Using a chemistry transport model to account for the spatial
variability of exposure-concentrations in epidemiologic air
pollution studies
Myrto Valari and Laurent Menut
Laboratoire de Météorologie Dynamique/IPSL, Ecole Polytechnique, Palaiseau,
France
Edouard Chatignoux
Regional Health Observatory of the Paris Ile-de-France Région (ORS)
Corresponding author address:
Myrto Valari
Laboratoire de Météorologie Dynamique,
Ecole Polytechnique, 91128, Palaiseau, FRANCE
E-mail: myrto.valari@lmd.polytechnique.fr
1
ABSTRACT
Environmental epidemiology has, traditionally, used area-aggregated pollutant concentrations
measured at outdoor sites as exposure surrogates in time-series studies of the association
between ambient air pollution and health outcomes. However, the spatial variability of exposure
across the study domain (cities) may contribute to biased estimates of the exposure-response
functions in time-series analyses. We propose an original methodology to compute daily,
population-weighted concentrations of NO2, O3 and PM2.5 as personal exposure surrogates for
use in time-series studies of air pollution short-term health effects. Pollutant concentrations are
modelled with the CHIMERE chemistry-transport model over the city of Paris. A Monte-Carlo
model is set up to create exposure event sequences for a sample population respecting the spatial
distributions of demographic data. Outdoor exposure-concentrations for each individual are
calculated from the time spent at each micro-environment and associated micro-environmental
concentrations. We propose a mixture-distributions model to fit to the time-series of 4-years
modelled data (2001-2004). This model provides a parametric way to account for the spatial
variability of outdoor-exposure over the study domain. We finally used the parametrized
distribution into a log-linear regression model to study short-term associations between NO2, O3
and PM2.5 and mortality. This study gives insight into the benefit of including information on
exposure’s spatial variability in the epidemiologic study. More specifically we show that the
time-series of the parametrized distribution of different pollutants are less correlated one with the
other than the area-aggregated concentrations. This results to less biased exposure-response
functions when co-pollutants are inserted simultaneously in the same regression model and
makes it possible to distinguish the effects of the different species.
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IMPLICATIONS
The methodology developed in this study suggests that spatially-resolved pollutant
concentrations combined with demographic data may be used in a parametric way in an
epidemiological time-series study and provide additional information on the spatial variability of
exposure. The principal advantage of the method is that the time-series of the exposure
surrogates used in the regression against health outcomes are less correlated between copollutants. It is, therefore, possible to estimate statistically significant associations between each
co-pollutant and the health outcome and thus, distinguish the corresponding health effects.
About the Authors
Myrto Valari is a Ph.D. student at the Laboratoire de Météorologie Dynamique / IPSL in
Palaiseau, France.
Laurent Menut is a research scientist at the Laboratoire de Météorologie Dynamique /IPSL /
CNRS in Palaiseau, France.
Edouard Chatignoux works at the Regional Health Observatory of the Paris Ile-de-France
Région, ORS, Paris, France.
INTRODUCTION
There is a long history of epidemiologic research on the relationship between spatial and
temporal variability in ambient concentrations (and exposure-concentrations) and adverse human
health effects (and ecosystems) 1 . The adverse health effects include acute and chronic effects of
both particulate and various gaseous air pollutants on morbidity and mortality 2 . Morbidity
effects of air pollution range from decreased lung function to exacerbation of symptoms of
3
asthma and chronic bronchitis to more serious cardiopulmonary events, such as myocardial
infarctions 3-5. There is, also, a variety of ambient air pollution epidemiology research
approaches. These studies vary by health data types and study design, but most of them make the
common assumption that exposure is homogeneous over the study domain. Under this
assumption pollutant concentrations measured at several monitoring sites are typically
aggregated over the geographical area of the study to a single exposure surrogate (e.g. 6,7 ).
However, these health studies have demonstrated that an accurate assessment of spatial
variations in ambient concentrations (or surrogates of exposures) is a critical research need. Over
urban areas such an assessment is only feasible by means of air-quality modelling. The
monitoring network is not capturing the sharp gradients in exposure due to high concentrations
near, for example, major roadways 8-10. There is, however, increasing evidence of the association
between adverse health effects and exposure to traffic pollution 11,12.
Also, a number of publications have already shown the importance of exposure
prediction errors in the interpretation of time-series epidemiology studies of PM mortality or
morbidity effects 13,14. The spatial variability of human exposure stems not only from the
variability in air pollution but also from human activities. These sources of exposure’s variability
lead to significant differences between the mean of personal exposures over the study population
and the area-aggregated concentration value that is used as exposure surrogate. This exposure
misclassification or exposure prediction errors lead in biased estimation of the exposure-response
function and may result in an attenuation of the ’real’ air pollution health effect 15,16. Thus, health
impact assessment requires concentration data resolved in time and space so that they may be
combined with demographic information and exposure event sequences and to provide more
accurate exposure estimates.
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A different, but related issue, concerns the inability of the time-series method to study the
independent health-effect of multiple, correlated pollutants 17,18. Given that on days of poor air
quality the concentration of all atmospheric contaminants is high, the area-aggregated
concentrations for different pollutants used in time-series studies are strongly correlated one with
the other. In addition, since the exposure surrogates are computed on the basis of discrete
monitor data they are very much influenced from the presence of emission sources in the vicinity
of the instrument. These results in enhanced correlation between species emitted from the same
source-type (e.g. NOx and PM, both emitted from the traffic network). In the presence of such
high correlation the time-series method can not provide independent exposure-response estimates
for each pollutant. The estimation may be biased towards or away from zero in a multiplepollutants regression model depending on both the direction and the magnitude of the correlation
between exposure prediction error and exposure surrogate 19,20. Thus, positive associations may
be found for an ’innocent’ species if it is correlated with another toxic species accounted for in
the same multiple-pollutants model. This kind of bias, in general, leads to over-estimated
exposure-response associations for the pollutants that are better measured (i.e. measured at more
sites). Since atmospheric contaminants are inhaled as complex mixtures, and there is evidence of
synergistic effects among co-pollutants 21, it is essential to be able to estimate both the over-all
health-effect of the mixture but also the independent effect of each species. Once more the use of
spatially-resolved information yielded by a grid-based air-quality model and combined with
demographic information and activity event sequences may help overcome the problem. While
daily variations in different pollutant concentrations may be correlated one with each other, it is
not necessarily the case for exposure-concentrations where other — demographic and activityrelated — parameters interfere in the computation. Spatially-resolved concentration data are also
5
less affected by local sources than discrete monitored concentrations. Thus, the likelihood to find
very strong correlations between the time-series of exposure-concentrations is lower when gridbased modelled data are used instead of measurements.
