Introduction
Acute and chronic lung function responses to ozone exposure have been
investigated extensively in a variety of epidemiological studies (1-14).
These studies, however, have a limited role in determining accurate dose-response
relationships for ozone (15,16). In most epidemiological studies,
population ozone exposures are assumed to be identical to the concentrations
measured at an ambient monitoring site. Lebowitz et al. (2) found
that this assumption may be flawed and concluded that personal ozone exposures
may be very different from those measured at both outdoor and indoor monitoring
sites. Fixed-location measurements do not account for the effects of spatial
variation in ozone concentrations, indoor/outdoor concentration differences
(17-22), and varying activity patterns on personal exposures
(21).
A major limitation of previous ozone exposure investigations is the lack
of a personal exposure or microenvironmental ozone monitor. With the recent
development of an ozone passive sampler by Koutrakis et al. (23),
personal, indoor microenvironmental, and outdoor concentrations can be measured
on a wide scale. This sampler has been validated in a variety of laboratory
conditions for temperature, relative humidity, wind velocity, ultraviolet
radiation, and other atmospheric oxidant interferences. Because of its low
cost and small size (weight = 7 g, size = 2 cm diameter x 3 cm), the passive
sampler is especially suited for characterizing the exposure pattern of
individuals in large-scale epidemiological studies.
This paper describes a pilot study conducted during summer 1991, in State
College, Pennsylvania, to assess ozone exposures using the passive ozone
sampler. This pilot study, which was performed in conjunction with an acid
aerosol monitoring study, enhances our understanding of ozone concentrations
in various outdoor and indoor environments and characterizes individual
ozone exposures. During the study, extensive personal measurements and detailed
time-activity information were collected for 23 children, and indoor and
outdoor concentrations were measured at their homes. Additional outdoor
measurements were taken at a stationary monitoring site. Factors affecting
variation of indoor and outdoor ozone concentrations as well as personal
ozone exposures were examined. Multivariate regression models and simple
micro-environmental exposure models were developed to provide a practical
means for estimating personal exposures.
Methods
Ozone concentrations were measured in State College, Pennsylvania, a
college town located approximately 240 km east of Pittsburgh, with a population
of 36,000. Indoor, outdoor, and personal monitoring was performed for 23
children (ages 10-11). All of these children lived in nonsmoking households
in one of six residential regions. Except for two children who lived in
apartment buildings, all the children lived in single-family residences.
Homes were located at altitudes ranging from 200 to 400 m. Two of the participants'
homes had gas stoves and 12 had air conditioners. However, only three of
these homes used air conditioning during the monitoring period. Of all the
sampled homes, 13 homes (including one air-conditioned home) used fans in
the sampling room for cooling, and the others used either air conditioning
(2 homes) or open windows (8 homes).
We also measured outdoor ozone concentrations at a stationary ambient
monitoring (SAM) site, located at the State College National Dry Deposition
Network Scotia Range site, approximately 6 km west of downtown State College.
Measurements from the SAM site served as reference levels for comparisons
to home-site concentrations.
We monitoried ozone from 8 July-27 August 1991. In general, three children
and their homes were monitored each week for up to 6 days (Fig. 1). For
each home, a maximum of six 12-hr indoor daytime, five 12-hr indoor nighttime,
four 24-hr outdoor, one 12-hr daytime outdoor, and six 12-hr personal daytime
samples were collected. For logistical reasons, we collected the 12-hr home
outdoor samples at the end of each monitoring week. In addition, 12-hr daytime
and nighttime outdoor passive samples were taken at the SAM site each day.
Indoor, outdoor, and personal samples were collected simultaneously, with
sampling times beginning or ending at 8 am or 8 pm.
Figure 1. Monitoring
plan and sampling duration. Typically, three children and their homes were
sampled each week, in addition to the SAM Site samples. Ind, indoor.
Passive samplers were clipped on a camera tripod and placed in the main
activity rooms of children's homes, at least 1 m from walls, windows, air
conditioners, and other ventilation devices to avoid excess air flow. Samplers
were located 1.2 m above the floor, so that ozone concentrations were measured
at about the height of a child's breathing zone.
A questionnaire regarding the ventilation conditions of the homes, including
use of air conditioning, hours of windows/doors opened, and percentage and
location of the open windows/doors, was administered at the end of each
sampling day. We also measured air exchange rates in each home using the
perfluorocarbon tracer gas method (24). Results from this analysis
will be discussed in forthcoming papers (Liu et al., in preparation).
On each sampling day, we suspended one passive sampler under a protective
cup, which was attached to a tripod in the front or backyard of each home.
