United States
Environmental Protection
Agency
Research and Development
Atmospheric Research and Exposure
Assessment Laboratory
Research Triangle Park NC 27711
EPA/600/S3-91/013 Mar. 1991
EPA Project Summary
Two Indoor Air Exposure
Modeling Studies: CONTAM
Modeling Results, and Serial
Correlation Effects
Ronald E. Stogner, John S. Irwin, William B. Petersen, Mourad Aissa and
Azzedine Lansari
Indoor exposures have become an
Important issue in recent years. It has
been suggested that human exposures
to pollutants indoors may significantly
differ from outdoor pollutant exposures.
There are, however, many factors that
affect indoor exposures, such as local
meteorology and building characteris-
tics, and it is often difficult to isolate
the individual factors affecting Indoor
pollution using only measurement data.
To aid in quantifying these factors,
therefore, numerical modeling studies
are sometimes used. In this report we
have undertaken such a study to better
characterize some of the factors influ-
encing indoor pollution and hence in-
door exposures.
Two mass balance computer models,
CONTAM87 and CONTAM88, were used
to estimate Indoor pollution levels for a
hypothetical four-story building ex-
posed to measured outdoor ozone,
concentrations for a 24-hour period. The
building contained 50 rooms; exterior
rooms contained windows, Interior
rooms did not. Modeled indoor con-
centrations indicated that ozone levels
generajly decreased along a partial
cross section from peripheral rooms to
interior rooms. CONTAM88 results in-
dicated, however, that under certain
conditions some peripheral rooms ac-
tually experienced lower ozone con-
centrations than interior rooms.
CONTAM88 analysis also revealed that
simple actions such as opening interior
office doors could significantly change
indoor ozone distributions.
Comparisons between CONTAM88
and a simple one-cell box model indi-
cated that the box model underesti-
mated building-averaged ozone con-
centrations when compared to
CONTAM88 results using the same air
exchange rates. Both models predicted
changes in average ozone concentra-
tions proportional to changes in air
exchange rates for the building.
CONTAM88 maximum ozone concen-
trations were less responsive to
changes in air exchange rates than
corresponding CONTAM88 mean con-
centrations.
Statistical methods of estimating
concentration values for use In human
exposure estimates have become in-
creasingly popular because of the
complexities in correlating the temporal
and spatial concentration variations
within microenvlronments with the lo-
cations of people. The number of vari-
ables and their associated uncertainty
make deterministic models difficult to
use. SHAPE and NEM, which include
statistical methods for cha'racterizing
the concentration values for different
microenvlronments, are examples of
human exposure models that combine
deterministic and statistical method-
ologies. In the simulations conducted '
thus far, these models have ignored
serial correlation effects and therefore
have tended to underestimate maximum
exposures. The purpose of the sensi-
tivity study conducted was to quantify
the factors affecting serial correlation
in indoor microenvironments. Further,
we Investigated in a very preliminary
Printed on Recycled Paper
-------
way the use of personal exposure
monitoring data to Infer the values of
variables needed to estimate Indoor
concentrations, such as the rates of air
exchange, pollutant removal, and pol-
lutant generation.
This Project Summary was developed
by EPA's Atmospheric Research and
Exposure Assessment Laboratory, Re-
search Triangle Park, NC, to announce
key findings of the research project
that Is fully documented In a separate
report of the same title (See Project
Report ordering Information at back).
Introduction
The quality of air within buildings has
become an important issue in recent years.
In particular, it is recognized that outdoor
pollutant concentration levels may not ac-
curately reflect pollutant exposures expe-
rienced by persons indoors. Since most
people spend a majority of their time in-
doors, especially sensitive groups such
as the young and the elderly, assess-
ments of indoor pollutant levels are
needed.
Pollutants within buildings are either
generated Indoors or enter the building
envelope from outside. Examples of in-
door-generated pollutants are nitrogen di-
oxide from gas-fired devices and formal-
dehyde from pressed-wood products. Ex-
amples of outdoor pollutants include sulfur
dioxide and ozone. Some pollutants are
not easy to classify as coming from either
indoor or outdoor sources, such as car-
bon monoxide, which can be generated
outdoors by automobiles and indoors by
burning tobacco products or residential
wood combustion.
