios that describe reasonable sets of potential exposure pathways,

given the types of people and activities that occur within the study area. The scenarios would

include specific exposure factors (e.g., body weight, fish consumption) based on the

particular activities identified above. The exposure scenarios should consider children, either

as a separate scenarios (e.g., a “resident child”) or as part of an overall scenario (e.g.,

someone who is born in the exposure area and lives there for 30 years, and thus experiences

exposure both during childhood and as an adult). The exposure factors could be set initially

at conservative levels for screening-level assessments and then at more site-specific levels for

higher tiers of analysis. Each exposure scenario also should appropriately consider studyspecific sub-populations that may experience different exposure conditions (e.g., because

they eat different foods or parts of foods at different rates than the general population).

• Using relatively simple multimedia modeling techniques (e.g., “linked modeling systems”

described in Chapter 18) to estimate exposure concentrations in the media and biota of

interest to each scenario. Monitoring (sampling or analysis) could be used to augment the

modeling effort. For screening-level analyses, the scenarios can be based on the locations
April 2004 Page 15-9

with the highest modeled concentrations or deposition rates. For example, a scenario

involving consumption of fish could be based on the same location as a residential scenario. 

In some cases, it may be appropriate to evaluate an exposure scenario assuming exposure

through ingestion of fish from one water body and drinking water from a different water

body. Such assumptions may need to be refined in subsequent modeling tiers.

• Quantifying the dietary intake for each scenario based on the modeled or estimated exposure

concentrations and the specific exposure factors for each scenario.

Several exposure scenarios are commonly used in multipathway risk assessments (Exhibit 15-4). 

A recent description of several scenarios is provided in EPA’s risk assessment guidance for

hazardous waste incinerators.(3) In addition to the commonly assessed scenarios provided in

Exhibit 15-4, other scenarios may be appropriate, depending on study-specific conditions. For

example, if a contaminated surface water body is used for bathing and swimming, incidental

ingestion, and dermal exposure from these activities may need to be considered. As another

example, the resuspension of contaminated soils (i.e., windblown dust) may be important in

some study areas. Note also that exposure of an infant to chlorinated dioxins/furans (and other

lipophilic contaminants) via the ingestion of breast milk may be evaluated as an additional

exposure pathway, separately from adult exposures, in each of the scenarios outlined below. 

Note also that in each of these scenarios, the risk assessment needs to look at both an adult

and child (for example, the “farmer” includes both an adult farmer and a farmer child).

Farmer. The farmer scenario is commonly evaluated to account for the combination of exposure

pathways to which a person may be exposed in a farm or ranch exposure setting. As indicated in

Exhibit 15-4, the farmer is commonly assumed to be exposed to air toxics through one or more of

the following exposure pathways:

C Direct inhalation of vapors and particles;

C Incidental ingestion of soil;

C Ingestion of drinking water from surface water sources;

C Ingestion of homegrown produce;

C Ingestion of homegrown beef;

C Ingestion of dairy products from homegrown livestock;

C Ingestion of homegrown chicken;

C Ingestion of eggs from homegrown chickens;

C Ingestion of homegrown pork; and

C Ingestion of breast milk (evaluated separately for an infant [for PCBs, dioxins, and furans]).

In a Tier 1 assessment, the farmer commonly is assumed to consume a certain amount daily (e.g.,

grams/day) of each food group (beef, pork, poultry, eggs, and milk) to make up a total

consumption rate, and amounts consumed are assumed to be homegrown. If site-specific

information is available that demonstrates that a farmer does not raise beef, poultry, or pork, and

that raising any of these livestock would not occur for a reasonable potential future farmer at a

location, then elimination of one or more of these exposure pathways could be justified. The

farmer scenario often does not include the fish ingestion exposure pathway. However, in some

areas of the country, it is common for farms to also have stock ponds which are fished on a

regular basis for the farmer’s consumption. Also, ingestion rates (e.g., food, incidental soil

ingestion) often are age-dependent.
April 2004 Page 15-10

Exhibit 15-4. Common Exposure Scenarios Used in Multipathway Exposure Assessments

Exposure Pathways

Scenariosa

F

arm

Resident

R

esid

e

nt

R

esid

e

nt with

G

a

r

d

e

n

L

ocal Fish

C

o

nsu

mers

Inhalation of Vapors and Particulatesb• • • •

Incidental Ingestion of Soil • • • •

Ingestion of Drinking Water from Surface Water Sources • • • •

Ingestion of Homegrown Produce • – • •

Ingestion of Homegrown Beef • – – –

Ingestion of Milk from Homegrown Cows • – – –

Ingestion of Homegrown Chicken • – – –

Ingestion of Eggs from Homegrown Chickens • d d d

Ingestion of Homegrown Pork • – – –

Ingestion of Fish d d d •

Ingestion of Breast Milk c c – c

Notes:

• Pathway is included in exposure scenario.

– Pathway is not included in exposure scenario.

a Exposure scenarios are defined as a combination of exposure pathways evaluated for a receptor at

a specific exposure scenario location. Note that these scenarios are not exhaustive (i.e., additional

or other scenarios may be relevant to a particular exposure assessment). Note also that within

each scenario, the quantitative exposure estimates will vary across age groups.

b Note that inhalation is included in the overall exposure assessment, but the inhalation exposure

assessment is performed separately (as described in Part II of this Reference Library). 

c

Infant exposure to dioxins and/or furans via the ingestion of their mother’s breast milk is evaluated

for infants as an additional, separate exposure pathway.

d

Regional specific exposure setting characteristics (e.g., presence of ponds on farms or within

semi-rural residential areas, presence of livestock within semi-rural residential areas) may warrant

inclusion of this exposure pathway when evaluating a recommended exposure scenario.

Resident. The resident scenario is commonly evaluated to account for the combination of

exposure pathways to which a person may be exposed in an urban or rural (non-farm) setting. As

indicated in Exhibit 15-4, the resident is commonly assumed to be exposed to air toxics through

the following exposure pathways:

C Direct inhalation of vapors and particles;

C Incidental ingestion of soil;

C Ingestion of drinking water from treated surface water sources; and

C Ingestion of breast milk (evaluated separately for an infant [for PCBs, dioxins, and furans]).
April 2004 Page 15-11

The resident scenario often does not include the fish ingestion exposure pathway. However, in

some areas of the country, ponds within semi-rural residential areas support fish for human

consumption. 

Resident with Garden. The resident with garden scenario is commonly evaluated to account for

people who may be exposed while gardening or through the consumption of produce grown in

their garden in an urban or rural (non-farm) setting. As indicated in Exhibit 15-4, the resident

with garden is commonly assumed to be exposed to air toxics through the same exposure

pathways as the resident scenario, with one additional exposure pathway:

C Ingestion of homegrown produce.

Local Fish Consumer. The local fish consumer scenario is evaluated to account for the

combination of exposure pathways to which a receptor may be exposed in an urban or rural

setting where fish is the main component of the person’s diet. As indicated in Exhibit 15-4, the

local fish consumer is commonly assumed to be exposed to air toxics through the following

exposure pathways:

C Direct inhalation of vapors and particles;

C Incidental ingestion of soil;

C Ingestion of drinking water from surface water sources;

C Ingestion of homegrown produce;

C Ingestion of fish; and

C Ingestion of breast milk (evaluated separately for an infant [for PCBs, dioxins, and furans]).

In many cases, local fish consumers are assumed to grow some of their own produce, but this

may or may not be relevant to a particular risk assessment. Also note that in some parts of the

country, a primary reliance on fishing as a source or dietary protein is common - a circumstance

known as “subsistence fishing.” Subsistence hunting may also be important for some groups.

15.4.2 Population-Based Approach

Multipathway exposures may be evaluated by tracking individual members of a population and

their inhalation and ingestion through time and space. Such analyses may incorporate a userspecified number of simulated individuals or population groups (cohorts) to represent the

population in the study area. A cohort is defined here as a group of people within a population

with the same demographic variables who are assumed to have similar exposures. In this

approach, the exposure analysis process consists of relating chemical concentrations in

environmental media (e.g., air, soil, water) to chemical concentrations in the exposure media

with which a person or population has contact (e.g., soil, food, household dust). Exposure is

estimated by tracking the movement of a population cohort through locations where chemical

exposure can occur according to a specific activity pattern.(4) Models such as the Stochastic

Human Exposure and Dose Simulation Model (SHEDS), Calendex, the Hazardous Air Pollution

Exposure Model (HAPEM), and the Total Risk Integrated Methodology, Exposure Event Model

(TRIM.Expo) incorporate this approach (see Chapter 19). The general approach, which is

analogous to the inhalation exposure modeling techniques described in Chapter 9, involves:
April 2004 Page 15-12

1. U.S. Environmental Protection Agency. 1995. New Policy on Evaluating Health Risks to

Children. Memorandum to Assistant Administrators, General Counsel, Inspector General,

Associate Administrators, Regional Administrators. From Carol M. Browner, Administrator,

and Fred Hansen, Deputy Administrator. Washington, DC., October 20, 1995.

• Defining exposure districts – geographic locations within the study area where there is

potential contact between humans and a pollutant – and estimating chemical concentrations

within each exposure district (through modeling and/or measurement).

• Preparing inventories of chemical concentrations in each microenvironment in each exposure

district at selected time intervals (e.g., days, hours). These inventories may be developed

using mass-balance approaches that predict the partitioning of chemicals throughout the

environment or through the use of microenvironment factors.

• Identifying the characteristics and activity patterns of each population cohort or set of

representative individuals (e.g., the demographic characteristics of the individual; the

locations where the individual lives, works, etc.; the amount of time spent in each location,

and the individual’s activities within a location). These activity patterns should be

representative of the exposed population of concern. For representative individuals or

cohorts, each simulated person/cohort is represented by a “personal profile” or “activity

pattern” developed by selecting from a set of variables that include:

– Demographic variables (e.g. age, sex), which are generated based on census data;

– Residential variables (e.g., where the person lives, works, etc., which are generated based

on sets of distribution data;

– Daily varying variables (e.g., how long a person works in a garden), which are generated

based on distribution data that change daily during the simulation period;

– Physiological variables (e.g., height, weight), which are generated based on age groupspecific distribution data; and

– Dietary variables (e.g., amount and type of food consumed), which are generated based

on sets of distribution data.

Profiles or activity patterns may be developed using probability density functions, allowing

the analysis to incorporate probabilistic techniques such as Monte Carlo analysis (see Part VII

of this reference manual).

• Summing the exposures for each exposure event over the assumed duration of exposure (e.g.,

lifetime or portion of a lifetime).

The primary advantage of the population-based approach is that it is a more realistic exposure

assessment that simulates how people actually live and work within the study area. It therefore

can provide a more complete characterization of the spatial and temporal patterns of exposure. 

The primary disadvantage of the cohort approach is the time and resources it requires, including

significant input data requirements.

References
April 2004 Page 15-13

2. U.S. Environmental Protection Agency. 2003. Guidance on Selecting the Appropriate Age

Groups for Assessing Childhood Exposures to Environmental Contaminants (External

Review Draft). Risk Assessment Forum, Washington, D.C., February 1, 2003. 

EPA/630/P-03/003A. Available at: http://cfpub.epa.gov/ncea/cfm/

recordisplay.cfm?deid=55887

3. U.S. Environmental Protection Agency. 1998. Human Health Risk Assessment Protocol for

Hazardous Waste Combustion Facilities, Volume 1. Peer Review Draft. Office of Solid

Waste and Emergency Response, Washington, D.C., July 1998. EPA/530/D-98/001A.

Available at: http://www.epa.gov/combustion/risk.htm.

4. See the Human Exposure Modeling page of EPA’s Fate, Exposure and Risk Analysis

(FERA) website: http://www.epa.gov/ttn/fera/human_gen.html.

See the TRIM.Expo web page at: http://www.epa.gov/ttn/fera/human_apex.html.

U.S. Environmental Protection Agency. 1999. Total Risk Integrated Methodology. 

TRIM.Expo Technical Support Document. External Review Draft. Office of Air Quality 

Planning and Standards, Research Triangle Park, NC, November 1999. EPA/453/D-99/001. 

Available at http://www.epa.gov/ttn/fera/trim_fate.html#1999historical.
Chapter 16 Quantification of Exposure: 

Development of the Emissions Inventory

for the Multipathway Risk Assessment

Table of Contents

16.1 Introduction ............ ............ ............ ............ ............ ...... 1

16.2 Developing the Emissions Inventory ............ ............ ............ .......... 1

April 2004 Page 16-1

16.1 Introduction

Chapter 7 provides an overview of the process used to develop an emissions inventory for an air

toxics risk assessment. As noted in that chapter:

• Emissions data are a source term for the risk assessment, primarily as a key input for

computer models that estimate the transport of chemicals in the atmosphere; if and how they

will be transformed by chemical or physical processes; how and where they will be

deposited; and how they will continue to partition and move through environmental media

following deposition.

• Developing the emissions inventory involves identifying the specific air toxics released from

the source and quantifying release characteristics (e.g., release rates, temperature, release

velocity).

• Local enhancements of existing air toxics emissions inventories may be advantageous to a

particular air toxics assessment effort as a very critical initial step, because air toxics

inventories are not always at the quality that would provide the results desired in a modeling

assessment.

16.2 Developing the Emissions Inventory

The process used to develop the emissions inventory for the multipathway risk assessment is

similar to the process for inhalation analyses (see Chapter 7 for a description of this process). 

However, there are a few additional considerations that may apply to a given multipathway

analysis (e.g., information on particulate/particle-bound/vapor fractions if ISCST3 is used). See

Chapter 18 for more discussion on inputs for models used in multimedia assessment.
Chapter 17 Quantification of Exposure: Chemical

and Physical Properties Affecting

Multimedia Fate and Transport

Table of Contents

17.1 Introduction ............ ............ ............ ............ ............ ...... 1

17.1.1 Measures of Persistence ............ ............ ............ ............ . . 1

17.1.2 Metrics of Bioaccumulation ............ ............ ............ .......... 5

17.2 Chemical and Physical Properties that Affect Persistence and Bioaccumulation ............ 8

17.3 Evaluating Persistence and Bioaccumulation in Exposure Assessments ............ ...... 10

References ............ ............ ............ ............ ............ ............ 11

April 2004 Page 17-1

17.1 Introduction

This chapter provides both an overview of the physical and chemical properties of chemicals that

persist in the environment long enough to be of potential concern for multimedia exposure and

information on their potential to bioaccumulate in biological tissues to levels that may result in

significant exposures. This chapter also provides a brief discussion of various approaches for

evaluating persistence and bioaccumulation in an air toxics exposure assessment.

• The term persistence refers to air toxics that are slow to degrade in the atmosphere or in the

soils, water, and/or sediments onto which they deposit and partition. Under certain

circumstances, persistent chemicals can increase in concentration over time. In addition,

some of these pollutants can bind tightly to soils and sediment and move from place to place

by erosion. Examples of persistent chemicals are metals (which never degrade) and

polychlorinated biphenyls (which degrade, but only over very long periods of time). 

Measures of persistence are described in Section 17.1.1.

• The term bioaccumulation refers to persistent chemicals that build up in the tissues of living

organisms to concentrations that are higher than in the surrounding environment. These

pollutants are often lipophilic in nature, which allows them to be taken up in and stored by fat

tissues. As an example, the group of chemicals known collectively as dioxins (usually some

mixture of chlorinated dioxins and furans) persists for long periods of time in the

environment and is strongly lipophilic. Repeated exposure to dioxin in food can lead to

increased body burdens in fat tissue and human milk. Measures of bioaccumulation are

discussed in 17.1.2.

• The term biomagnification refers to persistent chemicals that increase in concentration as

they transfer up the food chain so that they accumulate to higher concentration levels with

each successive food chain level (see Section 17.1.2).

The air pollutant’s physical and chemical properties and the characteristics of the environment to

which the pollutant is emitted affect its potential to persist and bioaccumulate. One of the most

important determinants in predicting persistence and accumulation in biota and other

environmental media is the partitioning behavior of the pollutant. Partitioning refers to where in

the environment a chemical will tend to reside and in what relative quantities. When released to

the air, chemicals may partition to air, water, soils, sediments, or biota, depending on a number

of chemical and site-specific factors. Section 17.2 highlights measures of partitioning and other

chemical and physical properties. Section 17.3 discusses how measures of persistence,

bioaccumulation, and partitioning are used in exposure assessment.

17.1.1 Measures of Persistence

Estimating the persistence of chemicals in the environment is a challenging exercise. Persistence

depends on basic processes such as how the chemicals are released (i.e., which environmental

media they are released to initially, in this case, air); how they move in the environment (i.e., to

which environmental media they tend to partition); and their tendency to degrade within those

specific media (i.e., their persistence in air vs. in water). These basic processes, in turn, depend

on a number of chemical-specific properties and site-specific conditions. However, despite the
April 2004 Page 17-2

Exhibit 17-1. Degradation Processes in

Environmental Media

Environmental

Medium

Applicable Degradation

Process(es)

Air

Photolysis

Photooxidation

Surface water

Aerobic biodegradation

Hydrolysis

Photolysis

Photooxidation

Soils

Aerobic biodegradation

Hydrolysis

Photolysis (surface soil)

Oxidation - reduction

reactions

Sediments Anaerobic biodegradation

complexity of the overall process, some general screening methods are available to get a sense of

persistence of chemicals in certain media. 

Within each environmental medium, several different degradation processes can influence the

persistence of chemicals. However, regardless of the specific degradation process, what

generally occurs is that organic chemicals (or organo-metallic compounds) are reduced in size

and complexity, and, if complete degradation occurs, converted to carbon dioxide, methane,

ammonia, water, sulfates, phosphates, nitrates, and other end products. Explanations of the

specific degradation processes and their applications within different environmental media are

summarized below and in Exhibit 17-1:

• Aerobic biodegradation is the breakdown of chemicals by microorganisms that utilize

oxygen;

• Hydrolysis is the breakdown of chemicals by reaction with water;

• Photolysis or photodegradation is the process by which chemicals can be degraded by the

energy in artificial light or sunlight;

• Oxidation or reduction (or redox)

reactions involving the exchange of

electrons between the pollutant and reactive

compounds found in the environment;

• Photooxidation is a process by which

oxidation and photolysis work jointly to

break down chemicals and refers to a

reaction with oxygen in the presence of light

(usually sunlight); and

• Anaerobic biodegradation is the

breakdown of chemicals by microorganisms

without the use oxygen (for example, in

sediments).

