Recent interest has focused on traffic-related air pollution
and the potential health effects associated with exposure (Kunzli
et al. 2000). The acute health effects of short-term exposures
to traffic-related pollution have been widely demonstrated, but
much less is known about the chronic effects of exposure. Several
studies have found associations between chronic morbidity or
mortality and traffic-related pollution (e.g., Brunekreef et
al. 1997; Heinrich and Wichmann 2004; Hoek et al. 2002a; Weiland
et al. 1994; Wjst et al. 1993). On the other hand, a number of
studies have found no detectable effects (Magnus et al. 1998;
Wilkinson et al. 1999). Thus, the extent to which the long-term
exposure to air pollution contributes to chronic health effects
remains unknown. Much of the uncertainty relates to the problems
of potential confounding variables and of reliable estimates
of exposure to traffic-related pollution at the individual or
small-area level, across large populations and cities. To date,
most assessments of the health impacts of long-term exposure
have involved between-city comparisons using a limited number
of monitors within each city. Such between-city comparisons are
subject to exposure misclassification because they rely on a
small number of monitors. A recently conducted study in four
European countries [SAVIAH (Small-Area Variation in Air Pollution
and Health)] found important variations in the concentrations
of nitrogen dioxide and sulfur dioxide on a small scale within
cities (Lebret et al. 2000). Several other studies have documented
important within-city variation of concentration, especially
related to nearness to motorized traffic and location within
the city--for example, center versus suburb (Bernard et
al. 1997; Cyrys et al. 1998; Raaschou-Nielsen et al. 2000).
To overcome these problems, some studies used surrogate variables,
such as distance to major road or traffic intensity (objectively
determined or self-reported) (Brunekreef et al. 1997; van Vliet
et al. 1997; Weiland et al. 1994; Wjst et al. 1993) to account
for within-city variability in exposure. A disadvantage of these
exposure indicators is that they are frequently not validated,
and it may therefore be unclear what the actual exposure contrast
is.
A potential solution to these problems is the use of geographic
information systems (GIS) in which geographic data can be either
used for the development of dispersion models (Bellander et al.
2001; Pershagen et al. 1995) or combined with concentration measurements
to estimate exposures for individual members of large study populations
by regression (stochastic) models (Brauer et al. 2003; Briggs
et al. 1997; Gehring et al. 2002).
So far, epidemiologic studies used either stochastic or dispersion
modeling, but not both in parallel. Only in the international
collaborative study on the risks of development of childhood
asthma and other allergic diseases [TRAPCA (Traffic-Related Air
Pollution on Childhood Asthma) study (Brauer et al. 2002; Gehring
et al. 2002)] were both approaches (stochastic and dispersion
modeling) used in parallel to predict the outdoor exposure to
NO2 and particulate matter (PM) for 1,669 study participants.
For the stochastic modeling, NO2 and particles collected
with an upper 50% cut point of 2.5 µm aerodynamic diameter
(PM2.5) were measured at 40 sites spread over the
city area to estimate the annual average concentrations of these
pollutants. This data set offers the unique opportunity to evaluate
the result of the dispersion and stochastic modeling. The aim
of the study is to compare the measured levels of the two pollutants
with the levels predicted by the two modeling approaches (for
the 40 measurement sites) and to compare the results of the stochastic
and dispersion modeling for all 1,669 study participants.
Study area and study cohort. The study was conducted
in the city of Munich, the capital of Bavaria, situated in the
south of Germany. In 1999 Munich had a population of approximately
1.32 millions inhabitants in an area of 310.4 km2,
and approximately 700,000 cars were registered (Statistic Agency
of the Provincial Capital Munich 2005).
Exposure to traffic-related air pollutants (NO2 and
PM) was modeled for two ongoing birth cohort studies [GINI (German
Infant Nutrition Intervention Programme) and LISA (Influence
of Lifestyle Factors on the Development of the Immune System
and Allergies in East and West Germany)] conducted in Munich.
A total of 1,757 infants--1,084 from the GINI cohort and
673 from the LISA cohort--were selected for this purpose.
