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|authors=M. Tainio, [[User:Jouni|Jouni T. Tuomisto]], J. Pekkanen, N. Karvosenoja, K. Kupiainen, P. Porvari, M. Sofiev, A. Karppinen, L. Kangas, J. Kukkonen
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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 [http://www.sciencedirect.com/science/article/pii/S1352231010001639 Atmospheric Environment 44 (2010)]'''. If referring to this text in scientific or other official papers, please refer to the published final version as: '''M. Tainio, J.T. Tuomisto, J. Pekkanen, N. Karvosenoja, K. Kupiainen, P. Porvari, M. Sofiev, A. Karppinen, L. Kangas, J. Kukkonen: Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland. Atmospheric Environment 44 (2010) 2125e2132 {{doi|10.1016/j.atmosenv.2010.02.036 }}'''.

==Title==

Editing Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland

==Authors and contact information==

:M. Tainio, correspondence author
:(Marko.Tainio@thl.fi)
:(Department of Environmental Health, National Institute for Health and Welfare (THL), Kuopio, Finland)
:(Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland)

:J.T. Tuomisto
:(Department of Environmental Health, National Institute for Health and Welfare (THL), Kuopio, Finland)
:(Academy of Finland, Helsinki, Finland)

:J. Pekkanen
:(Department of Environmental Health, National Institute for Health and Welfare (THL), Kuopio, Finland)
:(University of Eastern Finland, Kuopio, Finland)

:N. Karvosenoja
:(Finnish Environment Institute (SYKE), Helsinki, Finland)

:K. Kupiainen
:(Finnish Environment Institute (SYKE), Helsinki, Finland)

:P. Porvari
:(Finnish Environment Institute (SYKE), Helsinki, Finland)

:M. Sofiev
:(Finnish Meteorological Institute (FMI), Helsinki, Finland)

:A. Karppinen
:(Finnish Meteorological Institute (FMI), Helsinki, Finland)

:L. Kangas
:(Finnish Meteorological Institute (FMI), Helsinki, Finland)

:J. Kukkonen
:(Finnish Meteorological Institute (FMI), Helsinki, Finland)

==Article info==

Article history:

Received 4 June 2009

Received in revised form
18 January 2010

Accepted 24 February 2010

==Abstract==

The emission-exposure and exposure-response (toxicity) relationships are different for different emission
source categories of anthropogenic primary fine particulate matter (PM2.5). These variations have
a potentially crucial importance in the integrated assessment, when determining cost-effective abatement
strategies.We studied the importance of these variations by conducting a sensitivity analysis for an
integrated assessment model. The model was developed to estimate the adverse health effects to the
Finnish population attributable to primary PM2.5 emissions from the whole of Europe. The primary PM2.5
emissions in the whole of Europe and in more detail in Finland were evaluated using the inventory of the
European Monitoring and Evaluation Programme (EMEP) and the Finnish Regional Emission Scenario
model (FRES), respectively. The emission-exposure relationships for different primary PM2.5 emission
source categories in Finland have been previously evaluated and these values incorporated as intake
fractions into the integrated assessment model. The primary PM2.5 exposure-response functions and
toxicity differences for the pollution originating from different source categories were estimated in an
expert elicitation study performed by six European experts on air pollution health effects. The primary
PM2.5 emissions from Finnish and other European sources were estimated for the population of Finland
in 2000 to be responsible for 209 (mean, 95% confidence interval 6e739) and 357 (mean, 95% CI 8e1482)
premature deaths, respectively. The inclusion of emission-exposure and toxicity variation into the model
increased the predicted relative importance of traffic related primary PM2.5 emissions and correspondingly,
decreased the predicted relative importance of other emission source categories.We conclude that
the variations of emission-exposure relationship and toxicity between various source categories had
significant impacts for the assessment on premature deaths caused by primary PM2.5.

===Abbreviations===

CAFE, Clean Air For Europe-program; CI, Confidence interval;
EDM, Equal-weight decision maker; EMEP, European Monitoring and Evaluation
Programme; EPA, U.S. Environmental Protection Agency; ER, Exposure-response;
ExternE, Externalities of Energy; FRES, Finnish Regional Emission Scenario model;
ICD, International Classification of Disease; iF, Intake fraction; PDM, Performancebased
decision maker; PM, Particulate matter; PM2.5, Fine particulate matter;
RAINS, Regional Air Pollution Information and Simulation e model; SILAM, Air
Quality and Emergency Modeling System; WHO, World Health Organization.

