e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239

avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

Modelling concentrations of airborne primaryand secondary PM10 and PM2.5 with theBelEUROS-model in Belgium

Felix Deutscha,∗, Jean Vankerkoma, Liliane Janssena, Stijn Janssena, László Bencsb,c,René Van Griekenb, Frans Fierensd, Gerwin Dumontd, Clemens Mensinka

a VITO, Centre for Integrated Environmental Studies, Boeretang 200, B-2400 Mol, Belgiumb MiTAC, Department of Chemistry, University of Antwerp, Campus Drie Eiken, Universiteitsplein 1, B-2610 Antwerp, Belgiumc Research Institute for Solid State Physics and Optics, Hungarian Academy of Sciences, PO Box 49, H-1525 Budapest, Hungaryd Belgian Interregional Environment Agency (IRCEL), Kunstlaan 10-11, B-1210 Brussels, Belgium

a r t i c l e i n f o

Article history:

Published on line 17 July 2008

Keywords:

Particulate matter

Air quality modelling

PM10

PM2.5

Chemical species

Model validation

a b s t r a c t

The Eulerian Chemistry-Transport Model BelEUROS was used to calculate the concentrations

of airborne PM10 and PM2.5 over Europe. Both primary as well as secondary particulate mat-

ter in the respirable size-range was taken into account. Especially PM2.5 aerosols are often

formed in the atmosphere from gaseous precursor compounds. Comprehensive computer

codes for the calculation of gas phase chemical reactions and thermodynamic equilibria

between compounds in the gas-phase and the particulate phase had been implemented into

the BelEUROS-model. Calculated concentrations of PM10 and PM2.5 are compared to obser-

vations, including both the spatial and daily, temporal distribution of particulate matter

in Belgium for certain monitoring locations and periods. The concentrations of the sec-

ondary compounds ammonium, nitrate and sulfate have also been compared to observed

values. BelEUROS was found to reproduce the observed concentrations rather well. The

model was applied to assess the contribution of emissions derived from the sector agri-

culture in Flanders, the northern part of Belgium, to PM10- and PM2.5-concentrations. The

results demonstrate the importance of ammonia emissions in the formation of secondary

particulate matter. Hence, future European emission abatement policy should consider more

the role of ammonia in the formation of secondary particles.

However, this limit has been exceeded at 30 out of the 40 PM

1. Introduction

High concentrations of fine particulate matter (PM) in ambi-ent air are currently a major environmental problem in mostEuropean countries. These particles are associated with strong

adverse health effects (Dockery et al., 1993; Pope et al.,1995) and also the European Union states in her proposedThematic Strategy on Air Quality that fine particulate mat-

∗ Corresponding author. Tel.: +32 14 335964; fax: +32 14 321185.E-mail address: felix.deutsch@vito.be (F. Deutsch).

0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2008.06.003

© 2008 Elsevier B.V. All rights reserved.

ter is connected to considerable negative effects on humanhealth. According to the European Directive on Air Quality(1999/30/EG) the daily mean concentration of PM10 may notexceed a limit value of 50 �g/m3 on more than 35 days per year.

10

measurement locations in Belgium in the year of 2006. Themean number of days on which exceedances were recorded atthese stations amounted to 60, with a maximum of 175 days

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t one station (IRCEL, 2007). Hence, we are faced with a seriousxceedance of the European limit value for PM10 and adverseealth effects can be anticipated.

Advanced computer models including atmospheric trans-ort, turbulent diffusion, atmospheric chemistry and micro-hysics can help to understand the connections betweenmissions, chemical reactions, meteorological factors and theesulting concentrations of fine particulate matter in the air.ot only primary emissions of particles contribute to highM-concentrations, but also the formation of secondary par-iculate matter from the emissions of precursor compoundsontribute significantly to PM10- and PM2.5-concentrations.specially, the emissions of ammonia (NH3), nitrogen oxidesNOx), sulfur dioxide (SO2), and non-methane volatile organicompounds (NMVOC) are involved in these processes, leadingreferentially to small secondary particles of the size fractionM2.5.

