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Aerosol Science and Technology, 44:1–12, 2010Copyright © American Association for Aerosol ResearchISSN: 0278-6826 print / 1521-7388 onlineDOI: 10.1080/02786821003786048

Composition and Morphology of Individual Combustion,Biomass Burning, and Secondary Organic Particle TypesObtained Using Urban and Coastal ATOFMS andSTXM-NEXAFS Measurements

Ranjit Bahadur,1 Lynn M. Russell,1 and Kimberly Prather1,251Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA2Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California,USA

Complementary single particle measurements of organicaerosols using aerosol time-of-flight mass spectrometry (ATOFMS)and Scanning Transmission X-ray Microscopy—Near Edge X-rayAbsorption Fine Structure (STXM-NEXAFS) are compared to ex-10amine the relationships between particle morphologies and chemi-cal composition of particles having similar sources. ATOFMS mea-surements provide size-resolved chemical composition informationfor single particles. Measurements from field campaigns in pol-luted or urban (Riverside/SOAR 2005; Mexico City/MILAGRO152006; Port of Long Beach 2007) and clean or marine (ArabianSea/INDOEX 1999; Sea of Japan/ACE-Asia 2001; TrinidadHead/CIFEX 2004) locations illustrate regional differences. Themajority (>= 85%) of the number of submicron particles are car-bonaceous (including elemental and organic carbon), but represent20less than 10% of the number of supermicron particles. Organiccarbon (OC) particles are classified into three meta-classes corre-sponding to (1) combustion generated OC/EC internal mixtures, (2)biomass burning generated K/OC mixtures, and (3) OC/High MassOC (HMOC) mixtures containing secondary markers of atmo-25spheric processing. Normalized dot products are used to quantifysimilarity among fragment spectra and indicate that OC particletypes are consistent across (and within) platforms. Single parti-cle carbon STXM-NEXAFS measurements during ACE-Asia 2001and MILAGRO 2006 yield similar source categories based on rela-30tive abundances of aromatic, alkane, and carboxylic acid functionalgroups. All three organic particle types correspond to a variety

Received 4 December 2009; accepted 7 March 2009.This comparative study was funded by a grant from BP. Addi-

tional support from the National Science Foundation, the James S.McDonnell Foundation, and the California Air Resource Board fundedour participation in the field studies used in this work. The authorswould like to acknowledge all the members of the Russell and Pratherresearch groups at UCSD responsible for collecting the measurementssummarized here, including Satoshi Takahama, Ryan Moffett, SergioGuazzotti, Sharon Quin, Keith Coffee, John Holecek, Matt Spencer,Andy Ault, Cassandra Gaston, David Sodeman, and Monica Rivera, aswell as collaborators Patricia Quinn and Tim Bates of NOAA PMEL.

Address correspondence to Kimberly Prather, Department of Chem-istry and Biochemistry, University of California San Diego, La Jolla,California 92093-0314. E-mail: lmrussell@ucsd.edu

of very heterogeneous particle morphologies, although the highlyoxygenated OC particles with likely secondary organic contribu-tions frequently are nearly spherical, liquid-like particles. Size- 35resolved number fractions of the major ATOFMS OC particle typesshow qualitative agreement with OC particle types from STXM-NEXAFS analysis, indicating a correspondence of the OC/EC typewith the presence of strong aromatic groups, of the OC/HMOCtype with high carboxylic acid groups, and of the biomass burn- 40ing OC type with aromatic and carbonyl groups. ATOFMS mea-surements can be used to establish robust statistics for offline sin-gle particle techniques, providing the atmospheric context for thefunctional group and morphological information obtained fromSTXM-NEXAFS for an improved understanding of the climate 45impact of organic aerosols.

