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OCR for page 207
Assessment of Human
Exposure to Air Pollution:
Methods, Measurements,
en c! Moclels
KEN SEXTON
Health Effects Institute
P. BARRY RYAN
Harvard School of Public Health
Human Exposure: Introduction / 208
Definitions / 208
Concentration, Exposure, and Dose / 208 Components of
Exposure / 208 Types of Exposure Information / 209 Individual
Exposure Versus Population Exposure / 209
Methods / 211
Air Monitoring / 211 Biological Monitoring / 217 Research
Recommendations / 218
Measurements / 219
Air Monitoring / 220 Research Recommendation / 223 Biological
Monitoring / 225 Research Recommendation / 225
Modeling Human Exposure to Air Pollution / 226
Statistical Modeling / 226 Physical Modeling / 228
Physical-Stochastic Modeling / 229 Source Apportionment / 230
Validation and Generalization / 230 Research Recommendation / 231
Summary and Conclusions / 231
Summary of Research Recommendations / 232
Air Pollution, the Automobile' and Public Health. @) 1988 by the Health Ejects
Institute. National Academy Press, Washington, D.C.
207
OCR for page 208
208
Human Exposure to Air Pollution
Human Exposure: Introduction
Accurate estimates of human exposure to
inhaled air pollutants are necessary for a
realistic appraisal of the risks these pollut-
ants pose and for the design and implemen-
tation of strategies to control and limit
those risks. Except in occupational settings,
such estimates are usually based on mea-
surements of pollutant concentrations in
outside (ambient) air, recorded with out-
door fixed-site monitors.
Indeed, compliance with existing Na-
tional Ambient Air Quality Standards
(NAAQS), intended to protect public
health with an adequate margin of safety,
depends exclusively on outdoor measure-
ments of pollutants. But, such measure-
ments are subject to biases because most
people spend much more of their time
indoors than out, and air pollutant concen-
trations are often much higher inside build-
ings than outside (National Research
Council 1981; Spengler and Sexton 1983~.
In addition, available evidence indicates
that personal exposure to many pollutants
is not adequately characterized because the
time people spend in different locations and
their activities vary dramatically with age,
gender, occupation, and socioeconomic
status (National Research Council 1981;
World Health Organization 1982, 1983;
Yocum 1982; Spengler and Sexton 1983;
Spengler and Soczek 1985~.
In this chapter, the state of the art of air
pollution exposure assessment is discussed
with emphasis on gaps in our knowledge
and the implications of those gaps for fu-
ture research. First, important terms are
defined, and then the methods available for
monitoring exposure, the results of expo-
sure assessment studies, and the models for
exposure estimation are examined.
Definitions
Concentration, Exposure, and Dose
The concentration of a specific air pollutant
is the amount of material per unit volume
of air. Concentrations are most commonly
expressed as mass per unit volume (for
example, micrograms per cubic meter).
Concentrations of pollutant gases may be
reported as volume per unit volume (for
example, parts per million by volume) and
discrete particles as number per unit vol-
ume (for example, number of fibers per
cubic centimeter).
Exposure refers to any contact between
an airborne contaminant and a surface of
the human body, either outer (for example,
the skin) or inner (for example, respiratory
tract epithelium). Thus, exposure requires
the simultaneous occurrence of two events:
a pollutant concentration at a particular
place and time, and the presence of a person
at that place and time (Duan 1982; Ott
1985~. Exposure is expressed quantitatively
by a description of the duration of the
contact and the relevant pollutant concen
.
tratlon.
There is an important distinction be-
tween concentration and exposure. Con-
centration is a physical characteristic of the
environment at a certain place and time,
whereas strictly speaking, exposure de-
scribes an interaction between the environ-
ment and a living subject. Thus, a concen-
tration in a room with people present is a
surrogate measurement of exposure, but is
valid only to the degree that it approxi-
mates the concentrations actually experi-
enced by each individual in the room.
The distinction between exposure and
dose is also important. As stated above,
exposure is the pollutant concentration in
the air at the point of contact between the
body and the external environment. Dose
is the amount of the pollutant that actually
crosses one of the body's boundaries and
reaches the target tissue.
The difference between exposure and
dose is illustrated by considering two peo-
ple, one sedentary and one vigorously ac-
tive, in a room where the air pollutant
concentration is constant. Both have the
same nominal exposure. But because ot
faster and deeper breathing, the actual dose
of air pollution delivered to lung tissues is
greater in the active subject than in the
sedentary subject.
Components of Exposure
Three aspects of exposure are important
for determining related health conse-
quences.
OCR for page 209
Sexton and Ryan
209
1. Magnitude: What is the pollutant con-
centration?
2. Duration: How long does the expo-
sure last?
3. Frequency: How often do exposures
occur?
Magnitude is an important exposure pa-
rameter because concentration typically is
assumed to be directly proportional to dose
and ultimately to the health outcome. But
exposure implies a time component, and it
is essential to specify the duration of an
exposure. The health risks of exposure to a
specific concentration for 5 minutes are
likely to be different, all other factors being
equal, than exposure to the same concen-
tration for an hour. Similarly, the fre-
quency of exposure or the time between
subsequent exposures might have health im-
plications. Whether a person is exposed once
a week or several times a day can be an im-
portant determinant of air pollution injury.
A real-time air pollutant monitor carried
by a person for 24 hr would provide a
continuous exposure record for that period.
Depending on the pollutant and the per-
son's activities during that period, the
record might show some intervals of zero
exposure and some intervals of very high
exposure. The full record would contain all
exposure information for that day, but it is
often too complex to work with, as well as
too difficult and expensive to obtain.
It is common to rely on data summaries
(averages) that depend on the capabilities of
the available instruments. In most exposure
studies, magnitude is defined as the average
concentration over some specified time in-
terval (for example, 1, 8, or 24 fur). Dura-
tion is the time (or average time) from the
beginning to the end of a nonzero expo-
sure, and frequency is the number of expo-
sure episodes (of a specified duration) per
. .
unit ot time.
Types of Exposure Information
Data on human exposure can be presented
in several ways (Ott 1982, 1983-84, 1985~.
