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|authors= Pauliina Taimisto, Marko Tainio, Niko Karvosenoja, Kaarle Kupiainen, Petri Porvari, Ari Karppinen, Leena Kangas, Jaakko Kukkonen, [[User:Jouni|Jouni T. Tuomisto]]
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|moderator= Essi Vuorinen
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This page (including the files available for download at the bottom of this page) contains a draft version of a manuscript, whose '''final version is published and is available in the [Air quality, atmosphere & health http://www.springerlink.com/content/8521j0u04h865751/?MUD=MP]'''. If referring to this text in scientific or other official papers, please refer to the published final version as: '''Pauliina Taimisto, Marko Tainio, Niko Karvosenoja, Kaarle Kupiainen, Petri Porvari, Ari Karppinen, Leena Kangas, Jaakko Kukkonen, Jouni T. Tuomisto: Evaluation of intake fractions for different subpopulations due to primary fine particulate matter (PM2.5) emitted from domestic wood combustion and traffic in Finland. Air quality, atmosphere & health. Received: 29 October 2009 / Accepted: 8 February 2011 (c) Springer Science+Business Media B.V. 2011 {doi|10.1007/s11869-011-0138-3}'''.

==Title==

Editing Evaluation of intake fractions for different subpopulations due to primary fine particulate matter (PM2.5) emitted from domestic wood combustion and traffic in Finland

==Authors and contact information==

:Pauliina Taimisto, correspondence author
:(pauliina.taimisto@thl.fi
:(National Institute for Health and Welfare (THL), Kuopio, Finland)

:Marko Tainio
:(National Institute for Health and Welfare (THL), Kuopio, Finland)
:(Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland)

:Niko Karvosenoja
:(Finnish Environment Institute, Helsinki, Finland)

:Kaarle Kupiainen
:(Finnish Environment Institute, Helsinki, Finland)

:Petri Porvari
:(Finnish Environment Institute, Helsinki, Finland)

:Ari Karppinen
:(Finnish Meteorological Institute, Helsinki, Finland)

:Leena Kangas
:(Finnish Meteorological Institute, Helsinki, Finland)

:Jaakko Kukkonen
:(Finnish Meteorological Institute, Helsinki, Finland)

:Jouni T. Tuomisto
:(National Institute for Health and Welfare (THL), Kuopio, Finland)

==Abstract==

Domestic wood combustion and traffic are the
two most significant primary fine particulate matter (PM<sub>2.5</sub>)
emission source categories in Finland. We estimated
emission–exposure relationships for primary PM<sub>2.5</sub> emissions
from these source categories using intake fractions
(iF), which describes the fraction of an emission that is
ultimately inhaled by a target population. The iFs were
calculated for four different emission source subcategories
in Finland in 2000: (1) domestic wood combustion in
residential buildings, (2) domestic wood combustion in
recreational buildings, (3) traffic exhaust and wear emissions,
and (4) traffic resuspension emissions. The iFs were
estimated for both total population and for subpopulations
with different gender, age, and educational status. Primary
PM<sub>2.5</sub> emissions were based on the Finnish Regional
Emission Scenario model and the dispersion of particles
was calculated using the Urban Dispersion Modeling
system of Finnish Meteorological Institute. Both emissions
and dispersion were estimated on a 1 km spatial resolution.
The iFs for primary PM<sub>2.5</sub> emissions from (1) residential
and (2) recreational buildings were 3.4 and 0.6 per million,
respectively. The corresponding iF for (3) traffic exhaust
and wear and (4) traffic resuspension emissions were 9.7
and 9.5 per million, respectively. The differences in
population-weighted outdoor concentrations were significant
between subpopulations with different educational
status so that people with higher education were exposed
more to traffic-related PM<sub>2.5</sub>.

===Keywords===

iF, Intake fraction, Exposure, Particulate
matter, Domestic combustion, Traffic

==Introduction==

Exposure to air pollution, especially for fine particulate matter
(PM2.5; particles with aerodynamic diameter of less than
<sub>2.5</sub> μm), have been associated with increases in mortality and
hospital admissions due to respiratory and cardiovascular
diseases (Dockery et al. 1993; Pope et al. 2002; WHO 2003;
Beelen et al. 2008; Brunekreef and Holgate 2002). The Clean
Air for Europe program have estimated that PM<sub>2.5</sub> is
associated with more than 348,000 premature deaths and to
loss of nearly 3.7 Ma of life in Europe in 2000 (Watkiss et al.
2005). The other study, based on measured PM concentrations,
estimated that PM<sub>2.5</sub> air pollution causes 492,000
premature deaths and loss of 4.9 Ma of life lost in Europe in
2005 (de Leeuw and Horàlek 2009).