Persistence is usually described using a term

called half-life, which is the time required for

one-half of the original mass of the chemical to

be degraded, transformed, or destroyed in a

given medium. Half-life values may be

measured directly or estimated (e.g., with

computer models that predict half-life based on

chemical structure). Alternatively, the literature

may report a degradation or transformation rate

constant in units such as 1/day or day-1. Assuming the reaction is first-order, the rate constant

can be converted to the half-life and vice versa using the equation t1⁄2 = 0.693/k, where t1⁄2 is the

half-life (days) and k is the first-order rate constant (day-1). Exhibit 17-2 provides a list of data

sources for degradation half-life or rate constant values.
April 2004 Page 17-3

Exhibit 17-2. Data Sources for Half-Life and Rate Constant Values

Data Source Data Elements Comments

Howard et al.

Handbook

• High and low compartment

half-life values for soil, air,

surface water, and

sediment/ground water

based on measurements and

modeling estimations; and

• High and low measured and

estimated process-specific

half-life values, including

hydrolysis, reduction,

photolysis, photooxidation

(in water and air), and

biodegradation.

Howard et al.(1) compiled measured and estimated

environmental degradation rates for organic

chemicals, including polycyclic aromatic

hydrocarbons (PAHs), pesticides, and solvents.

Mackay et al

Handbooks

• Measured and estimated

half-life and rate constant

data for air, water, soil, and

sediment.

Mackay et al.(2) compiled measured and estimated

environmental degradation rates for organic

chemicals, including monoaromatic

hydrocarbons, chlorobenzenes, polychlorinated

biphenyls, polynuclear aromatic hydrocarbons,

polychlorinated dioxins and dibenzofurans,

volatile organic compounds (VOCs), pesticides,

and oxygen, nitrogen, and sulfur organic

compounds. 

Verschuren • Measured and estimated

half-life values in surface

water, ground water,

sediment, soil, and biota.

Verschuren(3) compiled measured and estimated

degradation rates for organic chemicals.

Mercury Study

Report to

Congress

• Measured demethylation

rate constants converted to

half-life data for mercury

(water, soil, sediment).

The Mercury Study Report to Congress(4) was

developed for a rulemaking and has undergone

extensive Agency peer review.

HYDROWIN • Estimated hydrolysis rate for

water.

HYDROWIN, the hydrolysis estimation program,

is part of EPA’s Estimation Program Interface

(EPI) Suite.(5) HYDROWIN uses a chemical’s

structure to estimate the acid- and base-catalyzed

rate constants for certain chemical classes (esters,

carbamates, epoxides, halomethanes, and certain

alkyl halides). Chemicals can be catalyzed

(broken down) by acids (hydronium) or bases

(hydroxide ions). The rate constants are used to

calculate hydrolysis half-lives at selected pHs.
April 2004 Page 17-4

Half-life values for the same substance in different media, and for different substances in the

same medium, can differ by several orders of magnitude. This is illustrated in Exhibit 17-3.

Exhibit 17-3. Example Illustrating Ranges of Half-life Values in Different Environmental Media

HAP

Measured or Estimated Half-life Value (Hours)(a)

Air Water Soil Sediment

acrolein 19 672 672 1,776

benzene 276 252 252 9,984

chlorobenzene 401 2,616 50 1,800

formaldehyde 4 192 96 384

methyl bromide 8,980 420 420 1,680

2,3,7,8-TCDD 200 768 12,096 14,400

Note: Values are for Illustrative Purposes Only

(a) Values represent the maximum measured or estimated values from the references cited in Exhibit 17-2.

The fate and transport models used for characterizing multipathway exposure use metrics of

persistence to account for loss of the chemical through degradation or transformation processes

described above. Typically, for each modeled media compartment, the fate and transport models

require an overall metric of persistence for that media compartment (e.g., a half-life value for air,

a half-life value for soil, a half-life value for surface water). The media compartment half-life is

the half-life associated with the most important or fastest degradation process or reaction. That

is, the process-specific half-life of the fastest degradation process is usually selected as the

overall media compartment half-life.

Metals may transform among different compounds or species (e.g., divalent mercury can undergo

methylation to yield methyl mercury, and it can be reduced to form elemental mercury). Some

metal species are more persistent and/or may be more toxic than others. Transformation half-life

values in different media are more useful for evaluating the persistence of metal pollutants of

concern. The speciation of metals is highly dependent on geochemical environment (and in some

cases, the presence of certain microbes). One species may dominate in water of one pH, Eh,

DOC concentration, etc., and an entirely different species may dominate in water of different

geochemistry. [Also note that a further complicating factor is that analytical laboratories often

report the total amount of a metal present in a sample, rather than the amount of the various

individual metal species present (e.g., Cr6+ versus Cr3+), which usually have different toxicities

and different persistence values. An understanding of the concentrations of various metal species

is therefore desirable; however, it is often not analytically achievable.](6) There are a number of

concerns regarding the assessment of risks posed by metals in the environment. Information on

this subject can be found at the following website:

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=51736.
April 2004 Page 17-5

Note that BAFs and BCFs are not calculated for

humans; rather, they are sometimes used to

estimate air toxics concentrations in the food items

eaten by people.

What is a Persistent Chemical?

Because environmental persistence is a complicated phenomenon, no single value is universally

accepted as indicating a “persistent” chemical. EPA has used a half-life value of two months (1,440

hours) in the Toxics Release Inventory (TRI) PBT final rule and the Premanufacturing Notice

evaluation process.(7) Other authors have suggested a half-life value of one month (720 hours), using

the following logic: Assuming that a chemical will degrade in approximately six half lives (when less

than two percent of the mass remains), a chemical with a half-life of one month would persist in the

environment for six months. This six month period would be long enough to encompass the sensitive

developmental life stages of many organisms.(8)

Note that the use of any single threshold value to define “persistent” chemicals may be

misleading. An important consideration is data quality (e.g., whether the half-life value is measured

or predicted and the overall quality of the experimental study or computer algorithm used to develop

the value). Assessors must consider other factors including the tendency of the chemical to partition

into various media. For example, vinyl chloride has a half-life of six months in surface water and two

years in sediment – values that might suggest this chemical is “persistent.” However, its half-life in air

is 53.4 hours and in soil is 10 days. Thus, depending on its relative tendency to partition between air,

soil, water, and sediment, a significant amount of the mass in air emissions might be degraded (in the

air and/or soil) prior to the chemical reaching surface water and sediment.

Ultimately, it is often difficult to select a single and reliable half-life because the half-life of a

chemical depends not only on its physical properties (see section 16.4), but also on the

environmental conditions of the site to which the chemical is emitted. Temperature, sunlight

intensity, the nature of the microbial community, and concentrations of reactive species such as

oxygen radicals can all affect the reactivity of a compound.(3)

17.1.2 Metrics of Bioaccumulation

Several chemical-specific metrics can be

used to evaluate the potential for a chemical

to bioaccumulate in plants and animals and

biomagnify in food webs.(9) These values

may be measured in laboratory tests or

estimated with computer models based on

chemical structure. These metrics include:

• Bioaccumulation Factor (BAF). The concentration of a substance in tissue of an organism

divided by its concentration in an environmental medium in situations where the organism

and its food are exposed (i.e., accounting for food chain exposure as well as direct chemical

uptake). Such values are often used to characterize the transfer of pollutants through

consumption of fish, beef, or dairy products.

(10)

• Bioconcentration Factor (BCF). The concentration of a substance in the tissue of an

organism divided by the concentration in an environmental medium (e.g., the concentration

of a substance in an aquatic organism [μg/kg body weight] divided by the concentration in

the ambient water [μg/L water], in situations where the organism is exposed through the

water only). The most commonly given value is an estimate of the relative concentrations in

water and whole fish (or, in some cases, fish fillets, since most people in the U.S. do not eat
April 2004 Page 17-6

whole fish), but BCFs for other organisms are also available. For plants, the Root

Concentration Factor (RCF) indicates how readily pollutants are taken up from soil into

underground tissues.

• Octanol/water partition coefficient. The ratio of a chemical’s solubility in n-octanol to its

solubility in water at equilibrium (in this test, n-octanol is used as a surrogate for lipophilic

tissue). This metric, usually expressed as a logarithm (log Kow or log P), is often used as a

surrogate for (and is an important basis for estimating) BAF or BCF.

As with persistence, bioaccumulation is a complicated phenomenon, and no single value is

universally accepted as indicating that a chemical has a high tendency to bioaccumulate, although 

EPA’s Office of Pollution Prevention and Toxics has used a threshold BCF value of 1,000 in

evaluating new and existing chemicals.(11) Exhibit 17-4 provides a list of sources for

bioaccumulation data.

Importance of Trophic Levels in Evaluating

Bioaccumulation in Aquatic Food Chains

An organism’s trophic position in the aquatic food web can have an important effect on the magnitude

of bioaccumulation of certain chemicals. Certain pollutants have the potential to biomagnify, or

increase in concentration at successive trophic levels through a series of predator-prey associations. 

Chapter 5 (Bioaccumulation) of EPA’s Methodology for Deriving Ambient Water Quality Criteria for

the Protection of Human Health provides guidance about deriving trophic level-specific

bioaccumulation factors.(17)
April 2004 Page 17-7

Exhibit 17-4. Sources of BCF and BAF Values

Data Source Data Element Comments

HWIR Technical

Support Document

Measured BAF

Measured BCF

Predicted BAF

Predicted BCF

The Hazardous Waste Identification Rule (HWIR)

Technical Support Document(12) data were developed for

a rulemaking. Estimates are available for different

classes of organisms (fish and invertebrates).

Mercury Study

Report to Congress

Measured BAF for

mercury

The Mercury Study Report to Congress(4) was developed

for a rulemaking and has undergone extensive Agency

peer review.

Ambient Water

Quality Criteria

documents

Measured BCF Ambient Water Quality Criteria documents(13) are

developed for rulemakings that establish concentration

limits for chemicals in the surface waters of the United

States. They have undergone extensive Agency peer

review.

AQUIRE Database Measured BCF EPA’s Office of Research and Development,

Environmental Research Laboratory in Duluth, MN,

maintains a database of citations and aquatic bioassay

data including residue measures.(14)

BCFWIN Predicted BCF BCFWIN, the Bioconcentration Factor Estimation

Program, is part of EPA’s EPI Suite.(5) This program

estimates BCFs based on log Kow data using the

estimation methodology presented in a 1997 study

prepared for EPA by Meylan et al.(15) The methodology

was formulated using a training set of 694 compounds

with measured BCF values for fish. In order of

preference, the program uses (1) the log Kow entered by

the user, (2) the experimental log Kow from the

experimental log Kow database for KOWWIN, and (3)

the KOWWIN estimated log Kow value.

BAF or BCF values are used in some fate and transport models to calculate how much of the

chemical will partition into organisms, such as fish or shellfish, that are consumed by humans or

ecological receptors of concern. The concentrations in these biota can then be used to calculate

the intake of the chemical by humans or ecological receptors of interest. It is recommended that

BAFs be derived separately for species of different trophic levels to account for different levels

of accumulation for members of different trophic levels.(16) EPA presents specific guidelines for

deriving BAF values, including how to estimate BAFs using BCF values in its revised

methodology for developing Ambient Water Quality Criteria.(17) National-level BAFs developed

by the Office of Water can be used in screening analyses. These can be refined based on sitespecific characteristics in subsequent tiers of the assessment, if necessary.

As with half-life values, BCF values vary significantly among air toxics. Exhibit 17-5 presents a

few representative BCF values for several HAPs. Note that these represent the highest values

identified in the references cited in Exhibit 17-4 and are meant for illustrative purposes only.
April 2004 Page 17-8

Exhibit 17-5. Example BCF Values

HAP BCF Value(a)

acrolein

carbon tetrachloride

chloroform

pentachlorophenol

2,3,7,8-TCDD

3

30

6

776

5,754

Note: values are for illustrative purposes only

(a) Values represent the maximum measured or estimated values

from the references cited in Exhibit 17-4.

Note that the use of any single threshold value to define “bioaccumulative” chemicals may

be misleading. As with persistence, data quality is an important consideration (e.g., measured

vs. predicted values; quality of the underlying study/algorithm used to develop the value). 

Moreover, the specific organisms and tissues in which bioaccumulation is measured or predicted

are important determinants of the relevance of a given BCF or BAF value for the exposure

assessment. Many substances that bioaccumulate tend to accumulate in lipid tissues (e.g., fat,

organs), which may not be the tissues that most people eat (e.g., fish fillet). Therefore, a high

BCF may not actually result in high exposures to humans.

17.2 Chemical and Physical Properties that Affect Persistence and Bioaccumulation

Exhibit 17-6 identifies both direct metrics of persistence (i.e., half-life values), as well as the

physical and chemical properties that are most important in determining persistence and

bioaccumulation.

In Exhibit 17-6, solubility and vapor pressure determine the propensity for pollutants to

dissolve in water and volatilize into the air, respectively. The Henry’s Law constant, which is

simply the ratio of the chemical’s water solubility to its vapor pressure, indicates whether a

compound will partition into air or water at equilibrium. The speed with which the equilibrium

occurs is affected by the diffusion constants in air and water.

The coefficients that are abbreviated with a capital “K” (e.g., the octanol-water partition

coefficient, Kow) in Exhibit 17-6 provide information on how strongly organic and inorganic

compounds are likely to bind to soil or sediment particles, or to partition into lipid versus

aqueous phase liquids. Strong binding indicates a high potential to persist and accumulate in

soils and sediments; soil/sediment binding also tends to be correlated with the potential to

bioaccumulate.

The 1998 Peer Review Draft of EPA’s Human Health Risk Assessment Protocol for Hazardous

Waste Combustion Facilities (HHRAP) provides (Appendix A) tables of recommended rate

constants and chemical/physical parameters for a large number of air toxics.(18) Also, the user’s

guide for EPA’s Risk Screening Environmental Indicators (RSEI) tool includes physicochemical

properties for TRI chemicals and chemical categories.

(19) Note, however, the values in these

sources may need to be updated.
April 2004 Page 17-9

Exhibit 17-6. Chemical and Physical Property Definitions

Property Definition Significance/Comments

Name Unique identifier Not always reliable (many synonyms)

CAS No. Unique identifier Much more reliable than the chemical name; some chemicals

have no CAS number; some CAS numbers refer to mixtures

MW Molecular weight In absence of data, can be used to calculate Da, Dw, etc.

Vp Vapor pressure Indicates volatility

S Solubility Indicates maximum concentration of a chemical that will

dissolve in water

H Henry’s Law constant Ratio of vapor phase concentration to the liquid phase

concentration of a gas; high values indicate tendency to

volatilize from water solution

Da Diffusion coefficient in air Used to calculate rate of volatilization from air

Dw Diffusion coefficient in water Used to calculate rate of volatilization from water

Kow Octanol-water partition

coefficient (log Kow is

frequently tabulated)

High value (> 1000, log Kow > 3) indicates strong tendency to

bioconcentrate

Koc Organic carbon partition

coefficient (log Koc often

tabulated)

High value indicates strong tendency to bind to soil/sediment;

Koc, Kow can be estimated from each other

Kdsoil Soil dissociation constant Indicates potential of inorganic ions/compounds to bind to soil;

varies for different ionic species, pH, soil types

Kdsed Sediment dissociation constant Indicates potential of inorganic ions/compounds to bind to

sediment (similar to Kdsoil)

t1/2soil Half-life in soil Indicates persistence in soil; generally soil type, conditions,

and degradation pathway(s) must be specified

t1/2sed Half-life in sediment Indicates persistence in sediment; same considerations as for

t1/2soil

t1/2sw Half-life in surface water Indicates persistence in surface water; for moderate to

high-vapor pressure compounds

BAF Bioaccumulation factor Indicates accumulation of a compound into tissues of an

organism from contact with contaminated water, contaminated

sediments, and ingestion of contaminated food 

BCF Bioconcentration factor Indicates accumulation of a compound into tissues of an

organism from contact with a contaminated medium.

RCF Root concentration factor Ratio of root to soil concentration, measures propensity to take

up pollutant from soil for defined plant species

B Biotransfer factors Describes propensity of pollutant to be transferred through

food chain; defined for specific crops and consuming

organisms (e.g., alfalfa => dairy cattle); generally correlates

with BCF, Kow; used primarily for detailed pathway modeling
April 2004 Page 17-10

Fugacity as a Determinant in the Fate and Transport of a Pollutant

Once a contaminant is emitted from its source, the fate of the contaminant is determined by a number

of factors (e.g., molecular weight, solubility, partition coefficients). In order to characterize the fate

of the pollutant, modelers have developed the concept of fugacity(20) to define the tendency of the gas

to escape to another phase in order to reach a steady-state equilibrium. This tendency to migrate then

leads to partitioning of the pollutant across environmental media based on its chemical and physical

characteristics. Fugacity models are distribution-based models incorporating all environmental

compartments (media) and are used to quantify steady-state fluxes of pollutants across compartment

interfaces. There exist three levels at which fugacity may be modeled. The Level I model depicts the

distribution of a known quantity of a chemical in a closed environment at equilibrium, in which no

degrading reaction occurs and no advective gain or loss is attained. A Level II model describes a

situation in which a chemical is continuously discharged at a constant rate and achieves steady-state

equilibrium, at which the input and output rates are equal. In Level II models, reaction and advection

may occur. A Level III model is similar to a Level II model in that the modeled chemical is

continuously discharged at a constant rate and achieves a steady state condition, in which the input

rate equals the output rate. Yet, a Level III model differs in that fugacity for the given chemical is

equal within a single compartment for all defined subcompartments, but is not equal between

compartments. Therefore, individual inputs for each medium must be defined separately in order to

determine appropriate pollutant partitioning among environmental compartments.

