Variations in the chemical composition of the submicron aerosol and

22 Pages·2016·4.29 MB·English

Atmos.Chem.Phys.,16,12875–12896,2016
www.atmos-chem-phys.net/16/12875/2016/
doi:10.5194/acp-16-12875-2016
©Author(s)2016.CCAttribution3.0License.
Variations in the chemical composition of the submicron aerosol
and in the sources of the organic fraction at a regional
background site of the Po Valley (Italy)
MichaelBressi1,FabriziaCavalli1,ClaudioA.Belis1,Jean-PhilippePutaud1,RomanFröhlich2,
SebastiaoMartinsdosSantos1,EttorePetralia3,AndréS.H.Prévôt2,MassimoBerico3,AntonellaMalaguti3,and
FrancescoCanonaco2
1EuropeanCommission,JointResearchCentre,InstituteforEnvironmentandSustainability,AirandClimateUnit,
ViaEnricoFermi2749,Ispra(VA)21027,Italy
2PaulScherrerInstitute,LaboratoryofAtmosphericChemistry,Villigen5232,Switzerland
3ItalianNationalAgencyforNewTechnologies,EnergyandSustainableEconomicDevelopment(ENEA),
ViaMartiridiMonteSole4,Bologna40129,Italy
Correspondenceto:MichaelBressi([email protected]),ClaudioA.Belis([email protected]),
andFabriziaCavalli([email protected])
Received:1February2016–PublishedinAtmos.Chem.Phys.Discuss.:22February2016
Revised:2August2016–Accepted:16August2016–Published:18October2016
Abstract. Fine particulate matter (PM) levels and result- monitoredatthestudysite(14.2µgm−3)isamongthehigh-
ing impacts on human health are in the Po Valley (Italy) est in Europe and is even comparable to levels reported in
among the highest in Europe. To build effective PM abate- urban areas like New York City and Tokyo. On the annual
mentstrategies,itisnecessarytocharacterizefinePMchem- basis,submicronparticlesareprimarilycomposedoforganic
icalcomposition,sourcesandatmosphericprocessesonlong aerosol (OA, 58% of NR-PM ). This fraction was appor-
1
timescales(>months),withshorttimeresolution(<day),and tioned into oxygenated OA (OOA, 66%), hydrocarbon-like
with particular emphasis on the predominant organic frac- OA (HOA, 11% of OA) and biomass burning OA (BBOA,
tion.Althoughpreviousstudieshavebeenconductedinthis 23%). Among the primary sources of OA, biomass burn-
region, none of them addressed all these aspects together. ing(23%)isthusbiggerthanfossilfuelcombustion(11%).
Forthe firsttimeinthe PoValley,weinvestigate thechem- Significant contributions of aged secondary organic aerosol
icalcompositionofnonrefractorysubmicronPM(NR-PM ) (OOA) are observed throughout the year. The unexpectedly
1
withatimeresolutionof30minattheregionalbackground high degree of oxygenation estimated during wintertime is
site of Ispra during 1 full year, using the Aerodyne Aerosol probablyduetothecontributionofsecondaryBBOAandthe
Chemical Speciation Monitor (ACSM) under the most up- enhancement of aqueous-phase production of OOA during
to-date and stringent quality assurance protocol. The iden- coldmonths.BBOAandnitratearetheonlycomponentsof
tification of the main components of the organic fraction is whichcontributionsincreasewiththeNR-PM levels.There-
1
madeusingtheMultilinear-Engine2algorithmimplemented fore, biomass burning and NO emission reductions would
x
withinthelatestversionoftheSoFitoolkit.Inaddition,with be particularly efficient in limiting submicron aerosol pol-
the aim of a potential implementation of ACSM measure- lution events. Abatement strategies conducted during cold
mentsinEuropeanairqualitynetworksasareplacementof seasons appear to be more efficient than annual-based poli-
traditional filter-based techniques, parallel multiple offline cies. In a broader context, further studies using high-time-
analyses were carried out to assess the performance of the resolutionanalyticaltechniquesonalong-termbasisforthe
ACSM in the determination of PM chemical species reg- characterizationoffineaerosolshouldhelpbettershapeour
ulated by air quality directives. The annual NR-PM level futureairqualitypolicies,whichconstantlyneedrefinement.
1
PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion.
12876 M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol
1 Introduction resolution(typically24h),thuspreventingthestudyofitsdi-
urnalvariationandshort-livedchemical–physicalprocesses.
ThePoValleyregion–locatedinnorthernItaly–isamongst Whendocumentedwithhighertimeresolutions(1horless),
the most polluted areas in Europe (van Donkelaar et al., aerosol chemical composition and its sources are usually
2010;EEA,2013).AnnualPM (particulatematterwithan characterized for intensive campaigns of a few weeks only,
2.5
aerodynamic diameter below 2.5µm) mean concentrations hencenotsuitabletodepicttheseasonaloryearlyairquality
can significantly exceed the European PM annual limit situation.Inaddition,thecomplexityofthefineorganicfrac-
2.5
value (25µgm−3 in 2015; European Directive 2008/50/EC) tion (e.g. Jimenezet al., 2009) requiresstate-of-the-art ana-
andtherecommendationsoftheWorldHealthOrganization lyticalandsourceapportionment(SA)techniquestoidentify
(PM annual average of 10µgm−3; WHO, 2006) at urban organicaerosol(OA)chemicalpropertiesandsources.
