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Raman Characterization of Ambient Airborne Soot andAssociated Mineral PhasesTiziano Catelani a , Giovanni Pratesi a b & Matteo Zoppi b ca Dipartimento di Scienze della Terra, Università degli Studi di Firenze , Firenze , Italiab Museo di Storia Naturale, Università degli Studi di Firenze , Firenze , Italiac Fondazione Prato Ricerche , Prato , ItaliaAccepted author version posted online: 23 Sep 2013.Published online: 23 Sep 2013.

To cite this article: Tiziano Catelani , Giovanni Pratesi & Matteo Zoppi (2014) Raman Characterization of Ambient AirborneSoot and Associated Mineral Phases, Aerosol Science and Technology, 48:1, 13-21, DOI: 10.1080/02786826.2013.847270

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Aerosol Science and Technology, 48:13–21, 2014Copyright C© American Association for Aerosol ResearchISSN: 0278-6826 print / 1521-7388 onlineDOI: 10.1080/02786826.2013.847270

Raman Characterization of Ambient Airborne Sootand Associated Mineral Phases

Tiziano Catelani,1 Giovanni Pratesi,1,2 and Matteo Zoppi2,3

1Dipartimento di Scienze della Terra, Universita degli Studi di Firenze, Firenze, Italia2Museo di Storia Naturale, Universita degli Studi di Firenze, Firenze, Italia3Fondazione Prato Ricerche, Prato, Italia

Airborne particulate matter samples were collected in an urbanand a rural–suburban monitoring stations of the city of Rome, Italy,and the particles were analyzed through the Raman microspec-troscopy. A careful examination of the spectral bands, performedwith a five-(Voigt) curve deconvolution model previously describedby the literature and here adapted to the purpose, lead to the char-acterization of the graphitic and carbonaceous material plus theidentification of the mineral particles associated with it. Statisticalanalysis of the full-width at half-maximum (FWHM) values of thebands, as well as of their intensity ratio, revealed the presence of twoclasses of soot particles that can be ascribed to a different degree ofcrystallinity. The population of soot collected at the urban site,where the vehicular emission component prevails, exhibits mostlycrystalline characteristics (with a D1 FWHM of 150–155 cm−1),whereas the population collected at the rural–suburban site, par-ticularly the coarse fraction, shows a prevailing amorphous nature(with a D1 FWHM of ∼175 cm−1). A similar aspect emerges forthe pure black carbon particles, mainly crystalline, and the blackcarbon particles associated with minerals, generally disordered.These results add useful information and characterization of thesoot, a relevant component of the ambient air, and its differentfeatures with respect to the urban or rural–suburban areas.

[Supplementary materials are available for this article. Go tothe publisher’s online edition of Aerosol Science and Technologyto view the free supplementary files.]

1. INTRODUCTIONSoot is widely regarded as one of the most important air

pollutants because of its occurrence in all urban areas and its

Received 28 January 2013; accepted 10 August 2013.The authors would like to thank Dr. Damiano Centioli of the Institute

for Environmental Protection and Research (ISPRA, former APAT) forhis support to the research work and Dr. Andrea Lucotti of Dipartimentodi Chimica, Materiali ed Ingegneria Chimica “G. Natta,” Politecnicodi Milano for supplying the standard material.

Address correspondence to Matteo Zoppi, Museo di Storia Naturale,Universita degli Studi di Firenze, Via G. La Pira 4, Firenze I-50121,Italia. E-mail: matteo.zoppi@unifi.it

impact on the environment, climate, and human health. Sev-eral authors have pointed out that the inhalation of particulatematter (PM) can cause acute and chronic toxicological effects,such as respiratory inflammations or lung disease on exposedpeople (Brunekreef and Forseberg 2005; Davidson et al. 2005;Englert 2005), and similar results are also shown by toxico-logical studies (Gualtieri et al. 2009, 2010). Therefore, due toits high content in the fine fraction and the ability to penetratedeeper in the lungs, soot particles are likely one of the mosttoxic components of ambient aerosol (Zerbi et al. 2008). Be-sides, the observed effects on the health are also correlated tothe soot properties and composition, in particular to the pres-ence of trace metals (Lee et al. 2006; Grobety et al. 2010),minerals (Giere et al. 2006), and organic compounds (Sakuraiet al. 2003), that are responsible of oxidative stress on biologi-cal systems (Sørensen et al. 2003; Risom et al. 2005; Gualtieriet al. 2009). Soot—sometimes referred to as “black carbon”(BC) because of its optical properties (Mertes et al. 2004)—isusually defined as the product of incomplete combustion of or-ganic matter (e.g., vehicular traffic, heating systems, industries,fossil-fueled power plants, incinerators), although it can also begenerated by combustion of biomasses or woods and, in do-mestic ambients, by cooking. Owing to the variability of theseprocesses, soot composition is given by a mix of graphitic car-bon (or elemental carbon—EC), amorphous carbon, graphenes,and other organic compounds, such as hydrocarbons, aromatics,alkanes, fullerenes, and polyenes (Sadezky et al. 2005).