For pollutants that enter the building
envelope from outside, the entry may be
either intentional or unintentional. If the
exchange of outside air and hence outside
pollutants is intentional, the process is
termed ventilation. Examples of ventila-
tion include open windows, fresh- air vents,
and heating, ventilating, and air condition-
ing (HVAC) systems. When air enters a
building unintentionally, the process is
called infiltration. Likewise, the uninten-
tional venting of inside air to the exterior
is called exfiltration. Infiltration/exfiltration
occurs due to defects in building con-
struction. Cracks in exterior walls and leaks
around windows and doors, for example,
allow outside air to enter and exit a build-
ing. The infiltration and exfiltration of air to
and from a building are driven primarily by
(1) the force of local winds on the building
exterior and (2) the difference between
inside and outside temperature. The force
of local winds on a building, termed wind
pressure, is proportional to the square of
the wind speed and is controlled by both
building geometry and local meteorologi-
cal factors. This wind pressure on the
building face induces a pressure gradient
across it, which results in an air flow
through any openings on the face; the
flow through an opening is related to the
nature of the opening. Flow through such
openings may also be induced by differ-
ences between inside and outside tem-
peratures that cause density differences
across the building face. This process is
referred to as the stack effect.
The total exchange of air between inside
and outside for a building during a given
time period, from both intentional and un-
intentional sources, is referred to as the
air exchange rate of that building. This
.exchange rate-is-usually expressed-in the
number of air changes per hour (ACH),
which represents the number of theoreti-
cal building volumes exchanged per hour.
Indoor pollutants may undergo chemical
reactions and loss by adsorption and ab-
sorption to building furnishings. Pollutants
are also dispersed through the building as
a function of interroom flows. The disper-
sion of pollutants through the building is a
function of many building and local envi-
ronment factors. Of course, new pollutants
of interest may be formed from any
chemical reactions that occur.
Attempts have been made to measure
indoor pollutant levels within buildings,
usually involving either personal exposure
monitors (PEMs) or complex tracer stud-
ies. However, the former method does not
readily lend itself to a detailed cause-and-
effect analysis, while the latter is difficult
and expensive to perform. Deducing how
individual parameters affect indoor air
concentration levels, therefore, is often
difficult. To aid in such analysis, physically
relevant models have been developed to
simulate indoor air concentration levels.
These models use mass balance concepts
to estimate pollutant dispersion within
buildings based on first- order chemical
kinetics. Such mass balance models re-
quire a detailed description of the building
to be modeled, as well as many inputs not
commonly available. To address such
complications, simpler statistical models
requiring less detailed inputs have also
been used in attempts to estimate indoor
air pollution levels.
This study, organized into two sections,
attempts to clarify some of the issues dis-
cussed above that affect indoor air quality.
In the first section results from two mass
balance models, CONTAM87 and
CONTAM88, are investigated. Both mod-
els were developed by the National Insti-
tute of Standards and Technology (NIST),
formerly the National Bureau of Standards
(NBS). The models were used here to
simulate indoor exposure to ozone con-
centrations in a hypothetical four-story of-
fice building. The section seeks to answer
some of the following questions concern-
ing indoor exposures:
1. Do modeled indoor air concentra-
tions indicate sufficient room-to- room
variation in pollutant concentrations
to justify the resources required to
use such sophisticated models?
2. If significant variations are indicated,
what factors influence them?
3: What impacts do possible variations
have on requirements for measuring
pollutants within indoor environ-
„ ... ments? .,,_,-.
4. How practical is it to use such mass
balance models?
The second section investigates the is-
sue of using statistical models to estimate
indoor exposures, particularly the need to
account for serial correlation in such
models.
Mass Balance Simulation of
Indoor Exposures
To investigate the effects of certain pa-
rameters on indoor air concentrations, two
mass balance simulation models were in-
vestigated CONTAM87 and CONTAM88.