2.5
(e.g. Bologna, 35.8µgm−3) and regional background sites The recently developed Aerosol Chemical Speciation
(e.g.Ispra,32.2µgm−3;Putaudetal.,2010).Consequently, Monitor(ACSM,AerodyneResearchInc.;Ngetal.,2011a)
PM impactsonhumanhealthareamongthemostseverein is suitable to fill these gaps by providing the chemical
2.5
Europe(EC,2005),whileimpactsonthelocalradiativeforc- compositionofnonrefractorysubmicronaerosols(NR-PM )
1
ing are substantial (Clerici and Mélin, 2008; Ferrero et al., with a time resolution of 30min, while operating on long
2014; Putaud et al., 2014b). Effective PM abatement strate- timescales. Even though promising results have been re-
giesarethusneededinthePoValleyandrequireanin-depth cently reported (e.g. Budisulistiorini et al., 2014; Canonaco
knowledgeofthechemicalcompositionoffinePMtoquan- etal.,2013,2015;Minguillónetal.,2015;Ngetal.,2011a;
tify its sources and the atmospheric processes leading to its Petit et al., 2015; Ripoll et al., 2015; Sun et al., 2012),
secondaryformation. this technique is still novel and requires additional field de-
In this region, high levels of fine aerosol are mostly due ployment to test its consistency with independent methods
to the conjunction of (i) high pollutant emissions related to for the monitoring of fine PM chemistry (e.g. filter mea-
industrial,transport,biomassburningandagriculturalactiv- surements). In addition, information on the accuracy of this
ities – the Po river basin hosting 37% of the Italian indus- technique is of paramount importance given the growing
tries,55%ofthelivestockandcontributing35%oftheItal- number of ACSMs in Europe and the necessity to build
ianagriculturalproduction(WMOetal.,2012)–and(ii)the a network of quality-assured and harmonized instruments
specificgeographyandtopographyofthisarea–aflatbasin for comparability of results – at present about 20 ACSMs
surroundedbytheAlpsandApennineMountainsdominated areinoperationinEurope(http://www.psi.ch/acsm-stations/
by weak winds that favour the accumulation of pollutants overview-full-period) within the frame of the EU ACTRIS
(Decesari et al., 2014; Kukkonen et al., 2005; Pernigotti et network (Aerosols, Clouds, and Traces gases Research In-
al.,2012).Asaconsequence,PMlevelsarehighnotonlyin fraStructure, http://www.actris.eu/). Moreover, by using re-
urban areas but also at regional and rural background sites, ceptor models, the apportionment of OA into its major
whicharekeylocationsforinvestigatingairpollutiondueto components – hydrocarbon-like (HOA), biomass burning
theirdistancefromlocalsourcesandlocalphenomena.Mea- (BBOA) and oxygenated OA (OOA) – can be performed
surementsoffinePMmassandchemicalcompositionatrural (Lanzetal.,2007;Zhangetal.,2011,andreferencestherein).
background sites are in addition specifically required in the In this study, we used an ACSM during 1 year with a
currentEuropeandirectiveonairquality(EU,2008). 30mintimeresolutionataregionalbackgroundsiteofthePo
Previous studies have investigated the properties of fine Valley and performed subsequent SA analyses with the aim
aerosolsatregionalandruralbackgroundsitesofthePoVal- of (i) describing the high-time-resolved chemical composi-
leyregion,includingtheirchemicalcharacteristics(e.g.Car- tionofNR-PM onalongtimescaletobetterunderstandthe
1
boneetal.,2014;Putaudetal.,2002,2010;Saarikoskietal., physicochemical processes driving its temporal variations,
2012), and their main sources (Belis et al., 2013; Gilardoni (ii) apportioning the organic fraction into its main sources,
et al., 2011; Larsen et al., 2012; Perrone et al., 2012). Fine (iii)identifyingPMabatementstrategiestoefficientlyreduce
aerosolsareprimarilymadeoforganics(30–80%offinePM NR-PM pollutioneventsatregionalbackgroundareasofthe
1
mass, depending on the site and season studied), followed PoValley,and(iv)assessingtheatmosphericconsistencyof
by ammonium nitrate and ammonium sulfate. Their main ACSM measurements when compared to independent ana-
sources are fossil fuel, biomass burning and biogenic emis- lyticalmethodstoevaluateitspossibleimplementationinfu-
sions to name a few. In addition, studies based on aerosol tureEuropeanairqualitynetworks.
massspectrometermeasurementshavebeenconductedinthe
Po Valley, with the aim of characterizing specific phenom-
ena(e.g.fogevents,cookingaerosols)orseasons(Dall’Osto
et al., 2015; Decesari et al., 2014; Gilardoni et al., 2014;
Saarikoski et al., 2012). In studies dealing with long time
series (entire season or year), the chemical composition of
fineaerosolisgenerallymeasuredwitharelativelylowtime
Atmos.Chem.Phys.,16,12875–12896,2016 www.atmos-chem-phys.net/16/12875/2016/
M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol 12877
2 Materialandmethods anddataanalysisoftheACSMs.Ammoniumnitratecalibra-
tionswereperformedseasonallyandusedforthedetermina-
2.1 Samplingsite tion of experimental nitrate response factors (RFs) and am-
monium relative ionization efficiencies (RIEs; see Sect. S1
Measurements were conducted at the European Commis- in the Supplement for further details). Annual average and
sion–JointResearchCentre(EC-JRC)Isprasite(45◦48(cid:48)N, season-dependentexperimentalRFandRIEvalueswereal-
8◦38(cid:48)E; 217ma.s.l.; Fig. S1 in the Supplement), which ternativelyappliedtoassesswhethertheACSMisstableover
is part of the European Monitoring and Evaluation Pro- multi-seasonalperiods(seeSect.3.1forresults).Seasonsare
gramme(EMEP)measurementnetwork(http://www.nilu.no/ definedasspring(MAM),summer(JJA),autumn(SON)and
projects/ccc/sitedescriptions/it/index.html) and the Global winter(DJF).RIEsfororganics,nitrateandchloride(1.4,1.1
Atmosphere Watch (GAW) regional stations (http://www. and 1.3, respectively) were taken from the literature (Cana-
wmo.int/pages/prog/arep/gaw/measurements.html). It is lo- garatna et al., 2007; Takegawa et al., 2005). RIE for sulfate
cated on the northwest edge of the Po Valley region, 60km wasexperimentallydeterminedbasedonammoniumsulfate
northwest of the Milan urban area. It can be regarded as a calibrations for ACSM2 and was taken from the literature
“regional/rural background” site following the criteria rec- forACSM1(seeSect.S1).Collectionefficiencies(CEs)set
ommended by the European Environment Agency (Larssen as (i) a fixed 0.5 value (e.g. Budisulistiorini et al., 2013) or
et al., 1999). For simplicity, the term “regional background (ii) following the composition-dependent CE algorithm in-
site”willbeusedinthefollowingalthoughcomparisonswith troducedbyMiddlebrooketal.(2012)werecomparedinor-
rural background sites from other studies will also be re- dertodeterminethemostappropriateCEs(seeSect.3.1for
ported. Further information on the study site can be found results).