INTRODUCTIONTropospheric aerosol particles vary in their composition

and phase and play a significant role in human health andthe climate system (Andreae and Crutzen 1997; Poschl 2005; 50Seinfeld 2004). Aerosol particles can be either directly emittedinto the atmosphere from combustion sources (fossil fuel andbiomass burning) and wind-driven processes (resuspension ofdust and creation of ocean-derived particles), or formed throughthe condensation of low volatility gas phase species produced 55photochemically, and through cloud processing (Prather et al.2008). Though the mechanisms of the aerosol direct (Jacobson2001; Liousse et al. 1996; Penner et al. 1998) and indirect ef-fects (Jones and Slingo 1997; Twomey 1977) are qualitativelyunderstood, there exists an uncertainty of as much as a factor 60of 2 in the radiative forcing magnitude (Chung and Seinfeld2002). A major contributing factor to this large uncertainty isthe limited knowledge of the specific chemical and morpho-logical characteristics of atmospheric aerosols, which, in turn,limits our understanding of their size distributions, optical and 65hygroscopic properties, and residence time in the atmosphere(Prather et al. 2008; Turpin et al. 2000). Carbonaceous parti-cles (elemental carbon, EC and organic carbon, OC) constitute

1

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2 R. BAHADUR ET AL.

between 20 and 80% of submicron aerosol mass (Jacobson et al.2000; Turpin and Lim 2001) with total anthropogenic emissions70estimated at 42 Tg yr−1 (Bond et al. 2004). The absorbing frac-tion of carbonaceous aerosols has been estimated as the sec-ond largest contributor to global warming (Jacobson et al. 2000;Ramanathan and Carmichael 2008), however the light absorbingproperties are strongly dependent on the mixing state of these75particles (Bond and Bergstrom 2006; Schnaiter et al. 2005).Therefore an understanding and chemical characterization ofindividual aerosol particles is essential to evaluate their impacton the climate.

Aerosol time-of-flight mass spectrometry (ATOFMS) with80laser desorption/ionization (LDI) has proven to be a valuableanalytical tool for online size-resolved chemical characteriza-tion of ambient aerosol samples (Sullivan and Prather 2005;Zimmermann et al. 2003). Using this technique, the classifi-cation of several different particle types is possible through85knowledge of their chemical fingerprints, and questions of mix-ing states between dust and crustal species, elemental carbon,as well as organic species that comprise the bulk of ambientaerosols can be addressed (Gross et al. 2000a; Gross et al.2000b; Silva and Prather 2000; Suess and Prather 1999). In90ATOFMS, organic and inorganic aerosol material is desorbedand ionized from single particles by an intense laser pulse (laserdesorption/ ionization process, LDI) and the positive and neg-ative ions are subsequently analyzed by a dual polarity time-of-flight mass spectrometer. The positive mass spectrum shows95cations such as metal ions, ammonium, and carbon-containingclusters while the negative spectrum contains carbon-containingclusters as well as additional peaks from secondary anions likesulfate, phosphate, nitrate, and certain organic species. In addi-tion to chemical heterogeneity, aerosol particles also exhibit var-100ied physical configurations (including spheres, agglomerations,inclusions, and shells) that have implications for their opticalproperties (Mishchenko et al. 2004; 2007) and hygroscopicity(Cruz and Pandis 1998; Sjogren et al. 2007). Scanning Trans-mission X-Ray Microscopy with Near-Edge X-ray Absorption105Fine Structure (STXM-NEXAFS) is a highly sensitive chemicalanalysis technique that can be used to non-destructively probeparticle morphology and functional group composition in in-dividual particles relevant to the atmosphere (Kilcoyne et al.2003; Russell et al. 2002; Takahama et al. 2007; Takahama110et al. 2008). STXM-NEXAFS provides advantages in higherchemical resolution for organic components compared to elec-tron microscopy—providing functional group composition at ascale of 30 nm, in addition to its capability for analysis at atmo-spheric pressure and the lower likelihood of radiation damage.115However, the number of particles that can be analyzed is limitedby access to a synchrotron radiation facility with a scanningx-ray microscopy end-station.

