Light absorbing carbon in Europe

55 Pages·2013·4.46 MB·English

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Light absorbing carbon in Europe – D J.Genbergetal.
measuremenBiotgeaosnciedncemsOpen Aodelling, wBioigtehosciaencfeosOpen Acus iscus
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1DivisionofNuclearPhysics,DGepeaorstcmieennttifiocfOpPhysics,LundUniversity,LGunedo,sSciwenetdifiecnOp per Back Close
2TNONetherlandsOrganisaItniosntrufomreAnptpaltiieodn SencientificResearch,UtreInchsttr,uthmeeNnteatthioenrl aennds
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3EMEPMSC-W,NorwegianMeMteeothrooldosg iacnadlIccenstitute,Oslo,Norway Methods andcce | FullScreen/Esc
4Dept.Earth&SpaceScienceDsa,tCah SaylmsteermssUssniv.Technology,GothenbuDragt,aS Swyesdteemnsss D
5DepartmentofAppliedEnvironmentalScience(ITM),StockholmUniversity,DSiswcuessdioensn isc Printer-friendlyVersion
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7SchoolofPhysics&CentreforClimateandAirPollutionStudies,RyanInstitute,National p
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8NILU,NorwegianInstituteforAirResearch,Kjeller,Norway Dis
9DepartmentofEnvironmentalSciences/CenterofExcellenceinEnvironmentalStudies,King cu ACPD
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AbdulazizUniversity,P.O.Box80203,Jeddah,21589,SaudiArabia s
10 FinnishMeteorologicalInstitute,AirQuality,P.O.Box503,00101Helsinki,Finland ion 13,9051–9105,2013
11 Leibniz-Institutfu¨rTropospha¨renforschung(TROPOS),Permoserstraße15,04318Leipzig, Pa
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Germany e Light absorbing
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12 DepartmentofChemistryandMicrobiology,UniversityofGothenburg,41296Gothenburg, carbon in Europe
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13 SwedishMeteorologicalandHydrologicalInstitute,60176Norrko¨ping,Sweden D J.Genbergetal.
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Received:21January2013–Accepted:18March2013–Published:4April2013 u
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Correspondenceto:R.Bergstro¨m([email protected]) io
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PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion. a
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Abstract D
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Theatmosphericconcentrationofelementalcarbon(EC)inEuropeduringthesix-year s
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period2005–2010hasbeensimulatedwiththeEMEPMSC-Wmodel.Themodelbias ion 13,9051–9105,2013
compared to EC measurements is less than 20% for most of the examined sites. The P
a
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5 modelresultssuggestthatfossilfuelcombustionisthedominantsourceofECinmost er Light absorbing
of Europe but there are important contributions also from residential wood burning carbon in Europe
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duringthecoldseasonsand,duringcertainepisodes,alsofromopenbiomassburning
(wildfiresandagriculturalfires).Themodelledcontributionsfromopenbiomassfiresto D J.Genbergetal.
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ground level concentrations of EC is small at the sites included in the present study, c
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<3% of the long-term average of EC in PM . The modelling of this EC-source is s
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subject to many uncertainties and for some episodes it is likely underestimated. n TitlePage
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EC measurements and modelled EC were also compared to optical measurements a
p Abstract Introduction
ofblackcarbon(BC).TherelationshipsbetweenECandBC(asgivenbymassabsorp- e
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tion cross section, MAC values) differed widely between the sites, and the correlation Conclusions References
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betweenobservedECandBCissometimespoor,makingitdifficulttocompareresults
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using the two techniques and limiting the comparability of BC measurements to model is
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EC results. u
Anewbottom-upemissioninventoryforcarbonaceousaerosolfromresidentialwood ssio J I
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combustion has been applied. For some countries the new inventory has substan- P J I
tially different EC emissions compared to earlier estimates. For Northern Europe the a
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most significant changes are much lower emissions in Norway and higher emissions r Back Close
inneighbouringSwedenandFinland.ForNorwayandSweden,comparisontosource-
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apportionmentdatafromwintercampaignsindicatethatthenewinventorymayimprove
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model calculated EC from wood burning. is
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25 Finally, three different model setups were tested with variable atmospheric lifetimes us
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of EC in order to evaluate the model sensitivity to the assumptions regarding hygro- io InteractiveDiscussion
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scopicityandatmosphericageingofEC.Thestandardageingschemeleadstoarapid P
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transformation of the emitted hydrophobic EC to hygroscopic particles and generates p
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similar results as assuming that all EC is aged at the point of emission. Assuming hy- D
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drophobic emissions and no ageing leads to higher EC concentrations. For the more c ACPD
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remote sites, the observed EC concentration was in between the modelled EC using s
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standard ageing and the scenario treating EC as hydrophobic. This could indicate too n
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rapid EC ageing in the model in relatively clean parts of the atmosphere. a
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carbon in Europe
1 Introduction |
D J.Genbergetal.
