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Journal of Quantitative Spectroscopy &Radiative Transfer

Journal of Quantitative Spectroscopy & Radiative Transfer 188 (2017) 103–117

http://d0022-40

n CorrE-m

journal homepage: www.elsevier.com/locate/jqsrt

Automated aerosol Raman spectrometer for semi-continuoussampling of atmospheric aerosol

David C. Doughty n, Steven C. HillUS Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783, USA

a r t i c l e i n f o

Article history:Received 24 February 2016Received in revised form28 June 2016Accepted 30 June 2016Available online 5 July 2016

Keywords:Raman SpectroscopyAerosolHULIS/Soot

x.doi.org/10.1016/j.jqsrt.2016.06.04273/Published by Elsevier Ltd. This is an ope

esponding author.ail address: david.c.doughty2.civ@mail.mil (D

a b s t r a c t

Raman spectroscopy (RS) is useful in characterizing atmospheric aerosol. It is not com-monly used in studying ambient particles partly because automated instrumentation foraerosol RS has not been available. Battelle (Columbus, Ohio, USA) has developed theResource Effective Bioidentification System (REBS) for automated detection of airbornebioagents based on RS. We use a version of the REBS that measures Raman spectra of oneset of particles while the next set of particles is collected from air, then moves the newlycollected particles to the analysis region and repeats. Here we investigate the use of theREBS as the core of a general-purpose automated Aerosol Raman Spectrometer (ARS) foratmospheric applications. This REBS-based ARS can be operated as a line-scanning Ramanimaging spectrometer. Spectra measured by this ARS for single particles made of poly-styrene, black carbon, and several other materials are clearly distinguishable. Ramanspectra from a 15 min ambient sample (approximately 35–50 particles, 158 spectra) wereanalyzed using a hierarchical clustering method to find that the cluster spectra are con-sistent with soot, inorganic aerosol, and other organic compounds. The ARS ran unat-tended, collecting atmospheric aerosol and measuring spectra for a 7 hr period at 15-minintervals. A total of 32,718 spectra were measured; 5892 exceeded a threshold and wereclustered during this time. The number of particles exhibiting the D-G bands of amor-phous carbon plotted vs time (at 15-min intervals) increases during the morning com-mute, then decreases. This data illustrates the potential of the ARS to measure thousandsof time resolved aerosol Raman spectra in the ambient atmosphere over the course ofseveral hours. The capability of this ARS for automated measurements of Raman spectrashould lead to more extensive RS-based studies of atmospheric aerosols.Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

1.1. Importance of atmospheric aerosol particles and theirsize and composition

Atmospheric aerosols can cause diseases, degrade visibi-lity, and affect the weather and global climate. Atmospheric

n access article under the C

.C. Doughty).

aerosols are associated with increased cardiovascular disease,chronic obstructive pulmonary disease, and other illnesses[1]. Many harmful components of aerosols are known, e.g.,carcinogens such as benzo[a]pyrene and other polycyclicaromatic hydrocarbons in soot, or heavy metals such asmercury in emissions from coal-fired power plants. How-ever, there are many unknowns regarding the compositionsand sizes of particles that contribute most to the harmfuleffects of aerosols on health [2–4]. Some diseases of humans(e.g., [5]), other animals (e.g. [6]) and many plant pathogens

C BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

D.C. Doughty, S.C. Hill / Journal of Quantitative Spectroscopy & Radiative Transfer 188 (2017) 103–117104

[7] are transmitted through the air by fungal spores, bacteria,and viruses. Aerosols affect climate and weather by scattering,absorbing, and reemitting radiation, and acting as cloud [8]and ice [9] condensation nuclei. Bioaerosols such as bacteriaand fungal spores may be significant contributors to icenucleation under certain conditions [10,11]. There are largeuncertainties regarding the effects of biological and otheraerosols on climate [12]. As a result, improved techniques forcharacterizing atmospheric aerosol size and composition areneeded for a better understanding of the effects of aerosols onhealth, agriculture, visibility, climate, and weather.

To address these needs, improved instruments that canrun continuously and provide information on the compo-sition and size of individual aerosol particles are needed.Single-particle measurements are important for quantify-ing minority species in a population, determining themixing state of aerosols [13,14] and its relation to climateforcing [15], and understanding the processing of particlesin the atmosphere. Examples of single-particle instru-ments include aerosol mass spectrometers [16], aerosolfluorescence spectrometers [17,18], and instrumentswhich measure Raman spectra [19] or electron-beamexcited x-rays [20] from collected particles. The topic ofthis paper is Raman spectroscopy (RS) for deriving infor-mation about single particles in the atmosphere using anautomated measurement method.

1.2. Raman spectroscopy for characterization of atmosphericaerosols

Because the intended audience for this paper includesresearchers who may have little familiarity with RS, westate here some of the features that make RS appealing forsingle-particle, and even semi-continuous, characteriza-tion, of atmospheric aerosol:

a) Raman spectra have a relatively high informationcontent because, like infrared (IR) absorption spectra,they are dependent on the vibrational and, whenapplicable, rotational frequencies of the molecules in asample. Typically, many or most of the vibrationalbands in IR spectra also appear in Raman spectra. InRS, an electron, in a ground state with frequency ω0, isexcited by a laser with photon energy hcωL, where h isthe Planck constant and c is the speed of light, to anintermediate transient state. The excited electronalmost immediately drops to an energy level withvibrational frequency ωn. To conserve energy, a pho-ton is emitted having an energy hcω¼hcωL�hcΔω,where Δω¼ωn�ω0. Vibrations that are very weak inIR spectra may be strong in RS and vice versa. Formany pure materials with molecular weights less thana few hundred daltons, Raman and/or IR spectra canserve as fingerprints. RS can differentiate betweenmany different bacteria [21], fungal spores [22], andpollens [23]. RS may also be useful for differentiatingtypes of soot [24], a feature important in studies ofclimate and the health effects of aerosols. The largemajority of materials have readily measured Ramanspectra. Although pure alkali halides (e.g., NaCl, KF)have negligible Raman spectra, sea spray aerosols,

which are primarily NaCl, contain other inorganic saltsand biological and other organic materials that appearin Raman spectra [25]. The fluorescence of most purematerials, on the other hand, is negligible or veryweak. There are limits on the information that can beobtained for complex mixtures using RS becauseweaker lines of primary compounds may overlap thestrong lines of secondary compounds [19]. Similarlimits apply when studying complex mixtures usingother high-information content techniques such asmass spectrometry (MS), IR spectroscopy, or nuclearmagnetic resonance spectroscopy.

b) Raman spectra can be generated using one laser andone detector array, where the photon energies of thelaser and the detected photons are far from the ener-gies of photons at the vibrational frequencies ofinterest. A single laser in the visible or near IR cangenerate a Raman spectrum from 200 to 4000 cm�1.If, for example, the Raman spectrum is generated usinglight at 640 nm, the detector must only be sensitive tolight at wavelengths from approximately 640 to860 nm in order to measure the Raman spectrum from0 to 4000 cm�1. Laser sources in the visible and nearIR are relatively inexpensive. Charge-coupled device(CCD) detectors throughout the visible and near IR canbe sensitive with low noise.