This paper presents a comparison of the chemical character-istics of the organic aerosol fraction measured using ATOFMS120from six field campaigns carried out between 1999 and 2007 indifferent geographic regions: INDOEX (Indian Ocean, March

1999), ACE-Asia (Sea of Japan, March–April 2001), CIFEX(Trinidad Head, CA, April 2004), SOAR-I and SOAR-II (River-side, CA, 2005), MILAGRO (Mexico City, March 2006), and 125in Long Beach, CA (August 2007). The dates, coordinates,and sampling environment for each project are summarized inTable 1. Nearby STXM-NEXAFS measurements from ACE-Asia and MILAGRO are compared to the ATOFMS measure-ments to examine the relationships between particle morpholo- 130gies and chemical compositions having similar source origins toimprove our understanding of organic aerosols further. The pur-pose of this study is to establish statistically robust trends in size,composition, and mixing states of organic aerosols essential forquantifying their climate impact and to link these trends to de- 135tailed particle morphology descriptions. Though not exhaustive,the subset of measurements considered here provides an essen-tial first step in the preparation of a complete global climatologyof single particle measurements from contrasting regions.

EXPERIMENTAL METHODS 140

The aerosol time-of-flight mass spectrometer (ATOFMS) asdescribed by Gard et al (1997) was used to determine the size andchemical composition of individual aerosol particles during theselected field campaigns. Ambient aerosol samples were drawninto a vacuum chamber through a nozzle inlet (with size cut- 145offs at 0.2 and 3 µm), resulting in a supersonic expansion thataccelerates individual particles to terminal velocities. The aero-dynamic diameter of each particle is determined by measuringthe time-of-flight between two continuous scattering Nd:YAGlasers operating at 532 nm. The firing of a third pulsed Nd:YAG 150laser (266 nm) is triggered based on the particle velocity, result-ing in the ablation and ionization of the particle in the centerof the mass spectrometer. Positive and negative ions producedfrom each particle are separately transferred to two flight tubes,and spectra are measured simultaneously using time-of-flight 155mass spectrometry. Recent versions of the ATOFMS’s data ac-quisition systems permit the analysis of up to a few hundredindividual particles per minute (Pratt et al. 2009). Project spe-cific details for the selected field studies used in this work aresummarized in Table 1. ATOFMS measurements were analyzed 160using the Matlab-based toolkit YAADA (www.yaada.org). Todetermine chemical characteristics, a subset of particles wereclassified using the ART-2a clustering algorithm (Song et al.1999) with resulting clusters matched to the entire dataset. Thesubset typically represented between 2.5 and 5% of all collected 165particles. Between 50 and 60 of the largest clusters represent inexcess of 90% of all particles, with the remainder comprising alarge number of sparsely populated clusters. These clusters weremanually grouped into a smaller number based on the major ionpeaks in their mass spectra (Guazzotti et al. 2003; Moffet et al. 1702008a; Su et al. 2005).

Particles for STXM-NEXAFS analysis were collected on sil-icon nitride windows (Si3N4; Silson Ltd.) mounted on a rotatingimpactor (Streaker; PIXE International, Inc.) by drawing air into

April 20, 2010 13:44 801 UAST_A_479126

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Nov

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Mex

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/EC

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3

April 20, 2010 13:44 801 UAST_A_479126

4 R. BAHADUR ET AL.

the impactor at 1 liter per minute (lpm). Sample windows were175analyzed at the Advanced Light Source at Lawrence Berke-ley National Laboratories (Berkeley, CA) in a He-filled cham-ber maintained at 1 atm. Photon transmission was measuredat energy levels between 278 and 305 eV and converted tooptical density as described in earlier work (Maria et al. 2004;180Russell et al. 2002; Takahama et al. 2007). Spectra were adjustedfor background absorbance (278 eV–283 eV), normalized usingthe total carbon content (Maria et al. 2004), and subsequentlyclassified using the Ward hierarchical clustering algorithm(Ward 1963). These clusters were manually condensed into a185smaller number representing similarities in atmospheric parti-cles based on qualitative information such as sampling times andconditions, chemical similarity between spectra, and location(Takahama et al. 2007).