Black carbon (BC) particles, a major component of soot, may heat the atmosphere is
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and thus have a warming effect on the climate. According to the latest IPCC report us
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(Forsteretal.,2007)thedirectradiativeforcing(RF)duetoBCfromfossilfuelburning io
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is estimated to be +0.2±0.15Wm−2 with a similar effect due to BC from biomass P
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burning. The total climate effect of BC is complex since it also contributes to different p Abstract Introduction
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semi-directeffectsonthecloudcover(e.g.KochandDelGenio,2010),thatcanbeboth r
Conclusions References
warming and cooling, and it also affects the surface albedo when deposited on snow |
andicecoveredsurfaces(e.g.HansenandNazarenko,2004;HadleyandKirchstetter, D Tables Figures
2012).ThetotalRFduetoBChasrecentlybeenestimatedto+1.1Wm−2 (Bondetal., isc
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2013). ss J I
Soot is also of interest because of its adverse health effects. Personal exposure to ion
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blackcarbonisassociatedwithoxidativestressinhumans(Sørensenetal.,2003)and a
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withexercise-inducedischemia(Lankietal.,2006).Janssenetal.(2011)haverecently e
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reviewed epidemiological studies of evaluated adverse health effects of PM mass and
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BlackCarbonParticles,BCP(hereBCPincludesBC,elementalcarbon(EC)andBlack FullScreen/Esc
Smoke).Theestimatedhealtheffectsper µgm−3 werefoundtobesubstantiallyhigher D
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for BCP than for PM10 or PM2.5. Another recent review of health effects of PM and cus Printer-friendlyVersion
its components (Rohr and Wyzga, 2012) also pointed out the importance of carbon- s
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containing PM components, i.e. both EC and OC (organic carbon). n
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Thereisanextensive,andsometimescontradictory,nomenclatureforvariousforms a
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oflightabsorbingcarbon,dependentonmeasurementtechniques(see,e.g.Bondand er
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Bergstrom,2006;AndreaeandGelencse´r,2006).Inthepresentstudyweusetheterm D
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ECforcarbonthatdoesnotvolatilizebelowadefinedtemperatureandBCforthemass c ACPD
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of light absorbing carbon determined by its light absorption (see Sects. 2.2 and 2.3). s
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– Inthermalanalysis,usedtomeasureEC,theparticlesarecollectedonafilterand P
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theOCisremovedbyheatingthesampleinaninertatmosphere,leavingonlythe p
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EC.SomeOCmay,however,charandformcompoundswhichwouldbedetected
carbon in Europe
as EC. The charred organics may be corrected for by monitoring the reflectance |
(Johnson et al., 1981) or transmission (Birch and Cary, 1996) of the filter during D J.Genbergetal.
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the analysis and the technique is then called thermal optical analysis (TOA). c
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10 – Todeterminethelightabsorptivepropertiesoftheaerosoltheparticlesareeither io
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collectedonafilterpriortotheanalysis,e.g.ParticleSootAbsorptionPhotometer
P
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(PSAP, Bond et al., 1999), Aethalometer (Hansen et al., 1982) and Multi Angle p Abstract Introduction
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Absorption Photometer MAAP (Petzold and Scho¨nlinner, 2004) or the absorption r
Conclusions References
can be directly measured in the aerosol, e.g. Photo-Acoustic Soot Spectrometer |
(PASS) (Arnott et al., 1999). A Mass Absorption Cross section (MAC) is used to
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transfer the optically measured light absorption (in units of m−1) into BC mass (in is
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−3 2 −1 u
unitsofµgm ).BondandBergstrom(2006)suggestedaMACvalueof7.5m g ss J I
for fresh BC and this value should increase with ageing of BC. However, a wide io
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rangeofMACvalues(from2.0to25.4)havebeenobtained(Liousseetal.,1993). P J I
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Optical measurements typically generate data at a higher time resolution than p
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filter based thermal techniques.