c) Raman spectra can be measured from particles withvolumes of less than 1 mm3, depending upon thematerial and the excitation and collection optics,because it is an inelastic scattering measurement.Raman microprobes, RS instruments with confocal, epi-illumination geometries, have been used for over 40years [26]. IR absorption spectroscopy, in contrast,typically requires larger sample volumes. Attempts todevelop techniques to measure the IR absorption bymeasuring and analyzing the elastic scattering patternsof individual particles have been made [27].

d) RS has relatively simple instrumentation requirementsthat are relatively inexpensive. RS only requires anarrowband laser, a good CCD, a spectrometer, ima-ging optics and optical filters, and a way to collect theaerosol and move the sample into the focal planeunder the excitation-collection optics. It does notrequire sample preparation or high vacuum.

e) Raman spectra include contributions from the molecularvibrations of molecules throughout the particles, at leastfor particles with little or no black carbon or other par-ticles that are not too large, e.g., greater than 30 mm,depending upon the excitation wavelengths. This abilityto include information about molecules throughout theparticle (when not too large or absorbing) arises becauseRaman spectra can typically be measured with excitationand emission wavelengths where most of the light is notabsorbed by the particle.

f) The measured Raman spectrum is the sum of the Ramanand fluorescence emission and therefore can also pro-vide information about molecules that are fluorescent atthe excitation wavelength. Although fluorescence spec-tra are broad and lack the informational content ofRaman, we suggest that their combination with RS canbe useful, especially regarding bacteria, pollens, and

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fungal spores. It can also be used to help determineregions to investigate further using RS. Because thefluorescence peaks are typically much broader thanRaman peaks, they can often be estimated and sub-tracted from the Raman spectrum. The Raman signalrelative to the fluorescence depends upon the particle’scomposition and the excitation wavelength. The fluor-escence can obscure the Raman spectral features if it istoo much larger than the Raman spectrum and does notphotobleach sufficiently within the time available for themeasurement.

g) RS is a mostly non-destructive technique, as long as thelaser intensity is kept below a threshold to avoid dama-ging the sample and the laser wavelength is not tooshort [28]. Thus, after the Raman spectra are measured,it is possible to further analyze the particles using, e.g.,antibodies, DNA hybridization, MS, or other techniques.However, many fluorophores are sensitive to light andmay be photobleached by the laser (Appendix A).

Because of the advantages listed above, RS has beenused to determine the composition of atmospheric aero-sols [19,28–36] including particles containing black carbon[28,33,34] and/or organic carbon and/or inorganic mate-rials [19,32]. Raman spectra can also aid in characterizingatmospheric fungal spores [22], pollens [37], and bacteria[21,38,39]. RS has been used to help understand thecomposition of desert dust [13,40], sea spray [25], andoceanic aerosols [32], as well as chemical reactions inaerosol droplets. Raman spectra obtained in these studieshave been used to assign particles to categories such asbacteria, black carbon, etc. (e.g. [31]). Single-particleRaman spectra have been used to analyze the propertiesof carbonaceous aerosols in Europe [34] and ambientvolcanic ash particles [35], and identify chemical agentsimulants [41]. Methods to assign measured Raman spec-tra to specific materials have been developed [42,21].

A main difficulty in using RS for studies of atmosphericaerosol is that Raman emission is typically several ordersof magnitude weaker than fluorescence from the samequantity of highly fluorescent molecules. RS is approxi-mately 1013 to 1014 times weaker than elastic scatteringfrom micrometer-sized particles of the same volume anddecreases with the fourth power of the wavelength. Theweak intensity of single-particle Raman scattering meansthat Raman spectral measurements of rapidly flowing mm-sized particles (measurements readily made with single-particle elastic scattering, fluorescence, or MS) are notcurrently feasible.

Extensive measurements of atmospheric aerosol usingsingle-particle RS are not done routinely, partly because ofthe time and effort required to collect or trap particles,transfer them if necessary to a RS system, and then measuretheir Raman spectra (e.g. [19,34]). Measuring atmosphericaerosol has been described as “tedious” [36] and “absolutelyimpractical for routine monitoring” [43]. As for making anautomated aerosol Raman spectrometer (ARS), the weak-ness of Raman scattering means that some method isrequired to collect (or trap) particles from air, either onto asurface or into a trap that holds the particle in air [36,44],and then, if necessary, move it to the focal region of the

excitation-collection optics [30,34]. Battelle (Columbus,Ohio, USA) has been developing instruments they term the“Resource Effective Bioidentification System” (REBS). Someof these, but not the one described in the only open-literature publication [21], collect particles from air, mea-sure their Raman spectrum, and then determine whetherspecific biological or chemical agents are in the particles.

1.3. This paper

The main objectives of this paper are to describe a REBS-based ARS and to suggest and illustrate how it appears to beuseful for characterizing size-composition distributions ofatmospheric aerosols. We describe the ARS we use andbriefly discuss some of the Battelle's software and some ofour own. We illustrate the use of the ARS, initially withuniformly sized polystyrene latex (PSL) spheres. We illus-trate the hyperspectral data-cube and spectra that areobtained using the instrument with carbohydrate, trypto-phan, inorganic materials, soot-like particles, and someambient aerosols. We analyze some ambient spectra mea-sured with the ARS in two ways: unstructured hierarchicalcluster analysis for a 15 min period; and the number ofsoot-like spectra in each 15 min period for a duration of 7 h.We also briefly discuss issues such as aerosol sample rate,analysis methodology, laser charring, and the ability tocontrol the instrument in detail.

2. Experimental

2.1. The REBS-based ARS and its operation

The core of the ARS we use, designed and manufacturedby Battelle, collects particles from air onto a metalized tapeand then measures the Raman spectra of those particleswhile the next set of particles is collected. Battelle terms thisinstrument the Resource Effective Bioidentification System(REBS), but also uses “REBS” for several other systems. Anewer REBS is the Rapid Enumerative Bioidentification Sys-tem. In a chapter by Ronningen et al., [21], the only openliterature scientific publication we know of describing aREBS, there was no mention of collecting particles from air ortransporting them into a collection region on tape. Instead,samples were pipetted onto an aluminized glass slide whichwas placed under the automated microscope system. Thatchapter emphasized the spectral analysis methods used todiscriminate between specific bioagents and other aerosols[21]. Because there have been multiple variants of the REBSover the past 10 years, and because we do not use the“bioidentification system,” but instead use software we havewritten for characterizing atmospheric aerosols, we call ourREBS-based automated Aerosol Raman Spectrometer an ARS.