The protective cups were made of opaque white polyvinyl chloride (PVC) pipe.
The cups were used to minimize face-velocity effects on the sampler collection
rate and to protect the samplers from rain (23). We placed samplers
approximately 1.2 m above ground to measure ozone concentrations at about
the level of a child's breathing zone.
The same sampling procedure was performed at the SAM site: one passive
sampler was placed under a PVC protective cup. The passive sampler was co-located
with the manifold inlet of a UV photometric ozone analyzer at a height of
3.5 m.
We clipped personal samplers onto the strap of a backpack worn by each
participant. (The backpack was used to hold a pump and a timer for the concurrent
acid aerosol measurements.) Samplers were placed at chest level to correspond
to breathing height. Participants were asked to wear the backpack throughout
the 12-hr daytime monitoring period.
On each day, participants recorded their activities and the amount of
time spent in different environments. To assure maximum accuracy and compliance,
we aggregated these records into 30-min periods and field technicians transferred
them onto formatted time-activity sheets with the help of the parents and
children at the end of each day. On the formatted time-activity sheet, locations
were divided into four groups: home, near home (within a few blocks of home),
school, and other.
The passive ozone sampler, developed by Koutrakis et al. (23),
consists of a badge clip supporting a barrel-shaped device developed by
Ogawa and Company. The sampler contains two glass-fiber filters coated with
potassium carbonate (K2CO3) and sodium nitrite (NaNO2).
The sampling technique is based on the oxidation reaction of nitrite (NO2-)
by ozone to produce nitrate (NO3-):
NO2- + O3 --> NO3-
+ O2
The amount of nitrate is determined using ion chromatography. The average
ozone concentration is calculated from the measured nitrate concentration
and a previously determined collection rate (25.5 cc/min) (23). The
passive samplers performed well in controlled laboratory tests at typical
ambient ozone levels (40 ppb-100 ppb) under relative humidities and temperatures
varying from 10 to 80% and 0 to 40°C, respectively (23). The
limit of detection (LOD), defined as three times the standard deviation
of the field blanks used in this study, was 18 ppb for 12-hr measurements.
A UV photometric ozone analyzer (Thermo-Electron Co. Model 49) used at
the SAM site is designated as an equivalent method for ambient ozone measurements
by the U.S. Environmental Protection Agency. The LOD of the UV method is
2 ppb, and the precision 2 ppb (25). We calibrated the continuous
analyzer once a week with span checks with zero air, 50, and 400 ppb, and
once a month with 0, 50, 100, 200, and 400 ppb of ozone.
We used SAS (26) software for all data management and statistical
analyses. Data capture rate was 85% of all possible samples. We analyzed
results in five ways. 1) Simple regression analysis and calculation of Pearson's
correlation coefficient for passive and continuous ozone measurements were
performed. The relative error of passive sampler measurements to continuous
measurements was also determined. (For all analyses except for the evaluation
of the passive sampler, we used continuous measurements as the SAM site
ozone concentrations.) 2) two-sample t-tests and one-way analysis
of variance (ANOVA) techniques were used to determine whether outdoor concentrations
varied spatially. 3) Diurnal variation in outdoor and indoor ozone concentrations
was determined using two-sample t-tests, comparing the means of daytime
and nighttime concentrations. 4) The ratio of indoor to outdoor ozone concentration
(I/O ratio) was calculated for each home. The variation in I/O ratios among
homes was further examined using ANOVA techniques. 5) Personal exposures
were compared to indoor and outdoor measurements using paired t-tests
and correlation analyses. We developed personal exposure models using multiple
linear regression analyses and the time-weighted microenvironmental concept.
The p-values associated with the tests of significance of regression
coefficients serve only as a guide because autocorrelation between repeated
measurements on the same subjects over time was not considered.
Results
A summary of results for samples collected is presented in Table 1. We
collected 47 daytime and 50 nighttime outdoor passive samples at the SAM
site. At home sites, we collected 68 outdoor (24 hr), 84 indoor daytime,
65 indoor nighttime, and 81 personal daytime samples. Simultaneous continuous
measurements were also tabulated. For comparison purposes, 24-hr continuous
measurements at the SAM site and estimated 12-hr home outdoor daytime concentrations
are also listed in Table 1.
In addition to ozone measurements, we collected 94 time-activity diaries
from the 23 participants during daytime sampling periods. On average, participants
spent 59 ± 22% of their time inside their homes, 11 ± 12%
of the time inside other microenvironments, and 30 ± 22% of the time
outdoors.