All simulations were centered on a hypo-
thetical four-story office building with out-
door ozone as the pollutant of interest.
CONTAM87 is an indoor air quality dis-
persion model developed in 1988 by the
National Institute of Standards and Tech-
nology (NIST). The model solves a series
of simultaneous nonlinear equations gov-
erning the dispersion of pollutants among
a network of well-mixed "zones." These
well-mixed zones typically represent indi-
vidual rooms, but may also represent other-
separate entities, such as garages and
HVAC systems. The zones are connected
by air flow elements that define the ex-
change of pollutant-laden air between
zones. The user must define the interzonal
flows, which is not a trivial task. In this
study, the interzonal floWs were estimated
using "rules of thumb" proposed by the
American Society of Heating, Refrigera-
tion and Air Conditioning Engineers.
CONTAM88 is a hybrid of the previous
model and was developed in 1990 by the
NIST. CONTAM88 also represents a
building as a collection of well-mixed zones
connected by flow elements and solves a
set of simultaneous equations to estimate
pollutant dispersion. CONTAM88, how-
-------
ever, uses a mixture of analytical and
empirical relationships for various building
components (doors, walls, etc.) to solve
for the interzonal flows, based upon out-
side meteorological conditions and physi-
cal building characteristics. These algo-
rithms were extracted from AIRMOVE, a
model developed by NIST in 1972.
CONTAM88 includes a preprocessor
NBSAVIS, which is a user interface that
allows one to "construct" a building from
its individual components.
The building modeled was a four-story
office building adequate for a total occu-
pancy of 131 persons. The building con-
sisted of four identical floors, each with
nine offices, two restrooms, and two stair-
way entrances. Each floor measures 26
m x 18 m x 3 m for a volume of 1404
cubic meters. On each floor, eight exterior
offices, two restrooms and two stairwells
open onto an inner hallway. The inner
hallway surrounds an interior office. Each
office, except for the interior office located
in the center of each floor, has one or
more exterior windows. The interior office,
both stairways, and both restrooms have
no windows. Entrance is gained into the
building via two exterior doors, one in
each first-floor stairway.
One of the first questions to be ad-
dressed was whether using such detailed
indoor air quality models is necessary.
Could a simpler, more manageable way
to calculate indoor exposure be used?
Findings from this study indicated that for
the office building considered there was
substantial spatial variation in modeled
indoor ozone concentrations. Modeled
concentrations showed an order-of-mag-
nitude variation from room to room, even
on the same floor. This suggests that in
some instances simple one-cell box mod-
els may not be adequate to simulate indoor
exposures for persons inside office build-
ings. Study results revealed that
CONTAM88 predicted maximum indoor
ozone concentrations 400% above those
predicted by the one-cell box model. The
study also indicated that the one-cell box
model underestimated mean building con-
centrations by 20% to 25% compared to
the more detailed mass balance model.
These results suggest that the complexity
of such models may be necessary to esti-
mate exposures within buildings, even for
calculating average exposures.
In addition to the above findings, we
also found that the building configuration
played a significant role in the level of
indoor exposure. The simple act of open-
ing all interior office doors nearly doubled
the air exchange rate of the building and
raised maximum ozone concentration lev-
els by 30%. The opening of interior doors
also changed the magnitude and location
of the maximum concentration. Compari-
sons with the one-cell box model indicated
that both models predicted average con-
centrations proportional to changes in the
overall air exchange rate. CONTAM88 re-
sults, however, indicated that maximum
concentration values responded less pro-
portionally to changes in the air exchange
rate than did mean concentration values.
More work is needed to examine such
factors as the effect of temporal wind di-
rection variation on modeled concentra-
tions. Future work is planned to address
such issues.
Our findings suggest that persons inter-
ested in measuring indoor pollutant levels
should carefully consider the placement
of monitors within the study building. Con-
ducting simulations using physically rel-
evant models prior to designing the ex-
periment is recommended. For example,
results from our study suggest a minimum
of five monitors per floor to accurately
characterize concentrations within the hy-
pothetical office building we modeled. The
study indicated that concentrations gener-
ally decreased in magnitude toward the
building interior, but also depended upon
wind direction and building configuration.