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Both optical and thermal measurement techniques are important since they comple-
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ment each other. Optical methods measure the climate relevant property of soot while is
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TOA measures the mass, a quantity which is likely to be related to the adverse health u
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25 effects. Thereare other methodsfor determining lightabsorbing and refractorycarbon sio InteractiveDiscussion
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suchastheSingleParticleSootPhotometer(Stephensetal.,2003)andTheSootPar- P
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ticle Aerosol Mass Spectrometer (Onasch et al., 2012) but none of these will be used p
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in the present study. r
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For chemical transport models, TOA results are of main interest since the emission D
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inventories used in the models are usually based on EC measurements. Several Eu- c ACPD
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ropean modelling studies of EC or BC have been published. Schaap et al. (2004) per- s
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formedaone-yearsimulationofanthropogenicBCandfineaerosol(for1995)withthe n
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LOTOSmodel.ComparisonsofcalculatedBC-concentrationstoavailableobservations a
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from the period 1980s–2001 were interpreted as indicating model underprediction of e Light absorbing
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BCbyaboutafactorof2.TheneedforbetterknowledgeofemissionfactorsforBCwas carbon in Europe
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pointedout.Tsyroetal.(2007)andSimpsonetal.(2007)performedmulti-yearsimula-
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tions (2002–2004) with the EMEP MSC-W model, including both anthropogenic emis- is
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sions and wildfire emissions (with low temporal resolution), and evaluated the model u
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resultsforECagainsttwolong-termmeasurementcampaigns(theCARBOSOLproject sio
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(Legrand and Puxbaum, 2007) and the EMEP EC/OC campaign (Yttri et al., 2007).
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The model generally overestimated EC at the northern measurement sites, especially a
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during winter. Emissions from residential wood combustion (RWC) were pointed out r
as especially uncertain. Bessagnet et al. (2008) modelled carbonaceous aerosol over Conclusions References
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parts of Europe (excluding the northern and eastern parts) for one year (2003) us-
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ing the Chimere model and included a more detailed emission inventory for wildfire is
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emissions with daily time resolution. Koch et al. (2009) evaluated 17 global models uss J I
that participated in the AeroCom project. Model calculated annual mean surface BC io
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20 concentrations (for 2000) were compared to surface observations and, for Europe, 13 P J I
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of the 17 models predicted higher mean BC concentrations than the observed annual p
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mean EC-concentrations from the EMEP EC/OC campaign of 2002–2003. However, r Back Close
individual models gave widely different results (model/observed ratio ranged from 0.5 |
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to 10). D
25 ThedominantremovalprocessforECiswetdeposition;Croftetal.(2005)estimated isc Printer-friendlyVersion
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that about 75% of the EC is removed by wet deposition and 25% by dry deposition, s
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based on global model runs. Particle hygroscopicity is important in order to account io InteractiveDiscussion
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for wet deposition. In modelling studies it is often assumed that at least part of the P
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EC is emitted as hydrophobic particles. A commonly used assumption is that 80% of pe
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the emitted EC is insoluble and 20% soluble (e.g. Cooke et al., 1999). After atmo- D
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spheric processing (ageing) the EC is transformed into more hygroscopic forms. The c ACPD
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ageing can be due to several different processes, condensation of organic and inor- ss
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ganic vapours on the particles, coagulation with hygroscopic particles and chemical n
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reactions on the surface (e.g. Croft et al., 2005). a
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In the present study, measurements of EC and BC from recent years (2005–2010) e Light absorbing
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are used to evaluate how the EC concentrations calculated by the EMEP MSC-W carbon in Europe
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model,combinedwithrecentlydevelopedemissioninventories,comparewiththemea-
D J.Genbergetal.
