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Atmospheric Environment 43 (2009) 3614–3620

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Atmospheric Environment

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Population exposure and mortality due to regional background PM in Europe –Long-term simulations of source region and shipping contributions

Camilla Andersson a,b,*, Robert Bergstrom b,c, Christer Johansson a,d

a Department of Applied Environmental Science, Stockholm University, SE-10691 Stockholm, Swedenb Swedish Meteorological and Hydrological Institute, SE-60176 Norrkoping, Swedenc Department of Chemistry, University of Gothenburg, SE-412 96 Goteborg, Swedend Environment & Health Administration, Box 8136, SE-10420 Stockholm, Sweden

a r t i c l e i n f o

Article history:Received 29 October 2008Received in revised form16 March 2009Accepted 18 March 2009

Keywords:Dispersion modellingEmissionsHealth effectsLong-range transportParticulate matter

* Corresponding author. Swedish MeteorologicalSE-60176 Norrkoping, Sweden. Tel.: þ46 11 4958203

E-mail address: camilla.andersson@smhi.se (C. An

1352-2310/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.atmosenv.2009.03.040

a b s t r a c t

This paper presents the contribution to population exposure (PE) of regional background fine primary(PPM2.5) and secondary inorganic (SIA) particulate matter and its impact on mortality in Europe during1997–2003 calculated with a chemistry transport model. Contributions to concentrations and PE due toemissions from shipping, Western (WEU), Eastern (EEU), and Northern Europe are compared.WEU contributes about 40% to both PPM2.5 and SIA concentrations, whereas the EEU contribution toPPM2.5 is much higher (43% of total PPM2.5) than to SIA (29% of total SIA). The population weightedaverage concentration (PWC) of PPM2.5 is a factor of 2.3 higher than average (non-weighted) concen-trations, whereas for SIA the PWC is only a factor 1.6 higher. This is due to PPM2.5 concentrations havinglarger gradients and being relatively high over densely populated areas, whereas SIA is formed outsidepopulated areas. WEU emissions contribute relatively more than EEU to PWC and mortality due to bothPPM2.5 and SIA in Europe.The number of premature deaths in Europe is estimated to 301 000 per year due to PPM2.5 exposure and245 000 due to SIA, despite 3.3 times higher average SIA concentrations. This is due to populationweighting and assumed (and uncertain) higher relative risk of mortality for PPM2.5 components (2.8times higher RR for PPM2.5). This study indicates that it might be more efficient, for the health of theEuropean population, to decrease primary PM emissions (especially in WEU) than to decrease precursorsof SIA, but more knowledge on the toxicity of different PM constituents is needed before firm conclusionscan be drawn.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Exposure to particulate matter (PM) leads to cardiopulmonaryand respiratory diseases and increased mortality (e.g. Annesi-Maesano et al., 2007; Maitre et al., 2006; Pope and Dockery, 2006).A challenge for health impacts studies is to differentiate the effectsof possible contributing factors (e.g. Schlesinger et al., 2006), suchas physical characteristics (size, surface, mass, number, surfacemorphology and charge), physio-chemical characteristics (hygro-scopicity, acidity, lipophilicity and bioavailability), and chemicalcontent (metals, biogenic, organic and inorganic compounds).Combinations of different gas- and aerosol-mixtures may be morehazardous than individual contributions (e.g. Valberg, 2004;

and Hydrological Institute,; fax: þ46 11 4958001.

dersson).

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Oberdorster, 2001; Reiss et al., 2007). Despite major efforts in bothtoxicology and epidemiology the relative contributions of specificproperties of PM to health outcomes are not resolved (Schlesingeret al., 2006).

The RAINS integrated assessment model (Amann et al., 2004),using country-by-country source receptor relations provided by theEuropean Monitoring and Evaluation Programme (EMEP; e.g.EMEP, 2003), was used in the CAFE Programme (Clean Air ForEurope; http://www.cafe-cba.org/) to estimate morbidity andmortality in Europe, but they did not distinguish between differ-ences in relative risk (RR) for different PM components (CAFEProgramme, 2005a). Other integrated assessment models forEurope are e.g., ASAM (ApSimon et al., 1994), MERLIN (Reis et al.,2005) and CASM (SEI-Y, 1996). None of these have publisheda study similar to this one. In this study we calculate anthropogenicPM components focussing on the contribution to concentrations,population exposure (PE) and mortality in Europe. We characterisethe exposure due to anthropogenic emissions in Eastern, Northern

C. Andersson et al. / Atmospheric Environment 43 (2009) 3614–3620 3615

and Western Europe, and international shipping in the watersaround Europe. We also estimate the interannual variability for thetime period 1997–2003.

The objective is to assess the European PE and provide an esti-mate of the effect on mortality, accounting for different RR ofmortality for primary and secondary particulate components. Wealso discuss how the exposure depends on the characteristics of theemissions in different parts of Europe. It is anticipated that thecomposition of long-range transported particulate exposure willdiffer significantly depending on the trajectory of the air mass anddifference in emissions in different geographical regions. Suchdifferences in exposure may be very important both from a healthpoint of view and for abatement strategies since the toxicity of theparticles probably depends on particle composition.

