RISK An IAQ Model for Windows

EP A/600/A-98/004
RISK - An IAQ Model for Windows
Leslie E. Sparks
U. S. EPA
National Risk Management Research Laboratory
Air Pollution Prevention and Control Division
Research Triangle Park, NC 27711
ABSTRACT
A computer model, called RISK, for calculating individual exposure to indoor air pollutants from
sources is presented. The model is designed to calculate exposure due to individual, as opposed to
population, activity patterns and exposure use. RISK is the third in a series of indoor air quality (IAQ)
models developed by the Indoor Environment Management Branch of U. S. EPA's National Risk
Management Research Laboratory. The model uses source emission and sink models developed as part
of EPA's indoor source characterization research program. The source emissions models provided by the
model include empirical first and second order decay models and mass-transfer models. The model
allows for consideration of the effects of room-to-room airflows, air exchange with the outdoors, and air
cleaners on the concentration/time history of pollutants. Comparisons of model predictions with data
from experiments conducted in EPA's IAQ test house are discussed. The model predictions are generally
in good agreement with the test house measurements.
INTRODUCTION
Indoor air quality (IAQ) is determined by the interactions of sources, sinks, and air movement
between rooms and between the building and the outdoors. Sources may be located in rooms, in the
heating, ventilation, and air-conditioning (HVAC) system, or outdoors. There may be sinks (i.e.,
materials that adsorb indoor pollutants) in the same locations. Sinks may also act as sources by re-
emitting the pollutants collected in them. Individual exposure to pollutants from indoor sources is
determined by the combination of indoor pollutant concentrations and individual activity patterns.
Several models have been developed to analyze the IAQ impacts of all these factors.
RISK (1) is the third in a series of IAQ models developed by the Indoor Environment Management
Branch of U. S. EPA's National Risk Management Research Laboratory. The first model, INDOOR (2),
was designed to calculate the indoor pollutant concentrations from indoor sources. The second model,
EXPOSURE (3), extended INDOOR to allow calculation of individual exposure. RISK extends
EXPOSURE to allow analysis of individual risk to indoor pollutant sources. The three models were all
developed as tools to carry out the mission of the engineering portion of the EPA's indoor air research
program, "To provide tools necessary to reduce individual exposure and risk to indoor air pollutants."
The three models reflect the status of EPA source and sink characterization research at the time the
models were written. RISK includes new empirical source models and mass-transfer-based source
models in addition to the common first order decay source models used in previous models. The mass-
transfer-based source models are particularly useful for gas-phase-limited mass-transfer situations. RISK
is the first version of the IAQ model designed for the Windows operating environment.
RELATIONSHIP BETWEEN MODELING AND SOURCE CHARACTERIZATION
The role of the model relative to source characterization can be seen in Figure 1. Data related to source
characterization are developed as part of EPA's indoor air source characterization program. These data
are used to develop source emission models that are used in this IAQ model. The source models are
updated whenever the source characterization research program develops new information.
MODELING DECISIONS
Several decisions were made in designing the model. Some of the major decisions were:
• The emphasis was on model ease of use.
• The data requirements were minimized as much as possible.

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• Data defaults would be provided as much as possible.
• Results of ongoing source and sink research would be incorporated into the model as soon as
possible.
• User would be responsible for balancing flows.
• Room-to-room flows and ventilation rates were model inputs and would not be calculated
from pressure/temperature data.
Most of these decisions were made to make the model easier to use. The requirement that the user
balance the flows was designed to help the user understand the input data. The computer will determine
if the flows balance, but it will not actually balance them. The purpose of this decision is to reinforce the
idea that mass must be conserved. The user must determine where air is coming from and where it goes.
THEORY
Mass balance equations
RISK is a multi-room model based on earlier models, INDOOR (2) and EXPOSURE (3). RISK
allows calculation of pollutant concentrations based on source emission rates, room-to-room air
movement, air exchange with the outdoors, and indoor sink behavior.
Each room is considered to be a well mixed reactor. The validity of the well-mixed assumption was
verified in several experiments in the EPA IAQ test house (3,4), and by data reported by Ma'.donado (5).
A mass balance for each room gives:
VidC/dt = CiiNQiiN- CiourQiouT + S; - R; (1)
where
Vj = the volume of the room
Cj = the pollutant concentration in the room
Cjin = the concentration entering the room
QjIN = the air flow into the room
CjouT = the concentration leaving the room
QjOUT = a'r f°w leaving the room
Sj = the source term
Rj = the removal term
and the subscript i refers to room i for a room in a set of multiple rooms, i = 1,2,... N where N is the
number of rooms. The removal term, Rj, includes pollutant removal by air cleaners and sinks.
From the well-mixed assumption Cjqut ecluals Cj. Equation (1) can be rewritten as:
VidCi/dt = CiiNQiiN - CiQiour + Si - Ri (2)
Equation (2) is one of a set of similar equations that must be solved simultaneously in a multiple room
model. RISK uses a fast discrete time step algorithm developed by Yamamoto et al. (6) to solve the
series of equations. The method is stable for all time steps and is accurate for sufficiently small time
steps. (The size of the time step depends on how rapidly concentrations are changing. In general a time
step of 1 minute is small enough when concentrations are changing rapidly, and time steps of several
minutes to hours are adequate when concentrations are near steady state.) The time step must be small
enough to capture the changing behavior of the ventilation system, the sources, the sinks, and the
individual activity patterns.