These infants were born in Munich (excluding surrounding communities,
postal codes 80000-81999) and remained in Munich at least
for the first year of life. For 1,756 study subjects, birth addresses
could be converted into geographic coordinates. However, because
some children shared the same home address, the final data set
for the present analysis consists of 1,669 different cohort addresses.
Exposure modeling. Because it was not feasible
to measure outdoor exposure for all 1,669 cohort addresses, we
used GIS-based stochastic and dispersion exposure modeling to
predict annual average concentrations for each cohort address.
Stochastic (regression) modeling. For the stochastic
modeling, we conducted a 1-year measurement program for NO2 and
PM2.5 at 40 measurement sites. To capture all of the
variation in air pollution concentrations that might be experienced
by the study subjects, we selected 17 street sites that were
located both at main roads and at side roads, and 23 background
sites. A detailed description of the site selection criteria
is provided elsewhere (Cyrys et al. 2003; Hoek et al. 2002b).
The measurement program was performed from 16 March 1999 to
21 July 2000. At each site, four 14-day measurements were conducted
such that each site was measured in each season once. PM2.5 samples
were collected with Harvard impactors (Marple et al. 1987), and
NO2 concentrations were measured by Palmes tubes
(Palmes et al. 1976). All measurements were conducted according
to a standard operating procedure (SOP) TRAPCA 2.0 (Hoek et al.
2001). A detailed description of the measurement program is provided
elsewhere (Cyrys et al. 2003; Hoek et al. 2002b; Lewne et al.
2004).
For all pollutants, we calculated annual averages as described
by Hoek et al. (2002b). In brief, measurements at the 40 sites
were not performed simultaneously. Therefore, differences among
the sites may have occurred because of temporal variation; because
we intended these measurements to incorporate spatial variability
only, the annual averages were adjusted for the impact of temporal
variability using data from one site where continuous measurements
were made over the entire study period.
In addition, we collected traffic-related variables (e.g.,
traffic intensity and population density) for the 40 measurement
sites and for all cohort addresses using GIS. The annual average
concentrations were then related to a set of predictor variables
obtained from a GIS, using stochastic modeling. The following
GIS variables were collected using GIS ARCVIEW (version 3.2;
ESRI, Redlands, CA, USA): traffic density and heavy vehicles
intensity in three different circular buffers around the measurement
sites (50, 250, and 1,000 m radius), and household density and
population density (300, 1,000, and 5,000 m radius). The relation
between the geographic variables (independent variables) and
the annual average air pollution concentrations (dependent variables)
for the 40 sites was analyzed by multiple linear regression.
The selection of the most relevant spatial scale for the geographic
variables (with the highest adjusted R2) is
described in detail by Brauer et al. (2003).
Table 1 |
The final linear regression models used for the calculation
of cohort exposures are presented in Table 1. These two models
include only variables that were also available for the cohort
addresses and therefore could be used for the calculation of
cohort exposures. Using these developed models, we obtained quantitative
estimates of exposure to outdoor NO
2 and PM
2.5 for
all study subjects.
We evaluated the validity of the regression models by a cross-validation
procedure. This involved fitting the regression model for 39
of the measurement sites to predict the concentration at the
remaining site. This procedure was conducted for each of the
40 sites, and these results were compared with the measured annual
average concentrations determined for each of the sites. The
root mean squared error (RMSE) was calculated as the square root
of the sum of the squared differences of the observed concentration
at site i and the predicted concentration at site i from
a model developed without site i (Hoek et al. 2001). The
RMSE was 1.35 µg/m3 for PM2.5 and
6.12 µg/m3 for NO2; that is, it was
small compared with the range in concentration across sites (11.18-19.69 µg/m3 for
PM2.5 and 15.86-50.64 µg/m3 for
NO2).
Dispersion modeling. We used a Gaussian multisource
dispersion model IMMISnet (IVU Umwelt GmbH, Sexau,
Germany) for the calculation of annual mean values for NO2 and
total suspended particles (TSP; defined as airborne particles
with a diameter < 30 µm) concentrations. The dispersion
models were developed on the basis of GIS data for the addresses
of the 40 measurement sites and for the 1,669 cohort addresses.