surements. The number of EC and BC observations has increased substantially dur- is
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ing the last decade and the increased interest in carbonaceous aerosol, both from u
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climate and health perspectives, makes it important to evaluate the most recent emis- sio
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sioninventories.Datafromeightnorthern/central/westernEuropeansitesareusedand
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bothECandBCdataareevaluatedwhenavailable.Newemissioninventoriesforboth a
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anthropogenic emissions (Denier van der Gon et al., 2013) and open biomass fires r
(Wiedinmyer et al., 2011) were included in the comparison. We have also investigated Conclusions References
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differentECprocessingschemesinthemodel,i.e.howageingofECaffectsthemodel
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results. The present work also highlights the severe problems in comparing different is
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measurement techniques used for estimating the concentrations of EC and BC in the uss J I
atmosphere. io
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20 2 Method er Back Close
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The EMEP MSC-W model was used to model EC concentrations in Europe for the FullScreen/Esc
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period2005–2010.ThemodelresultswerecomparedtomeasurementsofECandBC
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ateightsitesinEurope,asshowninFig.1.Differentmodelassumptionsweretestedto cu Printer-friendlyVersion
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studyhowageingofECinthemodelinfluencedtheresults.Also,twodifferentemission s
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inventories for residential wood combustion were compared. n
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2.1 Measurement stations D
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Measurement of EC and BC were collected from eight sites (Fig. 1). The stations s
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were chosen to cover Northern, Central and Western Europe and have preferably ion 13,9051–9105,2013
both EC and BC measurements. All stations, except Overtoom, are classified as ru- P
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5 ral background stations in the European Environment Agency EEA/Airbase database er Light absorbing
(http://air-climate.eionet.europa.eu/databases/airbase/),whichmeansthattheyideally carbon in Europe
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should be representative of a larger area and suitable for evaluation of the EMEP
50kmscale model. Melpitz is a rural site (Spindler et al., 2004) but it is located 41km D J.Genbergetal.
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NE from Leipzig (Herrmann et al., 2006). This means that it does not formally fulfil c
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the recommendations regarding minimum distance to emission sources in the EMEP s
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guidelines (EMEP/CCC) for siting criteria of regional background stations. However, n TitlePage
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duringtransportfromthesouth-westtothesite,turbulentmixingisusuallyefficientand a
p Abstract Introduction
the EMEP MSC-W model results for NO are in good agreement with observations e
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at Melpitz (Table S1), which indicates that influences from Leipzig are well captured Conclusions References
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by the model. Harwell is also a rural site but located in a densely populated region;
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it was classified as an agglomeration site by Henne et al. (2010) and could be less is
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representative for larger areas. Overtoom is an urban background station located in u
Amsterdam in the Netherlands. Mace Head, which is a background marine station, is ssio J I
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located on the west coast of Ireland and is a good site for investigating the clean ma- P J I
rine air during prevailing westerly/south-westerly winds, occurring more than 50% of a
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the time (Jennings et al., 2003). r Back Close
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2.2 EC data
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ECdatawereretrievedfromtheEBASdatabase(ebas.nilu.no,nowpartoftheACTRIS cu Printer-friendlyVersion
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data center, actris.nilu.no), except the data from Hyytia¨la¨ that were provided directly s
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from the Finnish Meteorological Institute (Aurela et al., 2011). All data is based on n
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thermal separation of OC from EC although the method used varies between the sites a
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(Table1).Allstations,exceptMelpitz,useTOAtechniquesforECquantification,which er
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corrects for OC charring in the initial heating phase. The VDI protocol (VDI2465-2, D
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1999; Gnauk et al., 2011), used at Melpitz, has no charring correction and is expected c ACPD
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to lead to higher EC values compared to TOA (Schmid et al., 2001) and to underesti- s
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mate OC (ten Brink et al., 2004). n
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The use of different TOA measurement protocols is known to produce differences a
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in results. The protocol QUARTZ (a version of NIOSH 5040, Birch and Cary, 1996) e Light absorbing
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uses a higher temperature in the initial He phase compared to EUSAAR-2 (Cavalli carbon in Europe
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et al., 2010); QUARTZ and NIOSH normally give lower EC compared to the EUSAAR-
D J.Genbergetal.