inPutaudetal.(2014b).
2.3 Additionalanalyticaltechniques
2.2 AerosolChemicalSpeciationMonitor
Additional measurements routinely performed at the JRC-
Ispra site are used in this study (see Putaud et al., 2014a,
The recently developed ACSM (Aerodyne Research Inc.,
for a full description). PM was sampled on quartz fibre
ARI)wasusedtomeasurethenonrefractorychemicalcom- 2.5
filters(Pall,2500QAT-UP)withaPartisolPLUS2025sam-
position (organics, nitrate, sulfate, ammonium, chloride)
pler equipped with a carbon honeycomb denuder operating
of submicron particles (PM ) with a 30min time resolu-
1 at 16.7Lmin−1 from 1 March 2013 to 28 February 2014
tion. The operating principle of the ACSM is similar to
+
with daily filter changes at 08:00UTC. Major ions (NH ,
thewidespreadAerodyneaerosolmassspectrometer(Cana- 4
garatna et al., 2007; Jayne et al., 2000), with the difference K+,NO−3,SO24−,etc.)areanalysedbyionchromatography
that the former does not inform on the size distribution of (Dionex DX 120 with electrochemical eluent suppression)
thechemicalcompositionofNR-PM .Afulldescriptionof after extraction in Milli-Q water (Millipore). Organic and
1
the ACSM can be found in Ng et al. (2011a). Briefly, an elemental carbon (OC and EC, respectively) are quantified
aerodynamiclensisusedtofocussubmicronparticles(50% by a thermal–optical method (Sunset Laboratory Inc. dual-
transmission range of 75–650nm; Liu et al., 2007), which optical lab thermal–optical carbon aerosol analyzer) using
arethenvaporizedinhighvacuum,ionizedbyelectronion- the EUSAAR-2 protocol (Cavalli et al., 2010). Equivalent
ization (at 70eV) and detected by a quadrupole mass spec- black carbon (BC) is measured by a multi-angle absorption
trometer (Pfeiffer Vacuum Prisma Plus RGA). Two differ- photometer(MAAP,ThermoScientific,model5012)apply-
entquadrupoleACSMs(Q-ACSMs)wereusedinthisstudy ing an absorption cross section of 6.6m2g−1 of equivalent
(from March 2013 to February 2014): Q-ACSM1 from 1 BC at the operation wavelength of 670nm. Particle volume
Marchto18August2013andQ-ACSM2from20June2013 concentrationsaredeterminedwithahomemadedifferential
to 28 February 2014. Note that Q-ACSM2 was not running mobilityparticlesizer(DMPS)combiningaVienna-typedif-
from 3 November to 18 December due to its participation ferentialmobilityanalyser(DMA)andacondensationparti-
in the first inter-ACSM comparison exercise (Crenn et al., clecounter(CPC,TSI3010),followingtheEuropeanSuper-
2015).Thereproducibilityandconsistencywithindependent sitesforAtmosphericAerosolResearch(EUSAAR)specifi-
measurements are discussed in Sect. 3.1. In the following, cationsforDMPSsystems(Wiedensohleretal.,2012).Me-
orthogonalregressionsarereportedunlessotherwisestated. teorologicalvariables(temperature,pressure,relativehumid-
Both ACSMs were operated with the latest Data Acqui- ity, precipitation, wind speed and direction) are determined
sition (DAQ 1.4.3.8 to 1.4.4.5) and Data Analysis (DAS fromaweathertransmitterWXT510(Vaisala,Finland).So-
1.5.3.0 to 1.5.3.2) software (ARI, https://sites.google.com/ larradiationismeasuredbyaCM11pyranometer(Kippand
site/ariacsm/mytemplate-sw) available at the time of use, Zonen,theNetherlands).
which are developed within Igor Pro 6.32A (Wavemetrics).