==Keywords==

Fine particulate matter, Intake fraction, Exposure-response, Integrated assessment, Sensitivity analysis

==Introduction==

Integrated assessment models can describe quantitative
dependences between emission sources and the adverse health
effects. This is essential to understand these dependences if one
wishes to devise effective emission mitigation strategies.

Fine particulate matter (PM2.5) air pollution has been associated
with several adverse health effects (e.g. Pope and Dockery, 2006).
The exposure to PM2.5 originating from different sources or source
categories can be estimated with atmospheric dispersion models or
with source apportionment of measured PM mass (Hopke et al.,
2006). Most of the integrated assessment studies for PM are
based on dispersion modeling. For example, Levy and Spengler (2002) demonstrated a modeling framework to estimate the
adverse health effects due to secondary PM2.5 formed from
precursor gas emissions from two power plants in Massachusetts,
U.S. Künzli et al. (2000) estimated the exposure for traffic related
PM in Austria, France and Switzerland. Both of the above
mentioned studies have focused on one or a few emission sources,
or a single emission source category.

The exposure assessment studies have rarely taken into account
PM emissions from several source categories. One such study is the
European Clean Air For Europe (CAFE) -program that evaluated the
adverse health effects due to PM2.5 in Europe, taking into account
all anthropogenic PM2.5 emission sources in Europe (Watkiss et al.,
2005). However, the CAFE program did not report any source
category specific results. Another study performed in the U.S. (Fann
et al., 2009), evaluated the effectiveness of emission reduction of
different air pollutants in 9 urban areas and for 5 emission source
categories. They concluded that reduction of carbonaceous PM
would generate the largest benefit in all locations, in comparison to
secondary PM.

The assessment studies for PM2.5 usually assume equal toxicity
for all PM2.5, regardless of the pollution source or chemical
composition. Toxicity is here defined to refer to the ability of air
pollution to cause adverse health effects to a human population.
The chemical and physical properties of PM are known to be
substantially different for various emission sources, and these
properties could modify the relative toxicity of PM2.5. For example,
several epidemiological studies have reported in both U.S. and
Europe that one obtains higher relative risk estimates for PM from
combustion sources (e.g., Laden et al., 2000; Lanki et al., 2006).
However, there is limited understanding of the differences of
toxicity in terms of PM properties. A World Health Organization
(WHO) workshop concluded in 2007 that the current scientific
knowledge is not sufficient to differentiate the toxicity of different
PM sources (WHO, 2007). Despite the lack of information, the
workshop acknowledged the need to perform sensitivity analyses
for toxicity differences in integrated assessments.

In this study, we describe an integrated assessment model to
estimate the adverse health effects due to anthropogenic primary
PM2.5 from six different emission source categories, taking into
account also the differences in emission-exposure relationships,
and the variations of toxicity between PM from different source
categories. In this study we did not take into account the formation
of secondary PM2.5 from anthropogenic or natural gaseous emissions.
We also did not include all the source categories of primary
PM2.5 from natural emissions (for instance, wild-land fires and sea
salt aerosols); however, fugitive dust was included.

The aims of this study were (i) to develop an integrated
assessment model that allows an estimation of the adverse health
effects caused by primary PM2.5, (ii) to estimate how emissionexposure
and toxicity differences for various primary PM2.5 source
categories influence the results of the assessments, (iii) to evaluate
the uncertainties in emissions, dispersion, and health effects, and
(iv) to estimate the primary PM2.5 induced premature deaths and
the change in life-expectancy in Finland in 2000. The modeling of
life-expectancy, the uncertainties of emissions, and emissionexposure
relationships have been previously published in Tainio
et al. (2007), Karvosenoja et al. (2008), and Tainio et al. (2009),
respectively.

==Materials and methods==

The schematic flowchart of the integrated assessment model is
presented in Fig. 1. Different modules that are part of the integrated
assessment model are described in the following chapters.

[[Image:Unc1.JPG|thumb|center|650px|'''Fig. 1.''' A schematic flowchart of the integrated assessment model. The arrows denote a flow of information, and the rectangles are the sub-models and the intermediate or final
results. Model uncertainties associated with different parts of the model are underlined. (PDM - Performance-based decision maker, EDM - Equal-weight decision maker).]]