For an individual, i, a plot of exposure
magnitude as a function of time, Civet),
typically covering a 24-hr period, is called
an exposure profile. As shown in figure la,
additional data about the subject's activities
can be combined with the exposure profile
to show when, where, and how the high-
est-magnitude exposures occurred.
Integrating the function CittJ with re-
spect to time t for a specified time period
yields the integrated exposure. The integra-
tion is represented graphically in figure lb
and shows a Bohr integrated exposure of
960 parts per billion-hour (ppb-hr). The
integrated exposure does not provide infor-
mation about the pattern of exposure over
subintervals of the averaging time, nor
does it reflect the magnitude of short-term
peaks in exposure. Figure lc shows several
examples of average exposure, ta' arrived at
by dividing the integrated exposure by the
period of integration. The figure gives
eight 3-fur averages, three 8-fur averages,
and a single Bohr average derived from the
same Bohr period of data.
In spite of the importance of these dis-
tinctions, it is common to refer to the
average exposure that is, the average
concentration during a specific measure-
ment period (for example, 24 hr) as the
exposure. In some instances, it is also com-
mon to refer to the average concentration
measured by a fixed-site monitor as the
exposure, even though no individual was
actually in the vicinity of the instrument
for the duration of the measurement
period. The blurring of these distinctions,
like those between weight and mass or
between heat and temperature, causes little
confusion for those well versed in the liter-
ature. For others, however, it is important
to keep in mind that a measurement of
air pollutant concentration is a surrogate
for exposure only to the degree that it
reflects actual concentrations experienced
by people.
Individual Exposure Versus Population
Exposure
The pollutant concentrations experienced
by an individual during normal daily activ-
ities are referred to as personal or individual
exposures. A personal exposure depends on
the air pollutant concentrations that are
present in the locations the person moves
through as well as on the time spent in each
location. Individual exposures for a speci
OCR for page 210
210
Human Exposure to Air Pollution
A
150 .
140
30 _
. _
120
Q 110
O 1 00
~ 90
z 80
c, 70
0 60
<) 50
o
z 40
30
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960
Q 880
Q 800
C' 720
O 640
x 560
O 480
c~ 400
~ 320
`3 240
'_ 1 60
- 80
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E
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ce
I I I 1 1 1
B
24hour
integrated
exposure
Plot of cumulative exposure
-
t~ = 24hour average
t`, = 8-hour average
t~ = ~hour average
-
-
I I I I I 1
0900 1200 1500 1800 2100 2400
TIME (hour of the day)
Figure 1. Examples of NO2 exposure information: A: a 24-hr exposure profile
and associated time-activity pattern data for a specific individual; B: a plot of
cumulative exposure and the calculated 24-hr integrated exposure; C: calculated
exposures averaged over 3 hr. 8 hr. and 24 hr.
. . . . . . . . .
OCR for page 211
Sexton and Ryan
211
fled group of people may vary widely
because of their different time-activity pat-
terns (Dockery and Spengler 1981; Quack-
enboss et al. 1982; Ott 1983-84; Sexton et
al. 1983; Letz et al. 1984; Spengler et al.
1985; Stock et al. 1985; Wallace et al.
1985a).
Measuring any one person's exposure is a
relatively straightforward procedure, but
from a public health perspective it is im-
portant to determine the population expo-
surc the aggregate exposure for a speci-
fied group of people, such as a community
or an occupational cohort. It is rarely nec-
essary or desirable to measure the exposure
of each member of the group. But some
measure of the distribution of individual
exposures is needed. This typically includes
at least a measure of the central tendency
(for example, mean exposure) and of its
variability (for example, variance). An ac-
curate and statistically valid characteriza-
tion of even these simple descriptors of
population exposure may require many
personal exposure measurements.
The upper tail of the distribution is fre-
quently of special interest, because it repre-
sents the segment of the population that has
much higher-than-average personal expo-
sures. Determination of the numbers and
kinds of people who experience exception-
ally high exposures can be critical for health
risk assessment. This is especially true
when the relationship between the pollut-
ant dose and resultant health effects is
highly nonlinear. Typically, more personal
exposure measurements are needed to ac-
curately estimate the tails of the distribu-
tion than are needed to estimate its mean
and variance.
Methods
Basically, there are two general approaches
to air pollution exposure assessment: (1) air
monitoring, which depends on either direct
measurements (personal monitors) or indi-
rect measurements (fixed-site monitors
combined with data on time-activity pat-
terns), and (2) biological measurements
that use biological markers to assess expo
sure. In the past, questionnaires have also
been used to estimate exposures, particu-
larly in epidemiologic studies. Typically,
questionnaires are used to categorize re-
spondents into two or more groups (for
example, exposed or unexposed, high ex-
posure or low exposure). This is a qualita-
tive, often retrospective, method for esti-
mating air pollution exposure. It depends
on a priori knowledge of exposures and
their determinants to develop effective
questionnaires (for example, high formal-
dehyde exposure for workers in certain
industries, or high carbon monoxide [CO]
and lead [Pb] exposure for traffic police-
men, bus drivers, and toll collectors). Most
often the information necessary to develop
. . .. . ~ .
qUeStlOnnalreS IS O ~talnec . trom previous
studies that used either air monitoring or
biological monitoring to measure expo-
sures. The questionnaire method is really a
way to extend the results of prior air mon-
itoring or biological measurements to a
larger or different population and is not a
separate approach.
Air Monitoring
Direct Approach to Exposure Assessment.
A personal monitor is a small, lightweight
device, such as a diffusion tube or a filter
with a battery-operated pump, that can
be carried or worn by a person during his
or her normal daily activities. Personal
monitors make it possible to measure ex-
posures for an identified subset of the
general population. Moreover, if study
participants maintain records of their activ-
ities, then locations where highest concen-
trations occur as well as the nature of
emission sources can often be inferred. The
major impediment to this type of assess-
ment has been the lack of suitable instru
ments.