In Finland, an integrated assessment model for primary
PM<sub>2.5</sub> air pollution has been developed to estimate
emissions, dispersion, and adverse health effects of primary
PM<sub>2.5</sub> air pollution (Kukkonen et al. 2008; Kauhaniemi et
al. 2008). In a previous assessment of the present authors, dispersion and exposure of primary PM2.5 were evaluated
in Finland using regional scale dispersion model system
SILAM (Sofiev et al. 2006) on the spatial resolution of
approximately 5 km (Tainio et al. 2009a) and the adverse
health effects were estimated for the whole population
(Tainio et al. 2010). The primary PM<sub>2.5</sub> emissions from
Finland were concluded to cause annually approximately
179 premature deaths in Finland (Tainio et al. 2010). Over
half of the deaths were associated with domestic wood
combustion and traffic emissions from Finland.

One of the main conclusions of Tainio et al. (2009a) was
that the emission–exposure calculations for emission sources
with low emission height, such as traffic and domestic wood
combustion, need to be refined. Traffic emissions occur to
considerable extent in densely populated urban environments,
while the wood combustion emissions occur both in
rural and in urban environments. This has been illustrated in
a study where the vicinity of primary PM<sub>2.5</sub> emissions and
population was evaluated over Finland (Tainio et al. 2009b).

Karvosenoja et al. (2010) compared population exposure
effects of traffic and domestic wood combustion using two
different source-receptor dispersion matrix set-ups: (1)
identical to this study on the horizontal grid resolution of
1 km, and (2) based on the SILAM atmospheric model
identical to Tainio et al. (2009a) on the resolution of 10 km.
The study pointed out the benefits of using finer spatial
resolution for low-altitude emissions especially in the
vicinity of high population densities.

Intake fraction describes the fraction of a pollutant
released from source and summed over exposed individuals
and divided by the emission (Bennett et al. 2002). It has
been used in a substantial number of air pollution studies
(e.g., Levy et al. 2002a; Greco et al. 2007). For example,
Stevens et al. (2007) have used the iF concept to compare
different exposure assessment methods with each other in
Mexico City due to PM<sub>2.5</sub> emissions from mobile sources.
In that study, the iFs for primary PM<sub>2.5</sub> varied from 23 to
120 per million depending on the method used.

Different subpopulations have different exposure levels.
This could be a result of living in different locations (e.g.,
families with children might live in different areas than the
elderly) or due to differences in time activity (e.g., adults may
spend more time in traffic than other subpopulations). In
previous studies, Levy et al. (2002b) have estimated that
people with higher education have lower premature mortality
due to air pollution. Greene and Morris (2006) evaluated the
health risks of PM<sub>2.5</sub> for different subpopulations and
concluded that females and adults had highest lifetime risks
for developing new lung cancer cases due to PM<sub>2.5</sub>. Beelen
et al. (2008) concluded that population with lower education
had higher adverse health effects due to traffic air pollution.

In this study, we estimated emission–exposure relationships
for primary PM<sub>2.5</sub> emissions due to domestic wood
combustion and traffic in Finland in 2000. Although, for
simplicity, concepts such as “emission–exposure relationship”
are used throughout this article, strictly speaking, the
term “exposure” refers here to population-weighted outdoor
concentrations (of PM<sub>2.5</sub>). The emissions and atmospheric
dispersion were estimated on a finer spatial resolution of
1 km. The emission–exposure relationships for both domestic
wood combustion and traffic were evaluated for two
emission source subcategories. We also estimated emission–
exposure relationships and the population-weighted outdoor
concentrations for subpopulations with different gender, age,
and educational status.

==Material and methods==

===Emissions===

Primary PM<sub>2.5</sub> emissions of domestic wood combustion and
road traffic were calculated with the Finnish Regional
Emission Scenario (FRES) model of the Finnish environment
institute (Karvosenoja et al. 2008). The emissions of
primary PM<sub>2.5</sub> are calculated by combining (1) activity
levels, (2) emission factors, and (3) emission control
technologies (for detailed description of these parameters,
see Karvosenoja et al. 2008). The basic spatial and temporal
domains of the model are Finland and 1 year, respectively,
which are then disaggregated to 1 km and 1 h resolutions,
respectively. The 1 km and hour resolutions were used in the
dispersion computations (see below). The national level
emission calculation and data sources for traffic exhaust and
domestic wood combustion are presented in Karvosenoja et
al. (2008), and for traffic non-exhaust in Kupiainen et al.
(2007). The basis for the spatial and temporal allocations of
emissions are presented in detail in Karvosenoja et al.
(2010). The description of emission source categories and
subcategories are presented in Tables 1 and 2.

Domestic wood combustion and traffic emissions are
calculated separately for various wood combustion appliance
and vehicle types, direct wear products of road, tires and brakes,
and particle resuspension from road surfaces. In this paper
emissions are presented aggregated to four categories based on
their different temporal variations: (1) wood heating in
residential buildings, (2) wood heating in recreational buildings
(3) traffic exhaust and wear, and (4) resuspension from road
surfaces. The temporal disaggregation in time was carried out
using typical temporal patterns based on data from wood use
and traffic surveys. Wood combustion emissions from residential
buildings are highest during winter, whereas for recreational
buildings the maximum occurs during non-winter months.
Hourly maximum of wood combustion emissions occurs at
early evening hours. For exhaust traffic emissions, monthly
variation is less pronounced, and for resuspension emissions, the
maximum occurs during winter and spring months (Kupiainen
2007). Traffic emissions have their maximum during the
morning and afternoon rush hours, and also the lower
emissions during weekends are taken into account.