2. Method

2.1. Model simulations

A Eulerian three-dimensional chemistry transport model (CTM)MATCH was used for calculations of particulate concentrations andexposure estimates. The MATCH model has been used previously onvarious spatial scales to calculate PM, photochemical species anddeposition of sulphur and nitrogen. Details on the model, modelinput (deposition velocities, land-use and boundary values) andmodel applications are given in Andersson et al. (2007). The wetdeposition scheme of primary PM was modified from completecloud condensation nuclei activation of particles to interstitial in thesmallest bin (0.002–0.01 micron) and 50% interstitial in the secondsmallest (0.01–0.1 micron). The third (0.1–2.5 micron) and fourth(2.5–10 micron) bins are completely activated. In this study wedescribe fine primary PM (PPM2.5; elemental carbon (EC), primaryorganic (OM) and primary inorganic matter (IM)) and secondaryinorganic aerosols (SIA, i.e. sulphate, nitrate and ammonium).Secondary organic aerosol (SOA) formation is not included.

The horizontal model resolution was 0.4� (ca. 44 km) and 22vertical layers were used; the lowest level being ca 20 m thick andthe top level at ca 5 km height. For meteorological data operationalfields from the European Centre of Medium-range Weather Fore-casts (ECMWF; http://www.ecmwf.int) were used for 2001–2003and ECMWF re-analysis, ERA40 (Uppala et al., 2005), for 1997–2000. The period studied here is 1997–2003. The use of ERA40 inCTM simulations has been discussed previously by Andersson et al.(2007). The temporal resolution of the original data is 6 h, inter-polated to 1-h resolution. The ERA40 and operational horizontalresolutions correspond to 125 km and 40 km respectively. Thesewere interpolated to the model grid.

EMEP expert emissions (Vestreng, 2003) of nitrogen oxides(NOx), sulphur oxides (SOx), non-methane volatile organiccompounds (NMVOC), carbon monoxide (CO), ammonia (NH3) andprimary PM with approximately 50 km resolution were used. Thesewere extracted from the EMEP home page (http://www.emep.int).In the supplements we show a summary of emission years used(Table S1), model level distribution of emissions (Table S2), distri-bution of primary PM emissions between EC, OM and IM (Table S3)and aggregated sector emissions (Table S4).

The ratio OM/OC (organic matter/organic carbon) of the primaryemissions was assumed to be one in this study, since the non-OCfraction is small and uncertain. The total emission of OM (OC) andEC is very close to the OC and EC emissions of Kupianen andKlimont (2007). The spatially distributed annual emissions fromEMEP were fed into the model with temporal variations dependenton emission sector, month, weekday and time of day (M. Roemer,TNO, 2002, personal communication).

2.2. Estimation of source contributions

The contributions of different source regions to the concentra-tions were calculated by reducing emissions and comparing theresults with concentrations in the standard emissions scenario.Emissions were decreased by 20% (instead of 100%) in the photo-chemical simulations of SIA in each scenario to minimise non-linearity effects. Then the contribution to SIA from a source regionwas obtained by scaling the difference between standard emissionand a scenario with 20% lower emissions by a factor of 5. To validatethis procedure we simulated one year with 10% emission decreasealso. It is expected that individual contributions from all regions tothe concentration will not add up to the total, since there arecontributions from boundaries and natural sources, and some non-linear effects in the chemistry. For primary PM the emissions weredecreased by 100%, since no non-linearity effects are expected fora passive tracer.

The source regions and sectors in the study are Sweden (SE),Denmark–Norway–Finland (DNF), Western Europe (WEU),Eastern Europe (EEU) and international shipping (SEA). WEU andEEU emissions are of similar magnitude but the composition ofthe emissions differs between the regions. It is interesting tocompare these regions, due to the difference in historicaldevelopment. The contributions from SE and DNF werecombined (SCA). SCA contains merely 3% of the European pop-ulation. Hence, this region can not be compared equally to WEUand EEU. A separate region for SCA is justified by itsgeographical location off-side from continental Europe and long-range transport being more important in this region, due tolower emissions. National shipping was included in the sourceregion scenarios. Natural emissions were not included. In thesupplements (Figure S1) the emissions of PPM2.5 are displayedfor the different source regions. This figure also illustrates whichcountries that belong to which region.

2.3. Estimation of health impact

PE to particles was estimated by multiplying concentrationswith the population density (population load, PL; Walker et al.,1999), by calculation of the population weighted average concen-tration (PWC). PWC is an estimate of the average concentrationa person in Europe is exposed to. The health risk of fine PM was alsoestimated in terms of death rate increase (DRI) and number ofpremature deaths,

DRIregioncomponent ¼

�RRcomponent � 1

�� PWCregion

10:

Premature death due to secondary components of SIA wasestimated using the RR from Pope et al. (2002), i.e. 6% increase inmortality per 10 mg m�3 PM2.5. For primary components the RRfrom Jerrett et al. (2005) was used, i.e. 17% per 10 mg m�3 PM2.5. Thisvalue is from a cohort study using variations in PM2.5 within LosAngeles, which to a larger degree represent primary emissions thanthe RR value of Pope et al. (2002). The higher RR in the Los Angelescohort may be interpreted as being due to effects of primarycomponents, which is in accordance with Reiss et al. (2007).

RRSIA ¼ 1:06

RRPPM2:5¼ 1:17

The population data for Europe was partially based on dataproduced by the Joint Research Centre (Population densitydisaggregated with Corine Land Cover 2000, CLC2000, http://dataservice.eea.europa.eu/dataservice/metadetails.asp?id¼830).