Small, quiet, portable personal exposure
monitors that are sensitive enough to mea-
sure ambient concentrations of some pol-
lutants are now available (Lautenberger et
al. 1981; Rose and Perkins 1982; Wallace
and Ott 1982; Bartley et al. 1983; Underhill
1984~. Pollutants that can be measured ac-
curately with personal monitors include
nitrogen dioxide (NO2) (Palmes et al. 1976;
OCR for page 212
212
Human Exposure to Air Pollution
Palmes and Tomczyk 1979; Woebkenberg
1982; Yanagisawa and Nishimura 1982),
respirable particles (Turner et al. 1979),
formaldehyde (Geisling et al. 1982; Kring
et al. 1984), sulfur dioxide (SO2) (Coleman
1983; Kring et al. 1983), organic vapors
(Feigley and Chastain 1982; Seifert and
Abraham 1983; Vo-Dinh and Miller 1983;
Compton et al. 1984; Reggin and Peterson
1985; Sheldon et al. 1985), and CO (Akland
et al. 1985; Ott et al. 1986~.
Personal monitors can be grouped into
two general categories: integrated samplers
that collect the pollutant over a specified
time period and then are returned to the
laboratory for analysis, and continuous
samplers that use a self-contained analytical
system to measure and record the pollutant
concentration on the spot. Instruments in
both categories can be either active or pas-
sive. Active monitors use a pump and a
power source to move air past a collector or
sensor. Passive monitors depend on diffu-
sion to bring the pollutants into contact
with the collector or sensor. Information
about personal monitors is summarized in
table 1 (Wallace and Ott 1982~.
Most personal monitors available today
are integrated samplers with sampling pe-
riods ranging from 8 hr to a week or more.
Active integrated sampling devices are
commonly used to obtain integrated expo-
sure measurements over an 8- to 24-hr
period. In general, they are bulky, noisy,
and require frequent calibration to ensure
the validity of the data they collect.
Passive samplers are simple, small, quiet,
inexpensive, and easy to use; but at ambient
concentrations normally require a longer
sampling period (for example, 1 or 2
weeks) to collect enough material for anal-
ysis. A passive sampler, therefore, cannot
be used to relate short-term exposures
(minutes or hours) to specific events or
sources. Moreover, passive samplers are
affected by temperature, relative humidity,
and air movement and tend to be less
accurate than active monitors. They are
most appropriate for large-scale surveys of
population exposure, where pinpoint accu-
racy is not required and long-term expo
r ~ .
sures are ot primary interest.
Although considerable progress has been
made in miniaturizing real-time analytical
monitors, much work remains to be done.
Continuous personal monitors have not
been developed for most of the important
pollutants (see table 1~. Those that are
available can provide data on measured
concentrations as a function of time
throughout the day. These data can be used
to construct exposure profiles (see figure 1)
and, when combined with time-activity
information, can be used to relate short-
term exposures to specific events and
sources. Because they record a large num-
ber of real-time measurements, continuous
personal monitors should log and store data
to be most effective.
Participants are typically asked to main-
tain a detailed record of their time-activity
patterns during the test period. The record
is usually a log or diary documenting the
subject's location and activity at particular
times. Recently, a small microprocessor-
based data logger was developed that auto-
matically computes and stores times and
average concentrations (Ott et al. 1986~.
The subject only records the type of activ-
ity engaged in and presses a button, and the
instrument stores all other information
electronically to be retrieved and analyzed
later.
As Wallace and Ott (1982) pointed out,
the direct measurement of exposures using
personal monitors raises several method-
ological issues. Personal monitoring studies
. . .
are comp. .ex, expensive, t~me-consum~ng,
and labor-intensive. They present prob-
lems because they generally require the
selection and recruitment of representative
subjects; the distribution, maintenance, and
retrieval of many monitors; either a labo-
ratory analysis of many air samples re-
turned from monitors in the field or cali-
bration and validation of many real-time
monitors; and the transcription and statis-
tical analysis of data on pollutant concen-
trations and time-activity patterns. The
problems raised by the three latter points
are fairly obvious, but the difficulties asso-
ciated with selecting and recruiting ~ test
sample require amplification.
. ~
, ~ .
Personal exposure monitoring is, by its
very nature, an intrusive event in the life of
the study participants. The degree of incon
OCR for page 213
Sexton and Ryan
213
Table 1. Personal Exposure Monitors Capable of Quantitative Pollutant Measurements at
Ambient Concentrations
Monitor
Type Pollutants Collection Method Analytical Method
Integrated, Respirable particles (sulfates, Pump/stack filter (2 size Microbalance chemical
Active nitrates, metals) fractions) analysis PIXE
Respirable particles (mass only) Pump-impactor/precipita- Piezoelectric
tor
Respirable particles (sulfates, Pump/filter Microbalance PIXE
nitrates, metals)
Sulfur dioxide, nitrogen Pump/impingers/filter Colorimetric gravimetric
dioxide, respirable particles
Nonpolar volatile organics Pump/Tenax cartridge Thermal desorption/
GC-MS
Organochlorine pesticides, Pump/polyurethane foam GC
polychlorinated biphenyls
Integrated, Carbon monoxide Diffusion Electrochemical
Passive
Nitrogen dioxide Diffusion tube (TEA) Colorimetric
adsorbent
Nitrogen dioxide Badge/TEA Colorimetric
Nitrogen dioxide Di~usion/dimethylsilicone Colorimetric
filter/TEA
Nitrogen dioxide Di~usion/TEA-impreg- Colorimetric
nated filter
Formaldehyde Permeable membrane MBTH, pararosaniline
Formaldehyde Diffusion badge Chromotropic acid
Polynuclear aromatics Diffusion badge Room-temperature
phosphorescence
Vinyl chloride Permeable membrane Solvent desorption/GC
badge/activated charcoal
Radon Plastic (records radiation Etching/microscopic
damage) examination
Continuous, Carbon monoxide Pump electrolyte Sulfuric acid
Active
Carbon monoxide Pump electrolyte Solid polymer
NOTE: GC = gas chromatography; GC-MS = gas chromatography-mass spectrometry; MBTH = 3-methyl-2-
benzothiozolinone; PIXE = proton-induced x-ray emission; TEA = triethanolamine.
SOURCE: Adapted with permission from Wallace and Ott 1982, and from the Air Pollution Control Association.
venience depends on the size, weight, ap-
pearance, and ease of operation of the mon-
itor, as well as other aspects of the study,
such as the need to fill out logs or diaries.