The ozone concentrations measured with the passive samplers at the SAM
site were in excellent agreement with those measured by the co-located continuous
monitor (Fig. 2). The Pearson's correlation coefficient for the passive
and continuous measurements was 0.95 (p<0.01). The relative error
of the passive measurements to the continuous measurements at the SAM site
decreased with increasing ozone concentrations (Table 2). For measurements
below or near the LOD, the relative errors reflect an uncertainty of only
4.5 ppb [i.e., 0.90(5) or 0.30(15) ppb]. In general, the uncertainty of
the passive sampler measurements was well below 10 ppb.
Figure 2. Ozone
concentrations measured by passive samplers and the continuous analyzer
at the stationary ambient monitoring site. Note that the graph is overlaid
by a 45° line.
Outdoor Spatial and Diurnal Variation
Outdoor (24 hr) ozone concentrations measured at home sites were highly
correlated with the SAM site ozone concentrations (r = 0.81, p<0.01).
Despite this agreement, there was a substantial difference in ozone concentrations
between the SAM site and home outdoor sites. The mean outdoor concentration
at the SAM site (37.8 ± 10.7 ppb) was significantly higher than that
for home sites (29.8 ± 14.3) using a two-sample t-test (p<0.01).
The mean ratio of home to SAM site outdoor (24 hr) concentrations was 0.80
± 0.25.
Spatial variation in outdoor concentrations was also observed when homes
were grouped into six residential regions. Region 1, which includes downtown
State College, has the greatest home, population, and traffic density. Regions
2-5 are populated residential areas but less dense compared to region 1.
Region 6 is the least densely populated community. The mean ratio of outdoor
home to SAM site concentration varied significantly by region (Table 3)
using ANOVA techniques (F-value = 3.06, p<0.05). When the
mean ratios were further examined using Tukey's pairwise comparison method
at the the 95% confidence level, the mean ratio of the most rural area,
region 6, was significantly higher than those for the densely populated
regions 1 and 4.
Attitude of the home sites (Table 3) was not correlated with the observed
spatial variation (r = 0.04, p = 0.75). Spatial variation
more likely resulted from differences in home density and traffic. Higher
density of homes may provide greater surface area for ozone depletion, whereas
higher traffic density may increase NO concentrations, which reacts with
ozone (27). This theory is supported by the similarity of the ozone
concentrations between region 6 and the SAM site. Even though these sites
are 13 km apart, their concentrations are comparable with a mean ratio approximately
equal to 1.
Both indoor and outdoor ozone concentrations exhibited a diurnal pattern
(Table 1), with daytime concentrations significantly higher than nighttime
concentrations (p<0.01). The Pearson's correlation coefficient
for home-site indoor and outdoor concentrations was highly significant (r
= 0.56, p<0.01). The similar diurnal patterns in both outdoor
and indoor ozone concentrations and this relatively high correlation strongly
suggest that ozone in homes originates primarily from outdoor sources.
Characterization of Indoor/Outdoor Ratios
The differences between outdoor and indoor concentrations, especially
inside homes, may significantly affect exposures (21,22). Characterization
of the I/O ratio is therefore important. The I/O ratio is a crucial parameter
for determining penetration rate of ozone and for estimating indoor ozone
concentrations when indoor measurements are otherwise unavailable.
We calculated I/O ratios for each home for 12-hr daytime periods. (Nighttime
I/O ratios were not determined because the indoor concentrations were well
below the LOD.) Ratios were determined using the measured 12-hr indoor concentrations
(Ci) divided by the measured 12-hr outdoor home concentrations.
When measured 12-hr outdoor concentrations were not available, we estimated
them using the expression:
(1)
where Co is the estimated 12-hr outdoor concentration
at the home site, Co24 is the measured 24-hr average outdoor
concentrations at the home sites, and Cc and Cc24
are the measured 12-hr and 24-hr average outdoor concentrations at the SAM
site, respectively. The I/O ratios for homes ranged from 0.07 to 1.16, with
a mean of 0.45 ± 0.23 (Table 4). The mean I/O ratios for homes, however,
differed significantly by home when analyzed by ANOVA (F = 3.76,
p<0.01). When excluding observations from air-conditioned homes,
significant differences in mean I/O ratios among homes were still found
(F =3.72, p<0.01).