Integrated exposure modelling systems have been developed during the last decade 22-25.
These models take into account detailed data on human activities, indoor and outdoor sources of
ambient pollutants and provide integrated estimates of the actual burden of contaminants
entering the respiratory tract. In contrast to these studies, we focus more on the development of a
general framework that takes into account the regional-scale spatial variability of exposure in the
evaluation of the exposure-response function through an epidemiologic model and less on the
exposure estimation module. Our goal is, therefore, twofold: first the issue of exposure’s spatial
variability over the urban environment is addressed, next this information is used into a Poisson
regression to study short-term associations between simultaneous exposure to three pollutants
(O3, NO2 and PM2.5) and mortality. We thus study how the incorporation of the new exposure
data-set influences the estimation of the exposure-response functions. The first goal is achieved
by combining concentrations modeled with the CHIMERE chemistry-transport model (CTM) at
a 3×3 km2 horizontal resolution with demographic data for the geographical region centered
around the city of Paris (Ile-de-France). To simulate personal outdoor-exposure-concentrations
(called OEC hereafter to avoid confusion with simple concentration data), we set up a MonteCarlo model (M-C model) that respects the distributions of the demographic data, but has the
additional advantage to follow individuals of a sample population along with their daily intraregional travel from home to work. A three-activity diary was created for each member of the MC simulation defining the time-segments spent by each person at each one of the three different
micro-environments involved in this study 1) at the place of residence, 2) at the place of work
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and 3) near roads (in transit from home to work and back). So we first propose a mixture-model
comprised of three normal distributions to account for the spatial variability of human exposure
over the region of Paris. Then we fitted successfully this model to 4-years of OEC data and
parametrized the spatial distribution of exposure accordingly.
The second goal of the study is to evaluate the benefit of adding the information on
exposure’s spatial variability into an epidemiologic model. For this reason we introduced the
time-series of the parametrized exposure distribution into a log-linear regression model. Finally,
we compare the exposure-response functions evaluated by regressing on mortality data 1) the
OEC distribution time-series calculated with the presented methodology and 2) the areaaggregated monitored concentration data as it is done in previous epidemiologic studies over the
same region and population 26,27. The document is organized beginning with a brief description
of different data and models used for the quantification of the exposure surrogate, followed by a
parametrization of the distribution of exposure among the population and completed by inserting
the results in an epidemiologic model studying the short-term associations between air pollution
and mortality. The outcomes of the study are finally summarized and discussed in the
Conclusions section.
METHODS AND DATA
Air Quality Modelling
On this study we focus on the greater Paris region (1.25 - 3.58ºE, 47.89 - 49.45ºN) with a
population of about 11,700,000 people from which more than 2,000,000 live in the city of Paris.
The area is situated away from the coast and is characterized by a uniform and low topography.
7
These characteristics make pollution events more sensitive to boundary conditions, than to local
meteorology 28. Under anticyclonic conditions, polluted air from north Europe enters the study
domain. Enhanced photochemical activity is favored by sunlight, leading to persisting ozone
plumes, especially in summertime 29. Ambient O3, NO2 and fine particulate matter (PM2.5)
concentrations are estimated with the Eulerian multi-scale CTM model CHIMERE
(http://www.lmd.polytechnique.fr/chimere/) from January 1, 2001 to December 31, 2004. The
modelling domain covers the European continental-scale area with nesting options for several
urban areas (see 30,31 for more details). In this study we only use concentrations modelled at the
nested domain over the Paris region, while the continental-scale version is used to provide
boundary conditions for several long-lived species (O3, NOy species, VOCs, CO peroxides etc.).
The model has been thoroughly evaluated for this region on a long-term basis with routine airquality network measurements (O3, NO, NO2, PM) and with observations from the ESQUIF
campaign 30-33 . The nested model domain (160×130 km2) is split into 53×43 grid-cells, centered
around the city of Paris, with 3 km horizontal resolution (Figure 1, left). On the vertical the
domain is divided into 10 layers from the ground to 500 hPa.
Chemical boundary conditions for the coarse resolution simulation are driven by
GOCART model monthly climatologies for aerosol species 34 and by the LMDz-INCA global
chemical weather forecast system for gas-phase species 35-37.
Dynamical calculations were provided by the non-hydrostatic WRF-Version 3 model
(http://www.wrf-model.org). The model is run at three-level two-way nested grids at horizontal
resolutions of 45, 15 and 5 km, with 31 vertical layers from surface (i.e. 900 hPa) to 100 hPa.
Initial and lateral boundary conditions for the meteorological simulations were obtained from 6hour analyses from the NCEP Global Final (FNL) at a resolution of 1º. The WRF model uses the
8
Yonsei University (YSU) Planetary Boundary Layer (PBL) scheme 38 and the Noah Land
Surface model scheme 39 with soil temperature and moisture in four layers, fractional snow cover
and frozen soil physics that provide heat and moisture fluxes for the PBL.