===Atmospheric dispersion===

The dispersion of primary PM<sub>2.5</sub> was evaluated with the
Urban Dispersion Modeling system (UDM-FMI) developed
at the Finnish Meteorological Institute (Karppinen et al.
2000a, b). It includes a multiple source Gaussian plume
model and a meteorological pre-processor MPP-FMI
(Karppinen et al. 1997, 1998). For the selected calculation
grid, the system can be used to compute an hourly time
series of concentrations. The parameters used in dispersion
calculations are presented in Table 2.

The MPP-FMI model uses synoptic weather observations
and meteorological soundings, and its output consists
of hourly time series of meteorological parameters, including
atmospheric turbulence parameters and the mixing
height of the atmospheric boundary layer. In order to have
input data representative for different areas in Finland,
meteorological observations from ten synoptic weather
stations were used in the calculations. The stations selected
were Helsinki, Turku, Lappeenranta, Jokioinen, Kankaanpää,
Jyväskylä, Kuopio, Oulu, Rovaniemi, and Sodankylä. The
corresponding sounding observations were taken from the
Jokioinen and Sodankylä observatories.

In this study, the UDM-FMI model was used to calculate
source-receptor matrices. These matrices describe the dispersion
of inert particles from a single emission source of unit
strength, and they are combined with the actual emissions to
compute the PM<sub>2.5</sub> concentrations. The size of the unit
source was set at 1×1 km2, corresponding to the resolution
of the emission inventory. The source was assumed to be in
the center of a square calculation grid of size 40×40 and
20×20 km<sup>2</sup> for domestic wood combustion and traffic
emissions, respectively. The size of the domain was chosen
so that it covers most of the substantially elevated concentrations
from both categories of sources. Detailed numerical
computations show that the computational domain includes
at least 85% and 90–95% of the pollutant mass for wood
combustion and traffic emissions, respectively. The diurnal
and seasonal variations of the emissions were taken into
account, as described above. Removal of particles through
deposition was neglected, due to the relatively small particle
size and the short time scales considered.

The source-receptor matrices were computed for four
different emission source subcategories (Table 1). The differences
between the various emission categories were the
different seasonal and diurnal variation of the emissions, and
varying source heights (Table 2). The emission heights
including the estimated initial plume rises were assumed to
be 7.5 and 2.0 m for domestic wood combustion and traffic,
respectively. The concentrations were calculated at the height
of 2.0 m. The dispersion matrices were calculated from
hourly concentrations as annual averages for the year 2000,
for the ten stations mentioned above, and for the above
mentioned four emission categories, i.e., altogether 40
matrices were computed.


{|{{prettytable}}
|+'''Table 1.''' Description of emission source categories and subcategories used in this study
|-
|Source category
|colspan=2|Traffic
|colspan=2|Domestic wood combustion
|-----
|Subcategory
|Traffic exhaust and wear
|Traffic resuspension
|Residential buildings
|Recreational buildings
|-----
|Description of source subcategory
|Primary PM<sub>2.5</sub> emissions from traffic exhaust and direct wear emissions from road, tires and brakes
|Primary PM<sub>2.5</sub> emissions caused by particle resuspension from road surfaces
|Primary PM<sub>2.5</sub> emissions from primary and supplementary wood heating in residential buildings
|Primary PM<sub>2.5</sub> emissions from wood heating in recreational buildings
|}


{|{{prettytable}}
|+'''Table 2.''' Parameters of emission source categories and subcategories for primary PM<sub>2.5</sub> used in this study
|-
!Source category
!colspan=2|Traffic
!colspan=2|Domestic wood combustion
|-----
!Subcategory
!Traffic exhaust and wear
!Traffic resuspension
!Residential buildings
!Recreational buildings
|-----
|Emission strength (whole of Finland) (g/s)
|146
|88
|198
|40
|-----
|Spatial resolution of emissions and source-receptor matrices (km)
|1
|1
|1
|1
|-----
|Emission height including initial plume rise (m)
|2
|2
|7.5
|7.5
|-----
|Dispersion domain used in the SRM computations (km2)
|20×20
|20×20
|40×40
|40×40
|}



There are differences between matrices especially between
different stations, due to varying meteorological conditions,
e.g., prevailing wind direction and speed. There are also
differences between the matrices for traffic and wood
combustion emissions, due to varying release heights. Traffic
emissions have lower release height, and therefore concentrations
in the center of the calculation area are higher than for
domestic wood combustion. Different seasonal variation
between the emission subcategories also results in some
differences in dispersion patterns. Some examples of sourcereceptor
matrices are given in Figs. 1 and 2 to illustrate the
different dilution of the emission for different cases. The
effect of different meteorological conditions between stations
is depicted in Fig. 1a–b. An example of differences in
dispersion due to different seasonal variation of emissions is
given in Fig. 2a–b for domestic wood combustion—
emissions from residential buildings are highest in winter,
whereas emissions from recreational buildings have their
maximum during non-winter months, which results in a
different dispersion pattern.