A different and important field of research concerns the roleof soot in the regulation of climate and environmental processes(Andreae and Gelencser 2006). Therefore, the understandingof the properties of air-dispersed soot in the atmosphere is im-portant for recognizing the sources and evaluating the differentimpact related to its properties. For this aim, the classical ap-proach to the airborne PM analysis (gas chromatography-massspectrometry for organics and scanning electron microscopyand energy dispersive spectrometry [SEM-EDS] for mineralsand trace metals) is not sufficient to obtain detailed infor-mation about soot properties and consequently the genetic

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14 T. CATELANI ET AL.

processes. Differently, transmission electron microscopy (TEM)analyses are particularly suitable to characterize the propertiesof soot (Buseck et al. 1987; Posfai et al. 1999; Mathis et al.2004; Chen et al. 2005). Many authors reported how the useof high-resolution transmission electron microscopy (HRTEM)images can highlight the presence of graphitic or amorphouscomponents (Adachi and Buseck 2008), and the different struc-tural frame of graphitic layers in the BC, related to differentcombustion processes such as pyrolysis of coal or combustionof liquid fuels (Su et al. 2004; Chen et al. 2005). This approachis very efficient, but it has the disadvantage of being hardly suit-able for systematic and quantitative studies of particles becauseof the difficulty of analyzing large numbers of particles andquantifying the graphitic and amorphous components in sootaggregates. Moreover, TEM is particularly useful for analyzingsmall and thin particles that constitute soot aggregates but maybe ineffective when trying to have information about the coarsesoot aggregates.

A complementary technique useful for this field of study isthe Raman microspectroscopy, employed for the characteriza-tion of graphitic materials (Sadezky et al. 2005) and soot parti-cles (Blaha et al. 1978; Sze et al. 2001; Ivleva et al. 2007a), forthe reason that the honeycomb carbon structure produces a typ-ical vibrational spectrum that allows to recognizing crystallinegraphite, ordered, disordered and amorphous carbon, as well asorganic matter. Using Raman micro-spectroscopy it is possibleto analyze single particles aggregates (Batonneau et al. 2006;Ivleva et al. 2007b; Stefaniak et al. 2009), collecting a spectrumfrom each one, and then performing a statistical analysis. Thisapproach has been applied to a case study of the city of Rome,where samples of PM2.5 and PM10 were collected in an urbanand a suburban–rural site, originally with the aim of assessingthe crustal (mineral) component during some episodes of Sa-haran dust outbreaks over Italy (Matassoni et al. 2011). Thosestudies employed SEM-EDS and particle-induced X-ray emis-sion (PIXE) techniques and here Raman microspectroscopy hasbeen used to show the differences of soot’s features at differentsites, with the aim of identifying different soot components andpossibly their sources.

2. EXPERIMENTAL SECTION

2.1. Particulate SamplingThe sampling program was performed with the collaboration

of ISPRA (Istituto Superiore per la Protezione e la RicercaAmbientale) and ARPA (Agenzia Regionale per la ProtezioneAmbientale) Lazio. Two sites were chosen for PM collection,both in the municipality of Rome: the urban sampling site atCorso Francia (CF), in the north outskirt, and the suburban–ruralsite at Castel Romano (CR), in the south-west countryside. Thetwo sites are distant from each other about 27 km. In thesesampling sites, both PM10 and PM2.5 were simultaneouslycollected, using a sampler Tecora Charlie at CR, and a TecoraSkypost PM at CF. The period of sampling we chose for this

study lasted 10 days, from 26 February to 7 March 2009 andincludes an episode of Saharan dust outbreaks in Italy. Thesample collection was originally performed, over a wider lengthof time, with the early aim of accomplishing a SEM-EDS char-acterization of single particles, and PIXE studies, for assessingthe Saharan dust contribution to PM10, PM2.5, and PM1 in ur-ban and suburban areas of Rome (Matassoni et al. 2011, 2012),thus optimizing the parameters of the collection to this purpose.