To investigate these processes, we extended the opera-ional Eulerian air-quality model EUROS, originally developedt RIVM in the Netherlands for the modelling of ozone, withwo special modular algorithms for atmospheric particles. TheelEUROS-model is now an operational tool for policy supportt the Interregional Environment Agency (IRCEL) in BrusselsDelobbe et al., 2002; Mensink et al., 2002). The base gridf BelEUROS covers nearly whole Europe with a resolutionf 60 km × 60 km. The part of the grid covering Belgium, theetherlands and parts of Germany, France and the UK is cal-ulated with a resolution of 15 km × 15 km or 7.5 km × 7.5 km.our vertical layers are used for the calculation of the chemicalrocesses and 14 layers for the advection–diffusion calcula-ions.

The reason for not running the BelEUROS-model withigher spatial and/or vertical resolution or using a moreophisticated Chemical Transport Model has to be seen in theact that the emphasis of this study lay on the calculation ofarious scenarios for which the necessary CPU time shouldot be too long. However, these scenarios should be calculated

or the entire model domain. Choosing a higher resolutionhan the one mentioned before would also interfere with theoncept of the BelEUROS-model which was constructed as aimplified 3D-model, especially in the vertical direction. Addi-ionally, the spatial resolution of the used emission input filess currently limited to 7.5 km.

A detailed emission module describes the emissions ofix pollutant categories (NOx, NMVOC, SO2, NH3, PM2.5 andM10–2.5) and 7 different emission sectors (traffic, residentialmissions, refineries, use of solvents, combustion, industrynd agriculture). Both point sources and area sources arencluded. As far as the meteorology is concerned, the modelses reanalyzed 3D datasets from ECMWF (European Centre

or Medium-Range Weather Forecasts, Reading, UK) for theemperature, relative humidity, wind velocity, wind direction,loud cover and precipitation. Additionally, the boundary layereight, calculated by IRCEL using the modified bulk Richard-on number method, is used.

Following an extended literature study, the Caltech Atmo-

pheric Chemistry Mechanism (CACM, Griffin et al., 2002) andhe Model of Aerosol Dynamics, Reaction, Ionization and Dis-olution (MADRID 2, Zhang et al., 2004) were selected andmplemented into BelEUROS. CACM is a gas phase chem-

7 ( 2 0 0 8 ) 230–239 231

ical mechanism describing the formation of precursors ofsecondary organic aerosols (SOA) in the atmosphere by amechanistic approach. The actual aerosol module, MADRID 2,treats the formation of secondary aerosols via equilibrium cal-culations between the gas phase and the aerosol phase. Alsodynamic processes (e.g. mass transfer between gas phase andparticulate phase and nucleation of particles) are included intoMADRID 2.

Currently, BelEUROS is able to model mass and chemi-cal composition of aerosols in two size fractions (PM2.5 andPMcoarse). The chemical composition is expressed in termsof seven components: ammonium, nitrate, sulfate, primaryinorganic compounds, elementary carbon, primary organiccompounds and SOA.

In Section 2, we shortly discuss the implementation ofCACM and MADRID 2 into the BelEUROS model. Section 3presents and discusses the results of calculations with theBelEUROS model for PM10 and PM2.5 as well as for the sec-ondary species ammonium, nitrate and sulfate for episodes.Additionally, the influence of emissions from the sector agri-culture on PM10- and PM2.5-concentrations is investigated.

2. Methodology

The “ozone-version” of BelEUROS uses the CB-IV gas phasechemical mechanism (Gery et al., 1989). For modellingaerosols, CACM is used instead as gas phase chemical mecha-nism. This mechanism comprises 361 reactions among 122components. With this, CACM contains basic ozone chem-istry, additional to the most important reactions of variousgenerations of organic compounds during which condensableproducts are formed. A limited liquid phase chemistry hasalso been implemented into the Chemistry-Aerosol-Moduleaccounting for the oxidation of S(IV) to S(VI) in the atmo-spheric liquid phase (i.e., clouds). In this way, liquid phaseformation of sulfate is taken into account. Heterogeneousreactions are not yet integrated into BelEUROS.

