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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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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.

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.

===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/)

===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:

iF_{s,i,j}=\sum(c_{s,i}*Pop_{i,j}*BR)

where
Qs is the total emission from the emission source
category or subcategory s
cs,i is the incremental outdoor concentration of PM
(grams per cubic meter) in a grid cell i (i=1..n,
where n is the total number of grid cells), caused
by Qs
Popi,j is the population size in the grid cell i for
subpopulation j
BR is the average individual breathing rate. A nominal
breathing rate of 20 m3 day−1 person−1
(∼0.00023 m3 s−1person−1) 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 PM2.5 was used as a proxy for the population
exposure while calculating the iFs. The multiplying of
population numbers with PM2.5 concentrations (C×Pop in