Airborne PM was collected on Millipore polycarbonate filterswith diameter of 47 mm and pore size of 0.8 μm. The samplingconditions were adjusted for collecting the optimal amount ofPM on the filter (about 30–50 μg/cm2) for SEM analyses asdescribed by the literature (Mamane et al. 2001; Casuccio et al.2002). The air flux rate and sampling time, for both PM10 andPM2.5, were calculated using the mean value of particulateconcentration at the two sites: 1 m3/h (0.28 l/s) for a total timeof 192 min per day at CF, and 2.3 m3/h (0.64 l/s) for a total timeof 240 min per day at CR. To get a good representativeness ofthe daily composition, the sampling was performed for 8 and10 min each hour, respectively.

2.2. Micro-Raman AnalysesRaman analyses were carried out using a confocal Raman

microprobe (Horiba Jobin-Yvon LabRam-IR) coupled with anoptical microscope. The Raman device was equipped with aHeNe laser source (λ0 = 632.8 nm), a monochromator withholographic notch filter, a spectrometer with diffraction gratingof 1800 g/mm, and a Peltier cooled CCD detector (1024 × 256pixels). The laser beam power at the source was 20 mW andfilters, with an attenuation factor from 1 to 10−4 of the sourcepower, were used. A confocal hole and a slit were employed toregulate the radiation flux in the objective and to the spectrome-ter, respectively. The coupled optical microscope was equippedwith three objectives with 10× (NA 0.25), 50× (NA 0.7), and100× (NA 0.9) magnification and an eyepiece of 10× magni-fication. The diameter of the laser spot on the sample surfacewas 1 μm for the fully focused laser beam at 100× objec-tive magnification. The spectral resolution was 1 cm−1 and theinstrument was calibrated against the Stokes Raman signal ofpure Si at 520 cm−1 using a silicon wafer. Instrument controland data acquisition, as well as the processing and analysis ofRaman spectra, were performed with the HORIBA Scientific’sLabSpec 5 software (http://www.horiba.com/scientific).

The aerosol samples were collected on polycarbonate filters,but it was not possible to obtain any useful Raman spectra fromthe particles because of the high interference of the polycar-bonate material; therefore, it was necessary to remove all theparticles from the filter. The operation has been done by carefullywashing a portion of the filter (5 × 5 mm) in a hollow cylinderapplied on a steel support and filled with 2 ml of isopropanol,letting the PM sediment at the bottom and, once the liquid wasfully evaporated (at the temperature of 20◦C), removing thecylinder. A steel support has been preferred, because it doesnot produce any Raman signal and well reduces sample heating

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(Sadezky et al. 2005; Ivleva et al. 2007a). For each sample,the Raman spectra of 40 particles have been collected: these par-ticles have been chosen starting from a point in the support andselecting the first 10 particles in the four directions. Among thePM10 samples, only those particles with a diameter included inthe range of 2.5–10 μm were selected, to assess a fraction com-plementary to the PM2.5 fraction, whereas for the latter onlythose particles with a diameter higher or equal to 1 μm wereselected. The Raman spectra, in the region of 800–1800 cm−1,have been acquired from soot particles (whole soot or sootassociated with other phases), in addition to several spectra ofmineral particles. The optimal signal-to-noise ratio spectra werecollected with the objective of 100× magnification, slit and holeaperture of 200 μm, grating of 1800 g/mm, integration time of10 seconds with 4 accumulated spectra, and the use of filtersfor reducing the laser beam intensity. The filters analyzed weretwo (PM10 and PM2.5) for each of the 10 days of sampling, fortwo sampling sites (CF and CR): that is 40 samples for a totalof 1527 particles successfully analyzed. Analyses have beencarried out also on particles selected from the standard NISTSRM 1650b diesel exhaust PM as to allow a direct comparisonwith the literature data. Spectra that showed changes during themeasurements were excluded, and second-order bands werenot analyzed in this work.

3. THEORETICAL BASES

3.1. Deconvolution ModelModels commonly used in spectral analysis of graphitic and

carbonaceous materials exploit integrated areas and peak heightsof spectra (Tommasini et al. 2011), or use a deconvolution pro-cess with different combinations of curves (Sze et al. 2001).In this work, we employed the five-curve deconvolution model,proposed by Sadezky et al. (2005), that has been proved to besuitable for analysis of Raman spectra of the soot particles asit yields a reproducible characterization (Ivleva et al. 2007a).Soot spectra generally display two wide and overlapping peakswith Raman shift values of about 1350 and 1600 cm−1, calledD and G band, respectively. However, according to the modelof Sadezky et al. (2005) the peaks are actually the convolutionof five bands named G, D1, D2, D3, and D4, as it is shown inFigure 1 for a case of our study.