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Source terms
The ability of any model to predict indoor air pollutant concentrations depends on the accuracy of the
source models incorporated into the model. RISK uses source models developed as a part of EPA's
source characterization research program and source models provided in the literature. The model
incorporates a wide range of emission characteristics to allow simulation of the range of sources
encountered in indoor spaces. Several sources are allowed in each room.
The model includes a database of source emission rates for these various sources based on research
conducted by the Indoor Environment Management Branch, National Risk Management Research
Laboratory of EPA. The user can override the database emission rates.
Generally sources can be divided into the following categories:
• Long term steady state sources such as moth cakes.
• On/off sources such as heaters.
• Rapidly decaying sources such as painted surfaces.
• Long term slowly decaying sources such as pressed wood products.
Source behavior can be described by empirical source models or by source models based on mass
transfer theory. The constants developed for empirical models are often affected by test conditions. For
example, if gas-phase mass transfer limits the mass transfer rate, the empirical constants are affected by
the air speed over the source. In general models based on mass transfer theory are easier to scale to new
situations than are empirical models. RISK allows the user to use both types of source models. See
References 7 and 8 for details of the mass transfer models.
Sink terms
Research in the EPA test house (2, 9, 10) and in the small chamber laboratory (10) has shown that
sinks (i.e., surfaces that remove pollutants from indoor air) play a major role in determining indoor
pollutant concentrations. These sinks may be reversible or irreversible. A reversible sink re-emits the
material collected in it, and an irreversible sink does not. Sink behavior depends on the pollutant, on the
nature of the sink, and on environmental factors such as temperature, air velocity, and humidity.
Considerable research is necessary to define the behavior of sinks. Tichenor et al. (10) and Axley (11)
have published sink models.
The sink model used in RISK is based on research of Tichenor et al.(10):
R« = kaCAsink - kdMs "Agjnk (3)
where
Rs = the rate to the sink (mass per unit time)
ka = the sink rate constant (length per time)
C = the in-room pollutant concentration (mass per length cubed)
Asink = the area of the sink (length squared)
kd = the re-emission or desorption rate constant (1/time if n =1)
Ms = the mass collected in the sink per unit area (mass per length squared)
n =an empirical constant. The recommended value of n, based on EPA research, is 1 (3).
Experimental data in the EPA test house and small chambers show that, for many gaseous organic
pollutants of interest in indoor air, ka ranges from about 0.1 to 0.5 m/h, and the sink re-emission rate, kj,
is about 0.008/h for carpet and 0.1/h for most other materials. The model allows up to four sinks in a
room.
The impact of sinks on individual exposure depends on the activity patterns. Sinks slightly reduce the
peak exposure of individuals spending 24 h/day in a building and have no impact on their cumulative
exposure. Sinks can have major impacts on the exposure of individuals with other activity patterns.