17.3 Evaluating Persistence and Bioaccumulation in Exposure Assessments

Characterizing the movements of air toxics that persist and which also may bioaccumulate

through various environmental media such as air, soils, water, sediments, and biota can be a

highly complex task, and generally available methods for evaluating these fate and transport

pathways have only recently been developed. Specifically, EPA and other regulatory agencies

have developed a number of models that estimate the concentrations of persistent and

bioaccumulative compounds over time in the various environmental “compartments” subsequent

to defined patterns of deposition. These models simulate both physical and chemical processes

such as air deposition to soil, runoff, leaching (dissolution), soil/sediment adsorption, and

chemical speciation (oxidation/reduction, precipitation reactions). Some models also evaluate

the biodegradation of organic pollutants by bacteria and other organisms in soil, sediment, and

surface water. These models require a large number of site-specific inputs and many measures of

the physical and chemical properties of pollutants. It is beyond the scope of this chapter to

discuss in detail all the processes that may be modeled in the assessment of indirect exposure

pathways; however, Mackay and his colleagues provide a good overview of the multimedia fate

and transport(20) and Conell and Emlay describe the principles governing the bioaccumulation of

pollutants in the environment.(21) Also, Chapter 18 provides a description of available

multimedia models that are generally recommended for use in air toxics risk assessment. The

documentation for these models provides detailed descriptions of the specific processes and

methods used in each model.
April 2004 Page 17-11

1. Howard, P., Boethling, R., Jarvis, W.F., Meylan, W. 1990. Handbook of Environmental

Degradation Rates. Lewis Publishers, Michigan.

2. Mackay D., Shiu W.Y., and Ma K.C. 1991-1997. Illustrated Handbook of PhysicalChemical Properties and Environmental Fate for Organic Chemicals, (5 volumes). Lewis

Publishers Inc., Boca Raton, Florida.

3. Verschueren, K. 1996. Handbook of Environmental Data on Organic Chemicals. 3rd ed. Van

Nostrand Reinhold Co., New York, NY.

4. U.S. Environmental Protection Agency. 1997. Mercury Study Report to Congress. Office of

Air Quality Planning and Standards; Office of Research and Development, Washington,

D.C., December 1997. EPA/452/R-976/003. Available at: 

http://www.epa.gov/ttn/atw/112nmerc/volume1.pdf.

5. U.S. Environmental Protection Agency. 2002. The Estimations Programs Interface for

Windows (EPIWIN) and SMILES. Updated June 20, 2002. Available at: http://www.epa.gov

/oppt/p2framework/docs/epiwin.htm. (Last accessed April, 2004).

6. Stumm, W. and Morgan, J.J. 1996. Aquatic Chemistry, 3rd Ed. John Wiley & Sons, Inc.,

New York.

Langmuir, D. and Klusman, R.L. 1997. Natural Background Concentrations of Metals That

Exceed Drinking Water Standards: Defining Background and the Effect of Sample Filtration.

Abstract. Geol. Soc. Am. Annual Meetings. Oct. 20-23. Salt Lake City UT: A-435.

Langmuir, D., Chrostowski, P., Chaney, R., and Vigneault, C. 2003. Draft Issue Paper on

the Environmental Chemistry of Metals. Submitted to EPA’s Risk Assessment Forum by

ERG, Lexington, MA. August, 2003.

7. U.S. Environmental Protection Agency. 1999. Persistent Bioaccumulative Toxic (PBT)

Chemicals (40 CFR Part 372). Final Rule. Federal Register 64:58665, October 29, 1999.

U.S. Environmental Protection Agency. 1999. Notice of Receipt of Requests for Amendments

to Delete Uses in Certain Pesticide Registrations. Federal Register 64:60193, November 4,

1999.

8. Boethling, R.S., P.H. Howard, W. Meylan, W. Stiteler, J. Beauman, and N. Tirado. 1994. 

Group contribution method for predicting probability and rate of aerobic biodegradation. 

Environmental Science and Technology. 28:459-465. 

9. U.S. Environmental Protection Agency. 1995. Great Lakes Water Quality Initiative

Technical Support Document for the Procedure to Determine Bioaccumulation Factors.

Office of Water, Washington, D.C., March 1995. EPA-820-B-95-005.

References
April 2004 Page 17-12

10. U.S. EPA. 1990. Interim Final Methodology for Assessing Health Risks Associated with

Indirect Exposure to Combustor Emissions. Environmental Criteria and Assessment Office, 

Office of Research and Development, Washington, D.C., January 1990. EPA-600-90-003. 

11. U.S. Environmental Protection Agency. 1992. Classification Criteria for Environmental

Toxicity and Fate of Industrial Chemicals. Office of Pollution Prevention and Toxics,

Washington, D.C.

12. U.S. Environmental Protection Agency. 1995. Technical Support Document for the

Hazardous Waste Identification Rule: Risk Assessment for Human and Ecological Receptors. 

Volume I. Office of Solid Waste, Washington, DC, August, 1995.

13. U.S. Environmental Protection Agency. 2003. Office of Water Shopping Cart Home Page. 

Available at: http://yosemite.epa.gov/water/owrccatalog.nsf/.

14. U.S. Environmental Protection Agency. 2003. AQUatic Information REtrieval (AQUIRE)

database, a part of the ECOTOX database. Version 3.0. Office of Research and

Development, National Health and Environmental Effects Laboratory, Mid-Continent

Ecology Division, Duluth, MN. Available at: http://www.epa.gov/ecotox/

15. Meylan, W. M., P. H. Howard, D. Aronson, H. Printup, and S. Gouchie. 1997. Improved

Method for Estimating Bioconcentration Factor (BCF) from Octanol-Water Partition

Coefficient. Third Update Report - August 1997. SRC-TR-97-006 (EPA Contract No. 68-

D5-0012). Prepared for Robert S. Boethling, U.S. EPA, OPPT, Washington, D.C.; Prepared

by SRC, Environmental Science Center, North Syracuse, NY 13212.

16. U.S. Environmental Protection Agency. 1998. Methodology for Assessing Health Risks 

 Associated with Multiple Pathways of Exposure to Combustor Emissions. Office of Research 

 and Development, National Center for Environmental Assessment, Cincinnati, OH. EPA 

 600/R-98/137.

17. U.S. Environmental Protection Agency. 2000. Methodology for Deriving Ambient Water

Quality Criteria for the Protection of Human Health. Office of Water, Washington, D.C.,

October 2000. EPA/822/B-00/004. Available at:

http://www.epa.gov/waterscience/humanhealth/method/method.html 

18. U.S. Environmental Protection Agency. 1998. Human Health Risk Assessment Protocol for

Hazardous Waste Combustion Facilities, Peer Review Draft. Office of Solid Waste and

Emergency Response, Washington, D.C., July 1998. EOA/30/D-98/001A. Available at: 

http://www.epa.gov/epaoswer/hazwaste/combust/risk.htm.

19. U.S. Environmental Protection Agency. 2002. Risk-Screening Environmental Indicators

User’s Manual RSEI Version 2.1 [1988-2000 TRI Data]. Office of Pollution Prevention and

Toxics, Washington, D.C., December, 2002. Available at:

http://www.epa.gov/opptintr/rsei/docs/users_manual.pdf.
April 2004 Page 17-13

20. Mackay, D. 1991. Multimedia Environmental Models. The Fugacity Approach. Lewis

Publishers, Chelsea, MI.

Mackay D, Paterson S, and WY Shiu. 1992. Generic models for evaluating the regional fate

of chemicals. Chemosphere 24(6):695-717.

Mackay D, DiGuardo A, Paterson S, and CE Cowan. 1996. Evaluating the environmental fate

of a variety of types of chemicals using the EQC Model. Environmental Toxicology and

Chemistry 15(9):1627-1637.

21. Connell, D.W. and D. Emlay. 1989. Bioaccumulation of Xenobiotic Compounds. CRC 

Press.
Chapter 18 Quantification of Exposure: Multimedia

Modeling

Table of Contents

18.1 Introduction ............ ............ ............ ............ ............ ...... 1

18.2 Multimedia Fate and Transport Modeling ............ ............ ............ ...... 1

18.2.1 Basis of Multimedia Models ............ ............ ............ .......... 1

18.2.2 Multimedia Exposure Models ............ ............ ............ ......... 2

18.3 Key Parameters/Inputs for Multimedia Models ............ ............ ............ . . 5

18.4 Examples of Multimedia Modeling ............ ............ ............ ........... 8

References ............ ............ ............ ............ ............ ............ 10

April 2004 Page 18-1

18.1 Introduction

This chapter summarizes the concepts and tools available for multimedia modeling to support a

multipathway human health risk assessment. The discussion is divided into three sections:

• Section 18.2 discusses multimedia fate and transport modeling used to estimate chemical

concentrations in abiotic and biotic media that indirectly result from air emissions;

• Section 18.3 discusses key parameters used as inputs to multimedia models; and

• Section 18.4 presents examples of the use of multimedia models in air toxics risk

assessments.

18.2 Multimedia Fate and Transport Modeling

Although the primary route of exposure to many air toxics is via inhalation, non-inhalation

exposure through soil, water, and food pathways can be a potential health concern for those air

toxics that persist and which also may bioaccumulate (see Chapter 4 for the list of persistent

bioaccumulative hazardous air pollutants (PB-HAP) chemicals). Therefore, risk assessments for

these substances often include multimedia modeling to predict the movement of these air toxics

in the environment. This section provides an overview of the multimedia fate and transport

models commonly used by EPA.

18.2.1 Basis of Multimedia Models

Multimedia fate and transport models take into account various physical and chemical processes

to predict the movement of pollutants within and between environmental media. Multimedia

models can be grouped into the following basic categories.

 

• Linked modeling systems are composed of several independent single-medium models. 

These systems typically consist of a “one-way” process through a series of linked singlemedium models or algorithms; that is, they calculate fate and transport by running a singlemedium model (e.g., an atmospheric model) and using the output as the input for the next

single-medium model (e.g., a soil or surface water model). One of the primary advantages of

linked modeling systems is that they can incorporate several highly sophisticated singlemedium models into a single modeling system. The primary drawbacks of these types of

models are (1) they do not always assure conservation of mass; (2) they lack dynamic

“feedback” loops; and (3) secondary pollutant transfers are not treated in a fully coupled

manner.

• Fully coupled, mass-conserving models estimate the fate and transport of pollutants

between and within media and are able to fully account for the distribution of pollutant mass

within a defined modeling region. In these types of models, each of the included media (e.g.,

soil, air, biota) are modeled simultaneously (i.e., fully coupled), and thus these models can

simulate dynamic “feedback” loops and secondary pollutant transfers. The primary drawback

of these types of models is that they typically involve some simplification relative to

sophisticated single-medium models due to the computational demands associated with

modeling multiple media simultaneously.
a Note that the MPE model and many of its variations are conceptual models used to describe fate and

transport, not “ready-to-run” computer models. Typically, users incorporate these conceptual models into

spreadsheets or other computer frameworks to create a usable model.

April 2004 Page 18-2

18.2.2 Multimedia Exposure Models

To date, EPA has used primarily the Multiple Pathways of Exposure (MPE) model and variations

of the MPE approach to conduct multimedia fate and transport modeling for air toxics. More

recently, EPA developed the Fate, Transport, and Ecological Exposure (TRIM.FaTE) model as a

component of the Total Risk Integrated Methodology (TRIM).(1) This section provides a

summary of the MPE model, the variations of the MPE approach, and the TRIM.FaTE model. 

During the development of the TRIM.FaTE model, EPA conducted a comprehensive review of

those multimedia fate and transport models that estimate exposures and risks from emissions of

air toxics that EPA and other organizations in the United States use. Exhibit 18-1 provides a

summary of the models included in this review, with models grouped into the two basic

categories described in the previous section. The TRIM.FaTE documentation provides a

description of each of these models. Also presented at the end of this section is a multimedia

model developed by the State of California called CalTOX.(2)

Exhibit 18-1. Multimedia Models Reviewed During TRIM.FaTE Development

Linked Modeling Systems Fully Coupled, Mass-Conserving Models

• Indirect Exposure Methodology

(IEM)/Multiple Pathways of Exposure (MPE),

developed by EPA’s National Center for

Environmental Assessment

• Multimedia Environmental Pollutant

Assessment System (MEPAS), developed by

the U.S. Department of Energy

• CalTOX, California Department of Toxic

Substance Control’s Multimedia Risk

Computerized Model

• SimpleBOX, developed by the Netherlands

National Institute of Public Health and the

Environment

• Modeling Multimedia Environmental

Distribution for Toxics (Mend-Tox)/ISMCM),

developed by EPA’s Office of Research and

Development

Multiple Pathways of Exposure Model (MPE)

The Multiple Pathways of Exposure model, formerly known as the Indirect Exposure

Methodology (IEM), primarily consists of a set of multimedia fate and exposure algorithms

developed by EPA’s Office of Research and Development (ORD).(a) ORD issued an interim

document describing this methodology in 1990, a major addendum was issued in 1993, and an

updated guidance document was issued in 1999 in response to comments it received during a

1994 Science Advisory Board review of the addendum.(3) The MPE documentation describes

fate and transport algorithms, exposure pathways, receptor scenarios, and dose algorithms.

The MPE approach includes procedures for estimating human exposures and health risks

resulting from the transfer of emitted pollutants from air to soil and surface water bodies and the
April 2004 Page 18-3

The Draft Guidance on the Development, Evaluation,

and Application of Regulatory Environmental

Models recommends best practices to help determine

when a model, despite its uncertainties, can be

appropriately used to inform a decision. The

Knowledge Base (KBase) is a web-accessible

database of information on some of EPA’s most

frequently used models. The draft guidance

recommends what information about models to

document, while the Knowledge Base is the

repository where this information is documented. 

Both products are available at the CREM internet site

at http://www.epa.gov/crem.

subsequent uptake by vegetation, animals, and humans. The methodology specifically addresses

exposures via inhalation; ingestion of food, water, and soil; and dermal contact. The MPE model

was designed to predict long-term, steady-state impacts from continuous sources, rather than

short-term, time-series estimates. It consists of a “one-way process” through a series of linked

models and algorithms, beginning with the modeling of the transport of pollutant emissions in air

and the subsequent deposition to soil and surface water and culminating in the uptake of the

emitted pollutant(s) into biota. The aspects of the MPE model that address exposure estimation

are described in more detail in Section 18.4 below.

EPA designed the MPE model to assess

human exposures to air toxics emitted

from stationary combustors, although

analysts can apply most aspects of the

approach to other types of stationary

sources. One can apply this model to one

or more sources at a single facility

simultaneously to estimate exposures

within 50 kilometers of the facility. The

MPE model will allow modeling of only

one chemical at a time, and there is no

tracking (i.e., carry through the analysis)

of transformation products of the modeled

chemical. To apply the MPE approach,

users must provide a significant number of site-specific inputs, such as source emission rate,

wind speed and direction, soil loss constant, and pollutant degradation rate.

EPA modified the MPE approach to multimedia fate and transport modeling for use in two

additional EPA models and modeling approaches. These models and approaches are as follows.

• IEM2M. In 1997, Office of Air Quality Planning and Standards (OAQPS) modified the

then-current version of the IEM model to create IEM2M. This revised version of IEM added

the functionality necessary to model transformation between the three key species of mercury

and track the concentrations throughout the modeled system for each of these species. This

model was applied to estimate nationwide exposures to mercury for the Mercury Study

Report to Congress.

(4)

• Human Health Risk Assessment Protocol (HHRAP). EPA’s Office of Solid Waste and

Emergency Response (OSWER) developed the HHRAP to provide guidance for conducting

multipathway exposure and risk assessments of emissions of air toxics from hazardous waste

combustion facilities. The suggested protocol for assessing multipathway exposures was

adapted from the MPE approach and the documentation of this protocol(5) compiles detailed

information on many of MPE’s input parameters and algorithms.

Two additional models and approaches used by EPA to assess multipathway exposures to air

toxics use many of the same fate and exposure algorithms and methodologies used in the MPE

model.
April 2004 Page 18-4

• Dioxin Reassessment Methodology. Many of the algorithms used in the MPE model have

been used for ongoing EPA efforts to characterize exposure and risks from dioxins,

particularly chlorinated dibenzodioxins and dibenzofurans, as part of the Dioxin

Reassessment project.(6)

• Multimedia, Multipathway, Multi-receptor Exposure and Risk Assessment Model

(3MRA). The 3MRA model is currently being developed by EPA’s Office of Solid Waste

and Emergency Response to support their Hazardous Waste Identification Rule (HWIR). 

Many of the fate and exposure algorithms used in 3MRA are similar to those used in MPE.

TRIM.FaTE

EPA developed the TRIM Fate, Transport, and Ecological Exposure (TRIM.FaTE) model(7) to

describe the movement and transformation of pollutants over time, through a user-defined,

bounded system of environmental compartments (i.e., abiotic media and organisms). The design

of the compartment system can encompass spatial interconnections (with some similarities to

grid-type Eulerian models) and ecological exposure-related relationships. TRIM.FaTE is

designed to generate both media concentrations relevant to human pollutant exposures and

exposure estimates relevant to ecological risk assessment primarily for air pollutants for which

non-inhalation exposures are important. 

In contrast to the IEM/MPE approach, TRIM.FaTE is a fully coupled multimedia model that

estimates the flow of pollutant through time among environmental compartments. TRIM.FaTE

offers the following important features that are not available using IEM/MPE.

• TRIM.FaTE is able to model mass-balanced “feedback” loops between media as well as

secondary emissions (e.g., re-emission of deposited pollutants).

• TRIM.FaTE has the ability to provide detailed time-series estimates of pollutant

concentrations in the environmental compartments.

• TRIM.FaTE maintains a full mass balance of the pollutant mass in the system (i.e., all the

pollutant introduced into the system is accounted for among all the environmental

compartments).

• TRIM.FaTE can model sensitivity of model results to variations in input parameters and 

perform probabilistic modeling such that uncertainty and variability in model results can be

characterized.

• TRIM.FaTE is designed with the flexibility to allow for implementation of nearly limitless

configurations (e.g., spatial resolution, types of biota), algorithms, and approaches. 

Simulations can range from quite simple analyses of pollutant distribution across abiotic

media and biota to more complex, spatially-refined assessments, with associated implications

with regard to user requirements.