C. Andersson et al. / Atmospheric Environment 43 (2009) 3614–36203616

For non-EU countries population data were extracted from theColumbia University data base ‘‘Gridded Population of the World,version 3 (GPWv3)’’ (CIESIN, 2005). For the exposure calculations in28 separate countries, population data from GPWv3 were used,since the CLC2000 data were not available for separate countries.By use of crude death rate and population from EUROSTAT (2008,http://epp.eurostat.ec.europa.eu), the number of deaths, P, inEU(27), averaged over 1997–2003, was calculated. For total Europenumber of deaths (8.09 million per annum) was calculated fromcrude death rate and population data in COE (2001).

The baseline mortality (P0) is

P0 ¼P

1þ DRI:

To calculate the number of premature death, P0 was multipliedby DRI for corresponding region (EU(27) or the whole domain). Thismethodology is based on Kunzli et al. (1999) under the assumptionof no threshold concentration for mortality impacts.

3. Evaluation

3.1. MATCH model

The MATCH model compares well with other similar state-of-the-art models (e.g., van Loon et al., 2007; Hass et al., 2003). Table 1shows an evaluation of model estimated SIA components, EC, OMand some gaseous components. All measurements were extractedfrom EMEP (http://www.emep.int).

For ozone (O3), measured hourly concentrations and dailymaximum (hourly) concentrations were compared to simulatedvalues. For the other components daily measurements werecompared. The validation of SIA components and gases was con-ducted for all years. For EC and OC daily concentrations, measuredonce a week, Aug. 2002 to July 2003, at 12 regional sites (Yttri et al.,2007), were compared to the modelled EC and primary OM. Theglobal correlation is a comparison of values at the highest availabletemporal resolution for all sites. Displayed are also correlations ofannual averages at all available sites (spatial correlation).

We used no factor for conversion of measured OC to OM, due tothe assumption made in the distribution of the emissions and noageing of primary aerosol in the model. The factor should rangebetween 1.2 and 1.7, and is probably even higher for aged aerosol

Table 1Comparison of measured and modelled concentrations of hourly ozone, and daily sulphur(S/N/EC/OM) or ppbv (ozone).

Meanobserved

Meanmodel

Modelbias (%)

Corrdaily

ECa 0.66 0.38 �43 0.63OM(¼OC)a 3.35 0.50 �85 0.53NO3� 0.39 0.38 �1.6 0.57

TNO3b 0.47 0.54 14.5 0.59

NO2 2.01 2.33 15.8 0.64HNO3 0.17 0.11 �36.2 0.20NH4þ 0.74 0.56 �24.3 0.56

TNHxc 1.23 1.26 2.8 0.49

NH3 1.67 0.94 �43.6 0.77SO4

2� 0.76 0.49 �35.3 0.55SO2 0.85 0.89 4.3 0.46O3 daily meand 30.62 27.10 �11.5 0.65O3 daily maxd 40.81 37.86 �7.2 0.77

a For EC and OM correlation of yearly averages is the spatial correlation since only onb TNO3 ¼ total nitrate: NO3

�(p) þ HNO3(g).c TNHx ¼ total NHx: NH4

þ(p) þ NH3(g).d For ozone only stations at altitudes within 250 m of the model altitude were include

(Turpin et al., 2000, Kupianen and Klimont, 2007). This means thatthe estimated exposure to OM2.5 is underestimated.

The underestimation of the EC concentrations is partly due towild fire emissions not taken into account. Tsyro et al. (2007)estimated the wild fire contribution to EC being up to 20–30% atsome sites (1–10% in general). They also indicated that the largestuncertainty in EC emissions lies in residential combustion, sug-gesting overestimations in Nordic countries and underestimationsin Southern Europe and possible underestimations of EC emissionsfrom traffic in some countries. However, the spatial correlation ofEC is good (0.93).

For OM the measurements also include SOA (excluded fromour calculations). Missing precursors to biogenic SOA shouldincrease the model bias during summer (Yttri et al., 2007).Uncertain wood burning emissions is also problematic for theprimary OM concentration (Simpson et al., 2007). Therefore thesimulation severely underestimates the OM concentration. Otherreasons for discrepancies are simplifications in division betweenprimary PM components, treatment of primary aerosol and wetdeposition.

The mean model biases for particulate nitrate and total nitrate(NO3

�(p) þ HNO3(g)) are relatively small, �2% and þ14%, respec-tively. For particulate ammonium the model underestimates theobserved concentrations by 24% but for stations that measure totalNHx (NH4

þ(p) þ NH3(g)) the mean model bias is small (3%). Themodel bias for sulphate is larger (average �35%). This underesti-mation could partly be due to emissions included being valid onlyfor latter years (the emissions are generally decreasing with time).

The correlation coefficients for the yearly average concentra-tions of the different SIA components at the measurement stationsare all reasonably high, from 0.74 for sulphate to 0.82 for nitrate.

3.2. Sensitivity to meteorology and non-linearity test

ERA40 meteorology has the advantage of being self-consistentover time (Andersson et al., 2007); hence for studying interannualvariability this data is superior to operational meteorology. On theother hand the operational meteorology has higher horizontalresolution.

By changing meteorological data type we introduce a bias in theinterannual variability. This bias was assessed by comparingconcentrations from simulations with ERA40 and operationalmeteorology for 2001. For SIA we note that the concentrations

ous and nitrogenous compounds and daily, once a week, for EC and OC. Units: mg m�3

elationvalues

#Cases dailyvalues

Correlationyearly averages

#Cases yearlyvalues

603 0.93 12603 0.91 12

62 520 0.82 189100 452 0.71 303133 418 0.75 40034 712 0.38 10454 878 0.76 17297 323 0.62 29525 951 0.99 81

178 981 0.74 538167 192 0.61 508

5 243 477 0.61 651224 180 0.78 651

e year of measurements exist.

d.