2protocol.EUSAAR-1usesfewertemperaturestepsintheECphasethanEUSAAR-2 is
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but the two are otherwise identical. All four TOA protocols use transmission to correct u
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for charring. It is well known that EC determination using even the same separation sio
protocol may produce more than 20% difference in EC results (Schmid et al., 2001). n TitlePage
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In addition to total EC data, we also use source apportioned biomass burning EC a
p Abstract Introduction
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data from five Nordic stations; Hurdal and Oslo in Norway (Yttri et al., 2011b), and r
Ra˚o¨, Gothenburg (Szidat et al., 2009) and Vavihill (Genberg et al., 2011) in Sweden. Conclusions References
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One month of levoglucosan data from Hyytia¨la¨ (Saarnio et al., 2010) was also used to
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evaluate the modelled EC from open biomass fires and residential wood burning. is
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2.3 BC data io
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BC or aerosol absorption coefficients were retrieved from the EBAS data base a
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20 (ebas.nilu.no)forallstationsexceptAspvreten,forwhichdataweretakendirectlyfrom er Back Close
the local data base in Stockholm. The method used for determining the aerosol ab-
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sorptionvaried(seeTable1).Atallsites,exceptMelpitz,BCdatawereacquiredusing
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aPSAPoranAethalometerwhichhavesimilarmeasurementtechniques.Theparticles is
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are collected on a filter and the attenuation is determined by measuring the transmis- u
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25 sion of a light beam through the filter. To retrieve aerosol absorption (Abs), corrections io InteractiveDiscussion
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havetobemadetoaccountforthefiltermaterialandscatteringinterference,whichare
P
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dependentonthemethodused(Bondetal.,1999).AtMelpitz,aMAAPwasused.The p
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MAAPmonitorsthescatteringpropertiesofthefilterduringsampling,whichotherwise r
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have to be estimated by measuring the scattering of the aerosol using, e.g. a neph- D
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elometer. Harwell and Hyytia¨la¨ had multi-wavelength Aethalometers. For Harwell BC c ACPD
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data880nmwasusedandforHyytia¨la¨ 520nm.Thecorrelationbetweenthemeasure- s
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ments at different wavelengths is high for the multi-wavelength instruments (r >0.95). n
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To determine the BC mass concentrations for Harwell, Hyytia¨la¨ and Mace Head a
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a pre-set MAC of 16.6m2g−1 was used for 880nm and an inverse wavelength de- e Light absorbing
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pendence was assumed for the other wavelengths. In addition to the Aethalometer, carbon in Europe
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a MAAP instrument has been deployed at Mace Head since 1 March 2005.
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Since the EMEP model is based on EC emissions, we used EC measurement data is
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to normalize BC, in accordance with the recommendation of Vignati et al. (2010). To u
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distinguish between BC deduced by MAC values and the ones normalized with EC, sio
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thelatter(“ECequivalentBC”)willbedenotedBC .Therelationshipsareexplainedby
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Eqs. (1)–(3). a
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BC=Abs/MAC (1) Conclusions References
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MAC =Abs/EC (2)
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BC =Abs/MAC (3) isc
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WedeterminethestationspecificMAC(MAC )byaleastabsolutedeviationfit(forced io
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through origin) between overlapping absorptions and EC measurements (see Fig. 2). P J I
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20 Tenhceeleoafsetxatbresmoleutevadlueevsia.tiTohnewdaisffeurseendc,ersatinheMrtAhCanvleaalusetssqbueatrweefietntintgh,etofitltiimngitmtheethinofldus- per Back Close
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were small for all stations except Aspvreten. Since we include data from Aspvreten | FullScreen/Esc
that were not fully quality controlled, they may contain some erroneous data points. To D
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beconsideredasoverlappingmeasurements,90%oftheECsamplingtimehadtobe c Printer-friendlyVersion
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25 covered by BC measurements. To be able to calculate MACe for Mace Head, the re- sio InteractiveDiscussion
quiredoverlapwasloweredto70%;thelackofECmeasurements(only5ECsamples n
with sufficient overlap) made the MACe determination rather uncertain. Pap
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