TRIM.FaTE can model multimedia fate and transport of air toxics from any type of stationary

source. It can be applied to multiple facilities, sources, and chemicals simultaneously to track the

fate and transport of emitted pollutants as well as transformation products of the emitted
April 2004 Page 18-5

pollutants. The amount of input data required by TRIM.FaTE is directly related to the

complexity of the user-specified modeling system; however, TRIM.FaTE analyses typically

require more input data than similar analyses conducted using the MPE approach. As noted in

Exhibit 18-2, TRIM.Expo is the exposure component of the TRIM modeling system (see Section

18.2.2.2).

California Total Exposure Model for Hazardous Waste Sites (CalTOX)

The Department of Toxic Substances Control (DTSC), within the California Environmental

Protection Agency, has the responsibility for managing the State’s hazardous-waste program. As

part of this program, the DTSC funded the development of the CalTOX program.(2) CalTOX has

been developed as a set of spreadsheet models and spreadsheet data sets to assist assessing

human exposures and defining soil clean-up levels at uncontrolled hazardous wastes sites. More

recently, CalTOX has been modified for use in establishing waste classification for landfills and

hazardous waste facilities in California. CalTOX addresses contaminated soils and the

contamination of adjacent air, surface water, sediments, and ground water. The modeling

components of CalTOX include a multimedia transport and transformation model, exposure

scenario models, and add-ins to quantify uncertainty and variability. The multimedia transport

and transformation model is a dynamic model that can assess time-varying concentrations of

pollutants introduced initially to soil layers or for pollutants released continuously to air, soil, or

water. This model assists the user in examining how chemical and landscape properties impact

both the ultimate route and quantity of human contact. Multimedia, multiple pathway exposure

models are used in CalTOX to estimate average daily doses within a human population. The

exposure modeling part of CalTOX is described further in Chapter 20.

18.3 Key Parameters/Inputs for Multimedia Models

For most air risk applications, multimedia modeling results are strongly dependent on the

emission rate of pollutants emitted to the air from the facility. For the MPE framework and

TRIM.FaTE model, transport of modeled pollutants and accumulation in media of interest result

directly from the emission of the chemical into the air from the facility, the dispersion or

advection of chemical through the air, and the subsequent deposition of the chemical onto land,

water, or other surfaces in the modeled region. In addition to emission rate, several other types

of data are often required by multimedia models to characterize the pollutants and site being

modeled. Generally, the data requirements for multimedia fate and transport models fall into the

following categories.

• Source characteristics for the sources that are modeled, such as location, emission rates for

the modeled pollutant(s), stack height, exit gas velocity, and exit gas temperature.

• Environmental setting characteristics for the abiotic media included in the modeling

scenario, such as water body dimensions, surface soil characteristics (e.g., organic carbon

content, porosity), and data related to local meteorology and hydrology (e.g., precipitation,

erosion, runoff rates).

• Abiotic chemical/physical data for the chemicals included in the modeling scenario, such as

Henry’s law constant and soil-water partition coefficients. EPA’s draft HHRAP provides

default values for many of these parameters.(5)
April 2004 Page 18-6

Exhibit 18-2. Role of the TRIM Modeling System

The Total Risk Integrated Methodology (TRIM) modeling system can be used to assess human

inhalation, human ingestion, and ecological risks. TRIM.FaTE accounts for movement of a chemical

through a comprehensive system of discrete compartments (e.g., media and biota) that represent

possible locations of the chemical in the physical and biological environments of the modeled

ecosystem and provides an inventory, over time, of a chemical throughout the entire system. In

addition to providing exposure estimates relevant to ecological risk assessment, TRIM.FaTE generates

media concentrations relevant to human ingestion exposures that can be used as input to the ingestion

component of the Exposure-Event module, TRIM.Expo. Measured concentrations also can be used as

inputs to TRIM.Expo. In the inhalation component of TRIM.Expo, human exposures are evaluated by

tracking randomly selected individuals that represent an area’s population and their inhalation and

ingestion through time and space. TRIM.ExpoInhalation can accept ambient air concentration estimates

from an external air quality model or monitoring data. In the Risk Characterization module,

TRIM.Risk, estimates of human exposures or doses are characterized with regard to potential risk

using the corresponding exposure- or dose-response relationships. The TRIM.Risk module is also

designed to characterize ecological risks from multimedia exposures. The output from TRIM.Risk is

intended to include documentation of the input data, assumptions in the analysis, and measures of

uncertainty/variability, as well as the results of risk calculations and exposure analysis. Information

on TRIM can be accessed at: http://www.epa.gov/ttn/fera/.

• Non-chemical-specific characteristics of biota for any organisms included in the modeling

scenario, such as feeding rates, body weight, and population density.

• Biotic chemical-specific data for any organisms included in the modeling scenario, such as

bioaccumulation and/or bioconcentration factors or assimilation efficiency values.
April 2004 Page 18-7

The Multiple Pathways of Exposure (MPE) model,(8) a commonly used model for multipathway

analyses, requires air concentrations, deposition rates, which are typically obtained via Industrial

Source Complex (e.g., ISCST3) modeling (see Chapter 9 for descriptions of these models). Risk

assessors would execute the ISCST3 modeling for multipathway in a similar fashion to how they

executed the modeling for inhalation. Specifically, the sources would be characterized in the

same way (e.g., vent height and diameter, release temperature and velocity, flow rate). The user

would provide the inputs necessary to calculate the deposition rates properly (e.g., particle size

distribution, scavenging coefficient). However, for multipathway analyses, the user should

execute the ISCST3 model with the “depletion option” (i.e., telling the model to subtract out the

mass of chemical deposited).

The user would need to know the particulate/particle-bound/vapor fractions of the emissions for

ISCST3 to calculate wet and dry deposition of vapors and particles. These would probably be

considered source-related, since although they are chemical-dependent, they also vary by source

(i.e., the industrial process affects the emissions profile).

For dry deposition of particles, the user would supply the following inputs (in addition to the

normal ISC inputs), including the:

• Array of particle diameters of the emissions;

• Array of mass fractions corresponding to the different particle diameters; and

• Array of particle densities corresponding to the different particle diameters.

For wet deposition of particles, the user would supply the following inputs (in addition to the

normal ISC inputs), including the:

• Particle scavenging coefficients for liquid precipitation corresponding to the different particle

diameters; and

• Particle scavenging coefficients for frozen precipitation corresponding to the different

particle diameters.

For wet deposition of gases, the user would supply the following inputs (in addition to the

normal ISC inputs), including the:

• Gaseous scavenging coefficient for liquid precipitation; and

• Gaseous scavenging coefficient for frozen precipitation.

The ISC user’s guide(9) provides more detailed information on the deposition algorithms and

required input data. There also is guidance for application of ISC for multipathway assessment

in the latest MPE documentation.(8)

The only facility-related/source term data points used by the TRIM Fate, Transport, and

Ecological Exposure (TRIM.FaTE) model are chemical emission rate, location (lat/long,

UTM), and emission height, which are available from the inhalation modeling. TRIM.FaTE

calculates all values internally for determining vapor/particle fractions and deposition rates based

on chemical-specific (not source-specific) properties.
April 2004 Page 18-8

Other multimedia models may require specific source characterization data and other

documentation that were not obtained for the inhalation analysis (the various user’s guides for

these models should be consulted for appropriate inputs).

It is important to note that the number and refinement of inputs to a multimedia model may vary

depending on the outputs of interest and level of detail entailed in the modeling.

18.4 Examples of Multimedia Modeling

TRIM.FaTE Test Case Application. As a test case application, the TRIM.FaTE model was

used to predict multimedia concentrations of mercury at a chlor-alkali facility in the northeastern

United States. Speciated mercury concentrations were calculated for various abiotic media (e.g.,

surface soil, surface water, lake sediment) and biota (e.g., fish for various trophic levels, birds,

mammalian predators) for the ecosystem surrounding the facility. A sensitivity and uncertainty

analysis using TRIM.FaTE tools and a model comparison involving the 3MRA modeling system

were also performed. The complete report on the test case will be available at the TRIM.FaTE

page of EPA’s Fate, Exposure, and Risk Analysis (FERA) website:

http://www.epa.gov/ttn/fera/trim_fate.html.

Paints Hazardous Waste Listing Determination Analysis. On April 4, 2002, EPA issued a

final determination not to list as hazardous certain wastes generated from the production of paint. 

EPA made this determination pursuant to the Resource Conservation and Recovery Act (RCRA),

which directs EPA to determine whether certain wastes from the paint production industry may

present a substantial hazard to human health or the environment. EPA proposed

concentration-based listings for certain paint waste solids (K179) and liquids (K180) on February

13, 2001 (66 Federal Register 10060). However, following a review of the public comments and

supplemental analyses based on public comments, EPA determined that the paint wastes

identified in the February 13, 2001, proposal did not present a substantial hazard to human health

or the environment. EPA conducted a multipathway risk assessment in support of this

determination.(10) EPA used a series of models to estimate concentrations of Chemicals of

Potential Concern (COPCs) in the environment with which human and ecological receptors may

come into contact. The analysis used a source partioning model to estimate environmental

releases of each COPC from a waste management unit for each waste stream, as appropriate. 

These estimated environmental releases provided input to the fate and transport models to

estimate media concentrations in air, soil, surface water, and groundwater. A farm food chain

model was used to estimate COPC concentrations in produce, beef, and dairy products. Aquatic

bioconcentration factors were used to estimate concentrations in fish.

Chlorinated Aliphatics Hazardous Waste Listing Determination. In support of a hazardous

waste listing determination for wastewaters and wastewater treatment sludges generated from the

production of certain chlorinated aliphatic chemicals, EPA conducted a multipathway human

health risk assessment.(11) EPA used the ISCST3 model to estimate dispersion and deposition of

vapors emitted from wastewater treatment tanks and landfills, and vapors and particulates

emitted from sludge land treatment units. EPA used a series of indirect exposure equations based

on the MPE approach to quantify the concentrations of contaminants that pass from contaminated

environmental media to the receptor indirectly. For example, EPA examined risks associated

with contaminant transport in air; deposition onto plants and soil; accumulation in forage, grain,
April 2004 Page 18-9

silage, and soil; subsequent ingestion by beef cattle and dairy cattle; and human ingestion of

contaminated beef and dairy products.

Hazardous Waste Combustor MACT Standard Analysis. A human health and ecological risk

assessment was performed in support of developing a Maximum Achievable Control Technology

(MACT) standard for hazardous waste combustor facilities.(12) The risk analysis included a

multimedia, multipathway assessment that addressed direct exposures to constituents released

into the atmosphere by hazardous waste combustor units and indirect exposures due to the

movement of air toxics in the food chain. The risk assessment addressed both human health risks

(cancer effects and noncancer effects) as well as ecological risks. Constituents assessed were

seven congeners of chlorinated dioxin and 10 congeners of chlorinated furan; three species of

mercury; 14 metals (antimony, chromium III, chromium VI, arsenic, lead, barium, nickel,

beryllium, selenium, cadmium, silver, thallium, cobalt, copper, and manganese); particulate

matter; hydrochloric acid; and chlorine gas. To the maximum extent possible, this risk

assessment followed the latest risk guidelines adopted by EPA and used the most recent data

available.

Columbus Waste-to-Energy Study. A risk assessment study using fate modeling was

performed by EPA’s NCEA for dioxin emissions at the Columbus, Ohio, Waste-to-Energy

incinerator facility.(13) In 1994, EPA headquarters, the Office of Research and Development, and

Region 5 conducted a screening assessment of indirect impacts, leading to the conclusion that

continued emissions “may pose an imminent endangerment to public health and the

environment.” Fate modeling used to support EPA’s position utilized the air-to-beef model

described in the draft Dioxin Exposure document (i.e., based on the principles included in the

MPE framework) and assumed a subsistence farming family scenario. Exposure pathways

considered beef, milk and vegetable ingestion; soil dermal contact and childhood soil ingestion;

and breast milk ingestion. The exposure duration for adults was assumed to be seventy years. 

Air concentrations used were the average from nine dairy farms located between five and twelve

miles from the incinerator. Overall exposure and cancer risk were estimated for each of the

exposure pathways, with cancer risk being highest for beef consumption (2×10-4) and lowest for

soil dermal contact (9×10-9). Exposure from breast milk ingestion was determined to be higher

by one order of magnitude than exposure from beef and milk consumption, and higher by two

orders of magnitude than exposure from inhalation. Breast milk exposure near the incinerator

site ranged between two and more than seven times the background dioxin levels. A

TRIM.FaTE case study has been developed based on this analysis, including a direct model

comparison component on the air-soil outputs, and will be available at:

http://www.epa.gov/ttn/fera/trim_fate.html.
April 2004 Page 18-10

1. A summary of this review can be found in: U.S. Environmental Protection Agency.

TRIM.FaTE Technical Support Document, Volume I (Chapter 2). Office of Air Quality

Planning and Standards, Research Triangle Park, NC, September 2002. EPA/453/R-02/011a.

Available at: http://www.epa.gov/ttnatw01/urban/trim/trimpg.html

2. McKone, T.E. 1993. CalTOX, a Multimedia Total-Exposure Model for Hazardous wastes

Sites Part I: Executive Summary. UCRL-CR-11456, Pt. I. 1993b. CalTOX, a Multimedia

Total-Exposure Model for Hazardous Wastes Sites Part II: the Dynamic Multimedia

Transport and Transformation Model. UCRL-CR-111456, Pt. II. 1993c. CalTOX, a

Multimedia Total-Exposure Model for Hazardous Wastes Sites Part III: The MultiplePathway Exposure Model. UCRL-CR-111456, Pt. III. Livermore, CA: Lawrence Livermore

National Laboratory.

3. U.S. Environmental Protection Agency. 1990. Methodology for Assessing Health Risks

Associated with Indirect Exposure to Combustor Emissions. Interim Final. Office of Health

and Environmental Assessment, Washington, D.C., EPA/600/6-90/003. 

U.S. Environmental Protection Agency. 1998. Methodology for Assessing Health Risks

Associated with Multiple Pathways of Exposure to Combustor Emissions. National Center for

Environmental Assessment, Washington, D.C. EPA 600/R-98/137. Both are available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525

4. U.S. Environmental Protection Agency. 1997. Mercury Study Report to Congress. Office of

Air Quality Planning and Standards, Research Triangle Park, NC and Office of Research and

Development, Washington, DC. EPA/452/R97/003. Available at:

http://www.epa.gov/ttn/atw/112nmerc/mercury.html

5. U.S. Environmental Protection Agency. 1998. Human Health Risk Assessment Protocol for

Hazardous Waste Combustion Facilities, Peer Review Draft. Office of Solid Waste,

Washington, D.C. EPA-530-D-98-001A. Available at:

http://www.epa.gov/epaoswer/hazwaste/combust/risk.htm. 

6. EPA is progressing toward completion of its comprehensive reassessment of dioxin exposure

and human health effects entitled, Exposure and Human Health Reassessment of

2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. The latest

information on the status of the reassessment is available at:

http://cfpub.epa.gov/ncea/cfm/dioxreass.cfm?ActType=default.

7. U. S. Environmental Protection Agency. 2002. Total Risk Integrated Methodology. 

TRIM.FaTE Technical Support Document. Volume 1: Description of Module. EPA-453/R02-011a; Volume 2: Description of Chemical Transport and Transformation Algorithms. 

EPA/453/R-02/011b. Evaluation of TRIM.FaTE. Volume 1: Approach and Initial Findings. 

EPA/453/R-02/012; TRIM.FaTE User’s Guide. Office of Air Quality Planning and

Standards, Research Triangle Park, NC. These documents and information are available at:

http://www.epa.gov/ttn/fera/trim_fate.html#current_user.

References
April 2004 Page 18-11

8. U.S. Environmental Protection Agency. 1998. Methodology for Assessing Health Risks

Associated with Multiple Pathways of Exposure to Combustor Emissions. Office of Research

and Development, National Center for Environmental Assessment, Cincinnati, OH, 1998.

EPA/600/R-98/137. Available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525. 

9. U.S. Environmental Protection Agency. 1995. User’s Guide for the Industrial Source

Complex (ISC) Dispersion Models. Volume 1 - User Instructions. Office of Air Quality

Planning and Standards, Research Triangle Park, NC, September 1995. EPA/454/B-95/003a.

Available at: http://www.epa.gov/scram001/userg/regmod/isc3v1.pdf

10. U.S. Environmental Protection Agency. 2001. Risk Assessment Technical Background

Document for the Paint and Coatings Hazardous Waste Listing Determination. Office of

Solid Waste, Washington D.C., January 2001. (see also the February 28, 2002 addendum).

Available at http://www.epa.gov/epaoswer/hazwaste/id/paint/index.htm.

11. U.S. Environmental Protection Agency. 1999. Risk Assessment Technical Background

Document for the Chlorinated Aliphatics Listing Determination. Office of Solid Waste,

Washington, D.C., July 1999. Available at:

http://www.epa.gov/epaoswer/hazwaste/id/chlorali/clordoc1.htm. (see also the September 29,

2000 addendum to the risk assessment. Available at:

http://www.epa.gov/epaoswer/hazwaste/id/chlorali/ca_risk.pdf).

12. U.S. Environmental Protection Agency. 1999. Human Health and Ecological Risk

Assessment Support to the Development of Technical Standards for Emissions from

Combustion Units Burning Hazardous Wastes: Background Document, Final Report. Office

of Solid Waste, Washington, D.C., July 1999. Available at:

http://www.epa.gov/epaoswer/hazwaste/combust/riskdoc1.htm.