Anthropogenic emissions are provided by the 1 km regional inventory developed by
AIRPARIF (http://www.airparif.asso.fr) for the ESMERALDA project (http://www.esmeraldaweb.fr). The elaboration of the inventory follows the European conventions on annual data-sets
of surface emissions based on a bottom-up approach for air-quality forecast. It considers NOx,
speciated volatile organic compounds, SO2, CO, PM10 and PM2.5 from fixed industrial sources,
mobile sources and biogenic sources.
The 3 km resolution of the CTM is considered as insufficient to represent concentration
gradients observed close to emission sources 40,41. Thus, the computation of source-specific
concentrations is essential for human exposure assessment. A special emission treatment is
implemented to the model to estimate source-specific concentrations, while the CTM provides
grid-averaged pollutant concentrations. The grid-cells are divided into four types of microenvironment: 1) traffic, 2) residential, 3) parks and 4) all other sources. Over each microenvironment only emissions of the corresponding sources are considered. The CTM simulation is
split at each model time-step into separate computations over each one of these four surfaces
yielding source-specific concentration components. The advantage of this implementation is that
in addition to the usual grid-averaged concentration provided by the CTM we obtain a
complementary information concerning its variability due to heterogeneous surface emissions
over the unresolved sub-grid scale.
Demographic data
9
Agglomerations include areas more or less densely populated, with people moving between them
during the day and being exposed to atmospheric mixtures of different chemical compositions.
Primary pollutant concentrations (e.g. NOx and PM) are high at urban centers and decrease with
distance from the sources, while secondary pollutants, like ozone, may be more abundant over
rural areas than close to the sources of its precursors. A realistic measure of the mean population
exposure at regional scale should, therefore account for the intra-urban variability of both
pollutant concentrations and population dynamics. Here, we use three types of population
distributions according to: 1) the place of residence, 2) the place of work, 3) incoming and
outgoing fluxes from one area to the other. These data were provided by the French National
Institute for Statistics and Economic Studies (INSEE, http://www.insee.fr/en/) for the eight
counties of the Paris region (Table 1).
Based on these data and applying a population density criterion we will adopt, hereafter,
a geographical division of the Paris region into three ‘sub-areas’: 1) the densely populated urban
center (city of Paris, county No 75), 2) the peri-urban area characterized by medium population
density (counties No 92, 93 and 94) and 3) the remote rural area of low population density
(counties No 77, 78, 91 and 95) (Figure 1, right). As shown in Table 1, the largest part of the
daily intra-regional travel focuses on the urban and peri-urban areas. A large amount of people
living in the peri-urban area work in the urban center and vice-versa. The movement between
rural and urban areas is significantly lower but not negligible. In general, most of the rural area’s
working population also lives in the same area. The non-working population fraction is similar
for the eight counties (~33%).
Exposure Event Sequences
10
We used these demographic data to create a three-activity diary for the parisian population. We
assumed that people are exposed to air pollution at three different micro-environments: 1) the
place of residence, 2) the place of work (for the active part of the population) and 3) near roads
during transit from home to work and back. Ambient pollutant concentrations at all three of these
micro-environments refer to outdoor concentration levels and they are calculated with the
CHIMERE model as described in the previous section. A sample population comprised of
100,000 people was created and we assigned at each individual of this ensemble a time-segment
of exposure to each one of the three micro-environments. The resolution of the demographic data
is lower than the CTM resolution. Thus, in order to exploit the spatial variability of the
concentration field yielded by the air-quality model we set up a Monte-Carlo model. The M-C
model also assigns to each member of the sample population different model grid-cells for its
residence and its place of work. The process of the assignment of time-segments of exposure to
the three micro-environmnets and of the air-quality model grid-cells for the place of residence
and work of each member of the M-C model simulation respects the initial distributions imposed
by the demographic data.
For the construction of the M-C model data-base we chose different time-segments of
exposure depending on the place of residence and activity of each person. For a typical 24-hour
day we consider that the first type of exposure occurs at the place of residence from 1:00 AM to
8:00 AM and from 7:00+δt PM to midnight. From 8:00+δt AM to 7:00 PM people are
considered to be exposed to air pollution at the place of work (for the active part of the
population). δt is a time-interval corresponding to the travel between home and working place
and it is a function of the distance between these two areas: 90 minutes for transit between the
rural and the urban areas, 60 minutes for transit between the rural and peri-urban areas or the
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peri-urban and urban areas, 30 minutes for travel inside the same area. During the time-interval,
δt, people are considered to be exposed to near-roads concentration levels calculated with the
CTM and the special emission treatment described in the ‘Air Quality Modelling’ section. For
the inactive part of the population a 30 minutes time-interval is assigned for exposure to near
roads pollution (δt) considering transportation for various reasons (shopping etc.).
Micro-environmental concentrations are calculated for each one of the three microenvironments with the CTM and then, for each member of the M-C model simulation, they are
weighted with the corresponding exposure time-intervals of the three-activity diary 42-44.
Considering the three-activity diary used here, the general expression of exposure is given by
(1)
with OEC the daily averaged personal exposure-concentrations, cres(t), cwrk(t) and ctrf (t)
modelled concentrations at the corresponding grid-cell on hour t and tres, twrk and ttrf the temporal
intervals of exposure at home, work and traffic (near roads) respectively.
Given the small number of degrees of freedom of the problem (i.e., place of residence,
place of work, and working activity) the distribution of the final output data set converges at a
relatively low population of 100,000 members (~1% of the population of Paris region) Figure 2.
SPATIAL VARIABILITY OF OUTDOOR EXPOSURECONCENTRATIONS
Distribution of Exposure
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4-years of OEC data (2001-2004)
are estimated with the described
methodology. The time-series of these estimates for O3, NO2 and PM2.5 and for all the members
of the M-C model simulation are shown in the 2-D histograms of Figure 3. The distribution of
OEC data among the population presents a trimodal structure through out the simulation period
for all three pollutants. These three modes correspond to:
• a high exposure mode (HEM) more clearly distinguishable for O3 and NO2 than for PM2.5.