C. Andersson et al. / Atmospheric Environment 43 (2009) 3614–3620 3617

decrease slightly in parts of Europe with the change from ERA40 tooperational meteorology (in general less than 2 mg m�3, largest overthe Mediterranean Sea, supplements, Figure S2). Increases (gener-ally less than 1 mg m�3) are seen in small regions in northern Italy,Romania and UK. For PPM2.5 the increase is mainly below 1 mg m�3

and limited to a few, small regions, with the largest increase (up to3 mg m�3) in Northern Italy (supplements, Figure S2). Thus,changing the type of meteorological data is of importance forstudies of interannual variability.

Figure S3 (supplements) demonstrates non-linear effects asa result of emission decreases. A small non-linearity is seen in theWEU and EEU scenarios. For WEU a non-linearity is visible alongthe North–West Mediterranean Sea and adjacent countries and inthe UK and the Netherlands. For EEU a non-linear effect is observedin Poland. The non-linearities are, however, negligible whencompared to the concentration changes due to 20% emissionchanges.

4. Source-region contribution

4.1. Contribution to emissions

Table 2 gives the total model domain emissions for the year2001 and the distribution between source regions. The emissions inEEU exceed those of WEU for primary particles and SOx. Fornitrogen emissions (NOx and NH3) and carbon species (CO, NMVOC)the emissions in WEU exceed those of EEU. The emission inventoryused is less complete for some Eastern European countries (Ves-treng et al., 2007a). Vestreng et al. (2007b) raise concerns regardingcomparisons between Eastern and Western Europe, due to unre-solved problems in some of the emission data. Different countriesmay have different procedures on how and what they report and towhich sector they attribute emissions. This might bias thecomparison between Eastern and Western European emissions.Therefore comparisons of sector distributions for the differentregions are uncertain.

The Swedish contributions to European emissions are 1–2%. Thecontributions of international shipping to the total Europeanemissions are relatively large: 8% (PPM2.5), 16.5% (NOx) and 11%(SOx). Natural emissions of sulphur (di-methyl sulphide andvolcanoes) contribute by a relatively large fraction of SOx.

EC and OM mainly originate from non-industrial combustionplants, road transport and shipping. IM is mainly from stationarysources, with largest contribution from production processes. Thetotal emission of PPM2.5 is 3.02 Tg yr�1 (EC: 666 Gg yr�1, OM:933 Gg yr�1 and IM: 1.42 Tg yr�1). Agriculture is the (single) largestcontributor to NH3. The largest sources of NOx are road transportand other mobile sources and machinery, though there is contri-bution also from combustion. SOx mainly originate from combus-tion and international shipping.

Table 2Total emissions in the model domain and distribution between source regions.Values are based on total EMEP expert emissions 2001. NOx in equivalents of NO2,SOx in equivalents of SO2. SE is Swedish contribution, DNF Danish, Norwegian andFinnish contribution, WEU Western European, EEU Eastern European contributionsand SEA is contribution from international shipping.

Total (Tg yr�1) SE (%) DNF (%) WEU (%) EEU (%) SEA (%) Other (%)

PPM2.5 3.02 1.5 3.9 37.4 49.1 8.0 0.0CO 51.8 1.3 3.4 52.2 41.8 0.6 0.7NH3 5.80 1.0 2.8 53.0 41.2 0.0 2.1NOx 19.2 1.1 3.2 47.5 31.1 16.5 0.6NMVOC 14.4 1.9 4.6 55.9 36.1 0.7 0.7SOx 20.1 0.3 0.7 27.7 45.8 11.0 14.5

4.2. Contribution to concentrations

Seven year average concentrations from different regions andinternational shipping are summarised in Table 3 (averages overthe model domain) and depicted in the supplements (Figure S4).Peak concentrations are found in the Moscow region (PPM2.5,18 mg m�3) and in the Milan region (SIA, 11 mg m�3). There isa gradient in SIA and PPM2.5 concentrations from continentalEurope to remote areas. High concentrations of sulphate exist in thewhole Mediterranean area and South-Eastern Europe (peak,6.5 mg m�3, Eastern Mediterranean Sea) due to high emissions fromSouth-Eastern Europe and Italy combined with volcanic emissions(Etna). Ammonium concentrations are high in North-Western(Benelux, the UK and Germany) and South-Eastern Europe and Italypeaking in the Milan region (2.4 mg m�3). Nitrate also peaks inMilan (6.3 mg m�3), having highest concentrations in North-Western, Central and Southern Europe.

The difference in spatial concentration pattern between the SIAcomponents is due to spatial differences in emissions of NH3, SOx

and NOx and meteorology. EC is higher in central Europe, and thespatial pattern is similar to that of Schaap et al. (2004). Primary OMshows similar pattern as EC.

SIA contributes more than PPM2.5 to PM2.5, despite highermaxima for PPM2.5. The WEU average contribution is larger for SIAwhereas EEU is contribute more to PPM2.5. This is partly expectedfrom the distribution of emissions. The effect of the prevailingSouth-Westerly winds over Europe is evident. The concentrationcontribution from Western to Eastern Europe is greater than therelative distribution of emissions. SIA originating from interna-tional shipping has higher concentrations off-shore than over landand affects a much larger land area than primary particles.