13. Lorber, M. 2002. Comments on the Columbus Waste-to-Energy Site. Presentation at EPA 

Regional/ORD Workshop on Air Toxics Exposure Assessment. June 25 - 27, 2002. San

Francisco, CA. National Center for Environmental Assessment. U.S. Environmental

Protection Agency.
Chapter 19 Quantification of Exposure: Multimedia

Monitoring

Table of Contents

19.1 Introduction ............ ............ ............ ............ ............ ...... 1

19.2 Why Monitor? ............ ............ ............ ............ ............ .... 1

19.3 Planning and Implementing Issues ............ ............ ............ ............ 1

19.4 Monitoring and Sampling Methods, Technologies and Costs ............ ............ ... 3

19.4.1 Method Selection ............ ............ ............ ............ ....... 3

19.4.2 Available Methods ............ ............ ............ ............ ...... 7

References ............ ............ ............ ............ ............ ............ 12

April 2004 Page 19-1

Monitoring for Evaluation of Multimedia

Modeling

For multipathway risk assessments, monitoring is

a valuable tool for evaluating model predictions

because multimedia modeling is more

complicated and involves more uncertainties than

does air quality modeling. When using samples

to evaluate model predictions, however, it is

important to realize that monitored

concentrations may be greater than model

predictions because sources other than those

being modeled may have contributed to the

contamination.

19.1 Introduction

As noted earlier, modeling is generally the recommended approach for estimating exposure

concentrations for air toxics risk assessments (for both inhalation and other pathways). However,

there may be circumstances in which monitoring is requested or recommended for a particular

multipathway risk assessment. This chapter provides an overview of multimedia monitoring,

including the reasons for monitoring (Section 19.2), planning and implementation issues (Section

19.3), and available monitoring methods (Section 19.4).

19.2 Why Monitor?

The reasons for monitoring for a

multipathway risk assessment are identical to

those noted earlier for inhalation risk

assessments (Chapter 10):

• Measuring existing concentrations of air

toxics in specific locations (e.g., soils in a

schoolyard) and/or food items (e.g., fish

from a lake within the study area) for

purposes of developing estimates of

exposure;

• Developing or refining values for specific

parameters needed by multimedia models;

• Evaluating the predictions of a model in specified circumstances (e.g., estimates of sediment

concentrations resulting from deposition and runoff);

• Closing gaps that might be present in existing data (e.g., gaps in emissions inventory); and

• Providing compliance/enforcement information as to whether a given facility or set of

sources is meeting regulatory or permit requirements.

19.3 Planning and Implementing Issues

The planning and implementation processes for multipathway risk assessment monitoring

programs are similar to those for air monitoring programs discussed in Chapter 10. The planning

process involves a step-wise integration of data quality and data sampling and analysis processes

that are consistent with the study-specific conceptual model (CM), quality assurance project plan

(QAPP), and data quality objectives (DQO) process. Many of the general planning and

implementation issues for air monitoring programs also apply to multimedia modeling. Some

additional considerations arise because the sampling and analysis program might include soils,

surface waters, sediments, fish, meat, vegetables, milk, and other human food items. The scale

and scope of monitoring could be much greater (e.g., multiple media could be sampled), and

issues specific to ingestion need to be considered (e.g., what parts of plants and animals do

people eat?).
April 2004 Page 19-2

• Monitoring or sampling methods should be appropriate for the compounds and

environmental media to be measured. They must have the sensitivity needed to monitor at

the levels likely to be of health and/or regulatory concern.

• Monitoring sites and frequency of monitoring should be appropriate for the spatial and

temporal variation of the chemical being measured and the monitoring objective. 

Typically, an exposure location (e.g., a water body, a property, an agricultural field) or source

(e.g., milk from cows on a specific farm) is defined for the risk assessment. The monitoring

program should be adequate to represent the spatial and temporal variation within the

location or source, given the particular measure(s) used to support the risk management

decision to be made (e.g., average exposure, maximum exposure). However, several aspects

of spatial and temporal variation are unique to air toxics that persist and which also may

bioaccumulate. For example:

– The temporal patterns of releases from sources may be less important because the

chemicals may slowly accumulate in media and biota over time;

– Spatial “hot spots” of contamination may occur (for example, if soils erode and collect in

low-lying areas);

– Chemicals generally accumulate in different tissues at different rates; therefore,

concentrations may be higher in certain parts of the plant or animal (which may or may

not be the parts that people tend to eat, and vice versa);

– Certain seasonal effects (e.g., growing season for plants, migratory movements in

animals) may be important sources of variation; and

– Age of the plant or animal being sampled may be important if it takes many months or

years for contaminants to reach equilibrium in biological tissues (or if equilibrium is

never reached). For example, mercury concentrations in fish tend to be higher in older,

larger fish.

• The monitoring effort should consider the relative contributions of the four main

sources of variability in measurements. As noted in Chapter 10, these are analytical,

sampling, temporal, and spatial.

• Standard operating procedures should be defined and followed both in the field (during

sample collection) and in the laboratory (during sample analysis). These include procedures

related to sample collection, sample transport and storage (including prevention of sample

degradation), sample analysis, “chain of custody,” audits, data validation, and data reporting. 

These procedures may be quite varied due to the range of possible media and biota that could

be sampled.

• Limits of quantitation or detection should be determined and compared against relevant

decision needs, including health benchmarks and likely environmental levels.

• Measurement processes should be properly calibrated to ensure the accuracy of the

method.

• Results must be adequately recorded and archived. The best monitoring program can be

compromised by a failure to keep proper records that can be made part of the public record. 
aAir sampling also may be conducted; however, that is discussed in Chapter 10.

April 2004 Page 19-3

A periodic, random check of the archived records (e.g. computer files) should be made

against “hard copies” to ensure the integrity of the process of recording the data.

Soil Depth: Issues for Sampling

The depth over which surface soils are sampled should reflect the type of exposure expected in the

study area, the type of receptors expected in the study area, the depth of biological activity and the

depth of potential contamination. Careful consideration of the size, shape, and orientation of sampling

volume is important since they have an effect on the reported measured contaminant concentration

values.(1) Selection of sampling design and methods can be accomplished by use of the Data Quality

Objectives (DQO) process discussed in Chapter 6. Additional soil sampling guidance that may be

consulted includes EPA’s Preparation of Soil Sampling Protocols: Sampling Techniques and

Strategies and Guidance for Data Usability in Risk Assessment and Soil Screening Guidance,

available at: http://www.epa.gov/superfund/programs/risk/tooltrad.htm#dbhh.

(2)

19.4 Monitoring and Sampling Methods, Technologies and Costs

19.4.1 Method Selection

Method selection for sample collection and analysis programs that are applicable to

multipathway human health risk assessments are dependent on numerous aspects of the project.

Factors such as media, sample types, sample program designs, lead regulatory authority, and

concentration ranges of concern all can impact the selection of the appropriate methods. While it

is not possible for this chapter to review all of the monitoring methods available for this broad

range of applications, several of the more important factors that generally influence decisions on

methods selected are discussed below.

The primary determining factor in selection of sample collection and analysis methodologies is

the sample media to be evaluated. Exhibit 19-1 presents several examples of the types of media

that might be sampled for a multipathway human health risk assessment.(a) Other factors that

affect selection of sample collection methods are sample type and sample program design. 

Specific factors in selection of sample collection methods may also be construction material of

the sampling devices, its design, decontamination, and proper use, site-specific conditions,

relative cost, and data quality limitations.

Sample collection methods may be categorized by sample type as discussed in Chapter 10. 

However, the distinction is not always clear (e.g., a single fish tissue sample might be considered

a grab sample because it is collected at a single location and time; however, because the

contaminant concentrations in its tissues accumulate over time, the sample could also be

considered a time-integrated sample). The more common types of samples used for non-air

sampling are provided below:

• Grab samples (also known as discrete samples) are collected at a specific location (and

generally instantaneous) time. Any technique where the sampling container is filled to

represent a snapshot of the concentration of target contaminants at a single specific time is
April 2004 Page 19-4

considered a grab sample. Where the population to be represented is demonstrated to be

homogenous or consistent, grab samples provide the maximum information. 

• Time-integrated samples are collected at a single location but over an extended period of

time. Similar to grab samples, analysis of time-integrated samples provides a snapshot of

that range of time and location as a single value. Only the total pollutant collected is

measured, and so only the average concentration during the sampling period can be

determined.

Exhibit 19-1. Examples of Environmental Media that May be Sampled for

Multipathway Human Health Risk Assessment (a)

Medium Comments

Surface Water Generally sampled only if used as a drinking water source

Soils Generally sampled within the top few inches of the surface, where

ingestion exposure or erosion may occur, but other considerations

may require a different soil sampling depth

Sediments Generally sampled to support assessments of bioaccumulation in

aquatic systems (in more rare instances, sediments might support a

dermal assessment of exposure to the sediments themselves)

Fish Generally focused on species and parts of the fish that people eat

(although this may vary regionally; e.g., some native cultures may

routinely eat the entire fish)

Vegetables and other

crops

Generally focused on the plants and parts of plants that people eat

and/or are fed to livestock

Dairy products and

other foods

Generally focuses on milk and other dairy products; eggs and meat are

also sometimes evaluated

(a) Note that this list is not exhaustive; additional types of samples might be appropriate for a

given risk assessment.

• Composite samples represent combinations of discrete samples, which may be collected

either at different times or from different locations, that are combined into a single sample for

analysis. Composite samples can be helpful when the amount of material that can be

obtained from a single sample is very small (e.g., fish tissue), and the analytical quantitation

limit can be lowered if the sample mass is increased (e.g., by combining multiple samples

into a single composite sample for analysis). Composite samples also can be helpful when

resources for laboratory analysis are limited, as they provide an estimate of average

concentration across multiple samples, with the analysis cost of only one sample. The

greatest drawback to composite samples is that they do not allow for an understanding of the

variation in concentration values among the individual samples collected.

• Continuous samples provide essentially real-time measurements over time from a single,

specific location. Continuous measurements typically involve real-time measurements,

because samples cannot be practically collected to provide true continuous data. Continuous

monitoring data frequently are evaluated as a function of concentration over the time period
April 2004 Page 19-5

analyzed. Depending on the application, maximum, median, time-weighted average, or

distribution curves may be applied to reduce the large amount of results obtained from true

continuous data to usable results which can be compared to decision criteria.

Sample collection methods may also be determined by the sample collection design methodology

(Exhibit 19-2). Sample design impacts method selection often by determining the number of

samples being collected.

Exhibit 19-2. Common Types of Sampling

Purposive sampling focuses the sampling effort in specific locations (in this example, the area

estimated to have the highest concentration). Grid sampling consists of regularly-spaced

samples in a predetermined grid. Random sampling consists of samples in locations selected

by chance.

• Purposive sampling involves focused sample collection based on previous knowledge of

release event locations. Purposive (also called biased) sampling is named such because the

person taking the sample willfully takes that sample at a time or place where, based on prior

knowledge, it is expected that concentrations will generally be biased high. Purposive

sampling may be desired in programs looking to verify expected model results. Purposive

sampling often targets maximum contaminant conditions to evaluate maximally impacted

areas. However, it may be used for reasons such as targeting specific species to calibrate

bioaccumulation models or defining the spatial extent of contamination.
April 2004 Page 19-6

• Systematic sampling consists of collecting samples at locations and times according to

specific patterns (e.g., grid sampling). Systematic sampling may use previous knowledge to

set frequency, density, or coverage of sampling.

• Random sampling involves collecting samples from locations in a manner such that each

location has an equal probability of being sampled and analyzed. Random sample collection

designs are an important aspect of certain statistical data evaluations.

The factors which primarily affect selection of preparation and analysis methods include target

contaminants, required reporting limits (i.e., concentration range of decision criteria), number of

samples, data quality limitations, method/instrument portability, previous data comparability,

acceptance/approval by regulators and stakeholders, and relative cost and availability. 

• Target contaminants. The specific contaminants being sampled may have a significant

impact on both budget and overall approach. For example, sampling and analytical

procedures for metals are different than those for organic chemicals. Careful evaluation

before inclusion of unwarranted parameters and establishment of a procedure for

identification and removal of chemicals of potential concern (COPCs) is critical to an

effective monitoring program. 

• Required reporting limits. Assessors should select analytical methods so that the reporting

limits (usually the estimated quantitation limits) are less than the effects concentrations of

interest. If the assessor does not select an adequately sensitive analytical method, the

quantitation limit for a given chemical could exceed the chemical’s effects benchmark

concentration of interest; in that case, monitoring information would not provide meaningful

input to the risk assessment.

• Number of samples. A sampling program that involves screening-level assessment of a

large number of samples may drive selection of certain methods for the bulk of samples in

order to allocate limited resources. In the opposite case, determination of low heterogeneity

of sample media, and extremely low risk-based concentrations of interest as decision criteria

may require fewer samples and more highly sophisticated methodologies.

• Data quality limitations. High data quality requirements imposed by high uncertainty or

other factors may influence the choice of sampling methods such as procedures that are more

stringent and more costly than usual procedures.

• Method/instrument portability. In-field or on-site analysis has begun to replace laboratorybased analysis in many monitoring programs. Certain preparation and analysis

methodologies are more portable than others, in part because of the sensitivity of the

instrumentation. However, considerable expertise in sampling and analysis is needed to

decide whether in-field or laboratory-based analysis is appropriate for the study.

• Previous data comparability. Previous data sets can affect selection of appropriate

methods. All other factors being equal, data comparability goals and objectives are more

easily met by use of consistent methods.
April 2004 Page 19-7

• Stakeholder input. Stakeholder preferences may influence method selection.

• Relative Cost/Availability. The reality of limited resources often impacts method selection.

Certain monitoring methods are commonly performed and available at numerous laboratories

or by readily available field instrumentation. Other more obscure methods may better meet

the needs of the project but are only available from highly specialized laboratories. In

addition to cost impact, low availability of some specific monitoring methods can impact data

quality due to lack of practice, market competition, appropriate standards, or certifications.

19.4.2 Available Methods

Hundreds of specific sampling, test, analysis, and quality assurance methods and procedures exist

for soil, water, sediment, and biota. The list of available methods changes frequently as new

methods are introduced and older methods are retired. It is not possible for this chapter to review

all of the monitoring methods available. Instead, this section provides an overview of several

key EPA resources and provides a listing of web sites that serve as sources of additional

information. Key EPA resources include the EPA Test Methods Index; the Contract Laboratory

Program (CLP); and the Fish and Wildlife Advisories Program.

• EPA Test Methods Index (http://www.epa.gov/epahome/Standards.html). EPA has

developed hundreds of specific sampling, test, analysis, and quality assurance methods and

procedures. In response to frequent requests for agency test methods, Region 1 Library staff

developed a methods index as a tool to help locate copies. Confirming that there was no

single volume containing all agency methods and no comprehensive list of them, the project

commenced and in 1988 printed the first EPA Test Methods Index.

(3) It has been updated

periodically to reflect new procedures and revoked methods, and the current edition includes

about 1,600 method references. The index includes only EPA methods, and its primary goal

remains as a reference tool to identify a source from which the actual method can be

obtained, either free or for a fee.

• EPA Contract Laboratory Program. The Contract Laboratory Program (CLP) is a national

network of EPA personnel, commercial laboratories, and support contractors whose

fundamental mission is to provide data of known and documented quality, primarily for the

Superfund program (http://www.epa.gov/superfund/programs/clp/about.htm). The Analytical

Operations/Data Quality Center (AOC) provides several tools to assist CLP clients,

laboratories, and samplers (http://www.epa.gov/superfund/programs/clp/tools.htm). These

tools were designed to use the Internet to facilitate many of the essential functions of the

CLP.
April 2004 Page 19-8

Available Guidance from EPA’s Contract Laboratory Program

Contract Laboratory Program National Functional Guidelines for Low Concentration Organic Data

Review EPA-540-R-00-006 June 2001

Contract Laboratory Program National Functional Guidelines for Organic Data Review

EPA-540/R-99-008 (PB99-963506) October 1999

Contract Laboratory Program National Functional Guidelines for Inorganic Data Review

EPA 540-R-01-008 July 2002

Contract Laboratory Program National Functional Guidelines for Chlorinated Dioxin/Furan Data

Review EPA-540-R-02-003 August 2002

Contract Laboratory Program Guidance for Field Samplers (Draft-Final) EPA-540-R-00-003 April

2003

This information, as well as methodology information is available from the CLP at:

http://www.epa.gov/superfund/programs/clp/services.htm

• EPA’s Fish and Wildlife Advisories Program (http://www.epa.gov/waterscience/fish/). 

EPA’s Office of Science and Technology provides technical and outreach material that

support efforts by state, local, and tribal (S/L/T) governments to protect their residents from

the health risks of consuming contaminated noncommercially caught fish. S/L/T

governments do this by issuing consumption advisories for the general population as well as

for specific vulnerable sub-populations. These advisories tell the public when high

concentrations of chemical contaminants have been found in local fish. They also include

recommendations to limit or avoid eating certain fish species from specific water bodies or

water body types. The program also provides Guidance for Assessing Chemical Contaminant

Data for Use in Fish Advisories (http://www.epa.gov/waterscience/fish/guidance.html), a set

of four volumes that provides guidance for assessing health risks associated with the

consumption of chemically contaminated non-commercial fish and wildlife. The set includes

Third Editions of Volume 1: Fish Sampling and Analysis and Volume 2: Risk Assessment

and Fish Consumption Limits. 