ATOFMS Particle Types190

Single particle clusters containing carbonaceous particleswere identified by the presence of carbon cluster fragments(Cn, with n typically between 1 and 3, but as large as 12 insome cases) in both the positive and negative ion spectra. Par-ticles with spectra lacking these markers can originate from195dust, combustion-generated metallic particles, or sea salt; buttheir spectra are omitted from this study. It is important to notethat the presence of water suppresses negative ion formation(Neubauer et al. 1998) occasionally resulting in particles thatdo not produce a negative spectrum (Ault et al. 2009; Moffet200et al. 2008b); these particles were excluded from this analysis.Carbonaceous particles that were dominated by strong Cn frag-ments with mass to charge ratio (m/z) 12, 24, and 36, and lackingtracer fragments associated with organic carbon: C2H3 (+27),C3H3 (+39), and C2H3O (+43) were classified as elemental car-205bon (EC), with the remainder constituting organic carbon (OC)particles (Moffet et al. 2008a; Sodeman et al. 2005; Su et al.2005). The presence (or absence) of alkali metal ions (suchas Na+, K+, or Ca+) and secondary tracers (such as Cl−, NO−

2 ,NO3

−, HSO4−, or SO4

−) was not considered in this preliminary210classification, and these species may be internally mixed witheither the non-carbonaceous, EC, or OC particles. The numberfractions of OC particles for the selected field campaigns aresummarized in Table 1.

Carbon-containing particles dominate the submicron size215fraction, contributing between 80% (INDOEX ’99) and 95%(Long Beach ’07) by number. Non-carbonaceous particles con-stitute the majority of supermicron particles in each data set withnumber fractions ranging from 80% in polluted urban environ-ments (SOAR ’05 and MILAGRO ’06) up to 98% in a clean220marine environment (ACE-Asia). Sea salt particles constitutedthe majority of both size fractions during ACE-Asia (Sullivanand Prather 2007), but the submicron number fraction of car-bonaceous particles was significantly higher (varying between20% and 40% depending on the air mass) compared to less225than 10% for the supermicron particles, consistent with the size

FIG. 1. Average normalized positive ion spectra for OC meta classes mea-sured using the ATOFMS corresponding to (a) EC/OC mixture, (b) K/OCbiomass burning, and (c) Processed/OC type of particles. The dashed linesindicate the standard deviation from the average spectra within each particleclass. Negative ion spectra are not illustrated.

partitioning of carbon-containing particles observed at the othersites. The submicron EC number fraction ranges between 5%(INDOEX ’99) and 23% (SOAR II ’05) and is typically smallerthan the OC number fraction in both size fractions for the field 230campaigns in this study.

The organic carbon particles were further resolved into threemeta-classes that were observed in different proportions duringeach of the field campaigns. The average positive ion mass spec-tra for all particles within these three meta-classes are illustrated 235in Figure 1, along with the standard deviation as a measure ofthe variability within each class. The differences in mass spec-tra correspond to chemical differences between the particles thatcan be related qualitatively to their sources to the atmosphere.

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ORGANIC PARTICLES FROM ATOFMS AND STXM-NEXAFS 5

FIG. 2. The similarity number defined by Equation (1) calculated for ATOFMS OC meta-classes separately for six different field campaigns, and for a grandaverage of all particles. Empty markers (left column in each set) show values calculated using entire spectrum, Solid markers (right in column in each set) showvalues calculated from positive ions only. Boxes are used to show approximate range of values. (Figure provided in color online.)

EC/OC mixture240

The Elemental Carbon/Organic Carbon (ECOC) type is char-acterized by a positive ion mass spectrum that is dominated bycarbon atom clusters (Cn) with smaller signal contributions fromOC, nitrate, sulfate, and ammonium tracer fragments. The simi-larities of these mass spectra to test spectra for vehicular exhaust245(Sodeman et al. 2005) and elemental carbon suggest that theseparticles are produced from primary sources of fossil fuel com-bustion, having undergone addition of secondary condensatesby atmospheric processing which is small relative to the ECamount.250

K/OC mixtureThese particles have a large peak at +39 that is attributed

to K+ in the positive ion spectra, and additionally may containnegative ion fragments at −45, −59, and −73 that have beenassociated with levoglucosan fragments. These tracers are typ-255ically associated with combustion products specific to biomassburning or biofuel combustion (Bein et al. 2008; Guazzotti et al.2003; Hudson et al. 2004; Silva et al. 1999; Silva and Prather2000) and indicate that this meta-class contains particles pro-duced from the burning of plant matter.260

OC/High mass OC mixtureThese particles are characterized by hydrocarbon ion series

starting with the base carbon peak at +12 but also containingmajor OC peaks at +27 (C2H3) and +43 (C2H3O), with the latteroxygenated fragment typical of atmospheric oxidation (Silva265and Prather 2000). Many of these particles also contain peaks at+39 (K or C3H3) suggesting possible condensation of secondaryorganic species on primary aerosol cores. A smaller fraction ofthese particles contain positive ion fragments up to and beyond

+150 indicative of high mass compounds that may be formed 270via aqueous phase processing during long range transport (Prattand Prather 2009).