IMMISnet is a model for calculating the spatial
extent of concentration levels of air pollution. The model describes
the dilution and transport of pollutants from point, line, and
area sources as a stationary process, using a Gaussian normal
distribution. Gaussian dispersion models are instruments that
have been tried and tested for many years within the framework
of plans for maintaining air quality, or planning permit procedures,
in line with the German Technical Directive on Air Pollution
Control TA-Luft 1986 (TA Luft 1986).
Based on the Gaussian smoke plume equation, the model calculates
concentration contributions from the emissions of the area, line,
or point sources considered. Statistical parameters, such as
the mean value or percentiles of the cumulative frequency, are
calculated for each of the defined receptors from the individual
concentrations determined for all the hours of the year. In addition,
IMMISnet can prepare all the background input
data for microscale street canyon models.
The input values in IMMISnet consist of the emission
data for the sources under consideration, broken down into a
number of polluter groups, and a climatologic frequency distribution
or a time series of meteorologic parameters. The model operates
chronologically; that is, the concentration contributions of
all the data sources considered are calculated for every hour
of the year. The representative meteorologic conditions for any
particular hour are selected randomly from the climatologic distribution
of meteorologic cases in a meteorologic frequency distribution.
The model determines hourly emissions from the annual emissions,
using polluter-group-specific monthly, weekly, and daily
cycles.
The specific emissions data of the different categories of
sources (traffic, industry, domestic fuel) were not available
for the measurement period from March 1999 to July 2000. Thus,
the data for the emissions of the traffic were determined based
on the road network of the city of Munich from 1997 (by the use
of the program IMMISem). Large single emitters such
as industrial plants or power stations were taken out of the
emission inventory for Munich from 1986. Because the emission
inventory contains only emissions data for TSP and not for PM2.5,
the dispersion model estimated TSP levels. The spatial distribution
of domestic heating emissions was obtained from the data for
energy consumption in Munich in 1997 and the data of the building
structure. Therefore, the estimated NO2 and TSP
levels are more valid for 1997 than for the study period (March
1999 through July 2000).
The annual concentrations are calculated for defined coordinates
including a 1.5-m height above ground level. The regional background
level was determined as the difference between the modeled and
the measured NOx and TSP concentrations (as measured
at the network station in Munich Johanneskirchen). The background
concentration was 21.5 µg/m3 for NOx and
33.2 µg/m3 for TSP. The NO2 values
were calculated from the estimated NOx values using
the following formula (Romberg et al. 1996):
To validate the IMMISnet/em model, we compared the
annual means of NO2 and TSP measured in 1997 at the
network stations in Munich (n = 7 for NO2 and n =
6 for TSP) with the estimated NO2 and TSP values.
The comparison showed that the mean difference between the measured
and modeled NO2 concentrations is 3.8 ± 4.8 µg/m3 (7.6 ± 10.2%).
The mean difference between the measured and modeled TSP levels
is -1.6 ± 9.7 µg/m3 (-3.6 ± 18.4%).
The coefficient of variation is 8.1% for NO2 and
12.9% for TSP.
Quality assurance. During each of the approximately
16 measurement periods, a PM2.5 field blank and field
duplicate were collected. The detection limit was 3.4 µg/m3,
and all samples were above the detection limit. The coefficient
of variance was low (3.3%); that is, the precision of PM2.5 was
good.
To answer the question whether the Palmes tube measurements
were not underestimating the true NO2, we compared
the Palmes tube measurements during every 2-week sampling period
with a chemiluminescence monitor (Ecophysics CLD 700 AL; Ecophysics
GmbH, Munich, Germany) at three sites. The Palmes tubes were
located in direct vicinity to the inlet of the chemiluminescence
equipment. There was a high correlation between 2-week average
NO2 concentrations from Palmes tubes and parallel
continuous monitoring measurements (r = 0.94). The overall
ratio of the Palmes tube reading and the corresponding chemiluminescence
value was 1.01. For more details, see Hoek et al. (2002b) and
Lewne et al. (2004).