The ARSs we describe here were made approximately5 years ago. We are not sure of the ways they differfrom presently available REBSs. An ARS instrument usedin this study is illustrated in Fig. 1. The dimensions are53�36�36 cm. The instrument weighs 20 kg. The outercovering is rainproof. We successfully operated the ARS inrain and high winds.

Fig. 1. a) The REBS in one of the sampling locations used for the mea-surements in this study. b) The REBS (side view) with the front and toppanels removed. Several components are labeled.

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The ARS operates as a semi-continuous automatedRaman spectrometer. There are two main operations: i)collection of particles from air onto a metalized tape andii) spectral measurement, where the Raman spectra of thecollected particles are measured with a CCD. Theseoperations occur concurrently, but on separate samples.The time for collection and the time for spectral mea-surement are the same, typically 15 min. After the collec-tion/measurement time period, the tape is moved forwardapproximately 14 cm so that the collected particles aremoved under the Raman microprobe. During the collec-tion/measurement time, the collection is occurring on anew area of the tape. Thus, during this time, both thespectral measurement and the collection are accomplishedsimultaneously.

ARS Parameters

Dimensions

53x36x36 cm Weight 20.4 kg Power o50 W CCD MITY CCD H7031 1007-

DS-LE 128X1024 pixels

Laser wavelength �640 nm Laser power �50 mW (max) Approximate dimensions of focalregion of laser beam on the tape

�40 mm�1 mm

Laser

Oz Optics Laser intensity in focal region (max) �1.2 mW/mm2

�1.2x105 W/cm2

Objective

Olympus 100� Objective Numerical Aperture 0.90

In the collection operation, the particles are drawn into

the ARS, where they are given a charge by a unipolarcorona charger. The particles are drawn by electrostaticforces to a grounded metallized tape (13 mm width) onwhich they are collected. The collection flow rate is nom-inally 8 lpm and is determined by the pump, which drawsthe sample air through the ARS. The inlet tubing for theARS is 1 inch in diameter. The particles are collected ontothe tape in a spot of Δx�1 cm by Δy�1.6 mm.

For the spectral measurements, the ARS used hereemploys an epi-illumination image-preserving spectro-graph. The excitation laser has a wavelength of approxi-mately 640 nm and maximum laser power of �50 mW.This laser is focused to a line with dimensions on the tapeof Δx�1 mm and Δy�40 mm. The elastic and Ramanscattering are collected by the same 100� objective usedto focus the laser onto the particles. The spectrally dis-persed emission is recorded by a 128x1024 pixel MITY CCDH7031 1007-DS-LE that is thermoelectrically cooled, typi-cally to �5 °C. The �40-mm line parallel to the y-dimen-sion on the tape is spread over approximately 40 of the128 pixels in the y-direction of the CCD. Once the tape hasbeen advanced (x-direction) from the collection region tothe spectral measurement region, a vacuum pump holdsthe tape against a block at the top of a motorized x–zstage. Because the tape is flexible, the short segment oftape held to the stage can then be positioned in the x and/or z direction, while the segment of tape held at the col-lector does not move. The stage is then adjusted in the z-direction until the laser line is focused onto the tape asindicated by the laser intensity at the peak. If desired, aparticle of interest could be studied further by steppingthrough several focal planes to obtain a z-scan. The firstspectrum is then recorded. Then the tape, held by the x–zstage, is stepped (typically 2 mm) in the x direction andheld fixed while the next set of (typically 40) spectra areacquired by the CCD. This procedure of step-tape-then-acquire-spectra is repeated until the (typically) 15-mintime period is finished. If, for example, three 10-s spectraare taken at each of the 2-mm steps, the total time isapproximately 30 s per step, and so in the 15 min timeperiod, approximately 15/0.5¼30 sets of spectra areobtained, and the laser was stepped over approximately60 mm in the x direction.

In this paper, two REBS-based ARSs are used. Oneinstrument (REBS serial number 13) is used for thelaboratory studies discussed in this paper, including thecalibration statistics in Section 3. The other instrument(REBS serial number 14) is used for the ambient studies.

2.2. Data handling and analysis procedures

The background spectrum is estimated in two ways.First, it is measured for a clean section of tape, beforecollecting any particles. Second, a time varying back-ground is also calculated because the background can vary,possibly because the CCD is not sufficiently cooled whenthe ARS is operated in direct sunlight on a hot day. Foreach 15 min period, the background is estimated by takingthe lowest tenth percentile of intensities measured in that

D.C. Doughty, S.C. Hill / Journal of Quantitative Spectroscopy & Radiative Transfer 188 (2017) 103–117 107

period at each wavelength.Bad pixels in the CCD are discovered by comparing

background spectra and spectra measured for broadlyfluorescent materials, looking for positive or negative spikes,and then keeping track of the locations of these spikes. Theintensities at these bad pixels are replaced by the average ofthe pixels on either side. Because multiple spectra are mea-sured at each position of the laser, cosmic rays are removedby comparing the second derivative of each spectrum. Incases where the second derivative at any pixel is above athreshold in one spectrum but not the other, those locationsare removed, along with one pixel on either side of thoselocations. This value is replaced by a linear fit between thevalues on either side of the removed pixels.

In this paper, fluorescence is only removed for thecluster analysis. All other spectra shown in this paper donot have fluorescence removed. Fluorescence backgroundswere also estimated using an Asymmetric Least Squares(ALS) algorithm [45]. ALS is similar to a least squares fit,but weights valleys more than peaks and uses an adjus-table parameter to change the curvature of the fit.

Cluster analysis is done using R [46]. Spectra from thepreprocessing are analyzed using the hyperSpec package[47]. Because large fluorescence values can cause featureswhich may be artificial in the Raman spectra, we removedall spectra with fluorescence above 2.87 (the 75th per-centile value of summed fluorescence), with a positiveratio of o0.00075 of maximum value after fluorescenceremoval to summed fluorescence. The goal is to removespectra with high fluorescence values, which have lowintensity peaks after the fluorescence has been removed.After cluster analysis, these spectra are added back in anadditional cluster ‘Fluorescent’. The remaining spectra areonly clustered if the maximum value is above 1% of thesaturation intensity so that noisy spectra are not analyzed.Each spectrum to be clustered is normalized to its meanvalue. Distance was assessed using R2 values between eachof the spectra. The hclust routine in R is used with Ward'smethod [48,49].

2.3. Air samples, test particles, and methods of aerosolizingparticles

The primary location sampled was an unused asphaltroad at the edge of a lawn and approximately 14 m from atwo-lane road with often heavy traffic, 0.8 km from theWashington, DC beltway with 4 or 5 lanes of traffic in eachdirection and 1.1 km from the intersection of interstatehighway I-95 with the Washington, DC beltway.