RecommendationsprovidedbyAerodyne(2010a,b)andNg
et al. (2011a) were followed for the operation, calibration
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12878 M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol
2.4 Apportionmentoftheorganicfraction 3 Qualityassurance/qualitycontrol
Theorganicfractionwasapportionedusingthepositivema- 3.1 Qualityassurance/qualitycontrolofACSM
trixfactorizationapproach(PMF;Lanzetal.,2007;Paatero measurements
andTapper,1994;Ulbrichetal.,2009;Zhangetal.,2011)by
applyingtheMultilinearEngine2algorithm(ME-2,Paatero, Ammonium nitrate calibrations performed on each ACSM
2000) implemented in the SoFi tool (v4.8, Canonaco et al., are shown in Fig. S2. RF and RIE do not
NO3 NH4
2013;Crippaetal.,2014).Detailsonthetheoryandapplica- present significant seasonal variability – e.g. for ACSM2,
tion of PMF and ME-2 can be found in the aforementioned RF =4.7E-11±0.2E-11A.µg−1m3 – suggesting con-
NO3
studies. Briefly, PMF aims at factorizing an initial X ma- stant calibration factors may be used throughout the
trix(representingthetemporalvariationofm/zsignalshere) campaign. However, calibration factors exhibit substan-
intotwoFandGmatrices(representingfactorprofiles(FPs) tial discrepancies between both ACSMs (e.g. RF
NO3
andcontributions,respectively),puttingaconstraintofnon- of 2.5E-11 and 4.7E-11A.µg−1m3 for ACSMs 1 and
negativityonFandGmatrices.Contrarytotheclassicalpro- 2, respectively), suggesting that instrument-specific fac-
gramusedtoresolvePMF(e.g.PMF2,PMF3),ME-2allows tors are necessary. Applying constant and composition-
anyelement oftheF andG matrices tobe constrainedwith dependent CEs does not lead to noticeable differences
a certain degree of freedom. This ME-2 approach has been (e.g. for NR-PM : r2=0.97, slope=1.00±0.00, y inter-
1
typicallyusedtoconstrainfullFPs(e.g.Amatoetal.,2009; cept=0.10±0.03µgm−3, n=14842) due to (i) low sam-
Crippa et al., 2014), specific elemental ratios (e.g. Sturtz et pling line RH (e.g. typically below 30% for ACSM2)
al., 2014) or specific species contribution (e.g. Crawford et and (ii) few high-nitrate-content events (only 5% of
al.,2005)inagivenFP. data exhibits ammonium nitrate mass fractions >40%,
Inourstudy,ME-2isappliedwithandwithoutconstrain- defined as high by Middlebrook et al., 2012). The
ing FPs, using the so-called a value approach (Canonaco et Middlebrook et al. (2012) algorithm is, however, pre-
al.,2013)intheformercase,whichcanbedescribedasfol- ferred since slightly acidic aerosols are observed at the
lows: study site (on average sulfate plus nitrate against am-
monium in µeqm−3: r2=0.96, slope=1.21±0.00, inter-
(f ) =(f ) ±a(f ) , (1)
k,j solution k,j reference k,j reference cept=0.01±0.00µeqm−3,n=14842).
wherekandj aretheindexesforthefactorsandthespecies, A comparison performed between the two ACSMs used
respectively,f istheelement(k,j)oftheFmatrix,thein- in this study during a 2-month summer period is shown in
k,j
dex“solution”standsforthePMFusersolution,“reference” Fig.S3.Verygoodcorrelationsareobservedforeverychem-
isthereferenceprofileand“a”isascalardefinedbetween0 ical component (0.91<r2<0.98, n=1402, hourly average)
and1(e.g.applyinganavalueof0.10lets±10%variability – chloride excluded – with slopes relatively close to one
toourFPsolutionwithrespecttothereferenceFP).Follow- (0.87<slopes<1.42),indicatingafairlygoodcomparability
ingCrippaetal.(2014),weperformasequenceofrunswith betweenbothinstruments.OneofthetwoACSMsalsopar-
(i) unconstrained PMF, (ii) fixed HOA, (iii) fixed HOA and ticipated in the first-ever inter-ACSM comparison exercise
BBOA,and(iv)fixedHOA,BBOAandcookingOA(COA) performedbetween13differentEuropeanQ-ACSMsduring
factorsbeforeselectingthemostappropriatesolution.Uncer- 3 weeks in Paris, France (Crenn et al., 2015). Satisfactory
taintiesarecalculatedusingtheDAS1.5.3.0versionfollow- performances – defined by z scores <2 – are reported for
ingthemethodologiesofAllanetal.,(2003a)andUlbrichet ourinstrumentregardingeverychemicalcomponentandNR-
al.(2009).m/z12and13areremovedforSAanalysissince PM mass, attesting the consistency of our measurements
1
negative signals are observed most of the time. Reference withotherEuropeansites.
factor profiles (RFPs) are taken from ambient deconvolved Measurements performed by the ACSM and independent
spectra from the aerosol mass spectrometry (AMS) spectral offline and online analytical techniques are compared in
database(Ulbrichetal.,2015).HOAandBBOAprofilesare Fig. 1 and Table 1. An overall good agreement is found
takenfromNgetal.(2011c)(averageofprofilesfrommul- for every major components throughout the year (typically
tiple studies) and COA from Crippa et al. (2013). Different r2>0.8), although discrepancies are observable for specific
a values are tested (see Sect. 3.2) applying (i) relative stan- speciesandseasons.Ontheannualscale,agoodagreement
darddeviationsofaveragedRFPsdefinedforeverym/z(i.e. (r2=0.77,n=317)isfoundbetweenorganicsfromACSM
assuming that the chosen averaged RFPs are representative and OC from filter measurements in spite of expected fil-
ofourdataset),(ii)recommendationsofCrippaetal.(2014) ter sampling artefacts (Maimone et al., 2011; Turpin et al.,
basedontheSAof25EuropeanAMSdatasetsand(iii)com- 2000; Watson et al., 2009). Even better agreements are ob-
parisonwithindependentmeasurements(e.g.NO ,CO,BC). served on a seasonal basis (r2∼0.9), with steeper slopes
x
Solutionsfromtwotoeightfactorsareinvestigatedinorder in summer compared with winter, which likely reflects the
to choose the appropriate number of factors (see Sects. S2 differentdegreesofoxygenationoforganicsamongseasons
and3.2). (leading to different ratios of organic matter (OM) to OC).
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M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol 12879
Figure 1. Comparison between measurements performed with the ACSM and other co-located analytical techniques. See Table 1 and
Sect.2.3formoredetails.