HEM is particularly highly populated in summertime for ozone.
• a medium exposure mode (MEM) covering a larger part of the OEC spectrum for O3 and NO2
than the other two modes (high and low). This mode often overlaps with the HEM for PM2.5.
• a low exposure mode (LEM) which is more narrow than the MEM but highly populated,
especially for NO2.
The presence of three well distinct exposure modes shows that the distribution of
exposure is not uniform among the population. The LEM and HEM may deviate from the MEM
by 75%. This remark stresses the need to represent this variability into a health-impact
epidemiologic model. The trimodal structure of the OEC distribution raises the question of
whether it is possible to associate each exposure mode to specific groups of the population. To
answer to this question we averaged the daily OEC estimates of all the inhabitants of the same
air-quality grid-cell in order to obtain the spatial distribution of modelled exposureconcentrations. Two examples of this representation of the OEC data are given by the surface
maps of a winter-day (15 February 2004) and of a summer-day (7 July 2004) (Figure 4). On both
examples the spatial distribution of exposure-concentrations presents also a trimodal pattern
which follows the location of the three aforementioned geographical ‘sub-areas’, namely the
13
urban, peri-urban and rural areas of the study-domain. For ozone a low exposure mode
corresponds to the urban center (Paris). A medium exposure mode relates to the peri-urban part
of the region while the inhabitants of the rural area are exposed to high ozone concentrations due
to photo-oxydant formation. For primary species like NO2 and PM2.5 the correspondence
between exposure modes and geographical ‘sub-area’ is directly linked to the spatial distribution
of the emission sources over the Paris region. The highest exposure occurs at the urban center
mainly due to traffic emissions.
The hypothesis that the three exposure modes observed in the distribution of the OEC
estimates (LEM, MEM and HEM) correspond to the inhabitants of each one of the urban, periurban and rural areas of the domain needs to be validated in a more robust way over the whole
data-set of modelled exposure-concentrations. Given the quasi-constant presence of the three
exposure modes in the distribution of the OEC data among the population we propose a mixturedistribution model to fit to the 4-years of modelled exposure-concentrations. The representation
of population exposure via a parametric distribution is not a new concept (e.g. 45,46). However,
both of the mentioned studies suggest a single, log-normal distribution to fit to their data. Given
the trimodal structure of the exposure distribution, we propose a mixture-model comprised of
three normal distributions for the parametrization:
(2)
with x the exposure-concentrations (μg=m3), μi and σi the mean and variance of each distribution
and fi the relative weight coefficient of each distribution in the composite formula (RWC
hereafter). RWCs represent the fractions of the population exposed to each mode of the
distribution.
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Parametrization
The three sets of mean, variance and RWC are treated as unknown parameters for the non-linear
fit. The result of the parametrization is a 4-years time-series for each one of the nine parameters
(three sets of three parameters). Figure 5 shows the result of the fit to the OEC distribution for
two days — the same days chosen for the representation of OEC spatial distribution through
surface maps (Figure 4). For both cases and for all three pollutants the mixture-model is able to
represent correctly the distribution of the OEC data (histograms). We note here, that the ranges
of each mode of the composite
distribution of Figure 5 match with
the ranges of the three exposure modes identified on the surface maps of Figure 4 — for all three
pollutants and both days. On days where the trimodal model was found inappropriate to fit to the
distribution we attempt to fit either a bi or a mono-normal distribution model over the OEC dataset. The former case occurred ~5% of the time while the latter ~1% for the three pollutants.
Given the low number of cases and in order to avoid discontinuities in the time-series of the
parameters of the distributions we did not remove the ‘vanishing’ modes. We calculated mean
RWC ratios from the rest of the parametrization and used them to share the data of the bi or
mono-normal distributions in three Gaussian parts. In the bimodal case we considered that the
distribution with the largest variance corresponds to the MEM and then we split it into two
distributions with the same means and variances respecting the fixed RWC ratio. In the mononormal distribution fit we shared all data in three distributions with the same means and
variances according to the mean RWC ratios.
The time-series of the means of the three normal distributions fitted to each mode of the
OEC data distribution are shown in Figure 6. The time-series were smoothed with a seven-days
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moving average in order to filter out short-term fluctuations and to focus on the same time-scale
as the epidemiologic model used to study short-term associations between air pollution and
health outcomes (i.e weekly variations). The three modes are clearly distinguishable through out
the period of the study for all three species. The means of the three exposure modes for ozone
follow a seasonal cycle with higher exposures during summer than during winter. The seasonal
trend is much less pronounced for both NO2 and PM2.5. For all three pollutants the means of the
HEM and LEM deviate from the MEM by more than 50%, showing once more the need to take
into account the heterogeneity of exposure among the population in health studies.
RWC (fi) corresponds to the fraction of the population exposed to the ith mode. Figure 7
shows the evolution of these three coefficients through time. The time-series of RWCs were
decomposed in different time-scales with a wavelet decomposition. Then the signal was
reconstructed after filtering out the time-scales with periods shorter than six months. Only interseasonal fluctuations are, therefore, represented in Figure 7, which allows us to compare the
relative weight of the three exposure modes over long time-scales. For all three pollutants more
than 60% of the population is exposed to the MEM all year long. For NO2 and PM2.5 the fraction
of the population exposed to the LEM and MEM. This is the sign of people moving between
these two exposure modes as a function of time. The lowest RWCs are observed for the HEM for
NO2 and PM2.5 with values ranging around 10%. As far as exposure to ozone is concerned we
observe an intra-annual movement of people between the HEM and MEM, with the
corresponding RWC values ranging around 20% and 70% respectively. In summertime the
relative importance of HEM increases compared to the lower RWC observed for this ozone
exposure mode during winter.