Statistical methods. The Pearson correlation
coefficients were calculated to describe the associations between
air pollutants concentration derived from the two different sets
of models.
To compare the stochastic and dispersion model, the modeled
concentrations were classified into 3 categories: high, middle,
and low concentrations for the two models separately. Tertiles
were used as cutoff values to ensure equal distribution of the
values between the three categories. Finally, the concordance
of the cohort address classification by the two models was considered.
Generalized additive models were used to investigate the functional
relationship between NO2 and PM concentrations estimated
by stochastic and dispersion modeling, respectively. We computed
LOESS smoothers with pointwise ± 2 SE bands and a span
of 0.4 for the smooth curves with S-Plus (version 6.0; Insightful
Corporation, Seattle, WA, USA).
Comparison of measured air pollution, stochastic-modeled
air pollution, and dispersion-modeled air pollution (for
40 measurements sites). The annual average air pollution
concentrations measured and estimated for the 40 measurement
sites are shown in Table 2. There is a substantial range
in annual average concentrations for NO
2 and for
PM. The ratio of the measured NO
2 concentrations
to the NO
2 levels estimated by the dispersion
model is 0.71. The ratio of the measured PM
2.5 concentrations
to the TSP values estimated by the dispersion model is 0.31.
Figure 1 shows the correlation between the measured concentration
of NO2 and PM and the levels modeled by the stochastic
or dispersion approach. The Pearson correlation coefficient between
the measured and modeled NO2 levels is 0.79 for the
stochastic model and 0.68 for the dispersion model. The Pearson
correlation coefficient between the measured PM2.5 and
modeled PM2.5 is 0.75 (stochastic modeling); between
the measured PM2.5 and modeled TSP, 0.60 (dispersion
modeling).
The relationship between the stochastic and dispersion NO2 values
is shown in Figure 2A. Figure 2B shows the relationship between
the stochastic PM2.5 and dispersion TSP levels. The
regression equation for NO2 differs significantly
from the one for PM2.5:TSP. The intercept of the regression
equation for NO2 is clearly higher than the intercept
of the regression equation for PM2.5:TSP (6.8 vs. -2.0).
The slope of the stochastic versus dispersion NO2 regression
equation is only slightly > 1, whereas the slope of the PM2.5 versus
TSP regression equation is > 3.
Note that, although the correlation between measured NO2 and
PM2.5 concentrations was 0.84, the correlation between
modeled NO2 and PM concentrations was almost
1 for both models (data not shown).
Comparison of stochastic-modeled air pollution and dispersion-modeled
air pollution (for 1,669 cohort addresses). We applied
the regression models described in Table 1 to the 1,669 home
addresses of the cohort, and we applied the dispersion model
to the home addresses of the cohort. A description of the
estimated exposure for the study cohort is presented in Table
3. The mean values estimated for the cohort are very similar
to those for the 40 measurement sites, whereas the range
of the estimated pollutant levels increased for the study
cohort. Apparently, the selection of 40 sampling sites did
not include some of the more extreme traffic conditions encountered
in the cohort. Exactly 18 cohort addresses were estimated
to have higher NO2 or PM values than the
highest measured values in the 40 measurement sites. All
18 addresses are located in the vicinity of the Munich city
circular highway (Mittlerer Ring), with an extremely high
traffic density, so the estimate for these addresses requires
extrapolation.
The relationship between the stochastic and dispersion NO2 values
for the whole study cohort is shown in Figure 3A. The estimated
LOESS smooth curve differs substantially from the linear regression
curve. The relation between the NO2 levels estimated
by means of the two models is nonlinear. However, the correlation
between the stochastic and dispersion NO2 levels
is strong. The Spearman rank-order correlation coefficient (instead
of Pearson correlation coefficient) is 0.86.
Figure 3B shows the relationship between the stochastic PM2.5 and
dispersion TSP levels for all study subjects. For PM the estimated
LOESS smooth curve does not differ substantially from the linear
regression curve. The linear regression equation for all study
subjects [TSP (dispersion) = 2.78 PM2.5 (stochastic)
+ 4.57] is similar to the regression equation found for the 40
measurement sites. The Pearson correlation coefficient (r =
0.79) has the same value as that for the 40 measurement sites.