===Emissions===

The emissions of anthropogenic primary PM2.5 from Finland and
from other European countries in 2000 were estimated using two
different emission datasets. The emissions of primary PM2.5 from
Finland were estimated with the Finnish Regional Emission
Scenario (FRES) model (Karvosenoja, 2008). The FRES model has
calculated primary PM2.5 emissions with detailed chemical and
size-segregation splits. The emissions are derived from 205 point
sources with detailed plant and stack characteristics, and area
sources with aggregation into 112 source categories and 15 fuels. In
this study, the emissions of anthropogenic primary PM2.5 in Finland
were divided into 6 emission source categories, which were further
divided into 13 sub-categories. These source categories and subcategories
are described in the supplementary material (Table S1).

The primary PM2.5 emission strength uncertainties for Finnish
emissions were estimated for all 13 sub-categories. The primary
PM2.5 emission strength uncertainties for traffic and domestic
wood combustion have been evaluated by Karvosenoja et al.
(2008). They concluded that the uncertainties of small-scale
domestic combustion emissions were responsible for most of the
emission uncertainties regarding primary PM2.5 from these two
source categories. The emission model used by Karvosenoja et al.
(2008) was used also in this study.

The primary PM2.5 emission strength uncertainties for other
emission source categories were estimated separately for activities
and emission factors, based on Karvosenoja (2008), Karvosenoja
et al. (2008) and Kupiainen et al. (2006). Both the uncertainties of
activities and emission factors were highest for emission source
categories ‘small power plants’ and ‘other sources’. The emission
strengths of annual average primary PM2.5 emissions from Finland
in 2000 and their respective uncertainties are presented in the
supplementary material (Table S1). Spatial uncertainty (i.e., the
location of emissions, which is not known with accuracy) was not
included in these uncertainties.

The emission strengths of anthropogenic primary PM2.5 for
other European countries were derived from the European Monitoring
and Evaluation Programme (EMEP) database (www.emep.
int). The emission strengths were estimated for all anthropogenic
sources combined in 2000 and separately for each of the 42
countries (Table S2, Supplementary material). Emission strength
uncertainties for these emissions have not been quantitatively
reported.

===Dispersion and exposure===

The population exposure for primary PM2.5 in Finland due to
primary PM2.5 emissions from Finland and elsewhere in Europe
was evaluated, and presented using the concept of intake fraction
(Bennett et al., 2002). The intake fraction is defined as “integrated
incremental intake of a pollutant released from a source category
and summed over all exposed individuals” (Bennett et al., 2002). In
this study, exposure estimates are based on outdoor concentration
of primary PM2.5. The exposures in this study are based on values
reported by Tainio et al. (2009); they calculated the emissionexposure
relationships for various primary PM2.5 emission source
categories in Europe using the iF methodology.

The atmospheric dispersion of primary PM2.5 was evaluated
using the dual-core Lagrangian-Eulerian dispersion model SILAM
(http://silam.fmi.fi), for the primary PM2.5 emissions in 2000. The
dispersion was computed for two different study domains. In the
European domain, the emissions of primary PM2.5 were based on
EMEP data and the concentrations were estimated with approximately
with a horizontal resolution of 30 km over the whole of
Europe. In the Northern European domain (that included most of Scandinavia and some surrounding regions), the primary PM2.5
emission strengths for Finnish sources were based on the values
provided by the FRES model and the concentrations were estimated
approximately with a horizontal resolution of 5 km. In the Northern
European domain, the PM2.5 concentrations were estimated separately
for six different emission source categories. A constant
breathing rate of 20 dm3 day�1 (w0.0002m3 s�1)was used in the iF
calculations.

The iF values were evaluated in Tainio et al. (2009) using:

<math>iF=\sum(Ci*Pop_i)*Br/Q</math>,

where Pop is the number of population (persons), C is the
concentration increase of pollutant due to a specified emission
source category or area of emissions (g m<sup>-3</sup>), Br is the breathing
rate (m<sup>3</sup>/s/person), and Q is the emission rate (g s<sup>-1</sup>). In this study,
we estimated the population-weighted average exposure for
primary PM2.5 matter in Finland from:
<math>
C=(Q\times iF)/(Pop\times Br)</math>

The primary PM2.5 emission strengths (Q) for Finnish emissions
were updated, in comparison to Tainio et al. (2009). For the primary
PM2.5 emission strengths for other European sources, for the population
(Pop) data and for the breathing rate (Br), we used the same
values as reported in Tainio et al. (2009). The iF data for primary
PM2.5 emissions originated from Finland and elsewhere in Europe
are summarized in the supplementary material (Tables S1eS2),
respectively, with other exposure uncertainties provided in
Table S3.