The demands of the project protocol and
the associated inconvenience may cause
many people to refuse to cooperate. It is
particularly difficult to get the cooperation
of schoolchildren, non-English-speaking
people, disadvantaged people, or those
with low socioeconomic status. The re-
sponse rate may be raised by offering in-
centives, but, even so, additional incentives
for those that complete the study may be
necessary to forestall high dropout rates. In
. . .
any case, simply wearing a monitor or
filling out a log can cause the participant to
change his or her behavior and conse-
quently introduce bias (Sexton et al. 1986a;
Ryan et al. 1987~.
Direct personal monitoring is the most
accurate means of exposure assessment, but
it is also the most expensive. Large-scale
personal monitoring studies are a recent
development, so many survey design, lo-
gistic, and technical problems remain to
be solved. More attention should be fo-
cused on these issues to make subsequent
OCR for page 214
214
Human Exposure to Air Pollution
personal exposure studies more cost-effec-
tive.
Indirect Approach to Exposure Assess-
ment. The indirect approach estimates in-
tegrated exposure by combining measure-
ments of pollutant concentrations at fixed
sites (for example, outdoors at a busy in-
tersection, indoors at home) with data logs
and diaries about the times people spend in
specific environments (Fugas et al. 1972;
Fugas 1975; Dockery and Spengler 1981;
Duan 1981, 1982; Ott 1982; Sexton et al.
1983, 1984b). The general form of the
equation used to calculate time-weighted
. .
1ntegratec exposure IS
J
Ei= ~ Cjtij
j
where Ei is the time-weighted integrated
exposure for person i over the specified
time period; Cj is the pollutant concentra
. . . . . .
lion in microenvironments tic IS t he aggre-
gate time that person i spends in microen-
vironment j; and r is the total number of
. .
microenvironments that person i moves
through during the specified time period.
A microenvironment is defined as a
three-dimensional space where the pollut-
ant level at some specified time is uniform
or has constant statistical properties. Out-
doors in a specific community, inside an
individual motor vehicle, and inside a par-
ticular residence are examples of locations
that can be defined, under appropriate con-
ditions, as microenvironments. Examples
of potentially important microenviron-
ments for exposure assessment are given in
table 2.
Several assumptions are implicit in the
application of equation 1:
1. The concentration Cj in microenvi-
ronmentj is assumed to be constant during
the time tic that person i is there. This is not
always the case. For example, it is likely
that air pollution levels inside one's resi-
dence will vary substantially during the 14
to 16 hr/day that most people spend at
home, because of variations in emission
rates and air exchange rates.
2. The concentration Cj within microen-
vironment j and the time ti that person i
spends there are assumed to be independent
events. This assumption is not universally
valid, however. Persons sensitive to pollut-
ants like tobacco smoke and formaldehyde,
or to noxious odors, such as those from
paint and cleansing solutions, are likely to
avoid microenvironments where concen-
trations of these pollutants are elevated.
3. The number of microenvironments
necessary to characterize personal exposure
adequately is assumed to be small, but in
fact, it is not clear how many are necessary.
Within the indoor residential environment,
for example, the variability in short-term
particle concentrations from activities such
as cooking, smoking, and cleaning might
(1) necessitate the inclusion of several addi
tional microenvironments in the model to
comply with assumption 1 above.
4. The time-weighted integrated expo-
sure, usually measured over 24 hr. is di-
rectly related to the health outcome. This
may not be the case for adverse health
effects due to short-term peak exposures
(hours, minutes, or in some cases seconds)
to pollutants such as formaldehyde, NO2,
or ozone (03~.
The concept of a time-weighted inte-
grated exposure is illustrated in figure 2. A
unit width is indicated on the j axis for each
of five microenvironments: indoors at
home, indoors at work, indoors in other
locations, in transit, and outdoors. The
concentration of respirable particles (RSP)
is displayed on the Y axis, and the fraction
of time that person i spends in each micro-
environment over the 2=hr period is plot-
ted on the t axis. The volumes of the boxes
shown in figure 2 represent contributions
from each of the five microenvironments to
the time-weighted integrated exposure.
The contribution of each microenviron-
ment is represented mathematically in the
table at the bottom of figure 2.
Even though respirable particle concen-
tration was low inside the home, it contrib-
uted significantly to the time-weighted ex-
posure because this person spent 18 out of
24 hr there. Conversely, the respirable par-
ticle concentration outdoors made only a
OCR for page 215
Sexton and Ryan
215
Table 2. Potentially Important Microenvironments for Air Pollution Exposure Assessment
Mi cro en viron men ts
Comments
Outdoors
Urban Metropolitan areas where air pollution levels are high as a result of a high
density of mobile and stationary sources.
Suburban Small- to medium-sized cities where air pollution levels tend to be lower than
metropolitan areas, although transport of urban pollution can affect local air
quality under certain conditions.
Rural Agricultural communities and small towns with few major anthropogenic
sources of air pollution. Air pollution levels tend to be low, although trans
port of urban and suburban pollution can affect local air quality under certain
, . .
conaltlons.
Indoors Occupational
Industrial Manufacturizing and production processes, such as those in petrochemical
plants, pulp mills, power plants, and smelters.
Nonindustrial Primarily service industries where workers are not involved in manufacturing
and production processes, such as insurance companies, law offices, and retail
sales outlets.
Indoors Nonoccupational
Residential Single-family houses, apartments, mobile homes, condominiums
Commercial Restaurants, retail stores, banks, supermarkets
Public Post offices, courthouses, sports arenas, convention halls
Institutional Schools, hospitals, convalescent homes
Indoors Transportation
Private Automobiles, private airplanes
Public Buses, subways, trains, commercial airplanes
minor contribution because this person was
outdoors less than half an hour during the
24-hr period.
This illustrates the general problems as-
sociated with attempts to define the limits
of microenvironments that are sufficiently
homogeneous, to identify which among
them are the significant contributors to
integrated exposure, and to measure or
estimate both the pollutant concentration
Cj and the average time, tic, the subject
spends In the microenvironment.
Better documentation of time-activity
patterns, as well as more information about
approximate indoor and outdoor pollutant
concentrations would help investigators
specify important microenvironments and
choose fixed monitoring sites. In most
cases, however, there is not enough infor
. . . .