As previously shown for the 40 measurements, we also found
for the study cohort very strong correlations between the stochastic
estimated levels of NO2 and PM2.5 (r =
0.98) as well as between NO2 and TSP levels estimated
by dispersion modeling (r = 0.99) (data not shown).
Numerous epidemiologic studies do not use individual exposure
estimates for NO2 for study subjects; rather, the
estimates are categorized in several groups, with each group
including a comparable number of subjects. For this reason, we
compare the categorization of the subjects made by means of the
results of both models. Table 4 shows the classification of the
study addresses into three categories (described in “Materials
and Methods”). For 70% of the cohort addresses, the exposure
estimates for NO2 remain in the same category; a change
between the highest and the lowest category is very rare (< 1%).
The changes between the highest and the middle or between the
middle and the lowest category were < 10% for the specific
relationship, but approximately 30% in total. A similar pattern
was observed for PM2.5:TSP (64% agreement). The highest
degree of disagreement is found for the middle-middle category
(45% for NO2 and 53% for PM), whereas the disagreement
in the low-low or high-high category is substantially
lower (between 20 and 30%).
Comparison of measured air pollution, stochastic-modeled
air pollution, and dispersion-modeled air pollution (for
40 measurements sites). The NO2 levels
estimated by the dispersion model are clearly higher than
the concentrations of NO2 at the 40 measurement
sites. For the comparison of the measured PM2.5 with
the modeled TSP levels, the typical PM2.5:TSP
ratio for Munich should be considered. To our knowledge,
there are no simultaneous measurements of PM2.5 and
TSP in Munich available at the present. However, one of our
40 measurement sites (background station where PM2.5 was
measured) was located approximately 2 km from the network
background station in Munich Johanniskirchen (where TSP was
measured). The calculated average PM2.5:TSP ratio
for those two stations is 0.40. The PM2.5(measured):
TSP(modeled) ratio estimated in our study
is lower (0.31), which suggests an overestimation of the
TSP levels by the dispersion model.
This assumption is supported by the consideration of the PM2.5:TSP
ratios observed for other European cities. Gomis´c´ek
et al. (2004) estimated the PM2.5:TSP ratios over
a 1-year period for three urban sites in Austria. The ratios
are 0.45 for Linz, 0.52 for Vienna, and 0.54 for Graz, with negligible
differences between the winter and the summer seasons. Similar
PM2.5:TSP ratios (0.46 ± 0.09 for the summer
and 0.59 ± 0.07 for the winter season) were estimated
for Erfurt, Germany, over a 5-year period from 1996 through 2000
(Heinrich J, personal communication). Lall et al. (2004) estimated
the mean PM2.5:TSP ratios for the United States based
on PM data collected over the last three decades (mean ratio
= 0.30). The PM2.5:TSP ratios show a strong spatial
trend across the United States, with the northeastern and eastern
parts of the country having among the highest fine mass fractions
(PM2.5:TSP between 0.45 and 0.55). The higher PM2.5:TSP
ratios in the eastern United States are consistent with the presence
of stronger sources of fine particulate emissions in the U.S.
east coast, with its high degree of urbanization. In the light
of the findings here, one can assume that the typical PM2.5:TSP
ratios expected for the Central European ambient air quality
situation as well as climatic conditions should be between 0.40
and 0.60.
The overestimation of the NO2 and TSP levels calculated
by the dispersion model could be caused by the use of older emission
data (emission inventory for industrial plants or power stations
from 1986, traffic and house fire emissions from 1997). It can
be assumed that especially the emissions from large single emitters
and domestic heating decreased significantly during the nineties.
However, even if the estimated levels of NO2 and
TSP could be overestimated, the within-city variability in concentrations
across the study participants does not change.
It seems that the difference between the stochastic- and dispersion-modeled
NO2 concentrations is rather constant for all measurement
sites (slope of the regression equation ~ 1), whereas the difference
between the stochastic-modeled PM2.5 levels and
dispersion-modeled TSP values is more site specific and increases
for higher PM concentrations (slope of the regression equation > 3).