===Exposure-response functions===

The exposure-response function describes the change in the
background health effect attributable to the change in the exposure
level. We used the term exposure-response function to emphasize
that although the exposure was estimated based on outdoor
concentrations of PM, the concentration is used as a proxy for
exposure.

The exposure-response functions for primary PM2.5 mortality
impacts were extracted from a formal elicitation of expert judgment
performed for six European air pollution experts (Cooke et al.,
2007; Tuomisto et al., 2008). In the expert elicitation study, experts
provided quantitative estimates of mortality impacts of hypothetical
short- and long-term changes in PM2.5 concentrations in US
and Europe, and for several other variables. The expert answers
were combined with two methods: with equal-weight between the
experts (equal-weight decision maker, EDM) and with assessing
weights based on seed questions on which answers were known
(performance-based decision maker, PDM). The experts also gave
their estimates for the least and most toxic element of the PM
mixture and defined those elements. All the experts assumed that
combustion PM were more toxic than the average PM2.5 mass
(Cooke et al., 2007). Two experts assumed that traffic related
combustion PM (referred as diesel or traffic PM) would be more
toxic than the average PM2.5 mass. Four experts claimed that
secondary PM (sulfate, nitrate or both) and two experts speculated
that PM2.5 from crustal sources would be less toxic than the average
PM2.5 mass. The uncertainties were recognized as being high.

For this study, we adopted exposure-response functions for
primary PM2.5 induced non-accidental mortality, caused by longterm
(chronic) exposure to PM, from the expert-elicitation study
(Table S3). Two additional assumptions on toxicity were made.
First, we assumed that all the primary PM2.5 possess equal toxicity,
regardless of source. For this, we used an average exposureresponse
function based on the expert elicitation study. Second, we
assumed from the experts’ estimates that the most toxic substances
originated from traffic tailpipe emissions (both for on-road and offroad
vehicles and machinery) and the least toxic substances are
from traffic non-tailpipe and agricultural emissions. For all the
other emission source sub-categories (Table S1, Supplementary
material) and for primary PM2.5 emissions from other European
countries, we used the average exposure-response function from
the expert elicitation study. The sensitivity of the model in the toxicity assumptions was tested in the sensitivity analysis (see
below).

===Health effects===

The premature death due to chronic exposure to primary PM2.5
was estimated with the equation:

D=C\times Cr\times B

where D is premature non-accidental death in 2000 due to PM2.5
exposure, C is the concentration increase of pollutant due to
a specified emission source category, Cr is percentage change
in non-accidental mortality due to permanent 1 mg m�3 change
in PM2.5 exposure and B background non-accidental mortality in
Finland in 2000.

Background mortality statistics for Finland for 2000 were
obtained from World Health Organization (WHO) Mortality Database
(http://www.who.int/healthinfo/morttables/). The non-accidental
mortality was calculated by subtracting accidental causes
(International Classification of Disease (ICD) version 10 codes V01-
Y89) from total mortality. The background hazard rates for one year
were estimated based on age categories of the WHO mortality
database and for United Nation World Population Prospects: The
2006 Revision and World Urbanization Prospects population data
for 2000 (UN, 2008).

The loss of life-expectancy attributable to primary PM2.5 exposure
was estimated with the life-table model described in Tainio
et al. (2007). Based on sensitivity analyses performed in Tainio
et al. (2007), we omitted the time lag calculations from the
model. In this case, lag is defined as the time elapsing between
a change in exposure and the ensuing change in the hazard rate.
The premature death was estimated for all emission source categories
separately and the life-expectancy to all sources combined.

===Sensitivity analysis===

The sensitivity analysis for input variables was performed by
calculating absolute rank-order correlations between input and
output variables. This sensitivity analysis method is called importance
analysis. Throughout the model, both parameter and model
uncertainties were evaluated based on the literature or author
judgment. Parameter uncertainties contain those related to the
input variables, such as the uncertainty of the annual emissions of
primary PM2.5 originating from domestic wood combustion. The
model uncertainties contain those caused by the physical and
chemical limitations of the models, such as using the same or
source category specific exposure-response functions in the model.
The uncertainties for emissions and exposure estimates are presented
in Tables S1eS3 (Supplementary material). All the uncertainties
were assumed to be independent of each other (thus,
uncorrelated).