The matrices were combined with the data regarding the
spatial distribution of emissions to calculate the concentration
levels due to nearby emissions of wood combustion
and road traffic for the whole of Finland. Each emission
value was multiplied with the appropriate value from the
source-receptor matrix, and the resulting concentration
fields were summed up. For most cases, the sourcereceptor
matrix of the nearest station was used, with two
exceptions. The border between Oulu and Rovaniemi, as
well as in southern Finland the border between the effect of
coastal stations (Helsinki and Turku) and inland stations,
were redefined in order to achieve a better compliance with
the borders of the climatic zones.

[[Image:Eva1.JPG|thumb|center|600px|'''Fig. 1''' An example of sourcereceptor matrices for different stations. Matrices for resuspened traffic emissions for Kuopio and Rovaniemi for the year 2000. Concentration of PM in micrograms per cubic meter assuming a source of size 1×1 km<sup>2</sup> and strength of 1,000 kg/a in the centre of the calculation area]]

[[Image:Eva2.JPG|thumb|center|600px|'''Fig. 2''' An example of source- b) receptor matrices for different source categories. Matrices for emissions from residential and recreational buildings for Helsinki for the year 2000. Concentration of PM in micrograms per cubic meter assuming a source of size 1×1 km2 and strength of 1,000 kg/a in the centre of the calculation area]]

===Population===

The population data were provided by the Statistics
Finland Grid Database (http://www.stat.fi/tup/ruututietokanta/
index_en.html). The dataset contained population numbers
for Finland on a resolution of 250×250 m<sup>2</sup> for 2004. The
same population data were used also in Tainio et al. (2009a).
In addition to population densities of the total population, the
dataset contained population densities for subpopulations
with different gender, age, and educational status (Table 3).
The population data were combined with the concentration
data so that population numbers of each grid were joined to
the nearest concentration point. The joining of data was done
with the ArcMap version 9.2 (http://www.esri.com/)


{|{{prettytable}}
|+'''Table 3.''' The description of subpopulations with different age, gender and educational status as used in the calculations
|-
!Subgroup
!Description
!Population
|-----
|Female
|Female
|2,664,334
|-----
|Male
|Male
|2,539,492
|-----
|Children (0–17)
|Children between 0 and 17 years old
|1,094,751
|-----
|Adults (18–64)
|Adults between 18 and 64 years old
|3,272,098
|-----
|Elderly (65–)
|People over 64 years old
|836,977
|-----
|Higher education
|People with an academy level education, and graduate school levels
|570,267
|-----
|High school
|People with matriculation
|325,831
|-----
|Vocational education
|People with middle level education, excluding matriculation
|1,793,804
|-----
|Comprehensive school
|People without education after comprehensive school level, or it is unknown
|1,398,225
|-----
|All
|Total population in Finland
|5,203,826
|}

===Emission–exposure relationship===

The emission–exposure relationships for different primary
PM2.5 emission source categories and subcategories were
estimated using intake fraction (iF) concept (Bennett et al.
2002). The iF is the fraction of an emission of a pollutant
that is inhaled by the population (Bennett et al. 2002). The
intake fraction iFs were calculated with Eq. 1:

<math>\frac{iF_{s,i,j}=\sum(c_{s,i}*Pop_{i,j}*BR)}{Q_s}</math>

where

:'''<math>Q_s</math>''' is the total emission from the emission source category or subcategory s

:'''<math>c_{s,i}</math>''' is the incremental outdoor concentration of PM (grams per cubic meter) in a grid cell <math>i (i=1..n,</math> where <math>n</math> is the total number of grid cells), caused by <math>Q_s</math>

:'''<math>Pop_{i,j}</math>''' is the population size in the grid cell <math>i</math> for subpopulation <math>j</math>

:'''<math>BR</math>''' is the average individual breathing rate. A nominal breathing rate of 20 m<sup>3</sup> day<sup>−1</sup> person<sup>−1</sup> (∼0.00023 m<sup>3</sup> s<sup>−1</sup>person<sup>−1</sup>) was adopted in this study. We used the same breathing rate for all subpopulations resulting differences in spatial distribution.

The total number of grid cells for the whole Finland (i)
was 783,900. The population–weighted outdoor concentration
of PM<sub>2.5</sub> was used as a proxy for the population
exposure while calculating the iFs. The multiplying of
population numbers with PM<sub>2.5</sub> concentrations (C×Pop in
Eq. 1) was done with ArcMap version 9.2 and rest of the iF
calculations with statistical computation and graphic system
R version 2.7.0 (The R Foundation for Statistical Computing,
http://www.r-project.org/).