G band stands for “graphitic band,”, being typical of or-dered graphitic materials: it is generated by the combinationof bending and stretching vibrational modes of carbon atomsin the honeycomb structure (hexagonal frame of sp2 C atoms)with E2g symmetry. Graphite crystals exhibit only this bandwhose Raman shift value is ∼1580 cm−1. D1 band, observedat ∼1320 cm−1, stands for “disorder band” and is due to thebreathing mode of hexagonal carbon atoms honeycomb frame-work. This vibrational mode is normally forbidden by Ramanselection rules in ordered graphite and the rising of this bandis strongly related to the presence of defective structures thatyields a change in the atomic symmetry from E2g to A1g (Wang

FIG. 1. Example of band deconvolution, with linear background removal, ofthe Raman spectrum (λ0 = 632.8 nm) of a particle of soot, as we applied fora case of analysis in this work (PM2.5 from CF). In the bottom, the residuals’curve.

et al. 1990; Castiglioni and Tommasini 2007). Soot particlesare characterized by such a defective structure because theirnucleation and growth takes place in disequilibrium conditions(Sadezky et al. 2005). The band called D2 (formerly D’), par-tially overlapped with G band, is placed at ∼1620 cm−1. Thisband arises from the same vibrational modes of G band, but it isinfluenced by the presence of incomplete graphitic structure, atthe borders of crystallites, that stretches C–C bonds producing alocally distorted symmetry E’2g, and its intensity is related to thesize of crystallites of soot particles (Sadezky et al. 2005; Tom-masini et al. 2011). The D3 band (also known as A band) occursat ∼1500 cm−1 and is related to the presence of amorphous car-bon and organic molecules, whereas the D4 band (also calledI band), located at ∼1200 cm−1, is linked to the presence ofimpurities in the BC such as ions, metals, and polyenes (organicmolecules with C=C bonds) (Sadezky et al. 2005).

3.2. Data ProcessingThe deconvolution of the collected Raman spectra were

performed using Fityk, a software for nonlinear curve fitting anddata analysis (Wojdyr 2010) applying the least-squares-basediterative algorithm Levenberg–Marquardt to optimize theparameters of the functions used in the modeling of the peaks.The fitting model of Sadezky et al. (2005) makes use of fourLorentzian curves (for G, D1, D2, and D4 bands) and a Gaussian(for D3 band) since their combination produces the better fitof carbonaceous materials and soot spectra. In this work, fiveVoigt functions have been used for the fitting procedure of theexperimental Raman spectra; in fact, according to the studiesof Zanatta and Ferri (2007), the Voigt function correspondsto the convolution of the phonon mode (Lorentzian profile)with the resolution of the optical setup (Gaussian profile).Moreover, as reported by Gołabczak and Konstantynowicz(2009), Raman peaks of crystalline carbonaceous materialsexhibit a Lorentzian profile while structural disorder producesa peak broadening with Gaussian profile. The choice of theband deconvolution procedure employing only Voigt functions

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was made on the basis of sound foundation, higher accuracy,and reproducibility of the fit, verified by a set of trials on sootand standard material spectra as explained below.

In our analysis, we excluded those spectra with intensity-of-the-peak/background ratio not sufficient for modeling and thosethat showed the presence of iron oxides. Such mineral phasesgenerate a broadband in the range of D1 band, and the fitting pro-cedure of these spectra fails due to the presence of too many pa-rameters to optimize. The Raman spectra were analyzed by firstremoving the background contribution using a linear function,then employing a common starting file with five Voigt peakshaving parameters as those reported by Sadezky et al. (2005).The following peak parameters were optimized: height, center,Gaussian FWHM, and shape (i.e., ratio of Lorentzian and Gaus-sian widths). The first stage of the modeling let the peaks varyall the parameters, except for the position that was optimizedafterward. This procedure allowed us to get a more reproducibleprocess of peak modeling that always reached convergence andyielded, in all cases, an R-squared higher than 95%. To assessingthe reliability of the scheme tests on a collected Raman spec-trum were performed applying both the five-Voigt curve modeland the one reported by Sadezky et al. (2005). Average valuesof the center and FWHM of the peaks, as well as the D/G ratio,with their standard deviations, were calculated for a selectedspectrum (Table S1). Ten iterative process of deconvolution hasbeen performed by starting from the same peaks position andvarying the following parameters: height, FWHM, and shape.The modeling of the pattern employing five Voigt curves wasapplied also on the spectra obtained from the particles of thestandard NIST SRM 1650b diesel exhaust PM and the resultsare shown Table S2.