Samples were introduced into the inlet in three ways: i)for ambient sampling, the factory inlet with a blackinverted cylinder with a rain shield (Fig. 1A) was used; ii)for dry materials, small particles were generated andsuspended in air by agitating bulk particulates in a con-tainer, typically using a coffee grinder, then removing thelid of the container near the inlet to the ARS, and allowingthe aerosols to be drawn into the ARS; and iii) for aqueoussuspensions, e.g., PSL spheres, the suspension was neb-ulized and dried using a Royco nebulizer system. In somecases, the ARS also drew room air into the inlet throughoutmuch of the collection period, but the number of test

aerosol particles greatly exceeded the numbers of back-ground aerosols that were also collected. For analysis ofbulk samples, the tape was removed from the micro-controller stage and the sample was either placed on amicroscope-slide cover or in a small plastic cup, either ofwhich was then placed on the stage. The bulk samplesplaced on the slide or in the cup were sufficiently thickthat spectral features of the plastic or glass could not beobserved in the measured spectra.

3. Results with test particles

3.1. Acquisition of Raman spectra and hyperspectral images

PSL spheres with a 2-mm diameter (2.01370.025 mm)were used in our initial illustration of the ARS's cap-abilities. Such spheres have been used extensively incharacterizing aerosol instruments. The PSL spheres wereaerosolized using a Royco nebulizer, collected on the tapeby the ARS, and their Raman spectra were measured.Fig. 2a illustrates the Raman intensity vs. spectral shift for38 pixels in the y-direction on the tape. The y direction isparallel to the axis of the focused laser line on the tape. Itis also perpendicular to the stepping direction for buildinga hyperspectral image. The pixel size in the y direction(vertical in Fig. 2b) is approximately 1 mm. The Raman shiftis approximately 3 cm�1 per pixel. Only 38 of the 128pixels at each shifted frequency are shown because theintensity of the illuminating laser line (and hence rowswhere Raman spectra can be measured) is small or negli-gible for the other pixels.

The strongest Raman line in Fig. 2a occurs at approxi-mately 1000 cm�1 and corresponds to the phenyl ringbreathing mode [50]. This line is seen for two particles: onewith a strong signal centered on line 26 and another with aweaker Raman intensity centered on line 3. The particle withthe strong Raman peak at 1000 cm�1 exhibits additionalpeaks associated with aliphatic and aromatic C–H stretchfrequencies. Fig. 2b shows the sum of all of the Raman shiftintensities for a given position on the laser line. The particlewith highest Raman intensity can be seen with the intensitymaximum at line 26. As indicated by the arrows in Fig. 2b, thefull-width at half of the maximumvalue is 2.3 pixels, which isconsistent with 2-mm particles and suggests that the two-dimensional cross-section of a particle can be estimated fromthe peak widths, and is consistent with the vertical binshaving a resolution of approximately 1 mm/bin. Two-dimensional cross sections of some test particles and atmo-spheric particles are illustrated in Fig. 3 and in Section 4,respectively. A key point of this section is that the ARS, withina 10-s imaging time, measures clear Raman spectra of indi-vidual 2-micron-sized aerosol particles made of polystyrene.

To obtain the surface plot in Fig. 3, the laser line isstepped in the x-direction across the sample, taking a setof spectra, such as the one shown in Fig. 2a, at eachposition. In this paper, the length of the step of the laser isapproximately 2 μm. Because the tape is stepped underthe excitation/collection optics, acquiring approximately40 Raman spectra at each step, an image can be built foreach wave number band. Fig. 3 illustrates an image from

Fig. 2. a) Raman spectra of 2-mm-diameter PSL spheres as acquired by the ARS using a 10-s acquisition time. Intensities from a subset (38�947 of the128�1024 pixels of the CCD) are shown. The vertical axis in (a) is the Raman intensity, where each spectrum is offset so the spectra do not overlap. b) Totalintensity, summed over all Raman frequency shifts shown in (a), vs. the y coordinate, which is parallel to the long axis of the laser line as focused onto thetape. The spectra near the bottom of (a) are weak because they are not centered within focal region of the laser or collection optics.

Fig. 3. Surface plot of 2 mm PSL spheres aerosolized using a Royconebulizer. The sum of the Raman emission between 995 and 1005 cm�1

is the quantity plotted on the z-axis. The acquisition time for eachspectrum was 12 s.

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the collection period when Fig. 2 was taken, focusing onthe 1000 cm�1 band. The interpolated sum of intensitiesbetween 995 and 1005 cm�1 is the value plotted at eachx–y position. Each spectrometer image corresponds to aline of values along the ordinate of a graph. As the spec-trometer steps in space, these line values are accumulatedto produce a 2-dimensional array of values over thiswavenumber band, as illustrated in Fig. 3. This surface plotin Fig. 3 suggests that the cross-sectional size and shape ofparticles could be estimated from ARS data for particleslarger than approximately 1.5 to 2 mm, for example usingthe full-width at half-maximum (see Fig. 2b). Because theRaman intensity for particles of a given material should be

approximately proportional to particle volume, for parti-cles that absorb only little of the illuminating intensity, therelative uniformity of the peak heights, or the totalintensity summed over the image of each particle, shouldalso be useful for estimating particle size. Further work isneeded to quantify how accurately the particle cross-sectional size can be determined from the images, andhow this accuracy depends upon particle size, shape andcomposition (including Raman cross sections, inhomo-geneity of the materials composing the particle, and thepolarization-sensitivity of different Raman bands of thematerial). Although that work is beyond the scope of thispaper, aspects of these problems and the problem ofestimating particle volume are briefly discussed in Section5.2.

Fig. 4 is our attempt to illustrate the high-intensityparts of the hyperspectral image, which includes the fullset of the data. In Fig. 4 there is a whole spectrum for eachilluminated point on the tape. Here, the image from Fig. 2ahas been rotated 90°, so that the y-axis is parallel to theaxis of the laser line-source. Also, the x-axis in Fig. 2a isnow the vertical axis (labeled ‘Raman Shift (cm�1)’). Thenthe other spectra similar to those in Fig. 2a, but obtainedfor different steps of the line source and collection opticsposition on the tape, are aligned parallel to this initialplane. Surfaces of constant intensity (arbitrary units) areindicated by blue, green, and red colors as intensityincreases. Thus, as the laser steps along the tape, a 3-Dmatrix of values is assembled. This matrix has twodimensions in physical space, and the third dimension isthe Raman shift. The phenyl ring breathing mode can beseen at the bottom of Fig. 4 for three PSL particles. Thearomatic and aliphatic C–H stretches show up in red at thetop of the graph. The particles have the appearance of‘tubes’ that change diameter and shape as Raman shift

Fig. 5. (a) Raman spectra of sucrose taken with the ARS (gray) and theestimated fluorescence background (blue). (b) Spectrum from the SPE-CARB database excited at 1064 nm (orange) and scaled REBS spectrumwith fluorescence subtracted (green). (For interpretation of the refer-ences to color in this figure legend, the reader is referred to the webversion of this article.)