Both parameter and model uncertainties were propagated
through the model by Monte Carlo simulation. The model uncertainty
was described with binary variables (Bernoulli distribution),
choosing between two alternative model branches. Parameter
uncertainty was described by using continuous distributions. The
effects of parameter and model uncertainties on model results were
studied using importance analysis. The importance analysis was
undertaken by calculating absolute rank-order correlations
between the input variables and the model results. The model was
implemented using Analytica � version 4.2. (Lumina Decision
Systems, Inc., CA) Monte Carlo simulation program and run with
50 000 iterations.

==Results==

The primary PM2.5 emissions from Finnish and European
anthropogenic sources, addressed in this study, were estimated be
responsible for 209 (mean, 95% CI 6-739) and 357 (mean, 95% CI 8-
1482) premature deaths, respectively, in the population of Finland
in 2000 and to lower the average life-expectancy by 0.12 (mean,
95% CI 0.00e0.48) years. The average exposures of Finnish population
to primary PM2.5 were estimated to be 0.33 (mean, 95% CI
0.18e0.55) due to Finnish and 0.65 mg m�3 (mean, 95% CI
0.34e1.08) due to European sources.

===Finnish emission sources===

The premature deaths due to primary PM2.5 emissions from
Finland were estimated taking account both emission-exposure
and toxicity differences, and model uncertainties. Fig. 2 illustrates
how the emission-exposure and toxicity variations between
primary PM2.5 emission source categories affected the relative
importance of these sources. The relative importance of trafficoriginated
primary PM2.5 emissions increased from approximately
30% (emissions) to 50% (premature deaths) when both emissionexposure
and toxicity variations were taken into account.

Table 1 describes how the mean premature death estimates
changed between source categories when emission-exposure,
toxicity variation or both were taken into account. The premature
deaths due to traffic emission were elevated by 240% and premature
deaths due to agriculture emissions decreased by 70% in
comparison to premature death estimates without source category
specification. For the other source categories, the impact of emission-
exposure and toxicity variation was smaller (Fig. 2, Table 1).

The exposure-response function uncertainty was the main
uncertainty in the integrated assessment with over 90% correlation
between the exposure-response function uncertainty and the
uncertainty of the premature death estimate (Fig. 3). The choice
between equal or emission source specific exposure-response
functions was the second most important uncertainty for those
sub-categories for which we assumed differential toxicity. The
main exposure uncertainty was the ability of the dispersion models
to predict primary PM2.5 concentrations. The emission uncertainties
had the greatest importance for source categories ‘other
sources’, ‘domestic combustion’ and ‘small power plants’ (Fig. 3).

The emission source category ‘traffic’ contributed almost a half
(85 premature deaths annually) of all the adverse health effects due
to Finnish primary PM2.5 emissions in Finland (Fig. 4). The second
most important emission source category was ‘domestic combustion’
(45 premature deaths) and the third most important was
‘power plants’ (29 premature deaths). These emission source
categories accounted for most of the adverse health effects,
regardless of the model assumptions (Table 1).

[[Image:Unc2.JPG|thumb|center|700px|'''Fig. 2.''' The fractions of emissions, exposure and health effects in Finland due to six Finnish primary PM2.5 emission source categories. For example, emission source category ‘Traffic’
causes approximately 30% of emissions, 40% of exposure and 50% of adverse health effects.]]

[[Image:Unc4.JPG|thumb|center|500px|'''Fig. 4.''' The premature deaths in Finland in 2000 due to anthropogenic primary PM2.5
emissions from Finland (mean and 95% CI). The premature death estimates have been
estimated separately for six emission source categories. The emission source categories
are described in Table S1 (Supplementary material). The premature death in Finland
due to other European primary PM2.5 emissions is presented in Table S2
(Supplementary material).]]

{|{{prettytable}}
|+'''Table 1.''' Comparison of mean premature death estimates for different primary PM2.5 emission source categories without and with source category specific emission-exposure (iF) and exposure-response (toxicity) variations. (iF = intake fraction).
|-
|-----
!
!colspan=4|Premature death
!colspan=3|Comparison in relation to model without source specific iF or toxicity
|-----
|
|Without source specific iF or toxicity
|Source specific iF specific iF or toxicity
|Source specific toxicity
|Source specific iF and toxicity
|Source specific iF
|Source specific toxicity
|Source specific iF and toxicity
|-----
|Traffic
|55
|72
|102
|133
|130%
|185%
|240%
|-----
|Domestic combustion
|50
|45
|50
|45
|92%
|100%
|92%
|-----
|Agriculture
|2
|2
|1
|1
|94%
|30%
|28%
|-----
|Large industrial processes
|21
|19
|21
|19
|92%
|100%
|92%
|-----
|Power plants
|33
|29
|33
|29
|89%
|100%
|89%
|-----
|Other
|19
|19
|19
|19
|100%
|100%
|100%
|-----
|Sum
|179
|186
|225
|246
|<nowiki>-</nowiki>
|<nowiki>-</nowiki>
|<nowiki>-</nowiki>
|}