The main advantage of using five Voigt curves with respectto the method proposed by Sadezky et al. (2005) is that the banddeconvolution process needs less constraints to reach a goodcurve fitting. In fact, using a starting model for the fitting proce-dure with five Voigt curves, only the initial positions and “shape”parameters (i.e., the Lorentzian and Gaussian contributions) arenecessary. Adopting the Sadezky’s method it is necessary con-straining the peaks positions in the first stages of the fitting. Inparticular, the Gaussian D3 band frequently shifts to a muchhigher or lower wavenumber values to better approximatingthe spectrum profile, thus introducing biased results. Using fiveVoigt curves faster and more stable profile fittings are obtained,as well as a better reproducibility with lower standard deviationsof the values of the center and FWHM of the peaks.

4. RESULTS

4.1. Soot CharacterizationThe spectral analysis of overall data yielded peak parameters,

summarized in the online supplemental information (Table S3),that show values of FWHM and center of the peaks in agree-ment with those reported by the literature about soot particles(Sadezky et al. 2005). Higher and lower values of the center

of the peaks are reported by the authors only for the G andD1 bands, respectively. To understand if some differences mayoccur in the typology of soot in the samples, all the collectedspectra were grouped in some classes according to the PM frac-tion (PM10 vs. PM2.5), the sampling site (CR vs. CF), and theparticles composition, that is particles composed of only BCor BC associated with mineral phases (BC vs. BC+min). Eachcouple represents two complementary classes. For every classthe mean peak parameters, as well as the average values forall the data, have been calculated and are reported in Table S4.If the peak parameters of each class are compared with thoseobtained from all the data, no significant difference is found forthe center of the band, whereas a slight difference is observed forthe FWHM values. However, all the data exhibit a high standarddeviation, thus it is difficult to correlate this shifts to possibledifferences.

The analysis of the frequency histogram plots of each sin-gle parameter yielded more interesting results. Despite thepeak height and area values show no significant distributionpattern, the FWHM values of D1 and G peaks show a dis-tinct bimodal distribution pattern for all the analyzed data. Asshown in Figure 2, the main FWHM modes are about 155 and

FIG. 2. Frequency histogram plots of the FWHM of D1 band (a), and G band(b) of all the peaks of the collected spectra.

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RAMAN CHARACTERIZATION OF AMBIENT AIRBORNE SOOT 17

FIG. 3. Frequency histograms of the FWHM of D1 bands for each class of particles: (a) PM2.5, (b) PM10, (c) BC, (d) BC+min, (e) CF, and (f) CR.

170 cm−1 for the D1 band, and 70 and 85 cm−1 for the Gband.

It has to be noticed that the uncertainties of the center or of theFWHM of the peaks, when calculated on a single measurement(Table S1), are lesser than the distance of the two mentionedmodes. Frequency histograms were also examined for each ofthe six classes of PM (Figure 3).

Regarding the FWHM of the D1 band, it appears that noneof each class of particles shows the same distribution patternas that of the “all data,” actually every class exhibits one of thetwo modes larger than the other. Furthermore, one may noticehow, within each couple of classes, one exhibits a complemen-tary pattern with respect to the other: the prevailing mode inthe PM2.5 class is the one at 155 cm−1, whereas in the PM10class it is the one at 175 cm−1. Regarding the classes of thesampling sites, the particles collected at CF exhibit a clear bi-modal distribution pattern where the prevailing mode is the onelocated around 150 cm−1, whereas the soot particles collectedat CR show the highest frequency of values around 175 cm−1.The same observations can be made about the classes BC andBC+min, respectively.

Concerning the areas of the five Raman bands the ratios ofeach D band with respect to the G band have been calculated,and the resulting average values are reported in Table S5. Be-cause of the slight differences between the values and the highstandard deviations, it is not possible to recognize major trends.

In addition, the frequency histograms and box plots were con-sidered. Statistical analysis shows that the D/G values exhibit alognormal distribution pattern thus the average values and thestandard deviations are calculated accordingly. Here, the dis-tribution pattern of D4/G, reported (as logarithm) in Figure 4,allows to distinguishing the different population of soot.