To summarize, particles are identified as OC by the pres-ence of peaks at +12,+24,+36, and +27; the subset of theseparticles with a peak at +43 are classified as OC/HMOC; the 275subset of remaining particles with a peak at +39 are classifiedas K/Biomass; and the remainder are classified as EC/OC mix-tures. The existence of the same OC meta-classes at each of thesix measurement sites allows for a cross-platform comparisonof OC particle spectra to address the similarity between organic 280particles derived from similar sources in polluted urban envi-ronments and clean marine environments. A simple quantitativemetric analogous to the vigilance factor used to classify the par-ticle spectra is the normalized Euclidean distance between ionfragment spectra within each cluster. If the spectra within each 285cluster are expressed as vectors then a similarity number α canbe defined as

α =∑

(Si · Sc)/∑

(Si · Si) [1]

where Si represents the spectra of particle i within each clusterand Sc is the average cluster spectrum. As defined, the similaritynumber is bounded between unity for a set of identical spectra, 290and 1/i for a set of orthogonal spectra. The similarity numbercalculated at the six measuring platforms individually, and forall platforms combined is illustrated in Figure 2 for the threeOC meta-classes. The value of α calculated for the positive ionspectra ranges from 0.87–0.96 for EC/OC particles, 0.84–0.91 295for OC/High mass particles, and 0.86–0.96 for K/Biomass par-ticles for the individual projects, with corresponding values of0.83, 0.81, and 0.84 respectively when calculated by combining

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6 R. BAHADUR ET AL.

FIG. 3. Size-resolved normalized number fraction of organic particles measured using the ATOFMS during (a) INDOEX 1999 (spring), (b) CIFEX 2004 (spring),(c) ACE-Asia 2001 (spring; polluted marine region), (d) SOAR-II 2005 (fall), (e) MILAGRO 2006 (spring), and (f) Long beach 2007 (fall). Bar segments frombottom to top illustrate of EC/OC, K/Biomass, and OC/HMOC fractions. (Figure provided in color online.)

spectra across platforms. The high values of α and agreementamong platforms for each of the OC classes indicates that con-300sistent fragmentation patterns are observed in the positive ionspectra in distinct sampling environments, indicating particlesin different regions have quite similar organic components. Thecalculated value of α decreases significantly when the negativeion spectra are included—this larger variability indicates that the305organic fractions are internally mixed to varying extents withsecondary ions (such as sulfate and nitrate), which show greatervariability among the samples collected in each environment.

This variability in negative ions reflects the difference in ageof the particles in each of these environments. The similarity 310numbers calculated between the average spectra correspondingto the three metaclasses are 0.70 for EC/OC-Biomass, 0.72 forEC/OC-OC/HMOC, and 0.77 for Biomass-OC/HMOC, provid-ing a metric of the range of variability associated with eachsource category. 315

Figure 3 illustrates the size-resolved distribution of OC meta-classes for PM2.5 particles for each field project, with the relativefractions normalized by the total number of OC type particles. A