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We can now use the parametrization of the three exposure modes to validate the
hypothesis that each mode corresponds to a specific geographical ‘sub-area’ of the studydomain, and therefore, to a specific group of the population. The OEC data were distributed back
to each exposure mode for the whole period of the study (after ruling out the bi and mononormal distribution cases) and we quantified the percentages of the inhabitants of the urban, periurban and rural areas over the total number of persons exposed to each one of the three modes
(Figure 8). The composition of the populations exposed to the LEM and HEM is dominated by
the inhabitants of a specific ‘sub-area’ (urban or rural). These two groups of the population
correspond probably to the people that were considered to stay in the same ‘sub-area’ during the
whole day. These people are, therefore, exposed all day long to high NO2 and PM2.5
concentration levels (urban population) or high ozone concentrations (rural population). More
than 50% of the people exposed to the MEM are residents of the peri-urban area. Peri-urban
areas, located downwind of the high VOC and NOx emissions of the city of Paris, are
characterized by medium NO2 and O3 levels due to photo-oxidant ozone build-up, which
transforms part of the emitted NO2 to O3. The MEM exposure mode is also the most
heterogeneous one in terms of population, with non-negligible population fractions
corresponding to the inhabitants of the urban and rural areas. This heterogeneity may be due to
the people that move between the urban and rural areas resulting to the mixing of the
corresponding high and low concentration levels in the mean population, daily-averaged
exposure burden. This remark shows the importance of taking into account the human activities
in the estimation of exposure. If the concentration was only combined with demographic data —
without taking into account the intra-regional travel of the population — we would not be able to
represent the heterogeneity of the MEM. To test the validity of this argument, we quantified the
17
distribution of a different OEC data-set, which was computed without using the activity-diary but
only considering the density of the population. The M-C model was run for the same number of
members but with the only a-priori condition to respect the distribution of the population
according to the place of residence. In this case outdoor exposure-concentration for each member
of the sample population corresponds to the daily-averaged concentration at the place of
residence. The comparison of the distributions of the OEC data computed with and without the
activity-diary shows clearly that the trimodal structure of the exposure-concentration distribution
among the population is the result of having considered that people move between different areas
inside the regional study-domain (Figure 9).
We note here, that for the quantification of the ‘geographical composition’ of the three
modes we divided the members of the M-C model simulation into the three groups of population
(urban, peri-urban and rural) according to their place of residence. The same procedure was
followed using the place of work for the definition of the three groups without changing the
overall distribution of the population in the three exposure modes.
EPIDEMIOLOGIC MODEL
Time-series method
The majority of studies on short-term associations between air pollution and mortality estimate
exposure-response functions by regressing area-aggregated daily mortality counts y = (y1, ...,
yn)n×1 against air pollution concentrations and a vector of q covariates, Z = (z1T, ... znT) 47 .
These covariates typically include smooth functions of calendar time and meteorological
conditions, which model
unmeasured risk factors that induce long-trends, seasonal variation, over-dispersion and temporal
18
correlation into the mortality data. Ambient pollution concentrations are measured by an airquality network at k fixed site monitors. Under the assumption of uniform exposure among the
study population the area-aggregated, daily-averaged concentration, xt = (1/k) ∑kj=1 xjt is
assumed to be strongly correlated to the mean of personal exposures over the study-domain. This
exposure surrogate is related to mortality counts using Poisson linear or additive models. A
typical implementation of the former is given by
(3)
where we assume a Poisson posterior for the distribution of mortality counts (yt) on day t with
mean (Yt) the value of mortality modelled from the air pollution (xt-l) and covariate (zTt-n) data on
day t and the unknown intercept (β0) and regression coefficients (α and γ). The exposure
surrogate (xt-l) and covariates zTt-n are introduced into the model at lags l and n with respect to
mortality.
In this epidemiologic model the association between the exposure surrogate (at lag l) and
mortality is expressed by γ, often referred to as log relative rate of mortality associated to a 10unit increase in xt-l. Thus, here, we will use this epidemiologic model at the same configuration
as in two previous health studies carried out over the region of Paris as part of the PSAS and
APHEA projects ( 26,27 respectively) but we will replace the monitor-based exposure surrogate
with the parametrized OEC distribution computed in the present study. More specifically we will
compare γ estimates for NO2, PM2.5 and O3 in order to study the potential benefit of having used
additional information on the spatial variability of concentration data and on population density
and activities.
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Model Set Up
The whole setting is based on General Additive Models (GAM) that gives the possibility of non
or semi-parametric relationships between the dependent and independent variables 47. The set of
covariates used for the present analysis are the same as in the reference studies (PSAS and
APHEA). These covariates are minimum and maximum temperature with lags 0 and 1
respectively, influenza and dummy variables such as week-days and holidays. Penalized splines
were used as smoothers for the non-parametric regression of temperature and influenza on
mortality data. This rules out long-terms, seasonal variations and the autocorrelation in model
residuals. Exposure surrogates are introduced into the model as linear terms at lag fixed to 1 (the
mean value of the exposure surrogate on the current day and the day before is calculated).