For the classes of PM2.5 and PM10, it is evident the presenceof two populations of soot. Also interesting to note the box plotof the D1/G area ratio versus the D1 FWHM (Figure 5) where

FIG. 4. Frequency distribution pattern of the ratio ID4/IG (as logarithm) of theFWHM for the PM2.5 and PM10.

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18 T. CATELANI ET AL.

FIG. 5. Box-whisker plot (95% confidence interval) of the D1/G area ratioversus the D1 FWHM.

it can be seen that the averages and the dispersion of the D1/Garea ratio increases according to the increase of the D1 FWHM.

4.2. Mineral Phases CharacterizationRaman analysis of the particles of PM has been also used

for the identification of a certain number of mineral phasesassociated with soot. For this purpose, the Raman microspec-troscopy is a powerful technique, useful for identifying exactlythe species present in the aggregates, with the only limitationthat, in some cases, the Raman peaks produced by the sootare much more intense than those of the mineral phases. Thismay not allow a proper identification of the mineral species,but the location of their main peak is often sufficient to distin-guish the mineral class (e.g., sulfates, carbonates, phosphates,nitrates). This is not a problem when oxides and silicates arepresent because they produce, at lower wavenumbers, strongpeaks that allow a correct identification. The most widespreadphases present in the samples analyzed are sulfates—easily rec-ognizable for the main peak at ∼1000 cm−1 (SO4 symmetricstretching) and mainly associated with soot—commonly gyp-sum and anhydrite, whereas ammonium sulfate and jarosite areless frequent. Another important class of minerals, althoughless frequently associated with soot, is carbonates, identifiedfor the strong peak at 1085 cm−1 (CO3 symmetric stretching):mainly calcite but also monohydrocalcite are present. Amongsilicates we mostly detected quartz, feldspars (plagioclase andorthoclase), pyroxenes, and rarely garnets. Ubiquitous in all thesamples are iron oxides, specifically hematite, magnetite, andFe-oxyhydroxides.

Other phases detected in the samples are phosphates (mainlyapatite), nitrates (ammonium nitrate), oxalates (whewellite andweddellite), and oxides (rutile). The relative amount of particles,as percentage by number, differentiated by mineral classes for“all data” and the categories BC and BC+min are shown inFigure 6.

FIG. 6. The relative amount of particles, as percentage by number, differen-tiated by mineral classes for “all data,” and the categories BC+min and min.Quartz particles, even belonging to silicates, have been counted separately fora more detailed information.

It is to notice that sulfates, as well as silicates and quartz,are mainly associated with soot, whereas carbonates and ironoxides are mainly found as isolated phases. Raman analyses onthe particles at the two sampling sites show about the same min-eralogical composition, with the exception of a higher amountof iron oxides at the urban sampling site CF, but the numberof mineral species detected did not allow obtaining sufficientstatistical data for further reflections.

5. DISCUSSIONAs concerns the analytical methods, it is worth mentioning

that the reason why the PM has been removed from the filtersis the necessary consequence of the original goals and strategyadopted for the samples collection, as explained in Matassoniet al. (2011). The PM10 and PM2.5 were collected on poly-carbonate filters with devices equipped with the conventionalsampling heads employed in Italy by the ARPAs to allow acomparison between the outcome of such studies and the PMvalues measured at the locations used for PM monitoring. As itis reported in Matassoni et al. (2011), and shown in the onlinesupplemental information (Figure S11), “The average particu-late fraction determined by PIXE analysis was 18% for PM10and 11% for PM2.5, with higher percentages during Saharanepisodes (in-dust days) due to the higher contribution of thesoil component and lower percentages during the most polluteddays when carbon compounds are predominant. The PM massfraction, which is not explained by PIXE, is mainly composed ofH, C, N, and O, major aerosol elements that cannot be detectedby this technique.” This implies that the carbonaceous mass is

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relevant and higher in the PM2.5 than in the PM10–2.5. Onlyconsequently to that study, a part of each filter was employedfor Raman analyses, and the only possible way to perform themeasurements was that of removing the particles from the filtersthrough carefully washing them, as it is carried out routinely inthe TEM analysis procedure. Besides, the efficacy of particle re-moving was high, and observing carefully the washed filters withan optical microscope at a magnification of 1000×, no particleswere spotted on the polycarbonate surface. Any aggregation ofthe particles removed was observed since their dimensions wereincluded within the ranges of the cutoff values of the samplers.This, and the lack of grouping of particles of unusual shape onthe steel support, let us suppose that the formation of particleaggregates is negligible. On the other hand, the fragmentationof particle aggregates was also considered unlikely since theultrasonic removal of the material from the filters was not nec-essary and the smooth surface of polycarbonate does not holdthe particles.