Exhibit 19-3 provides links to information on specific sampling and analysis methods,

summarized from key EPA compendia of methods. Methods are divided into four categories

(General, Analytical Method Index, Sample Collection, and Quality Assurance). Keywords are

added to help readers get to the area they are concerned with. Additional effort may be required

to “drill into” each site to view the relevant information. These links generally are limited to

government sites. Some non-EPA sites are included (e.g., Occupational Safety and Health

Administration (OSHA), National Institute of Standards and Technology (NIST), and National

Institute for Occupational Safety and Health (NIOSH)) to help fill specific information gaps.
April 2004 Page 19-9

Exhibit 19-3. Sources for Information on Specific Sampling and Analysis Methods

Keywords Description and URL Link

General References

Sample collection, analysis

method, criteria, water

General EPA Water page with links to analytical methods,

sampling guidance, and criteria for assessment of contamination.

http://www.epa.gov/waterscience/

Analysis methods EPA’ s Office of Ground Water and Drinking Water (OGWDW)

links to analysis methods.

http://www.epa.gov/OGWDW/methods/methods.html

Sample collection, analysis

methods, reference

NIOSH pocket guide to chemical hazards contains information by

analyte which can support field sample collection, analysis, and

determination of relevant criteria.

http://www.cdc.gov/niosh/npg/npg.html

Sample collection, analysis

methods, reference

NIST web book contains information by analyte which can

support field sample collection, analysis, and basic chemical

parameters from thermodynamic constants to reference mass

spectra. http://webbook.nist.gov/chemistry/

Sample collection, analysis

methods, reference

General EPA environmental test methods and guidelines page

with numerous links to other areas of information throughout EPA

web sites. http://www.epa.gov/epahome/Standards.html

Analysis Method Index

Analysis methods, sample

collection

Region I list of methods available as hardcopy and partial links to

analysis methods. http://www.epa.gov/epahome/index/

Analysis methods, sample

collection

Searchable online database of analysis methods. NEMI is a

project of the National Methods and Data Comparability Board, a

partnership of water quality experts from Federal agencies, States,

Tribes, municipalities, industry, and private organizations

supported by EPA and the U.S. Geological Survey.

http://www.nemi.gov

Analysis methods, sample

collection

National Exposure Research Laboratory (NERL) formerly EMSL, 

Manual of Manuals links to information about analysis methods;

summaries and ordering information for eight laboratory

analytical chemistry methods manuals published by the former

Environmental Monitoring Systems Laboratory-Cincinnati

(EMSL-Cincinnati) between 1988 and 1995.

http://www.epa.gov/nerlcwww/methmans.html

Analysis Methods

Analysis methods, water EPA’s Office of Water link to analysis methods. Laboratory

analytical methods that are used by industries and municipalities

to analyze the chemical and biological components of wastewater,

drinking water, sediment, and other environmental samples that

are required by regulations under the authority of the Clean Water

Act (CWA) and the Safe Drinking Water Act (SDWA).

http://www.epa.gov/waterscience/methods/

Analysis methods, water, 601,

602, 603, 604, 605, 606, 607,

608, 609, 610, 611, 612, 613,

624, 625, 1624, 1625

Methods for organic chemical analysis under the authority of the

Clean Water Act (CWA) and the Safe Drinking Water Act

(SDWA).

http://www.epa.gov/ostwater/methods/guide/methods.html
Exhibit 19-3. Sources for Information on Specific Sampling and Analysis Methods

Keywords Description and URL Link

April 2004 Page 19-10

Analysis methods, drinking water Recent drinking water methods from EPA’s Office of Research

and Development, National Exposure Research Laboratory

(NERL), formerly the Environmental Monitoring Systems

Laboratory (EMSL). http://www.epa.gov/nerlcwww/ordmeth.htm

Organic, analysis methods,

drinking water

Organic method index with hyperlink to method by analyte in

drinking water as maintained by Office of Ground Water and

Drinking Water.

http://www.epa.gov/OGWDW/methods/orch_tbl.html

Inorganic, metal, analysis

methods, drinking water

Inorganic and metal analysis methods in drinking water as

maintained by Office of Ground Water and Drinking Water.

http://www.epa.gov/OGWDW/methods/inch_tbl.html

Analysis methods, drinking

water, radionuclides

Radionuclides in drinking water as maintained by Office of

Ground Water and Drinking Water.

http://www.epa.gov/OGWDW/methods/rads.html (EPA)

http://www.epa.gov/OGWDW/methods/indrads.html (non-EPA)

Analysis methods, drinking

water, 

Approved methods for unregulated contaminants in drinking

water as maintained by Office of Ground Water and Drinking

Water. http://www.epa.gov/OGWDW/methods/unregtbl.html

Analysis methods, drinking

water, 

Secondary contaminants in drinking water as maintained by

Office of Ground Water and Drinking Water.

http://www.epa.gov/OGWDW/methods/2nd_tbl.html

Analysis methods, immunoassay Region 1 guidance on immunoassay methods.

http://www.epa.gov/region1/measure/ia/iaguide.html

Analysis methods, CLP, organic,

dioxin, inorganic, water, soil

Contract Laboratory Program (CLP) methods for organics,

inorganics, and dioxins/furans.

http://www.epa.gov/superfund/programs/clp/methods.htm

Analysis methods, air EPA Emissions Measurement Center (EMC) for methods related

to determination of airborne pollutants.

http://www.epa.gov/ttn/emc/

Analysis methods, pesticide, soil,

water

EPA’ Office of Pesticide Programs (OPP) database of

environmental chemistry, residual, and antimicrobial analysis

methods. http://www.epa.gov/oppbead1/methods/

Analysis methods, water, soil,

sediment, waste, air

EPA’s OSWER provides online updated SW-846 waste sampling

and analysis methods manual which is the source of many related

methods used in environmental sampling and analysis.

http://www.epa.gov/epaoswer/hazwaste/test/main.htm

Sample collection, analysis

methods, air

Occupational Safety and Health index of sampling and analysis

methods alphabetically by parameter and general information on

selection of methods and laboratories. http://www.oshaslc.gov/dts/sltc/methods/index.html

Sample collection, analysis

methods, air

EPA's Organic (TO) Compendium of methods for air toxics and

EPA's Inorganic (IO) Compendium methods.

http://www.epa.gov/ttn/amtic/airtox.html

Sample Collection

Sample collection, analysis, fish,

shellfish, biota

Methods for sampling and analyzing contaminants in fish and

shellfish tissue.

http://www.epa.gov/waterscience/fishadvice/volume1/index.html
Exhibit 19-3. Sources for Information on Specific Sampling and Analysis Methods

Keywords Description and URL Link

April 2004 Page 19-11

Sample collection Current manuals and protocols prepared by NERL-Cincinnati

scientists. NERL is the EPA’s scientific lead for the following

stream and source monitoring indicators: fish, macroinvertebrates,

periphyton, zooplankton, functional ecosystem indicators, water

and sediment toxicity and fish tissue contaminants. As part of

their indicator lead responsibilities NERL-Cincinnati scientists

prepare and update field and laboratory protocol and methods

manuals for these indicators.

http://www.epa.gov/nerleerd/methman.htm

Sample collection, monitoring

wells, low stress

Guidance for RCRA/Superfund groundwater sample collection

methodologies and the logical process for determining an

approach fit to site specifics.

http://www.epa.gov/tio/tsp/download/gw_sampling_guide.pdf

Sample collection, monitoring

wells, low stress

Generally well accepted low stress (low flow) ground water

sample collection guidance from EPA Region I. Several versions

exist across EPA regions and within other governmental and State

guidelines.

http://www.epa.gov/region1/measure/well/wellmon.html

Sample collection, field analysis EPA Environmental Response Team provides numerous sampling

and field analysis Standard Operating Procedures (SOPs) often

encountered in environmental responses including otherwise

atypical sample collections SOPs such as drum, wipe, and waste

pile sampling techniques. http://www.ertresponse.com/sops.asp

Sample collection, field analysis,

program design

EPA’s Office of Technology Innovation provides a web site with

information on proper sampling program design, QA/QC

concerns, and use of field methodologies to expedite information

collection without loss of data quality. http://clu-in.org

Quality Assurance

Quality assurance EPA Agency-wide quality system documents for EPA and nonEPA organizations plus general guidance. Documents are

available as PDFs. http://www.epa.gov/quality/qa_docs.html

Quality assurance Region I guidance includes quality assurance documents.

http://www.epa.gov/region1/lab/qa/qualsys.html
April 2004 Page 19-12

1. U.S. Environmental Protection Agency. 2000. Draft Ecological Soil Screening Level

Guidance. Office of Emergency and Remedial Response. July 10, 2000.

2. U.S. Environmental Protection Agency 1992. Guidance for Data Useability in Risk

Assessment (Part A). Office of Emergency and Remedial Response, Washington, D.C., April

1992. Publication 9285.7-09A, PB92-963356. Available at:

http://www.epa.gov/superfund/programs/risk/datause/parta.htm

U.S. Environmental Protection Agency 1992. Preparation of Soil Sampling Protocols:

Sampling Techniques and Strategies. Office of Research and Development.

EPA/600/R-92/128. Available at:

http://www.epa.gov/superfund/programs/risk/tooltrad.htm#dbh

U.S. Environmental Protection Agency 1996. Soil Screening Guidance: User’s Guide. Office

of Solid Waste and Emergency Response. Washington, D.C., July 1996. EPA540/R-96/018.

See especially Attachment B, Soil Screening DQOs for Surface Soils and Subsurface Soils.

Available at: http://www.epa.gov/superfund/resources/soil/index.htm#user

U.S. Environmental Protection Agency. 2002. Supplemental Guidance For Developing Soil

Screening Levels for Superfund Sites. Office of Solid Waste and Emergency Response,

Washington, D.C., December 2002. OSWER 9355.4-24. Available at:

http://www.epa.gov/superfund/programs/risk/tooltrad.htm#dbh

3. U.S. Environmental Protection Agency. 1988. EPA Test Methods Index. EPA 901/388/001.

References
Chapter 20 Exposure Metrics for Multimedia

Assessment

Table of Contents

20.1 Introduction ............ ............ ............ ............ ............ ...... 1

20.2 Generic Equation for Dietary Intake ............ ............ ............ ........... 3

20.3 Estimating Exposure Concentrations ............ ............ ............ .......... 4

20.4 Calculating Intake Variable Values ............ ............ ............ ........... 6

20.4.1 Consumption Rate ............ ............ ............ ............ ...... 7

20.4.2 Exposure Frequency ............ ............ ............ ............ ... 10

20.4.3 Exposure Duration ............ ............ ............ ............ ..... 10

20.4.4 Body Weight ............ ............ ............ ............ ......... 12

20.5 Calculating Averaging Time Value ............ ............ ............ .......... 12

20.6 Combining Exposure Estimates Across Pathways ............ ............ ........... 13

20.7 Exposure Models ............ ............ ............ ............ ............ 14

References ............ ............ ............ ............ ............ ............ 16

April 2004 Page 20-1

20.1 Introduction

This chapter concludes the exposure assessment component of the multipathway risk assessment

by describing how to develop estimates of intake (i.e., the metric of exposure) for the ingestion

pathways selected for analysis. Estimates of chemical intake via the inhalation pathway were

presented in Chapter 11. Exhibits 14-2 and 20-1 provide an overview of the potential

multimedia exposure pathways by which air toxics that persist and potentially bioaccumulate

may reach ecological and human receptors, respectively. Determination of chemical intake via

the ingestion exposure route combines the estimates of chemical of potential concern (COPC)

levels in food items and drinking water (discussed in Chapter 7) with estimates of consumption

rates (food, water), exposure frequency and duration, averaging time, and body weight to derive

estimates of the chemical intake rate (expressed generally as mg/kg-day).(1)

Exhibit 20-1. Potential Multimedia Exposure Pathways of Concern

This graphic illustrates many of the potential multimedia pathways of concern for air toxics. Air

toxics released from a source disperse through the air and eventually fall to the earth (atmospheric

deposition) via settling and/or precipitation. Air toxics deposited to soil may be absorbed by plants

that are then harvested for human consumption. Humans may be exposed via ingestion of

contaminated plants and soils, or by consuming contaminated terrestrial animals (e.g., beef, for those

air toxics that bioaccumulate and transfer up the terrestrial food web). Air toxics deposited to water

may be dissolved in the water column and/or may settle and be absorbed into aquatic sediments. Air

toxics in sediments and the water column may be absorbed by aquatic plants (uptake). Aquatic

organisms (e.g., fish) may be exposed directly to air toxics in the water column and/or by consuming

other contaminated aquatic organisms (for those air toxics that bioaccumulate and transfer up the

aquatic food web) or sediments. People may be exposed to air toxics by eating contaminated aquatic

plants, fish, or shellfish and/or by drinking contaminated water. Note also that, while in the

atmosphere, air toxics may also have direct impacts to humans via inhalation.
April 2004 Page 20-2

Exposure and Intake via Ingestion

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

followed by entry (crossing the boundary). Intake involves physically moving the chemical in

question through an opening in the outer boundary (usually the mouth), typically via eating or

drinking. Normally the chemical is contained in a medium that comes into contact with the body,

such as food or water, and the concentration of the chemical at this point of contact is called the

exposure concentration. The estimate of how much of the chemical enters into the body is based on

how much of the carrier medium enters the body. The chemical intake rate is the amount of

chemical crossing the outer boundary per unit time, and is the product of the exposure concentration

times the ingestion rate. Ingestion rate is the amount of the carrier medium crossing the boundary

per unit time, such as the number of kilograms of food ingested/day or liters of water consumed/day. 

Ingestion rates typically are not constant over time (they can vary over time and among individuals)

and are usually given (for deterministic analyses) as an average intake rate over some period of time.

In addition, the intake rates are usually normalized to body weight. Thus, a common intake rate

would take the form of milligrams of pollutant ingested per kilogram of body weight per day (or

mg/kg-d). A different ingestion rate would be developed for each type of person in the population

under study. For example, one intake rate could be developed to represent the average adult (male

and female) while a separate intake rate could be developed to represent children between the ages of

birth to four years old.

Chapter 7 described two general approaches for deriving the exposure concentration (EC) for an

inhalation risk assessment: (1) use of ambient air concentrations as a surrogate for the EC, and

(2) exposure modeling that combines estimates of ambient air concentrations with information

about the population of interest, including the types of people present (e.g., ethnicity, age, sex),

time spent in different microenvironments, and microenvironment concentrations. The first

approach (i.e., use of ambient concentrations in abiotic media such as soil, water, or

sediments) generally is not used for multipathway air toxics risk assessments. Instead, a

multipathway exposure assessment must involve some type of exposure modeling (e.g., at a

minimum simple scenarios to characterize persons who are exposed and the amount and duration

of their contact with the abiotic and biotic media).

Note that EPA has derived some human health screening-level concentration benchmarks for

surface water and soil (i.e., the Office of Water’s Ambient Water Quality Criteria for the

Protection of Human Health,(2) and the Superfund Program’s soil screening levels(3)). However,

these human health benchmarks are based on specific scenarios (e.g., how much water a person

drinks each day, how much they weigh) that were selected to meet different programmatic goals

and statutory requirements. Therefore, the scenarios on which these benchmarks are based may

not be appropriate for a specific air toxics risk assessment.

The way a chemical enters the body and eventually reaches the target organ is a complex process

(see box below). For most chemicals, however, it is not necessary to quantify anything beyond

the chemical intake rate, because the dose-response value (e.g., Reference Dose [RfD] or

Cancer Slope Factor [CSF]) is also based only on the amount of chemical ingested and not the

amount of chemical that has been absorbed into the bloodstream. 

The remainder of this chapter focuses on how to quantify ingestion exposure (intake) for

multipathway air toxics risk assessments. The corresponding chapter for inhalation analyses
April 2004 Page 20-3

(Chapter 11) discusses how to evaluate uncertainty in the exposure assessment and how to

present the exposure assessment results; this applies to all exposure evaluations (i.e., inhalation

and ingestion).

20.2 Generic Equation for Dietary Intake

Equation 20-1 is the generic equation used to calculate dietary chemical intake:(4)

(Equation 20-1)

where

I = Chemical intake rate, or the amount of pollutant ingested per unit time per body

weight (mass), expressed in units of mg/kg-day. For evaluating exposure to

noncarcinogens, the intake is referred to as Average Daily Dose (ADD); for evaluating

exposure to carcinogenic compounds, the intake is referred to as Lifetime Average

Daily Dose (LADD).

Chemical-related variable:

EC = Exposure concentration of the chemical in the medium of concern for the time

period being analyzed, expressed in units of mg/kg for soil and food or mg/L for

surface water or beverages (including milk).

Variables that describe the exposed population (also termed “intake variables”):

CR = Consumption rate, the amount of contaminated medium consumed per unit of time or

event (e.g., kg/day for soil and L/day for water).

EF = Exposure frequency (number of days exposed per year).

ED = Exposure duration (number of years exposed).

BW = Average body weight of the receptor over the exposure period (kg).

Assessment-determined variable:

AT = Averaging time, the period over which exposure is averaged (days). For carcinogens,

the averaging time is 25,550 days, based on an assumed lifetime exposure of 70 years;

for noncarcinogens, averaging time equals ED (years) multiplied by 365 days per

year.

The values of some exposure factors depend on site conditions as well as the characteristics of

the potentially exposed population (e.g., child vs. adult). Because of differences in physiology

and behavior, exposures among children are expected to be different than exposures among

adults. For example, body weight and consumption rate differ for children and adults. For the

evaluation of non-carcinogenic effects, intakes for children generally are estimated separately

(often for ages 0-6) than for adults (often from ages 6-beyond). For the evaluation of

carcinogenic effects, intake estimates are averaged over the assumed lifetime (70 years).
April 2004 Page 20-4

20.3 Estimating Exposure Concentrations

The exposure concentration for a chemical is calculated separately for each food item and

environmental medium of concern. The value of these variables may be determined by modeling

(Chapter 18), monitoring (Chapter 19), or a combination of both. The specific algorithms for

determining these concentrations will depend on the specific models and/or sampling and

analysis techniques used. For example, EPA has developed methodologies for estimating EC

values in soil, water, sediment, and various food items for releases from hazardous waste

combustion facilities (see Appendix L).(5)

For ingestion pathways, the specific media concentration values obtained from a multimedia

modeling simulation for use in deriving exposure concentrations depends on several important

decisions made during problem formulation, including:

• Choice of modeling duration for a model run;

• Choice of the year or years of the model run on which to base the EC; and

• Choice of a specific ED.

Exhibit 20-2 presents several different examples relevant to different purposes/objectives for an

assessment.

Exhibit 20-2. Example Decisions in Assessing Exposures Resulting From

Distribution of Air Toxics into Other Media

time (years)

In this hypothetical example, a modeling analysis was used to predict the concentrations of a persistent

bioaccumulative hazardous air pollutant (PB-HAP) in fish tissue during a 100-year emissions scenario

(annual average was estimated each year and is plotted here using a logarithmic scale). As discussed

below, the exposure scenario assessed will reflect several key choices including:

(1) choice of modeling duration for model run;

(2) choice of year or years of model run on which to base EC (i.e., the model outputs); and

(3) choice of specific ED.
April 2004 Page 20-5

Exhibit 20-2 (continued)

The modeling duration is a separate decision from the ED and is not related to the average human

lifespan.