We examined information on home ventilation conditions to help understand
factors that may affect I/O ratios. Three factors were considered: percentage
of open windows in the house, amount of time the windows were open, and
air conditioner use. During daytime periods, 98% of the windows in the homes
were open for a mean period of 11.5 hr. Three households used air conditioners
for an average period of 0.3 hr. Weak correlations were found for I/O ratios
with the amount of time the windows were open (r = 0.19, p =
0.10). Use of air conditioning was not correlated with I/O ratios (r
= -0.07, p = 0.57), which may be due to the small sample size
for air-conditioned homes. Results suggest that I/O ratios may increase
with greater air flow through the home. In addition, dissimilar housing
materials, such as painted walls, furniture, drapes, and books, may affect
ozone decay and as a result affect I/O ratios (28-31).
Comparison of Ozone Measurements
Daytime individual exposures (or Cp) were correlated
with daytime concentrations measured inside (r = 0.55, p<0.01)
and outside (r = 0.41, p<0.01) the home and at the SAM
site (r = 0.36, p<0.01; Fig. 3). The Cp
values, however, were significantly higher than the corresponding indoor
concentrations (p<0.05) and significantly lower than both home
outdoor (p<0.01) and SAM site outdoor concentrations (p<0.01)
using paired t-tests. The mean ratio of personal measurements to
home indoor measurements was 1.69 ± 3.03, indicating that home indoor
measurements underestimated personal exposures by 41% on average [i.e.,
100%(1-1.69)/1.69]. The mean ratios of personal measurements to the estimated
daytime home outdoor values and the SAM site measurements were 0.59 ±
0.52 and 0.44 ± 0.29, respectively. The ratios imply that outdoor
home measurements would overestimate personal exposures by 69% on average.
This overestimate would be greater (127% on average) if SAM site measurements
were used to approximate personal exposures. Ratios are summarized in Table
5.
Figure 3. Daytime
ozone measurements from the stationary ambient monitoring site continuous
analyzer, home outdoor, home indoor and personal passive samplers. Mean
concentrations from different homes or participants for each day were plotted.
Personal Exposure Models
We developed two types of daytime personal exposure models. Both models
used the measured daytime home indoor, personal, measured/estimated home
outdoor concentrations, and the daytime time-activity information to estimate
exposures. The first type of models were constructed using stepwise linear
regression techniques to determine the relative influences of indoor and
outdoor concentrations and time-activity patterns on personal exposures.
The measured or estimated daytime home outdoor concentrations (Co),
the measured daytime home indoor concentrations (Ci),
the fraction of time spent outdoors (Fo) within the daytime
sampling period, and the interaction terms, Ci(1-Fo)
and Co(Fo), were included as independent
variables in the model. Note that the fraction of time spent indoors (Fi)
was indirectly included in the regression analysis because it is inversely
correlated with the fraction of time spent outdoors (Fi =
1-Fo). Concentrations of all indoor microenvironments
were assumed to equal those measured inside the homes.
The stepwise variable selection technique suggests that indoor ozone
concentrations (Ci) were the most significant predictors
of personal exposures (Table 6, model 1). This is not surprising, given
the strong association between these two variables (r = 0.55). The
other important predictor variable added in the model was the interaction
term Co k Fo (Table 6, model 2), suggesting
that outdoor ozone concentrations were predictive when weighted by the fraction
of time spent outdoors. Model 2 explained 37% of the variability in personal
exposures and had a slightly smaller relative mean standard error than model
1. We tried different variable selection techniques for this analysis, including
forward stepwise and backward elimination procedures based on F statistics
for a variable's contribution to the model. Procedures based on maximizing
the adjusted R2 statistic were also performed. Each of
these procedures leads to the same final model 2.
Because the results from the above statistical models do not have an
intuitive interpretation, we constructed a second type of model based on
the microenvironmental exposure concept (41-43). A simple
prediction of daytime personal exposures (Ce) is the time-weighted
average of the outdoor and indoor exposures:
Estimates were regressed on measured
(2)
personal exposures, and the results are summarized as model 3 (Table
6). The multiple regression model incorporating an intercept term is summarized
as model 3 in Table 6. This model has a similar fit to that of model 2 (R2
= 0.35, relative mean standard error=13.68) and has a nonsignificant
intercept of 4.67 ± 3.70 (p = 0.21).
Because children generally spend time outdoors when ambient ozone concentrations
are highest, the time of day a child is outdoors may be an important determinant
of personal exposures. To incorporate this factor into the time-weighted
model, we divided concentration and activity data into 1-hr intervals. We
estimated hourly outdoor and indoor daytime concentrations using continuous
measurements from the SAM site. Hourly outdoor concentrations (Co,k)
were estimated for each home using the expression:
(3)
where Co24 is the 24-hr outdoor ozone concentration
measured at home sites, Cc24 is the 24-hr outdoor ozone
concentration measured at the SAM site, and Cc,k
is the daytime 1-hr outdoor ozone concentration measured at the SAM site
at hour k. Hourly indoor concentrations (Ci,k)
were estimated in a similar way:
(4)
where Ci/Co is the indoor/outdoor
ratio. Then, the daytime hourly microenvironmental model is as follows:
(5)
where Fo,k is the fraction of time spent outdoors
in the kth hour.