-
and spatial aspects of people's activity pat-
terns are reflected separately in the time
budgets and mobility patterns that sociol-
ogists, urban planners, economists, and
transportation analysts use. these data are
A ~ ,
. . . .
-- r ~ --- ~ ~ - - - 7
not in a form suitable for application to
exposure assessment. Only in the past few
years have both temporal and spatial as-
pects of people's everyday movements
. . . . . . . .
seen ~nvestlgatec . in conjunction wit ~ air
pollution measurements (Spengler et al.
1980, 1985; Dockery and Spengler 1981;
Dockery et al. 1981; Moschandreas 1981;
Ott and Flachsbart 1982; Sega and Fugas
1982; Sexton et al. 1983, 1984b; Flachsbart
and Brown 1985; Nagda and Koonz 1985;
Wallace et al. 1985a,b).
,
. . . . .
- - r
matron to determine wnlcn m1croenvlron-
ments are adequately defined, which can be
bypassed or lumped with others, which
should be subdivided, and which should
have their limits altered to ensure accurate
exposure estimates. Although the temporal
much ot what IS known about human
time-activity patterns can be traced to two
studies now more than a decade old (Szalai
1972; Chapin 1974~. A summary of mea-
sured 24-hr time-activity patterns from
these studies is provided in table 3. Both
studies found that on most days people are
inside their residences for an average of 65
OCR for page 216
216
t
Human Exposure to Air Pollution
RSP
CONCENTRATION
(regime)
130
120
110
100
90
80
70
60
50
40
30
20.
/ 10
rppcT1°/~ ~
~/~
0.9~
0,
~O, ~ ~ C~
i
RSP Time Microenvironment
Microenvironment Concentration Fractiona Cj x tij Contribution
Type (Cj, ~g/m ) (tij) (,ug/m3) to Ei (%)b
Indoors at Home 15 0.75 11.25 47
Indoors at Work 50 0.15 7.50 31
Indoors, Other 25 0.04 1.00 4
In Transit 90 0.04 3.60 15
Outdoors 40 0.02 0.80 3
Ei = ~ Cj x t<, = 24.15 ,ug/m3
a Fraction of 24 hr spent in each microenvironment.
b Percentage that each microenvironment contributes to the Bohr, time-weighted, integrated exposure (E,).
Figure 2. Examples of the relative contributions from specific microenvironments to an individual's time-
weighted, integrated exposure to respirable particles (RSP).
to 70 percent of the time, and indoors at
home, work, or elsewhere for more than 90
percent of the time. Although these values
vary with age, gender, occupation, socio-
economic status, and day of the week, it
has become clear that indoor microenvi-
ronments must be taken into account for a
realistic assessment of exposure to many air
pollutants (National Research Council
1981; World Health Organization 1982,
1983; Yocum 1982; Spengler and Sexton
1983; Lebowitz et al. 1984; De Bortoli et al.
1985; Stock et al. 1985), including NO2
(Quackenboss et al. 1982; Ryan et al. 1983;
Sexton et al. 1983; Spengler et al. 1983),
formaldehyde (Environmental Health Per-
spective 1984; Sexton et al. 1986a), CO
Jaeger 1981; Ott and Willits 1981; Ott and
Flachsbart 1982; Ziskind et al. 1982), respi-
rable particles (Spengler et al. 1981; Sexton
et al. 1984a,b; Sexton et al. 1986b), radon
(Nero and Lowder 1983), and organic va
OCR for page 217
Sexton and Ryan
217
Table 3. Summary of Average Time-Activity
Patterns for a 24-Hr Period
Hours in Each Location
Location Chapin (1974) Szalai (1972)
Indoors
Home
Work
Other
Subtotal
Outdoors
Home
Work
Other
Subtotal
In Transit
All modes
Total
16.03
4.61
1.31
21.95
0.27
0.27
0.54
1.16
23.65a
16.75
4.03
1.63
22.41
0.23
0.12
0.35
1.25
24.01
a Shortfall from 24 hr not explained by the author.
SOURCE: Adapted with permission from World
Health Organization 1982.
pars (Beau and Ulsamer 1981; Hollowell
and Miksch 1981; Parke et al. 1981; Miksch
et al. 1982; Molhave 1982; Otson et al.
1983; Wallace et al. 1984; Andelman 1985;
Wallace et al. 1985a; Sexton et al. 1986c).
In addition to the problems of identify-
ing important microenvironments and of
obtaining valid measurements of pollutant
concentrations, the indirect approach suf-
fers from the same problems as the direct
approach: the selection and recruitment of a
representative sample of people; the distri-
bution, maintenance, and retrieval of many
monitors; either a laboratory analysis of
many samples returned from monitors in
the field or calibration and validation of
many real-time monitors; and the tran-
scription and statistical analysis of data on
pollutant concentrations and time-activity
patterns.
Biological Monitoring
Air monitoring traditionally has been the
principal means of exposure assessment. A
major shortcoming of this approach is its
failure to take account of factors such as
respiration rate and depth of inspiration
that may cause two individuals with the
same measured exposure to receive vastly
different doses. Differences in dose at
equivalent exposures, coupled with varia-
tions in individual susceptibility, introduce
a large measure of uncertainty in the ex-
trapolation from air pollutant measure-
ments to the effects on human health. Thus
there is an acute need for methods that
provide better information about the inter-
relationships of exposure, dose, and health
effects.
Biological monitoring is the measure-
ment of environmental contaminants or
their biological consequences after the con-
taminants have crossed one of the body's
surfaces and entered tissues or fluids. There
are two kinds: measurements of environ-
mental contaminants or their metabolites
and derivatives in body fluids or excrete
(exposure markers); and measurements of
biological responses in cells and tissues
(exposure markers and effects markers).
Examples of the first type include direct
chemical analyses, immunoassays, and
bioassays specific for mutagenicity; these
methods can be used to measure chemicals
in the blood, urine, breast milk, saliva, and
semen. Examples of the second category
include immunologic and chemical meth-
ods to detect and quantify covalently
bound derivatives formed between acti-
vated chemicals and cellular macromol-
ecules such as nucleic acids and proteins, as
well as observations of mutation, sister
chromatic exchange, and chromosome ab-
errations (Wogan and Gorelick 1985~.