===Other European sources===

Over half of the premature deaths due to the exposure to
primary PM2.5 in Finland in 2000 were estimated to be due to
primary PM2.5 emissions outside Finland (Table S2, Supplementary
material). The primary PM2.5 emissions from the neighboring
countries of Russia, Sweden, and Estonia explained most of the
adverse health effects. Additionally, approximately 34 premature
deaths were associated with the primary PM2.5 emissions from
Ukraine.

These substantial contributions of the neighboring countries
and Ukraine are caused (i) on one hand by the relatively high iF
values (these are associated both with the geographic proximity
and the location of those countries with respect to the prevailing wind circulation patterns) and (ii) on the other hand by the relatively
high emission strengths (in case of Russia and Ukraine). The
premature death estimates due to European sources were sensitive
to exposure-response function uncertainties (data not shown).

==Discussion==

===Source category variation===

We have developed an integrated assessment model to evaluate
the premature deaths caused by primary PM2.5 from different
source categories. Fig. 2 and Table 1 present how the emissionexposure
and toxicity variation between source categories
substantially increased the relative importance of source category
‘traffic’ and decreased the relative importance of source category
‘agriculture’. Reliable information about relative contribution of
different emission sources to population health is important in
selecting the most effective mitigation actions.

The emission-exposure variation between different emission
source categories has been studied rarely. Wolff (2000) concluded
that taking into account the emission-exposure variations, the
rank-ordering of available mitigation actions changed in comparison
to mass based comparison. Fann et al. (2009) concluded that
a PM emission reduction is subject to high variation, depending on
the location of the emissions in U.S. In this study we have also
revealed that both emission-exposure and toxicity variation will
affect the results of integrated assessment and consequently the
rank-ordering of available mitigation actions.

There were only small differences for the emission-exposure
relationships between various primary PM2.5 source categories.
The emission-exposure relationships used in this study were based
on the results of a regional scale dispersion model with approximately
5 km spatial resolution over Finland (Tainio et al., 2009). As
discussed in that study, the finite resolution of the dispersion
model leads to an underestimation of the exposure, especially for
sources with low emission heights near to population hotspots. For
example, Wolff (2000) estimated a 4-fold difference in population
average iF between traffic and power plant emissions, while in this
study the difference was 1.4-fold. Thus, the iFs evaluated by Tainio
et al. (2009) tend to underestimate the emission-exposure relationships,
especially for traffic and possibly for domestic combustion
primary PM2.5 emissions, since emissions from these sources
are released from low emission heights and in most cases, in
relatively densely populated areas. This indicates that the emissionexposure
variations between sources may be potentially even
higher than those presented in this study, and that the emissionexposure
variation could well have even greater impacts on the
results of the integrated assessments.

The toxicity variation had major impacts on premature death
estimates (Table 1). The exposure-response functions for different
primary PM2.5 emission source categories were based on experts’
estimates of the most and least toxic substances in the PM2.5 mass
(Cooke et al., 2007; Tuomisto et al., 2008). The substances that
experts named as the least and the most toxic varied between the
experts and therefore the combined estimate (that was used in this
study) represents the maximum variation between the least and
the most toxic substance in PM2.5 mass. Clearly, there are inherent
uncertainties in using this kind of expert based approach, and the
results for different emission source categories should be viewed
more as indicative rather than quantitative.

Previous assessments have only rarely estimated toxicity
differences between different particles, although several epidemiological
studies have detected toxicity differences between PM
from different sources (e.g. Laden et al., 2000; Lanki et al., 2006). In
the European Externalities of Energy (ExternE) study, primary PM
from traffic was evaluated to be 1.5 times more toxic than the
average PM2.5 mass, while secondary sulfate and nitrate had
a lower toxicity than the average PM2.5 mass (ExternE, 2005). The
2005 update of ExternE methodology did not include a sensitivity
analysis and the impact of these toxicity variations in the assessment
was not evaluated. A recent study on regional background
exposure for PM2.5 in Europe used a 2.8 times higher RR estimate
for primary PM2.5 mass in comparison to secondary PM2.5 mass
(Andersson et al., 2009). They concluded that in Europe the exposure
for secondary PM2.5 is higher than exposure for primary PM2.5
but due to toxicity differences, the magnitude of adverse health
effects was similar.