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12880 M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol
Table1.ConsistencyofACSMmeasurements:comparisonbetweenACSMandindependentanalyticaltechniquesusingorthogonalregres-
sionanalyses.Slopesandinterceptsareindicated±uncertainties.
r2 Slope Intercept
Sp Su Au Wi An Sp Su Au Wi An Sp Su Au Wi An
Orgvs.OC 0.91 0.90 0.86 0.92 0.77 2.18±0.07 2.92±0.10 1.87±0.09 1.26±0.04 1.72±0.04 −0.29±0.37 −1.07±0.32 −0.28±0.36 0.74±0.37 0.61±0.25
Nitrate 0.95 0.53 0.96 0.92 0.91 1.37±0.03 4.27±0.25 1.28±0.03 0.86±0.03 1.28±0.02 0.42±0.18 0.64±0.11 0.48±0.10 0.62±0.11 0.48±0.09
Sulfate 0.96 0.97 0.92 0.86 0.95 1.05±0.02 0.98±0.02 0.96±0.04 1.38±0.06 1.00±0.01 −0.01±0.04 0.02±0.06 0.04±0.07 −0.25±0.06 0.00±0.03
Ammonium 0.92 0.70 0.91 0.95 0.90 1.03±0.03 1.00±0.06 0.93±0.04 0.81±0.02 0.99±0.02 −0.04±0.07 −0.04±0.07 −0.12±0.05 0.03±0.03 −0.08±0.03
Chloride 0.75 0.00 0.59 0.78 0.52 2.68±0.13 −0.13±0.09 0.68±0.06 1.13±0.07 1.75±0.06 0.04±0.01 0.03±0.00 0.04±0.00 −0.02±0.01 0.02±0.01
Massvs.volume 0.87 0.82 0.88 0.85 0.81 1.91±0.01 1.95±0.02 1.45±0.01 1.34±0.01 1.63±0.01 −1.16±0.19 −1.36±0.18 −2.45±0.19 −0.11±0.20 −1.09±0.11
Legend:Spisspring(March–April–May),Suissummer(June–July–August),Auisautumn(September–October–November),Wiiswinter(December–January–February),Anisannual.Independentanalyticaltechniquesreferto(i)EC–OCSunsetLaboratoryInc.analyzerforOCfrom
PM2.5sampling,(ii)ionchromatographyforionsfromPM2.5samplingand(iii)DMPSforvolumeconcentrations(seeSect.2.3formoredetails).MassreferstoNR-PM1+BC.Interceptsareinµgm−3.Slopesareingcm−3formassvs.volumeanddimensionlessotherwise.
However, these slopes cannot be directly regarded as the parison(i.e.slope)is1.6,whichistypicalofambientaerosol
OM-to-OC ratios due to (i) differences in size fractions be- particlesdensities(1.5–1.9inHandandKreidenweis,2002;
tween both methods (PM for ACSM and PM for filter Hu et al., 2012; McMurry et al., 2002; Pitz et al., 2003,
1 2.5
measurements) and (ii) uncertainties related to RIE for 2008).Thehigherdensitiesobservedduringspringandsum-
Org
ACSMmeasurements(Budisulistiorinietal.,2014;Ripollet mer (1.9–2.0) than autumn and winter (1.3–1.5) are likely
al., 2015).An estimation ofthe OM-to-OC ratiofor submi- due to the enhanced contribution of secondary aerosol and
cronorganicsapplyingthemethodologydescribedbyCana- agedparticlesduringtheformerperiod(Pitzetal.,2008).
garatna et al. (2015) is discussed in Sect. 4.2. Good corre-
lations are observed for nitrate during all seasons (r2>0.9) 3.2 Qualityassurance/qualitycontroloforganicsource
but summer (r2=0.5), which is most likely related to en- apportionment
hanced evaporative losses of ammonium nitrate from filter
during the latter season (Chow et al., 2005; Schaap et al., First, during the aforementioned inter-ACSM comparison
2004). Slopes range from 0.9 to 1.4 – summer excluded – study (Crenn et al., 2015), source apportionment of organ-
which is comparable to what is reported elsewhere (Bud- icswasperformedbasedondatafrom13Q-ACSMs(Fröh-
isulistiorini et al., 2014; Crenn et al., 2015; Ripoll et al., lich et al., 2015), including one ACSM used in the present
2015).Verygoodcorrelationsareobservedforsulfateinev- study. Satisfactory performances (z scores <2) are reported
ery season (r2=0.9–1.0) with slopes close to 1 (0.9–1.1, for our ACSM using a similar approach as adopted in this
winter excluded), consistent with its presence in the sub- study. This result demonstrates that our instrument and the
micronic size fraction and its low volatility leading to the associated data treatment, including the source apportion-
minimization of sampling artefacts. Note that discrepancies ment modelling, are capable of accurately identifying and
havebeenreportedwhencomparingsulfatemeasuredbythe quantifyingOAsources.