The interannual variability (cf. Table 3) is defined as the stan-dard deviation of annual averages (not de-trended) divided by theoverall average. It is largest for SIA since their formation dependson atmospheric conditions. The interannual variability of theregional contribution (supplements, Table S5) also follows thispattern. The highest interannual variability is displayed by WEUcontribution to average SIA (sulphate: 15%, ammonium: 12% andnitrate: 11%). For international shipping the interannual variabilityis as large for PPM2.5 components as for SIA components (6–7%).The interannual variability depends on both emissions and mete-orology in this study.

It is not straightforward to compare the results of this study tothe blame matrices of EMEP (e.g. EMEP, 2003). We aggregated theEMEP contributions for two countries: France and Sweden for theyear 2000, to compare to our average contributions for the sameyear, (Supplements, Table S6). In general, our model predictssmaller contributions than EMEP for the two countries. Thecontribution distribution is similar, e.g. for France the greatestcontribution is from WEU and second greatest is from SEA in bothstudies.

4.3. Contribution to exposure and mortality

In Fig. 1 contributions from source regions and international seatraffic to PL of PPM2.5 and SIA, averaged over 1997–2003, are dis-played. The PL of SIA is generally larger than of PPM2.5, despite thehigher concentration of PPM2.5 in densely populated areas. North-Western Europe has the largest PL, which is expected due to highemissions and large population density. The contribution to PL froma far distant region can be significant, e.g. the large contribution ofWEU to PL in Moscow. The contribution from international ship-ping to PL is largest where a lot of people live close to largeharbours such as in the Benelux region and UK.

Table 3European average concentration (1997–2003), contributions from emission regions and interannual variability for primary (EC/OC/IM) and secondary inorganic (SO4

2�/NO3�/

NH4þ) fine particulate components.

1997–2003 average concentration and contributions 1997–2003 interannual variability

Total (mg m�3) SE (%) DNF (%) WEU (%) EEU (%) SEA (%) Total (%)

EC2.5 0.23 1.2 3.8 43.9 31.7 19.2 3OM2.5 0.31 1.4 4.5 42.6 36.6 14.9 3IM2.5 0.42 1.5 3.6 39.7 55.0 0.0 4Total PPM2.5 0.97 1.4 4.0 41.7 43.5 9.4 3

SO42� 1.53 0.2 0.4 27.5 31.7 12.1 10

NO3� 1.24 0.9 2.6 52.8 23.9 14.9 7

NH4þ 0.47 0.6 1.6 47.2 37.3 6.2 7

Total SIA 3.23 0.5 1.4 40.0 29.5 12.3 8

C. Andersson et al. / Atmospheric Environment 43 (2009) 3614–36203618

Table 4 shows PWC of particulate components, and the cor-responding DRI. The PWC is much higher than the averageconcentration (compare Table 3) in Europe; a factor 2.3 forPPM2.5 and 1.6 for SIA. For most components the contribution toPWC (or DRI) from Western Europe is larger than from the otherregions, except for IM and sulphate for which the EEU contri-bution is larger. However, we do not account for the largerfraction transported out of the European area from EEU, affectingnon-European populations. The contribution from WEU is

Fig. 1. Average (1997–2003) population load of PPM2.5 and SIA in Europe (top row). The cinternational sea traffic (see Figure S1). Unit: #persons � ug m�3.

significantly higher for PWC than for average concentration, sinceWestern Europe is densely populated and atmospheric transportof pollutants also affects densely populated areas in EasternEurope to a large degree. The shipping, SE and DNF contributionsto PWC are lower compared to the average concentrationcontributions, since the emissions occur in less populated areas.Compared to the emission distributions, the contributions to PWCare skewed towards a larger Western contribution for allcomponents.

ontributions to PPM2.5 (middle row) and SIA (bottom row) from different regions and

Table 4European death rate increase (DRI) and population weighted average concentration (PWC) to particulate components for 1997–2003. Contributions from the emission regionsand interannual variability for death rate increase.

DRI (%) PWC (mg m�3) Contributions Interannual variability

SE (%) DNF (%) WEU (%) EEU (%) SEA (%) Total (%)

EC2.5 1.0 0.56 0.4 1.3 57.0 30.6 10.6 2OM2.5 1.2 0.73 0.5 1.6 54.5 34.9 8.5 2IM2.5 1.7 0.98 0.6 1.2 44.2 54.0 0.0 3Total PPM2.5 3.9 2.27 0.5 1.4 50.7 42.1 5.3 2

SO42� 1.3 2.17 0.1 0.3 31.2 37.2 9.8 10

NO3� 1.3 2.12 0.6 1.7 64.9 25.3 10.6 7

NH4þ 0.6 0.92 0.3 0.9 54.6 36.4 6.1 8

Total SIA 3.2 5.21 0.3 1.0 49.1 32.3 9.4 8

C. Andersson et al. / Atmospheric Environment 43 (2009) 3614–3620 3619

The relative contribution to PWC of sea traffic emissions is largerfor SIA (80% of total PWC) than for PPM2.5. This is not true for allindividual countries, e.g. for Iceland and the Netherlands, wherethe primary shipping contribution is more important (PWC and DRIfor individual countries is provided in the supplements, Tables S7and S8). SIA is more effectively transported to other regions thanPPM2.5. For PPM2.5 the contribution from the region to which thecountry belongs is largest. This is also true for SIA for the countriesin EEU and WEU. For Sweden, Norway and Denmark the largestcontributor is WEU and for Finland it is EEU showing the largerinfluence of long-range transport in the north.