Because of ozone’s marked seasonal cycle — with low concentrations during winter and high
concentrations during summer—and the fact that the relationship between ozone and mortality is
known to be non-linear (no effect has been observed for low ozone levels), we fit separately on
mortality data the two parts of ozone time-series corresponding to the winter period (October to
March) and the summer period (April to September). While the OEC data computed in our study
include the whole Paris region, the two studies we are using as point of reference (PSAS and
APHEA projects) focused their analyses only on the urban and peri-urban population. Ruling out
the rural area from the study domain increases the spatial homogeneity of the air pollution and
population data and consequently the statistical significance of the results yielded by the
epidemiologic model. Since we are interested in comparing our results with these studies we
modified the RWCs of the three modes in order to rule out the rural population. The period of the
study is from 01/01/2001 to 31/12/2004. The exposure surrogates that are finally introduced in
20
the epidemiologic model are calculated in three different ways and the corresponding γ estimates
are compared:
• MONITOR-model: (point of reference): The area-aggregated, monitor-based exposure
surrogate used in previous studies (PSAS and APHEA projects). This indicator aggregates
concentrations measured at background stations over the study-domain. Daily averaged
concentrations are used for NO2 and PM2.5. For ozone, we calculate the maximum of an 8-hour
moving average. This model serves as point of reference for the
comparison with the following modified versions.
• MEAN-model: The mean value of the trimodal OEC distribution given by eq 4. In order to
exclude the rural population from the composite distribution (and thus be able to compare
model results with the MONITOR-model), we kept only the urban and peri-urban population
fractions of each mode. Modified RWCs (fi´) are calculated for each pollutant, respecting the
composition of each mode’s population as it has been quantified in the ‘Parametrization’
section (see also Figure 8).
(4)
•
• with fi´ the modified RWC and μi the mean of the ith mode of the distribution.
• PARAM-model: The total three-normal-distribution model, where the rural part of the
population is excluded.
The implementation of the log-linear
regressions in the MONITOR and MEAN-models is given by:
(5)
21
In the case of the PARAM-model we also include higher order moments of the trimodal
distribution (i.e. variance) which leads to an extended version of the 1-Gauss distribution model
given by 48 which applies to the 3-Gauss mixture-model:
(6)
with μi, σi and f´i the mean, variance and RWC of the ith mode respectively.
Table 2 gives a quantitative comparison between the monitor-based and CTM-based
exposure surrogates used in the MONITOR and MEAN-models. For all three pollutants the
monitor-based exposure surrogates are higher than the CTM-based ones by 30, 22 and 33% for
NO2, PM2.5 and O3 respectively. This is due to the fact that the exposure surrogate in the MEANmodel is a population-weighted concentration and, on the other hand, because of differences
between modelled and measured concentration data. The mean and the median values are close
in all cases, which suggests that the values are symmetrically distributed. Comparing the low and
high percentiles of NO2 exposure distribution between the MEAN and PARAM-models we find
similar differences at low and high values (25th and 75th percentiles). For PM2.5 the differences
between the two models are more marked at low values (25th percentile), while for ozone they
are higher at high values (75th percentile) — probably due to an overestimation of ozone titration
by the CTM. Table 3 gives correlation coefficients between 1) monitor and model-based
exposure surrogates for the same pollutant (model vs. measurements) and 2) different pollutants
calculated with the same expression for the exposure surrogate (measurements vs. measurements
and model vs. model). The former comparison gives very similar results with previous model-to
measurements validation studies for the CHIMERE model (e.g. 49,50 ). The interest of studying
the correlation between co-pollutants lies on the interpretation of the regression coefficients and
thus the associations between exposure and mortality. Strong correlations among co-pollutants
22
prevent time-series health studies from distinguishing the health-effect corresponding to each
pollutant of the mixture. In the case of the three pollutants involved in the present study, namely
NO2, PM2.5 and O3, the strongest correlation is observed between the time-series of NO2 and
PM2.5 exposure surrogates. At the urban environment this correlation is explained because both
pollutants are released at large amounts from the traffic network. We note that the correlation is
stronger for the monitor-based exposure surrogate than for the model-based one. This is due to
the fact that the former is based on monitor data at a single measurement site inside the city. On
the contrary the exposure surrogate computed in the present study is based on grid-based model
simulation where concentration data at 81 grid-cells are provided over the same area. The joint
effect of having taken into account the spatial variability of pollutant concentrations and
weighting concentrations with population density and human activities for the estimation of OEC
data leads to exposure surrogates that are less correlated one with each other.
Comparison between Exposure-response Functions Evaluated by the Different
Epidemiological Models
We use two different model designs to estimate γ coefficients: 1) a mono-pollutant model, where
the time-series of each pollutant is introduced into the model separately and 2) a three-pollutants
model, where the time-series of NO2, PM2.5 and O3 are all three introduced into the model
simultaneously. This latter case, apart from being the most realistic one since pollutants are
inhaled as complex mixtures, also allows us to study the separate effect of each pollutant on
mortality. When the regression on mortality data is done separately for each species it is
impossible to know at what extent the estimated regression coefficient (log relative rate of
mortality, RR hereafter) corresponds to the specific species or to another correlated pollutant.
23
Table 4 gives the regression coefficients for the 3 mono-pollutant models (MONITOR, MEAN
and PARAM-models). These coefficients are expressed as % excess in mortality associated with
a 10 μg/m3 increase in the level of the corresponding exposure surrogate (%RR). The %RRs
yielded from the reference studies show a 1.2, 1.4 and 0.6 % increase in mortality due to a 10
μg/m3 increase in PM2.5, NO2 and O3 (only summer period) exposure respectively. All three
%RRs are smaller when they are estimated with the parametrization of the OEC data. The largest
difference between the results of our estimations and the studies of reference is observed for the
%RR corresponding to the exposure to NO2. This difference is not surprising given that the
correlation between PM2.5 and NO2 is lower in our exposure surrogates. The non-null regression
coefficient for NO2 is generally attributed to its strong correlation with other hazardous species
(e.g. PM) and not to its own toxicity. No laboratory experiment or cohort study has put in
evidence any toxic effect related to NO2 exposure up to now 51.