Fig. 4. Hyperspectral false color image of the Raman intensity vs. theRaman shift (cm�1) in the vertical direction) and vs. each pixel on thetape within the ranges shown (x from 0 to 64 μm and y from 0 to 44 mm).Illumination position corresponds to the position on the illuminationaxis. The total length of illumination is 44. The particles are 2-mm PSLspheres. The acquisition time is 25 s for each set of approximately 30.

D.C. Doughty, S.C. Hill / Journal of Quantitative Spectroscopy & Radiative Transfer 188 (2017) 103–117 109

varies because of fluorescence, which spans many wave-lengths. For inorganic particles with no fluorescence, thesetubes are replaced by disks at the Raman emission lines.

3.2. Spectra of aerosols from test particles

Spectra of sucrose (table sugar), an example carbohy-drate, are shown in Fig. 5. Dry, powdered sucrose wasaerosolized near the instrument, and 12-s Raman spectra ofthe collected aerosol particles were recorded. Since the par-ticle shown in Fig. 5 only appeared in two bins in the ydirection, and on no other steps of the tape, we surmise thatthis particle was probably less than 2 mm in diameter. Theexample spectrum shown in Fig. 5a is dominated by a broadfluorescence, with many smaller Raman peaks super-imposed. The fluorescence, estimated using an asymmetricleast squares fitting algorithm, is shown in the smooth line inFig. 5a. This fluorescence was subtracted from the total signalto obtain the Raman spectrum illustrated (green line) inFig. 5b, which was rescaled to be of comparable magnitudeto the 1064-nm excited reference sucrose spectrum (also inFig. 5b) from the SPECARB database (http://www.models.life.ku.dk/�specarb/). The C–H stretch in the aliphatic region atapproximately 2912 cm�1 in the ARS spectra also occurs inthe SPECARB (2913) spectrum and the sucrose spectrum in[51]. The strong peak near 850 cm�1 is also observed in ourspectrum (847 cm�1) as are many other peaks [51]. Ramanshifts from 15 peaks in Fig. 5 differ by �0.2 to 5 cm�1

(mean¼2.67 cm�1) except for the low peak near 943 cm�1

which had a difference of 6.6 cm�1. The differences might becaused by impurities in the non-reagent grade sugar used inthis experiment. The SPECARB resolution is listed as 4 cm�1.The ARS has a resolution of approximately 3 cm�1.

Light absorbing carbonaceous particles containing, e.g.,soot, humic-like substances (HULIS), and some humicsubstances, exhibit two broad peaks centered around 1350and 1600 cm�1, in bands labeled D and G. These broadpeaks are a combination of multiple narrower peaksincluding the graphite peak and several defect peaks [34].Fig. 6a shows an example of a Raman spectrum dominated

by these D and G peaks in an ambient sample takendownwind of a running gasoline-powered vehicle. Fig. 6band c illustrate spectra of inorganic particles in ArizonaTest Dust (ATD), (Powder Technology, Inc., Arden Hills,MN) that was aerosolized and then measured with theARS. The spectrum in Fig. 6b exhibits peaks at 481, 510,and 1067 cm�1 and is probably a particle with a combi-nation of NaNO3 (1067 cm�1) and albite (479 and507 cm�1). The spectrum in Fig. 6c has peaks at 714 and1087 cm�1 which are consistent with calcite.

Spectra with D and G peaks are common in atmosphericsamples [24,26]. Also, however, charring of organic particlesby lasers has been observed [28]. Ault et al. [25] assumedthat particles with D and G peaks in their sample of marineparticles had been charred by the laser used to generate theRaman spectra. Such charring is more likely to occur withexcitation wavelengths shorter than 540 nm instead of the640 nm laser used here. The concerns of Ault et al., [25] andthe presence of D and G peaks in spectra of atmosphericsamples obtained with the ARS, raise the question that someHULIS/Soot type spectra may occur because of charring bythe ARS. To look for evidence of charring of samples by thelaser in the ARS, we tested the laser at full power on bulksamples of tryptophan, leaf litter, and Cladosporium her-barum, and did not see evidence of charring. See Appendix Awhich illustrates results for tryptophan. A concern that thewavelength calibration of the ARS might vary with time isinvestigated in Appendix B, where the variation is seen to besmall over a period examined, i.e., 4 months.

4. Example results with atmospheric aerosol

4.1. Raman spectra of ambient aerosol particles with hier-archical cluster analysis

Figs. 7 and 8 illustrate Raman spectra of particles col-lected from air during a 15-min collection period

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beginning at 1 a.m. (EDT) on May 8, 2015. This period wasselected for analysis because it contained both inorganicand HULIS/soot spectra, as well as spectra with a peak atthe C–H stretch region, indicating organic materials. Twoimages were taken at each location (10 and 15 s). Of the1178 spectra obtained in this 15-min period, 158 spectrawere clustered using a hierarchical cluster analysis. Thirty-two spectra were classified as ‘Fluorescent’ and were notclustered. The mean and standard deviations of the Ramanspectra for each individual cluster are shown in Fig. 7.

The cluster in Fig. 7a (118 spectra) is named HULIS/Sootbecause spectra of soot and some HULIS have similar

Fig. 6. Raman spectra measured: (a) near a running automobile, and(b and c) from aerosolized Arizona Test Dust (ATD).

Fig. 7. Raman spectra (averages in color with standard deviations for each in lighto: (a) HULIS/Soot (gray); (b) Organic with a clear C–H stretch (orange); (c) Inoarbitrary units. (For interpretation of the references to color in this figure legen

peaks. These appear to be the D and G bands of amorphouscarbon. The spectra of the cluster in Fig. 7b (10 spectra)have a clear peak in the C–H stretch region, consistentwith organic carbon. The cluster in Fig. 7c (30 spectra) hasspectra with no peaks in the C–H stretch region and weakor no peaks in the D/G region, but has strong peaks near1000 cm�1, and so appears to be inorganic. Some of theseinorganic spectra also exhibit relatively weak D/G peaks.These classes of spectra are similar to those observed in[19], but without the TiO2 category. Fig. 8 illustrates sev-eral individual spectra selected from the clusters found.The two Raman spectra in Fig. 8a illustrate differing rela-tive heights of the D and G peaks. The spectrum in blue hasbeen increased by a factor of three to facilitate compar-isons of the spectral shapes of the two spectra. The spec-trum indicated by the yellow line in Fig. 8c has beenincreased by a factor of three to facilitate comparison withthe other spectrum. The two dominant peaks in Fig. 8c(which overlap in Figs. 7c and 8c because of the thicknessof the lines), near �1006 cm�1 and 1017 cm�1, are likelygypsum and anhydrite, respectively. It can be difficult toextract the exact composition of organic and inorganicspecies in marine aerosols from the aerosol Raman spec-trum, especially when the composition is mixed [19].However, Potgieter-Vermaak and Van Grieken in discuss-ing their study of atmospheric aerosol stated that, “themolecular identification of most inorganic particles is tri-vial” [52] .