CACM treats 42 condensable organic products originat-ing from anthropogenic or biogenic NMVOC-emissions. InMADRID 2, these condensable products are lumped into fivehydrophilic and five hydrophobic components which are sub-sequently equilibrated between the gas phase and the aerosolphase. ISORROPIA (Nenes et al., 1998) is used for inorganiccompounds, whereas organic compounds are treated by thenewly developed AEC-SOA-module (Pun et al., 2001, 2002).Nucleation of sulfate particles was treated by calculating therelative rates of new particle formation and condensation ofsecondary component mass on existing particles.

Emission data for the two size fractions of primary PM(PM2.5 and PM10–2.5) and for the precursor compounds of sec-ondary particulate matter (NOx, SO2, NMVOC and NH3) arederived from the EMEP-database for Europe (Vestreng et al.,2004) and for Flanders from recent emission inventories (VMM,2004). Model calculations with all emissions included in theinventories were carried out for the purpose of model vali-

dation and calculations without the emissions from Flemishagricultural sources were carried out to investigate the influ-ence of these emissions on PM2.5- and PM10-concentrations inBelgium.

232 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239

M10

Fig. 1 – Yearly averaged modelled P

More information on the implementation of the aerosolmodule into the BelEUROS-model, a description of the modeland results obtained can also be found in Deutsch et al.(2008a,b,c).

3. Results and discussion

3.1. Model validation

The validation of the model has been carried out for the years2002 and 2003. In this paper, examples are shown for the val-idation of time series of PM10, PM2.5 and of the secondaryinorganic components for certain episodes of the year 2002 aswell as for the validation of the spatial distribution of PM10 andPM2.5 for the year 2003. Subsequently, the model was appliedto assess the contribution of agricultural emissions to PM10-and PM2.5-levels for a winter- and a summer month of the year2003.

3.1.1. Comparison of modelled and observed PM10- andPM2.5-concentrations in BelgiumFig. 1 shows the calculated yearly averaged PM10-concentration map for Belgium for the year 2003. Thethree Belgian regions are shown on the map: the Flemishregion in the northern part of Belgium, the capital Brusselsin the centre and the Walloon region in the southern part ofBelgium.

The highest PM10-concentrations in Belgium were calcu-lated for the western part of both the Flemish and the Walloon

region. High concentrations of particulate matter were alsocalculated for the central part of Flanders. In contrast, lowerPM10-concentrations were calculated for the eastern part ofFlanders and especially for the south-eastern part of the Wal-

-concentration in Belgium in 2003.

loon region. The latter area is mainly covered by forests andgrassland without larger emission sources.

In comparison, Fig. 2 shows the map of the observed yearlyaveraged PM10-concentrations in Belgium in 2003. In order toobtain PM10-concentrations for the whole territory of Belgium(and not only for the locations of the measurement stations),concentrations were interpolated in between the measure-ment stations by means of the RIO interpolation technique,developed by VITO in collaboration with the Belgian Interre-gional Environment Agency IRCEL (Hooyberghs et al., 2006;Janssen et al., 2007).

The comparison shows that BelEUROS underestimates theobserved PM10-concentrations. Note that the concentrationscales used in Figs. 1 and 2 are different. However, this kind ofunderestimation is known from similar models for fine partic-ulate matter and can be traced back for an important part tothe underestimation of (diffuse) emissions of primary particu-late matter. However, the modelled geographical distributionof the PM10-concentration over Belgium agrees well with thegeographical distribution of the observed and interpolatedconcentrations. Especially, the gradient between NW- and SE-Belgium, with high concentrations in the northwest and lowconcentrations in the southeast, is reproduced well by themodel. Also, within Flanders, a gradient with higher concen-trations in the west and in the centre and lower concentrationsin the east can be noticed both from the observations and themodel data.