ACSM (Petit et al., 2015) or the AMS (Zhang, 2005) with
independent measurements. Our results suggest that ammo- 3.2.1 Modelconfigurations
niumsulfatecalibrationsshouldbeperformedtoexperimen-
tallydeterminesulfateRIEs,whichappeartobeinstrument Regarding our specific study, the configuration applied to
specific but stable over several months. Although aerosols reach the optimal SA of organics is thoroughly discussed
are slightly acidic on average at the study site, ammonium in Sect. S2 (constrained FPs, number of factors, a values
mostly neutralizes nitrate and sulfate throughout the cam- and integration-period durations). Briefly, constraining both
paign and thus exhibits behaviour in between the two latter HOAandBBOAfactorsresultinsatisfactorysolutionswith
compounds. Higher uncertainties are associated with chlo- relevant FPs, time series and daily cycles. Other configura-
ridefromfilterquantification,resultinginnoagreementwith tions (e.g. unconstrained factors) lead to unsatisfactory re-
ACSM measurements in summer when the concentrations sults with high seed variability, mixing of factors or ab-
are the lowest (r2=0.00) and fairly good agreement dur- sence of key fragments in identified profiles (e.g. absence
ing the other seasons (r2=0.64–0.77). The high slope ob- of m/z 43 and 44 in BBOA contrary to what is reported
served for ACSM1(e.g. 2.1 during spring)compared to the in Heringa et al., 2011, Fig. S4). Solutions applying differ-
fairly good slopes observed for ACSM2 (0.7–1.1) suggests entnumberoffactorsareinvestigated.Threefactors(HOA,
that chloride RIE might be instrument specific and require BBOA and OOA) are retained during spring, autumn and
appropriate calibrations for its accurate quantification (see winter,whereastwofactors(HOAandOOA)aremostsuit-
alsoRiffaultetal.,2013,onthistopic). able during summer. A lower number of factors results in
ThesumofNR-PM1 componentsandBChasbeencom- a mixing of them, whereas a higher number generates ad-
paredtothevolumeconcentrationofPM1.Goodagreement ditional factors – e.g. semi-volatile OOA (SV-OOA) during
is found between both variables (r2>0.8), giving further summer, OOA–BBOA during autumn – which are not sat-
confidenceontheconsistencyofourACSMmeasurements. isfactory – e.g. missing fragments or poor correlations with
Theannualaverageparticledensityestimatedfromthiscom- external data (see Table S1). BBOA cannot be clearly iden-
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M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol 12881
tified during summer; i.e. in this season agricultural waste residentialheatingemissions.ThelowBBOAconcentrations
burning contributions are estimated to be minor (maximum modelledduringlatespringandearlyautumn,aswellasthe
3–4% of OA, Sect. S2). Note that COA could not be evi- small increased contribution observed during the morning
denced,likelyduetothetypeofsitestudied(regionalback- also suggest residential heating emissions. OOA is identi-
+
ground) and the lower sensitivity, time and mass-to-charge fiedthankstothepredominantcontributionofm/z44(CO )
2
resolution of the ACSM compared to classical AMS in- and43(C H O+).Thehighercontributionoff (definedas
2 3 44
struments (further discussed in Sect. S2; see also Dall’Osto m/z44tototalorganicsignal;0.17–0.23dependingonsea-
et al., 2015, on this subject). Uncertainties associated with sons)withrespecttof (definedsimilarly;0.05–0.09)sug-
43
factor contributions are estimated by performing sensitiv- gests that this OOA factor is highly oxidized and presents
ity tests on a values, which are regarded as the most sub- low-volatile OOA (LV-OOA) rather than SV-OOA charac-
jective input parameters. Five scenarios putting very low to teristics(seeJimenezetal.,2009,andZhangetal.,2011,for
very high constraints on the reference FPs have been de- definitionsofthesecomponents).Thisstatementissupported
fined (see Table S2). Comparable solutions in terms of rel- by very good correlations (r2=0.96–0.99) found between
ative contributions (Fig. S5) and agreement with indepen- our unconstrained OOA profiles and the average LV-OOA
dentmeasurements(TableS2)arefoundwhenapplyinglow profilereportedbyNgetal.(2011c)fromsixAMSstudies.
to high constraints following the empiric recommendations Interestingly, our OOA profiles present slight seasonal dif-
of Crippa et al. (2014). Unsatisfactory solutions are gener- ferences that likely reflect changes in source contributions
allyreachedundertheextremescenarios(fullyfixedFPsand and/or physical–chemical processes in this factor. For in-
m/z specific standard deviations of RFPs). We decided to stance, f in OOA profiles is enhanced in winter (0.014)
60
apply low constraints (i.e. a values of 0.1 and 0.5 for HOA comparedwithotherseasons(0.001–0.004),whichsuggests
and BBOA, respectively) to let as much freedom as possi- that biomass burning contributes to this factor during the
ble to our FPs while remaining in the range of plausible aforementioned season, consistent with different studies re-
solutions. SA was performed on 3-month, 6-month and 1- portingf insecondaryOAfrombiomassburning(e.g.Cu-
60
year datasets. Although comparable solutions are found for bison et al., 2011; Heringa et al., 2011; see Sect. S2). Note
each configuration (number of factors, factor profiles, diur- that the mass spectral resemblance of primary humic-like
nalcycles,comparisonswithexternaldata),applyingSAon substancestoLV-OOAmightalsopartlyexplainthisobser-
seasonal datasets was preferred since (i) the seasonal vari- vation (e.g. Young et al., 2015), i.e. that a small fraction of
ability of FPs is captured and (ii) questionable results are primaryOAisfoundinthisfactor.Dailycyclesarecompa-
observed in summer for 6-month and 1-year configurations rableforallseasonswithabimodalpatterncharacterizedby
(see Sect. S2). When comparing the sum of OA factor con- a small peak during night-time and a prominent peak dur-
centrations and measured OA on the annual scale, OA is ingdaytime.Thelatterpeaksuggeststhatafractionof(LV-)
very well modelled (r2=0.97, slope=0.98±0.00, inter- OOAcouldbelocallyratherthanregionallyproducedonthe
cept=0.1±0.0µgm−3,n=14842). timescale of few hours only, likely due to enhanced photo-
chemical activities during daytime. The former peak could
3.2.2 Modeloptimalsolution be due to (i) the condensation of highly oxygenated semi-
volatilematerialfavouredbynight-timethermodynamiccon-
Factorprofiles,contributionsanddailycyclesoftheoptimal ditionsor(ii)acontributionofSV-OOAinourOOAfactor,
SA solution are presented in Fig. 2. Independent FPs and whichisgenerallydominatedbyLV-OOA.Theabsenceofan
time series are found for each season, which is a prerequi- f night-time peak (Sect. 4.2) suggests that the second as-
44
siteforhavingreliableSAsolutions.HOAisidentifieddur- sumptionismoreprobableimplyingthatbothSV-OOAand
ing every season and exhibits a profile dominated by alkyl LV-OOAinfluenceourOOAfactor.