===Population–weighted outdoor concentration===

The population–weighted outdoor concentrations of PM<sub>2.5</sub>
was estimated with Eq. 2:

<math>C_{s,j}=\frac{\sum(c_{s,i}*Pop_{i,j}}{\sum_jPop_j}</math>

where

:'''<math>C_{s,i,j} =</math>''' population–weighted outdoor concentration for primary PM2.5 for sector s and for subpopulation <math>j</math> (grams per cubic meter)

:'''<math>c_{s,i}</math>''' is the incremental outdoor concentration of PM (grams per cubic meter) in a grid cell <math>i (i=1..n,</math> where <math>n</math> is total number of grid cells), caused by <math>Q_s</math>

:'''<math>Pop_{i,j}</math>''' is the population size in the grid cell i for subpopulation <math>j</math>.

==Results==

The iFs for primary PM<sub>2.5</sub> emissions originated from
domestic wood combustion and traffic in Finland in 2000
were 2.9 and 9.6 per million, respectively (Table 4). The iFs
for the combined traffic exhaust and wear emissions, and
traffic resuspended emissions were 9.7 and 9.5 per million,
respectively. The small difference in iF values between these
two emission source subcategories was due to relatively
higher exhaust emissions in urban driving conditions.

The iFs for domestic wood combustion for residential
buildings and for recreational buildings were 3.4 and 0.6
per million, respectively (Table 4). The difference in iF
shows that the PM<sub>2.5</sub> emissions from these two subcategories
expose the population in a substantially different
efficiency. A practical implication of this result is that the
emission reduction of primary PM2.5 from residential
buildings has a relatively larger impact on exposure and
health, compared with the corresponding emission reductions
in recreational buildings.

The population–weighted outdoor concentration of the
different population subgroups for primary PM<sub>2.5</sub> emissions from domestic wood combustion and traffic are presented
in Fig. 3a–c. The corresponding iFs are presented in
Fig. 4a–c. The population–weighted outdoor concentrations
of PM<sub>2.5</sub> did not have significant relative differences
between populations with different gender and age status.
In subpopulations with different educational status, the
average concentrations varied between 1.1–1.7 and 0.7–
1.0 μg/m<sup>3</sup> for traffic exhaust and wear, and traffic
resuspensed emissions, respectively so that people with
higher education had higher average concentrations than
people with lower education (Fig. 3c). There were no
significant differences between different educational statuses
in population–weighted outdoor concentration of PM2.5 air
pollution due to domestic wood combustion.

The differences of the iF values between subpopulations
were higher than those for the population–weighted outdoor
concentrations (Fig. 4a–c). These differences can be
explained mainly by the size variations of the different
subpopulations (Table 3), as these iF values are defined as
the fraction of emission inhaled by all members of the
population subgroup (see Eq. 1).


{|{{prettytable}}
|+'''Table 4.''' Comparison of the estimated intake fractions (iF) corresponding to the emissions of primary PM<sub>2.5</sub> from Finland, between the present study and Tainio et al.’s (2009a) study
|-
!rowspan=2|Emission source category
!rowspan=2|Subcategory
!Intake fraction (per million)
|-----
!The present study
!Tainio et al.’s (2009a) study
|-----
|Traffic
|All<sup>a</sup>
|9.6
|0.7
|-----
|
|Traffic exhaust and wear
|9.7
|
|-----
|
|Traffic resuspended
|9.5
|
|-----
|Domestic combustion
|All<sup>b</sup>
|2.9
|0.5
|-----
|
|Residential building
|3.4
|
|-----
|
|Recreational buildings
|0.6
|
|}
:Tainio et al. (2009a) used the same emission and population data, but a different dispersion model. See Table 5 for details. We have compared
only the results regarding the same spatial area (Finland) and population (in Finland)

:<sup>a</sup> The iF value estimated in Tainio et al. (2009a) for traffic includes the emissions of tailpipe and direct wear emissions from road, tires, and brakes and
traffic resuspended emissions

:<sup>b</sup> The iF value estimated in Tainio et al. (2009a) for domestic combustion includes the emissions from both the residential and recreational wood
combustion