Biological measurements enable the de-
velopment of exposure markers related
qualitatively or quantitatively to measured
air pollution concentrations (Goldstein
1981; Miller 1983; Berlin et al. 1984; Na-
tional Institute of Environmental Health
Science 1984; Wogan and Gorelick 1985;
Ho and Dillion 1986~. Exposure markers
are not necessarily closely correlated with
subsequent health effects for two reasons:
first, the site and mechanism of toxic action
associated with adverse health effects are
not always fully understood; and second,
some identified sites of toxic action are not
accessible for analysis. For example, coti-
nine is a metabolite of nicotine that can be
detected in the blood of infants whose
mothers smoke as well as in the mothers
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228
Human Exposure to Air Pollution
Table 10. Different Approaches to Air Pollution Exposure Modeling
Model Type Examples References
Statistical Various epidemiologic studies, such as Ferris et al. (1979)
Harvard Air Pollution/Lung Health Speizer et al. (1980)
Study
Physical National Exposure Model (NEM) Biller et al. (1981, 1984)
Johnson and Paul (1983a,b)
Richmond and McCurdy (1985)
Stepwise physical models which include Tosteson et al. (1982)
physical parameters in stepwise regres- Sexton et al. (1984b)
signs Spengler et al. (1985)
Quackenboss et al. (1986)
Physical model of indoor air quality Ryan et al. (1983)
Sexton et al. (1983)
Nazaro~and Cass (1986)
Physical-stochastic Simulation of Human Air Pollution Ex- Ott (1981)
posure (SHAPE) Ott and Willits (1981)
Ott (1983-1984)
Simulation System (SIMSYS) Letz et al. (1984)
Ryan et al. (1986, 1987)
Statistical techniques such as factor anal-
ysis and cluster analysis can be used to
elucidate the basic, underlying processes
that determine air pollution exposure.
These methods allow exposure to be parti-
tioned into factors or clusters of correlated
independent variables that tend to act to-
gether. Such analyses are useful for inves-
tigating correlations among independent
variables and for understanding the relative
contribution of specific factors or clusters
of variables to the measured exposure.
Physical Modeling
The physical approach is based on the
investigator's interpretation of the underly-
ing processes that determine air pollution
exposure. This interpretation is expressed
as a quantitative description-mathematical
formula, computer program, numerical ta-
bles, or graph of the relationship between
exposure and the determinants thought to
be important. Since the model is chosen by
the investigator, it may produce biased
results because of the inadvertent inclusion
of inappropriate parameters or the im-
proper exclusion of critical determinants.
In the physical modeling approach, the
modeler begins with certain a priori as-
sumptions about the underlying physical
processes that determine air pollution ex
posure. These assumptions are the basis for
constructing a quantitative formulation
that constitutes a physical exposure model.
References and examples of the physical
modeling approach are given in table 10.
A simple physical model can be con-
structed by assuming that personal expo-
sure to air pollution is a strict function of
the outdoor, or ambient, concentration.
The mathematical form of this statement
can be expressed as
E = iamb (~2)
where E is exposure for a specific air con-
taminant, f denotes "a function of," and
Camb is the ambient (outdoor) concentra-
tion of the pollutant. This model would be
most appropriate for air pollutants that
result primarily from outdoor sources (see
table 8~.
An example of this basic model, which
assumes that exposure can be approximated
by a linear function, is
E = aCamb + b (3)
where E is exposure, a is the slope of the
line relating exposure to the ambient con-
centration, Camb is the measured ambient
concentration, and b is the exposure when
the ambient concentration is zero. Several
groups have combined this model with
data about personal exposures and ambient
OCR for page 229
Sexton and Ryan
229
concentrations to estimate values for a and
b in equation 3 (Tosteson et al. 1982; Ryan
et al. 1983; Sexton et al. 1983; Spengler et
al. 1985~. Further analysis has been carried
out to delineate the relationship between
the model parameters a and b and the
physical processes such as the air-exchange
rate, the first-order pollutant losses from
physicochemical processes, and the indoor
sources of air pollution (Ozkaynak et al.
1982; Ryan et al. 1983; Sexton et al. 1983;
Letz et al. 1984~.
The microenvironmental approach, dis-
cussed earlier in the Methods section under
the Indirect Approach to Exposure Assess-
ment, is a more complex model based on
similar ideas. Pioneered by Fugas (1975),
this approach assumes that a person's time-
weighted, integrated exposure is the prod-
uct of the air pollution concentration in
identified microenvironments and the time
spent in those microenvironments (see
equation 1~. Although this approach allows
comparison of the contributions of selected
microenvironments to the measured expo-
sure, the identification and monitoring of
. . . . ~ . . .
po ~ .utants In critical m~croenv~ronments Is
often difficult and expensive.
The NAAQS Exposure Model (NEM) is
a physical model that uses the microenvi-
ronmental approach (Biller et al. 1981,
1984; Johnson and Paul 1983a; Richmond
and McCurdy 1985~. The NEM also incor-
porates the concept of a population cohort
(a group of individuals having a statistical
factor in common, such as, live in the same
neighborhood or have the same commut-
ing pattern) an assumption that is analo-
gous to the requirement for spatial and
temporal uniformity of pollutant concen-
trations within a specific microenviron-
ment. The model is designed to estimate
the effect on population exposure that re-
sults from changes in air quality standards.
The NEM has been applied to CO
Johnson and Paul 1983a,b), SO2 (Biller et
al. 1984), and O3 (Richmond and McCurdy
1985~.
A common shortcoming of the physical
models described above is that while they
do estimate expected exposure, they do not
estimate the associated uncertainty. Evi-
dence suggests that there is substantial
. . . . . .
variation In t le time spent In venous
microenvironments (Sexton et al. 1984b;
Clausing et al. 1986; Quackenboss et al.
1986), as well as in the pollutant concen
. . . . .
tratlons wltnln eac :n microenvironment
(Spengler et al. 1983; Sexton et al. 1984a;
Akland et al. 1985; Sexton et al. 1986a,b).
Letz and his colleagues (1984) attempted
to estimate the uncertainty in predicted
exposure by including estimates of the
variance in each model parameter. The
variance in predicted exposure is estimated
by a Taylor-series expansion. Results of
this approach correlate well with findings
from personal monitoring studies.