The interannual variability of PWC is larger for the secondarythan for primary components (Table 4). There is slightly lowerinterannual variability in averages for the PPM2.5 components thanPWC, consistent with population and primary pollutant emissionsbeing correlated. The interannual variability may be affected by thechange in meteorological model.

For both SIA and PPM2.5 the largest PWC and DRI are found inthe Benelux region.

Despite larger PWC for SIA, the DRI is larger for PPM2.5, due tothe larger RR assumed for PPM2.5. The number of premature deathsin EU(27) are 177 000 and 139 000 for PPM2.5 and SIA respectively.For the whole of Europe these values are 301 000 and 245 000. TheCAFE Programme baseline estimate (347 900 premature deaths for2000; CAFE Programme, 2005b) for EU(25) is close to these values,despite the lower RR (they use 1.06 for all PM2.5) and slightlysmaller region. Using the same RR (1.06) for both SIA and PPM2.5,we obtain 203 000 premature deaths (for EU(27)), which is muchless than the CAFE estimate. The most important difference is likelythat the CAFE study include the urban enhancement effectincreasing the exposure estimate. They excluded the effect ofnatural sources, which we did not (in the premature death and PLestimates), which increases our estimate. Their emissions ofprimary PM differ somewhat from ours. They based the analysis onmeteorology from 1997 only, which could introduce some bias.Similarities include simulated particulate components, modelresolution and most of the emission data.

5. Discussion of uncertainties

There are a number of uncertainties associated with modellingPM concentrations. The above comparison with measurementsindicates that the uncertainty varies depending on substance. Inmost cases a bias between modelled and measured values can beexplained by missing or uncertain emissions. In addition the qualityof the emission data varies from sector to sector and from countryto country. Surface dust emissions are not well described. Theseparticles are, however, of minor importance for fine PM concen-trations and exposure, even though there is a fraction of road dustsmaller than 2.5 mm in some urban areas (e.g. Hussein et al., 2008).In this study we have not considered SOA formation.

Modelling of PE is associated with many uncertainties, and herewe have not considered indoor exposures or increased exposuresduring time spent in traffic environments etc. On the other handthe risk factors used are not valid for indoor experiments, but basedon urban background measurements. The model underestimationof sulphate (and ammonium) indicates that the PE of thiscompound will also be underestimated in this study.

Our calculated premature deaths due to PPM2.5 exposure arelikely an underestimate due to the coarse horizontal resolutionover urban areas, where gradients in PL may be very large (e.g.Johansson et al., 2007). For the mortality estimates there are largeuncertainties in the relative risk factors.

The estimated mortality effects are conditional to the assump-tion of different risk factors for primary and secondary PM. The RRfor primary PM is justified based on the higher RR factors seen instudies based on intra-city exposure gradients as in Los Angeles byJerrett et al. (2005) compared to inter-city exposure, as in the ACSstudy by Pope et al. (2002). Unfortunately, there are no studies thathave explicitly used primary PM as exposure indicator. Whether itis primary PM or the mixture of many primary pollutants or someother parameter correlating with primary pollution, causing thehigher RR in Jerrett et al. (2005) compared to ACS, is not known.Jerrett et al. (2005) argue that their higher RR is consistent withlarger health effects seen in studies based on intra-urban exposuresuch as Hoek et al. (2002) who found a near doubling of cardio-pulmonary mortality for people living near major roads in a Euro-pean study.

Other epidemiological studies for urban areas using nitrogenoxides (NOx) (Nafstad et al., 2004) or nitrogen dioxide (NO2) (Filleulet al., 2005; Scoggins et al., 2004; Gehring et al., 2006) as proxy forprimary vehicle exhaust exposure also indicate that a higher RRthan 1.06 should be applied for primary PM.

When estimating mortality among a large population due toexposure during several years, one should consider the life-tablesand calculate years of life lost among the population, rather thansimply the number of premature deaths per year. This wouldhowever not change our main conclusions regarding the differenteffects of primary versus secondary particles and the contributionsfrom emissions in different parts of Europe.

6. Conclusions

A three-dimensional chemistry transport model has been usedto estimate contribution from regions of Europe to populationexposure of regional background fine PM in Europe during 1997–2003. The main results from this study are:

European population weighted average concentration (PWC) ofsecondary inorganic aerosol (SIA) is higher (5.2 mg m�3) thanPPM2.5, (2.3 mg m�3). However, taking into account a possibledifference in relative risk for mortality for primary and secondaryPM the mortality effects of these are very similar. The exposure to

C. Andersson et al. / Atmospheric Environment 43 (2009) 3614–36203620

PPM2.5 and SIA in Europe is estimated to correspond to 301 000 and245 000 premature deaths per annum. The Benelux countries areespecially impacted by high exposure.

Western Europe contributes more to European average PM (sumof PPM2.5 and SIA) concentration and to European PWC andpremature deaths than Eastern Europe does, due to the prevailingwesterly wind direction. International shipping is estimated tocontribute 5% to the total PPM2.5 PWC and 9% to the SIA PWC.

Due to the differences in emissions of particles and gasesbetween different parts of Europe there are substantial differencesin exposure of different particle components.