As regards the experimental results, the Raman spectra haveshown that soot particles sampled in two sites of the city ofRome, within 10 days of sampling, have different characteris-tics that are supported by the analysis of the values of the FWHMof the D1 and G bands. Studies carried out by several authors(Sze et al. 2001; Sadezky et al. 2005; Ivleva et al. 2007a) reportthat the FWHM of the Raman bands typical of carbonaceousmaterial, such as D1 and G, is a parameter strictly correlatedwith the degree of crystallinity of the material, and it is mostsensitive to its modifications. In particular, the FWHM of thepeak is higher in materials with low degree of crystallinity andincreases in amorphous and microcrystalline carbon, due to themore disordered structure. Frequency histogram plots reportedin Figure 2 clearly suggest that at least two main different ty-pologies of soot are present in the aerosol of Rome: one withhigher crystalline degree and another one with a less orderedgraphitic structure. As shown by the frequency histogram plotsof Figure 3, the prevailing mode corresponding to more crys-talline carbonaceous particles, located around 155 cm−1, is typ-ical of the fine fraction (PM2.5) and the whole BC fraction.Differently, soot particles with greater size (PM10) and thoseassociated to mineral phases (BC+min) show a prevailing modecorresponding to the amorphous/microcrystalline carbon, witha D1 FWHM of about 175 cm−1. Despite it is less evident, D1/Gis another parameter sensitive to soot properties and is consis-tent with D1 FWHM trend, increasing as D1 FWHM increases(Figure 5).

The attempt of identifying possible BC sources from ourdata, as in the cases reported by Ivleva et al. (2007a) and Knaueret al. (2009), suffers of a lack of direct comparison of the inten-sities due to the use of a different laser source. Sadezky et al.(2005) reported that D1 FWHMs show no evidence of light-source dependence (Table S6), but we obtained substantiallydifferent values, both with Voigt and Lorentzian–Gaussian fit,for the same standard material NIST SRM 1650b (Table S2).Because of this, it is not possible to make a direct comparison

on the absolute FWHM values, but only on the relative ones.Ivleva et al. (2007a), analyzing both standard soot materials andaerosol containing black carbon, showed that D1 FWHM is di-rectly related to the Humic-like substances (HULIS) content indifferent typologies of soot reported as the apparent elementalcarbon to total carbon ratio (ECa/TC). Particularly the carbona-ceous component exhibits a mean D1 FWHM of about 175 cm−1

in the finer fraction, that correspond to the D1 FWHM of dieselNIST SRM 1650, and larger values for the coarser fraction, upto a value similar to spark discharge soot (>200 cm−1). Thisincrease of D1 FWHM is linked to the decrease of ECa content.From our data, it is evident that the main classes of the BCparticles analyzed have properties similar to those of soot mate-rials, that is consistent with the location of sampling sites, witha minor contribution of spark discharge soot-like materials. Inparticular, we found that the mode of the more crystalline par-ticles exhibits D1 FWHM values close to the diesel NIST SRM1650b, according to Ivleva et al. (2007a). Similarly to the out-comes of their research, the particles showing lower D1 FWHMvalues are prevailing in the finer fraction (1–2.5 μm), whereasthose with higher D1 FWHM values are prevailing in the coarserfraction (2.5–10 μm). The authors state that the intensity ratioof the D3 to the G band (ID3/IG) is another parameter highly sen-sitive to BC properties, but our results show no such evidence.This could be due to the experimental procedure adopted by theauthors, such as the different laser source and fitting strategy, aswell as to the samples properties due to the bulk analyses andthe analyzed fractions. In fact, the authors found the most evi-dent differences of ID3/IG for the PM fraction below 1 μm andthe coarser fraction over the 2.5 μm, in the summer samples,whereas, differently, our data relate to the fraction of PM greaterthan 1 μm sampled at the winter’s end. Moreover, we noticedthat the ID4/IG parameter is rather useful to detect the differ-ences among D1 FWHMs, and ultimately related to differentpopulations of soot, as for PM2.5 and PM10 (Figure 4). Al-though the ID3/IG parameter is considered suitable for revealingthe differences on the population of soot, nothing with a similarmeaning is reported by the literature about the ID4/IG parameter.Since the D4 band is linked to the amount of impurities, such asions, metals, and polyenes in the BC (Sadezky et al. 2005), theparameter ID4/IG might be reasonably assumed as a distinctivemarker.