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ORGANIC PARTICLES FROM ATOFMS AND STXM-NEXAFS 7

few general trends emerge based on the synthesis of these fieldmeasurements: (1) The OC/EC mixture class constitutes the320largest segment of OC particles at small sizes and decreases withincreasing aerodynamic diameters (following the same trend asEC particles); (2) OC/EC mixtures constitute a larger fraction ofOC particles in polluted urban environments (e.g., Mexico Cityand Riverside); (3) The OC/High Mass OC mixture class con-325stitutes a larger fraction of OC particles at larger diameters anddecreases at smaller sizes; (4) The OC/High mass OC mixturescontribute to the majority of particles in cleaner environments(INDOEX and CIFEX—consistent with the formation of largerparticles from cloud and fog processing during transport); and330(5) The K/Biomass mixtures typically make up less than 20% ofall OC particles, with the exception being in Mexico City wherethis class contributes in excess of 40% of OC particles—thislocal variation has been attributed to forest fires and emissionsfrom local trash burning being significant sources of organic335compounds during MILAGRO (DeCarlo et al. 2008; Moffetet al. 2008a; Yokelson et al. 2007). The relatively higher num-ber contribution of the oxygenated meta-classes (biomass burn-ing and OC/HMOC mixtures) in clean marine environmentscompared to polluted urban environments is consistent with340comparative studies using Aerosol Mass Spectrometry (AMS)measurements (Zhang et al. 2007) that find a higher fraction ofoxygenated organics in remote environments than in pollutedenvironments. The larger contribution of the EC/OC class com-pared to the OC/HMOC in the field studies considered here is345consistent with a smaller contribution of secondary aerosol tothe total organic mass during the spring months compared to thelate summer and fall (Shields et al. 2008; Zhang et al. 2007).

STXM-NEXAFS Particle TypesSpectra from STXM-NEXAFS were determined by aver-350

aging rasterized scans over each particle and classified usinga clustering algorithm following subtraction of the pre-carbonedge absorbance and normalization by the mean (Takahamaet al. 2007; Takahama et al. 2010). In addition to spectral classi-fication, the functional group composition of the carbonaceous355fraction was determined from the relative absorption measuredby fitting Gaussian peaks to the particle spectra (Braun 2005;Russell et al. 2002; Tivanski et al. 2007). Based on a trainingset of reference spectra, Takahama et al. (2007) categorized car-bonaceous particles into four source related meta-classes, with360a small number labeled “other.” The “ultisol” meta-class showssimilarities to soil particles collected in the Caribbean (Ade andUrquhart 2002) and is considered a subset of dust particles. Thethree remaining meta-classes constitute OC particles as definedwithin this study. The normalized absorption spectra for the OC365particle classes are illustrated in Figure 4.

Combustion ParticlesThe first meta-class of OC particles shows significant ab-

sorbance at 284 and 285 eV, which is attributed to the R(C C)R

FIG. 4. Average STXM-NEXAFS absorption spectra scaled by maximumabsorbance spectra for (a) Combustion generated, (b) Biomass Burning, and (c)Secondary particles. Average spectra for ACE-Asia 2001, MILAGRO 2006, andboth projects combined are illustrated using dark, light, and black lines. Dottedvertical lines show spectra features corresponding to R(C=C)R at 284 and285 eV, R(C=O)R at 286.7 eV, R(C–H)R at 287.7 eV, R(C=O)OH at 288.7 eV,RNH-ROH at 289.5 eV, and K+ at 297.4 and 299.9 eV. (Figure provided incolor online.)

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8 R. BAHADUR ET AL.

aromatic/alkene bond, indicating the presence of sp2 hybridized370carbon associated with soot and black carbon (Andreae andGelencser 2006; Maria et al. 2004; Takahama et al. 2007). Inaddition to the strong aromatic absorbance, particles in thisclass also showed peaks at 286.7 and 287.7 eV, attributed toR(C O)R carbonyl and R(C-H)R alkane groups respectively,375with a smaller relative abundance.

Biomass Burning ParticlesParticle spectra in this class showed multiple transitions with

absorption peaks corresponding to R(C C)R, R(C O)R, andR(C O)OH bonds (288.7 eV), although the peaks were weaker380than those identified for the soot containing particles (Takahamaet al. 2007). These particle types show spectral features similar toreported observations for phenols, biogenic fulvic acids, and theproducts of wood burning (Ade and Urquhart 2002; Braun 2005;Tivanski et al. 2007), and are therefore attributed to emissions385from biomass burning.