Statistical Modeling of Indoor
Exposures
Correlating the temporal and spatial
concentration variations within microenvi-
ronments with the locations of people is a
very complex process. As a result, statisti-
cal methods of estimating concentration
values for use in human exposure esti-
mates have become more popular. The
number of variables involved and their
associated uncertainties make determinis-
tic models difficult to use. SHAPE and
NEM, which include statistical methods
for characterizing concentration values for
different microenvironments, are examples
of human exposure models that combine
deterministic and statistical methodologies.
In SHAPE, concentrations are estimated
by sampling from a concentration distribu-
tion for a given microenvironment in a
Monte Carlo fashion. In model simulations
conducted thus far, however, correlation
effects have been ignored. The model
developers recognized that this tends to
underestimate maximum exposures, since
the concentration in a given microenviron-
ment is related to source terms and atmo-
spheric processes. The current sampling
procedures also tend to underestimate for
another reason, referred to as the
"intracluster" correlation effect. Intracluster
correlation occurs for individuals in some
"dirty" microenvironment, such as a com-
mute that exposes them to particularly
high carbon monoxide levels, because the
individuals tend to be exposed to these
high levels every commute. It has been
suggested that since Monte Carlo simula-
tions conducted thus far have made no
provision for intracluster correlation effects,
they have underestimated the highest ex-
posures and overestimated the lowest
exposures.
Recent investigations using Monte Carlo
simulations of exposure have suggested
that serial correlation may be too signifi-
cant to ignore. The purposes of our study
were to quantify the factors affecting se-
rial correlation in indoor microenvironments
and to investigate, in a very preliminary
way, how personal exposure monitoring
(PEM) data can be used to infer the values
of variables needed to estimate indoor •
concentrations.
Indoor concentration values were simu-
lated using a mass-consistent box model
driven by a 48-hour sulfur-dioxide data
set collected durihg the RAPS study. One
thousand 8-hour averages were gener-
ated for three different scenarios. The
Monte Carlo simulations underestimated
the highest concentrations by about 20%
and overestimated the lower concentra-
tions by about 30%. The difference be-
tween using each hour's actual air ex-
change rate versus an average air ex-
change rate for each microenvironment
during the 48-hour period was slight. This
suggests that a model like NEM could be
updated to include the effects of serial
correlation, since a mass- consistent box
model could be included rather easily.
We also conclude that personal expo-
sure monitoring data may be useful for
deriving order-of-magnitude estimates of
rate constants. More study is needed to
ascertain whether the positive correlations
and relative magnitudes of the time-aver-
aged cross-product terms would behave
as seen in our analysis if real-world data
were used. Our findings suggest that, due
to correlation effects, the use of PEM data
with corresponding fixed-site data would
underestimate air exchange rates, under-
estimate emission rates, and overestimate
removal rates where only one or at the
most two processes are in effect.
•&U. S. GOVERNMENT PRINTING OFFICE: 199 1/548-028/20204
-------
John S. Irwln (also the EPA Project Officer, see below), and WHHan B. Petersen,
are with the Atmospheric Research and Exposure Assessment Laboratory, Re-
search Triangle Park, NC27711; Ronald E. Stogner, Mourad Aissa and Azzedine
Lansariare with Computer Sciences Corporation, Research Triangle Park, NC
27709.
The complete report, entitled "Two Indoor Air Exposure Modeling Studies: CONTAM
Modeling Results, and Serial Correlation Effects," (Order No. PB91-159 707/AS;
Cost: $15.00, subject to change) will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Atmospheric Research and Exposure Assessment Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
United States
Environmental Protection
Agency
Center for Environmental
Research information ..
Cincinnati, OH 45268
BULK RATE
POSTAGE & FEES PAID
EPA
PERMIT No. G-35
Official Business
Penalty for Private Use $300
EPA/600/S3-91/013
------- |