Fig. 9 shows a map of the pixels which had Ramanspectra which were clustered or classified as fluorescent.The pixels are colored according to which cluster-type isdominant in the spectrum. Also in Fig. 9 are the contourlines for the interpolated total signal intensity (summed

t gray) for three clusters from one 15-min period. The spectra correspondrganic (green). The vertical scale indicates the Raman intensity, and is ind, the reader is referred to the web version of this article.)

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between 166 and 3185 cm�1). As in Fig. 3, Fig. 9 suggeststhat for particles larger than approximately 1.5 to 2 mm,the cross-sectional size and shape of particles could beestimated from the Raman signal and/or fluorescence.

The interpolated total signal, represented by the shadedcontours, yields information about the shapes and num-bers of particles present on the tape, at least in terms ofthe Ramanþfluorescence intensity. The fact that most of

Fig. 8. Example individual Raman spectra of ambient particles in each ofthe first three clusters: (a) HULIS/soot (note that the blue spectrum hasbeen scaled larger by a factor of 3 to have a similar magnitude to theother spectrum); (b) Organic; (c) inorganic particles (note that the yellowspectrum has been scaled larger by a factor of 3 to have similar magni-tude to the other spectrum).

Fig. 9. Map of particles by cluster category (bar on upper right) and by overall intmin period at approximately 1:00 a.m. on 8 May 2015. The red box indicates thethis figure legend, the reader is referred to the web version of this article.)

the clustered areas are centered on regions of high totalsignal suggests that our method for selecting spectra wasappropriate. We did not interpolate clusters, so Fig. 9 isplotted with a 1-mm gap separating each pair of lines withclustered spectra.

In Fig. 9, particles that appear to be clusters do notappear to be homogeneously mixed. Most of the particlesdo not appear to be relatively homogeneous. In the lowerleft of Fig. 9, a relatively large particle with a spectraclassified as organic (with a few spectra classified as‘fluorescent’) has one pixel with a spectrum of HULIS/Sooton its left-most edge. It might have been internally mixedin the atmosphere or might have been formed by twoparticles landing on the tape at different times.

The particle in the lower right hand corner of Fig. 9aappears to have spectra clustered as ‘inorganic’ in thecenter, and spectra clustered as ‘HULIS/Soot’ or ‘fluor-escent’ around the outside. Fig. 10 shows this particle inmore detail, showing spectra from vertical and horizontalslices across this particle. The spectra clustered as HULIS/Soot (labeled 11 and 2 in Fig. 10) appear to only have theD/G features. Of the spectra clustered as ‘inorganic’, somehave the D/G features (especially spectrum 12), in additionto peaks associated with inorganic material primarily.Although the spectra on three edges of this particle (11, 13,2) exhibit D/G features, spectra 8 and 9 at the ‘top’ edge ofthe particle do not have clear D/G peaks. This suggests thatthis is not an inorganic particle uniformly coated with sootor humic acid. Overall, there are many small and largeparticles with Raman dominated by HULIS/Soot; onelarge organic particle; one or two inorganic particles;several fluorescent particles; and some particles with thespectra indicating a mixed structure. Most of the particlesin Fig. 9 either have Raman spectra dominated by HULIS/

ensity (bar on lower right). This atmospheric sample was collected in a 15-region covered by Fig. 10a. (For interpretation of the references to color in

Fig. 10. (a) Expanded view of the small region delimited by the red box in Fig. 9. Colors represent cluster type, HULIS/Soot (gray), Inorganic (green),Fluorescent (blue). The spectra are numbered so they can be linked to their spectra (shown in 10b). (b) Raman spectra of the numbered pixels in 10a, wherethe number for each spectrum is shown on the far right side of 10b. The colors of these numbers (right side) indicate the cluster type as in 10a. The colors ofthe spectra are stepped in sequence to aid in discriminating between the different lines. (For interpretation of the references to color in this figure legend,the reader is referred to the web version of this article.)

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soot signatures, or are surrounded by material with aHULIS/Soot Raman signature.

This observation that soot appears more at the surfaceis consistent with the results in [14]. The prevalence ofspectra with the D and G peaks of amorphous carbonraises the question of whether small soot particles areagglomerating with other particles or whether the laser ischarring organic materials in the majority of aerosols.However, we note, again, that we could not find evidenceof charring in tests with different laser powers withtryptophan, table sugar, and dried leaves (see Appendix A).

The most prevalent spectral category in Figs. 8 and 9, asshown by the number of spectra falling into each cluster, isHULIS/Soot. The large number of spectra does not meanthat the particle mass is primarily HULIS/Soot. Materialswith extensive conjugation of π electrons tend to haveRaman cross sections that are many times larger thanmolecules with no or little conjugation. For example, the514.5-nm-excited Raman cross sections (1030 cm2 mole-cule sr�1), for selected peaks, increase from 5.6 for glu-cose, to 28.6 for benzene, to 82 for naphthalene, to 540 foranthracene, to 6200 for 1,4-bis(2-methylstyryl)benzene,and to 1.1�107 for β-carotene (see pp. 26–27 [53]). Theconjugation of π electrons in soot is extensive [54,55]. Also,Bladt et al., [56] showed that the Raman spectra of sootparticles containing 0 to 42% CaSO4 were essentiallyindistinguishable, and none exhibited Raman peaks ofCaSO4. Similarly, the signals from CeO, CeSO4 and Na2SO4

in soot were very weak compared to the D/G peaks. Inanother example illustrating the strength of the Ramansignal from soot, Blaha et al., [28] collected a NaNO3 par-ticle from the atmosphere and measured its Raman spec-trum before and after illumination with 514.5 nm light atapproximately 3.2 mW/mm2 (an intensity approximately2.7 times higher than the 640-nm, lower photon energy,light used here). After illumination, Blaha et al. [28],observed strong D and G peaks in the Raman spectrum.

However, under an optical microscope they observed “nochanges in the morphology or birefringent character of theparticle” [28]. The particle size was unchanged(2.5�3.5 mm). They concluded that the 3.2 mW/mm2 lightat 514.5-nm converted some organic material on theNaNO3 particle into amorphous, soot-like carbon. Blahaet al. [28], also coated an approximately 4.5 mm diameteranhydrite particle with a 20 nm coat of vitreous carbon,which has extensive sp2 graphitic type bonding [28]. Eventhough the ratio of the volume of vitreous carbon toanhydrite was only 0.013, the area under the D and Gpeaks of the Raman spectra exceeded that under theRaman peaks of the anhydrite. The data of McCreery [53],Bladt et al. [56], and Blaha et al. [28] suggest that a largenumber of D and G peaks should not be assumed toindicate that there is more mass of soot, HULIS or otherblack carbon than inorganic and non-aromatic species in asample.