Highly Oxygenated or Secondary ParticlesThe third class of OC particles shows the highest relative

abundance of absorbance due to a R(C O)OH carboxylic acidsignature, which is typically associated with the gas phase ox-390idation of volatile organic compounds that partition into theaerosol phase. Particles dominated by this signal are associatedwith either secondary organic aerosol or primary aerosol parti-cles that have undergone significant atmospheric processing andaging.395

Since STXM-NEXAFS allows non-destructive scanning, themorphology and heterogeneity of carbon distribution within sin-gle particles can be measured via image analysis. Figure 5 il-lustrates example single particle images for the three types ofOC particles observed in Mexico City during MILAGRO 2006.400A complete description of particle morphology analysis and re-sults based on particles collected during six field experimentshas been prepared by Takahama et al. (2010). Based on thismore extensive work, a few general trends relevant to the parti-cles considered here can be established—(1) OC particle types405show varied morphology (spherical, elongated, fractal aggre-gates) for all meta classes; (2) Combustion and biomass burningtype particles are typically heterogeneous, containing inclusionsand layers; (3) Secondary particles are often spherical and ho-mogenous; and (4) The small number of shell-core type particles410observed were all secondary.

Comparison of ATOFMS and STXM-NEXAFSThe analysis of ATOFMS and STXM measurements at mul-

tiple sites indicates that while the specific chemical compositionof organic aerosols is complex and varying, this system can be415simplified by its representation by a relatively small set of rep-resentative meta-classes. Both techniques yield three predom-inant types of organic particles associated with similar sourceprocesses, suggesting that a relationship exists between parti-

TABLE 2Positive ion fragments (ATOFMS) and organic functionalgroups (STXM-NEXAFS) used to identify corresponding

organic aerosol particle types

STXM-NEXAFSATOFMS fragments groups

EC/OC; Cn (12, 24, 36) R-(C C)-RCombustion C2H3 (27) R-(CH)n-RBiomass Burning K (39) R-(C O)-R

C3H3 (39) R-(CH)n-ROC/HMOC; Cn(12, 24, 36) R-(CH)n-RHighly Oxygenated C2H3 (27) R(C O)OHor Secondary C2H3O (43)

cles analyzed by these two complementary techniques. Based 420on their respective classification criteria, the particle classes canbe related to each other as: EC/OC mixtures from ATOFMSto Combustion from STXM, K/Biomass mixtures to Biomassburning, and OC/HMOC mixtures to Highly Oxygenated (orSecondary) particle types, as summarized in Table 2. 425

Figure 6 shows a size-resolved comparison of the num-ber fraction of the three OC particle types collected duringthe ACE-Asia and MILAGRO field campaigns at nearly co-located platforms. The same qualitative trends are observed atboth platforms, i.e., combustion and EC/OC type particles con- 430tribute a larger number fraction at smaller sizes, secondary andOC/HMOC type particles contribute a larger fraction at thelarger end of submicron sizes; and biomass burning type parti-cles constitute a much larger number fraction of organic particlesduring the MILAGRO study. Two significant caveats must be 435considered when conducting this comparison, which contributeto the lack of a close quantitative agreement between the meth-ods. First, the ATOFMS sizes particles based on aerodynamicdiameter, while STXM particle sizes are geometric measuresof the impacted size using image contrasts with the substrate. 440Second, the high sampling rate of the ATOFMS results in a ro-bust statistical distribution—a total of 14,552 and 126,611 OCparticles were sampled during ACE-Asia and MILAGRO, re-spectively, whereas the 101 and 81 particles analyzed by STXMprovide only an approximate characterization of the relative 445abundance of different types.

The existence of a qualitative agreement between OC particletypes from both methods and their attribution to similar sourcescan be used as justification for relating the functional groupsmeasured by STXM-NEXAFS to the tracer ion fragments from 450ATOFMS. For the combustion type particles, the R(C C)R andR(C H)R bonds can be linked to the formation of Cn and C2H3

fragments, and the R(C O)OH group in secondary particlescan be linked to the formation of the C2H3O oxygenated frag-ment. These relationships between chemical bonds and bond 455fragments are consistent with those observed from FourierTransform Infrared Spectroscopy (FTIR) and Aerosol Mass

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FIG. 5. STXM-NEXAFS generated images of particles collected during ACE-Asia 2001 and MILAGRO 2006. Darker pixels indicate higher scaled carbonabsorption relative to the Si3N4 collection windows. Example images illustrate (a–c) combustion particles, (d–f) biomass burning particles, and (g–i) secondaryparticles. (Figure provided in color online.)