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Exposure
The types of exposures of interest are instantaneous exposure and cumulative exposure. Instantaneous
exposure is the exposure at any time, t, and cumulative exposure is the total or integrated exposure over
the time of interest. The nature of the pollutant and the effects of the pollutant determine which type of
exposure is more important.
Individual exposure is determined by the time spent at a given pollutant concentration. Therefore, it is
a function of both the building concentration/time history and the individual activity pattern—that is,
where the individual is located at what time. Different activity patterns, for example, entering and leaving
a building at different times or moving from one room to another, result in different exposures to the
same building pollutant concentration/time history. Sparks (12) discusses exposure modeling.
Calculation of exposure requires the pollutant concentration, the time exposed to the concentration,
and (for inhalation exposure) the breathing rate and the volume per breath. The time exposed to the
concentration depends on the individual activity pattern.
An activity pattern, in the context of the model, is defined by providing the time a person enters and
leaves the various rooms of the building, or leaves the building for the outdoors. The model allows up to
10 room changes per day. The model is based on a 24-hour day. The activity patterns in the model repeat
from day to day.
The model provides instantaneous exposure time plots and cumulative exposure time plots for
individual activity patterns. Instantaneous exposure allows identification of high exposure situations and
of peak exposure.
While the model was designed to allow assessment of the impact of indoor air pollution sources and
sinks and IAQ control options on individual exposure from specific activities, it can also be used to help
estimate population exposures if data on population activity patterns are available. The model can be run
for each activity pattern and then the results can be weighted according to the population statistics.
Risk assessment
Risk assessment is a general term that includes four components: hazard identification, exposure
assessment, dose/response evaluation, and risk characterization. A risk assessment can be quantitative or
qualitative depending on the data available and the requirements for the assessment. RISK uses a risk
calculation scheme described in References 13, 14, 15, and 16. The calculation scheme developed by
these authors provides a systematic way for estimating risk.
Risk estimates based on currently available data are projections containing a great deal of
uncertainty. This is particularly true when using a model such as this one to calculate risk estimates for
individuals, because such numbers as carcinogenic potency, upon which the model depends for
calculating individual and population cancer risk, are projections ofpopulation risks based upon a
variety of extrapolations and assumptions. Risk estimates generated by models such as this one are
useful mainly for the purpose of comparing scenarios rather than for determining absolute risks to
individuals or populations.
M0lhave et al. (17) present an example of using an IAQ model to carry out a risk assessment. This
paper demonstrates the steps necessary to carry out a risk assessment. Melhave et al. show the
importance of obtaining the right data from source testing to carry out the risk assessment. They also
demonstrate the effects of the assumptions involved in risk assessment by providing examples of the
range of answers possible depending on the assumptions used. Sparks et al. (18) discuss source testing
necessary to obtain data required for conducting risk assessment.
Risk characterization framework
The predictive risk equation developed by Naugle and co-workers (13, 14, 15) is based on four general
equations. These equations relate source factors, activity patterns, dose factors, and dose response to
risk. The equations are:

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Exposure
Concentration x Duration = Exposure
Dose
Exposure x Dosimetry factors = Dose (note that biological effects are included in the dosimetry
factors and are not part of the exposure calculation)
Individual Risk
Dose x Dose/response relationship = Individual risk
Population Risk
Individual risk x Exposed population = Population risk
The framework subdivides the four components of the risk assessment process into 10 elements to
provide a refined and systematic way of describing the risk estimation process. These elements are:
• Source factors
The starting point for the risk analysis. The estimation of risk can be based on the study of a
single source emitting one or more pollutants of concern or the study of a pollutant or mixture
that is emitted from one or more sources.
• Pollutant concentration
The pollutant concentration of interest depends on the effect that is of interest. The concentration
may be the peak concentration, the average concentration, or some threshold concentration.
• Exposure duration and setting
The exposure duration and setting combines the setting in which exposure occurs and an
estimation of the time spent in that environment.
• Exposure
The outcome of exposure duration and setting is individual exposure.
• Dosimetry factor
The dosimetry factor addresses factors which influence how much of the exposure to a pollutant
is available to the body. For many air pollutants the major factor is inhalation rate.
• Dose
Dose represents the amount of a substance available for interaction with metabolic processes or
biologically significant receptors.
• Response factor
Response factor describes the magnitude of the response of an individual to a given dose of the
substance.
• Individual risk
Individual risk represents the risk of an individual exposed at the given concentration, duration,
etc. For cancer risk the individual risk is expressed as lifetime individual risk.
• Exposed population
Exposed population is the number of individuals who are exposed to the conditions covered by
the risk assessment.
• Risk to exposed population (Population risk)
The risk calculation framework can be placed in a spreadsheet format for ease of calculation. RISK
uses the spreadsheet format shown in Figure 2. The IAQ model calculates the concentration, duration,
and exposure. The user must independently provide the dosimetry factors, the response factors, and the
population.
TYPES OF RISK
Cancer risk
Most risk characterization studies of environmental pollutants have concentrated on cancer risk.
Cancer risk is typically expressed as lifetime risk—the probability of developing cancer over a lifetime for
the average individual in a defined population and for a defined exposure scenario.