Researchers have calibrated their Raman microprobesystems using different types of black carbon (see [57] andreferences cited therein). However, a further complicationin relating the Raman intensity of HULIS/Soot to the par-ticle mass is that the penetration depth of 640 nm lightinto a uniform sheet of black carbon is substantially lessthan 1 mm, and typically closer to 0.1 mm. As a result, forsolid BC particles smaller than approximately 0.1 mm, theintensities of the D and G peaks will be proportional toparticle volume, but for solid BC particles larger thanapproximately 0.5 mm, the intensities of the D and G peakswill be more proportional to cross sectional area. To makethe inverse problem even worse, the soot may be fractal,and of an unknown, maybe low density. A conclusion isthat estimating the mass of soot and HULIS from theamplitudes of the D and G peaks is non-trivial.

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4.2. Clusters in ambient aerosols: 7 hr total

A seven-hour period (5:26 a.m. to 12:30 p.m.) duringthe morning commute on May 8, 2015 was selected foranalysis. During this time, 32,718 spectra were measured.The spectra were clustered using the hierarchical methoddescribed in Sections 2.2 and 4.1, except that a higherthreshold for signal was used (0.025), seven clusters wereanalyzed, and total summed fluorescence intensityrequired for a spectrum to be ‘fluorescent’was increased. Atotal of 5892 spectra were clustered; also 2752 spectrawere considered as ‘fluorescent,’ and were not analyzedfurther. The mean and standard deviation of spectra ineach cluster can be seen in Fig. 11. In many cases thestandard deviations are within the thickness of the lineshowing the mean. Cluster 1 included HULIS/Soot(n¼5627). Cluster 2 (n¼18) is organic with a CH stretch.Clusters 3, 4, 5, and 7 have no CH stretch and are con-sidered to be ‘inorganic.’ The spectrum of cluster 4 has6 peaks (406, 492, 617, 667, 1004, 1132 cm�1) close tothose of CaSO4 �H2O. The spectrum of cluster 7 has 3 peaks(721, 1065, and 1382 cm�1) close to those of NaNO3.Clusters 3 and 5, and to a lesser extent, 4, have weak D/Gpeaks. Cluster 6 (n¼41) is the Raman spectrum of the tape(which appears when there are defects in the metalliza-tion of the tape). Cluster 8 is not shown because it includesthe fluorescent particles.

4.3. Time-series of numbers in clusters of ambient aerosols:7 hr total, 15-min intervals

Fig. 12 shows a time-series of the Fluorescent, HULIS/Soot, and inorganic clusters during the measurementperiod. Fig. 12 illustrates that the number of spectra inCluster 1 increases to a maximum around 9:00, thendecreases, but with a smaller spike at the imaging timestarting at 10:56. The increase and decrease of numbers ofspectra in cluster 1 is consistent with the expected beha-vior for a morning commute in an urban location, e.g. [58].In Ref. [58] the measured quantity is light absorption bythe sample, which is primarily useful for measurement ofsoot and HULIS. In contrast, here: i) Raman spectra areused to estimate the numbers of spectra dominated by theD/G peaks of HULIS/Soot, and ii) the analysis illustratedbarely begins to address the many questions that can beasked using a dataset with 5892 spectra above threshold,which should be useful for discriminating between variousbioparticles, organic and inorganic materials, and soot/HULIS. The spectra in cluster 4, 5, and 7 do not have anydiscernable trend due to the low numbers in that category.These low numbers may be due to the high thresholdused, which may have excluded spectra with weak inor-ganic signal. Fig. 12 illustrates that the ARS is able to obtainRaman spectra which provide information about thecomposition of the atmosphere and how that compositionchanges with a time resolution of 15 min.

5. Discussion

5.1. Estimates of maximum sample rates

Figs. 3 and 9 illustrate maps of particles collectedwithin a 15-min period and measured within the next15 min. In each of these figures, the y direction hasapproximately 40 points, each separated by approximately1 mm. In Fig. 3 the map has 63 points in the x direction,each separated by 2 mm, and so has approximately 2520pixels.

In Fig. 3 all the 19 particles detected are 2-mm PSL. It isnot difficult to imagine that four or five times as many 2-mm PSL spheres could be detected. However, with higherparticle densities there would be more aggregates of par-ticles, where it is not possible to determine whether theparticle was an aggregate in the atmosphere or formedwhen a particle was collected onto a particle already onthe tape. If we decide that a density of particles 2.5 timesthat in Fig. 3 is the largest density acceptable, then themaximum number of particles measured in a 15-minperiod is 50. That suggests a maximum of 200 particlesper hour, or 4800 particles per day, can be measured. Themap in Fig. 9 with 31 points in the x direction hasapproximately 1240 pixels. If the number of particlesmeasured in Fig. 9 is 35 and collections of similar densitywere collected all day, then the maximum number ofparticles counted in a day would be 3360. In cases wheresampling more particles is a higher priority than is ima-ging or obtaining multiple spectra of the same particle, theline source could be stepped more than 2 μm after eachspectrum in order to reduce the probability that any par-ticle is sampled more than once.

5.2. Effects of polarization on the ability to estimate particlecomposition and size

Many crystals illuminated by linearly polarized lighthave Raman spectra that depend upon the orientation ofthe crystal relative to the polarization of the illuminationbeam and on the polarization-dependence of the collec-tion optics and spectrograph. Because the laser illuminat-ing the particles in the ARS is linearly polarized, and someatmospheric aerosol have orientation-dependent Ramanspectra, the effect of polarization and particle orientationon the estimations of composition and particle size mustbe considered. First, soot, humic and humic-like sub-stances, biological materials and most other organic par-ticles likely to be in the atmosphere, are not likely to havetheir molecules sufficiently aligned to exhibit largepolarization-dependent effects. Also some of the primaryinorganic species in the atmosphere, e.g., ammoniumsulfate, ammonium nitrate, as well as other minerals [59],often do not exhibit strong effects of polarization, becausethey are agglomerates of differently-oriented crystals orthey crystalize with other materials. Second, most polar-ization sensitive bands are still apparent in all or almost allorientations. The method used to assign material type(s) tothe particles can be selected to emphasize the peak loca-tions not peak height. Not all lines in a Raman spectrum ofa pure crystal are polarization dependent in the same way.

Fig. 11. Raman spectra of clusters obtained for the 7 hr sampling periodby a road. Number of particles in each cluster are indicated. The clustertypes are: (1) HULIS/Soot (gray), (2) Organic; (3), (4), (5), (7) Inorganic;(6) the tape (polyethylene) where the metal did not cover it adequately.

Fig. 12. Number of Raman spectra in each 15 min sampling intervalwhich fall into cluster 1, the sum of clusters 4, 5 and 7, or into thefluorescent-particle cluster. The Raman spectra between 5:26 and 12:30were divided into four clusters, two of which are shown here in orangeand green.