Fig. 3 shows a comparison of an observed and a modelledPM10-concentration time series for the location of a subur-ban measurement station of the measurement network of the

Flemish Environment Agency (VMM). The comparison showsthat modelled and observed PM10-concentrations agree ratherwell over long intervals of the whole observation period ofapproximately 150 days (from 1st of January to 15th of May

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239 233

trati

2nbppowitsa“ciom

F(

Fig. 2 – Yearly averaged observed PM10-concen

002). Several episodes of high PM10-concentrations can beoticed, with periods of rather low PM10-concentrations inetween. These changes between periods of high and lowarticulate matter concentrations, i.e., the temporal PM10-attern, are reproduced quite well by the model for mostf the period studied. Also, the absolute values agree quiteell in several cases. However, there are also episodes dur-

ng which the model either underestimates or overestimateshe observed PM10-concentrations significantly. Several rea-ons can be accounted for these differences, among whichre differences between the used meteorological fields and thereal” fields, uncertainties in the emission inventories, espe-

ially concerning the emissions of primary particulates, andnsufficiencies in the representation of the various processesf dispersion and chemical transformations in the BelEUROSodel. However, a mean bias of −20.1% between the model

ig. 3 – Comparison of observed and modelled daily mean PM10-Vilvoorde) between January and May 2002.

on in 2003 in Belgium, interpolated with RIO.

results and the observations has to be addressed to as anacceptable model bias for PM10.

Fig. 4 shows the geographical pattern of the modelledyearly averaged PM2.5-concentrations in Belgium for the year2003. The differences between “Hot Spot”-areas and areas withlower concentrations seem to be larger for PM2.5 than for PM10.Higher concentrations were calculated for the central part ofFlanders (north of Brussels) and for the western part of theWalloon region. However, the western part of Flanders, whichshowed high modelled and observed PM10-concentrations,shows only quite low PM2.5-concentrations. The reason forthis has to be seen mainly in the relatively high emissions of

primary coarse particles in the size range PM10–2.5 by strongagricultural activities carried out in this region. The lowestPM2.5-concentrations were found, as for PM10, for the south-eastern part of the Walloon region.

concentrations at a suburban measurement station

234 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239

-con

Fig. 4 – Yearly averaged modelled PM2.5

A comparison to an interpolated map of observed PM2.5-concentrations is not possible yet, because currently there isnot a sufficiently high number of PM2.5 measurement stationspresent in Belgium in order to apply the RIO interpolationtechnique reliably.

Fig. 5 presents a comparison between an observed anda modelled time series of daily mean PM2.5-concentrationfor a suburban measurement station at Mechelen forthe time period between 1st of January to 15th of May2002.

The two measurement stations Vilvoorde (Fig. 3) andMechelen Zuid (Fig. 5) are located close to each other in two

neighboring model grid cells. Unfortunately, no PM10 had beenmeasured during the first 6 weeks of 2002 in Mechelen Zuid,and no PM2.5 is measured in Vilvoorde. Hence data from dif-ferent measurement stations have to be used.

Fig. 5 – Comparison of observed and modelled daily mean PM2.5

(Mechelen Zuid) for the period between January and May 2002.

centration in Belgium for the year 2003.

The mean bias between the modelled values and the mea-surements for PM2.5 amounts to −23.4%, hence, the underes-timation of the observations by the model is in the same orderof magnitude as for PM10. The comparison shows further thatthe model can also reproduce large parts of the variability ofthe observed PM2.5-concentrations and is also in this respectsimilar to the behavior for PM10. A comparison of Figs. 3 and 5shows that the model succeeds to reproduce the sameepisodes for PM10 and PM2.5 and it also fails to reproduce theobserved values during the same episodes. This fact suggeststhat the same parameter is responsible for a simulation to besuccessful or to be unsuccessful for both pollutants. Probably

this means that meteorological factors play the key role, as theemissions and the chemical transformations are different forthe two size-classes of particles to a large extent. PM2.5 is dom-inated by secondary aerosols, such as ammonium, nitrate, sul-

-concentrations at a suburban measurement station

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239 235

F oncef .

fPos

3cF2

ig. 6 – Comparison of observed and modelled daily mean craction for a measurement location in Zelzate, January 2002

ate and SOA and by primary emissions of elementary carbon.Mcoarse (i.e., PM10–2.5) in contrast is dominated by emissionsf primary inorganic and organic particles, partly from diffuseources, mainly from agriculture, industry and traffic.