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Non-cancer risk
Non-cancer risks have received less attention than cancer risks. Pierson et al. (15) recommend that
many of the mathematical operations used in the risk framework be dropped when it is applied to non-
cancer risks. Individual risk may be estimated as the probability of a response (adverse health effect) or
the degree to which exposure or dose exceeds a threshold for adverse health effects.
Irritant risk
An irritant response to a substance often occurs when the concentration exceeds some threshold value.
Examples of irritant effects include eye irritation, odor, and nasal irritation. The concentration required
to produce an irritant effect may depend on individual susceptibility. The duration of the irritant
exposure is often important. RISK provides information on the time spent above a user specified irritant
threshold concentration.
The avoidance of irritant risk is a major consideration in IAQ. For example, the American Society of
Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) ventilation rate standard (19) is
designed to ensure that no more than 20% of the occupants of a building express displeasure with the
IAQ. A major consideration in developing the standard was to provide sufficient outdoor air to avoid
complaints due to human body odor.
Chronic risk
Chronic risk is usually associated with long term exposure to a pollutant. Chronic risk is also often
associated with threshold-based doses. Little research has been published to quantify the chronic risk of
common indoor pollutants. Seifert (20) includes chronic risk as a factor in his ranking of sources. RISK
provides information on the time spent above a user specified threshold concentration.
MODEL VERIFICATION
Several experiments have been conducted to verify the model predictions. Most of these tests have
been conducted in the EPA IAQ test house. A full description of the test house can be found in
Reference 3. The floor plan of the IAQ test house is shown in Figure 3.
Two types of experiments have been conducted in the IAQ test house. One type of experiment used
tracer gas with known emission rates. These experiments test the model's ability to track pollutant
transport in the building and to respond to changes in factors such as room-to-room airflows [due for
example to heating and air-conditioning (HAC) system operation] and air exchange with the outdoors.
The second type of experiment uses real sources and tests the source and sink models as well as the
transport portions of the model. The results of the two types of experiments will be briefly discussed.
The American Society for Testing and Materials (ASTM) (21) has developed guidelines for comparing
model predictions with data. The ASTM criteria include:
• The slope and intercept of the best fit line between measured and predicted values. The ideal is
an intercept of 0 and a slope of 1. The ASTM guidelines recommend that the intercept be within
25% of the average and the slope be between 0.75 and 1.25.
• The correlation coefficient between measured and predicted concentrations. The recommended
value is 0.9 or greater.
• The normalized mean square error (NMSE) given by:
(C„ - cj2
NMSE = - (4)
c
p ^ m
where
Cp = the predicted concentration
Cm = the measured concentration
The bars indicate average; e.g., C is the average of all the predicted concentrations. The NMSE has
a value of 0 when there is perfect agreement for all pairs of measured and predicted concentrations.

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NMSE is near 0.25 for differences between measured and predicted of about 50%. ASTM
recommends that the value of NMSE for an adequate model be less than or equal to 0.25.
• The fractional bias, FB, is used to measure bias. The fractional bias is given by:
2 (C~-C~)
FB = —f — (5)
c„+cm
FB ranges from -2 to +2 with a value of 0 indicating perfect agreement. ASTM recommends that the
absolute value of FB for an adequate model be less than or equal to 0.25. ASTM recommends that
the model be judged based on all the criteria. It is possible to have an adequate model even if all the
criteria are not met.
Tracer gas experiments
Several tracer gas experiments have been conducted in the EPA IAQ test house. These experiments
were conducted under a range of house operating conditions such as HAC on continuously, HAC off,
HAC on an on/off cycle, and windows open. Comparison of model predictions with these data can be
used to verify model assumptions regarding air flow in the house and model response to changing
conditions independent of the source and sink effects. The ASTM criteria are given in Table 1. The
agreement between the model predictions and the measured data is excellent. These results demonstrate
that the model is able to capture the dynamics of air movement in the test house. A plot of predicated
versus measured tracer gas concentrations is shown in Figure 4.
Source sink experiments
When actual sources are used in the test house, the modeling must include the effects of sources and
sinks. Disagreement between the predictions and the measurements in these experiments is most likely
due to inadequacies in the source and or sink models. The ASTM criteria for the model for several test
house experiments using real sources are given in Table 2. Most of the experiments shown in Table 2 are
discussed in Reference 4.
CONCLUSIONS
A new easy-to-use IAQ model has been developed. The model includes the results of ongoing research
on sources and sinks. The model predictions meet the ASTM criteria for IAQ models.
MODEL AVAILABILITY
The model, including disks for Windows computers and the manual, is available from the National
Technical Information Service (NTIS) in Springfield, VA.
REFERENCES
1. Sparks, L. E.; IAQ Model for Windows RISK Version 1.0 User Manual, EPA-600/R-96-037 (NTIS
PB96-501929), Air Pollution Prevention and Control Division, Research Triangle Park, NC, March
1996.
2. Sparks, L. E.; Indoor Air Quality Model Version 1.0, EPA-600/8-88-097a (NTIS PB89-133607), Air
Pollution Prevention and Control Division, Research Triangle Park, NC, September 1988.
3. Sparks, L. E.; EXPOSURE Version 2: A Computer Model for Analyzing the Effects of Indoor Air
Pollutant Sources on Individual Exposure, EPA-600/8-91-013 (NTIS PB91-507764), Air Pollution
Prevention and Control Division, Research Triangle Park, NC, April 1991.