{|{{prettytable}}
|+'''Table 5.''' Comparison of input data and methods between the present study and Tainio et al.’s (2009a) study
|-
!Input
!The present study
!Tainio et al.’s (2009a) study
|-----
|Domain
|Finland
|Europe and Finland (the latter using a finer resolution)
|-----
|Emission data
|FRES
|FRES for Finland, EMEP for the whole of Europe
|-----
|Emission source categories
|4 source categories for traffic and domestic wood combustion (including subcategories for both)
|6 source categories including traffic and domestic wood combustion
|-----
|Dispersion model
|UDM-FMI (urban and local scale dispersion model)
|SILAM (regional and continental scale ispersion model)
|-----
|Meteorology
|MPP-FMI (meteorological preprocessing model, input data from synoptic weather observations and soundings)
|FMI variant of HIRLAM (numerical weather prediction model)
|-----
|Dispersion domain
|Square of size 20×20 km<sup>2</sup> or 40×40 km<sup>2</sup>
|Europe, or Finland and nearby areas
|-----
|Source size for area e missions
|1×1 km<sup>2</sup>
|30×30 km<sup>2</sup> for Europe and 5×5 km<sup>2</sup> for Finland
|-----
|Vertical mixing
|Emission height 2.0 m (traffic) or 7.5 m (domestic combustion) including initial plume rise, statistical concentration distribution
|Emission mixed to the whole boundary layer
|-----
|Horizontal resolution of the results
|1 km
|30 km for Europe and 5 km for Finland
|-----
|Exposed population
|The population in Finland
|The whole of Europe and the population in Finland
|-----
|Breathing rate
|20m<sup>3</sup> day<sup>−1</sup> person<sup>−1</sup>
|20 m<sup>3</sup> day<sup>−1</sup> per person<sup>−1</sup>
|}

==Discussion==

In this study we estimated iFs for primary PM<sub>2.5</sub> emissions
due to traffic and domestic wood combustion in Finland in
2000. We found out that iF is higher for traffic-related
primary PM<sub>2.5</sub> emissions than domestic wood combustion
related primary PM<sub>2.5</sub> emissions. We also found out that the
iF for residential building emissions is approximately six
times higher than the iF for recreational building emissions.
This difference is mainly caused by the much lower density
of population near recreational buildings on the average
than near other buildings with domestic combustion. These
results show that primary PM<sub>2.5</sub> emission reduction from
traffic sources reduces exposure more efficiently than
emission reduction from domestic wood combustion, and
emission reduction in residential buildings reduces exposure
more efficiently than emission reduction in recreational
buildings.

We also estimated population–weighted outdoor concentrations
for different subpopulations to find out if there are
substantial differences in exposure for primary PM<sub>2.5</sub> between
subpopulations. We found out only minor differences in
population–weighted outdoor concentrations between different
subpopulations. The largest difference in concentrations
was noted for PM<sub>2.5</sub> due to traffic and different education
statuses so that average concentration for people with higher
education was approximately 0.5 μg/m<sup>3</sup> higher than average
concentration for people with lower education. The results
indicate that people with higher educational status live nearer
traffic emission sources that other people. These results also
imply that gender or age does not significantly affect the
exposure of PM<sub>2.5</sub> due to low emission height sources.

===Comparison of results to previous studies===

The iFs calculated in this study are approximately an order
of magnitude higher than iFs published in Tainio et al.
(2009a) (Table 4). For traffic-related primary PM<sub>2.5</sub> emissions,
the iF in present study was 9.6 per million and in
Tainio et al. (2009a) 0.7 per million. For domestic wood
combustion, the corresponding iFs were 2.9 per million and
0.5 per million, respectively. The iF for recreational buildings,
0.6 per million, is closer the iF value of 0.5 per million
estimated in Tainio et al. (2009a). The present study and
Tainio et al. (2009a) have several linkages in the methodology and data (see summary in Table 5). Both of these studies are
based on primary PM<sub>2.5</sub> emission estimates made with FRES
model and both use the same population data to estimate the
population exposure. The main differences between the
studies are the different meteorological and dispersion models
and the resolution used in those models (Table 5).

The impact of horizontal grid resolution was studied in
the Karvosenoja et al. (2010) article. The source-receptor
matrices on the resolutions 1 and 10 km that were
compared in Karvosenoja et al. (2010) were based on the same dispersion model as the present study and Tainio et al.
(2009a), respectively. Based on the comparison, horizontal
grid resolution is an important factor in describing differences
between present study and Tainio et al. (2009a).
However, also other factors, e.g., vertical mixing of
emissions in the local and regional scale atmospheric
models used, play a significant role (for a detailed
discussion, see Karvosenoja et al. 2010).

===Traffic===

Several other studies have estimated emission–exposure
relationships for PM<sub>2.5</sub> emissions due to traffic and wood
smoke (Table 6). For traffic emissions, the published iFs
varied from 0.7 per million (Tainio et al. 2009a) to 120 per
million (Stevens et al. 2007). Most of the iF estimates are in
between 5 and 10 per million. In this comparison, the iF
values calculated in present study are slightly higher than iF
values estimated elsewhere.

Highest iF values for traffic emissions are reported by
Stevens et al. (2007). They compared five methods to
estimate iF for PM<sub>2.5</sub> emissions due to traffic in Mexico
City. The highest reported iF, 120 per million, was
estimated with model that used inventory-particle composition
method. In that model, iF was estimated from the
measured primary PM<sub>2.5</sub> fraction, and the estimated
primary PM<sub>2.5</sub> emissions if the chemical composition of
PM<sub>2.5</sub> was known. The lowest iF in Stevens et al. (2007),
26 per million, was estimated with regression model that
considers population within different distances from the
emission source. Clearly, Mexico City is large city with
several millions of inhabitants and high population density,
and therefore iF estimates from there are expected to be
clearly higher than iF values for traffic emissions in
Finland.