.1.2. Comparison of observed and modelledoncentrations of ammonium2.5, nitrate2.5 and sulfate2.5

ig. 6 shows a comparison of observed (Van Grieken et al.,003) and modelled daily mean concentrations of ammonium,

ntrations of ammonium, nitrate and sulfate in the PM2.5

nitrate, and sulfate in the PM2.5 fraction. The comparisonshows that observed and modelled concentrations for all threecomponents agree rather well. The model reproduces thegeneral trend with high values during the first part of theobservation period and significantly lower values during the

second part. However, the model has a tendency to overesti-mate the observed values. The following mean values of biaswere found: +33.6% (ammonium), +37.6% (nitrate) and +19.6%(sulfate).

236 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239

a cal

Fig. 7 – Relative difference in PM10-concentrations betweenFlemish agricultural emissions for July 2003.

The overestimation of the secondary components canresult from the fact that the formation of sulfate in theatmospheric liquid phase is only represented in a stronglyparameterized way in BelEUROS. This is due to the lack ofdetailed information about the clouds present in a given gridcell, such as the liquid water content of clouds and the volumeof the lower atmosphere covered by clouds. Unfortunately, themodelled concentrations of secondary organic aerosols (SOA)could not be compared to observations because of lackingdata.

Taking the negative bias of the model for PM2.5 (i.e., the sumof all seven modelled chemical components of this size-class)on the one hand and the positive bias for ammonium, nitrateand sulfate on the other hand into consideration, probablysome other components of PM2.5 (and PM10) are significantlyunderestimated by the model. First of all, natural contribu-tions to fine particulate matter such as sea salt aerosol and

resuspended dust are not yet included into the model. Sec-ond, most probably, the emissions of the primary componentsof PM2.5 and PM10 (such as primary inorganic and primaryorganic aerosol emissions) are underestimated in the used

Table 1 – Reduction of PM10-concentrations when removing theJanuary and July 2003 and mean contribution

PM10 Mean contribution (%)

Location January July Average Jan

Flanders 16.4 27.5 22.0 3Brussels 7.3 14.2 10.8 1Wallonia 2.4 4.2 3.3 1Belgium 8.7 14.6 11.7 3

culation with all emissions and a calculation without the

emission inventories. This concerns especially the diffuseemissions of these compounds. A third possible reason forthe observed underestimation of PM2.5 concentrations is anunderestimation of the concentrations of SOA by the model.

3.2. Application of the model for estimation of theimpact of agricultural emissions to PM10- andPM2.5-concentrations in Flanders

Fig. 7 shows the contribution of emissions of the sector “agri-culture” in Flanders to PM10-concentrations in Belgium for themonth of July 2003. This calculation was carried out by com-paring the results of a base model run including all emissionswith a second model calculation without the emissions fromthe sector “agriculture” in Flanders. These are mainly emis-sions of primary particulate matter (PM10–2.5) and ammonia(NH ). Afterwards, the difference between both results was

3

calculated. The calculation shows that the emissions of thissector contribute significantly to PM10-concentrations in largeparts of Flanders. Especially, in the western part of Flanders, ahigh contribution of up to around 45% is calculated. This high

emissions of the agricultural sector in Flanders for

Max. contribution (%) Min. contribution (%)

uary July January July

3.8 46.2 1.8 4.16.7 27.7 4.9 10.29.7 30.2 0.9 1.13.8 46.2 0.9 1.1

e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239 237

F a calF 003.

cmt

cm

FF

ig. 8 – Relative difference in PM2.5-concentrations betweenlemish emissions from the agricultural sector for January 2

ontribution can be explained by the high emissions of pri-ary coarse particles (PM10–2.5) due to agricultural sources in

he Flemish emission inventory (VMM, 2004).Table 1 gives an overview of the results obtained for the

alculation of the contribution of agricultural emissions toodelled PM10-concentrations for the months January and

ig. 9 – Relative difference in PM2.5-concentrations between a callemish emissions from the agricultural sector for July 2003.

culation with all emissions and a calculation without

July 2003 for the three Belgian regions. The mean, maxi-mum, and minimum contributions are shown, respectively.