In the theoretical case where exposures to all pollutants were predicted with the same
level of accuracy a multivariate regression of all co-pollutants against a health outcome would
allow us to jointly estimate the relationship between each pollutant (dependent variable) and the
health outcome (independent variable). We would then be able to compare the relative healtheffect of co-pollutants. However this is not the case and, for various reasons, errors in exposure
prediction are larger for certain pollutants than for others. For example in the case of the PSAS
and APHEA projects PM2.5 is measured at only one site over the study-area whereas 15 and 12
measurement sites are available for NO2 and O3 respectively. It has been shown that in presence
of exposure prediction errors, the regression coefficients will overestimate the association
between mortality and the pollutant which is ‘better’ measured even if another correlated copollutant is more hazardous 19,20,14. This model behavior is clearly shown in the MONITOR-
24
model when all three pollutants are introduced simultaneously in the three-pollutants regression
(Table 5). NO2 absorbs all the effect that should be, theoretically, attributed to PM2.5 because it
has a largest spatial monitoring coverage over the study-domain. Thus, a negative regression
coefficient is estimated for PM2.5, suggesting negative correlation between PM2.5 concentration
and mortality. On the contrary, the MEAN and PARAM-models do not seem to suffer from the
same bias. All coefficients are positive and the relative %RRs are more relevant to previous
laboratory experiments and cohort studies concerning the toxicity of those three pollutants. Even
if there exists no straightforward criterion to evaluate the relative ‘quality’ of the different fits we
note that R2 values and the total explained deviance are similar for the different statistical models
fitted to the mortality data (~0.44 and 45% respectively). For all three pollutants, the areaaggregated, monitor-based exposure surrogates used in the mono-pollutant MONITOR-model
have t-statistics closer to the 5% significance level than the OEC-based exposure surrogates of
the MEAN and PARAM-models. However, in the three-pollutants case, only NO2 comes out
with statistically significant t-scores for the MONITOR-model, whereas, all three pollutants
yield statistically significant estimates with the MEAN and PARAM-models. The advantage of
the multi-pollutants model lies on its ability to provide relevant regression coefficients for all
three pollutants and therefore making it possible to study separately the corresponding healtheffects.
We note that %RR estimates are identical for the MEAN and PARAM-models. This
means that adding the variances of the three modes of the exposure distribution into the
computation of the exposure surrogate does not have any impact on the regression estimates.
This is due to the fact that γ coefficients are very small and thus, the second order term of eq 6
(variance term) is insignificant compared to the first order term (mean term).
25
CONCLUSIONS
The principal motivation behind this work is to define a general framework within which
pollutant concentrations modelled with an air quality model may be introduced into an
epidemiologic model in order to provide reliable information on the independent health-effect of
contaminants present in the same atmospheric mixture. The advantage of using spatially-resolved
concentration fields instead of discrete monitoring data is that a significantly larger amount of
information is used for the regression of the exposure surrogate against health outcomes. In
addition, spatially-resolved information can be easily combined with demographic data and
population activities to provide more realistic exposure estimates compared to plain monitoring
data.
Four-years concentrations of O3, NO2 and PM2.5 were modelled with the CHIMERE
chemistry transport model over the greater Paris region. A Monte-Carlo model was set up to
combine demographic information on the density of the population over 8 counties of the studydomain with the daily travel of the population between three ‘sub-areas’ of the region of Paris in
order to create a three-activity diary for the study-population. A simple exposure model was
developed to compute personal outdoor exposure-concentrations. Three discrete modes were
identified in the distribution of outdoor exposure-concentrations among the population
highlighting the fact that exposure to atmospheric contaminants at the regional scale is not
uniform.
A three normal distributions model is used for the parametrization of the distribution of
exposure among the population. We quantified the geographical composition of the population
of the three exposure modes by mapping them on the urban, peri-urban and rural areas of the
domain. Each population group is exposed to atmospheric mixtures of different chemical
26
compositions. The parametrization provides time-series of the means, variances and population
fractions of each distribution mode. We note here that having taken into account exposure
indoors would have probably led to a different model parametrization but it is rather unlikely that
it would have modified the trimodal form of the distribution, and consequently the structure of
the mixture-model. This remark is especially valid for pollutants such as ozone, black carbon and
some VOC species with no significant indoor sources 52,53 but it is less justified in the case of
species for which indoor sources determine personal exposures (e.g. PM2.5) 54.
The time-series of these parameters were used to inform an epidemiologic model for the
evaluation of relative rates of mortality. We compared RRs estimated with previous
epidemiologic studies over the same area as for their ability to distinguish the health-effects
corresponding to three co-pollutants in a three-pollutants Poisson regression model. The use of
spatially-resolved pollutant concentrations combined with population dynamics instead of
discrete monitoring data leads to time-series of the exposure surrogates that are less correlated
between co-pollutants. This allows the estimation of positive and realistic associations between
all three pollutants involved into the multiple regression model against mortality counts for the
region of Paris.
In contrast to other exposure models 52,55 our study here does not aim at the modelling of
the most realistic personal exposure. Before developing a detailed exposure module we were
interested in studying the specific needs of the state-of-the-art epidemiologic method in modeling
exposure more accurately. Even though the modelling of personal exposures here is based on
simple input data and rather rough assumptions (for example we do not consider indoorexposure), the developed methodology is general and adapts successfully the new features (i.e.
the spatial variability of exposure) in a time-series health study. In this context, we demonstrated
27
a clear advantage of using spatially-resolved pollutant concentrations rather than area-aggregated
monitoring data for the evaluation of air pollution health-effects. First of all the use of additional
information on population activities and the spatial dispersion of pollutants reduces the
difference between the exposure surrogate and ’real’ human exposure, and hence, reduces
measurement error (compared to the classical epidemiologic studies). The need to use detailed
data on exposure event sequences for future studies in order to further improve the evaluation of
the mean population exposure became clear. The estimation of independent exposure-response
functions for different atmospheric pollutants is a crucial scientific need 20. In this context we
have shown with this study that grid-based chemistry transport models are the appropriate tools
to carry out health impact epidemiologic studies.