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4.	Sparks, L. E.; Tichenor, B. A.; White, J. B.; and Jackson, M. D.; "Comparison of data from an IAQ
test house with predictions of an IAQ computer model," Indoor Air, 1991 4, 577-592.
5.	Maldonado, E. A. B.; (1982) "A method to characterize air exchange in residences for evaluation of
indoor air quality," Ph.D. Dissertation in Mechanical Engineering, Iowa State University, Ames, IA,
1982.
6.	Yamamoto, T.; Ensor, D. S.; Lawless, P. A.; Damle, A. S.; Owen, M. K.; and Sparks, L. E.; "Fast
direct solution method for multizone indoor model," in Proceedings of Indoor Air Modeling,
Champaign, IL, 1988.
7.	Tichenor, B. A.; Guo, Z.; and Sparks, L. E.; "Fundamental mass transfer model for indoor air
emissions from surface coatings," Indoor Air, 1993 3, 263-268.
8.	Sparks, L. E.; Tichenor, B. A.; Chang, J.; and Guo, Z.; "Gas-phase mass transfer model for
predicting volatile organic compound (VOC) emission rates from indoor pollutant sources," Indoor
Air, 1996 6, 31-40.
9.	Tichenor, B. A.; Sparks, L. E.; White, J. B.; and Jackson, M. D.; "Evaluating sources of indoor air
pollution," Journal Air and Waste Management Association, 1990 40, 487.
10.	Tichenor, B. A.; Guo, Z.; Dunn, J. E.; Sparks, L. E.; and Mason, M. A.; "The interaction of vapour
phase organic compounds with indoor sinks," Indoor Air, 1991 1, 23-35.
11.	Axley, J. W.; "Adsorption modeling for building contaminant dispersal analysis," Indoor Air, 1991,
147.
12.	Sparks, L. E.; "Modeling indoor concentrations and exposures," in Sources of indoor air
contaminants characterizing emissions and health impacts, Tucker, W. G., B. P. Leaderer, L.
M0lhave, and W. S. Cain, Editors, Annals of the New York Academy of Sciences, 1992 641, 102.
13.	Naugle, D. F.; "Possibilities and limitations of indoor environment risk assessment," Presented at
Joint NATO/CCMS-European Cost 613 Project Joint Workshop on Methods of Risk Assessment,
Kolster Banz, Bavaria, Federal Republic of Germany, 1991.
14.	Naugle, D. F. and Pierson, T. K.; "A framework for risk characterization of environmental
pollutants," Journal of A ir and Waste Management Association, 1991 41: 1298.
15.	Pierson, T. K.; Hetes, R. G.; and Naugle, D. F.; "A risk characterization framework for noncancer
endpoints," Environmental Health Perspectives, 1991 95, 121.
16.	U. S. Environmental Protection Agency; "Indoor air assessment: A review of indoor air quality risk
characterization studies: United States, 1989-1990," EPA-600/8-90-044 (NTIS PB92-109107),
Environmental Criteria and Assessment Office, Research Triangle Park, NC, March 1991.
17. M0lhave, L.; Dueholm, S.; and Jensen, L. K; "Health assessment and risk evaluation of emissions
from furniture: a case study," Indoor Air, 1997 In press.