The other studies published in US have reported iF
values 1.2–9.4 per million (Greco et al. 2007; Marshall et
al. 2005; Evans et al. 2002; Levy et al. 2002a). In NATA
(National-scale Air Toxic Assessment) study, Marshall et al.
(2005) reported population–weighted iF mean value of 4.4
per million for urban on-road traffic based diesel PM<sub>2.5</sub>
emission sources among US urban counties. Marshall et al.
(2005) used Assessment System for Population Exposure
Nationwide (ASPEN) Gaussian plume dispersion model
with meteorological data to estimate ambient concentrations,
and probabilistic exposure model with ambient
concentrations, time-activity information of population
and differences between ambient and microenvironment
exposure concentrations. For breathing rate they used
12 m3/day. The iF results for traffic emissions was half of
the estimated iF for traffic in the present study. The results
are not easily comparable because of model differences in
emission category, areas, and breathing rate.

Greco et al. (2007) reported a value of 1.2 per million for
primary PM<sub>2.5</sub> as median iF value for emissions of urban
mobile source in the US. The result was based on a method
where concentrations of ambient PM2.5 in the US were
estimated by using a source-receptor matrix as a regressionbased
derivation of output from the Climatological Regional
Dispersion Model. In the source-receptor matrix they used
database of transfer factors that summarized the impact of
mobile sources of PM<sub>2.5</sub> and precursor emissions. Sourcereceptor
matrix resolution was limited to county level. We
estimated iF approximately eight times higher than was
found in Greco et al. (2007).

{|{{prettytable}}
|+'''Table 5.''' Comparison of input data and methods between the present study and Tainio et al.’s (2009a) study
|-
!Input
!The present study
!Tainio et al.’s (2009a) study
|-----
|Domain
|Finland
|Europe and Finland (the latter using a finer resolution)
|-----
|Emission data
|FRES
|FRES for Finland, EMEP for the whole of Europe
|-----
|Emission source categories
|4 source categories for traffic and domestic wood combustion (including subcategories for both)
|6 source categories including traffic and domestic wood combustion
|-----
|Dispersion model
|UDM-FMI (urban and local scale dispersion model)
|SILAM (regional and continental scale ispersion model)
|-----
|Meteorology
|MPP-FMI (meteorological preprocessing model, input data from synoptic weather observations and soundings)
|FMI variant of HIRLAM (numerical weather prediction model)
|-----
|Dispersion domain
|Square of size 20×20 km<sup>2</sup> or 40×40 km<sup>2</sup>
|Europe, or Finland and nearby areas
|-----
|Source size for area e missions
|1×1 km<sup>2</sup>
|30×30 km<sup>2</sup> for Europe and 5×5 km<sup>2</sup>
|-----
|Vertical mixing
|Emission height 2.0 m (traffic) or 7.5 m (domestic combustion) including initial plume rise, statistical concentration distribution
|Emission mixed to the whole boundary layer for Finland
|-----
|Horizontal resolution of the results
|1 km
|30 km for Europe and 5 km for Finland
|-----
|Exposed population
|The population in Finland
|The whole of Europe and the population in Finland
|-----
|Breathing rate
|20 m<sup>3</sup> day<sup>−1</sup> person<sup>−1</sup>
|20 m<sup>3</sup> day<sup>−1</sup> per person<sup>−1</sup>
|}

===Domestic wood combustion===

The emission–exposure relationship for the primary
PM2.5 originated from domestic wood combustion (wood
smoke) have been studied less extensively than those of
traffic sources. Ries et al. (2009) used modeled data to
estimate PM2.5 concentrations of residential wood smoke
in Vancouver. Data were fit to monitor data for calm and
cold winter periods. In addition, knowledge of estimation
of residential wood burning emissions was used in iF
evaluation. In the iF calculation, they used outdoor–indoor
infiltration factor that made this evaluation different
among all other iF studies, including ours. The iF result
reported in Ries et al. (2009) was more than four times
higher than in the present study.

===Subpopulations===

Exposure of different subpopulations to PM2.5 air pollution
has been studied in EXPOLIS study based on measured
population exposure in Helsinki Metropolitan Area, Finland
(Rotko et al. 2000). They observed that less educated and
young participants had greater exposures than more
educated and older participants in the Helsinki metropolitan
area (Rotko et al. 2000). Interestingly, our result is different.
This is probably due to differences in study design.
EXPOLIS directly studied personal exposures within a
large city and thus included micro environmental and
behavioral factors. Instead, our study did not assess these
things, and all differences between subpopulations in our
study are due to different home location patterns.