+
fragmentssuchasm/z55(fromtheC H ionseries)and
n 2n−1
+
m/z57(fromCnH2n+1 ionseries;Ngetal.,2011c).Itsrel- 3.2.3 Timeseriescomparisons
ative contribution is characteristic of traffic emissions, ex-
hibitingapeakinthemorningandhighercontributionsdur- Comparisons between our OA factors, m/z tracer and in-
ing weekdays than weekends (e.g. averages of 14 and 9%, dependent species time series are shown in Table 2. OOA
respectively,inautumn;Fig.S6).BBOAisfoundduringev- time series show very good agreement with Org_43 (or-
eryseasonexceptsummerandhasaprofilesimilartothatof ganic signal at m/z 43) and Org_44 (r2>0.8 and 0.9, re-
+
HOA,exceptforthehighcontributionofm/z60(C2H4O2) spectively) and relatively good agreement with secondary
and 73 (C H O+), which have been suggested as biomass inorganic species (e.g. r2≥0.5 for ammonium), indicating
3 5 2
burningmarkers(Leeetal.,2010,andreferencestherein).A thatthisfactorcanberegardedasasurrogateforsecondary
distinct daily cycle with higher contributions during night- organicaerosols.Comparisonswithsulfate(alow-volatility
time than daytime is observed, in addition to higher contri- species)andnitrate(asemi-volatilespecies)confirmthatour
butionsduringweekendsthanweekdays(e.g.averagesof24 OOAfactormightbeamixofSV-OOAandLV-OOA,since
and 21%, respectively, in spring; Fig. S6), consistent with better agreement is found with one or the other compound
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12882 M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol
Figure2.Organicsourceapportionmentpresentedbyseason:factorprofiles(left),timeseries(middle)anddailycycles(right,errorbars
represent1standarddeviation).Seasonsaredefinedasspring(MAM),summer(JJA),autumn(SON)andwinter(DJF).
dependingontheseasonstudied.BBOAexhibitsverygood portion of BC stems from biomass burning, consistent with
coefficients of determination when compared with its pre- previous findings at the study site (Gilardoni et al., 2011,
sumable fragment tracers Org_60 and Org_73 (r2>0.97), fromECmeasurements).Agoodagreementisalsoobserved
giving further confidence on its appropriate quantification. withCO(r2≥0.7),asalreadyreportedintheAlpinevalleys
Good correlations are generally found between BBOA and (e.g. Gaeggeler et al., 2008). HOA is not as well correlated
BC(r2≥0.5,exceptforsummer)indicatingthatalargepro- withexternaldataorspecificm/z,whichcouldberelatedto
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M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol 12883
Table2.Comparison(coefficientofdetermination,r2)betweenSAfactors,organicm/ztracersandindependentspeciestimeseries.BC
standsforblackcarbon;Org_istandsfororganicsignalatm/zi(i=43,44,60,67,73,81).
HOA BBOA OOA
Org_67 Org_81 NOx CO BC Org_60 Org_73 NOx CO BC Org_43 Org_44 NH4 SO4 NO3
Spring 0.60 0.55 0.03 0.08 0.28 0.99 0.97 0.32 0.81 0.70 0.88 0.94 0.76 0.43 0.77
Summer 0.90 0.91 0.07 0.40 0.52 – 0.97 0.94 0.54 0.60 0.19
Autumn 0.63 0.61 0.07 0.10 0.24 0.99 0.97 0.06 0.68 0.47 0.82 0.92 0.47 0.53 0.38
Winter 0.58 0.57 0.34 0.33 0.39 0.98 0.97 0.20 0.66 0.63 0.80 0.99 0.50 0.39 0.66
Figure3.OverviewofthechemicalcompositionofNR-PM1atIspra(PoValley,Italy):dailyabsolute(a)andrelative(b)chemicalcompo-
sition;seasonal(c)andannual(d)averages.
(i)theabsenceofclearm/ztracersforthisfactorduetosim- relevantdailycyclesand(iii)nosignificanta valuevariabil-
ilaritieswithBBOAprofile,(ii)theabsenceofclearexternal ity.
tracersduetoco-emissionsbyfossilfuelandbiomassburn-
ing activities of BC, CO and NO and (iii) possible uncer-
x
taintiesassociatedwiththeapportionmentbetweenHOAand 4 Resultsanddiscussion
BBOA.Thefirsttwoassumptionsaresupportedbythebetter
agreementobservedbetweenHOAandm/zfragmentsorin- The meteorological representativeness of this 1-year mea-
dependentdataduringsummer(e.g.r2=0.52,n=2208be- surementisassessedbycomparingthesolarirradiation,pre-
tweenHOAandBC)andspecificmonths(e.g.May,Septem- cipitationandtemperaturemonthlyaveragestotheonesmea-
ber),whenbiomassburningcontributionsarenegligible.Al- suredduring1990–2010atthestudysite(Fig.S7).Compa-
thoughuncertaintiesassociatedwiththeaccurateapportion- rableseasonalaveragesaregenerallyfoundinourstudyand
mentofHOAandBBOAcannotbeexcluded(e.g.duetoro- during the bidecadal reference period. Nevertheless, com-
tational ambiguity), several factors evidence the robustness paredto1990–2010,spring2013wasrainier,summer2013
oftheresultsandindicatethatamixingofbothfactorsisun- slightly warmer and sunnier and winter 2013–2014 rainier.
likely,sinceHOAandBBOApresent(i)independentfactor Furtherinformationregardingtherepresentativenessofmea-
timeseriesduringallseasons(r2=0.1–0.2),(ii)distinctand surements performed at the study site during the year 2013
canbefoundinPutaudetal.(2014a).