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18.	Sparks, L. E.; Molhave, L.; and Dueholm, S.; "Source testing and data analysis for exposure and risk
assessment of indoor pollutant sources," ASTM Symposium on methods for characterizing indoor
sources and sinks, Washington, DC, September 1994.
19.	ASTM Standard 62-1989: "Ventilation for acceptable indoor air quality," American Society of
Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, 1989.
20.	Seifert, B.; "Guidelines for material and product evaluation," in Sources of indoor air contaminants
characterizing emissions and health impacts, Tucker, W. G., B. P. Leaderer, L. Molhave, and W. S.
Cain, Editors, Annals of the New York Academy of Sciences, 1992 641, 125.
21.	ASTM; "Standard guide for evaluation of indoor air quality models," D5157-91, American Society
for Testing and Materials, Philadelphia, PA, 1991.
Figures
Risk analysis process
Source Source
Testing Modeling
Source &-
Size
Emissions
Ventilation	
Building factors (e.g., sinks).
Source usage	
Occupancy.
IAQ Exposure Risk
Modeling Modeling Modeling
Air
Concentration
Exposure
Occupant sensitivity.
Dose-response	
Risk
Figure 1. Risk Analysis process.
Concentration
C
Duration
t
Exposure
E=C x t
Dosimetry
factors, F
Dose
D=ExF
Response
Res
Lifetime Risk
LR=ResXD
Population
P
Population Risk
PR = PxLR













































Figure 2. Risk calculation framework.

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Master
y bath
Master bedroom
Main bath
r
y
Front corner
bedroom
Closet
A
Middle
bedroom
Oen
<0
Monitoring
room
Kitchen
Living room
Garage

C
/
\ „
Figure 3. Floor plan of EPA IAQ test house.
70
60
50
40
~ ~
30
~ ~
20
Prciiicted
.Perfect fit line
10
0
30
60
70
10
20
30
40
0
Measured value (ppm)
Figure 4. Comparison of predicted and measured tracer gas concentrations in EPA IAQ test house.

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Table 1. ASTM model criteria for tracer gas experiments
Criterion
Value
Recommended value
NMSE
0.038
<0.25
Correlation coefficient
0.99
>0.9
Fractional bias
0.01
Absolute value <0.25
Regression intercept
0.3
25% of average value of the
measurements
Regression slope
0.98
0.75 to 1.25
Table 2. ASTM model criteria for experiments using sources.
Source
NMSE
Fractional bias
Correlation coefficient
Aerosol
0.05
0.094
0.99
Floor wax
0.19
-0.07
0.96
Polyurethane
0.28
0.12
0.96
Wood stain
0.16
0.03
0.95
Latex paint (Ethylene
glycol)
0.08
0.04
0.97
Latex paint (Texanol)
0.21
-0.25
0.98

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TVTOTV/TOT T3T--™ -r. OOT TECHNICAL REPORT DATA
IN .ft IViX\ Lj~ Jtt 1 ir~ ZZl (Please read Instructions on the reverse before complet
1. REPORT 2.
EPA/600/A-98/004
3.
4. TITLE ANu o\jo i i i i_c
RISK--An IAQ Model for Windows
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Leslie E. Sparks
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
See Block 12
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
NA (Inhouse)
12. SPONSORING AGENCY NAME AND ADOHESS
EPA, Office of Research and Development
Air Pollution Prevention and Control Division
Research Triangle Park, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
Published Paper; 1/95—4/96
14. SPONSORING AGENCY CODE
EPA/600/13
15.supplementary	notes Author Sparks' phone number is 919/541-2458. His mail drop is
54. Presented at EPA/AWMA Meeting, Engineering Solutions to IAQ Problems.
July 21-23, 1997, Research Triangle Park, NC.	
16.	abstract paper presents a computer model, called RISK, for calculating indi-
vidual exposure to indoor air pollutants from sources. The model is designed to cal-
culate exposure due to individual, as opposed to population, activity patterns and ex-
posure use. RISK is the third in a series of indoor air quality (LAQ) models developed
by the Indoor Environment Branch of the U.S. EPA's National Risk Management Re-
search Laboratory. The model uses source emission and sink models developed as
part of EPA's indoor source characterization research program. The source emis-
sions models provided by the model include empirical first and second order decay
models and mass-transfer models. The model allows for consideration of the effects
of room-to-room airflows, air exchange with the outdoors, and air cleaners on the
concentration/time history of pollutants. Comparisons of model predictions with data
from experiments conducted in EPA's IAQ test house are discussed. The model pre-
dictions are generally in good agreement with the test house measurements.
17.	KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Air Pollution
Mathematical Models
Decay
Mass Transfer
Air Pollution Control
Stationary Sources
Indoor Air Quality
RISK
13 B
12 A
14G
18. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
20. SECURITY CLASS (This page)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)

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