Acknowledgements

This work is supported by the Swedish Road Administrationthrough EMFO by project IMPORT and the Swedish EPA by projectSCARP. Thanks to colleagues at Stockholm University and SMHI foruseful comments.

Appendix. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.atmosenv.2009.03.040.

References

Amann, M., Cofala, J., Heyes, C., Klimont, Z., Mechler, R., Posch, M., Schoepp, W.,2004. The RAINS model. Documentation of the model approach preparedfor the RAINS review. Internet URL: http://www.iiasa.ac.at/rains/review/review-full.pdf.

Andersson, C., Langner, J., Bergstrom, R., 2007. Interannual variation and trends inair pollution over Europe due to climate variability during 1958–2001 simu-lated with a regional CTM coupled to the ERA40 reanalysis. Tellus B 59, 77–98.

Annesi-Maesano, I., Forastiere, F., Kunzli, N., Brunekref, B., 2007. Particulate matter,science and EU policy. European Respiratory Journal 29, 428–431.

ApSimon, H., Warren, R., Wilson, J., 1994. The Abatement Strategies AssessmentModeldASAM: applications to reductions of sulphur dioxide emissions acrossEurope. Atmospheric Environment 24, 649–663.

CAFE Programme, Febuary 2005a. Methodology for the cost benefit analysis forCAFE: volume 2: health impact assessment. AEAT/ED51014/MethodologyVolume 2, Issue 2. Internet URL: http://www.cafe-cba.org/.

CAFE Programme, 2005b. CAFE CBA: baseline analysis 2000 to 2020. AEAT/ED51014/Baseline scenarios Issue 5. Internet URL: http://www.cafe-cba.org/.

CIESIN, 2005. Center for International Earth Science Information Network (CIESIN),Columbia University; and Centro Internacional de Agricultura Tropical (CIAT),2005. Gridded Population of the World Version 3 (GPWv3): Population Grids.Socioeconomic Data and Applications Center (SEDAC), Columbia University,Palisades, NY. Internet URL: http://sedac.ciesin.columbia.edu/gpw.

COE, 2001. Recent Demographic Developments in Europe. Council of EuropePublishing, ISBN 92-871-4783-3.

EMEP, 2003. Transboundary Acidification. Eutrophication and Ground Level Ozonein Europe Internet URL: http://www.emep.int, EMEP Report 1/2003. Part 3.102 pp.

Filleul, L., Rondeau, V., Vandentorren, S., Le Moual, N., Cantagrel, A., Annesi-Maesano, I., et al., 2005. Twenty five year mortality and air pollution: results fromthe French PAARC survey. Occupational Environmental Medicine 62, 453–460.

Gehring, U., Heinrich, J., Kramer, U., Grote, V., Hochadel, M., Sugiri, D., et al., 2006.Long-term exposure to ambient air pollution and cardiopulmonary mortality inwomen. Epidemiology 17, 545–551.

Hass, H., van Loon, M., Kessler, C., Stern, R., Matthijsen, J., Sauter, F., Zlatev, Z.,Langner, J., Foltescu, V., Schaap, M., 2003. Aerosol Modelling: Results andIntercomparison from European Regional-scale Modelling Systems. EUROTRACISS, Munich, EUROTRAC-2 Special Report. 77 pp.

Hoek, G., Brunekreef, B., Goldbohm, S., Fischer, P., van den Brandt, P.A., 2002.Association between mortality and indicators of traffic-related air pollution inthe Netherlands: a cohort study. Lancet 360 (9341), 1203–1209.

Hussein, T., Johansson, C., Karlsson, H., Hansson, H.-C., 2008. Factors affecting non-tailpipe aerosol particle emissions from paved roads: on road measurements inStockholm, Sweden. Atmospheric Environment 42, 688–702.

Jerrett, M., Burnett, R.T., Ma, R., Pope III, C.A., Krewski, D., Newbold, K.B.,Thurston, G., Shi, Y., Finkelstein, N., Calle, E.E., Thun, M.J., 2005. Spatial analysisof air pollution mortality in Los Angeles. Epidemiology 16 (6), 727–736.

Johansson, C., Norman, M., Gidhagen, L., 2007. Spatial & temporal variations of PM10and particle number concentrations in urban air. Environmental Monitoringand Assessment 127, 477–487. doi:10.1007/s10661-006-9296-4.

Kunzli, N., Kaiser, R., Medina, S., et al., 1999. Air pollution attributable cases: tech-nical report on epidemiology. In: Health Costs Due to Road Traffic-related AirPollution: An Impact Assessment Project of Austria, France, and Switzerland.Euro-pean Regional Office, World Health Organization, Bern, Switzerland.

Kupianen, K., Klimont, Z., 2007. Primary emissions of fine carbonaceous particles inEurope. Atmospheric Environment 41, 2156–2170.

van Loon, M., Vautard, R., Schaap, M., Bergstrom, R., Bessagnet, B., Brandt, J.,Builtjes, P.J.H., Christensen, J.H., Cuvelier, K., Graf, A., Jonson, J.E., Krol, M.,Langner, J., Roberts, P., Rouil, L., Stern, R., Tarrason, L., Thunis, P., Vignati, E.,White, L., Wind, P., 2007. Evaluation of long-term ozone simulations from sevenregional air quality models and their ensemble. Atmospheric Environment 41,2083–2097.

Maitre, A., Bonneterre, V., Huillard, L., Sebatier, P., de Gaudemaris, R., 2006. Impactof urban atmospheric pollution on coronary disease. European Heart Journal 27,2275–2284.