Such analytical approach lead to the identification of differ-ent typologies of BC particles in atmospheric soot, with a cleardifferentiation depending on their size (PM2.5 vs. PM10), sam-pling site (CR vs. CF), and phase association (BC vs. BC+min),as shown in Figure 3. These differences may be due to separatesources emitting soot particles with different size and graphiticcomponent such as vehicular emissions from the nearby ex-tended city and wood or biomass burning. The latter or its by-products released by the burning process for domestic heatingmight account for a part of the soot present in the ambient airof rural areas, although there is no evidence of such and activityat the CR sampling site. Apart from the source contributions,

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20 T. CATELANI ET AL.

that may be both present in the rural–suburban area examined,it has been clearly observed by HRTEM investigations on thesesamples—prepared with the Jaffe Wick Method—the occur-rence of amorphous aggregates associated to the crystalline car-bonaceous particles of the PM10 fraction (Catelani 2010). It hasbeen reported that secondary organic aerosol (SOA) particles,in an amorphous solid state, are formed in the atmosphere fromcondensable oxidation products of anthropogenic and biogenicvolatile organic compounds (Virtanen et al. 2010 and referencestherein), and the phenomenon of the aging of the organic con-densate of the soot particles might not be ruled out. Also, Adachiand Buseck (2008) described BC particles growth as the sumof two distinct processes: aggregation of nanoparticles of soot,of less than 100 nm, and organic matter condensation on theaggregates. Nanoparticles emitted directly from the source athigh temperature are more crystalline compared with the con-densation material that has a higher content of organics, clearlyexhibiting less ordered structures. Such particles are commonlycharacterized by a local crystal order—clearly evident in TEMhigh-resolution images—with concentric, onion like, arrange-ment of stacked graphitic layers (Buseck et al. 1987; Chen et al.2005). Subsequent growth of these particles is due to aggre-gation and condensation phenomena, at lower temperature, oforganic vapor phases produced during combustion processes,and other impurities. The larger the particles grow, due to theresiding time in the atmosphere, the more they increase thecoating from the condensation fraction (SOA). This mechanismgenerates soot particles with higher amount of amorphous car-bon and impurities whose presence is detectable through theinvestigation of the D1 FWHM and ID4/IG values, respectively(Adachi and Buseck 2008). Such an evolution process might ex-plain the differences between coarse and fine particles, relatingthem to a common source, as well as the differences betweensoot particles of the classes CR and CF. At CF, the main sourceis the exhaust of automotive engines of a high-traffic urban area,and the main process of soot particles growth is nanoparticlesaggregation, with lower condensation of organic matter. On theother hand, the particles sampled at CR exhibit generally a lowercrystallinity degree than those of CF because of the higher dis-tance of sampling from the main source, thus a likely higherrole of the condensation phenomenon of organic matter in theparticles growth. It has to be said that the area around the sam-pling site of CR, being at the proximity of the city of Rome,is not much affected by rural activities, and methane is widelyused as the source for domestic heating.

The hypothesis of the evolution process might explain thefact that those particles made up by both mineral phases and BCshow a higher amorphous content. This is probably related to thefact that solid particles may favor the carbon condensation in theatmosphere, forming an internally mixed mineral-soot particles.This is particularly true for sulfates and nitrates (Figure 6), thatare typical condensation phases. In fact, some authors (Posfaiet al. 1999; Su et al. 2004; Coz and Leck 2011) observed thatsulfates and soot form internally mixed aggregates from sub-

micrometric particles, where the soot largely affects the sulfatecondensation. For silicates and the other crustal phases, indeed,it is more likely a mechanism of scavenging soot particles andorganic compounds in the atmosphere, working as aggregationnuclei. On the other hand, our data let us suggest that silicatesare more efficient scavengers than carbonates and other mineralphases.

Peak analysis of micro-Raman spectra with band deconvo-lution procedure is useful tool for evaluating the nature of thecarbonaceous PM and yields information about the evolutionprocesses. Such characteristics have been found to be muchmore appreciable by evaluating the peak features relating todifferent classes of particles and are in agreement with what isreported by the literature on this subject. The present collectionand elaboration of data is suitable for giving a further pointof view to the airborne particulate analysis, despite it requires alarge amount of manual work, and a further effort may be done toimprove and mechanize the experimental and analytical process.

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