Physical-Stochastic Modeling
The physical-stochastic approach can be
thought of as a third type of exposure
model, even though it is a computational
method. It combines elements of both the
physical and the statistical approaches to
estimate exposure. A mathematical model
that describes the physical basis for air
pollution exposure is first constructed.
Then a random or stochastic component
that takes into account the imperfect
knowledge of the physical parameters that
determine exposure is introduced into the
model. The inclusion of the random com-
ponent limits the effect of investigator-
induced bias and allows for estimates of
population distributions of air pollutant
exposure. Misleading results can still be
produced if model parameters are selected
ineptly. In addition, the required knowl-
edge about distribution characteristics may
be difficult and expensive to obtain.
By the introduction of a stochastic com-
ponent into a physical model, the physical-
stochastic approach attempts to account
for the probabilistic nature of the physi-
cal processes that determine exposure. In
this way, the inherent uncertainty associ-
ated with a mathematical abstraction of
air pollution exposure is taken into ac-
count.
Two models, the Simulation of Human
Air Pollutant Exposure (SHAPE) model
and the Simulation System (SIMSYS)
model, are representative of the physical-
stochastic approach. Both models use the
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230
Human Exposure to Air Pollution
microenvironment concept discussed ear-
lier and use similar statistical approaches.
They differ primarily in their application
and intended use.
The SHAPE model focuses on estimat-
ing personal exposures (Ott 1981, 1983-
1984; Ott and Willits 1981~. Statistical tech-
niques are used to select the appropriate
characteristics of the individuals in the
study and the microenvironments through
which they move. Time-activity data are
generated by selecting the type of activity
as well as the duration of activity from
probability distributions. Air pollutant ex-
posure is modeled as the sum of 1-min
exposures that are experienced throughout
the course of an individual's daily activities.
The SHAPE model has two distinct ad-
vantages: less-detailed information on ti-
meactivity patterns is needed because one
must know only the probability of going
from one activity to another; and a small
number of microenvironments (14 in the
published version) is required to estimate
exposure. Disadvantages of this approach
include the potential bias introduced by the
modeler's selection of relevant microenvi-
ronments; the need for accurate data on the
probabilities of transitions between micro-
environments and the time spent in specific
microenvironments; and the difficulty of
obtaining the distribution of pollutant con-
centrations in important microenviron-
ments.
The SIMSYS model focuses on estimat-
ing the distribution of air pollutant expo-
sures within a population, with emphasis
on the contribution of specific microenvi-
ronments to the integrated exposure (Letz
et al. 1984; Ryan et al. 1986, 1987~. The
SIMSYS model is based on a physical de-
scription of exposure similar to equation 3.
Estimates of the probability distributions
for the model parameters are obtained from
the literature or from field studies. Basi-
cally, the SIMSYS approach is similar to
the SHAPES model and therefore shares
the same generic disadvantages. The ad-
vantage of this model is that it provides a
means of evaluating the effects on human
exposure of reducing air pollutant levels in
., . .
specific microenvironments
Source Apportionment
Before it is feasible to evaluate the adequacy
and cost-effectiveness of air pollution con-
trol strategies, it is necessary to obtain
more and better information about the rel-
ative contributions of indoor and outdoor
emission sources to measured personal ex-
posures. Models such as SHAPE and
SIMSYS are useful tools that aid in under-
standing where and how exposures occur.
They begin to address the issue of the
extent to which public health is protected
by the NAAQSs, which apply only to air
outside buildings.
As pointed out by Sexton and Hayward
(1986), informed decisions about appropri-
ate resource allocation to control air pollu-
tion require more than just data on health
effects. They depend also on adequate in-
formation about important emission
sources (source identification), chemical
and physical properties of emissions (emis-
sions characterization), and the effects of
important source categories on indoor and
outdoor air quality, as well as on personal
exposures (that is, source apportionment).
Although the major emission sources,
indoors as well as outdoors, have been
identified and work is progressing on the
characterization of airborne discharges, the
relative impact of indoor and outdoor
emissions on personal and population ex-
posures has not been addressed systemati-
cally and comprehensively.
Several types of source apportionment
models have been applied to outdoor (am-
bient) air, but their application to air pol-
lution inside buildings or to personal expo-
sures is just beginning. Consequently,
insufficient data are available to determine
the relative contributions of indoor and
outdoor sources to measured personal ex-
posures. This lack of information seriously
hinders attempts to evaluate the costs and
benefits of alternative control options
(Sexton and Hayward 1986~.
Validation and Generalization
The models described in the preceding sec-
tions are mathematical abstractions of
OCR for page 231
Sexton and Ryan
231
physical reality that may or may not pro-
vide adequate estimates of air pollution
exposure. The only way to be sure that a
model is capable of providing useful and
accurate information is by validation-
comparing model predictions with mea-
surements independent of the measure-
ments used to develop the model. More-
c~ver model validation is a necessary
precondition for the generalization of
model results to a different or larger popu-
lation.
In the statistical modeling approach, data
collection is an integral part of model con-
struction. If the data are known to be from
a statistically representative sample of the
population, there is no need for further
validation. If the results are to be extrapo-
lated beyond the range for which the exist-
ing data base provides a statistical descrip-
tion, validation is necessary. The physical
and physical-stochastic modeling ap-
proaches must be validated with actual data
from separately conducted field studies.
Care must be taken that the data used to
validate a model are not biased with respect
to crucial model parameters. The validation
step is useful only to the degree that the
sample population is representative of the
group to which results will be extrapolated.
. ,
Research Recommendation
Exposure Modeling.
Attempts to model
human exposures to air pollutants are rela-
tively recent. Models vary widely in com-
plexity and have not been validated ade-
quately. The lack of data on the variability
and covariance of time-activity patterns
among individuals is a critical hindrance to
model development.
Perhaps the most pressing need associ-
ated with modeling human exposure is the
necessity for the external review and vali-
dation of existing models. It is not clear, for
example, whether current exposure models
are adequate, or if a new generation of mod-
els needs to be developed. The validation of
existing models, using data sets other than
those from which they were generated, is
essential to answer this question.