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If the crystal is sufficiently large and pure so that it ispossible to identify it from its Raman spectrum, then itshould be possible to estimate the orientation of thecrystal, once the needed information is programmed intothe analysis code. This orientation should be of use inestimating the relative contributions of different materialsto the measured spectra. Third, the cross-sectional sizesand shapes of the particles are primarily estimated fromthe numbers of pixels and their positions. Raman intensityis used to help estimate the mass of the component orcomponents of the particle at that pixel. From the massand the density for that material, the particle volume can

be estimated if the Raman cross sections for the materialsare known. The problem of estimating mass from theRaman spectrum is not simple, in part because of thevariation of illumination intensity throughout particles ofdifferent sizes, and variations in collection efficiency fromdifferent regions of particles. Fourth, for applicationswhere the polarization effects are unacceptable, the pro-blem can largely be circumvented by circularly polarizingthe laser beam and then modifying as necessary the opticsused to block the elastic scattering from reaching thespectrometer.

5.3. Advantages of the REBS for an ARS

The ARS can increase the sampling rate compared withaerosol trapping techniques [36,44] because, as an image-preserving Raman spectrograph, it can obtain manyRaman spectra simultaneously Theoretically, particlescould be sampled from ambient air, held in a linearquadrupole trap [60,61], and their Raman spectra mea-sured using an image preserving Raman spectrograph.However, as far as we know, such measurements have notbeen reported. The REBS at the core of our ARS allows theuser to vary many parameters, e.g., sample times, spectralacquisition times, and number of spectra to record at eachlaser position. The ARS can be operated with differentparameters most suitable for different particle sizes. Also,with it, users have access to the raw data. Because the usercan write scripts that rapidly use the raw spectra to classifyparticles, these spectra and partial classifications can beused to make real-time decisions about when to takeadditional spectra, how far to step the tape, etc. The ima-ging capabilities allow users to more clearly determine if aparticle is homogeneous or an agglomerate, if a particle islayered, or if the spatial distribution of the Raman spec-trum from an inhomogeneous particle (e.g., a pollen) isconsistent with known values of that distribution.

6. Final comments

Investigations of the REBS-based ARS described heresuggest the potential of this ARS for multiple applications.

Fig. 13. (a) Raman spectra of bulk tryptophan (Sigma, 99.5%) measured atfour different times. The top curve was measured at t¼0. The otherspectra were measured at times 2, 4, and 6 min. Each spectrum wasacquired in 2 s. The lowest curve indicates the difference between the10 s and the 6-min spectrum. A cosmic ray appears at approx. 2580 cm�1

in the top spectrum and the difference spectrum.

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We demonstrated the ability of the ARS to measure Ramanspectra of 2-mm PSL, sucrose, tryptophan, CaSO4 and otherparticles, as well as Arizona test dust and HULIS/Soot.Raman peaks for sucrose were on average within2.67 cm�1 of standard values. Cluster analysis of spectrafrom one 15-min period indicated the following types ofmaterials: HULIS/Soot, organic, and fluorescent particletypes. Raman spectra near a road were measured andanalyzed for a 7 h period (15 min increments). The time-series of several spectral clusters showed that the HULIS/Soot category increased and decreased in a manner con-sistent with a morning commute. Neither the inorganicnor the fluorescent categories exhibited this temporalpattern. Even though additional work is needed to developthe processes to extract information from the Ramanspectra, we think that with additional work, ARSs shouldbe useful in characterizing time-resolved size and chemi-cal composition of atmospheric and laboratory-generatedaerosols, studying aerosol processing in the atmosphere,and in obtaining data needed to determine which particlecompositions are related to the various health effectscorrelated with atmospheric aerosol [1–3].

Acknowledgments

This work was supported by US Army ResearchLaboratory mission funds as part of the Atmospheric Sci-ence Center and the Meteorological Sensor Array. Twoanonymous reviewers provided feedback which sig-nificantly helped improve the quality of this manuscript.

Appendix A. Are particles charred by the laser in theARS?

If the laser intensity is too high and/or the laserwavelength is too short, the illumination to excite Ramancan convert carbonaceous materials to black carbon asevidenced by D and G peaks in the Raman spectra [28]. Toinvestigate whether the laser used here generates mate-rials with D and G peaks we tested it with several samples.In each case the sample was illuminated by the laser formore than six minutes. Fig. 13 shows the spectra from four2-s measurements of the Raman spectrum of bulk tryp-tophan over the course of 6 min. The laser was on con-tinuously over the time period (6 min and 2 s). The lowest(dark blue) line in Fig. 7 shows the difference betweenwhen the laser was first turned on and �6 min later. Nodiscernable amorphous carbon peaks appear in the Ramanspectra of this sample of tryptophan. Therefore, the spec-tra do not indicate the generation of amorphous carbon inthis bulk sample after 6 min of illumination at full power.The same test was done with leaf litter and with C. her-barum. Again, there was no evidence of generation ofamorphous carbon. Therefore, we suspect that the possi-bility for the lasers in the ARS to char aerosol samples islow. However, we plan to continue to look for evidence ofcharring and look for materials that may occur in atmo-spheric particles which may be especially prone to char-ring when illuminated 640 nm.

Blaha et al., [28] observed evidence of D and G peaks inthe Raman spectrum of a mostly NaNO3 particle collectedfrom the atmosphere. Spectra were measured before andafter illumination with 514.5-nm light at approximately3.2 mW/mm2, an intensity approximately 2.7 times higherthan the 640-nm light we use here. Also, the energy perphoton is 187 kJ/mole at 640 nm (ARS) and 232.5 kJ/molewith the 514.5 nm light used by Blaha et al. [28].

Appendix B. Stability of the calibration of emissionwavelengths

The ARS was calibrated by Battelle using the atomicemission lines from a neon lamp, as well as Raman shiftsfrom PSL block and a silicon (Si) wafer. We verified thecalibration using only a PSL block and Si wafer, using athird-order polynomial to fit the Raman shifts to spectro-meter bins. The PSL peaks used are at 620.9, 785.8, 1001.4,1031.8, 1155.3, 1450.5, 1602.3, 2852.4, 2904.5, and3054.3 cm�1, as found in the ASTM standard spectra forpolystyrene. A peak-finding algorithm was used to deter-mine the peaks from our ARS spectra. First, the secondderivative of the Raman spectrum was used to determineapproximate peak location. Then, for all peaks meetingspecified criteria for the absolute peak intensity and theslope of the second-derivative, a Gaussian function was fitto the spectrum in the vicinity of the peak to extract amore accurate peak location. Between 500 cm�1 and theupper limit of the Raman shift, the spectrometer variedless than 4 cm�1 from the factory calibration. Using simi-lar calibration methods (but different pieces of Si and PSL),the calibration varied less than 1 cm�1 between 9 July and19 November 2015, even though the instrument wastransported to several different locations.

Fig. 14 shows the difference between the factory cali-bration and two of our calibrations. As a check for thevalidity of Raman spectra, both in the field or in thelaboratory, PSL spheres are periodically tested in theinstrument.

Fig. 14. Difference in Raman shift between the factory calibration (20 Apr2015) and our calibrations on 9 July 2015 (blue) and 19 Nov 2015 (orange)The difference between our 9 July and 19 Nov calibrations is in green.

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