Note that in this example, the analyst assumed that the starting concentration was zero (i.e., the tissue

concentrations reflect only the sources being modeled). Some multimedia models (e.g., TRIM.FaTE)

can start with an initial concentration.

Modeling Duration. The analyst can choose to run a multimedia model for any period of time. 

Duration will usually be chosen to reflect the expected duration of emissions from the source(s) being

evaluated or, perhaps, that duration expected in order to reach steady-state conditions. A common

duration is 30 or 40 years (e.g., the expected lifespan of many facilities or processes). For this

example, a 100-year duration was selected.

Selection of Model Outputs. Usually the modeling duration will have been chosen with

consideration of the model outputs on which the exposure scenario is to be based and the exposure

duration. Some common examples follow:

• Year of maximum concentration. Screening-level analyses often use the maximum

concentration reached during the modeling period which, for a constant emissions scenario, will

usually be the final year of the modeling simulation. For this example (see figure), it would be the

100th year (at such time as the fish concentration was approximately 2,000 ng/kg).

– Exposure Duration. With use of the maximum model result, the analysis presumes no change

in fish concentration over the exposure duration (i.e., in this example EC = 2,000 ng/kg

throughout the exposure period).

C Initial years of simulation. In this case the exposure being assessed is that beginning with

initiation of emissions and extending through the duration selected for assessment.

– 30-year Exposure Duration. In this case, the analyst is basing the exposure duration near

the 95 percentile of how long people live in the same home.(6) If the analyst chose to examine

changing concentrations over time, the ECs would vary, reflecting the concentration outputs

from the first 30 years of the modeling duration.

– 70-year Exposure Duration. In this case, the analyst is using a lifetime exposure

assumption. The exposure scenario then may be based on the model outputs from the first 70

years of the modeling duration.

C Last years of simulation. In this case, the exposure being assessed is that which occurs during

the ending years of the simulation, with the number of years involved equal to the exposure

duration selected for assessment.

– 30 -year Exposure Duration. For this ED, the ECs would vary reflecting the predicted

concentrations from the last 30 years of the model simulation. 

– 70-year Exposure Duration. In this case, the analyst is using a lifetime exposure

assumption. The exposure scenario may then employ varying ECs reflecting the predicted

concentrations from the last 70 years of the model simulation.

Note: When using varying exposure concentrations for the exposure scenario, other variables included

in the calculation of ingestion exposure estimates (pollutant intake, mg/kg-day) for the population(s)

of interest may also vary. For example, if the exposure scenario includes exposure for cohorts aging

from birth - 30 years, other exposure factors (e.g., body weight, consumption rate) will also vary over

time.
aNCEA recently published a new compilation of consumption data from the 1994-1996 CSFII. This data

updates CSFII data in the 1997 Exposure Factors Handbook. 

See: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=56610.

April 2004 Page 20-6

20.4 Calculating Intake Variable Values

Each intake variable in Equation 20-1 (e.g., consumption rate, body weight) has a range of

potential values. Intake variable values for a given pathway may be selected so that the

combination of all intake variables results in an estimate for an individual at the “high-end” of

potential exposure levels. Alternatively, the intake variables may be selected to represent a

“central tendency” individual expected to receive an average exposure. In doing this, the

assessor needs to avoid combinations of parameter values that are inconsistent (e.g., low body

weight used in combination with high dietary intake rates), and must keep in mind the ultimate

objective of being within the distribution of actual expected exposures and doses, and not beyond

it. Commonly, both the central tendency and high end intakes are quantified. In some cases, the

distribution of intake rates in the population may be described using probabilistic risk assessment

methods (discussed in Part VI). 

EPA recommends values for intake variables for the U.S. population in the Exposure Factors

Handbook,

(7) the Child-Specific Exposure Factors Handbook,(8) and the Consolidated Human

Activity Database.

(9)(a) EPA also recently published draft guidance on selecting the appropriate

age groups for assessing childhood exposures.(10) Note, however, that there are likely to be

differences between recommended default, and regional and site-specific, exposure parameter

values. This may be especially true for consumption rate (see below).

For central tendency estimates, risk assessors commonly set all of the exposure factors in the

Equation 20-1 at central tendency values. If only limited information on the distribution of the

exposure or dose factors is available, risk assessors commonly approach the high-end estimates

by identifying the most sensitive variables and using high-end values for a subset of these

variables, leaving others at their central values. As mentioned earlier, the assessor needs to avoid

combinations of parameter values that are inconsistent (e.g., low body weight with high dietary

intake rates) and must keep in mind the ultimate objective of being within the distribution of

actual expected exposures and doses.

Maximizing all variables will in virtually all cases result in an estimate that is above the actual

values seen in the population. When the principal parameters of the dose equation (e.g.,

concentration [appropriately integrated over time], intake rate, and duration) are broken out into

sub-components, it may be necessary to use maximum values for more than two of these

sub-component parameters, depending on a sensitivity analysis.

For probabilistic analyses, values for exposure factors are commonly allowed to vary according

to specific assumed distributions of potential values.

Note that the high-end intake estimate is a plausible estimate of intake for those persons at the

upper end of the exposure distribution. This descriptor is intended to estimate the exposures

that are expected to occur in small but definable high-end segments of the subject population
April 2004 Page 20-7

(but not higher than the highest person in the population), but may not be appropriate for

estimating exposure for the population as a whole.

(1)

20.4.1 Consumption Rate

Consumption rate is the amount of contaminated food or medium consumed per event or unit of

time (e.g., amount of fish consumed per meal or per day). The consumption rate is multiplied by

a fraction of the total dietary intake for this type of food or medium, representing the amount

consumed from the study area. The specific fraction applied depends on the analysis.

C For screening-level analyses, it is common to assume that the person obtains 100 percent of

the food type from the study area (e.g., farm, water body) being evaluated. This assumption

also might be used for a subsistence-type receptor (e.g., a local fish consumer who only eats

fish caught from the study area).

C For higher tiers of analyses, it is common to assume that the person obtains some of the food

type from the study area (i.e., the contaminated fraction) and some of the food type from

other sources (e.g., at the grocery store). This latter fraction generally is assumed to be

uncontaminated by the source(s) under assessment. Thus, if a person is assumed to eat 1⁄2

pound of fish per day, but only 25 percent is caught within the study area, the assumed

consumption of contaminated fish would be 1/8 pound per day. 

The following pathway-specific considerations are important for estimating consumption rate.

C Food Ingestion. Plants and animals may accumulate COPCs that were deposited onto soil or

water. Humans may be exposed to these compounds via the food chain when they consume

these plants (and animals that consume these plants) as a food source. Human intake of

COPCs is quantified on the basis of the concentration of COPC in the food (Section 20.3)

and:

– The types of foods consumed, which vary with age (e.g., children and adults often eat

different things), geographical region, and sociocultural factors (e.g., ethnicity, cultural

factors);

– The amount of food consumed per day, which can vary with age, sex, and geographic

region, and also within these categories;

– The fraction of the diet contaminated by COPCs (which can vary by food type); and

– The effect of food preparation techniques on concentrations of COPCs in the food itself.

C Soil Ingestion. Children and adults may receive direct exposure to COPCs in soil when they

consume soil that has adhered to their hands (called incidental soil ingestion). Factors that

influence exposure by soil ingestion include concentration of the COPC in soil, the rate of

soil ingestion during the time of exposure, and the length of time spent in the vicinity of

contaminated soil. Soil ingestion rates in children are based on studies that measure the

quantities of nonabsorbable tracer minerals in the feces of young children. Ingestion rates for

adults are based on assumptions about exposed surface area and frequency of hand-to-mouth

transfer. Indoor dust and outdoor soil may both contribute to the total daily incidental

ingestion of soil (indoor dust is partially made up of outdoor soil that has been tracked

inside).
April 2004 Page 20-8

In addition, some young children – referred to as “pica” children – may intentionally eat soil. 

The typical medical and scientific use of the term “pica” refers to the ingestion of nonfood

items, such as soil, chalk, and crayons.(10) Such behavior is considered a temporary part of a

child’s development. For risk assessment purposes, pica is typically defined as “an

abnormally high soil ingestion rate” and is believed to be uncommon in the general

population. If available information indicates that there are children exhibiting pica behavior

in the assessment area, it may be appropriate to include these children as a separate group in

the exposure assessment. EPA’s Exposure Factors Handbook provides quantitative data on

soil ingestion rates related to pica.(11)

Inhalation of soil resulting from dust resuspension by wind erosion generally is not a

significant pathway of concern for air toxics.(5) However, it may be an issue for locations at

which there is little vegetative cover. Methodologies have been developed to assess the

exposure to pollutants resuspended by wind erosion for landfills and Superfund sites.(12) The

exposure estimate from resuspended soil would depend on moisture content of the soil,

fraction of vegetation cover, wind velocity, soil particle size, COPC concentration in the soil,

and size of the contaminated area.

Depth of Contaminated Soils: A Key Variable

When exposures to COPCs in soils are modeled for human health risk assessment, an important factor

affecting the exposure estimate is the depth of contaminated soils used to calculate soil concentrations. 

The same deposition rate will result in different soil concentrations depending on how deeply the

COPCs are assumed to mix or migrate into the soil. Mixing depth also may affect exposure estimates

via specific pathways. For example, in calculations of exposures resulting from uptake through plant

roots, the average concentration of COPCs over the depth of the plant root determines plant uptake. 

However, calculations that assess soil ingestion through hand-to-mouth activity commonly focus on 

only the top few centimeters of soil.

COPCs deposited onto undisturbed soils generally are assumed to remain in the shallow, upper soil

layer. However, COPCs deposited onto soil surfaces may be moved into lower soil profiles by tilling,

whether manually in a garden or mechanically in a large field. Other factors such as soil disturbance

by domestic animals (e.g., cattle in an enclosure) also may need to be considered. Some chemicals are

also highly soluble in water and may be carried deeper into soil along with infiltrating rainwater. The

key questions to ask therefore include:

• Are soils tilled, or is it reasonable to assume they are undisturbed?

• If soils are tilled, what mixing depth is reasonable to assume?

• What other factors might affect how deeply COPCs will be moved into soils?

EPA guidance and other references(5)(13) provide a more detailed discussion of depth of contaminated

soils, along with recommended values.
bNote that ingestion of contaminated groundwater generally is not a significant pathway of concern for air

toxics risk assessments because most air toxics that persist and may bioaccumulate tend to get bound up in soil and,

therefore, tend not to move readily into groundwater. However, if the groundwater pathway were a concern for a

specific study, it would be evaluated in generally the same way as the ingestion of surface water pathway (i.e., as a

drinking water source; however, depending on the circumstances, groundwater may or may not be treated to remove

particles prior to consumption).

April 2004 Page 20-9

• Ingestion of Drinking Water. In air toxics assessments, assessors only evaluate the

ingestion of drinking water when an affected surface water body or collected precipitation

(e.g., a cistern) is used as a drinking water source.(b) Important factors affecting the

concentration of COPCs in a surface water body include the location of the surface water

body or precipitation collection apparatus relative to emissions sources; concentrations of

COPCs in and characteristics of the soils (which affects runoff and leachate concentrations);

and the size and location of the watershed. For drinking water, the exposure estimate is

affected by:

– The concentration of the COPC in the water;

– The daily amount of drinkable water ingested; and

– The fraction of time that the individual spends in the area serviced by that water supply

system. (Note that for screening level analyses, 100% of drinking water may be

presumed to come from the contaminated source.)

Note that in estimated exposures associated with drinking water supplies, risk assessors

commonly assume that the drinking water undergoes at least a minimum level of treatment to

remove solids (i.e., particles in the water which are PB-HAPs or onto which PB-HAPs may

be absorbed). Therefore, the risk assessment commonly focuses on the dissolved

concentrations of PB-HAPs in drinking water sources.

Groundwater as a Source of Drinking Water

If site-specific circumstances suggest that groundwater may represent a potential concern (e.g., the

presence of extremely shallow aquifers used for drinking water purposes or a karst environment in

which the local surface water significantly affects the quality of ground water used as a drinking water

source), the TRIM.FaTE library includes a groundwater compartment that can be used to assess the

groundwater pathway. EPA’s Human Health Risk Assessment Protocol for Hazardous Waste

Combustion Facilities(14) and Draft Technical Background Document for Soil Screening Guidance(15)

discuss the methods for evaluating the groundwater pathway.

• Ingestion of Fish. Factors that affect human exposure by ingestion of fish from a surface

water body include:

– Sediment and water COPC concentrations;

– The types of fish and shellfish consumed;

– The portion of fish eaten (e.g., fillet only, fillet plus skin, whole body);

– The effect of food preparation techniques on concentrations of COPCs in the fish;

– Ingestion rates for the various fish and shellfish groups; and

– The fraction of dietary fish caught in the surface water body or bodies being evaluated. 

(Note that for screening level analyses, 100 percent of fish/shellfish is presumed to come

from the contaminated water body.)
April 2004 Page 20-10

The types of fish consumed will affect exposure because different types of fish and shellfish

accumulate COPCs at different rates. For example, fatty fish tend to accumulate lipophilic

organic compounds more readily than lean fish. The amount of fish consumed also affects

exposure because people who eat large amounts of fish will tend to have higher exposures. 

Fish consumption rates and the parts of the fish that are consumed can vary greatly,

depending on geographic region and social or cultural factors. Also, because all of a person’s

dietary fish may not originate from the surface water body near the source of the PB-HAP,

the fraction of locally caught fish is also a variable for exposure.

20.4.2 Exposure Frequency

The specific exposure frequency will depend on how the exposure analysis is set up. For

example, a scenario-based analysis would specify one or more exposure frequencies for each

defined scenario. A typical screening-level exposure frequency is 350 days per year; this number

is based on the assumption that all people spend a minimum of two weeks at a location other

than the exposure scenario location selected for analysis (e.g., on vacation).(1) (5) However, many

activities vary on a weekly and/or seasonal basis. For example, recreational fishing is more

likely to occur on weekends than on weekdays, and most areas in the U.S. have limited fishing

and hunting seasons.

20.4.3 Exposure Duration

Exposure duration is the length of time over which exposure occurs (e.g., a lifetime or a

particular residence time). As noted in Section 20.3 above, choice of ED will depend on many

factors, including the purpose of the assessment or risk management decision, the tier of analysis,

and the particular effect(s) of concern. There are no universally established ED values for risk

assessments because different EDs may be appropriate in different situations. Some commonly

used EDs include:

• Lifetime (70 years) – generally used for screening-level analyses;

• High-end number of years a person resides in a single location (about 30 years);

• Median number of years a person resides in a single location (about 9-10 years); and

• Seven years (ten percent of an assumed lifetime) – sometimes used for noncancer effects.

Although a source may remain in the same location for more than 70 years, and a person may

have a lifetime of exposure to emissions from that source, U.S. Bureau of the Census data on

population mobility indicate that many Americans do not always remain in the same area for

their assumed 70-year lifetime.(16) An estimate of the number of years that a person is likely to

spend in one area can be derived from information about mobility rate and median time in a

residence.

Analysts may use long EDs when conducting simple screening analyses performed to determine

if more complex analyses are necessary. The rationale for use of such EDs is that if risks are not

of concern when the exposure duration is long, then they would not be of concern given other,

shorter, exposure durations. (Typically analysts also make other conservative or “healthprotective” assumptions when conducting this type of screening analysis.) Analysts may use

specific EDs particular to the legal framework for the assessment. For example, the residual risk

section of the Clean Air Act (CAA) references an Agency rulemaking for which one prominent
c

The central tendency estimate of adult exposure duration commonly used in risk assessments is 9 years

(Section 15.4.3 and Table 15-174 in EPA’s Exposure Factors Handbook, Part III).(10) This estimate is a median

value based on national residential occupancy data for the general population. This estimate may not be appropriate

in certain situations, such as when population-specific data exist or when the analyst is evaluating a specific subpopulation that is expected to differ from the general population (e.g., farm families).

dAs described in Section 20.4, estimation of high-end exposure will sometimes involve setting exposure

duration at its high-end value. The high-end estimate of adult exposure duration typically used in risk assessments is

30 years (Section 15.4.3 and Table 15-174 in EPA’s Exposure Factors Handbook, Part III),(10) although this may

vary for specific sub-populations.

April 2004 Page 20-11

risk metric considered a 70-year exposure duration (see CAA section 112(f)(2) and 54 Federal

Register 38044).

The type of risk metric being derived also influences the consideration of exposure duration. For

example, when the analyst wants to describe central tendency risk based on a deterministic

analysis, s/he typically will use mean or median exposure assumptions to calculate risk.(c)

Similarly, when the analyst wants to describe high-end risk based on a deterministic analysis,

s/he may use high-end exposure assumptions or a combination of central tendency and high-end

exposure assumptions that provide a reasonable estimate of the individual risk for those persons

at the upper end of the risk distribution. As explained in EPA’s Policy on Risk

Characterization:

(17) “Conceptually, high-end exposure means exposure above about the 90th

percentile of the population distribution, but not higher than the individual in the population who

has the highest exposure.”(d) When the analyst wants to conduct a probabilistic analysis of risk,

s/he typically will use or develop a distribution of exposure durations from the available data

(e.g., see EPA’s Exposure Factors Handbook, Part III; Tables 15-164, 15-166, 15-167, and 15-

168).(10)

The areal extent of the impacted area(s) may also be a consideration. If a source of concern

occurs in the majority of communities, then it is possible that individuals may be exposed to the

source for a longer period of time than one might predict using standard estimates of exposure

duration. In this case, the analyst might assume that even though an individual changes

residence, the individual still would be exposed to the source of concern, and thus the

individual’s exposure duration would be greater than typically anticipated. Such an analysis

must consider whether the concentration of the pollutant at the multiple locations of exposure

would be equivalent. Because location-specific parameters such as meteorological conditions,

distance from the source, and the presence of certain pathways of exposure (e.g., surface water,

home-grown produce) may vary considerably by geographic area, the analyst likely will have to

estimate exposure concentrations for each geographic location or community of interest. 

Similarly, if a single source impacts a large geographic area, then it is possible that national

estimates of population mobility will not adequately capture an individual’s potential duration of

exposure. That is, an individual may move from one point of exposure associated with a

particular source to another point of exposure associated with that same source. For example,

data indicate 29 percent of home buyers move less than five miles to a new home (Table 15-171

in EPA’s Exposure Factors Handbook, Part III(10)). Similar to the caution expressed above, the

concentrations of pollutants within an area impacted by a single source may vary considerably. 