Health effect variation between subpopulations has been
studied in several settings (Levy et al. 2002b; Kan et al.
2008; Beelen et al. 2008). For example, Kan et al. (2008)
concluded in their epidemiological time-series analysis that
females and the elderly were more vulnerable to outdoor air
pollution. In general, these health studies proposed that the
adverse effects of air pollution were higher with people
with lower education compared with those with higher
education. It is unclear if differences in the observed risks
are due to exposure or sensitivity differences between
subpopulations.

===Model uncertainties===

This study estimated exposures on the spatial resolution of
1 km. As was found out, this resolution resulted in much
higher iF estimates for traffic and domestic wood combustion
emissions than the previous Tainio et al. (2009a) study
that used 5 km spatial resolution. In addition to different
horizontal computation grid size, there are several other
factors affecting the difference in the results obtained in the
two studies, e.g., the vertical mixing in the two models used
(Karvosenoja et al. 2010). However, it is likely that even a
1-km spatial resolution is too coarse to fully capture the
exposure for these sources. A review done by Zhou and
Levy (2007) concluded that the spatial extent of impact for
traffic PM emissions is 100–400 m around the road. The
review was done as meta-analysis by search of the peerreviewed
literature and government reports focusing on air
pollutants with study types of (bio)monitoring, air dispersion
modeling, land use regression, and epidemiology.

Emission uncertainties for traffic and domestic wood
combustion at country level have been assessed by
Karvosenoja et al. (2008). The uncertainties were found to
be considerable for traffic non-exhaust, i.e., resuspension
and wear (the limits of the 95% confidence interval equals
mean value minus 38% to the mean value plus 52%) and
domestic wood combustion emissions (minus, 36% and
plus, 50%), and less uncertain for traffic exhaust (±7%).
Additional uncertainties caused by the spatial emission
allocation have been qualitatively discussed by Tainio et al.
(2009b).

The other uncertainty relates to population location. We
assumed in this study that the home address represents the
place where the population is exposed. However, people
are, e.g., commuting from home to work and spend time in
traffic. It is unclear how much this simplification affects the
results of the present study.

The main uncertainty in the study relates to outdoor-toindoor
transportation of the particles. We did not take this
transportation into account in our calculations, but used
outdoor concentration as proxy for exposure. This will
overestimate calculated iF values, because only part of the
PM<sub>2.5</sub> penetrates inside the buildings and we therefore
overestimated the intake. There are considerable numbers
of studies with estimates of home indoor–outdoor PM<sub>2.5</sub>
concentrations (e.g., Hänninen et al. 2004; Molnar et al.
2005). For example, Hystad et al. (2009) have estimated
residential infiltration efficiencies of outdoor PM<sub>2.5</sub> by
using indoor–outdoor concentration measurements in
Seattle, USA and Victoria, Canada. Also, Ries et al.
(2009) used in iF calculations infiltration factor to account
for outdoor-to-indoor effect as fraction of ambient PM<sub>2.5</sub>
that penetrates indoors and remains resuspened.

==Conclusions==

The evaluation of emission–exposure relationships,
expressed as intake fractions, showed that in Finland the
traffic emissions expose people more efficiently than
domestic wood combustion emissions. Also, the iF for
residential emissions was six times higher than iF for
recreational buildings. These results show that residential
building emissions expose people more efficiently than
recreational building emissions. Thus, emission reduction
in residential building emission source category reduces the
population exposure more efficiently than emission reduction
in recreational building emissions. The population
average exposure for the emissions from traffic and
domestic wood combustion varied only slightly between
different subpopulations. The only exception was the
people with higher education, who were exposed more to
the traffic emissions.

The iFs for traffic were over an order of magnitude
higher in the present study compared with the
corresponding values evaluated in the previous study by
the same authors (Tainio et al. 2009b). The main differences
between these two studies were the different
dispersion models used, and the different horizontal spatial
resolution, 1 and 5 km, respectively. Clearly, increasing the
model resolution does not necessarily result in more
accurate concentration results. The physical reason for this
increase of the predicted exposure with finer model
resolution is that the higher concentrations and the higher
population densities are both spatially and temporally
closely associated. An implication of this result is that the
exposure values computed with fairly coarse resolution
models (such as, e.g., regional scale models) can actually
prove to be substantial underpredictions.

==Acknowledgments==

This study was performed as a part of the projects
PILTTI (funded by the Ministry of the Environment, Finland, grant no.
YM57/065/2005), INTARESE (funded by European Union, grant no.
018385–2), BIOHER (funded by Finnish Academy, grant no. 10155),
MEGAPOLI (funded by European Union, FP/2007-2011, grant no.
212520), and Climate change, air quality and housing—future challenges
to public health CLAIH (funded by Finnish Academy, grant no. 129355).
This work was a part of the work in the Centre for Environmental Health
Risk Analysis (jointly funded by the Academy of Finland, grants no.
53307, 111775, and 108571, and the National Technology Agency of
Finland (Tekes) grant no. 40715/01).
We would like to thank Mr. Kari Pasanen for his valuable help with
ArcGis calculations.

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