It becomes obvious that especially in July, high average andmaximum contributions of the Flemish agricultural emissionswere calculated. These high contributions are mainly due tothe high emissions of primary PM.

culation with all emissions and a calculation without

238 e c o l o g i c a l m o d e l l i n g 2 1 7 ( 2 0 0 8 ) 230–239

Table 2 – Reduction of PM2.5-concentrations when removing the emissions of the agricultural sector in Flanders forJanuary and July 2003 and mean contribution

PM2.5 Mean contribution (%) Max. contribution (%) Min. contribution (%)

Location January July Average January July January July

Flanders 7.5 16.9 12.2 17.1 32.3 0.7 2.36.78.8

17.1

r

Brussels 2.6 7.5 5.1Wallonia 1.2 2.7 2.0Belgium 4.0 9.0 6.5

Figs. 8 and 9 show the contribution of the Flemish agricul-tural emissions to PM2.5-concentrations in Belgium in Januaryand in July 2003, respectively. These results show that agri-cultural emissions contribute both locally and on average inwhole Flanders also to PM2.5-concentrations. As an averageover whole Flanders, 7.5% of PM2.5 is derived from agri-cultural sources in January and 16.9% in July 2003. Hence,agricultural emissions contribute significantly also to thePM2.5-fraction. This contribution is, in contrast to the one toPM10-concentrations, only for a small part due to emissionsof primary PM2.5, as only 11% of the Flemish primary PM2.5-emissions is derived from agricultural sources (VMM, 2004).

Much more important is the contribution of agriculturalemissions to the formation of secondary particulate matter,presumably mainly due to the formation of ammonium nitrateand ammonium sulfate in the size range of PM2.5. 95% ofall Flemish ammonia emissions are derived from agriculturalsources, making this sector responsible for an important partof secondary PM2.5-aerosols in Flanders.

Table 2 gives an overview of the obtained results concerningthe calculation of the contribution of the Flemish agriculturalemissions to the PM2.5-concentrations in the three Belgianregions. The mean contribution to PM2.5-concentrations isfound to be 12.2% in Flanders.

4. Conclusions

The calculations carried out using the BelEUROS model withimplemented aerosol module show that PM10- and PM2.5-concentrations are underestimated by the model, but themodelled geographical pattern agrees well with that oneobtained by observations. The comparison of observed andmodelled time series for PM10 and PM2.5 showed that most ofthe episodes of high particulate matter concentrations couldbe reproduced quite well by the model. However, during someepisodes the model failed to reproduce the observed values,which is presumably mainly due to problems connected toeither the meteorological input fields, or the meteorologicalcalculations within the model. The general underestimationof PM10 and PM2.5 could also be observed from the comparisonof the time series. The comparison of observed and mod-elled time series for ammonium, nitrate and sulfate in thesize fraction of PM2.5 showed a good agreement of the modelresults with the observations in general. In contrast to PM2.5,the model showed a positive bias for ammonium, nitrate and

sulfate.

The contribution of emissions derived from the agricul-tural sector was found to be rather high, both to PM10-, as wellas to PM2.5-concentrations. The high contribution to the first

15.8 1.9 5.717.9 0.5 0.832.3 0.5 0.8

size-class of airborne particles is predominantly due to highemissions of primary particles in the size range of PM10–2.5,especially in the western part of Flanders. The high contribu-tions to the size range of the fine particles have their originmainly in the formation of ammonium salts such as ammo-nium nitrate and ammonium sulfate in the atmosphere.Approximately 12% of the PM2.5-concentration in Flanders onaverage is due to Flemish agricultural emissions. For the smallsize range of particles, especially ammonia emissions areimportant. Hence, future emission reduction programs shouldconsider more the importance of an abatement of ammoniaemissions due to their potential in the formation of secondaryPM2.5 aerosols.

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