This study was based on toxicity variation between different
emission source categories. However, the toxicity is not dependent
per se on the emission sources but rather on the chemical and
physical properties of the inhaled PM. In an ideal case the exposureresponse
functions would be based on the chemical and physical
properties of inhaled PM. However, the current knowledge on the
adverse health effects of PM2.5 is inadequate to permit this kind of
exposure-response modeling.

===Uncertainties===

The uncertainties related to exposure-response functions were
identified to possess the highest importance in the health effect
estimates (Fig. 3). A similar result has been noted in our previous
assessment studies for PM2.5 air pollution (Leino et al., 2008; Tainio
et al., 2005, 2007) and in a number of other assessment studies (e.g.
Künzli et al., 2000; Levy and Spengler, 2002).

The uncertainties related to input variables and for the model
structure were evaluated with various methods. In a few cases, the
uncertainty was based on previously published studies (e.g. emission
uncertainties). For some of the input variables the uncertainty
was defined with expert estimate (e.g. exposure-response functions).
With respect to the other variables the uncertainty was
estimated based on author judgment. Thus, different uncertainties
were defined with different methods ranking from the expert study
to the crude guess of the modeler. This raises a doubt of the
comparativeness of different uncertainty estimates in the sensitivity
analysis. However, the purpose of this study was more to
demonstrate the model and to define those uncertainties that
require a more formal analysis in future assessments than provide
formal uncertainty assessment.

We assumed in this study much higher uncertainty to exposureresponse
functions than previous assessment studies. For example,
in the Pope et al., 2002 study, a <math>1 \mu g m^{-3}</math> change in PM2.5 concentration
was estimated to change non-accidental mortality between
0.15% and 1.00% (95% confidence interval). In this study, the corresponding
variationwas 0.03e4.57% (for average exposure-response function). When testing the sensitivity of the model for different
uncertainties by using similar uncertainty intervals as in Pope et al.
(2002), the relative importance of exposure-response function
uncertainties was still most important but only with small differences
e.g. compared to the importance of the uncertainty in the
dispersion model (data not presented). Thus, exposure-response
function uncertainty remained important also while using less
uncertain input variables.

We also assumed in this study that all the uncertainties are
independent from each other. Thus, we did not assume any correlation
between uncertainties. This could result in either under- or
overestimation of total uncertainty of the model. However, we
assume that this would not change significantly the results of this
study since we compared mainly uncertainties that are uncorrelated
(e.g. toxicity to iF).

[[Image:Unc3.JPG|thumb|center|600px|'''Fig. 3.''' The importance analysis results. The figure shows rankeorder correlation between seven input variables and the modeled premature deaths in Finland due to Finnish primary PM2.5 emissions in 2000 for the 13 emission source sub-categories. A high correlation indicates that the input variable has a strong impact on the model output. (iF - intake fraction, ER - Exposure-response, PDM - Performance-based decision maker, EDM - Equal-weight decision maker).]]

===Magnitude of health effects===

The primary PM2.5 originating from the whole of Europe were
estimated to cause in Finland approximately 566 premature deaths
and to lower the average life expectancy by 0.12 years in 2000. The
CAFE program has evaluated that PM2.5 were responsible for the
premature deaths of 1270 Finns (Watkiss et al., 2005). When one
takes into account that the CAFE study included both primary and
secondary PM2.5, the results between CAFE and this study are not in
disagreement. Over half of the adverse health effects in this study
were due to long-range transported primary PM2.5 from other
European countries. The exposure for local emission sources is
probably underestimated in this study due to reasons discussed
earlier.

We estimated that traffic-originated primary PM2.5 emissions
from Finland caused approximately 85 premature deaths in Finland
in 2000. In our previous studies, we have estimated that primary
PM2.5 from local buses in Helsinki region would be responsible for
18 premature deaths in 2020 (Tainio et al., 2005) and that the
primary PM2.5 due to the heavy-duty fleet (including buses)
account for 34 premature deaths per year in the Helsinki metropolitan
area (Leino et al., 2008). In our previous studies, the
exposure was based on personal measurements of PM2.5 and the
sources were identified with the source apportionment method.
The comparison of these two studies therefore indicate that this
study may underestimate the health effects for traffic-originated
primary PM2.5, most probably due to underestimated exposure.