Source apportionment of ambient air
pollution is a growing research field. Many
investigators are now studying ambient air
pollution to determine which pollution
sources are affecting which receptor and to
what degree. The work should go further
and determine which sources most directly
affect specific human populations. Future
studies should focus on determining the
relative contributions of indoor and out-
door emission sources to personal exposures.
Recommendation 5. Exposure Mod-
eling. Research is needed to assess the ade-
quacy of current exposure models through
external review and validation. Validation
of existing models is essential to determine
whether these models are adequate or if a
new generation of models should be devel-
oped. In addition, how to apportion con-
tributions of specific emission sources to
individual exposures requires further
study. The relevance of existing models
(outdoor air) for source apportionment of
personal exposures and of indoor air pollu-
tion needs to be evaluated and new models
need to be developed if existing models are
shown to be deficient.
Summary anc! Conclusions
In its examination of the state of the art in
air pollution exposure assessment, this
chapter describes the general methods avail-
able to determine exposure, the published
studies that report on measurements of
actual exposures, and the models that are
used to estimate individual and population
exposures. The goals are to help the reader
understand the rudiments of this emerging
field and to highlight the critical areas
where further research is needed. In addi-
tion, it attempts to impart an awareness of
the importance of obtaining information
about how, when, where, and why expo-
sures occur.
Evidence accumulated over the past few
years indicates that adequate estimates of
individual and population exposures for
most air pollutants, including regulated and
unregulated substances, cannot be derived
OCR for page 232
232
Human Exposure to Air Pollution
solely from measurements by traditional
outdoor monitoring stations. Depending
on the pollutant in question, exclusive re
.
fiance on outdoor measurements may over
or underestimate the magnitude, duration,
and frequency of exposures for the general
population, as well as for many potentially
susceptible subgroups. Although the rami
f~cations of these findings for the develop
ment and evaluation of air pollution controlHuman exposure data are obviously crucial
strategies have not been explored fully, it isto the calculation of air pollution health
clear that they raise policy issues thatrisks since this information is needed to
should be taken into account in future
regulatory decisions (Sexton and Repetto
1982; Sexton 1986~.
Perhaps the most important lesson to be
drawn from this chapter is the realization
that accurate estimation of human expo
sures is a prerequisite for realistic assess
ment of air pollution health risks. Quanti
tative risk assessment is rapidly becoming
an integral part of the regulatory decisions
that are aimed at protecting public health.
Too often, however, the availability of
suitable exposure data is taken for granted.
The generalized form of the equation
used to estimate health risks from environ
. . .
mental contaminants IS
Health Risk
(morbidity/mortality)
=
Potency x Exposure
(dose/response) (concentration)
x Exposed Population
(number of people exposed)
specify values for two terms in the equa-
tion: exposure (including magnitude, dura-
tion, and frequency) and exposed popula-
tion. Moreover, exposure assessment is a
critical element of epidemiologic studies,
which are often used to develop values for
the potency term in the equation. For ex-
ample, epidemiologic studies that fail to
account for indoor as well as outdoor ex-
posures are prone to systematic and ran-
dom bias and to the misclassification of
exposures. Such errors can lead to spurious
conclusions concerning dose/response rela-
tionships for airborne contaminants, and,
ultimately, to inappropriate estimation of
public health risks.
Summary of Research Recommendations
HIGH PRIORITY
Recommendation 1 Studies should be undertaken to provide information on the
Time-Activity spatial and temporal distributions of human populations as they
Patterns relate to exposure. The focus of these studies should be to construct
the frequency distribution of time spent in important microenvi
ronments, to identify the air pollution sources in those microenvi
ronments, to specify the time of day that people are in particular
microenvironments, and to determine the differences in time
activity patterns associated with demographic and socioeconomic
factors.
Recommendation 3 Studies are required to provide representative data on human
Exposure exposures and to investigate the link between measured exposures
Monitoring and adverse health effects. These efforts will require (a) the devel
opment of suitable instruments (for example, personal and indoor
monitors) and measurement techniques (for example, noninvasive
biological monitoring); (b) an application of the appropriate statis
tical survey design methods; (c) the creation of extensive data bases
on personal exposures, pollutant concentrations in important mi
OCR for page 233
Sexton and Ryan
233
croenvironments, and time-activity patterns; and (d) the develop-
ment and application of appropriate models to estimate human
exposure.
Recommendation 4 Research is required to adapt available biological measurement
Biological Markers techniques to community air pollution measurement and control.
of Exposure Studies that better define the nature of the relationship between
exposure and dose and between dose and health outcome are
needed. It is especially important to establish in controlled human
populations (a) the sources of error for a particular biological
measurement technique, (b) the validity of sample collection meth
odology, (c) the appropriateness of internal and external standards,
and (d) the adequacy of methods for quality control.
Recommendation 5 Research is needed to assess the adequacy of current exposure
Exposure Modeling models through external review and validation. Validation of
existing models is essential to determine whether these models are
adequate or if a new generation of models should be developed. In
addition, how to apportion the contributions of specific emissions
sources to individual exposures requires further study. The rele
vance of existing models (outdoor air) for source apportionment of
personal exposures and of indoor air pollution needs to be evalu
ated and new models need to be developed if existing models are
shown to be deficient.
MEDIUM PRIORITY
Recommendation 2 Respiration rate and mode (for example, mouth breathing versus
Breathing Patterns nose breathing) are important determinants of air pollutant dose
and therefore affect the health consequences of a measured expo
sure. The changes in respiration associated with repose, exercise,
standing, sitting, sleeping, talking, or any other important human
activity should be measured or estimated.
Acknowlecigments
We thank the following people for their
helpful comments on this manuscnpt: I. Bai-
lar, I. Evans, and I. Spengler, Harvard Uni-
versity; I. Goldstein, Columbia University;
W. Ott and L. Wallace, EPA; and A. Wat-
son, HEI. I. Schwartz and G. Raisbeck pro-
vided editorial assistance. The manuscript
was typed by M. E. Patten.
Correspondence should be addressed to Ken Sexton,
U. S. Environmental Protection Agency, Office of
Health Research, Washington, DC 20460, or P. Barry
Ryan, Harvard School of Public Health, Department
of Environmental Science and Physiology, 665 Hun-
tington Avenue, Boston MA 02115.
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Representative terms from entire chapter:
personal exposure