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TABLES
33
TABLE 1. Population distribution among the eight counties of the greater Paris region. One, two
and three stars notation corresponds to the rural, peri-urban and urban classification respectively.
Columns 1 and 2 give the distribution of the ~11,000,000 inhabitants according to their place of
residence and work respectively. Columns 3, 4 and 5 give: 3) the fraction of inhabitants of each
county that work in a different county; 4) the fraction of people working in a county but living at
a different county; 5) the inactive fraction of the inhabitants of each county. Source: l'INSEE
(http://www.insee.fr/)
TABLE 2. Distributions of the monitor and model-based exposure surrogates for NO2, PM2.5 and
O3 (μg/m3) during the study-period (2001-2004) at the urban and peri-urban areas of the studydomain.
NO2
PM2.5
O3
Mean
P25
P50
P75
Mean
P25
P50
P75
Mean
P25
P50
P75
Monitor
42.5
32.5
41.7
50.4
14.4
9.8
13.0
17.40
58.8
35.6
57.5
78.0
Model
30.80
23.5
30.3
37.0
11.95
7.1
9.9
14.4
45.2
30.0
47.40
59.7
P25 = 25th percentile
P50 = median
P75 = 75th percentile
TABLE 3. Correlation coefficients between the time-series of the (left) model and monitor-based
exposure surrogates, (middle) monitor-based exposure surrogates of different pollutants and
(right) model-based exposure surrogates of different pollutants.
34
Monitor vs. Monitror
Model vs.
monitor data
PM2.5
0.52
PM2.5
NO2
0.64
NO2
O3
(summer)
0.75
Model vs. Model
NO2
O3
(summer)
NO2
O3
(summer)
0.73
-0.13
0.60
-0.37
-0.32
-0.54
TABLE 4. Log relative rates of mortality (%RR) and 95% confidence intervals associated with a
10-unit increase in the air pollution exposure surrogate. PM2.5, NO2 and O3 exposure surrogates
are introduced to the log-linear regression model separately (mono-pollutant model).
MonoPollutant
PM2.5
NO2
O3
%RR
95% C.I.
%RR
95% C.I.
%RR
95% C.I.
MONITORMODEL
1.2
[ 0.2 ; 2.2]
1.4
[ 0.9 ; 1.9]
0.6
[ 0.1 ; 1.0]
MEANMODEL
0.9
[-0.2 ; 2.0]
0.3
[-0.4 ; 1.0]
0.4
[ 0.2 ; 1.2]
PARAMMODEL
0.9
[-0.2 ; 2.0]
0.3
[-0.4 ; 1.0]
0.4
[-0.4 ; 1.2]
TABLE 5. Log relative rates of mortality (%RR) and 95% confidence intervals associated with a
10-unit increase in the air pollution exposure surrogate. PM2.5, NO2 and O3 exposure surrogates
are introduced to the log-linear regression model simultaneously (three-pollutants model).
ThreePollutants
PM2.5
NO2
35
O3
%RR
95% C.I.
%RR
95% C.I.
%RR
95% C.I.
MONITORMODEL
-1.1
[-2.6 ; 0.5]
1.9
[ 1.1 ; 2.7]
0.4
[ 0.0 ; 0.9]
MEANMODEL
1.3
[0.0 ; 2.7]
0.4
[-0.6 ; 1.4]
0.9
[ 0.2 ; 1.2]
PARAMMODEL
1.3
[0.0 ; 2.7]
0.4
[-0.6 ; 1.4]
0.9
[-0.4 ; 1.2]
FIGURE CAPTIONS
1
Maps of the greater Paris region divided into 8 counties showing (left) the centers of the
grid-cells of the 3 km-resolution CHIMERE model mesh and the zip-codes of each county
and (right) population density per county expressed in number of inhabitants per square
kilometer.
36
2
Population-averaged OEC estimates as a function of the total number of the members of the M-C model showing the convergence of the simulation to a stable OEC
data-set. The error is calculated relatively to OEC estimates for an ensemble of
200,000 members.
3
Distribution of 4-years of OEC estimates (2001-2004) over the population for (top)
O3 , (middle) NO2 and (bottom) PM2.5 , modelled with the CHIMERE model over
the Paris region.
4
Maps of daily averaged exposure to (top) ambient O3 , (middle) NO2 and (bottom)
PM2.5 . Exposure units are expressed in µg/m3 . Maps on the left correspond to a
winter day (February 15, 2004) and maps on the right correspond to a summer day
(July 7, 2004).
5
Two-days’s example of the result of the fit of the trimodal distribution model on
the OEC data (histograms) with the red line showing the parametrized composite
distribution and the black lines illustrating the intermediate fit of single Gaussian
distributions on each one of the three modes of the distribution.
6
Time-series of the parametrized means of the (red) high, (green) medium and
(blue) low exposure modes to (top) O3 , (middle) NO2 and (bottom) PM2.5 . A
7-days moving average filter is used to smooth out short-term fluctuations.
7
Time-series of the parametrized relative weight coefficients (RWCs) showing the
fractions of the population exposed to the (red) high, (green) medium and (blue)
low exposure modes of (top) O3 , (middle) NO2 and (bottom) PM2.5 . Fine lines
represent daily variations, while thick lines show the result of a low-pass filter over
the data where only seasonal variations are represented. Short-term variability is
smoothed out by means of a wavelet decomposition.
37
8
Geographical composition of the population of each one of the three exposure
modes according to the division of the study-domain into three ’sub-areas’: (red)
urban area, (blue) peri-urban area and (yellow) rural area. The result represents a
mean value over the whole 4-years OEC data-set.
9
OEC distribution over the 100,000 members of the sample population when (left)
the three-activity diary is used for the computation of personal exposures and
(right) concentrations are simply weighted with population density to provide OEC
estimates.
38