The magnitude of underestimation in exposure can also be
estimated by comparing the measured PM concentrations to
modeled ones. In this study the average population exposure in
Finland for all primary PM2.5 emission sources included in the
computations combined was less than 1<math>\mu g m^{-3}</math>. For comparison, in
2001 the average PM2.5 concentrations, including secondary PM, in
Helsinki region varied between 8<math>\mu g m^{-3}</math> and 9<math>\mu g m^{-3}</math> at two
measurement stations (YTV, 2002). Although Helsinki is more
polluted than the rest of the Finland, a substantial fraction of the
measured PM2.5 are secondary PM, and present study did not
include non-anthropogenic sources, less than 1<math>\mu g m^{-3}</math> average
exposure seems an underestimation of exposure.

The Finnish anthropogenic primary PM2.5 emissions were estimated
in this study to be 30.8 (95% CI 27e36) Gg/a for the year
2000. In the European Thematic Strategy on Air Pollution assessment,
in the year 2000 the primary PM2.5 emissions for Finland
were 28 Gg/a based on the RAINS model (Amann et al., 2006). The
higher emission strength in this study in comparison to RAINS is
due to different emission source definitions of the traffic non-tailpipe
emissions (RAINS do not include traffic induced dust
suspension). Another emission inventory maintained by European
Monitoring and Evaluation Programme (EMEP) and based on
country submissions of national inventories estimated 38.2 Gg/
a primary PM2.5 emissions for Finland for 2000 (Vestreng et al.,
2006). The higher value is mainly due to the outdated estimate of
domestic wood combustion emissions in the EMEP database
(Karvosenoja, 2008).

We assumed no threshold for the primary PM2.5 induced
premature deaths. The previous assessments have shown that
a threshold value can have major impact on the results of the
assessment (e.g. Künzli et al., 2000). There have been several statistical
attempts to define a threshold for PM2.5 air pollution. For
example, Schwartz et al. (2002, 2008) have studied nonlinearities in
exposure-response functions in order to define a threshold for shortterm(
acute) and long-term (chronic) effect of PM2.5, respectively, but
neither of these studies detected any threshold value for PM2.5. The
World Health Organization working group stated in 2003 that
epidemiological studies have been unable to identify any threshold
for PM2.5 and that it is likely that the PM2.5 is harmful in the population
since all populations contain susceptible individuals (WHO,
2003). However, in the expert elicitation study conducted by U.S.
Environmental Protection Agency (EPA), a number of experts gave
separate exposure-response coefficients and/or plausibility for the
PM2.5 air pollution mortality impact of low exposure levels (usually
below 10<math>\mu g m^{-3}</math>) (Roman et al., 2008). Whether or not there is any
threshold remains one of the important undefined uncertainties.

==Conclusions==

We have utilized an integrated assessment model to estimate
the adverse health effects mainly due to anthropogenic primary
fine particulate matter originating from different emission source
categories. The variations in both emission-exposure and toxicities
between source categories had significant impacts for the assessment
results, especially for traffic-originated primary fine particulate
matter. This kind of information is important for the rank
ordering of the effectiveness of the available mitigation actions. The
main uncertainties in the model were related to exposure-response
functions and to the estimation of emission-exposure relationships
for different source categories.

==Acknowledgements==

This study was done as a part of the projects KOPRA (funded by
the Ministry of the Environment, Finland, Grant YM119/481/2002,
the National Technology Agency of Finland (Tekes), Grant 616/31/
02 and the Helsinki Metropolitan Area Council (YTV), Grant 135/
03), BIOHER (funded by the Academy of Finland, Grant 10155) and
PILTTI (funded by the Ministry of the Environment, Finland, Grant
YM57/065/2005). The work relates to projects INTARESE (funded
by European Union, Grant 018385-2), MEGAPOLI (funded by
European Union) and SCUD (funded by the Academy of Finland,
Grant 111775). Marko Tainio was supported by personal grant from
the Graduate School in Environmental Health. We would like to
thank Dr. Ewen MacDonald for checking the English language.

==Appendix. Supplementary material==

Supplementary data associated with this article can be found in
the online version at {{doi|10.1016/j.atmosenv.2010.02.036}}.

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