The correlations between the values obtained by the measurements
and the stochastic model were somewhat higher than the correlations
between the measured values and the dispersion values. This is
not unexpected, because the stochastic modeling includes the
multiple linear regression analysis based on the 40 measured
values. Notable is the very strong correlation between the exposure
estimates for NO2 and PM2.5 within
the two models. This could be explained by the similarity of
the predictors used for the two pollutants both in the regression
and in the dispersion modeling.
Comparison of stochastic-modeled air pollution and dispersion-modeled
air pollution (for 1,669 cohort addresses). The regression
equation for PM2.5 (stochastic) versus TSP (dispersion)
at the 1,669 cohort addresses is very similar to that observed
for the 40 measurement sites. Because the two models contain
different PM characteristics (PM2.5 or TSP), the
direct comparison of the two models is allowed only if the
spatial variation of TSP is to a large extent driven by the
PM2.5 spatial variation. It means that PM2.5 and
TSP should be strongly correlated over the whole study area.
Unfortunately, we do not have any information about the correlation
between PM2.5 and TSP in Munich. However, as shown
by Cyrys et al. (2003), the Pearson correlation coefficient
estimated on 36 sites across the whole TRAPCA study area
(Munich, Stockholm, and the Netherlands) between PM2.5 and
PM10 is 0.78. The correlation between PM2.5 and
PM10 restricted only to Munich (12 measurement
sites) is stronger (r = 0.95). This strong correlation
between annual averages of PM2.5 and PM10 documents
that a large portion of the spatial variation of PM10 was
caused by PM2.5. Although PM10 is not
TSP, we might assume that TSP is also strongly correlated
to PM2.5 in the urban area of Munich and that
the comparison of both variables (PM2.5 and TSP)
as shown in Figures 2A and 3B has some meaning.
Because of the similar classification of the study subject
generated by the two models, one would expect that the choice
of one model (regression or dispersion) should not affect the
results of the epidemiologic studies. In both cases, similar
results regarding the estimated association between health effects
and traffic-related pollutants are expected. This assumption
is valid only if simple categorization in tertiles is used for
epidemiologic studies. However, epidemiologic studies are also
using more than three exposure categories or even continuous
air pollution data that need to be considered.
In choosing between the two models, other aspects should also
be considered. The dispersion models require input data, specifically
for emissions and background pollution, which may not be readily
available. For this reason, we were able to estimate only the
TSP and not the PM2.5 concentrations by dispersion
modeling. On the other hand, the regression modeling requires
a monitoring program, which may be much more expensive because
of the high equipment and personnel costs.
Despite different assumptions and approaches made by the two
models, the NO2 and PM2.5 values predicted
by stochastic model were strongly correlated with the corresponding
NO2 and TSP concentrations predicted by the dispersion
model. Both models led to similar classifications of the cohort
addresses regarding the exposure to traffic-related air pollution.
Thus, we assume that similar results regarding the estimated
association between health effects and traffic-related pollutants
are expected by use of the two modeling approaches. However,
this assumption is valid only if similar categorization in tertiles
is used for epidemiologic analysis. Further verification of this
conclusion is needed--for example, an epidemiologic analysis
with continuous exposure data and comparison of the findings
coming from the two different approaches (stochastic and dispersion).
Other model aspects should be considered in choosing one specific
model. The regression modeling requires a monitoring program,
which may be very expensive because of high equipment and personnel
costs. On the other hand, the dispersion models require input
data, specifically for emissions and background pollution, which
may not be readily available. For this reason, we were not able
to estimate the PM2.5 concentrations by dispersion
modeling, but only the TSP levels.
Both models have common shortcomings: Because traffic intensity
and household density were the most important predictors for
both pollutants, the correlations between modeled NO2 and
PM2.5 (stochastic model) or between modeled NO2 and
TSP concentrations (dispersion model) were almost 1 for both
modeling methods. This does not allow a sufficient discrimination
of the two pollutants regarding their associations with the health
of the study cohort members.