Spectrometry (AMS) (Russell et al. 2009). This establishmentof analogous particle types can improve our understanding oforganic aerosols by combining the specific advantages of each460method—ATOFMS measurements can be used to establish ro-bust statistics for size resolved number fractions, which canbe related to functional group and morphological informationobtained from STXM-NEXAFS.

CONCLUSIONS AND FUTURE DIRECTIONS 465Consistent trends in organic aerosol composition and mor-

phology are shown from this synthesis of measurements fromsix field campaigns representing both polluted and clean en-vironments. Most carbonaceous aerosol particles (OC andEC) particles exist in the submicron size fraction, contribut- 470ing between 20% (ACE-Asia, marine environment) and 85%

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10 R. BAHADUR ET AL.

FIG. 6. Size resolved number fraction of organic particles collected during (a) and (b) ACE-Asia 2001 and (c) and (d) MILAGRO 2006. Bar segments frombottom to top in panels (a) and (c) show OC/EC, K/Biomass, and OC/HMOC particles as classified by the ATOFMS, and in panels (b) and (d) show combustion,biomass burning, and secondary particles classified from STXM-NEXAFS spectra. (Figure provided in color online.)

(MILAGRO/SOAR, polluted urban environments) of all par-ticles. OC particles can be consistently represented by threesource related meta-classes for ATOFMS and STXM-NEXAFSanalysis: combustion (fossil fuel burning), biomass burning, and475secondary aerosols (SOA—that may contain primary aerosolcores). Secondary aerosol particles contribute a significant num-ber fraction to the total organic aerosol, ranging between 20%(MILAGRO/SOAR, polluted urban environments) and 50%(INDOEX, clean marine environment). The ATOFMS submi-480cron mode in MILAGRO 2006 comprises 37% OC/EC, 30%OC/HMOC, and 33% biomass burning particles and the sub-micron mode in ACE-ASIA 2001 comprises 50% OC/EC, 17%OC/HMOC, and 33% biomass burning particles.

Single particle carbon spectra obtained from nearby Scan-485ning Transmission X-Ray Microscopy- Near Edge X-ray Ab-sorption Fine Structure (STXM-NEXAFS) measurements fromACE-ASIA 2001 and MILAGRO 2006 yield similar source cat-egories based on relative abundances of aromatic, alkane, andcarboxylic acid functional groups. The similarities in OC par-490ticle types observed using both techniques provides a basis forcombining the quantitative information on particle number dis-tribution and characteristic EC and OC fragments of ATOFMSwith the functional group, morphology, and heterogeneity char-

acteristics observed by STXM-NEXAFS. ATOFMS OC particle 495types and STXM-NEXAFS particle categories show a corre-spondence of the OC/EC type with strong aromatic group ab-sorbance, of the OC/HMOC mixture type with high carboxylicacid group absorbance, and of the biomass burning OC typewith aromatic and carbonyl groups. All three organic particle 500types correspond to a range of particle morphologies some ofwhich contain heterogeneous inclusions, although the highlyoxygenated or secondary OC type frequently are nearly spher-ical, liquid-like particles, sometimes with enhanced carboxylicacid groups near the surface. 505

The complete climate impact of organic aerosols can only beevaluated through the knowledge of the global distribution ofOC resolved in time, space, particle size, mixing state, as well aswater uptake and optical properties, which at present can only beestimated from global models. The uncertainty in model predic- 510tions is increased due to limitations in parameterizing organicaerosols—for example, most models appear to over-emphasizeprimary emissions and lack an explicit representation of sec-ondary aerosols (Kanakidou et al. 2005; Textor et al. 2006),though more recent work has improved the sophistication of 515climate models by explicitly including contributions from SOA(Beekmann et al. 2007; Matsui et al. 2009). Comparative studies

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of single particle measurements constitute a valuable resourceby providing not only general trends in aerosol composition invarious environments, but more complete information about the520mixing states and morphology of the aerosol. Information aboutthe relative contributions of different aerosol constituents, aswell as their distribution both within and among individual par-ticles, provides a greater understanding of atmospheric aerosolsthat can be used to better constrain model representations of525organic particles and their climate impact.

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