Nafstad, P., Haheim, L.L., Wisloff, T., Gram, F., Oftedal, B., Holme, I., et al., 2004. Urbanair pollution and mortality in a cohort of Norwegian men. EnvironmentalHealth Perspectives 112, 610–615.

Oberdorster, G., 2001. Pulmonary effects of inhaled ultrafine particles. InternationalArchives of Occupational and Environmental Health 74, 1–8.

Pope, C.A., Dockery, D.W., 2006. Health effects of fine particulate air pollution: linesthat connect. Journal of Waste Management Association 56, 709–742.

Pope III, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D.,2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fineparticulate air pollution. Journal of the American Medical Association 287,1137–1141.

Reis, S., Nitter, S., Friedrich, R., 2005. Innovative approaches in integrated assessmentmodelling of European air pollution control strategiesdimplications of dealingwith multi-pollutant multi-effect problems. Environmental Modelling &Software 20, 1524–1531.

Reiss, R., Andersson, E.L., Cross, C.E., Hidley, G., Hoel, D., McClellan, R.,Moolgavkar, S., 2007. Evidence of health impacts of sulfate- and nitrate-containing particles in ambient air. Inhalation Technology 19 (5), 419–449.

SEI-Y, 1996. The European Fossil-fueled Power Station Database Used in the SEICASM model Internet URL: http://www.sei.se/mediamanager/documents/Publications/Climate/theeuropean_fossil_fueled_power_station.pdf (accessedMarch 2009).

Schaap, M., Denier van der Gon, H.A.C., Dentener, F., Visschedijk, A.J.H., van Loon, M.,ten Brink, H.M., Putaud, J.-Ph., Guillame, B., Liousse, C., Builtjes, P.J.H., 2004.Anthropogenic black carbon and fine aerosol distribution over Europe. Journalof Geophysical Research 109, D18207. doi:10.1029/2003JD004330.

Schlesinger, R.B., Kunzli, N., Hidy, G.M., Gotschi, T., Jerrett, M., 2006. The healthrelevance of ambient particulate matter characteristics: coherence of toxico-logical and epidemiological interferences. Inhalation Toxicology 18, 95–125.

Scoggins, A., Kjellstrom, T., Fisher, G., Connor, J., Gimson, N., 2004. Spatial analysis ofannual air pollution exposure and mortality. Science of the Total Environment321, 71–85.

Simpson, D., Yttri, K.E., Klimont, Z., Kupianen, K., Caseiro, A., Gelencser, A., Pio, C.,Puxbaum, H., Legrand, M., 2007. Modeling carbonaceous aerosol over Europe:analysis of the CARBOSOL and EMEP EC/OC campaigns. Journal of GeophysicalResearch 112, D23S14. doi:10.1029/2006JD008158.

Tsyro, S., Simpson, D., Tarrason, L., Klimont, Z., Kupianen, K., Pio, C., Yttri, K.E., 2007.Modeling of elemental carbon over Europe. Journal of Geophysical Research112, D23S19. doi:10.1029/2006JD008164.

Turpin, B.J., Saxena, P., Andrews, E., 2000. Measuring and simulating particulateorganics in the atmosphere: problems and prospects. Atmospheric Environ-ment 34 (18), 2983–3013.

Uppala, S.M., Kållberg, P.W., Simmons, A.J., Andrae, U., da Costa Bechtold, V., et al.,2005. The ERA40 re-analysis. Quartly Journal of the Royal MeteorologicalSociety 131B, 2961–3012.

Valberg, P.A., 2004. Is PM more toxic than the sum of its parts? Risk assessmenttoxicity factors vs. PM mortality ‘‘effect functions’’. Inhalation Toxicology 16(Suppl. 1), 19–29.

Vestreng, V., 2003. Review and revision. Emission data reported to CLRTAP. MSC-WStatus report 2003. EMEP/MSC-W Note 1/2003. Retrieved in Aug 2008, fromInternet URL: http://www.emep.int.

Vestreng, V., Myhre, G., Fagerli, H., Reis, S., Tarrason, L., 2007a. Twenty-five years ofcontinuous sulphur dioxide emission reduction in Europe. AtmosphericChemistry and Physics 7, 3663–3681.

Vestreng, V., Mareckova, K., Kakareka, S., Malchykhina, A., Kukharchyk, T., 2007b.Inventory review 2007. Emission data reported to LRTAP Convention and NECDirective. Stage 1 and 2 review. Review of gridded data and Review of PMinventories in Belarus, Republic of Moldova, Russian Federation and Ukraine.Internet URL: http://www.emep.int.

Walker, S.E., Slordal, L.H., Guerreiro, C., Gram, F., Gronskei, K.E., 1999. Air pollutionexposure monitoring and estimation. Part II. Model evaluation and populationexposure. Journal of Environmental Monitoring 1, 321–326.

Yttri, K.E., Aas, W., Bjerke, A., Cape, J.N., Cavalli, F., Ceburnis, D., Dye, C., Emblico, L.,Facchini, M.C., Forster, C., Hanssen, J.E., Hansson, H.C., Jennings, S.G.,Maenhaut, W., Putaud, J.P., Torseth, K., 2007. Elemental and organic carbon inPM10: a one year measurement campaign within the European Monitoringand Evaluation Programme EMEP. Atmospheric Chemistry and Physics 7,5711–5725.