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indoor particles is very significant, and secondly that is rather consistent across all
the studies.
4 PM Exposure Modelling Applications in Europe
A number of PM exposure modelling studies based on air quality modelling results
have already been performed in Europe. Hanninen et al. [
31
] present the results of
the estimation of children (
<
15 years) and elderly (
>
ΒΌ
65 years) exposure in
Turin city area for a specific time period. An Eulerian mesoscale model was used to
obtain PM
10
ambient concentrations in a 1 km
1 km horizontal resolution grid,
which were then used to estimate the indoor concentrations necessary for exposure
calculations [
31
]. For the studied episode, average exposure levels experienced
by both population groups (43.5 and 45.8
gm
3
) were lower than the levels
m
gm
3
), as could be expected due to the
substantial fraction of time spent indoors. Within the whole modelling area, average
exposures ranged from 20 to 95
recorded at urban monitoring sites (111.3
m
gm
3
.
Exposure to PM
10
air concentrations was estimated for the municipality of Porto
(the second largest Portuguese city) based on the application of a mesoscale air
quality and exposure modelling system [
32
]. Data on population, time-activity
patterns, microenvironments characterisation and input/output empirical relations
were gathered. Figure
3
presents the annual averages of simulated PM
10
concentra-
tion and individual exposure fields for the study domain. The spatial distribution of
exposure levels follows the concentration field; however, exposure annual averages
(varying between 40 and 70
m
3
h) are much lower than outdoor average
gm
m
gm
3
. The results have also shown important
differences between outdoor and indoor concentrations, stressing the need to
include indoor concentrations quantification in the exposure assessment.
Some other studies were performed relating human exposure in urban areas
based on ambient PM air concentrations determined with computational fluid
dynamics (CFD) modelling applications. The three-dimensional CFD model
MISKAM has been successfully implemented to provide better assessment of
exposure to traffic-related air pollutants in urban areas [
33
].
Borrego et al. [
26
] describe a methodology (schematically shown in Fig.
4
)
to estimate population exposure to traffic-related PM in urban areas based
on the estimation of ambient pollutant concentrations with the CFD model
VADIS. This methodology combines information on concentrations at different
microenvironments and population time-activity pattern data. A downscaling from
a mesoscale meteorological and dispersion model to a local scale air quality model
was done to define the boundary conditions for the local scale application. Simple
I/O relations were used by Borrego et al. [
26
] to determine PM
10
indoor
concentrations from outdoor concentrations. The prime objective was the quantifi-
cation of an integrated exposure expressed as an accumulated population exposure
index (APEI). The APEI index was defined as the daily accumulated exposure over
concentrations, which reach 90
m