The analysis should reasonably account for such situations.
April 2004 Page 20-12

The persistence of the source-associated contamination may also be an important consideration

in the exposure duration for ingestion pathway exposure assessment. For example, the analyst

should not automatically assume that the exposure duration can be no greater than the operating

life of the source. Persistent pollutants may remain in the environment (e.g., soils and sediments)

for years after the primary source is discontinued. Nevertheless, in certain cases, once the source

of exposure stops, the pollutant concentrations in the affected media may diminish. Particularly

in more refined assessments, the exposure concentration may reflect any expected variations in

media or food concentrations over time.

When evaluating the risk of noncancer health effects from ingestion exposures (i.e., calculating

hazard quotients for ingestion exposures), we do not average pollutant dose over the lifetime of

an individual as we do when calculating carcinogenic risk. Rather, when calculating hazard

quotients for ingestion exposures, we average the dose over an averaging time equivalent to only

the period of exposure (i.e., we calculate an average daily dose rather than a lifetime average

daily dose). Consequently, the values for exposure duration and averaging time are the same,

and mathematically cancel each other out. Nevertheless, when calculating average daily dose,

the analyst must still consider exposure duration when selecting and computing food and media

intakes for use in the dose equation. EPA typically considers exposures of seven years or greater

as chronic exposures. Food and media intakes that represent time-weighted averages over a

seven-year period are reasonable for evaluating chronic non-cancer health effects. Durations as

short as one year are also commonly used, particularly in screening assessments, and for

childhood evaluations where intake on a per body weight basis may rapidly change from

year to year.

20.4.4 Body Weight

The choice of body weight for use in the exposure assessment depends on the definition of the

population group at potential risk. Because children have lower body weights, typical ingestion

exposures per unit of body weight, such as for soil, milk, and fruits, tend to be higher for

children. If a lifetime exposure duration (or an exposure duration over the childhood and adult

years) is being evaluated, it needs to be based on differing values for the different age groups. If

a less than a lifetime exposure estimate is being evaluated, it is important to include the

children’s age group in the specific scenarios or cohorts used. EPA’s Exposure Factors

Handbook(6) and Child-Specific Exposure Factors Handbook(7) provide age-specific values for

body weight and consumption rate per unit body weight.

20.5 Calculating Averaging Time Value

When evaluating exposure for the purposes of assessing hazard (vs. predicting cancer risk),

intakes are calculated by averaging intakes over the period of exposure (i.e., subchronic or

chronic durations) and result in average daily doses or ADDs for the duration of interest. For

evaluation of cancer risks, potential dose is calculated as the average daily dose over a lifetime

(i.e., chronic daily intakes, also called lifetime average daily doses or LADDs). The approach for

carcinogens is based on the premise that risk is proportional to total lifetime dose (i.e., a high

dose received over a short period of time is equivalent to a corresponding low dose spread over a

lifetime).(18) The basis for this approach becomes less strong as the exposures in question

become more intense but less frequent, especially when there is evidence that the agent has

shown age-related variations in carcinogenic potency, or a nonlinear dose-response relationship. 
April 2004 Page 20-13

In some cases, therefore, it may be necessary to consult a toxicologist to assess the level of

uncertainty associated with the exposure assessment for carcinogens.

Note that, even when the exposure of interest is a full lifetime, chronic hazards are generally

calculated separately for chronic exposures to age groups that differ substantially with regard to

pertinent exposure factors (e.g., ingestion rate or body weight) and are not combined (i.e., usually

the oral route hazards calculated for children are not added to the hazards posed to adults to

represent a “lifetime hazard”). Rather, both hazard quotients/indices are presented as chronic

hazard metrics relevant to the two groups. When assessing carcinogenic risks for a lifetime

exposure, on the other hand, cancer risk estimates are usually added across different age groups,

since the risk received over discrete periods of time (e.g., as a child, as a young adult, as an older

adult) are each considered to be fractions of the risk associated with a full lifetime of exposure. 

Note that in calculating LADDs, it is essential to account for differences in the values of different

intake variables (e.g., body weight, consumption rate) at different ages.

20.6 Combining Exposure Estimates Across Pathways

A given population may receive exposure to an individual chemical from several different

exposure pathways. For example, individuals may receive exposure via inhalation of the

chemical in the air and via ingestion of surface water and fish that have become contaminated

through deposition. The specific exposure scenarios or cohorts defined for the analysis may

include more than one pathway. The corresponding intake variables used in the analysis may

need to account for the number of pathways over which exposure will be combined. For

example, to develop a high-end estimate for a scenario that includes inhalation, ingestion of soil,

and ingestion of fish, it may be necessary to combine high-end exposure assumptions for all

pathways. In other cases, it may be more appropriate combine high-end exposure assumptions

for particular pathways with more central-tendency assumptions for others. Otherwise, the

estimate may represent an extreme situation in which the simulated behavior is assumed to result

in high exposures via all pathways.

Two steps are required to determine whether intake estimates should be combined for a single

scenario:

• Identify reasonable exposure pathway combinations. Identify exposure pathways that

have the potential to expose the same individual, cohort, or subpopulation at the key exposure

areas evaluated in the exposure assessment, making sure to consider areas of highest

exposure for each pathway. For each pathway, the intake estimates have been developed for

a particular exposure area and time period; they do not necessarily apply to other locations or

time periods. Hence, if two pathways do not affect the same individual, cohort, or

subpopulation, neither pathway’s exposure estimate affects the other, and exposures should

not be combined.

• Examine whether it is likely that the same individuals would consistently face a

reasonable central tendency or high-end exposure by more than one pathway. Once

reasonable exposure pathway combinations have been identified, it is necessary to examine

whether it is likely that the same individuals would consistently face central tendency or highend exposure conditions. As noted in Section 20.4 above, the exposure estimate for each

exposure pathway includes many conservative estimates. Also, some of the exposure
April 2004 Page 20-14

parameters are not completely predictable in space and/or time (e.g., the maximum

downwind concentration may shift compass direction). For real-world situations in which

contaminant concentrations vary over time and space, the same individual or cohort may or

may not experience central-tendency or high-end exposure conditions for more than one

pathway over the same period of time. Thus, it is important to clearly explain why the key

assumptions chosen for more than one pathway for an individual, subpopulation, or cohort

are set at central tendency and/or high-end exposure estimates. (Note that an important goal

in the analysis of high-end receptors is to identify exposures that are in the high-end of the

range - usually higher than the 90th percentile exposure - but not higher than the highest

exposure in the population.)

20.7 Exposure Models

Exposure models have been developed that automate the calculation of chemical intake. They

may simply calculate exposure for a set of individual scenarios, or they may draw upon activity

pattern and/or dietary survey databases to characterize cohort exposure within a population. 

Three exposure models are described below.

California Total Exposure Model for Hazardous Waste Sites (CalTOX)

As described previously in Part II, Chapter 9, the California Environmental Protection Agency

funded the development of the CalTOX program.(19) CalTOX has been developed as a set of

spreadsheet models and spreadsheet data sets to assist in assessing human exposures and defining

soil clean-up levels at uncontrolled hazardous wastes sites. CalTOX addresses contaminated

soils and the contamination of adjacent air, surface water, sediments, and ground water. The

modeling components of CalTOX include exposure scenario models. The exposure models

encompass twenty-three exposure pathways. The exposure assessment process consists of

relating pollutant concentrations in the multimedia model compartments to pollutant

concentrations in the media with which a human population has contact (e.g., personal air, tap

water, foods, household dusts, soils). The temporal resolution is either daily for inhalation and

dermal exposure or annual for ingestion. The aggregation period is variable, depending on the

duration of residence at a single location. The spatial resolution and modeling domain are userspecified, but generally encompass some vicinity around the waste site of interest. Activity data,

such as inhalation, ingestion, and dermal contact rates, are derived from EPA’s Exposure Factors

Handbook.(6)

TRIM.Expo

As discussed in Chapter 18, TRIM.Expo is the exposure component of the TRIM modeling

system. The ingestion component of TRIM.Expo (TRIM.ExpoIngestion) is designed to take input

values from TRIM.FaTE, but may also be operated independently with inputs from measurement

studies or alternative models. TRIM.ExpoIngestion will employ a scenario-based approach, based

on that used in the 3MRA modeling system, in its initial version. Information about the ingestion

component of TRIM.Expo is available on EPA’s Fate, Exposure and Risk Analysis (FERA) web

site: http://www.epa.gov/ttn/fera.
April 2004 Page 20-15

Stochastic Human Exposure and Dose Simulation Model (SHEDS)

The Stochastic Human Exposure and Dose Simulation (SHEDS) Model(20) is a probabilistic,

physically-based model that simulates aggregate exposure and dose for population cohorts and

multimedia pollutants of interest. It is being developed by EPA’s National Exposure Research

Laboratory (http://www.epa.gov/nerlpage/). At present the model is applied to assess children’s

exposures to pesticides (SHEDS-Pesticides) and population exposures to particulate matter

(SHEDS-PM).

SHEDS-Pesticides focuses on children’s aggregate population exposure to pesticides. Activity

data are selected from daily sequential time/location/activity diaries from surveys contained in

EPA’s Consolidated Human Activity Database (CHAD).(8) For each individual, SHEDSPesticides constructs daily exposure and dose time profiles for the inhalation, dietary and nondietary ingestion, and dermal contact exposure routes, and then aggregates the dose profiles

across routes. A pharmacokinetic component has been incorporated to predict pollutant or

metabolite concentrations in the blood compartment or eliminated urine. Exposure and dose

metrics of interest (e.g., peak, time-averaged, time-integrated) are extracted from the individual’s

profiles. Two-stage Monte-Carlo sampling is applied to predict the range and distribution of

aggregate doses within the specified population and identify the uncertainties associated with

percentiles of interest.

SHEDS-Pesticides is currently being refined to characterize both aggregate and cumulative dose

associated with human exposure (i.e., for both adults and children) to a variety of environmental

pollutants in addition to pesticides. SHEDS-Pesticides will eventually be expanded to include

source-to-concentration (i.e., fate and transport) models and more complete exposure-to-dose

models (i.e., pharmacokinetic or dosimetric models).

SHEDS-PM estimates the population distribution of particulate matter (PM) exposure by

sampling from distributions of ambient PM concentrations, distributions of emission strengths

for indoor sources of PM (e.g., cigarette smoking and cooking), and distributions of massbalance parameters (e.g., air exchange rate, penetration rate, deposition rate). A steady-state

mass balance equation is used to calculate PM concentrations for the residential and other

microenvironments. Additional model inputs include demographic and human activity pattern

data from the National Human Activity Pattern Survey (NHAPS). Output from the SHEDS-PM

model includes distributions of PM exposures in various microenvironments (e.g., in the home,

in vehicles, outdoors) and the relative contributions of these various microenvironments to the

total exposure.
April 2004 Page 20-16

1. U.S. Environmental Protection Agency. 1992. Guidelines for Exposure Assessment. 1992.

National Center for Environmental Assessment, Risk Assessment Forum. Washington, D.C.

EPA 600Z-92/001. 170 pp. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=15263.

U.S. Environmental Protection Agency. 2001. Risk Assessment Guidance for Superfund: 

Volume I. Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal

Assessment, Interim - Review Draft for Public Comment). Office of Emergency and

Remedial Response. Washington, D.C., EPA/540/R/99/005, available at:

http://www.epa.gov/superfund/programs/risk/ragse/index.htm.

2. U.S. Environmental Protection Agency. 1999. National Recommended Water Quality

Criteria - Correction. Office of Water. Washington, D.C. EPA 822-Z-99-001. April.

3. U.S. Environmental Protection Agency. 1996. Soil Screening Guidance: Technical

Background Document. Office of Superfund Remediation and Technology Innovation,

formerly the Office of Office of Emergency & Remedial Response. EPA/540/R-95/128.

Available at: http://www.epa.gov/superfund/resources/soil/introtbd.htm.

4. U.S. Environmental Protection Agency. 1989. Risk Assessment Guidance for Superfund: 

Volume I. Human Health Evaluation Manual (Part A). Office of Emergency and Remedial

Response. Washington, D.C., EPA/541/1-89/002, available at:

http://www.epa.gov/superfund/programs/risk/ragsa/index.htm

5. U.S. Environmental Protection Agency. 1990. Interim Final Methodology for Assessing

Health Risks Associated with Indirect Exposure to Combustor Emissions. Environmental

Criteria and Assessment Office, Office of Research and Development, January, 1990. 

EPA-600-90-003.

U.S. Environmental Protection Agency. 1993. Review Draft Addendum to the Methodology

for Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions.

OHEA, Office of Research and Development, November 1993. EPA/600-AP-93-003.

U.S. Environmental Protection Agency. 1998. Methodology for Assessing Health Risks

Associated with Multiple Pathways of Exposure to Combustor Emissions. Update to

EPA/600/6-90/003 Methodology for Assessing Health Risks Associated With Indirect

Exposure to Combustor Emissions. National Center for Environmental Assessment. EPA600/R-98-137; available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55525.

6. U.S. Environmental Protection Agency. 1997. Exposure Factors Handbook, Volume III. 

Activity Factors. Office of Research and Development, Washington, D.C., August 1997.

EPA/600/P-95/0002Fc, available at: http://www.epa.gov/ordntrnt/ORD/WebPubs/exposure/.

7. U.S. Environmental Protection Agency. 1989. Exposure Factors Handbook. Office of

Research and Development, National Center for Environmental Assessment, Washington,

D.C., May 1989. EPA/600/8-89/043, available at:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464.

References
April 2004 Page 20-17

8. U.S. Environmental Protection Agency. 2002. Child-Specific Exposure Factors Handbook

(Interim Report). Office of Research and Development, National Center for Environmental

Assessment, Washington, DC, September 2002. EPA-600-P-00-002B.

9. The Consolidated Human Activity Database (CHAD) is available at:

http://www.epa.gov/chadnet1/.

10. United States Environmental Protection Agency. 2003. Guidance on Selecting the

Appropriate Age Groups for Assessing Childhood Exposures to Environmental Contaminants

(External Review Draft). Risk Assessment Forum, Washington, D.C. September, 2003. 

EPA/630/P-03/003A; available at www.epa.gov/ncea.

11. U.S. Environmental Protection Agency. 1997. Exposure Factors Handbook, Volume II. 

Food Ingestion Factors. Office of Research and Development, Washington, D.C., August

1997. EPA/600/P-95/0002Fb, available at:

http://www.epa.gov/ordntrnt/ORD/WebPubs/exposure/.

12. U.S. Environmental Protection Agency. 1985. Rapid Assessment of Exposure to Particulate

Emissions from Surface Contamination Sites. Office of Health and Environmental

Assessment (OHEA). Washington, D.C. EPA/600/8-85/002. NTIS PB 85-192219.

U.S. Environmental Protection Agency. 1988. Superfund Exposure Assessment Manual.

Office of Emergency and Remedial Response. Washington, D.C. EPA/540/1-88/001. NTIS

PB 89-167985.

U.S. Environmental Protection Agency. 1994. Draft Technical Background Document for

Soil Screening Guidance. EPA/540/R-94/106. OSWER. Washington, D.C. December.

13. U.S. Environmental Protection Agency. 1990. Interim Final Methodology for Assessing

Health Risks Associated with Indirect Exposure to Combustor Emissions. Environmental

Criteria and Assessment Office, January 1990. ORD. EPA-600-90-003.

U.S. Environmental Protection Agency. 1992. Estimating Exposures to Dioxin-Like

Compounds. Draft Report. OHEA, Washington, D.C. EPA/600/6-88/005B, August 1992.

Brzusy, L.P. and Hites, R.A. 1995. Estimating the atmospheric deposition of polychlorinated

dibenzo-p-dioxins and dibenzofurans from soils. Environ. Sci. Technol. 29: 2090-2098.

14. U.S. Environmental Protection Agency. 1998. Human Health Risk Assessment Protocol for

Hazardous Waste Combustion Facilities, Peer Review Draft. Office of Solid Waste and

Emergency Response, Washington, D.C., July 1998. EOA/30/D-98/001A. Available at: 

http://www.epa.gov/epaoswer/hazwaste/combust/risk.htm

15. U.S. Environmental Protection Agency. 1994. Draft Technical Background Document for

Soil Screening Guidance. Office of Solid Waste and Emergency Response, Washington,

D.C., December 1994. EPA/540/R-94/106.
April 2004 Page 20-18

16. U.S. Bureau of the Census. 1986. Geographical Mobility: March 1983 to March 1984. 

Current Population Reports. Series P-20. Number 407. U.S. Government Printing Office. 

Washington, D.C.

17. U.S. Environmental Protection Agency. 1995. Policy for Risk Characterization (“Browner

Memorandum”). Science Policy Council, Washington, DC. Available at:

http://64.2.134.196/committees/aqph/rcpolicy.pdf

18. U.S. Environmental Protection Agency. 1999. Guidelines for Carcinogen Risk Assessment

(INTERIM), Risk Assessment Forum, Washington, DC NCEA-F-0644.

19. McKone, T.E. 1993a. CalTOX, a Multimedia Total-Exposure Model for Hazardous wastes

Sites Part I: Executive Summary. UCRL-CR-11456, Pt. I. 1993b. CalTOX, a Multimedia

Total-Exposure Model for Hazardous Wastes Sites Part II: the Dynamic Multimedia

Transport and Transformation Model. UCRL-CR-111456, Pt. II. 1993c. CalTOX, a

Multimedia Total-Exposure Model for Hazardous Wastes Sites Part III: The MultiplePathway Exposure Model. UCRL-CR-111456, Pt. III. Livermore, CA: Lawrence Livermore

National Laboratory.

20. Zartarian V.G., Ozkaynak H., Burke J.M., Zufall M.J., Rigas M.L., and Furtaw Jr. E.J. 2000.

A modeling framework for estimating children’s residential exposure and dose to

chlorpyrifos via dermal residue contact and non-dietary ingestion. Environmental Health

Perspectives 108:505-514.