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12884 M.Bressietal.:Variationsinthechemicalcompositionofthesubmicronaerosol
4.1 ChemicalcompositionofNR-PM ingemissions(duringcoldseasons)andlowertemperatures
1
favouringthepartitioningofsemi-volatileinorganic(mainly
An overview of the chemical composition of NR-PM re- ammonium nitrate) and organic material in the condensed
1
trievedduringthiscampaignisshowninFig.3.Theannual phase,tonameafew.Adistinctpeakis,however,observed
averagedNR-PM massreportedhere(14.2µgm−3)ranges around noon, probably caused by enhanced photochemical
1
amongstthehighestNR-PM levels(7thoutof41sites)re- production of secondary organic compounds and increased
1
portedatruralandurbandownwindsitesinEurope(Crippa BLHfavouringdownwardmixingofadvectedpollution,es-
et al., 2014) and worldwide (Jimenez et al., 2009; Zhang peciallyduringsummer(Fig.4;Decesarietal.,2014).Note
et al., 2007, 2011). Please note that these previous studies that this annual daily pattern is the combination of distinct
are based on typically 1 month of measurements in differ- daily cycles varying with the season studied (Fig. 4). In
ent seasons. It is comparable to NR-PM levels reported terms of relative chemical composition, organics dominate
1
during specific campaigns in the urban areas of New York NR-PM mass independently of the time of the day, with
1
City (USA, 12µgm−3; Weimer et al., 2006), Tokyo (Japan, mediancontributionsrangingfrom∼60to70%.Nitrateex-
12–15µgm−3; Takegawa et al., 2006) or Manchester (UK, hibitshighercontributionduringnight-timeduetoitsabove-
14µgm−3; Allan et al., 2003a, b). Our annual average NR- mentioned semi-volatile nature. Sulfate shows unexpected
PM mass is higher than the 10µgm−3 guideline given by dailycycleswithsignificantlydifferent(99.99%confidence
1
theWorldHealthOrganizationfortheannualaveragePM level) relative contributions – and absolute concentrations
2.5
mass (including refractory and nonrefractory compounds; – during daytime (∼15% of NR-PM mass around noon)
1
WHO, 2006). After similar conclusions have been drawn compared to night-time (∼10% around midnight, Fig. 4),
forPM andPM sizefractions(Putaudetal.,2010),the although its formation was expected to occur mainly over
2.5 10
Po Valley appears to be one of the most polluted regions longer timescales (i.e. days) in cloud droplets (Ervens et
in Europe with regard to NR-PM levels this time. Submi- al., 2011). This observation could be due to (i) local pro-
1
cron aerosol particles are mostly made of organics (58%), duction of sulfate with increased photochemical production
nitrate (21%), sulfate (12%) and ammonium (8%; Fig. 3). around noon at the study site and/or (ii) diurnal changes of
Thepredominanceoforganicsistypicalofurbandownwind theatmosphericstratificationinthePoValleyasdescribedby
sites (e.g. average of 52% reported in Zhang et al., 2011). Saarikoskietal.(2012)andDecesarietal.(2014),enhancing
However,thenoticeableproportionofnitrateischaracteris- aged particle contribution during the middle of the day and
tic of urban sites (18% in Zhang et al., 2011), which likely theafternoon.Nonrefractorychloride(mostlyNH Cl,Huang
4
reflects the substantial influence of anthropogenic activities etal.,2010)exhibitsverylowcontributionsindependentlyof
emissions at our regional site. As a result, sulfate exhibits thehouroftheday(mediansbelow0.5%ofNR-PM mass)
1
particularlylowcontributionsatthestudysitecomparedwith but with a slight increase at night, which is likely due to its
otherlocations(generally>20%inZhangetal.,2011). presumablesemi-volatilenaturehere.
NR-PM levelspresentaclearseasonalitywithhigherlev-
1
els during spring (∼18µgm−3) and winter (∼15µgm−3) 4.2 Focusonorganicaerosols
compared with summer and autumn (∼12µgm−3). Higher
levels were expected during cold months due to enhanced An overview of the contribution of HOA, BBOA and OOA
biomassburningemissionsandlowerboundarylayerheights toOAisshowninFig.5.Ontheannualaverage,theorganic
(BLHs), as previously observed at the study site (Putaud et fraction is dominated by the secondary component (OOA,
al.,2013).Expectedseasonalvariationsofthechemicalcom- 66%).AlthoughthisOOAcontributionissubstantial,higher
positionofNR-PM areobserved,with(i)highernitratecon- proportions are generally reported at rural and urban down-
1
tributionsduringthecoldseasonwhichfavoursitspartition- wind sites worldwide (90 and 82% of OA on average, re-
ing in the condensed phase (Clegg et al., 1998), (ii) higher spectively; Zhang et al., 2011). This lower relative contri-
sulfate contributions during summer, which can e.g. be as- bution of OOA is related to the higher contribution of (pri-
sociatedwithenhancedphotochemicalproduction(Seinfeld mary)BBOA inourstudy(23%of OAonthe annualaver-
andPandis,2006)andloweramountofrainout(Fig.S7),and age)comparedtothepreviousones.Considerablecontribu-
(iii) relatively stable contributions for ammonium (mainly tions ofBBOA areexplained by thespecific locationof the
neutralizing the two previous species) and organics (dis- studysiteinthevicinityoftheAlps,wherebiomassburning
cussedlateron). isamajorcontributortoOA(Belisetal.,2011;Herichetal.,
A focus will now be made on daily cycles of the chemi- 2014; Lanz et al., 2010). Biomass burning emissions hence
cal composition of NR-PM (Fig. 4), displayed for the first substantially affect OA levels on the annual scale here. The
1
time during the four seasons in the Po Valley, thanks to contribution of HOA is comparatively smaller (11%), indi-
the high time resolution and stability of the ACSM. On the cating that despite the expected large contributions of fos-
annual scale, daily cycles of NR-PM levels are character- silfuelemissions(i.e.trafficandindustrialemissions),those
1
izedbysignificantlyhigherconcentrationsduringnight-time are not the major sources of primary OA at the study site.
than daytime, likely due to lower BLH, higher wood burn- However,itislikelythatfossilfuelemissionsofvolatileor-
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