When personal exposures (Ce) estimated by the model
were regressed on measured personal exposures (Cp), the
hourly microenvironmental model (model 4 in Table 6; Fig. 4) explained a
slightly higher percentage of the variability in measured personal exposures
(R2 = 0.40) and had a smaller relative mean standard error
than the 12-hr simple microenvironmental model (model 3).
Figure 4. Predicted personal
exposures using estimated hourly home indoor and home outdoor concentrations
versus measured personal exposures. Shaded dots represent observations from
children who spent at least 95% of the day in or near their homes. Note
that the graph is overlaid by a 45° line.
Further improvements in the predictive power of the hourly microenvironmental
ozone model may be achieved by accounting for the contribution of diverse
outdoor and indoor microenvironments to personal ozone exposures. We anticipated
that the regression model would have the most predictive power for those
days on which individuals spent most of the time indoors and outdoors near
the home for which corresponding exposure measurements were available. Support
for this hypothesis is evidenced by the fact that model 5 predicted exposures
more accurately for participants who spent at least 95% of the day in or
near their homes than for those who did not (Fig. 4). When the fitted analogue
of model 5 (Table 6) only to the 14 observations from participants who stayed
at or near their home for at least 95% of the monitoring period, 76% of
the variability in personal ozone exposures was explained.
Discussion and Conclusions
Results from this pilot study indicate that the traditional method of
using fixed-site measurements to represent individual exposures may not
be appropriate. Our results showed a significant spatial variation in outdoor
ozone concentrations for a small college town, with densely populated regions
having lower ozone concentrations than rural regions. The spatial variation
may be due to differences in density of houses and/or population, traffic
intensity, and availability of NO sources. Ignoring the spatial variation
and using the fixed-site measurements alone to estimate personal exposures
can result in an error as high as 127% . Had the SAM site been located in
one of the residential areas of town, the error in personal exposure estimates
may have been lower.
Indoor/outdoor (I/O) ratios varied by home, with typical mean ratios
ranging from 0.20 to 0.74. Results from simple regression and correlation
analyses suggest that the I/O ratio differences may be due partly to house
ventilation conditions and dissimilar housing materials. Other studies also
have shown that the I/O ratios of other indoor microenvironments vary widely.
For example, Thompson et al. (17) showed that in hospitals, the mean
I/O ratios for total oxidants ranged between 0.5 and 0.67. In office buildings,
mean I/O ratios ranged from 0.3 to 0.8 (17,21,35-37),
while the mean I/O ratios were approximately 0.3 and 0.6 for air-conditioned
and non-air-conditioned school classrooms, respectively. For a large shopping
mall, where outside air was minimal, the mean I/O ratio was approximately
zero (17).
As a result of the variations in outdoor spatial and indoor air, the
ability to predict personal exposures from outdoor and indoor concentrations
was poor (r2 = 0.35), even when time-weighted concentrations
were used (r2 = 0.40). The inability of the simple microenvironmental
model to estimate personal exposures may have resulted from the consideration
of only two microenvironments, indoor home and outdoor home, by the model.
However, when activities were limited to locations in or near the home,
the accuracy of the simple microenvironmental model improved substantially
(r2 = 0.76). It is evident that contributions from diverse
indoor and outdoor microenvironments must be considered to estimate personal
ozone exposures accurately.
To improve our ability to model personal ozone exposures, future studies
should characterize indoor and outdoor concentrations in a variety of indoor
and outdoor microenvironments within the same community. This effort should
examine factors that affect indoor and outdoor ozone concentrations. For
indoor concentrations, these factors may include air exchange rates, housing
materials, gas stove use, home volumes, home interior surface type. For
outdoor concentrations, the effects of NO sources and/or traffic density,
house density, and population density should be investigated. In this regard,
we have continued investigating factors affecting variations in indoor and
outdoor ozone concentrations. In the Canadian Research on Exposure Assessment
and Modeling Project, we measured outdoor ozone concentrations at different
locations in Toronto, Canada, and collected indoor ozone samples in a variety
of indoor environments, such as schools, office buildings, and retail stores.
The results from this study will be presented in forthcoming papers (Liu
et al., in preparation).
